I'\n\n\t# Get metadata for images\n\timages.read(4) # skip the magic_number\n\tnumber_of_images = images.read(4)\n\tnumber_of_images = unpack('>I', number_of_images)[0]\n\trows = images.read(4)\n\trows = unpack('>I', rows)[0]#28\n\tcols = images.read(4)\n\tcols = unpack('>I', cols)[0]#28\n\n\t# Get metadata for labels\n\tlabels.read(4) # skip the magic_number\n\tN = labels.read(4)\n\tN = unpack('>I', N)[0] #60000\n\t# print(number_of_images);\n\n\tif number_of_images != N:\n\t raise Exception('number of labels did not match the number of images')\n\n\t# Get the data\n\tx = zeros((N, rows, cols), dtype=float32) # Initialize numpy array #60000X28X28\n\ty = zeros((N, 1), dtype=uint8) # Initialize numpy array\n\tfor i in range(N):\n\t if i % 1000 == 0:\n\t print(\"i: %i\" % i)\n\t for row in range(rows):\n\t for col in range(cols):\n\t tmp_pixel = images.read(1) # Just a single byte\n\t tmp_pixel = unpack('>B', tmp_pixel)[0]\n\t x[i][row][col] = tmp_pixel\n\t tmp_label = labels.read(1)\n\t y[i] = unpack('>B', tmp_label)[0]\n\t # print(y.shape)#60000X1\n\treturn (x, y)","def load(filename):\n img = image.load_img(filename, target_size=(299, 299))\n np_image = image.img_to_array(img)\n np_image = np.array(np_image).astype('float32')/255\n\n # Make to a rank 4 tensor (1, 299, 299, 3) -> 1 is for the batch size\n np_image = np.expand_dims(np_image, axis=0)\n\n return np_image","def load_fashion_mnist():\n # List of image file names\n dataset_directory = os.path.join(root_directory,'Fashion_MNIST')\n filenames = os.listdir(dataset_directory)\n filenames.sort()\n\n # List of numpy array; each row is a Image of the dataset\n data = []\n\n # Numpy array of labels associated to each class of image\n target = np.empty([len(filenames), ])\n\n previous_label = ''\n class_num = -1\n index = 0\n\n for index, filename in enumerate(filenames):\n data.append(Bitmap(io.imread(os.path.join(dataset_directory, filename))))\n file_label = filename.split('-')[0]\n\n if(previous_label != file_label):\n previous_label = file_label\n class_num += 1\n target[index] = class_num\n else:\n target[index] = class_num\n\n return {'bitmaps': data, 'targets': target}","def load_EMNIST_data(file, verbose = False, standarized = False): \n mat = sio.loadmat(file)\n data = mat[\"dataset\"]\n \n X_train = data['train'][0,0]['images'][0,0]\n X_train = X_train.reshape((X_train.shape[0], 28, 28), order = \"F\")\n y_train = data['train'][0,0]['labels'][0,0]\n y_train = np.squeeze(y_train)\n y_train -= 1 #y_train is zero-based\n \n X_test = data['test'][0,0]['images'][0,0]\n X_test= X_test.reshape((X_test.shape[0], 28, 28), order = \"F\")\n y_test = data['test'][0,0]['labels'][0,0]\n y_test = np.squeeze(y_test)\n y_test -= 1 #y_test is zero-based\n \n if standarized: \n X_train = X_train/255\n X_test = X_test/255\n mean_image = np.mean(X_train, axis=0)\n X_train -= mean_image\n X_test -= mean_image\n \n\n if verbose == True: \n print(\"EMNIST-letter dataset ... \")\n print(\"X_train shape :\", X_train.shape)\n print(\"X_test shape :\", X_test.shape)\n print(\"y_train shape :\", y_train.shape)\n print(\"y_test shape :\", y_test.shape)\n \n return X_train, y_train, X_test, y_test","def mnist_training():\n mndata = MNIST(MNIST_PATH)\n train_ims, train_labels = mndata.load_training()\n train_X = np.array(train_ims).T\n train_y = np.array(train_labels).T\n return train_X, train_y","def load_batch(filename: str) -> Tuple[ndarray, ndarray, ndarray]:\n dataDict = unpickle(filename)\n print(\"1\", dataDict[b\"data\"][1, :])\n X = (dataDict[b\"data\"] / 255).T\n print(\"2\", X[:, 1])\n y = np.array(dataDict[b\"labels\"])\n Y = np.eye(10)[y].T\n return X, Y, y","def load_image_as_np(filename):\n try:\n img = image.load_img(filename, color_mode='grayscale') # for newer versions, use \"color_mode='grayscale'\"; For older versions, use \"grayscale=True\"\n return np.atleast_3d(img)\n except Exception as error:\n logging.error(traceback.format_exc())","def bin2matrix(filename):\n filename = realpath(filename)\n Xy = np.load(filename)\n if len(Xy.shape) == 3:\n X = Xy[:,:,:-1]\n y = Xy[:,:,-1]\n else:\n X = Xy[:,:-1]\n y = Xy[:,-1]\n return X, y","def load_data(path='alex_mnist_data.npz'):\n with np.load(path, allow_pickle=True) as f:\n x_train, y_train = f['alex_train_data'], f['alex_train_label']\n x_test, y_test = f['alex_test_data'], f['alex_test_label']\n return (x_train, y_train),(x_test, y_test)","def readDataFromFile():\n image_size = 28 # each image is 28x28\n\n num_images = 60000 # there are 60k images\n with gzip.open(r'train-images-idx3-ubyte.gz', 'r') as f: # 60k train & valid\n f.read(16) # reading by 16-byte double\n buffer_Train_Images = f.read(image_size * image_size * num_images)\n f.close()\n data_Train_Images = np.frombuffer(buffer_Train_Images, dtype=np.uint8).astype(\n np.int32) # translating into 0 to 255\n data_Train_Images = data_Train_Images.reshape(num_images,\n image_size * image_size) # Data = 60k x 28 x 28 with 1 value in it\n\n with gzip.open('train-labels-idx1-ubyte.gz', 'r') as f: # 60k train & valid - labels\n f.read(8) # reading by 16-byte double\n buffer_Train_Labels = f.read(num_images)\n data_Train_Labels = np.frombuffer(buffer_Train_Labels, dtype=np.uint8).astype(\n np.int32) # translating into 0 to 255\n\n num_images = 10000 # there are 10k images\n with gzip.open('t10k-images-idx3-ubyte.gz', 'r') as f: # 10k tests\n f.read(16) # reading by 16-byte double\n buffer_Test_Image = f.read(image_size * image_size * num_images)\n data_Test_Image = np.frombuffer(buffer_Test_Image, dtype=np.uint8).astype(\n np.uint8) # translating into 0 to 255\n data_Test_Image = data_Test_Image.reshape(num_images, image_size * image_size) # Data = 60k x 28 x 28 with\n\n with gzip.open('t10k-labels-idx1-ubyte.gz', 'r') as f: # 10k tests - lbles\n f.read(8) # reading by 16-byte double\n buffer_Test_Label = f.read(num_images)\n data_Test_Labels = np.frombuffer(buffer_Test_Label, dtype=np.uint8).astype(\n np.int32) # translating into 0 to 255\n\n return data_Train_Images, data_Train_Labels, data_Test_Image, data_Test_Labels","def load_dataset(ipc = 20000):\n files = os.listdir(\"..\\\\data\")\n ind = 0\n xs = []\n ys = []\n classNames = []\n for file in files:\n fileSplit = file.split('.')\n print('--Loading ' + fileSplit[0][18:] + ' data.')\n classNames.append(fileSplit[0][18:])\n x = np.load(\"..\\\\data\\\\\" + file)\n x = x.astype('float32')/255\n xs.append(x[0:ipc, :])\n y = np.array([float(ind) for i in range(ipc)])\n ys.append(y.reshape(ipc, 1))\n ind += 1\n\n xs = np.array(xs)\n ys = np.array(ys)\n xs = xs.reshape(xs.shape[0]*xs.shape[1], xs.shape[2])\n ys = ys.reshape(ys.shape[0]*ys.shape[1], ys.shape[2])\n return xs, ys, classNames","def load(filename):\n lines = [l.strip('\\r\\n ') for l in open(filename, 'r').readlines()]\n lines = [l for l in lines if l != '']\n dims = [re.split(r'\\s+', l) for l in lines]\n f = np.array([[float(f) for f in d] for d in dims])\n return f","def load_data(filename):\n assert os.path.exists(filename)==True\n dat = scipy.io.loadmat(filename)\n inputs = dat['inputs']\n #print len(inputs)\n targets = dat['targets']\n #print len(targets)\n assert len(inputs)==len(targets)\n\n global alldata\n global indim \n global outdim\n\n indim = len(inputs[0])\n outdim = 1\n #print indim\n alldata = ClassificationDataSet(indim, outdim, nb_classes = 8)\n alldata.setField('input',inputs)\n alldata.setField('target',targets)\n\n assert len(alldata['input'])==len(alldata['target'])\n print type(alldata)","def load_data():\n f = gzip.open('mnist.pkl.gz', 'rb')\n training_data, validation_data, test_data = pickle.load(f, encoding=\"latin1\")\n f.close()\n \n X_train = [np.reshape(x, (784, 1)) for x in training_data[0]]\n Y_train = [vectorized_result(y) for y in training_data[1]]\n \n X_validation = [np.reshape(x, (784, 1)) for x in validation_data[0]]\n Y_validation = validation_data[1]\n \n X_test = [np.reshape(x, (784, 1)) for x in test_data[0]]\n Y_test = test_data[1]\n \n return (X_train, Y_train, X_validation, Y_validation, X_test, Y_test)","def load_X():\n x1 = np.asarray([-1, -1, 1, -1, 1, -1, -1, 1]).reshape((1, 8))\n x2 = np.asarray([-1, -1, -1, -1, -1, 1, -1, -1]).reshape((1, 8))\n x3 = np.asarray([-1, 1, 1, -1, -1, 1, -1, 1]).reshape((1, 8))\n X = np.vstack([x1, x2, x3])\n\n return X","def load_mnist (images_fn_gz, labels_fn_gz, digits=None, path=None, asbytes=False, selection=None, return_labels=True, return_indices=False):\n\n # We can skip the labels file only if digits aren't specified and labels aren't asked for\n if return_labels or digits is not None:\n flbl = gzip.open (labels_fn_gz, 'rb')\n magic_nr, size = struct.unpack(\">II\", flbl.read(8))\n labels_raw = pyarray(\"b\", flbl.read())\n flbl.close()\n\n fimg = gzip.open(images_fn_gz, 'rb')\n magic_nr, size, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n images_raw = pyarray(\"B\", fimg.read())\n fimg.close()\n\n if digits:\n indices = [k for k in range(size) if labels_raw[k] in digits]\n else:\n indices = range(size)\n\n if selection:\n indices = indices[selection] \n N = len(indices)\n\n images = zeros((N, rows, cols), dtype=uint8)\n\n if return_labels:\n labels = zeros((N), dtype=int8)\n for i, index in enumerate(indices):\n images[i] = array(images_raw[ indices[i]*rows*cols : (indices[i]+1)*rows*cols ]).reshape((rows, cols))\n if return_labels:\n labels[i] = labels_raw[indices[i]]\n\n if not asbytes:\n images = images.astype(float)/255.0\n\n ret = (images,)\n if return_labels:\n ret += (labels,)\n if return_indices:\n ret += (indices,)\n if len(ret) == 1:\n return ret[0] # Don't return a tuple of one\n else:\n return ret","def load_data():\n\n \"\"\"The ``training_data`` is returned as a tuple with two entries.\n The first entry contains the actual training images. This is a\n numpy ndarray with 50,000 entries. Each entry is, in turn, a\n numpy ndarray with 784 values, representing the 28 * 28 = 784\n pixels in a single MNIST image.\"\"\"\n\n \"\"\"The second entry in the ``training_data`` tuple is a numpy ndarray\n containing 50,000 entries. Those entries are just the digit\n values (0...9) for the corresponding images contained in the first\n entry of the tuple.\"\"\"\n\n \"\"\"The ``validation_data`` and ``test_data`` are similar, except\n each contains only 10,000 images.\"\"\"\n f = gzip.open('MNIST/data/mnist.pkl.gz', 'rb')\n training_data, validation_data, test_data = Pickle.load(f, encoding='bytes'\n )\n f.close()\n return (training_data, validation_data, test_data)","def imagesMatrix(path,imageSize = 10304,byteorder = '>'):\n listing = os.listdir(path)\n listing.sort()\n count = 0\n docFiles = []\n for infile in listing:\n count = count + 1\n docFiles.append(infile)\n matrix = np.zeros((imageSize,count))\n for i in range(len(listing)):\n matrix[:,i]=np.asarray(read_pgm(join(path,listing[i]),byteorder)).reshape(-1)\n return matrix,listing","def load_rgb_image_as_3d_array(filename):\n image = np.asarray(Image.open(filename))\n image_shape = image.shape\n image_dim = len(image_shape)\n assert(image_dim == 2 or image_dim == 3)\n if(image_dim == 2):\n image = np.expand_dims(image, axis = 0)\n else:\n # transpose rgb image from [H, W, C] to [C, H, W]\n assert(image_shape[2] == 3 or image_shape[2] == 4)\n if(image_shape[2] == 4):\n image = image[:, :, range(3)]\n image = np.transpose(image, axes = [2, 0, 1])\n output = {}\n output['data_array'] = image\n output['origin'] = (0, 0)\n output['spacing'] = (1.0, 1.0)\n output['direction'] = 0\n return output","def load_CIFAR_batch(filename):\n with open(filename, 'rb')as f:\n datadict = p.load(f, encoding='iso-8859-1')\n X = datadict['data']\n Y = datadict['labels']\n X = X.reshape(10000, 3, 32, 32)\n Y = np.array(Y)\n return X, Y","def _images(path):\r\n with gzip.open(path) as f:\r\n # First 16 bytes are magic_number, n_imgs, n_rows, n_cols\r\n pixels = np.frombuffer(f.read(), 'B', offset=16)\r\n return pixels.reshape(-1, 784).astype('float32') / 255","def load_data_in_folder(self):\n print('loading files in data folder')\n n = len(self.filenames)\n idx_max = n // self.batch_size\n for idx in range(0, idx_max-1):\n data = []\n for f in self.filenames[idx:idx+64]:\n img = cv2.imread(f, int(self.color))\n if not self.color:\n img = np.expand_dims(img, axis=-1)\n data.append(img)\n data = np.array(data)\n data = data.astype('float32')\n data = (data - 127.5)/127.5\n np.save(op.join(self.data_path, str(idx)), data)\n # TODO last batch ?\n self.data_filenames = sorted(glob(op.join(self.data_path, '*.npy')))","def tns3d_batch(self, tensorlist, output_resolution):\n dimensions = 3\n formats = 5\n filenames = []\n\n ## record the file dir\n filelist = open(tensorlist)\n for line in filelist:\n list_seg = line.split()\n filenames.append(list_seg[-1])\n filelist.close()\n\n ## get the batch data of 3d tensors and their basic info\n batch_size = len(filenames)\n tensor_batch = np.zeros((batch_size, dimensions, output_resolution, output_resolution), dtype='int32')\n for findex in range(0, batch_size):\n img = self.tns3d_Sample(filenames[findex], RES)\n tensor_batch[findex, :, :, :] = img\n print('**** the {}th tns flattening sampled finished ({} in total) ****'.format(findex, batch_size))\n return tensor_batch","def mnist(path):\n with open(path, 'r') as f:\n for line in f:\n data = line.strip().split(',')\n\n # Label is a vector with one element per class\n label = [0.0] * 10\n label[int(data[0])] = 1.0 \n\n # The data are images of 28x28 pixels\n image_array = np.asfarray(data[1:]).reshape((28, 28))\n # Normalize the pictures \n image_array = image_array / 255.0\n\n #plt.imshow(image_array, cmap='Greys', interpolation='None')\n yield (image_array, label)","def load_data(path,size, scale = True):\n images = os.listdir(path)\n images.sort()\n\n X = []\n for i, img in enumerate(images):\n photo = plt.imread(os.path.join(path,img))\n if size:\n photo = tf.image.resize(photo, (size, size))\n X.append(photo)\n \n X = np.array(X)\n if scale:\n X = X/X.max() \n return X","def load_MNIST_data():\n mnist = input_data.read_data_sets('data', one_hot=True)\n return {'train': mnist.train.images,\n 'validation': mnist.validation.images,\n 'test': mnist.test.images}","def load_data(flatten=True):\n if flatten:\n reshape = _flatten\n else:\n reshape = _square\n\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n x_train = reshape(x_train)\n x_test = reshape(x_test)\n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n x_train /= 255\n x_test /= 255\n y_train = to_categorical(y_train, 10)\n y_test = to_categorical(y_test, 10)\n return x_train, y_train, x_test, y_test","def _load_dataset(self, path):\n\t\twhile True:\n\t\t\t\n\t\t\ttry:\n\t\t\t\tX_test = np.load(\"data/X_test.npy\")\n\t\t\t\tY_test = np.load(\"data/Y_test.npy\")\n\t\t\t\tbreak\n\n\t\t\texcept FileNotFoundError:\n\n\t\t\t\tX_test = np.zeros((10000,64,64,3))\n\t\t\t\tY_test = []\n\n\t\t\t\t\n\t\t\t\twith open(path, 'rb') as fo:\n\t\t\t\t\ttemp_element = pickle.load(fo, encoding='bytes')\n\n\t\t\t\ttemp_data = temp_element[b'data']\n\t\t\t\tY_test.extend(temp_element[b'labels'])\n\n\t\t\t\tfor j in range(10000):\n\t\t\t\t\tX_test[j] = self._reshape(temp_data[j])\n\n\t\t\t\tY_test = np.eye(10)[np.array(Y_test)]\n\t\t\t\t\n\t\t\t\tnp.save(\"./data/X_test\", X_test)\n\t\t\t\tnp.save(\"./data/Y_test\", Y_test)\n\n\t\t\t\tbreak\n\n\n\t\treturn X_test, Y_test","def load_data():\n data = gzip.open(\"mnist.pkl.gz\", \"rb\")\n train_set, valid_set, test_set = cPickle.load(data)\n data.close()\n\n # Combine validation and train folds to recreate the master 60k set.\n new_images = numpy.concatenate((train_set[0], valid_set[0]))\n new_labels = numpy.concatenate((train_set[1], valid_set[1]))\n\n train_set = (new_images, new_labels)\n \n return (train_set, test_set)","def loadtrainData():\n train_x = []\n train_y = []\n fileIn = open(PATH + 'traindata_Subtask4.txt')\n for line in fileIn.readlines():\n lineArr = line.strip().split()\n train_x.append([float(lineArr[i]) for i in range(len(lineArr) - 1)])\n train_y.append(int(lineArr[-1]))\n return np.mat(train_x), np.mat(train_y).transpose()","def mnist(path):\n with open(path, 'r') as f:\n for line in f:\n data = line.strip().split(',')\n\n # Label is a vector with one element per class\n label = [0.01] * 10\n label[int(data[0])] = 0.99\n\n # The data are images of 28x28 pixels\n #image_array = np.asfarray(data[1:]).reshape((28, 28))\n image_array = np.asfarray(data[1:])\n # Normalize all values between [0.01, 1.0]\n image_array = ((image_array) / 255.0 * 0.99) + 0.01\n\n #plt.imshow(image_array, cmap='Greys', interpolation='None')\n yield (image_array, label)","def load_tmp_atlas(filename):\n fbase, ext = osp.splitext(filename)\n fimg = None\n if osp.isfile(fbase+\".nii\"): fimg = fbase+\".nii\"\n if osp.isfile(fbase+\".nii.gz\"): fimg = fbase+\".nii.gz\" \n\n try:\n img = nib.load(fimg)\n except ValueError as e:\n print(\"error {0}, cannot find file {1} .nii or .nii.gz \".format(fbase, e.errno))\n\n fjson = None\n if osp.isfile(fbase+\".txt\"): fjson= fbase+\".txt\"\n if osp.isfile(fbase+\".json\"): fjson= fbase+\".json\"\n\n if fjson == None:\n warn(\"cannot find file %s .txt or .json\" % fbase)\n return None\n\n with open(fjson) as f:\n j_labels = json.load(f)\n\n a_labels = [label[1] for label in j_labels]\n \n return (img.get_data(), img.get_affine(), a_labels)","def load_mnist(data_path=None, data_home=None, subsets=None):\n if data_path is None:\n data_path = _utils.validate_data_home(data_home)\n data_path /= 'mnist.npz'\n url = 'https://s3.amazonaws.com/img-datasets/mnist.npz'\n _ds_utils.get_file(data_path, url)\n \n if subsets is None:\n subsets = ['training', 'test']\n subsets = _ds_utils.validate_tvt(subsets, return_list=True)\n X, Y = [], []\n with np.load(data_path) as f:\n for subset in subsets:\n if subset == 'training':\n X.append(f['x_train'])\n Y.append(f['y_train'])\n elif subset == 'test':\n X.append(f['x_test'])\n Y.append(f['y_test'])\n else:\n raise ValueError('Subset:', subset, ' not supported.')\n return np.concatenate(X), np.concatenate(Y)","def read_image(images_root):\n im_array = np.load(images_root)\n return im_array","def load_CIFAR_batch(filename):\r\n with open(filename, 'rb')as f:\r\n datadict = p.load(f)\r\n \r\n X = datadict['data']\r\n Y = datadict['labels']\r\n \r\n print X.shape\r\n X = X.reshape(X.shape[0], SHAPE[0], SHAPE[1], SHAPE[2])\r\n Y = np.array(Y)\r\n return X, Y","def load_image(file_name):\n if not osp.exists(file_name):\n print('{} not exist'.format(file_name))\n return\n image = np.asarray(io.imread(file_name))\n if len(image.shape)==3 and image.shape[2]>3:\n image = image[:, :, :3]\n # print(image.shape) #should be (x, x, 3)\n return image","def tns3d_batch(self, tensorlist, output_resolution):\n dimensions = 3\n formats = 5\n filenames = []\n\n ## record the file dir and corresponding labels\n filelist = open(tensorlist)\n for line in filelist:\n list_seg = line.split()\n filenames.append(list_seg[-1])\n filelist.close()\n\n ## get the batch data of 3d tensors and their basic info\n batch_size = len(filenames)\n tensor_batch = np.zeros((batch_size, dimensions, output_resolution, output_resolution), dtype='int32')\n for findex in range(0, batch_size):\n mapImgs = self.tns3d_Sample(filenames[findex], RES)\n for i in range(0, 3):\n tensor_batch[findex, i, :, :] = mapImgs[i]\n print('**** the {}th tns mapping sampled finished ({} in total) ****'.format(findex, batch_size))\n return tensor_batch","def read_and_normalize(im_path):\n im3d = io.imread(im_path, plugin=\"tifffile\").astype(np.uint8)\n sh = np.shape(im3d)\n if len(sh) > 3:\n im3d = im3d.reshape(sh[1], sh[2], sh[3])\n return im3d / 255","def image_load(path) -> numpy.ndarray:\n # file\n na = numpy.array(Image.open(path))\n # fix shape\n na = numpy.moveaxis(na, [2,0,1], [0,1,2])\n # shape is now (3,h,w), add 1\n na = na.reshape(1,3,na.shape[1],na.shape[2])\n # change type\n na = na.astype(\"float32\") / 255.0\n return na","def create_MNIST_arrays(\n alb_transforms=None, aug_number=1, target_dir=\".\", batch_size=256, num_workers=1\n):\n\n dataset_alb = AlbMNIST(os.path.join(target_dir, \"MNIST\"), download=True)\n dataloader = DataLoader(\n dataset_alb, batch_size=len(dataset_alb), shuffle=False, num_workers=num_workers\n )\n\n print(\"Fetching original dataset...\", end=\" \")\n originals_array = next(iter(dataloader))[\"original\"].numpy()\n labels_array = next(iter(dataloader))[\"label\"].numpy()\n print(\"Done!\")\n\n dataset_alb.set_transofrms(alb_transforms)\n aug_arrays = []\n dataloader = DataLoader(\n dataset_alb, batch_size=batch_size, shuffle=False, num_workers=num_workers\n )\n\n for aug_idx in range(aug_number):\n print(\"Making aug #%i\" % aug_idx)\n aug_arrays.append(compose_array_from_dataloader(dataloader, key=\"augmented\"))\n\n return originals_array, labels_array, aug_arrays","def load_images(input_dir=\"/tmp/mapswipe/project-1\", n_images=2000, seed=1):\n class_map = {1: \"1\", 0: \"5\"}\n output_dir = \"/Users/thead/git/dreamview/data/\"\n\n X_ = []\n y_ = []\n for new_klass in class_map:\n images = []\n for klass in class_map[new_klass]:\n for img in glob.glob(input_dir + \"/%s/*/*/*/aerial.jpeg\" % klass):\n if os.stat(img).st_size > 0:\n images.append(img)\n\n images = shuffle(images, random_state=seed+42+new_klass)\n images = images[:n_images]\n X_ += images\n y_ += [new_klass] * len(images)\n\n # XXX deduce array size from an actual image\n X = np.zeros((2*n_images, 256*256), dtype=np.ubyte)\n y = np.zeros(2*n_images, dtype=np.int)\n\n for n, (img_path, klass) in enumerate(zip(X_, y_)):\n # the order of these OPs has been chosen on purpose, don't mess\n # without checking what happens\n img = imread(img_path)\n img = equalize_adapthist(img)\n img = rgb2grey(img)\n img = img_as_ubyte(img)\n\n if not n % 10:\n fname = os.path.split(img_path)[:-1]\n fname = os.path.join(*fname, \"aerial-processed.jpeg\")\n imsave(fname, img)\n\n X[n,:] = img.ravel()\n y[n] = klass\n\n return X, y","def load_CIFAR_batch(filename):\r\n with open(filename, 'rb') as f:\r\n datadict = pickle.load(f, encoding='latin1')\r\n X = datadict['data']\r\n Y = datadict['labels']\r\n X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype(\"float\")\r\n Y = np.array(Y)\r\n return X, Y","def load_CIFAR_batch(filename):\n with open(filename, 'rb')as f:\n # datadict = p.load(f)\n datadict = pickle.load(f, encoding = 'bytes')\n X = datadict[b'data']\n Y = datadict[b'labels']\n X = X.reshape(10000, 3, 32, 32)\n Y = np.array(Y)\n return X, Y","def load_images(mraw, h, w, N, bit=16, roll_axis=True):\n\n if int(bit) == 16:\n images = np.memmap(mraw, dtype=np.uint16, mode='r', shape=(N, h, w))\n elif int(bit) == 8:\n images = np.memmap(mraw, dtype=np.uint8, mode='r', shape=(N, h, w))\n elif int(bit) == 12:\n warnings.warn(\"12bit images will be loaded into memory!\")\n #images = _read_uint12_video(mraw, (N, h, w))\n images = _read_uint12_video_prec(mraw, (N, h, w))\n else:\n raise Exception(f\"Unsupported bit depth: {bit}\")\n\n\n #images=np.fromfile(mraw, dtype=np.uint16, count=h * w * N).reshape(N, h, w) # about a 1/3 slower than memmap when loading to RAM. Also memmap doesn't need to read to RAM but can read from disc when needed.\n if roll_axis:\n return np.rollaxis(images, 0, 3)\n else:\n return images","def load_images(filelist):\n # pixel value range 0-255\n if not isinstance(filelist, list):\n im = Image.open(filelist).convert('L')\n return np.array(im).reshape(1, im.size[1], im.size[0], 1)\n data = []\n for file in filelist:\n im = Image.open(file).convert('L')\n data.append(np.array(im).reshape(1, im.size[1], im.size[0], 1))\n return data","def read_local(path):\n files = os.listdir(path)\n imgs = []\n for f in files:\n if f.endswith(\".tiff\") or f.endswith(\".tif\"):\n img = Image.open(os.path.join(path, f))\n imgs.append(np.array(img))\n return imgs","def load_CIFAR_batch(filename):\n with open(filename, 'rb') as f:\n datadict = pickle.load(f, encoding='latin1')\n X = datadict['data']\n Y = datadict['labels']\n X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype(\"float64\")\n Y = np.array(Y)\n return X, Y"],"string":"[\n \"def load_mnist(kind='train'):\\r\\n with open('%s-labels.idx1-ubyte' % kind, 'rb') as lbpath:\\r\\n magic, n = struct.unpack('>II', lbpath.read(8))\\r\\n labels = np.fromfile(lbpath, dtype=np.uint8)\\r\\n\\r\\n with open('%s-images.idx3-ubyte' % kind, 'rb') as imgpath:\\r\\n magic, num, rows, cols = struct.unpack('>IIII', imgpath.read(16))\\r\\n images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)\\r\\n\\r\\n return images, labels\",\n \"def load_mnist(path='mnist/mnist.npz'):\\n\\n with np.load(path) as f:\\n x_train, y_train = f['x_train'], f['y_train']\\n x_test, y_test = f['x_test'], f['y_test']\\n x_train = x_train.astype(np.float32) / 255.\\n y_train = y_train.astype(np.int32)\\n x_test = x_test.astype(np.float32) / 255.\\n y_test = y_test.astype(np.int32)\\n \\n return (x_train, y_train), (x_test, y_test)\",\n \"def load_mnist(path, kind='train'):\\n labels_path = os.path.join(path,'%s-labels-idx1-ubyte.gz'% kind)\\n\\n images_path = os.path.join(path,'%s-images-idx3-ubyte.gz'% kind)\\n\\n with gzip.open(labels_path, 'rb') as lbpath:\\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8,offset=8)\\n\\n with gzip.open(images_path, 'rb') as imgpath:\\n images = np.frombuffer(imgpath.read(), dtype=np.uint8,offset=16).reshape(len(labels), 784)\\n\\n print(\\\"Dataset Loaded\\\")\\n \\n return images, labels\",\n \"def load_mnist(path, kind='train'):\\n\\tlabels_path = os.path.join(path,'%s-labels.idx1-ubyte'%kind)\\n\\timages_path = os.path.join(path,'%s-images.idx3-ubyte'%kind)\\n\\t\\n\\twith open(labels_path, 'rb') as lbpath:\\n\\t\\tmagic, n = struct.unpack('>II', lbpath.read(8))\\n\\t\\tlabels = np.fromfile(lbpath, dtype=np.uint8)\\n\\t\\t\\n\\twith open(images_path, 'rb') as imgpath:\\n\\t\\tmagic, num, row, cols = struct.unpack('>IIII', imgpath.read(16))\\n\\t\\timages = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)\\n\\t\\n\\treturn images, labels\",\n \"def load_mnist(path, kind='train'):\\n '''ref: http://yann.lecun.com/exdb/mnist/ '''\\n ''' each hand write is 28x28 = 784, a 1 dim vector'''\\n labels_path = os.path.join(path,\\n '%s-labels-idx1-ubyte'\\n % kind)\\n images_path = os.path.join(path,\\n '%s-images-idx3-ubyte'\\n % kind)\\n\\n # check the offical doc to know how to extract the content\\n '''\\n [offset] [type] [value] [description]\\n 0000 32 bit integer 0x00000801(2049) magic number (MSB first)\\n 0004 32 bit integer 60000 number of items\\n 0008 unsigned byte ?? label\\n 0009 unsigned byte ?? label\\n ........\\n xxxx unsigned byte ?? label\\n The labels values are 0 to 9.\\n '''\\n with open(labels_path, 'rb') as lbpath:\\n magic, n = struct.unpack('>II',\\n lbpath.read(8))\\n labels = np.fromfile(lbpath,\\n dtype=np.uint8)\\n\\n '''\\n [offset] [type] [value] [description]\\n 0000 32 bit integer 0x00000803(2051) magic number\\n 0004 32 bit integer 60000 number of images\\n 0008 32 bit integer 28 number of rows\\n 0012 32 bit integer 28 number of columns\\n 0016 unsigned byte ?? pixel\\n 0017 unsigned byte ?? pixel\\n ........\\n xxxx unsigned byte ?? pixel\\n Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).\\n '''\\n with open(images_path, 'rb') as imgpath:\\n magic, num, rows, cols = struct.unpack(\\\">IIII\\\",\\n imgpath.read(16))\\n ''' each hand write is 28x28 = 784, a 1 dim vector'''\\n images = np.fromfile(imgpath,\\n dtype=np.uint8).reshape(len(labels), 784)\\n\\n return images, labels\",\n \"def load_mnist(path, kind='train'):\\n labels_path = os.path.join(path,\\n '%s-labels-idx1-ubyte.gz'\\n % kind)\\n images_path = os.path.join(path,\\n '%s-images-idx3-ubyte.gz'\\n % kind)\\n\\n with gzip.open(labels_path, 'rb') as lbpath:\\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8,\\n offset=8)\\n\\n with gzip.open(images_path, 'rb') as imgpath:\\n images = np.frombuffer(imgpath.read(), dtype=np.uint8,\\n offset=16).reshape(len(labels), 784)\\n\\n return images, labels\",\n \"def load_mnist(path, kind='train'):\\n\\n labels_path = os.path.join(path,\\n '%s-labels-idx1-ubyte.gz'\\n % kind)\\n images_path = os.path.join(path,\\n '%s-images-idx3-ubyte.gz'\\n % kind)\\n\\n with gzip.open(labels_path, 'rb') as lbpath:\\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8,\\n offset=8)\\n\\n with gzip.open(images_path, 'rb') as imgpath:\\n images = np.frombuffer(imgpath.read(), dtype=np.uint8,\\n offset=16).reshape(len(labels), 784)\\n\\n return images, labels\",\n \"def load_mnist(path, kind='train'):\\n labels_path = os.path.join(path,\\n '%s-labels-idx1-ubyte' % kind)\\n images_path = os.path.join(path,\\n '%s-images-idx3-ubyte' % kind)\\n \\n with open(labels_path, 'rb') as lbpath:\\n magic, n = struct.unpack('>II',\\n lbpath.read(8))\\n labels = np.fromfile(lbpath,\\n dtype=np.uint8)\\n \\n with open(images_path, 'rb') as imgpath:\\n magic, num, rows, cols = struct.unpack(\\\">IIII\\\",\\n imgpath.read(16))\\n images = np.fromfile(imgpath,\\n dtype=np.uint8).reshape(len(labels), 784)\\n \\n return images, labels\",\n \"def load_mnist(path, kind = 'train'):\\n label_path = os.path.join(path, '%s-labels-idx1-ubyte' % kind)\\n images_path = os.path.join(path, '%s-images-idx3-ubyte' % kind)\\n\\n\\n with open(label_path, 'rb') as lbpath:\\n magic, n = struct.unpack('>II', lbpath.read(8))\\n\\n labels = np.fromfile(lbpath, dtype= np.uint8)\\n\\n with open(images_path, 'rb') as imgpath:\\n magic, num, rows, cols = struct.unpack('>IIII', imgpath.read(16))\\n\\n images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels),784)\\n\\n\\n return images, labels\",\n \"def load_data():\\n dirname = os.path.join('datasets', 'fashion-mnist')\\n base = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'\\n files = [\\n 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',\\n 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'\\n ]\\n\\n paths = []\\n for fname in files:\\n paths.append(get_file(fname, origin=base + fname, cache_subdir=dirname))\\n\\n with gzip.open(paths[0], 'rb') as lbpath:\\n y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)\\n\\n with gzip.open(paths[1], 'rb') as imgpath:\\n x_train = np.frombuffer(\\n imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)\\n\\n with gzip.open(paths[2], 'rb') as lbpath:\\n y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)\\n\\n with gzip.open(paths[3], 'rb') as imgpath:\\n x_test = np.frombuffer(\\n imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)\\n\\n return (x_train, y_train), (x_test, y_test)\",\n \"def load_mnist(path, kind='train'):\\n labels_path = os.path.join(path,'{}-labels-idx1-ubyte'.format(kind))\\n images_path = os.path.join(path,'{}-images-idx3-ubyte'.format(kind))\\n with open(labels_path, 'rb') as lbpath:\\n magic, n = struct.unpack('>II',\\n lbpath.read(8))\\n labels = np.fromfile(lbpath,\\n dtype=np.uint8).reshape(n)\\n\\n with open(images_path, 'rb') as imgpath:\\n magic, num, rows, cols = struct.unpack('>IIII',\\n imgpath.read(16))\\n images = np.fromfile(imgpath,\\n dtype=np.uint8).reshape((num,1,rows,cols))\\n print(kind)\\n print(\\\"label num:\\\",n)\\n print(\\\"image num:\\\",num)\\n print(\\\"image rows:\\\",rows)\\n print(\\\"image cols:\\\",cols)\\n images = images/255\\n return images, labels\",\n \"def read_mnist_images(filename, dtype=None):\\n with gzip.open(filename, 'rb') as f:\\n magic, number, rows, cols = struct.unpack('>iiii', f.read(16))\\n if magic != MNIST_IMAGE_MAGIC:\\n raise ValueError(\\\"Wrong magic number reading MNIST image file\\\")\\n array = numpy.frombuffer(f.read(), dtype='uint8')\\n array = array.reshape((number, 1, rows, cols))\\n if dtype:\\n dtype = numpy.dtype(dtype)\\n\\n if dtype.kind == 'b':\\n # If the user wants Booleans, threshold at half the range.\\n array = array >= 128\\n elif dtype.kind == 'f':\\n # Otherwise, just convert.\\n array = array.astype(dtype)\\n array /= 255.\\n else:\\n raise ValueError(\\\"Unknown dtype to convert MNIST to\\\")\\n return array\",\n \"def extract_images(filename):\\n\\tprint('Extracting', filename)\\n\\twith gzip.open(filename) as bytestream:\\n\\t\\tmagic = _read32(bytestream)\\n\\t\\tif magic != 2051:\\n\\t\\t\\traise ValueError('Invalid magic number %d in MNIST image file: %s' %(magic, filename))\\n\\t\\tnum_images = _read32(bytestream)\\n\\t\\trows = _read32(bytestream)\\n\\t\\tcols = _read32(bytestream)\\n\\t\\tbuf = bytestream.read(rows * cols * num_images)\\n\\t\\tdata = numpy.frombuffer(buf, dtype=numpy.uint8)\\n\\t\\tdata = data.reshape(num_images, rows, cols, 1)\\n\\t\\treturn data\",\n \"def _extract_images(self, filename):\\n log.info('Extracting', filename)\\n with gzip.open(filename) as bytestream:\\n magic = self._read32(bytestream)\\n if magic != 2051:\\n raise ValueError(\\n 'Invalid magic number %d in MNIST image file: %s' %\\n (magic, filename))\\n num_images = self._read32(bytestream)\\n rows = self._read32(bytestream)\\n cols = self._read32(bytestream)\\n buf = bytestream.read(rows * cols * num_images)\\n data = np.frombuffer(buf, dtype=np.uint8)\\n data = data.reshape(num_images, rows, cols, 1)\\n return data\",\n \"def load_data():\\n prefix = 'mnist_data/'\\n train_data = np.load(prefix + 'mnist_train_images.npy')\\n train_labels = np.load(prefix + 'mnist_train_labels.npy')\\n val_data = np.load(prefix + 'mnist_validation_images.npy')\\n val_labels = np.load(prefix + 'mnist_validation_labels.npy')\\n test_data = np.load(prefix + 'mnist_test_images.npy')\\n test_labels = np.load(prefix + 'mnist_test_labels.npy')\\n assert train_data.shape == (55000, 784) and train_labels.shape == (55000, 10)\\n assert val_data.shape == (5000, 784) and val_labels.shape == (5000, 10)\\n assert test_data.shape == (10000, 784) and test_labels.shape == (10000, 10)\\n return train_data, train_labels, val_data, val_labels, test_data, test_labels\",\n \"def extract_images(filename):\\n print('Extracting', filename)\\n with gzip.open(filename) as bytestream:\\n magic = _read32(bytestream)\\n if magic != 2051:\\n raise ValueError(\\n 'Invalid magic number %d in MNIST image file: %s' %\\n (magic, filename))\\n num_images = _read32(bytestream)\\n rows = _read32(bytestream)\\n cols = _read32(bytestream)\\n buf = bytestream.read(rows * cols * num_images)\\n data = np.frombuffer(buf, dtype=np.uint8)\\n data = data.reshape(num_images, rows, cols, 1)\\n return data\",\n \"def extract_images(filename):\\n print('Extracting', filename)\\n with gzip.open(filename) as bytestream:\\n magic = _read32(bytestream)\\n if magic != 2051:\\n raise ValueError(\\n 'Invalid magic number %d in MNIST image file: %s' %\\n (magic, filename))\\n num_images = _read32(bytestream)[0]\\n rows = _read32(bytestream)[0]\\n cols = _read32(bytestream)[0]\\n #print('check', magic, num_images, rows, cols, rows * cols * num_images)\\n buf = bytestream.read(rows * cols * num_images)\\n data = numpy.frombuffer(buf, dtype=numpy.uint8)\\n data = data.reshape(num_images, rows, cols, 1)\\n return data\",\n \"def load_data(m=5000, n=100, path='D:/file/vscode/py/data/mnist.npz'):\\r\\n f = np.load(path)\\r\\n x_train, y_train = f['x_train'], f['y_train']\\r\\n\\r\\n x_test, y_test = f['x_test'], f['y_test']\\r\\n\\r\\n f.close()\\r\\n return (x_train, y_train), (x_test, y_test)\",\n \"def extract_images(f):\\n with gzip.GzipFile(fileobj=f) as bytestream:\\n magic = _read32(bytestream)\\n if magic != 2051:\\n raise ValueError('Invalid magic number %d in MNIST image file: %s' %\\n (magic, f.name))\\n num_images = _read32(bytestream)\\n rows = _read32(bytestream)\\n cols = _read32(bytestream)\\n buf = bytestream.read(rows * cols * num_images)\\n data = numpy.frombuffer(buf, dtype=numpy.uint8)\\n data = data.reshape(num_images, rows, cols, 1)\\n return data\",\n \"def load_fashion_mnist(path, kind='train'):\\n labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind)\\n images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind)\\n with gzip.open(labels_path, 'rb') as lbpath:\\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8)\\n with gzip.open(images_path, 'rb') as imgpath:\\n images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784)\\n return images, labels\",\n \"def load_fmnist(path, kind='train'):\\n labels_path = os.path.join(path,\\n '%s-labels-idx1-ubyte.gz'\\n % kind)\\n images_path = os.path.join(path,\\n '%s-images-idx3-ubyte.gz'\\n % kind)\\n\\n with gzip.open(labels_path, 'rb') as lbpath:\\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8,\\n offset=8)\\n\\n with gzip.open(images_path, 'rb') as imgpath:\\n images = np.frombuffer(imgpath.read(), dtype=np.uint8,\\n offset=16).reshape(len(labels), 784)\\n\\n return images, labels\",\n \"def load_data():\\n\\n training_files_dir = \\\"digits/trainingDigits\\\"\\n training_files = os.listdir(training_files_dir)\\n file_num = len(training_files)\\n hw_labels = []\\n\\n training_mat = zeros((file_num, 32 * 32))\\n for i in xrange(file_num):\\n filename = training_files[i]\\n file_label = int((filename.split(\\\".\\\")[0]).split(\\\"_\\\")[0])\\n hw_labels.append(file_label)\\n training_mat[i, :] = img2vector(training_files_dir + '/' + filename)\\n\\n return training_mat, hw_labels\",\n \"def read_mnist_labels(filename):\\n with gzip.open(filename, 'rb') as f:\\n magic, _ = struct.unpack('>ii', f.read(8))\\n if magic != MNIST_LABEL_MAGIC:\\n raise ValueError(\\\"Wrong magic number reading MNIST label file\\\")\\n array = numpy.frombuffer(f.read(), dtype='uint8')\\n array = array.reshape(array.size, 1)\\n return array\",\n \"def load_images(filename='training_images'): \\n file_path = os.path.join(DATA_DIR, filename)\\n with open(file_path, 'rb') as f:\\n b = f.read() # hope ya get it all\\n\\n # grab the first four numbers ...\\n # fmt='>i' means big-endian int32\\n magic, n_images, n_rows, n_cols = (struct.unpack('>i', b[i*4:(i+1)*4]) for i in range(4))\\n\\n # i am a god-fearing man\\n assert magic[0] == 2051, \\\"bad magic number, what do?\\\"\\n\\n\\n # so i think you can use the standard libary's \\\"array\\\" for this, just\\n # because binary data of any sort is kinda dodgy, but this grabs 'the rest'\\n # format='B' means unsigned char === 'uint8', and apparently endianness doesn't matter\\n image_stream = array.array('B', b[16:])\\n\\n # so each 28*28 byte portion of image_stream is a flattened image. these two\\n # numpy.reshape calls get it into the desired shape for A. maybe could\\n # combine it into one call, idk. anyway, each flattened image appears as a\\n # row, and there is a row for each image.\\n image_first = numpy.reshape(image_stream, (n_images[0], n_rows[0], n_cols[0]))\\n images = image_first.reshape(n_images[0], n_rows[0]*n_cols[0])\\n\\n # convert to float in [0,1]\\n images = images.astype('f') / 255\\n\\n return images\",\n \"def MNIST_data():\\n\\n # Pobieramy macierze numpy z cyframi\\n # images[i,j,k] <=> piksel (j,k) z i-tego obrazka w zbiorze danych\\n images, labels = get_MNIST_dataset(range(10), \\\"training\\\") #pierwszy argument to\\n\\n # a) Ilosc przykladow i rozmiary danych\\n print \\\"Raw training data dimensions \\\", images.shape\\n print \\\"Labels dimensions \\\",labels.shape\\n\\n # b) Ile jest cyfr 2?\\n print \\\"Counting 2 in training dataset \\\",len(filter(lambda x: x == 2, labels))\\n\\n # c) Jaki jest sredni obrazek 2 ? (Usrednienie wszystkich macierzy ktore sa 2)\\n\\n #1. Pobierzmy wszystkie dwojki, fajny sposob indeksowania\\n print labels == 2\\n only_2 = images[labels == 2, :, :]\\n print \\\"Checking number of 2s \\\", only_2.shape\\n\\n #2. TODO: Usrednienie (matrix.mean moze byc przydatne)\\n\\n #3. TODO: narysowanie usrednionej cyfry (zobacz pl.imshow)\\n\\n # d) Ostatnie - przetworzmy ostatnia cyfre do 1 wymiarowego wektora\\n vectorized = np.reshape(images[-1], newshape=(images[-1].shape[0]*images[-1].shape[1]))\\n print \\\"Vectorized last digit \\\", vectorized\",\n \"def extract_images(f):\\n print('Extracting', f.name)\\n with gzip.GzipFile(fileobj=f) as bytestream:\\n magic = _read32(bytestream)\\n if magic != 2051:\\n raise ValueError('Invalid magic number %d in MNIST image file: %s' %(magic, f.name))\\n num_images = _read32(bytestream)\\n rows = _read32(bytestream)\\n cols = _read32(bytestream)\\n buf = bytestream.read(rows * cols * num_images)\\n data = np.frombuffer(buf, dtype=np.uint8)\\n data = data.reshape(num_images, rows, cols, 1)\\n return data\",\n \"def extract_images(filename,lx):\\n print('Extracting', filename,'aaaaaa')\\n \\n data=numpy.loadtxt(filename,dtype='int64')\\n dim=data.shape[0]\\n data=data.reshape(dim, lx, lx, 1) \\n # Convert shape from [num examples, rows, columns, depth]\\n # to [num examples, rows*columns] (assuming depth == 1)\\n data = data.reshape(data.shape[0],\\n data.shape[1] * data.shape[2])\\n # Convert from [0, 255] -> [0.0, 1.0].\\n data = data.astype(numpy.float64)\\n # images = numpy.multiply(images, 1.0 / 255.0) # commented since it is ising variables\\n data = numpy.multiply(data, 1.0 ) # multiply by one, instead\\n print(data.shape)\\n return data\",\n \"def extract_images(f):\\n print('Extracting', f.name)\\n with gzip.GzipFile(fileobj=f) as bytestream:\\n magic = _read32(bytestream)\\n if magic != 2051:\\n raise ValueError('Invalid magic number %d in MNIST image file: %s' %\\n (magic, f.name))\\n num_images = _read32(bytestream)\\n rows = _read32(bytestream)\\n cols = _read32(bytestream)\\n buf = bytestream.read(rows * cols * num_images)\\n data = np.frombuffer(buf, dtype=np.uint8)\\n data = data.reshape(num_images, rows, cols, 1)\\n return data\",\n \"def load_images(folder_path, num_images):\\n imgs = np.zeros(shape=[num_images, 400, 400, 3])\\n for i in range(1, num_images + 1):\\n image_name = \\\"satImage_%.3d\\\" % i\\n image_path = folder_path + image_name + \\\".png\\\"\\n if os.path.isfile(image_path):\\n print('Loading ' + image_path)\\n img = mpimg.imread(image_path)\\n\\n #imgs[i - 1] = np.asarray(img).reshape(400, 400, 3)\\n imgs[i - 1] = img.reshape(400, 400, 3)\\n else:\\n print('File ' + image_path + ' does not exist')\\n return imgs\",\n \"def extract_images(f):\\n print('Extracting', f.name)\\n with gzip.GzipFile(fileobj=f) as bytestream:\\n magic = _read32(bytestream)\\n if magic != 2051:\\n raise ValueError('Invalid magic number %d in MNIST image file: %s' %\\n (magic, f.name))\\n num_images = _read32(bytestream)\\n rows = _read32(bytestream)\\n cols = _read32(bytestream)\\n buf = bytestream.read(rows * cols * num_images)\\n data = np.frombuffer(buf, dtype=np.uint8)\\n data = data.reshape(num_images, rows, cols, 1)\\n return data\",\n \"def extract_images(f):\\n\\tprint('Extracting', f.name)\\n\\twith gzip.GzipFile(fileobj=f) as bytestream:\\n\\t\\tmagic = _read32(bytestream)\\n\\t\\tif magic != 2051:\\n\\t\\t\\traise ValueError('Invalid magic number %d in MNIST image file: %s' %\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t (magic, f.name))\\n\\t\\tnum_images = _read32(bytestream)\\n\\t\\trows = _read32(bytestream)\\n\\t\\tcols = _read32(bytestream)\\n\\t\\tbuf = bytestream.read(rows * cols * num_images)\\n\\t\\tdata = numpy.frombuffer(buf, dtype=numpy.uint8)\\n\\t\\tdata = data.reshape(num_images, rows, cols, 1)\\n\\t\\treturn data\",\n \"def as_mnist(filename, imwidth):\\n\\n images = []\\n labels = []\\n \\n if filename.find(\\\"devel\\\") != -1:\\n print(\\\"we're working with the development set: \\\" + filename)\\n\\n for cls, data in enumerate(load(filename)):\\n for example in data:\\n labels.append(cls)\\n image = numpy.zeros(shape=(imwidth, imwidth), dtype='uint8')\\n for (x, y) in example:\\n x_ = int(round(imwidth * x))\\n y_ = int(round(1-(imwidth * y)))\\n image[y_, x_] = 255\\n images.append(image.flatten())\\n\\n return numpy.vstack(images).T.copy(), numpy.array(labels)\",\n \"def extract_images(f):\\n print('Extracting', f.name)\\n with gzip.GzipFile(fileobj=f) as bytestream:\\n magic = _read32(bytestream)\\n if magic != 2051:\\n raise ValueError('Invalid magic number %d in MNIST image file: %s' %\\n (magic, f.name))\\n num_images = _read32(bytestream)\\n rows = _read32(bytestream)\\n cols = _read32(bytestream)\\n buf = bytestream.read(rows * cols * num_images)\\n data = numpy.frombuffer(buf, dtype=numpy.uint8)\\n data = data.reshape(num_images, rows, cols, 1)\\n return data\",\n \"def load_train_small():\\n with open('mnist_train_small.npz', 'rb') as f:\\n train_set_small = np.load(f)\\n train_inputs_small = train_set_small['train_inputs_small']\\n train_targets_small = train_set_small['train_targets_small']\\n return train_inputs_small, train_targets_small\",\n \"def load_data():\\n (trainx, trainy), (valx, valy), (testx, testy) = pickle.load(gzip.open(\\\"data/mnist_one_hot.pkl.gz\\\"),\\n encoding=\\\"latin1\\\")\\n trainy = np.argmax(trainy, axis=1)\\n valy = np.argmax(valy, axis=1)\\n testy = np.argmax(testy, axis=1)\\n trainx = trainx * 2 - 1\\n valx = valx * 2 - 1\\n testx = testx * 2 - 1\\n return (trainx.reshape(-1, 1, 28, 28), trainy), (valx.reshape(-1, 1, 28, 28), valy), (testx.reshape(-1, 1, 28, 28),\\n testy)\",\n \"def import_mnist():\\n\\turl_mnist = \\\"http://deeplearning.net/data/mnist/mnist.pkl.gz\\\"\\n\\tfile_name = \\\"mnist.pkl.gz\\\"\\n\\twork_directory = \\\"mnist\\\"\\n\\tfile_path = maybe_download(url=url_mnist, file_name=file_name, work_directory=work_directory)\\n\\n\\timport pickle\\n\\twith gzip.open(file_path,'rb') as ff :\\n\\t\\tu = pickle._Unpickler( ff )\\n\\t\\tu.encoding = 'latin1'\\n\\t\\ttrain, val, test = u.load()\\n\\t\\ttrainX = np.array(train[0])\\n\\t\\ttrainY = np.reshape(train[1], [50000, 1])\\n\\t\\tvalX = np.array(val[0])\\n\\t\\tvalY = np.reshape(val[1], [10000, 1])\\n\\t\\ttestX = np.array(test[0])\\n\\t\\ttestY = np.reshape(test[1], [10000, 1])\\n\\t\\ttrainX = np.concatenate((trainX, valX), axis = 0)\\n\\t\\ttrainY = np.concatenate((trainY, valY), axis = 0)\\n\\treturn trainX, trainY, testX, testY\",\n \"def load_mnist_dataset(shape=(-1,784)):\\n # We first define a download function, supporting both Python 2 and 3.\\n if sys.version_info[0] == 2:\\n from urllib import urlretrieve\\n else:\\n from urllib.request import urlretrieve\\n\\n def download(filename, source='http://yann.lecun.com/exdb/mnist/'):\\n print(\\\"Downloading %s\\\" % filename)\\n urlretrieve(source + filename, filename)\\n\\n # We then define functions for loading MNIST images and labels.\\n # For convenience, they also download the requested files if needed.\\n import gzip\\n\\n def load_mnist_images(filename):\\n if not os.path.exists(filename):\\n download(filename)\\n # Read the inputs in Yann LeCun's binary format.\\n with gzip.open(filename, 'rb') as f:\\n data = np.frombuffer(f.read(), np.uint8, offset=16)\\n # The inputs are vectors now, we reshape them to monochrome 2D images,\\n # following the shape convention: (examples, channels, rows, columns)\\n data = data.reshape(shape)\\n # data = data.reshape(-1, 1, 28, 28) # for lasagne\\n # data = data.reshape(-1, 28, 28, 1) # for tensorflow\\n # data = data.reshape(-1, 784) # for tensorflow\\n # The inputs come as bytes, we convert them to float32 in range [0,1].\\n # (Actually to range [0, 255/256], for compatibility to the version\\n # provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.)\\n return data / np.float32(256)\\n\\n def load_mnist_labels(filename):\\n if not os.path.exists(filename):\\n download(filename)\\n # Read the labels in Yann LeCun's binary format.\\n with gzip.open(filename, 'rb') as f:\\n data = np.frombuffer(f.read(), np.uint8, offset=8)\\n # The labels are vectors of integers now, that's exactly what we want.\\n return data\\n\\n # We can now download and read the training and test set images and labels.\\n ## you may want to change the path\\n data_dir = '' #os.getcwd() + '/lasagne_tutorial/'\\n # print('data_dir > %s' % data_dir)\\n\\n X_train = load_mnist_images(data_dir+'train-images-idx3-ubyte.gz')\\n y_train = load_mnist_labels(data_dir+'train-labels-idx1-ubyte.gz')\\n X_test = load_mnist_images(data_dir+'t10k-images-idx3-ubyte.gz')\\n y_test = load_mnist_labels(data_dir+'t10k-labels-idx1-ubyte.gz')\\n\\n # We reserve the last 10000 training examples for validation.\\n X_train, X_val = X_train[:-10000], X_train[-10000:]\\n y_train, y_val = y_train[:-10000], y_train[-10000:]\\n\\n ## you may want to plot one example\\n # print('X_train[0][0] >', X_train[0][0].shape, type(X_train[0][0])) # for lasagne\\n # print('X_train[0] >', X_train[0].shape, type(X_train[0])) # for tensorflow\\n # # exit()\\n # # [[..],[..]] (28, 28) numpy.ndarray\\n # # plt.imshow 只支持 (28, 28)格式，不支持 (1, 28, 28),所以用 [0][0]\\n # fig = plt.figure()\\n # #plotwindow = fig.add_subplot(111)\\n # # plt.imshow(X_train[0][0], cmap='gray') # for lasagne (-1, 1, 28, 28)\\n # plt.imshow(X_train[0].reshape(28,28), cmap='gray') # for tensorflow (-1, 28, 28, 1)\\n # plt.title('A training image')\\n # plt.show()\\n\\n # We just return all the arrays in order, as expected in main().\\n # (It doesn't matter how we do this as long as we can read them again.)\\n return X_train, y_train, X_val, y_val, X_test, y_test\",\n \"def readmnist(dataset = \\\"training\\\", path = \\\".\\\"):\\n\\n if dataset is \\\"training\\\":\\n fname_img = os.path.join(path, 'train-images.idx3-ubyte')\\n fname_lbl = os.path.join(path, 'train-labels.idx1-ubyte')\\n elif dataset is \\\"testing\\\":\\n fname_img = os.path.join(path, 't10k-images.idx3-ubyte')\\n fname_lbl = os.path.join(path, 't10k-labels.idx1-ubyte')\\n # else:\\n # raise ValueError, \\\"dataset must be 'testing' or 'training'\\\"\\n\\n # Load everything in some numpy arrays\\n with open(fname_lbl, 'rb') as flbl:\\n magic, num = struct.unpack(\\\">II\\\", flbl.read(8))\\n lbl = np.fromfile(flbl, dtype=np.int8)\\n\\n with open(fname_img, 'rb') as fimg:\\n magic, num, rows, cols = struct.unpack(\\\">IIII\\\", fimg.read(16))\\n img = np.fromfile(fimg, dtype=np.uint8).reshape(len(lbl), rows, cols)\\n\\n get_img = lambda idx: (lbl[idx], img[idx])\\n\\n # Create an iterator which returns each image in turn\\n for i in xrange(len(lbl)):\\n yield get_img(i)\",\n \"def load_from_array():\\n\\n x = np.load(settings.data(\\\"x.npy\\\")).reshape(-1, 1, 224, 224)\\n y = np.load(settings.data(\\\"y.npy\\\"))\\n\\n return x, y\",\n \"def readData():\\n\\tN = 800\\n\\tD = 28*28\\n\\tX = np.zeros((N, D), dtype=np.uint8)\\n\\n\\tf = open(\\\"data/a012_images.dat\\\", 'rb')\\n\\n\\tfor i in range(0, N):\\n\\t\\tX[i, :] = np.fromstring(f.read(D), dtype='uint8')\\n\\n\\tf.close()\\n\\n\\treturn X\",\n \"def load_data(path='mnist.npz'):\\n origin_folder = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'\\n path = get_file(\\n path,\\n origin=origin_folder + 'mnist.npz',\\n file_hash=\\n '731c5ac602752760c8e48fbffcf8c3b850d9dc2a2aedcf2cc48468fc17b673d1')\\n print('############################################' + path) \\n with np.load(path, allow_pickle=True) as f: # pylint: disable=unexpected-keyword-arg\\n x_train, y_train = f['x_train'], f['y_train']\\n x_test, y_test = f['x_test'], f['y_test']\\n\\n return (x_train, y_train), (x_test, y_test)\",\n \"def load_groundtruths(folder_path, num_images):\\n imgs = []\\n for i in range(1, num_images + 1):\\n image_name = \\\"satImage_%.3d\\\" % i\\n image_path = folder_path + image_name + \\\".png\\\"\\n if os.path.isfile(image_path):\\n print('Loading ' + image_path)\\n img = mpimg.imread(image_path)\\n # See if it is better to use dtype = int\\n hot_img = convert_image_to_hot(img)\\n imgs.append(hot_img)\\n else:\\n print('File ' + image_path + ' does not exist')\\n #imgs = np.around(imgs) # Uncomment if we want to round values.\\n imgs_array = np.asarray(imgs)\\n return imgs_array\",\n \"def _load_mnist(path, dataset=\\\"training\\\", digits=None, asbytes=False,\\n selection=None, return_labels=True, return_indices=False):\\n\\n # The files are assumed to have these names and should be found in 'path'\\n files = {\\n 'training': ('train-images-idx3-ubyte', 'train-labels-idx1-ubyte'),\\n 'testing': ('t10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte'),\\n }\\n\\n try:\\n images_fname = os.path.join(path, files[dataset][0])\\n labels_fname = os.path.join(path, files[dataset][1])\\n except KeyError:\\n raise ValueError(\\\"Data set must be 'testing' or 'training'\\\")\\n\\n # We can skip the labels file only if digits aren't specified and labels\\n # aren't asked for\\n if return_labels or digits is not None:\\n flbl = open(labels_fname, 'rb')\\n magic_nr, size = struct.unpack(\\\">II\\\", flbl.read(8))\\n labels_raw = pyarray(\\\"b\\\", flbl.read())\\n flbl.close()\\n\\n fimg = open(images_fname, 'rb')\\n magic_nr, size, rows, cols = struct.unpack(\\\">IIII\\\", fimg.read(16))\\n images_raw = pyarray(\\\"B\\\", fimg.read())\\n fimg.close()\\n\\n if digits:\\n indices = [k for k in range(size) if labels_raw[k] in digits]\\n else:\\n indices = range(size)\\n\\n if selection:\\n indices = indices[selection]\\n\\n images = np.zeros((len(indices), rows, cols), dtype=np.uint8)\\n\\n if return_labels:\\n labels = np.zeros((len(indices)), dtype=np.int8)\\n for i in range(len(indices)):\\n images[i] = np.array(images_raw[indices[i] * rows * cols:(indices[i] + 1) * rows * cols]).reshape((rows, cols))\\n if return_labels:\\n labels[i] = labels_raw[indices[i]]\\n\\n if not asbytes:\\n images = images.astype(float)/255.0\\n\\n ret = (images,)\\n if return_labels:\\n ret += (labels,)\\n if return_indices:\\n ret += (indices,)\\n\\n if len(ret) == 1:\\n return ret[0] # Don't return a tuple of one\\n\\n return ret\",\n \"def load_images(self, image_path):\\n X_train = []\\n\\n # Load all files from the image path using Image.open.\\n for i in recursive_list(image_path):\\n # Open images as ???\\n img = Image.open(i)\\n # Convert to NP array.\\n img = np.asarray(img)\\n # Append them into higher order array.\\n if img.shape == (128, 128, 3):\\n X_train.append(img)\\n\\n # return all the images concatenated as a 4D array\\n return np.asarray(X_train)\",\n \"def mnist(path=None):\\r\\n url = 'http://yann.lecun.com/exdb/mnist/'\\r\\n files = ['train-images-idx3-ubyte.gz',\\r\\n 'train-labels-idx1-ubyte.gz',\\r\\n 't10k-images-idx3-ubyte.gz',\\r\\n 't10k-labels-idx1-ubyte.gz']\\r\\n\\r\\n if path is None:\\r\\n # Set path to /home/USER/data/mnist or C:\\\\Users\\\\USER\\\\data\\\\mnist\\r\\n path = os.path.join(os.path.expanduser('~'), 'data', 'mnist')\\r\\n\\r\\n # Create path if it doesn't exist\\r\\n os.makedirs(path, exist_ok=True)\\r\\n\\r\\n # Download any missing files\\r\\n for file in files:\\r\\n if file not in os.listdir(path):\\r\\n urlretrieve(url + file, os.path.join(path, file))\\r\\n print(\\\"Downloaded %s to %s\\\" % (file, path))\\r\\n\\r\\n def _images(path):\\r\\n \\\"\\\"\\\"Return images loaded locally.\\\"\\\"\\\"\\r\\n with gzip.open(path) as f:\\r\\n # First 16 bytes are magic_number, n_imgs, n_rows, n_cols\\r\\n pixels = np.frombuffer(f.read(), 'B', offset=16)\\r\\n return pixels.reshape(-1, 784).astype('float32') / 255\\r\\n\\r\\n def _labels(path):\\r\\n \\\"\\\"\\\"Return labels loaded locally.\\\"\\\"\\\"\\r\\n with gzip.open(path) as f:\\r\\n # First 8 bytes are magic_number, n_labels\\r\\n integer_labels = np.frombuffer(f.read(), 'B', offset=8)\\r\\n\\r\\n def _onehot(integer_labels):\\r\\n \\\"\\\"\\\"Return matrix whose rows are onehot encodings of integers.\\\"\\\"\\\"\\r\\n n_rows = len(integer_labels)\\r\\n n_cols = integer_labels.max() + 1\\r\\n onehot = np.zeros((n_rows, n_cols), dtype='uint8')\\r\\n onehot[np.arange(n_rows), integer_labels] = 1\\r\\n return onehot\\r\\n\\r\\n return _onehot(integer_labels)\\r\\n\\r\\n train_images = _images(os.path.join(path, files[0]))\\r\\n train_labels = _labels(os.path.join(path, files[1]))\\r\\n test_images = _images(os.path.join(path, files[2]))\\r\\n test_labels = _labels(os.path.join(path, files[3]))\\r\\n\\r\\n return train_images, train_labels, test_images, test_labels\",\n \"def readpil3d(self):\\r\\n\\r\\n # Read the data in as an array.\\r\\n res = np.loadtxt(self.name, delimiter=' ')\\r\\n\\r\\n # Split into useful chunks\\r\\n self.pos = res[:, 0:3] # Grid point locations\\r\\n self.Pn = res[:, 3:4] # Normal pressure [Pa]\\r\\n self.flux = res[:, -1] # Flux\\r\",\n \"def load_data(filename):\\n emnist = loadmat(filename)\\n\\n # Load training images and labels\\n train_images_unshuffled = emnist['train_images']\\n train_labels_unshuffled = emnist['train_labels']\\n\\n # Combine labels and training data\\n combined_training = np.hstack((train_images_unshuffled, train_labels_unshuffled))\\n\\n # Shuffle data\\n np.random.shuffle(combined_training)\\n\\n # Seperate into data and labels\\n # Split into training and validation sets\\n train_images = combined_training[:20800,:-1] / 255 # Normalize data, values are now between 0 and 1\\n train_labels = combined_training[:20800,-1][...,None] # Turns back into column vector\\n validation_images = combined_training[20800:,:-1] / 255 # Normalize data, values are now between 0 and 1\\n validation_labels = combined_training[20800:,-1][...,None] # Turns back into column vector\\n\\n # Load training images and labels\\n test_images = emnist['test_images'] / 255 # Normalize data, values are now between 0 and 1\\n test_labels = emnist['test_labels']\\n\\n return train_images, train_labels, test_images, test_labels, validation_images, validation_labels\",\n \"def load_dataset(path_test, width, height):\\n tot_images = 0\\n for label in listdir(path_test):\\n label_full = join(path_test, label)\\n for img_name in listdir(label_full):\\n tot_images += 1\\n\\n # allocate the memory\\n # THE DTYPE is float, should be the right one\\n all_images = np.zeros((tot_images, width, height, 3))\\n\\n true_labels = []\\n num_images = 0\\n for label in listdir(path_test):\\n label_full = join(path_test, label)\\n for img_name in listdir(label_full):\\n # for img_name in listdir(label_full)[:10]:\\n img_name_full = join(label_full, img_name)\\n print(f\\\"Opening {img_name_full} {width}\\\")\\n\\n image = cv2.imread(img_name_full)\\n\\n image = cv2.resize(image, (width, height))\\n\\n # scale the pixel values to [0, 1]\\n image = image.astype(\\\"float\\\") / 255.0\\n\\n all_images[num_images, :, :, :] = image\\n\\n num_images += 1\\n true_labels.append(label)\\n\\n print(f\\\"All_images.shape {all_images.shape}\\\")\\n\\n # cv2.imshow('Resized all_images[0]', all_images[0])\\n # cv2.waitKey(0)\\n\\n return all_images, true_labels\",\n \"def readMNISTData():\\n mnist = input_data.read_data_sets(\\\"MNIST_data\\\",one_hot=True) \\n return mnist\",\n \"def load_images(file):\\n\\timage_list = [] # List for storing all the images\\n\\ttargets = []\\n\\t\\n\\tfor filename in glob.glob(file + '/*.png'):\\n\\t\\t# ==================\\n\\t\\t# Reading the image\\n\\t\\t# ==================\\n\\t\\timage = scipy.misc.imread(filename).astype(np.float32)\\n\\t\\t\\n\\t\\t# ================================\\n\\t\\t# Converting the image to a vector\\n\\t\\t# ================================\\n\\t\\timage = image.flatten() # (784, )\\n\\t\\t\\n\\t\\t# ==============================\\n\\t\\t# Normalizing the image to numpy\\n\\t\\t# ==============================\\n\\t\\timage = image / 255.0\\n\\t\\timage = image - 0.5\\n\\t\\timage = image * 2.0\\n\\t\\t\\n\\t\\t# ===============================\\n\\t\\t# Appending the image to the list\\n\\t\\t# ===============================\\n\\t\\timage_list.append(image)\\n\\t\\t\\n\\t\\t_, value = filename.split('\\\\\\\\')\\n\\t\\t# print(value[0])\\n\\t\\ttargets.append(int(value[0]))\\n\\t\\n\\timage_list = np.array(image_list)\\n\\ttargets = np.array(targets)\\n\\t\\n\\t# ================================================\\n\\t# \\t\\t\\tShuffling the data\\n\\t# ================================================\\n\\timage_list, targets = shuffle(image_list, targets)\\n\\t\\n\\ttrain_images, test_images, train_targets, test_targets = split(image_list, targets)\\n\\treturn train_images, test_images, train_targets, test_targets\",\n \"def load_food_image_batch(filename, num):\\n with open(filename, 'rb') as f:\\n datadict = pickle.load(f)\\n url_parts = datadict['Image URL'].split(\\\"/\\\")\\n img_fn = url_parts[-1]\\n with open(img_fn):\\n X = f.read()\\n Y = datadict['coarse_labels']\\n X = X.reshape(num, 3, 32, 32).transpose(0,2,3,1).astype(\\\"float\\\")\\n Y = np.array(Y)\\n return X, Y\",\n \"def read_picture_data(filename):\\n file_name = os.path.join('.', 'datas', filename)\\n\\n try:\\n with open(file_name, 'rb') as file:\\n read_data = file.read()\\n except FileNotFoundError:\\n print(f'Oups, the file {filename} was not found')\\n\\n try:\\n if filename == 'train-images.idx3-ubyte':\\n number_of_pics = 60000\\n else:\\n number_of_pics = 10000\\n except LookupError:\\n print(f'Oups, the file {filename} was not named as a MNist file')\\n\\n picture_data = np.zeros((number_of_pics, 28 * 28)) # 28*28 = 784\\n\\n s = 0\\n for n in range(16, number_of_pics * 784, 784): # 16 header bytes being dumped\\n for t, byte in enumerate(read_data[n: n + 784]):\\n picture_data[s, t] = byte\\n s += 1\\n\\n print(f'\\\\nPicture data read from {filename}\\\\n')\\n\\n return picture_data\",\n \"def load_dataset():\\n temp = gzip.open('mnist.pkl.gz')\\n train, val , test = pickle.load(temp,encoding='latin1')\\n temp.close()\\n train_inp = [np.reshape(x, (784,1)) for x in train[0]]\\n train_outp = [one_hot(y) for y in train[1]]\\n training_data = zip(train_inp, train_outp)\\n validation_inp = [np.reshape(x, (784, 1)) for x in val[0]]\\n validation_data = zip(validation_inp, val[1])\\n test_inp = [np.reshape(x, (784, 1)) for x in test[0]]\\n test_data = zip(test_inp, test[1])\\n return (training_data,validation_data,test_data)\",\n \"def read_gz(images,labels):\\n\\t# Open the images with gzip in read binary mode\\n\\t# images = gzip.open('../MNIST-data/train-images-idx3-ubyte.gz', 'rb')\\n\\t# labels = gzip.open('../MNIST-data/train-labels-idx1-ubyte.gz', 'rb')\\n\\n\\t# Read the binary data\\n\\n\\t# We have to get big endian unsigned int. So we need '>I'\\n\\n\\t# Get metadata for images\\n\\timages.read(4) # skip the magic_number\\n\\tnumber_of_images = images.read(4)\\n\\tnumber_of_images = unpack('>I', number_of_images)[0]\\n\\trows = images.read(4)\\n\\trows = unpack('>I', rows)[0]#28\\n\\tcols = images.read(4)\\n\\tcols = unpack('>I', cols)[0]#28\\n\\n\\t# Get metadata for labels\\n\\tlabels.read(4) # skip the magic_number\\n\\tN = labels.read(4)\\n\\tN = unpack('>I', N)[0] #60000\\n\\t# print(number_of_images);\\n\\n\\tif number_of_images != N:\\n\\t raise Exception('number of labels did not match the number of images')\\n\\n\\t# Get the data\\n\\tx = zeros((N, rows, cols), dtype=float32) # Initialize numpy array #60000X28X28\\n\\ty = zeros((N, 1), dtype=uint8) # Initialize numpy array\\n\\tfor i in range(N):\\n\\t if i % 1000 == 0:\\n\\t print(\\\"i: %i\\\" % i)\\n\\t for row in range(rows):\\n\\t for col in range(cols):\\n\\t tmp_pixel = images.read(1) # Just a single byte\\n\\t tmp_pixel = unpack('>B', tmp_pixel)[0]\\n\\t x[i][row][col] = tmp_pixel\\n\\t tmp_label = labels.read(1)\\n\\t y[i] = unpack('>B', tmp_label)[0]\\n\\t # print(y.shape)#60000X1\\n\\treturn (x, y)\",\n \"def load(filename):\\n img = image.load_img(filename, target_size=(299, 299))\\n np_image = image.img_to_array(img)\\n np_image = np.array(np_image).astype('float32')/255\\n\\n # Make to a rank 4 tensor (1, 299, 299, 3) -> 1 is for the batch size\\n np_image = np.expand_dims(np_image, axis=0)\\n\\n return np_image\",\n \"def load_fashion_mnist():\\n # List of image file names\\n dataset_directory = os.path.join(root_directory,'Fashion_MNIST')\\n filenames = os.listdir(dataset_directory)\\n filenames.sort()\\n\\n # List of numpy array; each row is a Image of the dataset\\n data = []\\n\\n # Numpy array of labels associated to each class of image\\n target = np.empty([len(filenames), ])\\n\\n previous_label = ''\\n class_num = -1\\n index = 0\\n\\n for index, filename in enumerate(filenames):\\n data.append(Bitmap(io.imread(os.path.join(dataset_directory, filename))))\\n file_label = filename.split('-')[0]\\n\\n if(previous_label != file_label):\\n previous_label = file_label\\n class_num += 1\\n target[index] = class_num\\n else:\\n target[index] = class_num\\n\\n return {'bitmaps': data, 'targets': target}\",\n \"def load_EMNIST_data(file, verbose = False, standarized = False): \\n mat = sio.loadmat(file)\\n data = mat[\\\"dataset\\\"]\\n \\n X_train = data['train'][0,0]['images'][0,0]\\n X_train = X_train.reshape((X_train.shape[0], 28, 28), order = \\\"F\\\")\\n y_train = data['train'][0,0]['labels'][0,0]\\n y_train = np.squeeze(y_train)\\n y_train -= 1 #y_train is zero-based\\n \\n X_test = data['test'][0,0]['images'][0,0]\\n X_test= X_test.reshape((X_test.shape[0], 28, 28), order = \\\"F\\\")\\n y_test = data['test'][0,0]['labels'][0,0]\\n y_test = np.squeeze(y_test)\\n y_test -= 1 #y_test is zero-based\\n \\n if standarized: \\n X_train = X_train/255\\n X_test = X_test/255\\n mean_image = np.mean(X_train, axis=0)\\n X_train -= mean_image\\n X_test -= mean_image\\n \\n\\n if verbose == True: \\n print(\\\"EMNIST-letter dataset ... \\\")\\n print(\\\"X_train shape :\\\", X_train.shape)\\n print(\\\"X_test shape :\\\", X_test.shape)\\n print(\\\"y_train shape :\\\", y_train.shape)\\n print(\\\"y_test shape :\\\", y_test.shape)\\n \\n return X_train, y_train, X_test, y_test\",\n \"def mnist_training():\\n mndata = MNIST(MNIST_PATH)\\n train_ims, train_labels = mndata.load_training()\\n train_X = np.array(train_ims).T\\n train_y = np.array(train_labels).T\\n return train_X, train_y\",\n \"def load_batch(filename: str) -> Tuple[ndarray, ndarray, ndarray]:\\n dataDict = unpickle(filename)\\n print(\\\"1\\\", dataDict[b\\\"data\\\"][1, :])\\n X = (dataDict[b\\\"data\\\"] / 255).T\\n print(\\\"2\\\", X[:, 1])\\n y = np.array(dataDict[b\\\"labels\\\"])\\n Y = np.eye(10)[y].T\\n return X, Y, y\",\n \"def load_image_as_np(filename):\\n try:\\n img = image.load_img(filename, color_mode='grayscale') # for newer versions, use \\\"color_mode='grayscale'\\\"; For older versions, use \\\"grayscale=True\\\"\\n return np.atleast_3d(img)\\n except Exception as error:\\n logging.error(traceback.format_exc())\",\n \"def bin2matrix(filename):\\n filename = realpath(filename)\\n Xy = np.load(filename)\\n if len(Xy.shape) == 3:\\n X = Xy[:,:,:-1]\\n y = Xy[:,:,-1]\\n else:\\n X = Xy[:,:-1]\\n y = Xy[:,-1]\\n return X, y\",\n \"def load_data(path='alex_mnist_data.npz'):\\n with np.load(path, allow_pickle=True) as f:\\n x_train, y_train = f['alex_train_data'], f['alex_train_label']\\n x_test, y_test = f['alex_test_data'], f['alex_test_label']\\n return (x_train, y_train),(x_test, y_test)\",\n \"def readDataFromFile():\\n image_size = 28 # each image is 28x28\\n\\n num_images = 60000 # there are 60k images\\n with gzip.open(r'train-images-idx3-ubyte.gz', 'r') as f: # 60k train & valid\\n f.read(16) # reading by 16-byte double\\n buffer_Train_Images = f.read(image_size * image_size * num_images)\\n f.close()\\n data_Train_Images = np.frombuffer(buffer_Train_Images, dtype=np.uint8).astype(\\n np.int32) # translating into 0 to 255\\n data_Train_Images = data_Train_Images.reshape(num_images,\\n image_size * image_size) # Data = 60k x 28 x 28 with 1 value in it\\n\\n with gzip.open('train-labels-idx1-ubyte.gz', 'r') as f: # 60k train & valid - labels\\n f.read(8) # reading by 16-byte double\\n buffer_Train_Labels = f.read(num_images)\\n data_Train_Labels = np.frombuffer(buffer_Train_Labels, dtype=np.uint8).astype(\\n np.int32) # translating into 0 to 255\\n\\n num_images = 10000 # there are 10k images\\n with gzip.open('t10k-images-idx3-ubyte.gz', 'r') as f: # 10k tests\\n f.read(16) # reading by 16-byte double\\n buffer_Test_Image = f.read(image_size * image_size * num_images)\\n data_Test_Image = np.frombuffer(buffer_Test_Image, dtype=np.uint8).astype(\\n np.uint8) # translating into 0 to 255\\n data_Test_Image = data_Test_Image.reshape(num_images, image_size * image_size) # Data = 60k x 28 x 28 with\\n\\n with gzip.open('t10k-labels-idx1-ubyte.gz', 'r') as f: # 10k tests - lbles\\n f.read(8) # reading by 16-byte double\\n buffer_Test_Label = f.read(num_images)\\n data_Test_Labels = np.frombuffer(buffer_Test_Label, dtype=np.uint8).astype(\\n np.int32) # translating into 0 to 255\\n\\n return data_Train_Images, data_Train_Labels, data_Test_Image, data_Test_Labels\",\n \"def load_dataset(ipc = 20000):\\n files = os.listdir(\\\"..\\\\\\\\data\\\")\\n ind = 0\\n xs = []\\n ys = []\\n classNames = []\\n for file in files:\\n fileSplit = file.split('.')\\n print('--Loading ' + fileSplit[0][18:] + ' data.')\\n classNames.append(fileSplit[0][18:])\\n x = np.load(\\\"..\\\\\\\\data\\\\\\\\\\\" + file)\\n x = x.astype('float32')/255\\n xs.append(x[0:ipc, :])\\n y = np.array([float(ind) for i in range(ipc)])\\n ys.append(y.reshape(ipc, 1))\\n ind += 1\\n\\n xs = np.array(xs)\\n ys = np.array(ys)\\n xs = xs.reshape(xs.shape[0]*xs.shape[1], xs.shape[2])\\n ys = ys.reshape(ys.shape[0]*ys.shape[1], ys.shape[2])\\n return xs, ys, classNames\",\n \"def load(filename):\\n lines = [l.strip('\\\\r\\\\n ') for l in open(filename, 'r').readlines()]\\n lines = [l for l in lines if l != '']\\n dims = [re.split(r'\\\\s+', l) for l in lines]\\n f = np.array([[float(f) for f in d] for d in dims])\\n return f\",\n \"def load_data(filename):\\n assert os.path.exists(filename)==True\\n dat = scipy.io.loadmat(filename)\\n inputs = dat['inputs']\\n #print len(inputs)\\n targets = dat['targets']\\n #print len(targets)\\n assert len(inputs)==len(targets)\\n\\n global alldata\\n global indim \\n global outdim\\n\\n indim = len(inputs[0])\\n outdim = 1\\n #print indim\\n alldata = ClassificationDataSet(indim, outdim, nb_classes = 8)\\n alldata.setField('input',inputs)\\n alldata.setField('target',targets)\\n\\n assert len(alldata['input'])==len(alldata['target'])\\n print type(alldata)\",\n \"def load_data():\\n f = gzip.open('mnist.pkl.gz', 'rb')\\n training_data, validation_data, test_data = pickle.load(f, encoding=\\\"latin1\\\")\\n f.close()\\n \\n X_train = [np.reshape(x, (784, 1)) for x in training_data[0]]\\n Y_train = [vectorized_result(y) for y in training_data[1]]\\n \\n X_validation = [np.reshape(x, (784, 1)) for x in validation_data[0]]\\n Y_validation = validation_data[1]\\n \\n X_test = [np.reshape(x, (784, 1)) for x in test_data[0]]\\n Y_test = test_data[1]\\n \\n return (X_train, Y_train, X_validation, Y_validation, X_test, Y_test)\",\n \"def load_X():\\n x1 = np.asarray([-1, -1, 1, -1, 1, -1, -1, 1]).reshape((1, 8))\\n x2 = np.asarray([-1, -1, -1, -1, -1, 1, -1, -1]).reshape((1, 8))\\n x3 = np.asarray([-1, 1, 1, -1, -1, 1, -1, 1]).reshape((1, 8))\\n X = np.vstack([x1, x2, x3])\\n\\n return X\",\n \"def load_mnist (images_fn_gz, labels_fn_gz, digits=None, path=None, asbytes=False, selection=None, return_labels=True, return_indices=False):\\n\\n # We can skip the labels file only if digits aren't specified and labels aren't asked for\\n if return_labels or digits is not None:\\n flbl = gzip.open (labels_fn_gz, 'rb')\\n magic_nr, size = struct.unpack(\\\">II\\\", flbl.read(8))\\n labels_raw = pyarray(\\\"b\\\", flbl.read())\\n flbl.close()\\n\\n fimg = gzip.open(images_fn_gz, 'rb')\\n magic_nr, size, rows, cols = struct.unpack(\\\">IIII\\\", fimg.read(16))\\n images_raw = pyarray(\\\"B\\\", fimg.read())\\n fimg.close()\\n\\n if digits:\\n indices = [k for k in range(size) if labels_raw[k] in digits]\\n else:\\n indices = range(size)\\n\\n if selection:\\n indices = indices[selection] \\n N = len(indices)\\n\\n images = zeros((N, rows, cols), dtype=uint8)\\n\\n if return_labels:\\n labels = zeros((N), dtype=int8)\\n for i, index in enumerate(indices):\\n images[i] = array(images_raw[ indices[i]*rows*cols : (indices[i]+1)*rows*cols ]).reshape((rows, cols))\\n if return_labels:\\n labels[i] = labels_raw[indices[i]]\\n\\n if not asbytes:\\n images = images.astype(float)/255.0\\n\\n ret = (images,)\\n if return_labels:\\n ret += (labels,)\\n if return_indices:\\n ret += (indices,)\\n if len(ret) == 1:\\n return ret[0] # Don't return a tuple of one\\n else:\\n return ret\",\n \"def load_data():\\n\\n \\\"\\\"\\\"The ``training_data`` is returned as a tuple with two entries.\\n The first entry contains the actual training images. This is a\\n numpy ndarray with 50,000 entries. Each entry is, in turn, a\\n numpy ndarray with 784 values, representing the 28 * 28 = 784\\n pixels in a single MNIST image.\\\"\\\"\\\"\\n\\n \\\"\\\"\\\"The second entry in the ``training_data`` tuple is a numpy ndarray\\n containing 50,000 entries. Those entries are just the digit\\n values (0...9) for the corresponding images contained in the first\\n entry of the tuple.\\\"\\\"\\\"\\n\\n \\\"\\\"\\\"The ``validation_data`` and ``test_data`` are similar, except\\n each contains only 10,000 images.\\\"\\\"\\\"\\n f = gzip.open('MNIST/data/mnist.pkl.gz', 'rb')\\n training_data, validation_data, test_data = Pickle.load(f, encoding='bytes'\\n )\\n f.close()\\n return (training_data, validation_data, test_data)\",\n \"def imagesMatrix(path,imageSize = 10304,byteorder = '>'):\\n listing = os.listdir(path)\\n listing.sort()\\n count = 0\\n docFiles = []\\n for infile in listing:\\n count = count + 1\\n docFiles.append(infile)\\n matrix = np.zeros((imageSize,count))\\n for i in range(len(listing)):\\n matrix[:,i]=np.asarray(read_pgm(join(path,listing[i]),byteorder)).reshape(-1)\\n return matrix,listing\",\n \"def load_rgb_image_as_3d_array(filename):\\n image = np.asarray(Image.open(filename))\\n image_shape = image.shape\\n image_dim = len(image_shape)\\n assert(image_dim == 2 or image_dim == 3)\\n if(image_dim == 2):\\n image = np.expand_dims(image, axis = 0)\\n else:\\n # transpose rgb image from [H, W, C] to [C, H, W]\\n assert(image_shape[2] == 3 or image_shape[2] == 4)\\n if(image_shape[2] == 4):\\n image = image[:, :, range(3)]\\n image = np.transpose(image, axes = [2, 0, 1])\\n output = {}\\n output['data_array'] = image\\n output['origin'] = (0, 0)\\n output['spacing'] = (1.0, 1.0)\\n output['direction'] = 0\\n return output\",\n \"def load_CIFAR_batch(filename):\\n with open(filename, 'rb')as f:\\n datadict = p.load(f, encoding='iso-8859-1')\\n X = datadict['data']\\n Y = datadict['labels']\\n X = X.reshape(10000, 3, 32, 32)\\n Y = np.array(Y)\\n return X, Y\",\n \"def _images(path):\\r\\n with gzip.open(path) as f:\\r\\n # First 16 bytes are magic_number, n_imgs, n_rows, n_cols\\r\\n pixels = np.frombuffer(f.read(), 'B', offset=16)\\r\\n return pixels.reshape(-1, 784).astype('float32') / 255\",\n \"def load_data_in_folder(self):\\n print('loading files in data folder')\\n n = len(self.filenames)\\n idx_max = n // self.batch_size\\n for idx in range(0, idx_max-1):\\n data = []\\n for f in self.filenames[idx:idx+64]:\\n img = cv2.imread(f, int(self.color))\\n if not self.color:\\n img = np.expand_dims(img, axis=-1)\\n data.append(img)\\n data = np.array(data)\\n data = data.astype('float32')\\n data = (data - 127.5)/127.5\\n np.save(op.join(self.data_path, str(idx)), data)\\n # TODO last batch ?\\n self.data_filenames = sorted(glob(op.join(self.data_path, '*.npy')))\",\n \"def tns3d_batch(self, tensorlist, output_resolution):\\n dimensions = 3\\n formats = 5\\n filenames = []\\n\\n ## record the file dir\\n filelist = open(tensorlist)\\n for line in filelist:\\n list_seg = line.split()\\n filenames.append(list_seg[-1])\\n filelist.close()\\n\\n ## get the batch data of 3d tensors and their basic info\\n batch_size = len(filenames)\\n tensor_batch = np.zeros((batch_size, dimensions, output_resolution, output_resolution), dtype='int32')\\n for findex in range(0, batch_size):\\n img = self.tns3d_Sample(filenames[findex], RES)\\n tensor_batch[findex, :, :, :] = img\\n print('**** the {}th tns flattening sampled finished ({} in total) ****'.format(findex, batch_size))\\n return tensor_batch\",\n \"def mnist(path):\\n with open(path, 'r') as f:\\n for line in f:\\n data = line.strip().split(',')\\n\\n # Label is a vector with one element per class\\n label = [0.0] * 10\\n label[int(data[0])] = 1.0 \\n\\n # The data are images of 28x28 pixels\\n image_array = np.asfarray(data[1:]).reshape((28, 28))\\n # Normalize the pictures \\n image_array = image_array / 255.0\\n\\n #plt.imshow(image_array, cmap='Greys', interpolation='None')\\n yield (image_array, label)\",\n \"def load_data(path,size, scale = True):\\n images = os.listdir(path)\\n images.sort()\\n\\n X = []\\n for i, img in enumerate(images):\\n photo = plt.imread(os.path.join(path,img))\\n if size:\\n photo = tf.image.resize(photo, (size, size))\\n X.append(photo)\\n \\n X = np.array(X)\\n if scale:\\n X = X/X.max() \\n return X\",\n \"def load_MNIST_data():\\n mnist = input_data.read_data_sets('data', one_hot=True)\\n return {'train': mnist.train.images,\\n 'validation': mnist.validation.images,\\n 'test': mnist.test.images}\",\n \"def load_data(flatten=True):\\n if flatten:\\n reshape = _flatten\\n else:\\n reshape = _square\\n\\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\\n x_train = reshape(x_train)\\n x_test = reshape(x_test)\\n x_train = x_train.astype('float32')\\n x_test = x_test.astype('float32')\\n x_train /= 255\\n x_test /= 255\\n y_train = to_categorical(y_train, 10)\\n y_test = to_categorical(y_test, 10)\\n return x_train, y_train, x_test, y_test\",\n \"def _load_dataset(self, path):\\n\\t\\twhile True:\\n\\t\\t\\t\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tX_test = np.load(\\\"data/X_test.npy\\\")\\n\\t\\t\\t\\tY_test = np.load(\\\"data/Y_test.npy\\\")\\n\\t\\t\\t\\tbreak\\n\\n\\t\\t\\texcept FileNotFoundError:\\n\\n\\t\\t\\t\\tX_test = np.zeros((10000,64,64,3))\\n\\t\\t\\t\\tY_test = []\\n\\n\\t\\t\\t\\t\\n\\t\\t\\t\\twith open(path, 'rb') as fo:\\n\\t\\t\\t\\t\\ttemp_element = pickle.load(fo, encoding='bytes')\\n\\n\\t\\t\\t\\ttemp_data = temp_element[b'data']\\n\\t\\t\\t\\tY_test.extend(temp_element[b'labels'])\\n\\n\\t\\t\\t\\tfor j in range(10000):\\n\\t\\t\\t\\t\\tX_test[j] = self._reshape(temp_data[j])\\n\\n\\t\\t\\t\\tY_test = np.eye(10)[np.array(Y_test)]\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tnp.save(\\\"./data/X_test\\\", X_test)\\n\\t\\t\\t\\tnp.save(\\\"./data/Y_test\\\", Y_test)\\n\\n\\t\\t\\t\\tbreak\\n\\n\\n\\t\\treturn X_test, Y_test\",\n \"def load_data():\\n data = gzip.open(\\\"mnist.pkl.gz\\\", \\\"rb\\\")\\n train_set, valid_set, test_set = cPickle.load(data)\\n data.close()\\n\\n # Combine validation and train folds to recreate the master 60k set.\\n new_images = numpy.concatenate((train_set[0], valid_set[0]))\\n new_labels = numpy.concatenate((train_set[1], valid_set[1]))\\n\\n train_set = (new_images, new_labels)\\n \\n return (train_set, test_set)\",\n \"def loadtrainData():\\n train_x = []\\n train_y = []\\n fileIn = open(PATH + 'traindata_Subtask4.txt')\\n for line in fileIn.readlines():\\n lineArr = line.strip().split()\\n train_x.append([float(lineArr[i]) for i in range(len(lineArr) - 1)])\\n train_y.append(int(lineArr[-1]))\\n return np.mat(train_x), np.mat(train_y).transpose()\",\n \"def mnist(path):\\n with open(path, 'r') as f:\\n for line in f:\\n data = line.strip().split(',')\\n\\n # Label is a vector with one element per class\\n label = [0.01] * 10\\n label[int(data[0])] = 0.99\\n\\n # The data are images of 28x28 pixels\\n #image_array = np.asfarray(data[1:]).reshape((28, 28))\\n image_array = np.asfarray(data[1:])\\n # Normalize all values between [0.01, 1.0]\\n image_array = ((image_array) / 255.0 * 0.99) + 0.01\\n\\n #plt.imshow(image_array, cmap='Greys', interpolation='None')\\n yield (image_array, label)\",\n \"def load_tmp_atlas(filename):\\n fbase, ext = osp.splitext(filename)\\n fimg = None\\n if osp.isfile(fbase+\\\".nii\\\"): fimg = fbase+\\\".nii\\\"\\n if osp.isfile(fbase+\\\".nii.gz\\\"): fimg = fbase+\\\".nii.gz\\\" \\n\\n try:\\n img = nib.load(fimg)\\n except ValueError as e:\\n print(\\\"error {0}, cannot find file {1} .nii or .nii.gz \\\".format(fbase, e.errno))\\n\\n fjson = None\\n if osp.isfile(fbase+\\\".txt\\\"): fjson= fbase+\\\".txt\\\"\\n if osp.isfile(fbase+\\\".json\\\"): fjson= fbase+\\\".json\\\"\\n\\n if fjson == None:\\n warn(\\\"cannot find file %s .txt or .json\\\" % fbase)\\n return None\\n\\n with open(fjson) as f:\\n j_labels = json.load(f)\\n\\n a_labels = [label[1] for label in j_labels]\\n \\n return (img.get_data(), img.get_affine(), a_labels)\",\n \"def load_mnist(data_path=None, data_home=None, subsets=None):\\n if data_path is None:\\n data_path = _utils.validate_data_home(data_home)\\n data_path /= 'mnist.npz'\\n url = 'https://s3.amazonaws.com/img-datasets/mnist.npz'\\n _ds_utils.get_file(data_path, url)\\n \\n if subsets is None:\\n subsets = ['training', 'test']\\n subsets = _ds_utils.validate_tvt(subsets, return_list=True)\\n X, Y = [], []\\n with np.load(data_path) as f:\\n for subset in subsets:\\n if subset == 'training':\\n X.append(f['x_train'])\\n Y.append(f['y_train'])\\n elif subset == 'test':\\n X.append(f['x_test'])\\n Y.append(f['y_test'])\\n else:\\n raise ValueError('Subset:', subset, ' not supported.')\\n return np.concatenate(X), np.concatenate(Y)\",\n \"def read_image(images_root):\\n im_array = np.load(images_root)\\n return im_array\",\n \"def load_CIFAR_batch(filename):\\r\\n with open(filename, 'rb')as f:\\r\\n datadict = p.load(f)\\r\\n \\r\\n X = datadict['data']\\r\\n Y = datadict['labels']\\r\\n \\r\\n print X.shape\\r\\n X = X.reshape(X.shape[0], SHAPE[0], SHAPE[1], SHAPE[2])\\r\\n Y = np.array(Y)\\r\\n return X, Y\",\n \"def load_image(file_name):\\n if not osp.exists(file_name):\\n print('{} not exist'.format(file_name))\\n return\\n image = np.asarray(io.imread(file_name))\\n if len(image.shape)==3 and image.shape[2]>3:\\n image = image[:, :, :3]\\n # print(image.shape) #should be (x, x, 3)\\n return image\",\n \"def tns3d_batch(self, tensorlist, output_resolution):\\n dimensions = 3\\n formats = 5\\n filenames = []\\n\\n ## record the file dir and corresponding labels\\n filelist = open(tensorlist)\\n for line in filelist:\\n list_seg = line.split()\\n filenames.append(list_seg[-1])\\n filelist.close()\\n\\n ## get the batch data of 3d tensors and their basic info\\n batch_size = len(filenames)\\n tensor_batch = np.zeros((batch_size, dimensions, output_resolution, output_resolution), dtype='int32')\\n for findex in range(0, batch_size):\\n mapImgs = self.tns3d_Sample(filenames[findex], RES)\\n for i in range(0, 3):\\n tensor_batch[findex, i, :, :] = mapImgs[i]\\n print('**** the {}th tns mapping sampled finished ({} in total) ****'.format(findex, batch_size))\\n return tensor_batch\",\n \"def read_and_normalize(im_path):\\n im3d = io.imread(im_path, plugin=\\\"tifffile\\\").astype(np.uint8)\\n sh = np.shape(im3d)\\n if len(sh) > 3:\\n im3d = im3d.reshape(sh[1], sh[2], sh[3])\\n return im3d / 255\",\n \"def image_load(path) -> numpy.ndarray:\\n # file\\n na = numpy.array(Image.open(path))\\n # fix shape\\n na = numpy.moveaxis(na, [2,0,1], [0,1,2])\\n # shape is now (3,h,w), add 1\\n na = na.reshape(1,3,na.shape[1],na.shape[2])\\n # change type\\n na = na.astype(\\\"float32\\\") / 255.0\\n return na\",\n \"def create_MNIST_arrays(\\n alb_transforms=None, aug_number=1, target_dir=\\\".\\\", batch_size=256, num_workers=1\\n):\\n\\n dataset_alb = AlbMNIST(os.path.join(target_dir, \\\"MNIST\\\"), download=True)\\n dataloader = DataLoader(\\n dataset_alb, batch_size=len(dataset_alb), shuffle=False, num_workers=num_workers\\n )\\n\\n print(\\\"Fetching original dataset...\\\", end=\\\" \\\")\\n originals_array = next(iter(dataloader))[\\\"original\\\"].numpy()\\n labels_array = next(iter(dataloader))[\\\"label\\\"].numpy()\\n print(\\\"Done!\\\")\\n\\n dataset_alb.set_transofrms(alb_transforms)\\n aug_arrays = []\\n dataloader = DataLoader(\\n dataset_alb, batch_size=batch_size, shuffle=False, num_workers=num_workers\\n )\\n\\n for aug_idx in range(aug_number):\\n print(\\\"Making aug #%i\\\" % aug_idx)\\n aug_arrays.append(compose_array_from_dataloader(dataloader, key=\\\"augmented\\\"))\\n\\n return originals_array, labels_array, aug_arrays\",\n \"def load_images(input_dir=\\\"/tmp/mapswipe/project-1\\\", n_images=2000, seed=1):\\n class_map = {1: \\\"1\\\", 0: \\\"5\\\"}\\n output_dir = \\\"/Users/thead/git/dreamview/data/\\\"\\n\\n X_ = []\\n y_ = []\\n for new_klass in class_map:\\n images = []\\n for klass in class_map[new_klass]:\\n for img in glob.glob(input_dir + \\\"/%s/*/*/*/aerial.jpeg\\\" % klass):\\n if os.stat(img).st_size > 0:\\n images.append(img)\\n\\n images = shuffle(images, random_state=seed+42+new_klass)\\n images = images[:n_images]\\n X_ += images\\n y_ += [new_klass] * len(images)\\n\\n # XXX deduce array size from an actual image\\n X = np.zeros((2*n_images, 256*256), dtype=np.ubyte)\\n y = np.zeros(2*n_images, dtype=np.int)\\n\\n for n, (img_path, klass) in enumerate(zip(X_, y_)):\\n # the order of these OPs has been chosen on purpose, don't mess\\n # without checking what happens\\n img = imread(img_path)\\n img = equalize_adapthist(img)\\n img = rgb2grey(img)\\n img = img_as_ubyte(img)\\n\\n if not n % 10:\\n fname = os.path.split(img_path)[:-1]\\n fname = os.path.join(*fname, \\\"aerial-processed.jpeg\\\")\\n imsave(fname, img)\\n\\n X[n,:] = img.ravel()\\n y[n] = klass\\n\\n return X, y\",\n \"def load_CIFAR_batch(filename):\\r\\n with open(filename, 'rb') as f:\\r\\n datadict = pickle.load(f, encoding='latin1')\\r\\n X = datadict['data']\\r\\n Y = datadict['labels']\\r\\n X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype(\\\"float\\\")\\r\\n Y = np.array(Y)\\r\\n return X, Y\",\n \"def load_CIFAR_batch(filename):\\n with open(filename, 'rb')as f:\\n # datadict = p.load(f)\\n datadict = pickle.load(f, encoding = 'bytes')\\n X = datadict[b'data']\\n Y = datadict[b'labels']\\n X = X.reshape(10000, 3, 32, 32)\\n Y = np.array(Y)\\n return X, Y\",\n \"def load_images(mraw, h, w, N, bit=16, roll_axis=True):\\n\\n if int(bit) == 16:\\n images = np.memmap(mraw, dtype=np.uint16, mode='r', shape=(N, h, w))\\n elif int(bit) == 8:\\n images = np.memmap(mraw, dtype=np.uint8, mode='r', shape=(N, h, w))\\n elif int(bit) == 12:\\n warnings.warn(\\\"12bit images will be loaded into memory!\\\")\\n #images = _read_uint12_video(mraw, (N, h, w))\\n images = _read_uint12_video_prec(mraw, (N, h, w))\\n else:\\n raise Exception(f\\\"Unsupported bit depth: {bit}\\\")\\n\\n\\n #images=np.fromfile(mraw, dtype=np.uint16, count=h * w * N).reshape(N, h, w) # about a 1/3 slower than memmap when loading to RAM. Also memmap doesn't need to read to RAM but can read from disc when needed.\\n if roll_axis:\\n return np.rollaxis(images, 0, 3)\\n else:\\n return images\",\n \"def load_images(filelist):\\n # pixel value range 0-255\\n if not isinstance(filelist, list):\\n im = Image.open(filelist).convert('L')\\n return np.array(im).reshape(1, im.size[1], im.size[0], 1)\\n data = []\\n for file in filelist:\\n im = Image.open(file).convert('L')\\n data.append(np.array(im).reshape(1, im.size[1], im.size[0], 1))\\n return data\",\n \"def read_local(path):\\n files = os.listdir(path)\\n imgs = []\\n for f in files:\\n if f.endswith(\\\".tiff\\\") or f.endswith(\\\".tif\\\"):\\n img = Image.open(os.path.join(path, f))\\n imgs.append(np.array(img))\\n return imgs\",\n \"def load_CIFAR_batch(filename):\\n with open(filename, 'rb') as f:\\n datadict = pickle.load(f, encoding='latin1')\\n X = datadict['data']\\n Y = datadict['labels']\\n X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype(\\\"float64\\\")\\n Y = np.array(Y)\\n return X, Y\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6996989","0.69236135","0.68585235","0.6848802","0.68018305","0.6801796","0.6785937","0.67511636","0.6737762","0.6649627","0.6620649","0.6572148","0.65664387","0.6564884","0.6544595","0.65435225","0.6539307","0.65093267","0.6489088","0.6486471","0.6466393","0.6442627","0.642544","0.63891107","0.63820785","0.63783324","0.63695645","0.63624835","0.6358522","0.6350845","0.6327597","0.63234466","0.63218576","0.6306377","0.6283855","0.6274831","0.6261332","0.6259758","0.6251833","0.6244885","0.62292695","0.6207699","0.6184362","0.61696994","0.61560726","0.614638","0.61440074","0.6129719","0.61212415","0.61165553","0.60984194","0.6097963","0.60717356","0.6049447","0.6036963","0.6031613","0.5996555","0.59681153","0.5965679","0.59484005","0.5943711","0.59437066","0.59207684","0.59113955","0.5900979","0.58840895","0.58830106","0.58818","0.5880251","0.586815","0.5853163","0.5852128","0.5840969","0.5837958","0.5836616","0.5836456","0.58296245","0.5827238","0.5820289","0.5813015","0.5807307","0.5799579","0.579813","0.57899207","0.57870394","0.5784477","0.5782243","0.57811606","0.57786447","0.57761085","0.57737607","0.5767407","0.57669175","0.5762911","0.5759293","0.5757081","0.57570505","0.57541317","0.5742615","0.5730597"],"string":"[\n \"0.6996989\",\n \"0.69236135\",\n \"0.68585235\",\n \"0.6848802\",\n \"0.68018305\",\n \"0.6801796\",\n \"0.6785937\",\n \"0.67511636\",\n \"0.6737762\",\n \"0.6649627\",\n \"0.6620649\",\n \"0.6572148\",\n \"0.65664387\",\n \"0.6564884\",\n \"0.6544595\",\n \"0.65435225\",\n \"0.6539307\",\n \"0.65093267\",\n \"0.6489088\",\n \"0.6486471\",\n \"0.6466393\",\n \"0.6442627\",\n \"0.642544\",\n \"0.63891107\",\n \"0.63820785\",\n \"0.63783324\",\n \"0.63695645\",\n \"0.63624835\",\n \"0.6358522\",\n \"0.6350845\",\n \"0.6327597\",\n \"0.63234466\",\n \"0.63218576\",\n \"0.6306377\",\n \"0.6283855\",\n \"0.6274831\",\n \"0.6261332\",\n \"0.6259758\",\n \"0.6251833\",\n \"0.6244885\",\n \"0.62292695\",\n \"0.6207699\",\n \"0.6184362\",\n \"0.61696994\",\n \"0.61560726\",\n \"0.614638\",\n \"0.61440074\",\n \"0.6129719\",\n \"0.61212415\",\n \"0.61165553\",\n \"0.60984194\",\n \"0.6097963\",\n \"0.60717356\",\n \"0.6049447\",\n \"0.6036963\",\n \"0.6031613\",\n \"0.5996555\",\n \"0.59681153\",\n \"0.5965679\",\n \"0.59484005\",\n \"0.5943711\",\n \"0.59437066\",\n \"0.59207684\",\n \"0.59113955\",\n \"0.5900979\",\n \"0.58840895\",\n \"0.58830106\",\n \"0.58818\",\n \"0.5880251\",\n \"0.586815\",\n \"0.5853163\",\n \"0.5852128\",\n \"0.5840969\",\n \"0.5837958\",\n \"0.5836616\",\n \"0.5836456\",\n \"0.58296245\",\n \"0.5827238\",\n \"0.5820289\",\n \"0.5813015\",\n \"0.5807307\",\n \"0.5799579\",\n \"0.579813\",\n \"0.57899207\",\n \"0.57870394\",\n \"0.5784477\",\n \"0.5782243\",\n \"0.57811606\",\n \"0.57786447\",\n \"0.57761085\",\n \"0.57737607\",\n \"0.5767407\",\n \"0.57669175\",\n \"0.5762911\",\n \"0.5759293\",\n \"0.5757081\",\n \"0.57570505\",\n \"0.57541317\",\n \"0.5742615\",\n \"0.5730597\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64559424"},"document_rank":{"kind":"string","value":"21"}}},{"rowIdx":876,"cells":{"query":{"kind":"string","value":"Calculate the squared L2 norm of the pattern."},"document":{"kind":"string","value":"def l2_norm(pattern):\n return np.linalg.norm(pattern)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def L2_norm(self):\n analyticals = self.analytical(self.x_values, self.C, self.D)\n error = analyticals - self.numerical\n self.L2 = np.sqrt((1/self.gp)*np.sum(error**2))","def L2norm(m):\n return np.sqrt(np.sum(m**2))","def squared_norm(self) -> float:\n return self.__real**2 + self.__img[0]**2 + self.__img[1]**2 + self.__img[2]**2","def norm_l2(v):\n return np.sqrt((v**2).sum())","def norm_L2(u):\n return norm_l2(u)/sqrt(float(u.size))","def l2_norm(self):\n return (self.x**2 + self.y**2 + self.z**2)**0.5","def l2_norm(params):\n flattened, _ = flatten(params)\n return np.dot(flattened, flattened)","def _l2_norm_squared(self, z, theta):\n norms = np.zeros(shape=(len(z), self.n_states))\n\n for j in range(self.n_states):\n diff = theta[:, j] - z # ndarray of shape (n_samples, n_states) with differences\n norms[:, j] = np.square(np.linalg.norm(diff, axis=1)) # squared state conditional l2 norms\n\n return norms # squared l2 norm.","def norm2(self):\n\t\treturn self.x ** 2 + self.y ** 2 + self.z ** 2","def l2_norm(self, input):\n input_size = input.size()\n buffer = torch.pow(input, 2)\n normp = torch.sum(buffer, 1).add_(1e-10)\n norm = torch.sqrt(normp)\n _output = torch.div(input, norm.view(-1, 1).expand_as(input))\n output = _output.view(input_size)\n\n return output","def norm(self):\n mag_squared = self._sum_of_squares()\n return sqrt(mag_squared)","def l2(vec):\n return np.linalg.norm(vec)","def L2norm(self, array):\n norm = torch.sqrt(torch.sum(array * array))\n return norm","def l2(weights):\n\treturn np.sqrt(np.sum(weights * weights))","def norm(self):\n\t\treturn math.sqrt(self.norm2())","def norm2d(self) -> float:\n\n return self.v2ddict.norm2d()","def norm2(point):\n return np.sum(point**2, -1)","def l2_norm(v):\n res = 0\n for e in v:\n res += e * e\n return math.sqrt(res)","def l2norm_(X, Xstar):\n return cdist(X, Xstar)","def l2_regularization_penalty(self):\n return self.l2 * (np.linalg.norm(self.weights)**2)","def norm_l2(u):\n return linalg.norm(u.ravel())","def l2_norm_batch(pattern_stack):\n\n return np.linalg.norm(pattern_stack, axis=0)","def norm(self):\n\t\treturn np.sqrt(self.normSq())","def l2norm(array1,array2):\r\n tot = np.sum(np.abs(array1)**2)\r\n return np.sqrt(np.sum(np.abs(array1-array2)**2)/tot)","def _l2s(self, params):\n return [np.linalg.norm(param) for param in params]","def normF2(X):\r\n # pass\r\n if X.shape[0]*X.shape[1] == 0:\r\n return 0\r\n return LA.norm(X, 'fro')**2","def normsq(self):\n return sum(x**2 for x in self.data)","def compute_L2_normalization(xx):\r\n\treturn np.sum(xx ** 2, axis=1)","def tree_l2_norm(tree_x, squared=False):\n squared_tree = tree_map(jnp.square, tree_x)\n sqnorm = tree_sum(squared_tree)\n if squared:\n return sqnorm\n else:\n return jnp.sqrt(sqnorm)","def norm(self):\n return sqrt(self.dot(self))","def normSq(self):\n\t\treturn self.x*self.x+self.y*self.y","def L2_norm(x, axis=-1):\n return keras.backend.l2_normalize(x, axis=axis)","def norm(x):\n return np.sqrt(norm2(x))","def _l2_normalize(x, axis=None, eps=1e-12):\n return x * jax.lax.rsqrt((x * x).sum(axis=axis, keepdims=True) + eps)","def sigma_norm2( self):\n return self._sigma2","def calculate_error_l2_norm(self, dY):\n solutions = []\n norm = 0.\n for mi in range(len(self._meshes)):\n for ei in range(len(self._meshes[mi].elements)):\n e = self._meshes[mi].elements[ei]\n # change this to gauss points:\n x_vals, w = p_roots(20)\n norm_e_squared = 0.\n for i, x in enumerate(x_vals):\n norm_e_squared += w[i] * \\\n self.get_sol_value(mi, ei, dY, x,\n count_lift=False)**2\n norm_e_squared *= e.jacobian\n norm += norm_e_squared\n return sqrt(norm)","def norm(self) -> float:\n return self.squared_norm()**0.5","def norm_with_l2(original_mat):\n normed_mat = np.zeros(original_mat.shape, dtype=np.float32)\n if len(original_mat.shape) == 2:\n for ind_r in range(original_mat.shape[0]):\n a = np.square(original_mat[ind_r]*1.0)\n b = np.sum(a)\n c = np.sqrt(b)\n normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / c\n # normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / np.sqrt(np.sum(np.square(original_mat[ind_r])*1.0))\n return normed_mat","def norm2_r(self, a: np.ndarray) -> float:\n return a.T @ a","def norm(self):\n return math.sqrt(self.dotProduct(self))","def l2norm(X):\n norm = np.linalg.norm(X, axis=1, keepdims=True)\n return 1.0 * X / norm","def normsq(self):\n return abs(sum(self._ar * self._ar))","def norm2(self):\n return getattr(self, self.norm2_name)","def norm(self):\n return np.sqrt(np.dot(self._data, self._data))","def inner_product_to_normalized_L2_square(matrix):\n\n length = matrix.shape[0]\n norm = np.divide(1, np.sqrt(l2_square_from_inner_product(matrix)))\n\n normalized_inner_product = np.multiply(np.multiply(np.reshape(norm, [length, 1]), matrix),\n np.reshape(norm, [1, length]))\n return 2 - 2 * normalized_inner_product","def test_scale_features_L2_norm(self):\n data = array([[0.564, 20.661], [-18.512, 41.168], [-0.009, 20.440]])\n cdata = CData(data)\n\n # correct answer computed in Mathematica\n # TODO: can we compute the right answer in Python?\n answer = array([[0.0304526, 0.409996], [-0.999536, 0.816936], [-0.000485946, 0.40561]])\n\n # perform L2 normalization and check answer\n cdata.scale_features('L2 norm')\n self.assertTrue(allclose(cdata.data, answer))","def L2_normalize(xx):\r\n\tZx = compute_L2_normalization(xx)\r\n\treturn xx / np.sqrt(Zx[:, np.newaxis])","def inner_product_to_L2_square(matrix):\n\n length = matrix.shape[0]\n squared_norm = np.reshape(np.diag(matrix), (length, 1))\n\n return squared_norm + np.transpose(squared_norm) - 2 * matrix","def square_norm(x):\n return np.linalg.norm(x) ** 2","def two_norm(v):\n return math.sqrt(dot_product(v, v))","def norm(x):\n return inner_prod(x, x)[0].sqrt_()","def norm(self):\n C = np.prod([F.T @ F for F in self.factors], axis=0)\n return np.sqrt(np.sum(C))","def get_sqrt_2():\n return 1.41421356","def norm(self):\n return math.sqrt(sum([x*x for x in self.mV]))","def l2_norm(input_x, epsilon=1e-12):\n input_x_norm = input_x/(tf.reduce_sum(input_x**2)**0.5 + epsilon)\n return input_x_norm","def norm(point):\n return np.sqrt(norm2(point))","def fast_2_norm(A):\n v = np.random.rand(A.shape[1], 1)\n return la.norm(A.dot(v))","def normalizeL2(f):\r\n \r\n f=np.array(f)\r\n fsum=np.sum(np.abs(f))\r\n if fsum==0:\r\n fnorm=f\r\n else:\r\n fnorm=f/np.sqrt(np.sum(np.abs(f)**2))\r\n \r\n return fnorm","def Norm(self):\n \n return sqrt(sum([sum(abs(x)**2) for x in self.__ObjList]))","def norm(x):\r\n return sqrt(np.numerical.sum(x**2))","def norm(self):","def normalize_l2(x):\n return x / (npla.norm(x))","def norm(self):\n if self._coord_format != constants.MatrixCoordinateDefault:\n self._logger.error(\"invalid coordinate format\")\n raise NotImplementedError(\"invalid coordinate format\")\n\n if self._dtype == complex:\n def __map(m):\n return m[2].real ** 2 + m[2].imag ** 2\n else:\n def __map(m):\n return m[2] ** 2\n\n n = self._data.map(\n __map\n ).reduce(\n lambda a, b: a + b\n )\n\n return math.sqrt(n)","def normr(Mat):\n B = normalize(Mat, norm='l2', axis=1)\n return B","def norm_sqr(x):\n return inner_prod(x, x)[0]","def l2(v, axis=None):\n length = v.shape[0]\n return np.sqrt(np.sum(np.square(v), axis=axis) / length)","def get_norm(self, l):\n return self._W.norm(l)","def get_norm(self, l):\n return self._W.norm(l)","def squared_norm(self, vector, base_point=None):\n sq_norm = self.embedding_metric.squared_norm(vector)\n return sq_norm","def norm(x):\n return np.sqrt(np.sum(x ** 2))","def squared_norm(self, vector, base_point=None):\n sq_norm = self.inner_product(vector, vector, base_point)\n return gs.real(sq_norm)","def norm(self):\n return np.linalg.norm(self.ravel())","def l2norm(X):\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n X = torch.div(X, norm)\n return X","def l2norm(X):\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n X = torch.div(X, norm)\n return X","def l2norm(X):\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n X = torch.div(X, norm)\n return X","def norm(self):\n norm = self.scalarProduct(self) ** 0.5\n return norm","def squared_norm(self, vector, base_point=None):\n args = {\n \"vector\": vector,\n \"base_point\": base_point,\n }\n sq_norms = self._iterate_over_factors(\"squared_norm\", args)\n return sum(sq_norms)","def l2_norm(vec_or_matrix):\n if len(vec_or_matrix.shape) == 1:\n # linear vector\n return vec_or_matrix / np.linalg.norm(vec_or_matrix)\n elif len(vec_or_matrix.shape) == 2:\n return vec_or_matrix / np.linalg.norm(vec_or_matrix, axis=1, ord=2)[:, np.newaxis]\n else:\n raise ValueError('Wrong number of dimensions, 1 or 2 is supported, not %i.' % len(vec_or_matrix.shape))","def l2(x1, x2):\n return np.sqrt((x1 - x2)**2)","def weight_l2_norm():\n cumulated_l2_norm = tf.constant(0., dtype=tf.float32)\n for trainable_variable in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):\n name = trainable_variable.name.split('/')[-1]\n if name.startswith('weights'):\n cumulated_l2_norm += tf.nn.l2_loss(trainable_variable)\n return cumulated_l2_norm","def residualNorm2(self):\n r2 = (np.dot(self.x,np.dot(self.AtA,self.x)-2.0*self.Atb) + self.btb)*self.scale\n if self.regularizationLambda > 0:\n r2 -= self.regularizationLambda*np.dot(self.x,self.x)\n return r2","def norm(self):\n raise NotImplementedError","def getNorm(self, norm=lambda l: (sum(map(lambda x: x ** 2, l))) ** (1 / 2)):\n return norm(self.components)","def l2norm(X): \n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n a = norm.expand_as(X) + 1e-8\n X = torch.div(X, a) \n return X","def dist_squared(self, vec2):\n if type(vec2) != Vector:\n raise TypeError(\"Not a vector\")\n\n return (self - vec2) * (self - vec2)","def norm2d(w_in):\n return nn.BatchNorm2d(num_features=w_in, eps=cfg.BN.EPS, momentum=cfg.BN.MOM)","def l2norm_1d(new, old):\n\tdiff = 0\n\tnx = len(new)\n\tfor i, n in enumerate(new):\n\t\tif n:\n\t\t\tdiff += ((n - old[i])/n)**2\n\tnorm = scipy.sqrt(diff/nx)\n\treturn norm","def project_L2(w, l):\n return w * min(1, 1 / (l ** (1 / 2.0) * np.linalg.norm(w, 2)))","def P2l_rec_norm(ells, cost):\n P22 = 3. * (1. - cost**2)\n P23 = 15. * cost * (1. - cost**2)\n P2l = np.zeros(len(ells))\n P2l[0] = 0.\n P2l[1] = 0.\n P2l[2] = P22\n P2l[3] = P23\n P2l_norm = np.copy(P2l)\n P2l_norm[2] *= P2l_norm_prefac(2)\n P2l_norm[3] *= P2l_norm_prefac(3)\n for ell in ells[4:]:\n # print ell, P2l[ell-1], P2l[ell-2]\n a = np.sqrt((4 * ell**2 - 1.) / (ell**2 - 4))\n b = cost * P2l_norm[ell - 1]\n c = np.sqrt(((ell - 1.)**2 - 4) /\n (4 * (ell - 1.)**2 - 1)) * P2l_norm[ell - 2]\n # print a,b,c\n P2l_norm[ell] = a * (b - c)\n # print ell, P2l_norm[ell], P2l_norm_prefac(ell)\n P2l[ell] = P2l_norm[ell] / P2l_norm_prefac(ell)\n return P2l","def func_val_l1_norm(w):\n\treturn np.linalg.norm(w,ord = 1)","def norm(self) -> float:\n return numpy.linalg.norm(self.coeff)","def squaredDistance(vec1, vec2):\n return (distance.euclidean(vec1, vec2))**2","def norm(self):\n return np.linalg.norm(self.values)","def l2_normalize(data, axis=-1, eps=1e-6):\n ret = data / (np.linalg.norm(data, axis=axis, keepdims=True) + eps)\n return ret","def squared_distance_calculator(position1, position2):\r\n difference_vector = position2 - position1\r\n return np.dot(difference_vector, difference_vector)","def vec_2norm (x):\n return math.sqrt (sum ([x_i**2 for x_i in x]))","def norm(self):\n return numpy.linalg.norm(self.values)","def normalize_l2norm(data,tol=0):\n data_sqrt=np.sqrt(np.square(data).sum(axis=1))\n data_sqrt.shape=(data_sqrt.shape[0],1)\n #tol=0#1e-8\n data=data/(data_sqrt+tol)\n return data","def model_norm(self, order=2) -> float:\n # L-n norm of model where we treat the model as a flat other\n return math.pow(sum([\n torch.pow(layer, order).sum().item()\n for layer in self.parameters\n ]), 1.0 / order)","def l2_normalize(data, eps, axis=None):\n return cpp.nn.l2_normalize(data, eps, axis)"],"string":"[\n \"def L2_norm(self):\\n analyticals = self.analytical(self.x_values, self.C, self.D)\\n error = analyticals - self.numerical\\n self.L2 = np.sqrt((1/self.gp)*np.sum(error**2))\",\n \"def L2norm(m):\\n return np.sqrt(np.sum(m**2))\",\n \"def squared_norm(self) -> float:\\n return self.__real**2 + self.__img[0]**2 + self.__img[1]**2 + self.__img[2]**2\",\n \"def norm_l2(v):\\n return np.sqrt((v**2).sum())\",\n \"def norm_L2(u):\\n return norm_l2(u)/sqrt(float(u.size))\",\n \"def l2_norm(self):\\n return (self.x**2 + self.y**2 + self.z**2)**0.5\",\n \"def l2_norm(params):\\n flattened, _ = flatten(params)\\n return np.dot(flattened, flattened)\",\n \"def _l2_norm_squared(self, z, theta):\\n norms = np.zeros(shape=(len(z), self.n_states))\\n\\n for j in range(self.n_states):\\n diff = theta[:, j] - z # ndarray of shape (n_samples, n_states) with differences\\n norms[:, j] = np.square(np.linalg.norm(diff, axis=1)) # squared state conditional l2 norms\\n\\n return norms # squared l2 norm.\",\n \"def norm2(self):\\n\\t\\treturn self.x ** 2 + self.y ** 2 + self.z ** 2\",\n \"def l2_norm(self, input):\\n input_size = input.size()\\n buffer = torch.pow(input, 2)\\n normp = torch.sum(buffer, 1).add_(1e-10)\\n norm = torch.sqrt(normp)\\n _output = torch.div(input, norm.view(-1, 1).expand_as(input))\\n output = _output.view(input_size)\\n\\n return output\",\n \"def norm(self):\\n mag_squared = self._sum_of_squares()\\n return sqrt(mag_squared)\",\n \"def l2(vec):\\n return np.linalg.norm(vec)\",\n \"def L2norm(self, array):\\n norm = torch.sqrt(torch.sum(array * array))\\n return norm\",\n \"def l2(weights):\\n\\treturn np.sqrt(np.sum(weights * weights))\",\n \"def norm(self):\\n\\t\\treturn math.sqrt(self.norm2())\",\n \"def norm2d(self) -> float:\\n\\n return self.v2ddict.norm2d()\",\n \"def norm2(point):\\n return np.sum(point**2, -1)\",\n \"def l2_norm(v):\\n res = 0\\n for e in v:\\n res += e * e\\n return math.sqrt(res)\",\n \"def l2norm_(X, Xstar):\\n return cdist(X, Xstar)\",\n \"def l2_regularization_penalty(self):\\n return self.l2 * (np.linalg.norm(self.weights)**2)\",\n \"def norm_l2(u):\\n return linalg.norm(u.ravel())\",\n \"def l2_norm_batch(pattern_stack):\\n\\n return np.linalg.norm(pattern_stack, axis=0)\",\n \"def norm(self):\\n\\t\\treturn np.sqrt(self.normSq())\",\n \"def l2norm(array1,array2):\\r\\n tot = np.sum(np.abs(array1)**2)\\r\\n return np.sqrt(np.sum(np.abs(array1-array2)**2)/tot)\",\n \"def _l2s(self, params):\\n return [np.linalg.norm(param) for param in params]\",\n \"def normF2(X):\\r\\n # pass\\r\\n if X.shape[0]*X.shape[1] == 0:\\r\\n return 0\\r\\n return LA.norm(X, 'fro')**2\",\n \"def normsq(self):\\n return sum(x**2 for x in self.data)\",\n \"def compute_L2_normalization(xx):\\r\\n\\treturn np.sum(xx ** 2, axis=1)\",\n \"def tree_l2_norm(tree_x, squared=False):\\n squared_tree = tree_map(jnp.square, tree_x)\\n sqnorm = tree_sum(squared_tree)\\n if squared:\\n return sqnorm\\n else:\\n return jnp.sqrt(sqnorm)\",\n \"def norm(self):\\n return sqrt(self.dot(self))\",\n \"def normSq(self):\\n\\t\\treturn self.x*self.x+self.y*self.y\",\n \"def L2_norm(x, axis=-1):\\n return keras.backend.l2_normalize(x, axis=axis)\",\n \"def norm(x):\\n return np.sqrt(norm2(x))\",\n \"def _l2_normalize(x, axis=None, eps=1e-12):\\n return x * jax.lax.rsqrt((x * x).sum(axis=axis, keepdims=True) + eps)\",\n \"def sigma_norm2( self):\\n return self._sigma2\",\n \"def calculate_error_l2_norm(self, dY):\\n solutions = []\\n norm = 0.\\n for mi in range(len(self._meshes)):\\n for ei in range(len(self._meshes[mi].elements)):\\n e = self._meshes[mi].elements[ei]\\n # change this to gauss points:\\n x_vals, w = p_roots(20)\\n norm_e_squared = 0.\\n for i, x in enumerate(x_vals):\\n norm_e_squared += w[i] * \\\\\\n self.get_sol_value(mi, ei, dY, x,\\n count_lift=False)**2\\n norm_e_squared *= e.jacobian\\n norm += norm_e_squared\\n return sqrt(norm)\",\n \"def norm(self) -> float:\\n return self.squared_norm()**0.5\",\n \"def norm_with_l2(original_mat):\\n normed_mat = np.zeros(original_mat.shape, dtype=np.float32)\\n if len(original_mat.shape) == 2:\\n for ind_r in range(original_mat.shape[0]):\\n a = np.square(original_mat[ind_r]*1.0)\\n b = np.sum(a)\\n c = np.sqrt(b)\\n normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / c\\n # normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / np.sqrt(np.sum(np.square(original_mat[ind_r])*1.0))\\n return normed_mat\",\n \"def norm2_r(self, a: np.ndarray) -> float:\\n return a.T @ a\",\n \"def norm(self):\\n return math.sqrt(self.dotProduct(self))\",\n \"def l2norm(X):\\n norm = np.linalg.norm(X, axis=1, keepdims=True)\\n return 1.0 * X / norm\",\n \"def normsq(self):\\n return abs(sum(self._ar * self._ar))\",\n \"def norm2(self):\\n return getattr(self, self.norm2_name)\",\n \"def norm(self):\\n return np.sqrt(np.dot(self._data, self._data))\",\n \"def inner_product_to_normalized_L2_square(matrix):\\n\\n length = matrix.shape[0]\\n norm = np.divide(1, np.sqrt(l2_square_from_inner_product(matrix)))\\n\\n normalized_inner_product = np.multiply(np.multiply(np.reshape(norm, [length, 1]), matrix),\\n np.reshape(norm, [1, length]))\\n return 2 - 2 * normalized_inner_product\",\n \"def test_scale_features_L2_norm(self):\\n data = array([[0.564, 20.661], [-18.512, 41.168], [-0.009, 20.440]])\\n cdata = CData(data)\\n\\n # correct answer computed in Mathematica\\n # TODO: can we compute the right answer in Python?\\n answer = array([[0.0304526, 0.409996], [-0.999536, 0.816936], [-0.000485946, 0.40561]])\\n\\n # perform L2 normalization and check answer\\n cdata.scale_features('L2 norm')\\n self.assertTrue(allclose(cdata.data, answer))\",\n \"def L2_normalize(xx):\\r\\n\\tZx = compute_L2_normalization(xx)\\r\\n\\treturn xx / np.sqrt(Zx[:, np.newaxis])\",\n \"def inner_product_to_L2_square(matrix):\\n\\n length = matrix.shape[0]\\n squared_norm = np.reshape(np.diag(matrix), (length, 1))\\n\\n return squared_norm + np.transpose(squared_norm) - 2 * matrix\",\n \"def square_norm(x):\\n return np.linalg.norm(x) ** 2\",\n \"def two_norm(v):\\n return math.sqrt(dot_product(v, v))\",\n \"def norm(x):\\n return inner_prod(x, x)[0].sqrt_()\",\n \"def norm(self):\\n C = np.prod([F.T @ F for F in self.factors], axis=0)\\n return np.sqrt(np.sum(C))\",\n \"def get_sqrt_2():\\n return 1.41421356\",\n \"def norm(self):\\n return math.sqrt(sum([x*x for x in self.mV]))\",\n \"def l2_norm(input_x, epsilon=1e-12):\\n input_x_norm = input_x/(tf.reduce_sum(input_x**2)**0.5 + epsilon)\\n return input_x_norm\",\n \"def norm(point):\\n return np.sqrt(norm2(point))\",\n \"def fast_2_norm(A):\\n v = np.random.rand(A.shape[1], 1)\\n return la.norm(A.dot(v))\",\n \"def normalizeL2(f):\\r\\n \\r\\n f=np.array(f)\\r\\n fsum=np.sum(np.abs(f))\\r\\n if fsum==0:\\r\\n fnorm=f\\r\\n else:\\r\\n fnorm=f/np.sqrt(np.sum(np.abs(f)**2))\\r\\n \\r\\n return fnorm\",\n \"def Norm(self):\\n \\n return sqrt(sum([sum(abs(x)**2) for x in self.__ObjList]))\",\n \"def norm(x):\\r\\n return sqrt(np.numerical.sum(x**2))\",\n \"def norm(self):\",\n \"def normalize_l2(x):\\n return x / (npla.norm(x))\",\n \"def norm(self):\\n if self._coord_format != constants.MatrixCoordinateDefault:\\n self._logger.error(\\\"invalid coordinate format\\\")\\n raise NotImplementedError(\\\"invalid coordinate format\\\")\\n\\n if self._dtype == complex:\\n def __map(m):\\n return m[2].real ** 2 + m[2].imag ** 2\\n else:\\n def __map(m):\\n return m[2] ** 2\\n\\n n = self._data.map(\\n __map\\n ).reduce(\\n lambda a, b: a + b\\n )\\n\\n return math.sqrt(n)\",\n \"def normr(Mat):\\n B = normalize(Mat, norm='l2', axis=1)\\n return B\",\n \"def norm_sqr(x):\\n return inner_prod(x, x)[0]\",\n \"def l2(v, axis=None):\\n length = v.shape[0]\\n return np.sqrt(np.sum(np.square(v), axis=axis) / length)\",\n \"def get_norm(self, l):\\n return self._W.norm(l)\",\n \"def get_norm(self, l):\\n return self._W.norm(l)\",\n \"def squared_norm(self, vector, base_point=None):\\n sq_norm = self.embedding_metric.squared_norm(vector)\\n return sq_norm\",\n \"def norm(x):\\n return np.sqrt(np.sum(x ** 2))\",\n \"def squared_norm(self, vector, base_point=None):\\n sq_norm = self.inner_product(vector, vector, base_point)\\n return gs.real(sq_norm)\",\n \"def norm(self):\\n return np.linalg.norm(self.ravel())\",\n \"def l2norm(X):\\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n X = torch.div(X, norm)\\n return X\",\n \"def l2norm(X):\\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n X = torch.div(X, norm)\\n return X\",\n \"def l2norm(X):\\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n X = torch.div(X, norm)\\n return X\",\n \"def norm(self):\\n norm = self.scalarProduct(self) ** 0.5\\n return norm\",\n \"def squared_norm(self, vector, base_point=None):\\n args = {\\n \\\"vector\\\": vector,\\n \\\"base_point\\\": base_point,\\n }\\n sq_norms = self._iterate_over_factors(\\\"squared_norm\\\", args)\\n return sum(sq_norms)\",\n \"def l2_norm(vec_or_matrix):\\n if len(vec_or_matrix.shape) == 1:\\n # linear vector\\n return vec_or_matrix / np.linalg.norm(vec_or_matrix)\\n elif len(vec_or_matrix.shape) == 2:\\n return vec_or_matrix / np.linalg.norm(vec_or_matrix, axis=1, ord=2)[:, np.newaxis]\\n else:\\n raise ValueError('Wrong number of dimensions, 1 or 2 is supported, not %i.' % len(vec_or_matrix.shape))\",\n \"def l2(x1, x2):\\n return np.sqrt((x1 - x2)**2)\",\n \"def weight_l2_norm():\\n cumulated_l2_norm = tf.constant(0., dtype=tf.float32)\\n for trainable_variable in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):\\n name = trainable_variable.name.split('/')[-1]\\n if name.startswith('weights'):\\n cumulated_l2_norm += tf.nn.l2_loss(trainable_variable)\\n return cumulated_l2_norm\",\n \"def residualNorm2(self):\\n r2 = (np.dot(self.x,np.dot(self.AtA,self.x)-2.0*self.Atb) + self.btb)*self.scale\\n if self.regularizationLambda > 0:\\n r2 -= self.regularizationLambda*np.dot(self.x,self.x)\\n return r2\",\n \"def norm(self):\\n raise NotImplementedError\",\n \"def getNorm(self, norm=lambda l: (sum(map(lambda x: x ** 2, l))) ** (1 / 2)):\\n return norm(self.components)\",\n \"def l2norm(X): \\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n a = norm.expand_as(X) + 1e-8\\n X = torch.div(X, a) \\n return X\",\n \"def dist_squared(self, vec2):\\n if type(vec2) != Vector:\\n raise TypeError(\\\"Not a vector\\\")\\n\\n return (self - vec2) * (self - vec2)\",\n \"def norm2d(w_in):\\n return nn.BatchNorm2d(num_features=w_in, eps=cfg.BN.EPS, momentum=cfg.BN.MOM)\",\n \"def l2norm_1d(new, old):\\n\\tdiff = 0\\n\\tnx = len(new)\\n\\tfor i, n in enumerate(new):\\n\\t\\tif n:\\n\\t\\t\\tdiff += ((n - old[i])/n)**2\\n\\tnorm = scipy.sqrt(diff/nx)\\n\\treturn norm\",\n \"def project_L2(w, l):\\n return w * min(1, 1 / (l ** (1 / 2.0) * np.linalg.norm(w, 2)))\",\n \"def P2l_rec_norm(ells, cost):\\n P22 = 3. * (1. - cost**2)\\n P23 = 15. * cost * (1. - cost**2)\\n P2l = np.zeros(len(ells))\\n P2l[0] = 0.\\n P2l[1] = 0.\\n P2l[2] = P22\\n P2l[3] = P23\\n P2l_norm = np.copy(P2l)\\n P2l_norm[2] *= P2l_norm_prefac(2)\\n P2l_norm[3] *= P2l_norm_prefac(3)\\n for ell in ells[4:]:\\n # print ell, P2l[ell-1], P2l[ell-2]\\n a = np.sqrt((4 * ell**2 - 1.) / (ell**2 - 4))\\n b = cost * P2l_norm[ell - 1]\\n c = np.sqrt(((ell - 1.)**2 - 4) /\\n (4 * (ell - 1.)**2 - 1)) * P2l_norm[ell - 2]\\n # print a,b,c\\n P2l_norm[ell] = a * (b - c)\\n # print ell, P2l_norm[ell], P2l_norm_prefac(ell)\\n P2l[ell] = P2l_norm[ell] / P2l_norm_prefac(ell)\\n return P2l\",\n \"def func_val_l1_norm(w):\\n\\treturn np.linalg.norm(w,ord = 1)\",\n \"def norm(self) -> float:\\n return numpy.linalg.norm(self.coeff)\",\n \"def squaredDistance(vec1, vec2):\\n return (distance.euclidean(vec1, vec2))**2\",\n \"def norm(self):\\n return np.linalg.norm(self.values)\",\n \"def l2_normalize(data, axis=-1, eps=1e-6):\\n ret = data / (np.linalg.norm(data, axis=axis, keepdims=True) + eps)\\n return ret\",\n \"def squared_distance_calculator(position1, position2):\\r\\n difference_vector = position2 - position1\\r\\n return np.dot(difference_vector, difference_vector)\",\n \"def vec_2norm (x):\\n return math.sqrt (sum ([x_i**2 for x_i in x]))\",\n \"def norm(self):\\n return numpy.linalg.norm(self.values)\",\n \"def normalize_l2norm(data,tol=0):\\n data_sqrt=np.sqrt(np.square(data).sum(axis=1))\\n data_sqrt.shape=(data_sqrt.shape[0],1)\\n #tol=0#1e-8\\n data=data/(data_sqrt+tol)\\n return data\",\n \"def model_norm(self, order=2) -> float:\\n # L-n norm of model where we treat the model as a flat other\\n return math.pow(sum([\\n torch.pow(layer, order).sum().item()\\n for layer in self.parameters\\n ]), 1.0 / order)\",\n \"def l2_normalize(data, eps, axis=None):\\n return cpp.nn.l2_normalize(data, eps, axis)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7689054","0.7469105","0.7411847","0.7393104","0.73873013","0.73019034","0.7256206","0.72420824","0.71961755","0.7140405","0.71325195","0.71182483","0.71150076","0.7069896","0.7034822","0.7023806","0.7019462","0.69990593","0.69920164","0.69680566","0.6951413","0.69096494","0.6767838","0.6760333","0.673882","0.67322636","0.6731022","0.6729424","0.6721004","0.6703303","0.6668874","0.66516876","0.66384107","0.65615237","0.6560211","0.65599376","0.6558396","0.65547085","0.65506625","0.65485436","0.6518504","0.65143293","0.6474202","0.646358","0.64603555","0.64586043","0.6408987","0.6408933","0.6386254","0.63810736","0.63801146","0.6365494","0.63620234","0.63610953","0.6342854","0.6337362","0.6331644","0.6296248","0.62948173","0.62751955","0.62665683","0.62654275","0.6245794","0.62226516","0.62120557","0.621056","0.62048686","0.62048686","0.61481684","0.6129907","0.6125986","0.61256516","0.61035","0.61035","0.61035","0.6101315","0.60767424","0.6068372","0.60672754","0.6066929","0.6063615","0.6049005","0.60451674","0.6041332","0.6040449","0.60394794","0.6038446","0.60287476","0.6020228","0.6018523","0.601738","0.6011987","0.60094947","0.6003854","0.59972584","0.59932375","0.5973853","0.5969759","0.5957874","0.59449786"],"string":"[\n \"0.7689054\",\n \"0.7469105\",\n \"0.7411847\",\n \"0.7393104\",\n \"0.73873013\",\n \"0.73019034\",\n \"0.7256206\",\n \"0.72420824\",\n \"0.71961755\",\n \"0.7140405\",\n \"0.71325195\",\n \"0.71182483\",\n \"0.71150076\",\n \"0.7069896\",\n \"0.7034822\",\n \"0.7023806\",\n \"0.7019462\",\n \"0.69990593\",\n \"0.69920164\",\n \"0.69680566\",\n \"0.6951413\",\n \"0.69096494\",\n \"0.6767838\",\n \"0.6760333\",\n \"0.673882\",\n \"0.67322636\",\n \"0.6731022\",\n \"0.6729424\",\n \"0.6721004\",\n \"0.6703303\",\n \"0.6668874\",\n \"0.66516876\",\n \"0.66384107\",\n \"0.65615237\",\n \"0.6560211\",\n \"0.65599376\",\n \"0.6558396\",\n \"0.65547085\",\n \"0.65506625\",\n \"0.65485436\",\n \"0.6518504\",\n \"0.65143293\",\n \"0.6474202\",\n \"0.646358\",\n \"0.64603555\",\n \"0.64586043\",\n \"0.6408987\",\n \"0.6408933\",\n \"0.6386254\",\n \"0.63810736\",\n \"0.63801146\",\n \"0.6365494\",\n \"0.63620234\",\n \"0.63610953\",\n \"0.6342854\",\n \"0.6337362\",\n \"0.6331644\",\n \"0.6296248\",\n \"0.62948173\",\n \"0.62751955\",\n \"0.62665683\",\n \"0.62654275\",\n \"0.6245794\",\n \"0.62226516\",\n \"0.62120557\",\n \"0.621056\",\n \"0.62048686\",\n \"0.62048686\",\n \"0.61481684\",\n \"0.6129907\",\n \"0.6125986\",\n \"0.61256516\",\n \"0.61035\",\n \"0.61035\",\n \"0.61035\",\n \"0.6101315\",\n \"0.60767424\",\n \"0.6068372\",\n \"0.60672754\",\n \"0.6066929\",\n \"0.6063615\",\n \"0.6049005\",\n \"0.60451674\",\n \"0.6041332\",\n \"0.6040449\",\n \"0.60394794\",\n \"0.6038446\",\n \"0.60287476\",\n \"0.6020228\",\n \"0.6018523\",\n \"0.601738\",\n \"0.6011987\",\n \"0.60094947\",\n \"0.6003854\",\n \"0.59972584\",\n \"0.59932375\",\n \"0.5973853\",\n \"0.5969759\",\n \"0.5957874\",\n \"0.59449786\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8329601"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":877,"cells":{"query":{"kind":"string","value":"Calculate the l2 norm of a stack of patterns."},"document":{"kind":"string","value":"def l2_norm_batch(pattern_stack):\n\n return np.linalg.norm(pattern_stack, axis=0)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def l2_norm(pattern):\n return np.linalg.norm(pattern)","def l2(weights):\n\treturn np.sqrt(np.sum(weights * weights))","def l2_norm(params):\n flattened, _ = flatten(params)\n return np.dot(flattened, flattened)","def L2norm(m):\n return np.sqrt(np.sum(m**2))","def L2_norm(self):\n analyticals = self.analytical(self.x_values, self.C, self.D)\n error = analyticals - self.numerical\n self.L2 = np.sqrt((1/self.gp)*np.sum(error**2))","def _l2_norm_squared(self, z, theta):\n norms = np.zeros(shape=(len(z), self.n_states))\n\n for j in range(self.n_states):\n diff = theta[:, j] - z # ndarray of shape (n_samples, n_states) with differences\n norms[:, j] = np.square(np.linalg.norm(diff, axis=1)) # squared state conditional l2 norms\n\n return norms # squared l2 norm.","def l2norm(array1,array2):\r\n tot = np.sum(np.abs(array1)**2)\r\n return np.sqrt(np.sum(np.abs(array1-array2)**2)/tot)","def norm_L2(u):\n return norm_l2(u)/sqrt(float(u.size))","def l2_norm(self):\n return (self.x**2 + self.y**2 + self.z**2)**0.5","def _l2s(self, params):\n return [np.linalg.norm(param) for param in params]","def l2(vec):\n return np.linalg.norm(vec)","def norm_l2(u):\n return linalg.norm(u.ravel())","def tree_l2_norm(tree_x, squared=False):\n squared_tree = tree_map(jnp.square, tree_x)\n sqnorm = tree_sum(squared_tree)\n if squared:\n return sqnorm\n else:\n return jnp.sqrt(sqnorm)","def L2_norm(x, axis=-1):\n return keras.backend.l2_normalize(x, axis=axis)","def compute_L2_normalization(xx):\r\n\treturn np.sum(xx ** 2, axis=1)","def norm_l2(v):\n return np.sqrt((v**2).sum())","def L2norm(self, array):\n norm = torch.sqrt(torch.sum(array * array))\n return norm","def norm_with_l2(original_mat):\n normed_mat = np.zeros(original_mat.shape, dtype=np.float32)\n if len(original_mat.shape) == 2:\n for ind_r in range(original_mat.shape[0]):\n a = np.square(original_mat[ind_r]*1.0)\n b = np.sum(a)\n c = np.sqrt(b)\n normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / c\n # normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / np.sqrt(np.sum(np.square(original_mat[ind_r])*1.0))\n return normed_mat","def l2_norm(self, input):\n input_size = input.size()\n buffer = torch.pow(input, 2)\n normp = torch.sum(buffer, 1).add_(1e-10)\n norm = torch.sqrt(normp)\n _output = torch.div(input, norm.view(-1, 1).expand_as(input))\n output = _output.view(input_size)\n\n return output","def l2_norm(v):\n res = 0\n for e in v:\n res += e * e\n return math.sqrt(res)","def norm2(self):\n\t\treturn self.x ** 2 + self.y ** 2 + self.z ** 2","def l2_regularization_penalty(self):\n return self.l2 * (np.linalg.norm(self.weights)**2)","def l2norm_(X, Xstar):\n return cdist(X, Xstar)","def weight_l2_norm():\n cumulated_l2_norm = tf.constant(0., dtype=tf.float32)\n for trainable_variable in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):\n name = trainable_variable.name.split('/')[-1]\n if name.startswith('weights'):\n cumulated_l2_norm += tf.nn.l2_loss(trainable_variable)\n return cumulated_l2_norm","def normF2(X):\r\n # pass\r\n if X.shape[0]*X.shape[1] == 0:\r\n return 0\r\n return LA.norm(X, 'fro')**2","def norm2(point):\n return np.sum(point**2, -1)","def norm2d(self) -> float:\n\n return self.v2ddict.norm2d()","def inner_product_to_normalized_L2_square(matrix):\n\n length = matrix.shape[0]\n norm = np.divide(1, np.sqrt(l2_square_from_inner_product(matrix)))\n\n normalized_inner_product = np.multiply(np.multiply(np.reshape(norm, [length, 1]), matrix),\n np.reshape(norm, [1, length]))\n return 2 - 2 * normalized_inner_product","def _l2_normalize(x, axis=None, eps=1e-12):\n return x * jax.lax.rsqrt((x * x).sum(axis=axis, keepdims=True) + eps)","def l2norm(X):\n norm = np.linalg.norm(X, axis=1, keepdims=True)\n return 1.0 * X / norm","def l2_normalize(data, eps, axis=None):\n return cpp.nn.l2_normalize(data, eps, axis)","def calculate_error_l2_norm(self, dY):\n solutions = []\n norm = 0.\n for mi in range(len(self._meshes)):\n for ei in range(len(self._meshes[mi].elements)):\n e = self._meshes[mi].elements[ei]\n # change this to gauss points:\n x_vals, w = p_roots(20)\n norm_e_squared = 0.\n for i, x in enumerate(x_vals):\n norm_e_squared += w[i] * \\\n self.get_sol_value(mi, ei, dY, x,\n count_lift=False)**2\n norm_e_squared *= e.jacobian\n norm += norm_e_squared\n return sqrt(norm)","def l2norm(X, dim=-1, eps=1e-8):\r\n norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps\r\n X = torch.div(X, norm)\r\n return X","def l2(v, axis=None):\n length = v.shape[0]\n return np.sqrt(np.sum(np.square(v), axis=axis) / length)","def get_norms(self):\n l1_sum = 0\n l2_sum = 0\n actives = 0\n for lbl in self.labels:\n for fid in self.w[lbl]:\n # apply and remaing L1 penalities at the end of training.\n alpha = self.s - self.lastW[lbl].get(fid,0)\n self.w[lbl][fid] = self.w[lbl].get(fid, 0) - alpha\n weight = self.w[lbl][fid]\n l1_sum += weight if weight > 0 else -weight\n l2_sum += weight * weight\n if weight != 0:\n actives += 1\n l2_sum = math.sqrt(l2_sum)\n return (l1_sum,l2_sum,actives)","def normr(Mat):\n B = normalize(Mat, norm='l2', axis=1)\n return B","def l2_norm(vec_or_matrix):\n if len(vec_or_matrix.shape) == 1:\n # linear vector\n return vec_or_matrix / np.linalg.norm(vec_or_matrix)\n elif len(vec_or_matrix.shape) == 2:\n return vec_or_matrix / np.linalg.norm(vec_or_matrix, axis=1, ord=2)[:, np.newaxis]\n else:\n raise ValueError('Wrong number of dimensions, 1 or 2 is supported, not %i.' % len(vec_or_matrix.shape))","def l2_normalize(data, axis=-1, eps=1e-6):\n ret = data / (np.linalg.norm(data, axis=axis, keepdims=True) + eps)\n return ret","def P2l_rec_norm(ells, cost):\n P22 = 3. * (1. - cost**2)\n P23 = 15. * cost * (1. - cost**2)\n P2l = np.zeros(len(ells))\n P2l[0] = 0.\n P2l[1] = 0.\n P2l[2] = P22\n P2l[3] = P23\n P2l_norm = np.copy(P2l)\n P2l_norm[2] *= P2l_norm_prefac(2)\n P2l_norm[3] *= P2l_norm_prefac(3)\n for ell in ells[4:]:\n # print ell, P2l[ell-1], P2l[ell-2]\n a = np.sqrt((4 * ell**2 - 1.) / (ell**2 - 4))\n b = cost * P2l_norm[ell - 1]\n c = np.sqrt(((ell - 1.)**2 - 4) /\n (4 * (ell - 1.)**2 - 1)) * P2l_norm[ell - 2]\n # print a,b,c\n P2l_norm[ell] = a * (b - c)\n # print ell, P2l_norm[ell], P2l_norm_prefac(ell)\n P2l[ell] = P2l_norm[ell] / P2l_norm_prefac(ell)\n return P2l","def sigma_norm2( self):\n return self._sigma2","def l2_normalization(inputs, scaling=True):\n with tf.variable_scope('L2Normalization'):\n inputs_shape = inputs.get_shape()\n channel_shape = inputs_shape[-1:]\n # cal l2_norm on channel\n outputs = tf.nn.l2_normalize(inputs, 3, epsilon=1e-12)\n # scalling\n if scaling:\n # scale.shape == channel.shape\n scale = slim.variable('gamma', channel_shape, tf.float32, tf.constant_initializer(1.0))\n outputs = tf.multiply(outputs, scale)\n\n return outputs","def fast_2_norm(A):\n v = np.random.rand(A.shape[1], 1)\n return la.norm(A.dot(v))","def test_scale_features_L2_norm(self):\n data = array([[0.564, 20.661], [-18.512, 41.168], [-0.009, 20.440]])\n cdata = CData(data)\n\n # correct answer computed in Mathematica\n # TODO: can we compute the right answer in Python?\n answer = array([[0.0304526, 0.409996], [-0.999536, 0.816936], [-0.000485946, 0.40561]])\n\n # perform L2 normalization and check answer\n cdata.scale_features('L2 norm')\n self.assertTrue(allclose(cdata.data, answer))","def norms(Z):\n return Z.view(Z.shape[0], -1).norm(dim=1)[:,None,None,None]","def norm2_r(self, a: np.ndarray) -> float:\n return a.T @ a","def normalizeL2(f):\r\n \r\n f=np.array(f)\r\n fsum=np.sum(np.abs(f))\r\n if fsum==0:\r\n fnorm=f\r\n else:\r\n fnorm=f/np.sqrt(np.sum(np.abs(f)**2))\r\n \r\n return fnorm","def L2_func(x):\n return K.expand_dims(K.sqrt(K.sum(K.pow(x,2), axis=1)))","def L2_normalize(xx):\r\n\tZx = compute_L2_normalization(xx)\r\n\treturn xx / np.sqrt(Zx[:, np.newaxis])","def inner_product_to_L2_square(matrix):\n\n length = matrix.shape[0]\n squared_norm = np.reshape(np.diag(matrix), (length, 1))\n\n return squared_norm + np.transpose(squared_norm) - 2 * matrix","def project_L2(w, l):\n return w * min(1, 1 / (l ** (1 / 2.0) * np.linalg.norm(w, 2)))","def l2_distance(v1, v2):\n\treturn np.linalg.norm(np.array(v1) - np.array(v2))","def l2_norm(input_x, epsilon=1e-12):\n input_x_norm = input_x/(tf.reduce_sum(input_x**2)**0.5 + epsilon)\n return input_x_norm","def l2norm_1d(new, old):\n\tdiff = 0\n\tnx = len(new)\n\tfor i, n in enumerate(new):\n\t\tif n:\n\t\t\tdiff += ((n - old[i])/n)**2\n\tnorm = scipy.sqrt(diff/nx)\n\treturn norm","def l2(x1, x2):\n return np.sqrt((x1 - x2)**2)","def normalize_l2(x):\n return x / (npla.norm(x))","def l2norm(X):\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n X = torch.div(X, norm)\n return X","def l2norm(X):\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n X = torch.div(X, norm)\n return X","def l2norm(X):\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n X = torch.div(X, norm)\n return X","def l2norm(X): \n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n a = norm.expand_as(X) + 1e-8\n X = torch.div(X, a) \n return X","def two_norm(v):\n return math.sqrt(dot_product(v, v))","def gradient_nD(stack):\n # Convert for 64-bit to avoid large number problems in squares.\n stack = np.copy(stack)\n stack = stack.astype(np.float64)\n sumsq = ndi.filters.sobel(stack, axis=0) ** 2\n for d in range(1, stack.ndim):\n sumsq = sumsq + (ndi.filters.sobel(stack, axis=d) ** 2)\n gradient = np.sqrt(sumsq)\n return gradient","def norm2d(w_in):\n return nn.BatchNorm2d(num_features=w_in, eps=cfg.BN.EPS, momentum=cfg.BN.MOM)","def compute_model_diff_squared_norm(model1: torch.nn.Module, model2: torch.nn.Module):\n tensor1 = list(model1.parameters())\n tensor2 = list(model2.parameters())\n norm = sum([torch.sum((tensor1[i] - tensor2[i]) ** 2) for i in range(len(tensor1))])\n\n return norm","def model_norm(self, order=2) -> float:\n # L-n norm of model where we treat the model as a flat other\n return math.pow(sum([\n torch.pow(layer, order).sum().item()\n for layer in self.parameters\n ]), 1.0 / order)","def normalize_l2norm(data,tol=0):\n data_sqrt=np.sqrt(np.square(data).sum(axis=1))\n data_sqrt.shape=(data_sqrt.shape[0],1)\n #tol=0#1e-8\n data=data/(data_sqrt+tol)\n return data","def norm(self):","def l2(weights, name=None):\n with ops.op_scope([weights], name, 'l2_regularizer') as scope:\n my_scale = ops.convert_to_tensor(scale,\n dtype=weights.dtype.base_dtype,\n name='scale')\n return standard_ops.mul(my_scale, nn.l2_loss(weights), name=scope)","def l2_norm(x, filters=None, epsilon=1e-6, name=None, reuse=None):\n if filters is None:\n filters = shape_list(x)[-1]\n with tf.variable_scope(name, default_name=\"l2_norm\", values=[x], reuse=reuse):\n scale = tf.get_variable(\n \"l2_norm_scale\", [filters], initializer=tf.ones_initializer())\n bias = tf.get_variable(\n \"l2_norm_bias\", [filters], initializer=tf.zeros_initializer())\n epsilon, scale, bias = [cast_like(t, x)\n for t in [epsilon, scale, bias]]\n mean = tf.reduce_mean(x, axis=[-1], keepdims=True)\n l2norm = tf.reduce_sum(tf.square(x - mean), axis=[-1], keepdims=True)\n norm_x = (x - mean) * tf.rsqrt(l2norm + epsilon)\n return norm_x * scale + bias","def L2_dists(x, y):\n #print(x.shape)\n #print(y.shape)\n dists = -2 * np.matmul(x, y.T)\n dists += np.sum(x**2)[np.newaxis]\n dists += np.sum(y**2)\n return np.sqrt(dists)","def l2_reg_cost(cost, lambtha, weights, L, m):\n enorm = 0\n for i in range(1, L + 1):\n layer = 'W{}'.format(i)\n enorm += np.linalg.norm(weights[layer])\n return cost + (lambtha / (2 * m)) * enorm","def test_normalization(self):\n u = np.array([np.array([0.7, 1.2]), np.array([0.5, 1.6])])\n with tf.Session() as sess:\n n = sess.run(AbstractModel.l2_normalization_layer(u, axis=1))\n magnitude = np.linalg.norm(n, axis=1)\n np.testing.assert_allclose(magnitude, np.array([1.0, 1.0]))","def norm(self):\n\t\treturn math.sqrt(self.norm2())","def normalisation_l2(x):\n res = np.zeros(x.shape)\n print(x.shape)\n for i in range(x.shape[0]):\n res[i] = x[i]/(np.linalg.norm(x[i],2)+1e-5)\n std = res.std()\n mean = res.mean()\n print(\"normalisation done\")\n return(mean,std,res)","def norm2(self):\n return getattr(self, self.norm2_name)","def getNormLaplacian(W):\n\td=[np.sum(row) for row in W]\n\tD=np.diag(d)\n\tL=D-W\n\t#Dn=D^(-1/2)\n\tDn=np.power(np.linalg.matrix_power(D,-1),0.5)\n\tLbar=np.dot(np.dot(Dn,L),Dn)\n\treturn Lbar","def l2_regularization(W, reg_strength):\n # TODO: Copy from the previous assignment\n loss = reg_strength*np.sum(W*W)\n grad = 2*reg_strength*W\n return loss, grad","def norm(self):\n mag_squared = self._sum_of_squares()\n return sqrt(mag_squared)","def squared_norm(self) -> float:\n return self.__real**2 + self.__img[0]**2 + self.__img[1]**2 + self.__img[2]**2","def difference_models_norm_2(model_1, model_2):\n \n tensor_1=list(model_1.parameters())\n tensor_2=list(model_2.parameters())\n \n norm=sum([torch.sum((tensor_1[i]-tensor_2[i])**2) \n for i in range(len(tensor_1))])\n \n return norm","def _calc_r2(self):\n sse = np.sum((self.data.y - self.predict(self.data.x))**2)\n sst = np.sum((self.data.y - self.data.y.mean())**2)\n return (1. - sse/sst)","def normsq(self):\n return sum(x**2 for x in self.data)","def norm(imagestack, mean, std):\n \n new_im = (imagestack - mean)/std \n \n return new_im","def tlbr_norm(self):\n ret = self.tlwh()\n ret[2:] += ret[:2] # tlbr\n ret[0] /= self.im_shape[1] # im_shape: [height, width]\n ret[1] /= self.im_shape[0]\n ret[2] /= self.im_shape[1]\n ret[3] /= self.im_shape[0]\n\n return ret","def l2_reg_cost(cost, lambtha, weights, L, m):\n w_norm = 0\n for i in range(1, L + 1):\n w_norm += np.linalg.norm(weights['W' + str(i)])\n L2 = cost + (lambtha / (2 * m) * w_norm)\n return L2","def l2distance(X, Z=None):\n\n if Z is None:\n n, d = X.shape\n s1 = np.sum(np.power(X, 2), axis=1).reshape(-1,1)\n D1 = -2 * np.dot(X, X.T) + repmat(s1, 1, n)\n D = D1 + repmat(s1.T, n, 1)\n np.fill_diagonal(D, 0)\n D = np.sqrt(np.maximum(D, 0))\n else:\n n, d = X.shape\n m, _ = Z.shape\n s1 = np.sum(np.power(X, 2), axis=1).reshape(-1,1)\n s2 = np.sum(np.power(Z, 2), axis=1).reshape(1,-1)\n D1 = -2 * np.dot(X, Z.T) + repmat(s1, 1, m)\n D = D1 + repmat(s2, n, 1)\n D = np.sqrt(np.maximum(D, 0))\n return D","def l2_reg_cost(cost, lambtha, weights, L, m):\n f = 0\n while (L):\n index = \"W{}\".format(L)\n weight = weights[index]\n f += np.linalg.norm(weight)\n L -= 1\n return cost + lambtha / (2 * m) * f","def norm(self):\n return sqrt(self.dot(self))","def getNorm(self, norm=lambda l: (sum(map(lambda x: x ** 2, l))) ** (1 / 2)):\n return norm(self.components)","def l2_regularization(cg, rate=0.01):\n W = VariableFilter(roles=[WEIGHT])(cg.variables)\n L2_cost = rate * l2_norm(W)\n\n return L2_cost","def layer_norm(self, index, order=2) -> float:\n # L-n norms of layer where we treat each layer as a flat other\n return math.pow(torch.pow(self.parameters[index], order).sum().item(), 1.0 / order)","def compute_Rg(L):\n\treturn np.sqrt(np.dot(L,L))","def get_norm(self, l):\n return self._W.norm(l)","def get_norm(self, l):\n return self._W.norm(l)","def square_dist(X, X2=None, ls=1.):\n N, D = X.shape\n\n X = X / ls\n Xs = tf.reduce_sum(tf.square(X), axis=1)\n\n if X2 is None:\n dist = -2 * tf.matmul(X, X, transpose_b=True)\n dist += tf.reshape(Xs, (-1, 1)) + tf.reshape(Xs, (1, -1))\n return tf.clip_by_value(dist, 0., np.inf)\n\n N2, D2 = X2.shape\n if D != D2:\n raise ValueError('Dimension of X and X2 does not match.')\n\n X2 = X2 / ls\n X2s = tf.reduce_sum(tf.square(X2), axis=1)\n dist = -2 * tf.matmul(X, X2, transpose_b=True)\n dist += tf.reshape(Xs, (-1, 1)) + tf.reshape(X2s, (1, -1))\n return tf.clip_by_value(dist, 0., np.inf)","def compute_dist(self, s1, s2):\n return sp_linalg.norm(self.wrap(s1, s2))","def compute_dist(self, s1, s2):\n return sp_linalg.norm(self.wrap(s1, s2))","def normsq(self):\n return abs(sum(self._ar * self._ar))","def l2_reg_cost(cost, lambtha, weights, L, m):\n sumWeights = 0\n for i in range(1, L + 1):\n sumWeights += np.linalg.norm(weights['W' + str(i)])\n return cost + sumWeights * lambtha / (2 * m)","def Norm(self):\n \n return sqrt(sum([sum(abs(x)**2) for x in self.__ObjList]))","def norm_distance(self):\n graph_size = self.N + self.M\n return self.distance() / (1. * graph_size)"],"string":"[\n \"def l2_norm(pattern):\\n return np.linalg.norm(pattern)\",\n \"def l2(weights):\\n\\treturn np.sqrt(np.sum(weights * weights))\",\n \"def l2_norm(params):\\n flattened, _ = flatten(params)\\n return np.dot(flattened, flattened)\",\n \"def L2norm(m):\\n return np.sqrt(np.sum(m**2))\",\n \"def L2_norm(self):\\n analyticals = self.analytical(self.x_values, self.C, self.D)\\n error = analyticals - self.numerical\\n self.L2 = np.sqrt((1/self.gp)*np.sum(error**2))\",\n \"def _l2_norm_squared(self, z, theta):\\n norms = np.zeros(shape=(len(z), self.n_states))\\n\\n for j in range(self.n_states):\\n diff = theta[:, j] - z # ndarray of shape (n_samples, n_states) with differences\\n norms[:, j] = np.square(np.linalg.norm(diff, axis=1)) # squared state conditional l2 norms\\n\\n return norms # squared l2 norm.\",\n \"def l2norm(array1,array2):\\r\\n tot = np.sum(np.abs(array1)**2)\\r\\n return np.sqrt(np.sum(np.abs(array1-array2)**2)/tot)\",\n \"def norm_L2(u):\\n return norm_l2(u)/sqrt(float(u.size))\",\n \"def l2_norm(self):\\n return (self.x**2 + self.y**2 + self.z**2)**0.5\",\n \"def _l2s(self, params):\\n return [np.linalg.norm(param) for param in params]\",\n \"def l2(vec):\\n return np.linalg.norm(vec)\",\n \"def norm_l2(u):\\n return linalg.norm(u.ravel())\",\n \"def tree_l2_norm(tree_x, squared=False):\\n squared_tree = tree_map(jnp.square, tree_x)\\n sqnorm = tree_sum(squared_tree)\\n if squared:\\n return sqnorm\\n else:\\n return jnp.sqrt(sqnorm)\",\n \"def L2_norm(x, axis=-1):\\n return keras.backend.l2_normalize(x, axis=axis)\",\n \"def compute_L2_normalization(xx):\\r\\n\\treturn np.sum(xx ** 2, axis=1)\",\n \"def norm_l2(v):\\n return np.sqrt((v**2).sum())\",\n \"def L2norm(self, array):\\n norm = torch.sqrt(torch.sum(array * array))\\n return norm\",\n \"def norm_with_l2(original_mat):\\n normed_mat = np.zeros(original_mat.shape, dtype=np.float32)\\n if len(original_mat.shape) == 2:\\n for ind_r in range(original_mat.shape[0]):\\n a = np.square(original_mat[ind_r]*1.0)\\n b = np.sum(a)\\n c = np.sqrt(b)\\n normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / c\\n # normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / np.sqrt(np.sum(np.square(original_mat[ind_r])*1.0))\\n return normed_mat\",\n \"def l2_norm(self, input):\\n input_size = input.size()\\n buffer = torch.pow(input, 2)\\n normp = torch.sum(buffer, 1).add_(1e-10)\\n norm = torch.sqrt(normp)\\n _output = torch.div(input, norm.view(-1, 1).expand_as(input))\\n output = _output.view(input_size)\\n\\n return output\",\n \"def l2_norm(v):\\n res = 0\\n for e in v:\\n res += e * e\\n return math.sqrt(res)\",\n \"def norm2(self):\\n\\t\\treturn self.x ** 2 + self.y ** 2 + self.z ** 2\",\n \"def l2_regularization_penalty(self):\\n return self.l2 * (np.linalg.norm(self.weights)**2)\",\n \"def l2norm_(X, Xstar):\\n return cdist(X, Xstar)\",\n \"def weight_l2_norm():\\n cumulated_l2_norm = tf.constant(0., dtype=tf.float32)\\n for trainable_variable in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):\\n name = trainable_variable.name.split('/')[-1]\\n if name.startswith('weights'):\\n cumulated_l2_norm += tf.nn.l2_loss(trainable_variable)\\n return cumulated_l2_norm\",\n \"def normF2(X):\\r\\n # pass\\r\\n if X.shape[0]*X.shape[1] == 0:\\r\\n return 0\\r\\n return LA.norm(X, 'fro')**2\",\n \"def norm2(point):\\n return np.sum(point**2, -1)\",\n \"def norm2d(self) -> float:\\n\\n return self.v2ddict.norm2d()\",\n \"def inner_product_to_normalized_L2_square(matrix):\\n\\n length = matrix.shape[0]\\n norm = np.divide(1, np.sqrt(l2_square_from_inner_product(matrix)))\\n\\n normalized_inner_product = np.multiply(np.multiply(np.reshape(norm, [length, 1]), matrix),\\n np.reshape(norm, [1, length]))\\n return 2 - 2 * normalized_inner_product\",\n \"def _l2_normalize(x, axis=None, eps=1e-12):\\n return x * jax.lax.rsqrt((x * x).sum(axis=axis, keepdims=True) + eps)\",\n \"def l2norm(X):\\n norm = np.linalg.norm(X, axis=1, keepdims=True)\\n return 1.0 * X / norm\",\n \"def l2_normalize(data, eps, axis=None):\\n return cpp.nn.l2_normalize(data, eps, axis)\",\n \"def calculate_error_l2_norm(self, dY):\\n solutions = []\\n norm = 0.\\n for mi in range(len(self._meshes)):\\n for ei in range(len(self._meshes[mi].elements)):\\n e = self._meshes[mi].elements[ei]\\n # change this to gauss points:\\n x_vals, w = p_roots(20)\\n norm_e_squared = 0.\\n for i, x in enumerate(x_vals):\\n norm_e_squared += w[i] * \\\\\\n self.get_sol_value(mi, ei, dY, x,\\n count_lift=False)**2\\n norm_e_squared *= e.jacobian\\n norm += norm_e_squared\\n return sqrt(norm)\",\n \"def l2norm(X, dim=-1, eps=1e-8):\\r\\n norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps\\r\\n X = torch.div(X, norm)\\r\\n return X\",\n \"def l2(v, axis=None):\\n length = v.shape[0]\\n return np.sqrt(np.sum(np.square(v), axis=axis) / length)\",\n \"def get_norms(self):\\n l1_sum = 0\\n l2_sum = 0\\n actives = 0\\n for lbl in self.labels:\\n for fid in self.w[lbl]:\\n # apply and remaing L1 penalities at the end of training.\\n alpha = self.s - self.lastW[lbl].get(fid,0)\\n self.w[lbl][fid] = self.w[lbl].get(fid, 0) - alpha\\n weight = self.w[lbl][fid]\\n l1_sum += weight if weight > 0 else -weight\\n l2_sum += weight * weight\\n if weight != 0:\\n actives += 1\\n l2_sum = math.sqrt(l2_sum)\\n return (l1_sum,l2_sum,actives)\",\n \"def normr(Mat):\\n B = normalize(Mat, norm='l2', axis=1)\\n return B\",\n \"def l2_norm(vec_or_matrix):\\n if len(vec_or_matrix.shape) == 1:\\n # linear vector\\n return vec_or_matrix / np.linalg.norm(vec_or_matrix)\\n elif len(vec_or_matrix.shape) == 2:\\n return vec_or_matrix / np.linalg.norm(vec_or_matrix, axis=1, ord=2)[:, np.newaxis]\\n else:\\n raise ValueError('Wrong number of dimensions, 1 or 2 is supported, not %i.' % len(vec_or_matrix.shape))\",\n \"def l2_normalize(data, axis=-1, eps=1e-6):\\n ret = data / (np.linalg.norm(data, axis=axis, keepdims=True) + eps)\\n return ret\",\n \"def P2l_rec_norm(ells, cost):\\n P22 = 3. * (1. - cost**2)\\n P23 = 15. * cost * (1. - cost**2)\\n P2l = np.zeros(len(ells))\\n P2l[0] = 0.\\n P2l[1] = 0.\\n P2l[2] = P22\\n P2l[3] = P23\\n P2l_norm = np.copy(P2l)\\n P2l_norm[2] *= P2l_norm_prefac(2)\\n P2l_norm[3] *= P2l_norm_prefac(3)\\n for ell in ells[4:]:\\n # print ell, P2l[ell-1], P2l[ell-2]\\n a = np.sqrt((4 * ell**2 - 1.) / (ell**2 - 4))\\n b = cost * P2l_norm[ell - 1]\\n c = np.sqrt(((ell - 1.)**2 - 4) /\\n (4 * (ell - 1.)**2 - 1)) * P2l_norm[ell - 2]\\n # print a,b,c\\n P2l_norm[ell] = a * (b - c)\\n # print ell, P2l_norm[ell], P2l_norm_prefac(ell)\\n P2l[ell] = P2l_norm[ell] / P2l_norm_prefac(ell)\\n return P2l\",\n \"def sigma_norm2( self):\\n return self._sigma2\",\n \"def l2_normalization(inputs, scaling=True):\\n with tf.variable_scope('L2Normalization'):\\n inputs_shape = inputs.get_shape()\\n channel_shape = inputs_shape[-1:]\\n # cal l2_norm on channel\\n outputs = tf.nn.l2_normalize(inputs, 3, epsilon=1e-12)\\n # scalling\\n if scaling:\\n # scale.shape == channel.shape\\n scale = slim.variable('gamma', channel_shape, tf.float32, tf.constant_initializer(1.0))\\n outputs = tf.multiply(outputs, scale)\\n\\n return outputs\",\n \"def fast_2_norm(A):\\n v = np.random.rand(A.shape[1], 1)\\n return la.norm(A.dot(v))\",\n \"def test_scale_features_L2_norm(self):\\n data = array([[0.564, 20.661], [-18.512, 41.168], [-0.009, 20.440]])\\n cdata = CData(data)\\n\\n # correct answer computed in Mathematica\\n # TODO: can we compute the right answer in Python?\\n answer = array([[0.0304526, 0.409996], [-0.999536, 0.816936], [-0.000485946, 0.40561]])\\n\\n # perform L2 normalization and check answer\\n cdata.scale_features('L2 norm')\\n self.assertTrue(allclose(cdata.data, answer))\",\n \"def norms(Z):\\n return Z.view(Z.shape[0], -1).norm(dim=1)[:,None,None,None]\",\n \"def norm2_r(self, a: np.ndarray) -> float:\\n return a.T @ a\",\n \"def normalizeL2(f):\\r\\n \\r\\n f=np.array(f)\\r\\n fsum=np.sum(np.abs(f))\\r\\n if fsum==0:\\r\\n fnorm=f\\r\\n else:\\r\\n fnorm=f/np.sqrt(np.sum(np.abs(f)**2))\\r\\n \\r\\n return fnorm\",\n \"def L2_func(x):\\n return K.expand_dims(K.sqrt(K.sum(K.pow(x,2), axis=1)))\",\n \"def L2_normalize(xx):\\r\\n\\tZx = compute_L2_normalization(xx)\\r\\n\\treturn xx / np.sqrt(Zx[:, np.newaxis])\",\n \"def inner_product_to_L2_square(matrix):\\n\\n length = matrix.shape[0]\\n squared_norm = np.reshape(np.diag(matrix), (length, 1))\\n\\n return squared_norm + np.transpose(squared_norm) - 2 * matrix\",\n \"def project_L2(w, l):\\n return w * min(1, 1 / (l ** (1 / 2.0) * np.linalg.norm(w, 2)))\",\n \"def l2_distance(v1, v2):\\n\\treturn np.linalg.norm(np.array(v1) - np.array(v2))\",\n \"def l2_norm(input_x, epsilon=1e-12):\\n input_x_norm = input_x/(tf.reduce_sum(input_x**2)**0.5 + epsilon)\\n return input_x_norm\",\n \"def l2norm_1d(new, old):\\n\\tdiff = 0\\n\\tnx = len(new)\\n\\tfor i, n in enumerate(new):\\n\\t\\tif n:\\n\\t\\t\\tdiff += ((n - old[i])/n)**2\\n\\tnorm = scipy.sqrt(diff/nx)\\n\\treturn norm\",\n \"def l2(x1, x2):\\n return np.sqrt((x1 - x2)**2)\",\n \"def normalize_l2(x):\\n return x / (npla.norm(x))\",\n \"def l2norm(X):\\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n X = torch.div(X, norm)\\n return X\",\n \"def l2norm(X):\\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n X = torch.div(X, norm)\\n return X\",\n \"def l2norm(X):\\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n X = torch.div(X, norm)\\n return X\",\n \"def l2norm(X): \\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n a = norm.expand_as(X) + 1e-8\\n X = torch.div(X, a) \\n return X\",\n \"def two_norm(v):\\n return math.sqrt(dot_product(v, v))\",\n \"def gradient_nD(stack):\\n # Convert for 64-bit to avoid large number problems in squares.\\n stack = np.copy(stack)\\n stack = stack.astype(np.float64)\\n sumsq = ndi.filters.sobel(stack, axis=0) ** 2\\n for d in range(1, stack.ndim):\\n sumsq = sumsq + (ndi.filters.sobel(stack, axis=d) ** 2)\\n gradient = np.sqrt(sumsq)\\n return gradient\",\n \"def norm2d(w_in):\\n return nn.BatchNorm2d(num_features=w_in, eps=cfg.BN.EPS, momentum=cfg.BN.MOM)\",\n \"def compute_model_diff_squared_norm(model1: torch.nn.Module, model2: torch.nn.Module):\\n tensor1 = list(model1.parameters())\\n tensor2 = list(model2.parameters())\\n norm = sum([torch.sum((tensor1[i] - tensor2[i]) ** 2) for i in range(len(tensor1))])\\n\\n return norm\",\n \"def model_norm(self, order=2) -> float:\\n # L-n norm of model where we treat the model as a flat other\\n return math.pow(sum([\\n torch.pow(layer, order).sum().item()\\n for layer in self.parameters\\n ]), 1.0 / order)\",\n \"def normalize_l2norm(data,tol=0):\\n data_sqrt=np.sqrt(np.square(data).sum(axis=1))\\n data_sqrt.shape=(data_sqrt.shape[0],1)\\n #tol=0#1e-8\\n data=data/(data_sqrt+tol)\\n return data\",\n \"def norm(self):\",\n \"def l2(weights, name=None):\\n with ops.op_scope([weights], name, 'l2_regularizer') as scope:\\n my_scale = ops.convert_to_tensor(scale,\\n dtype=weights.dtype.base_dtype,\\n name='scale')\\n return standard_ops.mul(my_scale, nn.l2_loss(weights), name=scope)\",\n \"def l2_norm(x, filters=None, epsilon=1e-6, name=None, reuse=None):\\n if filters is None:\\n filters = shape_list(x)[-1]\\n with tf.variable_scope(name, default_name=\\\"l2_norm\\\", values=[x], reuse=reuse):\\n scale = tf.get_variable(\\n \\\"l2_norm_scale\\\", [filters], initializer=tf.ones_initializer())\\n bias = tf.get_variable(\\n \\\"l2_norm_bias\\\", [filters], initializer=tf.zeros_initializer())\\n epsilon, scale, bias = [cast_like(t, x)\\n for t in [epsilon, scale, bias]]\\n mean = tf.reduce_mean(x, axis=[-1], keepdims=True)\\n l2norm = tf.reduce_sum(tf.square(x - mean), axis=[-1], keepdims=True)\\n norm_x = (x - mean) * tf.rsqrt(l2norm + epsilon)\\n return norm_x * scale + bias\",\n \"def L2_dists(x, y):\\n #print(x.shape)\\n #print(y.shape)\\n dists = -2 * np.matmul(x, y.T)\\n dists += np.sum(x**2)[np.newaxis]\\n dists += np.sum(y**2)\\n return np.sqrt(dists)\",\n \"def l2_reg_cost(cost, lambtha, weights, L, m):\\n enorm = 0\\n for i in range(1, L + 1):\\n layer = 'W{}'.format(i)\\n enorm += np.linalg.norm(weights[layer])\\n return cost + (lambtha / (2 * m)) * enorm\",\n \"def test_normalization(self):\\n u = np.array([np.array([0.7, 1.2]), np.array([0.5, 1.6])])\\n with tf.Session() as sess:\\n n = sess.run(AbstractModel.l2_normalization_layer(u, axis=1))\\n magnitude = np.linalg.norm(n, axis=1)\\n np.testing.assert_allclose(magnitude, np.array([1.0, 1.0]))\",\n \"def norm(self):\\n\\t\\treturn math.sqrt(self.norm2())\",\n \"def normalisation_l2(x):\\n res = np.zeros(x.shape)\\n print(x.shape)\\n for i in range(x.shape[0]):\\n res[i] = x[i]/(np.linalg.norm(x[i],2)+1e-5)\\n std = res.std()\\n mean = res.mean()\\n print(\\\"normalisation done\\\")\\n return(mean,std,res)\",\n \"def norm2(self):\\n return getattr(self, self.norm2_name)\",\n \"def getNormLaplacian(W):\\n\\td=[np.sum(row) for row in W]\\n\\tD=np.diag(d)\\n\\tL=D-W\\n\\t#Dn=D^(-1/2)\\n\\tDn=np.power(np.linalg.matrix_power(D,-1),0.5)\\n\\tLbar=np.dot(np.dot(Dn,L),Dn)\\n\\treturn Lbar\",\n \"def l2_regularization(W, reg_strength):\\n # TODO: Copy from the previous assignment\\n loss = reg_strength*np.sum(W*W)\\n grad = 2*reg_strength*W\\n return loss, grad\",\n \"def norm(self):\\n mag_squared = self._sum_of_squares()\\n return sqrt(mag_squared)\",\n \"def squared_norm(self) -> float:\\n return self.__real**2 + self.__img[0]**2 + self.__img[1]**2 + self.__img[2]**2\",\n \"def difference_models_norm_2(model_1, model_2):\\n \\n tensor_1=list(model_1.parameters())\\n tensor_2=list(model_2.parameters())\\n \\n norm=sum([torch.sum((tensor_1[i]-tensor_2[i])**2) \\n for i in range(len(tensor_1))])\\n \\n return norm\",\n \"def _calc_r2(self):\\n sse = np.sum((self.data.y - self.predict(self.data.x))**2)\\n sst = np.sum((self.data.y - self.data.y.mean())**2)\\n return (1. - sse/sst)\",\n \"def normsq(self):\\n return sum(x**2 for x in self.data)\",\n \"def norm(imagestack, mean, std):\\n \\n new_im = (imagestack - mean)/std \\n \\n return new_im\",\n \"def tlbr_norm(self):\\n ret = self.tlwh()\\n ret[2:] += ret[:2] # tlbr\\n ret[0] /= self.im_shape[1] # im_shape: [height, width]\\n ret[1] /= self.im_shape[0]\\n ret[2] /= self.im_shape[1]\\n ret[3] /= self.im_shape[0]\\n\\n return ret\",\n \"def l2_reg_cost(cost, lambtha, weights, L, m):\\n w_norm = 0\\n for i in range(1, L + 1):\\n w_norm += np.linalg.norm(weights['W' + str(i)])\\n L2 = cost + (lambtha / (2 * m) * w_norm)\\n return L2\",\n \"def l2distance(X, Z=None):\\n\\n if Z is None:\\n n, d = X.shape\\n s1 = np.sum(np.power(X, 2), axis=1).reshape(-1,1)\\n D1 = -2 * np.dot(X, X.T) + repmat(s1, 1, n)\\n D = D1 + repmat(s1.T, n, 1)\\n np.fill_diagonal(D, 0)\\n D = np.sqrt(np.maximum(D, 0))\\n else:\\n n, d = X.shape\\n m, _ = Z.shape\\n s1 = np.sum(np.power(X, 2), axis=1).reshape(-1,1)\\n s2 = np.sum(np.power(Z, 2), axis=1).reshape(1,-1)\\n D1 = -2 * np.dot(X, Z.T) + repmat(s1, 1, m)\\n D = D1 + repmat(s2, n, 1)\\n D = np.sqrt(np.maximum(D, 0))\\n return D\",\n \"def l2_reg_cost(cost, lambtha, weights, L, m):\\n f = 0\\n while (L):\\n index = \\\"W{}\\\".format(L)\\n weight = weights[index]\\n f += np.linalg.norm(weight)\\n L -= 1\\n return cost + lambtha / (2 * m) * f\",\n \"def norm(self):\\n return sqrt(self.dot(self))\",\n \"def getNorm(self, norm=lambda l: (sum(map(lambda x: x ** 2, l))) ** (1 / 2)):\\n return norm(self.components)\",\n \"def l2_regularization(cg, rate=0.01):\\n W = VariableFilter(roles=[WEIGHT])(cg.variables)\\n L2_cost = rate * l2_norm(W)\\n\\n return L2_cost\",\n \"def layer_norm(self, index, order=2) -> float:\\n # L-n norms of layer where we treat each layer as a flat other\\n return math.pow(torch.pow(self.parameters[index], order).sum().item(), 1.0 / order)\",\n \"def compute_Rg(L):\\n\\treturn np.sqrt(np.dot(L,L))\",\n \"def get_norm(self, l):\\n return self._W.norm(l)\",\n \"def get_norm(self, l):\\n return self._W.norm(l)\",\n \"def square_dist(X, X2=None, ls=1.):\\n N, D = X.shape\\n\\n X = X / ls\\n Xs = tf.reduce_sum(tf.square(X), axis=1)\\n\\n if X2 is None:\\n dist = -2 * tf.matmul(X, X, transpose_b=True)\\n dist += tf.reshape(Xs, (-1, 1)) + tf.reshape(Xs, (1, -1))\\n return tf.clip_by_value(dist, 0., np.inf)\\n\\n N2, D2 = X2.shape\\n if D != D2:\\n raise ValueError('Dimension of X and X2 does not match.')\\n\\n X2 = X2 / ls\\n X2s = tf.reduce_sum(tf.square(X2), axis=1)\\n dist = -2 * tf.matmul(X, X2, transpose_b=True)\\n dist += tf.reshape(Xs, (-1, 1)) + tf.reshape(X2s, (1, -1))\\n return tf.clip_by_value(dist, 0., np.inf)\",\n \"def compute_dist(self, s1, s2):\\n return sp_linalg.norm(self.wrap(s1, s2))\",\n \"def compute_dist(self, s1, s2):\\n return sp_linalg.norm(self.wrap(s1, s2))\",\n \"def normsq(self):\\n return abs(sum(self._ar * self._ar))\",\n \"def l2_reg_cost(cost, lambtha, weights, L, m):\\n sumWeights = 0\\n for i in range(1, L + 1):\\n sumWeights += np.linalg.norm(weights['W' + str(i)])\\n return cost + sumWeights * lambtha / (2 * m)\",\n \"def Norm(self):\\n \\n return sqrt(sum([sum(abs(x)**2) for x in self.__ObjList]))\",\n \"def norm_distance(self):\\n graph_size = self.N + self.M\\n return self.distance() / (1. * graph_size)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74098885","0.6808609","0.67808706","0.67259216","0.65520793","0.65451896","0.6460118","0.64029825","0.6392266","0.62797666","0.627647","0.626877","0.62292206","0.6221982","0.6214867","0.62103754","0.61965305","0.61522037","0.6128999","0.6110056","0.6074128","0.6052297","0.60296553","0.6021897","0.60171294","0.5888251","0.58822685","0.58049625","0.5804344","0.5796051","0.5784968","0.5784316","0.57818305","0.5780563","0.5775568","0.5771007","0.5754276","0.57529444","0.574019","0.5738888","0.57220197","0.5706875","0.56947917","0.56229496","0.5589051","0.5576343","0.556107","0.5560761","0.5553708","0.5543217","0.55342054","0.5530855","0.5519885","0.54833406","0.54800487","0.54642594","0.54642594","0.54642594","0.5454233","0.54460615","0.54442286","0.54216623","0.54165","0.5406305","0.53955823","0.53784823","0.53740174","0.5368133","0.5360109","0.5359022","0.5339425","0.5325685","0.53218764","0.53174543","0.5314816","0.5304645","0.5288519","0.5283318","0.5281344","0.52803016","0.52776283","0.5276568","0.52735907","0.52517354","0.52136445","0.5182811","0.5175183","0.516102","0.51528597","0.51504815","0.513328","0.5125897","0.5125897","0.5123865","0.51198125","0.51198125","0.51187766","0.51139176","0.51117694","0.5109401"],"string":"[\n \"0.74098885\",\n \"0.6808609\",\n \"0.67808706\",\n \"0.67259216\",\n \"0.65520793\",\n \"0.65451896\",\n \"0.6460118\",\n \"0.64029825\",\n \"0.6392266\",\n \"0.62797666\",\n \"0.627647\",\n \"0.626877\",\n \"0.62292206\",\n \"0.6221982\",\n \"0.6214867\",\n \"0.62103754\",\n \"0.61965305\",\n \"0.61522037\",\n \"0.6128999\",\n \"0.6110056\",\n \"0.6074128\",\n \"0.6052297\",\n \"0.60296553\",\n \"0.6021897\",\n \"0.60171294\",\n \"0.5888251\",\n \"0.58822685\",\n \"0.58049625\",\n \"0.5804344\",\n \"0.5796051\",\n \"0.5784968\",\n \"0.5784316\",\n \"0.57818305\",\n \"0.5780563\",\n \"0.5775568\",\n \"0.5771007\",\n \"0.5754276\",\n \"0.57529444\",\n \"0.574019\",\n \"0.5738888\",\n \"0.57220197\",\n \"0.5706875\",\n \"0.56947917\",\n \"0.56229496\",\n \"0.5589051\",\n \"0.5576343\",\n \"0.556107\",\n \"0.5560761\",\n \"0.5553708\",\n \"0.5543217\",\n \"0.55342054\",\n \"0.5530855\",\n \"0.5519885\",\n \"0.54833406\",\n \"0.54800487\",\n \"0.54642594\",\n \"0.54642594\",\n \"0.54642594\",\n \"0.5454233\",\n \"0.54460615\",\n \"0.54442286\",\n \"0.54216623\",\n \"0.54165\",\n \"0.5406305\",\n \"0.53955823\",\n \"0.53784823\",\n \"0.53740174\",\n \"0.5368133\",\n \"0.5360109\",\n \"0.5359022\",\n \"0.5339425\",\n \"0.5325685\",\n \"0.53218764\",\n \"0.53174543\",\n \"0.5314816\",\n \"0.5304645\",\n \"0.5288519\",\n \"0.5283318\",\n \"0.5281344\",\n \"0.52803016\",\n \"0.52776283\",\n \"0.5276568\",\n \"0.52735907\",\n \"0.52517354\",\n \"0.52136445\",\n \"0.5182811\",\n \"0.5175183\",\n \"0.516102\",\n \"0.51528597\",\n \"0.51504815\",\n \"0.513328\",\n \"0.5125897\",\n \"0.5125897\",\n \"0.5123865\",\n \"0.51198125\",\n \"0.51198125\",\n \"0.51187766\",\n \"0.51139176\",\n \"0.51117694\",\n \"0.5109401\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.83394605"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":878,"cells":{"query":{"kind":"string","value":"Calculate the inner product of the two patterns as a vecter."},"document":{"kind":"string","value":"def inner_product(pattern_one, pattern_two):\n\n return np.sum(np.multiply(pattern_one, pattern_two))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def inner_product_batch(pattern_stack_one, pattern_num_one, pattern_stack_two, pattern_num_two):\n\n \"\"\"\n Notice that the two stacks can be different. So we can not deduce the lower triangular pattern from the \n other half.\n \"\"\"\n holder = np.zeros((pattern_num_one, pattern_num_two))\n for l in range(pattern_num_one):\n for m in range(pattern_num_two):\n holder[l, m] = np.sum(np.multiply(pattern_stack_one[l], pattern_stack_two[m]))\n\n return holder","def vectordot(a, b):\n return np.sum(a * b, 1)","def dotProduct(v1, v2):\n return sum((a * b) for a, b in zip(v1, v2))","def dotproduct(v1, v2):\n\treturn sum(imap(operator.mul, v1, v2))","def outer_product(x,y):\n\n return x[:,0]*y[:,1] -x[:,1]*y[:,0]","def __mul__(self, other):\n return Vec2d(self.v[0] * other, self.v[1] * other)","def inner_prod(x, y):\n z = torch.zeros(2, dtype=torch.double, device=x.device)\n\n if len(list(x.size())) == 2 and len(list(y.size())) == 2:\n z[0] = torch.dot(x[0], y[0]) - torch.dot(-x[1], y[1])\n z[1] = torch.dot(x[0], y[1]) + torch.dot(-x[1], y[0])\n\n if len(list(x.size())) == 1 and len(list(y.size())) == 1:\n z[0] = (x[0] * y[0]) - (-x[1] * y[1])\n z[1] = (x[0] * y[1]) + (-x[1] * y[0])\n\n return z","def _outer(a, b):\n a_flat = torch.reshape(a, [-1])\n b_flat = torch.reshape(b, [-1])\n a_mul = torch.unsqueeze(a_flat, dim=-1)\n b_mul = torch.unsqueeze(b_flat, dim=0)\n return a_mul * b_mul","def dot_product(a,b):\n return sum(pairwise_mult(a,b))","def inner_product(state_1, state_2):\n return numpy.dot(state_1.conjugate(), state_2)","def __mul__(self, othertr):\n res = self.dot(othertr)\n return res","def inner_product_similarity(a: torch.Tensor, b: torch.Tensor, dim=1) -> torch.Tensor:\n outputs = (a * b).sum(dim=dim)\n return outputs","def dot_product(v1, v2):\n return v1[0]*v2[0] + v1[1]*v2[1] + v1[2]*v2[2]","def dotproduct(x, y):\n return sum(imap(operator.mul, x, y))","def dotProduct(vectorA, vectorB):\r\n product =0\r\n for i in range(len(vectorA)):\r\n product += eval(vectorA[i])*eval(vectorB[i])\r\n return product","def dotproduct(vec1, vec2):\n import operator\n return sum(map(operator.mul, vec1, vec2))","def outer_prod(x, y):\n if len(list(x.size())) != 2 or len(list(y.size())) != 2:\n raise ValueError(\"An input is not of the right dimension.\")\n\n z = torch.zeros(2, x.size()[1], y.size()[1], dtype=torch.double, device=x.device)\n z[0] = torch.ger(x[0], y[0]) - torch.ger(x[1], -y[1])\n z[1] = torch.ger(x[0], -y[1]) + torch.ger(x[1], y[0])\n\n return z","def __mul__(self, other): \n if isinstance(other, Iterable):\n # dot product\n return self.x * other[0] + self.y * other[1]\n else:\n # scalar product\n return Vector(self.x * other, self.y * other)","def _inner_product_a2(\n self, tangent_vec_a, tangent_vec_b, base_point, vertex_areas_bp\n ):\n laplacian_at_base_point = self._space.laplacian(base_point)\n return self.a2 * gs.sum(\n gs.einsum(\n \"...bi,...bi->...b\",\n laplacian_at_base_point(tangent_vec_a),\n laplacian_at_base_point(tangent_vec_b),\n )\n / vertex_areas_bp,\n axis=-1,\n )","def _inner_product_c1(self, point_a, point_b, normals_bp, areas_bp):\n dna = self._space.normals(point_a) - normals_bp\n dnb = self._space.normals(point_b) - normals_bp\n return self.c1 * gs.sum(\n gs.einsum(\"...bi,...bi->...b\", dna, dnb) * areas_bp, axis=-1\n )","def dot_product(vector1, vector2):\n return [reduce_by_multiplication(pair) for pair in zip(vector1, vector2)]","def dot_product(v1, v2):\n return v1[0] * v2[0] + v1[1] * v2[1]","def dot_product(v, w):\n return sum(v_i * w_i for v_i, w_i in zip(v, w))","def dot4(a,b):\n return a[0]*b[0]+a[1]*b[1]+a[2]*b[2]+a[3]*b[3]","def _mulVectors(X1,X2):\n _checkSize(X1,X2)\n return sum([ X1[i] * X2[i] for i in range(len(X1))])","def _z2matmul(self, left, right):\n prod = np.mod(np.dot(left, right), 2)\n return prod","def inner_product(self, tangent_vec_a, tangent_vec_b, base_point):\n inner_prod_mat = self.metric_matrix(base_point)\n aux = gs.einsum(\"...j,...jk->...k\", gs.conj(tangent_vec_a), inner_prod_mat)\n return gs.dot(aux, tangent_vec_b)","def _qij_vec_inner(a: int, b: int, i: int, j: int):\n vec_dagger = _qij_vec_dagger(a, b)\n vec = _qij_vec(i, j)\n sum_result = FermionOperator()\n for idx, term in enumerate(vec):\n sum_result += term * vec_dagger[idx]\n return sum_result","def vec_dot(x, y):\r\n return sum(a * b for a, b in zip(x, y))","def dot_product(first_vector, second_vector):\n first_unpacker = VectorUnpacker(first_vector)\n second_unpacker = VectorUnpacker(second_vector)\n if first_unpacker.unpacked_vector_length != second_unpacker.unpacked_vector_length:\n raise ApplicationError(\"Unpacked vector sizes are unequal\")\n\n # looks better than a 'map' one-liner to me\n value = 0\n for piece in zip(first_unpacker(), second_unpacker()):\n value += piece[0] * piece[1]\n\n return value","def inner_product(alpha, F, beta):\n return np.dot(alpha, np.dot(F, beta))","def dotproduct(vec1, vec2):\n return sum((a*b) for a, b in zip(vec1, vec2))","def dot_product(v,w):\n return v[0] * w[0] + v[1] * w[1]","def dotProduct(v1, v2):\n n1 = normalize(v1)\n n2 = normalize(v2)\n return n1[0] * n2[0] + n1[1] * n2[1] + n1[2] * n2[2]","def __mul__(self,v2):\n\t\tif(isinstance(v2,Vect2D)):\n\t\t\treturn np.dot(self._vec,v2._vec)\n\t\telse:\n\t\t\treturn Vect2D(v2*self._vec)","def product(self, x, y):\n return self( x.lift() * y.lift() )","def dot(self,other):\n if len(self) == len(other):\n res = 0\n for a,b in zip(self,other):\n res += a*b\n return res\n else: \n raise ValueError(\"The length is not matched\")","def dot(a, b):\n return sum([a[i]*b[i] for i in range(2)])","def _inner_product_d1(\n self, one_forms_a, one_forms_b, one_forms_bp, areas_bp, inv_surface_metrics_bp\n ):\n one_forms_bp_t = gs.transpose(one_forms_bp, (0, 2, 1))\n\n one_forms_a_t = gs.transpose(one_forms_a, (0, 1, 3, 2))\n xa = one_forms_a_t - one_forms_bp_t\n\n xa_0 = gs.matmul(\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\n gs.matmul(gs.transpose(xa, (0, 1, 3, 2)), one_forms_bp_t)\n - gs.matmul(one_forms_bp, xa),\n )\n\n one_forms_b_t = gs.transpose(one_forms_b, (0, 1, 3, 2))\n xb = one_forms_b_t - one_forms_bp_t\n xb_0 = gs.matmul(\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\n gs.matmul(gs.transpose(xb, (0, 1, 3, 2)), one_forms_bp_t)\n - gs.matmul(one_forms_bp, xb),\n )\n\n return self.d1 * gs.sum(\n gs.einsum(\n \"...bii->...b\",\n gs.matmul(\n xa_0,\n gs.matmul(\n inv_surface_metrics_bp, gs.transpose(xb_0, axes=(0, 1, 3, 2))\n ),\n ),\n )\n * areas_bp\n )","def dot( v1, v2 ):\n return sum( x*y for x,y in izip(v1,v2) )","def dot_prod(u,v):\n each_product = []\n for i in range(len(u)):\n each_product.append(u[i] * v[i])\n return sum(each_product)","def dot_product(v1, v2):\n #print(v1, v2)\n sum = 0\n\n for i in range(len(v1)):\n #print(v1[i], v2[i])\n sum += v1[i] * v2[i]\n return sum","def dot_product(a, b):\n dp = 0.0\n for i, j in zip(a, b):\n dp += i * j\n return dp","def dot(v,w):\n return sum(v_i * w_i for v_i,w_i in zip(v,w))","def mul(Z,X,Y):","def dot(v,w):\r\n return sum(v_i * w_i\r\n for v_i, w_i in zip(v, w))","def inner_product(self, tangent_vec_a, tangent_vec_b, base_point):\n args = {\n \"tangent_vec_a\": tangent_vec_a,\n \"tangent_vec_b\": tangent_vec_b,\n \"base_point\": base_point,\n }\n inner_products = self._iterate_over_factors(\"inner_product\", args)\n return sum(inner_products)","def __mul__(self, other):\n x = self.x * other\n y = self.y * other\n return vec(x, y)","def prod(self, x, y):\n return (self.basic_operation.reduce(x.original+y.original),\n self.operation1.prod(x.left, y.left),\n self.operation2.prod(x.right, y.right))","def dotproduct(first, other):\n if isinstance(first,FreeCAD.Vector) and isinstance(other,FreeCAD.Vector):\n return (first.x*other.x + first.y*other.y + first.z*other.z)","def crossproduct(first, other=FreeCAD.Vector(0,0,1)):\n if isinstance(first,FreeCAD.Vector) and isinstance(other,FreeCAD.Vector):\n return FreeCAD.Vector(first.y*other.z - first.z*other.y, first.z*other.x - first.x*other.z, first.x*other.y - first.y*other.x)","def inner(self, a: np.ndarray, b: np.ndarray) -> float:\n return a.T @ (self.mass @ b)","def pair_product(x1, x2):\n return np.multiply(x1, x2)","def _mul(*args):\n\treturn functools.reduce(numpy.dot, args)","def innerprod_q2(q1, q2):\n T = q1.shape[1]\n val = sum(sum(q1 * q2)) / T\n\n return (val)","def _inner_product_a1(self, ginvdga, ginvdgb, areas_bp):\n return self.a1 * gs.sum(\n gs.einsum(\"...bii->...b\", gs.matmul(ginvdga, ginvdgb)) * areas_bp,\n axis=-1,\n )","def vec_dot_star(v1,v2):\r\n \r\n dot_star = v1[0]*(v2[1])-v1[1]*v2[0]\r\n return dot_star","def vecDot(a, b):\n ret=0.0\n for i in range(len(a)):\n ret+=a[i]*b[i]\n return ret","def sparseVectorDotProduct(v1, v2):\n # BEGIN_YOUR_CODE (our solution is 4 lines of code, but don't worry if you deviate from this)\n return sum(v1[k]*v2[k] for k in v1 and v2)\n # END_YOUR_CODE","def dot(v,w):\n return sum(v_i * w_i\n for v_i, w_i in zip(v,w))","def _prod_vectorized(M1, M2):\n sh1 = M1.shape\n sh2 = M2.shape\n assert len(sh1) >= 2\n assert len(sh2) >= 2\n assert sh1[-1] == sh2[-2]\n\n ndim1 = len(sh1)\n t1_index = list(xrange(ndim1-2)) + [ndim1-1, ndim1-2]\n return np.sum(np.transpose(M1, t1_index)[..., np.newaxis] *\n M2[..., np.newaxis, :], -3)","def dot_product(vec_1:tuple, vec_2:tuple)->float:\n return vec_1[0] * vec_2[0] + vec_1[1] * vec_2[1]","def same_side_product(p, q, a, b):\n return line_ccw(a, b, p) * line_ccw(a, b, q)","def vdot(a, b):\n return np.vdot(a.ravel(), b.ravel())","def __mul__(self,other):\n if type(other) is Vector:\n return(self.x*other.x + self.y*other.y + self.z*other.z)\n else:\n return(Vector(self.x*other,self.y*other,self.z*other))","def _dot(a, b):\n return np.einsum('ijk,ikl->ijl', a, b)","def sparseVectorDotProduct(a, b):\n # BEGIN_YOUR_CODE (our solution is 4 lines of code, but don't worry if you deviate from this)\n ref = a if len(a)>len(b) else b\n return sum(a[key]*b[key] for key in ref)\n # END_YOUR_CODE","def basic_geometric_product(obj1, obj2):\n def mul_table(b1, b2):\n return MV.base_mul_table[(b1, b2)]\n\n obj12 = bilinear_product(obj1 * obj2, mul_table)\n\n return obj12","def __mul__(self, other):\n return sum(self._ar * other._ar)","def get_dot_product(v1,v2):\n #sets default dot product\n dot_product = 0\n \n for key in v2:\n if key in v1:\n # updates the dot product if key is present in both vectors\n dot_product += v1[key]*v2[key]\n #returns final dot product\n return dot_product","def dot(a,b):\n return a[0]*b[0]+a[1]*b[1]+a[2]*b[2]","def _inner_product_a0(self, tangent_vec_a, tangent_vec_b, vertex_areas_bp):\n return self.a0 * gs.sum(\n vertex_areas_bp\n * gs.einsum(\"...bi,...bi->...b\", tangent_vec_a, tangent_vec_b),\n axis=-1,\n )","def dot(self,v, w):\n return sum(v_i * w_i for v_i, w_i in zip(v, w))","def test_l2_metric_inner_product_vectorization(\n self,\n l2_metric_s2,\n times,\n n_landmark_sets,\n landmarks_a,\n landmarks_b,\n landmarks_c,\n ):\n landmarks_ab = l2_metric_s2.geodesic(landmarks_a, landmarks_b)\n landmarks_bc = l2_metric_s2.geodesic(landmarks_b, landmarks_c)\n landmarks_ab = landmarks_ab(times)\n landmarks_bc = landmarks_bc(times)\n\n tangent_vecs = l2_metric_s2.log(point=landmarks_bc, base_point=landmarks_ab)\n\n result = l2_metric_s2.inner_product(tangent_vecs, tangent_vecs, landmarks_ab)\n\n self.assertAllClose(gs.shape(result), (n_landmark_sets,))","def complex_mul2d(a, b):\n op = partial(torch.einsum, \"bixy,ioxy->boxy\")\n return torch.stack([\n op(a[..., 0], b[..., 0]) - op(a[..., 1], b[..., 1]),\n op(a[..., 1], b[..., 0]) + op(a[..., 0], b[..., 1])\n ],\n dim=-1)","def dot(v, w):\n return sum(v_i * w_i for v_i, w_i in zip(v, w))","def dot(v, w):\n return sum(v_i * w_i for v_i, w_i in zip(v, w))","def dot(v, w):\n return sum(v_i * w_i for v_i, w_i in zip(v, w))","def dot(v, w):\n return sum(v_i * w_i for v_i, w_i in zip(v, w))","def dot(v, w):\n return sum(v_i * w_i for v_i, w_i in zip(v, w))","def __mul__(self, other):\n\n newlist = [v for v in self.args]\n for i, v in enumerate(newlist):\n newlist[i] = (sympify(other) * newlist[i][0], newlist[i][1])\n return Vector(newlist)","def __mul__(self, other):\n if isinstance(other, Vector):\n return self.dot(other)\n else:\n raise TypeError(other)","def __mul__(self, vs) -> List[Tuple[np.ndarray, np.ndarray]]:\n return sum([[*map(lambda u: (u, v), self.__elements)] for v in vs], [])","def robust_outer_product(vec_1, vec_2):\n mantissa_1, exponents_1 = np.frexp(vec_1)\n mantissa_2, exponents_2 = np.frexp(vec_2)\n new_mantissas = mantissa_1[None, :] * mantissa_2[:, None]\n new_exponents = exponents_1[None, :] + exponents_2[:, None]\n return new_mantissas * np.exp2(new_exponents)","def product(self, x, y):\n return self._cached_product(x.value, y.value)","def outer_product(A, B): \n print(A)\n print(B)\n A_rows = len(A)\n A_columns = len(A[0])\n\n B_rows = len(B)\n B_columns = len(B[0])\n \n if A_columns == 1 and B_rows == 1:\n \n outer_product = []\n\n # multi-line list comprehension for outer product\n [outer_product.append([A[i][0] * B[0][j] for j in range(B_columns)]) \n for i in range(A_rows)]\n\n return outer_product\n\n else:\n print(\"dimensions of vector do not match.\")","def _inner_product_b1(self, ginvdga, ginvdgb, areas_bp):\n return self.b1 * gs.sum(\n gs.einsum(\"...bii->...b\", ginvdga)\n * gs.einsum(\"...bii->...b\", ginvdgb)\n * areas_bp,\n axis=-1,\n )","def product(self):\n return self.right[self.i:] + self.left[:self.i], self.left[self.i:] + self.right[:self.i]","def dot_product(u, v):\n sum_of_products = 0\n if u!= None:\n if v!= None:\n for combo in zip(u, v):\n sum_of_products += (combo[0] * combo[1])\n return sum_of_products","def dot(a, b):\n return a[0]*b[0] + a[1]*b[1] + a[2]*b[2]","def product_on_basis(self, t1, t2):\n return tensor( (module.monomial(x1)*module.monomial(x2) for (module, x1, x2) in zip(self._sets, t1, t2)) ) #.","def proyeccion(v1, v2):\n prod = np.dot(v1, v2)\n N = np.zeros((len(prod), len(v2)))\n\n for i in range(len(N)):\n N[i, :] = prod[i] * v2\n return N","def dot_product(A, B):\n A_rows = len(A)\n A_columns = len(A[0])\n\n B_rows = len(B)\n B_columns = len(B[0])\n\n if (A_columns == B_rows) and (A_rows == 1 and B_columns == 1):\n\n dot_product = []\n \n dot_product.append(sum([A[0][i]*B[i][0] for i in range(A_columns)]))\n\n return float(dot_product)\n \n else:\n print(\"dimensions of vector do not match.\")","def vec_product(vec1: List[int], vec2: List[int]) -> int:\n return sum(map(lambda v1, v2: v1 * v2, vec1, vec2))","def dot(v, w):\n\treturn sum(v_i * w_i for v_i, w_i in zip(v, w))","def __mul__(self, other):\r\n return self.prod(other)","def tensor_outer_product(tensor1, tensor2):\n shape_1 = tensor1.shape\n shape_2 = tensor2.shape\n s1 = len(shape_1)\n s2 = len(shape_2)\n \n shape_1 = shape_1 + (1, )*s2\n shape_2 = (1, )*s1 + shape_2\n return np.reshape(tensor1, shape_1) * np.reshape(tensor2, shape_2)","def dot(v, w):\n return sum(v_i * w_i\n for v_i, w_i in zip(v, w))","def inner_product(self, tangent_vec_a, tangent_vec_b, base_point):\n to_squeeze = False\n if tangent_vec_a.ndim == 2 and tangent_vec_b.ndim == 2:\n to_squeeze = True\n if tangent_vec_a.ndim == 2:\n tangent_vec_a = gs.expand_dims(tangent_vec_a, axis=0)\n if tangent_vec_b.ndim == 2:\n tangent_vec_b = gs.expand_dims(tangent_vec_b, axis=0)\n\n point_a = base_point + tangent_vec_a\n point_b = base_point + tangent_vec_b\n inner_prod = gs.zeros((gs.maximum(len(tangent_vec_a), len(tangent_vec_b)), 1))\n if self.a0 > 0 or self.a2 > 0:\n vertex_areas_bp = self._space.vertex_areas(base_point)\n if self.a0 > 0:\n inner_prod += self._inner_product_a0(\n tangent_vec_a, tangent_vec_b, vertex_areas_bp=vertex_areas_bp\n )\n if self.a2 > 0:\n inner_prod += self._inner_product_a2(\n tangent_vec_a,\n tangent_vec_b,\n base_point=base_point,\n vertex_areas_bp=vertex_areas_bp,\n )\n if self.a1 > 0 or self.b1 > 0 or self.c1 > 0 or self.b1 > 0:\n one_forms_bp = self._space.surface_one_forms(base_point)\n surface_metrics_bp = self._space._surface_metric_matrices_from_one_forms(\n one_forms_bp\n )\n normals_bp = self._space.normals(base_point)\n areas_bp = gs.sqrt(gs.linalg.det(surface_metrics_bp))\n\n if self.c1 > 0:\n inner_prod += self._inner_product_c1(\n point_a, point_b, normals_bp, areas_bp\n )\n if self.d1 > 0 or self.b1 > 0 or self.a1 > 0:\n ginv_bp = gs.linalg.inv(surface_metrics_bp)\n one_forms_a = self._space.surface_one_forms(point_a)\n one_forms_b = self._space.surface_one_forms(point_b)\n if self.d1 > 0:\n inner_prod += self._inner_product_d1(\n one_forms_a,\n one_forms_b,\n one_forms_bp,\n areas_bp=areas_bp,\n inv_surface_metrics_bp=ginv_bp,\n )\n\n if self.b1 > 0 or self.a1 > 0:\n dga = (\n gs.matmul(\n one_forms_a, gs.transpose(one_forms_a, axes=(0, 1, 3, 2))\n )\n - surface_metrics_bp\n )\n dgb = (\n gs.matmul(\n one_forms_b, gs.transpose(one_forms_b, axes=(0, 1, 3, 2))\n )\n - surface_metrics_bp\n )\n ginvdga = gs.matmul(ginv_bp, dga)\n ginvdgb = gs.matmul(ginv_bp, dgb)\n inner_prod += self._inner_product_a1(ginvdga, ginvdgb, areas_bp)\n inner_prod += self._inner_product_b1(ginvdga, ginvdgb, areas_bp)\n return gs.squeeze(inner_prod, axis=0) if to_squeeze else inner_prod","def multiply_vector(self, dv, spm):\n product = []\n for a, b in zip(dv, spm):\n product.append(a * b)\n return product"],"string":"[\n \"def inner_product_batch(pattern_stack_one, pattern_num_one, pattern_stack_two, pattern_num_two):\\n\\n \\\"\\\"\\\"\\n Notice that the two stacks can be different. So we can not deduce the lower triangular pattern from the \\n other half.\\n \\\"\\\"\\\"\\n holder = np.zeros((pattern_num_one, pattern_num_two))\\n for l in range(pattern_num_one):\\n for m in range(pattern_num_two):\\n holder[l, m] = np.sum(np.multiply(pattern_stack_one[l], pattern_stack_two[m]))\\n\\n return holder\",\n \"def vectordot(a, b):\\n return np.sum(a * b, 1)\",\n \"def dotProduct(v1, v2):\\n return sum((a * b) for a, b in zip(v1, v2))\",\n \"def dotproduct(v1, v2):\\n\\treturn sum(imap(operator.mul, v1, v2))\",\n \"def outer_product(x,y):\\n\\n return x[:,0]*y[:,1] -x[:,1]*y[:,0]\",\n \"def __mul__(self, other):\\n return Vec2d(self.v[0] * other, self.v[1] * other)\",\n \"def inner_prod(x, y):\\n z = torch.zeros(2, dtype=torch.double, device=x.device)\\n\\n if len(list(x.size())) == 2 and len(list(y.size())) == 2:\\n z[0] = torch.dot(x[0], y[0]) - torch.dot(-x[1], y[1])\\n z[1] = torch.dot(x[0], y[1]) + torch.dot(-x[1], y[0])\\n\\n if len(list(x.size())) == 1 and len(list(y.size())) == 1:\\n z[0] = (x[0] * y[0]) - (-x[1] * y[1])\\n z[1] = (x[0] * y[1]) + (-x[1] * y[0])\\n\\n return z\",\n \"def _outer(a, b):\\n a_flat = torch.reshape(a, [-1])\\n b_flat = torch.reshape(b, [-1])\\n a_mul = torch.unsqueeze(a_flat, dim=-1)\\n b_mul = torch.unsqueeze(b_flat, dim=0)\\n return a_mul * b_mul\",\n \"def dot_product(a,b):\\n return sum(pairwise_mult(a,b))\",\n \"def inner_product(state_1, state_2):\\n return numpy.dot(state_1.conjugate(), state_2)\",\n \"def __mul__(self, othertr):\\n res = self.dot(othertr)\\n return res\",\n \"def inner_product_similarity(a: torch.Tensor, b: torch.Tensor, dim=1) -> torch.Tensor:\\n outputs = (a * b).sum(dim=dim)\\n return outputs\",\n \"def dot_product(v1, v2):\\n return v1[0]*v2[0] + v1[1]*v2[1] + v1[2]*v2[2]\",\n \"def dotproduct(x, y):\\n return sum(imap(operator.mul, x, y))\",\n \"def dotProduct(vectorA, vectorB):\\r\\n product =0\\r\\n for i in range(len(vectorA)):\\r\\n product += eval(vectorA[i])*eval(vectorB[i])\\r\\n return product\",\n \"def dotproduct(vec1, vec2):\\n import operator\\n return sum(map(operator.mul, vec1, vec2))\",\n \"def outer_prod(x, y):\\n if len(list(x.size())) != 2 or len(list(y.size())) != 2:\\n raise ValueError(\\\"An input is not of the right dimension.\\\")\\n\\n z = torch.zeros(2, x.size()[1], y.size()[1], dtype=torch.double, device=x.device)\\n z[0] = torch.ger(x[0], y[0]) - torch.ger(x[1], -y[1])\\n z[1] = torch.ger(x[0], -y[1]) + torch.ger(x[1], y[0])\\n\\n return z\",\n \"def __mul__(self, other): \\n if isinstance(other, Iterable):\\n # dot product\\n return self.x * other[0] + self.y * other[1]\\n else:\\n # scalar product\\n return Vector(self.x * other, self.y * other)\",\n \"def _inner_product_a2(\\n self, tangent_vec_a, tangent_vec_b, base_point, vertex_areas_bp\\n ):\\n laplacian_at_base_point = self._space.laplacian(base_point)\\n return self.a2 * gs.sum(\\n gs.einsum(\\n \\\"...bi,...bi->...b\\\",\\n laplacian_at_base_point(tangent_vec_a),\\n laplacian_at_base_point(tangent_vec_b),\\n )\\n / vertex_areas_bp,\\n axis=-1,\\n )\",\n \"def _inner_product_c1(self, point_a, point_b, normals_bp, areas_bp):\\n dna = self._space.normals(point_a) - normals_bp\\n dnb = self._space.normals(point_b) - normals_bp\\n return self.c1 * gs.sum(\\n gs.einsum(\\\"...bi,...bi->...b\\\", dna, dnb) * areas_bp, axis=-1\\n )\",\n \"def dot_product(vector1, vector2):\\n return [reduce_by_multiplication(pair) for pair in zip(vector1, vector2)]\",\n \"def dot_product(v1, v2):\\n return v1[0] * v2[0] + v1[1] * v2[1]\",\n \"def dot_product(v, w):\\n return sum(v_i * w_i for v_i, w_i in zip(v, w))\",\n \"def dot4(a,b):\\n return a[0]*b[0]+a[1]*b[1]+a[2]*b[2]+a[3]*b[3]\",\n \"def _mulVectors(X1,X2):\\n _checkSize(X1,X2)\\n return sum([ X1[i] * X2[i] for i in range(len(X1))])\",\n \"def _z2matmul(self, left, right):\\n prod = np.mod(np.dot(left, right), 2)\\n return prod\",\n \"def inner_product(self, tangent_vec_a, tangent_vec_b, base_point):\\n inner_prod_mat = self.metric_matrix(base_point)\\n aux = gs.einsum(\\\"...j,...jk->...k\\\", gs.conj(tangent_vec_a), inner_prod_mat)\\n return gs.dot(aux, tangent_vec_b)\",\n \"def _qij_vec_inner(a: int, b: int, i: int, j: int):\\n vec_dagger = _qij_vec_dagger(a, b)\\n vec = _qij_vec(i, j)\\n sum_result = FermionOperator()\\n for idx, term in enumerate(vec):\\n sum_result += term * vec_dagger[idx]\\n return sum_result\",\n \"def vec_dot(x, y):\\r\\n return sum(a * b for a, b in zip(x, y))\",\n \"def dot_product(first_vector, second_vector):\\n first_unpacker = VectorUnpacker(first_vector)\\n second_unpacker = VectorUnpacker(second_vector)\\n if first_unpacker.unpacked_vector_length != second_unpacker.unpacked_vector_length:\\n raise ApplicationError(\\\"Unpacked vector sizes are unequal\\\")\\n\\n # looks better than a 'map' one-liner to me\\n value = 0\\n for piece in zip(first_unpacker(), second_unpacker()):\\n value += piece[0] * piece[1]\\n\\n return value\",\n \"def inner_product(alpha, F, beta):\\n return np.dot(alpha, np.dot(F, beta))\",\n \"def dotproduct(vec1, vec2):\\n return sum((a*b) for a, b in zip(vec1, vec2))\",\n \"def dot_product(v,w):\\n return v[0] * w[0] + v[1] * w[1]\",\n \"def dotProduct(v1, v2):\\n n1 = normalize(v1)\\n n2 = normalize(v2)\\n return n1[0] * n2[0] + n1[1] * n2[1] + n1[2] * n2[2]\",\n \"def __mul__(self,v2):\\n\\t\\tif(isinstance(v2,Vect2D)):\\n\\t\\t\\treturn np.dot(self._vec,v2._vec)\\n\\t\\telse:\\n\\t\\t\\treturn Vect2D(v2*self._vec)\",\n \"def product(self, x, y):\\n return self( x.lift() * y.lift() )\",\n \"def dot(self,other):\\n if len(self) == len(other):\\n res = 0\\n for a,b in zip(self,other):\\n res += a*b\\n return res\\n else: \\n raise ValueError(\\\"The length is not matched\\\")\",\n \"def dot(a, b):\\n return sum([a[i]*b[i] for i in range(2)])\",\n \"def _inner_product_d1(\\n self, one_forms_a, one_forms_b, one_forms_bp, areas_bp, inv_surface_metrics_bp\\n ):\\n one_forms_bp_t = gs.transpose(one_forms_bp, (0, 2, 1))\\n\\n one_forms_a_t = gs.transpose(one_forms_a, (0, 1, 3, 2))\\n xa = one_forms_a_t - one_forms_bp_t\\n\\n xa_0 = gs.matmul(\\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\\n gs.matmul(gs.transpose(xa, (0, 1, 3, 2)), one_forms_bp_t)\\n - gs.matmul(one_forms_bp, xa),\\n )\\n\\n one_forms_b_t = gs.transpose(one_forms_b, (0, 1, 3, 2))\\n xb = one_forms_b_t - one_forms_bp_t\\n xb_0 = gs.matmul(\\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\\n gs.matmul(gs.transpose(xb, (0, 1, 3, 2)), one_forms_bp_t)\\n - gs.matmul(one_forms_bp, xb),\\n )\\n\\n return self.d1 * gs.sum(\\n gs.einsum(\\n \\\"...bii->...b\\\",\\n gs.matmul(\\n xa_0,\\n gs.matmul(\\n inv_surface_metrics_bp, gs.transpose(xb_0, axes=(0, 1, 3, 2))\\n ),\\n ),\\n )\\n * areas_bp\\n )\",\n \"def dot( v1, v2 ):\\n return sum( x*y for x,y in izip(v1,v2) )\",\n \"def dot_prod(u,v):\\n each_product = []\\n for i in range(len(u)):\\n each_product.append(u[i] * v[i])\\n return sum(each_product)\",\n \"def dot_product(v1, v2):\\n #print(v1, v2)\\n sum = 0\\n\\n for i in range(len(v1)):\\n #print(v1[i], v2[i])\\n sum += v1[i] * v2[i]\\n return sum\",\n \"def dot_product(a, b):\\n dp = 0.0\\n for i, j in zip(a, b):\\n dp += i * j\\n return dp\",\n \"def dot(v,w):\\n return sum(v_i * w_i for v_i,w_i in zip(v,w))\",\n \"def mul(Z,X,Y):\",\n \"def dot(v,w):\\r\\n return sum(v_i * w_i\\r\\n for v_i, w_i in zip(v, w))\",\n \"def inner_product(self, tangent_vec_a, tangent_vec_b, base_point):\\n args = {\\n \\\"tangent_vec_a\\\": tangent_vec_a,\\n \\\"tangent_vec_b\\\": tangent_vec_b,\\n \\\"base_point\\\": base_point,\\n }\\n inner_products = self._iterate_over_factors(\\\"inner_product\\\", args)\\n return sum(inner_products)\",\n \"def __mul__(self, other):\\n x = self.x * other\\n y = self.y * other\\n return vec(x, y)\",\n \"def prod(self, x, y):\\n return (self.basic_operation.reduce(x.original+y.original),\\n self.operation1.prod(x.left, y.left),\\n self.operation2.prod(x.right, y.right))\",\n \"def dotproduct(first, other):\\n if isinstance(first,FreeCAD.Vector) and isinstance(other,FreeCAD.Vector):\\n return (first.x*other.x + first.y*other.y + first.z*other.z)\",\n \"def crossproduct(first, other=FreeCAD.Vector(0,0,1)):\\n if isinstance(first,FreeCAD.Vector) and isinstance(other,FreeCAD.Vector):\\n return FreeCAD.Vector(first.y*other.z - first.z*other.y, first.z*other.x - first.x*other.z, first.x*other.y - first.y*other.x)\",\n \"def inner(self, a: np.ndarray, b: np.ndarray) -> float:\\n return a.T @ (self.mass @ b)\",\n \"def pair_product(x1, x2):\\n return np.multiply(x1, x2)\",\n \"def _mul(*args):\\n\\treturn functools.reduce(numpy.dot, args)\",\n \"def innerprod_q2(q1, q2):\\n T = q1.shape[1]\\n val = sum(sum(q1 * q2)) / T\\n\\n return (val)\",\n \"def _inner_product_a1(self, ginvdga, ginvdgb, areas_bp):\\n return self.a1 * gs.sum(\\n gs.einsum(\\\"...bii->...b\\\", gs.matmul(ginvdga, ginvdgb)) * areas_bp,\\n axis=-1,\\n )\",\n \"def vec_dot_star(v1,v2):\\r\\n \\r\\n dot_star = v1[0]*(v2[1])-v1[1]*v2[0]\\r\\n return dot_star\",\n \"def vecDot(a, b):\\n ret=0.0\\n for i in range(len(a)):\\n ret+=a[i]*b[i]\\n return ret\",\n \"def sparseVectorDotProduct(v1, v2):\\n # BEGIN_YOUR_CODE (our solution is 4 lines of code, but don't worry if you deviate from this)\\n return sum(v1[k]*v2[k] for k in v1 and v2)\\n # END_YOUR_CODE\",\n \"def dot(v,w):\\n return sum(v_i * w_i\\n for v_i, w_i in zip(v,w))\",\n \"def _prod_vectorized(M1, M2):\\n sh1 = M1.shape\\n sh2 = M2.shape\\n assert len(sh1) >= 2\\n assert len(sh2) >= 2\\n assert sh1[-1] == sh2[-2]\\n\\n ndim1 = len(sh1)\\n t1_index = list(xrange(ndim1-2)) + [ndim1-1, ndim1-2]\\n return np.sum(np.transpose(M1, t1_index)[..., np.newaxis] *\\n M2[..., np.newaxis, :], -3)\",\n \"def dot_product(vec_1:tuple, vec_2:tuple)->float:\\n return vec_1[0] * vec_2[0] + vec_1[1] * vec_2[1]\",\n \"def same_side_product(p, q, a, b):\\n return line_ccw(a, b, p) * line_ccw(a, b, q)\",\n \"def vdot(a, b):\\n return np.vdot(a.ravel(), b.ravel())\",\n \"def __mul__(self,other):\\n if type(other) is Vector:\\n return(self.x*other.x + self.y*other.y + self.z*other.z)\\n else:\\n return(Vector(self.x*other,self.y*other,self.z*other))\",\n \"def _dot(a, b):\\n return np.einsum('ijk,ikl->ijl', a, b)\",\n \"def sparseVectorDotProduct(a, b):\\n # BEGIN_YOUR_CODE (our solution is 4 lines of code, but don't worry if you deviate from this)\\n ref = a if len(a)>len(b) else b\\n return sum(a[key]*b[key] for key in ref)\\n # END_YOUR_CODE\",\n \"def basic_geometric_product(obj1, obj2):\\n def mul_table(b1, b2):\\n return MV.base_mul_table[(b1, b2)]\\n\\n obj12 = bilinear_product(obj1 * obj2, mul_table)\\n\\n return obj12\",\n \"def __mul__(self, other):\\n return sum(self._ar * other._ar)\",\n \"def get_dot_product(v1,v2):\\n #sets default dot product\\n dot_product = 0\\n \\n for key in v2:\\n if key in v1:\\n # updates the dot product if key is present in both vectors\\n dot_product += v1[key]*v2[key]\\n #returns final dot product\\n return dot_product\",\n \"def dot(a,b):\\n return a[0]*b[0]+a[1]*b[1]+a[2]*b[2]\",\n \"def _inner_product_a0(self, tangent_vec_a, tangent_vec_b, vertex_areas_bp):\\n return self.a0 * gs.sum(\\n vertex_areas_bp\\n * gs.einsum(\\\"...bi,...bi->...b\\\", tangent_vec_a, tangent_vec_b),\\n axis=-1,\\n )\",\n \"def dot(self,v, w):\\n return sum(v_i * w_i for v_i, w_i in zip(v, w))\",\n \"def test_l2_metric_inner_product_vectorization(\\n self,\\n l2_metric_s2,\\n times,\\n n_landmark_sets,\\n landmarks_a,\\n landmarks_b,\\n landmarks_c,\\n ):\\n landmarks_ab = l2_metric_s2.geodesic(landmarks_a, landmarks_b)\\n landmarks_bc = l2_metric_s2.geodesic(landmarks_b, landmarks_c)\\n landmarks_ab = landmarks_ab(times)\\n landmarks_bc = landmarks_bc(times)\\n\\n tangent_vecs = l2_metric_s2.log(point=landmarks_bc, base_point=landmarks_ab)\\n\\n result = l2_metric_s2.inner_product(tangent_vecs, tangent_vecs, landmarks_ab)\\n\\n self.assertAllClose(gs.shape(result), (n_landmark_sets,))\",\n \"def complex_mul2d(a, b):\\n op = partial(torch.einsum, \\\"bixy,ioxy->boxy\\\")\\n return torch.stack([\\n op(a[..., 0], b[..., 0]) - op(a[..., 1], b[..., 1]),\\n op(a[..., 1], b[..., 0]) + op(a[..., 0], b[..., 1])\\n ],\\n dim=-1)\",\n \"def dot(v, w):\\n return sum(v_i * w_i for v_i, w_i in zip(v, w))\",\n \"def dot(v, w):\\n return sum(v_i * w_i for v_i, w_i in zip(v, w))\",\n \"def dot(v, w):\\n return sum(v_i * w_i for v_i, w_i in zip(v, w))\",\n \"def dot(v, w):\\n return sum(v_i * w_i for v_i, w_i in zip(v, w))\",\n \"def dot(v, w):\\n return sum(v_i * w_i for v_i, w_i in zip(v, w))\",\n \"def __mul__(self, other):\\n\\n newlist = [v for v in self.args]\\n for i, v in enumerate(newlist):\\n newlist[i] = (sympify(other) * newlist[i][0], newlist[i][1])\\n return Vector(newlist)\",\n \"def __mul__(self, other):\\n if isinstance(other, Vector):\\n return self.dot(other)\\n else:\\n raise TypeError(other)\",\n \"def __mul__(self, vs) -> List[Tuple[np.ndarray, np.ndarray]]:\\n return sum([[*map(lambda u: (u, v), self.__elements)] for v in vs], [])\",\n \"def robust_outer_product(vec_1, vec_2):\\n mantissa_1, exponents_1 = np.frexp(vec_1)\\n mantissa_2, exponents_2 = np.frexp(vec_2)\\n new_mantissas = mantissa_1[None, :] * mantissa_2[:, None]\\n new_exponents = exponents_1[None, :] + exponents_2[:, None]\\n return new_mantissas * np.exp2(new_exponents)\",\n \"def product(self, x, y):\\n return self._cached_product(x.value, y.value)\",\n \"def outer_product(A, B): \\n print(A)\\n print(B)\\n A_rows = len(A)\\n A_columns = len(A[0])\\n\\n B_rows = len(B)\\n B_columns = len(B[0])\\n \\n if A_columns == 1 and B_rows == 1:\\n \\n outer_product = []\\n\\n # multi-line list comprehension for outer product\\n [outer_product.append([A[i][0] * B[0][j] for j in range(B_columns)]) \\n for i in range(A_rows)]\\n\\n return outer_product\\n\\n else:\\n print(\\\"dimensions of vector do not match.\\\")\",\n \"def _inner_product_b1(self, ginvdga, ginvdgb, areas_bp):\\n return self.b1 * gs.sum(\\n gs.einsum(\\\"...bii->...b\\\", ginvdga)\\n * gs.einsum(\\\"...bii->...b\\\", ginvdgb)\\n * areas_bp,\\n axis=-1,\\n )\",\n \"def product(self):\\n return self.right[self.i:] + self.left[:self.i], self.left[self.i:] + self.right[:self.i]\",\n \"def dot_product(u, v):\\n sum_of_products = 0\\n if u!= None:\\n if v!= None:\\n for combo in zip(u, v):\\n sum_of_products += (combo[0] * combo[1])\\n return sum_of_products\",\n \"def dot(a, b):\\n return a[0]*b[0] + a[1]*b[1] + a[2]*b[2]\",\n \"def product_on_basis(self, t1, t2):\\n return tensor( (module.monomial(x1)*module.monomial(x2) for (module, x1, x2) in zip(self._sets, t1, t2)) ) #.\",\n \"def proyeccion(v1, v2):\\n prod = np.dot(v1, v2)\\n N = np.zeros((len(prod), len(v2)))\\n\\n for i in range(len(N)):\\n N[i, :] = prod[i] * v2\\n return N\",\n \"def dot_product(A, B):\\n A_rows = len(A)\\n A_columns = len(A[0])\\n\\n B_rows = len(B)\\n B_columns = len(B[0])\\n\\n if (A_columns == B_rows) and (A_rows == 1 and B_columns == 1):\\n\\n dot_product = []\\n \\n dot_product.append(sum([A[0][i]*B[i][0] for i in range(A_columns)]))\\n\\n return float(dot_product)\\n \\n else:\\n print(\\\"dimensions of vector do not match.\\\")\",\n \"def vec_product(vec1: List[int], vec2: List[int]) -> int:\\n return sum(map(lambda v1, v2: v1 * v2, vec1, vec2))\",\n \"def dot(v, w):\\n\\treturn sum(v_i * w_i for v_i, w_i in zip(v, w))\",\n \"def __mul__(self, other):\\r\\n return self.prod(other)\",\n \"def tensor_outer_product(tensor1, tensor2):\\n shape_1 = tensor1.shape\\n shape_2 = tensor2.shape\\n s1 = len(shape_1)\\n s2 = len(shape_2)\\n \\n shape_1 = shape_1 + (1, )*s2\\n shape_2 = (1, )*s1 + shape_2\\n return np.reshape(tensor1, shape_1) * np.reshape(tensor2, shape_2)\",\n \"def dot(v, w):\\n return sum(v_i * w_i\\n for v_i, w_i in zip(v, w))\",\n \"def inner_product(self, tangent_vec_a, tangent_vec_b, base_point):\\n to_squeeze = False\\n if tangent_vec_a.ndim == 2 and tangent_vec_b.ndim == 2:\\n to_squeeze = True\\n if tangent_vec_a.ndim == 2:\\n tangent_vec_a = gs.expand_dims(tangent_vec_a, axis=0)\\n if tangent_vec_b.ndim == 2:\\n tangent_vec_b = gs.expand_dims(tangent_vec_b, axis=0)\\n\\n point_a = base_point + tangent_vec_a\\n point_b = base_point + tangent_vec_b\\n inner_prod = gs.zeros((gs.maximum(len(tangent_vec_a), len(tangent_vec_b)), 1))\\n if self.a0 > 0 or self.a2 > 0:\\n vertex_areas_bp = self._space.vertex_areas(base_point)\\n if self.a0 > 0:\\n inner_prod += self._inner_product_a0(\\n tangent_vec_a, tangent_vec_b, vertex_areas_bp=vertex_areas_bp\\n )\\n if self.a2 > 0:\\n inner_prod += self._inner_product_a2(\\n tangent_vec_a,\\n tangent_vec_b,\\n base_point=base_point,\\n vertex_areas_bp=vertex_areas_bp,\\n )\\n if self.a1 > 0 or self.b1 > 0 or self.c1 > 0 or self.b1 > 0:\\n one_forms_bp = self._space.surface_one_forms(base_point)\\n surface_metrics_bp = self._space._surface_metric_matrices_from_one_forms(\\n one_forms_bp\\n )\\n normals_bp = self._space.normals(base_point)\\n areas_bp = gs.sqrt(gs.linalg.det(surface_metrics_bp))\\n\\n if self.c1 > 0:\\n inner_prod += self._inner_product_c1(\\n point_a, point_b, normals_bp, areas_bp\\n )\\n if self.d1 > 0 or self.b1 > 0 or self.a1 > 0:\\n ginv_bp = gs.linalg.inv(surface_metrics_bp)\\n one_forms_a = self._space.surface_one_forms(point_a)\\n one_forms_b = self._space.surface_one_forms(point_b)\\n if self.d1 > 0:\\n inner_prod += self._inner_product_d1(\\n one_forms_a,\\n one_forms_b,\\n one_forms_bp,\\n areas_bp=areas_bp,\\n inv_surface_metrics_bp=ginv_bp,\\n )\\n\\n if self.b1 > 0 or self.a1 > 0:\\n dga = (\\n gs.matmul(\\n one_forms_a, gs.transpose(one_forms_a, axes=(0, 1, 3, 2))\\n )\\n - surface_metrics_bp\\n )\\n dgb = (\\n gs.matmul(\\n one_forms_b, gs.transpose(one_forms_b, axes=(0, 1, 3, 2))\\n )\\n - surface_metrics_bp\\n )\\n ginvdga = gs.matmul(ginv_bp, dga)\\n ginvdgb = gs.matmul(ginv_bp, dgb)\\n inner_prod += self._inner_product_a1(ginvdga, ginvdgb, areas_bp)\\n inner_prod += self._inner_product_b1(ginvdga, ginvdgb, areas_bp)\\n return gs.squeeze(inner_prod, axis=0) if to_squeeze else inner_prod\",\n \"def multiply_vector(self, dv, spm):\\n product = []\\n for a, b in zip(dv, spm):\\n product.append(a * b)\\n return product\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6906152","0.6847398","0.68069786","0.67931485","0.6750435","0.6661231","0.665229","0.6584153","0.6543558","0.6477087","0.645507","0.64223343","0.64149445","0.64062977","0.64052427","0.6394551","0.6389129","0.6387153","0.6369647","0.6364393","0.6364216","0.63563746","0.6349533","0.6338918","0.6337371","0.6333778","0.63327885","0.6304801","0.6301042","0.6294226","0.62937057","0.6278996","0.6254354","0.6249698","0.62419444","0.62408894","0.62312293","0.6223513","0.6194338","0.61871403","0.615942","0.6153975","0.6143662","0.6125205","0.61245793","0.61241776","0.6123317","0.6119969","0.6117163","0.6109324","0.61043894","0.61042815","0.6097835","0.60862917","0.6082641","0.60778964","0.607604","0.60723877","0.6049616","0.60438526","0.6033628","0.6028014","0.6025679","0.60188544","0.60178226","0.6011806","0.6011655","0.6008298","0.59859014","0.59810317","0.5980731","0.59763557","0.59692574","0.5964098","0.59478","0.5943066","0.5943066","0.5943066","0.5943066","0.5943066","0.5942895","0.5942147","0.5942145","0.59349096","0.59289217","0.5925027","0.59164935","0.59005314","0.58936363","0.58843505","0.5882821","0.5874659","0.58651155","0.58648306","0.5863016","0.5861122","0.5861097","0.5857336","0.5848796","0.58481383"],"string":"[\n \"0.6906152\",\n \"0.6847398\",\n \"0.68069786\",\n \"0.67931485\",\n \"0.6750435\",\n \"0.6661231\",\n \"0.665229\",\n \"0.6584153\",\n \"0.6543558\",\n \"0.6477087\",\n \"0.645507\",\n \"0.64223343\",\n \"0.64149445\",\n \"0.64062977\",\n \"0.64052427\",\n \"0.6394551\",\n \"0.6389129\",\n \"0.6387153\",\n \"0.6369647\",\n \"0.6364393\",\n \"0.6364216\",\n \"0.63563746\",\n \"0.6349533\",\n \"0.6338918\",\n \"0.6337371\",\n \"0.6333778\",\n \"0.63327885\",\n \"0.6304801\",\n \"0.6301042\",\n \"0.6294226\",\n \"0.62937057\",\n \"0.6278996\",\n \"0.6254354\",\n \"0.6249698\",\n \"0.62419444\",\n \"0.62408894\",\n \"0.62312293\",\n \"0.6223513\",\n \"0.6194338\",\n \"0.61871403\",\n \"0.615942\",\n \"0.6153975\",\n \"0.6143662\",\n \"0.6125205\",\n \"0.61245793\",\n \"0.61241776\",\n \"0.6123317\",\n \"0.6119969\",\n \"0.6117163\",\n \"0.6109324\",\n \"0.61043894\",\n \"0.61042815\",\n \"0.6097835\",\n \"0.60862917\",\n \"0.6082641\",\n \"0.60778964\",\n \"0.607604\",\n \"0.60723877\",\n \"0.6049616\",\n \"0.60438526\",\n \"0.6033628\",\n \"0.6028014\",\n \"0.6025679\",\n \"0.60188544\",\n \"0.60178226\",\n \"0.6011806\",\n \"0.6011655\",\n \"0.6008298\",\n \"0.59859014\",\n \"0.59810317\",\n \"0.5980731\",\n \"0.59763557\",\n \"0.59692574\",\n \"0.5964098\",\n \"0.59478\",\n \"0.5943066\",\n \"0.5943066\",\n \"0.5943066\",\n \"0.5943066\",\n \"0.5943066\",\n \"0.5942895\",\n \"0.5942147\",\n \"0.5942145\",\n \"0.59349096\",\n \"0.59289217\",\n \"0.5925027\",\n \"0.59164935\",\n \"0.59005314\",\n \"0.58936363\",\n \"0.58843505\",\n \"0.5882821\",\n \"0.5874659\",\n \"0.58651155\",\n \"0.58648306\",\n \"0.5863016\",\n \"0.5861122\",\n \"0.5861097\",\n \"0.5857336\",\n \"0.5848796\",\n \"0.58481383\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.83498394"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":879,"cells":{"query":{"kind":"string","value":"Calculate the inner product pair of each pattern in batch one and batch two. Notice that the pattern_stack_one variable represent the pattern along the zero dimension while the pattern_stack_two variable represent patterns along dimension one in the final distance matrix."},"document":{"kind":"string","value":"def inner_product_batch(pattern_stack_one, pattern_num_one, pattern_stack_two, pattern_num_two):\n\n \"\"\"\n Notice that the two stacks can be different. So we can not deduce the lower triangular pattern from the \n other half.\n \"\"\"\n holder = np.zeros((pattern_num_one, pattern_num_two))\n for l in range(pattern_num_one):\n for m in range(pattern_num_two):\n holder[l, m] = np.sum(np.multiply(pattern_stack_one[l], pattern_stack_two[m]))\n\n return holder"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def inner_product(pattern_one, pattern_two):\n\n return np.sum(np.multiply(pattern_one, pattern_two))","def l2_norm_batch(pattern_stack):\n\n return np.linalg.norm(pattern_stack, axis=0)","def generate_pattern_grid(words1, words2):\n # Convert word lists to integer arrays\n w1, w2 = (\n np.array([[ord(c) for c in w] for w in words], dtype=np.uint8)\n for words in (words1, words2)\n )\n\n if len(w1) == 0 or len(w2) == 0:\n return np.zeros((len(w1), len(w2)), dtype=np.uint8)\n\n # equality_grid[a, b, i, j] represents whether the ith letter\n # of words1[a] equals the jth letter of words2[b]\n equality_grid = np.zeros((len(w1), len(w2), 5, 5), dtype=bool)\n for i, j in it.product(range(5), range(5)):\n equality_grid[:, :, i, j] = np.equal.outer(w1[:, i], w2[:, j])\n\n patterns = np.zeros((len(w1), len(w2)), dtype=np.uint8)\n three_pows = (3**np.arange(5)).astype(np.uint8)\n for i, tp in enumerate(three_pows):\n # This accounts for yellow squares\n patterns[:, :] += tp * equality_grid[:, :, i, :].any(2)\n # This accounts for green squares\n patterns[:, :] += tp * equality_grid[:, :, i, i]\n\n return patterns","def pair_product(x1, x2):\n return np.multiply(x1, x2)","def dim_mul(dims1, dims2):\n return (\n dims1[0] + dims2[0],\n dims1[1] + dims2[1],\n dims1[2] + dims2[2],\n dims1[3] + dims2[3],\n dims1[4] + dims2[4],\n dims1[5] + dims2[5],\n dims1[6] + dims2[6],\n )","def axis_element_wise_multiplication(t1, t2, which_axis):\n # assert len(K.int_shape(t1)) == len(K.int_shape(t2)) + 1, \"rank(t1) should be rank(t2) + 1\"\n slices = tf.unstack(t1, axis=which_axis)\n # assert K.int_shape(slices[0]) == K.int_shape(t2), \"Slices of t1 were not the same shape as t2\"\n multiplies = []\n for s in slices:\n multiplies.append(t2 * s)\n return tf.stack(multiplies, axis=2)","def _prod_vectorized(M1, M2):\n sh1 = M1.shape\n sh2 = M2.shape\n assert len(sh1) >= 2\n assert len(sh2) >= 2\n assert sh1[-1] == sh2[-2]\n\n ndim1 = len(sh1)\n t1_index = list(xrange(ndim1-2)) + [ndim1-1, ndim1-2]\n return np.sum(np.transpose(M1, t1_index)[..., np.newaxis] *\n M2[..., np.newaxis, :], -3)","def double_chop_pairs(\n x1, y1, z1, w1, cell1, x2, y2, z2, w2, indx2, rbins_squared, result):\n start = cuda.grid(1)\n stride = cuda.gridsize(1)\n\n n1 = x1.shape[0]\n nbins = rbins_squared.shape[0]\n\n for i in range(start, n1, stride):\n px = x1[i]\n py = y1[i]\n pz = z1[i]\n pw = w1[i]\n\n cell1_i = cell1[i]\n first = indx2[cell1_i]\n last = indx2[cell1_i+1]\n\n for j in range(first, last):\n qx = x2[j]\n qy = y2[j]\n qz = z2[j]\n qw = w2[j]\n\n dx = px-qx\n dy = py-qy\n dz = pz-qz\n wprod = pw*qw\n dsq = dx*dx + dy*dy + dz*dz\n\n k = nbins-1\n while dsq <= rbins_squared[k]:\n cuda.atomic.add(result, k-1, wprod)\n k = k-1\n if k <= 0:\n break","def inner_prod(x, y):\n z = torch.zeros(2, dtype=torch.double, device=x.device)\n\n if len(list(x.size())) == 2 and len(list(y.size())) == 2:\n z[0] = torch.dot(x[0], y[0]) - torch.dot(-x[1], y[1])\n z[1] = torch.dot(x[0], y[1]) + torch.dot(-x[1], y[0])\n\n if len(list(x.size())) == 1 and len(list(y.size())) == 1:\n z[0] = (x[0] * y[0]) - (-x[1] * y[1])\n z[1] = (x[0] * y[1]) + (-x[1] * y[0])\n\n return z","def tensor_outer_product(tensor1, tensor2):\n shape_1 = tensor1.shape\n shape_2 = tensor2.shape\n s1 = len(shape_1)\n s2 = len(shape_2)\n \n shape_1 = shape_1 + (1, )*s2\n shape_2 = (1, )*s1 + shape_2\n return np.reshape(tensor1, shape_1) * np.reshape(tensor2, shape_2)","def mult(m1, m2):\n assert np.shape(m1) == (2, 3)\n assert np.shape(m2) == (2, 3)\n\n m1_temp = np.vstack((m1, [0, 0, 1]))\n m2_temp = np.vstack((m2, [0, 0, 1]))\n result = m1_temp * m2_temp\n\n return result[:2, :]","def _inner_product_d1(\n self, one_forms_a, one_forms_b, one_forms_bp, areas_bp, inv_surface_metrics_bp\n ):\n one_forms_bp_t = gs.transpose(one_forms_bp, (0, 2, 1))\n\n one_forms_a_t = gs.transpose(one_forms_a, (0, 1, 3, 2))\n xa = one_forms_a_t - one_forms_bp_t\n\n xa_0 = gs.matmul(\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\n gs.matmul(gs.transpose(xa, (0, 1, 3, 2)), one_forms_bp_t)\n - gs.matmul(one_forms_bp, xa),\n )\n\n one_forms_b_t = gs.transpose(one_forms_b, (0, 1, 3, 2))\n xb = one_forms_b_t - one_forms_bp_t\n xb_0 = gs.matmul(\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\n gs.matmul(gs.transpose(xb, (0, 1, 3, 2)), one_forms_bp_t)\n - gs.matmul(one_forms_bp, xb),\n )\n\n return self.d1 * gs.sum(\n gs.einsum(\n \"...bii->...b\",\n gs.matmul(\n xa_0,\n gs.matmul(\n inv_surface_metrics_bp, gs.transpose(xb_0, axes=(0, 1, 3, 2))\n ),\n ),\n )\n * areas_bp\n )","def _kronecker_product(mat1: tf.Tensor, mat2: tf.Tensor) -> tf.Tensor:\n m1, n1 = mat1.get_shape().as_list()\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\n m2, n2 = mat2.get_shape().as_list()\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])","def dot_product(a,b):\n return sum(pairwise_mult(a,b))","def _z2matmul(self, left, right):\n prod = np.mod(np.dot(left, right), 2)\n return prod","def _outer(a, b):\n a_flat = torch.reshape(a, [-1])\n b_flat = torch.reshape(b, [-1])\n a_mul = torch.unsqueeze(a_flat, dim=-1)\n b_mul = torch.unsqueeze(b_flat, dim=0)\n return a_mul * b_mul","def inner_product_similarity(a: torch.Tensor, b: torch.Tensor, dim=1) -> torch.Tensor:\n outputs = (a * b).sum(dim=dim)\n return outputs","def gradient_merge_arrays(cls, image_one, image_two):\n if image_one.shape != image_two.shape:\n raise AttributeError(\"shapes do not match: {} vs {}\".format(image_one.shape, image_two.shape))\n height = image_one.shape[0]\n vector_one = numpy.array([1.0 - float(i + 1) / (height + 1) for i in range(height)])\n vector_two = numpy.array([float(i + 1) / (height + 1) for i in range(height)])\n return (image_one * vector_one[:, numpy.newaxis]) + (image_two * vector_two[:, numpy.newaxis])","def mul(self,mat1,mat2):\n if(isinstance(mat2,int)==True):\n result = [[mat1[i][j] * mat2 for j in range(len(mat1[0]))] for i in range(len(mat1))]\n self.out = result\n return self.out\n elif(len(mat1[0])==len(mat2)):\n result = [[sum(a*b for a,b in zip(i,j)) for j in zip(*mat2)] for i in mat1]\n self.out = result\n return self.out","def batch_outer_product(a, b):\n a, b = normalize_and_check_ndim([a, b], 2)\n # This is a batchwise version of the matrix multiplication approach\n # used for outer_product(), see explanation there.\n return a[:, :, np.newaxis] * b[:, np.newaxis, :]","def dotproduct(x, y):\n return sum(imap(operator.mul, x, y))","def _layerwise_dot_product(x_s, y_s):\n return [torch.sum(x * y).item() for x, y in zip(x_s, y_s)]","def pairwise_distances(a, b, p=2):\n squeezed = False\n if len(a.shape) == 2 and len(b.shape) == 2:\n a = tf.expand_dims(a,0) #[np.newaxis, :, :]\n b = tf.expand_dims(a,0) #b[np.newaxis, :, :]\n squeezed = True\n \n ret = tf.reduce_sum(tf.keras.backend.pow(tf.math.abs(tf.expand_dims(a,2) - tf.expand_dims(b,1)), p), 3)\n #[:, :, np.newaxis, :], [:, np.newaxis, :, :]\n if squeezed:\n ret = tf.squeeze(ret)\n\n return ret","def pair_eval(self, X, Y):\n d1 = self.d1\n K1 = self.k1.pair_eval(X[:, :d1], Y[:, :d1])\n K2 = self.k2.pair_eval(X[:, d1:], Y[:, d1:])\n return K1 * K2","def inner_product(state_1, state_2):\n return numpy.dot(state_1.conjugate(), state_2)","def dot_product(vector1, vector2):\n return [reduce_by_multiplication(pair) for pair in zip(vector1, vector2)]","def product_2(m1, m2):\r\n return make_mono_admissible_2(list(m1) + list(m2))","def innerprod_q2(q1, q2):\n T = q1.shape[1]\n val = sum(sum(q1 * q2)) / T\n\n return (val)","def dist_matrix(self, group1, group2):\n \n tmps = []\n for i in group2:\n tmps.append([])\n for j in group1:\n mi, label = self.distance(i, j)\n tmps[-1].append(mi)\n return tmps","def _interleave_ecdfs(\n x1: np.ndarray,\n y1: np.ndarray,\n x2: np.ndarray,\n y2: np.ndarray\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n assert len(x1.shape) == len(x2.shape) == 1\n assert x1.shape == y1.shape\n assert x2.shape == y2.shape\n\n x = np.sort(np.concatenate([x1, x2]))\n y1 = np.insert(y1, 0, [0])\n y2 = np.insert(y2, 0, [0])\n return x, y1[np.searchsorted(x1, x, side='right')], y2[np.searchsorted(x2, x, side='right')]","def alignPairShapes(s1,s2,weights):\n\n\n s1=np.asarray(s1)\n s2=np.asarray(s2)\n \n x1k=s1[:,0]\n y1k=s1[:,1]\n x2k=s2[:,0]\n y2k=s2[:,1]\n\n X1=sum(x1k*weights) \n X2=sum(x2k*weights)\n\n Y1=sum(y1k*weights)\n Y2=sum(y2k*weights)\n\n Z=sum(weights*(pow(x2k,2)+pow(y2k,2)))\n\n W=sum(weights)\n\n C1=sum(weights*(x1k*x2k+y1k*y2k))\n\n C2=sum(weights*(y1k*x2k-x1k*y2k))\n \n a=np.asarray([[X2,-Y2,W,0],[Y2,X2,0,W],[Z,0,X2,Y2],[0,Z,-Y2,X2]])\n b=np.asarray([X1,Y1,C1,C2])\n\n x=np.linalg.solve(a,b)\n\n ax=x[0]\n ay=x[1]\n tx=x[2]\n ty=x[3]\n return ax,ay,tx,ty","def strassen(m1, m2):\n \n if ((m1.shape[0] % 2 == 0) or (m1.shape[0] == 1)):\n n = m1.shape[0] \n else:\n n = m1.shape[0] + 1\n result = np.zeros((n, n), dtype = int)\n \n if (n == 1):\n result[0][0] = m1[0][0] * m2[0][0]\n else:\n new = n//2\n \n a11, a12, a21, a22 = m1[:new, :new], m1[new:, :new], m1[:new, new:], m1[new:, new:]\n b11, b12, b21, b22 = m2[:new, :new], m2[new:, :new], m2[:new, new:], m2[new:, new:]\n \n p1 = strassen(a11, b12 - b22)\n p2 = strassen(a11 + a12, b22)\n p3 = strassen(a21 + a22, b11)\n p4 = strassen(a22, b21 - b11)\n p5 = strassen(a11 + a22, b11 + b22)\n p6 = strassen(a12 - a22, b21 + b22)\n p7 = strassen(a11 - a21, b11 + b12)\n \n result[:new, :new] = p5 + p4 - p2 + p6\n result[new:, :new] = p1 + p2\n result[:new, new:] = p3 + p4 \n result[new:, new:] = p5 + p1 - p3 - p7\n \n return result","def matrix_mult(m1, m2):\n\ttemp = []\n\tfor i in range(len(m1)):\n\t\te = []\n\t\tfor j in range(len(m2[0])):\n\t\t\te.append(row_times_column(m1,i,m2,j))\n\t\ttemp.append(e)\n\treturn temp","def solve_part_two(self):\n return self.outputs[0] * self.outputs[1] * self.outputs[2]","def kronecker_product(mat1, mat2):\n m1, n1 = mat1.get_shape().as_list()\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\n m2, n2 = mat2.get_shape().as_list()\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])","def multiplicand_2(p):\n m2 = cddr(p) # (m2 m3 ...)\n rests = cdr(m2) # (m3...)\n if isNull(rests):\n return car(m2)\n else:\n restp = convertToPythonList(cdr(rests))\n return make_product_2(car(m2), car(rests), *restp)","def _mulVectors(X1,X2):\n _checkSize(X1,X2)\n return sum([ X1[i] * X2[i] for i in range(len(X1))])","def complex_mul2d(a, b):\n op = partial(torch.einsum, \"bixy,ioxy->boxy\")\n return torch.stack([\n op(a[..., 0], b[..., 0]) - op(a[..., 1], b[..., 1]),\n op(a[..., 1], b[..., 0]) + op(a[..., 0], b[..., 1])\n ],\n dim=-1)","def __call__(self, sample_1, sample_2, alphas, ret_matrix=False):\n sample_12 = torch.cat((sample_1, sample_2), 0)\n distances = pdist(sample_12, sample_12, norm=2)\n\n kernels = None\n for alpha in alphas:\n kernels_a = torch.exp(- alpha * distances ** 2)\n if kernels is None:\n kernels = kernels_a\n else:\n kernels = kernels + kernels_a\n\n k_1 = kernels[:self.n_1, :self.n_1]\n k_2 = kernels[self.n_1:, self.n_1:]\n k_12 = kernels[:self.n_1, self.n_1:]\n\n mmd = (2 * self.a01 * k_12.sum() +\n self.a00 * (k_1.sum() - torch.trace(k_1)) +\n self.a11 * (k_2.sum() - torch.trace(k_2)))\n if ret_matrix:\n return mmd, kernels\n else:\n return mmd","def product_map(xs1, xs2):\n return jax.vmap(lambda x1: jax.vmap(lambda x2: pair_product(x1, x2))(xs2))(xs1)","def Multiply(M1,M2):\r\n M3=[]\r\n w=0\r\n while w<len(M2[0]):\r\n tap=[]\r\n t=0\r\n while t<len(M2):\r\n tap.append(M2[t][w])\r\n t=t+1\r\n M3.append(tap)\r\n w=w+1\r\n M=[]\r\n # Multiplying matrices\r\n k=0\r\n sums=0\r\n while k<len(M1):\r\n j=0\r\n mpy=[]\r\n while j<len(M3):\r\n p=0\r\n sums=0\r\n while p<len(M3[j]):\r\n temp = (M1[k][p])*(M3[j][p])\r\n sums=sums+temp\r\n p=p+1\r\n mpy.append(sums)\r\n j=j+1\r\n M.append(mpy)\r\n k=k+1\r\n return M","def mat_mul(mat1, mat2):\n\n rows1 = len(mat1)\n cols1 = len(mat1[0])\n rows2 = len(mat2)\n cols2 = len(mat2[0])\n\n if cols1 != rows2:\n return None\n else:\n new_matrix = []\n for x in range(rows1):\n aux_row = []\n for y in range(cols2):\n aux_sum = []\n for z in range(cols1):\n aux_sum.append(mat1[x][z] * mat2[z][y])\n aux_row.append(sum(aux_sum))\n new_matrix.append(aux_row)\n\n return new_matrix","def unfolded_product(m1, m2, coords = None, mx = None):\n if coords is None:\n mx = np.max( np.array([np.max(np.abs(m1.coords), axis = 0), \n np.max(np.abs(m2.coords), axis = 0)]), axis = 0)\n\n coords = lattice_coords(mx)\n else:\n if mx is not None:\n coords = lattice_coords(mx)\n #print(coords, coords[int(len(coords)/2)])\n M1 = m1.tofull(m1,coords,coords)\n M2 = m2.tofull(m2,coords,coords)\n \n RET = np.dot(M1, M2)\n rnx = m1.blocks.shape[1]\n rny = m2.blocks.shape[2]\n rc = int(len(coords)/2)\n retblocks = RET[rnx*rc:rnx*(rc+1),:].reshape(rnx, coords.shape[0], rny).swapaxes(0,1)\n \n ret = tmat()\n ret.load_nparray(retblocks, coords)\n return ret","def recursive_multiply(a, b):\n if len(a) == 2:\n return naive_multiply(a, b)\n\n a11 = a[0:int(len(a) / 2)]\n for index, row in enumerate(a11):\n a11[index] = row[0:int(len(row) / 2)]\n\n a12 = a[0:int(len(a) / 2)]\n for index, row in enumerate(a12):\n a12[index] = row[int(len(a) / 2):len(a)]\n\n a21 = a[int(len(a) / 2):len(a)]\n for index, row in enumerate(a21):\n a21[index] = row[0:int(len(row) / 2)]\n\n a22 = a[int(len(a) / 2):len(a)]\n for index, row in enumerate(a22):\n a22[index] = row[int(len(a) / 2):len(a)]\n\n b11 = b[0:int(len(b) / 2)]\n for index, row in enumerate(b11):\n b11[index] = row[0:int(len(row) / 2)]\n\n b12 = b[0:int(len(b) / 2)]\n for index, row in enumerate(b12):\n b12[index] = row[int(len(b) / 2):len(b)]\n\n b21 = b[int(len(b) / 2):len(b)]\n for index, row in enumerate(b21):\n b21[index] = row[0:int(len(row) / 2)]\n\n b22 = b[int(len(b) / 2):len(b)]\n for index, row in enumerate(b22):\n b22[index] = row[int(len(b) / 2):len(b)]\n\n c11 = matrix_add(recursive_multiply(a11, b11), recursive_multiply(a12, b21)) # C11 = A11*B11 + A12*B21\n c12 = matrix_add(recursive_multiply(a11, b12), recursive_multiply(a12, b22)) # C12 = A11*B12 + A12*B22\n c21 = matrix_add(recursive_multiply(a21, b11), recursive_multiply(a22, b21)) # C21 = A21*B11 + A22*B21\n c22 = matrix_add(recursive_multiply(a21, b12), recursive_multiply(a22, b22)) # C22 = A21*B12 + A22*B22\n\n # Append c12 to c11\n for row_index, row in enumerate(c11):\n for col_index, col in enumerate(c12):\n row.append(c12[row_index][col_index])\n\n # Append c22 to c21\n for row_index, row in enumerate(c21):\n for col_index, col in enumerate(c12):\n row.append(c22[row_index][col_index])\n\n # Append c21 to c11\n for i in c21:\n c11.append(i)\n\n return c11","def squared_distance_calculator(position1, position2):\r\n difference_vector = position2 - position1\r\n return np.dot(difference_vector, difference_vector)","def multiply(A, B, *args, **kwargs):\n dim=A.__len__()\n assert(dim==B.__len__())\n C=[]\n for ii in range(dim):\n shape=(A[ii].shape[0]*B[ii].shape[0], A[ii].shape[1])\n val=np.empty(shape)\n for iimn, (mm, nn) in enumerate(itertools.product(list(range(A[ii].shape[0])), list(range(B[ii].shape[0])))):\n val[iimn] = A[ii][mm]*B[ii][nn]\n C.append(val)\n return C","def mul(Z,X,Y):","def _mps_AA(self, A1, A2):\n Dl, d1, _ = A1.shape\n _, d2, Dr = A2.shape\n return np.reshape(np.tensordot(A1, A2, axes=(2, 0)), [Dl, d1 * d2, Dr])","def brute_multiply(x, y):\n \n n = x.shape[0]\n res = np.zeros(x.shape)\n \n for i in range(n):\n for j in range(n):\n for k in range(n):\n res[i, j] += x[i, k] * y[k, j]\n \n return res","def compute_surface_distances(mask1, mask2, voxel_dimensions=1):\n structuring_el_size = tuple(3 for _ in mask1.shape)\n grad1 = morphological_gradient(mask1.astype(int), size=structuring_el_size)\n grad2 = morphological_gradient(mask2.astype(int), size=structuring_el_size)\n\n if not _is_iterable(voxel_dimensions):\n voxel_dimensions = [voxel_dimensions for _ in mask1.shape]\n voxel_dimensions = np.array(voxel_dimensions).reshape(1, -1)\n\n nonzeros_1 = np.array(np.nonzero(grad1)).T * voxel_dimensions\n nonzeros_2 = np.array(np.nonzero(grad2)).T * voxel_dimensions\n return np.sort(_compute_set_distances(nonzeros_1, nonzeros_2))","def relate_pattern(a, b, pattern, **kwargs):\n return lib.relate_pattern(a, b, pattern, **kwargs)","def _inner_product_c1(self, point_a, point_b, normals_bp, areas_bp):\n dna = self._space.normals(point_a) - normals_bp\n dnb = self._space.normals(point_b) - normals_bp\n return self.c1 * gs.sum(\n gs.einsum(\"...bi,...bi->...b\", dna, dnb) * areas_bp, axis=-1\n )","def outer_prod(x, y):\n if len(list(x.size())) != 2 or len(list(y.size())) != 2:\n raise ValueError(\"An input is not of the right dimension.\")\n\n z = torch.zeros(2, x.size()[1], y.size()[1], dtype=torch.double, device=x.device)\n z[0] = torch.ger(x[0], y[0]) - torch.ger(x[1], -y[1])\n z[1] = torch.ger(x[0], -y[1]) + torch.ger(x[1], y[0])\n\n return z","def product_on_basis(self, t1, t2):\n return tensor( (module.monomial(x1)*module.monomial(x2) for (module, x1, x2) in zip(self._sets, t1, t2)) ) #.","def complex_mul(x1, x2):\n assert x1.size(-1) == 2 and x2.size(-1) == 2\n\n res = torch.stack(\n (x1[..., 0]*x2[..., 0]-x1[..., 1]*x2[..., 1],\n x1[..., 0]*x2[..., 1] + x1[..., 1]*x2[..., 0]), -1)\n\n return res","def kronecker_prod(x, y):\n if len(list(x.size())) != 3 or len(list(y.size())) != 3:\n raise ValueError(\"An input is not of the right dimension.\")\n\n z = torch.zeros(\n 2,\n x.size()[1] * y.size()[1],\n x.size()[2] * y.size()[2],\n dtype=torch.double,\n device=x.device,\n )\n\n row_count = 0\n\n for i in range(x.size()[1]):\n for k in range(y.size()[1]):\n column_count = 0\n for j in range(x.size()[2]):\n for l in range(y.size()[2]):\n\n z[0][row_count][column_count] = (x[0][i][j] * y[0][k][l]) - (\n x[1][i][j] * y[1][k][l]\n )\n z[1][row_count][column_count] = (x[0][i][j] * y[1][k][l]) + (\n x[1][i][j] * y[0][k][l]\n )\n\n column_count += 1\n row_count += 1\n\n return z","def squared_dist(x1: np.ndarray, x2: np.ndarray) -> np.ndarray:\n return (\n np.sum(x1 ** 2, 1).reshape(-1, 1) +\n np.sum(x2 ** 2, 1) -\n 2 * np.dot(x1, x2.T)\n )","def product(self):\n return self.right[self.i:] + self.left[:self.i], self.left[self.i:] + self.right[:self.i]","def mat_mul(mat1, mat2):\n\n if len(mat1[0]) == len(mat2):\n\n mat2 = matrix_transpose(mat2)\n response = []\n\n for row in range(len(mat1)):\n response.append(\n [\n sum(dot_product(mat1[row], mat2[column]))\n for column in range(len(mat2))\n ]\n )\n\n return response\n\n else:\n return None","def __mul__(self, other):\n #\n # TODO - your code here\n #\n final_matrix = []\n for i in range(self.h):\n temp_row = []\n for j in range(other.w):\n # take dot-product of row of\n # matrix in 1st arg with col of\n # matrix in 2nd arg\n temp_row.append(dot_product(get_row(self.g, i), get_col(other.g, j)))\n final_matrix.append(temp_row)\n return Matrix(final_matrix)\n # TODO - your code here","def dot_product(a, b):\n dp = 0.0\n for i, j in zip(a, b):\n dp += i * j\n return dp","def hadmardProduct(self, a,b):\n\t\tnumRows = len(a)\n\t\tnumCols = len(a[0])\n\t\n\t\treturn [[a[j][i] * b[j][i] for i in range(numCols)] for j in range(numRows)]","def __surface_distances(result, reference, voxelspacing=None, connectivity=1):\n result = np.atleast_1d(result.astype(np.bool))\n reference = np.atleast_1d(reference.astype(np.bool))\n if voxelspacing is not None:\n voxelspacing = _ni_support._normalize_sequence(voxelspacing, result.ndim)\n voxelspacing = np.asarray(voxelspacing, dtype=np.float64)\n if not voxelspacing.flags.contiguous:\n voxelspacing = voxelspacing.copy()\n \n # binary structure\n footprint = generate_binary_structure(result.ndim, connectivity)\n \n # test for emptiness\n if 0 == np.count_nonzero(result): \n raise RuntimeError('The first supplied array does not contain any binary object.')\n if 0 == np.count_nonzero(reference): \n raise RuntimeError('The second supplied array does not contain any binary object.') \n \n # extract only 1-pixel border line of objects\n result_border = result ^ binary_erosion(result, structure=footprint, iterations=1)\n # print(result_border+0)\n # result_border = (result_border+0).astype(np.float32)\n # imsave(\"./test_comp1/\" +\"truth.jpg\", result_border)\n reference_border = reference ^ binary_erosion(reference, structure=footprint, iterations=1)\n # print(reference_border)\n # reference_border = (reference_border + 0).astype(np.float32)\n # imsave(\"./test_comp1/\" +\"truth.jpg\", reference_border)\n # compute average surface distance \n # Note: scipys distance transform is calculated only inside the borders of the\n # foreground objects, therefore the input has to be reversed\n dt = distance_transform_edt(~reference_border, sampling=voxelspacing)\n # print(dt)\n reference_border = (reference_border + 0).astype(np.float32)\n # imsave(\"./test_comp1/\" +\"truth.jpg\", reference_border)\n sds = dt[result_border]\n \n return sds","def dotProduct(v1, v2):\n return sum((a * b) for a, b in zip(v1, v2))","def outer_product(x,y):\n\n return x[:,0]*y[:,1] -x[:,1]*y[:,0]","def products(q_1: Qs, q_2: Qs, kind: str = \"\", reverse: bool = False) -> Qs:\n\n q_1_copy = deepcopy(q_1)\n q_2_copy = deepcopy(q_2)\n qs_left, qs_right = Qs(), Qs()\n\n # Diagonalize if need be.\n if ((q_1.rows == q_2.rows) and (q_1.columns == q_2.columns)) or (\n \"scalar_q\" in [q_1.qs_type, q_2.qs_type]\n ):\n\n if q_1.columns == 1:\n qs_right = q_2_copy\n qs_left = diagonal(q_1_copy, qs_right.rows)\n\n elif q_2.rows == 1:\n qs_left = q_1_copy\n qs_right = diagonal(q_2_copy, qs_left.columns)\n\n else:\n qs_left = q_1_copy\n qs_right = q_2_copy\n\n # Typical matrix multiplication criteria.\n elif q_1.columns == q_2.rows:\n qs_left = q_1_copy\n qs_right = q_2_copy\n\n else:\n print(\n \"Oops, cannot multiply series with row/column dimensions of {}/{} to {}/{}\".format(\n q_1.rows, q_1.columns, q_2.rows, q_2.columns\n )\n )\n\n # Operator products need to be transposed.\n operator_flag = False\n if qs_left in [\"op\", \"operator\"] and qs_right in [\"op\", \"operator\"]:\n operator_flag = True\n\n outer_row_max = qs_left.rows\n outer_column_max = qs_right.columns\n shared_inner_max = qs_left.columns\n projector_flag = (\n (shared_inner_max == 1) and (outer_row_max > 1) and (outer_column_max > 1)\n )\n\n result = [\n [q0(q_type=\"\") for _i in range(outer_column_max)]\n for _j in range(outer_row_max)\n ]\n\n for outer_row in range(outer_row_max):\n for outer_column in range(outer_column_max):\n for shared_inner in range(shared_inner_max):\n\n # For projection operators.\n left_index = outer_row\n right_index = outer_column\n\n if outer_row_max >= 1 and shared_inner_max > 1:\n left_index = outer_row + shared_inner * outer_row_max\n\n if outer_column_max >= 1 and shared_inner_max > 1:\n right_index = shared_inner + outer_column * shared_inner_max\n\n result[outer_row][outer_column] = add(result[outer_row][outer_column],\n product(qs_left.qs[left_index],\n qs_right.qs[right_index], kind=kind, reverse=reverse\n )\n )\n\n # Flatten the list.\n new_qs = [item for sublist in result for item in sublist]\n new_states = Qs(new_qs, rows=outer_row_max, columns=outer_column_max)\n\n if projector_flag or operator_flag:\n return transpose(new_states)\n\n else:\n return new_states","def test_l2_metric_inner_product_vectorization(\n self,\n l2_metric_s2,\n times,\n n_landmark_sets,\n landmarks_a,\n landmarks_b,\n landmarks_c,\n ):\n landmarks_ab = l2_metric_s2.geodesic(landmarks_a, landmarks_b)\n landmarks_bc = l2_metric_s2.geodesic(landmarks_b, landmarks_c)\n landmarks_ab = landmarks_ab(times)\n landmarks_bc = landmarks_bc(times)\n\n tangent_vecs = l2_metric_s2.log(point=landmarks_bc, base_point=landmarks_ab)\n\n result = l2_metric_s2.inner_product(tangent_vecs, tangent_vecs, landmarks_ab)\n\n self.assertAllClose(gs.shape(result), (n_landmark_sets,))","def product(self, x, y):\n return self( x.lift() * y.lift() )","def _dot_product_attention_inner_relative(x, y, z, transpose):\n batch_size, heads, length, _ = x.size()\n\n # xy_matmul is [batch_size, heads, length, length or depth]\n xy_matmul = torch.matmul(x, y if not transpose else y.transpose(-2, -1))\n # x_t is [length, batch_size, heads, length or depth]\n x_t = x.permute(2, 0, 1, 3)\n # x_t_r is [length, batch_size * heads, length or depth]\n x_t_r = x_t.view(length, batch_size * heads, -1)\n # x_tz_matmul is [length, batch_size * heads, length or depth]\n x_tz_matmul = torch.matmul(x_t_r, z if not transpose else z.transpose(-2, -1))\n # x_tz_matmul_r is [length, batch_size, heads, length or depth]\n x_tz_matmul_r = x_tz_matmul.view(length, batch_size, heads, -1)\n # x_tz_matmul_r_t is [batch_size, heads, length, length or depth]\n x_tz_matmul_r_t = x_tz_matmul_r.permute(1, 2, 0, 3)\n\n return xy_matmul + x_tz_matmul_r_t","def apply_feature_matching(desc1: np.ndarray, desc2: np.ndarray,\n match_calculator: Callable[[list, list], float]) -> list:\n\n # Check descriptors dimensions are 2\n assert desc1.ndim == 2, \"Descriptor 1 shape is not 2\"\n assert desc2.ndim == 2, \"Descriptor 2 shape is not 2\"\n\n # Check that the two features have the same descriptor type\n assert desc1.shape[1] == desc2.shape[1], \"Descriptors shapes are not equal\"\n\n # If there is not key points in any of the images\n if desc1.shape[0] == 0 or desc2.shape[0] == 0:\n return []\n\n # number of key points in each image\n num_key_points1 = desc1.shape[0]\n num_key_points2 = desc2.shape[0]\n\n # List to store the matches scores\n matches = []\n\n # Loop over each key point in image1\n # We need to calculate similarity with each key point in image2\n for kp1 in range(num_key_points1):\n # Initial variables which will be updated in the loop\n distance = -np.inf\n y_index = -1\n\n # Loop over each key point in image2\n for kp2 in range(num_key_points2):\n\n # Match features between the 2 vectors\n value = match_calculator(desc1[kp1], desc2[kp2])\n\n # SSD values examples: (50, 200, 70), we need to minimize SSD (Choose 50)\n # In case of SSD matching: (value is returned as a \"negative\" number) (-50, -200, -70)\n # So we compare it with -np.inf. (The sorting will be reversed later)\n\n # NCC values examples: (0.58, 0.87, 0.42), we need to maximize NCC (Choose 0.87)\n # In case of NCC matching: (value is returned as a \"positive\" number) (-58, -0.87, -0.42)\n # So we compare it with -np.inf. (The sorting will be reversed later)\n\n if value > distance:\n distance = value\n y_index = kp2\n\n # Create a cv2.DMatch object for each match and set attributes:\n # queryIdx: The index of the feature in the first image\n # trainIdx: The index of the feature in the second image\n # distance: The distance between the two features\n cur = cv2.DMatch()\n cur.queryIdx = kp1\n cur.trainIdx = y_index\n cur.distance = distance\n matches.append(cur)\n\n return matches","def euclidean_squared_distance(input1, input2):\n m, n = input1.size(0), input2.size(0)\n mat1 = torch.pow(input1, 2).sum(dim=1, keepdim=True).expand(m, n)\n mat2 = torch.pow(input2, 2).sum(dim=1, keepdim=True).expand(n, m).t()\n distmat = mat1 + mat2\n distmat.addmm_(input1, input2.t(), beta=1, alpha=-2)\n return distmat","def interior_tensor_product(mx, dim_a, dim_b, e=None):\n assert _np.shape(mx) == (dim_a * dim_b, dim_a * dim_b), \"Dimensions do not agree with matrix size\"\n assert _np.shape(e)[0] == _np.shape(e)[1], \"e should be a square matrix\"\n basis_a = matrix_units(dim_a)\n basis_b = matrix_units(dim_b)\n return sum((_np.trace(_np.dot(mx, _np.kron(unit_a, unit_b).T)) * multikron([unit_a, e, unit_b])\n for unit_a in basis_a for unit_b in basis_b))","def matmul(xs: List[List[float]],\n ys: List[List[float]]) -> List[List[float]]:\n product = []\n for x_row in range(len(xs)):\n row = []\n for y_col in range(len(ys[0])):\n col = [ys[y_row][y_col] for y_row in range(len(ys))]\n row.append(Math.dot(xs[x_row], col))\n product.append(row)\n return product","def dotproduct(vec1, vec2):\n return sum((a*b) for a, b in zip(vec1, vec2))","def dist_squared (a, b):\n return sum(map(lambda (x,y): (x-y)**2, zip(a, b)))","def times(self, other):\r\n result = TriangularArray(self.num_rows)\r\n for i in range(self.num_rows):\r\n for j in range(self.num_rows):\r\n # Calculate the [i, j] entry.\r\n total = 0\r\n for k in range(j, i + 1):\r\n total += self[(i, k)] * other[(k, j)]\r\n result[(i, j)] = total\r\n return result","def dot_product(A, B):\n # Section 1: Ensure A and B dimensions are the same\n rowsA = len(A); colsA = len(A[0])\n rowsB = len(B); colsB = len(B[0])\n if rowsA != rowsB or colsA != colsB:\n raise ArithmeticError('Matrices are NOT the same size.')\n\n # Section 2: Sum the products \n total = 0\n for i in range(rowsA):\n for j in range(colsB):\n total += A[i][j] * B[i][j]\n\n return total","def distance(f1, f2):\n\n return np.sum((np.sum([f1, -f2], axis=0))**2, axis=1)","def divide(m1,m2):\n \n if ((m1.shape[0] % 2 == 0) or (m1.shape[0] == 1)):\n n = m1.shape[0]\n else:\n n = m1.shape[0] + 1\n result = np.zeros((n, n), dtype = int)\n \n if (n == 1):\n result[0][0] = m1[0][0] * m2[0][0]\n else:\n new = n//2\n \n a11, a12, a21, a22 = m1[:new, :new], m1[new:, :new], m1[:new, new:], m1[new:, new:]\n b11, b12, b21, b22 = m2[:new, :new], m2[new:, :new], m2[:new, new:], m2[new:, new:]\n \n result[:new, :new] = divide(a11,b11) + divide(a12,b21)\n result[new:, :new] = divide(a11,b12) + divide(a12,b22)\n result[:new, new:] = divide(a21,b11) + divide(a22,b21)\n result[new:, new:] = divide(a21,b12) + divide(a22,b22)\n \n return result","def __mul__(self, other):\n # \n # TODO - your code here\n #\n \n result = [];\n row_result = [];\n product = 0;\n \n if(self.w != other.h):\n raise(ValueError, \"Matrices can not multiply for their dimesion doesn't match\"); \n \n for row in self.g:\n row_result = [];\n for j in range(other.w):\n product = dot_product(row,other.get_column(j));\n row_result.append(product);\n result.append(row_result);\n \n return Matrix(result);","def matrix_product(mat1: List[List[int]], mat2: List[List[int]]):\n if len(mat1) == 0 or len(mat2) == 0:\n raise ValueError(\"One of matrix is empty\")\n n, k1 = len(mat1), len(mat1[0])\n k2, m = len(mat2), len(mat2[0])\n if k1 != k2:\n raise ValueError(\n f\"Can't multiply two matrices with shapes {n}x{k1} and {k2}x{m}\"\n )\n mat2_t = matrix_transpose(mat2)\n return [[vec_product(vec1, vec2) for vec2 in mat2_t] for vec1 in mat1]","def prod(self, x, y):\n return (self.basic_operation.reduce(x.original+y.original),\n self.operation1.prod(x.left, y.left),\n self.operation2.prod(x.right, y.right))","def tile_calculation(xi, yi, axi, ayi, positions, weights):\n for j in range(cuda.blockDim.x):\n xj = positions[j,0]\n yj = positions[j,1]\n wj = weights[j]\n axi, ayi = body_body_interaction(xi, yi, xj, yj, wj, axi, ayi)\n return axi, ayi","def dot(pepx1, pepx2):\n\n Ls = pepx1.shape\n assert pepx2.shape==Ls, '[dot]: sizes of pepx1 and pepx2 are not equal'\n new_pepx = np.empty(Ls, dtype=np.object)\n new_lams = np.empty(pepx1.lambdas.shape, dtype=np.object)\n\n # if np.all([ pepx1[i].ndim==3 and pepx2[i].ndim==3 for i in np.ndenumerate(pepx1) ]):\n # return peps_dot(pepx1,pepx2)\n # else:\n for idx in np.ndindex(Ls):\n len_dp1 = len(pepx1.phys_bonds[idx])\n len_dp2 = len(pepx2.phys_bonds[idx])\n ax1 = [0,2,4,6] + range(8, 8+len_dp1)\n ax2 = [1,3,5,7] + range(8+len_dp1-1,8+len_dp1+len_dp2-1)\n ax2[-len_dp2] = ax1[-1] # contract vertical bonds (mpx1 down with mpx2 up)\n new_site = np.einsum(pepx1[idx],ax1,pepx2[idx],ax2)\n new_pepx[idx] = tf.reshape(new_site,'ii,ii,ii,ii,...',group_ellipsis=False)\n\n i,j = idx\n for xx in range(new_lams.shape[2]):\n new_lams[i,j,xx] = np.outer(pepx1.lambdas[i,j,xx], pepx2.lambdas[i,j,xx]).reshape(-1)\n # print new_lams[i,j,xx].shape\n\n return PEPX_GL(new_pepx,new_lams) #,pepx1.phys_bonds)","def _mul(*args):\n\treturn functools.reduce(numpy.dot, args)","def pairwise_dot_product_similarity(x, y):\n return torch.mm(x, torch.transpose(y, 1, 0))","def dotproduct(vec1, vec2):\n import operator\n return sum(map(operator.mul, vec1, vec2))","def classical(m1,m2):\n \n n = m1.shape\n result = np.zeros(n, dtype = int)\n\n for i in range(n[0]):\n for j in range(n[0]):\n for k in range(n[0]):\n result[i][j] += m1[i][k] * m2[k][j]\n return result","def dot_product(a, b):\n a1, a2, a3 = a\n b1, b2, b3 = b\n return a1 * b1 + a2 * b2 + a3 * b3","def _product(self, args):\n pools = map(tuple, args) #within original version args defined as *args\n result = [[]]\n for pool in pools:\n result = [x + [y] for x in result for y in pool]\n return result","def linear_interpolation(atoms1, atoms2, N):\n \n pos1 = atoms1.positions\n pos2 = atoms2.positions\n images = [atoms1]\n for n in range(N):\n nn = n + 1\n atoms_tmp = atoms1.copy()\n atoms_tmp.positions = ( (N+1-nn)*pos1 + nn*pos2 ) / (N+1)\n images += [atoms_tmp]\n images += [atoms2]\n return images","def euclidean_distance_2d(XA: np.ndarray, XB: np.ndarray):\n out = np.empty((XA.shape[0], XB.shape[0]), dtype=XA.dtype)\n for i in numba.prange(XA.shape[0]):\n for j in range(XB.shape[0]):\n out[i, j] = np.sqrt((XA[i, 0] - XB[j, 0]) ** 2 + (XA[i, 1] - XB[j, 1]) ** 2)\n return out","def sqeuclidean_distance_2d(XA: np.ndarray, XB: np.ndarray):\n out = np.empty((XA.shape[0], XB.shape[0]), dtype=XA.dtype)\n for i in numba.prange(XA.shape[0]):\n for j in range(XB.shape[0]):\n out[i, j] = (XA[i, 0] - XB[j, 0]) ** 2 + (XA[i, 1] - XB[j, 1]) ** 2\n return out","def __surface_distances(result, reference, voxelspacing=None, connectivity=1):\n result = np.atleast_1d(result.astype(np.bool))\n reference = np.atleast_1d(reference.astype(np.bool))\n if voxelspacing is not None:\n voxelspacing = _ni_support._normalize_sequence(voxelspacing, result.ndim)\n voxelspacing = np.asarray(voxelspacing, dtype=np.float64)\n if not voxelspacing.flags.contiguous:\n voxelspacing = voxelspacing.copy()\n \n # binary structure\n footprint = generate_binary_structure(result.ndim, connectivity)\n \n # test for emptiness\n if 0 == np.count_nonzero(result): \n raise RuntimeError('The first supplied array does not contain any binary object.')\n if 0 == np.count_nonzero(reference): \n raise RuntimeError('The second supplied array does not contain any binary object.') \n \n # extract only 1-pixel border line of objects\n result_border = result ^ binary_erosion(result, structure=footprint, iterations=1)\n\n reference_border = reference ^ binary_erosion(reference, structure=footprint, iterations=1)\n\n dt = distance_transform_edt(~reference_border, sampling=voxelspacing)\n # print(dt)\n reference_border = (reference_border + 0).astype(np.float32)\n\n sds = dt[result_border]\n \n return sds","def _tensor_batch_dot(t1: Tensor, t2: Tensor) -> Tensor:\n\n msg = (\n \"Please ensure each batch member has the same feature dimension. \"\n f\"First input has {torch.numel(t1) / t1.shape[0]} features, and \"\n f\"second input has {torch.numel(t2) / t2.shape[0]} features.\"\n )\n assert torch.numel(t1) / t1.shape[0] == torch.numel(t2) / t2.shape[0], msg\n\n return torch.mm(\n t1.view(t1.shape[0], -1),\n t2.view(t2.shape[0], -1).T,\n )","def __mul__(self,other):\n # \n # 注意矩阵的A的列 与 相乘矩阵B的行必须相等，才能进行运算\n height = 0\n width = 0\n if isinstance(other, list): # 判断other是否是矩阵，即list形式的矩阵\n height = len(other)\n width = len(other[0])\n else:\n # 如果是对象，则直接获取行列值\n height = other.h\n width = other.w\n\n\n my_mul = zeroes(self.h, self.w)\n if self.w == height: # 两个矩阵的行列值需要相等 才能相乘\n for i in range(self.h):\n for j in range(width):\n my_sum = 0\n for k in range(height):\n if isinstance(other, list):\n my_sum += self.g[i][k] * other[k][j]\n # 通过3个循环变量取所有矩阵的行列值\n else:\n my_sum += self.g[i][k] * other.g[k][j]\n my_mul[i][j] = my_sum\n return my_mul \n else:\n return NotImplementedError","def __mul__ (self, other):\n return perm(*(self._getcycles() + other._getcycles()))","def product(self, x, y):\n return self._cached_product(x.value, y.value)","def distance(brd1,brd2):\n\n step=brd1[1,0]-brd1[0,0]\n return np.sum(np.abs(brd1[:,1]-brd2[:,1]))*step","def map_feature(X1, X2):\n degree = 6\n out = np.ones(X1.shape)\n \n for i in range(1, degree + 1):\n for j in range(0, i + 1):\n out = np.concatenate((out, np.power(X1, (i - j)) * np.power(X2, j)), axis=-1)\n\n return out"],"string":"[\n \"def inner_product(pattern_one, pattern_two):\\n\\n return np.sum(np.multiply(pattern_one, pattern_two))\",\n \"def l2_norm_batch(pattern_stack):\\n\\n return np.linalg.norm(pattern_stack, axis=0)\",\n \"def generate_pattern_grid(words1, words2):\\n # Convert word lists to integer arrays\\n w1, w2 = (\\n np.array([[ord(c) for c in w] for w in words], dtype=np.uint8)\\n for words in (words1, words2)\\n )\\n\\n if len(w1) == 0 or len(w2) == 0:\\n return np.zeros((len(w1), len(w2)), dtype=np.uint8)\\n\\n # equality_grid[a, b, i, j] represents whether the ith letter\\n # of words1[a] equals the jth letter of words2[b]\\n equality_grid = np.zeros((len(w1), len(w2), 5, 5), dtype=bool)\\n for i, j in it.product(range(5), range(5)):\\n equality_grid[:, :, i, j] = np.equal.outer(w1[:, i], w2[:, j])\\n\\n patterns = np.zeros((len(w1), len(w2)), dtype=np.uint8)\\n three_pows = (3**np.arange(5)).astype(np.uint8)\\n for i, tp in enumerate(three_pows):\\n # This accounts for yellow squares\\n patterns[:, :] += tp * equality_grid[:, :, i, :].any(2)\\n # This accounts for green squares\\n patterns[:, :] += tp * equality_grid[:, :, i, i]\\n\\n return patterns\",\n \"def pair_product(x1, x2):\\n return np.multiply(x1, x2)\",\n \"def dim_mul(dims1, dims2):\\n return (\\n dims1[0] + dims2[0],\\n dims1[1] + dims2[1],\\n dims1[2] + dims2[2],\\n dims1[3] + dims2[3],\\n dims1[4] + dims2[4],\\n dims1[5] + dims2[5],\\n dims1[6] + dims2[6],\\n )\",\n \"def axis_element_wise_multiplication(t1, t2, which_axis):\\n # assert len(K.int_shape(t1)) == len(K.int_shape(t2)) + 1, \\\"rank(t1) should be rank(t2) + 1\\\"\\n slices = tf.unstack(t1, axis=which_axis)\\n # assert K.int_shape(slices[0]) == K.int_shape(t2), \\\"Slices of t1 were not the same shape as t2\\\"\\n multiplies = []\\n for s in slices:\\n multiplies.append(t2 * s)\\n return tf.stack(multiplies, axis=2)\",\n \"def _prod_vectorized(M1, M2):\\n sh1 = M1.shape\\n sh2 = M2.shape\\n assert len(sh1) >= 2\\n assert len(sh2) >= 2\\n assert sh1[-1] == sh2[-2]\\n\\n ndim1 = len(sh1)\\n t1_index = list(xrange(ndim1-2)) + [ndim1-1, ndim1-2]\\n return np.sum(np.transpose(M1, t1_index)[..., np.newaxis] *\\n M2[..., np.newaxis, :], -3)\",\n \"def double_chop_pairs(\\n x1, y1, z1, w1, cell1, x2, y2, z2, w2, indx2, rbins_squared, result):\\n start = cuda.grid(1)\\n stride = cuda.gridsize(1)\\n\\n n1 = x1.shape[0]\\n nbins = rbins_squared.shape[0]\\n\\n for i in range(start, n1, stride):\\n px = x1[i]\\n py = y1[i]\\n pz = z1[i]\\n pw = w1[i]\\n\\n cell1_i = cell1[i]\\n first = indx2[cell1_i]\\n last = indx2[cell1_i+1]\\n\\n for j in range(first, last):\\n qx = x2[j]\\n qy = y2[j]\\n qz = z2[j]\\n qw = w2[j]\\n\\n dx = px-qx\\n dy = py-qy\\n dz = pz-qz\\n wprod = pw*qw\\n dsq = dx*dx + dy*dy + dz*dz\\n\\n k = nbins-1\\n while dsq <= rbins_squared[k]:\\n cuda.atomic.add(result, k-1, wprod)\\n k = k-1\\n if k <= 0:\\n break\",\n \"def inner_prod(x, y):\\n z = torch.zeros(2, dtype=torch.double, device=x.device)\\n\\n if len(list(x.size())) == 2 and len(list(y.size())) == 2:\\n z[0] = torch.dot(x[0], y[0]) - torch.dot(-x[1], y[1])\\n z[1] = torch.dot(x[0], y[1]) + torch.dot(-x[1], y[0])\\n\\n if len(list(x.size())) == 1 and len(list(y.size())) == 1:\\n z[0] = (x[0] * y[0]) - (-x[1] * y[1])\\n z[1] = (x[0] * y[1]) + (-x[1] * y[0])\\n\\n return z\",\n \"def tensor_outer_product(tensor1, tensor2):\\n shape_1 = tensor1.shape\\n shape_2 = tensor2.shape\\n s1 = len(shape_1)\\n s2 = len(shape_2)\\n \\n shape_1 = shape_1 + (1, )*s2\\n shape_2 = (1, )*s1 + shape_2\\n return np.reshape(tensor1, shape_1) * np.reshape(tensor2, shape_2)\",\n \"def mult(m1, m2):\\n assert np.shape(m1) == (2, 3)\\n assert np.shape(m2) == (2, 3)\\n\\n m1_temp = np.vstack((m1, [0, 0, 1]))\\n m2_temp = np.vstack((m2, [0, 0, 1]))\\n result = m1_temp * m2_temp\\n\\n return result[:2, :]\",\n \"def _inner_product_d1(\\n self, one_forms_a, one_forms_b, one_forms_bp, areas_bp, inv_surface_metrics_bp\\n ):\\n one_forms_bp_t = gs.transpose(one_forms_bp, (0, 2, 1))\\n\\n one_forms_a_t = gs.transpose(one_forms_a, (0, 1, 3, 2))\\n xa = one_forms_a_t - one_forms_bp_t\\n\\n xa_0 = gs.matmul(\\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\\n gs.matmul(gs.transpose(xa, (0, 1, 3, 2)), one_forms_bp_t)\\n - gs.matmul(one_forms_bp, xa),\\n )\\n\\n one_forms_b_t = gs.transpose(one_forms_b, (0, 1, 3, 2))\\n xb = one_forms_b_t - one_forms_bp_t\\n xb_0 = gs.matmul(\\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\\n gs.matmul(gs.transpose(xb, (0, 1, 3, 2)), one_forms_bp_t)\\n - gs.matmul(one_forms_bp, xb),\\n )\\n\\n return self.d1 * gs.sum(\\n gs.einsum(\\n \\\"...bii->...b\\\",\\n gs.matmul(\\n xa_0,\\n gs.matmul(\\n inv_surface_metrics_bp, gs.transpose(xb_0, axes=(0, 1, 3, 2))\\n ),\\n ),\\n )\\n * areas_bp\\n )\",\n \"def _kronecker_product(mat1: tf.Tensor, mat2: tf.Tensor) -> tf.Tensor:\\n m1, n1 = mat1.get_shape().as_list()\\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\\n m2, n2 = mat2.get_shape().as_list()\\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])\",\n \"def dot_product(a,b):\\n return sum(pairwise_mult(a,b))\",\n \"def _z2matmul(self, left, right):\\n prod = np.mod(np.dot(left, right), 2)\\n return prod\",\n \"def _outer(a, b):\\n a_flat = torch.reshape(a, [-1])\\n b_flat = torch.reshape(b, [-1])\\n a_mul = torch.unsqueeze(a_flat, dim=-1)\\n b_mul = torch.unsqueeze(b_flat, dim=0)\\n return a_mul * b_mul\",\n \"def inner_product_similarity(a: torch.Tensor, b: torch.Tensor, dim=1) -> torch.Tensor:\\n outputs = (a * b).sum(dim=dim)\\n return outputs\",\n \"def gradient_merge_arrays(cls, image_one, image_two):\\n if image_one.shape != image_two.shape:\\n raise AttributeError(\\\"shapes do not match: {} vs {}\\\".format(image_one.shape, image_two.shape))\\n height = image_one.shape[0]\\n vector_one = numpy.array([1.0 - float(i + 1) / (height + 1) for i in range(height)])\\n vector_two = numpy.array([float(i + 1) / (height + 1) for i in range(height)])\\n return (image_one * vector_one[:, numpy.newaxis]) + (image_two * vector_two[:, numpy.newaxis])\",\n \"def mul(self,mat1,mat2):\\n if(isinstance(mat2,int)==True):\\n result = [[mat1[i][j] * mat2 for j in range(len(mat1[0]))] for i in range(len(mat1))]\\n self.out = result\\n return self.out\\n elif(len(mat1[0])==len(mat2)):\\n result = [[sum(a*b for a,b in zip(i,j)) for j in zip(*mat2)] for i in mat1]\\n self.out = result\\n return self.out\",\n \"def batch_outer_product(a, b):\\n a, b = normalize_and_check_ndim([a, b], 2)\\n # This is a batchwise version of the matrix multiplication approach\\n # used for outer_product(), see explanation there.\\n return a[:, :, np.newaxis] * b[:, np.newaxis, :]\",\n \"def dotproduct(x, y):\\n return sum(imap(operator.mul, x, y))\",\n \"def _layerwise_dot_product(x_s, y_s):\\n return [torch.sum(x * y).item() for x, y in zip(x_s, y_s)]\",\n \"def pairwise_distances(a, b, p=2):\\n squeezed = False\\n if len(a.shape) == 2 and len(b.shape) == 2:\\n a = tf.expand_dims(a,0) #[np.newaxis, :, :]\\n b = tf.expand_dims(a,0) #b[np.newaxis, :, :]\\n squeezed = True\\n \\n ret = tf.reduce_sum(tf.keras.backend.pow(tf.math.abs(tf.expand_dims(a,2) - tf.expand_dims(b,1)), p), 3)\\n #[:, :, np.newaxis, :], [:, np.newaxis, :, :]\\n if squeezed:\\n ret = tf.squeeze(ret)\\n\\n return ret\",\n \"def pair_eval(self, X, Y):\\n d1 = self.d1\\n K1 = self.k1.pair_eval(X[:, :d1], Y[:, :d1])\\n K2 = self.k2.pair_eval(X[:, d1:], Y[:, d1:])\\n return K1 * K2\",\n \"def inner_product(state_1, state_2):\\n return numpy.dot(state_1.conjugate(), state_2)\",\n \"def dot_product(vector1, vector2):\\n return [reduce_by_multiplication(pair) for pair in zip(vector1, vector2)]\",\n \"def product_2(m1, m2):\\r\\n return make_mono_admissible_2(list(m1) + list(m2))\",\n \"def innerprod_q2(q1, q2):\\n T = q1.shape[1]\\n val = sum(sum(q1 * q2)) / T\\n\\n return (val)\",\n \"def dist_matrix(self, group1, group2):\\n \\n tmps = []\\n for i in group2:\\n tmps.append([])\\n for j in group1:\\n mi, label = self.distance(i, j)\\n tmps[-1].append(mi)\\n return tmps\",\n \"def _interleave_ecdfs(\\n x1: np.ndarray,\\n y1: np.ndarray,\\n x2: np.ndarray,\\n y2: np.ndarray\\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\\n assert len(x1.shape) == len(x2.shape) == 1\\n assert x1.shape == y1.shape\\n assert x2.shape == y2.shape\\n\\n x = np.sort(np.concatenate([x1, x2]))\\n y1 = np.insert(y1, 0, [0])\\n y2 = np.insert(y2, 0, [0])\\n return x, y1[np.searchsorted(x1, x, side='right')], y2[np.searchsorted(x2, x, side='right')]\",\n \"def alignPairShapes(s1,s2,weights):\\n\\n\\n s1=np.asarray(s1)\\n s2=np.asarray(s2)\\n \\n x1k=s1[:,0]\\n y1k=s1[:,1]\\n x2k=s2[:,0]\\n y2k=s2[:,1]\\n\\n X1=sum(x1k*weights) \\n X2=sum(x2k*weights)\\n\\n Y1=sum(y1k*weights)\\n Y2=sum(y2k*weights)\\n\\n Z=sum(weights*(pow(x2k,2)+pow(y2k,2)))\\n\\n W=sum(weights)\\n\\n C1=sum(weights*(x1k*x2k+y1k*y2k))\\n\\n C2=sum(weights*(y1k*x2k-x1k*y2k))\\n \\n a=np.asarray([[X2,-Y2,W,0],[Y2,X2,0,W],[Z,0,X2,Y2],[0,Z,-Y2,X2]])\\n b=np.asarray([X1,Y1,C1,C2])\\n\\n x=np.linalg.solve(a,b)\\n\\n ax=x[0]\\n ay=x[1]\\n tx=x[2]\\n ty=x[3]\\n return ax,ay,tx,ty\",\n \"def strassen(m1, m2):\\n \\n if ((m1.shape[0] % 2 == 0) or (m1.shape[0] == 1)):\\n n = m1.shape[0] \\n else:\\n n = m1.shape[0] + 1\\n result = np.zeros((n, n), dtype = int)\\n \\n if (n == 1):\\n result[0][0] = m1[0][0] * m2[0][0]\\n else:\\n new = n//2\\n \\n a11, a12, a21, a22 = m1[:new, :new], m1[new:, :new], m1[:new, new:], m1[new:, new:]\\n b11, b12, b21, b22 = m2[:new, :new], m2[new:, :new], m2[:new, new:], m2[new:, new:]\\n \\n p1 = strassen(a11, b12 - b22)\\n p2 = strassen(a11 + a12, b22)\\n p3 = strassen(a21 + a22, b11)\\n p4 = strassen(a22, b21 - b11)\\n p5 = strassen(a11 + a22, b11 + b22)\\n p6 = strassen(a12 - a22, b21 + b22)\\n p7 = strassen(a11 - a21, b11 + b12)\\n \\n result[:new, :new] = p5 + p4 - p2 + p6\\n result[new:, :new] = p1 + p2\\n result[:new, new:] = p3 + p4 \\n result[new:, new:] = p5 + p1 - p3 - p7\\n \\n return result\",\n \"def matrix_mult(m1, m2):\\n\\ttemp = []\\n\\tfor i in range(len(m1)):\\n\\t\\te = []\\n\\t\\tfor j in range(len(m2[0])):\\n\\t\\t\\te.append(row_times_column(m1,i,m2,j))\\n\\t\\ttemp.append(e)\\n\\treturn temp\",\n \"def solve_part_two(self):\\n return self.outputs[0] * self.outputs[1] * self.outputs[2]\",\n \"def kronecker_product(mat1, mat2):\\n m1, n1 = mat1.get_shape().as_list()\\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\\n m2, n2 = mat2.get_shape().as_list()\\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])\",\n \"def multiplicand_2(p):\\n m2 = cddr(p) # (m2 m3 ...)\\n rests = cdr(m2) # (m3...)\\n if isNull(rests):\\n return car(m2)\\n else:\\n restp = convertToPythonList(cdr(rests))\\n return make_product_2(car(m2), car(rests), *restp)\",\n \"def _mulVectors(X1,X2):\\n _checkSize(X1,X2)\\n return sum([ X1[i] * X2[i] for i in range(len(X1))])\",\n \"def complex_mul2d(a, b):\\n op = partial(torch.einsum, \\\"bixy,ioxy->boxy\\\")\\n return torch.stack([\\n op(a[..., 0], b[..., 0]) - op(a[..., 1], b[..., 1]),\\n op(a[..., 1], b[..., 0]) + op(a[..., 0], b[..., 1])\\n ],\\n dim=-1)\",\n \"def __call__(self, sample_1, sample_2, alphas, ret_matrix=False):\\n sample_12 = torch.cat((sample_1, sample_2), 0)\\n distances = pdist(sample_12, sample_12, norm=2)\\n\\n kernels = None\\n for alpha in alphas:\\n kernels_a = torch.exp(- alpha * distances ** 2)\\n if kernels is None:\\n kernels = kernels_a\\n else:\\n kernels = kernels + kernels_a\\n\\n k_1 = kernels[:self.n_1, :self.n_1]\\n k_2 = kernels[self.n_1:, self.n_1:]\\n k_12 = kernels[:self.n_1, self.n_1:]\\n\\n mmd = (2 * self.a01 * k_12.sum() +\\n self.a00 * (k_1.sum() - torch.trace(k_1)) +\\n self.a11 * (k_2.sum() - torch.trace(k_2)))\\n if ret_matrix:\\n return mmd, kernels\\n else:\\n return mmd\",\n \"def product_map(xs1, xs2):\\n return jax.vmap(lambda x1: jax.vmap(lambda x2: pair_product(x1, x2))(xs2))(xs1)\",\n \"def Multiply(M1,M2):\\r\\n M3=[]\\r\\n w=0\\r\\n while w<len(M2[0]):\\r\\n tap=[]\\r\\n t=0\\r\\n while t<len(M2):\\r\\n tap.append(M2[t][w])\\r\\n t=t+1\\r\\n M3.append(tap)\\r\\n w=w+1\\r\\n M=[]\\r\\n # Multiplying matrices\\r\\n k=0\\r\\n sums=0\\r\\n while k<len(M1):\\r\\n j=0\\r\\n mpy=[]\\r\\n while j<len(M3):\\r\\n p=0\\r\\n sums=0\\r\\n while p<len(M3[j]):\\r\\n temp = (M1[k][p])*(M3[j][p])\\r\\n sums=sums+temp\\r\\n p=p+1\\r\\n mpy.append(sums)\\r\\n j=j+1\\r\\n M.append(mpy)\\r\\n k=k+1\\r\\n return M\",\n \"def mat_mul(mat1, mat2):\\n\\n rows1 = len(mat1)\\n cols1 = len(mat1[0])\\n rows2 = len(mat2)\\n cols2 = len(mat2[0])\\n\\n if cols1 != rows2:\\n return None\\n else:\\n new_matrix = []\\n for x in range(rows1):\\n aux_row = []\\n for y in range(cols2):\\n aux_sum = []\\n for z in range(cols1):\\n aux_sum.append(mat1[x][z] * mat2[z][y])\\n aux_row.append(sum(aux_sum))\\n new_matrix.append(aux_row)\\n\\n return new_matrix\",\n \"def unfolded_product(m1, m2, coords = None, mx = None):\\n if coords is None:\\n mx = np.max( np.array([np.max(np.abs(m1.coords), axis = 0), \\n np.max(np.abs(m2.coords), axis = 0)]), axis = 0)\\n\\n coords = lattice_coords(mx)\\n else:\\n if mx is not None:\\n coords = lattice_coords(mx)\\n #print(coords, coords[int(len(coords)/2)])\\n M1 = m1.tofull(m1,coords,coords)\\n M2 = m2.tofull(m2,coords,coords)\\n \\n RET = np.dot(M1, M2)\\n rnx = m1.blocks.shape[1]\\n rny = m2.blocks.shape[2]\\n rc = int(len(coords)/2)\\n retblocks = RET[rnx*rc:rnx*(rc+1),:].reshape(rnx, coords.shape[0], rny).swapaxes(0,1)\\n \\n ret = tmat()\\n ret.load_nparray(retblocks, coords)\\n return ret\",\n \"def recursive_multiply(a, b):\\n if len(a) == 2:\\n return naive_multiply(a, b)\\n\\n a11 = a[0:int(len(a) / 2)]\\n for index, row in enumerate(a11):\\n a11[index] = row[0:int(len(row) / 2)]\\n\\n a12 = a[0:int(len(a) / 2)]\\n for index, row in enumerate(a12):\\n a12[index] = row[int(len(a) / 2):len(a)]\\n\\n a21 = a[int(len(a) / 2):len(a)]\\n for index, row in enumerate(a21):\\n a21[index] = row[0:int(len(row) / 2)]\\n\\n a22 = a[int(len(a) / 2):len(a)]\\n for index, row in enumerate(a22):\\n a22[index] = row[int(len(a) / 2):len(a)]\\n\\n b11 = b[0:int(len(b) / 2)]\\n for index, row in enumerate(b11):\\n b11[index] = row[0:int(len(row) / 2)]\\n\\n b12 = b[0:int(len(b) / 2)]\\n for index, row in enumerate(b12):\\n b12[index] = row[int(len(b) / 2):len(b)]\\n\\n b21 = b[int(len(b) / 2):len(b)]\\n for index, row in enumerate(b21):\\n b21[index] = row[0:int(len(row) / 2)]\\n\\n b22 = b[int(len(b) / 2):len(b)]\\n for index, row in enumerate(b22):\\n b22[index] = row[int(len(b) / 2):len(b)]\\n\\n c11 = matrix_add(recursive_multiply(a11, b11), recursive_multiply(a12, b21)) # C11 = A11*B11 + A12*B21\\n c12 = matrix_add(recursive_multiply(a11, b12), recursive_multiply(a12, b22)) # C12 = A11*B12 + A12*B22\\n c21 = matrix_add(recursive_multiply(a21, b11), recursive_multiply(a22, b21)) # C21 = A21*B11 + A22*B21\\n c22 = matrix_add(recursive_multiply(a21, b12), recursive_multiply(a22, b22)) # C22 = A21*B12 + A22*B22\\n\\n # Append c12 to c11\\n for row_index, row in enumerate(c11):\\n for col_index, col in enumerate(c12):\\n row.append(c12[row_index][col_index])\\n\\n # Append c22 to c21\\n for row_index, row in enumerate(c21):\\n for col_index, col in enumerate(c12):\\n row.append(c22[row_index][col_index])\\n\\n # Append c21 to c11\\n for i in c21:\\n c11.append(i)\\n\\n return c11\",\n \"def squared_distance_calculator(position1, position2):\\r\\n difference_vector = position2 - position1\\r\\n return np.dot(difference_vector, difference_vector)\",\n \"def multiply(A, B, *args, **kwargs):\\n dim=A.__len__()\\n assert(dim==B.__len__())\\n C=[]\\n for ii in range(dim):\\n shape=(A[ii].shape[0]*B[ii].shape[0], A[ii].shape[1])\\n val=np.empty(shape)\\n for iimn, (mm, nn) in enumerate(itertools.product(list(range(A[ii].shape[0])), list(range(B[ii].shape[0])))):\\n val[iimn] = A[ii][mm]*B[ii][nn]\\n C.append(val)\\n return C\",\n \"def mul(Z,X,Y):\",\n \"def _mps_AA(self, A1, A2):\\n Dl, d1, _ = A1.shape\\n _, d2, Dr = A2.shape\\n return np.reshape(np.tensordot(A1, A2, axes=(2, 0)), [Dl, d1 * d2, Dr])\",\n \"def brute_multiply(x, y):\\n \\n n = x.shape[0]\\n res = np.zeros(x.shape)\\n \\n for i in range(n):\\n for j in range(n):\\n for k in range(n):\\n res[i, j] += x[i, k] * y[k, j]\\n \\n return res\",\n \"def compute_surface_distances(mask1, mask2, voxel_dimensions=1):\\n structuring_el_size = tuple(3 for _ in mask1.shape)\\n grad1 = morphological_gradient(mask1.astype(int), size=structuring_el_size)\\n grad2 = morphological_gradient(mask2.astype(int), size=structuring_el_size)\\n\\n if not _is_iterable(voxel_dimensions):\\n voxel_dimensions = [voxel_dimensions for _ in mask1.shape]\\n voxel_dimensions = np.array(voxel_dimensions).reshape(1, -1)\\n\\n nonzeros_1 = np.array(np.nonzero(grad1)).T * voxel_dimensions\\n nonzeros_2 = np.array(np.nonzero(grad2)).T * voxel_dimensions\\n return np.sort(_compute_set_distances(nonzeros_1, nonzeros_2))\",\n \"def relate_pattern(a, b, pattern, **kwargs):\\n return lib.relate_pattern(a, b, pattern, **kwargs)\",\n \"def _inner_product_c1(self, point_a, point_b, normals_bp, areas_bp):\\n dna = self._space.normals(point_a) - normals_bp\\n dnb = self._space.normals(point_b) - normals_bp\\n return self.c1 * gs.sum(\\n gs.einsum(\\\"...bi,...bi->...b\\\", dna, dnb) * areas_bp, axis=-1\\n )\",\n \"def outer_prod(x, y):\\n if len(list(x.size())) != 2 or len(list(y.size())) != 2:\\n raise ValueError(\\\"An input is not of the right dimension.\\\")\\n\\n z = torch.zeros(2, x.size()[1], y.size()[1], dtype=torch.double, device=x.device)\\n z[0] = torch.ger(x[0], y[0]) - torch.ger(x[1], -y[1])\\n z[1] = torch.ger(x[0], -y[1]) + torch.ger(x[1], y[0])\\n\\n return z\",\n \"def product_on_basis(self, t1, t2):\\n return tensor( (module.monomial(x1)*module.monomial(x2) for (module, x1, x2) in zip(self._sets, t1, t2)) ) #.\",\n \"def complex_mul(x1, x2):\\n assert x1.size(-1) == 2 and x2.size(-1) == 2\\n\\n res = torch.stack(\\n (x1[..., 0]*x2[..., 0]-x1[..., 1]*x2[..., 1],\\n x1[..., 0]*x2[..., 1] + x1[..., 1]*x2[..., 0]), -1)\\n\\n return res\",\n \"def kronecker_prod(x, y):\\n if len(list(x.size())) != 3 or len(list(y.size())) != 3:\\n raise ValueError(\\\"An input is not of the right dimension.\\\")\\n\\n z = torch.zeros(\\n 2,\\n x.size()[1] * y.size()[1],\\n x.size()[2] * y.size()[2],\\n dtype=torch.double,\\n device=x.device,\\n )\\n\\n row_count = 0\\n\\n for i in range(x.size()[1]):\\n for k in range(y.size()[1]):\\n column_count = 0\\n for j in range(x.size()[2]):\\n for l in range(y.size()[2]):\\n\\n z[0][row_count][column_count] = (x[0][i][j] * y[0][k][l]) - (\\n x[1][i][j] * y[1][k][l]\\n )\\n z[1][row_count][column_count] = (x[0][i][j] * y[1][k][l]) + (\\n x[1][i][j] * y[0][k][l]\\n )\\n\\n column_count += 1\\n row_count += 1\\n\\n return z\",\n \"def squared_dist(x1: np.ndarray, x2: np.ndarray) -> np.ndarray:\\n return (\\n np.sum(x1 ** 2, 1).reshape(-1, 1) +\\n np.sum(x2 ** 2, 1) -\\n 2 * np.dot(x1, x2.T)\\n )\",\n \"def product(self):\\n return self.right[self.i:] + self.left[:self.i], self.left[self.i:] + self.right[:self.i]\",\n \"def mat_mul(mat1, mat2):\\n\\n if len(mat1[0]) == len(mat2):\\n\\n mat2 = matrix_transpose(mat2)\\n response = []\\n\\n for row in range(len(mat1)):\\n response.append(\\n [\\n sum(dot_product(mat1[row], mat2[column]))\\n for column in range(len(mat2))\\n ]\\n )\\n\\n return response\\n\\n else:\\n return None\",\n \"def __mul__(self, other):\\n #\\n # TODO - your code here\\n #\\n final_matrix = []\\n for i in range(self.h):\\n temp_row = []\\n for j in range(other.w):\\n # take dot-product of row of\\n # matrix in 1st arg with col of\\n # matrix in 2nd arg\\n temp_row.append(dot_product(get_row(self.g, i), get_col(other.g, j)))\\n final_matrix.append(temp_row)\\n return Matrix(final_matrix)\\n # TODO - your code here\",\n \"def dot_product(a, b):\\n dp = 0.0\\n for i, j in zip(a, b):\\n dp += i * j\\n return dp\",\n \"def hadmardProduct(self, a,b):\\n\\t\\tnumRows = len(a)\\n\\t\\tnumCols = len(a[0])\\n\\t\\n\\t\\treturn [[a[j][i] * b[j][i] for i in range(numCols)] for j in range(numRows)]\",\n \"def __surface_distances(result, reference, voxelspacing=None, connectivity=1):\\n result = np.atleast_1d(result.astype(np.bool))\\n reference = np.atleast_1d(reference.astype(np.bool))\\n if voxelspacing is not None:\\n voxelspacing = _ni_support._normalize_sequence(voxelspacing, result.ndim)\\n voxelspacing = np.asarray(voxelspacing, dtype=np.float64)\\n if not voxelspacing.flags.contiguous:\\n voxelspacing = voxelspacing.copy()\\n \\n # binary structure\\n footprint = generate_binary_structure(result.ndim, connectivity)\\n \\n # test for emptiness\\n if 0 == np.count_nonzero(result): \\n raise RuntimeError('The first supplied array does not contain any binary object.')\\n if 0 == np.count_nonzero(reference): \\n raise RuntimeError('The second supplied array does not contain any binary object.') \\n \\n # extract only 1-pixel border line of objects\\n result_border = result ^ binary_erosion(result, structure=footprint, iterations=1)\\n # print(result_border+0)\\n # result_border = (result_border+0).astype(np.float32)\\n # imsave(\\\"./test_comp1/\\\" +\\\"truth.jpg\\\", result_border)\\n reference_border = reference ^ binary_erosion(reference, structure=footprint, iterations=1)\\n # print(reference_border)\\n # reference_border = (reference_border + 0).astype(np.float32)\\n # imsave(\\\"./test_comp1/\\\" +\\\"truth.jpg\\\", reference_border)\\n # compute average surface distance \\n # Note: scipys distance transform is calculated only inside the borders of the\\n # foreground objects, therefore the input has to be reversed\\n dt = distance_transform_edt(~reference_border, sampling=voxelspacing)\\n # print(dt)\\n reference_border = (reference_border + 0).astype(np.float32)\\n # imsave(\\\"./test_comp1/\\\" +\\\"truth.jpg\\\", reference_border)\\n sds = dt[result_border]\\n \\n return sds\",\n \"def dotProduct(v1, v2):\\n return sum((a * b) for a, b in zip(v1, v2))\",\n \"def outer_product(x,y):\\n\\n return x[:,0]*y[:,1] -x[:,1]*y[:,0]\",\n \"def products(q_1: Qs, q_2: Qs, kind: str = \\\"\\\", reverse: bool = False) -> Qs:\\n\\n q_1_copy = deepcopy(q_1)\\n q_2_copy = deepcopy(q_2)\\n qs_left, qs_right = Qs(), Qs()\\n\\n # Diagonalize if need be.\\n if ((q_1.rows == q_2.rows) and (q_1.columns == q_2.columns)) or (\\n \\\"scalar_q\\\" in [q_1.qs_type, q_2.qs_type]\\n ):\\n\\n if q_1.columns == 1:\\n qs_right = q_2_copy\\n qs_left = diagonal(q_1_copy, qs_right.rows)\\n\\n elif q_2.rows == 1:\\n qs_left = q_1_copy\\n qs_right = diagonal(q_2_copy, qs_left.columns)\\n\\n else:\\n qs_left = q_1_copy\\n qs_right = q_2_copy\\n\\n # Typical matrix multiplication criteria.\\n elif q_1.columns == q_2.rows:\\n qs_left = q_1_copy\\n qs_right = q_2_copy\\n\\n else:\\n print(\\n \\\"Oops, cannot multiply series with row/column dimensions of {}/{} to {}/{}\\\".format(\\n q_1.rows, q_1.columns, q_2.rows, q_2.columns\\n )\\n )\\n\\n # Operator products need to be transposed.\\n operator_flag = False\\n if qs_left in [\\\"op\\\", \\\"operator\\\"] and qs_right in [\\\"op\\\", \\\"operator\\\"]:\\n operator_flag = True\\n\\n outer_row_max = qs_left.rows\\n outer_column_max = qs_right.columns\\n shared_inner_max = qs_left.columns\\n projector_flag = (\\n (shared_inner_max == 1) and (outer_row_max > 1) and (outer_column_max > 1)\\n )\\n\\n result = [\\n [q0(q_type=\\\"\\\") for _i in range(outer_column_max)]\\n for _j in range(outer_row_max)\\n ]\\n\\n for outer_row in range(outer_row_max):\\n for outer_column in range(outer_column_max):\\n for shared_inner in range(shared_inner_max):\\n\\n # For projection operators.\\n left_index = outer_row\\n right_index = outer_column\\n\\n if outer_row_max >= 1 and shared_inner_max > 1:\\n left_index = outer_row + shared_inner * outer_row_max\\n\\n if outer_column_max >= 1 and shared_inner_max > 1:\\n right_index = shared_inner + outer_column * shared_inner_max\\n\\n result[outer_row][outer_column] = add(result[outer_row][outer_column],\\n product(qs_left.qs[left_index],\\n qs_right.qs[right_index], kind=kind, reverse=reverse\\n )\\n )\\n\\n # Flatten the list.\\n new_qs = [item for sublist in result for item in sublist]\\n new_states = Qs(new_qs, rows=outer_row_max, columns=outer_column_max)\\n\\n if projector_flag or operator_flag:\\n return transpose(new_states)\\n\\n else:\\n return new_states\",\n \"def test_l2_metric_inner_product_vectorization(\\n self,\\n l2_metric_s2,\\n times,\\n n_landmark_sets,\\n landmarks_a,\\n landmarks_b,\\n landmarks_c,\\n ):\\n landmarks_ab = l2_metric_s2.geodesic(landmarks_a, landmarks_b)\\n landmarks_bc = l2_metric_s2.geodesic(landmarks_b, landmarks_c)\\n landmarks_ab = landmarks_ab(times)\\n landmarks_bc = landmarks_bc(times)\\n\\n tangent_vecs = l2_metric_s2.log(point=landmarks_bc, base_point=landmarks_ab)\\n\\n result = l2_metric_s2.inner_product(tangent_vecs, tangent_vecs, landmarks_ab)\\n\\n self.assertAllClose(gs.shape(result), (n_landmark_sets,))\",\n \"def product(self, x, y):\\n return self( x.lift() * y.lift() )\",\n \"def _dot_product_attention_inner_relative(x, y, z, transpose):\\n batch_size, heads, length, _ = x.size()\\n\\n # xy_matmul is [batch_size, heads, length, length or depth]\\n xy_matmul = torch.matmul(x, y if not transpose else y.transpose(-2, -1))\\n # x_t is [length, batch_size, heads, length or depth]\\n x_t = x.permute(2, 0, 1, 3)\\n # x_t_r is [length, batch_size * heads, length or depth]\\n x_t_r = x_t.view(length, batch_size * heads, -1)\\n # x_tz_matmul is [length, batch_size * heads, length or depth]\\n x_tz_matmul = torch.matmul(x_t_r, z if not transpose else z.transpose(-2, -1))\\n # x_tz_matmul_r is [length, batch_size, heads, length or depth]\\n x_tz_matmul_r = x_tz_matmul.view(length, batch_size, heads, -1)\\n # x_tz_matmul_r_t is [batch_size, heads, length, length or depth]\\n x_tz_matmul_r_t = x_tz_matmul_r.permute(1, 2, 0, 3)\\n\\n return xy_matmul + x_tz_matmul_r_t\",\n \"def apply_feature_matching(desc1: np.ndarray, desc2: np.ndarray,\\n match_calculator: Callable[[list, list], float]) -> list:\\n\\n # Check descriptors dimensions are 2\\n assert desc1.ndim == 2, \\\"Descriptor 1 shape is not 2\\\"\\n assert desc2.ndim == 2, \\\"Descriptor 2 shape is not 2\\\"\\n\\n # Check that the two features have the same descriptor type\\n assert desc1.shape[1] == desc2.shape[1], \\\"Descriptors shapes are not equal\\\"\\n\\n # If there is not key points in any of the images\\n if desc1.shape[0] == 0 or desc2.shape[0] == 0:\\n return []\\n\\n # number of key points in each image\\n num_key_points1 = desc1.shape[0]\\n num_key_points2 = desc2.shape[0]\\n\\n # List to store the matches scores\\n matches = []\\n\\n # Loop over each key point in image1\\n # We need to calculate similarity with each key point in image2\\n for kp1 in range(num_key_points1):\\n # Initial variables which will be updated in the loop\\n distance = -np.inf\\n y_index = -1\\n\\n # Loop over each key point in image2\\n for kp2 in range(num_key_points2):\\n\\n # Match features between the 2 vectors\\n value = match_calculator(desc1[kp1], desc2[kp2])\\n\\n # SSD values examples: (50, 200, 70), we need to minimize SSD (Choose 50)\\n # In case of SSD matching: (value is returned as a \\\"negative\\\" number) (-50, -200, -70)\\n # So we compare it with -np.inf. (The sorting will be reversed later)\\n\\n # NCC values examples: (0.58, 0.87, 0.42), we need to maximize NCC (Choose 0.87)\\n # In case of NCC matching: (value is returned as a \\\"positive\\\" number) (-58, -0.87, -0.42)\\n # So we compare it with -np.inf. (The sorting will be reversed later)\\n\\n if value > distance:\\n distance = value\\n y_index = kp2\\n\\n # Create a cv2.DMatch object for each match and set attributes:\\n # queryIdx: The index of the feature in the first image\\n # trainIdx: The index of the feature in the second image\\n # distance: The distance between the two features\\n cur = cv2.DMatch()\\n cur.queryIdx = kp1\\n cur.trainIdx = y_index\\n cur.distance = distance\\n matches.append(cur)\\n\\n return matches\",\n \"def euclidean_squared_distance(input1, input2):\\n m, n = input1.size(0), input2.size(0)\\n mat1 = torch.pow(input1, 2).sum(dim=1, keepdim=True).expand(m, n)\\n mat2 = torch.pow(input2, 2).sum(dim=1, keepdim=True).expand(n, m).t()\\n distmat = mat1 + mat2\\n distmat.addmm_(input1, input2.t(), beta=1, alpha=-2)\\n return distmat\",\n \"def interior_tensor_product(mx, dim_a, dim_b, e=None):\\n assert _np.shape(mx) == (dim_a * dim_b, dim_a * dim_b), \\\"Dimensions do not agree with matrix size\\\"\\n assert _np.shape(e)[0] == _np.shape(e)[1], \\\"e should be a square matrix\\\"\\n basis_a = matrix_units(dim_a)\\n basis_b = matrix_units(dim_b)\\n return sum((_np.trace(_np.dot(mx, _np.kron(unit_a, unit_b).T)) * multikron([unit_a, e, unit_b])\\n for unit_a in basis_a for unit_b in basis_b))\",\n \"def matmul(xs: List[List[float]],\\n ys: List[List[float]]) -> List[List[float]]:\\n product = []\\n for x_row in range(len(xs)):\\n row = []\\n for y_col in range(len(ys[0])):\\n col = [ys[y_row][y_col] for y_row in range(len(ys))]\\n row.append(Math.dot(xs[x_row], col))\\n product.append(row)\\n return product\",\n \"def dotproduct(vec1, vec2):\\n return sum((a*b) for a, b in zip(vec1, vec2))\",\n \"def dist_squared (a, b):\\n return sum(map(lambda (x,y): (x-y)**2, zip(a, b)))\",\n \"def times(self, other):\\r\\n result = TriangularArray(self.num_rows)\\r\\n for i in range(self.num_rows):\\r\\n for j in range(self.num_rows):\\r\\n # Calculate the [i, j] entry.\\r\\n total = 0\\r\\n for k in range(j, i + 1):\\r\\n total += self[(i, k)] * other[(k, j)]\\r\\n result[(i, j)] = total\\r\\n return result\",\n \"def dot_product(A, B):\\n # Section 1: Ensure A and B dimensions are the same\\n rowsA = len(A); colsA = len(A[0])\\n rowsB = len(B); colsB = len(B[0])\\n if rowsA != rowsB or colsA != colsB:\\n raise ArithmeticError('Matrices are NOT the same size.')\\n\\n # Section 2: Sum the products \\n total = 0\\n for i in range(rowsA):\\n for j in range(colsB):\\n total += A[i][j] * B[i][j]\\n\\n return total\",\n \"def distance(f1, f2):\\n\\n return np.sum((np.sum([f1, -f2], axis=0))**2, axis=1)\",\n \"def divide(m1,m2):\\n \\n if ((m1.shape[0] % 2 == 0) or (m1.shape[0] == 1)):\\n n = m1.shape[0]\\n else:\\n n = m1.shape[0] + 1\\n result = np.zeros((n, n), dtype = int)\\n \\n if (n == 1):\\n result[0][0] = m1[0][0] * m2[0][0]\\n else:\\n new = n//2\\n \\n a11, a12, a21, a22 = m1[:new, :new], m1[new:, :new], m1[:new, new:], m1[new:, new:]\\n b11, b12, b21, b22 = m2[:new, :new], m2[new:, :new], m2[:new, new:], m2[new:, new:]\\n \\n result[:new, :new] = divide(a11,b11) + divide(a12,b21)\\n result[new:, :new] = divide(a11,b12) + divide(a12,b22)\\n result[:new, new:] = divide(a21,b11) + divide(a22,b21)\\n result[new:, new:] = divide(a21,b12) + divide(a22,b22)\\n \\n return result\",\n \"def __mul__(self, other):\\n # \\n # TODO - your code here\\n #\\n \\n result = [];\\n row_result = [];\\n product = 0;\\n \\n if(self.w != other.h):\\n raise(ValueError, \\\"Matrices can not multiply for their dimesion doesn't match\\\"); \\n \\n for row in self.g:\\n row_result = [];\\n for j in range(other.w):\\n product = dot_product(row,other.get_column(j));\\n row_result.append(product);\\n result.append(row_result);\\n \\n return Matrix(result);\",\n \"def matrix_product(mat1: List[List[int]], mat2: List[List[int]]):\\n if len(mat1) == 0 or len(mat2) == 0:\\n raise ValueError(\\\"One of matrix is empty\\\")\\n n, k1 = len(mat1), len(mat1[0])\\n k2, m = len(mat2), len(mat2[0])\\n if k1 != k2:\\n raise ValueError(\\n f\\\"Can't multiply two matrices with shapes {n}x{k1} and {k2}x{m}\\\"\\n )\\n mat2_t = matrix_transpose(mat2)\\n return [[vec_product(vec1, vec2) for vec2 in mat2_t] for vec1 in mat1]\",\n \"def prod(self, x, y):\\n return (self.basic_operation.reduce(x.original+y.original),\\n self.operation1.prod(x.left, y.left),\\n self.operation2.prod(x.right, y.right))\",\n \"def tile_calculation(xi, yi, axi, ayi, positions, weights):\\n for j in range(cuda.blockDim.x):\\n xj = positions[j,0]\\n yj = positions[j,1]\\n wj = weights[j]\\n axi, ayi = body_body_interaction(xi, yi, xj, yj, wj, axi, ayi)\\n return axi, ayi\",\n \"def dot(pepx1, pepx2):\\n\\n Ls = pepx1.shape\\n assert pepx2.shape==Ls, '[dot]: sizes of pepx1 and pepx2 are not equal'\\n new_pepx = np.empty(Ls, dtype=np.object)\\n new_lams = np.empty(pepx1.lambdas.shape, dtype=np.object)\\n\\n # if np.all([ pepx1[i].ndim==3 and pepx2[i].ndim==3 for i in np.ndenumerate(pepx1) ]):\\n # return peps_dot(pepx1,pepx2)\\n # else:\\n for idx in np.ndindex(Ls):\\n len_dp1 = len(pepx1.phys_bonds[idx])\\n len_dp2 = len(pepx2.phys_bonds[idx])\\n ax1 = [0,2,4,6] + range(8, 8+len_dp1)\\n ax2 = [1,3,5,7] + range(8+len_dp1-1,8+len_dp1+len_dp2-1)\\n ax2[-len_dp2] = ax1[-1] # contract vertical bonds (mpx1 down with mpx2 up)\\n new_site = np.einsum(pepx1[idx],ax1,pepx2[idx],ax2)\\n new_pepx[idx] = tf.reshape(new_site,'ii,ii,ii,ii,...',group_ellipsis=False)\\n\\n i,j = idx\\n for xx in range(new_lams.shape[2]):\\n new_lams[i,j,xx] = np.outer(pepx1.lambdas[i,j,xx], pepx2.lambdas[i,j,xx]).reshape(-1)\\n # print new_lams[i,j,xx].shape\\n\\n return PEPX_GL(new_pepx,new_lams) #,pepx1.phys_bonds)\",\n \"def _mul(*args):\\n\\treturn functools.reduce(numpy.dot, args)\",\n \"def pairwise_dot_product_similarity(x, y):\\n return torch.mm(x, torch.transpose(y, 1, 0))\",\n \"def dotproduct(vec1, vec2):\\n import operator\\n return sum(map(operator.mul, vec1, vec2))\",\n \"def classical(m1,m2):\\n \\n n = m1.shape\\n result = np.zeros(n, dtype = int)\\n\\n for i in range(n[0]):\\n for j in range(n[0]):\\n for k in range(n[0]):\\n result[i][j] += m1[i][k] * m2[k][j]\\n return result\",\n \"def dot_product(a, b):\\n a1, a2, a3 = a\\n b1, b2, b3 = b\\n return a1 * b1 + a2 * b2 + a3 * b3\",\n \"def _product(self, args):\\n pools = map(tuple, args) #within original version args defined as *args\\n result = [[]]\\n for pool in pools:\\n result = [x + [y] for x in result for y in pool]\\n return result\",\n \"def linear_interpolation(atoms1, atoms2, N):\\n \\n pos1 = atoms1.positions\\n pos2 = atoms2.positions\\n images = [atoms1]\\n for n in range(N):\\n nn = n + 1\\n atoms_tmp = atoms1.copy()\\n atoms_tmp.positions = ( (N+1-nn)*pos1 + nn*pos2 ) / (N+1)\\n images += [atoms_tmp]\\n images += [atoms2]\\n return images\",\n \"def euclidean_distance_2d(XA: np.ndarray, XB: np.ndarray):\\n out = np.empty((XA.shape[0], XB.shape[0]), dtype=XA.dtype)\\n for i in numba.prange(XA.shape[0]):\\n for j in range(XB.shape[0]):\\n out[i, j] = np.sqrt((XA[i, 0] - XB[j, 0]) ** 2 + (XA[i, 1] - XB[j, 1]) ** 2)\\n return out\",\n \"def sqeuclidean_distance_2d(XA: np.ndarray, XB: np.ndarray):\\n out = np.empty((XA.shape[0], XB.shape[0]), dtype=XA.dtype)\\n for i in numba.prange(XA.shape[0]):\\n for j in range(XB.shape[0]):\\n out[i, j] = (XA[i, 0] - XB[j, 0]) ** 2 + (XA[i, 1] - XB[j, 1]) ** 2\\n return out\",\n \"def __surface_distances(result, reference, voxelspacing=None, connectivity=1):\\n result = np.atleast_1d(result.astype(np.bool))\\n reference = np.atleast_1d(reference.astype(np.bool))\\n if voxelspacing is not None:\\n voxelspacing = _ni_support._normalize_sequence(voxelspacing, result.ndim)\\n voxelspacing = np.asarray(voxelspacing, dtype=np.float64)\\n if not voxelspacing.flags.contiguous:\\n voxelspacing = voxelspacing.copy()\\n \\n # binary structure\\n footprint = generate_binary_structure(result.ndim, connectivity)\\n \\n # test for emptiness\\n if 0 == np.count_nonzero(result): \\n raise RuntimeError('The first supplied array does not contain any binary object.')\\n if 0 == np.count_nonzero(reference): \\n raise RuntimeError('The second supplied array does not contain any binary object.') \\n \\n # extract only 1-pixel border line of objects\\n result_border = result ^ binary_erosion(result, structure=footprint, iterations=1)\\n\\n reference_border = reference ^ binary_erosion(reference, structure=footprint, iterations=1)\\n\\n dt = distance_transform_edt(~reference_border, sampling=voxelspacing)\\n # print(dt)\\n reference_border = (reference_border + 0).astype(np.float32)\\n\\n sds = dt[result_border]\\n \\n return sds\",\n \"def _tensor_batch_dot(t1: Tensor, t2: Tensor) -> Tensor:\\n\\n msg = (\\n \\\"Please ensure each batch member has the same feature dimension. \\\"\\n f\\\"First input has {torch.numel(t1) / t1.shape[0]} features, and \\\"\\n f\\\"second input has {torch.numel(t2) / t2.shape[0]} features.\\\"\\n )\\n assert torch.numel(t1) / t1.shape[0] == torch.numel(t2) / t2.shape[0], msg\\n\\n return torch.mm(\\n t1.view(t1.shape[0], -1),\\n t2.view(t2.shape[0], -1).T,\\n )\",\n \"def __mul__(self,other):\\n # \\n # 注意矩阵的A的列 与 相乘矩阵B的行必须相等，才能进行运算\\n height = 0\\n width = 0\\n if isinstance(other, list): # 判断other是否是矩阵，即list形式的矩阵\\n height = len(other)\\n width = len(other[0])\\n else:\\n # 如果是对象，则直接获取行列值\\n height = other.h\\n width = other.w\\n\\n\\n my_mul = zeroes(self.h, self.w)\\n if self.w == height: # 两个矩阵的行列值需要相等 才能相乘\\n for i in range(self.h):\\n for j in range(width):\\n my_sum = 0\\n for k in range(height):\\n if isinstance(other, list):\\n my_sum += self.g[i][k] * other[k][j]\\n # 通过3个循环变量取所有矩阵的行列值\\n else:\\n my_sum += self.g[i][k] * other.g[k][j]\\n my_mul[i][j] = my_sum\\n return my_mul \\n else:\\n return NotImplementedError\",\n \"def __mul__ (self, other):\\n return perm(*(self._getcycles() + other._getcycles()))\",\n \"def product(self, x, y):\\n return self._cached_product(x.value, y.value)\",\n \"def distance(brd1,brd2):\\n\\n step=brd1[1,0]-brd1[0,0]\\n return np.sum(np.abs(brd1[:,1]-brd2[:,1]))*step\",\n \"def map_feature(X1, X2):\\n degree = 6\\n out = np.ones(X1.shape)\\n \\n for i in range(1, degree + 1):\\n for j in range(0, i + 1):\\n out = np.concatenate((out, np.power(X1, (i - j)) * np.power(X2, j)), axis=-1)\\n\\n return out\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.773317","0.5649336","0.5526998","0.54341394","0.53960305","0.5378274","0.5336186","0.5309336","0.529776","0.5289525","0.5283448","0.52754015","0.52056116","0.5135578","0.50863296","0.5050334","0.5024294","0.49954486","0.4992397","0.49735647","0.49717188","0.4961927","0.4959781","0.4940064","0.49399367","0.4932615","0.49212724","0.49055746","0.48976764","0.48897642","0.4866107","0.4843088","0.48255998","0.48251253","0.4819184","0.4816141","0.4810712","0.4809668","0.48034745","0.47891194","0.47860396","0.47842","0.47838998","0.47635022","0.47585952","0.4753178","0.475048","0.4735247","0.47303593","0.4726892","0.47224283","0.47199464","0.47169435","0.4713793","0.47126818","0.46998528","0.46976426","0.46967214","0.46959233","0.4695676","0.46955204","0.46913534","0.4683207","0.46802652","0.46719408","0.4671064","0.46693748","0.46641195","0.46614587","0.46605617","0.46574455","0.4656189","0.46512765","0.46493345","0.46483332","0.46456802","0.4631644","0.46263385","0.46255046","0.46240968","0.46236375","0.46200195","0.46048486","0.4601232","0.45925498","0.45832348","0.45785397","0.45778146","0.4576788","0.45632055","0.45613247","0.4555602","0.45501024","0.45486438","0.45476693","0.45455432","0.4541269","0.45396024","0.45381728","0.45372927"],"string":"[\n \"0.773317\",\n \"0.5649336\",\n \"0.5526998\",\n \"0.54341394\",\n \"0.53960305\",\n \"0.5378274\",\n \"0.5336186\",\n \"0.5309336\",\n \"0.529776\",\n \"0.5289525\",\n \"0.5283448\",\n \"0.52754015\",\n \"0.52056116\",\n \"0.5135578\",\n \"0.50863296\",\n \"0.5050334\",\n \"0.5024294\",\n \"0.49954486\",\n \"0.4992397\",\n \"0.49735647\",\n \"0.49717188\",\n \"0.4961927\",\n \"0.4959781\",\n \"0.4940064\",\n \"0.49399367\",\n \"0.4932615\",\n \"0.49212724\",\n \"0.49055746\",\n \"0.48976764\",\n \"0.48897642\",\n \"0.4866107\",\n \"0.4843088\",\n \"0.48255998\",\n \"0.48251253\",\n \"0.4819184\",\n \"0.4816141\",\n \"0.4810712\",\n \"0.4809668\",\n \"0.48034745\",\n \"0.47891194\",\n \"0.47860396\",\n \"0.47842\",\n \"0.47838998\",\n \"0.47635022\",\n \"0.47585952\",\n \"0.4753178\",\n \"0.475048\",\n \"0.4735247\",\n \"0.47303593\",\n \"0.4726892\",\n \"0.47224283\",\n \"0.47199464\",\n \"0.47169435\",\n \"0.4713793\",\n \"0.47126818\",\n \"0.46998528\",\n \"0.46976426\",\n \"0.46967214\",\n \"0.46959233\",\n \"0.4695676\",\n \"0.46955204\",\n \"0.46913534\",\n \"0.4683207\",\n \"0.46802652\",\n \"0.46719408\",\n \"0.4671064\",\n \"0.46693748\",\n \"0.46641195\",\n \"0.46614587\",\n \"0.46605617\",\n \"0.46574455\",\n \"0.4656189\",\n \"0.46512765\",\n \"0.46493345\",\n \"0.46483332\",\n \"0.46456802\",\n \"0.4631644\",\n \"0.46263385\",\n \"0.46255046\",\n \"0.46240968\",\n \"0.46236375\",\n \"0.46200195\",\n \"0.46048486\",\n \"0.4601232\",\n \"0.45925498\",\n \"0.45832348\",\n \"0.45785397\",\n \"0.45778146\",\n \"0.4576788\",\n \"0.45632055\",\n \"0.45613247\",\n \"0.4555602\",\n \"0.45501024\",\n \"0.45486438\",\n \"0.45476693\",\n \"0.45455432\",\n \"0.4541269\",\n \"0.45396024\",\n \"0.45381728\",\n \"0.45372927\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.87116134"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":880,"cells":{"query":{"kind":"string","value":"This function extract the squared l2 norm from the inner product matrix."},"document":{"kind":"string","value":"def l2_square_from_inner_product(matrix):\n return np.diag(matrix)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def inner_product_to_normalized_L2_square(matrix):\n\n length = matrix.shape[0]\n norm = np.divide(1, np.sqrt(l2_square_from_inner_product(matrix)))\n\n normalized_inner_product = np.multiply(np.multiply(np.reshape(norm, [length, 1]), matrix),\n np.reshape(norm, [1, length]))\n return 2 - 2 * normalized_inner_product","def inner_product_to_L2_square(matrix):\n\n length = matrix.shape[0]\n squared_norm = np.reshape(np.diag(matrix), (length, 1))\n\n return squared_norm + np.transpose(squared_norm) - 2 * matrix","def L2norm(m):\n return np.sqrt(np.sum(m**2))","def L2_norm(self):\n analyticals = self.analytical(self.x_values, self.C, self.D)\n error = analyticals - self.numerical\n self.L2 = np.sqrt((1/self.gp)*np.sum(error**2))","def norm_L2(u):\n return norm_l2(u)/sqrt(float(u.size))","def l2_norm(params):\n flattened, _ = flatten(params)\n return np.dot(flattened, flattened)","def l2(vec):\n return np.linalg.norm(vec)","def norm_l2(v):\n return np.sqrt((v**2).sum())","def l2_norm(self):\n return (self.x**2 + self.y**2 + self.z**2)**0.5","def norm_l2(u):\n return linalg.norm(u.ravel())","def l2_norm(v):\n res = 0\n for e in v:\n res += e * e\n return math.sqrt(res)","def _l2_norm_squared(self, z, theta):\n norms = np.zeros(shape=(len(z), self.n_states))\n\n for j in range(self.n_states):\n diff = theta[:, j] - z # ndarray of shape (n_samples, n_states) with differences\n norms[:, j] = np.square(np.linalg.norm(diff, axis=1)) # squared state conditional l2 norms\n\n return norms # squared l2 norm.","def squared_norm(self) -> float:\n return self.__real**2 + self.__img[0]**2 + self.__img[1]**2 + self.__img[2]**2","def tree_l2_norm(tree_x, squared=False):\n squared_tree = tree_map(jnp.square, tree_x)\n sqnorm = tree_sum(squared_tree)\n if squared:\n return sqnorm\n else:\n return jnp.sqrt(sqnorm)","def norm_with_l2(original_mat):\n normed_mat = np.zeros(original_mat.shape, dtype=np.float32)\n if len(original_mat.shape) == 2:\n for ind_r in range(original_mat.shape[0]):\n a = np.square(original_mat[ind_r]*1.0)\n b = np.sum(a)\n c = np.sqrt(b)\n normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / c\n # normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / np.sqrt(np.sum(np.square(original_mat[ind_r])*1.0))\n return normed_mat","def calculate_error_l2_norm(self, dY):\n solutions = []\n norm = 0.\n for mi in range(len(self._meshes)):\n for ei in range(len(self._meshes[mi].elements)):\n e = self._meshes[mi].elements[ei]\n # change this to gauss points:\n x_vals, w = p_roots(20)\n norm_e_squared = 0.\n for i, x in enumerate(x_vals):\n norm_e_squared += w[i] * \\\n self.get_sol_value(mi, ei, dY, x,\n count_lift=False)**2\n norm_e_squared *= e.jacobian\n norm += norm_e_squared\n return sqrt(norm)","def norm2(self):\n\t\treturn self.x ** 2 + self.y ** 2 + self.z ** 2","def norm2d(self) -> float:\n\n return self.v2ddict.norm2d()","def l2_norm(vec_or_matrix):\n if len(vec_or_matrix.shape) == 1:\n # linear vector\n return vec_or_matrix / np.linalg.norm(vec_or_matrix)\n elif len(vec_or_matrix.shape) == 2:\n return vec_or_matrix / np.linalg.norm(vec_or_matrix, axis=1, ord=2)[:, np.newaxis]\n else:\n raise ValueError('Wrong number of dimensions, 1 or 2 is supported, not %i.' % len(vec_or_matrix.shape))","def l2_norm(pattern):\n return np.linalg.norm(pattern)","def l2(v, axis=None):\n length = v.shape[0]\n return np.sqrt(np.sum(np.square(v), axis=axis) / length)","def l2(weights):\n\treturn np.sqrt(np.sum(weights * weights))","def _l2s(self, params):\n return [np.linalg.norm(param) for param in params]","def norm_sqr(x):\n return inner_prod(x, x)[0]","def compute_L2_normalization(xx):\r\n\treturn np.sum(xx ** 2, axis=1)","def test_scale_features_L2_norm(self):\n data = array([[0.564, 20.661], [-18.512, 41.168], [-0.009, 20.440]])\n cdata = CData(data)\n\n # correct answer computed in Mathematica\n # TODO: can we compute the right answer in Python?\n answer = array([[0.0304526, 0.409996], [-0.999536, 0.816936], [-0.000485946, 0.40561]])\n\n # perform L2 normalization and check answer\n cdata.scale_features('L2 norm')\n self.assertTrue(allclose(cdata.data, answer))","def norm2(point):\n return np.sum(point**2, -1)","def l2_norm(self, input):\n input_size = input.size()\n buffer = torch.pow(input, 2)\n normp = torch.sum(buffer, 1).add_(1e-10)\n norm = torch.sqrt(normp)\n _output = torch.div(input, norm.view(-1, 1).expand_as(input))\n output = _output.view(input_size)\n\n return output","def sigma_norm2( self):\n return self._sigma2","def _l2_normalize(x, axis=None, eps=1e-12):\n return x * jax.lax.rsqrt((x * x).sum(axis=axis, keepdims=True) + eps)","def L2norm(self, array):\n norm = torch.sqrt(torch.sum(array * array))\n return norm","def norm2(self):\n return getattr(self, self.norm2_name)","def norm(x):\n return inner_prod(x, x)[0].sqrt_()","def squared_norm(self, vector, base_point=None):\n sq_norm = self.inner_product(vector, vector, base_point)\n return gs.real(sq_norm)","def two_norm(v):\n return math.sqrt(dot_product(v, v))","def l2norm(array1,array2):\r\n tot = np.sum(np.abs(array1)**2)\r\n return np.sqrt(np.sum(np.abs(array1-array2)**2)/tot)","def norm(self):\n\t\treturn math.sqrt(self.norm2())","def norm(self):\n mag_squared = self._sum_of_squares()\n return sqrt(mag_squared)","def normF2(X):\r\n # pass\r\n if X.shape[0]*X.shape[1] == 0:\r\n return 0\r\n return LA.norm(X, 'fro')**2","def fast_2_norm(A):\n v = np.random.rand(A.shape[1], 1)\n return la.norm(A.dot(v))","def norm2_r(self, a: np.ndarray) -> float:\n return a.T @ a","def l2norm(X):\n norm = np.linalg.norm(X, axis=1, keepdims=True)\n return 1.0 * X / norm","def squared_norm(self, vector, base_point=None):\n sq_norm = self.embedding_metric.squared_norm(vector)\n return sq_norm","def square_norm(x):\n return np.linalg.norm(x) ** 2","def normSq(self):\n\t\treturn self.x*self.x+self.y*self.y","def L2_norm(x, axis=-1):\n return keras.backend.l2_normalize(x, axis=axis)","def l2norm_(X, Xstar):\n return cdist(X, Xstar)","def normsq(self):\n return sum(x**2 for x in self.data)","def norm2d(w_in):\n return nn.BatchNorm2d(num_features=w_in, eps=cfg.BN.EPS, momentum=cfg.BN.MOM)","def L2_normalize(xx):\r\n\tZx = compute_L2_normalization(xx)\r\n\treturn xx / np.sqrt(Zx[:, np.newaxis])","def l2_normalize(data, axis=-1, eps=1e-6):\n ret = data / (np.linalg.norm(data, axis=axis, keepdims=True) + eps)\n return ret","def norm(self):\n\t\treturn np.sqrt(self.normSq())","def vec_2norm (x):\n return math.sqrt (sum ([x_i**2 for x_i in x]))","def normr(Mat):\n B = normalize(Mat, norm='l2', axis=1)\n return B","def norm(self):\n return sqrt(self.dot(self))","def norm(self):\n return math.sqrt(self.dotProduct(self))","def squared_norm(self, vector, base_point=None):\n args = {\n \"vector\": vector,\n \"base_point\": base_point,\n }\n sq_norms = self._iterate_over_factors(\"squared_norm\", args)\n return sum(sq_norms)","def norm(x):\n return np.sqrt(norm2(x))","def get_sqrt_2():\n return 1.41421356","def l2(x1, x2):\n return np.sqrt((x1 - x2)**2)","def l2_regularization_penalty(self):\n return self.l2 * (np.linalg.norm(self.weights)**2)","def l2_norm_batch(pattern_stack):\n\n return np.linalg.norm(pattern_stack, axis=0)","def squaredDistance(vec1, vec2):\n return (distance.euclidean(vec1, vec2))**2","def normalize_l2norm(data,tol=0):\n data_sqrt=np.sqrt(np.square(data).sum(axis=1))\n data_sqrt.shape=(data_sqrt.shape[0],1)\n #tol=0#1e-8\n data=data/(data_sqrt+tol)\n return data","def L2_dists(x, y):\n #print(x.shape)\n #print(y.shape)\n dists = -2 * np.matmul(x, y.T)\n dists += np.sum(x**2)[np.newaxis]\n dists += np.sum(y**2)\n return np.sqrt(dists)","def L2_func(x):\n return K.expand_dims(K.sqrt(K.sum(K.pow(x,2), axis=1)))","def l2_normalize(data, eps, axis=None):\n return cpp.nn.l2_normalize(data, eps, axis)","def get_L(self, X):\n if issparse(X):\n return slinalg.norm(X, axis=1) ** 2\n else:\n return norm(X, axis=1) ** 2","def getNorm(self, norm=lambda l: (sum(map(lambda x: x ** 2, l))) ** (1 / 2)):\n return norm(self.components)","def getNormLaplacian(W):\n\td=[np.sum(row) for row in W]\n\tD=np.diag(d)\n\tL=D-W\n\t#Dn=D^(-1/2)\n\tDn=np.power(np.linalg.matrix_power(D,-1),0.5)\n\tLbar=np.dot(np.dot(Dn,L),Dn)\n\treturn Lbar","def project_L2(w, l):\n return w * min(1, 1 / (l ** (1 / 2.0) * np.linalg.norm(w, 2)))","def normalisation_l2(x):\n res = np.zeros(x.shape)\n print(x.shape)\n for i in range(x.shape[0]):\n res[i] = x[i]/(np.linalg.norm(x[i],2)+1e-5)\n std = res.std()\n mean = res.mean()\n print(\"normalisation done\")\n return(mean,std,res)","def getL2Error(self,exactSolution):\n value = 0\n error = np.array(self.solution)-np.array([exactSolution(x) for x in self.triangulation.points])\n for ele,triPoints in enumerate(self.triangulation.simplices):\n transformMatrix,translateVector = self.calculateTransform(ele)\n determinant = abs(np.linalg.det(transformMatrix))\n #Last vector is the precalculated integral of the basisfunctions over a reference element\n value+=determinant*np.dot(error[triPoints]**2,np.array([1/6.,1/3.,1/3.]))\n return(math.sqrt(value))","def l21_norm_bound(X, Y, loss='square'):\n if loss.lower() == 'square':\n # In this case max_tau := 2/n * max(||[X^T * Y]s||_2)\n # First compute the 2-norm of each row of X^T * Y\n norm2 = map(lambda x: np.linalg.norm(x, ord=2), X.T.dot(Y))\n return np.max(norm2) * (2.0/X.shape[0])\n else:\n print('Only square loss implemented so far.')\n sys.exit(-1)","def norm(self):\n C = np.prod([F.T @ F for F in self.factors], axis=0)\n return np.sqrt(np.sum(C))","def calculate_dist_mat_2(A: np.ndarray, B: np.array, norm: int) -> np.ndarray:\n kwargs = {'p': norm}\n dist_mat = cdist(A, B, metric='minkowski', **kwargs)\n return dist_mat","def P2l_rec_norm(ells, cost):\n P22 = 3. * (1. - cost**2)\n P23 = 15. * cost * (1. - cost**2)\n P2l = np.zeros(len(ells))\n P2l[0] = 0.\n P2l[1] = 0.\n P2l[2] = P22\n P2l[3] = P23\n P2l_norm = np.copy(P2l)\n P2l_norm[2] *= P2l_norm_prefac(2)\n P2l_norm[3] *= P2l_norm_prefac(3)\n for ell in ells[4:]:\n # print ell, P2l[ell-1], P2l[ell-2]\n a = np.sqrt((4 * ell**2 - 1.) / (ell**2 - 4))\n b = cost * P2l_norm[ell - 1]\n c = np.sqrt(((ell - 1.)**2 - 4) /\n (4 * (ell - 1.)**2 - 1)) * P2l_norm[ell - 2]\n # print a,b,c\n P2l_norm[ell] = a * (b - c)\n # print ell, P2l_norm[ell], P2l_norm_prefac(ell)\n P2l[ell] = P2l_norm[ell] / P2l_norm_prefac(ell)\n return P2l","def get_norm(self, l):\n return self._W.norm(l)","def get_norm(self, l):\n return self._W.norm(l)","def norm(point):\n return np.sqrt(norm2(point))","def l2norm(X):\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n X = torch.div(X, norm)\n return X","def l2norm(X):\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n X = torch.div(X, norm)\n return X","def l2norm(X):\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n X = torch.div(X, norm)\n return X","def sqnorm(v):\n res = 0\n for elt in v:\n for coef in elt:\n res += coef ** 2\n return res","def l2norm(X): \n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\n a = norm.expand_as(X) + 1e-8\n X = torch.div(X, a) \n return X","def L2_dists_vectorized(x, y):\n dists = -2 * np.matmul(x, y.T)\n dists += np.sum(x**2, axis=1)[:, np.newaxis]\n dists += np.sum(y**2, axis=1)\n return np.sqrt(dists)","def norm(self):\n norm = self.scalarProduct(self) ** 0.5\n return norm","def normalize_l2(x):\n return x / (npla.norm(x))","def l2norm(X, dim=-1, eps=1e-8):\r\n norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps\r\n X = torch.div(X, norm)\r\n return X","def normsq(self):\n return abs(sum(self._ar * self._ar))","def dist_squared(self, vec2):\n if type(vec2) != Vector:\n raise TypeError(\"Not a vector\")\n\n return (self - vec2) * (self - vec2)","def norm(self):\n if self._coord_format != constants.MatrixCoordinateDefault:\n self._logger.error(\"invalid coordinate format\")\n raise NotImplementedError(\"invalid coordinate format\")\n\n if self._dtype == complex:\n def __map(m):\n return m[2].real ** 2 + m[2].imag ** 2\n else:\n def __map(m):\n return m[2] ** 2\n\n n = self._data.map(\n __map\n ).reduce(\n lambda a, b: a + b\n )\n\n return math.sqrt(n)","def squared_norm(self, x_tensors=None):\n if x_tensors is None:\n x_tensors = self.x_tensors()\n\n return numpy.sum([squared_L2_norm(t) for t in x_tensors])","def matrix_L2(l, omega, S, cn):\n zt = omega * S / cn['t']\n L = np.array(wN2(l, zt), wN4(l, zt))\n return L.T","def l2p(x, y):\n \n return square_size[0]*x, square_size[1]*y","def norm(alpha, F):\n return inner_product(alpha, F, alpha)","def norm(self) -> float:\n return self.squared_norm()**0.5","def norm(self):\n return np.sqrt(np.dot(self._data, self._data))","def l2norm_1d(new, old):\n\tdiff = 0\n\tnx = len(new)\n\tfor i, n in enumerate(new):\n\t\tif n:\n\t\t\tdiff += ((n - old[i])/n)**2\n\tnorm = scipy.sqrt(diff/nx)\n\treturn norm","def l2_distance(v1, v2):\n\treturn np.linalg.norm(np.array(v1) - np.array(v2))"],"string":"[\n \"def inner_product_to_normalized_L2_square(matrix):\\n\\n length = matrix.shape[0]\\n norm = np.divide(1, np.sqrt(l2_square_from_inner_product(matrix)))\\n\\n normalized_inner_product = np.multiply(np.multiply(np.reshape(norm, [length, 1]), matrix),\\n np.reshape(norm, [1, length]))\\n return 2 - 2 * normalized_inner_product\",\n \"def inner_product_to_L2_square(matrix):\\n\\n length = matrix.shape[0]\\n squared_norm = np.reshape(np.diag(matrix), (length, 1))\\n\\n return squared_norm + np.transpose(squared_norm) - 2 * matrix\",\n \"def L2norm(m):\\n return np.sqrt(np.sum(m**2))\",\n \"def L2_norm(self):\\n analyticals = self.analytical(self.x_values, self.C, self.D)\\n error = analyticals - self.numerical\\n self.L2 = np.sqrt((1/self.gp)*np.sum(error**2))\",\n \"def norm_L2(u):\\n return norm_l2(u)/sqrt(float(u.size))\",\n \"def l2_norm(params):\\n flattened, _ = flatten(params)\\n return np.dot(flattened, flattened)\",\n \"def l2(vec):\\n return np.linalg.norm(vec)\",\n \"def norm_l2(v):\\n return np.sqrt((v**2).sum())\",\n \"def l2_norm(self):\\n return (self.x**2 + self.y**2 + self.z**2)**0.5\",\n \"def norm_l2(u):\\n return linalg.norm(u.ravel())\",\n \"def l2_norm(v):\\n res = 0\\n for e in v:\\n res += e * e\\n return math.sqrt(res)\",\n \"def _l2_norm_squared(self, z, theta):\\n norms = np.zeros(shape=(len(z), self.n_states))\\n\\n for j in range(self.n_states):\\n diff = theta[:, j] - z # ndarray of shape (n_samples, n_states) with differences\\n norms[:, j] = np.square(np.linalg.norm(diff, axis=1)) # squared state conditional l2 norms\\n\\n return norms # squared l2 norm.\",\n \"def squared_norm(self) -> float:\\n return self.__real**2 + self.__img[0]**2 + self.__img[1]**2 + self.__img[2]**2\",\n \"def tree_l2_norm(tree_x, squared=False):\\n squared_tree = tree_map(jnp.square, tree_x)\\n sqnorm = tree_sum(squared_tree)\\n if squared:\\n return sqnorm\\n else:\\n return jnp.sqrt(sqnorm)\",\n \"def norm_with_l2(original_mat):\\n normed_mat = np.zeros(original_mat.shape, dtype=np.float32)\\n if len(original_mat.shape) == 2:\\n for ind_r in range(original_mat.shape[0]):\\n a = np.square(original_mat[ind_r]*1.0)\\n b = np.sum(a)\\n c = np.sqrt(b)\\n normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / c\\n # normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / np.sqrt(np.sum(np.square(original_mat[ind_r])*1.0))\\n return normed_mat\",\n \"def calculate_error_l2_norm(self, dY):\\n solutions = []\\n norm = 0.\\n for mi in range(len(self._meshes)):\\n for ei in range(len(self._meshes[mi].elements)):\\n e = self._meshes[mi].elements[ei]\\n # change this to gauss points:\\n x_vals, w = p_roots(20)\\n norm_e_squared = 0.\\n for i, x in enumerate(x_vals):\\n norm_e_squared += w[i] * \\\\\\n self.get_sol_value(mi, ei, dY, x,\\n count_lift=False)**2\\n norm_e_squared *= e.jacobian\\n norm += norm_e_squared\\n return sqrt(norm)\",\n \"def norm2(self):\\n\\t\\treturn self.x ** 2 + self.y ** 2 + self.z ** 2\",\n \"def norm2d(self) -> float:\\n\\n return self.v2ddict.norm2d()\",\n \"def l2_norm(vec_or_matrix):\\n if len(vec_or_matrix.shape) == 1:\\n # linear vector\\n return vec_or_matrix / np.linalg.norm(vec_or_matrix)\\n elif len(vec_or_matrix.shape) == 2:\\n return vec_or_matrix / np.linalg.norm(vec_or_matrix, axis=1, ord=2)[:, np.newaxis]\\n else:\\n raise ValueError('Wrong number of dimensions, 1 or 2 is supported, not %i.' % len(vec_or_matrix.shape))\",\n \"def l2_norm(pattern):\\n return np.linalg.norm(pattern)\",\n \"def l2(v, axis=None):\\n length = v.shape[0]\\n return np.sqrt(np.sum(np.square(v), axis=axis) / length)\",\n \"def l2(weights):\\n\\treturn np.sqrt(np.sum(weights * weights))\",\n \"def _l2s(self, params):\\n return [np.linalg.norm(param) for param in params]\",\n \"def norm_sqr(x):\\n return inner_prod(x, x)[0]\",\n \"def compute_L2_normalization(xx):\\r\\n\\treturn np.sum(xx ** 2, axis=1)\",\n \"def test_scale_features_L2_norm(self):\\n data = array([[0.564, 20.661], [-18.512, 41.168], [-0.009, 20.440]])\\n cdata = CData(data)\\n\\n # correct answer computed in Mathematica\\n # TODO: can we compute the right answer in Python?\\n answer = array([[0.0304526, 0.409996], [-0.999536, 0.816936], [-0.000485946, 0.40561]])\\n\\n # perform L2 normalization and check answer\\n cdata.scale_features('L2 norm')\\n self.assertTrue(allclose(cdata.data, answer))\",\n \"def norm2(point):\\n return np.sum(point**2, -1)\",\n \"def l2_norm(self, input):\\n input_size = input.size()\\n buffer = torch.pow(input, 2)\\n normp = torch.sum(buffer, 1).add_(1e-10)\\n norm = torch.sqrt(normp)\\n _output = torch.div(input, norm.view(-1, 1).expand_as(input))\\n output = _output.view(input_size)\\n\\n return output\",\n \"def sigma_norm2( self):\\n return self._sigma2\",\n \"def _l2_normalize(x, axis=None, eps=1e-12):\\n return x * jax.lax.rsqrt((x * x).sum(axis=axis, keepdims=True) + eps)\",\n \"def L2norm(self, array):\\n norm = torch.sqrt(torch.sum(array * array))\\n return norm\",\n \"def norm2(self):\\n return getattr(self, self.norm2_name)\",\n \"def norm(x):\\n return inner_prod(x, x)[0].sqrt_()\",\n \"def squared_norm(self, vector, base_point=None):\\n sq_norm = self.inner_product(vector, vector, base_point)\\n return gs.real(sq_norm)\",\n \"def two_norm(v):\\n return math.sqrt(dot_product(v, v))\",\n \"def l2norm(array1,array2):\\r\\n tot = np.sum(np.abs(array1)**2)\\r\\n return np.sqrt(np.sum(np.abs(array1-array2)**2)/tot)\",\n \"def norm(self):\\n\\t\\treturn math.sqrt(self.norm2())\",\n \"def norm(self):\\n mag_squared = self._sum_of_squares()\\n return sqrt(mag_squared)\",\n \"def normF2(X):\\r\\n # pass\\r\\n if X.shape[0]*X.shape[1] == 0:\\r\\n return 0\\r\\n return LA.norm(X, 'fro')**2\",\n \"def fast_2_norm(A):\\n v = np.random.rand(A.shape[1], 1)\\n return la.norm(A.dot(v))\",\n \"def norm2_r(self, a: np.ndarray) -> float:\\n return a.T @ a\",\n \"def l2norm(X):\\n norm = np.linalg.norm(X, axis=1, keepdims=True)\\n return 1.0 * X / norm\",\n \"def squared_norm(self, vector, base_point=None):\\n sq_norm = self.embedding_metric.squared_norm(vector)\\n return sq_norm\",\n \"def square_norm(x):\\n return np.linalg.norm(x) ** 2\",\n \"def normSq(self):\\n\\t\\treturn self.x*self.x+self.y*self.y\",\n \"def L2_norm(x, axis=-1):\\n return keras.backend.l2_normalize(x, axis=axis)\",\n \"def l2norm_(X, Xstar):\\n return cdist(X, Xstar)\",\n \"def normsq(self):\\n return sum(x**2 for x in self.data)\",\n \"def norm2d(w_in):\\n return nn.BatchNorm2d(num_features=w_in, eps=cfg.BN.EPS, momentum=cfg.BN.MOM)\",\n \"def L2_normalize(xx):\\r\\n\\tZx = compute_L2_normalization(xx)\\r\\n\\treturn xx / np.sqrt(Zx[:, np.newaxis])\",\n \"def l2_normalize(data, axis=-1, eps=1e-6):\\n ret = data / (np.linalg.norm(data, axis=axis, keepdims=True) + eps)\\n return ret\",\n \"def norm(self):\\n\\t\\treturn np.sqrt(self.normSq())\",\n \"def vec_2norm (x):\\n return math.sqrt (sum ([x_i**2 for x_i in x]))\",\n \"def normr(Mat):\\n B = normalize(Mat, norm='l2', axis=1)\\n return B\",\n \"def norm(self):\\n return sqrt(self.dot(self))\",\n \"def norm(self):\\n return math.sqrt(self.dotProduct(self))\",\n \"def squared_norm(self, vector, base_point=None):\\n args = {\\n \\\"vector\\\": vector,\\n \\\"base_point\\\": base_point,\\n }\\n sq_norms = self._iterate_over_factors(\\\"squared_norm\\\", args)\\n return sum(sq_norms)\",\n \"def norm(x):\\n return np.sqrt(norm2(x))\",\n \"def get_sqrt_2():\\n return 1.41421356\",\n \"def l2(x1, x2):\\n return np.sqrt((x1 - x2)**2)\",\n \"def l2_regularization_penalty(self):\\n return self.l2 * (np.linalg.norm(self.weights)**2)\",\n \"def l2_norm_batch(pattern_stack):\\n\\n return np.linalg.norm(pattern_stack, axis=0)\",\n \"def squaredDistance(vec1, vec2):\\n return (distance.euclidean(vec1, vec2))**2\",\n \"def normalize_l2norm(data,tol=0):\\n data_sqrt=np.sqrt(np.square(data).sum(axis=1))\\n data_sqrt.shape=(data_sqrt.shape[0],1)\\n #tol=0#1e-8\\n data=data/(data_sqrt+tol)\\n return data\",\n \"def L2_dists(x, y):\\n #print(x.shape)\\n #print(y.shape)\\n dists = -2 * np.matmul(x, y.T)\\n dists += np.sum(x**2)[np.newaxis]\\n dists += np.sum(y**2)\\n return np.sqrt(dists)\",\n \"def L2_func(x):\\n return K.expand_dims(K.sqrt(K.sum(K.pow(x,2), axis=1)))\",\n \"def l2_normalize(data, eps, axis=None):\\n return cpp.nn.l2_normalize(data, eps, axis)\",\n \"def get_L(self, X):\\n if issparse(X):\\n return slinalg.norm(X, axis=1) ** 2\\n else:\\n return norm(X, axis=1) ** 2\",\n \"def getNorm(self, norm=lambda l: (sum(map(lambda x: x ** 2, l))) ** (1 / 2)):\\n return norm(self.components)\",\n \"def getNormLaplacian(W):\\n\\td=[np.sum(row) for row in W]\\n\\tD=np.diag(d)\\n\\tL=D-W\\n\\t#Dn=D^(-1/2)\\n\\tDn=np.power(np.linalg.matrix_power(D,-1),0.5)\\n\\tLbar=np.dot(np.dot(Dn,L),Dn)\\n\\treturn Lbar\",\n \"def project_L2(w, l):\\n return w * min(1, 1 / (l ** (1 / 2.0) * np.linalg.norm(w, 2)))\",\n \"def normalisation_l2(x):\\n res = np.zeros(x.shape)\\n print(x.shape)\\n for i in range(x.shape[0]):\\n res[i] = x[i]/(np.linalg.norm(x[i],2)+1e-5)\\n std = res.std()\\n mean = res.mean()\\n print(\\\"normalisation done\\\")\\n return(mean,std,res)\",\n \"def getL2Error(self,exactSolution):\\n value = 0\\n error = np.array(self.solution)-np.array([exactSolution(x) for x in self.triangulation.points])\\n for ele,triPoints in enumerate(self.triangulation.simplices):\\n transformMatrix,translateVector = self.calculateTransform(ele)\\n determinant = abs(np.linalg.det(transformMatrix))\\n #Last vector is the precalculated integral of the basisfunctions over a reference element\\n value+=determinant*np.dot(error[triPoints]**2,np.array([1/6.,1/3.,1/3.]))\\n return(math.sqrt(value))\",\n \"def l21_norm_bound(X, Y, loss='square'):\\n if loss.lower() == 'square':\\n # In this case max_tau := 2/n * max(||[X^T * Y]s||_2)\\n # First compute the 2-norm of each row of X^T * Y\\n norm2 = map(lambda x: np.linalg.norm(x, ord=2), X.T.dot(Y))\\n return np.max(norm2) * (2.0/X.shape[0])\\n else:\\n print('Only square loss implemented so far.')\\n sys.exit(-1)\",\n \"def norm(self):\\n C = np.prod([F.T @ F for F in self.factors], axis=0)\\n return np.sqrt(np.sum(C))\",\n \"def calculate_dist_mat_2(A: np.ndarray, B: np.array, norm: int) -> np.ndarray:\\n kwargs = {'p': norm}\\n dist_mat = cdist(A, B, metric='minkowski', **kwargs)\\n return dist_mat\",\n \"def P2l_rec_norm(ells, cost):\\n P22 = 3. * (1. - cost**2)\\n P23 = 15. * cost * (1. - cost**2)\\n P2l = np.zeros(len(ells))\\n P2l[0] = 0.\\n P2l[1] = 0.\\n P2l[2] = P22\\n P2l[3] = P23\\n P2l_norm = np.copy(P2l)\\n P2l_norm[2] *= P2l_norm_prefac(2)\\n P2l_norm[3] *= P2l_norm_prefac(3)\\n for ell in ells[4:]:\\n # print ell, P2l[ell-1], P2l[ell-2]\\n a = np.sqrt((4 * ell**2 - 1.) / (ell**2 - 4))\\n b = cost * P2l_norm[ell - 1]\\n c = np.sqrt(((ell - 1.)**2 - 4) /\\n (4 * (ell - 1.)**2 - 1)) * P2l_norm[ell - 2]\\n # print a,b,c\\n P2l_norm[ell] = a * (b - c)\\n # print ell, P2l_norm[ell], P2l_norm_prefac(ell)\\n P2l[ell] = P2l_norm[ell] / P2l_norm_prefac(ell)\\n return P2l\",\n \"def get_norm(self, l):\\n return self._W.norm(l)\",\n \"def get_norm(self, l):\\n return self._W.norm(l)\",\n \"def norm(point):\\n return np.sqrt(norm2(point))\",\n \"def l2norm(X):\\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n X = torch.div(X, norm)\\n return X\",\n \"def l2norm(X):\\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n X = torch.div(X, norm)\\n return X\",\n \"def l2norm(X):\\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n X = torch.div(X, norm)\\n return X\",\n \"def sqnorm(v):\\n res = 0\\n for elt in v:\\n for coef in elt:\\n res += coef ** 2\\n return res\",\n \"def l2norm(X): \\n norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt()\\n a = norm.expand_as(X) + 1e-8\\n X = torch.div(X, a) \\n return X\",\n \"def L2_dists_vectorized(x, y):\\n dists = -2 * np.matmul(x, y.T)\\n dists += np.sum(x**2, axis=1)[:, np.newaxis]\\n dists += np.sum(y**2, axis=1)\\n return np.sqrt(dists)\",\n \"def norm(self):\\n norm = self.scalarProduct(self) ** 0.5\\n return norm\",\n \"def normalize_l2(x):\\n return x / (npla.norm(x))\",\n \"def l2norm(X, dim=-1, eps=1e-8):\\r\\n norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps\\r\\n X = torch.div(X, norm)\\r\\n return X\",\n \"def normsq(self):\\n return abs(sum(self._ar * self._ar))\",\n \"def dist_squared(self, vec2):\\n if type(vec2) != Vector:\\n raise TypeError(\\\"Not a vector\\\")\\n\\n return (self - vec2) * (self - vec2)\",\n \"def norm(self):\\n if self._coord_format != constants.MatrixCoordinateDefault:\\n self._logger.error(\\\"invalid coordinate format\\\")\\n raise NotImplementedError(\\\"invalid coordinate format\\\")\\n\\n if self._dtype == complex:\\n def __map(m):\\n return m[2].real ** 2 + m[2].imag ** 2\\n else:\\n def __map(m):\\n return m[2] ** 2\\n\\n n = self._data.map(\\n __map\\n ).reduce(\\n lambda a, b: a + b\\n )\\n\\n return math.sqrt(n)\",\n \"def squared_norm(self, x_tensors=None):\\n if x_tensors is None:\\n x_tensors = self.x_tensors()\\n\\n return numpy.sum([squared_L2_norm(t) for t in x_tensors])\",\n \"def matrix_L2(l, omega, S, cn):\\n zt = omega * S / cn['t']\\n L = np.array(wN2(l, zt), wN4(l, zt))\\n return L.T\",\n \"def l2p(x, y):\\n \\n return square_size[0]*x, square_size[1]*y\",\n \"def norm(alpha, F):\\n return inner_product(alpha, F, alpha)\",\n \"def norm(self) -> float:\\n return self.squared_norm()**0.5\",\n \"def norm(self):\\n return np.sqrt(np.dot(self._data, self._data))\",\n \"def l2norm_1d(new, old):\\n\\tdiff = 0\\n\\tnx = len(new)\\n\\tfor i, n in enumerate(new):\\n\\t\\tif n:\\n\\t\\t\\tdiff += ((n - old[i])/n)**2\\n\\tnorm = scipy.sqrt(diff/nx)\\n\\treturn norm\",\n \"def l2_distance(v1, v2):\\n\\treturn np.linalg.norm(np.array(v1) - np.array(v2))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8038655","0.79935986","0.7481027","0.7333756","0.71862626","0.7168722","0.71422154","0.71272767","0.7005889","0.6986283","0.6936558","0.689807","0.6859919","0.68299115","0.6818152","0.678622","0.67799747","0.6730021","0.67066455","0.6701476","0.6701306","0.6688216","0.66403055","0.6579155","0.6573765","0.6558734","0.6543347","0.6512711","0.6489012","0.64723676","0.64642733","0.64614713","0.6459877","0.6446045","0.6443651","0.63937837","0.6317596","0.628092","0.6263609","0.6259707","0.6255387","0.6230586","0.6226179","0.6217083","0.62118757","0.6187874","0.6169813","0.61610216","0.6136922","0.6123859","0.609898","0.6096916","0.6053245","0.60363936","0.60154194","0.601107","0.59963846","0.59841037","0.5964484","0.59570616","0.5944093","0.5933876","0.5932054","0.5931658","0.5924176","0.5907942","0.59000313","0.5875353","0.5841822","0.5826781","0.58147866","0.58085686","0.58085084","0.5793662","0.578605","0.5772651","0.5759394","0.57579535","0.57579535","0.57414836","0.5711277","0.5711277","0.5711277","0.5704038","0.5691994","0.5691083","0.5689474","0.5684701","0.56827873","0.568219","0.5681298","0.5679438","0.5676075","0.56741947","0.5667119","0.5660092","0.5649604","0.56457275","0.5636244","0.56249326"],"string":"[\n \"0.8038655\",\n \"0.79935986\",\n \"0.7481027\",\n \"0.7333756\",\n \"0.71862626\",\n \"0.7168722\",\n \"0.71422154\",\n \"0.71272767\",\n \"0.7005889\",\n \"0.6986283\",\n \"0.6936558\",\n \"0.689807\",\n \"0.6859919\",\n \"0.68299115\",\n \"0.6818152\",\n \"0.678622\",\n \"0.67799747\",\n \"0.6730021\",\n \"0.67066455\",\n \"0.6701476\",\n \"0.6701306\",\n \"0.6688216\",\n \"0.66403055\",\n \"0.6579155\",\n \"0.6573765\",\n \"0.6558734\",\n \"0.6543347\",\n \"0.6512711\",\n \"0.6489012\",\n \"0.64723676\",\n \"0.64642733\",\n \"0.64614713\",\n \"0.6459877\",\n \"0.6446045\",\n \"0.6443651\",\n \"0.63937837\",\n \"0.6317596\",\n \"0.628092\",\n \"0.6263609\",\n \"0.6259707\",\n \"0.6255387\",\n \"0.6230586\",\n \"0.6226179\",\n \"0.6217083\",\n \"0.62118757\",\n \"0.6187874\",\n \"0.6169813\",\n \"0.61610216\",\n \"0.6136922\",\n \"0.6123859\",\n \"0.609898\",\n \"0.6096916\",\n \"0.6053245\",\n \"0.60363936\",\n \"0.60154194\",\n \"0.601107\",\n \"0.59963846\",\n \"0.59841037\",\n \"0.5964484\",\n \"0.59570616\",\n \"0.5944093\",\n \"0.5933876\",\n \"0.5932054\",\n \"0.5931658\",\n \"0.5924176\",\n \"0.5907942\",\n \"0.59000313\",\n \"0.5875353\",\n \"0.5841822\",\n \"0.5826781\",\n \"0.58147866\",\n \"0.58085686\",\n \"0.58085084\",\n \"0.5793662\",\n \"0.578605\",\n \"0.5772651\",\n \"0.5759394\",\n \"0.57579535\",\n \"0.57579535\",\n \"0.57414836\",\n \"0.5711277\",\n \"0.5711277\",\n \"0.5711277\",\n \"0.5704038\",\n \"0.5691994\",\n \"0.5691083\",\n \"0.5689474\",\n \"0.5684701\",\n \"0.56827873\",\n \"0.568219\",\n \"0.5681298\",\n \"0.5679438\",\n \"0.5676075\",\n \"0.56741947\",\n \"0.5667119\",\n \"0.5660092\",\n \"0.5649604\",\n \"0.56457275\",\n \"0.5636244\",\n \"0.56249326\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7382337"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":881,"cells":{"query":{"kind":"string","value":"Turns the inner product matrix into the |pattern1 pattern2|_{2}^2 matrix. The matrix has to be a square matrix"},"document":{"kind":"string","value":"def inner_product_to_L2_square(matrix):\n\n length = matrix.shape[0]\n squared_norm = np.reshape(np.diag(matrix), (length, 1))\n\n return squared_norm + np.transpose(squared_norm) - 2 * matrix"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def inner_product(pattern_one, pattern_two):\n\n return np.sum(np.multiply(pattern_one, pattern_two))","def inner_product_batch(pattern_stack_one, pattern_num_one, pattern_stack_two, pattern_num_two):\n\n \"\"\"\n Notice that the two stacks can be different. So we can not deduce the lower triangular pattern from the \n other half.\n \"\"\"\n holder = np.zeros((pattern_num_one, pattern_num_two))\n for l in range(pattern_num_one):\n for m in range(pattern_num_two):\n holder[l, m] = np.sum(np.multiply(pattern_stack_one[l], pattern_stack_two[m]))\n\n return holder","def l2_square_from_inner_product(matrix):\n return np.diag(matrix)","def matrix_subs(matrix_2x2, point):\n arr = []\n for el in matrix_2x2:\n arr.append(el.subs(x1, point[0]).subs(x2, point[1]))\n \n M = Matrix([[arr[0], arr[1]], [arr[2], arr[3]]])\n\n return M","def matrix_mult(m1, m2):\n\ttemp = []\n\tfor i in range(len(m1)):\n\t\te = []\n\t\tfor j in range(len(m2[0])):\n\t\t\te.append(row_times_column(m1,i,m2,j))\n\t\ttemp.append(e)\n\treturn temp","def python_nonsquare_matrix_mult(matrix):\n\n transposed_matrix = np.zeros([matrix.shape[1],matrix.shape[0]])\n start = time.time()\n # for i in range(matrix.shape[0]):\n # for j in range(matrix.shape[1]):\n # transposed_matrix[j,i] = matrix[i,j]\n\n transposed_matrix = np.transpose(matrix)\n product = matrix.dot(transposed_matrix)\n\n # transposed_matrix = np.transpose(matrix)\n end = time.time()-start\n\n # print(\"Python Golden Transpose: %s\" % product)\n # print('python transpose time: %.2E' % end)\n return [product, end]","def inner_product_to_normalized_L2_square(matrix):\n\n length = matrix.shape[0]\n norm = np.divide(1, np.sqrt(l2_square_from_inner_product(matrix)))\n\n normalized_inner_product = np.multiply(np.multiply(np.reshape(norm, [length, 1]), matrix),\n np.reshape(norm, [1, length]))\n return 2 - 2 * normalized_inner_product","def mat_mul(mat1, mat2):\n\n rows1 = len(mat1)\n cols1 = len(mat1[0])\n rows2 = len(mat2)\n cols2 = len(mat2[0])\n\n if cols1 != rows2:\n return None\n else:\n new_matrix = []\n for x in range(rows1):\n aux_row = []\n for y in range(cols2):\n aux_sum = []\n for z in range(cols1):\n aux_sum.append(mat1[x][z] * mat2[z][y])\n aux_row.append(sum(aux_sum))\n new_matrix.append(aux_row)\n\n return new_matrix","def inner_product(state_1, state_2):\n return numpy.dot(state_1.conjugate(), state_2)","def _kronecker_product(mat1: tf.Tensor, mat2: tf.Tensor) -> tf.Tensor:\n m1, n1 = mat1.get_shape().as_list()\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\n m2, n2 = mat2.get_shape().as_list()\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])","def __mul__(self, other):\n #\n # TODO - your code here\n #\n final_matrix = []\n for i in range(self.h):\n temp_row = []\n for j in range(other.w):\n # take dot-product of row of\n # matrix in 1st arg with col of\n # matrix in 2nd arg\n temp_row.append(dot_product(get_row(self.g, i), get_col(other.g, j)))\n final_matrix.append(temp_row)\n return Matrix(final_matrix)\n # TODO - your code here","def Mxform(x1,y1,x2,y2):\n return Jones.toMueller([[np.dot(x2,x1), np.dot(x2, y1)], [np.dot(y2,x1), np.dot(y2,y1)]])","def matrix_mult(m1, m2):\n pass","def rotate2D(self, matrix) -> None:\n N = len(matrix)\n\n # In case of N is odd, the innermost square belt is just one cell, no need of rotating.\n for i in range(0,int(N/2)): # outer loop for each square belt\t\t\t\n for j in range(i,N-i-1): # N-i group in the i-th square belt\n #print(i,j)\n tmp = matrix[i][j]\n matrix[i][j] = matrix[N-j-1][i]\n matrix[N-j-1][i] = matrix[N-i-1][N-j-1]\n matrix[N-i-1][N-j-1] = matrix[j][N-i-1]\n matrix[j][N-i-1] = tmp\n #print(matrix)","def matrixMul(self, matrix, matrix2):\n matrix0 = matrix[:]\n matrix[0] = matrix0[0] * matrix2[0] + matrix0[2]*matrix2[1] # + matrix0[4]*0\n matrix[1] = matrix0[1] * matrix2[0] + matrix0[3]*matrix2[1] # + matrix0[5]*0\n matrix[2] = matrix0[0] * matrix2[2] + matrix0[2]*matrix2[3] # + matrix0[4]*0\n matrix[3] = matrix0[1] * matrix2[2] + matrix0[3]*matrix2[3] # + matrix0[5]*0\n matrix[4] = matrix0[0] * matrix2[4] + matrix0[2]*matrix2[5] + matrix0[4]\n matrix[5] = matrix0[1] * matrix2[4] + matrix0[3]*matrix2[5] + matrix0[5]","def _z2matmul(self, left, right):\n prod = np.mod(np.dot(left, right), 2)\n return prod","def form_square_block_matrix(mat1,mat2):\n if mat1.cols==1:\n mat3 = mp.matrix(mat1.rows+mat2.rows,1)\n mat3[:mat1.rows] = mat1[:]\n mat3[mat1.rows:mat3.rows] = mat2[:]\n else:\n mat3 = mp.matrix(mat1.rows+mat2.rows, mat1.rows+mat2.rows)\n mat3[:mat1.rows,:mat1.rows] = mat1[:,:]\n mat3[mat1.rows:mat3.rows,mat1.rows:mat3.rows] = mat2[:,:]\n return mat3","def outer_product(x,y):\n\n return x[:,0]*y[:,1] -x[:,1]*y[:,0]","def mul(self,mat1,mat2):\n if(isinstance(mat2,int)==True):\n result = [[mat1[i][j] * mat2 for j in range(len(mat1[0]))] for i in range(len(mat1))]\n self.out = result\n return self.out\n elif(len(mat1[0])==len(mat2)):\n result = [[sum(a*b for a,b in zip(i,j)) for j in zip(*mat2)] for i in mat1]\n self.out = result\n return self.out","def matrix_product(mat1: List[List[int]], mat2: List[List[int]]):\n if len(mat1) == 0 or len(mat2) == 0:\n raise ValueError(\"One of matrix is empty\")\n n, k1 = len(mat1), len(mat1[0])\n k2, m = len(mat2), len(mat2[0])\n if k1 != k2:\n raise ValueError(\n f\"Can't multiply two matrices with shapes {n}x{k1} and {k2}x{m}\"\n )\n mat2_t = matrix_transpose(mat2)\n return [[vec_product(vec1, vec2) for vec2 in mat2_t] for vec1 in mat1]","def kronecker_product(mat1, mat2):\n m1, n1 = mat1.get_shape().as_list()\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\n m2, n2 = mat2.get_shape().as_list()\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])","def mmultiply(self, matrix):\n try:\n result_matrix = [[0 for row in range(len(self.matrix))] for col in range(len(matrix[0]))]\n for i in range(len(self.matrix)):\n for j in range(len(matrix[0])):\n for k in range(len(matrix)):\n result_matrix[i][j] += self.matrix[i][k] * matrix[k][j]\n self.matrix = result_matrix\n except IndexError:\n pass\n pass","def StrassenMatrixM(a, b):\r\n if len(a) != 2 or len(a[0]) != 2 or len(b) != 2 or len(b[0]) != 2:\r\n raise Exception('Matrices should be 2x2!')\r\n print(a[0][0] * b[0][1] + a[0][1] * b[1][1])\r\n matrix = [[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],\r\n [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]]]\r\n\r\n return matrix","def matrix_mult_matrix(matrix_a, matrix_b):\n m = len(matrix_a)\n n = len(matrix_b)\n result = []\n matrix_b_t = transpose_matrix(matrix_b)\n for i in xrange(m):\n row = []\n\tfor j in xrange(m):\n row.append(dot_product(matrix_a[i], matrix_b_t[j]))\n\tresult.append(row)\n return result","def __mul__(self,m):\n if type(m) != Matrix:\n raise TypeError('The second argument is not a matrix lol')\n if self.ncols != m.nrows:\n raise ValueError('matrix dot argument has incorrect number of rows')\n new = Matrix(self.nrows,m.ncols)\n columns = m.getCols()\n rowindex = 0\n colindex = 0 \n for row in self.matrix:\n colindex = 0 \n for col in columns:\n summ = 0\n for i,j in zip(row,col):\n summ+= i*j \n new.matrix[rowindex][colindex] = summ\n print new.matrix\n colindex += 1 \n rowindex+=1\n return new","def matrix_multiplication_loop(x_matrix, y_matrix):\n result = []\n for i, row in enumerate(x_matrix):\n row_vector = []\n for j in range(len(y_matrix[0])):\n product = 0\n for k in range(len(row)):\n product += x_matrix[i][k] * y_matrix[k][j]\n row_vector.append(product)\n result.append(row_vector)\n return result","def prod_mat(self,other):\n [rs,cs],[ro,co] = self.D,other.D\n assert cs == ro, \"tailles incompatibles\"\n return Mat([rs,co], lambda i,j : prod_scal(self.ligne(i),other.col(j)))","def tensor_outer_product(tensor1, tensor2):\n shape_1 = tensor1.shape\n shape_2 = tensor2.shape\n s1 = len(shape_1)\n s2 = len(shape_2)\n \n shape_1 = shape_1 + (1, )*s2\n shape_2 = (1, )*s1 + shape_2\n return np.reshape(tensor1, shape_1) * np.reshape(tensor2, shape_2)","def mat_mul(mat1, mat2):\n\n if len(mat1[0]) == len(mat2):\n\n mat2 = matrix_transpose(mat2)\n response = []\n\n for row in range(len(mat1)):\n response.append(\n [\n sum(dot_product(mat1[row], mat2[column]))\n for column in range(len(mat2))\n ]\n )\n\n return response\n\n else:\n return None","def multiply_by_left_matrix(matrix, img):\n first = np.inner(matrix[0], img)\n second = np.inner(matrix[1], img)\n third = np.inner(matrix[2], img)\n\n res = np.dstack((first, second, third))\n return res","def __mul__(self, other):\n # \n # TODO - your code here\n #\n \n result = [];\n row_result = [];\n product = 0;\n \n if(self.w != other.h):\n raise(ValueError, \"Matrices can not multiply for their dimesion doesn't match\"); \n \n for row in self.g:\n row_result = [];\n for j in range(other.w):\n product = dot_product(row,other.get_column(j));\n row_result.append(product);\n result.append(row_result);\n \n return Matrix(result);","def square_matrix_multiply(a, b):\n n = len(a)\n c = [[0]*n for _ in range(n)]\n for i in range(n):\n for j in range(n):\n sm = 0\n for k in range(n):\n sm += (a[i][k] * b[k][j])\n c[i][j] = sm\n\n return c","def matrix_mult(A,B):\n\n m = len(A)\n p = len(B)\n n = len(B[0])\n AB = []\n for i in range(m):\n AB.append([])\n for j in range(n):\n total = 0\n for k in range(p):\n total += A[i][k] * B[k][j]\n AB[i].append(total)\n return AB","def solve_2x2(matrix: FieldMatrix,\n rhs: Fields) -> OutputFields:\n _validate_matrix_shape(matrix, (2, 2))\n\n a, b = matrix[0]\n c, d = matrix[1]\n e, f = rhs\n\n inv_factor = det_2x2(matrix)\n\n return [\n tf.nest.map_structure(tf.math.divide_no_nan, det_2x2([\n [e, b],\n [f, d],\n ]), inv_factor),\n tf.nest.map_structure(tf.math.divide_no_nan, det_2x2([\n [a, e],\n [c, f],\n ]), inv_factor),\n ]","def outer_product(A, B): \n print(A)\n print(B)\n A_rows = len(A)\n A_columns = len(A[0])\n\n B_rows = len(B)\n B_columns = len(B[0])\n \n if A_columns == 1 and B_rows == 1:\n \n outer_product = []\n\n # multi-line list comprehension for outer product\n [outer_product.append([A[i][0] * B[0][j] for j in range(B_columns)]) \n for i in range(A_rows)]\n\n return outer_product\n\n else:\n print(\"dimensions of vector do not match.\")","def matrix_mult(m1, m2):\n output = []\n for rowIndex, row in enumerate(m1): #go through rows in m1\n new_row = []\n for columnIndex in range(len(m2[0])): #go through indices for each column of m2\n sum = 0\n for index3 in range(len(row)):\n product = m1[rowIndex][index3] * m2[index3][columnIndex]\n sum += product\n new_row.append(sum)\n output.append(new_row)\n return output\n \n \n #output = []\n #first for loop corresponds to the rows of my output matrix and loops through the rows of m1 (enumerate)\n #create an empty new row\n # second for loop, loops through columns of m2\n # create sum variable, initialize it with zero\n # third for loop, multiplies the index of the row in m1 times the index of the column in m2\n # add sum to product and assign this to the sum variable\n # append sum to new row\n # append new row to output\n # return output","def transformation_2d(vertices, kernels=KERNELS):\n\t# calculate the transpose matrix of vertices\n\ttranspose = vertices.transpose()\n\t# insert a row of ones in the transpose matrix's end, then insert the result in 'matrices' list\n\tkernels.append(np.append(transpose, [np.ones(len(transpose[0]))], axis=0))\n\t# multiply matrices into 'kernels' list,\n\t# remove the last row (of ones) and calculate the transpose matrix of the result\n\tfinal_transformation_result = np.delete(np.linalg.multi_dot(kernels), 2, 0).transpose()\n\tKERNELS.clear()\n\treturn final_transformation_result","def matrix_apply_to_2d(data, matrix: np.matrix):\n from scipy import mgrid\n\n cx = data.shape[0] / 2\n cy = data.shape[1] / 2\n\n # Calculate the new coordinates of every point\n grid = mgrid[-cx:data.shape[0]-cx, -cy:data.shape[1]-cy]\n temp = grid.reshape((2, grid.size / 2))\n # Add the fourth dimension (just 1s but needed for the computations)\n newrow = np.ones(grid.size / 2)\n temp = np.vstack([temp, newrow])\n # Use the matrix to calculate the new positions of every point\n temp = np.dot(matrix, temp)\n # Delete the fourth dimension\n temp = np.delete(temp, 2, axis=0)\n temp = np.array(temp)\n grid = np.reshape(temp, (2, data.shape[0], data.shape[1]))\n\n grid[0] += cx\n grid[1] += cy\n\n from scipy.ndimage.interpolation import map_coordinates\n d = map_coordinates(data, grid, order=3)\n\n return d","def cat_matrices2D(mat1, mat2, axis=0):\n res = []\n if axis == 1:\n if len(mat1) == len(mat2):\n for i in mat1:\n for j in mat2:\n res.append(i+[j[0]])\n mat2.remove(j)\n else:\n return None\n else:\n for i in mat2:\n if len(i) == len(mat1[0]):\n res.append(mat1 + [i])\n else:\n return None\n return (res)","def add_matrices2D(mat1, mat2):\n if len(mat1) != len(mat2):\n return None\n if len(mat1[0]) != len(mat2[0]):\n return None\n return [[ele1 + ele2 for ele1, ele2 in zip(row1, row2)]\n for row1, row2 in zip(mat1, mat2)]","def recursive_multiply(a, b):\n if len(a) == 2:\n return naive_multiply(a, b)\n\n a11 = a[0:int(len(a) / 2)]\n for index, row in enumerate(a11):\n a11[index] = row[0:int(len(row) / 2)]\n\n a12 = a[0:int(len(a) / 2)]\n for index, row in enumerate(a12):\n a12[index] = row[int(len(a) / 2):len(a)]\n\n a21 = a[int(len(a) / 2):len(a)]\n for index, row in enumerate(a21):\n a21[index] = row[0:int(len(row) / 2)]\n\n a22 = a[int(len(a) / 2):len(a)]\n for index, row in enumerate(a22):\n a22[index] = row[int(len(a) / 2):len(a)]\n\n b11 = b[0:int(len(b) / 2)]\n for index, row in enumerate(b11):\n b11[index] = row[0:int(len(row) / 2)]\n\n b12 = b[0:int(len(b) / 2)]\n for index, row in enumerate(b12):\n b12[index] = row[int(len(b) / 2):len(b)]\n\n b21 = b[int(len(b) / 2):len(b)]\n for index, row in enumerate(b21):\n b21[index] = row[0:int(len(row) / 2)]\n\n b22 = b[int(len(b) / 2):len(b)]\n for index, row in enumerate(b22):\n b22[index] = row[int(len(b) / 2):len(b)]\n\n c11 = matrix_add(recursive_multiply(a11, b11), recursive_multiply(a12, b21)) # C11 = A11*B11 + A12*B21\n c12 = matrix_add(recursive_multiply(a11, b12), recursive_multiply(a12, b22)) # C12 = A11*B12 + A12*B22\n c21 = matrix_add(recursive_multiply(a21, b11), recursive_multiply(a22, b21)) # C21 = A21*B11 + A22*B21\n c22 = matrix_add(recursive_multiply(a21, b12), recursive_multiply(a22, b22)) # C22 = A21*B12 + A22*B22\n\n # Append c12 to c11\n for row_index, row in enumerate(c11):\n for col_index, col in enumerate(c12):\n row.append(c12[row_index][col_index])\n\n # Append c22 to c21\n for row_index, row in enumerate(c21):\n for col_index, col in enumerate(c12):\n row.append(c22[row_index][col_index])\n\n # Append c21 to c11\n for i in c21:\n c11.append(i)\n\n return c11","def get_molecular_matrix(single_body, two_body):\n x, y = single_body.shape\n func = np.vectorize(round_custom)\n _new_dim = x * y\n single_one_dim = single_body.reshape(_new_dim, 1)\n two_body_two_dim = func(two_body.reshape(_new_dim, _new_dim))\n idx = 0\n x, _ = two_body_two_dim.shape\n while idx < x:\n two_body_two_dim[idx][idx] = round_custom(single_one_dim[idx][0])\n idx += 1\n return two_body_two_dim","def strassen_dot(x, y):\n# X = (A B) Y = (E F)\n# (C D) (G H)\n# \n# A through H -- (n/2 x n/2) matrices\n# \n# let P1 = A(F-H), P2 = (A+B)H, P3 = (C+D)E, P4 = D(G-E), \n# \n# P5 = (A+D)(E+H), P6 = (B-D)(G+H), P7 = (A-C)(E+F)\n# \n# XY = (AE+BG AF+BH) = (P5+P4-P2+P6 P1+P2 )\n# (CE+DG CF+DH) (P3+P4 P1+P5-P3-P7)\n\n n = x.shape[0]\n originalN = n\n \n # base case (n<=8)\n if n <= 8:\n return brute_multiply(x, y)\n \n n_is_power_of_two = (n & (n-1) == 0)\n\n if not n_is_power_of_two:\n # count the closest power of 2 above n\n # and resize x and y matrices\n newN = upper_pow_2(n)\n \n n = newN\n \n nX = np.zeros( (n, n) )\n# mapping x on to nX:\n nX[:originalN, :originalN] = x\n x = nX\n \n nY = np.zeros( (n, n) )\n# mapping y on to nY:\n nY[:originalN, :originalN] = y\n y = nY\n \n \n a = x[ 0:n//2, 0:n//2 ]\n b = x[ 0:n//2, n//2: ]\n c = x[ n//2:, 0:n//2 ]\n d = x[ n//2:, n//2: ]\n\n e = y[ 0:n//2, 0:n//2 ]\n f = y[ 0:n//2, n//2: ]\n g = y[ n//2:, 0:n//2 ]\n h = y[ n//2:, n//2: ]\n \n# let P1 = A(F-H), P2 = (A+B)H, P3 = (C+D)E, P4 = D(G-E), \n# \n# P5 = (A+D)(E+H), P6 = (B-D)(G+H), P7 = (A-C)(E+F)\n \n # recursively compute 7 (SEVEN) products P1 .. P7\n p1 = strassen_dot(a, f-h)\n p2 = strassen_dot(a+b, h)\n p3 = strassen_dot(c+d, e)\n p4 = strassen_dot(d, g-e)\n p5 = strassen_dot(a+d, e+h)\n p6 = strassen_dot(b-d, g+h)\n p7 = strassen_dot(a-c, e+f)\n \n# do the necessary (clever) additions & substractions with P1..P7\n \n# XY = (AE+BG AF+BH) = (P5+P4-P2+P6 P1+P2 )\n# (CE+DG CF+DH) (P3+P4 P1+P5-P3-P7)\n res1 = np.hstack( (p5+p4-p2+p6, p1+p2) )\n res2 = np.hstack( (p3+p4, p1+p5-p3-p7) )\n res = np.vstack( (res1, res2) )\n\n return res[:originalN, :originalN]","def inner_product(alpha, F, beta):\n return np.dot(alpha, np.dot(F, beta))","def matrixMultiplication(firstMatrix, secondMatrix):\n if len(firstMatrix[0]) == len(secondMatrix): # Checks whether the matrices can be multiplied or not or not\n finalMatrix = []\n for y in range(len(firstMatrix)): # 2\n currentMatrix = []\n for i in range(len(secondMatrix[0])):\n currentSum = 0\n for j in range(len(secondMatrix)):\n currentSum += secondMatrix[j][i] * firstMatrix[y][j]\n currentMatrix.append(currentSum)\n print(\"This is my current matrix: \" + str(currentMatrix))\n finalMatrix.append(currentMatrix)\n print(\"This product of the two matrices is :) \" + str(finalMatrix))\n else:\n print(\"This operation cannot be done, make sure the rows of the first matrix is the same as the number of columns in the second matrix\")","def _inner_product_d1(\n self, one_forms_a, one_forms_b, one_forms_bp, areas_bp, inv_surface_metrics_bp\n ):\n one_forms_bp_t = gs.transpose(one_forms_bp, (0, 2, 1))\n\n one_forms_a_t = gs.transpose(one_forms_a, (0, 1, 3, 2))\n xa = one_forms_a_t - one_forms_bp_t\n\n xa_0 = gs.matmul(\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\n gs.matmul(gs.transpose(xa, (0, 1, 3, 2)), one_forms_bp_t)\n - gs.matmul(one_forms_bp, xa),\n )\n\n one_forms_b_t = gs.transpose(one_forms_b, (0, 1, 3, 2))\n xb = one_forms_b_t - one_forms_bp_t\n xb_0 = gs.matmul(\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\n gs.matmul(gs.transpose(xb, (0, 1, 3, 2)), one_forms_bp_t)\n - gs.matmul(one_forms_bp, xb),\n )\n\n return self.d1 * gs.sum(\n gs.einsum(\n \"...bii->...b\",\n gs.matmul(\n xa_0,\n gs.matmul(\n inv_surface_metrics_bp, gs.transpose(xb_0, axes=(0, 1, 3, 2))\n ),\n ),\n )\n * areas_bp\n )","def mult(m1, m2):\n assert np.shape(m1) == (2, 3)\n assert np.shape(m2) == (2, 3)\n\n m1_temp = np.vstack((m1, [0, 0, 1]))\n m2_temp = np.vstack((m2, [0, 0, 1]))\n result = m1_temp * m2_temp\n\n return result[:2, :]","def pair_product(x1, x2):\n return np.multiply(x1, x2)","def Multiply(M1,M2):\r\n M3=[]\r\n w=0\r\n while w<len(M2[0]):\r\n tap=[]\r\n t=0\r\n while t<len(M2):\r\n tap.append(M2[t][w])\r\n t=t+1\r\n M3.append(tap)\r\n w=w+1\r\n M=[]\r\n # Multiplying matrices\r\n k=0\r\n sums=0\r\n while k<len(M1):\r\n j=0\r\n mpy=[]\r\n while j<len(M3):\r\n p=0\r\n sums=0\r\n while p<len(M3[j]):\r\n temp = (M1[k][p])*(M3[j][p])\r\n sums=sums+temp\r\n p=p+1\r\n mpy.append(sums)\r\n j=j+1\r\n M.append(mpy)\r\n k=k+1\r\n return M","def generate_pattern_grid(words1, words2):\n # Convert word lists to integer arrays\n w1, w2 = (\n np.array([[ord(c) for c in w] for w in words], dtype=np.uint8)\n for words in (words1, words2)\n )\n\n if len(w1) == 0 or len(w2) == 0:\n return np.zeros((len(w1), len(w2)), dtype=np.uint8)\n\n # equality_grid[a, b, i, j] represents whether the ith letter\n # of words1[a] equals the jth letter of words2[b]\n equality_grid = np.zeros((len(w1), len(w2), 5, 5), dtype=bool)\n for i, j in it.product(range(5), range(5)):\n equality_grid[:, :, i, j] = np.equal.outer(w1[:, i], w2[:, j])\n\n patterns = np.zeros((len(w1), len(w2)), dtype=np.uint8)\n three_pows = (3**np.arange(5)).astype(np.uint8)\n for i, tp in enumerate(three_pows):\n # This accounts for yellow squares\n patterns[:, :] += tp * equality_grid[:, :, i, :].any(2)\n # This accounts for green squares\n patterns[:, :] += tp * equality_grid[:, :, i, i]\n\n return patterns","def complex_mul2d(a, b):\n op = partial(torch.einsum, \"bixy,ioxy->boxy\")\n return torch.stack([\n op(a[..., 0], b[..., 0]) - op(a[..., 1], b[..., 1]),\n op(a[..., 1], b[..., 0]) + op(a[..., 0], b[..., 1])\n ],\n dim=-1)","def matmul(xs: List[List[float]],\n ys: List[List[float]]) -> List[List[float]]:\n product = []\n for x_row in range(len(xs)):\n row = []\n for y_col in range(len(ys[0])):\n col = [ys[y_row][y_col] for y_row in range(len(ys))]\n row.append(Math.dot(xs[x_row], col))\n product.append(row)\n return product","def multiplicand_2(p):\n m2 = cddr(p) # (m2 m3 ...)\n rests = cdr(m2) # (m3...)\n if isNull(rests):\n return car(m2)\n else:\n restp = convertToPythonList(cdr(rests))\n return make_product_2(car(m2), car(rests), *restp)","def strassen(m1, m2):\n \n if ((m1.shape[0] % 2 == 0) or (m1.shape[0] == 1)):\n n = m1.shape[0] \n else:\n n = m1.shape[0] + 1\n result = np.zeros((n, n), dtype = int)\n \n if (n == 1):\n result[0][0] = m1[0][0] * m2[0][0]\n else:\n new = n//2\n \n a11, a12, a21, a22 = m1[:new, :new], m1[new:, :new], m1[:new, new:], m1[new:, new:]\n b11, b12, b21, b22 = m2[:new, :new], m2[new:, :new], m2[:new, new:], m2[new:, new:]\n \n p1 = strassen(a11, b12 - b22)\n p2 = strassen(a11 + a12, b22)\n p3 = strassen(a21 + a22, b11)\n p4 = strassen(a22, b21 - b11)\n p5 = strassen(a11 + a22, b11 + b22)\n p6 = strassen(a12 - a22, b21 + b22)\n p7 = strassen(a11 - a21, b11 + b12)\n \n result[:new, :new] = p5 + p4 - p2 + p6\n result[new:, :new] = p1 + p2\n result[:new, new:] = p3 + p4 \n result[new:, new:] = p5 + p1 - p3 - p7\n \n return result","def interior_tensor_product(mx, dim_a, dim_b, e=None):\n assert _np.shape(mx) == (dim_a * dim_b, dim_a * dim_b), \"Dimensions do not agree with matrix size\"\n assert _np.shape(e)[0] == _np.shape(e)[1], \"e should be a square matrix\"\n basis_a = matrix_units(dim_a)\n basis_b = matrix_units(dim_b)\n return sum((_np.trace(_np.dot(mx, _np.kron(unit_a, unit_b).T)) * multikron([unit_a, e, unit_b])\n for unit_a in basis_a for unit_b in basis_b))","def cat_matrices2D(mat1, mat2, axis=0):\n mat11 = [[i for i in j] for j in mat1]\n mat22 = [[i for i in j] for j in mat2]\n if axis == 0:\n if len(mat1[0]) != len(mat2[0]):\n return\n return mat11 + mat22\n if axis == 1:\n if len(mat1) != len(mat2):\n return\n return [mat11[i] + mat22[i] for i in range(len(mat11))]","def inner_prod(x, y):\n z = torch.zeros(2, dtype=torch.double, device=x.device)\n\n if len(list(x.size())) == 2 and len(list(y.size())) == 2:\n z[0] = torch.dot(x[0], y[0]) - torch.dot(-x[1], y[1])\n z[1] = torch.dot(x[0], y[1]) + torch.dot(-x[1], y[0])\n\n if len(list(x.size())) == 1 and len(list(y.size())) == 1:\n z[0] = (x[0] * y[0]) - (-x[1] * y[1])\n z[1] = (x[0] * y[1]) + (-x[1] * y[0])\n\n return z","def rotate_2d_matrix(matrix):\n\n L = len(matrix)\n for i in range(L // 2):\n for j in range(i, L - i - 1):\n temp = matrix[i][j]\n matrix[i][j] = matrix[L - 1 - j][i]\n matrix[L - 1 - j][i] = matrix[L - 1 - i][L - 1 - j]\n matrix[L - 1 - i][L - 1 - j] = matrix[j][L - 1 - i]\n matrix[j][L - 1 - i] = temp","def inner_product_similarity(a: torch.Tensor, b: torch.Tensor, dim=1) -> torch.Tensor:\n outputs = (a * b).sum(dim=dim)\n return outputs","def classical(m1,m2):\n \n n = m1.shape\n result = np.zeros(n, dtype = int)\n\n for i in range(n[0]):\n for j in range(n[0]):\n for k in range(n[0]):\n result[i][j] += m1[i][k] * m2[k][j]\n return result","def to_matrix(self, system, second_system=None):\n\n if second_system is None:\n second_system = system\n\n return Matrix([i.dot(self).dot(j) for i in system for j in\n second_system]).reshape(3, 3)","def matrix_as_psfm(matrix):\n return [[2**(ep-2) for ep in row] for row in matrix]","def matrix_vector_prod(m,u):\n each_product = []\n for v in m:\n each_product.append(dot_prod(v, u))\n return each_product","def symmetric_matrix_from_2vectors(v1=[1,1,1],v2=[0,0,0]):\n a,b,c=v1\n u,v,w=v2\n return np.complex128(np.matrix([[a,u,v],[u,b,w],[v,w,c]]))","def partial_transpose(matrix):\n size = len(matrix)\n res_matrix = np.zeros((size,) * 4, dtype=complex)\n for p1 in range(size):\n for p2 in range(size):\n for p1_ in range(size):\n for p2_ in range(size):\n res_matrix[p1, p2, p1_, p2_] = matrix[p1, p2_, p1_, p2]\n return res_matrix","def mult_img_matrix(imgs, matrix):\n nimgs, ny, nx = imgs.shape\n\n vec = np.reshape(imgs, [nimgs, ny * nx])\n vec_out = matrix.dot(vec)\n imgs_out = np.reshape(vec_out, [nimgs, ny, nx])\n\n return imgs_out","def _calc_matrix(self):\n\t\tz = self.zoom\n\t\talloc = self.allocation\n\t\tif self.image:\n\t\t\tiw, ih = self.image.get_width(), self.image.get_height()\n\t\telse:\n\t\t\tiw, ih = 0, 0\n#\t\tif __debug__: print self._vadj.lower, self._vadj.value, self._vadj.upper\n\t\t\n\t\ti2w = cairo.Matrix(\n\t\t\tz,0,\n\t\t\t0,z,\n\t\t\t-self._hadj.value if alloc.width < iw*z else (alloc.width - iw*z)/2, \n\t\t\t-self._vadj.value if alloc.height < ih*z else (alloc.height - ih*z)/2,\n\t\t\t)\n\t\t\n\t\tself._i2w_matrix = i2w\n\t\t\n\t\tw2i = cairo.Matrix(*i2w) #copy\n\t\tw2i.invert()\n\t\tself._w2i_matrix = w2i","def quadratic_expansion(matrix):\n arr = np.copy(matrix)\n arr = np.array([x + x ** 2 for x in arr])\n return np.concatenate((matrix, arr), axis=1)","def scale(matrix, s):\n x, y = matrix.shape\n print(matrix.shape)\n\n print(np.ones(matrix.shape))\n # b = np.ones(x, y)\n # print(\"ones:\", b)\n return np.kron(a, np.ones((x, y)))","def _surface_metric_matrices_from_one_forms(one_forms):\n ndim = one_forms.ndim\n transpose_axes = tuple(range(ndim - 2)) + tuple(reversed(range(ndim - 2, ndim)))\n transposed_one_forms = gs.transpose(one_forms, axes=transpose_axes)\n return gs.matmul(one_forms, transposed_one_forms)","def snorm(x):\n return np.dot(x.flatten().T, x.flatten())","def row_matrix_col_4d(a, b, A):\n\n\treturn (a[0]*A[0][0]*b[0] + a[1]*A[1][0]*b[0] + a[2]*A[2][0]*b[0] + a[3]*A[3][0]*b[0] +\n\t a[0]*A[0][1]*b[1] + a[1]*A[1][1]*b[1] + a[2]*A[2][1]*b[1] + a[3]*A[3][1]*b[1] +\n\t a[0]*A[0][2]*b[2] + a[1]*A[1][2]*b[2] + a[2]*A[2][2]*b[2] + a[3]*A[3][2]*b[2] +\n\t a[0]*A[0][3]*b[3] + a[1]*A[1][3]*b[3] + a[2]*A[2][3]*b[3] + a[3]*A[3][3]*b[3])","def lazy_matrix_mul(m_a, m_b):\n return np.dot(m_a, m_b)","def pattern_mat(x, m):\n x = np.asarray(x).ravel()\n if m == 1:\n return x\n else:\n N = len(x)\n patterns = np.zeros((m, N-m+1))\n for i in range(m):\n patterns[i, :] = x[i:N-m+i+1]\n return patterns","def cat_matrices2D(mat1, mat2, axis=0):\n new_matrix = []\n if axis == 0:\n if len(mat1[0]) != len(mat2[0]):\n return None\n for row in mat1:\n new_matrix.append(list(row))\n for row in mat2:\n new_matrix.append(list(row))\n if axis == 1:\n if len(mat1) != len(mat2):\n return None\n for row, element in zip(mat1, mat2):\n new_matrix.append(list(row) + element)\n return new_matrix","def __mul__(left, right):\n \n if isinstance(left, Plucker) and isinstance(right, Plucker):\n # reciprocal product\n return np.dot(left.uw, right.v) + np.dot(right.uw, left.v)\n elif isinstance(left, Plucker) and arg.ismatrix(right, (4,None)):\n return left.skew @ right; # postmultiply by 4xN","def _mps_AA(self, A1, A2):\n Dl, d1, _ = A1.shape\n _, d2, Dr = A2.shape\n return np.reshape(np.tensordot(A1, A2, axes=(2, 0)), [Dl, d1 * d2, Dr])","def _prod_vectorized(M1, M2):\n sh1 = M1.shape\n sh2 = M2.shape\n assert len(sh1) >= 2\n assert len(sh2) >= 2\n assert sh1[-1] == sh2[-2]\n\n ndim1 = len(sh1)\n t1_index = list(xrange(ndim1-2)) + [ndim1-1, ndim1-2]\n return np.sum(np.transpose(M1, t1_index)[..., np.newaxis] *\n M2[..., np.newaxis, :], -3)","def process_matrix(row_count, column_count, operator):\n\n result = []\n for i in range(row_count):\n row = []\n for j in range(column_count):\n row.append(operator(i, j))\n result.append(row)\n\n return result","def _outer(a, b):\n a_flat = torch.reshape(a, [-1])\n b_flat = torch.reshape(b, [-1])\n a_mul = torch.unsqueeze(a_flat, dim=-1)\n b_mul = torch.unsqueeze(b_flat, dim=0)\n return a_mul * b_mul","def mk_single_diffy():\n # make matrix:\n mat = zeros((M, M), dtype='d')\n for m in range(M):\n for p in range(m+1, M, 2):\n mat[m,p] = 2*p*oneOverC[m]\n\n return mat","def multiply_matrices(a, b):\n try:\n x = len(b[0])\n except:\n b = make_2D(b)\n try:\n x = len(a[0])\n except:\n a = make_2D(a)\n if len(a[0]) != len(b):\n print 'error: matrices cannot be multiplied'\n return\n out = np.zeros((len(a), len(b[0])))\n for i in range(len(out)):\n for j in range(len(out[0])):\n sum = 0\n for k in range(len(a[i])):\n sum += a[i][k] * b[k][j]\n out[i][j] = sum\n return out","def multiM(*args):\r\n filas_1,filas_2 = len(args[0]),len(args[1])\r\n columnas_1,columnas_2 = len(args[0][0]),len(args[1][0])\r\n matriz_r = []\r\n for k in range(filas_1):\r\n matriz_r.append([0]*columnas_2)\r\n for i in range(columnas_2):\r\n matriz_r[k][i] = 0\r\n for i in range(filas_1):\r\n for j in range(columnas_1):\r\n for k in range(columnas_2):\r\n matriz_r[i][k] = matriz_r[i][k] + args[0][i][j] * args[1][j][k]\r\n return matriz_r","def matrix_multiply(m1, m2):\n\n\tproduct = numpy.matmul(m1, m2)\n\tif type(product) == numpy.int64:\n\t\treturn float(product)\n\telse:\n\t\tresult = list(product)\n\t\treturn result","def row_matrix_col(a, b, A):\n\n\treturn (a[0]*A[0][0]*b[0] + a[1]*A[1][0]*b[0] + a[2]*A[2][0]*b[0] + \n\t a[0]*A[0][1]*b[1] + a[1]*A[1][1]*b[1] + a[2]*A[2][1]*b[1] + \n\t a[0]*A[0][2]*b[2] + a[1]*A[1][2]*b[2] + a[2]*A[2][2]*b[2])","def frobenius_inner_prod(mat1, mat2):\n assert mat1.shape==mat2.shape\n # assert isinstance(mat1, Variable) and isinstance(mat2, Variable))\n f = mat1.mul(mat2).sum()\n return f","def hadmardProduct(self, a,b):\n\t\tnumRows = len(a)\n\t\tnumCols = len(a[0])\n\t\n\t\treturn [[a[j][i] * b[j][i] for i in range(numCols)] for j in range(numRows)]","def multiply_matrices(list):\n # Section 1: Start matrix product using 1st matrix in list\n matrix_product = list[0]\n\n # Section 2: Loop thru list to create product\n for matrix in list[1:]:\n matrix_product = matrix_multiply(matrix_product, matrix)\n\n return matrix_product","def __mul__(self, other):\n if self.n != other.m:\n raise TypeError(\"Illegal dimensions for mul operator\")\n tmp = [[0 for _ in xrange(self.n)] for _ in xrange(other.m)]\n for i in xrange(self.n):\n for j in xrange(other.m):\n for k in xrange(other.n):\n tmp[i][j] += self.values[i][k] * other.values[k][j]\n res = []\n for i in tmp:\n res += i\n return simplematrix(self.n, other.m, res)","def chain_matmul_square(As):\n\n As_matmul = As\n while As_matmul.shape[0] > 1:\n if As_matmul.shape[0] % 2:\n A_last = As_matmul[-1:]\n else:\n A_last = None\n \n As_matmul = torch.matmul(As_matmul[0:-1:2], As_matmul[1::2])\n if A_last is not None:\n As_matmul = torch.cat([As_matmul, A_last], dim=0)\n \n return As_matmul.squeeze(0)","def cayley_menger_mat(x2, y2, z2, xb2, yb2, zb2):\n one = np.ones_like(x2)\n zero = np.zeros_like(x2)\n mat = np.array([[zero, x2, y2, z2, one], \n [x2, zero, zb2, yb2, one], \n [y2, zb2, zero, xb2, one], \n [z2, yb2, xb2, zero, one], \n [one, one, one, one, zero]\n ]).T\n return mat","def matrixMult( self, matrix0, matrix1 ):\r\n result = {}\r\n keys = sorted( set( matrix0.keys() ) )\r\n count = range( len( matrix0.keys() ) )\r\n \r\n for key in keys:\r\n result[ key ] = []\r\n for i in count:\r\n sum = 0\r\n for j in count:\r\n sum += matrix0[ key ][j] * matrix1[ keys[j] ][i]\r\n result[ key ].insert( i, sum )\r\n \r\n return result","def form_matrix_yt(w):\r\n M = np.zeros((len(w),len(w)))\r\n for i in range(len(w)):\r\n for j in range(len(w)):\r\n M[i,j] = YoungTableaux(w[i],w[j]).CMNR()\r\n return M","def row_times_column(m1, row, m2, column):\n\n sum = 0\n for index in range(len(m1)):\n product = m1[row][index] * m2[index][column]\n sum += product\n return sum","def commute_matrix(A):\n R = resistance_matrix(A)\n E = A.sum() / 2 # number of edges in graph\n C = 2 * E * R\n return C","def add_matrices2D(mat1, mat2):\n if matrix_shape(mat1) != matrix_shape(mat2):\n return None\n shape = matrix_shape(mat1)\n matrix = []\n for axis1, axis2 in zip(mat1, mat2):\n matrix.append(add_arrays(axis1, axis2))\n return matrix","def MultiplyMatrix(matrixA, matrixB):\r\n # result matrix initialized as singularity matrix\r\n result = [[0 for y in range(len(matrixB[0]))] for x in range(len(matrixA))]\r\n for i in range(len(matrixA)):\r\n # iterate through columns of Y\r\n for j in range(len(matrixB[0])):\r\n # iterate through rows of Y\r\n for k in range(len(matrixB)):\r\n result[i][j] += matrixA[i][k] * matrixB[k][j]\r\n return result","def cat_matrices2D(mat1, mat2, axis=0):\n if (len(mat1[0]) == len(mat2[0])) and (axis == 0):\n concat = [ele.copy() for ele in mat1]\n concat += [ele.copy() for ele in mat2]\n return concat\n elif (len(mat1) == len(mat2)) and (axis == 1):\n concat = [mat1[j] + mat2[j] for j in range(len(mat1))]\n return concat\n else:\n return None","def python_square_matrix(matrix):\n\n transposed_matrix = np.zeros([matrix.shape[0],matrix.shape[0]])\n start = time.time()\n # for i in range(matrix.shape[0]):\n # for j in range(matrix.shape[0]):\n # transposed_matrix[i,j] = matrix[j,i]\n transposed_matrix = np.transpose(matrix)\n end = time.time()-start\n\n #Testing\n if not(np.allclose(transposed_matrix,np.transpose(matrix))):\n print(transposed_matrix)\n\n # print('python transpose time: %.2E' % end)\n return [transposed_matrix, end]","def add_matrices2D(mat1, mat2):\n\n if matrix_shape(mat1) != matrix_shape(mat2):\n return None\n\n range_ax0 = range(len(mat1)) # range of axis 0\n range_ax1 = range(len(mat1[0])) # range of axis 1\n\n return [[mat1[i][j] + mat2[i][j] for j in range_ax1] for i in range_ax0]"],"string":"[\n \"def inner_product(pattern_one, pattern_two):\\n\\n return np.sum(np.multiply(pattern_one, pattern_two))\",\n \"def inner_product_batch(pattern_stack_one, pattern_num_one, pattern_stack_two, pattern_num_two):\\n\\n \\\"\\\"\\\"\\n Notice that the two stacks can be different. So we can not deduce the lower triangular pattern from the \\n other half.\\n \\\"\\\"\\\"\\n holder = np.zeros((pattern_num_one, pattern_num_two))\\n for l in range(pattern_num_one):\\n for m in range(pattern_num_two):\\n holder[l, m] = np.sum(np.multiply(pattern_stack_one[l], pattern_stack_two[m]))\\n\\n return holder\",\n \"def l2_square_from_inner_product(matrix):\\n return np.diag(matrix)\",\n \"def matrix_subs(matrix_2x2, point):\\n arr = []\\n for el in matrix_2x2:\\n arr.append(el.subs(x1, point[0]).subs(x2, point[1]))\\n \\n M = Matrix([[arr[0], arr[1]], [arr[2], arr[3]]])\\n\\n return M\",\n \"def matrix_mult(m1, m2):\\n\\ttemp = []\\n\\tfor i in range(len(m1)):\\n\\t\\te = []\\n\\t\\tfor j in range(len(m2[0])):\\n\\t\\t\\te.append(row_times_column(m1,i,m2,j))\\n\\t\\ttemp.append(e)\\n\\treturn temp\",\n \"def python_nonsquare_matrix_mult(matrix):\\n\\n transposed_matrix = np.zeros([matrix.shape[1],matrix.shape[0]])\\n start = time.time()\\n # for i in range(matrix.shape[0]):\\n # for j in range(matrix.shape[1]):\\n # transposed_matrix[j,i] = matrix[i,j]\\n\\n transposed_matrix = np.transpose(matrix)\\n product = matrix.dot(transposed_matrix)\\n\\n # transposed_matrix = np.transpose(matrix)\\n end = time.time()-start\\n\\n # print(\\\"Python Golden Transpose: %s\\\" % product)\\n # print('python transpose time: %.2E' % end)\\n return [product, end]\",\n \"def inner_product_to_normalized_L2_square(matrix):\\n\\n length = matrix.shape[0]\\n norm = np.divide(1, np.sqrt(l2_square_from_inner_product(matrix)))\\n\\n normalized_inner_product = np.multiply(np.multiply(np.reshape(norm, [length, 1]), matrix),\\n np.reshape(norm, [1, length]))\\n return 2 - 2 * normalized_inner_product\",\n \"def mat_mul(mat1, mat2):\\n\\n rows1 = len(mat1)\\n cols1 = len(mat1[0])\\n rows2 = len(mat2)\\n cols2 = len(mat2[0])\\n\\n if cols1 != rows2:\\n return None\\n else:\\n new_matrix = []\\n for x in range(rows1):\\n aux_row = []\\n for y in range(cols2):\\n aux_sum = []\\n for z in range(cols1):\\n aux_sum.append(mat1[x][z] * mat2[z][y])\\n aux_row.append(sum(aux_sum))\\n new_matrix.append(aux_row)\\n\\n return new_matrix\",\n \"def inner_product(state_1, state_2):\\n return numpy.dot(state_1.conjugate(), state_2)\",\n \"def _kronecker_product(mat1: tf.Tensor, mat2: tf.Tensor) -> tf.Tensor:\\n m1, n1 = mat1.get_shape().as_list()\\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\\n m2, n2 = mat2.get_shape().as_list()\\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])\",\n \"def __mul__(self, other):\\n #\\n # TODO - your code here\\n #\\n final_matrix = []\\n for i in range(self.h):\\n temp_row = []\\n for j in range(other.w):\\n # take dot-product of row of\\n # matrix in 1st arg with col of\\n # matrix in 2nd arg\\n temp_row.append(dot_product(get_row(self.g, i), get_col(other.g, j)))\\n final_matrix.append(temp_row)\\n return Matrix(final_matrix)\\n # TODO - your code here\",\n \"def Mxform(x1,y1,x2,y2):\\n return Jones.toMueller([[np.dot(x2,x1), np.dot(x2, y1)], [np.dot(y2,x1), np.dot(y2,y1)]])\",\n \"def matrix_mult(m1, m2):\\n pass\",\n \"def rotate2D(self, matrix) -> None:\\n N = len(matrix)\\n\\n # In case of N is odd, the innermost square belt is just one cell, no need of rotating.\\n for i in range(0,int(N/2)): # outer loop for each square belt\\t\\t\\t\\n for j in range(i,N-i-1): # N-i group in the i-th square belt\\n #print(i,j)\\n tmp = matrix[i][j]\\n matrix[i][j] = matrix[N-j-1][i]\\n matrix[N-j-1][i] = matrix[N-i-1][N-j-1]\\n matrix[N-i-1][N-j-1] = matrix[j][N-i-1]\\n matrix[j][N-i-1] = tmp\\n #print(matrix)\",\n \"def matrixMul(self, matrix, matrix2):\\n matrix0 = matrix[:]\\n matrix[0] = matrix0[0] * matrix2[0] + matrix0[2]*matrix2[1] # + matrix0[4]*0\\n matrix[1] = matrix0[1] * matrix2[0] + matrix0[3]*matrix2[1] # + matrix0[5]*0\\n matrix[2] = matrix0[0] * matrix2[2] + matrix0[2]*matrix2[3] # + matrix0[4]*0\\n matrix[3] = matrix0[1] * matrix2[2] + matrix0[3]*matrix2[3] # + matrix0[5]*0\\n matrix[4] = matrix0[0] * matrix2[4] + matrix0[2]*matrix2[5] + matrix0[4]\\n matrix[5] = matrix0[1] * matrix2[4] + matrix0[3]*matrix2[5] + matrix0[5]\",\n \"def _z2matmul(self, left, right):\\n prod = np.mod(np.dot(left, right), 2)\\n return prod\",\n \"def form_square_block_matrix(mat1,mat2):\\n if mat1.cols==1:\\n mat3 = mp.matrix(mat1.rows+mat2.rows,1)\\n mat3[:mat1.rows] = mat1[:]\\n mat3[mat1.rows:mat3.rows] = mat2[:]\\n else:\\n mat3 = mp.matrix(mat1.rows+mat2.rows, mat1.rows+mat2.rows)\\n mat3[:mat1.rows,:mat1.rows] = mat1[:,:]\\n mat3[mat1.rows:mat3.rows,mat1.rows:mat3.rows] = mat2[:,:]\\n return mat3\",\n \"def outer_product(x,y):\\n\\n return x[:,0]*y[:,1] -x[:,1]*y[:,0]\",\n \"def mul(self,mat1,mat2):\\n if(isinstance(mat2,int)==True):\\n result = [[mat1[i][j] * mat2 for j in range(len(mat1[0]))] for i in range(len(mat1))]\\n self.out = result\\n return self.out\\n elif(len(mat1[0])==len(mat2)):\\n result = [[sum(a*b for a,b in zip(i,j)) for j in zip(*mat2)] for i in mat1]\\n self.out = result\\n return self.out\",\n \"def matrix_product(mat1: List[List[int]], mat2: List[List[int]]):\\n if len(mat1) == 0 or len(mat2) == 0:\\n raise ValueError(\\\"One of matrix is empty\\\")\\n n, k1 = len(mat1), len(mat1[0])\\n k2, m = len(mat2), len(mat2[0])\\n if k1 != k2:\\n raise ValueError(\\n f\\\"Can't multiply two matrices with shapes {n}x{k1} and {k2}x{m}\\\"\\n )\\n mat2_t = matrix_transpose(mat2)\\n return [[vec_product(vec1, vec2) for vec2 in mat2_t] for vec1 in mat1]\",\n \"def kronecker_product(mat1, mat2):\\n m1, n1 = mat1.get_shape().as_list()\\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\\n m2, n2 = mat2.get_shape().as_list()\\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])\",\n \"def mmultiply(self, matrix):\\n try:\\n result_matrix = [[0 for row in range(len(self.matrix))] for col in range(len(matrix[0]))]\\n for i in range(len(self.matrix)):\\n for j in range(len(matrix[0])):\\n for k in range(len(matrix)):\\n result_matrix[i][j] += self.matrix[i][k] * matrix[k][j]\\n self.matrix = result_matrix\\n except IndexError:\\n pass\\n pass\",\n \"def StrassenMatrixM(a, b):\\r\\n if len(a) != 2 or len(a[0]) != 2 or len(b) != 2 or len(b[0]) != 2:\\r\\n raise Exception('Matrices should be 2x2!')\\r\\n print(a[0][0] * b[0][1] + a[0][1] * b[1][1])\\r\\n matrix = [[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],\\r\\n [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]]]\\r\\n\\r\\n return matrix\",\n \"def matrix_mult_matrix(matrix_a, matrix_b):\\n m = len(matrix_a)\\n n = len(matrix_b)\\n result = []\\n matrix_b_t = transpose_matrix(matrix_b)\\n for i in xrange(m):\\n row = []\\n\\tfor j in xrange(m):\\n row.append(dot_product(matrix_a[i], matrix_b_t[j]))\\n\\tresult.append(row)\\n return result\",\n \"def __mul__(self,m):\\n if type(m) != Matrix:\\n raise TypeError('The second argument is not a matrix lol')\\n if self.ncols != m.nrows:\\n raise ValueError('matrix dot argument has incorrect number of rows')\\n new = Matrix(self.nrows,m.ncols)\\n columns = m.getCols()\\n rowindex = 0\\n colindex = 0 \\n for row in self.matrix:\\n colindex = 0 \\n for col in columns:\\n summ = 0\\n for i,j in zip(row,col):\\n summ+= i*j \\n new.matrix[rowindex][colindex] = summ\\n print new.matrix\\n colindex += 1 \\n rowindex+=1\\n return new\",\n \"def matrix_multiplication_loop(x_matrix, y_matrix):\\n result = []\\n for i, row in enumerate(x_matrix):\\n row_vector = []\\n for j in range(len(y_matrix[0])):\\n product = 0\\n for k in range(len(row)):\\n product += x_matrix[i][k] * y_matrix[k][j]\\n row_vector.append(product)\\n result.append(row_vector)\\n return result\",\n \"def prod_mat(self,other):\\n [rs,cs],[ro,co] = self.D,other.D\\n assert cs == ro, \\\"tailles incompatibles\\\"\\n return Mat([rs,co], lambda i,j : prod_scal(self.ligne(i),other.col(j)))\",\n \"def tensor_outer_product(tensor1, tensor2):\\n shape_1 = tensor1.shape\\n shape_2 = tensor2.shape\\n s1 = len(shape_1)\\n s2 = len(shape_2)\\n \\n shape_1 = shape_1 + (1, )*s2\\n shape_2 = (1, )*s1 + shape_2\\n return np.reshape(tensor1, shape_1) * np.reshape(tensor2, shape_2)\",\n \"def mat_mul(mat1, mat2):\\n\\n if len(mat1[0]) == len(mat2):\\n\\n mat2 = matrix_transpose(mat2)\\n response = []\\n\\n for row in range(len(mat1)):\\n response.append(\\n [\\n sum(dot_product(mat1[row], mat2[column]))\\n for column in range(len(mat2))\\n ]\\n )\\n\\n return response\\n\\n else:\\n return None\",\n \"def multiply_by_left_matrix(matrix, img):\\n first = np.inner(matrix[0], img)\\n second = np.inner(matrix[1], img)\\n third = np.inner(matrix[2], img)\\n\\n res = np.dstack((first, second, third))\\n return res\",\n \"def __mul__(self, other):\\n # \\n # TODO - your code here\\n #\\n \\n result = [];\\n row_result = [];\\n product = 0;\\n \\n if(self.w != other.h):\\n raise(ValueError, \\\"Matrices can not multiply for their dimesion doesn't match\\\"); \\n \\n for row in self.g:\\n row_result = [];\\n for j in range(other.w):\\n product = dot_product(row,other.get_column(j));\\n row_result.append(product);\\n result.append(row_result);\\n \\n return Matrix(result);\",\n \"def square_matrix_multiply(a, b):\\n n = len(a)\\n c = [[0]*n for _ in range(n)]\\n for i in range(n):\\n for j in range(n):\\n sm = 0\\n for k in range(n):\\n sm += (a[i][k] * b[k][j])\\n c[i][j] = sm\\n\\n return c\",\n \"def matrix_mult(A,B):\\n\\n m = len(A)\\n p = len(B)\\n n = len(B[0])\\n AB = []\\n for i in range(m):\\n AB.append([])\\n for j in range(n):\\n total = 0\\n for k in range(p):\\n total += A[i][k] * B[k][j]\\n AB[i].append(total)\\n return AB\",\n \"def solve_2x2(matrix: FieldMatrix,\\n rhs: Fields) -> OutputFields:\\n _validate_matrix_shape(matrix, (2, 2))\\n\\n a, b = matrix[0]\\n c, d = matrix[1]\\n e, f = rhs\\n\\n inv_factor = det_2x2(matrix)\\n\\n return [\\n tf.nest.map_structure(tf.math.divide_no_nan, det_2x2([\\n [e, b],\\n [f, d],\\n ]), inv_factor),\\n tf.nest.map_structure(tf.math.divide_no_nan, det_2x2([\\n [a, e],\\n [c, f],\\n ]), inv_factor),\\n ]\",\n \"def outer_product(A, B): \\n print(A)\\n print(B)\\n A_rows = len(A)\\n A_columns = len(A[0])\\n\\n B_rows = len(B)\\n B_columns = len(B[0])\\n \\n if A_columns == 1 and B_rows == 1:\\n \\n outer_product = []\\n\\n # multi-line list comprehension for outer product\\n [outer_product.append([A[i][0] * B[0][j] for j in range(B_columns)]) \\n for i in range(A_rows)]\\n\\n return outer_product\\n\\n else:\\n print(\\\"dimensions of vector do not match.\\\")\",\n \"def matrix_mult(m1, m2):\\n output = []\\n for rowIndex, row in enumerate(m1): #go through rows in m1\\n new_row = []\\n for columnIndex in range(len(m2[0])): #go through indices for each column of m2\\n sum = 0\\n for index3 in range(len(row)):\\n product = m1[rowIndex][index3] * m2[index3][columnIndex]\\n sum += product\\n new_row.append(sum)\\n output.append(new_row)\\n return output\\n \\n \\n #output = []\\n #first for loop corresponds to the rows of my output matrix and loops through the rows of m1 (enumerate)\\n #create an empty new row\\n # second for loop, loops through columns of m2\\n # create sum variable, initialize it with zero\\n # third for loop, multiplies the index of the row in m1 times the index of the column in m2\\n # add sum to product and assign this to the sum variable\\n # append sum to new row\\n # append new row to output\\n # return output\",\n \"def transformation_2d(vertices, kernels=KERNELS):\\n\\t# calculate the transpose matrix of vertices\\n\\ttranspose = vertices.transpose()\\n\\t# insert a row of ones in the transpose matrix's end, then insert the result in 'matrices' list\\n\\tkernels.append(np.append(transpose, [np.ones(len(transpose[0]))], axis=0))\\n\\t# multiply matrices into 'kernels' list,\\n\\t# remove the last row (of ones) and calculate the transpose matrix of the result\\n\\tfinal_transformation_result = np.delete(np.linalg.multi_dot(kernels), 2, 0).transpose()\\n\\tKERNELS.clear()\\n\\treturn final_transformation_result\",\n \"def matrix_apply_to_2d(data, matrix: np.matrix):\\n from scipy import mgrid\\n\\n cx = data.shape[0] / 2\\n cy = data.shape[1] / 2\\n\\n # Calculate the new coordinates of every point\\n grid = mgrid[-cx:data.shape[0]-cx, -cy:data.shape[1]-cy]\\n temp = grid.reshape((2, grid.size / 2))\\n # Add the fourth dimension (just 1s but needed for the computations)\\n newrow = np.ones(grid.size / 2)\\n temp = np.vstack([temp, newrow])\\n # Use the matrix to calculate the new positions of every point\\n temp = np.dot(matrix, temp)\\n # Delete the fourth dimension\\n temp = np.delete(temp, 2, axis=0)\\n temp = np.array(temp)\\n grid = np.reshape(temp, (2, data.shape[0], data.shape[1]))\\n\\n grid[0] += cx\\n grid[1] += cy\\n\\n from scipy.ndimage.interpolation import map_coordinates\\n d = map_coordinates(data, grid, order=3)\\n\\n return d\",\n \"def cat_matrices2D(mat1, mat2, axis=0):\\n res = []\\n if axis == 1:\\n if len(mat1) == len(mat2):\\n for i in mat1:\\n for j in mat2:\\n res.append(i+[j[0]])\\n mat2.remove(j)\\n else:\\n return None\\n else:\\n for i in mat2:\\n if len(i) == len(mat1[0]):\\n res.append(mat1 + [i])\\n else:\\n return None\\n return (res)\",\n \"def add_matrices2D(mat1, mat2):\\n if len(mat1) != len(mat2):\\n return None\\n if len(mat1[0]) != len(mat2[0]):\\n return None\\n return [[ele1 + ele2 for ele1, ele2 in zip(row1, row2)]\\n for row1, row2 in zip(mat1, mat2)]\",\n \"def recursive_multiply(a, b):\\n if len(a) == 2:\\n return naive_multiply(a, b)\\n\\n a11 = a[0:int(len(a) / 2)]\\n for index, row in enumerate(a11):\\n a11[index] = row[0:int(len(row) / 2)]\\n\\n a12 = a[0:int(len(a) / 2)]\\n for index, row in enumerate(a12):\\n a12[index] = row[int(len(a) / 2):len(a)]\\n\\n a21 = a[int(len(a) / 2):len(a)]\\n for index, row in enumerate(a21):\\n a21[index] = row[0:int(len(row) / 2)]\\n\\n a22 = a[int(len(a) / 2):len(a)]\\n for index, row in enumerate(a22):\\n a22[index] = row[int(len(a) / 2):len(a)]\\n\\n b11 = b[0:int(len(b) / 2)]\\n for index, row in enumerate(b11):\\n b11[index] = row[0:int(len(row) / 2)]\\n\\n b12 = b[0:int(len(b) / 2)]\\n for index, row in enumerate(b12):\\n b12[index] = row[int(len(b) / 2):len(b)]\\n\\n b21 = b[int(len(b) / 2):len(b)]\\n for index, row in enumerate(b21):\\n b21[index] = row[0:int(len(row) / 2)]\\n\\n b22 = b[int(len(b) / 2):len(b)]\\n for index, row in enumerate(b22):\\n b22[index] = row[int(len(b) / 2):len(b)]\\n\\n c11 = matrix_add(recursive_multiply(a11, b11), recursive_multiply(a12, b21)) # C11 = A11*B11 + A12*B21\\n c12 = matrix_add(recursive_multiply(a11, b12), recursive_multiply(a12, b22)) # C12 = A11*B12 + A12*B22\\n c21 = matrix_add(recursive_multiply(a21, b11), recursive_multiply(a22, b21)) # C21 = A21*B11 + A22*B21\\n c22 = matrix_add(recursive_multiply(a21, b12), recursive_multiply(a22, b22)) # C22 = A21*B12 + A22*B22\\n\\n # Append c12 to c11\\n for row_index, row in enumerate(c11):\\n for col_index, col in enumerate(c12):\\n row.append(c12[row_index][col_index])\\n\\n # Append c22 to c21\\n for row_index, row in enumerate(c21):\\n for col_index, col in enumerate(c12):\\n row.append(c22[row_index][col_index])\\n\\n # Append c21 to c11\\n for i in c21:\\n c11.append(i)\\n\\n return c11\",\n \"def get_molecular_matrix(single_body, two_body):\\n x, y = single_body.shape\\n func = np.vectorize(round_custom)\\n _new_dim = x * y\\n single_one_dim = single_body.reshape(_new_dim, 1)\\n two_body_two_dim = func(two_body.reshape(_new_dim, _new_dim))\\n idx = 0\\n x, _ = two_body_two_dim.shape\\n while idx < x:\\n two_body_two_dim[idx][idx] = round_custom(single_one_dim[idx][0])\\n idx += 1\\n return two_body_two_dim\",\n \"def strassen_dot(x, y):\\n# X = (A B) Y = (E F)\\n# (C D) (G H)\\n# \\n# A through H -- (n/2 x n/2) matrices\\n# \\n# let P1 = A(F-H), P2 = (A+B)H, P3 = (C+D)E, P4 = D(G-E), \\n# \\n# P5 = (A+D)(E+H), P6 = (B-D)(G+H), P7 = (A-C)(E+F)\\n# \\n# XY = (AE+BG AF+BH) = (P5+P4-P2+P6 P1+P2 )\\n# (CE+DG CF+DH) (P3+P4 P1+P5-P3-P7)\\n\\n n = x.shape[0]\\n originalN = n\\n \\n # base case (n<=8)\\n if n <= 8:\\n return brute_multiply(x, y)\\n \\n n_is_power_of_two = (n & (n-1) == 0)\\n\\n if not n_is_power_of_two:\\n # count the closest power of 2 above n\\n # and resize x and y matrices\\n newN = upper_pow_2(n)\\n \\n n = newN\\n \\n nX = np.zeros( (n, n) )\\n# mapping x on to nX:\\n nX[:originalN, :originalN] = x\\n x = nX\\n \\n nY = np.zeros( (n, n) )\\n# mapping y on to nY:\\n nY[:originalN, :originalN] = y\\n y = nY\\n \\n \\n a = x[ 0:n//2, 0:n//2 ]\\n b = x[ 0:n//2, n//2: ]\\n c = x[ n//2:, 0:n//2 ]\\n d = x[ n//2:, n//2: ]\\n\\n e = y[ 0:n//2, 0:n//2 ]\\n f = y[ 0:n//2, n//2: ]\\n g = y[ n//2:, 0:n//2 ]\\n h = y[ n//2:, n//2: ]\\n \\n# let P1 = A(F-H), P2 = (A+B)H, P3 = (C+D)E, P4 = D(G-E), \\n# \\n# P5 = (A+D)(E+H), P6 = (B-D)(G+H), P7 = (A-C)(E+F)\\n \\n # recursively compute 7 (SEVEN) products P1 .. P7\\n p1 = strassen_dot(a, f-h)\\n p2 = strassen_dot(a+b, h)\\n p3 = strassen_dot(c+d, e)\\n p4 = strassen_dot(d, g-e)\\n p5 = strassen_dot(a+d, e+h)\\n p6 = strassen_dot(b-d, g+h)\\n p7 = strassen_dot(a-c, e+f)\\n \\n# do the necessary (clever) additions & substractions with P1..P7\\n \\n# XY = (AE+BG AF+BH) = (P5+P4-P2+P6 P1+P2 )\\n# (CE+DG CF+DH) (P3+P4 P1+P5-P3-P7)\\n res1 = np.hstack( (p5+p4-p2+p6, p1+p2) )\\n res2 = np.hstack( (p3+p4, p1+p5-p3-p7) )\\n res = np.vstack( (res1, res2) )\\n\\n return res[:originalN, :originalN]\",\n \"def inner_product(alpha, F, beta):\\n return np.dot(alpha, np.dot(F, beta))\",\n \"def matrixMultiplication(firstMatrix, secondMatrix):\\n if len(firstMatrix[0]) == len(secondMatrix): # Checks whether the matrices can be multiplied or not or not\\n finalMatrix = []\\n for y in range(len(firstMatrix)): # 2\\n currentMatrix = []\\n for i in range(len(secondMatrix[0])):\\n currentSum = 0\\n for j in range(len(secondMatrix)):\\n currentSum += secondMatrix[j][i] * firstMatrix[y][j]\\n currentMatrix.append(currentSum)\\n print(\\\"This is my current matrix: \\\" + str(currentMatrix))\\n finalMatrix.append(currentMatrix)\\n print(\\\"This product of the two matrices is :) \\\" + str(finalMatrix))\\n else:\\n print(\\\"This operation cannot be done, make sure the rows of the first matrix is the same as the number of columns in the second matrix\\\")\",\n \"def _inner_product_d1(\\n self, one_forms_a, one_forms_b, one_forms_bp, areas_bp, inv_surface_metrics_bp\\n ):\\n one_forms_bp_t = gs.transpose(one_forms_bp, (0, 2, 1))\\n\\n one_forms_a_t = gs.transpose(one_forms_a, (0, 1, 3, 2))\\n xa = one_forms_a_t - one_forms_bp_t\\n\\n xa_0 = gs.matmul(\\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\\n gs.matmul(gs.transpose(xa, (0, 1, 3, 2)), one_forms_bp_t)\\n - gs.matmul(one_forms_bp, xa),\\n )\\n\\n one_forms_b_t = gs.transpose(one_forms_b, (0, 1, 3, 2))\\n xb = one_forms_b_t - one_forms_bp_t\\n xb_0 = gs.matmul(\\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\\n gs.matmul(gs.transpose(xb, (0, 1, 3, 2)), one_forms_bp_t)\\n - gs.matmul(one_forms_bp, xb),\\n )\\n\\n return self.d1 * gs.sum(\\n gs.einsum(\\n \\\"...bii->...b\\\",\\n gs.matmul(\\n xa_0,\\n gs.matmul(\\n inv_surface_metrics_bp, gs.transpose(xb_0, axes=(0, 1, 3, 2))\\n ),\\n ),\\n )\\n * areas_bp\\n )\",\n \"def mult(m1, m2):\\n assert np.shape(m1) == (2, 3)\\n assert np.shape(m2) == (2, 3)\\n\\n m1_temp = np.vstack((m1, [0, 0, 1]))\\n m2_temp = np.vstack((m2, [0, 0, 1]))\\n result = m1_temp * m2_temp\\n\\n return result[:2, :]\",\n \"def pair_product(x1, x2):\\n return np.multiply(x1, x2)\",\n \"def Multiply(M1,M2):\\r\\n M3=[]\\r\\n w=0\\r\\n while w<len(M2[0]):\\r\\n tap=[]\\r\\n t=0\\r\\n while t<len(M2):\\r\\n tap.append(M2[t][w])\\r\\n t=t+1\\r\\n M3.append(tap)\\r\\n w=w+1\\r\\n M=[]\\r\\n # Multiplying matrices\\r\\n k=0\\r\\n sums=0\\r\\n while k<len(M1):\\r\\n j=0\\r\\n mpy=[]\\r\\n while j<len(M3):\\r\\n p=0\\r\\n sums=0\\r\\n while p<len(M3[j]):\\r\\n temp = (M1[k][p])*(M3[j][p])\\r\\n sums=sums+temp\\r\\n p=p+1\\r\\n mpy.append(sums)\\r\\n j=j+1\\r\\n M.append(mpy)\\r\\n k=k+1\\r\\n return M\",\n \"def generate_pattern_grid(words1, words2):\\n # Convert word lists to integer arrays\\n w1, w2 = (\\n np.array([[ord(c) for c in w] for w in words], dtype=np.uint8)\\n for words in (words1, words2)\\n )\\n\\n if len(w1) == 0 or len(w2) == 0:\\n return np.zeros((len(w1), len(w2)), dtype=np.uint8)\\n\\n # equality_grid[a, b, i, j] represents whether the ith letter\\n # of words1[a] equals the jth letter of words2[b]\\n equality_grid = np.zeros((len(w1), len(w2), 5, 5), dtype=bool)\\n for i, j in it.product(range(5), range(5)):\\n equality_grid[:, :, i, j] = np.equal.outer(w1[:, i], w2[:, j])\\n\\n patterns = np.zeros((len(w1), len(w2)), dtype=np.uint8)\\n three_pows = (3**np.arange(5)).astype(np.uint8)\\n for i, tp in enumerate(three_pows):\\n # This accounts for yellow squares\\n patterns[:, :] += tp * equality_grid[:, :, i, :].any(2)\\n # This accounts for green squares\\n patterns[:, :] += tp * equality_grid[:, :, i, i]\\n\\n return patterns\",\n \"def complex_mul2d(a, b):\\n op = partial(torch.einsum, \\\"bixy,ioxy->boxy\\\")\\n return torch.stack([\\n op(a[..., 0], b[..., 0]) - op(a[..., 1], b[..., 1]),\\n op(a[..., 1], b[..., 0]) + op(a[..., 0], b[..., 1])\\n ],\\n dim=-1)\",\n \"def matmul(xs: List[List[float]],\\n ys: List[List[float]]) -> List[List[float]]:\\n product = []\\n for x_row in range(len(xs)):\\n row = []\\n for y_col in range(len(ys[0])):\\n col = [ys[y_row][y_col] for y_row in range(len(ys))]\\n row.append(Math.dot(xs[x_row], col))\\n product.append(row)\\n return product\",\n \"def multiplicand_2(p):\\n m2 = cddr(p) # (m2 m3 ...)\\n rests = cdr(m2) # (m3...)\\n if isNull(rests):\\n return car(m2)\\n else:\\n restp = convertToPythonList(cdr(rests))\\n return make_product_2(car(m2), car(rests), *restp)\",\n \"def strassen(m1, m2):\\n \\n if ((m1.shape[0] % 2 == 0) or (m1.shape[0] == 1)):\\n n = m1.shape[0] \\n else:\\n n = m1.shape[0] + 1\\n result = np.zeros((n, n), dtype = int)\\n \\n if (n == 1):\\n result[0][0] = m1[0][0] * m2[0][0]\\n else:\\n new = n//2\\n \\n a11, a12, a21, a22 = m1[:new, :new], m1[new:, :new], m1[:new, new:], m1[new:, new:]\\n b11, b12, b21, b22 = m2[:new, :new], m2[new:, :new], m2[:new, new:], m2[new:, new:]\\n \\n p1 = strassen(a11, b12 - b22)\\n p2 = strassen(a11 + a12, b22)\\n p3 = strassen(a21 + a22, b11)\\n p4 = strassen(a22, b21 - b11)\\n p5 = strassen(a11 + a22, b11 + b22)\\n p6 = strassen(a12 - a22, b21 + b22)\\n p7 = strassen(a11 - a21, b11 + b12)\\n \\n result[:new, :new] = p5 + p4 - p2 + p6\\n result[new:, :new] = p1 + p2\\n result[:new, new:] = p3 + p4 \\n result[new:, new:] = p5 + p1 - p3 - p7\\n \\n return result\",\n \"def interior_tensor_product(mx, dim_a, dim_b, e=None):\\n assert _np.shape(mx) == (dim_a * dim_b, dim_a * dim_b), \\\"Dimensions do not agree with matrix size\\\"\\n assert _np.shape(e)[0] == _np.shape(e)[1], \\\"e should be a square matrix\\\"\\n basis_a = matrix_units(dim_a)\\n basis_b = matrix_units(dim_b)\\n return sum((_np.trace(_np.dot(mx, _np.kron(unit_a, unit_b).T)) * multikron([unit_a, e, unit_b])\\n for unit_a in basis_a for unit_b in basis_b))\",\n \"def cat_matrices2D(mat1, mat2, axis=0):\\n mat11 = [[i for i in j] for j in mat1]\\n mat22 = [[i for i in j] for j in mat2]\\n if axis == 0:\\n if len(mat1[0]) != len(mat2[0]):\\n return\\n return mat11 + mat22\\n if axis == 1:\\n if len(mat1) != len(mat2):\\n return\\n return [mat11[i] + mat22[i] for i in range(len(mat11))]\",\n \"def inner_prod(x, y):\\n z = torch.zeros(2, dtype=torch.double, device=x.device)\\n\\n if len(list(x.size())) == 2 and len(list(y.size())) == 2:\\n z[0] = torch.dot(x[0], y[0]) - torch.dot(-x[1], y[1])\\n z[1] = torch.dot(x[0], y[1]) + torch.dot(-x[1], y[0])\\n\\n if len(list(x.size())) == 1 and len(list(y.size())) == 1:\\n z[0] = (x[0] * y[0]) - (-x[1] * y[1])\\n z[1] = (x[0] * y[1]) + (-x[1] * y[0])\\n\\n return z\",\n \"def rotate_2d_matrix(matrix):\\n\\n L = len(matrix)\\n for i in range(L // 2):\\n for j in range(i, L - i - 1):\\n temp = matrix[i][j]\\n matrix[i][j] = matrix[L - 1 - j][i]\\n matrix[L - 1 - j][i] = matrix[L - 1 - i][L - 1 - j]\\n matrix[L - 1 - i][L - 1 - j] = matrix[j][L - 1 - i]\\n matrix[j][L - 1 - i] = temp\",\n \"def inner_product_similarity(a: torch.Tensor, b: torch.Tensor, dim=1) -> torch.Tensor:\\n outputs = (a * b).sum(dim=dim)\\n return outputs\",\n \"def classical(m1,m2):\\n \\n n = m1.shape\\n result = np.zeros(n, dtype = int)\\n\\n for i in range(n[0]):\\n for j in range(n[0]):\\n for k in range(n[0]):\\n result[i][j] += m1[i][k] * m2[k][j]\\n return result\",\n \"def to_matrix(self, system, second_system=None):\\n\\n if second_system is None:\\n second_system = system\\n\\n return Matrix([i.dot(self).dot(j) for i in system for j in\\n second_system]).reshape(3, 3)\",\n \"def matrix_as_psfm(matrix):\\n return [[2**(ep-2) for ep in row] for row in matrix]\",\n \"def matrix_vector_prod(m,u):\\n each_product = []\\n for v in m:\\n each_product.append(dot_prod(v, u))\\n return each_product\",\n \"def symmetric_matrix_from_2vectors(v1=[1,1,1],v2=[0,0,0]):\\n a,b,c=v1\\n u,v,w=v2\\n return np.complex128(np.matrix([[a,u,v],[u,b,w],[v,w,c]]))\",\n \"def partial_transpose(matrix):\\n size = len(matrix)\\n res_matrix = np.zeros((size,) * 4, dtype=complex)\\n for p1 in range(size):\\n for p2 in range(size):\\n for p1_ in range(size):\\n for p2_ in range(size):\\n res_matrix[p1, p2, p1_, p2_] = matrix[p1, p2_, p1_, p2]\\n return res_matrix\",\n \"def mult_img_matrix(imgs, matrix):\\n nimgs, ny, nx = imgs.shape\\n\\n vec = np.reshape(imgs, [nimgs, ny * nx])\\n vec_out = matrix.dot(vec)\\n imgs_out = np.reshape(vec_out, [nimgs, ny, nx])\\n\\n return imgs_out\",\n \"def _calc_matrix(self):\\n\\t\\tz = self.zoom\\n\\t\\talloc = self.allocation\\n\\t\\tif self.image:\\n\\t\\t\\tiw, ih = self.image.get_width(), self.image.get_height()\\n\\t\\telse:\\n\\t\\t\\tiw, ih = 0, 0\\n#\\t\\tif __debug__: print self._vadj.lower, self._vadj.value, self._vadj.upper\\n\\t\\t\\n\\t\\ti2w = cairo.Matrix(\\n\\t\\t\\tz,0,\\n\\t\\t\\t0,z,\\n\\t\\t\\t-self._hadj.value if alloc.width < iw*z else (alloc.width - iw*z)/2, \\n\\t\\t\\t-self._vadj.value if alloc.height < ih*z else (alloc.height - ih*z)/2,\\n\\t\\t\\t)\\n\\t\\t\\n\\t\\tself._i2w_matrix = i2w\\n\\t\\t\\n\\t\\tw2i = cairo.Matrix(*i2w) #copy\\n\\t\\tw2i.invert()\\n\\t\\tself._w2i_matrix = w2i\",\n \"def quadratic_expansion(matrix):\\n arr = np.copy(matrix)\\n arr = np.array([x + x ** 2 for x in arr])\\n return np.concatenate((matrix, arr), axis=1)\",\n \"def scale(matrix, s):\\n x, y = matrix.shape\\n print(matrix.shape)\\n\\n print(np.ones(matrix.shape))\\n # b = np.ones(x, y)\\n # print(\\\"ones:\\\", b)\\n return np.kron(a, np.ones((x, y)))\",\n \"def _surface_metric_matrices_from_one_forms(one_forms):\\n ndim = one_forms.ndim\\n transpose_axes = tuple(range(ndim - 2)) + tuple(reversed(range(ndim - 2, ndim)))\\n transposed_one_forms = gs.transpose(one_forms, axes=transpose_axes)\\n return gs.matmul(one_forms, transposed_one_forms)\",\n \"def snorm(x):\\n return np.dot(x.flatten().T, x.flatten())\",\n \"def row_matrix_col_4d(a, b, A):\\n\\n\\treturn (a[0]*A[0][0]*b[0] + a[1]*A[1][0]*b[0] + a[2]*A[2][0]*b[0] + a[3]*A[3][0]*b[0] +\\n\\t a[0]*A[0][1]*b[1] + a[1]*A[1][1]*b[1] + a[2]*A[2][1]*b[1] + a[3]*A[3][1]*b[1] +\\n\\t a[0]*A[0][2]*b[2] + a[1]*A[1][2]*b[2] + a[2]*A[2][2]*b[2] + a[3]*A[3][2]*b[2] +\\n\\t a[0]*A[0][3]*b[3] + a[1]*A[1][3]*b[3] + a[2]*A[2][3]*b[3] + a[3]*A[3][3]*b[3])\",\n \"def lazy_matrix_mul(m_a, m_b):\\n return np.dot(m_a, m_b)\",\n \"def pattern_mat(x, m):\\n x = np.asarray(x).ravel()\\n if m == 1:\\n return x\\n else:\\n N = len(x)\\n patterns = np.zeros((m, N-m+1))\\n for i in range(m):\\n patterns[i, :] = x[i:N-m+i+1]\\n return patterns\",\n \"def cat_matrices2D(mat1, mat2, axis=0):\\n new_matrix = []\\n if axis == 0:\\n if len(mat1[0]) != len(mat2[0]):\\n return None\\n for row in mat1:\\n new_matrix.append(list(row))\\n for row in mat2:\\n new_matrix.append(list(row))\\n if axis == 1:\\n if len(mat1) != len(mat2):\\n return None\\n for row, element in zip(mat1, mat2):\\n new_matrix.append(list(row) + element)\\n return new_matrix\",\n \"def __mul__(left, right):\\n \\n if isinstance(left, Plucker) and isinstance(right, Plucker):\\n # reciprocal product\\n return np.dot(left.uw, right.v) + np.dot(right.uw, left.v)\\n elif isinstance(left, Plucker) and arg.ismatrix(right, (4,None)):\\n return left.skew @ right; # postmultiply by 4xN\",\n \"def _mps_AA(self, A1, A2):\\n Dl, d1, _ = A1.shape\\n _, d2, Dr = A2.shape\\n return np.reshape(np.tensordot(A1, A2, axes=(2, 0)), [Dl, d1 * d2, Dr])\",\n \"def _prod_vectorized(M1, M2):\\n sh1 = M1.shape\\n sh2 = M2.shape\\n assert len(sh1) >= 2\\n assert len(sh2) >= 2\\n assert sh1[-1] == sh2[-2]\\n\\n ndim1 = len(sh1)\\n t1_index = list(xrange(ndim1-2)) + [ndim1-1, ndim1-2]\\n return np.sum(np.transpose(M1, t1_index)[..., np.newaxis] *\\n M2[..., np.newaxis, :], -3)\",\n \"def process_matrix(row_count, column_count, operator):\\n\\n result = []\\n for i in range(row_count):\\n row = []\\n for j in range(column_count):\\n row.append(operator(i, j))\\n result.append(row)\\n\\n return result\",\n \"def _outer(a, b):\\n a_flat = torch.reshape(a, [-1])\\n b_flat = torch.reshape(b, [-1])\\n a_mul = torch.unsqueeze(a_flat, dim=-1)\\n b_mul = torch.unsqueeze(b_flat, dim=0)\\n return a_mul * b_mul\",\n \"def mk_single_diffy():\\n # make matrix:\\n mat = zeros((M, M), dtype='d')\\n for m in range(M):\\n for p in range(m+1, M, 2):\\n mat[m,p] = 2*p*oneOverC[m]\\n\\n return mat\",\n \"def multiply_matrices(a, b):\\n try:\\n x = len(b[0])\\n except:\\n b = make_2D(b)\\n try:\\n x = len(a[0])\\n except:\\n a = make_2D(a)\\n if len(a[0]) != len(b):\\n print 'error: matrices cannot be multiplied'\\n return\\n out = np.zeros((len(a), len(b[0])))\\n for i in range(len(out)):\\n for j in range(len(out[0])):\\n sum = 0\\n for k in range(len(a[i])):\\n sum += a[i][k] * b[k][j]\\n out[i][j] = sum\\n return out\",\n \"def multiM(*args):\\r\\n filas_1,filas_2 = len(args[0]),len(args[1])\\r\\n columnas_1,columnas_2 = len(args[0][0]),len(args[1][0])\\r\\n matriz_r = []\\r\\n for k in range(filas_1):\\r\\n matriz_r.append([0]*columnas_2)\\r\\n for i in range(columnas_2):\\r\\n matriz_r[k][i] = 0\\r\\n for i in range(filas_1):\\r\\n for j in range(columnas_1):\\r\\n for k in range(columnas_2):\\r\\n matriz_r[i][k] = matriz_r[i][k] + args[0][i][j] * args[1][j][k]\\r\\n return matriz_r\",\n \"def matrix_multiply(m1, m2):\\n\\n\\tproduct = numpy.matmul(m1, m2)\\n\\tif type(product) == numpy.int64:\\n\\t\\treturn float(product)\\n\\telse:\\n\\t\\tresult = list(product)\\n\\t\\treturn result\",\n \"def row_matrix_col(a, b, A):\\n\\n\\treturn (a[0]*A[0][0]*b[0] + a[1]*A[1][0]*b[0] + a[2]*A[2][0]*b[0] + \\n\\t a[0]*A[0][1]*b[1] + a[1]*A[1][1]*b[1] + a[2]*A[2][1]*b[1] + \\n\\t a[0]*A[0][2]*b[2] + a[1]*A[1][2]*b[2] + a[2]*A[2][2]*b[2])\",\n \"def frobenius_inner_prod(mat1, mat2):\\n assert mat1.shape==mat2.shape\\n # assert isinstance(mat1, Variable) and isinstance(mat2, Variable))\\n f = mat1.mul(mat2).sum()\\n return f\",\n \"def hadmardProduct(self, a,b):\\n\\t\\tnumRows = len(a)\\n\\t\\tnumCols = len(a[0])\\n\\t\\n\\t\\treturn [[a[j][i] * b[j][i] for i in range(numCols)] for j in range(numRows)]\",\n \"def multiply_matrices(list):\\n # Section 1: Start matrix product using 1st matrix in list\\n matrix_product = list[0]\\n\\n # Section 2: Loop thru list to create product\\n for matrix in list[1:]:\\n matrix_product = matrix_multiply(matrix_product, matrix)\\n\\n return matrix_product\",\n \"def __mul__(self, other):\\n if self.n != other.m:\\n raise TypeError(\\\"Illegal dimensions for mul operator\\\")\\n tmp = [[0 for _ in xrange(self.n)] for _ in xrange(other.m)]\\n for i in xrange(self.n):\\n for j in xrange(other.m):\\n for k in xrange(other.n):\\n tmp[i][j] += self.values[i][k] * other.values[k][j]\\n res = []\\n for i in tmp:\\n res += i\\n return simplematrix(self.n, other.m, res)\",\n \"def chain_matmul_square(As):\\n\\n As_matmul = As\\n while As_matmul.shape[0] > 1:\\n if As_matmul.shape[0] % 2:\\n A_last = As_matmul[-1:]\\n else:\\n A_last = None\\n \\n As_matmul = torch.matmul(As_matmul[0:-1:2], As_matmul[1::2])\\n if A_last is not None:\\n As_matmul = torch.cat([As_matmul, A_last], dim=0)\\n \\n return As_matmul.squeeze(0)\",\n \"def cayley_menger_mat(x2, y2, z2, xb2, yb2, zb2):\\n one = np.ones_like(x2)\\n zero = np.zeros_like(x2)\\n mat = np.array([[zero, x2, y2, z2, one], \\n [x2, zero, zb2, yb2, one], \\n [y2, zb2, zero, xb2, one], \\n [z2, yb2, xb2, zero, one], \\n [one, one, one, one, zero]\\n ]).T\\n return mat\",\n \"def matrixMult( self, matrix0, matrix1 ):\\r\\n result = {}\\r\\n keys = sorted( set( matrix0.keys() ) )\\r\\n count = range( len( matrix0.keys() ) )\\r\\n \\r\\n for key in keys:\\r\\n result[ key ] = []\\r\\n for i in count:\\r\\n sum = 0\\r\\n for j in count:\\r\\n sum += matrix0[ key ][j] * matrix1[ keys[j] ][i]\\r\\n result[ key ].insert( i, sum )\\r\\n \\r\\n return result\",\n \"def form_matrix_yt(w):\\r\\n M = np.zeros((len(w),len(w)))\\r\\n for i in range(len(w)):\\r\\n for j in range(len(w)):\\r\\n M[i,j] = YoungTableaux(w[i],w[j]).CMNR()\\r\\n return M\",\n \"def row_times_column(m1, row, m2, column):\\n\\n sum = 0\\n for index in range(len(m1)):\\n product = m1[row][index] * m2[index][column]\\n sum += product\\n return sum\",\n \"def commute_matrix(A):\\n R = resistance_matrix(A)\\n E = A.sum() / 2 # number of edges in graph\\n C = 2 * E * R\\n return C\",\n \"def add_matrices2D(mat1, mat2):\\n if matrix_shape(mat1) != matrix_shape(mat2):\\n return None\\n shape = matrix_shape(mat1)\\n matrix = []\\n for axis1, axis2 in zip(mat1, mat2):\\n matrix.append(add_arrays(axis1, axis2))\\n return matrix\",\n \"def MultiplyMatrix(matrixA, matrixB):\\r\\n # result matrix initialized as singularity matrix\\r\\n result = [[0 for y in range(len(matrixB[0]))] for x in range(len(matrixA))]\\r\\n for i in range(len(matrixA)):\\r\\n # iterate through columns of Y\\r\\n for j in range(len(matrixB[0])):\\r\\n # iterate through rows of Y\\r\\n for k in range(len(matrixB)):\\r\\n result[i][j] += matrixA[i][k] * matrixB[k][j]\\r\\n return result\",\n \"def cat_matrices2D(mat1, mat2, axis=0):\\n if (len(mat1[0]) == len(mat2[0])) and (axis == 0):\\n concat = [ele.copy() for ele in mat1]\\n concat += [ele.copy() for ele in mat2]\\n return concat\\n elif (len(mat1) == len(mat2)) and (axis == 1):\\n concat = [mat1[j] + mat2[j] for j in range(len(mat1))]\\n return concat\\n else:\\n return None\",\n \"def python_square_matrix(matrix):\\n\\n transposed_matrix = np.zeros([matrix.shape[0],matrix.shape[0]])\\n start = time.time()\\n # for i in range(matrix.shape[0]):\\n # for j in range(matrix.shape[0]):\\n # transposed_matrix[i,j] = matrix[j,i]\\n transposed_matrix = np.transpose(matrix)\\n end = time.time()-start\\n\\n #Testing\\n if not(np.allclose(transposed_matrix,np.transpose(matrix))):\\n print(transposed_matrix)\\n\\n # print('python transpose time: %.2E' % end)\\n return [transposed_matrix, end]\",\n \"def add_matrices2D(mat1, mat2):\\n\\n if matrix_shape(mat1) != matrix_shape(mat2):\\n return None\\n\\n range_ax0 = range(len(mat1)) # range of axis 0\\n range_ax1 = range(len(mat1[0])) # range of axis 1\\n\\n return [[mat1[i][j] + mat2[i][j] for j in range_ax1] for i in range_ax0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74826276","0.7294018","0.6634914","0.6595926","0.62142396","0.5985415","0.59326464","0.58786714","0.5865172","0.5857749","0.58491534","0.5798571","0.5797253","0.57930493","0.57865286","0.57813066","0.57709336","0.57322204","0.5704064","0.5663638","0.5653181","0.5643115","0.5623832","0.56153333","0.5583555","0.55788","0.5569403","0.5546664","0.55460435","0.5541713","0.55014354","0.5499286","0.5475835","0.54653025","0.5454078","0.54442793","0.5419662","0.54076743","0.5377725","0.5368803","0.5361725","0.5356933","0.5344811","0.5342129","0.532544","0.5322299","0.53211844","0.5319832","0.53178275","0.5301389","0.5285707","0.5285187","0.5266845","0.5260758","0.52591664","0.5254037","0.52435106","0.52276844","0.52141124","0.5188914","0.5182223","0.5179323","0.51643175","0.51628023","0.51574415","0.51329607","0.51257557","0.5121921","0.511521","0.510496","0.5103772","0.51001793","0.5093926","0.50928617","0.5090669","0.5088742","0.50837785","0.5079877","0.507776","0.5072599","0.50715786","0.5070323","0.5070017","0.5065328","0.506243","0.5046779","0.5046372","0.50429356","0.5042517","0.50415987","0.5040749","0.50350493","0.50325584","0.5032039","0.50318533","0.5030212","0.5029228","0.5024405","0.5020256","0.5014971"],"string":"[\n \"0.74826276\",\n \"0.7294018\",\n \"0.6634914\",\n \"0.6595926\",\n \"0.62142396\",\n \"0.5985415\",\n \"0.59326464\",\n \"0.58786714\",\n \"0.5865172\",\n \"0.5857749\",\n \"0.58491534\",\n \"0.5798571\",\n \"0.5797253\",\n \"0.57930493\",\n \"0.57865286\",\n \"0.57813066\",\n \"0.57709336\",\n \"0.57322204\",\n \"0.5704064\",\n \"0.5663638\",\n \"0.5653181\",\n \"0.5643115\",\n \"0.5623832\",\n \"0.56153333\",\n \"0.5583555\",\n \"0.55788\",\n \"0.5569403\",\n \"0.5546664\",\n \"0.55460435\",\n \"0.5541713\",\n \"0.55014354\",\n \"0.5499286\",\n \"0.5475835\",\n \"0.54653025\",\n \"0.5454078\",\n \"0.54442793\",\n \"0.5419662\",\n \"0.54076743\",\n \"0.5377725\",\n \"0.5368803\",\n \"0.5361725\",\n \"0.5356933\",\n \"0.5344811\",\n \"0.5342129\",\n \"0.532544\",\n \"0.5322299\",\n \"0.53211844\",\n \"0.5319832\",\n \"0.53178275\",\n \"0.5301389\",\n \"0.5285707\",\n \"0.5285187\",\n \"0.5266845\",\n \"0.5260758\",\n \"0.52591664\",\n \"0.5254037\",\n \"0.52435106\",\n \"0.52276844\",\n \"0.52141124\",\n \"0.5188914\",\n \"0.5182223\",\n \"0.5179323\",\n \"0.51643175\",\n \"0.51628023\",\n \"0.51574415\",\n \"0.51329607\",\n \"0.51257557\",\n \"0.5121921\",\n \"0.511521\",\n \"0.510496\",\n \"0.5103772\",\n \"0.51001793\",\n \"0.5093926\",\n \"0.50928617\",\n \"0.5090669\",\n \"0.5088742\",\n \"0.50837785\",\n \"0.5079877\",\n \"0.507776\",\n \"0.5072599\",\n \"0.50715786\",\n \"0.5070323\",\n \"0.5070017\",\n \"0.5065328\",\n \"0.506243\",\n \"0.5046779\",\n \"0.5046372\",\n \"0.50429356\",\n \"0.5042517\",\n \"0.50415987\",\n \"0.5040749\",\n \"0.50350493\",\n \"0.50325584\",\n \"0.5032039\",\n \"0.50318533\",\n \"0.5030212\",\n \"0.5029228\",\n \"0.5024405\",\n \"0.5020256\",\n \"0.5014971\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6709533"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":882,"cells":{"query":{"kind":"string","value":"Turns the inner product matrix into the |pattern1 pattern2|_{2}^2 matrix. Here the pattern is normalized. Therefore, it can be reformulated into |pattern1 pattern2|_{2}^2 = 2 2 / |pattern1||pattern2|"},"document":{"kind":"string","value":"def inner_product_to_normalized_L2_square(matrix):\n\n length = matrix.shape[0]\n norm = np.divide(1, np.sqrt(l2_square_from_inner_product(matrix)))\n\n normalized_inner_product = np.multiply(np.multiply(np.reshape(norm, [length, 1]), matrix),\n np.reshape(norm, [1, length]))\n return 2 - 2 * normalized_inner_product"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def inner_product(pattern_one, pattern_two):\n\n return np.sum(np.multiply(pattern_one, pattern_two))","def inner_product_batch(pattern_stack_one, pattern_num_one, pattern_stack_two, pattern_num_two):\n\n \"\"\"\n Notice that the two stacks can be different. So we can not deduce the lower triangular pattern from the \n other half.\n \"\"\"\n holder = np.zeros((pattern_num_one, pattern_num_two))\n for l in range(pattern_num_one):\n for m in range(pattern_num_two):\n holder[l, m] = np.sum(np.multiply(pattern_stack_one[l], pattern_stack_two[m]))\n\n return holder","def inner_product_to_L2_square(matrix):\n\n length = matrix.shape[0]\n squared_norm = np.reshape(np.diag(matrix), (length, 1))\n\n return squared_norm + np.transpose(squared_norm) - 2 * matrix","def l2_square_from_inner_product(matrix):\n return np.diag(matrix)","def _z2matmul(self, left, right):\n prod = np.mod(np.dot(left, right), 2)\n return prod","def snorm(x):\n return np.dot(x.flatten().T, x.flatten())","def matrix_subs(matrix_2x2, point):\n arr = []\n for el in matrix_2x2:\n arr.append(el.subs(x1, point[0]).subs(x2, point[1]))\n \n M = Matrix([[arr[0], arr[1]], [arr[2], arr[3]]])\n\n return M","def inner_product(state_1, state_2):\n return numpy.dot(state_1.conjugate(), state_2)","def _inner_product_d1(\n self, one_forms_a, one_forms_b, one_forms_bp, areas_bp, inv_surface_metrics_bp\n ):\n one_forms_bp_t = gs.transpose(one_forms_bp, (0, 2, 1))\n\n one_forms_a_t = gs.transpose(one_forms_a, (0, 1, 3, 2))\n xa = one_forms_a_t - one_forms_bp_t\n\n xa_0 = gs.matmul(\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\n gs.matmul(gs.transpose(xa, (0, 1, 3, 2)), one_forms_bp_t)\n - gs.matmul(one_forms_bp, xa),\n )\n\n one_forms_b_t = gs.transpose(one_forms_b, (0, 1, 3, 2))\n xb = one_forms_b_t - one_forms_bp_t\n xb_0 = gs.matmul(\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\n gs.matmul(gs.transpose(xb, (0, 1, 3, 2)), one_forms_bp_t)\n - gs.matmul(one_forms_bp, xb),\n )\n\n return self.d1 * gs.sum(\n gs.einsum(\n \"...bii->...b\",\n gs.matmul(\n xa_0,\n gs.matmul(\n inv_surface_metrics_bp, gs.transpose(xb_0, axes=(0, 1, 3, 2))\n ),\n ),\n )\n * areas_bp\n )","def matrix_mult(m1, m2):\n\ttemp = []\n\tfor i in range(len(m1)):\n\t\te = []\n\t\tfor j in range(len(m2[0])):\n\t\t\te.append(row_times_column(m1,i,m2,j))\n\t\ttemp.append(e)\n\treturn temp","def outer_product(x,y):\n\n return x[:,0]*y[:,1] -x[:,1]*y[:,0]","def l2_norm(pattern):\n return np.linalg.norm(pattern)","def l2_norm_batch(pattern_stack):\n\n return np.linalg.norm(pattern_stack, axis=0)","def Mxform(x1,y1,x2,y2):\n return Jones.toMueller([[np.dot(x2,x1), np.dot(x2, y1)], [np.dot(y2,x1), np.dot(y2,y1)]])","def strassen(m1, m2):\n \n if ((m1.shape[0] % 2 == 0) or (m1.shape[0] == 1)):\n n = m1.shape[0] \n else:\n n = m1.shape[0] + 1\n result = np.zeros((n, n), dtype = int)\n \n if (n == 1):\n result[0][0] = m1[0][0] * m2[0][0]\n else:\n new = n//2\n \n a11, a12, a21, a22 = m1[:new, :new], m1[new:, :new], m1[:new, new:], m1[new:, new:]\n b11, b12, b21, b22 = m2[:new, :new], m2[new:, :new], m2[:new, new:], m2[new:, new:]\n \n p1 = strassen(a11, b12 - b22)\n p2 = strassen(a11 + a12, b22)\n p3 = strassen(a21 + a22, b11)\n p4 = strassen(a22, b21 - b11)\n p5 = strassen(a11 + a22, b11 + b22)\n p6 = strassen(a12 - a22, b21 + b22)\n p7 = strassen(a11 - a21, b11 + b12)\n \n result[:new, :new] = p5 + p4 - p2 + p6\n result[new:, :new] = p1 + p2\n result[:new, new:] = p3 + p4 \n result[new:, new:] = p5 + p1 - p3 - p7\n \n return result","def __mul__(self, other):\n #\n # TODO - your code here\n #\n final_matrix = []\n for i in range(self.h):\n temp_row = []\n for j in range(other.w):\n # take dot-product of row of\n # matrix in 1st arg with col of\n # matrix in 2nd arg\n temp_row.append(dot_product(get_row(self.g, i), get_col(other.g, j)))\n final_matrix.append(temp_row)\n return Matrix(final_matrix)\n # TODO - your code here","def strassen_dot(x, y):\n# X = (A B) Y = (E F)\n# (C D) (G H)\n# \n# A through H -- (n/2 x n/2) matrices\n# \n# let P1 = A(F-H), P2 = (A+B)H, P3 = (C+D)E, P4 = D(G-E), \n# \n# P5 = (A+D)(E+H), P6 = (B-D)(G+H), P7 = (A-C)(E+F)\n# \n# XY = (AE+BG AF+BH) = (P5+P4-P2+P6 P1+P2 )\n# (CE+DG CF+DH) (P3+P4 P1+P5-P3-P7)\n\n n = x.shape[0]\n originalN = n\n \n # base case (n<=8)\n if n <= 8:\n return brute_multiply(x, y)\n \n n_is_power_of_two = (n & (n-1) == 0)\n\n if not n_is_power_of_two:\n # count the closest power of 2 above n\n # and resize x and y matrices\n newN = upper_pow_2(n)\n \n n = newN\n \n nX = np.zeros( (n, n) )\n# mapping x on to nX:\n nX[:originalN, :originalN] = x\n x = nX\n \n nY = np.zeros( (n, n) )\n# mapping y on to nY:\n nY[:originalN, :originalN] = y\n y = nY\n \n \n a = x[ 0:n//2, 0:n//2 ]\n b = x[ 0:n//2, n//2: ]\n c = x[ n//2:, 0:n//2 ]\n d = x[ n//2:, n//2: ]\n\n e = y[ 0:n//2, 0:n//2 ]\n f = y[ 0:n//2, n//2: ]\n g = y[ n//2:, 0:n//2 ]\n h = y[ n//2:, n//2: ]\n \n# let P1 = A(F-H), P2 = (A+B)H, P3 = (C+D)E, P4 = D(G-E), \n# \n# P5 = (A+D)(E+H), P6 = (B-D)(G+H), P7 = (A-C)(E+F)\n \n # recursively compute 7 (SEVEN) products P1 .. P7\n p1 = strassen_dot(a, f-h)\n p2 = strassen_dot(a+b, h)\n p3 = strassen_dot(c+d, e)\n p4 = strassen_dot(d, g-e)\n p5 = strassen_dot(a+d, e+h)\n p6 = strassen_dot(b-d, g+h)\n p7 = strassen_dot(a-c, e+f)\n \n# do the necessary (clever) additions & substractions with P1..P7\n \n# XY = (AE+BG AF+BH) = (P5+P4-P2+P6 P1+P2 )\n# (CE+DG CF+DH) (P3+P4 P1+P5-P3-P7)\n res1 = np.hstack( (p5+p4-p2+p6, p1+p2) )\n res2 = np.hstack( (p3+p4, p1+p5-p3-p7) )\n res = np.vstack( (res1, res2) )\n\n return res[:originalN, :originalN]","def inner_product_similarity(a: torch.Tensor, b: torch.Tensor, dim=1) -> torch.Tensor:\n outputs = (a * b).sum(dim=dim)\n return outputs","def normalize(self):\n det = self._mat[0][0]*self._mat[1][1] - self._mat[0][1]*self._mat[1][0]\n for i in range(2):\n for j in range(2):\n self._mat[i][j] = (self._mat[i][j])/(np.sqrt(det))","def inner_product(alpha, F, beta):\n return np.dot(alpha, np.dot(F, beta))","def tensor_outer_product(tensor1, tensor2):\n shape_1 = tensor1.shape\n shape_2 = tensor2.shape\n s1 = len(shape_1)\n s2 = len(shape_2)\n \n shape_1 = shape_1 + (1, )*s2\n shape_2 = (1, )*s1 + shape_2\n return np.reshape(tensor1, shape_1) * np.reshape(tensor2, shape_2)","def StrassenMatrixM(a, b):\r\n if len(a) != 2 or len(a[0]) != 2 or len(b) != 2 or len(b[0]) != 2:\r\n raise Exception('Matrices should be 2x2!')\r\n print(a[0][0] * b[0][1] + a[0][1] * b[1][1])\r\n matrix = [[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],\r\n [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]]]\r\n\r\n return matrix","def inner_prod(x, y):\n z = torch.zeros(2, dtype=torch.double, device=x.device)\n\n if len(list(x.size())) == 2 and len(list(y.size())) == 2:\n z[0] = torch.dot(x[0], y[0]) - torch.dot(-x[1], y[1])\n z[1] = torch.dot(x[0], y[1]) + torch.dot(-x[1], y[0])\n\n if len(list(x.size())) == 1 and len(list(y.size())) == 1:\n z[0] = (x[0] * y[0]) - (-x[1] * y[1])\n z[1] = (x[0] * y[1]) + (-x[1] * y[0])\n\n return z","def outer_product(A, B): \n print(A)\n print(B)\n A_rows = len(A)\n A_columns = len(A[0])\n\n B_rows = len(B)\n B_columns = len(B[0])\n \n if A_columns == 1 and B_rows == 1:\n \n outer_product = []\n\n # multi-line list comprehension for outer product\n [outer_product.append([A[i][0] * B[0][j] for j in range(B_columns)]) \n for i in range(A_rows)]\n\n return outer_product\n\n else:\n print(\"dimensions of vector do not match.\")","def solve_2x2(matrix: FieldMatrix,\n rhs: Fields) -> OutputFields:\n _validate_matrix_shape(matrix, (2, 2))\n\n a, b = matrix[0]\n c, d = matrix[1]\n e, f = rhs\n\n inv_factor = det_2x2(matrix)\n\n return [\n tf.nest.map_structure(tf.math.divide_no_nan, det_2x2([\n [e, b],\n [f, d],\n ]), inv_factor),\n tf.nest.map_structure(tf.math.divide_no_nan, det_2x2([\n [a, e],\n [c, f],\n ]), inv_factor),\n ]","def _kronecker_product(mat1: tf.Tensor, mat2: tf.Tensor) -> tf.Tensor:\n m1, n1 = mat1.get_shape().as_list()\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\n m2, n2 = mat2.get_shape().as_list()\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])","def prod_mat(self,other):\n [rs,cs],[ro,co] = self.D,other.D\n assert cs == ro, \"tailles incompatibles\"\n return Mat([rs,co], lambda i,j : prod_scal(self.ligne(i),other.col(j)))","def _outer(a, b):\n a_flat = torch.reshape(a, [-1])\n b_flat = torch.reshape(b, [-1])\n a_mul = torch.unsqueeze(a_flat, dim=-1)\n b_mul = torch.unsqueeze(b_flat, dim=0)\n return a_mul * b_mul","def pfm(pattern_sequence):\n if isinstance(pattern_sequence, Pattern):\n sequences = []\n for match_sequences in pattern_sequence.matchtable_pset.match_sequences.itervalues():\n for strand, sequence in match_sequences:\n if strand == 2:\n sequences.append(revcomp(sequence))\n else:\n sequences.append(sequence)\n else:\n sequences = pattern_sequence\n\n ncol = len(sequences[0])\n matrix = {\n 'a': [0] * ncol,\n 't': [0] * ncol,\n 'c': [0] * ncol,\n 'g': [0] * ncol,\n }\n total = [0] * ncol\n\n for s in sequences:\n for i, j in enumerate(s):\n matrix.get(j)[i] += 1\n total[i] += 1\n\n # Normalization\n for i in xrange(ncol):\n matrix.get('a')[i] = float(matrix.get('a')[i]) / total[i]\n matrix.get('t')[i] = float(matrix.get('t')[i]) / total[i]\n matrix.get('c')[i] = float(matrix.get('c')[i]) / total[i]\n matrix.get('g')[i] = float(matrix.get('g')[i]) / total[i]\n\n return matrix","def recursive_multiply(a, b):\n if len(a) == 2:\n return naive_multiply(a, b)\n\n a11 = a[0:int(len(a) / 2)]\n for index, row in enumerate(a11):\n a11[index] = row[0:int(len(row) / 2)]\n\n a12 = a[0:int(len(a) / 2)]\n for index, row in enumerate(a12):\n a12[index] = row[int(len(a) / 2):len(a)]\n\n a21 = a[int(len(a) / 2):len(a)]\n for index, row in enumerate(a21):\n a21[index] = row[0:int(len(row) / 2)]\n\n a22 = a[int(len(a) / 2):len(a)]\n for index, row in enumerate(a22):\n a22[index] = row[int(len(a) / 2):len(a)]\n\n b11 = b[0:int(len(b) / 2)]\n for index, row in enumerate(b11):\n b11[index] = row[0:int(len(row) / 2)]\n\n b12 = b[0:int(len(b) / 2)]\n for index, row in enumerate(b12):\n b12[index] = row[int(len(b) / 2):len(b)]\n\n b21 = b[int(len(b) / 2):len(b)]\n for index, row in enumerate(b21):\n b21[index] = row[0:int(len(row) / 2)]\n\n b22 = b[int(len(b) / 2):len(b)]\n for index, row in enumerate(b22):\n b22[index] = row[int(len(b) / 2):len(b)]\n\n c11 = matrix_add(recursive_multiply(a11, b11), recursive_multiply(a12, b21)) # C11 = A11*B11 + A12*B21\n c12 = matrix_add(recursive_multiply(a11, b12), recursive_multiply(a12, b22)) # C12 = A11*B12 + A12*B22\n c21 = matrix_add(recursive_multiply(a21, b11), recursive_multiply(a22, b21)) # C21 = A21*B11 + A22*B21\n c22 = matrix_add(recursive_multiply(a21, b12), recursive_multiply(a22, b22)) # C22 = A21*B12 + A22*B22\n\n # Append c12 to c11\n for row_index, row in enumerate(c11):\n for col_index, col in enumerate(c12):\n row.append(c12[row_index][col_index])\n\n # Append c22 to c21\n for row_index, row in enumerate(c21):\n for col_index, col in enumerate(c12):\n row.append(c22[row_index][col_index])\n\n # Append c21 to c11\n for i in c21:\n c11.append(i)\n\n return c11","def outer_product(x):\n return keras.backend.batch_dot(\n x[0]\n , x[1]\n , axes=[1,1]\n ) / x[0].get_shape().as_list()[1]","def _inner_product_c1(self, point_a, point_b, normals_bp, areas_bp):\n dna = self._space.normals(point_a) - normals_bp\n dnb = self._space.normals(point_b) - normals_bp\n return self.c1 * gs.sum(\n gs.einsum(\"...bi,...bi->...b\", dna, dnb) * areas_bp, axis=-1\n )","def robust_outer_product(vec_1, vec_2):\n mantissa_1, exponents_1 = np.frexp(vec_1)\n mantissa_2, exponents_2 = np.frexp(vec_2)\n new_mantissas = mantissa_1[None, :] * mantissa_2[:, None]\n new_exponents = exponents_1[None, :] + exponents_2[:, None]\n return new_mantissas * np.exp2(new_exponents)","def __mul__(self, other):\n # \n # TODO - your code here\n #\n \n result = [];\n row_result = [];\n product = 0;\n \n if(self.w != other.h):\n raise(ValueError, \"Matrices can not multiply for their dimesion doesn't match\"); \n \n for row in self.g:\n row_result = [];\n for j in range(other.w):\n product = dot_product(row,other.get_column(j));\n row_result.append(product);\n result.append(row_result);\n \n return Matrix(result);","def matrix_mult(m1, m2):\n pass","def generate_pattern_grid(words1, words2):\n # Convert word lists to integer arrays\n w1, w2 = (\n np.array([[ord(c) for c in w] for w in words], dtype=np.uint8)\n for words in (words1, words2)\n )\n\n if len(w1) == 0 or len(w2) == 0:\n return np.zeros((len(w1), len(w2)), dtype=np.uint8)\n\n # equality_grid[a, b, i, j] represents whether the ith letter\n # of words1[a] equals the jth letter of words2[b]\n equality_grid = np.zeros((len(w1), len(w2), 5, 5), dtype=bool)\n for i, j in it.product(range(5), range(5)):\n equality_grid[:, :, i, j] = np.equal.outer(w1[:, i], w2[:, j])\n\n patterns = np.zeros((len(w1), len(w2)), dtype=np.uint8)\n three_pows = (3**np.arange(5)).astype(np.uint8)\n for i, tp in enumerate(three_pows):\n # This accounts for yellow squares\n patterns[:, :] += tp * equality_grid[:, :, i, :].any(2)\n # This accounts for green squares\n patterns[:, :] += tp * equality_grid[:, :, i, i]\n\n return patterns","def interior_tensor_product(mx, dim_a, dim_b, e=None):\n assert _np.shape(mx) == (dim_a * dim_b, dim_a * dim_b), \"Dimensions do not agree with matrix size\"\n assert _np.shape(e)[0] == _np.shape(e)[1], \"e should be a square matrix\"\n basis_a = matrix_units(dim_a)\n basis_b = matrix_units(dim_b)\n return sum((_np.trace(_np.dot(mx, _np.kron(unit_a, unit_b).T)) * multikron([unit_a, e, unit_b])\n for unit_a in basis_a for unit_b in basis_b))","def mult(m1, m2):\n assert np.shape(m1) == (2, 3)\n assert np.shape(m2) == (2, 3)\n\n m1_temp = np.vstack((m1, [0, 0, 1]))\n m2_temp = np.vstack((m2, [0, 0, 1]))\n result = m1_temp * m2_temp\n\n return result[:2, :]","def mat_mul(mat1, mat2):\n\n rows1 = len(mat1)\n cols1 = len(mat1[0])\n rows2 = len(mat2)\n cols2 = len(mat2[0])\n\n if cols1 != rows2:\n return None\n else:\n new_matrix = []\n for x in range(rows1):\n aux_row = []\n for y in range(cols2):\n aux_sum = []\n for z in range(cols1):\n aux_sum.append(mat1[x][z] * mat2[z][y])\n aux_row.append(sum(aux_sum))\n new_matrix.append(aux_row)\n\n return new_matrix","def frobenius_inner_prod(mat1, mat2):\n assert mat1.shape==mat2.shape\n # assert isinstance(mat1, Variable) and isinstance(mat2, Variable))\n f = mat1.mul(mat2).sum()\n return f","def transformation_2d(vertices, kernels=KERNELS):\n\t# calculate the transpose matrix of vertices\n\ttranspose = vertices.transpose()\n\t# insert a row of ones in the transpose matrix's end, then insert the result in 'matrices' list\n\tkernels.append(np.append(transpose, [np.ones(len(transpose[0]))], axis=0))\n\t# multiply matrices into 'kernels' list,\n\t# remove the last row (of ones) and calculate the transpose matrix of the result\n\tfinal_transformation_result = np.delete(np.linalg.multi_dot(kernels), 2, 0).transpose()\n\tKERNELS.clear()\n\treturn final_transformation_result","def classical(m1,m2):\n \n n = m1.shape\n result = np.zeros(n, dtype = int)\n\n for i in range(n[0]):\n for j in range(n[0]):\n for k in range(n[0]):\n result[i][j] += m1[i][k] * m2[k][j]\n return result","def complex_mul2d(a, b):\n op = partial(torch.einsum, \"bixy,ioxy->boxy\")\n return torch.stack([\n op(a[..., 0], b[..., 0]) - op(a[..., 1], b[..., 1]),\n op(a[..., 1], b[..., 0]) + op(a[..., 0], b[..., 1])\n ],\n dim=-1)","def __mul__(self,m):\n if type(m) != Matrix:\n raise TypeError('The second argument is not a matrix lol')\n if self.ncols != m.nrows:\n raise ValueError('matrix dot argument has incorrect number of rows')\n new = Matrix(self.nrows,m.ncols)\n columns = m.getCols()\n rowindex = 0\n colindex = 0 \n for row in self.matrix:\n colindex = 0 \n for col in columns:\n summ = 0\n for i,j in zip(row,col):\n summ+= i*j \n new.matrix[rowindex][colindex] = summ\n print new.matrix\n colindex += 1 \n rowindex+=1\n return new","def matrix_mult(A,B):\n\n m = len(A)\n p = len(B)\n n = len(B[0])\n AB = []\n for i in range(m):\n AB.append([])\n for j in range(n):\n total = 0\n for k in range(p):\n total += A[i][k] * B[k][j]\n AB[i].append(total)\n return AB","def _e_2d_(p, a):\r\n diff = a - p[np.newaxis, :]\r\n return np.einsum('ij,ij->i', diff, diff)","def mul(self,mat1,mat2):\n if(isinstance(mat2,int)==True):\n result = [[mat1[i][j] * mat2 for j in range(len(mat1[0]))] for i in range(len(mat1))]\n self.out = result\n return self.out\n elif(len(mat1[0])==len(mat2)):\n result = [[sum(a*b for a,b in zip(i,j)) for j in zip(*mat2)] for i in mat1]\n self.out = result\n return self.out","def mmultiply(self, matrix):\n try:\n result_matrix = [[0 for row in range(len(self.matrix))] for col in range(len(matrix[0]))]\n for i in range(len(self.matrix)):\n for j in range(len(matrix[0])):\n for k in range(len(matrix)):\n result_matrix[i][j] += self.matrix[i][k] * matrix[k][j]\n self.matrix = result_matrix\n except IndexError:\n pass\n pass","def test_two_qubit_weyl_decomposition_bgate(self):\n for k1l, k1r, k2l, k2r in K1K2S:\n k1 = np.kron(k1l.data, k1r.data)\n k2 = np.kron(k2l.data, k2r.data)\n a = Ud(np.pi / 4, np.pi / 8, 0)\n self.check_two_qubit_weyl_decomposition(k1 @ a @ k2)","def matrix_as_psfm(matrix):\n return [[2**(ep-2) for ep in row] for row in matrix]","def multiply_by_left_matrix(matrix, img):\n first = np.inner(matrix[0], img)\n second = np.inner(matrix[1], img)\n third = np.inner(matrix[2], img)\n\n res = np.dstack((first, second, third))\n return res","def matrixMul(self, matrix, matrix2):\n matrix0 = matrix[:]\n matrix[0] = matrix0[0] * matrix2[0] + matrix0[2]*matrix2[1] # + matrix0[4]*0\n matrix[1] = matrix0[1] * matrix2[0] + matrix0[3]*matrix2[1] # + matrix0[5]*0\n matrix[2] = matrix0[0] * matrix2[2] + matrix0[2]*matrix2[3] # + matrix0[4]*0\n matrix[3] = matrix0[1] * matrix2[2] + matrix0[3]*matrix2[3] # + matrix0[5]*0\n matrix[4] = matrix0[0] * matrix2[4] + matrix0[2]*matrix2[5] + matrix0[4]\n matrix[5] = matrix0[1] * matrix2[4] + matrix0[3]*matrix2[5] + matrix0[5]","def python_nonsquare_matrix_mult(matrix):\n\n transposed_matrix = np.zeros([matrix.shape[1],matrix.shape[0]])\n start = time.time()\n # for i in range(matrix.shape[0]):\n # for j in range(matrix.shape[1]):\n # transposed_matrix[j,i] = matrix[i,j]\n\n transposed_matrix = np.transpose(matrix)\n product = matrix.dot(transposed_matrix)\n\n # transposed_matrix = np.transpose(matrix)\n end = time.time()-start\n\n # print(\"Python Golden Transpose: %s\" % product)\n # print('python transpose time: %.2E' % end)\n return [product, end]","def matmul(xs: List[List[float]],\n ys: List[List[float]]) -> List[List[float]]:\n product = []\n for x_row in range(len(xs)):\n row = []\n for y_col in range(len(ys[0])):\n col = [ys[y_row][y_col] for y_row in range(len(ys))]\n row.append(Math.dot(xs[x_row], col))\n product.append(row)\n return product","def matrix_vector_prod(m,u):\n each_product = []\n for v in m:\n each_product.append(dot_prod(v, u))\n return each_product","def _calc_matrix(self):\n\t\tz = self.zoom\n\t\talloc = self.allocation\n\t\tif self.image:\n\t\t\tiw, ih = self.image.get_width(), self.image.get_height()\n\t\telse:\n\t\t\tiw, ih = 0, 0\n#\t\tif __debug__: print self._vadj.lower, self._vadj.value, self._vadj.upper\n\t\t\n\t\ti2w = cairo.Matrix(\n\t\t\tz,0,\n\t\t\t0,z,\n\t\t\t-self._hadj.value if alloc.width < iw*z else (alloc.width - iw*z)/2, \n\t\t\t-self._vadj.value if alloc.height < ih*z else (alloc.height - ih*z)/2,\n\t\t\t)\n\t\t\n\t\tself._i2w_matrix = i2w\n\t\t\n\t\tw2i = cairo.Matrix(*i2w) #copy\n\t\tw2i.invert()\n\t\tself._w2i_matrix = w2i","def test_l2_metric_inner_product_vectorization(\n self,\n l2_metric_s2,\n times,\n n_landmark_sets,\n landmarks_a,\n landmarks_b,\n landmarks_c,\n ):\n landmarks_ab = l2_metric_s2.geodesic(landmarks_a, landmarks_b)\n landmarks_bc = l2_metric_s2.geodesic(landmarks_b, landmarks_c)\n landmarks_ab = landmarks_ab(times)\n landmarks_bc = landmarks_bc(times)\n\n tangent_vecs = l2_metric_s2.log(point=landmarks_bc, base_point=landmarks_ab)\n\n result = l2_metric_s2.inner_product(tangent_vecs, tangent_vecs, landmarks_ab)\n\n self.assertAllClose(gs.shape(result), (n_landmark_sets,))","def __mul__(left, right):\n \n if isinstance(left, Plucker) and isinstance(right, Plucker):\n # reciprocal product\n return np.dot(left.uw, right.v) + np.dot(right.uw, left.v)\n elif isinstance(left, Plucker) and arg.ismatrix(right, (4,None)):\n return left.skew @ right; # postmultiply by 4xN","def kronecker_product(mat1, mat2):\n m1, n1 = mat1.get_shape().as_list()\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\n m2, n2 = mat2.get_shape().as_list()\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])","def outer_prod(x, y):\n if len(list(x.size())) != 2 or len(list(y.size())) != 2:\n raise ValueError(\"An input is not of the right dimension.\")\n\n z = torch.zeros(2, x.size()[1], y.size()[1], dtype=torch.double, device=x.device)\n z[0] = torch.ger(x[0], y[0]) - torch.ger(x[1], -y[1])\n z[1] = torch.ger(x[0], -y[1]) + torch.ger(x[1], y[0])\n\n return z","def _prod_vectorized(M1, M2):\n sh1 = M1.shape\n sh2 = M2.shape\n assert len(sh1) >= 2\n assert len(sh2) >= 2\n assert sh1[-1] == sh2[-2]\n\n ndim1 = len(sh1)\n t1_index = list(xrange(ndim1-2)) + [ndim1-1, ndim1-2]\n return np.sum(np.transpose(M1, t1_index)[..., np.newaxis] *\n M2[..., np.newaxis, :], -3)","def _mps_AA(self, A1, A2):\n Dl, d1, _ = A1.shape\n _, d2, Dr = A2.shape\n return np.reshape(np.tensordot(A1, A2, axes=(2, 0)), [Dl, d1 * d2, Dr])","def hadmardProduct(self, a,b):\n\t\tnumRows = len(a)\n\t\tnumCols = len(a[0])\n\t\n\t\treturn [[a[j][i] * b[j][i] for i in range(numCols)] for j in range(numRows)]","def _inner_product_a1(self, ginvdga, ginvdgb, areas_bp):\n return self.a1 * gs.sum(\n gs.einsum(\"...bii->...b\", gs.matmul(ginvdga, ginvdgb)) * areas_bp,\n axis=-1,\n )","def divide(m1,m2):\n \n if ((m1.shape[0] % 2 == 0) or (m1.shape[0] == 1)):\n n = m1.shape[0]\n else:\n n = m1.shape[0] + 1\n result = np.zeros((n, n), dtype = int)\n \n if (n == 1):\n result[0][0] = m1[0][0] * m2[0][0]\n else:\n new = n//2\n \n a11, a12, a21, a22 = m1[:new, :new], m1[new:, :new], m1[:new, new:], m1[new:, new:]\n b11, b12, b21, b22 = m2[:new, :new], m2[new:, :new], m2[:new, new:], m2[new:, new:]\n \n result[:new, :new] = divide(a11,b11) + divide(a12,b21)\n result[new:, :new] = divide(a11,b12) + divide(a12,b22)\n result[:new, new:] = divide(a21,b11) + divide(a22,b21)\n result[new:, new:] = divide(a21,b12) + divide(a22,b22)\n \n return result","def get_molecular_matrix(single_body, two_body):\n x, y = single_body.shape\n func = np.vectorize(round_custom)\n _new_dim = x * y\n single_one_dim = single_body.reshape(_new_dim, 1)\n two_body_two_dim = func(two_body.reshape(_new_dim, _new_dim))\n idx = 0\n x, _ = two_body_two_dim.shape\n while idx < x:\n two_body_two_dim[idx][idx] = round_custom(single_one_dim[idx][0])\n idx += 1\n return two_body_two_dim","def innerprod_q2(q1, q2):\n T = q1.shape[1]\n val = sum(sum(q1 * q2)) / T\n\n return (val)","def matrix_mult(m1, m2):\n output = []\n for rowIndex, row in enumerate(m1): #go through rows in m1\n new_row = []\n for columnIndex in range(len(m2[0])): #go through indices for each column of m2\n sum = 0\n for index3 in range(len(row)):\n product = m1[rowIndex][index3] * m2[index3][columnIndex]\n sum += product\n new_row.append(sum)\n output.append(new_row)\n return output\n \n \n #output = []\n #first for loop corresponds to the rows of my output matrix and loops through the rows of m1 (enumerate)\n #create an empty new row\n # second for loop, loops through columns of m2\n # create sum variable, initialize it with zero\n # third for loop, multiplies the index of the row in m1 times the index of the column in m2\n # add sum to product and assign this to the sum variable\n # append sum to new row\n # append new row to output\n # return output","def matrix_mult_matrix(matrix_a, matrix_b):\n m = len(matrix_a)\n n = len(matrix_b)\n result = []\n matrix_b_t = transpose_matrix(matrix_b)\n for i in xrange(m):\n row = []\n\tfor j in xrange(m):\n row.append(dot_product(matrix_a[i], matrix_b_t[j]))\n\tresult.append(row)\n return result","def multiplicand_2(p):\n m2 = cddr(p) # (m2 m3 ...)\n rests = cdr(m2) # (m3...)\n if isNull(rests):\n return car(m2)\n else:\n restp = convertToPythonList(cdr(rests))\n return make_product_2(car(m2), car(rests), *restp)","def similarity_transform_2d(v, mapping, alpha = 1):\r\n p_wgt = vec2(0, 0)\r\n q_wgt = vec2(0, 0)\r\n w = len(mapping)*[None]\r\n w_sum = 0\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n x = mp[0].x - v.x\r\n y = mp[0].y - v.y\r\n if (x == 0 and y == 0): return mp[1]\r\n w[i] = 1/((x*x + y*y) ** alpha)\r\n p_wgt += mp[0]*w[i]\r\n q_wgt += mp[1]*w[i]\r\n w_sum += w[i]\r\n p_wgt /= w_sum\r\n q_wgt /= w_sum\r\n mu = 0\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n p_adj = mp[0] - p_wgt\r\n mu += w[i]*(p_adj.dot(p_adj))\r\n A_fac = mat2([v.x - p_wgt.x, v.y - p_wgt.y, v.y - p_wgt.y, p_wgt.x - v.x])\r\n v_out = vec2(0, 0)\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n p_adj = mp[0] - p_wgt\r\n q_adj = mp[1] - q_wgt\r\n A = mat2([p_adj.x, p_adj.y, p_adj.y, -p_adj.x])*A_fac*w[i]\r\n A = A.transpose()\r\n v_out += A*q_adj/mu\r\n v_out += q_wgt\r\n return v_out","def outer_subspace_product(subspace1: list, subspace2: list, reverse: bool = False) -> list:\n assert isinstance(subspace1, list), 'Please provide subspace as a list of str.'\n assert isinstance(subspace2, list), 'Please provide subspace as a list of str.'\n\n product_subspace = []\n if subspace1 == []: return subspace2\n if subspace2 == []: return subspace1\n\n for basisvector1 in subspace1:\n for basisvector2 in subspace2:\n if reverse:\n product_subspace += [basisvector2 + basisvector1]\n else:\n product_subspace += [basisvector1 + basisvector2]\n return product_subspace","def mosaicing(img, pattern):\n imout = np.zeros((img.shape[0], img.shape[1]))\n for i in range(2):\n for j in range(2):\n imout[i::2, j::2] = img[i::2, j::2, pattern[i][j]]\n \n return imout","def mat_mul(mat1, mat2):\n\n if len(mat1[0]) == len(mat2):\n\n mat2 = matrix_transpose(mat2)\n response = []\n\n for row in range(len(mat1)):\n response.append(\n [\n sum(dot_product(mat1[row], mat2[column]))\n for column in range(len(mat2))\n ]\n )\n\n return response\n\n else:\n return None","def form_square_block_matrix(mat1,mat2):\n if mat1.cols==1:\n mat3 = mp.matrix(mat1.rows+mat2.rows,1)\n mat3[:mat1.rows] = mat1[:]\n mat3[mat1.rows:mat3.rows] = mat2[:]\n else:\n mat3 = mp.matrix(mat1.rows+mat2.rows, mat1.rows+mat2.rows)\n mat3[:mat1.rows,:mat1.rows] = mat1[:,:]\n mat3[mat1.rows:mat3.rows,mat1.rows:mat3.rows] = mat2[:,:]\n return mat3","def _prepare_outer_matrix(self):\n self._mat_plane = numpy.array([\n self._scaling[0], 0, 0, 0,\n 0, self._scaling[1], 0, 0,\n 0, 0, 1, 0,\n self.i_border[0], -self.i_border[1], 0, 1\n ], dtype=numpy.float32)","def pattern_modifier(pattern, X, y):\n mod_pattern = pattern.reshape(n_channels, n_samples)\n mod_pattern = mod_pattern * kernel[np.newaxis, :]\n return mod_pattern.reshape(pattern.shape)","def pair_product(x1, x2):\n return np.multiply(x1, x2)","def det_matrix_2x2(m: list):\n return m[0][0]*m[1][1] - m[0][1]*m[1][0]","def __mul__(self,v2):\n\t\tif(isinstance(v2,Vect2D)):\n\t\t\treturn np.dot(self._vec,v2._vec)\n\t\telse:\n\t\t\treturn Vect2D(v2*self._vec)","def rotate2D(self, matrix) -> None:\n N = len(matrix)\n\n # In case of N is odd, the innermost square belt is just one cell, no need of rotating.\n for i in range(0,int(N/2)): # outer loop for each square belt\t\t\t\n for j in range(i,N-i-1): # N-i group in the i-th square belt\n #print(i,j)\n tmp = matrix[i][j]\n matrix[i][j] = matrix[N-j-1][i]\n matrix[N-j-1][i] = matrix[N-i-1][N-j-1]\n matrix[N-i-1][N-j-1] = matrix[j][N-i-1]\n matrix[j][N-i-1] = tmp\n #print(matrix)","def _surface_metric_matrices_from_one_forms(one_forms):\n ndim = one_forms.ndim\n transpose_axes = tuple(range(ndim - 2)) + tuple(reversed(range(ndim - 2, ndim)))\n transposed_one_forms = gs.transpose(one_forms, axes=transpose_axes)\n return gs.matmul(one_forms, transposed_one_forms)","def basic_geometric_product(obj1, obj2):\n def mul_table(b1, b2):\n return MV.base_mul_table[(b1, b2)]\n\n obj12 = bilinear_product(obj1 * obj2, mul_table)\n\n return obj12","def _inner_product_a2(\n self, tangent_vec_a, tangent_vec_b, base_point, vertex_areas_bp\n ):\n laplacian_at_base_point = self._space.laplacian(base_point)\n return self.a2 * gs.sum(\n gs.einsum(\n \"...bi,...bi->...b\",\n laplacian_at_base_point(tangent_vec_a),\n laplacian_at_base_point(tangent_vec_b),\n )\n / vertex_areas_bp,\n axis=-1,\n )","def _pmatrix(kn_u, kn_d, thickness):\n p = np.zeros((kn_u.size, 4, 4), np.complex128)\n\n p0 = np.exp(complex(0, 1) * kn_u * thickness)\n p1 = np.exp(complex(0, 1) * kn_d * thickness)\n\n p[:, 0, 0] = 1 / p0\n p[:, 1, 1] = p0\n p[:, 2, 2] = 1 / p1\n p[:, 3, 3] = p1\n\n return p","def _inner_product_b1(self, ginvdga, ginvdgb, areas_bp):\n return self.b1 * gs.sum(\n gs.einsum(\"...bii->...b\", ginvdga)\n * gs.einsum(\"...bii->...b\", ginvdgb)\n * areas_bp,\n axis=-1,\n )","def _z2matvecmul(self, mat, vec):\n prod = np.mod(np.dot(mat, vec), 2)\n return prod","def row_matrix_col_4d(a, b, A):\n\n\treturn (a[0]*A[0][0]*b[0] + a[1]*A[1][0]*b[0] + a[2]*A[2][0]*b[0] + a[3]*A[3][0]*b[0] +\n\t a[0]*A[0][1]*b[1] + a[1]*A[1][1]*b[1] + a[2]*A[2][1]*b[1] + a[3]*A[3][1]*b[1] +\n\t a[0]*A[0][2]*b[2] + a[1]*A[1][2]*b[2] + a[2]*A[2][2]*b[2] + a[3]*A[3][2]*b[2] +\n\t a[0]*A[0][3]*b[3] + a[1]*A[1][3]*b[3] + a[2]*A[2][3]*b[3] + a[3]*A[3][3]*b[3])","def same_side_product(p, q, a, b):\n return line_ccw(a, b, p) * line_ccw(a, b, q)","def _pseudo_inv22sym_vectorized(M):\n assert M.ndim == 3\n assert M.shape[-2:] == (2, 2)\n M_inv = np.empty_like(M)\n prod1 = M[:, 0, 0]*M[:, 1, 1]\n delta = prod1 - M[:, 0, 1]*M[:, 1, 0]\n rank2 = (np.abs(delta) > 1e-8*np.abs(prod1))\n\n if np.all(rank2):\n # Normal 'optimized' flow.\n M_inv[:, 0, 0] = M[:, 1, 1] / delta\n M_inv[:, 0, 1] = -M[:, 0, 1] / delta\n M_inv[:, 1, 0] = -M[:, 1, 0] / delta\n M_inv[:, 1, 1] = M[:, 0, 0] / delta\n else:\n # 'Pathologic' flow.\n # Here we have to deal with 2 sub-cases\n # 1) First sub-case: matrices of rank 2:\n delta = delta[rank2]\n M_inv[rank2, 0, 0] = M[rank2, 1, 1] / delta\n M_inv[rank2, 0, 1] = -M[rank2, 0, 1] / delta\n M_inv[rank2, 1, 0] = -M[rank2, 1, 0] / delta\n M_inv[rank2, 1, 1] = M[rank2, 0, 0] / delta\n # 2) Second sub-case: rank-deficient matrices of rank 0 and 1:\n rank01 = ~rank2\n tr = M[rank01, 0, 0] + M[rank01, 1, 1]\n tr_zeros = (np.abs(tr) < 1.e-8)\n sq_tr_inv = (1.-tr_zeros) / (tr**2+tr_zeros)\n #sq_tr_inv = 1. / tr**2\n M_inv[rank01, 0, 0] = M[rank01, 0, 0] * sq_tr_inv\n M_inv[rank01, 0, 1] = M[rank01, 0, 1] * sq_tr_inv\n M_inv[rank01, 1, 0] = M[rank01, 1, 0] * sq_tr_inv\n M_inv[rank01, 1, 1] = M[rank01, 1, 1] * sq_tr_inv\n\n return M_inv","def square_matrix_multiply(a, b):\n n = len(a)\n c = [[0]*n for _ in range(n)]\n for i in range(n):\n for j in range(n):\n sm = 0\n for k in range(n):\n sm += (a[i][k] * b[k][j])\n c[i][j] = sm\n\n return c","def batch_outer_product(a, b):\n a, b = normalize_and_check_ndim([a, b], 2)\n # This is a batchwise version of the matrix multiplication approach\n # used for outer_product(), see explanation there.\n return a[:, :, np.newaxis] * b[:, np.newaxis, :]","def _mult_poly_matrix_poly(p, mat_y):\n\n mult_op = lambda q: np.convolve(p, q)\n p_times_y = np.apply_along_axis(mult_op, 2, mat_y)\n return p_times_y","def similarity_transformation(rot, mat):\n return np.dot(rot, np.dot(mat, np.linalg.inv(rot)))","def similarity_transformation(rot, mat):\n return np.dot(rot, np.dot(mat, np.linalg.inv(rot)))","def normalize_adj(adj):\n\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = np.diag(d_inv_sqrt)\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt)","def flat_to_2d(data, det_width):\n return data.reshape((data.shape[0], data.shape[1], det_width, det_width))","def reconstruct(A, B, z):\n f = factorint(igcd(A, B))\n for p, e in f.items():\n if e != 1:\n raise ValueError('a and b should be square-free')\n z *= p\n return z","def matrix_multiplication_loop(x_matrix, y_matrix):\n result = []\n for i, row in enumerate(x_matrix):\n row_vector = []\n for j in range(len(y_matrix[0])):\n product = 0\n for k in range(len(row)):\n product += x_matrix[i][k] * y_matrix[k][j]\n row_vector.append(product)\n result.append(row_vector)\n return result","def norm2_r(self, a: np.ndarray) -> float:\n return a.T @ a"],"string":"[\n \"def inner_product(pattern_one, pattern_two):\\n\\n return np.sum(np.multiply(pattern_one, pattern_two))\",\n \"def inner_product_batch(pattern_stack_one, pattern_num_one, pattern_stack_two, pattern_num_two):\\n\\n \\\"\\\"\\\"\\n Notice that the two stacks can be different. So we can not deduce the lower triangular pattern from the \\n other half.\\n \\\"\\\"\\\"\\n holder = np.zeros((pattern_num_one, pattern_num_two))\\n for l in range(pattern_num_one):\\n for m in range(pattern_num_two):\\n holder[l, m] = np.sum(np.multiply(pattern_stack_one[l], pattern_stack_two[m]))\\n\\n return holder\",\n \"def inner_product_to_L2_square(matrix):\\n\\n length = matrix.shape[0]\\n squared_norm = np.reshape(np.diag(matrix), (length, 1))\\n\\n return squared_norm + np.transpose(squared_norm) - 2 * matrix\",\n \"def l2_square_from_inner_product(matrix):\\n return np.diag(matrix)\",\n \"def _z2matmul(self, left, right):\\n prod = np.mod(np.dot(left, right), 2)\\n return prod\",\n \"def snorm(x):\\n return np.dot(x.flatten().T, x.flatten())\",\n \"def matrix_subs(matrix_2x2, point):\\n arr = []\\n for el in matrix_2x2:\\n arr.append(el.subs(x1, point[0]).subs(x2, point[1]))\\n \\n M = Matrix([[arr[0], arr[1]], [arr[2], arr[3]]])\\n\\n return M\",\n \"def inner_product(state_1, state_2):\\n return numpy.dot(state_1.conjugate(), state_2)\",\n \"def _inner_product_d1(\\n self, one_forms_a, one_forms_b, one_forms_bp, areas_bp, inv_surface_metrics_bp\\n ):\\n one_forms_bp_t = gs.transpose(one_forms_bp, (0, 2, 1))\\n\\n one_forms_a_t = gs.transpose(one_forms_a, (0, 1, 3, 2))\\n xa = one_forms_a_t - one_forms_bp_t\\n\\n xa_0 = gs.matmul(\\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\\n gs.matmul(gs.transpose(xa, (0, 1, 3, 2)), one_forms_bp_t)\\n - gs.matmul(one_forms_bp, xa),\\n )\\n\\n one_forms_b_t = gs.transpose(one_forms_b, (0, 1, 3, 2))\\n xb = one_forms_b_t - one_forms_bp_t\\n xb_0 = gs.matmul(\\n gs.matmul(one_forms_bp_t, inv_surface_metrics_bp),\\n gs.matmul(gs.transpose(xb, (0, 1, 3, 2)), one_forms_bp_t)\\n - gs.matmul(one_forms_bp, xb),\\n )\\n\\n return self.d1 * gs.sum(\\n gs.einsum(\\n \\\"...bii->...b\\\",\\n gs.matmul(\\n xa_0,\\n gs.matmul(\\n inv_surface_metrics_bp, gs.transpose(xb_0, axes=(0, 1, 3, 2))\\n ),\\n ),\\n )\\n * areas_bp\\n )\",\n \"def matrix_mult(m1, m2):\\n\\ttemp = []\\n\\tfor i in range(len(m1)):\\n\\t\\te = []\\n\\t\\tfor j in range(len(m2[0])):\\n\\t\\t\\te.append(row_times_column(m1,i,m2,j))\\n\\t\\ttemp.append(e)\\n\\treturn temp\",\n \"def outer_product(x,y):\\n\\n return x[:,0]*y[:,1] -x[:,1]*y[:,0]\",\n \"def l2_norm(pattern):\\n return np.linalg.norm(pattern)\",\n \"def l2_norm_batch(pattern_stack):\\n\\n return np.linalg.norm(pattern_stack, axis=0)\",\n \"def Mxform(x1,y1,x2,y2):\\n return Jones.toMueller([[np.dot(x2,x1), np.dot(x2, y1)], [np.dot(y2,x1), np.dot(y2,y1)]])\",\n \"def strassen(m1, m2):\\n \\n if ((m1.shape[0] % 2 == 0) or (m1.shape[0] == 1)):\\n n = m1.shape[0] \\n else:\\n n = m1.shape[0] + 1\\n result = np.zeros((n, n), dtype = int)\\n \\n if (n == 1):\\n result[0][0] = m1[0][0] * m2[0][0]\\n else:\\n new = n//2\\n \\n a11, a12, a21, a22 = m1[:new, :new], m1[new:, :new], m1[:new, new:], m1[new:, new:]\\n b11, b12, b21, b22 = m2[:new, :new], m2[new:, :new], m2[:new, new:], m2[new:, new:]\\n \\n p1 = strassen(a11, b12 - b22)\\n p2 = strassen(a11 + a12, b22)\\n p3 = strassen(a21 + a22, b11)\\n p4 = strassen(a22, b21 - b11)\\n p5 = strassen(a11 + a22, b11 + b22)\\n p6 = strassen(a12 - a22, b21 + b22)\\n p7 = strassen(a11 - a21, b11 + b12)\\n \\n result[:new, :new] = p5 + p4 - p2 + p6\\n result[new:, :new] = p1 + p2\\n result[:new, new:] = p3 + p4 \\n result[new:, new:] = p5 + p1 - p3 - p7\\n \\n return result\",\n \"def __mul__(self, other):\\n #\\n # TODO - your code here\\n #\\n final_matrix = []\\n for i in range(self.h):\\n temp_row = []\\n for j in range(other.w):\\n # take dot-product of row of\\n # matrix in 1st arg with col of\\n # matrix in 2nd arg\\n temp_row.append(dot_product(get_row(self.g, i), get_col(other.g, j)))\\n final_matrix.append(temp_row)\\n return Matrix(final_matrix)\\n # TODO - your code here\",\n \"def strassen_dot(x, y):\\n# X = (A B) Y = (E F)\\n# (C D) (G H)\\n# \\n# A through H -- (n/2 x n/2) matrices\\n# \\n# let P1 = A(F-H), P2 = (A+B)H, P3 = (C+D)E, P4 = D(G-E), \\n# \\n# P5 = (A+D)(E+H), P6 = (B-D)(G+H), P7 = (A-C)(E+F)\\n# \\n# XY = (AE+BG AF+BH) = (P5+P4-P2+P6 P1+P2 )\\n# (CE+DG CF+DH) (P3+P4 P1+P5-P3-P7)\\n\\n n = x.shape[0]\\n originalN = n\\n \\n # base case (n<=8)\\n if n <= 8:\\n return brute_multiply(x, y)\\n \\n n_is_power_of_two = (n & (n-1) == 0)\\n\\n if not n_is_power_of_two:\\n # count the closest power of 2 above n\\n # and resize x and y matrices\\n newN = upper_pow_2(n)\\n \\n n = newN\\n \\n nX = np.zeros( (n, n) )\\n# mapping x on to nX:\\n nX[:originalN, :originalN] = x\\n x = nX\\n \\n nY = np.zeros( (n, n) )\\n# mapping y on to nY:\\n nY[:originalN, :originalN] = y\\n y = nY\\n \\n \\n a = x[ 0:n//2, 0:n//2 ]\\n b = x[ 0:n//2, n//2: ]\\n c = x[ n//2:, 0:n//2 ]\\n d = x[ n//2:, n//2: ]\\n\\n e = y[ 0:n//2, 0:n//2 ]\\n f = y[ 0:n//2, n//2: ]\\n g = y[ n//2:, 0:n//2 ]\\n h = y[ n//2:, n//2: ]\\n \\n# let P1 = A(F-H), P2 = (A+B)H, P3 = (C+D)E, P4 = D(G-E), \\n# \\n# P5 = (A+D)(E+H), P6 = (B-D)(G+H), P7 = (A-C)(E+F)\\n \\n # recursively compute 7 (SEVEN) products P1 .. P7\\n p1 = strassen_dot(a, f-h)\\n p2 = strassen_dot(a+b, h)\\n p3 = strassen_dot(c+d, e)\\n p4 = strassen_dot(d, g-e)\\n p5 = strassen_dot(a+d, e+h)\\n p6 = strassen_dot(b-d, g+h)\\n p7 = strassen_dot(a-c, e+f)\\n \\n# do the necessary (clever) additions & substractions with P1..P7\\n \\n# XY = (AE+BG AF+BH) = (P5+P4-P2+P6 P1+P2 )\\n# (CE+DG CF+DH) (P3+P4 P1+P5-P3-P7)\\n res1 = np.hstack( (p5+p4-p2+p6, p1+p2) )\\n res2 = np.hstack( (p3+p4, p1+p5-p3-p7) )\\n res = np.vstack( (res1, res2) )\\n\\n return res[:originalN, :originalN]\",\n \"def inner_product_similarity(a: torch.Tensor, b: torch.Tensor, dim=1) -> torch.Tensor:\\n outputs = (a * b).sum(dim=dim)\\n return outputs\",\n \"def normalize(self):\\n det = self._mat[0][0]*self._mat[1][1] - self._mat[0][1]*self._mat[1][0]\\n for i in range(2):\\n for j in range(2):\\n self._mat[i][j] = (self._mat[i][j])/(np.sqrt(det))\",\n \"def inner_product(alpha, F, beta):\\n return np.dot(alpha, np.dot(F, beta))\",\n \"def tensor_outer_product(tensor1, tensor2):\\n shape_1 = tensor1.shape\\n shape_2 = tensor2.shape\\n s1 = len(shape_1)\\n s2 = len(shape_2)\\n \\n shape_1 = shape_1 + (1, )*s2\\n shape_2 = (1, )*s1 + shape_2\\n return np.reshape(tensor1, shape_1) * np.reshape(tensor2, shape_2)\",\n \"def StrassenMatrixM(a, b):\\r\\n if len(a) != 2 or len(a[0]) != 2 or len(b) != 2 or len(b[0]) != 2:\\r\\n raise Exception('Matrices should be 2x2!')\\r\\n print(a[0][0] * b[0][1] + a[0][1] * b[1][1])\\r\\n matrix = [[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],\\r\\n [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]]]\\r\\n\\r\\n return matrix\",\n \"def inner_prod(x, y):\\n z = torch.zeros(2, dtype=torch.double, device=x.device)\\n\\n if len(list(x.size())) == 2 and len(list(y.size())) == 2:\\n z[0] = torch.dot(x[0], y[0]) - torch.dot(-x[1], y[1])\\n z[1] = torch.dot(x[0], y[1]) + torch.dot(-x[1], y[0])\\n\\n if len(list(x.size())) == 1 and len(list(y.size())) == 1:\\n z[0] = (x[0] * y[0]) - (-x[1] * y[1])\\n z[1] = (x[0] * y[1]) + (-x[1] * y[0])\\n\\n return z\",\n \"def outer_product(A, B): \\n print(A)\\n print(B)\\n A_rows = len(A)\\n A_columns = len(A[0])\\n\\n B_rows = len(B)\\n B_columns = len(B[0])\\n \\n if A_columns == 1 and B_rows == 1:\\n \\n outer_product = []\\n\\n # multi-line list comprehension for outer product\\n [outer_product.append([A[i][0] * B[0][j] for j in range(B_columns)]) \\n for i in range(A_rows)]\\n\\n return outer_product\\n\\n else:\\n print(\\\"dimensions of vector do not match.\\\")\",\n \"def solve_2x2(matrix: FieldMatrix,\\n rhs: Fields) -> OutputFields:\\n _validate_matrix_shape(matrix, (2, 2))\\n\\n a, b = matrix[0]\\n c, d = matrix[1]\\n e, f = rhs\\n\\n inv_factor = det_2x2(matrix)\\n\\n return [\\n tf.nest.map_structure(tf.math.divide_no_nan, det_2x2([\\n [e, b],\\n [f, d],\\n ]), inv_factor),\\n tf.nest.map_structure(tf.math.divide_no_nan, det_2x2([\\n [a, e],\\n [c, f],\\n ]), inv_factor),\\n ]\",\n \"def _kronecker_product(mat1: tf.Tensor, mat2: tf.Tensor) -> tf.Tensor:\\n m1, n1 = mat1.get_shape().as_list()\\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\\n m2, n2 = mat2.get_shape().as_list()\\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])\",\n \"def prod_mat(self,other):\\n [rs,cs],[ro,co] = self.D,other.D\\n assert cs == ro, \\\"tailles incompatibles\\\"\\n return Mat([rs,co], lambda i,j : prod_scal(self.ligne(i),other.col(j)))\",\n \"def _outer(a, b):\\n a_flat = torch.reshape(a, [-1])\\n b_flat = torch.reshape(b, [-1])\\n a_mul = torch.unsqueeze(a_flat, dim=-1)\\n b_mul = torch.unsqueeze(b_flat, dim=0)\\n return a_mul * b_mul\",\n \"def pfm(pattern_sequence):\\n if isinstance(pattern_sequence, Pattern):\\n sequences = []\\n for match_sequences in pattern_sequence.matchtable_pset.match_sequences.itervalues():\\n for strand, sequence in match_sequences:\\n if strand == 2:\\n sequences.append(revcomp(sequence))\\n else:\\n sequences.append(sequence)\\n else:\\n sequences = pattern_sequence\\n\\n ncol = len(sequences[0])\\n matrix = {\\n 'a': [0] * ncol,\\n 't': [0] * ncol,\\n 'c': [0] * ncol,\\n 'g': [0] * ncol,\\n }\\n total = [0] * ncol\\n\\n for s in sequences:\\n for i, j in enumerate(s):\\n matrix.get(j)[i] += 1\\n total[i] += 1\\n\\n # Normalization\\n for i in xrange(ncol):\\n matrix.get('a')[i] = float(matrix.get('a')[i]) / total[i]\\n matrix.get('t')[i] = float(matrix.get('t')[i]) / total[i]\\n matrix.get('c')[i] = float(matrix.get('c')[i]) / total[i]\\n matrix.get('g')[i] = float(matrix.get('g')[i]) / total[i]\\n\\n return matrix\",\n \"def recursive_multiply(a, b):\\n if len(a) == 2:\\n return naive_multiply(a, b)\\n\\n a11 = a[0:int(len(a) / 2)]\\n for index, row in enumerate(a11):\\n a11[index] = row[0:int(len(row) / 2)]\\n\\n a12 = a[0:int(len(a) / 2)]\\n for index, row in enumerate(a12):\\n a12[index] = row[int(len(a) / 2):len(a)]\\n\\n a21 = a[int(len(a) / 2):len(a)]\\n for index, row in enumerate(a21):\\n a21[index] = row[0:int(len(row) / 2)]\\n\\n a22 = a[int(len(a) / 2):len(a)]\\n for index, row in enumerate(a22):\\n a22[index] = row[int(len(a) / 2):len(a)]\\n\\n b11 = b[0:int(len(b) / 2)]\\n for index, row in enumerate(b11):\\n b11[index] = row[0:int(len(row) / 2)]\\n\\n b12 = b[0:int(len(b) / 2)]\\n for index, row in enumerate(b12):\\n b12[index] = row[int(len(b) / 2):len(b)]\\n\\n b21 = b[int(len(b) / 2):len(b)]\\n for index, row in enumerate(b21):\\n b21[index] = row[0:int(len(row) / 2)]\\n\\n b22 = b[int(len(b) / 2):len(b)]\\n for index, row in enumerate(b22):\\n b22[index] = row[int(len(b) / 2):len(b)]\\n\\n c11 = matrix_add(recursive_multiply(a11, b11), recursive_multiply(a12, b21)) # C11 = A11*B11 + A12*B21\\n c12 = matrix_add(recursive_multiply(a11, b12), recursive_multiply(a12, b22)) # C12 = A11*B12 + A12*B22\\n c21 = matrix_add(recursive_multiply(a21, b11), recursive_multiply(a22, b21)) # C21 = A21*B11 + A22*B21\\n c22 = matrix_add(recursive_multiply(a21, b12), recursive_multiply(a22, b22)) # C22 = A21*B12 + A22*B22\\n\\n # Append c12 to c11\\n for row_index, row in enumerate(c11):\\n for col_index, col in enumerate(c12):\\n row.append(c12[row_index][col_index])\\n\\n # Append c22 to c21\\n for row_index, row in enumerate(c21):\\n for col_index, col in enumerate(c12):\\n row.append(c22[row_index][col_index])\\n\\n # Append c21 to c11\\n for i in c21:\\n c11.append(i)\\n\\n return c11\",\n \"def outer_product(x):\\n return keras.backend.batch_dot(\\n x[0]\\n , x[1]\\n , axes=[1,1]\\n ) / x[0].get_shape().as_list()[1]\",\n \"def _inner_product_c1(self, point_a, point_b, normals_bp, areas_bp):\\n dna = self._space.normals(point_a) - normals_bp\\n dnb = self._space.normals(point_b) - normals_bp\\n return self.c1 * gs.sum(\\n gs.einsum(\\\"...bi,...bi->...b\\\", dna, dnb) * areas_bp, axis=-1\\n )\",\n \"def robust_outer_product(vec_1, vec_2):\\n mantissa_1, exponents_1 = np.frexp(vec_1)\\n mantissa_2, exponents_2 = np.frexp(vec_2)\\n new_mantissas = mantissa_1[None, :] * mantissa_2[:, None]\\n new_exponents = exponents_1[None, :] + exponents_2[:, None]\\n return new_mantissas * np.exp2(new_exponents)\",\n \"def __mul__(self, other):\\n # \\n # TODO - your code here\\n #\\n \\n result = [];\\n row_result = [];\\n product = 0;\\n \\n if(self.w != other.h):\\n raise(ValueError, \\\"Matrices can not multiply for their dimesion doesn't match\\\"); \\n \\n for row in self.g:\\n row_result = [];\\n for j in range(other.w):\\n product = dot_product(row,other.get_column(j));\\n row_result.append(product);\\n result.append(row_result);\\n \\n return Matrix(result);\",\n \"def matrix_mult(m1, m2):\\n pass\",\n \"def generate_pattern_grid(words1, words2):\\n # Convert word lists to integer arrays\\n w1, w2 = (\\n np.array([[ord(c) for c in w] for w in words], dtype=np.uint8)\\n for words in (words1, words2)\\n )\\n\\n if len(w1) == 0 or len(w2) == 0:\\n return np.zeros((len(w1), len(w2)), dtype=np.uint8)\\n\\n # equality_grid[a, b, i, j] represents whether the ith letter\\n # of words1[a] equals the jth letter of words2[b]\\n equality_grid = np.zeros((len(w1), len(w2), 5, 5), dtype=bool)\\n for i, j in it.product(range(5), range(5)):\\n equality_grid[:, :, i, j] = np.equal.outer(w1[:, i], w2[:, j])\\n\\n patterns = np.zeros((len(w1), len(w2)), dtype=np.uint8)\\n three_pows = (3**np.arange(5)).astype(np.uint8)\\n for i, tp in enumerate(three_pows):\\n # This accounts for yellow squares\\n patterns[:, :] += tp * equality_grid[:, :, i, :].any(2)\\n # This accounts for green squares\\n patterns[:, :] += tp * equality_grid[:, :, i, i]\\n\\n return patterns\",\n \"def interior_tensor_product(mx, dim_a, dim_b, e=None):\\n assert _np.shape(mx) == (dim_a * dim_b, dim_a * dim_b), \\\"Dimensions do not agree with matrix size\\\"\\n assert _np.shape(e)[0] == _np.shape(e)[1], \\\"e should be a square matrix\\\"\\n basis_a = matrix_units(dim_a)\\n basis_b = matrix_units(dim_b)\\n return sum((_np.trace(_np.dot(mx, _np.kron(unit_a, unit_b).T)) * multikron([unit_a, e, unit_b])\\n for unit_a in basis_a for unit_b in basis_b))\",\n \"def mult(m1, m2):\\n assert np.shape(m1) == (2, 3)\\n assert np.shape(m2) == (2, 3)\\n\\n m1_temp = np.vstack((m1, [0, 0, 1]))\\n m2_temp = np.vstack((m2, [0, 0, 1]))\\n result = m1_temp * m2_temp\\n\\n return result[:2, :]\",\n \"def mat_mul(mat1, mat2):\\n\\n rows1 = len(mat1)\\n cols1 = len(mat1[0])\\n rows2 = len(mat2)\\n cols2 = len(mat2[0])\\n\\n if cols1 != rows2:\\n return None\\n else:\\n new_matrix = []\\n for x in range(rows1):\\n aux_row = []\\n for y in range(cols2):\\n aux_sum = []\\n for z in range(cols1):\\n aux_sum.append(mat1[x][z] * mat2[z][y])\\n aux_row.append(sum(aux_sum))\\n new_matrix.append(aux_row)\\n\\n return new_matrix\",\n \"def frobenius_inner_prod(mat1, mat2):\\n assert mat1.shape==mat2.shape\\n # assert isinstance(mat1, Variable) and isinstance(mat2, Variable))\\n f = mat1.mul(mat2).sum()\\n return f\",\n \"def transformation_2d(vertices, kernels=KERNELS):\\n\\t# calculate the transpose matrix of vertices\\n\\ttranspose = vertices.transpose()\\n\\t# insert a row of ones in the transpose matrix's end, then insert the result in 'matrices' list\\n\\tkernels.append(np.append(transpose, [np.ones(len(transpose[0]))], axis=0))\\n\\t# multiply matrices into 'kernels' list,\\n\\t# remove the last row (of ones) and calculate the transpose matrix of the result\\n\\tfinal_transformation_result = np.delete(np.linalg.multi_dot(kernels), 2, 0).transpose()\\n\\tKERNELS.clear()\\n\\treturn final_transformation_result\",\n \"def classical(m1,m2):\\n \\n n = m1.shape\\n result = np.zeros(n, dtype = int)\\n\\n for i in range(n[0]):\\n for j in range(n[0]):\\n for k in range(n[0]):\\n result[i][j] += m1[i][k] * m2[k][j]\\n return result\",\n \"def complex_mul2d(a, b):\\n op = partial(torch.einsum, \\\"bixy,ioxy->boxy\\\")\\n return torch.stack([\\n op(a[..., 0], b[..., 0]) - op(a[..., 1], b[..., 1]),\\n op(a[..., 1], b[..., 0]) + op(a[..., 0], b[..., 1])\\n ],\\n dim=-1)\",\n \"def __mul__(self,m):\\n if type(m) != Matrix:\\n raise TypeError('The second argument is not a matrix lol')\\n if self.ncols != m.nrows:\\n raise ValueError('matrix dot argument has incorrect number of rows')\\n new = Matrix(self.nrows,m.ncols)\\n columns = m.getCols()\\n rowindex = 0\\n colindex = 0 \\n for row in self.matrix:\\n colindex = 0 \\n for col in columns:\\n summ = 0\\n for i,j in zip(row,col):\\n summ+= i*j \\n new.matrix[rowindex][colindex] = summ\\n print new.matrix\\n colindex += 1 \\n rowindex+=1\\n return new\",\n \"def matrix_mult(A,B):\\n\\n m = len(A)\\n p = len(B)\\n n = len(B[0])\\n AB = []\\n for i in range(m):\\n AB.append([])\\n for j in range(n):\\n total = 0\\n for k in range(p):\\n total += A[i][k] * B[k][j]\\n AB[i].append(total)\\n return AB\",\n \"def _e_2d_(p, a):\\r\\n diff = a - p[np.newaxis, :]\\r\\n return np.einsum('ij,ij->i', diff, diff)\",\n \"def mul(self,mat1,mat2):\\n if(isinstance(mat2,int)==True):\\n result = [[mat1[i][j] * mat2 for j in range(len(mat1[0]))] for i in range(len(mat1))]\\n self.out = result\\n return self.out\\n elif(len(mat1[0])==len(mat2)):\\n result = [[sum(a*b for a,b in zip(i,j)) for j in zip(*mat2)] for i in mat1]\\n self.out = result\\n return self.out\",\n \"def mmultiply(self, matrix):\\n try:\\n result_matrix = [[0 for row in range(len(self.matrix))] for col in range(len(matrix[0]))]\\n for i in range(len(self.matrix)):\\n for j in range(len(matrix[0])):\\n for k in range(len(matrix)):\\n result_matrix[i][j] += self.matrix[i][k] * matrix[k][j]\\n self.matrix = result_matrix\\n except IndexError:\\n pass\\n pass\",\n \"def test_two_qubit_weyl_decomposition_bgate(self):\\n for k1l, k1r, k2l, k2r in K1K2S:\\n k1 = np.kron(k1l.data, k1r.data)\\n k2 = np.kron(k2l.data, k2r.data)\\n a = Ud(np.pi / 4, np.pi / 8, 0)\\n self.check_two_qubit_weyl_decomposition(k1 @ a @ k2)\",\n \"def matrix_as_psfm(matrix):\\n return [[2**(ep-2) for ep in row] for row in matrix]\",\n \"def multiply_by_left_matrix(matrix, img):\\n first = np.inner(matrix[0], img)\\n second = np.inner(matrix[1], img)\\n third = np.inner(matrix[2], img)\\n\\n res = np.dstack((first, second, third))\\n return res\",\n \"def matrixMul(self, matrix, matrix2):\\n matrix0 = matrix[:]\\n matrix[0] = matrix0[0] * matrix2[0] + matrix0[2]*matrix2[1] # + matrix0[4]*0\\n matrix[1] = matrix0[1] * matrix2[0] + matrix0[3]*matrix2[1] # + matrix0[5]*0\\n matrix[2] = matrix0[0] * matrix2[2] + matrix0[2]*matrix2[3] # + matrix0[4]*0\\n matrix[3] = matrix0[1] * matrix2[2] + matrix0[3]*matrix2[3] # + matrix0[5]*0\\n matrix[4] = matrix0[0] * matrix2[4] + matrix0[2]*matrix2[5] + matrix0[4]\\n matrix[5] = matrix0[1] * matrix2[4] + matrix0[3]*matrix2[5] + matrix0[5]\",\n \"def python_nonsquare_matrix_mult(matrix):\\n\\n transposed_matrix = np.zeros([matrix.shape[1],matrix.shape[0]])\\n start = time.time()\\n # for i in range(matrix.shape[0]):\\n # for j in range(matrix.shape[1]):\\n # transposed_matrix[j,i] = matrix[i,j]\\n\\n transposed_matrix = np.transpose(matrix)\\n product = matrix.dot(transposed_matrix)\\n\\n # transposed_matrix = np.transpose(matrix)\\n end = time.time()-start\\n\\n # print(\\\"Python Golden Transpose: %s\\\" % product)\\n # print('python transpose time: %.2E' % end)\\n return [product, end]\",\n \"def matmul(xs: List[List[float]],\\n ys: List[List[float]]) -> List[List[float]]:\\n product = []\\n for x_row in range(len(xs)):\\n row = []\\n for y_col in range(len(ys[0])):\\n col = [ys[y_row][y_col] for y_row in range(len(ys))]\\n row.append(Math.dot(xs[x_row], col))\\n product.append(row)\\n return product\",\n \"def matrix_vector_prod(m,u):\\n each_product = []\\n for v in m:\\n each_product.append(dot_prod(v, u))\\n return each_product\",\n \"def _calc_matrix(self):\\n\\t\\tz = self.zoom\\n\\t\\talloc = self.allocation\\n\\t\\tif self.image:\\n\\t\\t\\tiw, ih = self.image.get_width(), self.image.get_height()\\n\\t\\telse:\\n\\t\\t\\tiw, ih = 0, 0\\n#\\t\\tif __debug__: print self._vadj.lower, self._vadj.value, self._vadj.upper\\n\\t\\t\\n\\t\\ti2w = cairo.Matrix(\\n\\t\\t\\tz,0,\\n\\t\\t\\t0,z,\\n\\t\\t\\t-self._hadj.value if alloc.width < iw*z else (alloc.width - iw*z)/2, \\n\\t\\t\\t-self._vadj.value if alloc.height < ih*z else (alloc.height - ih*z)/2,\\n\\t\\t\\t)\\n\\t\\t\\n\\t\\tself._i2w_matrix = i2w\\n\\t\\t\\n\\t\\tw2i = cairo.Matrix(*i2w) #copy\\n\\t\\tw2i.invert()\\n\\t\\tself._w2i_matrix = w2i\",\n \"def test_l2_metric_inner_product_vectorization(\\n self,\\n l2_metric_s2,\\n times,\\n n_landmark_sets,\\n landmarks_a,\\n landmarks_b,\\n landmarks_c,\\n ):\\n landmarks_ab = l2_metric_s2.geodesic(landmarks_a, landmarks_b)\\n landmarks_bc = l2_metric_s2.geodesic(landmarks_b, landmarks_c)\\n landmarks_ab = landmarks_ab(times)\\n landmarks_bc = landmarks_bc(times)\\n\\n tangent_vecs = l2_metric_s2.log(point=landmarks_bc, base_point=landmarks_ab)\\n\\n result = l2_metric_s2.inner_product(tangent_vecs, tangent_vecs, landmarks_ab)\\n\\n self.assertAllClose(gs.shape(result), (n_landmark_sets,))\",\n \"def __mul__(left, right):\\n \\n if isinstance(left, Plucker) and isinstance(right, Plucker):\\n # reciprocal product\\n return np.dot(left.uw, right.v) + np.dot(right.uw, left.v)\\n elif isinstance(left, Plucker) and arg.ismatrix(right, (4,None)):\\n return left.skew @ right; # postmultiply by 4xN\",\n \"def kronecker_product(mat1, mat2):\\n m1, n1 = mat1.get_shape().as_list()\\n mat1_rsh = tf.reshape(mat1, [m1, 1, n1, 1])\\n m2, n2 = mat2.get_shape().as_list()\\n mat2_rsh = tf.reshape(mat2, [1, m2, 1, n2])\\n return tf.reshape(mat1_rsh * mat2_rsh, [m1 * m2, n1 * n2])\",\n \"def outer_prod(x, y):\\n if len(list(x.size())) != 2 or len(list(y.size())) != 2:\\n raise ValueError(\\\"An input is not of the right dimension.\\\")\\n\\n z = torch.zeros(2, x.size()[1], y.size()[1], dtype=torch.double, device=x.device)\\n z[0] = torch.ger(x[0], y[0]) - torch.ger(x[1], -y[1])\\n z[1] = torch.ger(x[0], -y[1]) + torch.ger(x[1], y[0])\\n\\n return z\",\n \"def _prod_vectorized(M1, M2):\\n sh1 = M1.shape\\n sh2 = M2.shape\\n assert len(sh1) >= 2\\n assert len(sh2) >= 2\\n assert sh1[-1] == sh2[-2]\\n\\n ndim1 = len(sh1)\\n t1_index = list(xrange(ndim1-2)) + [ndim1-1, ndim1-2]\\n return np.sum(np.transpose(M1, t1_index)[..., np.newaxis] *\\n M2[..., np.newaxis, :], -3)\",\n \"def _mps_AA(self, A1, A2):\\n Dl, d1, _ = A1.shape\\n _, d2, Dr = A2.shape\\n return np.reshape(np.tensordot(A1, A2, axes=(2, 0)), [Dl, d1 * d2, Dr])\",\n \"def hadmardProduct(self, a,b):\\n\\t\\tnumRows = len(a)\\n\\t\\tnumCols = len(a[0])\\n\\t\\n\\t\\treturn [[a[j][i] * b[j][i] for i in range(numCols)] for j in range(numRows)]\",\n \"def _inner_product_a1(self, ginvdga, ginvdgb, areas_bp):\\n return self.a1 * gs.sum(\\n gs.einsum(\\\"...bii->...b\\\", gs.matmul(ginvdga, ginvdgb)) * areas_bp,\\n axis=-1,\\n )\",\n \"def divide(m1,m2):\\n \\n if ((m1.shape[0] % 2 == 0) or (m1.shape[0] == 1)):\\n n = m1.shape[0]\\n else:\\n n = m1.shape[0] + 1\\n result = np.zeros((n, n), dtype = int)\\n \\n if (n == 1):\\n result[0][0] = m1[0][0] * m2[0][0]\\n else:\\n new = n//2\\n \\n a11, a12, a21, a22 = m1[:new, :new], m1[new:, :new], m1[:new, new:], m1[new:, new:]\\n b11, b12, b21, b22 = m2[:new, :new], m2[new:, :new], m2[:new, new:], m2[new:, new:]\\n \\n result[:new, :new] = divide(a11,b11) + divide(a12,b21)\\n result[new:, :new] = divide(a11,b12) + divide(a12,b22)\\n result[:new, new:] = divide(a21,b11) + divide(a22,b21)\\n result[new:, new:] = divide(a21,b12) + divide(a22,b22)\\n \\n return result\",\n \"def get_molecular_matrix(single_body, two_body):\\n x, y = single_body.shape\\n func = np.vectorize(round_custom)\\n _new_dim = x * y\\n single_one_dim = single_body.reshape(_new_dim, 1)\\n two_body_two_dim = func(two_body.reshape(_new_dim, _new_dim))\\n idx = 0\\n x, _ = two_body_two_dim.shape\\n while idx < x:\\n two_body_two_dim[idx][idx] = round_custom(single_one_dim[idx][0])\\n idx += 1\\n return two_body_two_dim\",\n \"def innerprod_q2(q1, q2):\\n T = q1.shape[1]\\n val = sum(sum(q1 * q2)) / T\\n\\n return (val)\",\n \"def matrix_mult(m1, m2):\\n output = []\\n for rowIndex, row in enumerate(m1): #go through rows in m1\\n new_row = []\\n for columnIndex in range(len(m2[0])): #go through indices for each column of m2\\n sum = 0\\n for index3 in range(len(row)):\\n product = m1[rowIndex][index3] * m2[index3][columnIndex]\\n sum += product\\n new_row.append(sum)\\n output.append(new_row)\\n return output\\n \\n \\n #output = []\\n #first for loop corresponds to the rows of my output matrix and loops through the rows of m1 (enumerate)\\n #create an empty new row\\n # second for loop, loops through columns of m2\\n # create sum variable, initialize it with zero\\n # third for loop, multiplies the index of the row in m1 times the index of the column in m2\\n # add sum to product and assign this to the sum variable\\n # append sum to new row\\n # append new row to output\\n # return output\",\n \"def matrix_mult_matrix(matrix_a, matrix_b):\\n m = len(matrix_a)\\n n = len(matrix_b)\\n result = []\\n matrix_b_t = transpose_matrix(matrix_b)\\n for i in xrange(m):\\n row = []\\n\\tfor j in xrange(m):\\n row.append(dot_product(matrix_a[i], matrix_b_t[j]))\\n\\tresult.append(row)\\n return result\",\n \"def multiplicand_2(p):\\n m2 = cddr(p) # (m2 m3 ...)\\n rests = cdr(m2) # (m3...)\\n if isNull(rests):\\n return car(m2)\\n else:\\n restp = convertToPythonList(cdr(rests))\\n return make_product_2(car(m2), car(rests), *restp)\",\n \"def similarity_transform_2d(v, mapping, alpha = 1):\\r\\n p_wgt = vec2(0, 0)\\r\\n q_wgt = vec2(0, 0)\\r\\n w = len(mapping)*[None]\\r\\n w_sum = 0\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n x = mp[0].x - v.x\\r\\n y = mp[0].y - v.y\\r\\n if (x == 0 and y == 0): return mp[1]\\r\\n w[i] = 1/((x*x + y*y) ** alpha)\\r\\n p_wgt += mp[0]*w[i]\\r\\n q_wgt += mp[1]*w[i]\\r\\n w_sum += w[i]\\r\\n p_wgt /= w_sum\\r\\n q_wgt /= w_sum\\r\\n mu = 0\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n p_adj = mp[0] - p_wgt\\r\\n mu += w[i]*(p_adj.dot(p_adj))\\r\\n A_fac = mat2([v.x - p_wgt.x, v.y - p_wgt.y, v.y - p_wgt.y, p_wgt.x - v.x])\\r\\n v_out = vec2(0, 0)\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n p_adj = mp[0] - p_wgt\\r\\n q_adj = mp[1] - q_wgt\\r\\n A = mat2([p_adj.x, p_adj.y, p_adj.y, -p_adj.x])*A_fac*w[i]\\r\\n A = A.transpose()\\r\\n v_out += A*q_adj/mu\\r\\n v_out += q_wgt\\r\\n return v_out\",\n \"def outer_subspace_product(subspace1: list, subspace2: list, reverse: bool = False) -> list:\\n assert isinstance(subspace1, list), 'Please provide subspace as a list of str.'\\n assert isinstance(subspace2, list), 'Please provide subspace as a list of str.'\\n\\n product_subspace = []\\n if subspace1 == []: return subspace2\\n if subspace2 == []: return subspace1\\n\\n for basisvector1 in subspace1:\\n for basisvector2 in subspace2:\\n if reverse:\\n product_subspace += [basisvector2 + basisvector1]\\n else:\\n product_subspace += [basisvector1 + basisvector2]\\n return product_subspace\",\n \"def mosaicing(img, pattern):\\n imout = np.zeros((img.shape[0], img.shape[1]))\\n for i in range(2):\\n for j in range(2):\\n imout[i::2, j::2] = img[i::2, j::2, pattern[i][j]]\\n \\n return imout\",\n \"def mat_mul(mat1, mat2):\\n\\n if len(mat1[0]) == len(mat2):\\n\\n mat2 = matrix_transpose(mat2)\\n response = []\\n\\n for row in range(len(mat1)):\\n response.append(\\n [\\n sum(dot_product(mat1[row], mat2[column]))\\n for column in range(len(mat2))\\n ]\\n )\\n\\n return response\\n\\n else:\\n return None\",\n \"def form_square_block_matrix(mat1,mat2):\\n if mat1.cols==1:\\n mat3 = mp.matrix(mat1.rows+mat2.rows,1)\\n mat3[:mat1.rows] = mat1[:]\\n mat3[mat1.rows:mat3.rows] = mat2[:]\\n else:\\n mat3 = mp.matrix(mat1.rows+mat2.rows, mat1.rows+mat2.rows)\\n mat3[:mat1.rows,:mat1.rows] = mat1[:,:]\\n mat3[mat1.rows:mat3.rows,mat1.rows:mat3.rows] = mat2[:,:]\\n return mat3\",\n \"def _prepare_outer_matrix(self):\\n self._mat_plane = numpy.array([\\n self._scaling[0], 0, 0, 0,\\n 0, self._scaling[1], 0, 0,\\n 0, 0, 1, 0,\\n self.i_border[0], -self.i_border[1], 0, 1\\n ], dtype=numpy.float32)\",\n \"def pattern_modifier(pattern, X, y):\\n mod_pattern = pattern.reshape(n_channels, n_samples)\\n mod_pattern = mod_pattern * kernel[np.newaxis, :]\\n return mod_pattern.reshape(pattern.shape)\",\n \"def pair_product(x1, x2):\\n return np.multiply(x1, x2)\",\n \"def det_matrix_2x2(m: list):\\n return m[0][0]*m[1][1] - m[0][1]*m[1][0]\",\n \"def __mul__(self,v2):\\n\\t\\tif(isinstance(v2,Vect2D)):\\n\\t\\t\\treturn np.dot(self._vec,v2._vec)\\n\\t\\telse:\\n\\t\\t\\treturn Vect2D(v2*self._vec)\",\n \"def rotate2D(self, matrix) -> None:\\n N = len(matrix)\\n\\n # In case of N is odd, the innermost square belt is just one cell, no need of rotating.\\n for i in range(0,int(N/2)): # outer loop for each square belt\\t\\t\\t\\n for j in range(i,N-i-1): # N-i group in the i-th square belt\\n #print(i,j)\\n tmp = matrix[i][j]\\n matrix[i][j] = matrix[N-j-1][i]\\n matrix[N-j-1][i] = matrix[N-i-1][N-j-1]\\n matrix[N-i-1][N-j-1] = matrix[j][N-i-1]\\n matrix[j][N-i-1] = tmp\\n #print(matrix)\",\n \"def _surface_metric_matrices_from_one_forms(one_forms):\\n ndim = one_forms.ndim\\n transpose_axes = tuple(range(ndim - 2)) + tuple(reversed(range(ndim - 2, ndim)))\\n transposed_one_forms = gs.transpose(one_forms, axes=transpose_axes)\\n return gs.matmul(one_forms, transposed_one_forms)\",\n \"def basic_geometric_product(obj1, obj2):\\n def mul_table(b1, b2):\\n return MV.base_mul_table[(b1, b2)]\\n\\n obj12 = bilinear_product(obj1 * obj2, mul_table)\\n\\n return obj12\",\n \"def _inner_product_a2(\\n self, tangent_vec_a, tangent_vec_b, base_point, vertex_areas_bp\\n ):\\n laplacian_at_base_point = self._space.laplacian(base_point)\\n return self.a2 * gs.sum(\\n gs.einsum(\\n \\\"...bi,...bi->...b\\\",\\n laplacian_at_base_point(tangent_vec_a),\\n laplacian_at_base_point(tangent_vec_b),\\n )\\n / vertex_areas_bp,\\n axis=-1,\\n )\",\n \"def _pmatrix(kn_u, kn_d, thickness):\\n p = np.zeros((kn_u.size, 4, 4), np.complex128)\\n\\n p0 = np.exp(complex(0, 1) * kn_u * thickness)\\n p1 = np.exp(complex(0, 1) * kn_d * thickness)\\n\\n p[:, 0, 0] = 1 / p0\\n p[:, 1, 1] = p0\\n p[:, 2, 2] = 1 / p1\\n p[:, 3, 3] = p1\\n\\n return p\",\n \"def _inner_product_b1(self, ginvdga, ginvdgb, areas_bp):\\n return self.b1 * gs.sum(\\n gs.einsum(\\\"...bii->...b\\\", ginvdga)\\n * gs.einsum(\\\"...bii->...b\\\", ginvdgb)\\n * areas_bp,\\n axis=-1,\\n )\",\n \"def _z2matvecmul(self, mat, vec):\\n prod = np.mod(np.dot(mat, vec), 2)\\n return prod\",\n \"def row_matrix_col_4d(a, b, A):\\n\\n\\treturn (a[0]*A[0][0]*b[0] + a[1]*A[1][0]*b[0] + a[2]*A[2][0]*b[0] + a[3]*A[3][0]*b[0] +\\n\\t a[0]*A[0][1]*b[1] + a[1]*A[1][1]*b[1] + a[2]*A[2][1]*b[1] + a[3]*A[3][1]*b[1] +\\n\\t a[0]*A[0][2]*b[2] + a[1]*A[1][2]*b[2] + a[2]*A[2][2]*b[2] + a[3]*A[3][2]*b[2] +\\n\\t a[0]*A[0][3]*b[3] + a[1]*A[1][3]*b[3] + a[2]*A[2][3]*b[3] + a[3]*A[3][3]*b[3])\",\n \"def same_side_product(p, q, a, b):\\n return line_ccw(a, b, p) * line_ccw(a, b, q)\",\n \"def _pseudo_inv22sym_vectorized(M):\\n assert M.ndim == 3\\n assert M.shape[-2:] == (2, 2)\\n M_inv = np.empty_like(M)\\n prod1 = M[:, 0, 0]*M[:, 1, 1]\\n delta = prod1 - M[:, 0, 1]*M[:, 1, 0]\\n rank2 = (np.abs(delta) > 1e-8*np.abs(prod1))\\n\\n if np.all(rank2):\\n # Normal 'optimized' flow.\\n M_inv[:, 0, 0] = M[:, 1, 1] / delta\\n M_inv[:, 0, 1] = -M[:, 0, 1] / delta\\n M_inv[:, 1, 0] = -M[:, 1, 0] / delta\\n M_inv[:, 1, 1] = M[:, 0, 0] / delta\\n else:\\n # 'Pathologic' flow.\\n # Here we have to deal with 2 sub-cases\\n # 1) First sub-case: matrices of rank 2:\\n delta = delta[rank2]\\n M_inv[rank2, 0, 0] = M[rank2, 1, 1] / delta\\n M_inv[rank2, 0, 1] = -M[rank2, 0, 1] / delta\\n M_inv[rank2, 1, 0] = -M[rank2, 1, 0] / delta\\n M_inv[rank2, 1, 1] = M[rank2, 0, 0] / delta\\n # 2) Second sub-case: rank-deficient matrices of rank 0 and 1:\\n rank01 = ~rank2\\n tr = M[rank01, 0, 0] + M[rank01, 1, 1]\\n tr_zeros = (np.abs(tr) < 1.e-8)\\n sq_tr_inv = (1.-tr_zeros) / (tr**2+tr_zeros)\\n #sq_tr_inv = 1. / tr**2\\n M_inv[rank01, 0, 0] = M[rank01, 0, 0] * sq_tr_inv\\n M_inv[rank01, 0, 1] = M[rank01, 0, 1] * sq_tr_inv\\n M_inv[rank01, 1, 0] = M[rank01, 1, 0] * sq_tr_inv\\n M_inv[rank01, 1, 1] = M[rank01, 1, 1] * sq_tr_inv\\n\\n return M_inv\",\n \"def square_matrix_multiply(a, b):\\n n = len(a)\\n c = [[0]*n for _ in range(n)]\\n for i in range(n):\\n for j in range(n):\\n sm = 0\\n for k in range(n):\\n sm += (a[i][k] * b[k][j])\\n c[i][j] = sm\\n\\n return c\",\n \"def batch_outer_product(a, b):\\n a, b = normalize_and_check_ndim([a, b], 2)\\n # This is a batchwise version of the matrix multiplication approach\\n # used for outer_product(), see explanation there.\\n return a[:, :, np.newaxis] * b[:, np.newaxis, :]\",\n \"def _mult_poly_matrix_poly(p, mat_y):\\n\\n mult_op = lambda q: np.convolve(p, q)\\n p_times_y = np.apply_along_axis(mult_op, 2, mat_y)\\n return p_times_y\",\n \"def similarity_transformation(rot, mat):\\n return np.dot(rot, np.dot(mat, np.linalg.inv(rot)))\",\n \"def similarity_transformation(rot, mat):\\n return np.dot(rot, np.dot(mat, np.linalg.inv(rot)))\",\n \"def normalize_adj(adj):\\n\\n rowsum = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = np.diag(d_inv_sqrt)\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt)\",\n \"def flat_to_2d(data, det_width):\\n return data.reshape((data.shape[0], data.shape[1], det_width, det_width))\",\n \"def reconstruct(A, B, z):\\n f = factorint(igcd(A, B))\\n for p, e in f.items():\\n if e != 1:\\n raise ValueError('a and b should be square-free')\\n z *= p\\n return z\",\n \"def matrix_multiplication_loop(x_matrix, y_matrix):\\n result = []\\n for i, row in enumerate(x_matrix):\\n row_vector = []\\n for j in range(len(y_matrix[0])):\\n product = 0\\n for k in range(len(row)):\\n product += x_matrix[i][k] * y_matrix[k][j]\\n row_vector.append(product)\\n result.append(row_vector)\\n return result\",\n \"def norm2_r(self, a: np.ndarray) -> float:\\n return a.T @ a\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7459909","0.69026655","0.62501264","0.59727603","0.58457905","0.56935406","0.56712896","0.5659509","0.5638323","0.562021","0.55866134","0.5575207","0.5560609","0.55025274","0.5491025","0.5439844","0.5381539","0.53541124","0.5343363","0.53310347","0.532383","0.5305266","0.5295224","0.5279774","0.5244985","0.5239988","0.52263427","0.5226059","0.52230066","0.52128536","0.5181816","0.5176417","0.5169206","0.5167986","0.515937","0.5158919","0.5122471","0.511444","0.51063055","0.5102178","0.50925946","0.50807035","0.5063364","0.5060758","0.5054657","0.50530577","0.5046044","0.5039231","0.50386906","0.5034785","0.50297636","0.50193775","0.5017412","0.49813205","0.49807236","0.49756384","0.4971787","0.49711654","0.49709055","0.49507028","0.4949671","0.4949146","0.49441355","0.49376434","0.49333808","0.49317148","0.49287587","0.4920873","0.4915144","0.49130973","0.49079886","0.49035653","0.49025","0.49016878","0.490036","0.48962334","0.4892124","0.48908907","0.48675817","0.48663434","0.486304","0.48628187","0.48535436","0.4853111","0.48429668","0.48423478","0.48405617","0.4834119","0.48292828","0.48246852","0.48211947","0.48206893","0.4818812","0.48181638","0.48181638","0.4814729","0.4810796","0.48060337","0.48024935","0.48022324"],"string":"[\n \"0.7459909\",\n \"0.69026655\",\n \"0.62501264\",\n \"0.59727603\",\n \"0.58457905\",\n \"0.56935406\",\n \"0.56712896\",\n \"0.5659509\",\n \"0.5638323\",\n \"0.562021\",\n \"0.55866134\",\n \"0.5575207\",\n \"0.5560609\",\n \"0.55025274\",\n \"0.5491025\",\n \"0.5439844\",\n \"0.5381539\",\n \"0.53541124\",\n \"0.5343363\",\n \"0.53310347\",\n \"0.532383\",\n \"0.5305266\",\n \"0.5295224\",\n \"0.5279774\",\n \"0.5244985\",\n \"0.5239988\",\n \"0.52263427\",\n \"0.5226059\",\n \"0.52230066\",\n \"0.52128536\",\n \"0.5181816\",\n \"0.5176417\",\n \"0.5169206\",\n \"0.5167986\",\n \"0.515937\",\n \"0.5158919\",\n \"0.5122471\",\n \"0.511444\",\n \"0.51063055\",\n \"0.5102178\",\n \"0.50925946\",\n \"0.50807035\",\n \"0.5063364\",\n \"0.5060758\",\n \"0.5054657\",\n \"0.50530577\",\n \"0.5046044\",\n \"0.5039231\",\n \"0.50386906\",\n \"0.5034785\",\n \"0.50297636\",\n \"0.50193775\",\n \"0.5017412\",\n \"0.49813205\",\n \"0.49807236\",\n \"0.49756384\",\n \"0.4971787\",\n \"0.49711654\",\n \"0.49709055\",\n \"0.49507028\",\n \"0.4949671\",\n \"0.4949146\",\n \"0.49441355\",\n \"0.49376434\",\n \"0.49333808\",\n \"0.49317148\",\n \"0.49287587\",\n \"0.4920873\",\n \"0.4915144\",\n \"0.49130973\",\n \"0.49079886\",\n \"0.49035653\",\n \"0.49025\",\n \"0.49016878\",\n \"0.490036\",\n \"0.48962334\",\n \"0.4892124\",\n \"0.48908907\",\n \"0.48675817\",\n \"0.48663434\",\n \"0.486304\",\n \"0.48628187\",\n \"0.48535436\",\n \"0.4853111\",\n \"0.48429668\",\n \"0.48423478\",\n \"0.48405617\",\n \"0.4834119\",\n \"0.48292828\",\n \"0.48246852\",\n \"0.48211947\",\n \"0.48206893\",\n \"0.4818812\",\n \"0.48181638\",\n \"0.48181638\",\n \"0.4814729\",\n \"0.4810796\",\n \"0.48060337\",\n \"0.48024935\",\n \"0.48022324\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5993504"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":883,"cells":{"query":{"kind":"string","value":"Apply np.exp( matrix/two_sigma_square) elementwise."},"document":{"kind":"string","value":"def gaussian_dense(matrix, two_sigma_square):\n\n return np.exp(- matrix / two_sigma_square)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def exp(tensor):\n return _elementary_op(tensor, np.exp, np.exp)","def expval(op, dm):\n return np.tensordot(op, dm, ([0, 1], [0, 1]))","def Exp(A, B):\n return A.dot(expm(B))","def kernel_sqExp(a,b, ls=1, sv=1):\n a = a.T/ls\n b = b.T/ls\n D, n = np.shape(a)\n d, m = np.shape(b)\n sqdist = np.tile((a**2).T, [1, m]) + np.tile(b*b, [n, 1]) - 2*np.dot(a.T,b)\n my_kernel = (sv**2) * np.exp(-0.5*sqdist)\n \n # written all out to illustrate (need to make sure a, b are in original dimensions):\n# my_kernel2 = np.zeros((n, m))\n# for i in range(n):\n# for j in range(m):\n# ai = a[i]\n# bj = b[j]\n# my_kernel2[i, j] = np.exp(-1/(2*ls**2) * (ai-bj)**2 )\n# my_kernel2 = my_kernel2 * (sv**2)\n \n return my_kernel","def expms(A, eig=np.linalg.eigh):\r\n # TODO: check that this works reliably for low rank matrices\r\n # first: symmetrize A\r\n D, B = eig(A)\r\n return np.dot(B, (np.exp(D) * B).T)","def exponentialfcn(x: np.ndarray) -> np.ndarray:\n\n x2 = x**2\n scores = -np.exp(-0.5 * np.sum(x2, axis=1))\n return scores","def ga_exp(B):\n if np.sum(np.abs(B.value)) < np.finfo(float).eps:\n return cf.MultiVector(layout, unit_scalar_mv.value)\n return cf.MultiVector(layout, val_exp(B.value))","def gaussian_smearing(E: NDArray[Shape['Ngrid'], Number],\n E0: NDArray[Shape['*, ...'], Number],\n sigma: float):\n return 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(\n -(np.broadcast_to(E, E0.shape + E.shape) - np.expand_dims(E0, len(E0.shape))) ** 2 / (2 * sigma ** 2))","def exp_env(N, sr, lam = 3):\n return np.exp(-lam*np.arange(N)/sr)","def get_est_exp_discount_function(self,params):\n params = params[0:5]\n df = pd.DataFrame(self.maturity.apply(lambda x: x ** i) for i in range(1, 6)).T\n df.columns = ['M1', 'M2', 'M3', 'M4', 'M5']\n return np.exp(df.dot(params))","def sum_exp(self,time):\n sum = np.sum([self.A[i]*np.exp(-1.*self.lambdas[i]*time)\\\n for i in xrange(self.m)])\n return sum","def compute_MSE(e):\n\n return 1/2*np.mean(e**2)","def gauss_2d(N, sigma = 0.25):\r\n x, y = make_xy(N)\r\n sigma_pixel = sigma * np.float(N)\r\n arrayout = np.exp(-(x**2 + y**2) / sigma_pixel**2) / (np.pi * sigma_pixel**2)\r\n return arrayout","def calculate_mse(e):\n return 1/2*np.mean(e.dot(e))","def test_perform_sigm_times_exp(self):\r\n x, y, z, t = tensor.vectors('x', 'y', 'z', 't')\r\n exp = tensor.exp\r\n\r\n def ok(expr1, expr2):\r\n trees = [parse_mul_tree(e) for e in (expr1, expr2)]\r\n perform_sigm_times_exp(trees[0])\r\n trees[0] = simplify_mul(trees[0])\r\n good = theano.gof.graph.is_same_graph(\r\n compute_mul(trees[0]),\r\n compute_mul(trees[1]))\r\n if not good:\r\n print trees[0]\r\n print trees[1]\r\n print '***'\r\n theano.printing.debugprint(compute_mul(trees[0]))\r\n print '***'\r\n theano.printing.debugprint(compute_mul(trees[1]))\r\n assert good\r\n ok(sigmoid(x) * exp(-x), sigmoid(-x))\r\n ok(-x * sigmoid(x) * (y * (-1 * z) * exp(-x)),\r\n -x * sigmoid(-x) * (y * (-1 * z)))\r\n ok(-sigmoid(-x) *\r\n (exp(y) * (-exp(-z) * 3 * -exp(x)) *\r\n (y * 2 * (-sigmoid(-y) * (z + t) * exp(z)) * sigmoid(z))) *\r\n -sigmoid(x),\r\n sigmoid(x) *\r\n (-sigmoid(y) * (-sigmoid(-z) * 3) * (y * 2 * ((z + t) * exp(z)))) *\r\n -sigmoid(x))\r\n ok(exp(-x) * -exp(-x) * (-sigmoid(x) * -sigmoid(x)),\r\n -sigmoid(-x) * sigmoid(-x))\r\n ok(-exp(x) * -sigmoid(-x) * -exp(-x),\r\n -sigmoid(-x))","def matlab_style_gauss2D(shape=(3, 3), sigma=0.5):\n m, n = [(ss - 1.) / 2. for ss in shape]\n y, x = np.ogrid[-m:m + 1, -n:n + 1]\n h = np.exp(-(x * x + y * y) / (2. * sigma * sigma))\n h[h < np.finfo(h.dtype).eps * h.max()] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def func(self, X, a, b):\n return a*np.exp(-b*X)","def cal_gaussian_process(b, sigma2, X_train, y_train, X_test):\n n = X_train.shape[0]\n p = X_test.shape[0]\n\n K_n = np.array([[kernel(X_train[i], X_train[j], b) for i in range(n)] for j in range(n)])\n inv = np.linalg.inv(np.diag([sigma2] * n) + K_n)\n miu = np.zeros(p)\n Sigma = np.zeros(p)\n \n for j in range(p): # for every new point x0 in testing data.\n x0 = X_test[j]\n K_Dn = np.zeros(n) # initialize K_Dn \n for i in range(n):\n K_Dn[i] = kernel(X_train[i], x0, b) # calculate every item in K_Dn\n \n miu[j] = K_Dn.dot(inv).dot(y_train)[0] # calculate new distribution parameters\n Sigma[j] = sigma2 + kernel(x0, x0, b) - K_Dn.dot(inv).dot(K_Dn.T)\n \n return miu, Sigma","def _sigma_2(gam, eps):\n s0 = r0**2 * alpha / (3 * eps) / mec2_unit\n\n s1_1 = 16 * (1 - eps + eps**2) * np.log(gam / eps)\n s1_2 = -1 / eps**2 + 3 / eps - 4 - 4 * eps - 8 * eps**2\n s1_3 = -2 * (1 - 2 * eps) * np.log(1 - 2 * eps)\n s1_4 = 1 / (4 * eps**3) - 1 / (2 * eps**2) + 3 / eps - 2 + 4 * eps\n s1 = s1_1 + s1_2 + s1_3 * s1_4\n\n s2_1 = 2 / eps\n s2_2 = (4 - 1 / eps + 1 / (4 * eps**2)) * np.log(2 * gam)\n s2_3 = -2 + 2 / eps - 5 / (8 * eps**2)\n s2 = s2_1 * (s2_2 + s2_3)\n\n return s0 * np.where(eps <= 0.5, s1, s2) * heaviside(gam - eps)","def exp(self: Float[LinearOperator, \"*batch M N\"]) -> Float[LinearOperator, \"*batch M N\"]:\n return self.__class__(self._diag.exp())","def compute_kernel_matrix(x,y,sigma):\n m = len(x)\n\n s = np.zeros((m,m))\n for i in range(len(x)):\n for j in range(i+1):\n s[i,j] = np.exp(-((x[i]-y[j])**2)/(2*sigma**2))\n for i in range(2,m):\n for j in range(0,i):\n s[i,j] = s[j,i]\n return s","def eval(self, X, Y):\n sigma2 = self.sigma2\n sumx2 = torch.sum(X**2, dim=1).view(-1, 1)\n sumy2 = torch.sum(Y**2, dim=1).view(1, -1)\n D2 = sumx2 - 2.0*torch.matmul(X, Y.transpose(1, 0)) + sumy2\n K = torch.exp(-D2/(2.0*sigma2))\n return K","def compute_dual_energy(b_i, phi_ij, psi_i, lambda_ij, gamma_ij, N):\n # Compute lambdas for all pair states (0,0),(1,0),(0,1) and (1,1)\n lmbd_tmp = numpy.zeros([N, N, 4])\n lmbd_tmp[:, :, 0] = lambda_ij[:, :, 0] + lambda_ij[:, :, 0].T\n lmbd_tmp[:, :, 1] = lambda_ij[:, :, 0] + lambda_ij[:, :, 1].T\n lmbd_tmp[:, :, 2] = lambda_ij[:, :, 1] + lambda_ij[:, :, 0].T\n lmbd_tmp[:, :, 3] = lambda_ij[:, :, 1] + lambda_ij[:, :, 1].T\n # Compute dual energy\n dual_energy = -numpy.sum(phi_ij * numpy.exp(-1. - lmbd_tmp -\n gamma_ij[:, :, numpy.newaxis]))\n dual_energy -= numpy.sum(\n psi_i * numpy.exp(-1. + (N - 1.) + numpy.sum(lambda_ij, axis=0)) *\n (b_i / psi_i) ** (N - 1))\n dual_energy -= numpy.sum(gamma_ij)\n return dual_energy","def calculate_mse(e):\r\n return 1/2*np.mean(e**2)","def gauss_2d(shape=(3, 3), sigma=0.5):\n m, n = [(ss - 1.) / 2. for ss in shape]\n y, x = np.ogrid[-m:m + 1, -n:n + 1]\n h = np.exp(-(x * x + y * y) / (2. * sigma * sigma))\n h[h < np.finfo(h.dtype).eps * h.max()] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def gauss_2d(shape=(3, 3), sigma=0.5):\n m, n = [(ss - 1.) / 2. for ss in shape]\n y, x = np.ogrid[-m:m + 1, -n:n + 1]\n h = np.exp(-(x * x + y * y) / (2. * sigma * sigma))\n h[h < np.finfo(h.dtype).eps * h.max()] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def uni_gaussian(X, mu, sigma2):\n p = (1 / sqrt(2 * pi * sigma2))\n p = p * exp(-power(X - mu, 2) / (2 * sigma2))\n\n def prod(x, y):\n return x * y\n p = array([[reduce(prod, el)] for el in p])\n\n return p","def mse(image1: np.ndarray, image2: np.ndarray) -> np.ndarray:\n return np.sqrt(np.power((image1 - image2), 2).mean(axis=(-1, -2)))","def similarity_matrix(points, sigma):\n distances_squared = spherical_distances(points, points)**2\n\n \n return np.exp( -distances_squared / (2.0 * sigma) )","def test_l2_metric_exp_vectorization(\n self, l2_metric_s2, times, landmarks_a, landmarks_b, landmarks_c\n ):\n landmarks_ab = l2_metric_s2.geodesic(landmarks_a, landmarks_b)\n landmarks_bc = l2_metric_s2.geodesic(landmarks_b, landmarks_c)\n landmarks_ab = landmarks_ab(times)\n landmarks_bc = landmarks_bc(times)\n\n tangent_vecs = l2_metric_s2.log(point=landmarks_bc, base_point=landmarks_ab)\n\n result = l2_metric_s2.exp(tangent_vec=tangent_vecs, base_point=landmarks_ab)\n self.assertAllClose(gs.shape(result), gs.shape(landmarks_ab))","def matlab_style_gauss2D(shape=(3,3),sigma=0.5):\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def matlab_style_gauss2D(shape=(3,3),sigma=0.5):\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def TwoE(A, B, C, D, Rab2, Rcd2, Rpq2):\n return 2.0 * (np.pi ** 2.5) / ((A + B) * (C + D) * np.sqrt(A + B + C + D)) * F0(\n (A + B) * (C + D) * Rpq2 / (A + B + C + D)) * np.exp(-A * B * Rab2 / (A + B) - C * D * Rcd2 / (C + D))","def evaluate(self, X1, X2):\r\n\r\n \"\"\"YOUR CODE HERE FOR Q1.1\"\"\"\r\n # raise NotImplementedError()\r\n n1, d = X1.shape\r\n n2, _ = X2.shape\r\n D = euclidean_dist_squared(X1, X2)\r\n return np.exp(-D / (2 * self.sigma**2))","def psfVal(ix, iy, x, y, sigma1, sigma2, b):\n return (math.exp (-0.5*((ix - x)**2 + (iy - y)**2)/sigma1**2) +\n b*math.exp (-0.5*((ix - x)**2 + (iy - y)**2)/sigma2**2))/(1 + b)","def matlab_style_gauss2D(shape,sigma):\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def log_mean_exp(x, dim):\n return log_sum_exp(x, dim) - np.log(x.size(dim))","def M_sigma(self):\n\n print(\"\", file=self.logfile)\n print(\"Updating sigma_2\", file=self.logfile)\n self.sigma2 = 1/self.L_h*(np.square(np.linalg.norm(self.w)) + np.trace(self.R))","def exp(data):\n return _make.exp(data)","def sse(matrix,motif):\n return sum([site_error(matrix,site)**2\n for site in motif])","def seToSE( x ):\n x = asarray(x,dtype=float)\n if x.shape != (6,):\n raise ValueError(\"shape must be (6,); got %s\" % str(x.shape))\n #\n return expM(screw(x))","def easomfcn(x: np.ndarray) -> np.ndarray:\n\n n = x.shape[1]\n assert n == 2, \"The Easom's function is only defined on a 2D space.\"\n X = x[:, 0]\n Y = x[:, 1]\n\n scores = (\n -np.cos(X) * np.cos(Y) * np.exp(-((X - np.pi) ** 2 + (Y - np.pi) ** 2))\n )\n return scores","def func_exp(x, a, b, c):\n return a * np.exp(b * x) + c","def MSE(a,b,axis):\n return ((a-b)**2).mean(axis=axis)","def mse(sim, obs, dim=\"time\"):\n # wrap numpy function\n kwargs = dict(\n input_core_dims=[[dim], [dim]], dask=\"parallelized\", output_dtypes=[float]\n )\n mse = xr.apply_ufunc(_mse, sim, obs, **kwargs)\n mse.name = \"mse\"\n return mse","def gaussian2d(x, y, A, sigma, x0):\n Z = A * np.exp(-( (x-x0[0])**2/(2*sigma[0]**2) + (y-x0[1])**2/(2*sigma[1]**2)))\n return Z","def softmax(x, axis=1):\n sf = np.exp(x)\n sf = sf/np.sum(sf, axis=axis)[:,np.newaxis]\n return sf","def softmax(x):\n if type(x) == list:\n dim=len(x)\n norm = np.sum(np.exp(x))\n for idx in range(dim):\n x[idx] = np.exp(x[idx])/norm\n elif type(x) == np.ndarray:\n dim=x.shape\n for col in range(dim[1]):\n norm = np.sum(np.exp(x[:, col]))\n for idx in range(dim[0]):\n x[idx, col] = np.exp(x[idx, col])/norm\n else:\n raise Exception('incorrect input')\n return x","def exp(q):\n normv = amplitude(q[:,:3])\n res = np.zeros_like(q)\n res[:,3:] = np.exp(q[:,3:]) * np.cos(normv)\n res[:,:3] = np.exp(q[:,3:]) * q[:,:3] / normv \n res[:,:3] *= np.sin(normv)\n return res","def compute_RMSE(e):\n \"\"\"Corresponds to sqrt(2*MSE)\"\"\"\n \n return np.sqrt(2*compute_MSE(e))","def _mse2(self, trace, **inputs):\n exp = np.dot(inputs['gwas_gen'],\n trace['beta_med'].mean(axis=0).T)\n phen_pred = exp * trace['alpha'].mean()\n mse = np.mean((inputs['gwas_phen'] - phen_pred) ** 2)\n return mse","def perform_sigm_times_exp(tree, exp_x=None, exp_minus_x=None, sigm_x=None,\r\n sigm_minus_x=None, parent=None, child_idx=None,\r\n full_tree=None):\r\n\r\n if exp_x is None:\r\n exp_x = []\r\n if exp_minus_x is None:\r\n exp_minus_x = []\r\n if sigm_x is None:\r\n sigm_x = []\r\n if sigm_minus_x is None:\r\n sigm_minus_x = []\r\n if full_tree is None:\r\n full_tree = tree\r\n if False: # Debug code.\r\n print '<perform_sigm_times_exp>'\r\n print ' full_tree = %s' % full_tree\r\n print ' tree = %s' % tree\r\n print ' exp_x = %s' % exp_x\r\n print ' exp_minus_x = %s' % exp_minus_x\r\n print ' sigm_x = %s' % sigm_x\r\n print ' sigm_minus_x= %s' % sigm_minus_x\r\n neg, inputs = tree\r\n if isinstance(inputs, list):\r\n # Recurse through inputs of the multiplication.\r\n rval = False\r\n for sub_idx, sub_tree in enumerate(inputs):\r\n rval |= perform_sigm_times_exp(\r\n tree=sub_tree, parent=tree, child_idx=sub_idx,\r\n exp_x=exp_x, exp_minus_x=exp_minus_x, sigm_x=sigm_x,\r\n sigm_minus_x=sigm_minus_x, full_tree=full_tree)\r\n return rval\r\n else:\r\n # Reached a leaf: if it is an exponential or a sigmoid, then we\r\n # first attempt to find a match in leaves already visited.\r\n # If there is such a match, we modify the already-visited leaf\r\n # accordingly: for instance if we visited a leaf sigmoid(x), then\r\n # find later a -exp(-x), we replace the previous leaf by\r\n # -sigmoid(-x) and remove the -exp(-x) from the tree.\r\n # If no match is found, then we register this leaf so that it can\r\n # be found later while walking the tree.\r\n var = inputs\r\n keep_it = False\r\n exp_info = is_exp(var)\r\n if exp_info is not None:\r\n exp_neg, exp_arg = exp_info\r\n neg ^= exp_neg\r\n neg_arg = is_neg(exp_arg)\r\n if neg_arg is None:\r\n if not replace_leaf(exp_arg, sigm_minus_x, sigm_x,\r\n sigmoid, neg):\r\n exp_x.append((exp_arg, tree))\r\n keep_it = True\r\n else:\r\n if not replace_leaf(neg_arg, sigm_x, sigm_minus_x,\r\n lambda x: sigmoid(-x), neg):\r\n exp_minus_x.append((neg_arg, tree))\r\n keep_it = True\r\n elif var.owner and var.owner.op == sigmoid:\r\n sigm_arg = var.owner.inputs[0]\r\n neg_arg = is_neg(sigm_arg)\r\n if neg_arg is None:\r\n if not replace_leaf(sigm_arg, exp_minus_x, sigm_minus_x,\r\n lambda x: sigmoid(-x), neg):\r\n sigm_x.append((sigm_arg, tree))\r\n keep_it = True\r\n else:\r\n if not replace_leaf(neg_arg, exp_x, sigm_x, sigmoid, neg):\r\n sigm_minus_x.append((neg_arg, tree))\r\n keep_it = True\r\n else:\r\n # It is not an exponential nor a sigmoid.\r\n keep_it = True\r\n if not keep_it:\r\n # Delete this leaf, i.e. replace it by [False, None] (corresponding\r\n # to a multiplication by 1).\r\n assert parent is not None\r\n parent[1][child_idx] = [False, None]\r\n return not keep_it","def dim_pow(dims, exp):\n return (\n dims[0] * exp,\n dims[1] * exp,\n dims[2] * exp,\n dims[3] * exp,\n dims[4] * exp,\n dims[5] * exp,\n dims[6] * exp,\n )","def exp_mean_dense(x):\n # convert out of compressed sparse matrix\n return np.log((np.sum(np.exp(x)-1)/x.shape[1]) + 1)","def compute_e(f_mat, m_mat):\r\n return m_mat.T @ f_mat @ m_mat","def mse(A, B):\n return ((A - B) ** 2).mean(axis=0)","def exp(x):\n raise NotImplementedError","def expgaussian(mu, wid, timeconstant, x): \n # Gaussian signal broadened by an exponetial signal\n g = gaussian(mu, wid, x)\n \n hly = np.round( len(g) / 2.0 )\n ey = np.r_[np.zeros(hly),g,np.zeros(hly)]\n fy = np.fft.fft(ey)\n a = np.exp(-(np.arange(len(fy))) / timeconstant )\n fa = np.fft.fft(a)\n fy1 = fy * fa\n ybz = np.real(np.fft.ifft(fy1)) / np.sum(a)\n yb = ybz[hly:len(ybz)-hly]\n \n return yb","def sse_optimized(matrix,motif):\n #Hoisted computation of K out of site_error\n K = 1/beta * sum([log(sum([exp(-beta*matrix[i][base_dict[b]])\n for b in \"ACGT\"]))\n for i in range(L)])\n return sum([(site_error_optimized(matrix,site)+K)**2\n for site in motif])","def exp(self: Float[LinearOperator, \"*batch M N\"]) -> Float[LinearOperator, \"*batch M N\"]:\n return ConstantDiagLinearOperator(self.diag_values.exp(), diag_shape=self.diag_shape)","def fspecial_gaussian(shape=(3, 3), sigma=0.5):\n m, n = [(ss - 1.) / 2. for ss in shape]\n y, x = np.ogrid[-m:m + 1, -n:n + 1]\n h = np.exp(-(x * x + y * y) / (2. * sigma * sigma))\n h[h < np.finfo(h.dtype).eps * h.max()] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h","def __bivariate_gaussian(self, sig1, sig2, mu1, mu2, x1, x2, rho):\n Z1 = ((x1 - mu1) / sig1) ** 2\n Z2 = ((x2 - mu2) / sig2) ** 2\n Z3 = 2 * rho * (x1 - mu1) * (x2 - mu2) / (sig1 * sig2)\n Z = Z1 + Z2 - Z3\n\n pi_const = torch.tensor([np.pi]).to(self.__device)\n\n return torch.exp(-Z / (2 * (1 - rho ** 2))).to(self.__device) / (\n 2 * pi_const * sig1 * sig2 * torch.sqrt(1 - rho ** 2)\n )","def exp(X):\n X = np.maximum(X,100)\n return np.exp(X)","def frobenius_inner_prod(mat1, mat2):\n assert mat1.shape==mat2.shape\n # assert isinstance(mat1, Variable) and isinstance(mat2, Variable))\n f = mat1.mul(mat2).sum()\n return f","def __bivariate_gaussian(self, sig1, sig2, mu1, mu2, x1, x2, rho):\n Z1 = ((x1 - mu1) / sig1) ** 2\n Z2 = ((x2 - mu2) / sig2) ** 2\n Z3 = 2 * rho * (x1 - mu1) * (x2 - mu2) / (sig1 * sig2)\n\n Z = Z1 + Z2 - Z3\n\n pi_const = torch.tensor([np.pi]).to(self.__device)\n\n return torch.exp(-Z / (2 * (1 - rho ** 2))).to(self.__device) / (\n 2 * pi_const * sig1 * sig2 * torch.sqrt(1 - rho ** 2)\n )","def mse(x1, x2, axis=0):\n x1 = np.asanyarray(x1)\n x2 = np.asanyarray(x2)\n return np.mean((x1 - x2) ** 2, axis=axis)","def exp(t,tau):\n return np.exp(-t/tau)","def MSE(actual, noisy):\n mean_squared_error(actual, noisy)","def apply_gaussian(X, sigma):\n return np.array([ndimage.gaussian_filter(x, sigma) for x in X])","def exp(module, x):\n _import_modules()\n if module in [np, ma]:\n return np.exp(x)\n elif module == torch:\n return torch.exp(x)\n elif module == jnp:\n return jnp.exp(x)\n elif module == tf:\n return tf.math.exp(x)\n raise UnknownModuleException(f\"Module {module.__name__} not supported.\")","def softmax(x):\n sf = np.exp(x)\n sf = sf / np.sum(sf, axis=0)\n return sf","def softmax(x):\n sf = np.exp(x)\n sf = sf / np.sum(sf, axis=0)\n return sf","def exp_fun(self, xs, *args, **kwargs):\n raise NotImplementedError","def compute_sigma_star_2(self, shape, array, j):\n array_lag = np.roll(array[:,j], 1)\n array_lag[0] = array[0,j]\n scale = (0.01 + sum((array[:,j] - array_lag)**2))**(-1)\n return invgamma.rvs(a = shape, scale = scale, size = 1)[0]","def model_2exp(a1, a2, t1, t2, s,**kwargs):\n # auxilary function taking a time array as argument.\n def aux(array_t, details=False):\n exp1 = model_1exp(a1, t1, s, **kwargs)(array_t)\n exp2 = model_1exp(a2, t2, s, **kwargs)(array_t)\n exp_tot = exp1+exp2\n\n # can return exponential components\n if details == True:\n return exp_tot, exp1, exp2\n elif details == False:\n return exp_tot\n\n return aux","def _sigma_ep(self,gam,eps):\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n return self._sigma_1(gam,eps)","def test_local_sigm_times_exp(self):\r\n def match(func, ops):\r\n #print [node.op.scalar_op for node in func.maker.fgraph.toposort()]\r\n assert [node.op for node in func.maker.fgraph.toposort()] == ops\r\n m = self.get_mode(excluding=['local_elemwise_fusion', 'inplace'])\r\n x, y = tensor.vectors('x', 'y')\r\n\r\n f = theano.function([x], sigmoid(-x) * tensor.exp(x), mode=m)\r\n match(f, [sigmoid])\r\n\r\n f = theano.function([x], sigmoid(x) * tensor.exp(-x), mode=m)\r\n match(f, [tensor.neg, sigmoid])\r\n\r\n f = theano.function([x], -(-(-(sigmoid(x)))) * tensor.exp(-x), mode=m)\r\n match(f, [tensor.neg, sigmoid, tensor.neg])\r\n\r\n f = theano.function(\r\n [x, y],\r\n (sigmoid(x) * sigmoid(-y) * -tensor.exp(-x) *\r\n tensor.exp(x * y) * tensor.exp(y)),\r\n mode=m)\r\n match(f, [sigmoid, tensor.mul, tensor.neg, tensor.exp, sigmoid,\r\n tensor.mul])","def rmse_matrices(A: np.ndarray, B: np.ndarray, element_wise: bool = False) -> np.ndarray:\n A = np.copy(A)\n B = np.copy(B)\n if A.shape != B.shape:\n raise ValueError(\"Both arrays must have the same shape.\")\n if A.ndim == 2:\n return np.sqrt(np.mean((A - B)**2))\n if element_wise:\n return np.sqrt(np.mean((A - B)**2, axis=0))\n return np.sqrt(np.mean(np.mean((A - B)**2, axis=2), axis=1))","def logsumexp2(z):\n return logsumexp(z, axis=2)","def ema(matrix, alpha):\n\n # declare empty EMA numpy array\n e = np.zeros(matrix.shape[0])\n\n # set the value of the first element in the EMA array\n e[0] = matrix[0]\n\n # use the EMA formula to calculate the value of each point in the EMA array\n for t in range(1, matrix.shape[0]):\n e[t] = alpha*matrix[t] + (1 - alpha)*e[t - 1]\n\n return e","def ema(matrix, alpha):\n\n # declare empty EMA numpy array\n e = np.zeros(matrix.shape[0])\n\n # set the value of the first element in the EMA array\n e[0] = matrix[0]\n\n # use the EMA formula to calculate the value of each point in the EMA array\n for t in range(1, matrix.shape[0]):\n e[t] = alpha*matrix[t] + (1 - alpha)*e[t - 1]\n\n return e","def doubleexp2(params, t):\n # 2011-10-27 15:50 IJMC: Created\n\n if len(params)==3:\n return (1. - params[0] * exp(-t/params[1])) * exp(-t/params[2])\n else:\n return (1. - params[0] * exp(-t/params[1])) * exp(-t/params[2]) * params[3]","def get_matrix_of_eigs(w: np.ndarray) -> np.ndarray:\n transform_eigs = np.zeros((w.shape[0], w.shape[0]),\n dtype=np.complex128)\n for i, j in product(range(w.shape[0]), repeat=2):\n if np.isclose(abs(w[i] - w[j]), 0):\n transform_eigs[i, j] = 1\n else:\n transform_eigs[i, j] = (np.exp(1j * (w[i] - w[j])) - 1) / (\n 1j * (w[i] - w[j]))\n return transform_eigs","def _calc_Em(self):\n return (self.parameters.E0 +\n self.x * sqrt2 * self.parameters.sigma * self.mt)","def test_exp():\n x = np.linspace(-3,3,13)\n\n default_use_numexpr = accel_math._USE_NUMEXPR\n\n accel_math._USE_NUMEXPR = True\n r1 = accel_math._exp(x)\n\n accel_math._USE_NUMEXPR = False\n r2 = accel_math._exp(x)\n\n np.testing.assert_almost_equal(r1,r2)\n\n accel_math._USE_NUMEXPR = default_use_numexpr","def logmatmulexp(mat1: torch.Tensor, mat2: torch.Tensor, use_mm: bool = False) -> torch.Tensor:\n mat1_shape = mat1.size()\n mat2_shape = mat2.size()\n mat1 = mat1.contiguous().view(-1, mat1_shape[-1])\n mat2 = move_dim(mat2, 0, -1)\n mat2 = mat2.contiguous().view(-1, mat2_shape[0])\n\n if use_mm:\n mat1_max = mat1.max(dim=-1, keepdim=True)[0]\n mat2_max = mat2.max(dim=-1, keepdim=True)[0]\n mat1 = mat1 - mat1_max\n mat2 = mat2 - mat2_max\n\n out = _safe_log(torch.matmul(mat1.exp(), mat2.exp().t()))\n out = out + mat1_max + mat2_max.t()\n else:\n out_sum = mat1.unsqueeze(1) + mat2.unsqueeze(0)\n out = logsumexp(out_sum, dim=-1)\n\n return out.view(concat_shape(mat1_shape[:-1], mat2_shape[1:]))","def test_exp_vectorization(self, space, point, tangent_vecs):\n space.equip_with_metric(self.Metric)\n end_points = space.metric.exp(\n tangent_vec=tangent_vecs,\n base_point=point,\n )\n result = end_points.shape\n expected = (tangent_vecs.shape[0], 2)\n self.assertAllClose(result, expected)","def test_exp_square():\n\timport odelab.scheme.exponential as E\n\tfor name in dir(E):\n\t\tcls = getattr(E, name)\n\t\tif hasattr(cls, 'general_linear_z'):\n\t\t\tobj = cls()\n\t\t\ta,b = obj.general_linear_z(np.eye(2))\n\t\t\tnb_stages = len(a)\n\t\t\ttail_length = obj.tail_length\n\t\t\tyield CheckSquare(name),name, a,b, nb_stages, tail_length","def exponentiate_and_normalize(values, dim=0):\n\n return torch.exp(lognormexp(values, dim=dim))","def exponentiate_and_normalize(values, dim=0):\n\n return torch.exp(lognormexp(values, dim=dim))","def MVgaussian(size,mu1=0,mu2=0, sigma1=3,sigma2 = 1):\n kernel = np.zeros((size, size), dtype=np.float32)\n \n size = int(size) // 2\n X = np.arange(-size,size+1)\n Y = np.arange(-size,size+1)\n \n for x in X:\n for y in Y:\n Gx = np.exp(-((x-mu1)**2)/(2*(sigma1**2)))\n Gy = np.exp(-((y-mu2)**2)/(2*(sigma2**2)))\n Gx = math.exp(-(math.pow(x-mu1,2))/(2*math.pow(sigma1,2)))\n Gy = math.exp(-(math.pow(y-mu2,2))/(2*math.pow(sigma2,2)))\n kernel[x+size,y+size] = Gx*Gy\n return kernel","def spher_harm_exp_v2(V,Yj,scale):\n # Convert the 3D DC potential into 1D array.\n # Numerically invert, here the actual expansion takes place and we obtain the expansion coefficients M_{j}.\n sz = np.array(V).shape\n npts = sz[0]*sz[1]*sz[2]\n W=np.reshape(V,npts) # 1D array of all potential values\n W=np.array([W]).T # make into column array\n Mj=np.linalg.lstsq(Yj,W)\n Mj=Mj[0] # array of coefficients\n Order = np.sqrt(len(Mj))-1\n # rescale to original units\n i = 0\n Order = int(np.sqrt(len(Mj))-1)\n for n in range(1,Order+1):\n for m in range(1,2*n+1):\n i += 1\n Mj[i] = Mj[i]/(scale**n)\n return Mj","def _sigmoid_m(self, X):\n result = np.zeros((X.shape[0], X.shape[1]), dtype='float32')\n for i in range(X.shape[0]):\n for j in range(X.shape[1]):\n result[i, j] = 1.0 / (1.0 + np.exp(-X[i, j]))\n return result","def np_elementwise(mat1, mat2):\n return (mat1 + mat2, mat1-mat2, mat1 * mat2, mat1/mat2)","def exp(self):\n return Factor().__build( VarSet(self.v) , np.exp(self.t) )","def multivariateGaussian(X, mu, Sigma2):\n k = mu.shape[0]\n\n if Sigma2.shape[1] == 1 or Sigma2.shape[0] == 1:\n Sigma2 = np.diag(Sigma2[:, 0])\n\n X = (X-mu.T).copy()\n p = (2*np.pi)**(-k/2)*np.linalg.det(Sigma2)**-0.5\n p = p*np.exp(-0.5*(X.dot(np.linalg.pinv(Sigma2))*X).sum(1, keepdims=True))\n return p","def exp2_inplace(a):","def run_numpy(self):\n return np.linalg.eigh(self.mat)","def softmax(x): \n return np.exp(x) / np.sum(np.exp(x), axis=0)","def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\n U, V = fourier_meshgrid(image)\n D = fourier_distance(U, V)\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\n \n if kind == 'high' or kind == 'highpass':\n H = 1.0 - H\n \n return H"],"string":"[\n \"def exp(tensor):\\n return _elementary_op(tensor, np.exp, np.exp)\",\n \"def expval(op, dm):\\n return np.tensordot(op, dm, ([0, 1], [0, 1]))\",\n \"def Exp(A, B):\\n return A.dot(expm(B))\",\n \"def kernel_sqExp(a,b, ls=1, sv=1):\\n a = a.T/ls\\n b = b.T/ls\\n D, n = np.shape(a)\\n d, m = np.shape(b)\\n sqdist = np.tile((a**2).T, [1, m]) + np.tile(b*b, [n, 1]) - 2*np.dot(a.T,b)\\n my_kernel = (sv**2) * np.exp(-0.5*sqdist)\\n \\n # written all out to illustrate (need to make sure a, b are in original dimensions):\\n# my_kernel2 = np.zeros((n, m))\\n# for i in range(n):\\n# for j in range(m):\\n# ai = a[i]\\n# bj = b[j]\\n# my_kernel2[i, j] = np.exp(-1/(2*ls**2) * (ai-bj)**2 )\\n# my_kernel2 = my_kernel2 * (sv**2)\\n \\n return my_kernel\",\n \"def expms(A, eig=np.linalg.eigh):\\r\\n # TODO: check that this works reliably for low rank matrices\\r\\n # first: symmetrize A\\r\\n D, B = eig(A)\\r\\n return np.dot(B, (np.exp(D) * B).T)\",\n \"def exponentialfcn(x: np.ndarray) -> np.ndarray:\\n\\n x2 = x**2\\n scores = -np.exp(-0.5 * np.sum(x2, axis=1))\\n return scores\",\n \"def ga_exp(B):\\n if np.sum(np.abs(B.value)) < np.finfo(float).eps:\\n return cf.MultiVector(layout, unit_scalar_mv.value)\\n return cf.MultiVector(layout, val_exp(B.value))\",\n \"def gaussian_smearing(E: NDArray[Shape['Ngrid'], Number],\\n E0: NDArray[Shape['*, ...'], Number],\\n sigma: float):\\n return 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(\\n -(np.broadcast_to(E, E0.shape + E.shape) - np.expand_dims(E0, len(E0.shape))) ** 2 / (2 * sigma ** 2))\",\n \"def exp_env(N, sr, lam = 3):\\n return np.exp(-lam*np.arange(N)/sr)\",\n \"def get_est_exp_discount_function(self,params):\\n params = params[0:5]\\n df = pd.DataFrame(self.maturity.apply(lambda x: x ** i) for i in range(1, 6)).T\\n df.columns = ['M1', 'M2', 'M3', 'M4', 'M5']\\n return np.exp(df.dot(params))\",\n \"def sum_exp(self,time):\\n sum = np.sum([self.A[i]*np.exp(-1.*self.lambdas[i]*time)\\\\\\n for i in xrange(self.m)])\\n return sum\",\n \"def compute_MSE(e):\\n\\n return 1/2*np.mean(e**2)\",\n \"def gauss_2d(N, sigma = 0.25):\\r\\n x, y = make_xy(N)\\r\\n sigma_pixel = sigma * np.float(N)\\r\\n arrayout = np.exp(-(x**2 + y**2) / sigma_pixel**2) / (np.pi * sigma_pixel**2)\\r\\n return arrayout\",\n \"def calculate_mse(e):\\n return 1/2*np.mean(e.dot(e))\",\n \"def test_perform_sigm_times_exp(self):\\r\\n x, y, z, t = tensor.vectors('x', 'y', 'z', 't')\\r\\n exp = tensor.exp\\r\\n\\r\\n def ok(expr1, expr2):\\r\\n trees = [parse_mul_tree(e) for e in (expr1, expr2)]\\r\\n perform_sigm_times_exp(trees[0])\\r\\n trees[0] = simplify_mul(trees[0])\\r\\n good = theano.gof.graph.is_same_graph(\\r\\n compute_mul(trees[0]),\\r\\n compute_mul(trees[1]))\\r\\n if not good:\\r\\n print trees[0]\\r\\n print trees[1]\\r\\n print '***'\\r\\n theano.printing.debugprint(compute_mul(trees[0]))\\r\\n print '***'\\r\\n theano.printing.debugprint(compute_mul(trees[1]))\\r\\n assert good\\r\\n ok(sigmoid(x) * exp(-x), sigmoid(-x))\\r\\n ok(-x * sigmoid(x) * (y * (-1 * z) * exp(-x)),\\r\\n -x * sigmoid(-x) * (y * (-1 * z)))\\r\\n ok(-sigmoid(-x) *\\r\\n (exp(y) * (-exp(-z) * 3 * -exp(x)) *\\r\\n (y * 2 * (-sigmoid(-y) * (z + t) * exp(z)) * sigmoid(z))) *\\r\\n -sigmoid(x),\\r\\n sigmoid(x) *\\r\\n (-sigmoid(y) * (-sigmoid(-z) * 3) * (y * 2 * ((z + t) * exp(z)))) *\\r\\n -sigmoid(x))\\r\\n ok(exp(-x) * -exp(-x) * (-sigmoid(x) * -sigmoid(x)),\\r\\n -sigmoid(-x) * sigmoid(-x))\\r\\n ok(-exp(x) * -sigmoid(-x) * -exp(-x),\\r\\n -sigmoid(-x))\",\n \"def matlab_style_gauss2D(shape=(3, 3), sigma=0.5):\\n m, n = [(ss - 1.) / 2. for ss in shape]\\n y, x = np.ogrid[-m:m + 1, -n:n + 1]\\n h = np.exp(-(x * x + y * y) / (2. * sigma * sigma))\\n h[h < np.finfo(h.dtype).eps * h.max()] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def func(self, X, a, b):\\n return a*np.exp(-b*X)\",\n \"def cal_gaussian_process(b, sigma2, X_train, y_train, X_test):\\n n = X_train.shape[0]\\n p = X_test.shape[0]\\n\\n K_n = np.array([[kernel(X_train[i], X_train[j], b) for i in range(n)] for j in range(n)])\\n inv = np.linalg.inv(np.diag([sigma2] * n) + K_n)\\n miu = np.zeros(p)\\n Sigma = np.zeros(p)\\n \\n for j in range(p): # for every new point x0 in testing data.\\n x0 = X_test[j]\\n K_Dn = np.zeros(n) # initialize K_Dn \\n for i in range(n):\\n K_Dn[i] = kernel(X_train[i], x0, b) # calculate every item in K_Dn\\n \\n miu[j] = K_Dn.dot(inv).dot(y_train)[0] # calculate new distribution parameters\\n Sigma[j] = sigma2 + kernel(x0, x0, b) - K_Dn.dot(inv).dot(K_Dn.T)\\n \\n return miu, Sigma\",\n \"def _sigma_2(gam, eps):\\n s0 = r0**2 * alpha / (3 * eps) / mec2_unit\\n\\n s1_1 = 16 * (1 - eps + eps**2) * np.log(gam / eps)\\n s1_2 = -1 / eps**2 + 3 / eps - 4 - 4 * eps - 8 * eps**2\\n s1_3 = -2 * (1 - 2 * eps) * np.log(1 - 2 * eps)\\n s1_4 = 1 / (4 * eps**3) - 1 / (2 * eps**2) + 3 / eps - 2 + 4 * eps\\n s1 = s1_1 + s1_2 + s1_3 * s1_4\\n\\n s2_1 = 2 / eps\\n s2_2 = (4 - 1 / eps + 1 / (4 * eps**2)) * np.log(2 * gam)\\n s2_3 = -2 + 2 / eps - 5 / (8 * eps**2)\\n s2 = s2_1 * (s2_2 + s2_3)\\n\\n return s0 * np.where(eps <= 0.5, s1, s2) * heaviside(gam - eps)\",\n \"def exp(self: Float[LinearOperator, \\\"*batch M N\\\"]) -> Float[LinearOperator, \\\"*batch M N\\\"]:\\n return self.__class__(self._diag.exp())\",\n \"def compute_kernel_matrix(x,y,sigma):\\n m = len(x)\\n\\n s = np.zeros((m,m))\\n for i in range(len(x)):\\n for j in range(i+1):\\n s[i,j] = np.exp(-((x[i]-y[j])**2)/(2*sigma**2))\\n for i in range(2,m):\\n for j in range(0,i):\\n s[i,j] = s[j,i]\\n return s\",\n \"def eval(self, X, Y):\\n sigma2 = self.sigma2\\n sumx2 = torch.sum(X**2, dim=1).view(-1, 1)\\n sumy2 = torch.sum(Y**2, dim=1).view(1, -1)\\n D2 = sumx2 - 2.0*torch.matmul(X, Y.transpose(1, 0)) + sumy2\\n K = torch.exp(-D2/(2.0*sigma2))\\n return K\",\n \"def compute_dual_energy(b_i, phi_ij, psi_i, lambda_ij, gamma_ij, N):\\n # Compute lambdas for all pair states (0,0),(1,0),(0,1) and (1,1)\\n lmbd_tmp = numpy.zeros([N, N, 4])\\n lmbd_tmp[:, :, 0] = lambda_ij[:, :, 0] + lambda_ij[:, :, 0].T\\n lmbd_tmp[:, :, 1] = lambda_ij[:, :, 0] + lambda_ij[:, :, 1].T\\n lmbd_tmp[:, :, 2] = lambda_ij[:, :, 1] + lambda_ij[:, :, 0].T\\n lmbd_tmp[:, :, 3] = lambda_ij[:, :, 1] + lambda_ij[:, :, 1].T\\n # Compute dual energy\\n dual_energy = -numpy.sum(phi_ij * numpy.exp(-1. - lmbd_tmp -\\n gamma_ij[:, :, numpy.newaxis]))\\n dual_energy -= numpy.sum(\\n psi_i * numpy.exp(-1. + (N - 1.) + numpy.sum(lambda_ij, axis=0)) *\\n (b_i / psi_i) ** (N - 1))\\n dual_energy -= numpy.sum(gamma_ij)\\n return dual_energy\",\n \"def calculate_mse(e):\\r\\n return 1/2*np.mean(e**2)\",\n \"def gauss_2d(shape=(3, 3), sigma=0.5):\\n m, n = [(ss - 1.) / 2. for ss in shape]\\n y, x = np.ogrid[-m:m + 1, -n:n + 1]\\n h = np.exp(-(x * x + y * y) / (2. * sigma * sigma))\\n h[h < np.finfo(h.dtype).eps * h.max()] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def gauss_2d(shape=(3, 3), sigma=0.5):\\n m, n = [(ss - 1.) / 2. for ss in shape]\\n y, x = np.ogrid[-m:m + 1, -n:n + 1]\\n h = np.exp(-(x * x + y * y) / (2. * sigma * sigma))\\n h[h < np.finfo(h.dtype).eps * h.max()] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def uni_gaussian(X, mu, sigma2):\\n p = (1 / sqrt(2 * pi * sigma2))\\n p = p * exp(-power(X - mu, 2) / (2 * sigma2))\\n\\n def prod(x, y):\\n return x * y\\n p = array([[reduce(prod, el)] for el in p])\\n\\n return p\",\n \"def mse(image1: np.ndarray, image2: np.ndarray) -> np.ndarray:\\n return np.sqrt(np.power((image1 - image2), 2).mean(axis=(-1, -2)))\",\n \"def similarity_matrix(points, sigma):\\n distances_squared = spherical_distances(points, points)**2\\n\\n \\n return np.exp( -distances_squared / (2.0 * sigma) )\",\n \"def test_l2_metric_exp_vectorization(\\n self, l2_metric_s2, times, landmarks_a, landmarks_b, landmarks_c\\n ):\\n landmarks_ab = l2_metric_s2.geodesic(landmarks_a, landmarks_b)\\n landmarks_bc = l2_metric_s2.geodesic(landmarks_b, landmarks_c)\\n landmarks_ab = landmarks_ab(times)\\n landmarks_bc = landmarks_bc(times)\\n\\n tangent_vecs = l2_metric_s2.log(point=landmarks_bc, base_point=landmarks_ab)\\n\\n result = l2_metric_s2.exp(tangent_vec=tangent_vecs, base_point=landmarks_ab)\\n self.assertAllClose(gs.shape(result), gs.shape(landmarks_ab))\",\n \"def matlab_style_gauss2D(shape=(3,3),sigma=0.5):\\n m,n = [(ss-1.)/2. for ss in shape]\\n y,x = np.ogrid[-m:m+1,-n:n+1]\\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def matlab_style_gauss2D(shape=(3,3),sigma=0.5):\\n m,n = [(ss-1.)/2. for ss in shape]\\n y,x = np.ogrid[-m:m+1,-n:n+1]\\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def TwoE(A, B, C, D, Rab2, Rcd2, Rpq2):\\n return 2.0 * (np.pi ** 2.5) / ((A + B) * (C + D) * np.sqrt(A + B + C + D)) * F0(\\n (A + B) * (C + D) * Rpq2 / (A + B + C + D)) * np.exp(-A * B * Rab2 / (A + B) - C * D * Rcd2 / (C + D))\",\n \"def evaluate(self, X1, X2):\\r\\n\\r\\n \\\"\\\"\\\"YOUR CODE HERE FOR Q1.1\\\"\\\"\\\"\\r\\n # raise NotImplementedError()\\r\\n n1, d = X1.shape\\r\\n n2, _ = X2.shape\\r\\n D = euclidean_dist_squared(X1, X2)\\r\\n return np.exp(-D / (2 * self.sigma**2))\",\n \"def psfVal(ix, iy, x, y, sigma1, sigma2, b):\\n return (math.exp (-0.5*((ix - x)**2 + (iy - y)**2)/sigma1**2) +\\n b*math.exp (-0.5*((ix - x)**2 + (iy - y)**2)/sigma2**2))/(1 + b)\",\n \"def matlab_style_gauss2D(shape,sigma):\\n m,n = [(ss-1.)/2. for ss in shape]\\n y,x = np.ogrid[-m:m+1,-n:n+1]\\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def log_mean_exp(x, dim):\\n return log_sum_exp(x, dim) - np.log(x.size(dim))\",\n \"def M_sigma(self):\\n\\n print(\\\"\\\", file=self.logfile)\\n print(\\\"Updating sigma_2\\\", file=self.logfile)\\n self.sigma2 = 1/self.L_h*(np.square(np.linalg.norm(self.w)) + np.trace(self.R))\",\n \"def exp(data):\\n return _make.exp(data)\",\n \"def sse(matrix,motif):\\n return sum([site_error(matrix,site)**2\\n for site in motif])\",\n \"def seToSE( x ):\\n x = asarray(x,dtype=float)\\n if x.shape != (6,):\\n raise ValueError(\\\"shape must be (6,); got %s\\\" % str(x.shape))\\n #\\n return expM(screw(x))\",\n \"def easomfcn(x: np.ndarray) -> np.ndarray:\\n\\n n = x.shape[1]\\n assert n == 2, \\\"The Easom's function is only defined on a 2D space.\\\"\\n X = x[:, 0]\\n Y = x[:, 1]\\n\\n scores = (\\n -np.cos(X) * np.cos(Y) * np.exp(-((X - np.pi) ** 2 + (Y - np.pi) ** 2))\\n )\\n return scores\",\n \"def func_exp(x, a, b, c):\\n return a * np.exp(b * x) + c\",\n \"def MSE(a,b,axis):\\n return ((a-b)**2).mean(axis=axis)\",\n \"def mse(sim, obs, dim=\\\"time\\\"):\\n # wrap numpy function\\n kwargs = dict(\\n input_core_dims=[[dim], [dim]], dask=\\\"parallelized\\\", output_dtypes=[float]\\n )\\n mse = xr.apply_ufunc(_mse, sim, obs, **kwargs)\\n mse.name = \\\"mse\\\"\\n return mse\",\n \"def gaussian2d(x, y, A, sigma, x0):\\n Z = A * np.exp(-( (x-x0[0])**2/(2*sigma[0]**2) + (y-x0[1])**2/(2*sigma[1]**2)))\\n return Z\",\n \"def softmax(x, axis=1):\\n sf = np.exp(x)\\n sf = sf/np.sum(sf, axis=axis)[:,np.newaxis]\\n return sf\",\n \"def softmax(x):\\n if type(x) == list:\\n dim=len(x)\\n norm = np.sum(np.exp(x))\\n for idx in range(dim):\\n x[idx] = np.exp(x[idx])/norm\\n elif type(x) == np.ndarray:\\n dim=x.shape\\n for col in range(dim[1]):\\n norm = np.sum(np.exp(x[:, col]))\\n for idx in range(dim[0]):\\n x[idx, col] = np.exp(x[idx, col])/norm\\n else:\\n raise Exception('incorrect input')\\n return x\",\n \"def exp(q):\\n normv = amplitude(q[:,:3])\\n res = np.zeros_like(q)\\n res[:,3:] = np.exp(q[:,3:]) * np.cos(normv)\\n res[:,:3] = np.exp(q[:,3:]) * q[:,:3] / normv \\n res[:,:3] *= np.sin(normv)\\n return res\",\n \"def compute_RMSE(e):\\n \\\"\\\"\\\"Corresponds to sqrt(2*MSE)\\\"\\\"\\\"\\n \\n return np.sqrt(2*compute_MSE(e))\",\n \"def _mse2(self, trace, **inputs):\\n exp = np.dot(inputs['gwas_gen'],\\n trace['beta_med'].mean(axis=0).T)\\n phen_pred = exp * trace['alpha'].mean()\\n mse = np.mean((inputs['gwas_phen'] - phen_pred) ** 2)\\n return mse\",\n \"def perform_sigm_times_exp(tree, exp_x=None, exp_minus_x=None, sigm_x=None,\\r\\n sigm_minus_x=None, parent=None, child_idx=None,\\r\\n full_tree=None):\\r\\n\\r\\n if exp_x is None:\\r\\n exp_x = []\\r\\n if exp_minus_x is None:\\r\\n exp_minus_x = []\\r\\n if sigm_x is None:\\r\\n sigm_x = []\\r\\n if sigm_minus_x is None:\\r\\n sigm_minus_x = []\\r\\n if full_tree is None:\\r\\n full_tree = tree\\r\\n if False: # Debug code.\\r\\n print '<perform_sigm_times_exp>'\\r\\n print ' full_tree = %s' % full_tree\\r\\n print ' tree = %s' % tree\\r\\n print ' exp_x = %s' % exp_x\\r\\n print ' exp_minus_x = %s' % exp_minus_x\\r\\n print ' sigm_x = %s' % sigm_x\\r\\n print ' sigm_minus_x= %s' % sigm_minus_x\\r\\n neg, inputs = tree\\r\\n if isinstance(inputs, list):\\r\\n # Recurse through inputs of the multiplication.\\r\\n rval = False\\r\\n for sub_idx, sub_tree in enumerate(inputs):\\r\\n rval |= perform_sigm_times_exp(\\r\\n tree=sub_tree, parent=tree, child_idx=sub_idx,\\r\\n exp_x=exp_x, exp_minus_x=exp_minus_x, sigm_x=sigm_x,\\r\\n sigm_minus_x=sigm_minus_x, full_tree=full_tree)\\r\\n return rval\\r\\n else:\\r\\n # Reached a leaf: if it is an exponential or a sigmoid, then we\\r\\n # first attempt to find a match in leaves already visited.\\r\\n # If there is such a match, we modify the already-visited leaf\\r\\n # accordingly: for instance if we visited a leaf sigmoid(x), then\\r\\n # find later a -exp(-x), we replace the previous leaf by\\r\\n # -sigmoid(-x) and remove the -exp(-x) from the tree.\\r\\n # If no match is found, then we register this leaf so that it can\\r\\n # be found later while walking the tree.\\r\\n var = inputs\\r\\n keep_it = False\\r\\n exp_info = is_exp(var)\\r\\n if exp_info is not None:\\r\\n exp_neg, exp_arg = exp_info\\r\\n neg ^= exp_neg\\r\\n neg_arg = is_neg(exp_arg)\\r\\n if neg_arg is None:\\r\\n if not replace_leaf(exp_arg, sigm_minus_x, sigm_x,\\r\\n sigmoid, neg):\\r\\n exp_x.append((exp_arg, tree))\\r\\n keep_it = True\\r\\n else:\\r\\n if not replace_leaf(neg_arg, sigm_x, sigm_minus_x,\\r\\n lambda x: sigmoid(-x), neg):\\r\\n exp_minus_x.append((neg_arg, tree))\\r\\n keep_it = True\\r\\n elif var.owner and var.owner.op == sigmoid:\\r\\n sigm_arg = var.owner.inputs[0]\\r\\n neg_arg = is_neg(sigm_arg)\\r\\n if neg_arg is None:\\r\\n if not replace_leaf(sigm_arg, exp_minus_x, sigm_minus_x,\\r\\n lambda x: sigmoid(-x), neg):\\r\\n sigm_x.append((sigm_arg, tree))\\r\\n keep_it = True\\r\\n else:\\r\\n if not replace_leaf(neg_arg, exp_x, sigm_x, sigmoid, neg):\\r\\n sigm_minus_x.append((neg_arg, tree))\\r\\n keep_it = True\\r\\n else:\\r\\n # It is not an exponential nor a sigmoid.\\r\\n keep_it = True\\r\\n if not keep_it:\\r\\n # Delete this leaf, i.e. replace it by [False, None] (corresponding\\r\\n # to a multiplication by 1).\\r\\n assert parent is not None\\r\\n parent[1][child_idx] = [False, None]\\r\\n return not keep_it\",\n \"def dim_pow(dims, exp):\\n return (\\n dims[0] * exp,\\n dims[1] * exp,\\n dims[2] * exp,\\n dims[3] * exp,\\n dims[4] * exp,\\n dims[5] * exp,\\n dims[6] * exp,\\n )\",\n \"def exp_mean_dense(x):\\n # convert out of compressed sparse matrix\\n return np.log((np.sum(np.exp(x)-1)/x.shape[1]) + 1)\",\n \"def compute_e(f_mat, m_mat):\\r\\n return m_mat.T @ f_mat @ m_mat\",\n \"def mse(A, B):\\n return ((A - B) ** 2).mean(axis=0)\",\n \"def exp(x):\\n raise NotImplementedError\",\n \"def expgaussian(mu, wid, timeconstant, x): \\n # Gaussian signal broadened by an exponetial signal\\n g = gaussian(mu, wid, x)\\n \\n hly = np.round( len(g) / 2.0 )\\n ey = np.r_[np.zeros(hly),g,np.zeros(hly)]\\n fy = np.fft.fft(ey)\\n a = np.exp(-(np.arange(len(fy))) / timeconstant )\\n fa = np.fft.fft(a)\\n fy1 = fy * fa\\n ybz = np.real(np.fft.ifft(fy1)) / np.sum(a)\\n yb = ybz[hly:len(ybz)-hly]\\n \\n return yb\",\n \"def sse_optimized(matrix,motif):\\n #Hoisted computation of K out of site_error\\n K = 1/beta * sum([log(sum([exp(-beta*matrix[i][base_dict[b]])\\n for b in \\\"ACGT\\\"]))\\n for i in range(L)])\\n return sum([(site_error_optimized(matrix,site)+K)**2\\n for site in motif])\",\n \"def exp(self: Float[LinearOperator, \\\"*batch M N\\\"]) -> Float[LinearOperator, \\\"*batch M N\\\"]:\\n return ConstantDiagLinearOperator(self.diag_values.exp(), diag_shape=self.diag_shape)\",\n \"def fspecial_gaussian(shape=(3, 3), sigma=0.5):\\n m, n = [(ss - 1.) / 2. for ss in shape]\\n y, x = np.ogrid[-m:m + 1, -n:n + 1]\\n h = np.exp(-(x * x + y * y) / (2. * sigma * sigma))\\n h[h < np.finfo(h.dtype).eps * h.max()] = 0\\n sumh = h.sum()\\n if sumh != 0:\\n h /= sumh\\n return h\",\n \"def __bivariate_gaussian(self, sig1, sig2, mu1, mu2, x1, x2, rho):\\n Z1 = ((x1 - mu1) / sig1) ** 2\\n Z2 = ((x2 - mu2) / sig2) ** 2\\n Z3 = 2 * rho * (x1 - mu1) * (x2 - mu2) / (sig1 * sig2)\\n Z = Z1 + Z2 - Z3\\n\\n pi_const = torch.tensor([np.pi]).to(self.__device)\\n\\n return torch.exp(-Z / (2 * (1 - rho ** 2))).to(self.__device) / (\\n 2 * pi_const * sig1 * sig2 * torch.sqrt(1 - rho ** 2)\\n )\",\n \"def exp(X):\\n X = np.maximum(X,100)\\n return np.exp(X)\",\n \"def frobenius_inner_prod(mat1, mat2):\\n assert mat1.shape==mat2.shape\\n # assert isinstance(mat1, Variable) and isinstance(mat2, Variable))\\n f = mat1.mul(mat2).sum()\\n return f\",\n \"def __bivariate_gaussian(self, sig1, sig2, mu1, mu2, x1, x2, rho):\\n Z1 = ((x1 - mu1) / sig1) ** 2\\n Z2 = ((x2 - mu2) / sig2) ** 2\\n Z3 = 2 * rho * (x1 - mu1) * (x2 - mu2) / (sig1 * sig2)\\n\\n Z = Z1 + Z2 - Z3\\n\\n pi_const = torch.tensor([np.pi]).to(self.__device)\\n\\n return torch.exp(-Z / (2 * (1 - rho ** 2))).to(self.__device) / (\\n 2 * pi_const * sig1 * sig2 * torch.sqrt(1 - rho ** 2)\\n )\",\n \"def mse(x1, x2, axis=0):\\n x1 = np.asanyarray(x1)\\n x2 = np.asanyarray(x2)\\n return np.mean((x1 - x2) ** 2, axis=axis)\",\n \"def exp(t,tau):\\n return np.exp(-t/tau)\",\n \"def MSE(actual, noisy):\\n mean_squared_error(actual, noisy)\",\n \"def apply_gaussian(X, sigma):\\n return np.array([ndimage.gaussian_filter(x, sigma) for x in X])\",\n \"def exp(module, x):\\n _import_modules()\\n if module in [np, ma]:\\n return np.exp(x)\\n elif module == torch:\\n return torch.exp(x)\\n elif module == jnp:\\n return jnp.exp(x)\\n elif module == tf:\\n return tf.math.exp(x)\\n raise UnknownModuleException(f\\\"Module {module.__name__} not supported.\\\")\",\n \"def softmax(x):\\n sf = np.exp(x)\\n sf = sf / np.sum(sf, axis=0)\\n return sf\",\n \"def softmax(x):\\n sf = np.exp(x)\\n sf = sf / np.sum(sf, axis=0)\\n return sf\",\n \"def exp_fun(self, xs, *args, **kwargs):\\n raise NotImplementedError\",\n \"def compute_sigma_star_2(self, shape, array, j):\\n array_lag = np.roll(array[:,j], 1)\\n array_lag[0] = array[0,j]\\n scale = (0.01 + sum((array[:,j] - array_lag)**2))**(-1)\\n return invgamma.rvs(a = shape, scale = scale, size = 1)[0]\",\n \"def model_2exp(a1, a2, t1, t2, s,**kwargs):\\n # auxilary function taking a time array as argument.\\n def aux(array_t, details=False):\\n exp1 = model_1exp(a1, t1, s, **kwargs)(array_t)\\n exp2 = model_1exp(a2, t2, s, **kwargs)(array_t)\\n exp_tot = exp1+exp2\\n\\n # can return exponential components\\n if details == True:\\n return exp_tot, exp1, exp2\\n elif details == False:\\n return exp_tot\\n\\n return aux\",\n \"def _sigma_ep(self,gam,eps):\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n return self._sigma_1(gam,eps)\",\n \"def test_local_sigm_times_exp(self):\\r\\n def match(func, ops):\\r\\n #print [node.op.scalar_op for node in func.maker.fgraph.toposort()]\\r\\n assert [node.op for node in func.maker.fgraph.toposort()] == ops\\r\\n m = self.get_mode(excluding=['local_elemwise_fusion', 'inplace'])\\r\\n x, y = tensor.vectors('x', 'y')\\r\\n\\r\\n f = theano.function([x], sigmoid(-x) * tensor.exp(x), mode=m)\\r\\n match(f, [sigmoid])\\r\\n\\r\\n f = theano.function([x], sigmoid(x) * tensor.exp(-x), mode=m)\\r\\n match(f, [tensor.neg, sigmoid])\\r\\n\\r\\n f = theano.function([x], -(-(-(sigmoid(x)))) * tensor.exp(-x), mode=m)\\r\\n match(f, [tensor.neg, sigmoid, tensor.neg])\\r\\n\\r\\n f = theano.function(\\r\\n [x, y],\\r\\n (sigmoid(x) * sigmoid(-y) * -tensor.exp(-x) *\\r\\n tensor.exp(x * y) * tensor.exp(y)),\\r\\n mode=m)\\r\\n match(f, [sigmoid, tensor.mul, tensor.neg, tensor.exp, sigmoid,\\r\\n tensor.mul])\",\n \"def rmse_matrices(A: np.ndarray, B: np.ndarray, element_wise: bool = False) -> np.ndarray:\\n A = np.copy(A)\\n B = np.copy(B)\\n if A.shape != B.shape:\\n raise ValueError(\\\"Both arrays must have the same shape.\\\")\\n if A.ndim == 2:\\n return np.sqrt(np.mean((A - B)**2))\\n if element_wise:\\n return np.sqrt(np.mean((A - B)**2, axis=0))\\n return np.sqrt(np.mean(np.mean((A - B)**2, axis=2), axis=1))\",\n \"def logsumexp2(z):\\n return logsumexp(z, axis=2)\",\n \"def ema(matrix, alpha):\\n\\n # declare empty EMA numpy array\\n e = np.zeros(matrix.shape[0])\\n\\n # set the value of the first element in the EMA array\\n e[0] = matrix[0]\\n\\n # use the EMA formula to calculate the value of each point in the EMA array\\n for t in range(1, matrix.shape[0]):\\n e[t] = alpha*matrix[t] + (1 - alpha)*e[t - 1]\\n\\n return e\",\n \"def ema(matrix, alpha):\\n\\n # declare empty EMA numpy array\\n e = np.zeros(matrix.shape[0])\\n\\n # set the value of the first element in the EMA array\\n e[0] = matrix[0]\\n\\n # use the EMA formula to calculate the value of each point in the EMA array\\n for t in range(1, matrix.shape[0]):\\n e[t] = alpha*matrix[t] + (1 - alpha)*e[t - 1]\\n\\n return e\",\n \"def doubleexp2(params, t):\\n # 2011-10-27 15:50 IJMC: Created\\n\\n if len(params)==3:\\n return (1. - params[0] * exp(-t/params[1])) * exp(-t/params[2])\\n else:\\n return (1. - params[0] * exp(-t/params[1])) * exp(-t/params[2]) * params[3]\",\n \"def get_matrix_of_eigs(w: np.ndarray) -> np.ndarray:\\n transform_eigs = np.zeros((w.shape[0], w.shape[0]),\\n dtype=np.complex128)\\n for i, j in product(range(w.shape[0]), repeat=2):\\n if np.isclose(abs(w[i] - w[j]), 0):\\n transform_eigs[i, j] = 1\\n else:\\n transform_eigs[i, j] = (np.exp(1j * (w[i] - w[j])) - 1) / (\\n 1j * (w[i] - w[j]))\\n return transform_eigs\",\n \"def _calc_Em(self):\\n return (self.parameters.E0 +\\n self.x * sqrt2 * self.parameters.sigma * self.mt)\",\n \"def test_exp():\\n x = np.linspace(-3,3,13)\\n\\n default_use_numexpr = accel_math._USE_NUMEXPR\\n\\n accel_math._USE_NUMEXPR = True\\n r1 = accel_math._exp(x)\\n\\n accel_math._USE_NUMEXPR = False\\n r2 = accel_math._exp(x)\\n\\n np.testing.assert_almost_equal(r1,r2)\\n\\n accel_math._USE_NUMEXPR = default_use_numexpr\",\n \"def logmatmulexp(mat1: torch.Tensor, mat2: torch.Tensor, use_mm: bool = False) -> torch.Tensor:\\n mat1_shape = mat1.size()\\n mat2_shape = mat2.size()\\n mat1 = mat1.contiguous().view(-1, mat1_shape[-1])\\n mat2 = move_dim(mat2, 0, -1)\\n mat2 = mat2.contiguous().view(-1, mat2_shape[0])\\n\\n if use_mm:\\n mat1_max = mat1.max(dim=-1, keepdim=True)[0]\\n mat2_max = mat2.max(dim=-1, keepdim=True)[0]\\n mat1 = mat1 - mat1_max\\n mat2 = mat2 - mat2_max\\n\\n out = _safe_log(torch.matmul(mat1.exp(), mat2.exp().t()))\\n out = out + mat1_max + mat2_max.t()\\n else:\\n out_sum = mat1.unsqueeze(1) + mat2.unsqueeze(0)\\n out = logsumexp(out_sum, dim=-1)\\n\\n return out.view(concat_shape(mat1_shape[:-1], mat2_shape[1:]))\",\n \"def test_exp_vectorization(self, space, point, tangent_vecs):\\n space.equip_with_metric(self.Metric)\\n end_points = space.metric.exp(\\n tangent_vec=tangent_vecs,\\n base_point=point,\\n )\\n result = end_points.shape\\n expected = (tangent_vecs.shape[0], 2)\\n self.assertAllClose(result, expected)\",\n \"def test_exp_square():\\n\\timport odelab.scheme.exponential as E\\n\\tfor name in dir(E):\\n\\t\\tcls = getattr(E, name)\\n\\t\\tif hasattr(cls, 'general_linear_z'):\\n\\t\\t\\tobj = cls()\\n\\t\\t\\ta,b = obj.general_linear_z(np.eye(2))\\n\\t\\t\\tnb_stages = len(a)\\n\\t\\t\\ttail_length = obj.tail_length\\n\\t\\t\\tyield CheckSquare(name),name, a,b, nb_stages, tail_length\",\n \"def exponentiate_and_normalize(values, dim=0):\\n\\n return torch.exp(lognormexp(values, dim=dim))\",\n \"def exponentiate_and_normalize(values, dim=0):\\n\\n return torch.exp(lognormexp(values, dim=dim))\",\n \"def MVgaussian(size,mu1=0,mu2=0, sigma1=3,sigma2 = 1):\\n kernel = np.zeros((size, size), dtype=np.float32)\\n \\n size = int(size) // 2\\n X = np.arange(-size,size+1)\\n Y = np.arange(-size,size+1)\\n \\n for x in X:\\n for y in Y:\\n Gx = np.exp(-((x-mu1)**2)/(2*(sigma1**2)))\\n Gy = np.exp(-((y-mu2)**2)/(2*(sigma2**2)))\\n Gx = math.exp(-(math.pow(x-mu1,2))/(2*math.pow(sigma1,2)))\\n Gy = math.exp(-(math.pow(y-mu2,2))/(2*math.pow(sigma2,2)))\\n kernel[x+size,y+size] = Gx*Gy\\n return kernel\",\n \"def spher_harm_exp_v2(V,Yj,scale):\\n # Convert the 3D DC potential into 1D array.\\n # Numerically invert, here the actual expansion takes place and we obtain the expansion coefficients M_{j}.\\n sz = np.array(V).shape\\n npts = sz[0]*sz[1]*sz[2]\\n W=np.reshape(V,npts) # 1D array of all potential values\\n W=np.array([W]).T # make into column array\\n Mj=np.linalg.lstsq(Yj,W)\\n Mj=Mj[0] # array of coefficients\\n Order = np.sqrt(len(Mj))-1\\n # rescale to original units\\n i = 0\\n Order = int(np.sqrt(len(Mj))-1)\\n for n in range(1,Order+1):\\n for m in range(1,2*n+1):\\n i += 1\\n Mj[i] = Mj[i]/(scale**n)\\n return Mj\",\n \"def _sigmoid_m(self, X):\\n result = np.zeros((X.shape[0], X.shape[1]), dtype='float32')\\n for i in range(X.shape[0]):\\n for j in range(X.shape[1]):\\n result[i, j] = 1.0 / (1.0 + np.exp(-X[i, j]))\\n return result\",\n \"def np_elementwise(mat1, mat2):\\n return (mat1 + mat2, mat1-mat2, mat1 * mat2, mat1/mat2)\",\n \"def exp(self):\\n return Factor().__build( VarSet(self.v) , np.exp(self.t) )\",\n \"def multivariateGaussian(X, mu, Sigma2):\\n k = mu.shape[0]\\n\\n if Sigma2.shape[1] == 1 or Sigma2.shape[0] == 1:\\n Sigma2 = np.diag(Sigma2[:, 0])\\n\\n X = (X-mu.T).copy()\\n p = (2*np.pi)**(-k/2)*np.linalg.det(Sigma2)**-0.5\\n p = p*np.exp(-0.5*(X.dot(np.linalg.pinv(Sigma2))*X).sum(1, keepdims=True))\\n return p\",\n \"def exp2_inplace(a):\",\n \"def run_numpy(self):\\n return np.linalg.eigh(self.mat)\",\n \"def softmax(x): \\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\\n U, V = fourier_meshgrid(image)\\n D = fourier_distance(U, V)\\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\\n \\n if kind == 'high' or kind == 'highpass':\\n H = 1.0 - H\\n \\n return H\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60777706","0.6070397","0.6037772","0.60048586","0.59793174","0.58501387","0.58492404","0.58091897","0.57963014","0.57812047","0.57540333","0.57262975","0.57174045","0.5708458","0.5661374","0.5589162","0.55674833","0.5548229","0.5532804","0.5519702","0.54916966","0.54835385","0.5474528","0.54680383","0.5456158","0.5456158","0.5452313","0.5418749","0.5401341","0.5395118","0.53858733","0.53858733","0.5362595","0.5361635","0.53553575","0.5350479","0.5348321","0.5338657","0.5320004","0.53186786","0.531603","0.5303144","0.5300526","0.52781266","0.5272391","0.5269842","0.52651966","0.52643055","0.52437574","0.52425915","0.52404386","0.5231633","0.5224332","0.52087235","0.5203727","0.51982546","0.51969475","0.5196199","0.5192377","0.51873386","0.51837546","0.5182736","0.51772773","0.51747644","0.5172783","0.51697797","0.51679283","0.5167631","0.5153145","0.5150834","0.5140671","0.5140671","0.5135554","0.5118057","0.5108687","0.5106982","0.50947094","0.5091585","0.50888526","0.50856847","0.50856847","0.5081475","0.50761914","0.5068948","0.50680614","0.5064137","0.5055759","0.50543016","0.5050767","0.5050767","0.50507647","0.50478894","0.5047238","0.5041728","0.50408405","0.50406796","0.50393873","0.5034815","0.5033738","0.5030269"],"string":"[\n \"0.60777706\",\n \"0.6070397\",\n \"0.6037772\",\n \"0.60048586\",\n \"0.59793174\",\n \"0.58501387\",\n \"0.58492404\",\n \"0.58091897\",\n \"0.57963014\",\n \"0.57812047\",\n \"0.57540333\",\n \"0.57262975\",\n \"0.57174045\",\n \"0.5708458\",\n \"0.5661374\",\n \"0.5589162\",\n \"0.55674833\",\n \"0.5548229\",\n \"0.5532804\",\n \"0.5519702\",\n \"0.54916966\",\n \"0.54835385\",\n \"0.5474528\",\n \"0.54680383\",\n \"0.5456158\",\n \"0.5456158\",\n \"0.5452313\",\n \"0.5418749\",\n \"0.5401341\",\n \"0.5395118\",\n \"0.53858733\",\n \"0.53858733\",\n \"0.5362595\",\n \"0.5361635\",\n \"0.53553575\",\n \"0.5350479\",\n \"0.5348321\",\n \"0.5338657\",\n \"0.5320004\",\n \"0.53186786\",\n \"0.531603\",\n \"0.5303144\",\n \"0.5300526\",\n \"0.52781266\",\n \"0.5272391\",\n \"0.5269842\",\n \"0.52651966\",\n \"0.52643055\",\n \"0.52437574\",\n \"0.52425915\",\n \"0.52404386\",\n \"0.5231633\",\n \"0.5224332\",\n \"0.52087235\",\n \"0.5203727\",\n \"0.51982546\",\n \"0.51969475\",\n \"0.5196199\",\n \"0.5192377\",\n \"0.51873386\",\n \"0.51837546\",\n \"0.5182736\",\n \"0.51772773\",\n \"0.51747644\",\n \"0.5172783\",\n \"0.51697797\",\n \"0.51679283\",\n \"0.5167631\",\n \"0.5153145\",\n \"0.5150834\",\n \"0.5140671\",\n \"0.5140671\",\n \"0.5135554\",\n \"0.5118057\",\n \"0.5108687\",\n \"0.5106982\",\n \"0.50947094\",\n \"0.5091585\",\n \"0.50888526\",\n \"0.50856847\",\n \"0.50856847\",\n \"0.5081475\",\n \"0.50761914\",\n \"0.5068948\",\n \"0.50680614\",\n \"0.5064137\",\n \"0.5055759\",\n \"0.50543016\",\n \"0.5050767\",\n \"0.5050767\",\n \"0.50507647\",\n \"0.50478894\",\n \"0.5047238\",\n \"0.5041728\",\n \"0.50408405\",\n \"0.50406796\",\n \"0.50393873\",\n \"0.5034815\",\n \"0.5033738\",\n \"0.5030269\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7608249"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":884,"cells":{"query":{"kind":"string","value":"Each row of the matrix, let's say the jth row, represents the distance between the other data point from the jth point. This function returns the indexes for the points with the smallest distances with respect to each point represented by that specified row. By row, I mean the 0th dimension. Also notice that this function does not include the target particle, i.e. the diagonal element along the matrix is set to zero."},"document":{"kind":"string","value":"def nearest_points_indexes_without_self(matrix, num_to_keep):\n\n # Set the diagonal to 0\n np.fill_diagonal(matrix, 0)\n # Get the position for the resulted values\n sort_arg = np.argsort(matrix, axis=1)\n\n return sort_arg[:, : num_to_keep]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def nearest_min(dist_matrix):\n # much faster than np.where\n i, j = np.unravel_index(\n np.argmin(dist_matrix), \n dims=dist_matrix.shape\n )\n return i, j","def nearest_points_indexes_with_self(matrix, num_to_keep):\n\n # Set the diagonal to 1\n np.fill_diagonal(matrix, 1)\n # Get the position for the resulted values\n sort_arg = np.argsort(matrix, axis=1)\n\n return sort_arg[:, : num_to_keep]","def find_min_distance():\n return np.argmin(d)","def smallest_distance(self, clusters):\n i, j = numpy.unravel_index(numpy.argmin(clusters), clusters.shape)\n return clusters[i, j], i, j","def nearest_points_values_with_self(matrix, num_to_keep):\n\n # Set the diagonal to 1\n np.fill_diagonal(matrix, 1)\n # Get the position for the resulted values\n sort = np.sort(matrix, axis=1)\n\n return sort[:, : num_to_keep]","def nearest_neighbour(matrix, start=0):\n path = [start]\n while len(matrix) != len(path):\n matrix[:, start] = numpy.inf\n start = numpy.argmin(matrix[start])\n path.append(start)\n return path","def nearest_cluster(X,c):\n K = np.size(c,0)\n idx = np.zeros((np.size(X,0),1))\n arr = np.empty((np.size(X,0),1))\n for i in range(0,K):\n y = c[i]\n temp = np.ones((np.size(X,0),1))*y\n b = np.power(np.subtract(X,temp),2)\n a = np.sum(b,axis = 1)\n a.resize((np.size(X,0),1))\n arr = np.append(arr, a, axis=1)\n arr = np.delete(arr,0,axis=1)\n idx = np.argmin(arr, axis=1)\n return idx","def nearest_vertex_to(self, point):\n distances = self.distances_to(point)\n idx = np.argmin(distances)\n return idx","def find_min(self):\n\n\n min_x = 1000\n min_y = 1000\n k = len(self.__col_lista)\n for i in range(k):\n x, y = self.__col_lista[i]\n if x < min_x:\n min_x = x\n if y < min_y:\n min_y = y\n return min_x, min_y","def _find_min_pair(pandas_matrix):\n numpy_matrix = pandas_matrix.values\n mins = np.where(numpy_matrix == np.nanmin(numpy_matrix))\n min_col_idx = mins[0][0]\n min_row_idx = mins[1][0]\n (min_col, min_row) = (pandas_matrix.index[min_col_idx], \n pandas_matrix.columns[min_row_idx])\n\n return (min_col, min_row)","def find_closest_index(traj, point):\n\n\t#TODO: vectorise function to receive any length of points.\n\n\tdistances = np.subtract(np.array(point),traj) \n\tdistances = distances.reshape(-1,2)\n\t#distances = distances[~np.isnan(distances)].reshape(-1,2)\n\n\t#print(\"distances\")\n\t#pprint(distances)\n\tdist_array = np.linalg.norm(distances, axis = 1)\n\t#pprint(dist_array)\n\t#dist_array = np.sqrt((distances[:,0]**2)+(distances[:,1]**2)) #array of distances from trajectory to gaze landing point in world. \n\tidx = np.nanargmin(abs(dist_array)) #find smallest difference in pythag distance from 0,0 to get closest point. \n\tdists = distances[idx, :]\n\tdist = dist_array[idx]\n\n\treturn idx#, dists, dist\n\t#return idx","def getclosest_ij(lats,lons,latpt,lonpt):\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\n dist_lon = (lons-lonpt)**2\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\n minindex_lon = dist_lon.argmin()\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index","def getclosest_ij(lats,lons,latpt,lonpt):\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\n dist_lon = (lons-lonpt)**2\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\n minindex_lon = dist_lon.argmin()\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index","def nearest_points_values_without_self(matrix, num_to_keep):\n\n # Set the diagonal to 0\n np.fill_diagonal(matrix, 0)\n # Get the position for the resulted values\n sort = np.sort(matrix, axis=1)\n\n return sort[:, : num_to_keep]","def find_closest(distances, threshold):\n n = len(distances)\n person_1 = []\n person_2 = []\n d = []\n\n for i in range(n):\n for j in range(i+1, n):\n if distances[i][j] <= threshold:\n person_1.append(i)\n person_2.append(j)\n d.append(distances[i][j])\n\n return person_1, person_2, d","def getRowHeuristics(matrix):\n row, col = matrix.shape\n rHeuristic = np.zeros((row,2)) # Dos columnas. La primera para indicar la columna la segunda para la Heuristica\n for i in range(0,row):\n rHeuristic[i,0] = int(i)\n #print (i,sum(matrix[:,i]), pesos[i], float(pesos[i]/sum(matrix[:,i])))\n rHeuristic[i,1] = 1/sum(matrix[i,:])\n return rHeuristic[rHeuristic[:,1].argsort()]","def nearestNeighbor(self, coords, my_index, blacklist):\n min_index, min_distance = 0, 999999999\n my_col, my_row = coords[my_index]\n for i, (col, row) in enumerate(coords):\n if i in blacklist:\n continue\n distance = math.sqrt((my_col-col)**2 + (my_row-row)**2)\n if distance < min_distance:\n min_index, min_distance = i, distance\n return min_index","def _nearest_cluster_distance(distances_row, labels, i):\n label = labels[i]\n b = np.min([np.mean(distances_row[labels == cur_label])\n for cur_label in set(labels) if not cur_label == label])\n return b","def find_closest_trajectory_pose(self):\n np_state = numpy.array([[self.x], [self.y]])\n temp_distance = numpy.sum(\n (self.np_trajectory[0:2, :] - np_state) ** 2,\n axis=0)\n best_idx = numpy.argmin(temp_distance)\n return best_idx","def findknn(xTr,xTe,k):\n\n # YOUR CODE HERE\n if k > len(xTr):\n k = len(xTr)\n \n D=l2distance(xTe, xTr)\n (m,n) = D.shape\n \n indices = []\n dists = []\n for i in range(m):\n smallest_indices = np.argsort(D[i])\n ind = smallest_indices[:k]\n dis = D[i,smallest_indices[:k]]\n indices.append(ind)\n dists.append(dis)\n \n indices = np.transpose(np.array(indices))\n dists = np.transpose(np.array(dists))\n return indices, dists","def getNearestSampleIndex(test, trainX):\n dist_matrix = test - trainX\n dist_square = dist_matrix ** 2\n dist_sums = dist_square.sum(axis=1)\n distance_vector = np.sqrt(dist_sums)\n return (distance_vector).argmin()","def _first_index_with_smaller_neighbour(P):\n i = len(P) - 1\n while i > 0 and P[i-1] <= P[i]:\n i -= 1\n return i","def saddle_points(matrix):\n if not all(len(row) == len(matrix[0]) for row in matrix[1:]):\n raise ValueError('Provided matrix is irregular.')\n columns = [col for col in zip(*matrix)]\n points = set()\n for ridx, row in enumerate(matrix):\n for cidx, element in enumerate(row):\n if element == max(row) and element == min(columns[cidx]):\n points.add((ridx, cidx))\n return points","def _compute_euclidean_neigh_matrix(src, d_matrix, radius):\n\n n_max = 100\n n_min = 3\n reached_points = np.array([0])\n counter = 0\n n_neigh = []\n list_neigh = []\n\n while counter < reached_points.shape[0] < src.shape[0]:\n P = reached_points[counter]\n aux = np.array(sorted(\n np.where(d_matrix[P] <= radius)[0],\n key=lambda k: d_matrix[P, k]))\n n_neigh.append(aux.shape[0])\n\n # Check the number of neighbours\n if n_neigh[-1] < n_min:\n raise ValueError('Computation of neighbours aborted since '\n 'their minimum number is too small.\\n'\n 'Please choose a higher radius.')\n elif n_neigh[-1] > n_max:\n raise ValueError('Computation of neighbours aborted since'\n 'their maximum number is too big.\\n'\n 'Please choose a lower radius.')\n list_neigh.append(aux)\n reached_points = np.append(reached_points,\n aux[~np.in1d(aux, reached_points)])\n counter += 1\n\n if counter >= reached_points.shape[0]:\n raise ValueError('Too small value of the radius:'\n 'the neighbour-matrix is not connected')\n elif src.shape[0] == reached_points.shape[0]:\n while counter < src.shape[0]:\n P = reached_points[counter]\n aux = np.array(sorted(\n np.where(d_matrix[P] <= radius)[0],\n key=lambda k: d_matrix[P, k]))\n n_neigh.append(aux.shape[0])\n\n if n_neigh[-1] < n_min:\n raise ValueError('Computation of neighbours aborted since '\n 'their minimum number is too small.\\n'\n 'Please choose a higher radius.')\n elif n_neigh[-1] > n_max:\n raise ValueError('Computation of neighbours aborted since'\n 'their maximum number is too big.\\n'\n 'Please choose a lower radius.')\n\n list_neigh.append(aux)\n counter += 1\n\n n_neigh_max = max(n_neigh)\n n_matrix = np.zeros([src.shape[0],\n n_neigh_max], dtype=int) - 1\n for i in range(src.shape[0]):\n n_matrix[i, 0:list_neigh[i].shape[0]] = list_neigh[i]\n index_ord = np.argsort(n_matrix[:, 0])\n n_matrix = n_matrix[index_ord]\n return n_matrix\n else:\n raise RuntimeError(\"Some problems during\"\n \"computation of neighbours.\")","def min_distance_vertex(distance, visited):\n vertices = len(visited)\n min_distance = INF\n min_index = None\n for v in range(vertices):\n if not visited[v] and distance[v] <= min_distance:\n min_distance = distance[v]\n min_index = v\n return min_index","def shortestDistance(self, grid):\n # return self.house_oriented_TLE(grid)\n # One axis\n row_count = [sum(row) for row in grid]\n col_count = [0]* len(grid[0])\n row_dist = [0]* len(grid)\n col_dist = [0]* len(grid[0])\n output = sys.maxsize\n for i in range(len(grid)): \n for j in range(len(grid[0])):\n col_count[j] += grid[i][j]\n \n for index_p in range(len(row_count)):\n for index_h in range(len(row_count)):\n row_dist[index_p] += abs(index_h - index_p) * row_count[index_h]\n \n for index_p in range(len(col_count)):\n for index_h in range(len(col_count)):\n col_dist[index_p] += abs(index_h - index_p) * col_count[index_h]\n \n # print(row_count)\n # print(col_count)\n # print(row_dist)\n # print(col_dist)\n for i in range(len(grid)):\n for j in range(len(grid[0])):\n if grid[i][j] == 1:\n continue\n output = min(output, row_dist[i] + col_dist[j])\n return output","def closest_points_naive(self, x, y):\r\n # Running time: O(n ** 2)\r\n\r\n dist = []\r\n for i in range(len(x)):\r\n for j in range(i+1, len(x)):\r\n d = self.get_euclidean_distance(x[i], x[j], y[i], y[j])\r\n dist.append(d)\r\n \r\n return min(dist)","def find_min_point(points):\r\n smallest_point_index = 0\r\n for i in range(1, len(points)):\r\n if points[i][1] < points[smallest_point_index][1]:\r\n smallest_point_index = i\r\n elif points[i][0] > points[smallest_point_index][0] and points[i][1] == points[smallest_point_index][1]:\r\n smallest_point_index = i\r\n return smallest_point_index","def find_nearest_neighbors_idx(X, x, k):\n ## homework:start\n result = \n ## homework:end\n return result","def _findMin(p, A):\n\n m=(-1, (0,0))\n for p0 in A:\n dist = np.linalg.norm(p0-np.array(p))\n if m[0]==-1 or m[0]>dist:\n m = (dist, p0)\n \n return tuple(m[1])","def get_idx(lons, lats, lon, lat):\n dist = ((lons - lon) ** 2 + (lats - lat) ** 2) ** 0.5\n return np.unravel_index(dist.argmin(), dist.shape)","def get_min_dist(x0, y0, arr):\n dist = np.hypot(arr.T[0] - x0, arr.T[1] - y0)\n min_dist = np.min(dist)\n val = np.argmin(dist)\n return min_dist, arr[val]","def dist(dm, sm, neighbors):\n\n # Initialize list of possible distances\n distances = []\n\n # loop over all neighbors of the cell\n for neighbor in neighbors:\n # If the neighbor is valid\n if dm[neighbor[0], neighbor[1]] != -1:\n # add neighbor distance + 1 to possible distances\n distances.append(dm[neighbor[0], neighbor[1]] + 1)\n\n # return minimal distance\n return np.min(distances)","def nodes_min_energy_index(self, node):\n idx = -1\n curr_energy = np.inf\n for i in range(self.cost_matrix.shape[1]):\n new_energy = self.cost_matrix[node][i]\n if new_energy < curr_energy:\n curr_energy = new_energy\n idx = i\n return idx","def pair_idx(rows, comm=None):\n raise Exception(\"Not implemented\")\n \n if comm == None:\n comm = MPI.COMM_WORLD\n \n total = comb(rows,2,exact=True)\n size = comm.Get_size()\n \n size = 1000\n \n print(total / size)\n \n target = total / size\n \n current_row = 0\n calc_list = []\n row_list = [[] for x in range(size)]\n for rank in range(size):\n row_list[rank].append(current_row)\n \n current_calcs = 0\n \n for value in range(current_row, rows):\n current_calcs += value\n if current_calcs > target:\n if rank == size-1:\n pass\n else:\n break\n \n calc_list.append(current_calcs)\n row_list[rank].append(value)\n current_row = value\n \n return row_list,calc_list","def get_maxmin_index_from_row(\n distance_matrix: np.ndarray,\n row: int,\n previous_indexes: List,\n type: str,\n )-> int:\n distance_matrix = distance_matrix.copy()\n arr = distance_matrix[row].astype(float)\n \n aux_list = range(arr.shape[0])\n aux_list_2 = []\n for i in aux_list:\n if i in previous_indexes:\n aux_list_2.append(True)\n else:\n aux_list_2.append(False)\n previous_indexes_bool = aux_list_2\n \n if type == 'max':\n arr[previous_indexes_bool] = -1\n target_index = np.argmax(arr)\n if type == 'min':\n arr[previous_indexes_bool] = np.Inf\n target_index = np.argmin(arr)\n \n return target_index","def get_nearest_atom_inds_per_mol(self):\n self.closest_at_per_mol = np.zeros((self.nmol,\n self.at_per_mol,\n self.at_per_mol-1), dtype=int)\n\n # Get and sort distances\n all_at_inds = np.arange(self.at_per_mol)\n for imol in range(self.nmol):\n for iat in range(self.at_per_mol):\n at_inds = all_at_inds[all_at_inds != iat]\n dist = self.all_dist_per_mol[imol, iat, at_inds]\n\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\n self.closest_at_per_mol[imol, iat] = at_inds","def distance_matrix(n_row, n_col):\n\n n_pop = int(n_row * n_col)\n center = int(n_row/2*(n_col+1))\n\n pop_idx = np.arange(n_pop)\n pop_idx_col = np.remainder(pop_idx, n_col)\n pop_idx_row = pop_idx // n_row\n\n pos = np.vstack((pop_idx_col,pop_idx_row)).T\n distance = spa.distance.cdist([pos[center]], pos)[0]\n\n return distance","def find_min(self, A, w):\n import numpy as np\n\n vcost = self.INFINITY\n vto = vfrom = -1\n for v in w:\n # Get array offset of minimum of this vertex\n i = np.argmin(A[v,:])\n if A[v,i] < vcost:\n vcost = A[v,i]\n vto = i\n vfrom = v\n return (vfrom, vto, vcost)","def find_nearest_neighbours_SURF(averageDistance,inst,distanceArray,maxInst): \r\n NN=[]\r\n min_indices=[] \r\n\r\n for j in range(maxInst):\r\n if inst != j:\r\n locator = [inst,j]\r\n locator = sorted(locator, reverse=True) #Access corect half of table (result of removed table redundancy)\r\n d = distanceArray[locator[0]][locator[1]]\r\n if d<averageDistance:\r\n min_indices.append(j)\r\n \r\n for j in range(len(min_indices)):\r\n NN.append(min_indices[j])\r\n \r\n return NN","def min(weightData , dataSetVector ):\r\n # weightData: pass the whole weightData array.\r\n # dataSetVector: pass the a data vector to compare with weightdata array, to find its closest match\r\n winnerIndex = 0 #flag for initalizing the winner index\r\n minValue = EcuDist(dataSetVector,weightData[0]) # initalize the minValue\r\n # iterate through all weighdata rows to find the closest match, depending on ecu. distance,\r\n #and then return the index of the closest match(winner)\r\n for i in range(weightData.shape[0]):\r\n if(EcuDist(dataSetVector,weightData[i]) < minValue):\r\n minValue = EcuDist(dataSetVector,weightData[i])\r\n winnerIndex = i\r\n return winnerIndex","def get_nearest_atom_inds(self):\n # Create empty data structure\n self.closest_ats = np.zeros((self.natom, self.natom-1), dtype=int)\n\n # Get and sort distances\n all_at_inds = np.arange(self.natom)\n for iat in range(self.natom):\n at_inds = all_at_inds[all_at_inds != iat]\n dist = self.all_dist[iat, at_inds]\n\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\n self.closest_ats[iat] = at_inds","def find_nearest_neighbors(p, points, k=5):\n dist = np.zeros(points.shape[0])\n for i in range(len(dist)):\n dist[i] = distance(p, points[i])\n ind = np.argsort(dist)\n return ind[0:k]","def update_distmatrix(min_idx, dist_matrix):\n i, j = min_idx\n new_cluster_dist = np.minimum(dist_matrix[i, :], dist_matrix[j, :])\n new_cluster_dist[i] = np.inf\n \n dist_matrix[i, :] = new_cluster_dist\n dist_matrix[:, i] = new_cluster_dist\n \n dist_matrix[j, :] = np.inf\n dist_matrix[:, j] = np.inf\n \n return dist_matrix","def getNeighbors(training_data, test_row, k):\n\n distances = list()\n for training_row in training_data:\n dist = euclidianDistance(training_row, test_row)\n distances.append([training_row, dist])\n \n #Sort on the basis of dist\n distances.sort(key=lambda row:row[1])\n\n neighbors = list()\n\n for i in range(int(k)):\n neighbors.append(distances[i][0])\n\n return neighbors","def get_nearest_node_index(node_list, random_node):\n\n dist_list = [\n (node.x - random_node.x) ** 2 + (node.y - random_node.y) ** 2\n for node in node_list\n ]\n minind = dist_list.index(min(dist_list))\n\n return minind","def findSmallest(distancesWithNames):\n smallest = distancesWithNames[0][2]\n smallestIndex = -1\n for i in range(len(distancesWithNames)):\n if smallest >= distancesWithNames[i][2]:\n smallest = distancesWithNames[i][2]\n smallestIndex = i\n return smallestIndex","def closestCluster(p, centers):\n\tbestIndex = 0\n closest = float(\"+inf\")\n for i in range(len(centers)):\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\n if distance < closest:\n closest = distance\n bestIndex = i\n\treturn bestIndex","def get_min_shannon_entropy(grid):\r\n curr_min = math.inf\r\n curr_best = []\r\n for i in range(len(grid[0])):\r\n for j in range(len(grid)):\r\n if not grid[j][i].collapsed:\r\n w = grid[j][i].block_weights\r\n shannon_entropy = sum([-math.log(el) for el in w] )\r\n if shannon_entropy < curr_min:\r\n curr_min = shannon_entropy\r\n curr_best = [(i,j)]\r\n elif shannon_entropy == curr_min:\r\n curr_best.append((i,j))\r\n idx = np.random.choice(range(len(curr_best))) #choose randomly if theres a tie\r\n return curr_best[idx] #x,y\r","def detec_mfo_dist(betas):\n k = len(betas)\n min_dist = np.inf\n for i in range(k-1):\n for j in range(i+1,k):\n distance = np.sum((betas[i,:] - betas[j,:])**2)\n if distance < min_dist:\n MFO_index = [i,j]\n min_dist = distance\n return MFO_index","def smallest_cert(pts):\n # 1. project the points\n def projected_flattened_pt(pt):\n mag = distance(pt)\n distance_wanted = rect_dist\n return distance_wanted/mag*pt[1], \\\n distance_wanted/mag*pt[2]\n\n flat_pts = np.array([[projected_flattened_pt(pt), pt] for pt in pts])\n\n # 2. identify rows of data\n data = {} \n # maps height (from ground) to the \"row\" (scan) list of\n # horizontal-only (1D) data\n for pt, origpt in flat_pts:\n for row_height in data:\n if abs(pt[1]-row_height) < dist_bt_rows/2:\n data[row_height].append([pt[0], origpt])\n break\n else:\n data[pt[1]] = [[pt[0], origpt]]\n\n #3. build up final_data so each row has enough point x-density\n def get_left_i(pts, left_edge):\n # get last index <= left_edge\n for i, pt in enumerate(pts):\n if pt[0] > left_edge:\n return i - 1\n\n def get_right_i(pts, right_edge):\n # get first index >= right_edge\n i = len(pts)-1\n while True:\n pt = pts[i]\n if pt[0] < right_edge:\n return i + 1\n i -= 1\n\n final_data = []\n for _, subdata in data.items():\n subdata = sorted(subdata, key=lambda t: t[0])\n i = get_left_i(subdata, lane_left)\n end = get_right_i(subdata, lane_right)\n\n final_data = [subdata[i][1]]\n while i < end:\n next_i = get_left_i(subdata[i:], \n subdata[i][0]+max_xpt_separation)\n if not next_i:\n return False\n i += next_i\n final_data.append(subdata[i][1])\n\n return final_data","def _lowestDistanceToCluster(self, clusters: ndarray, sequenceIdx: int) -> Tuple[int, float]:\n lowestClusterIdx = -1\n lowestDistance = np.inf\n for cIdx in range(self.numClusters):\n distance = self._distanceToCluster(clusters[cIdx], sequenceIdx)\n if distance < lowestDistance:\n lowestClusterIdx = cIdx\n lowestDistance = distance\n return lowestClusterIdx, lowestDistance","def eeg_findnearest(x,X):\t\n\t#x array or vector and X a scalar\n\tabsdif = np.abs(x-X)\n\tval = np.min(absdif)\n\tidx = absdif.argmin()\n\treturn val,idx","def get_index_under_point(self, event):\r\n xy = np.asarray(list(zip(self.xs, self.ys)))\r\n xyt = self.line.get_transform().transform(xy)\r\n xt, yt = xyt[:, 0], xyt[:, 1]\r\n d = np.sqrt((xt - event.x) ** 2 + (yt - event.y) ** 2)\r\n pt_idx = np.argmin(d)\r\n if d[pt_idx] >= self.max_pixels_from_vertex:\r\n pt_idx = None\r\n return pt_idx","def _kth_nearest_neighbor_dist(\n distance_matrix: Union[np.ndarray, scipy.sparse.csr_matrix], k\n) -> np.ndarray:\n\n if not is_integer(k):\n raise ValueError(f\"parameter 'k={k}' must be a positive integer\")\n else:\n # make sure we deal with Python built-in\n k = int(k)\n\n if not (0 <= k <= distance_matrix.shape[1]):\n raise ValueError(\n \"'k' must be an integer between 1 and \"\n f\"distance_matrix.shape[1]={distance_matrix.shape[1]}\"\n )\n\n if isinstance(distance_matrix, np.ndarray):\n dist_knn = np.partition(distance_matrix, k - 1, axis=1)[:, k - 1]\n elif isinstance(distance_matrix, scipy.sparse.csr_matrix):\n # see mircobenchmark_kth_nn.py for a comparison of implementations for the\n # sparse case\n\n def _get_kth_largest_elements_sparse(\n data: np.ndarray,\n indptr: np.ndarray,\n row_nnz,\n k_neighbor: int,\n ):\n dist_knn = np.zeros(len(row_nnz))\n for i in range(len(row_nnz)):\n start_row = indptr[i]\n dist_knn[i] = np.partition(\n data[start_row : start_row + row_nnz[i]], k_neighbor - 1\n )[k_neighbor - 1]\n\n return dist_knn\n\n row_nnz = distance_matrix.getnnz(axis=1)\n\n if (row_nnz < k).any():\n raise ValueError(\n f\"There are {(row_nnz < k).sum()} points that \"\n f\"do not have at least k_neighbor={k}.\"\n )\n\n dist_knn = _get_kth_largest_elements_sparse(\n distance_matrix.data,\n distance_matrix.indptr,\n row_nnz,\n k,\n )\n else:\n raise TypeError(f\"type {type(distance_matrix)} not supported\")\n\n return dist_knn","def closestCentroids(self, points , centroids ):\n dists = scipy.spatial.distance.cdist(points,centroids)\n # 1 is dimension\n minIds = numpy.argmin(dists, 1)\n return minIds","def closestClusterAndDistance(p, centers):\n\tbestIndex = 0\n closest = float(\"+inf\")\n for i in range(len(centers)):\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\n if distance < closest:\n closest = distance\n bestIndex = i\n return (bestIndex, closest)","def get_nearest_indices(vector, matrix, num=5):\n if len(matrix) < num:\n num = len(matrix)\n\n m, n = matrix.shape\n diff_matrix = np.tile(vector, (m, 1)) - matrix\n diff_matrix = abs(diff_matrix)\n\n distance = diff_matrix.sum(axis=1)\n sortIndices = np.argsort(distance)\n sortIndices = sortIndices[0:num]\n\n return sortIndices[random.randint(0, num - 1)]","def exhaustive_search(X, z):\n #initialize shortest path and distance to the first row\n d_star = la.norm(z - X[0])\n x_star = X[0]\n #check the distance from each row of X and if shortest thus far, save it\n for i in range(X.shape[0]):\n #calculate distance from current row to target z\n x = X[i,:]\n cur_dist = la.norm(x - z)\n #if the distance is smallest thus far, save the row and distance\n if cur_dist < d_star:\n x_star = x\n d_star = cur_dist\n return x_star, d_star","def find_min_path(s, t, dist):\n\n rows = len(dist) - 1\n cols = len(dist[0]) - 1\n col = cols\n row = rows\n pos_str = \"Position: (row={} col={}) -> (row={} col={})\"\n cst_str = \"Cost: {}\"\n prev_row = row\n prev_col = col\n\n # init sparse path matrix\n sparse_path = [[\" \" for x in range(cols + 1)] for x in range(rows + 1)]\n sparse_path[0][0] = \"0\"\n\n # start with operation at (rows, cols) and work backwards\n sparse_path[rows][cols] = dist[rows][cols]\n\n if verbose == 2:\n print()\n print(\"Initial Minimum Path Matrix:\")\n print_matrix(s, t, sparse_path)\n\n while True:\n\n # bail out if we are in the corner\n if row == 0 and col == 0:\n break\n\n # if we are not at a matrix boundary\n if row != 0 and col != 0: # if at left edge or top row, cannot move diagonally\n\n # diagonal\n if (dist[row - 1][col - 1] == min(dist[row - 1][col],\n dist[row][col - 1],\n dist[row - 1][col - 1])) and (dist[row - 1][col - 1] == dist[row][col] or dist[row - 1][col - 1] == dist[row][col] - 1):\n sparse_path[row - 1][col - 1] = dist[row - 1][col - 1]\n temp_cost = dist[row - 1][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # left\n elif dist[row][col - 1] <= dist[row][col]:\n sparse_path[row][col - 1] = dist[row][col - 1]\n temp_cost = dist[row][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # above\n else:\n sparse_path[row - 1][col] = dist[row - 1][col]\n temp_cost = dist[row - 1][col]\n\n # move current cell\n prev_row = row\n prev_col = col\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # if at matrix edge, can only move up\n elif col == 0:\n # above\n sparse_path[row - 1][col] = dist[row - 1][col]\n temp_cost = dist[row - 1][col]\n\n # move current cell\n prev_row = row\n prev_col = col\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # must be at row boundary, can only move left\n else:\n # left\n if dist[row][col - 1] <= dist[row][col]:\n sparse_path[row][col - 1] = dist[row][col - 1]\n temp_cost = dist[row][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # print matrix\n if verbose == 2:\n print_matrix(s, t, sparse_path)\n\n return sparse_path","def find_neighbor_indices(atoms, probe, k):\r\n coords_all = atoms.getCoords()\r\n neighbor_indices = []\r\n atom_k = atoms[k]\r\n radius = atom_k.getRadius() + probe + probe\r\n\r\n indices = range(k)\r\n indices.extend(range(k+1, len(atoms)))\r\n\r\n for i in indices:\r\n\tdist = pos_distance(coords_all[k], coords_all[i])\r\n\t#dist = np.linalg.norm(coords_all[k] - coords_all[i])\r\n\tif dist < radius + atoms[i].getRadius():\r\n neighbor_indices.append(i)\r\n\r\n return neighbor_indices","def find_closest_atom(coords1, coords2):\n\n coords1 = np.array(coords1)\n coords2 = np.array(coords2)\n diff = coords2[:, np.newaxis] - coords1[np.newaxis, :]\n dist = np.einsum('ijk->ij', diff**2)**0.5\n index = np.argmin(dist)\n return index","def djikstre(connection_mat):\n n = connection_mat.shape[0]\n dist, prev = {}, {}\n Q = list(range(n))\n \n for i in Q:\n dist[i] = np.inf\n dist[n-2] = 0.0\n \n while(len(Q)>0):\n\n min_dist = min([dist[key] for key in Q])\n u = [key for key in Q if dist[key] == min_dist][0]\n Q.remove(u)\n\n for v in np.nonzero(connection_mat[:, u])[0]:\n \n alt = dist[u]+connection_mat[v, u]\n \n if alt < dist[v]:\n dist[v] = alt\n prev[v] = u\n \n return dist, prev","def _first_index_with_bigger_neighbour(P):\n i = len(P) - 1\n while i > 0 and P[i-1] >= P[i]:\n i -= 1\n return i","def neirest_neighbour(business, cells):\n array = cells.get_neighbours(business, num=1)\n neighbours = pd.DataFrame(array).set_index('index')\n index = neighbours['distance'].idxmin()\n return neighbours.loc[index]","def _retrieve_neighbors(df, i_point, point, eps, column):\n neigborhood = []\n for index, row in df.iterrows():\n if index != i_point:\n a = np.array(point[column])\n b = np.array([row[column]])\n distance = np.linalg.norm(a - b)\n if distance <= eps:\n neigborhood.append(index)\n\n return neigborhood","def find_nearest_neighbors(p, points, k):\n\timport numpy as np\n\tdistances = np.zeros(points.shape[0])\n\tfor i in range(len(distances)):\n\t\tdistances[i] = distance(p,points[i])\n\tind = np.argsort(distances)\n\treturn ind[0:k]","def get_nearest_row(self):\n return (self.rect.top - (self.screen.get_height() // 12)) // self.maze.block_size","def find_nearest_neighbour_from_point(point_cloud:np.ndarray, point:int) -> int:\n pass","def _nearest_point_index(points, point):\n distance = sys.float_info.max\n index = None\n for i, p in enumerate(points):\n temp = _vec_distance(p, point)\n if temp < distance:\n distance = temp\n index = i\n return index, distance","def nearest_neighbor(data):\n features = set([i for i, x in enumerate(data[0][1])])\n return leave_one_out_cross_validation(data, features)","def shortestpathij(self, i, j):\n pathlist = []\n self.pathij(i, j, pathlist)\n distance = []\n \n for i in range(len(pathlist)):\n Temp = 0\n for j in range(len(pathlist[i]) - 1):\n Temp += self.Dismatrix[pathlist[i][j], pathlist[i][j+1]]\n distance.append(Temp)\n \n if(len(distance) == 0):\n return None\n else:\n return min(distance)","def get_nearest_neighbours(self, cell):\n\t\tneighs = self.get_neighbours(cell)\n\t\ti, j = cell.find_id()\n\t\tneighbours = []\n\t\tfor neigh in neighs:\n\t\t\tx, y = neigh.find_id()\n\t\t\tif abs(x-i)+abs(y-j) <= 1: \n\t\t\t\tneighbours.append(self.space[y,x])\n\t\treturn neighbours","def closest_node(node, nodes):\n nodes = np.asarray(nodes)\n deltas = nodes - node\n dist_2 = np.einsum(\"ij,ij->i\", deltas, deltas)\n return np.argmin(dist_2), np.min(dist_2)","def extract_min(H, ds):\n minDist = approxInf\n u = None # min vertex unknown\n i = 0\n for v in H:\n if ds[v] <= minDist:\n minDist = ds[v]\n u = v # note that u is unused (instead returned by pop)\n imin = i\n i += 1\n return(H.pop(imin)) # return [u, d]","def min_distance(s1, s2):\n n = len(s1)\n m = len(s2)\n matrix = [([0]*(m+1)) for i in xrange(n+1)]\n for i in xrange(m+1):\n matrix[0][i] = i\n for i in xrange(n+1):\n matrix[i][0] = i\n for i in xrange(1,n+1):\n for j in xrange(1,m+1):\n temp = min(matrix[i-1][j]+1, matrix[i][j-1]+1)\n d = 0 if s1[i-1]==s2[j-1] else 1\n matrix[i][j] = min(temp, matrix[i-1][j-1]+d)\n return matrix[n][m]","def getNearestLineIndex(row, tagLineNumbers):\n # DOC {{{\n # }}}\n\n # CODE {{{\n # initialize local auxiliary variables {{{\n nearestLineNumber = -1\n nearestLineIndex = -1\n # }}}\n\n # go through all tag line numbers and find the one nearest to the specified row {{{\n for lineIndex, lineNumber in enumerate(tagLineNumbers):\n # if the current line is nearer the current cursor position, take it {{{\n if (nearestLineNumber < lineNumber <= row):\n nearestLineNumber = lineNumber\n nearestLineIndex = lineIndex\n # }}}\n\n # if we've got past the current cursor position, let's end the search {{{\n if (lineNumber >= row):\n break\n # }}}\n # }}}\n\n # return index of the line with the nearest tag\n return nearestLineIndex\n # }}}","def _low_tri_indices(rowCount):\n for col in range(rowCount):\n for row in range(col, rowCount):\n yield (row, col)","def closest(self, x, y):\n if self.direction == 'horizontal':\n p_pts = np.array([\n self.ax.transData.transform((p, 0))[0] for p in self.positions\n ])\n dist = abs(p_pts - x)\n else:\n p_pts = np.array([\n self.ax.transData.transform((0, p))[1] for p in self.positions\n ])\n dist = abs(p_pts - y)\n index = np.argmin(dist)\n return index, dist[index]","def findNearset(x,y,lon,lat):\n dist = np.sqrt( (lon - x)**2 + (lat - y)**2)\n\n return np.argwhere(dist==dist.min())[0][0]","def min_horizontal_dist_meters(coords, targets, is_geo=False):\n xe = coords[:, 0]\n ye = coords[:, 1]\n n = len(xe)\n d = np.zeros(n)\n for j in range(n):\n d1 = dist_in_meters(targets, [xe[j], ye[j]], is_geo=is_geo)\n d[j] = d1.min()\n return d","def findminpath(tab, gxtab, gytab, pixtab):\n\n pathdist = 2 # the number of points each points on a ray can related to on the previous ray\n pathdist_penalty = 0.3 # penalty of the difference of the pathdist\n pathpix_penalty = 2 # penalty of the difference of pixel values between the point and the previous point\n nray = tab.shape[1]\n\n #tab = np.hstack((tab,tab[:, 0].reshape(tab.shape[0], 1)))\n #pixtab = np.hstack((pixtab,pixtab[:, 0].reshape(pixtab.shape[0], 1)))\n #gxtab = np.hstack((gxtab,gxtab[:, 0].reshape(gxtab.shape[0], 1)))\n #gytab = np.hstack((gytab,gytab[:, 0].reshape(gytab.shape[0], 1)))\n\n tab = np.hstack((tab,tab,tab)) # horizontally stack the tab matrix to prepare for the filtering on the result\n pixtab = np.hstack((pixtab,pixtab,pixtab))\n gxtab = np.hstack((gxtab,gxtab,gxtab))\n gytab = np.hstack((gytab,gytab,gytab))\n\n tab = (tab - tab.min()) / (tab.max() - tab.min()) # noralize the tab matrix\n pixtab = (pixtab - pixtab.min()) / (pixtab.max() - pixtab.min()) * -1 # for we want to find the white contour of the cell so we multipy -1 on the pixtab\n # tab = tab / np.median(tab)\n # pixtab = pixtab / np.median(pixtab)\n path = np.zeros(tab.shape)\n path[:, 0] = np.array(range(0, tab.shape[0]))\n score = np.zeros(tab.shape)\n score[:, 1] = tab[:, 1]\n\n for i in range(1, tab.shape[1]):\n for j in range(tab.shape[0]):\n mins = np.Inf # record the min value of the ray\n minat = 0\n for k in range(-pathdist, pathdist+1):\n if(0 <= (j+k) and (j+k) < tab.shape[0]):\n s = pixtab[j, i]\n pixdiff = abs(pixtab[j, i] - pixtab[j+k, i-1])\n s += pixdiff * pathpix_penalty # two kinds of penalty\n s += abs(k) * pathdist_penalty\n s += score[j+k, i-1]\n\n if(s < mins):\n mins = s\n minat = j + k\n path[j, i] = minat\n score[j, i]= mins\n\n start = int(np.argmin(score[:, -1]))\n path = path.astype(np.int32)\n minpath = [start]\n for i in range(tab.shape[1]-1, 0, -1):\n minpath.append(path[minpath[-1], i])\n minpath = minpath[::-1]\n # print(len(minpath))\n minpath = savgol_filter(minpath, 15, 3) # apply a Savitzky-Golay filter to the raw minpath signal\n minpath = minpath[nray:nray*2] # cut the middle part of minpath whose length is nray\n return np.array(minpath).astype(np.int32)","def nearest_sparse(self, query):\n self.best_dist = float(\"inf\")\n self.best_element = None\n self._register_best_element = self._register_best_element_single \n self._nearest_sparse_recursive(self._sparse2seq(query), self.root, 0.0)\n return self.best_element,self.best_dist","def numNeighbors(minesSet, row_index, cols_index, num_cols, num_rows):\n mines = 0\n for j in np.arange(max(0, cols_index-1), min(num_cols-1, cols_index+1)+1):\n for i in np.arange(max(0, row_index-1), min(num_rows-1, row_index+1)+1):\n if ((i, j) in minesSet):\n mines+=1\n return mines","def find_all_nearest_neighbours(point_cloud:np.ndarray) -> np.ndarray:\n pass","def _intra_cluster_distance(distances_row, labels, i):\n mask = labels == labels[i]\n mask[i] = False\n if not np.any(mask):\n # cluster of size 1\n return 0\n a = np.mean(distances_row[mask])\n return a","def matrix_idx(n_hist, n_req, n_rows):\n\n flat_idx = []\n for i in range(n_rows):\n flat_idx.extend(range(i * n_req, (i + 1) * n_req + n_hist))\n # idx = np.unravel_index(flat_idx, (n_rows, n_hist + n_req))\n\n idx_matrix = np.reshape(flat_idx, (n_rows, n_hist + n_req))\n idxX = idx_matrix[:, n_req:]\n idxY = idx_matrix[:, :n_req]\n\n return idxX, idxY","def closest_waypoint(self, location: pylot.utils.Location):\n min_dist = np.infty\n min_index = 0\n for index, waypoint in enumerate(self.waypoints):\n dist = waypoint.location.distance(location)\n if dist < min_dist:\n min_dist = dist\n min_index = index\n return min_index","def __find_immediate_predecessors(storm_object_table, target_row):\n\n error_checking.assert_is_integer(target_row)\n error_checking.assert_is_geq(target_row, 0)\n error_checking.assert_is_less_than(\n target_row, len(storm_object_table.index)\n )\n\n predecessor_sec_id_strings = [\n storm_object_table[c].values[target_row]\n for c in PREV_SECONDARY_ID_COLUMNS\n if storm_object_table[c].values[target_row] != ''\n ]\n\n num_predecessors = len(predecessor_sec_id_strings)\n if num_predecessors == 0:\n return numpy.array([], dtype=int)\n\n target_time_unix_sec = storm_object_table[\n tracking_utils.VALID_TIME_COLUMN].values[target_row]\n\n predecessor_rows = numpy.full(num_predecessors, -1, dtype=int)\n\n for i in range(num_predecessors):\n these_rows = numpy.where(numpy.logical_and(\n storm_object_table[tracking_utils.SECONDARY_ID_COLUMN].values ==\n predecessor_sec_id_strings[i],\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values <\n target_time_unix_sec\n ))[0]\n\n if len(these_rows) == 0:\n continue\n\n this_subrow = numpy.argmax(\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values[\n these_rows]\n )\n\n predecessor_rows[i] = these_rows[this_subrow]\n\n return predecessor_rows[predecessor_rows >= 0]","def _partition_nearest(self, medoids, dists, only_these=set()):\n if len(only_these) == 0:\n allowed_inds = self._not_ignored_inds\n else:\n allowed_inds = self._not_ignored_inds & only_these\n closest_medoid_ind = np.argmin(dists[:,medoids], 1) # If len(medoids)==3, would look like [2,1,1,0,1,2,...].\n clusts = [[] for i in medoids]\n for node_ind, med_ind in enumerate(closest_medoid_ind):\n if node_ind in allowed_inds:\n clusts[med_ind].append(node_ind)\n return clusts","def index_condensed_matrix(n, i, j):\n if i == j:\n main_warning(\"Diagonal elements (i=j) are not stored in condensed matrices.\")\n return None\n elif i > j:\n i, j = j, i\n return int(i * (n - (i + 3) * 0.5) + j - 1)","def find_closest_pt(ref_lon, ref_lat, tlon, tlat):\n\n # compute great circle distance from location to model grid points\n dist = gc_dist(ref_lon, ref_lat, tlon, tlat)\n\n # find j index of closest grid point\n work = N.take(dist,N.argmin(dist,0),0).diagonal()\n jj = N.argsort(work)[0]\n\n # find i index of closest grid point\n work = N.take(dist,N.argmin(dist,1),1).diagonal()\n ii = N.argsort(work)[0]\n\n return ii, jj","def nearest_neighbors(self):\n neighbor_distances_and_indices = []\n for idx, data_point in enumerate(self.data):\n distance = self.euclidean_dis(data_point[:-1], self.query) # Calculate the distance between the query\n # example and the current example from the data.\n\n neighbor_distances_and_indices.append((distance, idx)) # Add the distance and the index of the example\n # to an ordered collection\n\n sorted_neighbor_distances_and_indices = sorted(neighbor_distances_and_indices, key=lambda x: x[0]) #\n # Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by\n # the distances\n\n k_nearest_distances_and_indices = sorted_neighbor_distances_and_indices[:self.k] # Pick the first K\n # entries from the sorted collection\n\n k_nearest_labels = [self.data[i][1] for distance, i in k_nearest_distances_and_indices] # Get the labels of\n # the selected K entries\n\n return k_nearest_labels, self.mode(k_nearest_labels)","def closest_centroid(x,centroids):\n\tK =len(centroids)\n\tN = len(x)\n\tDistance = np.zeros((N,K))\n\tfor j in range(K):\n\t\tmu = centroids[j]\n\t\tDistance[:,j] = np.linalg.norm(x-mu,axis=1)\n\tout = np.argmin(Distance,axis=1) \n\treturn out","def medoid_ft(ftvectors,pairwise_dist_matrix):\r\n N = len(ftvectors)\r\n if N == 1 : \r\n return 0 \r\n # pairwise_dist_matrix = calculate_dist_matrix_ft(ftvectors) \r\n sum_dist = np.sum(pairwise_dist_matrix, axis = 0)\r\n min_idx = np.argmin(sum_dist)\r\n return min_idx","def nn_dists(coords, spherical=True):\n\n full_dist_matrix = all_dists(coords, spherical)\n\n # find all minimum distances\n # apply min over ranges of the dist array\n min_dists = np.min(full_dist_matrix, axis=1)\n min_ix = np.argmin(full_dist_matrix, axis=1)\n\n return min_dists, min_ix","def find_indices(colorhs, centres):\n\n indices = np.zeros(colorhs.shape[0], dtype=np.uint8)\n i = 0\n\n for hs in colorhs:\n # Past Euclidian distance\n past_ed = float(\"inf\")\n for cluster in range(centres.shape[0]):\n # Current Euclidian distance\n curr_ed = (sum((hs - centres[cluster, :]) ** 2)) ** 1/2\n # A frame belongs to the cluster with the minimum ed value.\n if curr_ed <= past_ed:\n past_ed = curr_ed\n indices[i] = cluster\n i += 1\n return indices","def minesweeper(matrix):\n \n num_rows = len(matrix)\n num_cols = len(matrix[0])\n \n adj_mines = []\n \n adj_row = [0]*num_cols\n \n for i in range(num_rows):\n adj_mines.append(adj_row[:])\n \n for r in range(num_rows):\n for c in range(num_cols):\n if matrix[r][c] == True:\n if (r-1) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r-1][c-1] += 1\n if (r-1) in range(num_rows) and (c) in range(num_cols):\n adj_mines[r-1][c] += 1\n if (r-1) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r-1][c+1] += 1\n if (r) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r][c-1] += 1\n if (r) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r][c+1] += 1\n if (r+1) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r+1][c-1] += 1\n if (r+1) in range(num_rows) and (c) in range(num_cols):\n adj_mines[r+1][c] += 1\n if (r+1) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r+1][c+1] += 1\n\n \n return adj_mines","def findClosetCentroids(X, centroids):\n\tm, n = X.shape\n\tK = centroids.shape[0]\n\tidx = np.zeros(m) # m\n\n\tfor i in range(m):\n\t\ttemp = np.tile(X[i, :], K).reshape(centroids.shape)\n\t\tidx[i] = np.argmin(np.sum((centroids - temp) ** 2, axis=1))\n\treturn idx","def find_nearest(ref_array,target_array):\n ref_tree = scipy.spatial.cKDTree(ref_array)\n dist, indices = ref_tree.query(target_array, k=1)\n return indices"],"string":"[\n \"def nearest_min(dist_matrix):\\n # much faster than np.where\\n i, j = np.unravel_index(\\n np.argmin(dist_matrix), \\n dims=dist_matrix.shape\\n )\\n return i, j\",\n \"def nearest_points_indexes_with_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 1\\n np.fill_diagonal(matrix, 1)\\n # Get the position for the resulted values\\n sort_arg = np.argsort(matrix, axis=1)\\n\\n return sort_arg[:, : num_to_keep]\",\n \"def find_min_distance():\\n return np.argmin(d)\",\n \"def smallest_distance(self, clusters):\\n i, j = numpy.unravel_index(numpy.argmin(clusters), clusters.shape)\\n return clusters[i, j], i, j\",\n \"def nearest_points_values_with_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 1\\n np.fill_diagonal(matrix, 1)\\n # Get the position for the resulted values\\n sort = np.sort(matrix, axis=1)\\n\\n return sort[:, : num_to_keep]\",\n \"def nearest_neighbour(matrix, start=0):\\n path = [start]\\n while len(matrix) != len(path):\\n matrix[:, start] = numpy.inf\\n start = numpy.argmin(matrix[start])\\n path.append(start)\\n return path\",\n \"def nearest_cluster(X,c):\\n K = np.size(c,0)\\n idx = np.zeros((np.size(X,0),1))\\n arr = np.empty((np.size(X,0),1))\\n for i in range(0,K):\\n y = c[i]\\n temp = np.ones((np.size(X,0),1))*y\\n b = np.power(np.subtract(X,temp),2)\\n a = np.sum(b,axis = 1)\\n a.resize((np.size(X,0),1))\\n arr = np.append(arr, a, axis=1)\\n arr = np.delete(arr,0,axis=1)\\n idx = np.argmin(arr, axis=1)\\n return idx\",\n \"def nearest_vertex_to(self, point):\\n distances = self.distances_to(point)\\n idx = np.argmin(distances)\\n return idx\",\n \"def find_min(self):\\n\\n\\n min_x = 1000\\n min_y = 1000\\n k = len(self.__col_lista)\\n for i in range(k):\\n x, y = self.__col_lista[i]\\n if x < min_x:\\n min_x = x\\n if y < min_y:\\n min_y = y\\n return min_x, min_y\",\n \"def _find_min_pair(pandas_matrix):\\n numpy_matrix = pandas_matrix.values\\n mins = np.where(numpy_matrix == np.nanmin(numpy_matrix))\\n min_col_idx = mins[0][0]\\n min_row_idx = mins[1][0]\\n (min_col, min_row) = (pandas_matrix.index[min_col_idx], \\n pandas_matrix.columns[min_row_idx])\\n\\n return (min_col, min_row)\",\n \"def find_closest_index(traj, point):\\n\\n\\t#TODO: vectorise function to receive any length of points.\\n\\n\\tdistances = np.subtract(np.array(point),traj) \\n\\tdistances = distances.reshape(-1,2)\\n\\t#distances = distances[~np.isnan(distances)].reshape(-1,2)\\n\\n\\t#print(\\\"distances\\\")\\n\\t#pprint(distances)\\n\\tdist_array = np.linalg.norm(distances, axis = 1)\\n\\t#pprint(dist_array)\\n\\t#dist_array = np.sqrt((distances[:,0]**2)+(distances[:,1]**2)) #array of distances from trajectory to gaze landing point in world. \\n\\tidx = np.nanargmin(abs(dist_array)) #find smallest difference in pythag distance from 0,0 to get closest point. \\n\\tdists = distances[idx, :]\\n\\tdist = dist_array[idx]\\n\\n\\treturn idx#, dists, dist\\n\\t#return idx\",\n \"def getclosest_ij(lats,lons,latpt,lonpt):\\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\\n dist_lon = (lons-lonpt)**2\\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\\n minindex_lon = dist_lon.argmin()\\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index\",\n \"def getclosest_ij(lats,lons,latpt,lonpt):\\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\\n dist_lon = (lons-lonpt)**2\\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\\n minindex_lon = dist_lon.argmin()\\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index\",\n \"def nearest_points_values_without_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 0\\n np.fill_diagonal(matrix, 0)\\n # Get the position for the resulted values\\n sort = np.sort(matrix, axis=1)\\n\\n return sort[:, : num_to_keep]\",\n \"def find_closest(distances, threshold):\\n n = len(distances)\\n person_1 = []\\n person_2 = []\\n d = []\\n\\n for i in range(n):\\n for j in range(i+1, n):\\n if distances[i][j] <= threshold:\\n person_1.append(i)\\n person_2.append(j)\\n d.append(distances[i][j])\\n\\n return person_1, person_2, d\",\n \"def getRowHeuristics(matrix):\\n row, col = matrix.shape\\n rHeuristic = np.zeros((row,2)) # Dos columnas. La primera para indicar la columna la segunda para la Heuristica\\n for i in range(0,row):\\n rHeuristic[i,0] = int(i)\\n #print (i,sum(matrix[:,i]), pesos[i], float(pesos[i]/sum(matrix[:,i])))\\n rHeuristic[i,1] = 1/sum(matrix[i,:])\\n return rHeuristic[rHeuristic[:,1].argsort()]\",\n \"def nearestNeighbor(self, coords, my_index, blacklist):\\n min_index, min_distance = 0, 999999999\\n my_col, my_row = coords[my_index]\\n for i, (col, row) in enumerate(coords):\\n if i in blacklist:\\n continue\\n distance = math.sqrt((my_col-col)**2 + (my_row-row)**2)\\n if distance < min_distance:\\n min_index, min_distance = i, distance\\n return min_index\",\n \"def _nearest_cluster_distance(distances_row, labels, i):\\n label = labels[i]\\n b = np.min([np.mean(distances_row[labels == cur_label])\\n for cur_label in set(labels) if not cur_label == label])\\n return b\",\n \"def find_closest_trajectory_pose(self):\\n np_state = numpy.array([[self.x], [self.y]])\\n temp_distance = numpy.sum(\\n (self.np_trajectory[0:2, :] - np_state) ** 2,\\n axis=0)\\n best_idx = numpy.argmin(temp_distance)\\n return best_idx\",\n \"def findknn(xTr,xTe,k):\\n\\n # YOUR CODE HERE\\n if k > len(xTr):\\n k = len(xTr)\\n \\n D=l2distance(xTe, xTr)\\n (m,n) = D.shape\\n \\n indices = []\\n dists = []\\n for i in range(m):\\n smallest_indices = np.argsort(D[i])\\n ind = smallest_indices[:k]\\n dis = D[i,smallest_indices[:k]]\\n indices.append(ind)\\n dists.append(dis)\\n \\n indices = np.transpose(np.array(indices))\\n dists = np.transpose(np.array(dists))\\n return indices, dists\",\n \"def getNearestSampleIndex(test, trainX):\\n dist_matrix = test - trainX\\n dist_square = dist_matrix ** 2\\n dist_sums = dist_square.sum(axis=1)\\n distance_vector = np.sqrt(dist_sums)\\n return (distance_vector).argmin()\",\n \"def _first_index_with_smaller_neighbour(P):\\n i = len(P) - 1\\n while i > 0 and P[i-1] <= P[i]:\\n i -= 1\\n return i\",\n \"def saddle_points(matrix):\\n if not all(len(row) == len(matrix[0]) for row in matrix[1:]):\\n raise ValueError('Provided matrix is irregular.')\\n columns = [col for col in zip(*matrix)]\\n points = set()\\n for ridx, row in enumerate(matrix):\\n for cidx, element in enumerate(row):\\n if element == max(row) and element == min(columns[cidx]):\\n points.add((ridx, cidx))\\n return points\",\n \"def _compute_euclidean_neigh_matrix(src, d_matrix, radius):\\n\\n n_max = 100\\n n_min = 3\\n reached_points = np.array([0])\\n counter = 0\\n n_neigh = []\\n list_neigh = []\\n\\n while counter < reached_points.shape[0] < src.shape[0]:\\n P = reached_points[counter]\\n aux = np.array(sorted(\\n np.where(d_matrix[P] <= radius)[0],\\n key=lambda k: d_matrix[P, k]))\\n n_neigh.append(aux.shape[0])\\n\\n # Check the number of neighbours\\n if n_neigh[-1] < n_min:\\n raise ValueError('Computation of neighbours aborted since '\\n 'their minimum number is too small.\\\\n'\\n 'Please choose a higher radius.')\\n elif n_neigh[-1] > n_max:\\n raise ValueError('Computation of neighbours aborted since'\\n 'their maximum number is too big.\\\\n'\\n 'Please choose a lower radius.')\\n list_neigh.append(aux)\\n reached_points = np.append(reached_points,\\n aux[~np.in1d(aux, reached_points)])\\n counter += 1\\n\\n if counter >= reached_points.shape[0]:\\n raise ValueError('Too small value of the radius:'\\n 'the neighbour-matrix is not connected')\\n elif src.shape[0] == reached_points.shape[0]:\\n while counter < src.shape[0]:\\n P = reached_points[counter]\\n aux = np.array(sorted(\\n np.where(d_matrix[P] <= radius)[0],\\n key=lambda k: d_matrix[P, k]))\\n n_neigh.append(aux.shape[0])\\n\\n if n_neigh[-1] < n_min:\\n raise ValueError('Computation of neighbours aborted since '\\n 'their minimum number is too small.\\\\n'\\n 'Please choose a higher radius.')\\n elif n_neigh[-1] > n_max:\\n raise ValueError('Computation of neighbours aborted since'\\n 'their maximum number is too big.\\\\n'\\n 'Please choose a lower radius.')\\n\\n list_neigh.append(aux)\\n counter += 1\\n\\n n_neigh_max = max(n_neigh)\\n n_matrix = np.zeros([src.shape[0],\\n n_neigh_max], dtype=int) - 1\\n for i in range(src.shape[0]):\\n n_matrix[i, 0:list_neigh[i].shape[0]] = list_neigh[i]\\n index_ord = np.argsort(n_matrix[:, 0])\\n n_matrix = n_matrix[index_ord]\\n return n_matrix\\n else:\\n raise RuntimeError(\\\"Some problems during\\\"\\n \\\"computation of neighbours.\\\")\",\n \"def min_distance_vertex(distance, visited):\\n vertices = len(visited)\\n min_distance = INF\\n min_index = None\\n for v in range(vertices):\\n if not visited[v] and distance[v] <= min_distance:\\n min_distance = distance[v]\\n min_index = v\\n return min_index\",\n \"def shortestDistance(self, grid):\\n # return self.house_oriented_TLE(grid)\\n # One axis\\n row_count = [sum(row) for row in grid]\\n col_count = [0]* len(grid[0])\\n row_dist = [0]* len(grid)\\n col_dist = [0]* len(grid[0])\\n output = sys.maxsize\\n for i in range(len(grid)): \\n for j in range(len(grid[0])):\\n col_count[j] += grid[i][j]\\n \\n for index_p in range(len(row_count)):\\n for index_h in range(len(row_count)):\\n row_dist[index_p] += abs(index_h - index_p) * row_count[index_h]\\n \\n for index_p in range(len(col_count)):\\n for index_h in range(len(col_count)):\\n col_dist[index_p] += abs(index_h - index_p) * col_count[index_h]\\n \\n # print(row_count)\\n # print(col_count)\\n # print(row_dist)\\n # print(col_dist)\\n for i in range(len(grid)):\\n for j in range(len(grid[0])):\\n if grid[i][j] == 1:\\n continue\\n output = min(output, row_dist[i] + col_dist[j])\\n return output\",\n \"def closest_points_naive(self, x, y):\\r\\n # Running time: O(n ** 2)\\r\\n\\r\\n dist = []\\r\\n for i in range(len(x)):\\r\\n for j in range(i+1, len(x)):\\r\\n d = self.get_euclidean_distance(x[i], x[j], y[i], y[j])\\r\\n dist.append(d)\\r\\n \\r\\n return min(dist)\",\n \"def find_min_point(points):\\r\\n smallest_point_index = 0\\r\\n for i in range(1, len(points)):\\r\\n if points[i][1] < points[smallest_point_index][1]:\\r\\n smallest_point_index = i\\r\\n elif points[i][0] > points[smallest_point_index][0] and points[i][1] == points[smallest_point_index][1]:\\r\\n smallest_point_index = i\\r\\n return smallest_point_index\",\n \"def find_nearest_neighbors_idx(X, x, k):\\n ## homework:start\\n result = \\n ## homework:end\\n return result\",\n \"def _findMin(p, A):\\n\\n m=(-1, (0,0))\\n for p0 in A:\\n dist = np.linalg.norm(p0-np.array(p))\\n if m[0]==-1 or m[0]>dist:\\n m = (dist, p0)\\n \\n return tuple(m[1])\",\n \"def get_idx(lons, lats, lon, lat):\\n dist = ((lons - lon) ** 2 + (lats - lat) ** 2) ** 0.5\\n return np.unravel_index(dist.argmin(), dist.shape)\",\n \"def get_min_dist(x0, y0, arr):\\n dist = np.hypot(arr.T[0] - x0, arr.T[1] - y0)\\n min_dist = np.min(dist)\\n val = np.argmin(dist)\\n return min_dist, arr[val]\",\n \"def dist(dm, sm, neighbors):\\n\\n # Initialize list of possible distances\\n distances = []\\n\\n # loop over all neighbors of the cell\\n for neighbor in neighbors:\\n # If the neighbor is valid\\n if dm[neighbor[0], neighbor[1]] != -1:\\n # add neighbor distance + 1 to possible distances\\n distances.append(dm[neighbor[0], neighbor[1]] + 1)\\n\\n # return minimal distance\\n return np.min(distances)\",\n \"def nodes_min_energy_index(self, node):\\n idx = -1\\n curr_energy = np.inf\\n for i in range(self.cost_matrix.shape[1]):\\n new_energy = self.cost_matrix[node][i]\\n if new_energy < curr_energy:\\n curr_energy = new_energy\\n idx = i\\n return idx\",\n \"def pair_idx(rows, comm=None):\\n raise Exception(\\\"Not implemented\\\")\\n \\n if comm == None:\\n comm = MPI.COMM_WORLD\\n \\n total = comb(rows,2,exact=True)\\n size = comm.Get_size()\\n \\n size = 1000\\n \\n print(total / size)\\n \\n target = total / size\\n \\n current_row = 0\\n calc_list = []\\n row_list = [[] for x in range(size)]\\n for rank in range(size):\\n row_list[rank].append(current_row)\\n \\n current_calcs = 0\\n \\n for value in range(current_row, rows):\\n current_calcs += value\\n if current_calcs > target:\\n if rank == size-1:\\n pass\\n else:\\n break\\n \\n calc_list.append(current_calcs)\\n row_list[rank].append(value)\\n current_row = value\\n \\n return row_list,calc_list\",\n \"def get_maxmin_index_from_row(\\n distance_matrix: np.ndarray,\\n row: int,\\n previous_indexes: List,\\n type: str,\\n )-> int:\\n distance_matrix = distance_matrix.copy()\\n arr = distance_matrix[row].astype(float)\\n \\n aux_list = range(arr.shape[0])\\n aux_list_2 = []\\n for i in aux_list:\\n if i in previous_indexes:\\n aux_list_2.append(True)\\n else:\\n aux_list_2.append(False)\\n previous_indexes_bool = aux_list_2\\n \\n if type == 'max':\\n arr[previous_indexes_bool] = -1\\n target_index = np.argmax(arr)\\n if type == 'min':\\n arr[previous_indexes_bool] = np.Inf\\n target_index = np.argmin(arr)\\n \\n return target_index\",\n \"def get_nearest_atom_inds_per_mol(self):\\n self.closest_at_per_mol = np.zeros((self.nmol,\\n self.at_per_mol,\\n self.at_per_mol-1), dtype=int)\\n\\n # Get and sort distances\\n all_at_inds = np.arange(self.at_per_mol)\\n for imol in range(self.nmol):\\n for iat in range(self.at_per_mol):\\n at_inds = all_at_inds[all_at_inds != iat]\\n dist = self.all_dist_per_mol[imol, iat, at_inds]\\n\\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\\n self.closest_at_per_mol[imol, iat] = at_inds\",\n \"def distance_matrix(n_row, n_col):\\n\\n n_pop = int(n_row * n_col)\\n center = int(n_row/2*(n_col+1))\\n\\n pop_idx = np.arange(n_pop)\\n pop_idx_col = np.remainder(pop_idx, n_col)\\n pop_idx_row = pop_idx // n_row\\n\\n pos = np.vstack((pop_idx_col,pop_idx_row)).T\\n distance = spa.distance.cdist([pos[center]], pos)[0]\\n\\n return distance\",\n \"def find_min(self, A, w):\\n import numpy as np\\n\\n vcost = self.INFINITY\\n vto = vfrom = -1\\n for v in w:\\n # Get array offset of minimum of this vertex\\n i = np.argmin(A[v,:])\\n if A[v,i] < vcost:\\n vcost = A[v,i]\\n vto = i\\n vfrom = v\\n return (vfrom, vto, vcost)\",\n \"def find_nearest_neighbours_SURF(averageDistance,inst,distanceArray,maxInst): \\r\\n NN=[]\\r\\n min_indices=[] \\r\\n\\r\\n for j in range(maxInst):\\r\\n if inst != j:\\r\\n locator = [inst,j]\\r\\n locator = sorted(locator, reverse=True) #Access corect half of table (result of removed table redundancy)\\r\\n d = distanceArray[locator[0]][locator[1]]\\r\\n if d<averageDistance:\\r\\n min_indices.append(j)\\r\\n \\r\\n for j in range(len(min_indices)):\\r\\n NN.append(min_indices[j])\\r\\n \\r\\n return NN\",\n \"def min(weightData , dataSetVector ):\\r\\n # weightData: pass the whole weightData array.\\r\\n # dataSetVector: pass the a data vector to compare with weightdata array, to find its closest match\\r\\n winnerIndex = 0 #flag for initalizing the winner index\\r\\n minValue = EcuDist(dataSetVector,weightData[0]) # initalize the minValue\\r\\n # iterate through all weighdata rows to find the closest match, depending on ecu. distance,\\r\\n #and then return the index of the closest match(winner)\\r\\n for i in range(weightData.shape[0]):\\r\\n if(EcuDist(dataSetVector,weightData[i]) < minValue):\\r\\n minValue = EcuDist(dataSetVector,weightData[i])\\r\\n winnerIndex = i\\r\\n return winnerIndex\",\n \"def get_nearest_atom_inds(self):\\n # Create empty data structure\\n self.closest_ats = np.zeros((self.natom, self.natom-1), dtype=int)\\n\\n # Get and sort distances\\n all_at_inds = np.arange(self.natom)\\n for iat in range(self.natom):\\n at_inds = all_at_inds[all_at_inds != iat]\\n dist = self.all_dist[iat, at_inds]\\n\\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\\n self.closest_ats[iat] = at_inds\",\n \"def find_nearest_neighbors(p, points, k=5):\\n dist = np.zeros(points.shape[0])\\n for i in range(len(dist)):\\n dist[i] = distance(p, points[i])\\n ind = np.argsort(dist)\\n return ind[0:k]\",\n \"def update_distmatrix(min_idx, dist_matrix):\\n i, j = min_idx\\n new_cluster_dist = np.minimum(dist_matrix[i, :], dist_matrix[j, :])\\n new_cluster_dist[i] = np.inf\\n \\n dist_matrix[i, :] = new_cluster_dist\\n dist_matrix[:, i] = new_cluster_dist\\n \\n dist_matrix[j, :] = np.inf\\n dist_matrix[:, j] = np.inf\\n \\n return dist_matrix\",\n \"def getNeighbors(training_data, test_row, k):\\n\\n distances = list()\\n for training_row in training_data:\\n dist = euclidianDistance(training_row, test_row)\\n distances.append([training_row, dist])\\n \\n #Sort on the basis of dist\\n distances.sort(key=lambda row:row[1])\\n\\n neighbors = list()\\n\\n for i in range(int(k)):\\n neighbors.append(distances[i][0])\\n\\n return neighbors\",\n \"def get_nearest_node_index(node_list, random_node):\\n\\n dist_list = [\\n (node.x - random_node.x) ** 2 + (node.y - random_node.y) ** 2\\n for node in node_list\\n ]\\n minind = dist_list.index(min(dist_list))\\n\\n return minind\",\n \"def findSmallest(distancesWithNames):\\n smallest = distancesWithNames[0][2]\\n smallestIndex = -1\\n for i in range(len(distancesWithNames)):\\n if smallest >= distancesWithNames[i][2]:\\n smallest = distancesWithNames[i][2]\\n smallestIndex = i\\n return smallestIndex\",\n \"def closestCluster(p, centers):\\n\\tbestIndex = 0\\n closest = float(\\\"+inf\\\")\\n for i in range(len(centers)):\\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\\n if distance < closest:\\n closest = distance\\n bestIndex = i\\n\\treturn bestIndex\",\n \"def get_min_shannon_entropy(grid):\\r\\n curr_min = math.inf\\r\\n curr_best = []\\r\\n for i in range(len(grid[0])):\\r\\n for j in range(len(grid)):\\r\\n if not grid[j][i].collapsed:\\r\\n w = grid[j][i].block_weights\\r\\n shannon_entropy = sum([-math.log(el) for el in w] )\\r\\n if shannon_entropy < curr_min:\\r\\n curr_min = shannon_entropy\\r\\n curr_best = [(i,j)]\\r\\n elif shannon_entropy == curr_min:\\r\\n curr_best.append((i,j))\\r\\n idx = np.random.choice(range(len(curr_best))) #choose randomly if theres a tie\\r\\n return curr_best[idx] #x,y\\r\",\n \"def detec_mfo_dist(betas):\\n k = len(betas)\\n min_dist = np.inf\\n for i in range(k-1):\\n for j in range(i+1,k):\\n distance = np.sum((betas[i,:] - betas[j,:])**2)\\n if distance < min_dist:\\n MFO_index = [i,j]\\n min_dist = distance\\n return MFO_index\",\n \"def smallest_cert(pts):\\n # 1. project the points\\n def projected_flattened_pt(pt):\\n mag = distance(pt)\\n distance_wanted = rect_dist\\n return distance_wanted/mag*pt[1], \\\\\\n distance_wanted/mag*pt[2]\\n\\n flat_pts = np.array([[projected_flattened_pt(pt), pt] for pt in pts])\\n\\n # 2. identify rows of data\\n data = {} \\n # maps height (from ground) to the \\\"row\\\" (scan) list of\\n # horizontal-only (1D) data\\n for pt, origpt in flat_pts:\\n for row_height in data:\\n if abs(pt[1]-row_height) < dist_bt_rows/2:\\n data[row_height].append([pt[0], origpt])\\n break\\n else:\\n data[pt[1]] = [[pt[0], origpt]]\\n\\n #3. build up final_data so each row has enough point x-density\\n def get_left_i(pts, left_edge):\\n # get last index <= left_edge\\n for i, pt in enumerate(pts):\\n if pt[0] > left_edge:\\n return i - 1\\n\\n def get_right_i(pts, right_edge):\\n # get first index >= right_edge\\n i = len(pts)-1\\n while True:\\n pt = pts[i]\\n if pt[0] < right_edge:\\n return i + 1\\n i -= 1\\n\\n final_data = []\\n for _, subdata in data.items():\\n subdata = sorted(subdata, key=lambda t: t[0])\\n i = get_left_i(subdata, lane_left)\\n end = get_right_i(subdata, lane_right)\\n\\n final_data = [subdata[i][1]]\\n while i < end:\\n next_i = get_left_i(subdata[i:], \\n subdata[i][0]+max_xpt_separation)\\n if not next_i:\\n return False\\n i += next_i\\n final_data.append(subdata[i][1])\\n\\n return final_data\",\n \"def _lowestDistanceToCluster(self, clusters: ndarray, sequenceIdx: int) -> Tuple[int, float]:\\n lowestClusterIdx = -1\\n lowestDistance = np.inf\\n for cIdx in range(self.numClusters):\\n distance = self._distanceToCluster(clusters[cIdx], sequenceIdx)\\n if distance < lowestDistance:\\n lowestClusterIdx = cIdx\\n lowestDistance = distance\\n return lowestClusterIdx, lowestDistance\",\n \"def eeg_findnearest(x,X):\\t\\n\\t#x array or vector and X a scalar\\n\\tabsdif = np.abs(x-X)\\n\\tval = np.min(absdif)\\n\\tidx = absdif.argmin()\\n\\treturn val,idx\",\n \"def get_index_under_point(self, event):\\r\\n xy = np.asarray(list(zip(self.xs, self.ys)))\\r\\n xyt = self.line.get_transform().transform(xy)\\r\\n xt, yt = xyt[:, 0], xyt[:, 1]\\r\\n d = np.sqrt((xt - event.x) ** 2 + (yt - event.y) ** 2)\\r\\n pt_idx = np.argmin(d)\\r\\n if d[pt_idx] >= self.max_pixels_from_vertex:\\r\\n pt_idx = None\\r\\n return pt_idx\",\n \"def _kth_nearest_neighbor_dist(\\n distance_matrix: Union[np.ndarray, scipy.sparse.csr_matrix], k\\n) -> np.ndarray:\\n\\n if not is_integer(k):\\n raise ValueError(f\\\"parameter 'k={k}' must be a positive integer\\\")\\n else:\\n # make sure we deal with Python built-in\\n k = int(k)\\n\\n if not (0 <= k <= distance_matrix.shape[1]):\\n raise ValueError(\\n \\\"'k' must be an integer between 1 and \\\"\\n f\\\"distance_matrix.shape[1]={distance_matrix.shape[1]}\\\"\\n )\\n\\n if isinstance(distance_matrix, np.ndarray):\\n dist_knn = np.partition(distance_matrix, k - 1, axis=1)[:, k - 1]\\n elif isinstance(distance_matrix, scipy.sparse.csr_matrix):\\n # see mircobenchmark_kth_nn.py for a comparison of implementations for the\\n # sparse case\\n\\n def _get_kth_largest_elements_sparse(\\n data: np.ndarray,\\n indptr: np.ndarray,\\n row_nnz,\\n k_neighbor: int,\\n ):\\n dist_knn = np.zeros(len(row_nnz))\\n for i in range(len(row_nnz)):\\n start_row = indptr[i]\\n dist_knn[i] = np.partition(\\n data[start_row : start_row + row_nnz[i]], k_neighbor - 1\\n )[k_neighbor - 1]\\n\\n return dist_knn\\n\\n row_nnz = distance_matrix.getnnz(axis=1)\\n\\n if (row_nnz < k).any():\\n raise ValueError(\\n f\\\"There are {(row_nnz < k).sum()} points that \\\"\\n f\\\"do not have at least k_neighbor={k}.\\\"\\n )\\n\\n dist_knn = _get_kth_largest_elements_sparse(\\n distance_matrix.data,\\n distance_matrix.indptr,\\n row_nnz,\\n k,\\n )\\n else:\\n raise TypeError(f\\\"type {type(distance_matrix)} not supported\\\")\\n\\n return dist_knn\",\n \"def closestCentroids(self, points , centroids ):\\n dists = scipy.spatial.distance.cdist(points,centroids)\\n # 1 is dimension\\n minIds = numpy.argmin(dists, 1)\\n return minIds\",\n \"def closestClusterAndDistance(p, centers):\\n\\tbestIndex = 0\\n closest = float(\\\"+inf\\\")\\n for i in range(len(centers)):\\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\\n if distance < closest:\\n closest = distance\\n bestIndex = i\\n return (bestIndex, closest)\",\n \"def get_nearest_indices(vector, matrix, num=5):\\n if len(matrix) < num:\\n num = len(matrix)\\n\\n m, n = matrix.shape\\n diff_matrix = np.tile(vector, (m, 1)) - matrix\\n diff_matrix = abs(diff_matrix)\\n\\n distance = diff_matrix.sum(axis=1)\\n sortIndices = np.argsort(distance)\\n sortIndices = sortIndices[0:num]\\n\\n return sortIndices[random.randint(0, num - 1)]\",\n \"def exhaustive_search(X, z):\\n #initialize shortest path and distance to the first row\\n d_star = la.norm(z - X[0])\\n x_star = X[0]\\n #check the distance from each row of X and if shortest thus far, save it\\n for i in range(X.shape[0]):\\n #calculate distance from current row to target z\\n x = X[i,:]\\n cur_dist = la.norm(x - z)\\n #if the distance is smallest thus far, save the row and distance\\n if cur_dist < d_star:\\n x_star = x\\n d_star = cur_dist\\n return x_star, d_star\",\n \"def find_min_path(s, t, dist):\\n\\n rows = len(dist) - 1\\n cols = len(dist[0]) - 1\\n col = cols\\n row = rows\\n pos_str = \\\"Position: (row={} col={}) -> (row={} col={})\\\"\\n cst_str = \\\"Cost: {}\\\"\\n prev_row = row\\n prev_col = col\\n\\n # init sparse path matrix\\n sparse_path = [[\\\" \\\" for x in range(cols + 1)] for x in range(rows + 1)]\\n sparse_path[0][0] = \\\"0\\\"\\n\\n # start with operation at (rows, cols) and work backwards\\n sparse_path[rows][cols] = dist[rows][cols]\\n\\n if verbose == 2:\\n print()\\n print(\\\"Initial Minimum Path Matrix:\\\")\\n print_matrix(s, t, sparse_path)\\n\\n while True:\\n\\n # bail out if we are in the corner\\n if row == 0 and col == 0:\\n break\\n\\n # if we are not at a matrix boundary\\n if row != 0 and col != 0: # if at left edge or top row, cannot move diagonally\\n\\n # diagonal\\n if (dist[row - 1][col - 1] == min(dist[row - 1][col],\\n dist[row][col - 1],\\n dist[row - 1][col - 1])) and (dist[row - 1][col - 1] == dist[row][col] or dist[row - 1][col - 1] == dist[row][col] - 1):\\n sparse_path[row - 1][col - 1] = dist[row - 1][col - 1]\\n temp_cost = dist[row - 1][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # left\\n elif dist[row][col - 1] <= dist[row][col]:\\n sparse_path[row][col - 1] = dist[row][col - 1]\\n temp_cost = dist[row][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # above\\n else:\\n sparse_path[row - 1][col] = dist[row - 1][col]\\n temp_cost = dist[row - 1][col]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # if at matrix edge, can only move up\\n elif col == 0:\\n # above\\n sparse_path[row - 1][col] = dist[row - 1][col]\\n temp_cost = dist[row - 1][col]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # must be at row boundary, can only move left\\n else:\\n # left\\n if dist[row][col - 1] <= dist[row][col]:\\n sparse_path[row][col - 1] = dist[row][col - 1]\\n temp_cost = dist[row][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # print matrix\\n if verbose == 2:\\n print_matrix(s, t, sparse_path)\\n\\n return sparse_path\",\n \"def find_neighbor_indices(atoms, probe, k):\\r\\n coords_all = atoms.getCoords()\\r\\n neighbor_indices = []\\r\\n atom_k = atoms[k]\\r\\n radius = atom_k.getRadius() + probe + probe\\r\\n\\r\\n indices = range(k)\\r\\n indices.extend(range(k+1, len(atoms)))\\r\\n\\r\\n for i in indices:\\r\\n\\tdist = pos_distance(coords_all[k], coords_all[i])\\r\\n\\t#dist = np.linalg.norm(coords_all[k] - coords_all[i])\\r\\n\\tif dist < radius + atoms[i].getRadius():\\r\\n neighbor_indices.append(i)\\r\\n\\r\\n return neighbor_indices\",\n \"def find_closest_atom(coords1, coords2):\\n\\n coords1 = np.array(coords1)\\n coords2 = np.array(coords2)\\n diff = coords2[:, np.newaxis] - coords1[np.newaxis, :]\\n dist = np.einsum('ijk->ij', diff**2)**0.5\\n index = np.argmin(dist)\\n return index\",\n \"def djikstre(connection_mat):\\n n = connection_mat.shape[0]\\n dist, prev = {}, {}\\n Q = list(range(n))\\n \\n for i in Q:\\n dist[i] = np.inf\\n dist[n-2] = 0.0\\n \\n while(len(Q)>0):\\n\\n min_dist = min([dist[key] for key in Q])\\n u = [key for key in Q if dist[key] == min_dist][0]\\n Q.remove(u)\\n\\n for v in np.nonzero(connection_mat[:, u])[0]:\\n \\n alt = dist[u]+connection_mat[v, u]\\n \\n if alt < dist[v]:\\n dist[v] = alt\\n prev[v] = u\\n \\n return dist, prev\",\n \"def _first_index_with_bigger_neighbour(P):\\n i = len(P) - 1\\n while i > 0 and P[i-1] >= P[i]:\\n i -= 1\\n return i\",\n \"def neirest_neighbour(business, cells):\\n array = cells.get_neighbours(business, num=1)\\n neighbours = pd.DataFrame(array).set_index('index')\\n index = neighbours['distance'].idxmin()\\n return neighbours.loc[index]\",\n \"def _retrieve_neighbors(df, i_point, point, eps, column):\\n neigborhood = []\\n for index, row in df.iterrows():\\n if index != i_point:\\n a = np.array(point[column])\\n b = np.array([row[column]])\\n distance = np.linalg.norm(a - b)\\n if distance <= eps:\\n neigborhood.append(index)\\n\\n return neigborhood\",\n \"def find_nearest_neighbors(p, points, k):\\n\\timport numpy as np\\n\\tdistances = np.zeros(points.shape[0])\\n\\tfor i in range(len(distances)):\\n\\t\\tdistances[i] = distance(p,points[i])\\n\\tind = np.argsort(distances)\\n\\treturn ind[0:k]\",\n \"def get_nearest_row(self):\\n return (self.rect.top - (self.screen.get_height() // 12)) // self.maze.block_size\",\n \"def find_nearest_neighbour_from_point(point_cloud:np.ndarray, point:int) -> int:\\n pass\",\n \"def _nearest_point_index(points, point):\\n distance = sys.float_info.max\\n index = None\\n for i, p in enumerate(points):\\n temp = _vec_distance(p, point)\\n if temp < distance:\\n distance = temp\\n index = i\\n return index, distance\",\n \"def nearest_neighbor(data):\\n features = set([i for i, x in enumerate(data[0][1])])\\n return leave_one_out_cross_validation(data, features)\",\n \"def shortestpathij(self, i, j):\\n pathlist = []\\n self.pathij(i, j, pathlist)\\n distance = []\\n \\n for i in range(len(pathlist)):\\n Temp = 0\\n for j in range(len(pathlist[i]) - 1):\\n Temp += self.Dismatrix[pathlist[i][j], pathlist[i][j+1]]\\n distance.append(Temp)\\n \\n if(len(distance) == 0):\\n return None\\n else:\\n return min(distance)\",\n \"def get_nearest_neighbours(self, cell):\\n\\t\\tneighs = self.get_neighbours(cell)\\n\\t\\ti, j = cell.find_id()\\n\\t\\tneighbours = []\\n\\t\\tfor neigh in neighs:\\n\\t\\t\\tx, y = neigh.find_id()\\n\\t\\t\\tif abs(x-i)+abs(y-j) <= 1: \\n\\t\\t\\t\\tneighbours.append(self.space[y,x])\\n\\t\\treturn neighbours\",\n \"def closest_node(node, nodes):\\n nodes = np.asarray(nodes)\\n deltas = nodes - node\\n dist_2 = np.einsum(\\\"ij,ij->i\\\", deltas, deltas)\\n return np.argmin(dist_2), np.min(dist_2)\",\n \"def extract_min(H, ds):\\n minDist = approxInf\\n u = None # min vertex unknown\\n i = 0\\n for v in H:\\n if ds[v] <= minDist:\\n minDist = ds[v]\\n u = v # note that u is unused (instead returned by pop)\\n imin = i\\n i += 1\\n return(H.pop(imin)) # return [u, d]\",\n \"def min_distance(s1, s2):\\n n = len(s1)\\n m = len(s2)\\n matrix = [([0]*(m+1)) for i in xrange(n+1)]\\n for i in xrange(m+1):\\n matrix[0][i] = i\\n for i in xrange(n+1):\\n matrix[i][0] = i\\n for i in xrange(1,n+1):\\n for j in xrange(1,m+1):\\n temp = min(matrix[i-1][j]+1, matrix[i][j-1]+1)\\n d = 0 if s1[i-1]==s2[j-1] else 1\\n matrix[i][j] = min(temp, matrix[i-1][j-1]+d)\\n return matrix[n][m]\",\n \"def getNearestLineIndex(row, tagLineNumbers):\\n # DOC {{{\\n # }}}\\n\\n # CODE {{{\\n # initialize local auxiliary variables {{{\\n nearestLineNumber = -1\\n nearestLineIndex = -1\\n # }}}\\n\\n # go through all tag line numbers and find the one nearest to the specified row {{{\\n for lineIndex, lineNumber in enumerate(tagLineNumbers):\\n # if the current line is nearer the current cursor position, take it {{{\\n if (nearestLineNumber < lineNumber <= row):\\n nearestLineNumber = lineNumber\\n nearestLineIndex = lineIndex\\n # }}}\\n\\n # if we've got past the current cursor position, let's end the search {{{\\n if (lineNumber >= row):\\n break\\n # }}}\\n # }}}\\n\\n # return index of the line with the nearest tag\\n return nearestLineIndex\\n # }}}\",\n \"def _low_tri_indices(rowCount):\\n for col in range(rowCount):\\n for row in range(col, rowCount):\\n yield (row, col)\",\n \"def closest(self, x, y):\\n if self.direction == 'horizontal':\\n p_pts = np.array([\\n self.ax.transData.transform((p, 0))[0] for p in self.positions\\n ])\\n dist = abs(p_pts - x)\\n else:\\n p_pts = np.array([\\n self.ax.transData.transform((0, p))[1] for p in self.positions\\n ])\\n dist = abs(p_pts - y)\\n index = np.argmin(dist)\\n return index, dist[index]\",\n \"def findNearset(x,y,lon,lat):\\n dist = np.sqrt( (lon - x)**2 + (lat - y)**2)\\n\\n return np.argwhere(dist==dist.min())[0][0]\",\n \"def min_horizontal_dist_meters(coords, targets, is_geo=False):\\n xe = coords[:, 0]\\n ye = coords[:, 1]\\n n = len(xe)\\n d = np.zeros(n)\\n for j in range(n):\\n d1 = dist_in_meters(targets, [xe[j], ye[j]], is_geo=is_geo)\\n d[j] = d1.min()\\n return d\",\n \"def findminpath(tab, gxtab, gytab, pixtab):\\n\\n pathdist = 2 # the number of points each points on a ray can related to on the previous ray\\n pathdist_penalty = 0.3 # penalty of the difference of the pathdist\\n pathpix_penalty = 2 # penalty of the difference of pixel values between the point and the previous point\\n nray = tab.shape[1]\\n\\n #tab = np.hstack((tab,tab[:, 0].reshape(tab.shape[0], 1)))\\n #pixtab = np.hstack((pixtab,pixtab[:, 0].reshape(pixtab.shape[0], 1)))\\n #gxtab = np.hstack((gxtab,gxtab[:, 0].reshape(gxtab.shape[0], 1)))\\n #gytab = np.hstack((gytab,gytab[:, 0].reshape(gytab.shape[0], 1)))\\n\\n tab = np.hstack((tab,tab,tab)) # horizontally stack the tab matrix to prepare for the filtering on the result\\n pixtab = np.hstack((pixtab,pixtab,pixtab))\\n gxtab = np.hstack((gxtab,gxtab,gxtab))\\n gytab = np.hstack((gytab,gytab,gytab))\\n\\n tab = (tab - tab.min()) / (tab.max() - tab.min()) # noralize the tab matrix\\n pixtab = (pixtab - pixtab.min()) / (pixtab.max() - pixtab.min()) * -1 # for we want to find the white contour of the cell so we multipy -1 on the pixtab\\n # tab = tab / np.median(tab)\\n # pixtab = pixtab / np.median(pixtab)\\n path = np.zeros(tab.shape)\\n path[:, 0] = np.array(range(0, tab.shape[0]))\\n score = np.zeros(tab.shape)\\n score[:, 1] = tab[:, 1]\\n\\n for i in range(1, tab.shape[1]):\\n for j in range(tab.shape[0]):\\n mins = np.Inf # record the min value of the ray\\n minat = 0\\n for k in range(-pathdist, pathdist+1):\\n if(0 <= (j+k) and (j+k) < tab.shape[0]):\\n s = pixtab[j, i]\\n pixdiff = abs(pixtab[j, i] - pixtab[j+k, i-1])\\n s += pixdiff * pathpix_penalty # two kinds of penalty\\n s += abs(k) * pathdist_penalty\\n s += score[j+k, i-1]\\n\\n if(s < mins):\\n mins = s\\n minat = j + k\\n path[j, i] = minat\\n score[j, i]= mins\\n\\n start = int(np.argmin(score[:, -1]))\\n path = path.astype(np.int32)\\n minpath = [start]\\n for i in range(tab.shape[1]-1, 0, -1):\\n minpath.append(path[minpath[-1], i])\\n minpath = minpath[::-1]\\n # print(len(minpath))\\n minpath = savgol_filter(minpath, 15, 3) # apply a Savitzky-Golay filter to the raw minpath signal\\n minpath = minpath[nray:nray*2] # cut the middle part of minpath whose length is nray\\n return np.array(minpath).astype(np.int32)\",\n \"def nearest_sparse(self, query):\\n self.best_dist = float(\\\"inf\\\")\\n self.best_element = None\\n self._register_best_element = self._register_best_element_single \\n self._nearest_sparse_recursive(self._sparse2seq(query), self.root, 0.0)\\n return self.best_element,self.best_dist\",\n \"def numNeighbors(minesSet, row_index, cols_index, num_cols, num_rows):\\n mines = 0\\n for j in np.arange(max(0, cols_index-1), min(num_cols-1, cols_index+1)+1):\\n for i in np.arange(max(0, row_index-1), min(num_rows-1, row_index+1)+1):\\n if ((i, j) in minesSet):\\n mines+=1\\n return mines\",\n \"def find_all_nearest_neighbours(point_cloud:np.ndarray) -> np.ndarray:\\n pass\",\n \"def _intra_cluster_distance(distances_row, labels, i):\\n mask = labels == labels[i]\\n mask[i] = False\\n if not np.any(mask):\\n # cluster of size 1\\n return 0\\n a = np.mean(distances_row[mask])\\n return a\",\n \"def matrix_idx(n_hist, n_req, n_rows):\\n\\n flat_idx = []\\n for i in range(n_rows):\\n flat_idx.extend(range(i * n_req, (i + 1) * n_req + n_hist))\\n # idx = np.unravel_index(flat_idx, (n_rows, n_hist + n_req))\\n\\n idx_matrix = np.reshape(flat_idx, (n_rows, n_hist + n_req))\\n idxX = idx_matrix[:, n_req:]\\n idxY = idx_matrix[:, :n_req]\\n\\n return idxX, idxY\",\n \"def closest_waypoint(self, location: pylot.utils.Location):\\n min_dist = np.infty\\n min_index = 0\\n for index, waypoint in enumerate(self.waypoints):\\n dist = waypoint.location.distance(location)\\n if dist < min_dist:\\n min_dist = dist\\n min_index = index\\n return min_index\",\n \"def __find_immediate_predecessors(storm_object_table, target_row):\\n\\n error_checking.assert_is_integer(target_row)\\n error_checking.assert_is_geq(target_row, 0)\\n error_checking.assert_is_less_than(\\n target_row, len(storm_object_table.index)\\n )\\n\\n predecessor_sec_id_strings = [\\n storm_object_table[c].values[target_row]\\n for c in PREV_SECONDARY_ID_COLUMNS\\n if storm_object_table[c].values[target_row] != ''\\n ]\\n\\n num_predecessors = len(predecessor_sec_id_strings)\\n if num_predecessors == 0:\\n return numpy.array([], dtype=int)\\n\\n target_time_unix_sec = storm_object_table[\\n tracking_utils.VALID_TIME_COLUMN].values[target_row]\\n\\n predecessor_rows = numpy.full(num_predecessors, -1, dtype=int)\\n\\n for i in range(num_predecessors):\\n these_rows = numpy.where(numpy.logical_and(\\n storm_object_table[tracking_utils.SECONDARY_ID_COLUMN].values ==\\n predecessor_sec_id_strings[i],\\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values <\\n target_time_unix_sec\\n ))[0]\\n\\n if len(these_rows) == 0:\\n continue\\n\\n this_subrow = numpy.argmax(\\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values[\\n these_rows]\\n )\\n\\n predecessor_rows[i] = these_rows[this_subrow]\\n\\n return predecessor_rows[predecessor_rows >= 0]\",\n \"def _partition_nearest(self, medoids, dists, only_these=set()):\\n if len(only_these) == 0:\\n allowed_inds = self._not_ignored_inds\\n else:\\n allowed_inds = self._not_ignored_inds & only_these\\n closest_medoid_ind = np.argmin(dists[:,medoids], 1) # If len(medoids)==3, would look like [2,1,1,0,1,2,...].\\n clusts = [[] for i in medoids]\\n for node_ind, med_ind in enumerate(closest_medoid_ind):\\n if node_ind in allowed_inds:\\n clusts[med_ind].append(node_ind)\\n return clusts\",\n \"def index_condensed_matrix(n, i, j):\\n if i == j:\\n main_warning(\\\"Diagonal elements (i=j) are not stored in condensed matrices.\\\")\\n return None\\n elif i > j:\\n i, j = j, i\\n return int(i * (n - (i + 3) * 0.5) + j - 1)\",\n \"def find_closest_pt(ref_lon, ref_lat, tlon, tlat):\\n\\n # compute great circle distance from location to model grid points\\n dist = gc_dist(ref_lon, ref_lat, tlon, tlat)\\n\\n # find j index of closest grid point\\n work = N.take(dist,N.argmin(dist,0),0).diagonal()\\n jj = N.argsort(work)[0]\\n\\n # find i index of closest grid point\\n work = N.take(dist,N.argmin(dist,1),1).diagonal()\\n ii = N.argsort(work)[0]\\n\\n return ii, jj\",\n \"def nearest_neighbors(self):\\n neighbor_distances_and_indices = []\\n for idx, data_point in enumerate(self.data):\\n distance = self.euclidean_dis(data_point[:-1], self.query) # Calculate the distance between the query\\n # example and the current example from the data.\\n\\n neighbor_distances_and_indices.append((distance, idx)) # Add the distance and the index of the example\\n # to an ordered collection\\n\\n sorted_neighbor_distances_and_indices = sorted(neighbor_distances_and_indices, key=lambda x: x[0]) #\\n # Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by\\n # the distances\\n\\n k_nearest_distances_and_indices = sorted_neighbor_distances_and_indices[:self.k] # Pick the first K\\n # entries from the sorted collection\\n\\n k_nearest_labels = [self.data[i][1] for distance, i in k_nearest_distances_and_indices] # Get the labels of\\n # the selected K entries\\n\\n return k_nearest_labels, self.mode(k_nearest_labels)\",\n \"def closest_centroid(x,centroids):\\n\\tK =len(centroids)\\n\\tN = len(x)\\n\\tDistance = np.zeros((N,K))\\n\\tfor j in range(K):\\n\\t\\tmu = centroids[j]\\n\\t\\tDistance[:,j] = np.linalg.norm(x-mu,axis=1)\\n\\tout = np.argmin(Distance,axis=1) \\n\\treturn out\",\n \"def medoid_ft(ftvectors,pairwise_dist_matrix):\\r\\n N = len(ftvectors)\\r\\n if N == 1 : \\r\\n return 0 \\r\\n # pairwise_dist_matrix = calculate_dist_matrix_ft(ftvectors) \\r\\n sum_dist = np.sum(pairwise_dist_matrix, axis = 0)\\r\\n min_idx = np.argmin(sum_dist)\\r\\n return min_idx\",\n \"def nn_dists(coords, spherical=True):\\n\\n full_dist_matrix = all_dists(coords, spherical)\\n\\n # find all minimum distances\\n # apply min over ranges of the dist array\\n min_dists = np.min(full_dist_matrix, axis=1)\\n min_ix = np.argmin(full_dist_matrix, axis=1)\\n\\n return min_dists, min_ix\",\n \"def find_indices(colorhs, centres):\\n\\n indices = np.zeros(colorhs.shape[0], dtype=np.uint8)\\n i = 0\\n\\n for hs in colorhs:\\n # Past Euclidian distance\\n past_ed = float(\\\"inf\\\")\\n for cluster in range(centres.shape[0]):\\n # Current Euclidian distance\\n curr_ed = (sum((hs - centres[cluster, :]) ** 2)) ** 1/2\\n # A frame belongs to the cluster with the minimum ed value.\\n if curr_ed <= past_ed:\\n past_ed = curr_ed\\n indices[i] = cluster\\n i += 1\\n return indices\",\n \"def minesweeper(matrix):\\n \\n num_rows = len(matrix)\\n num_cols = len(matrix[0])\\n \\n adj_mines = []\\n \\n adj_row = [0]*num_cols\\n \\n for i in range(num_rows):\\n adj_mines.append(adj_row[:])\\n \\n for r in range(num_rows):\\n for c in range(num_cols):\\n if matrix[r][c] == True:\\n if (r-1) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r-1][c-1] += 1\\n if (r-1) in range(num_rows) and (c) in range(num_cols):\\n adj_mines[r-1][c] += 1\\n if (r-1) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r-1][c+1] += 1\\n if (r) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r][c-1] += 1\\n if (r) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r][c+1] += 1\\n if (r+1) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r+1][c-1] += 1\\n if (r+1) in range(num_rows) and (c) in range(num_cols):\\n adj_mines[r+1][c] += 1\\n if (r+1) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r+1][c+1] += 1\\n\\n \\n return adj_mines\",\n \"def findClosetCentroids(X, centroids):\\n\\tm, n = X.shape\\n\\tK = centroids.shape[0]\\n\\tidx = np.zeros(m) # m\\n\\n\\tfor i in range(m):\\n\\t\\ttemp = np.tile(X[i, :], K).reshape(centroids.shape)\\n\\t\\tidx[i] = np.argmin(np.sum((centroids - temp) ** 2, axis=1))\\n\\treturn idx\",\n \"def find_nearest(ref_array,target_array):\\n ref_tree = scipy.spatial.cKDTree(ref_array)\\n dist, indices = ref_tree.query(target_array, k=1)\\n return indices\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76758415","0.73902124","0.64999825","0.6483356","0.6479489","0.64227754","0.6417629","0.6413701","0.6329621","0.63124496","0.6312059","0.6303333","0.6287571","0.62725973","0.6206513","0.6193971","0.61772275","0.61472964","0.6100118","0.60979205","0.6076559","0.6044639","0.60424477","0.60383624","0.5994276","0.5977324","0.59745604","0.59316355","0.5921004","0.59207225","0.59187675","0.5914445","0.59131867","0.5908921","0.5902639","0.5874711","0.5851722","0.58390146","0.5798481","0.5793911","0.5791052","0.5787699","0.5767839","0.5752606","0.5741009","0.5733529","0.5732233","0.57233125","0.56989825","0.5698967","0.56981534","0.56805634","0.5673394","0.56583023","0.5656402","0.5655434","0.565437","0.56487834","0.5646905","0.56460285","0.5636113","0.56331956","0.563113","0.5623121","0.56203","0.561111","0.5604995","0.55974776","0.55969167","0.5584674","0.5579417","0.55760306","0.5572063","0.55564016","0.55543065","0.5553647","0.55494267","0.5549265","0.5548724","0.5543832","0.55302244","0.551905","0.5507722","0.55050474","0.5502348","0.5491812","0.54882824","0.5487834","0.54852426","0.5484282","0.5482726","0.54819846","0.5475506","0.54594016","0.5456662","0.5451533","0.5442221","0.543894","0.5433832","0.5427434"],"string":"[\n \"0.76758415\",\n \"0.73902124\",\n \"0.64999825\",\n \"0.6483356\",\n \"0.6479489\",\n \"0.64227754\",\n \"0.6417629\",\n \"0.6413701\",\n \"0.6329621\",\n \"0.63124496\",\n \"0.6312059\",\n \"0.6303333\",\n \"0.6287571\",\n \"0.62725973\",\n \"0.6206513\",\n \"0.6193971\",\n \"0.61772275\",\n \"0.61472964\",\n \"0.6100118\",\n \"0.60979205\",\n \"0.6076559\",\n \"0.6044639\",\n \"0.60424477\",\n \"0.60383624\",\n \"0.5994276\",\n \"0.5977324\",\n \"0.59745604\",\n \"0.59316355\",\n \"0.5921004\",\n \"0.59207225\",\n \"0.59187675\",\n \"0.5914445\",\n \"0.59131867\",\n \"0.5908921\",\n \"0.5902639\",\n \"0.5874711\",\n \"0.5851722\",\n \"0.58390146\",\n \"0.5798481\",\n \"0.5793911\",\n \"0.5791052\",\n \"0.5787699\",\n \"0.5767839\",\n \"0.5752606\",\n \"0.5741009\",\n \"0.5733529\",\n \"0.5732233\",\n \"0.57233125\",\n \"0.56989825\",\n \"0.5698967\",\n \"0.56981534\",\n \"0.56805634\",\n \"0.5673394\",\n \"0.56583023\",\n \"0.5656402\",\n \"0.5655434\",\n \"0.565437\",\n \"0.56487834\",\n \"0.5646905\",\n \"0.56460285\",\n \"0.5636113\",\n \"0.56331956\",\n \"0.563113\",\n \"0.5623121\",\n \"0.56203\",\n \"0.561111\",\n \"0.5604995\",\n \"0.55974776\",\n \"0.55969167\",\n \"0.5584674\",\n \"0.5579417\",\n \"0.55760306\",\n \"0.5572063\",\n \"0.55564016\",\n \"0.55543065\",\n \"0.5553647\",\n \"0.55494267\",\n \"0.5549265\",\n \"0.5548724\",\n \"0.5543832\",\n \"0.55302244\",\n \"0.551905\",\n \"0.5507722\",\n \"0.55050474\",\n \"0.5502348\",\n \"0.5491812\",\n \"0.54882824\",\n \"0.5487834\",\n \"0.54852426\",\n \"0.5484282\",\n \"0.5482726\",\n \"0.54819846\",\n \"0.5475506\",\n \"0.54594016\",\n \"0.5456662\",\n \"0.5451533\",\n \"0.5442221\",\n \"0.543894\",\n \"0.5433832\",\n \"0.5427434\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72165185"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":885,"cells":{"query":{"kind":"string","value":"Each row of the matrix, let's say the jth row, represents the distance between the other data point from the jth point. This function returns the indexes for the points with the smallest distances with respect to each point represented by that specified row. By row, I mean the 0th dimension. Also notice that this function includes the target particle, i.e. the diagonal element along the matrix is set to 1."},"document":{"kind":"string","value":"def nearest_points_indexes_with_self(matrix, num_to_keep):\n\n # Set the diagonal to 1\n np.fill_diagonal(matrix, 1)\n # Get the position for the resulted values\n sort_arg = np.argsort(matrix, axis=1)\n\n return sort_arg[:, : num_to_keep]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def nearest_min(dist_matrix):\n # much faster than np.where\n i, j = np.unravel_index(\n np.argmin(dist_matrix), \n dims=dist_matrix.shape\n )\n return i, j","def nearest_points_indexes_without_self(matrix, num_to_keep):\n\n # Set the diagonal to 0\n np.fill_diagonal(matrix, 0)\n # Get the position for the resulted values\n sort_arg = np.argsort(matrix, axis=1)\n\n return sort_arg[:, : num_to_keep]","def find_min_distance():\n return np.argmin(d)","def nearest_neighbour(matrix, start=0):\n path = [start]\n while len(matrix) != len(path):\n matrix[:, start] = numpy.inf\n start = numpy.argmin(matrix[start])\n path.append(start)\n return path","def nearest_points_values_with_self(matrix, num_to_keep):\n\n # Set the diagonal to 1\n np.fill_diagonal(matrix, 1)\n # Get the position for the resulted values\n sort = np.sort(matrix, axis=1)\n\n return sort[:, : num_to_keep]","def nearest_vertex_to(self, point):\n distances = self.distances_to(point)\n idx = np.argmin(distances)\n return idx","def smallest_distance(self, clusters):\n i, j = numpy.unravel_index(numpy.argmin(clusters), clusters.shape)\n return clusters[i, j], i, j","def nearest_cluster(X,c):\n K = np.size(c,0)\n idx = np.zeros((np.size(X,0),1))\n arr = np.empty((np.size(X,0),1))\n for i in range(0,K):\n y = c[i]\n temp = np.ones((np.size(X,0),1))*y\n b = np.power(np.subtract(X,temp),2)\n a = np.sum(b,axis = 1)\n a.resize((np.size(X,0),1))\n arr = np.append(arr, a, axis=1)\n arr = np.delete(arr,0,axis=1)\n idx = np.argmin(arr, axis=1)\n return idx","def find_min(self):\n\n\n min_x = 1000\n min_y = 1000\n k = len(self.__col_lista)\n for i in range(k):\n x, y = self.__col_lista[i]\n if x < min_x:\n min_x = x\n if y < min_y:\n min_y = y\n return min_x, min_y","def find_closest_index(traj, point):\n\n\t#TODO: vectorise function to receive any length of points.\n\n\tdistances = np.subtract(np.array(point),traj) \n\tdistances = distances.reshape(-1,2)\n\t#distances = distances[~np.isnan(distances)].reshape(-1,2)\n\n\t#print(\"distances\")\n\t#pprint(distances)\n\tdist_array = np.linalg.norm(distances, axis = 1)\n\t#pprint(dist_array)\n\t#dist_array = np.sqrt((distances[:,0]**2)+(distances[:,1]**2)) #array of distances from trajectory to gaze landing point in world. \n\tidx = np.nanargmin(abs(dist_array)) #find smallest difference in pythag distance from 0,0 to get closest point. \n\tdists = distances[idx, :]\n\tdist = dist_array[idx]\n\n\treturn idx#, dists, dist\n\t#return idx","def getclosest_ij(lats,lons,latpt,lonpt):\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\n dist_lon = (lons-lonpt)**2\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\n minindex_lon = dist_lon.argmin()\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index","def getclosest_ij(lats,lons,latpt,lonpt):\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\n dist_lon = (lons-lonpt)**2\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\n minindex_lon = dist_lon.argmin()\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index","def _find_min_pair(pandas_matrix):\n numpy_matrix = pandas_matrix.values\n mins = np.where(numpy_matrix == np.nanmin(numpy_matrix))\n min_col_idx = mins[0][0]\n min_row_idx = mins[1][0]\n (min_col, min_row) = (pandas_matrix.index[min_col_idx], \n pandas_matrix.columns[min_row_idx])\n\n return (min_col, min_row)","def find_closest(distances, threshold):\n n = len(distances)\n person_1 = []\n person_2 = []\n d = []\n\n for i in range(n):\n for j in range(i+1, n):\n if distances[i][j] <= threshold:\n person_1.append(i)\n person_2.append(j)\n d.append(distances[i][j])\n\n return person_1, person_2, d","def nearest_points_values_without_self(matrix, num_to_keep):\n\n # Set the diagonal to 0\n np.fill_diagonal(matrix, 0)\n # Get the position for the resulted values\n sort = np.sort(matrix, axis=1)\n\n return sort[:, : num_to_keep]","def getRowHeuristics(matrix):\n row, col = matrix.shape\n rHeuristic = np.zeros((row,2)) # Dos columnas. La primera para indicar la columna la segunda para la Heuristica\n for i in range(0,row):\n rHeuristic[i,0] = int(i)\n #print (i,sum(matrix[:,i]), pesos[i], float(pesos[i]/sum(matrix[:,i])))\n rHeuristic[i,1] = 1/sum(matrix[i,:])\n return rHeuristic[rHeuristic[:,1].argsort()]","def nearestNeighbor(self, coords, my_index, blacklist):\n min_index, min_distance = 0, 999999999\n my_col, my_row = coords[my_index]\n for i, (col, row) in enumerate(coords):\n if i in blacklist:\n continue\n distance = math.sqrt((my_col-col)**2 + (my_row-row)**2)\n if distance < min_distance:\n min_index, min_distance = i, distance\n return min_index","def _nearest_cluster_distance(distances_row, labels, i):\n label = labels[i]\n b = np.min([np.mean(distances_row[labels == cur_label])\n for cur_label in set(labels) if not cur_label == label])\n return b","def getNearestSampleIndex(test, trainX):\n dist_matrix = test - trainX\n dist_square = dist_matrix ** 2\n dist_sums = dist_square.sum(axis=1)\n distance_vector = np.sqrt(dist_sums)\n return (distance_vector).argmin()","def find_closest_trajectory_pose(self):\n np_state = numpy.array([[self.x], [self.y]])\n temp_distance = numpy.sum(\n (self.np_trajectory[0:2, :] - np_state) ** 2,\n axis=0)\n best_idx = numpy.argmin(temp_distance)\n return best_idx","def _compute_euclidean_neigh_matrix(src, d_matrix, radius):\n\n n_max = 100\n n_min = 3\n reached_points = np.array([0])\n counter = 0\n n_neigh = []\n list_neigh = []\n\n while counter < reached_points.shape[0] < src.shape[0]:\n P = reached_points[counter]\n aux = np.array(sorted(\n np.where(d_matrix[P] <= radius)[0],\n key=lambda k: d_matrix[P, k]))\n n_neigh.append(aux.shape[0])\n\n # Check the number of neighbours\n if n_neigh[-1] < n_min:\n raise ValueError('Computation of neighbours aborted since '\n 'their minimum number is too small.\\n'\n 'Please choose a higher radius.')\n elif n_neigh[-1] > n_max:\n raise ValueError('Computation of neighbours aborted since'\n 'their maximum number is too big.\\n'\n 'Please choose a lower radius.')\n list_neigh.append(aux)\n reached_points = np.append(reached_points,\n aux[~np.in1d(aux, reached_points)])\n counter += 1\n\n if counter >= reached_points.shape[0]:\n raise ValueError('Too small value of the radius:'\n 'the neighbour-matrix is not connected')\n elif src.shape[0] == reached_points.shape[0]:\n while counter < src.shape[0]:\n P = reached_points[counter]\n aux = np.array(sorted(\n np.where(d_matrix[P] <= radius)[0],\n key=lambda k: d_matrix[P, k]))\n n_neigh.append(aux.shape[0])\n\n if n_neigh[-1] < n_min:\n raise ValueError('Computation of neighbours aborted since '\n 'their minimum number is too small.\\n'\n 'Please choose a higher radius.')\n elif n_neigh[-1] > n_max:\n raise ValueError('Computation of neighbours aborted since'\n 'their maximum number is too big.\\n'\n 'Please choose a lower radius.')\n\n list_neigh.append(aux)\n counter += 1\n\n n_neigh_max = max(n_neigh)\n n_matrix = np.zeros([src.shape[0],\n n_neigh_max], dtype=int) - 1\n for i in range(src.shape[0]):\n n_matrix[i, 0:list_neigh[i].shape[0]] = list_neigh[i]\n index_ord = np.argsort(n_matrix[:, 0])\n n_matrix = n_matrix[index_ord]\n return n_matrix\n else:\n raise RuntimeError(\"Some problems during\"\n \"computation of neighbours.\")","def findknn(xTr,xTe,k):\n\n # YOUR CODE HERE\n if k > len(xTr):\n k = len(xTr)\n \n D=l2distance(xTe, xTr)\n (m,n) = D.shape\n \n indices = []\n dists = []\n for i in range(m):\n smallest_indices = np.argsort(D[i])\n ind = smallest_indices[:k]\n dis = D[i,smallest_indices[:k]]\n indices.append(ind)\n dists.append(dis)\n \n indices = np.transpose(np.array(indices))\n dists = np.transpose(np.array(dists))\n return indices, dists","def saddle_points(matrix):\n if not all(len(row) == len(matrix[0]) for row in matrix[1:]):\n raise ValueError('Provided matrix is irregular.')\n columns = [col for col in zip(*matrix)]\n points = set()\n for ridx, row in enumerate(matrix):\n for cidx, element in enumerate(row):\n if element == max(row) and element == min(columns[cidx]):\n points.add((ridx, cidx))\n return points","def _first_index_with_smaller_neighbour(P):\n i = len(P) - 1\n while i > 0 and P[i-1] <= P[i]:\n i -= 1\n return i","def min_distance_vertex(distance, visited):\n vertices = len(visited)\n min_distance = INF\n min_index = None\n for v in range(vertices):\n if not visited[v] and distance[v] <= min_distance:\n min_distance = distance[v]\n min_index = v\n return min_index","def find_nearest_neighbors_idx(X, x, k):\n ## homework:start\n result = \n ## homework:end\n return result","def closest_points_naive(self, x, y):\r\n # Running time: O(n ** 2)\r\n\r\n dist = []\r\n for i in range(len(x)):\r\n for j in range(i+1, len(x)):\r\n d = self.get_euclidean_distance(x[i], x[j], y[i], y[j])\r\n dist.append(d)\r\n \r\n return min(dist)","def shortestDistance(self, grid):\n # return self.house_oriented_TLE(grid)\n # One axis\n row_count = [sum(row) for row in grid]\n col_count = [0]* len(grid[0])\n row_dist = [0]* len(grid)\n col_dist = [0]* len(grid[0])\n output = sys.maxsize\n for i in range(len(grid)): \n for j in range(len(grid[0])):\n col_count[j] += grid[i][j]\n \n for index_p in range(len(row_count)):\n for index_h in range(len(row_count)):\n row_dist[index_p] += abs(index_h - index_p) * row_count[index_h]\n \n for index_p in range(len(col_count)):\n for index_h in range(len(col_count)):\n col_dist[index_p] += abs(index_h - index_p) * col_count[index_h]\n \n # print(row_count)\n # print(col_count)\n # print(row_dist)\n # print(col_dist)\n for i in range(len(grid)):\n for j in range(len(grid[0])):\n if grid[i][j] == 1:\n continue\n output = min(output, row_dist[i] + col_dist[j])\n return output","def find_min_point(points):\r\n smallest_point_index = 0\r\n for i in range(1, len(points)):\r\n if points[i][1] < points[smallest_point_index][1]:\r\n smallest_point_index = i\r\n elif points[i][0] > points[smallest_point_index][0] and points[i][1] == points[smallest_point_index][1]:\r\n smallest_point_index = i\r\n return smallest_point_index","def dist(dm, sm, neighbors):\n\n # Initialize list of possible distances\n distances = []\n\n # loop over all neighbors of the cell\n for neighbor in neighbors:\n # If the neighbor is valid\n if dm[neighbor[0], neighbor[1]] != -1:\n # add neighbor distance + 1 to possible distances\n distances.append(dm[neighbor[0], neighbor[1]] + 1)\n\n # return minimal distance\n return np.min(distances)","def get_min_dist(x0, y0, arr):\n dist = np.hypot(arr.T[0] - x0, arr.T[1] - y0)\n min_dist = np.min(dist)\n val = np.argmin(dist)\n return min_dist, arr[val]","def nodes_min_energy_index(self, node):\n idx = -1\n curr_energy = np.inf\n for i in range(self.cost_matrix.shape[1]):\n new_energy = self.cost_matrix[node][i]\n if new_energy < curr_energy:\n curr_energy = new_energy\n idx = i\n return idx","def get_maxmin_index_from_row(\n distance_matrix: np.ndarray,\n row: int,\n previous_indexes: List,\n type: str,\n )-> int:\n distance_matrix = distance_matrix.copy()\n arr = distance_matrix[row].astype(float)\n \n aux_list = range(arr.shape[0])\n aux_list_2 = []\n for i in aux_list:\n if i in previous_indexes:\n aux_list_2.append(True)\n else:\n aux_list_2.append(False)\n previous_indexes_bool = aux_list_2\n \n if type == 'max':\n arr[previous_indexes_bool] = -1\n target_index = np.argmax(arr)\n if type == 'min':\n arr[previous_indexes_bool] = np.Inf\n target_index = np.argmin(arr)\n \n return target_index","def get_idx(lons, lats, lon, lat):\n dist = ((lons - lon) ** 2 + (lats - lat) ** 2) ** 0.5\n return np.unravel_index(dist.argmin(), dist.shape)","def pair_idx(rows, comm=None):\n raise Exception(\"Not implemented\")\n \n if comm == None:\n comm = MPI.COMM_WORLD\n \n total = comb(rows,2,exact=True)\n size = comm.Get_size()\n \n size = 1000\n \n print(total / size)\n \n target = total / size\n \n current_row = 0\n calc_list = []\n row_list = [[] for x in range(size)]\n for rank in range(size):\n row_list[rank].append(current_row)\n \n current_calcs = 0\n \n for value in range(current_row, rows):\n current_calcs += value\n if current_calcs > target:\n if rank == size-1:\n pass\n else:\n break\n \n calc_list.append(current_calcs)\n row_list[rank].append(value)\n current_row = value\n \n return row_list,calc_list","def _findMin(p, A):\n\n m=(-1, (0,0))\n for p0 in A:\n dist = np.linalg.norm(p0-np.array(p))\n if m[0]==-1 or m[0]>dist:\n m = (dist, p0)\n \n return tuple(m[1])","def distance_matrix(n_row, n_col):\n\n n_pop = int(n_row * n_col)\n center = int(n_row/2*(n_col+1))\n\n pop_idx = np.arange(n_pop)\n pop_idx_col = np.remainder(pop_idx, n_col)\n pop_idx_row = pop_idx // n_row\n\n pos = np.vstack((pop_idx_col,pop_idx_row)).T\n distance = spa.distance.cdist([pos[center]], pos)[0]\n\n return distance","def min(weightData , dataSetVector ):\r\n # weightData: pass the whole weightData array.\r\n # dataSetVector: pass the a data vector to compare with weightdata array, to find its closest match\r\n winnerIndex = 0 #flag for initalizing the winner index\r\n minValue = EcuDist(dataSetVector,weightData[0]) # initalize the minValue\r\n # iterate through all weighdata rows to find the closest match, depending on ecu. distance,\r\n #and then return the index of the closest match(winner)\r\n for i in range(weightData.shape[0]):\r\n if(EcuDist(dataSetVector,weightData[i]) < minValue):\r\n minValue = EcuDist(dataSetVector,weightData[i])\r\n winnerIndex = i\r\n return winnerIndex","def find_min(self, A, w):\n import numpy as np\n\n vcost = self.INFINITY\n vto = vfrom = -1\n for v in w:\n # Get array offset of minimum of this vertex\n i = np.argmin(A[v,:])\n if A[v,i] < vcost:\n vcost = A[v,i]\n vto = i\n vfrom = v\n return (vfrom, vto, vcost)","def get_nearest_atom_inds_per_mol(self):\n self.closest_at_per_mol = np.zeros((self.nmol,\n self.at_per_mol,\n self.at_per_mol-1), dtype=int)\n\n # Get and sort distances\n all_at_inds = np.arange(self.at_per_mol)\n for imol in range(self.nmol):\n for iat in range(self.at_per_mol):\n at_inds = all_at_inds[all_at_inds != iat]\n dist = self.all_dist_per_mol[imol, iat, at_inds]\n\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\n self.closest_at_per_mol[imol, iat] = at_inds","def find_nearest_neighbours_SURF(averageDistance,inst,distanceArray,maxInst): \r\n NN=[]\r\n min_indices=[] \r\n\r\n for j in range(maxInst):\r\n if inst != j:\r\n locator = [inst,j]\r\n locator = sorted(locator, reverse=True) #Access corect half of table (result of removed table redundancy)\r\n d = distanceArray[locator[0]][locator[1]]\r\n if d<averageDistance:\r\n min_indices.append(j)\r\n \r\n for j in range(len(min_indices)):\r\n NN.append(min_indices[j])\r\n \r\n return NN","def find_nearest_neighbors(p, points, k=5):\n dist = np.zeros(points.shape[0])\n for i in range(len(dist)):\n dist[i] = distance(p, points[i])\n ind = np.argsort(dist)\n return ind[0:k]","def update_distmatrix(min_idx, dist_matrix):\n i, j = min_idx\n new_cluster_dist = np.minimum(dist_matrix[i, :], dist_matrix[j, :])\n new_cluster_dist[i] = np.inf\n \n dist_matrix[i, :] = new_cluster_dist\n dist_matrix[:, i] = new_cluster_dist\n \n dist_matrix[j, :] = np.inf\n dist_matrix[:, j] = np.inf\n \n return dist_matrix","def get_nearest_node_index(node_list, random_node):\n\n dist_list = [\n (node.x - random_node.x) ** 2 + (node.y - random_node.y) ** 2\n for node in node_list\n ]\n minind = dist_list.index(min(dist_list))\n\n return minind","def getNeighbors(training_data, test_row, k):\n\n distances = list()\n for training_row in training_data:\n dist = euclidianDistance(training_row, test_row)\n distances.append([training_row, dist])\n \n #Sort on the basis of dist\n distances.sort(key=lambda row:row[1])\n\n neighbors = list()\n\n for i in range(int(k)):\n neighbors.append(distances[i][0])\n\n return neighbors","def eeg_findnearest(x,X):\t\n\t#x array or vector and X a scalar\n\tabsdif = np.abs(x-X)\n\tval = np.min(absdif)\n\tidx = absdif.argmin()\n\treturn val,idx","def get_nearest_atom_inds(self):\n # Create empty data structure\n self.closest_ats = np.zeros((self.natom, self.natom-1), dtype=int)\n\n # Get and sort distances\n all_at_inds = np.arange(self.natom)\n for iat in range(self.natom):\n at_inds = all_at_inds[all_at_inds != iat]\n dist = self.all_dist[iat, at_inds]\n\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\n self.closest_ats[iat] = at_inds","def closestCluster(p, centers):\n\tbestIndex = 0\n closest = float(\"+inf\")\n for i in range(len(centers)):\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\n if distance < closest:\n closest = distance\n bestIndex = i\n\treturn bestIndex","def get_min_shannon_entropy(grid):\r\n curr_min = math.inf\r\n curr_best = []\r\n for i in range(len(grid[0])):\r\n for j in range(len(grid)):\r\n if not grid[j][i].collapsed:\r\n w = grid[j][i].block_weights\r\n shannon_entropy = sum([-math.log(el) for el in w] )\r\n if shannon_entropy < curr_min:\r\n curr_min = shannon_entropy\r\n curr_best = [(i,j)]\r\n elif shannon_entropy == curr_min:\r\n curr_best.append((i,j))\r\n idx = np.random.choice(range(len(curr_best))) #choose randomly if theres a tie\r\n return curr_best[idx] #x,y\r","def findSmallest(distancesWithNames):\n smallest = distancesWithNames[0][2]\n smallestIndex = -1\n for i in range(len(distancesWithNames)):\n if smallest >= distancesWithNames[i][2]:\n smallest = distancesWithNames[i][2]\n smallestIndex = i\n return smallestIndex","def get_index_under_point(self, event):\r\n xy = np.asarray(list(zip(self.xs, self.ys)))\r\n xyt = self.line.get_transform().transform(xy)\r\n xt, yt = xyt[:, 0], xyt[:, 1]\r\n d = np.sqrt((xt - event.x) ** 2 + (yt - event.y) ** 2)\r\n pt_idx = np.argmin(d)\r\n if d[pt_idx] >= self.max_pixels_from_vertex:\r\n pt_idx = None\r\n return pt_idx","def find_nearest_neighbour_from_point(point_cloud:np.ndarray, point:int) -> int:\n pass","def find_min_path(s, t, dist):\n\n rows = len(dist) - 1\n cols = len(dist[0]) - 1\n col = cols\n row = rows\n pos_str = \"Position: (row={} col={}) -> (row={} col={})\"\n cst_str = \"Cost: {}\"\n prev_row = row\n prev_col = col\n\n # init sparse path matrix\n sparse_path = [[\" \" for x in range(cols + 1)] for x in range(rows + 1)]\n sparse_path[0][0] = \"0\"\n\n # start with operation at (rows, cols) and work backwards\n sparse_path[rows][cols] = dist[rows][cols]\n\n if verbose == 2:\n print()\n print(\"Initial Minimum Path Matrix:\")\n print_matrix(s, t, sparse_path)\n\n while True:\n\n # bail out if we are in the corner\n if row == 0 and col == 0:\n break\n\n # if we are not at a matrix boundary\n if row != 0 and col != 0: # if at left edge or top row, cannot move diagonally\n\n # diagonal\n if (dist[row - 1][col - 1] == min(dist[row - 1][col],\n dist[row][col - 1],\n dist[row - 1][col - 1])) and (dist[row - 1][col - 1] == dist[row][col] or dist[row - 1][col - 1] == dist[row][col] - 1):\n sparse_path[row - 1][col - 1] = dist[row - 1][col - 1]\n temp_cost = dist[row - 1][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # left\n elif dist[row][col - 1] <= dist[row][col]:\n sparse_path[row][col - 1] = dist[row][col - 1]\n temp_cost = dist[row][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # above\n else:\n sparse_path[row - 1][col] = dist[row - 1][col]\n temp_cost = dist[row - 1][col]\n\n # move current cell\n prev_row = row\n prev_col = col\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # if at matrix edge, can only move up\n elif col == 0:\n # above\n sparse_path[row - 1][col] = dist[row - 1][col]\n temp_cost = dist[row - 1][col]\n\n # move current cell\n prev_row = row\n prev_col = col\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # must be at row boundary, can only move left\n else:\n # left\n if dist[row][col - 1] <= dist[row][col]:\n sparse_path[row][col - 1] = dist[row][col - 1]\n temp_cost = dist[row][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # print matrix\n if verbose == 2:\n print_matrix(s, t, sparse_path)\n\n return sparse_path","def closestClusterAndDistance(p, centers):\n\tbestIndex = 0\n closest = float(\"+inf\")\n for i in range(len(centers)):\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\n if distance < closest:\n closest = distance\n bestIndex = i\n return (bestIndex, closest)","def get_nearest_indices(vector, matrix, num=5):\n if len(matrix) < num:\n num = len(matrix)\n\n m, n = matrix.shape\n diff_matrix = np.tile(vector, (m, 1)) - matrix\n diff_matrix = abs(diff_matrix)\n\n distance = diff_matrix.sum(axis=1)\n sortIndices = np.argsort(distance)\n sortIndices = sortIndices[0:num]\n\n return sortIndices[random.randint(0, num - 1)]","def detec_mfo_dist(betas):\n k = len(betas)\n min_dist = np.inf\n for i in range(k-1):\n for j in range(i+1,k):\n distance = np.sum((betas[i,:] - betas[j,:])**2)\n if distance < min_dist:\n MFO_index = [i,j]\n min_dist = distance\n return MFO_index","def get_nearest_row(self):\n return (self.rect.top - (self.screen.get_height() // 12)) // self.maze.block_size","def smallest_cert(pts):\n # 1. project the points\n def projected_flattened_pt(pt):\n mag = distance(pt)\n distance_wanted = rect_dist\n return distance_wanted/mag*pt[1], \\\n distance_wanted/mag*pt[2]\n\n flat_pts = np.array([[projected_flattened_pt(pt), pt] for pt in pts])\n\n # 2. identify rows of data\n data = {} \n # maps height (from ground) to the \"row\" (scan) list of\n # horizontal-only (1D) data\n for pt, origpt in flat_pts:\n for row_height in data:\n if abs(pt[1]-row_height) < dist_bt_rows/2:\n data[row_height].append([pt[0], origpt])\n break\n else:\n data[pt[1]] = [[pt[0], origpt]]\n\n #3. build up final_data so each row has enough point x-density\n def get_left_i(pts, left_edge):\n # get last index <= left_edge\n for i, pt in enumerate(pts):\n if pt[0] > left_edge:\n return i - 1\n\n def get_right_i(pts, right_edge):\n # get first index >= right_edge\n i = len(pts)-1\n while True:\n pt = pts[i]\n if pt[0] < right_edge:\n return i + 1\n i -= 1\n\n final_data = []\n for _, subdata in data.items():\n subdata = sorted(subdata, key=lambda t: t[0])\n i = get_left_i(subdata, lane_left)\n end = get_right_i(subdata, lane_right)\n\n final_data = [subdata[i][1]]\n while i < end:\n next_i = get_left_i(subdata[i:], \n subdata[i][0]+max_xpt_separation)\n if not next_i:\n return False\n i += next_i\n final_data.append(subdata[i][1])\n\n return final_data","def _lowestDistanceToCluster(self, clusters: ndarray, sequenceIdx: int) -> Tuple[int, float]:\n lowestClusterIdx = -1\n lowestDistance = np.inf\n for cIdx in range(self.numClusters):\n distance = self._distanceToCluster(clusters[cIdx], sequenceIdx)\n if distance < lowestDistance:\n lowestClusterIdx = cIdx\n lowestDistance = distance\n return lowestClusterIdx, lowestDistance","def find_closest_atom(coords1, coords2):\n\n coords1 = np.array(coords1)\n coords2 = np.array(coords2)\n diff = coords2[:, np.newaxis] - coords1[np.newaxis, :]\n dist = np.einsum('ijk->ij', diff**2)**0.5\n index = np.argmin(dist)\n return index","def closestCentroids(self, points , centroids ):\n dists = scipy.spatial.distance.cdist(points,centroids)\n # 1 is dimension\n minIds = numpy.argmin(dists, 1)\n return minIds","def find_neighbor_indices(atoms, probe, k):\r\n coords_all = atoms.getCoords()\r\n neighbor_indices = []\r\n atom_k = atoms[k]\r\n radius = atom_k.getRadius() + probe + probe\r\n\r\n indices = range(k)\r\n indices.extend(range(k+1, len(atoms)))\r\n\r\n for i in indices:\r\n\tdist = pos_distance(coords_all[k], coords_all[i])\r\n\t#dist = np.linalg.norm(coords_all[k] - coords_all[i])\r\n\tif dist < radius + atoms[i].getRadius():\r\n neighbor_indices.append(i)\r\n\r\n return neighbor_indices","def exhaustive_search(X, z):\n #initialize shortest path and distance to the first row\n d_star = la.norm(z - X[0])\n x_star = X[0]\n #check the distance from each row of X and if shortest thus far, save it\n for i in range(X.shape[0]):\n #calculate distance from current row to target z\n x = X[i,:]\n cur_dist = la.norm(x - z)\n #if the distance is smallest thus far, save the row and distance\n if cur_dist < d_star:\n x_star = x\n d_star = cur_dist\n return x_star, d_star","def _retrieve_neighbors(df, i_point, point, eps, column):\n neigborhood = []\n for index, row in df.iterrows():\n if index != i_point:\n a = np.array(point[column])\n b = np.array([row[column]])\n distance = np.linalg.norm(a - b)\n if distance <= eps:\n neigborhood.append(index)\n\n return neigborhood","def closest_node(node, nodes):\n nodes = np.asarray(nodes)\n deltas = nodes - node\n dist_2 = np.einsum(\"ij,ij->i\", deltas, deltas)\n return np.argmin(dist_2), np.min(dist_2)","def find_nearest_neighbors(p, points, k):\n\timport numpy as np\n\tdistances = np.zeros(points.shape[0])\n\tfor i in range(len(distances)):\n\t\tdistances[i] = distance(p,points[i])\n\tind = np.argsort(distances)\n\treturn ind[0:k]","def nearest_neighbor(data):\n features = set([i for i, x in enumerate(data[0][1])])\n return leave_one_out_cross_validation(data, features)","def djikstre(connection_mat):\n n = connection_mat.shape[0]\n dist, prev = {}, {}\n Q = list(range(n))\n \n for i in Q:\n dist[i] = np.inf\n dist[n-2] = 0.0\n \n while(len(Q)>0):\n\n min_dist = min([dist[key] for key in Q])\n u = [key for key in Q if dist[key] == min_dist][0]\n Q.remove(u)\n\n for v in np.nonzero(connection_mat[:, u])[0]:\n \n alt = dist[u]+connection_mat[v, u]\n \n if alt < dist[v]:\n dist[v] = alt\n prev[v] = u\n \n return dist, prev","def neirest_neighbour(business, cells):\n array = cells.get_neighbours(business, num=1)\n neighbours = pd.DataFrame(array).set_index('index')\n index = neighbours['distance'].idxmin()\n return neighbours.loc[index]","def closest(self, x, y):\n if self.direction == 'horizontal':\n p_pts = np.array([\n self.ax.transData.transform((p, 0))[0] for p in self.positions\n ])\n dist = abs(p_pts - x)\n else:\n p_pts = np.array([\n self.ax.transData.transform((0, p))[1] for p in self.positions\n ])\n dist = abs(p_pts - y)\n index = np.argmin(dist)\n return index, dist[index]","def _nearest_point_index(points, point):\n distance = sys.float_info.max\n index = None\n for i, p in enumerate(points):\n temp = _vec_distance(p, point)\n if temp < distance:\n distance = temp\n index = i\n return index, distance","def _kth_nearest_neighbor_dist(\n distance_matrix: Union[np.ndarray, scipy.sparse.csr_matrix], k\n) -> np.ndarray:\n\n if not is_integer(k):\n raise ValueError(f\"parameter 'k={k}' must be a positive integer\")\n else:\n # make sure we deal with Python built-in\n k = int(k)\n\n if not (0 <= k <= distance_matrix.shape[1]):\n raise ValueError(\n \"'k' must be an integer between 1 and \"\n f\"distance_matrix.shape[1]={distance_matrix.shape[1]}\"\n )\n\n if isinstance(distance_matrix, np.ndarray):\n dist_knn = np.partition(distance_matrix, k - 1, axis=1)[:, k - 1]\n elif isinstance(distance_matrix, scipy.sparse.csr_matrix):\n # see mircobenchmark_kth_nn.py for a comparison of implementations for the\n # sparse case\n\n def _get_kth_largest_elements_sparse(\n data: np.ndarray,\n indptr: np.ndarray,\n row_nnz,\n k_neighbor: int,\n ):\n dist_knn = np.zeros(len(row_nnz))\n for i in range(len(row_nnz)):\n start_row = indptr[i]\n dist_knn[i] = np.partition(\n data[start_row : start_row + row_nnz[i]], k_neighbor - 1\n )[k_neighbor - 1]\n\n return dist_knn\n\n row_nnz = distance_matrix.getnnz(axis=1)\n\n if (row_nnz < k).any():\n raise ValueError(\n f\"There are {(row_nnz < k).sum()} points that \"\n f\"do not have at least k_neighbor={k}.\"\n )\n\n dist_knn = _get_kth_largest_elements_sparse(\n distance_matrix.data,\n distance_matrix.indptr,\n row_nnz,\n k,\n )\n else:\n raise TypeError(f\"type {type(distance_matrix)} not supported\")\n\n return dist_knn","def _first_index_with_bigger_neighbour(P):\n i = len(P) - 1\n while i > 0 and P[i-1] >= P[i]:\n i -= 1\n return i","def getNearestLineIndex(row, tagLineNumbers):\n # DOC {{{\n # }}}\n\n # CODE {{{\n # initialize local auxiliary variables {{{\n nearestLineNumber = -1\n nearestLineIndex = -1\n # }}}\n\n # go through all tag line numbers and find the one nearest to the specified row {{{\n for lineIndex, lineNumber in enumerate(tagLineNumbers):\n # if the current line is nearer the current cursor position, take it {{{\n if (nearestLineNumber < lineNumber <= row):\n nearestLineNumber = lineNumber\n nearestLineIndex = lineIndex\n # }}}\n\n # if we've got past the current cursor position, let's end the search {{{\n if (lineNumber >= row):\n break\n # }}}\n # }}}\n\n # return index of the line with the nearest tag\n return nearestLineIndex\n # }}}","def shortestpathij(self, i, j):\n pathlist = []\n self.pathij(i, j, pathlist)\n distance = []\n \n for i in range(len(pathlist)):\n Temp = 0\n for j in range(len(pathlist[i]) - 1):\n Temp += self.Dismatrix[pathlist[i][j], pathlist[i][j+1]]\n distance.append(Temp)\n \n if(len(distance) == 0):\n return None\n else:\n return min(distance)","def min_horizontal_dist_meters(coords, targets, is_geo=False):\n xe = coords[:, 0]\n ye = coords[:, 1]\n n = len(xe)\n d = np.zeros(n)\n for j in range(n):\n d1 = dist_in_meters(targets, [xe[j], ye[j]], is_geo=is_geo)\n d[j] = d1.min()\n return d","def findNearset(x,y,lon,lat):\n dist = np.sqrt( (lon - x)**2 + (lat - y)**2)\n\n return np.argwhere(dist==dist.min())[0][0]","def get_nearest_neighbours(self, cell):\n\t\tneighs = self.get_neighbours(cell)\n\t\ti, j = cell.find_id()\n\t\tneighbours = []\n\t\tfor neigh in neighs:\n\t\t\tx, y = neigh.find_id()\n\t\t\tif abs(x-i)+abs(y-j) <= 1: \n\t\t\t\tneighbours.append(self.space[y,x])\n\t\treturn neighbours","def _low_tri_indices(rowCount):\n for col in range(rowCount):\n for row in range(col, rowCount):\n yield (row, col)","def min_distance(s1, s2):\n n = len(s1)\n m = len(s2)\n matrix = [([0]*(m+1)) for i in xrange(n+1)]\n for i in xrange(m+1):\n matrix[0][i] = i\n for i in xrange(n+1):\n matrix[i][0] = i\n for i in xrange(1,n+1):\n for j in xrange(1,m+1):\n temp = min(matrix[i-1][j]+1, matrix[i][j-1]+1)\n d = 0 if s1[i-1]==s2[j-1] else 1\n matrix[i][j] = min(temp, matrix[i-1][j-1]+d)\n return matrix[n][m]","def extract_min(H, ds):\n minDist = approxInf\n u = None # min vertex unknown\n i = 0\n for v in H:\n if ds[v] <= minDist:\n minDist = ds[v]\n u = v # note that u is unused (instead returned by pop)\n imin = i\n i += 1\n return(H.pop(imin)) # return [u, d]","def findminpath(tab, gxtab, gytab, pixtab):\n\n pathdist = 2 # the number of points each points on a ray can related to on the previous ray\n pathdist_penalty = 0.3 # penalty of the difference of the pathdist\n pathpix_penalty = 2 # penalty of the difference of pixel values between the point and the previous point\n nray = tab.shape[1]\n\n #tab = np.hstack((tab,tab[:, 0].reshape(tab.shape[0], 1)))\n #pixtab = np.hstack((pixtab,pixtab[:, 0].reshape(pixtab.shape[0], 1)))\n #gxtab = np.hstack((gxtab,gxtab[:, 0].reshape(gxtab.shape[0], 1)))\n #gytab = np.hstack((gytab,gytab[:, 0].reshape(gytab.shape[0], 1)))\n\n tab = np.hstack((tab,tab,tab)) # horizontally stack the tab matrix to prepare for the filtering on the result\n pixtab = np.hstack((pixtab,pixtab,pixtab))\n gxtab = np.hstack((gxtab,gxtab,gxtab))\n gytab = np.hstack((gytab,gytab,gytab))\n\n tab = (tab - tab.min()) / (tab.max() - tab.min()) # noralize the tab matrix\n pixtab = (pixtab - pixtab.min()) / (pixtab.max() - pixtab.min()) * -1 # for we want to find the white contour of the cell so we multipy -1 on the pixtab\n # tab = tab / np.median(tab)\n # pixtab = pixtab / np.median(pixtab)\n path = np.zeros(tab.shape)\n path[:, 0] = np.array(range(0, tab.shape[0]))\n score = np.zeros(tab.shape)\n score[:, 1] = tab[:, 1]\n\n for i in range(1, tab.shape[1]):\n for j in range(tab.shape[0]):\n mins = np.Inf # record the min value of the ray\n minat = 0\n for k in range(-pathdist, pathdist+1):\n if(0 <= (j+k) and (j+k) < tab.shape[0]):\n s = pixtab[j, i]\n pixdiff = abs(pixtab[j, i] - pixtab[j+k, i-1])\n s += pixdiff * pathpix_penalty # two kinds of penalty\n s += abs(k) * pathdist_penalty\n s += score[j+k, i-1]\n\n if(s < mins):\n mins = s\n minat = j + k\n path[j, i] = minat\n score[j, i]= mins\n\n start = int(np.argmin(score[:, -1]))\n path = path.astype(np.int32)\n minpath = [start]\n for i in range(tab.shape[1]-1, 0, -1):\n minpath.append(path[minpath[-1], i])\n minpath = minpath[::-1]\n # print(len(minpath))\n minpath = savgol_filter(minpath, 15, 3) # apply a Savitzky-Golay filter to the raw minpath signal\n minpath = minpath[nray:nray*2] # cut the middle part of minpath whose length is nray\n return np.array(minpath).astype(np.int32)","def find_all_nearest_neighbours(point_cloud:np.ndarray) -> np.ndarray:\n pass","def numNeighbors(minesSet, row_index, cols_index, num_cols, num_rows):\n mines = 0\n for j in np.arange(max(0, cols_index-1), min(num_cols-1, cols_index+1)+1):\n for i in np.arange(max(0, row_index-1), min(num_rows-1, row_index+1)+1):\n if ((i, j) in minesSet):\n mines+=1\n return mines","def index_condensed_matrix(n, i, j):\n if i == j:\n main_warning(\"Diagonal elements (i=j) are not stored in condensed matrices.\")\n return None\n elif i > j:\n i, j = j, i\n return int(i * (n - (i + 3) * 0.5) + j - 1)","def nearest_sparse(self, query):\n self.best_dist = float(\"inf\")\n self.best_element = None\n self._register_best_element = self._register_best_element_single \n self._nearest_sparse_recursive(self._sparse2seq(query), self.root, 0.0)\n return self.best_element,self.best_dist","def find_closest_pt(ref_lon, ref_lat, tlon, tlat):\n\n # compute great circle distance from location to model grid points\n dist = gc_dist(ref_lon, ref_lat, tlon, tlat)\n\n # find j index of closest grid point\n work = N.take(dist,N.argmin(dist,0),0).diagonal()\n jj = N.argsort(work)[0]\n\n # find i index of closest grid point\n work = N.take(dist,N.argmin(dist,1),1).diagonal()\n ii = N.argsort(work)[0]\n\n return ii, jj","def find_nearest(ref_array,target_array):\n ref_tree = scipy.spatial.cKDTree(ref_array)\n dist, indices = ref_tree.query(target_array, k=1)\n return indices","def locate_source(p,d):\n # M = sensors, n = dimensions\n M, n = p.shape\n p = np.matrix( p ).T\n\n # pick closest receiver\n c = np.argmin(d)\n #sensors delta time relative to sensor c\n d = d - min(d)\n\n indices = list(range(M))\n del indices[c]\n\n A = np.zeros([M-2,n])\n b = np.zeros([M-2,1])\n\n i = indices[0]\n for row,j in enumerate(indices[1:]):\n A[row,:] = 2*( (d[j])*(p[:,i]-p[:,c]).T - \\\n (d[i])*(p[:,j]-p[:,c]).T )\n b[row,0] = (d[i])*((d[j])**2-p[:,j].T*p[:,j]) + \\\n ((d[i])-(d[j]))*p[:,c].T*p[:,c] + \\\n (d[j])*(p[:,i].T*p[:,i]-(d[i])**2)\n\n\n x = np.asarray( np.linalg.lstsq(A,b)[0] )[:,0]\n return x","def _intra_cluster_distance(distances_row, labels, i):\n mask = labels == labels[i]\n mask[i] = False\n if not np.any(mask):\n # cluster of size 1\n return 0\n a = np.mean(distances_row[mask])\n return a","def matrix_idx(n_hist, n_req, n_rows):\n\n flat_idx = []\n for i in range(n_rows):\n flat_idx.extend(range(i * n_req, (i + 1) * n_req + n_hist))\n # idx = np.unravel_index(flat_idx, (n_rows, n_hist + n_req))\n\n idx_matrix = np.reshape(flat_idx, (n_rows, n_hist + n_req))\n idxX = idx_matrix[:, n_req:]\n idxY = idx_matrix[:, :n_req]\n\n return idxX, idxY","def minesweeper(matrix):\n \n num_rows = len(matrix)\n num_cols = len(matrix[0])\n \n adj_mines = []\n \n adj_row = [0]*num_cols\n \n for i in range(num_rows):\n adj_mines.append(adj_row[:])\n \n for r in range(num_rows):\n for c in range(num_cols):\n if matrix[r][c] == True:\n if (r-1) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r-1][c-1] += 1\n if (r-1) in range(num_rows) and (c) in range(num_cols):\n adj_mines[r-1][c] += 1\n if (r-1) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r-1][c+1] += 1\n if (r) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r][c-1] += 1\n if (r) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r][c+1] += 1\n if (r+1) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r+1][c-1] += 1\n if (r+1) in range(num_rows) and (c) in range(num_cols):\n adj_mines[r+1][c] += 1\n if (r+1) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r+1][c+1] += 1\n\n \n return adj_mines","def nearest_neighbors(self):\n neighbor_distances_and_indices = []\n for idx, data_point in enumerate(self.data):\n distance = self.euclidean_dis(data_point[:-1], self.query) # Calculate the distance between the query\n # example and the current example from the data.\n\n neighbor_distances_and_indices.append((distance, idx)) # Add the distance and the index of the example\n # to an ordered collection\n\n sorted_neighbor_distances_and_indices = sorted(neighbor_distances_and_indices, key=lambda x: x[0]) #\n # Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by\n # the distances\n\n k_nearest_distances_and_indices = sorted_neighbor_distances_and_indices[:self.k] # Pick the first K\n # entries from the sorted collection\n\n k_nearest_labels = [self.data[i][1] for distance, i in k_nearest_distances_and_indices] # Get the labels of\n # the selected K entries\n\n return k_nearest_labels, self.mode(k_nearest_labels)","def __find_immediate_predecessors(storm_object_table, target_row):\n\n error_checking.assert_is_integer(target_row)\n error_checking.assert_is_geq(target_row, 0)\n error_checking.assert_is_less_than(\n target_row, len(storm_object_table.index)\n )\n\n predecessor_sec_id_strings = [\n storm_object_table[c].values[target_row]\n for c in PREV_SECONDARY_ID_COLUMNS\n if storm_object_table[c].values[target_row] != ''\n ]\n\n num_predecessors = len(predecessor_sec_id_strings)\n if num_predecessors == 0:\n return numpy.array([], dtype=int)\n\n target_time_unix_sec = storm_object_table[\n tracking_utils.VALID_TIME_COLUMN].values[target_row]\n\n predecessor_rows = numpy.full(num_predecessors, -1, dtype=int)\n\n for i in range(num_predecessors):\n these_rows = numpy.where(numpy.logical_and(\n storm_object_table[tracking_utils.SECONDARY_ID_COLUMN].values ==\n predecessor_sec_id_strings[i],\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values <\n target_time_unix_sec\n ))[0]\n\n if len(these_rows) == 0:\n continue\n\n this_subrow = numpy.argmax(\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values[\n these_rows]\n )\n\n predecessor_rows[i] = these_rows[this_subrow]\n\n return predecessor_rows[predecessor_rows >= 0]","def closest_waypoint(self, location: pylot.utils.Location):\n min_dist = np.infty\n min_index = 0\n for index, waypoint in enumerate(self.waypoints):\n dist = waypoint.location.distance(location)\n if dist < min_dist:\n min_dist = dist\n min_index = index\n return min_index","def medoid_ft(ftvectors,pairwise_dist_matrix):\r\n N = len(ftvectors)\r\n if N == 1 : \r\n return 0 \r\n # pairwise_dist_matrix = calculate_dist_matrix_ft(ftvectors) \r\n sum_dist = np.sum(pairwise_dist_matrix, axis = 0)\r\n min_idx = np.argmin(sum_dist)\r\n return min_idx","def _partition_nearest(self, medoids, dists, only_these=set()):\n if len(only_these) == 0:\n allowed_inds = self._not_ignored_inds\n else:\n allowed_inds = self._not_ignored_inds & only_these\n closest_medoid_ind = np.argmin(dists[:,medoids], 1) # If len(medoids)==3, would look like [2,1,1,0,1,2,...].\n clusts = [[] for i in medoids]\n for node_ind, med_ind in enumerate(closest_medoid_ind):\n if node_ind in allowed_inds:\n clusts[med_ind].append(node_ind)\n return clusts","def closest(self, x, y):\n pts = np.column_stack([self.x, self.y])\n # Transform data coordinates to pixel coordinates.\n pts = self.ax.transData.transform(pts)\n diff = pts - [x, y]\n dist = np.hypot(*diff.T)\n min_index = np.argmin(dist)\n return min_index, dist[min_index]","def search_matrix(to_search: int, a_matrix: list) -> (int, int):\n row = 0\n column = len(a_matrix[0]) - 1\n while row < len(a_matrix) and column >= 0:\n if to_search < a_matrix[row][column]:\n column -= 1\n elif to_search > a_matrix[row][column]:\n row += 1\n else:\n return row, column\n return -1, -1","def closest_centroid(x,centroids):\n\tK =len(centroids)\n\tN = len(x)\n\tDistance = np.zeros((N,K))\n\tfor j in range(K):\n\t\tmu = centroids[j]\n\t\tDistance[:,j] = np.linalg.norm(x-mu,axis=1)\n\tout = np.argmin(Distance,axis=1) \n\treturn out"],"string":"[\n \"def nearest_min(dist_matrix):\\n # much faster than np.where\\n i, j = np.unravel_index(\\n np.argmin(dist_matrix), \\n dims=dist_matrix.shape\\n )\\n return i, j\",\n \"def nearest_points_indexes_without_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 0\\n np.fill_diagonal(matrix, 0)\\n # Get the position for the resulted values\\n sort_arg = np.argsort(matrix, axis=1)\\n\\n return sort_arg[:, : num_to_keep]\",\n \"def find_min_distance():\\n return np.argmin(d)\",\n \"def nearest_neighbour(matrix, start=0):\\n path = [start]\\n while len(matrix) != len(path):\\n matrix[:, start] = numpy.inf\\n start = numpy.argmin(matrix[start])\\n path.append(start)\\n return path\",\n \"def nearest_points_values_with_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 1\\n np.fill_diagonal(matrix, 1)\\n # Get the position for the resulted values\\n sort = np.sort(matrix, axis=1)\\n\\n return sort[:, : num_to_keep]\",\n \"def nearest_vertex_to(self, point):\\n distances = self.distances_to(point)\\n idx = np.argmin(distances)\\n return idx\",\n \"def smallest_distance(self, clusters):\\n i, j = numpy.unravel_index(numpy.argmin(clusters), clusters.shape)\\n return clusters[i, j], i, j\",\n \"def nearest_cluster(X,c):\\n K = np.size(c,0)\\n idx = np.zeros((np.size(X,0),1))\\n arr = np.empty((np.size(X,0),1))\\n for i in range(0,K):\\n y = c[i]\\n temp = np.ones((np.size(X,0),1))*y\\n b = np.power(np.subtract(X,temp),2)\\n a = np.sum(b,axis = 1)\\n a.resize((np.size(X,0),1))\\n arr = np.append(arr, a, axis=1)\\n arr = np.delete(arr,0,axis=1)\\n idx = np.argmin(arr, axis=1)\\n return idx\",\n \"def find_min(self):\\n\\n\\n min_x = 1000\\n min_y = 1000\\n k = len(self.__col_lista)\\n for i in range(k):\\n x, y = self.__col_lista[i]\\n if x < min_x:\\n min_x = x\\n if y < min_y:\\n min_y = y\\n return min_x, min_y\",\n \"def find_closest_index(traj, point):\\n\\n\\t#TODO: vectorise function to receive any length of points.\\n\\n\\tdistances = np.subtract(np.array(point),traj) \\n\\tdistances = distances.reshape(-1,2)\\n\\t#distances = distances[~np.isnan(distances)].reshape(-1,2)\\n\\n\\t#print(\\\"distances\\\")\\n\\t#pprint(distances)\\n\\tdist_array = np.linalg.norm(distances, axis = 1)\\n\\t#pprint(dist_array)\\n\\t#dist_array = np.sqrt((distances[:,0]**2)+(distances[:,1]**2)) #array of distances from trajectory to gaze landing point in world. \\n\\tidx = np.nanargmin(abs(dist_array)) #find smallest difference in pythag distance from 0,0 to get closest point. \\n\\tdists = distances[idx, :]\\n\\tdist = dist_array[idx]\\n\\n\\treturn idx#, dists, dist\\n\\t#return idx\",\n \"def getclosest_ij(lats,lons,latpt,lonpt):\\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\\n dist_lon = (lons-lonpt)**2\\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\\n minindex_lon = dist_lon.argmin()\\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index\",\n \"def getclosest_ij(lats,lons,latpt,lonpt):\\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\\n dist_lon = (lons-lonpt)**2\\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\\n minindex_lon = dist_lon.argmin()\\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index\",\n \"def _find_min_pair(pandas_matrix):\\n numpy_matrix = pandas_matrix.values\\n mins = np.where(numpy_matrix == np.nanmin(numpy_matrix))\\n min_col_idx = mins[0][0]\\n min_row_idx = mins[1][0]\\n (min_col, min_row) = (pandas_matrix.index[min_col_idx], \\n pandas_matrix.columns[min_row_idx])\\n\\n return (min_col, min_row)\",\n \"def find_closest(distances, threshold):\\n n = len(distances)\\n person_1 = []\\n person_2 = []\\n d = []\\n\\n for i in range(n):\\n for j in range(i+1, n):\\n if distances[i][j] <= threshold:\\n person_1.append(i)\\n person_2.append(j)\\n d.append(distances[i][j])\\n\\n return person_1, person_2, d\",\n \"def nearest_points_values_without_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 0\\n np.fill_diagonal(matrix, 0)\\n # Get the position for the resulted values\\n sort = np.sort(matrix, axis=1)\\n\\n return sort[:, : num_to_keep]\",\n \"def getRowHeuristics(matrix):\\n row, col = matrix.shape\\n rHeuristic = np.zeros((row,2)) # Dos columnas. La primera para indicar la columna la segunda para la Heuristica\\n for i in range(0,row):\\n rHeuristic[i,0] = int(i)\\n #print (i,sum(matrix[:,i]), pesos[i], float(pesos[i]/sum(matrix[:,i])))\\n rHeuristic[i,1] = 1/sum(matrix[i,:])\\n return rHeuristic[rHeuristic[:,1].argsort()]\",\n \"def nearestNeighbor(self, coords, my_index, blacklist):\\n min_index, min_distance = 0, 999999999\\n my_col, my_row = coords[my_index]\\n for i, (col, row) in enumerate(coords):\\n if i in blacklist:\\n continue\\n distance = math.sqrt((my_col-col)**2 + (my_row-row)**2)\\n if distance < min_distance:\\n min_index, min_distance = i, distance\\n return min_index\",\n \"def _nearest_cluster_distance(distances_row, labels, i):\\n label = labels[i]\\n b = np.min([np.mean(distances_row[labels == cur_label])\\n for cur_label in set(labels) if not cur_label == label])\\n return b\",\n \"def getNearestSampleIndex(test, trainX):\\n dist_matrix = test - trainX\\n dist_square = dist_matrix ** 2\\n dist_sums = dist_square.sum(axis=1)\\n distance_vector = np.sqrt(dist_sums)\\n return (distance_vector).argmin()\",\n \"def find_closest_trajectory_pose(self):\\n np_state = numpy.array([[self.x], [self.y]])\\n temp_distance = numpy.sum(\\n (self.np_trajectory[0:2, :] - np_state) ** 2,\\n axis=0)\\n best_idx = numpy.argmin(temp_distance)\\n return best_idx\",\n \"def _compute_euclidean_neigh_matrix(src, d_matrix, radius):\\n\\n n_max = 100\\n n_min = 3\\n reached_points = np.array([0])\\n counter = 0\\n n_neigh = []\\n list_neigh = []\\n\\n while counter < reached_points.shape[0] < src.shape[0]:\\n P = reached_points[counter]\\n aux = np.array(sorted(\\n np.where(d_matrix[P] <= radius)[0],\\n key=lambda k: d_matrix[P, k]))\\n n_neigh.append(aux.shape[0])\\n\\n # Check the number of neighbours\\n if n_neigh[-1] < n_min:\\n raise ValueError('Computation of neighbours aborted since '\\n 'their minimum number is too small.\\\\n'\\n 'Please choose a higher radius.')\\n elif n_neigh[-1] > n_max:\\n raise ValueError('Computation of neighbours aborted since'\\n 'their maximum number is too big.\\\\n'\\n 'Please choose a lower radius.')\\n list_neigh.append(aux)\\n reached_points = np.append(reached_points,\\n aux[~np.in1d(aux, reached_points)])\\n counter += 1\\n\\n if counter >= reached_points.shape[0]:\\n raise ValueError('Too small value of the radius:'\\n 'the neighbour-matrix is not connected')\\n elif src.shape[0] == reached_points.shape[0]:\\n while counter < src.shape[0]:\\n P = reached_points[counter]\\n aux = np.array(sorted(\\n np.where(d_matrix[P] <= radius)[0],\\n key=lambda k: d_matrix[P, k]))\\n n_neigh.append(aux.shape[0])\\n\\n if n_neigh[-1] < n_min:\\n raise ValueError('Computation of neighbours aborted since '\\n 'their minimum number is too small.\\\\n'\\n 'Please choose a higher radius.')\\n elif n_neigh[-1] > n_max:\\n raise ValueError('Computation of neighbours aborted since'\\n 'their maximum number is too big.\\\\n'\\n 'Please choose a lower radius.')\\n\\n list_neigh.append(aux)\\n counter += 1\\n\\n n_neigh_max = max(n_neigh)\\n n_matrix = np.zeros([src.shape[0],\\n n_neigh_max], dtype=int) - 1\\n for i in range(src.shape[0]):\\n n_matrix[i, 0:list_neigh[i].shape[0]] = list_neigh[i]\\n index_ord = np.argsort(n_matrix[:, 0])\\n n_matrix = n_matrix[index_ord]\\n return n_matrix\\n else:\\n raise RuntimeError(\\\"Some problems during\\\"\\n \\\"computation of neighbours.\\\")\",\n \"def findknn(xTr,xTe,k):\\n\\n # YOUR CODE HERE\\n if k > len(xTr):\\n k = len(xTr)\\n \\n D=l2distance(xTe, xTr)\\n (m,n) = D.shape\\n \\n indices = []\\n dists = []\\n for i in range(m):\\n smallest_indices = np.argsort(D[i])\\n ind = smallest_indices[:k]\\n dis = D[i,smallest_indices[:k]]\\n indices.append(ind)\\n dists.append(dis)\\n \\n indices = np.transpose(np.array(indices))\\n dists = np.transpose(np.array(dists))\\n return indices, dists\",\n \"def saddle_points(matrix):\\n if not all(len(row) == len(matrix[0]) for row in matrix[1:]):\\n raise ValueError('Provided matrix is irregular.')\\n columns = [col for col in zip(*matrix)]\\n points = set()\\n for ridx, row in enumerate(matrix):\\n for cidx, element in enumerate(row):\\n if element == max(row) and element == min(columns[cidx]):\\n points.add((ridx, cidx))\\n return points\",\n \"def _first_index_with_smaller_neighbour(P):\\n i = len(P) - 1\\n while i > 0 and P[i-1] <= P[i]:\\n i -= 1\\n return i\",\n \"def min_distance_vertex(distance, visited):\\n vertices = len(visited)\\n min_distance = INF\\n min_index = None\\n for v in range(vertices):\\n if not visited[v] and distance[v] <= min_distance:\\n min_distance = distance[v]\\n min_index = v\\n return min_index\",\n \"def find_nearest_neighbors_idx(X, x, k):\\n ## homework:start\\n result = \\n ## homework:end\\n return result\",\n \"def closest_points_naive(self, x, y):\\r\\n # Running time: O(n ** 2)\\r\\n\\r\\n dist = []\\r\\n for i in range(len(x)):\\r\\n for j in range(i+1, len(x)):\\r\\n d = self.get_euclidean_distance(x[i], x[j], y[i], y[j])\\r\\n dist.append(d)\\r\\n \\r\\n return min(dist)\",\n \"def shortestDistance(self, grid):\\n # return self.house_oriented_TLE(grid)\\n # One axis\\n row_count = [sum(row) for row in grid]\\n col_count = [0]* len(grid[0])\\n row_dist = [0]* len(grid)\\n col_dist = [0]* len(grid[0])\\n output = sys.maxsize\\n for i in range(len(grid)): \\n for j in range(len(grid[0])):\\n col_count[j] += grid[i][j]\\n \\n for index_p in range(len(row_count)):\\n for index_h in range(len(row_count)):\\n row_dist[index_p] += abs(index_h - index_p) * row_count[index_h]\\n \\n for index_p in range(len(col_count)):\\n for index_h in range(len(col_count)):\\n col_dist[index_p] += abs(index_h - index_p) * col_count[index_h]\\n \\n # print(row_count)\\n # print(col_count)\\n # print(row_dist)\\n # print(col_dist)\\n for i in range(len(grid)):\\n for j in range(len(grid[0])):\\n if grid[i][j] == 1:\\n continue\\n output = min(output, row_dist[i] + col_dist[j])\\n return output\",\n \"def find_min_point(points):\\r\\n smallest_point_index = 0\\r\\n for i in range(1, len(points)):\\r\\n if points[i][1] < points[smallest_point_index][1]:\\r\\n smallest_point_index = i\\r\\n elif points[i][0] > points[smallest_point_index][0] and points[i][1] == points[smallest_point_index][1]:\\r\\n smallest_point_index = i\\r\\n return smallest_point_index\",\n \"def dist(dm, sm, neighbors):\\n\\n # Initialize list of possible distances\\n distances = []\\n\\n # loop over all neighbors of the cell\\n for neighbor in neighbors:\\n # If the neighbor is valid\\n if dm[neighbor[0], neighbor[1]] != -1:\\n # add neighbor distance + 1 to possible distances\\n distances.append(dm[neighbor[0], neighbor[1]] + 1)\\n\\n # return minimal distance\\n return np.min(distances)\",\n \"def get_min_dist(x0, y0, arr):\\n dist = np.hypot(arr.T[0] - x0, arr.T[1] - y0)\\n min_dist = np.min(dist)\\n val = np.argmin(dist)\\n return min_dist, arr[val]\",\n \"def nodes_min_energy_index(self, node):\\n idx = -1\\n curr_energy = np.inf\\n for i in range(self.cost_matrix.shape[1]):\\n new_energy = self.cost_matrix[node][i]\\n if new_energy < curr_energy:\\n curr_energy = new_energy\\n idx = i\\n return idx\",\n \"def get_maxmin_index_from_row(\\n distance_matrix: np.ndarray,\\n row: int,\\n previous_indexes: List,\\n type: str,\\n )-> int:\\n distance_matrix = distance_matrix.copy()\\n arr = distance_matrix[row].astype(float)\\n \\n aux_list = range(arr.shape[0])\\n aux_list_2 = []\\n for i in aux_list:\\n if i in previous_indexes:\\n aux_list_2.append(True)\\n else:\\n aux_list_2.append(False)\\n previous_indexes_bool = aux_list_2\\n \\n if type == 'max':\\n arr[previous_indexes_bool] = -1\\n target_index = np.argmax(arr)\\n if type == 'min':\\n arr[previous_indexes_bool] = np.Inf\\n target_index = np.argmin(arr)\\n \\n return target_index\",\n \"def get_idx(lons, lats, lon, lat):\\n dist = ((lons - lon) ** 2 + (lats - lat) ** 2) ** 0.5\\n return np.unravel_index(dist.argmin(), dist.shape)\",\n \"def pair_idx(rows, comm=None):\\n raise Exception(\\\"Not implemented\\\")\\n \\n if comm == None:\\n comm = MPI.COMM_WORLD\\n \\n total = comb(rows,2,exact=True)\\n size = comm.Get_size()\\n \\n size = 1000\\n \\n print(total / size)\\n \\n target = total / size\\n \\n current_row = 0\\n calc_list = []\\n row_list = [[] for x in range(size)]\\n for rank in range(size):\\n row_list[rank].append(current_row)\\n \\n current_calcs = 0\\n \\n for value in range(current_row, rows):\\n current_calcs += value\\n if current_calcs > target:\\n if rank == size-1:\\n pass\\n else:\\n break\\n \\n calc_list.append(current_calcs)\\n row_list[rank].append(value)\\n current_row = value\\n \\n return row_list,calc_list\",\n \"def _findMin(p, A):\\n\\n m=(-1, (0,0))\\n for p0 in A:\\n dist = np.linalg.norm(p0-np.array(p))\\n if m[0]==-1 or m[0]>dist:\\n m = (dist, p0)\\n \\n return tuple(m[1])\",\n \"def distance_matrix(n_row, n_col):\\n\\n n_pop = int(n_row * n_col)\\n center = int(n_row/2*(n_col+1))\\n\\n pop_idx = np.arange(n_pop)\\n pop_idx_col = np.remainder(pop_idx, n_col)\\n pop_idx_row = pop_idx // n_row\\n\\n pos = np.vstack((pop_idx_col,pop_idx_row)).T\\n distance = spa.distance.cdist([pos[center]], pos)[0]\\n\\n return distance\",\n \"def min(weightData , dataSetVector ):\\r\\n # weightData: pass the whole weightData array.\\r\\n # dataSetVector: pass the a data vector to compare with weightdata array, to find its closest match\\r\\n winnerIndex = 0 #flag for initalizing the winner index\\r\\n minValue = EcuDist(dataSetVector,weightData[0]) # initalize the minValue\\r\\n # iterate through all weighdata rows to find the closest match, depending on ecu. distance,\\r\\n #and then return the index of the closest match(winner)\\r\\n for i in range(weightData.shape[0]):\\r\\n if(EcuDist(dataSetVector,weightData[i]) < minValue):\\r\\n minValue = EcuDist(dataSetVector,weightData[i])\\r\\n winnerIndex = i\\r\\n return winnerIndex\",\n \"def find_min(self, A, w):\\n import numpy as np\\n\\n vcost = self.INFINITY\\n vto = vfrom = -1\\n for v in w:\\n # Get array offset of minimum of this vertex\\n i = np.argmin(A[v,:])\\n if A[v,i] < vcost:\\n vcost = A[v,i]\\n vto = i\\n vfrom = v\\n return (vfrom, vto, vcost)\",\n \"def get_nearest_atom_inds_per_mol(self):\\n self.closest_at_per_mol = np.zeros((self.nmol,\\n self.at_per_mol,\\n self.at_per_mol-1), dtype=int)\\n\\n # Get and sort distances\\n all_at_inds = np.arange(self.at_per_mol)\\n for imol in range(self.nmol):\\n for iat in range(self.at_per_mol):\\n at_inds = all_at_inds[all_at_inds != iat]\\n dist = self.all_dist_per_mol[imol, iat, at_inds]\\n\\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\\n self.closest_at_per_mol[imol, iat] = at_inds\",\n \"def find_nearest_neighbours_SURF(averageDistance,inst,distanceArray,maxInst): \\r\\n NN=[]\\r\\n min_indices=[] \\r\\n\\r\\n for j in range(maxInst):\\r\\n if inst != j:\\r\\n locator = [inst,j]\\r\\n locator = sorted(locator, reverse=True) #Access corect half of table (result of removed table redundancy)\\r\\n d = distanceArray[locator[0]][locator[1]]\\r\\n if d<averageDistance:\\r\\n min_indices.append(j)\\r\\n \\r\\n for j in range(len(min_indices)):\\r\\n NN.append(min_indices[j])\\r\\n \\r\\n return NN\",\n \"def find_nearest_neighbors(p, points, k=5):\\n dist = np.zeros(points.shape[0])\\n for i in range(len(dist)):\\n dist[i] = distance(p, points[i])\\n ind = np.argsort(dist)\\n return ind[0:k]\",\n \"def update_distmatrix(min_idx, dist_matrix):\\n i, j = min_idx\\n new_cluster_dist = np.minimum(dist_matrix[i, :], dist_matrix[j, :])\\n new_cluster_dist[i] = np.inf\\n \\n dist_matrix[i, :] = new_cluster_dist\\n dist_matrix[:, i] = new_cluster_dist\\n \\n dist_matrix[j, :] = np.inf\\n dist_matrix[:, j] = np.inf\\n \\n return dist_matrix\",\n \"def get_nearest_node_index(node_list, random_node):\\n\\n dist_list = [\\n (node.x - random_node.x) ** 2 + (node.y - random_node.y) ** 2\\n for node in node_list\\n ]\\n minind = dist_list.index(min(dist_list))\\n\\n return minind\",\n \"def getNeighbors(training_data, test_row, k):\\n\\n distances = list()\\n for training_row in training_data:\\n dist = euclidianDistance(training_row, test_row)\\n distances.append([training_row, dist])\\n \\n #Sort on the basis of dist\\n distances.sort(key=lambda row:row[1])\\n\\n neighbors = list()\\n\\n for i in range(int(k)):\\n neighbors.append(distances[i][0])\\n\\n return neighbors\",\n \"def eeg_findnearest(x,X):\\t\\n\\t#x array or vector and X a scalar\\n\\tabsdif = np.abs(x-X)\\n\\tval = np.min(absdif)\\n\\tidx = absdif.argmin()\\n\\treturn val,idx\",\n \"def get_nearest_atom_inds(self):\\n # Create empty data structure\\n self.closest_ats = np.zeros((self.natom, self.natom-1), dtype=int)\\n\\n # Get and sort distances\\n all_at_inds = np.arange(self.natom)\\n for iat in range(self.natom):\\n at_inds = all_at_inds[all_at_inds != iat]\\n dist = self.all_dist[iat, at_inds]\\n\\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\\n self.closest_ats[iat] = at_inds\",\n \"def closestCluster(p, centers):\\n\\tbestIndex = 0\\n closest = float(\\\"+inf\\\")\\n for i in range(len(centers)):\\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\\n if distance < closest:\\n closest = distance\\n bestIndex = i\\n\\treturn bestIndex\",\n \"def get_min_shannon_entropy(grid):\\r\\n curr_min = math.inf\\r\\n curr_best = []\\r\\n for i in range(len(grid[0])):\\r\\n for j in range(len(grid)):\\r\\n if not grid[j][i].collapsed:\\r\\n w = grid[j][i].block_weights\\r\\n shannon_entropy = sum([-math.log(el) for el in w] )\\r\\n if shannon_entropy < curr_min:\\r\\n curr_min = shannon_entropy\\r\\n curr_best = [(i,j)]\\r\\n elif shannon_entropy == curr_min:\\r\\n curr_best.append((i,j))\\r\\n idx = np.random.choice(range(len(curr_best))) #choose randomly if theres a tie\\r\\n return curr_best[idx] #x,y\\r\",\n \"def findSmallest(distancesWithNames):\\n smallest = distancesWithNames[0][2]\\n smallestIndex = -1\\n for i in range(len(distancesWithNames)):\\n if smallest >= distancesWithNames[i][2]:\\n smallest = distancesWithNames[i][2]\\n smallestIndex = i\\n return smallestIndex\",\n \"def get_index_under_point(self, event):\\r\\n xy = np.asarray(list(zip(self.xs, self.ys)))\\r\\n xyt = self.line.get_transform().transform(xy)\\r\\n xt, yt = xyt[:, 0], xyt[:, 1]\\r\\n d = np.sqrt((xt - event.x) ** 2 + (yt - event.y) ** 2)\\r\\n pt_idx = np.argmin(d)\\r\\n if d[pt_idx] >= self.max_pixels_from_vertex:\\r\\n pt_idx = None\\r\\n return pt_idx\",\n \"def find_nearest_neighbour_from_point(point_cloud:np.ndarray, point:int) -> int:\\n pass\",\n \"def find_min_path(s, t, dist):\\n\\n rows = len(dist) - 1\\n cols = len(dist[0]) - 1\\n col = cols\\n row = rows\\n pos_str = \\\"Position: (row={} col={}) -> (row={} col={})\\\"\\n cst_str = \\\"Cost: {}\\\"\\n prev_row = row\\n prev_col = col\\n\\n # init sparse path matrix\\n sparse_path = [[\\\" \\\" for x in range(cols + 1)] for x in range(rows + 1)]\\n sparse_path[0][0] = \\\"0\\\"\\n\\n # start with operation at (rows, cols) and work backwards\\n sparse_path[rows][cols] = dist[rows][cols]\\n\\n if verbose == 2:\\n print()\\n print(\\\"Initial Minimum Path Matrix:\\\")\\n print_matrix(s, t, sparse_path)\\n\\n while True:\\n\\n # bail out if we are in the corner\\n if row == 0 and col == 0:\\n break\\n\\n # if we are not at a matrix boundary\\n if row != 0 and col != 0: # if at left edge or top row, cannot move diagonally\\n\\n # diagonal\\n if (dist[row - 1][col - 1] == min(dist[row - 1][col],\\n dist[row][col - 1],\\n dist[row - 1][col - 1])) and (dist[row - 1][col - 1] == dist[row][col] or dist[row - 1][col - 1] == dist[row][col] - 1):\\n sparse_path[row - 1][col - 1] = dist[row - 1][col - 1]\\n temp_cost = dist[row - 1][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # left\\n elif dist[row][col - 1] <= dist[row][col]:\\n sparse_path[row][col - 1] = dist[row][col - 1]\\n temp_cost = dist[row][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # above\\n else:\\n sparse_path[row - 1][col] = dist[row - 1][col]\\n temp_cost = dist[row - 1][col]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # if at matrix edge, can only move up\\n elif col == 0:\\n # above\\n sparse_path[row - 1][col] = dist[row - 1][col]\\n temp_cost = dist[row - 1][col]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # must be at row boundary, can only move left\\n else:\\n # left\\n if dist[row][col - 1] <= dist[row][col]:\\n sparse_path[row][col - 1] = dist[row][col - 1]\\n temp_cost = dist[row][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # print matrix\\n if verbose == 2:\\n print_matrix(s, t, sparse_path)\\n\\n return sparse_path\",\n \"def closestClusterAndDistance(p, centers):\\n\\tbestIndex = 0\\n closest = float(\\\"+inf\\\")\\n for i in range(len(centers)):\\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\\n if distance < closest:\\n closest = distance\\n bestIndex = i\\n return (bestIndex, closest)\",\n \"def get_nearest_indices(vector, matrix, num=5):\\n if len(matrix) < num:\\n num = len(matrix)\\n\\n m, n = matrix.shape\\n diff_matrix = np.tile(vector, (m, 1)) - matrix\\n diff_matrix = abs(diff_matrix)\\n\\n distance = diff_matrix.sum(axis=1)\\n sortIndices = np.argsort(distance)\\n sortIndices = sortIndices[0:num]\\n\\n return sortIndices[random.randint(0, num - 1)]\",\n \"def detec_mfo_dist(betas):\\n k = len(betas)\\n min_dist = np.inf\\n for i in range(k-1):\\n for j in range(i+1,k):\\n distance = np.sum((betas[i,:] - betas[j,:])**2)\\n if distance < min_dist:\\n MFO_index = [i,j]\\n min_dist = distance\\n return MFO_index\",\n \"def get_nearest_row(self):\\n return (self.rect.top - (self.screen.get_height() // 12)) // self.maze.block_size\",\n \"def smallest_cert(pts):\\n # 1. project the points\\n def projected_flattened_pt(pt):\\n mag = distance(pt)\\n distance_wanted = rect_dist\\n return distance_wanted/mag*pt[1], \\\\\\n distance_wanted/mag*pt[2]\\n\\n flat_pts = np.array([[projected_flattened_pt(pt), pt] for pt in pts])\\n\\n # 2. identify rows of data\\n data = {} \\n # maps height (from ground) to the \\\"row\\\" (scan) list of\\n # horizontal-only (1D) data\\n for pt, origpt in flat_pts:\\n for row_height in data:\\n if abs(pt[1]-row_height) < dist_bt_rows/2:\\n data[row_height].append([pt[0], origpt])\\n break\\n else:\\n data[pt[1]] = [[pt[0], origpt]]\\n\\n #3. build up final_data so each row has enough point x-density\\n def get_left_i(pts, left_edge):\\n # get last index <= left_edge\\n for i, pt in enumerate(pts):\\n if pt[0] > left_edge:\\n return i - 1\\n\\n def get_right_i(pts, right_edge):\\n # get first index >= right_edge\\n i = len(pts)-1\\n while True:\\n pt = pts[i]\\n if pt[0] < right_edge:\\n return i + 1\\n i -= 1\\n\\n final_data = []\\n for _, subdata in data.items():\\n subdata = sorted(subdata, key=lambda t: t[0])\\n i = get_left_i(subdata, lane_left)\\n end = get_right_i(subdata, lane_right)\\n\\n final_data = [subdata[i][1]]\\n while i < end:\\n next_i = get_left_i(subdata[i:], \\n subdata[i][0]+max_xpt_separation)\\n if not next_i:\\n return False\\n i += next_i\\n final_data.append(subdata[i][1])\\n\\n return final_data\",\n \"def _lowestDistanceToCluster(self, clusters: ndarray, sequenceIdx: int) -> Tuple[int, float]:\\n lowestClusterIdx = -1\\n lowestDistance = np.inf\\n for cIdx in range(self.numClusters):\\n distance = self._distanceToCluster(clusters[cIdx], sequenceIdx)\\n if distance < lowestDistance:\\n lowestClusterIdx = cIdx\\n lowestDistance = distance\\n return lowestClusterIdx, lowestDistance\",\n \"def find_closest_atom(coords1, coords2):\\n\\n coords1 = np.array(coords1)\\n coords2 = np.array(coords2)\\n diff = coords2[:, np.newaxis] - coords1[np.newaxis, :]\\n dist = np.einsum('ijk->ij', diff**2)**0.5\\n index = np.argmin(dist)\\n return index\",\n \"def closestCentroids(self, points , centroids ):\\n dists = scipy.spatial.distance.cdist(points,centroids)\\n # 1 is dimension\\n minIds = numpy.argmin(dists, 1)\\n return minIds\",\n \"def find_neighbor_indices(atoms, probe, k):\\r\\n coords_all = atoms.getCoords()\\r\\n neighbor_indices = []\\r\\n atom_k = atoms[k]\\r\\n radius = atom_k.getRadius() + probe + probe\\r\\n\\r\\n indices = range(k)\\r\\n indices.extend(range(k+1, len(atoms)))\\r\\n\\r\\n for i in indices:\\r\\n\\tdist = pos_distance(coords_all[k], coords_all[i])\\r\\n\\t#dist = np.linalg.norm(coords_all[k] - coords_all[i])\\r\\n\\tif dist < radius + atoms[i].getRadius():\\r\\n neighbor_indices.append(i)\\r\\n\\r\\n return neighbor_indices\",\n \"def exhaustive_search(X, z):\\n #initialize shortest path and distance to the first row\\n d_star = la.norm(z - X[0])\\n x_star = X[0]\\n #check the distance from each row of X and if shortest thus far, save it\\n for i in range(X.shape[0]):\\n #calculate distance from current row to target z\\n x = X[i,:]\\n cur_dist = la.norm(x - z)\\n #if the distance is smallest thus far, save the row and distance\\n if cur_dist < d_star:\\n x_star = x\\n d_star = cur_dist\\n return x_star, d_star\",\n \"def _retrieve_neighbors(df, i_point, point, eps, column):\\n neigborhood = []\\n for index, row in df.iterrows():\\n if index != i_point:\\n a = np.array(point[column])\\n b = np.array([row[column]])\\n distance = np.linalg.norm(a - b)\\n if distance <= eps:\\n neigborhood.append(index)\\n\\n return neigborhood\",\n \"def closest_node(node, nodes):\\n nodes = np.asarray(nodes)\\n deltas = nodes - node\\n dist_2 = np.einsum(\\\"ij,ij->i\\\", deltas, deltas)\\n return np.argmin(dist_2), np.min(dist_2)\",\n \"def find_nearest_neighbors(p, points, k):\\n\\timport numpy as np\\n\\tdistances = np.zeros(points.shape[0])\\n\\tfor i in range(len(distances)):\\n\\t\\tdistances[i] = distance(p,points[i])\\n\\tind = np.argsort(distances)\\n\\treturn ind[0:k]\",\n \"def nearest_neighbor(data):\\n features = set([i for i, x in enumerate(data[0][1])])\\n return leave_one_out_cross_validation(data, features)\",\n \"def djikstre(connection_mat):\\n n = connection_mat.shape[0]\\n dist, prev = {}, {}\\n Q = list(range(n))\\n \\n for i in Q:\\n dist[i] = np.inf\\n dist[n-2] = 0.0\\n \\n while(len(Q)>0):\\n\\n min_dist = min([dist[key] for key in Q])\\n u = [key for key in Q if dist[key] == min_dist][0]\\n Q.remove(u)\\n\\n for v in np.nonzero(connection_mat[:, u])[0]:\\n \\n alt = dist[u]+connection_mat[v, u]\\n \\n if alt < dist[v]:\\n dist[v] = alt\\n prev[v] = u\\n \\n return dist, prev\",\n \"def neirest_neighbour(business, cells):\\n array = cells.get_neighbours(business, num=1)\\n neighbours = pd.DataFrame(array).set_index('index')\\n index = neighbours['distance'].idxmin()\\n return neighbours.loc[index]\",\n \"def closest(self, x, y):\\n if self.direction == 'horizontal':\\n p_pts = np.array([\\n self.ax.transData.transform((p, 0))[0] for p in self.positions\\n ])\\n dist = abs(p_pts - x)\\n else:\\n p_pts = np.array([\\n self.ax.transData.transform((0, p))[1] for p in self.positions\\n ])\\n dist = abs(p_pts - y)\\n index = np.argmin(dist)\\n return index, dist[index]\",\n \"def _nearest_point_index(points, point):\\n distance = sys.float_info.max\\n index = None\\n for i, p in enumerate(points):\\n temp = _vec_distance(p, point)\\n if temp < distance:\\n distance = temp\\n index = i\\n return index, distance\",\n \"def _kth_nearest_neighbor_dist(\\n distance_matrix: Union[np.ndarray, scipy.sparse.csr_matrix], k\\n) -> np.ndarray:\\n\\n if not is_integer(k):\\n raise ValueError(f\\\"parameter 'k={k}' must be a positive integer\\\")\\n else:\\n # make sure we deal with Python built-in\\n k = int(k)\\n\\n if not (0 <= k <= distance_matrix.shape[1]):\\n raise ValueError(\\n \\\"'k' must be an integer between 1 and \\\"\\n f\\\"distance_matrix.shape[1]={distance_matrix.shape[1]}\\\"\\n )\\n\\n if isinstance(distance_matrix, np.ndarray):\\n dist_knn = np.partition(distance_matrix, k - 1, axis=1)[:, k - 1]\\n elif isinstance(distance_matrix, scipy.sparse.csr_matrix):\\n # see mircobenchmark_kth_nn.py for a comparison of implementations for the\\n # sparse case\\n\\n def _get_kth_largest_elements_sparse(\\n data: np.ndarray,\\n indptr: np.ndarray,\\n row_nnz,\\n k_neighbor: int,\\n ):\\n dist_knn = np.zeros(len(row_nnz))\\n for i in range(len(row_nnz)):\\n start_row = indptr[i]\\n dist_knn[i] = np.partition(\\n data[start_row : start_row + row_nnz[i]], k_neighbor - 1\\n )[k_neighbor - 1]\\n\\n return dist_knn\\n\\n row_nnz = distance_matrix.getnnz(axis=1)\\n\\n if (row_nnz < k).any():\\n raise ValueError(\\n f\\\"There are {(row_nnz < k).sum()} points that \\\"\\n f\\\"do not have at least k_neighbor={k}.\\\"\\n )\\n\\n dist_knn = _get_kth_largest_elements_sparse(\\n distance_matrix.data,\\n distance_matrix.indptr,\\n row_nnz,\\n k,\\n )\\n else:\\n raise TypeError(f\\\"type {type(distance_matrix)} not supported\\\")\\n\\n return dist_knn\",\n \"def _first_index_with_bigger_neighbour(P):\\n i = len(P) - 1\\n while i > 0 and P[i-1] >= P[i]:\\n i -= 1\\n return i\",\n \"def getNearestLineIndex(row, tagLineNumbers):\\n # DOC {{{\\n # }}}\\n\\n # CODE {{{\\n # initialize local auxiliary variables {{{\\n nearestLineNumber = -1\\n nearestLineIndex = -1\\n # }}}\\n\\n # go through all tag line numbers and find the one nearest to the specified row {{{\\n for lineIndex, lineNumber in enumerate(tagLineNumbers):\\n # if the current line is nearer the current cursor position, take it {{{\\n if (nearestLineNumber < lineNumber <= row):\\n nearestLineNumber = lineNumber\\n nearestLineIndex = lineIndex\\n # }}}\\n\\n # if we've got past the current cursor position, let's end the search {{{\\n if (lineNumber >= row):\\n break\\n # }}}\\n # }}}\\n\\n # return index of the line with the nearest tag\\n return nearestLineIndex\\n # }}}\",\n \"def shortestpathij(self, i, j):\\n pathlist = []\\n self.pathij(i, j, pathlist)\\n distance = []\\n \\n for i in range(len(pathlist)):\\n Temp = 0\\n for j in range(len(pathlist[i]) - 1):\\n Temp += self.Dismatrix[pathlist[i][j], pathlist[i][j+1]]\\n distance.append(Temp)\\n \\n if(len(distance) == 0):\\n return None\\n else:\\n return min(distance)\",\n \"def min_horizontal_dist_meters(coords, targets, is_geo=False):\\n xe = coords[:, 0]\\n ye = coords[:, 1]\\n n = len(xe)\\n d = np.zeros(n)\\n for j in range(n):\\n d1 = dist_in_meters(targets, [xe[j], ye[j]], is_geo=is_geo)\\n d[j] = d1.min()\\n return d\",\n \"def findNearset(x,y,lon,lat):\\n dist = np.sqrt( (lon - x)**2 + (lat - y)**2)\\n\\n return np.argwhere(dist==dist.min())[0][0]\",\n \"def get_nearest_neighbours(self, cell):\\n\\t\\tneighs = self.get_neighbours(cell)\\n\\t\\ti, j = cell.find_id()\\n\\t\\tneighbours = []\\n\\t\\tfor neigh in neighs:\\n\\t\\t\\tx, y = neigh.find_id()\\n\\t\\t\\tif abs(x-i)+abs(y-j) <= 1: \\n\\t\\t\\t\\tneighbours.append(self.space[y,x])\\n\\t\\treturn neighbours\",\n \"def _low_tri_indices(rowCount):\\n for col in range(rowCount):\\n for row in range(col, rowCount):\\n yield (row, col)\",\n \"def min_distance(s1, s2):\\n n = len(s1)\\n m = len(s2)\\n matrix = [([0]*(m+1)) for i in xrange(n+1)]\\n for i in xrange(m+1):\\n matrix[0][i] = i\\n for i in xrange(n+1):\\n matrix[i][0] = i\\n for i in xrange(1,n+1):\\n for j in xrange(1,m+1):\\n temp = min(matrix[i-1][j]+1, matrix[i][j-1]+1)\\n d = 0 if s1[i-1]==s2[j-1] else 1\\n matrix[i][j] = min(temp, matrix[i-1][j-1]+d)\\n return matrix[n][m]\",\n \"def extract_min(H, ds):\\n minDist = approxInf\\n u = None # min vertex unknown\\n i = 0\\n for v in H:\\n if ds[v] <= minDist:\\n minDist = ds[v]\\n u = v # note that u is unused (instead returned by pop)\\n imin = i\\n i += 1\\n return(H.pop(imin)) # return [u, d]\",\n \"def findminpath(tab, gxtab, gytab, pixtab):\\n\\n pathdist = 2 # the number of points each points on a ray can related to on the previous ray\\n pathdist_penalty = 0.3 # penalty of the difference of the pathdist\\n pathpix_penalty = 2 # penalty of the difference of pixel values between the point and the previous point\\n nray = tab.shape[1]\\n\\n #tab = np.hstack((tab,tab[:, 0].reshape(tab.shape[0], 1)))\\n #pixtab = np.hstack((pixtab,pixtab[:, 0].reshape(pixtab.shape[0], 1)))\\n #gxtab = np.hstack((gxtab,gxtab[:, 0].reshape(gxtab.shape[0], 1)))\\n #gytab = np.hstack((gytab,gytab[:, 0].reshape(gytab.shape[0], 1)))\\n\\n tab = np.hstack((tab,tab,tab)) # horizontally stack the tab matrix to prepare for the filtering on the result\\n pixtab = np.hstack((pixtab,pixtab,pixtab))\\n gxtab = np.hstack((gxtab,gxtab,gxtab))\\n gytab = np.hstack((gytab,gytab,gytab))\\n\\n tab = (tab - tab.min()) / (tab.max() - tab.min()) # noralize the tab matrix\\n pixtab = (pixtab - pixtab.min()) / (pixtab.max() - pixtab.min()) * -1 # for we want to find the white contour of the cell so we multipy -1 on the pixtab\\n # tab = tab / np.median(tab)\\n # pixtab = pixtab / np.median(pixtab)\\n path = np.zeros(tab.shape)\\n path[:, 0] = np.array(range(0, tab.shape[0]))\\n score = np.zeros(tab.shape)\\n score[:, 1] = tab[:, 1]\\n\\n for i in range(1, tab.shape[1]):\\n for j in range(tab.shape[0]):\\n mins = np.Inf # record the min value of the ray\\n minat = 0\\n for k in range(-pathdist, pathdist+1):\\n if(0 <= (j+k) and (j+k) < tab.shape[0]):\\n s = pixtab[j, i]\\n pixdiff = abs(pixtab[j, i] - pixtab[j+k, i-1])\\n s += pixdiff * pathpix_penalty # two kinds of penalty\\n s += abs(k) * pathdist_penalty\\n s += score[j+k, i-1]\\n\\n if(s < mins):\\n mins = s\\n minat = j + k\\n path[j, i] = minat\\n score[j, i]= mins\\n\\n start = int(np.argmin(score[:, -1]))\\n path = path.astype(np.int32)\\n minpath = [start]\\n for i in range(tab.shape[1]-1, 0, -1):\\n minpath.append(path[minpath[-1], i])\\n minpath = minpath[::-1]\\n # print(len(minpath))\\n minpath = savgol_filter(minpath, 15, 3) # apply a Savitzky-Golay filter to the raw minpath signal\\n minpath = minpath[nray:nray*2] # cut the middle part of minpath whose length is nray\\n return np.array(minpath).astype(np.int32)\",\n \"def find_all_nearest_neighbours(point_cloud:np.ndarray) -> np.ndarray:\\n pass\",\n \"def numNeighbors(minesSet, row_index, cols_index, num_cols, num_rows):\\n mines = 0\\n for j in np.arange(max(0, cols_index-1), min(num_cols-1, cols_index+1)+1):\\n for i in np.arange(max(0, row_index-1), min(num_rows-1, row_index+1)+1):\\n if ((i, j) in minesSet):\\n mines+=1\\n return mines\",\n \"def index_condensed_matrix(n, i, j):\\n if i == j:\\n main_warning(\\\"Diagonal elements (i=j) are not stored in condensed matrices.\\\")\\n return None\\n elif i > j:\\n i, j = j, i\\n return int(i * (n - (i + 3) * 0.5) + j - 1)\",\n \"def nearest_sparse(self, query):\\n self.best_dist = float(\\\"inf\\\")\\n self.best_element = None\\n self._register_best_element = self._register_best_element_single \\n self._nearest_sparse_recursive(self._sparse2seq(query), self.root, 0.0)\\n return self.best_element,self.best_dist\",\n \"def find_closest_pt(ref_lon, ref_lat, tlon, tlat):\\n\\n # compute great circle distance from location to model grid points\\n dist = gc_dist(ref_lon, ref_lat, tlon, tlat)\\n\\n # find j index of closest grid point\\n work = N.take(dist,N.argmin(dist,0),0).diagonal()\\n jj = N.argsort(work)[0]\\n\\n # find i index of closest grid point\\n work = N.take(dist,N.argmin(dist,1),1).diagonal()\\n ii = N.argsort(work)[0]\\n\\n return ii, jj\",\n \"def find_nearest(ref_array,target_array):\\n ref_tree = scipy.spatial.cKDTree(ref_array)\\n dist, indices = ref_tree.query(target_array, k=1)\\n return indices\",\n \"def locate_source(p,d):\\n # M = sensors, n = dimensions\\n M, n = p.shape\\n p = np.matrix( p ).T\\n\\n # pick closest receiver\\n c = np.argmin(d)\\n #sensors delta time relative to sensor c\\n d = d - min(d)\\n\\n indices = list(range(M))\\n del indices[c]\\n\\n A = np.zeros([M-2,n])\\n b = np.zeros([M-2,1])\\n\\n i = indices[0]\\n for row,j in enumerate(indices[1:]):\\n A[row,:] = 2*( (d[j])*(p[:,i]-p[:,c]).T - \\\\\\n (d[i])*(p[:,j]-p[:,c]).T )\\n b[row,0] = (d[i])*((d[j])**2-p[:,j].T*p[:,j]) + \\\\\\n ((d[i])-(d[j]))*p[:,c].T*p[:,c] + \\\\\\n (d[j])*(p[:,i].T*p[:,i]-(d[i])**2)\\n\\n\\n x = np.asarray( np.linalg.lstsq(A,b)[0] )[:,0]\\n return x\",\n \"def _intra_cluster_distance(distances_row, labels, i):\\n mask = labels == labels[i]\\n mask[i] = False\\n if not np.any(mask):\\n # cluster of size 1\\n return 0\\n a = np.mean(distances_row[mask])\\n return a\",\n \"def matrix_idx(n_hist, n_req, n_rows):\\n\\n flat_idx = []\\n for i in range(n_rows):\\n flat_idx.extend(range(i * n_req, (i + 1) * n_req + n_hist))\\n # idx = np.unravel_index(flat_idx, (n_rows, n_hist + n_req))\\n\\n idx_matrix = np.reshape(flat_idx, (n_rows, n_hist + n_req))\\n idxX = idx_matrix[:, n_req:]\\n idxY = idx_matrix[:, :n_req]\\n\\n return idxX, idxY\",\n \"def minesweeper(matrix):\\n \\n num_rows = len(matrix)\\n num_cols = len(matrix[0])\\n \\n adj_mines = []\\n \\n adj_row = [0]*num_cols\\n \\n for i in range(num_rows):\\n adj_mines.append(adj_row[:])\\n \\n for r in range(num_rows):\\n for c in range(num_cols):\\n if matrix[r][c] == True:\\n if (r-1) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r-1][c-1] += 1\\n if (r-1) in range(num_rows) and (c) in range(num_cols):\\n adj_mines[r-1][c] += 1\\n if (r-1) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r-1][c+1] += 1\\n if (r) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r][c-1] += 1\\n if (r) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r][c+1] += 1\\n if (r+1) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r+1][c-1] += 1\\n if (r+1) in range(num_rows) and (c) in range(num_cols):\\n adj_mines[r+1][c] += 1\\n if (r+1) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r+1][c+1] += 1\\n\\n \\n return adj_mines\",\n \"def nearest_neighbors(self):\\n neighbor_distances_and_indices = []\\n for idx, data_point in enumerate(self.data):\\n distance = self.euclidean_dis(data_point[:-1], self.query) # Calculate the distance between the query\\n # example and the current example from the data.\\n\\n neighbor_distances_and_indices.append((distance, idx)) # Add the distance and the index of the example\\n # to an ordered collection\\n\\n sorted_neighbor_distances_and_indices = sorted(neighbor_distances_and_indices, key=lambda x: x[0]) #\\n # Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by\\n # the distances\\n\\n k_nearest_distances_and_indices = sorted_neighbor_distances_and_indices[:self.k] # Pick the first K\\n # entries from the sorted collection\\n\\n k_nearest_labels = [self.data[i][1] for distance, i in k_nearest_distances_and_indices] # Get the labels of\\n # the selected K entries\\n\\n return k_nearest_labels, self.mode(k_nearest_labels)\",\n \"def __find_immediate_predecessors(storm_object_table, target_row):\\n\\n error_checking.assert_is_integer(target_row)\\n error_checking.assert_is_geq(target_row, 0)\\n error_checking.assert_is_less_than(\\n target_row, len(storm_object_table.index)\\n )\\n\\n predecessor_sec_id_strings = [\\n storm_object_table[c].values[target_row]\\n for c in PREV_SECONDARY_ID_COLUMNS\\n if storm_object_table[c].values[target_row] != ''\\n ]\\n\\n num_predecessors = len(predecessor_sec_id_strings)\\n if num_predecessors == 0:\\n return numpy.array([], dtype=int)\\n\\n target_time_unix_sec = storm_object_table[\\n tracking_utils.VALID_TIME_COLUMN].values[target_row]\\n\\n predecessor_rows = numpy.full(num_predecessors, -1, dtype=int)\\n\\n for i in range(num_predecessors):\\n these_rows = numpy.where(numpy.logical_and(\\n storm_object_table[tracking_utils.SECONDARY_ID_COLUMN].values ==\\n predecessor_sec_id_strings[i],\\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values <\\n target_time_unix_sec\\n ))[0]\\n\\n if len(these_rows) == 0:\\n continue\\n\\n this_subrow = numpy.argmax(\\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values[\\n these_rows]\\n )\\n\\n predecessor_rows[i] = these_rows[this_subrow]\\n\\n return predecessor_rows[predecessor_rows >= 0]\",\n \"def closest_waypoint(self, location: pylot.utils.Location):\\n min_dist = np.infty\\n min_index = 0\\n for index, waypoint in enumerate(self.waypoints):\\n dist = waypoint.location.distance(location)\\n if dist < min_dist:\\n min_dist = dist\\n min_index = index\\n return min_index\",\n \"def medoid_ft(ftvectors,pairwise_dist_matrix):\\r\\n N = len(ftvectors)\\r\\n if N == 1 : \\r\\n return 0 \\r\\n # pairwise_dist_matrix = calculate_dist_matrix_ft(ftvectors) \\r\\n sum_dist = np.sum(pairwise_dist_matrix, axis = 0)\\r\\n min_idx = np.argmin(sum_dist)\\r\\n return min_idx\",\n \"def _partition_nearest(self, medoids, dists, only_these=set()):\\n if len(only_these) == 0:\\n allowed_inds = self._not_ignored_inds\\n else:\\n allowed_inds = self._not_ignored_inds & only_these\\n closest_medoid_ind = np.argmin(dists[:,medoids], 1) # If len(medoids)==3, would look like [2,1,1,0,1,2,...].\\n clusts = [[] for i in medoids]\\n for node_ind, med_ind in enumerate(closest_medoid_ind):\\n if node_ind in allowed_inds:\\n clusts[med_ind].append(node_ind)\\n return clusts\",\n \"def closest(self, x, y):\\n pts = np.column_stack([self.x, self.y])\\n # Transform data coordinates to pixel coordinates.\\n pts = self.ax.transData.transform(pts)\\n diff = pts - [x, y]\\n dist = np.hypot(*diff.T)\\n min_index = np.argmin(dist)\\n return min_index, dist[min_index]\",\n \"def search_matrix(to_search: int, a_matrix: list) -> (int, int):\\n row = 0\\n column = len(a_matrix[0]) - 1\\n while row < len(a_matrix) and column >= 0:\\n if to_search < a_matrix[row][column]:\\n column -= 1\\n elif to_search > a_matrix[row][column]:\\n row += 1\\n else:\\n return row, column\\n return -1, -1\",\n \"def closest_centroid(x,centroids):\\n\\tK =len(centroids)\\n\\tN = len(x)\\n\\tDistance = np.zeros((N,K))\\n\\tfor j in range(K):\\n\\t\\tmu = centroids[j]\\n\\t\\tDistance[:,j] = np.linalg.norm(x-mu,axis=1)\\n\\tout = np.argmin(Distance,axis=1) \\n\\treturn out\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76846826","0.7152678","0.65159774","0.6458269","0.64570606","0.6453487","0.64491487","0.6418172","0.6344188","0.6331945","0.62991154","0.62834746","0.62771547","0.62474954","0.6215967","0.6195537","0.6191709","0.6147389","0.6129545","0.6113139","0.60623944","0.606233","0.6058435","0.60123354","0.5976111","0.5973001","0.5966138","0.5958277","0.59407294","0.5921387","0.5908705","0.59075737","0.5906178","0.59027755","0.5898496","0.58922964","0.58238596","0.5817136","0.58046806","0.5794629","0.5786032","0.57790613","0.5760545","0.57487136","0.5729835","0.5727794","0.57262546","0.5723319","0.57142335","0.5699363","0.56863594","0.5669504","0.5668976","0.5659022","0.5657898","0.5656562","0.5640429","0.56398696","0.56386065","0.5635183","0.56332725","0.5628614","0.5613239","0.56091344","0.5606968","0.56068265","0.56064546","0.5605219","0.5593027","0.559182","0.5589653","0.5584512","0.55826277","0.5568779","0.55642515","0.5558048","0.55453014","0.5542451","0.55262434","0.5525691","0.551633","0.55148125","0.55112","0.5510455","0.5500535","0.5499876","0.54814476","0.5480102","0.5476696","0.54706466","0.54668593","0.54622704","0.5459552","0.5458548","0.5455434","0.54476726","0.5441773","0.5437488","0.54363096","0.5430931"],"string":"[\n \"0.76846826\",\n \"0.7152678\",\n \"0.65159774\",\n \"0.6458269\",\n \"0.64570606\",\n \"0.6453487\",\n \"0.64491487\",\n \"0.6418172\",\n \"0.6344188\",\n \"0.6331945\",\n \"0.62991154\",\n \"0.62834746\",\n \"0.62771547\",\n \"0.62474954\",\n \"0.6215967\",\n \"0.6195537\",\n \"0.6191709\",\n \"0.6147389\",\n \"0.6129545\",\n \"0.6113139\",\n \"0.60623944\",\n \"0.606233\",\n \"0.6058435\",\n \"0.60123354\",\n \"0.5976111\",\n \"0.5973001\",\n \"0.5966138\",\n \"0.5958277\",\n \"0.59407294\",\n \"0.5921387\",\n \"0.5908705\",\n \"0.59075737\",\n \"0.5906178\",\n \"0.59027755\",\n \"0.5898496\",\n \"0.58922964\",\n \"0.58238596\",\n \"0.5817136\",\n \"0.58046806\",\n \"0.5794629\",\n \"0.5786032\",\n \"0.57790613\",\n \"0.5760545\",\n \"0.57487136\",\n \"0.5729835\",\n \"0.5727794\",\n \"0.57262546\",\n \"0.5723319\",\n \"0.57142335\",\n \"0.5699363\",\n \"0.56863594\",\n \"0.5669504\",\n \"0.5668976\",\n \"0.5659022\",\n \"0.5657898\",\n \"0.5656562\",\n \"0.5640429\",\n \"0.56398696\",\n \"0.56386065\",\n \"0.5635183\",\n \"0.56332725\",\n \"0.5628614\",\n \"0.5613239\",\n \"0.56091344\",\n \"0.5606968\",\n \"0.56068265\",\n \"0.56064546\",\n \"0.5605219\",\n \"0.5593027\",\n \"0.559182\",\n \"0.5589653\",\n \"0.5584512\",\n \"0.55826277\",\n \"0.5568779\",\n \"0.55642515\",\n \"0.5558048\",\n \"0.55453014\",\n \"0.5542451\",\n \"0.55262434\",\n \"0.5525691\",\n \"0.551633\",\n \"0.55148125\",\n \"0.55112\",\n \"0.5510455\",\n \"0.5500535\",\n \"0.5499876\",\n \"0.54814476\",\n \"0.5480102\",\n \"0.5476696\",\n \"0.54706466\",\n \"0.54668593\",\n \"0.54622704\",\n \"0.5459552\",\n \"0.5458548\",\n \"0.5455434\",\n \"0.54476726\",\n \"0.5441773\",\n \"0.5437488\",\n \"0.54363096\",\n \"0.5430931\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7358082"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":886,"cells":{"query":{"kind":"string","value":"Each row of the matrix, let's say the jth row, represents the distance between the other data point from the jth point. This function returns the indexes for the points with the smallest distances with respect to each point represented by that specified row. By row, I mean the 0th dimension. Also notice that this function does not include the target particle, i.e. the diagonal element along the matrix is set to zero."},"document":{"kind":"string","value":"def nearest_points_values_without_self(matrix, num_to_keep):\n\n # Set the diagonal to 0\n np.fill_diagonal(matrix, 0)\n # Get the position for the resulted values\n sort = np.sort(matrix, axis=1)\n\n return sort[:, : num_to_keep]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def nearest_min(dist_matrix):\n # much faster than np.where\n i, j = np.unravel_index(\n np.argmin(dist_matrix), \n dims=dist_matrix.shape\n )\n return i, j","def nearest_points_indexes_with_self(matrix, num_to_keep):\n\n # Set the diagonal to 1\n np.fill_diagonal(matrix, 1)\n # Get the position for the resulted values\n sort_arg = np.argsort(matrix, axis=1)\n\n return sort_arg[:, : num_to_keep]","def nearest_points_indexes_without_self(matrix, num_to_keep):\n\n # Set the diagonal to 0\n np.fill_diagonal(matrix, 0)\n # Get the position for the resulted values\n sort_arg = np.argsort(matrix, axis=1)\n\n return sort_arg[:, : num_to_keep]","def find_min_distance():\n return np.argmin(d)","def smallest_distance(self, clusters):\n i, j = numpy.unravel_index(numpy.argmin(clusters), clusters.shape)\n return clusters[i, j], i, j","def nearest_points_values_with_self(matrix, num_to_keep):\n\n # Set the diagonal to 1\n np.fill_diagonal(matrix, 1)\n # Get the position for the resulted values\n sort = np.sort(matrix, axis=1)\n\n return sort[:, : num_to_keep]","def nearest_neighbour(matrix, start=0):\n path = [start]\n while len(matrix) != len(path):\n matrix[:, start] = numpy.inf\n start = numpy.argmin(matrix[start])\n path.append(start)\n return path","def nearest_cluster(X,c):\n K = np.size(c,0)\n idx = np.zeros((np.size(X,0),1))\n arr = np.empty((np.size(X,0),1))\n for i in range(0,K):\n y = c[i]\n temp = np.ones((np.size(X,0),1))*y\n b = np.power(np.subtract(X,temp),2)\n a = np.sum(b,axis = 1)\n a.resize((np.size(X,0),1))\n arr = np.append(arr, a, axis=1)\n arr = np.delete(arr,0,axis=1)\n idx = np.argmin(arr, axis=1)\n return idx","def nearest_vertex_to(self, point):\n distances = self.distances_to(point)\n idx = np.argmin(distances)\n return idx","def find_min(self):\n\n\n min_x = 1000\n min_y = 1000\n k = len(self.__col_lista)\n for i in range(k):\n x, y = self.__col_lista[i]\n if x < min_x:\n min_x = x\n if y < min_y:\n min_y = y\n return min_x, min_y","def _find_min_pair(pandas_matrix):\n numpy_matrix = pandas_matrix.values\n mins = np.where(numpy_matrix == np.nanmin(numpy_matrix))\n min_col_idx = mins[0][0]\n min_row_idx = mins[1][0]\n (min_col, min_row) = (pandas_matrix.index[min_col_idx], \n pandas_matrix.columns[min_row_idx])\n\n return (min_col, min_row)","def find_closest_index(traj, point):\n\n\t#TODO: vectorise function to receive any length of points.\n\n\tdistances = np.subtract(np.array(point),traj) \n\tdistances = distances.reshape(-1,2)\n\t#distances = distances[~np.isnan(distances)].reshape(-1,2)\n\n\t#print(\"distances\")\n\t#pprint(distances)\n\tdist_array = np.linalg.norm(distances, axis = 1)\n\t#pprint(dist_array)\n\t#dist_array = np.sqrt((distances[:,0]**2)+(distances[:,1]**2)) #array of distances from trajectory to gaze landing point in world. \n\tidx = np.nanargmin(abs(dist_array)) #find smallest difference in pythag distance from 0,0 to get closest point. \n\tdists = distances[idx, :]\n\tdist = dist_array[idx]\n\n\treturn idx#, dists, dist\n\t#return idx","def getclosest_ij(lats,lons,latpt,lonpt):\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\n dist_lon = (lons-lonpt)**2\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\n minindex_lon = dist_lon.argmin()\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index","def getclosest_ij(lats,lons,latpt,lonpt):\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\n dist_lon = (lons-lonpt)**2\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\n minindex_lon = dist_lon.argmin()\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index","def find_closest(distances, threshold):\n n = len(distances)\n person_1 = []\n person_2 = []\n d = []\n\n for i in range(n):\n for j in range(i+1, n):\n if distances[i][j] <= threshold:\n person_1.append(i)\n person_2.append(j)\n d.append(distances[i][j])\n\n return person_1, person_2, d","def getRowHeuristics(matrix):\n row, col = matrix.shape\n rHeuristic = np.zeros((row,2)) # Dos columnas. La primera para indicar la columna la segunda para la Heuristica\n for i in range(0,row):\n rHeuristic[i,0] = int(i)\n #print (i,sum(matrix[:,i]), pesos[i], float(pesos[i]/sum(matrix[:,i])))\n rHeuristic[i,1] = 1/sum(matrix[i,:])\n return rHeuristic[rHeuristic[:,1].argsort()]","def nearestNeighbor(self, coords, my_index, blacklist):\n min_index, min_distance = 0, 999999999\n my_col, my_row = coords[my_index]\n for i, (col, row) in enumerate(coords):\n if i in blacklist:\n continue\n distance = math.sqrt((my_col-col)**2 + (my_row-row)**2)\n if distance < min_distance:\n min_index, min_distance = i, distance\n return min_index","def _nearest_cluster_distance(distances_row, labels, i):\n label = labels[i]\n b = np.min([np.mean(distances_row[labels == cur_label])\n for cur_label in set(labels) if not cur_label == label])\n return b","def find_closest_trajectory_pose(self):\n np_state = numpy.array([[self.x], [self.y]])\n temp_distance = numpy.sum(\n (self.np_trajectory[0:2, :] - np_state) ** 2,\n axis=0)\n best_idx = numpy.argmin(temp_distance)\n return best_idx","def findknn(xTr,xTe,k):\n\n # YOUR CODE HERE\n if k > len(xTr):\n k = len(xTr)\n \n D=l2distance(xTe, xTr)\n (m,n) = D.shape\n \n indices = []\n dists = []\n for i in range(m):\n smallest_indices = np.argsort(D[i])\n ind = smallest_indices[:k]\n dis = D[i,smallest_indices[:k]]\n indices.append(ind)\n dists.append(dis)\n \n indices = np.transpose(np.array(indices))\n dists = np.transpose(np.array(dists))\n return indices, dists","def getNearestSampleIndex(test, trainX):\n dist_matrix = test - trainX\n dist_square = dist_matrix ** 2\n dist_sums = dist_square.sum(axis=1)\n distance_vector = np.sqrt(dist_sums)\n return (distance_vector).argmin()","def _first_index_with_smaller_neighbour(P):\n i = len(P) - 1\n while i > 0 and P[i-1] <= P[i]:\n i -= 1\n return i","def saddle_points(matrix):\n if not all(len(row) == len(matrix[0]) for row in matrix[1:]):\n raise ValueError('Provided matrix is irregular.')\n columns = [col for col in zip(*matrix)]\n points = set()\n for ridx, row in enumerate(matrix):\n for cidx, element in enumerate(row):\n if element == max(row) and element == min(columns[cidx]):\n points.add((ridx, cidx))\n return points","def _compute_euclidean_neigh_matrix(src, d_matrix, radius):\n\n n_max = 100\n n_min = 3\n reached_points = np.array([0])\n counter = 0\n n_neigh = []\n list_neigh = []\n\n while counter < reached_points.shape[0] < src.shape[0]:\n P = reached_points[counter]\n aux = np.array(sorted(\n np.where(d_matrix[P] <= radius)[0],\n key=lambda k: d_matrix[P, k]))\n n_neigh.append(aux.shape[0])\n\n # Check the number of neighbours\n if n_neigh[-1] < n_min:\n raise ValueError('Computation of neighbours aborted since '\n 'their minimum number is too small.\\n'\n 'Please choose a higher radius.')\n elif n_neigh[-1] > n_max:\n raise ValueError('Computation of neighbours aborted since'\n 'their maximum number is too big.\\n'\n 'Please choose a lower radius.')\n list_neigh.append(aux)\n reached_points = np.append(reached_points,\n aux[~np.in1d(aux, reached_points)])\n counter += 1\n\n if counter >= reached_points.shape[0]:\n raise ValueError('Too small value of the radius:'\n 'the neighbour-matrix is not connected')\n elif src.shape[0] == reached_points.shape[0]:\n while counter < src.shape[0]:\n P = reached_points[counter]\n aux = np.array(sorted(\n np.where(d_matrix[P] <= radius)[0],\n key=lambda k: d_matrix[P, k]))\n n_neigh.append(aux.shape[0])\n\n if n_neigh[-1] < n_min:\n raise ValueError('Computation of neighbours aborted since '\n 'their minimum number is too small.\\n'\n 'Please choose a higher radius.')\n elif n_neigh[-1] > n_max:\n raise ValueError('Computation of neighbours aborted since'\n 'their maximum number is too big.\\n'\n 'Please choose a lower radius.')\n\n list_neigh.append(aux)\n counter += 1\n\n n_neigh_max = max(n_neigh)\n n_matrix = np.zeros([src.shape[0],\n n_neigh_max], dtype=int) - 1\n for i in range(src.shape[0]):\n n_matrix[i, 0:list_neigh[i].shape[0]] = list_neigh[i]\n index_ord = np.argsort(n_matrix[:, 0])\n n_matrix = n_matrix[index_ord]\n return n_matrix\n else:\n raise RuntimeError(\"Some problems during\"\n \"computation of neighbours.\")","def min_distance_vertex(distance, visited):\n vertices = len(visited)\n min_distance = INF\n min_index = None\n for v in range(vertices):\n if not visited[v] and distance[v] <= min_distance:\n min_distance = distance[v]\n min_index = v\n return min_index","def shortestDistance(self, grid):\n # return self.house_oriented_TLE(grid)\n # One axis\n row_count = [sum(row) for row in grid]\n col_count = [0]* len(grid[0])\n row_dist = [0]* len(grid)\n col_dist = [0]* len(grid[0])\n output = sys.maxsize\n for i in range(len(grid)): \n for j in range(len(grid[0])):\n col_count[j] += grid[i][j]\n \n for index_p in range(len(row_count)):\n for index_h in range(len(row_count)):\n row_dist[index_p] += abs(index_h - index_p) * row_count[index_h]\n \n for index_p in range(len(col_count)):\n for index_h in range(len(col_count)):\n col_dist[index_p] += abs(index_h - index_p) * col_count[index_h]\n \n # print(row_count)\n # print(col_count)\n # print(row_dist)\n # print(col_dist)\n for i in range(len(grid)):\n for j in range(len(grid[0])):\n if grid[i][j] == 1:\n continue\n output = min(output, row_dist[i] + col_dist[j])\n return output","def closest_points_naive(self, x, y):\r\n # Running time: O(n ** 2)\r\n\r\n dist = []\r\n for i in range(len(x)):\r\n for j in range(i+1, len(x)):\r\n d = self.get_euclidean_distance(x[i], x[j], y[i], y[j])\r\n dist.append(d)\r\n \r\n return min(dist)","def find_min_point(points):\r\n smallest_point_index = 0\r\n for i in range(1, len(points)):\r\n if points[i][1] < points[smallest_point_index][1]:\r\n smallest_point_index = i\r\n elif points[i][0] > points[smallest_point_index][0] and points[i][1] == points[smallest_point_index][1]:\r\n smallest_point_index = i\r\n return smallest_point_index","def _findMin(p, A):\n\n m=(-1, (0,0))\n for p0 in A:\n dist = np.linalg.norm(p0-np.array(p))\n if m[0]==-1 or m[0]>dist:\n m = (dist, p0)\n \n return tuple(m[1])","def find_nearest_neighbors_idx(X, x, k):\n ## homework:start\n result = \n ## homework:end\n return result","def get_idx(lons, lats, lon, lat):\n dist = ((lons - lon) ** 2 + (lats - lat) ** 2) ** 0.5\n return np.unravel_index(dist.argmin(), dist.shape)","def get_min_dist(x0, y0, arr):\n dist = np.hypot(arr.T[0] - x0, arr.T[1] - y0)\n min_dist = np.min(dist)\n val = np.argmin(dist)\n return min_dist, arr[val]","def dist(dm, sm, neighbors):\n\n # Initialize list of possible distances\n distances = []\n\n # loop over all neighbors of the cell\n for neighbor in neighbors:\n # If the neighbor is valid\n if dm[neighbor[0], neighbor[1]] != -1:\n # add neighbor distance + 1 to possible distances\n distances.append(dm[neighbor[0], neighbor[1]] + 1)\n\n # return minimal distance\n return np.min(distances)","def nodes_min_energy_index(self, node):\n idx = -1\n curr_energy = np.inf\n for i in range(self.cost_matrix.shape[1]):\n new_energy = self.cost_matrix[node][i]\n if new_energy < curr_energy:\n curr_energy = new_energy\n idx = i\n return idx","def pair_idx(rows, comm=None):\n raise Exception(\"Not implemented\")\n \n if comm == None:\n comm = MPI.COMM_WORLD\n \n total = comb(rows,2,exact=True)\n size = comm.Get_size()\n \n size = 1000\n \n print(total / size)\n \n target = total / size\n \n current_row = 0\n calc_list = []\n row_list = [[] for x in range(size)]\n for rank in range(size):\n row_list[rank].append(current_row)\n \n current_calcs = 0\n \n for value in range(current_row, rows):\n current_calcs += value\n if current_calcs > target:\n if rank == size-1:\n pass\n else:\n break\n \n calc_list.append(current_calcs)\n row_list[rank].append(value)\n current_row = value\n \n return row_list,calc_list","def get_maxmin_index_from_row(\n distance_matrix: np.ndarray,\n row: int,\n previous_indexes: List,\n type: str,\n )-> int:\n distance_matrix = distance_matrix.copy()\n arr = distance_matrix[row].astype(float)\n \n aux_list = range(arr.shape[0])\n aux_list_2 = []\n for i in aux_list:\n if i in previous_indexes:\n aux_list_2.append(True)\n else:\n aux_list_2.append(False)\n previous_indexes_bool = aux_list_2\n \n if type == 'max':\n arr[previous_indexes_bool] = -1\n target_index = np.argmax(arr)\n if type == 'min':\n arr[previous_indexes_bool] = np.Inf\n target_index = np.argmin(arr)\n \n return target_index","def get_nearest_atom_inds_per_mol(self):\n self.closest_at_per_mol = np.zeros((self.nmol,\n self.at_per_mol,\n self.at_per_mol-1), dtype=int)\n\n # Get and sort distances\n all_at_inds = np.arange(self.at_per_mol)\n for imol in range(self.nmol):\n for iat in range(self.at_per_mol):\n at_inds = all_at_inds[all_at_inds != iat]\n dist = self.all_dist_per_mol[imol, iat, at_inds]\n\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\n self.closest_at_per_mol[imol, iat] = at_inds","def distance_matrix(n_row, n_col):\n\n n_pop = int(n_row * n_col)\n center = int(n_row/2*(n_col+1))\n\n pop_idx = np.arange(n_pop)\n pop_idx_col = np.remainder(pop_idx, n_col)\n pop_idx_row = pop_idx // n_row\n\n pos = np.vstack((pop_idx_col,pop_idx_row)).T\n distance = spa.distance.cdist([pos[center]], pos)[0]\n\n return distance","def find_min(self, A, w):\n import numpy as np\n\n vcost = self.INFINITY\n vto = vfrom = -1\n for v in w:\n # Get array offset of minimum of this vertex\n i = np.argmin(A[v,:])\n if A[v,i] < vcost:\n vcost = A[v,i]\n vto = i\n vfrom = v\n return (vfrom, vto, vcost)","def find_nearest_neighbours_SURF(averageDistance,inst,distanceArray,maxInst): \r\n NN=[]\r\n min_indices=[] \r\n\r\n for j in range(maxInst):\r\n if inst != j:\r\n locator = [inst,j]\r\n locator = sorted(locator, reverse=True) #Access corect half of table (result of removed table redundancy)\r\n d = distanceArray[locator[0]][locator[1]]\r\n if d<averageDistance:\r\n min_indices.append(j)\r\n \r\n for j in range(len(min_indices)):\r\n NN.append(min_indices[j])\r\n \r\n return NN","def min(weightData , dataSetVector ):\r\n # weightData: pass the whole weightData array.\r\n # dataSetVector: pass the a data vector to compare with weightdata array, to find its closest match\r\n winnerIndex = 0 #flag for initalizing the winner index\r\n minValue = EcuDist(dataSetVector,weightData[0]) # initalize the minValue\r\n # iterate through all weighdata rows to find the closest match, depending on ecu. distance,\r\n #and then return the index of the closest match(winner)\r\n for i in range(weightData.shape[0]):\r\n if(EcuDist(dataSetVector,weightData[i]) < minValue):\r\n minValue = EcuDist(dataSetVector,weightData[i])\r\n winnerIndex = i\r\n return winnerIndex","def get_nearest_atom_inds(self):\n # Create empty data structure\n self.closest_ats = np.zeros((self.natom, self.natom-1), dtype=int)\n\n # Get and sort distances\n all_at_inds = np.arange(self.natom)\n for iat in range(self.natom):\n at_inds = all_at_inds[all_at_inds != iat]\n dist = self.all_dist[iat, at_inds]\n\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\n self.closest_ats[iat] = at_inds","def find_nearest_neighbors(p, points, k=5):\n dist = np.zeros(points.shape[0])\n for i in range(len(dist)):\n dist[i] = distance(p, points[i])\n ind = np.argsort(dist)\n return ind[0:k]","def update_distmatrix(min_idx, dist_matrix):\n i, j = min_idx\n new_cluster_dist = np.minimum(dist_matrix[i, :], dist_matrix[j, :])\n new_cluster_dist[i] = np.inf\n \n dist_matrix[i, :] = new_cluster_dist\n dist_matrix[:, i] = new_cluster_dist\n \n dist_matrix[j, :] = np.inf\n dist_matrix[:, j] = np.inf\n \n return dist_matrix","def getNeighbors(training_data, test_row, k):\n\n distances = list()\n for training_row in training_data:\n dist = euclidianDistance(training_row, test_row)\n distances.append([training_row, dist])\n \n #Sort on the basis of dist\n distances.sort(key=lambda row:row[1])\n\n neighbors = list()\n\n for i in range(int(k)):\n neighbors.append(distances[i][0])\n\n return neighbors","def get_nearest_node_index(node_list, random_node):\n\n dist_list = [\n (node.x - random_node.x) ** 2 + (node.y - random_node.y) ** 2\n for node in node_list\n ]\n minind = dist_list.index(min(dist_list))\n\n return minind","def findSmallest(distancesWithNames):\n smallest = distancesWithNames[0][2]\n smallestIndex = -1\n for i in range(len(distancesWithNames)):\n if smallest >= distancesWithNames[i][2]:\n smallest = distancesWithNames[i][2]\n smallestIndex = i\n return smallestIndex","def closestCluster(p, centers):\n\tbestIndex = 0\n closest = float(\"+inf\")\n for i in range(len(centers)):\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\n if distance < closest:\n closest = distance\n bestIndex = i\n\treturn bestIndex","def smallest_cert(pts):\n # 1. project the points\n def projected_flattened_pt(pt):\n mag = distance(pt)\n distance_wanted = rect_dist\n return distance_wanted/mag*pt[1], \\\n distance_wanted/mag*pt[2]\n\n flat_pts = np.array([[projected_flattened_pt(pt), pt] for pt in pts])\n\n # 2. identify rows of data\n data = {} \n # maps height (from ground) to the \"row\" (scan) list of\n # horizontal-only (1D) data\n for pt, origpt in flat_pts:\n for row_height in data:\n if abs(pt[1]-row_height) < dist_bt_rows/2:\n data[row_height].append([pt[0], origpt])\n break\n else:\n data[pt[1]] = [[pt[0], origpt]]\n\n #3. build up final_data so each row has enough point x-density\n def get_left_i(pts, left_edge):\n # get last index <= left_edge\n for i, pt in enumerate(pts):\n if pt[0] > left_edge:\n return i - 1\n\n def get_right_i(pts, right_edge):\n # get first index >= right_edge\n i = len(pts)-1\n while True:\n pt = pts[i]\n if pt[0] < right_edge:\n return i + 1\n i -= 1\n\n final_data = []\n for _, subdata in data.items():\n subdata = sorted(subdata, key=lambda t: t[0])\n i = get_left_i(subdata, lane_left)\n end = get_right_i(subdata, lane_right)\n\n final_data = [subdata[i][1]]\n while i < end:\n next_i = get_left_i(subdata[i:], \n subdata[i][0]+max_xpt_separation)\n if not next_i:\n return False\n i += next_i\n final_data.append(subdata[i][1])\n\n return final_data","def get_min_shannon_entropy(grid):\r\n curr_min = math.inf\r\n curr_best = []\r\n for i in range(len(grid[0])):\r\n for j in range(len(grid)):\r\n if not grid[j][i].collapsed:\r\n w = grid[j][i].block_weights\r\n shannon_entropy = sum([-math.log(el) for el in w] )\r\n if shannon_entropy < curr_min:\r\n curr_min = shannon_entropy\r\n curr_best = [(i,j)]\r\n elif shannon_entropy == curr_min:\r\n curr_best.append((i,j))\r\n idx = np.random.choice(range(len(curr_best))) #choose randomly if theres a tie\r\n return curr_best[idx] #x,y\r","def detec_mfo_dist(betas):\n k = len(betas)\n min_dist = np.inf\n for i in range(k-1):\n for j in range(i+1,k):\n distance = np.sum((betas[i,:] - betas[j,:])**2)\n if distance < min_dist:\n MFO_index = [i,j]\n min_dist = distance\n return MFO_index","def _lowestDistanceToCluster(self, clusters: ndarray, sequenceIdx: int) -> Tuple[int, float]:\n lowestClusterIdx = -1\n lowestDistance = np.inf\n for cIdx in range(self.numClusters):\n distance = self._distanceToCluster(clusters[cIdx], sequenceIdx)\n if distance < lowestDistance:\n lowestClusterIdx = cIdx\n lowestDistance = distance\n return lowestClusterIdx, lowestDistance","def eeg_findnearest(x,X):\t\n\t#x array or vector and X a scalar\n\tabsdif = np.abs(x-X)\n\tval = np.min(absdif)\n\tidx = absdif.argmin()\n\treturn val,idx","def get_index_under_point(self, event):\r\n xy = np.asarray(list(zip(self.xs, self.ys)))\r\n xyt = self.line.get_transform().transform(xy)\r\n xt, yt = xyt[:, 0], xyt[:, 1]\r\n d = np.sqrt((xt - event.x) ** 2 + (yt - event.y) ** 2)\r\n pt_idx = np.argmin(d)\r\n if d[pt_idx] >= self.max_pixels_from_vertex:\r\n pt_idx = None\r\n return pt_idx","def _kth_nearest_neighbor_dist(\n distance_matrix: Union[np.ndarray, scipy.sparse.csr_matrix], k\n) -> np.ndarray:\n\n if not is_integer(k):\n raise ValueError(f\"parameter 'k={k}' must be a positive integer\")\n else:\n # make sure we deal with Python built-in\n k = int(k)\n\n if not (0 <= k <= distance_matrix.shape[1]):\n raise ValueError(\n \"'k' must be an integer between 1 and \"\n f\"distance_matrix.shape[1]={distance_matrix.shape[1]}\"\n )\n\n if isinstance(distance_matrix, np.ndarray):\n dist_knn = np.partition(distance_matrix, k - 1, axis=1)[:, k - 1]\n elif isinstance(distance_matrix, scipy.sparse.csr_matrix):\n # see mircobenchmark_kth_nn.py for a comparison of implementations for the\n # sparse case\n\n def _get_kth_largest_elements_sparse(\n data: np.ndarray,\n indptr: np.ndarray,\n row_nnz,\n k_neighbor: int,\n ):\n dist_knn = np.zeros(len(row_nnz))\n for i in range(len(row_nnz)):\n start_row = indptr[i]\n dist_knn[i] = np.partition(\n data[start_row : start_row + row_nnz[i]], k_neighbor - 1\n )[k_neighbor - 1]\n\n return dist_knn\n\n row_nnz = distance_matrix.getnnz(axis=1)\n\n if (row_nnz < k).any():\n raise ValueError(\n f\"There are {(row_nnz < k).sum()} points that \"\n f\"do not have at least k_neighbor={k}.\"\n )\n\n dist_knn = _get_kth_largest_elements_sparse(\n distance_matrix.data,\n distance_matrix.indptr,\n row_nnz,\n k,\n )\n else:\n raise TypeError(f\"type {type(distance_matrix)} not supported\")\n\n return dist_knn","def closestCentroids(self, points , centroids ):\n dists = scipy.spatial.distance.cdist(points,centroids)\n # 1 is dimension\n minIds = numpy.argmin(dists, 1)\n return minIds","def closestClusterAndDistance(p, centers):\n\tbestIndex = 0\n closest = float(\"+inf\")\n for i in range(len(centers)):\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\n if distance < closest:\n closest = distance\n bestIndex = i\n return (bestIndex, closest)","def get_nearest_indices(vector, matrix, num=5):\n if len(matrix) < num:\n num = len(matrix)\n\n m, n = matrix.shape\n diff_matrix = np.tile(vector, (m, 1)) - matrix\n diff_matrix = abs(diff_matrix)\n\n distance = diff_matrix.sum(axis=1)\n sortIndices = np.argsort(distance)\n sortIndices = sortIndices[0:num]\n\n return sortIndices[random.randint(0, num - 1)]","def exhaustive_search(X, z):\n #initialize shortest path and distance to the first row\n d_star = la.norm(z - X[0])\n x_star = X[0]\n #check the distance from each row of X and if shortest thus far, save it\n for i in range(X.shape[0]):\n #calculate distance from current row to target z\n x = X[i,:]\n cur_dist = la.norm(x - z)\n #if the distance is smallest thus far, save the row and distance\n if cur_dist < d_star:\n x_star = x\n d_star = cur_dist\n return x_star, d_star","def find_min_path(s, t, dist):\n\n rows = len(dist) - 1\n cols = len(dist[0]) - 1\n col = cols\n row = rows\n pos_str = \"Position: (row={} col={}) -> (row={} col={})\"\n cst_str = \"Cost: {}\"\n prev_row = row\n prev_col = col\n\n # init sparse path matrix\n sparse_path = [[\" \" for x in range(cols + 1)] for x in range(rows + 1)]\n sparse_path[0][0] = \"0\"\n\n # start with operation at (rows, cols) and work backwards\n sparse_path[rows][cols] = dist[rows][cols]\n\n if verbose == 2:\n print()\n print(\"Initial Minimum Path Matrix:\")\n print_matrix(s, t, sparse_path)\n\n while True:\n\n # bail out if we are in the corner\n if row == 0 and col == 0:\n break\n\n # if we are not at a matrix boundary\n if row != 0 and col != 0: # if at left edge or top row, cannot move diagonally\n\n # diagonal\n if (dist[row - 1][col - 1] == min(dist[row - 1][col],\n dist[row][col - 1],\n dist[row - 1][col - 1])) and (dist[row - 1][col - 1] == dist[row][col] or dist[row - 1][col - 1] == dist[row][col] - 1):\n sparse_path[row - 1][col - 1] = dist[row - 1][col - 1]\n temp_cost = dist[row - 1][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # left\n elif dist[row][col - 1] <= dist[row][col]:\n sparse_path[row][col - 1] = dist[row][col - 1]\n temp_cost = dist[row][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # above\n else:\n sparse_path[row - 1][col] = dist[row - 1][col]\n temp_cost = dist[row - 1][col]\n\n # move current cell\n prev_row = row\n prev_col = col\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # if at matrix edge, can only move up\n elif col == 0:\n # above\n sparse_path[row - 1][col] = dist[row - 1][col]\n temp_cost = dist[row - 1][col]\n\n # move current cell\n prev_row = row\n prev_col = col\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # must be at row boundary, can only move left\n else:\n # left\n if dist[row][col - 1] <= dist[row][col]:\n sparse_path[row][col - 1] = dist[row][col - 1]\n temp_cost = dist[row][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # print matrix\n if verbose == 2:\n print_matrix(s, t, sparse_path)\n\n return sparse_path","def find_closest_atom(coords1, coords2):\n\n coords1 = np.array(coords1)\n coords2 = np.array(coords2)\n diff = coords2[:, np.newaxis] - coords1[np.newaxis, :]\n dist = np.einsum('ijk->ij', diff**2)**0.5\n index = np.argmin(dist)\n return index","def find_neighbor_indices(atoms, probe, k):\r\n coords_all = atoms.getCoords()\r\n neighbor_indices = []\r\n atom_k = atoms[k]\r\n radius = atom_k.getRadius() + probe + probe\r\n\r\n indices = range(k)\r\n indices.extend(range(k+1, len(atoms)))\r\n\r\n for i in indices:\r\n\tdist = pos_distance(coords_all[k], coords_all[i])\r\n\t#dist = np.linalg.norm(coords_all[k] - coords_all[i])\r\n\tif dist < radius + atoms[i].getRadius():\r\n neighbor_indices.append(i)\r\n\r\n return neighbor_indices","def djikstre(connection_mat):\n n = connection_mat.shape[0]\n dist, prev = {}, {}\n Q = list(range(n))\n \n for i in Q:\n dist[i] = np.inf\n dist[n-2] = 0.0\n \n while(len(Q)>0):\n\n min_dist = min([dist[key] for key in Q])\n u = [key for key in Q if dist[key] == min_dist][0]\n Q.remove(u)\n\n for v in np.nonzero(connection_mat[:, u])[0]:\n \n alt = dist[u]+connection_mat[v, u]\n \n if alt < dist[v]:\n dist[v] = alt\n prev[v] = u\n \n return dist, prev","def _first_index_with_bigger_neighbour(P):\n i = len(P) - 1\n while i > 0 and P[i-1] >= P[i]:\n i -= 1\n return i","def neirest_neighbour(business, cells):\n array = cells.get_neighbours(business, num=1)\n neighbours = pd.DataFrame(array).set_index('index')\n index = neighbours['distance'].idxmin()\n return neighbours.loc[index]","def _retrieve_neighbors(df, i_point, point, eps, column):\n neigborhood = []\n for index, row in df.iterrows():\n if index != i_point:\n a = np.array(point[column])\n b = np.array([row[column]])\n distance = np.linalg.norm(a - b)\n if distance <= eps:\n neigborhood.append(index)\n\n return neigborhood","def find_nearest_neighbors(p, points, k):\n\timport numpy as np\n\tdistances = np.zeros(points.shape[0])\n\tfor i in range(len(distances)):\n\t\tdistances[i] = distance(p,points[i])\n\tind = np.argsort(distances)\n\treturn ind[0:k]","def get_nearest_row(self):\n return (self.rect.top - (self.screen.get_height() // 12)) // self.maze.block_size","def find_nearest_neighbour_from_point(point_cloud:np.ndarray, point:int) -> int:\n pass","def _nearest_point_index(points, point):\n distance = sys.float_info.max\n index = None\n for i, p in enumerate(points):\n temp = _vec_distance(p, point)\n if temp < distance:\n distance = temp\n index = i\n return index, distance","def nearest_neighbor(data):\n features = set([i for i, x in enumerate(data[0][1])])\n return leave_one_out_cross_validation(data, features)","def shortestpathij(self, i, j):\n pathlist = []\n self.pathij(i, j, pathlist)\n distance = []\n \n for i in range(len(pathlist)):\n Temp = 0\n for j in range(len(pathlist[i]) - 1):\n Temp += self.Dismatrix[pathlist[i][j], pathlist[i][j+1]]\n distance.append(Temp)\n \n if(len(distance) == 0):\n return None\n else:\n return min(distance)","def get_nearest_neighbours(self, cell):\n\t\tneighs = self.get_neighbours(cell)\n\t\ti, j = cell.find_id()\n\t\tneighbours = []\n\t\tfor neigh in neighs:\n\t\t\tx, y = neigh.find_id()\n\t\t\tif abs(x-i)+abs(y-j) <= 1: \n\t\t\t\tneighbours.append(self.space[y,x])\n\t\treturn neighbours","def closest_node(node, nodes):\n nodes = np.asarray(nodes)\n deltas = nodes - node\n dist_2 = np.einsum(\"ij,ij->i\", deltas, deltas)\n return np.argmin(dist_2), np.min(dist_2)","def extract_min(H, ds):\n minDist = approxInf\n u = None # min vertex unknown\n i = 0\n for v in H:\n if ds[v] <= minDist:\n minDist = ds[v]\n u = v # note that u is unused (instead returned by pop)\n imin = i\n i += 1\n return(H.pop(imin)) # return [u, d]","def min_distance(s1, s2):\n n = len(s1)\n m = len(s2)\n matrix = [([0]*(m+1)) for i in xrange(n+1)]\n for i in xrange(m+1):\n matrix[0][i] = i\n for i in xrange(n+1):\n matrix[i][0] = i\n for i in xrange(1,n+1):\n for j in xrange(1,m+1):\n temp = min(matrix[i-1][j]+1, matrix[i][j-1]+1)\n d = 0 if s1[i-1]==s2[j-1] else 1\n matrix[i][j] = min(temp, matrix[i-1][j-1]+d)\n return matrix[n][m]","def getNearestLineIndex(row, tagLineNumbers):\n # DOC {{{\n # }}}\n\n # CODE {{{\n # initialize local auxiliary variables {{{\n nearestLineNumber = -1\n nearestLineIndex = -1\n # }}}\n\n # go through all tag line numbers and find the one nearest to the specified row {{{\n for lineIndex, lineNumber in enumerate(tagLineNumbers):\n # if the current line is nearer the current cursor position, take it {{{\n if (nearestLineNumber < lineNumber <= row):\n nearestLineNumber = lineNumber\n nearestLineIndex = lineIndex\n # }}}\n\n # if we've got past the current cursor position, let's end the search {{{\n if (lineNumber >= row):\n break\n # }}}\n # }}}\n\n # return index of the line with the nearest tag\n return nearestLineIndex\n # }}}","def closest(self, x, y):\n if self.direction == 'horizontal':\n p_pts = np.array([\n self.ax.transData.transform((p, 0))[0] for p in self.positions\n ])\n dist = abs(p_pts - x)\n else:\n p_pts = np.array([\n self.ax.transData.transform((0, p))[1] for p in self.positions\n ])\n dist = abs(p_pts - y)\n index = np.argmin(dist)\n return index, dist[index]","def _low_tri_indices(rowCount):\n for col in range(rowCount):\n for row in range(col, rowCount):\n yield (row, col)","def findNearset(x,y,lon,lat):\n dist = np.sqrt( (lon - x)**2 + (lat - y)**2)\n\n return np.argwhere(dist==dist.min())[0][0]","def min_horizontal_dist_meters(coords, targets, is_geo=False):\n xe = coords[:, 0]\n ye = coords[:, 1]\n n = len(xe)\n d = np.zeros(n)\n for j in range(n):\n d1 = dist_in_meters(targets, [xe[j], ye[j]], is_geo=is_geo)\n d[j] = d1.min()\n return d","def findminpath(tab, gxtab, gytab, pixtab):\n\n pathdist = 2 # the number of points each points on a ray can related to on the previous ray\n pathdist_penalty = 0.3 # penalty of the difference of the pathdist\n pathpix_penalty = 2 # penalty of the difference of pixel values between the point and the previous point\n nray = tab.shape[1]\n\n #tab = np.hstack((tab,tab[:, 0].reshape(tab.shape[0], 1)))\n #pixtab = np.hstack((pixtab,pixtab[:, 0].reshape(pixtab.shape[0], 1)))\n #gxtab = np.hstack((gxtab,gxtab[:, 0].reshape(gxtab.shape[0], 1)))\n #gytab = np.hstack((gytab,gytab[:, 0].reshape(gytab.shape[0], 1)))\n\n tab = np.hstack((tab,tab,tab)) # horizontally stack the tab matrix to prepare for the filtering on the result\n pixtab = np.hstack((pixtab,pixtab,pixtab))\n gxtab = np.hstack((gxtab,gxtab,gxtab))\n gytab = np.hstack((gytab,gytab,gytab))\n\n tab = (tab - tab.min()) / (tab.max() - tab.min()) # noralize the tab matrix\n pixtab = (pixtab - pixtab.min()) / (pixtab.max() - pixtab.min()) * -1 # for we want to find the white contour of the cell so we multipy -1 on the pixtab\n # tab = tab / np.median(tab)\n # pixtab = pixtab / np.median(pixtab)\n path = np.zeros(tab.shape)\n path[:, 0] = np.array(range(0, tab.shape[0]))\n score = np.zeros(tab.shape)\n score[:, 1] = tab[:, 1]\n\n for i in range(1, tab.shape[1]):\n for j in range(tab.shape[0]):\n mins = np.Inf # record the min value of the ray\n minat = 0\n for k in range(-pathdist, pathdist+1):\n if(0 <= (j+k) and (j+k) < tab.shape[0]):\n s = pixtab[j, i]\n pixdiff = abs(pixtab[j, i] - pixtab[j+k, i-1])\n s += pixdiff * pathpix_penalty # two kinds of penalty\n s += abs(k) * pathdist_penalty\n s += score[j+k, i-1]\n\n if(s < mins):\n mins = s\n minat = j + k\n path[j, i] = minat\n score[j, i]= mins\n\n start = int(np.argmin(score[:, -1]))\n path = path.astype(np.int32)\n minpath = [start]\n for i in range(tab.shape[1]-1, 0, -1):\n minpath.append(path[minpath[-1], i])\n minpath = minpath[::-1]\n # print(len(minpath))\n minpath = savgol_filter(minpath, 15, 3) # apply a Savitzky-Golay filter to the raw minpath signal\n minpath = minpath[nray:nray*2] # cut the middle part of minpath whose length is nray\n return np.array(minpath).astype(np.int32)","def nearest_sparse(self, query):\n self.best_dist = float(\"inf\")\n self.best_element = None\n self._register_best_element = self._register_best_element_single \n self._nearest_sparse_recursive(self._sparse2seq(query), self.root, 0.0)\n return self.best_element,self.best_dist","def find_all_nearest_neighbours(point_cloud:np.ndarray) -> np.ndarray:\n pass","def numNeighbors(minesSet, row_index, cols_index, num_cols, num_rows):\n mines = 0\n for j in np.arange(max(0, cols_index-1), min(num_cols-1, cols_index+1)+1):\n for i in np.arange(max(0, row_index-1), min(num_rows-1, row_index+1)+1):\n if ((i, j) in minesSet):\n mines+=1\n return mines","def _intra_cluster_distance(distances_row, labels, i):\n mask = labels == labels[i]\n mask[i] = False\n if not np.any(mask):\n # cluster of size 1\n return 0\n a = np.mean(distances_row[mask])\n return a","def closest_waypoint(self, location: pylot.utils.Location):\n min_dist = np.infty\n min_index = 0\n for index, waypoint in enumerate(self.waypoints):\n dist = waypoint.location.distance(location)\n if dist < min_dist:\n min_dist = dist\n min_index = index\n return min_index","def __find_immediate_predecessors(storm_object_table, target_row):\n\n error_checking.assert_is_integer(target_row)\n error_checking.assert_is_geq(target_row, 0)\n error_checking.assert_is_less_than(\n target_row, len(storm_object_table.index)\n )\n\n predecessor_sec_id_strings = [\n storm_object_table[c].values[target_row]\n for c in PREV_SECONDARY_ID_COLUMNS\n if storm_object_table[c].values[target_row] != ''\n ]\n\n num_predecessors = len(predecessor_sec_id_strings)\n if num_predecessors == 0:\n return numpy.array([], dtype=int)\n\n target_time_unix_sec = storm_object_table[\n tracking_utils.VALID_TIME_COLUMN].values[target_row]\n\n predecessor_rows = numpy.full(num_predecessors, -1, dtype=int)\n\n for i in range(num_predecessors):\n these_rows = numpy.where(numpy.logical_and(\n storm_object_table[tracking_utils.SECONDARY_ID_COLUMN].values ==\n predecessor_sec_id_strings[i],\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values <\n target_time_unix_sec\n ))[0]\n\n if len(these_rows) == 0:\n continue\n\n this_subrow = numpy.argmax(\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values[\n these_rows]\n )\n\n predecessor_rows[i] = these_rows[this_subrow]\n\n return predecessor_rows[predecessor_rows >= 0]","def matrix_idx(n_hist, n_req, n_rows):\n\n flat_idx = []\n for i in range(n_rows):\n flat_idx.extend(range(i * n_req, (i + 1) * n_req + n_hist))\n # idx = np.unravel_index(flat_idx, (n_rows, n_hist + n_req))\n\n idx_matrix = np.reshape(flat_idx, (n_rows, n_hist + n_req))\n idxX = idx_matrix[:, n_req:]\n idxY = idx_matrix[:, :n_req]\n\n return idxX, idxY","def _partition_nearest(self, medoids, dists, only_these=set()):\n if len(only_these) == 0:\n allowed_inds = self._not_ignored_inds\n else:\n allowed_inds = self._not_ignored_inds & only_these\n closest_medoid_ind = np.argmin(dists[:,medoids], 1) # If len(medoids)==3, would look like [2,1,1,0,1,2,...].\n clusts = [[] for i in medoids]\n for node_ind, med_ind in enumerate(closest_medoid_ind):\n if node_ind in allowed_inds:\n clusts[med_ind].append(node_ind)\n return clusts","def find_closest_pt(ref_lon, ref_lat, tlon, tlat):\n\n # compute great circle distance from location to model grid points\n dist = gc_dist(ref_lon, ref_lat, tlon, tlat)\n\n # find j index of closest grid point\n work = N.take(dist,N.argmin(dist,0),0).diagonal()\n jj = N.argsort(work)[0]\n\n # find i index of closest grid point\n work = N.take(dist,N.argmin(dist,1),1).diagonal()\n ii = N.argsort(work)[0]\n\n return ii, jj","def index_condensed_matrix(n, i, j):\n if i == j:\n main_warning(\"Diagonal elements (i=j) are not stored in condensed matrices.\")\n return None\n elif i > j:\n i, j = j, i\n return int(i * (n - (i + 3) * 0.5) + j - 1)","def nearest_neighbors(self):\n neighbor_distances_and_indices = []\n for idx, data_point in enumerate(self.data):\n distance = self.euclidean_dis(data_point[:-1], self.query) # Calculate the distance between the query\n # example and the current example from the data.\n\n neighbor_distances_and_indices.append((distance, idx)) # Add the distance and the index of the example\n # to an ordered collection\n\n sorted_neighbor_distances_and_indices = sorted(neighbor_distances_and_indices, key=lambda x: x[0]) #\n # Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by\n # the distances\n\n k_nearest_distances_and_indices = sorted_neighbor_distances_and_indices[:self.k] # Pick the first K\n # entries from the sorted collection\n\n k_nearest_labels = [self.data[i][1] for distance, i in k_nearest_distances_and_indices] # Get the labels of\n # the selected K entries\n\n return k_nearest_labels, self.mode(k_nearest_labels)","def closest_centroid(x,centroids):\n\tK =len(centroids)\n\tN = len(x)\n\tDistance = np.zeros((N,K))\n\tfor j in range(K):\n\t\tmu = centroids[j]\n\t\tDistance[:,j] = np.linalg.norm(x-mu,axis=1)\n\tout = np.argmin(Distance,axis=1) \n\treturn out","def medoid_ft(ftvectors,pairwise_dist_matrix):\r\n N = len(ftvectors)\r\n if N == 1 : \r\n return 0 \r\n # pairwise_dist_matrix = calculate_dist_matrix_ft(ftvectors) \r\n sum_dist = np.sum(pairwise_dist_matrix, axis = 0)\r\n min_idx = np.argmin(sum_dist)\r\n return min_idx","def nn_dists(coords, spherical=True):\n\n full_dist_matrix = all_dists(coords, spherical)\n\n # find all minimum distances\n # apply min over ranges of the dist array\n min_dists = np.min(full_dist_matrix, axis=1)\n min_ix = np.argmin(full_dist_matrix, axis=1)\n\n return min_dists, min_ix","def find_indices(colorhs, centres):\n\n indices = np.zeros(colorhs.shape[0], dtype=np.uint8)\n i = 0\n\n for hs in colorhs:\n # Past Euclidian distance\n past_ed = float(\"inf\")\n for cluster in range(centres.shape[0]):\n # Current Euclidian distance\n curr_ed = (sum((hs - centres[cluster, :]) ** 2)) ** 1/2\n # A frame belongs to the cluster with the minimum ed value.\n if curr_ed <= past_ed:\n past_ed = curr_ed\n indices[i] = cluster\n i += 1\n return indices","def minesweeper(matrix):\n \n num_rows = len(matrix)\n num_cols = len(matrix[0])\n \n adj_mines = []\n \n adj_row = [0]*num_cols\n \n for i in range(num_rows):\n adj_mines.append(adj_row[:])\n \n for r in range(num_rows):\n for c in range(num_cols):\n if matrix[r][c] == True:\n if (r-1) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r-1][c-1] += 1\n if (r-1) in range(num_rows) and (c) in range(num_cols):\n adj_mines[r-1][c] += 1\n if (r-1) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r-1][c+1] += 1\n if (r) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r][c-1] += 1\n if (r) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r][c+1] += 1\n if (r+1) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r+1][c-1] += 1\n if (r+1) in range(num_rows) and (c) in range(num_cols):\n adj_mines[r+1][c] += 1\n if (r+1) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r+1][c+1] += 1\n\n \n return adj_mines","def findClosetCentroids(X, centroids):\n\tm, n = X.shape\n\tK = centroids.shape[0]\n\tidx = np.zeros(m) # m\n\n\tfor i in range(m):\n\t\ttemp = np.tile(X[i, :], K).reshape(centroids.shape)\n\t\tidx[i] = np.argmin(np.sum((centroids - temp) ** 2, axis=1))\n\treturn idx","def find_nearest(ref_array,target_array):\n ref_tree = scipy.spatial.cKDTree(ref_array)\n dist, indices = ref_tree.query(target_array, k=1)\n return indices"],"string":"[\n \"def nearest_min(dist_matrix):\\n # much faster than np.where\\n i, j = np.unravel_index(\\n np.argmin(dist_matrix), \\n dims=dist_matrix.shape\\n )\\n return i, j\",\n \"def nearest_points_indexes_with_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 1\\n np.fill_diagonal(matrix, 1)\\n # Get the position for the resulted values\\n sort_arg = np.argsort(matrix, axis=1)\\n\\n return sort_arg[:, : num_to_keep]\",\n \"def nearest_points_indexes_without_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 0\\n np.fill_diagonal(matrix, 0)\\n # Get the position for the resulted values\\n sort_arg = np.argsort(matrix, axis=1)\\n\\n return sort_arg[:, : num_to_keep]\",\n \"def find_min_distance():\\n return np.argmin(d)\",\n \"def smallest_distance(self, clusters):\\n i, j = numpy.unravel_index(numpy.argmin(clusters), clusters.shape)\\n return clusters[i, j], i, j\",\n \"def nearest_points_values_with_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 1\\n np.fill_diagonal(matrix, 1)\\n # Get the position for the resulted values\\n sort = np.sort(matrix, axis=1)\\n\\n return sort[:, : num_to_keep]\",\n \"def nearest_neighbour(matrix, start=0):\\n path = [start]\\n while len(matrix) != len(path):\\n matrix[:, start] = numpy.inf\\n start = numpy.argmin(matrix[start])\\n path.append(start)\\n return path\",\n \"def nearest_cluster(X,c):\\n K = np.size(c,0)\\n idx = np.zeros((np.size(X,0),1))\\n arr = np.empty((np.size(X,0),1))\\n for i in range(0,K):\\n y = c[i]\\n temp = np.ones((np.size(X,0),1))*y\\n b = np.power(np.subtract(X,temp),2)\\n a = np.sum(b,axis = 1)\\n a.resize((np.size(X,0),1))\\n arr = np.append(arr, a, axis=1)\\n arr = np.delete(arr,0,axis=1)\\n idx = np.argmin(arr, axis=1)\\n return idx\",\n \"def nearest_vertex_to(self, point):\\n distances = self.distances_to(point)\\n idx = np.argmin(distances)\\n return idx\",\n \"def find_min(self):\\n\\n\\n min_x = 1000\\n min_y = 1000\\n k = len(self.__col_lista)\\n for i in range(k):\\n x, y = self.__col_lista[i]\\n if x < min_x:\\n min_x = x\\n if y < min_y:\\n min_y = y\\n return min_x, min_y\",\n \"def _find_min_pair(pandas_matrix):\\n numpy_matrix = pandas_matrix.values\\n mins = np.where(numpy_matrix == np.nanmin(numpy_matrix))\\n min_col_idx = mins[0][0]\\n min_row_idx = mins[1][0]\\n (min_col, min_row) = (pandas_matrix.index[min_col_idx], \\n pandas_matrix.columns[min_row_idx])\\n\\n return (min_col, min_row)\",\n \"def find_closest_index(traj, point):\\n\\n\\t#TODO: vectorise function to receive any length of points.\\n\\n\\tdistances = np.subtract(np.array(point),traj) \\n\\tdistances = distances.reshape(-1,2)\\n\\t#distances = distances[~np.isnan(distances)].reshape(-1,2)\\n\\n\\t#print(\\\"distances\\\")\\n\\t#pprint(distances)\\n\\tdist_array = np.linalg.norm(distances, axis = 1)\\n\\t#pprint(dist_array)\\n\\t#dist_array = np.sqrt((distances[:,0]**2)+(distances[:,1]**2)) #array of distances from trajectory to gaze landing point in world. \\n\\tidx = np.nanargmin(abs(dist_array)) #find smallest difference in pythag distance from 0,0 to get closest point. \\n\\tdists = distances[idx, :]\\n\\tdist = dist_array[idx]\\n\\n\\treturn idx#, dists, dist\\n\\t#return idx\",\n \"def getclosest_ij(lats,lons,latpt,lonpt):\\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\\n dist_lon = (lons-lonpt)**2\\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\\n minindex_lon = dist_lon.argmin()\\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index\",\n \"def getclosest_ij(lats,lons,latpt,lonpt):\\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\\n dist_lon = (lons-lonpt)**2\\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\\n minindex_lon = dist_lon.argmin()\\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index\",\n \"def find_closest(distances, threshold):\\n n = len(distances)\\n person_1 = []\\n person_2 = []\\n d = []\\n\\n for i in range(n):\\n for j in range(i+1, n):\\n if distances[i][j] <= threshold:\\n person_1.append(i)\\n person_2.append(j)\\n d.append(distances[i][j])\\n\\n return person_1, person_2, d\",\n \"def getRowHeuristics(matrix):\\n row, col = matrix.shape\\n rHeuristic = np.zeros((row,2)) # Dos columnas. La primera para indicar la columna la segunda para la Heuristica\\n for i in range(0,row):\\n rHeuristic[i,0] = int(i)\\n #print (i,sum(matrix[:,i]), pesos[i], float(pesos[i]/sum(matrix[:,i])))\\n rHeuristic[i,1] = 1/sum(matrix[i,:])\\n return rHeuristic[rHeuristic[:,1].argsort()]\",\n \"def nearestNeighbor(self, coords, my_index, blacklist):\\n min_index, min_distance = 0, 999999999\\n my_col, my_row = coords[my_index]\\n for i, (col, row) in enumerate(coords):\\n if i in blacklist:\\n continue\\n distance = math.sqrt((my_col-col)**2 + (my_row-row)**2)\\n if distance < min_distance:\\n min_index, min_distance = i, distance\\n return min_index\",\n \"def _nearest_cluster_distance(distances_row, labels, i):\\n label = labels[i]\\n b = np.min([np.mean(distances_row[labels == cur_label])\\n for cur_label in set(labels) if not cur_label == label])\\n return b\",\n \"def find_closest_trajectory_pose(self):\\n np_state = numpy.array([[self.x], [self.y]])\\n temp_distance = numpy.sum(\\n (self.np_trajectory[0:2, :] - np_state) ** 2,\\n axis=0)\\n best_idx = numpy.argmin(temp_distance)\\n return best_idx\",\n \"def findknn(xTr,xTe,k):\\n\\n # YOUR CODE HERE\\n if k > len(xTr):\\n k = len(xTr)\\n \\n D=l2distance(xTe, xTr)\\n (m,n) = D.shape\\n \\n indices = []\\n dists = []\\n for i in range(m):\\n smallest_indices = np.argsort(D[i])\\n ind = smallest_indices[:k]\\n dis = D[i,smallest_indices[:k]]\\n indices.append(ind)\\n dists.append(dis)\\n \\n indices = np.transpose(np.array(indices))\\n dists = np.transpose(np.array(dists))\\n return indices, dists\",\n \"def getNearestSampleIndex(test, trainX):\\n dist_matrix = test - trainX\\n dist_square = dist_matrix ** 2\\n dist_sums = dist_square.sum(axis=1)\\n distance_vector = np.sqrt(dist_sums)\\n return (distance_vector).argmin()\",\n \"def _first_index_with_smaller_neighbour(P):\\n i = len(P) - 1\\n while i > 0 and P[i-1] <= P[i]:\\n i -= 1\\n return i\",\n \"def saddle_points(matrix):\\n if not all(len(row) == len(matrix[0]) for row in matrix[1:]):\\n raise ValueError('Provided matrix is irregular.')\\n columns = [col for col in zip(*matrix)]\\n points = set()\\n for ridx, row in enumerate(matrix):\\n for cidx, element in enumerate(row):\\n if element == max(row) and element == min(columns[cidx]):\\n points.add((ridx, cidx))\\n return points\",\n \"def _compute_euclidean_neigh_matrix(src, d_matrix, radius):\\n\\n n_max = 100\\n n_min = 3\\n reached_points = np.array([0])\\n counter = 0\\n n_neigh = []\\n list_neigh = []\\n\\n while counter < reached_points.shape[0] < src.shape[0]:\\n P = reached_points[counter]\\n aux = np.array(sorted(\\n np.where(d_matrix[P] <= radius)[0],\\n key=lambda k: d_matrix[P, k]))\\n n_neigh.append(aux.shape[0])\\n\\n # Check the number of neighbours\\n if n_neigh[-1] < n_min:\\n raise ValueError('Computation of neighbours aborted since '\\n 'their minimum number is too small.\\\\n'\\n 'Please choose a higher radius.')\\n elif n_neigh[-1] > n_max:\\n raise ValueError('Computation of neighbours aborted since'\\n 'their maximum number is too big.\\\\n'\\n 'Please choose a lower radius.')\\n list_neigh.append(aux)\\n reached_points = np.append(reached_points,\\n aux[~np.in1d(aux, reached_points)])\\n counter += 1\\n\\n if counter >= reached_points.shape[0]:\\n raise ValueError('Too small value of the radius:'\\n 'the neighbour-matrix is not connected')\\n elif src.shape[0] == reached_points.shape[0]:\\n while counter < src.shape[0]:\\n P = reached_points[counter]\\n aux = np.array(sorted(\\n np.where(d_matrix[P] <= radius)[0],\\n key=lambda k: d_matrix[P, k]))\\n n_neigh.append(aux.shape[0])\\n\\n if n_neigh[-1] < n_min:\\n raise ValueError('Computation of neighbours aborted since '\\n 'their minimum number is too small.\\\\n'\\n 'Please choose a higher radius.')\\n elif n_neigh[-1] > n_max:\\n raise ValueError('Computation of neighbours aborted since'\\n 'their maximum number is too big.\\\\n'\\n 'Please choose a lower radius.')\\n\\n list_neigh.append(aux)\\n counter += 1\\n\\n n_neigh_max = max(n_neigh)\\n n_matrix = np.zeros([src.shape[0],\\n n_neigh_max], dtype=int) - 1\\n for i in range(src.shape[0]):\\n n_matrix[i, 0:list_neigh[i].shape[0]] = list_neigh[i]\\n index_ord = np.argsort(n_matrix[:, 0])\\n n_matrix = n_matrix[index_ord]\\n return n_matrix\\n else:\\n raise RuntimeError(\\\"Some problems during\\\"\\n \\\"computation of neighbours.\\\")\",\n \"def min_distance_vertex(distance, visited):\\n vertices = len(visited)\\n min_distance = INF\\n min_index = None\\n for v in range(vertices):\\n if not visited[v] and distance[v] <= min_distance:\\n min_distance = distance[v]\\n min_index = v\\n return min_index\",\n \"def shortestDistance(self, grid):\\n # return self.house_oriented_TLE(grid)\\n # One axis\\n row_count = [sum(row) for row in grid]\\n col_count = [0]* len(grid[0])\\n row_dist = [0]* len(grid)\\n col_dist = [0]* len(grid[0])\\n output = sys.maxsize\\n for i in range(len(grid)): \\n for j in range(len(grid[0])):\\n col_count[j] += grid[i][j]\\n \\n for index_p in range(len(row_count)):\\n for index_h in range(len(row_count)):\\n row_dist[index_p] += abs(index_h - index_p) * row_count[index_h]\\n \\n for index_p in range(len(col_count)):\\n for index_h in range(len(col_count)):\\n col_dist[index_p] += abs(index_h - index_p) * col_count[index_h]\\n \\n # print(row_count)\\n # print(col_count)\\n # print(row_dist)\\n # print(col_dist)\\n for i in range(len(grid)):\\n for j in range(len(grid[0])):\\n if grid[i][j] == 1:\\n continue\\n output = min(output, row_dist[i] + col_dist[j])\\n return output\",\n \"def closest_points_naive(self, x, y):\\r\\n # Running time: O(n ** 2)\\r\\n\\r\\n dist = []\\r\\n for i in range(len(x)):\\r\\n for j in range(i+1, len(x)):\\r\\n d = self.get_euclidean_distance(x[i], x[j], y[i], y[j])\\r\\n dist.append(d)\\r\\n \\r\\n return min(dist)\",\n \"def find_min_point(points):\\r\\n smallest_point_index = 0\\r\\n for i in range(1, len(points)):\\r\\n if points[i][1] < points[smallest_point_index][1]:\\r\\n smallest_point_index = i\\r\\n elif points[i][0] > points[smallest_point_index][0] and points[i][1] == points[smallest_point_index][1]:\\r\\n smallest_point_index = i\\r\\n return smallest_point_index\",\n \"def _findMin(p, A):\\n\\n m=(-1, (0,0))\\n for p0 in A:\\n dist = np.linalg.norm(p0-np.array(p))\\n if m[0]==-1 or m[0]>dist:\\n m = (dist, p0)\\n \\n return tuple(m[1])\",\n \"def find_nearest_neighbors_idx(X, x, k):\\n ## homework:start\\n result = \\n ## homework:end\\n return result\",\n \"def get_idx(lons, lats, lon, lat):\\n dist = ((lons - lon) ** 2 + (lats - lat) ** 2) ** 0.5\\n return np.unravel_index(dist.argmin(), dist.shape)\",\n \"def get_min_dist(x0, y0, arr):\\n dist = np.hypot(arr.T[0] - x0, arr.T[1] - y0)\\n min_dist = np.min(dist)\\n val = np.argmin(dist)\\n return min_dist, arr[val]\",\n \"def dist(dm, sm, neighbors):\\n\\n # Initialize list of possible distances\\n distances = []\\n\\n # loop over all neighbors of the cell\\n for neighbor in neighbors:\\n # If the neighbor is valid\\n if dm[neighbor[0], neighbor[1]] != -1:\\n # add neighbor distance + 1 to possible distances\\n distances.append(dm[neighbor[0], neighbor[1]] + 1)\\n\\n # return minimal distance\\n return np.min(distances)\",\n \"def nodes_min_energy_index(self, node):\\n idx = -1\\n curr_energy = np.inf\\n for i in range(self.cost_matrix.shape[1]):\\n new_energy = self.cost_matrix[node][i]\\n if new_energy < curr_energy:\\n curr_energy = new_energy\\n idx = i\\n return idx\",\n \"def pair_idx(rows, comm=None):\\n raise Exception(\\\"Not implemented\\\")\\n \\n if comm == None:\\n comm = MPI.COMM_WORLD\\n \\n total = comb(rows,2,exact=True)\\n size = comm.Get_size()\\n \\n size = 1000\\n \\n print(total / size)\\n \\n target = total / size\\n \\n current_row = 0\\n calc_list = []\\n row_list = [[] for x in range(size)]\\n for rank in range(size):\\n row_list[rank].append(current_row)\\n \\n current_calcs = 0\\n \\n for value in range(current_row, rows):\\n current_calcs += value\\n if current_calcs > target:\\n if rank == size-1:\\n pass\\n else:\\n break\\n \\n calc_list.append(current_calcs)\\n row_list[rank].append(value)\\n current_row = value\\n \\n return row_list,calc_list\",\n \"def get_maxmin_index_from_row(\\n distance_matrix: np.ndarray,\\n row: int,\\n previous_indexes: List,\\n type: str,\\n )-> int:\\n distance_matrix = distance_matrix.copy()\\n arr = distance_matrix[row].astype(float)\\n \\n aux_list = range(arr.shape[0])\\n aux_list_2 = []\\n for i in aux_list:\\n if i in previous_indexes:\\n aux_list_2.append(True)\\n else:\\n aux_list_2.append(False)\\n previous_indexes_bool = aux_list_2\\n \\n if type == 'max':\\n arr[previous_indexes_bool] = -1\\n target_index = np.argmax(arr)\\n if type == 'min':\\n arr[previous_indexes_bool] = np.Inf\\n target_index = np.argmin(arr)\\n \\n return target_index\",\n \"def get_nearest_atom_inds_per_mol(self):\\n self.closest_at_per_mol = np.zeros((self.nmol,\\n self.at_per_mol,\\n self.at_per_mol-1), dtype=int)\\n\\n # Get and sort distances\\n all_at_inds = np.arange(self.at_per_mol)\\n for imol in range(self.nmol):\\n for iat in range(self.at_per_mol):\\n at_inds = all_at_inds[all_at_inds != iat]\\n dist = self.all_dist_per_mol[imol, iat, at_inds]\\n\\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\\n self.closest_at_per_mol[imol, iat] = at_inds\",\n \"def distance_matrix(n_row, n_col):\\n\\n n_pop = int(n_row * n_col)\\n center = int(n_row/2*(n_col+1))\\n\\n pop_idx = np.arange(n_pop)\\n pop_idx_col = np.remainder(pop_idx, n_col)\\n pop_idx_row = pop_idx // n_row\\n\\n pos = np.vstack((pop_idx_col,pop_idx_row)).T\\n distance = spa.distance.cdist([pos[center]], pos)[0]\\n\\n return distance\",\n \"def find_min(self, A, w):\\n import numpy as np\\n\\n vcost = self.INFINITY\\n vto = vfrom = -1\\n for v in w:\\n # Get array offset of minimum of this vertex\\n i = np.argmin(A[v,:])\\n if A[v,i] < vcost:\\n vcost = A[v,i]\\n vto = i\\n vfrom = v\\n return (vfrom, vto, vcost)\",\n \"def find_nearest_neighbours_SURF(averageDistance,inst,distanceArray,maxInst): \\r\\n NN=[]\\r\\n min_indices=[] \\r\\n\\r\\n for j in range(maxInst):\\r\\n if inst != j:\\r\\n locator = [inst,j]\\r\\n locator = sorted(locator, reverse=True) #Access corect half of table (result of removed table redundancy)\\r\\n d = distanceArray[locator[0]][locator[1]]\\r\\n if d<averageDistance:\\r\\n min_indices.append(j)\\r\\n \\r\\n for j in range(len(min_indices)):\\r\\n NN.append(min_indices[j])\\r\\n \\r\\n return NN\",\n \"def min(weightData , dataSetVector ):\\r\\n # weightData: pass the whole weightData array.\\r\\n # dataSetVector: pass the a data vector to compare with weightdata array, to find its closest match\\r\\n winnerIndex = 0 #flag for initalizing the winner index\\r\\n minValue = EcuDist(dataSetVector,weightData[0]) # initalize the minValue\\r\\n # iterate through all weighdata rows to find the closest match, depending on ecu. distance,\\r\\n #and then return the index of the closest match(winner)\\r\\n for i in range(weightData.shape[0]):\\r\\n if(EcuDist(dataSetVector,weightData[i]) < minValue):\\r\\n minValue = EcuDist(dataSetVector,weightData[i])\\r\\n winnerIndex = i\\r\\n return winnerIndex\",\n \"def get_nearest_atom_inds(self):\\n # Create empty data structure\\n self.closest_ats = np.zeros((self.natom, self.natom-1), dtype=int)\\n\\n # Get and sort distances\\n all_at_inds = np.arange(self.natom)\\n for iat in range(self.natom):\\n at_inds = all_at_inds[all_at_inds != iat]\\n dist = self.all_dist[iat, at_inds]\\n\\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\\n self.closest_ats[iat] = at_inds\",\n \"def find_nearest_neighbors(p, points, k=5):\\n dist = np.zeros(points.shape[0])\\n for i in range(len(dist)):\\n dist[i] = distance(p, points[i])\\n ind = np.argsort(dist)\\n return ind[0:k]\",\n \"def update_distmatrix(min_idx, dist_matrix):\\n i, j = min_idx\\n new_cluster_dist = np.minimum(dist_matrix[i, :], dist_matrix[j, :])\\n new_cluster_dist[i] = np.inf\\n \\n dist_matrix[i, :] = new_cluster_dist\\n dist_matrix[:, i] = new_cluster_dist\\n \\n dist_matrix[j, :] = np.inf\\n dist_matrix[:, j] = np.inf\\n \\n return dist_matrix\",\n \"def getNeighbors(training_data, test_row, k):\\n\\n distances = list()\\n for training_row in training_data:\\n dist = euclidianDistance(training_row, test_row)\\n distances.append([training_row, dist])\\n \\n #Sort on the basis of dist\\n distances.sort(key=lambda row:row[1])\\n\\n neighbors = list()\\n\\n for i in range(int(k)):\\n neighbors.append(distances[i][0])\\n\\n return neighbors\",\n \"def get_nearest_node_index(node_list, random_node):\\n\\n dist_list = [\\n (node.x - random_node.x) ** 2 + (node.y - random_node.y) ** 2\\n for node in node_list\\n ]\\n minind = dist_list.index(min(dist_list))\\n\\n return minind\",\n \"def findSmallest(distancesWithNames):\\n smallest = distancesWithNames[0][2]\\n smallestIndex = -1\\n for i in range(len(distancesWithNames)):\\n if smallest >= distancesWithNames[i][2]:\\n smallest = distancesWithNames[i][2]\\n smallestIndex = i\\n return smallestIndex\",\n \"def closestCluster(p, centers):\\n\\tbestIndex = 0\\n closest = float(\\\"+inf\\\")\\n for i in range(len(centers)):\\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\\n if distance < closest:\\n closest = distance\\n bestIndex = i\\n\\treturn bestIndex\",\n \"def smallest_cert(pts):\\n # 1. project the points\\n def projected_flattened_pt(pt):\\n mag = distance(pt)\\n distance_wanted = rect_dist\\n return distance_wanted/mag*pt[1], \\\\\\n distance_wanted/mag*pt[2]\\n\\n flat_pts = np.array([[projected_flattened_pt(pt), pt] for pt in pts])\\n\\n # 2. identify rows of data\\n data = {} \\n # maps height (from ground) to the \\\"row\\\" (scan) list of\\n # horizontal-only (1D) data\\n for pt, origpt in flat_pts:\\n for row_height in data:\\n if abs(pt[1]-row_height) < dist_bt_rows/2:\\n data[row_height].append([pt[0], origpt])\\n break\\n else:\\n data[pt[1]] = [[pt[0], origpt]]\\n\\n #3. build up final_data so each row has enough point x-density\\n def get_left_i(pts, left_edge):\\n # get last index <= left_edge\\n for i, pt in enumerate(pts):\\n if pt[0] > left_edge:\\n return i - 1\\n\\n def get_right_i(pts, right_edge):\\n # get first index >= right_edge\\n i = len(pts)-1\\n while True:\\n pt = pts[i]\\n if pt[0] < right_edge:\\n return i + 1\\n i -= 1\\n\\n final_data = []\\n for _, subdata in data.items():\\n subdata = sorted(subdata, key=lambda t: t[0])\\n i = get_left_i(subdata, lane_left)\\n end = get_right_i(subdata, lane_right)\\n\\n final_data = [subdata[i][1]]\\n while i < end:\\n next_i = get_left_i(subdata[i:], \\n subdata[i][0]+max_xpt_separation)\\n if not next_i:\\n return False\\n i += next_i\\n final_data.append(subdata[i][1])\\n\\n return final_data\",\n \"def get_min_shannon_entropy(grid):\\r\\n curr_min = math.inf\\r\\n curr_best = []\\r\\n for i in range(len(grid[0])):\\r\\n for j in range(len(grid)):\\r\\n if not grid[j][i].collapsed:\\r\\n w = grid[j][i].block_weights\\r\\n shannon_entropy = sum([-math.log(el) for el in w] )\\r\\n if shannon_entropy < curr_min:\\r\\n curr_min = shannon_entropy\\r\\n curr_best = [(i,j)]\\r\\n elif shannon_entropy == curr_min:\\r\\n curr_best.append((i,j))\\r\\n idx = np.random.choice(range(len(curr_best))) #choose randomly if theres a tie\\r\\n return curr_best[idx] #x,y\\r\",\n \"def detec_mfo_dist(betas):\\n k = len(betas)\\n min_dist = np.inf\\n for i in range(k-1):\\n for j in range(i+1,k):\\n distance = np.sum((betas[i,:] - betas[j,:])**2)\\n if distance < min_dist:\\n MFO_index = [i,j]\\n min_dist = distance\\n return MFO_index\",\n \"def _lowestDistanceToCluster(self, clusters: ndarray, sequenceIdx: int) -> Tuple[int, float]:\\n lowestClusterIdx = -1\\n lowestDistance = np.inf\\n for cIdx in range(self.numClusters):\\n distance = self._distanceToCluster(clusters[cIdx], sequenceIdx)\\n if distance < lowestDistance:\\n lowestClusterIdx = cIdx\\n lowestDistance = distance\\n return lowestClusterIdx, lowestDistance\",\n \"def eeg_findnearest(x,X):\\t\\n\\t#x array or vector and X a scalar\\n\\tabsdif = np.abs(x-X)\\n\\tval = np.min(absdif)\\n\\tidx = absdif.argmin()\\n\\treturn val,idx\",\n \"def get_index_under_point(self, event):\\r\\n xy = np.asarray(list(zip(self.xs, self.ys)))\\r\\n xyt = self.line.get_transform().transform(xy)\\r\\n xt, yt = xyt[:, 0], xyt[:, 1]\\r\\n d = np.sqrt((xt - event.x) ** 2 + (yt - event.y) ** 2)\\r\\n pt_idx = np.argmin(d)\\r\\n if d[pt_idx] >= self.max_pixels_from_vertex:\\r\\n pt_idx = None\\r\\n return pt_idx\",\n \"def _kth_nearest_neighbor_dist(\\n distance_matrix: Union[np.ndarray, scipy.sparse.csr_matrix], k\\n) -> np.ndarray:\\n\\n if not is_integer(k):\\n raise ValueError(f\\\"parameter 'k={k}' must be a positive integer\\\")\\n else:\\n # make sure we deal with Python built-in\\n k = int(k)\\n\\n if not (0 <= k <= distance_matrix.shape[1]):\\n raise ValueError(\\n \\\"'k' must be an integer between 1 and \\\"\\n f\\\"distance_matrix.shape[1]={distance_matrix.shape[1]}\\\"\\n )\\n\\n if isinstance(distance_matrix, np.ndarray):\\n dist_knn = np.partition(distance_matrix, k - 1, axis=1)[:, k - 1]\\n elif isinstance(distance_matrix, scipy.sparse.csr_matrix):\\n # see mircobenchmark_kth_nn.py for a comparison of implementations for the\\n # sparse case\\n\\n def _get_kth_largest_elements_sparse(\\n data: np.ndarray,\\n indptr: np.ndarray,\\n row_nnz,\\n k_neighbor: int,\\n ):\\n dist_knn = np.zeros(len(row_nnz))\\n for i in range(len(row_nnz)):\\n start_row = indptr[i]\\n dist_knn[i] = np.partition(\\n data[start_row : start_row + row_nnz[i]], k_neighbor - 1\\n )[k_neighbor - 1]\\n\\n return dist_knn\\n\\n row_nnz = distance_matrix.getnnz(axis=1)\\n\\n if (row_nnz < k).any():\\n raise ValueError(\\n f\\\"There are {(row_nnz < k).sum()} points that \\\"\\n f\\\"do not have at least k_neighbor={k}.\\\"\\n )\\n\\n dist_knn = _get_kth_largest_elements_sparse(\\n distance_matrix.data,\\n distance_matrix.indptr,\\n row_nnz,\\n k,\\n )\\n else:\\n raise TypeError(f\\\"type {type(distance_matrix)} not supported\\\")\\n\\n return dist_knn\",\n \"def closestCentroids(self, points , centroids ):\\n dists = scipy.spatial.distance.cdist(points,centroids)\\n # 1 is dimension\\n minIds = numpy.argmin(dists, 1)\\n return minIds\",\n \"def closestClusterAndDistance(p, centers):\\n\\tbestIndex = 0\\n closest = float(\\\"+inf\\\")\\n for i in range(len(centers)):\\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\\n if distance < closest:\\n closest = distance\\n bestIndex = i\\n return (bestIndex, closest)\",\n \"def get_nearest_indices(vector, matrix, num=5):\\n if len(matrix) < num:\\n num = len(matrix)\\n\\n m, n = matrix.shape\\n diff_matrix = np.tile(vector, (m, 1)) - matrix\\n diff_matrix = abs(diff_matrix)\\n\\n distance = diff_matrix.sum(axis=1)\\n sortIndices = np.argsort(distance)\\n sortIndices = sortIndices[0:num]\\n\\n return sortIndices[random.randint(0, num - 1)]\",\n \"def exhaustive_search(X, z):\\n #initialize shortest path and distance to the first row\\n d_star = la.norm(z - X[0])\\n x_star = X[0]\\n #check the distance from each row of X and if shortest thus far, save it\\n for i in range(X.shape[0]):\\n #calculate distance from current row to target z\\n x = X[i,:]\\n cur_dist = la.norm(x - z)\\n #if the distance is smallest thus far, save the row and distance\\n if cur_dist < d_star:\\n x_star = x\\n d_star = cur_dist\\n return x_star, d_star\",\n \"def find_min_path(s, t, dist):\\n\\n rows = len(dist) - 1\\n cols = len(dist[0]) - 1\\n col = cols\\n row = rows\\n pos_str = \\\"Position: (row={} col={}) -> (row={} col={})\\\"\\n cst_str = \\\"Cost: {}\\\"\\n prev_row = row\\n prev_col = col\\n\\n # init sparse path matrix\\n sparse_path = [[\\\" \\\" for x in range(cols + 1)] for x in range(rows + 1)]\\n sparse_path[0][0] = \\\"0\\\"\\n\\n # start with operation at (rows, cols) and work backwards\\n sparse_path[rows][cols] = dist[rows][cols]\\n\\n if verbose == 2:\\n print()\\n print(\\\"Initial Minimum Path Matrix:\\\")\\n print_matrix(s, t, sparse_path)\\n\\n while True:\\n\\n # bail out if we are in the corner\\n if row == 0 and col == 0:\\n break\\n\\n # if we are not at a matrix boundary\\n if row != 0 and col != 0: # if at left edge or top row, cannot move diagonally\\n\\n # diagonal\\n if (dist[row - 1][col - 1] == min(dist[row - 1][col],\\n dist[row][col - 1],\\n dist[row - 1][col - 1])) and (dist[row - 1][col - 1] == dist[row][col] or dist[row - 1][col - 1] == dist[row][col] - 1):\\n sparse_path[row - 1][col - 1] = dist[row - 1][col - 1]\\n temp_cost = dist[row - 1][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # left\\n elif dist[row][col - 1] <= dist[row][col]:\\n sparse_path[row][col - 1] = dist[row][col - 1]\\n temp_cost = dist[row][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # above\\n else:\\n sparse_path[row - 1][col] = dist[row - 1][col]\\n temp_cost = dist[row - 1][col]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # if at matrix edge, can only move up\\n elif col == 0:\\n # above\\n sparse_path[row - 1][col] = dist[row - 1][col]\\n temp_cost = dist[row - 1][col]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # must be at row boundary, can only move left\\n else:\\n # left\\n if dist[row][col - 1] <= dist[row][col]:\\n sparse_path[row][col - 1] = dist[row][col - 1]\\n temp_cost = dist[row][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # print matrix\\n if verbose == 2:\\n print_matrix(s, t, sparse_path)\\n\\n return sparse_path\",\n \"def find_closest_atom(coords1, coords2):\\n\\n coords1 = np.array(coords1)\\n coords2 = np.array(coords2)\\n diff = coords2[:, np.newaxis] - coords1[np.newaxis, :]\\n dist = np.einsum('ijk->ij', diff**2)**0.5\\n index = np.argmin(dist)\\n return index\",\n \"def find_neighbor_indices(atoms, probe, k):\\r\\n coords_all = atoms.getCoords()\\r\\n neighbor_indices = []\\r\\n atom_k = atoms[k]\\r\\n radius = atom_k.getRadius() + probe + probe\\r\\n\\r\\n indices = range(k)\\r\\n indices.extend(range(k+1, len(atoms)))\\r\\n\\r\\n for i in indices:\\r\\n\\tdist = pos_distance(coords_all[k], coords_all[i])\\r\\n\\t#dist = np.linalg.norm(coords_all[k] - coords_all[i])\\r\\n\\tif dist < radius + atoms[i].getRadius():\\r\\n neighbor_indices.append(i)\\r\\n\\r\\n return neighbor_indices\",\n \"def djikstre(connection_mat):\\n n = connection_mat.shape[0]\\n dist, prev = {}, {}\\n Q = list(range(n))\\n \\n for i in Q:\\n dist[i] = np.inf\\n dist[n-2] = 0.0\\n \\n while(len(Q)>0):\\n\\n min_dist = min([dist[key] for key in Q])\\n u = [key for key in Q if dist[key] == min_dist][0]\\n Q.remove(u)\\n\\n for v in np.nonzero(connection_mat[:, u])[0]:\\n \\n alt = dist[u]+connection_mat[v, u]\\n \\n if alt < dist[v]:\\n dist[v] = alt\\n prev[v] = u\\n \\n return dist, prev\",\n \"def _first_index_with_bigger_neighbour(P):\\n i = len(P) - 1\\n while i > 0 and P[i-1] >= P[i]:\\n i -= 1\\n return i\",\n \"def neirest_neighbour(business, cells):\\n array = cells.get_neighbours(business, num=1)\\n neighbours = pd.DataFrame(array).set_index('index')\\n index = neighbours['distance'].idxmin()\\n return neighbours.loc[index]\",\n \"def _retrieve_neighbors(df, i_point, point, eps, column):\\n neigborhood = []\\n for index, row in df.iterrows():\\n if index != i_point:\\n a = np.array(point[column])\\n b = np.array([row[column]])\\n distance = np.linalg.norm(a - b)\\n if distance <= eps:\\n neigborhood.append(index)\\n\\n return neigborhood\",\n \"def find_nearest_neighbors(p, points, k):\\n\\timport numpy as np\\n\\tdistances = np.zeros(points.shape[0])\\n\\tfor i in range(len(distances)):\\n\\t\\tdistances[i] = distance(p,points[i])\\n\\tind = np.argsort(distances)\\n\\treturn ind[0:k]\",\n \"def get_nearest_row(self):\\n return (self.rect.top - (self.screen.get_height() // 12)) // self.maze.block_size\",\n \"def find_nearest_neighbour_from_point(point_cloud:np.ndarray, point:int) -> int:\\n pass\",\n \"def _nearest_point_index(points, point):\\n distance = sys.float_info.max\\n index = None\\n for i, p in enumerate(points):\\n temp = _vec_distance(p, point)\\n if temp < distance:\\n distance = temp\\n index = i\\n return index, distance\",\n \"def nearest_neighbor(data):\\n features = set([i for i, x in enumerate(data[0][1])])\\n return leave_one_out_cross_validation(data, features)\",\n \"def shortestpathij(self, i, j):\\n pathlist = []\\n self.pathij(i, j, pathlist)\\n distance = []\\n \\n for i in range(len(pathlist)):\\n Temp = 0\\n for j in range(len(pathlist[i]) - 1):\\n Temp += self.Dismatrix[pathlist[i][j], pathlist[i][j+1]]\\n distance.append(Temp)\\n \\n if(len(distance) == 0):\\n return None\\n else:\\n return min(distance)\",\n \"def get_nearest_neighbours(self, cell):\\n\\t\\tneighs = self.get_neighbours(cell)\\n\\t\\ti, j = cell.find_id()\\n\\t\\tneighbours = []\\n\\t\\tfor neigh in neighs:\\n\\t\\t\\tx, y = neigh.find_id()\\n\\t\\t\\tif abs(x-i)+abs(y-j) <= 1: \\n\\t\\t\\t\\tneighbours.append(self.space[y,x])\\n\\t\\treturn neighbours\",\n \"def closest_node(node, nodes):\\n nodes = np.asarray(nodes)\\n deltas = nodes - node\\n dist_2 = np.einsum(\\\"ij,ij->i\\\", deltas, deltas)\\n return np.argmin(dist_2), np.min(dist_2)\",\n \"def extract_min(H, ds):\\n minDist = approxInf\\n u = None # min vertex unknown\\n i = 0\\n for v in H:\\n if ds[v] <= minDist:\\n minDist = ds[v]\\n u = v # note that u is unused (instead returned by pop)\\n imin = i\\n i += 1\\n return(H.pop(imin)) # return [u, d]\",\n \"def min_distance(s1, s2):\\n n = len(s1)\\n m = len(s2)\\n matrix = [([0]*(m+1)) for i in xrange(n+1)]\\n for i in xrange(m+1):\\n matrix[0][i] = i\\n for i in xrange(n+1):\\n matrix[i][0] = i\\n for i in xrange(1,n+1):\\n for j in xrange(1,m+1):\\n temp = min(matrix[i-1][j]+1, matrix[i][j-1]+1)\\n d = 0 if s1[i-1]==s2[j-1] else 1\\n matrix[i][j] = min(temp, matrix[i-1][j-1]+d)\\n return matrix[n][m]\",\n \"def getNearestLineIndex(row, tagLineNumbers):\\n # DOC {{{\\n # }}}\\n\\n # CODE {{{\\n # initialize local auxiliary variables {{{\\n nearestLineNumber = -1\\n nearestLineIndex = -1\\n # }}}\\n\\n # go through all tag line numbers and find the one nearest to the specified row {{{\\n for lineIndex, lineNumber in enumerate(tagLineNumbers):\\n # if the current line is nearer the current cursor position, take it {{{\\n if (nearestLineNumber < lineNumber <= row):\\n nearestLineNumber = lineNumber\\n nearestLineIndex = lineIndex\\n # }}}\\n\\n # if we've got past the current cursor position, let's end the search {{{\\n if (lineNumber >= row):\\n break\\n # }}}\\n # }}}\\n\\n # return index of the line with the nearest tag\\n return nearestLineIndex\\n # }}}\",\n \"def closest(self, x, y):\\n if self.direction == 'horizontal':\\n p_pts = np.array([\\n self.ax.transData.transform((p, 0))[0] for p in self.positions\\n ])\\n dist = abs(p_pts - x)\\n else:\\n p_pts = np.array([\\n self.ax.transData.transform((0, p))[1] for p in self.positions\\n ])\\n dist = abs(p_pts - y)\\n index = np.argmin(dist)\\n return index, dist[index]\",\n \"def _low_tri_indices(rowCount):\\n for col in range(rowCount):\\n for row in range(col, rowCount):\\n yield (row, col)\",\n \"def findNearset(x,y,lon,lat):\\n dist = np.sqrt( (lon - x)**2 + (lat - y)**2)\\n\\n return np.argwhere(dist==dist.min())[0][0]\",\n \"def min_horizontal_dist_meters(coords, targets, is_geo=False):\\n xe = coords[:, 0]\\n ye = coords[:, 1]\\n n = len(xe)\\n d = np.zeros(n)\\n for j in range(n):\\n d1 = dist_in_meters(targets, [xe[j], ye[j]], is_geo=is_geo)\\n d[j] = d1.min()\\n return d\",\n \"def findminpath(tab, gxtab, gytab, pixtab):\\n\\n pathdist = 2 # the number of points each points on a ray can related to on the previous ray\\n pathdist_penalty = 0.3 # penalty of the difference of the pathdist\\n pathpix_penalty = 2 # penalty of the difference of pixel values between the point and the previous point\\n nray = tab.shape[1]\\n\\n #tab = np.hstack((tab,tab[:, 0].reshape(tab.shape[0], 1)))\\n #pixtab = np.hstack((pixtab,pixtab[:, 0].reshape(pixtab.shape[0], 1)))\\n #gxtab = np.hstack((gxtab,gxtab[:, 0].reshape(gxtab.shape[0], 1)))\\n #gytab = np.hstack((gytab,gytab[:, 0].reshape(gytab.shape[0], 1)))\\n\\n tab = np.hstack((tab,tab,tab)) # horizontally stack the tab matrix to prepare for the filtering on the result\\n pixtab = np.hstack((pixtab,pixtab,pixtab))\\n gxtab = np.hstack((gxtab,gxtab,gxtab))\\n gytab = np.hstack((gytab,gytab,gytab))\\n\\n tab = (tab - tab.min()) / (tab.max() - tab.min()) # noralize the tab matrix\\n pixtab = (pixtab - pixtab.min()) / (pixtab.max() - pixtab.min()) * -1 # for we want to find the white contour of the cell so we multipy -1 on the pixtab\\n # tab = tab / np.median(tab)\\n # pixtab = pixtab / np.median(pixtab)\\n path = np.zeros(tab.shape)\\n path[:, 0] = np.array(range(0, tab.shape[0]))\\n score = np.zeros(tab.shape)\\n score[:, 1] = tab[:, 1]\\n\\n for i in range(1, tab.shape[1]):\\n for j in range(tab.shape[0]):\\n mins = np.Inf # record the min value of the ray\\n minat = 0\\n for k in range(-pathdist, pathdist+1):\\n if(0 <= (j+k) and (j+k) < tab.shape[0]):\\n s = pixtab[j, i]\\n pixdiff = abs(pixtab[j, i] - pixtab[j+k, i-1])\\n s += pixdiff * pathpix_penalty # two kinds of penalty\\n s += abs(k) * pathdist_penalty\\n s += score[j+k, i-1]\\n\\n if(s < mins):\\n mins = s\\n minat = j + k\\n path[j, i] = minat\\n score[j, i]= mins\\n\\n start = int(np.argmin(score[:, -1]))\\n path = path.astype(np.int32)\\n minpath = [start]\\n for i in range(tab.shape[1]-1, 0, -1):\\n minpath.append(path[minpath[-1], i])\\n minpath = minpath[::-1]\\n # print(len(minpath))\\n minpath = savgol_filter(minpath, 15, 3) # apply a Savitzky-Golay filter to the raw minpath signal\\n minpath = minpath[nray:nray*2] # cut the middle part of minpath whose length is nray\\n return np.array(minpath).astype(np.int32)\",\n \"def nearest_sparse(self, query):\\n self.best_dist = float(\\\"inf\\\")\\n self.best_element = None\\n self._register_best_element = self._register_best_element_single \\n self._nearest_sparse_recursive(self._sparse2seq(query), self.root, 0.0)\\n return self.best_element,self.best_dist\",\n \"def find_all_nearest_neighbours(point_cloud:np.ndarray) -> np.ndarray:\\n pass\",\n \"def numNeighbors(minesSet, row_index, cols_index, num_cols, num_rows):\\n mines = 0\\n for j in np.arange(max(0, cols_index-1), min(num_cols-1, cols_index+1)+1):\\n for i in np.arange(max(0, row_index-1), min(num_rows-1, row_index+1)+1):\\n if ((i, j) in minesSet):\\n mines+=1\\n return mines\",\n \"def _intra_cluster_distance(distances_row, labels, i):\\n mask = labels == labels[i]\\n mask[i] = False\\n if not np.any(mask):\\n # cluster of size 1\\n return 0\\n a = np.mean(distances_row[mask])\\n return a\",\n \"def closest_waypoint(self, location: pylot.utils.Location):\\n min_dist = np.infty\\n min_index = 0\\n for index, waypoint in enumerate(self.waypoints):\\n dist = waypoint.location.distance(location)\\n if dist < min_dist:\\n min_dist = dist\\n min_index = index\\n return min_index\",\n \"def __find_immediate_predecessors(storm_object_table, target_row):\\n\\n error_checking.assert_is_integer(target_row)\\n error_checking.assert_is_geq(target_row, 0)\\n error_checking.assert_is_less_than(\\n target_row, len(storm_object_table.index)\\n )\\n\\n predecessor_sec_id_strings = [\\n storm_object_table[c].values[target_row]\\n for c in PREV_SECONDARY_ID_COLUMNS\\n if storm_object_table[c].values[target_row] != ''\\n ]\\n\\n num_predecessors = len(predecessor_sec_id_strings)\\n if num_predecessors == 0:\\n return numpy.array([], dtype=int)\\n\\n target_time_unix_sec = storm_object_table[\\n tracking_utils.VALID_TIME_COLUMN].values[target_row]\\n\\n predecessor_rows = numpy.full(num_predecessors, -1, dtype=int)\\n\\n for i in range(num_predecessors):\\n these_rows = numpy.where(numpy.logical_and(\\n storm_object_table[tracking_utils.SECONDARY_ID_COLUMN].values ==\\n predecessor_sec_id_strings[i],\\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values <\\n target_time_unix_sec\\n ))[0]\\n\\n if len(these_rows) == 0:\\n continue\\n\\n this_subrow = numpy.argmax(\\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values[\\n these_rows]\\n )\\n\\n predecessor_rows[i] = these_rows[this_subrow]\\n\\n return predecessor_rows[predecessor_rows >= 0]\",\n \"def matrix_idx(n_hist, n_req, n_rows):\\n\\n flat_idx = []\\n for i in range(n_rows):\\n flat_idx.extend(range(i * n_req, (i + 1) * n_req + n_hist))\\n # idx = np.unravel_index(flat_idx, (n_rows, n_hist + n_req))\\n\\n idx_matrix = np.reshape(flat_idx, (n_rows, n_hist + n_req))\\n idxX = idx_matrix[:, n_req:]\\n idxY = idx_matrix[:, :n_req]\\n\\n return idxX, idxY\",\n \"def _partition_nearest(self, medoids, dists, only_these=set()):\\n if len(only_these) == 0:\\n allowed_inds = self._not_ignored_inds\\n else:\\n allowed_inds = self._not_ignored_inds & only_these\\n closest_medoid_ind = np.argmin(dists[:,medoids], 1) # If len(medoids)==3, would look like [2,1,1,0,1,2,...].\\n clusts = [[] for i in medoids]\\n for node_ind, med_ind in enumerate(closest_medoid_ind):\\n if node_ind in allowed_inds:\\n clusts[med_ind].append(node_ind)\\n return clusts\",\n \"def find_closest_pt(ref_lon, ref_lat, tlon, tlat):\\n\\n # compute great circle distance from location to model grid points\\n dist = gc_dist(ref_lon, ref_lat, tlon, tlat)\\n\\n # find j index of closest grid point\\n work = N.take(dist,N.argmin(dist,0),0).diagonal()\\n jj = N.argsort(work)[0]\\n\\n # find i index of closest grid point\\n work = N.take(dist,N.argmin(dist,1),1).diagonal()\\n ii = N.argsort(work)[0]\\n\\n return ii, jj\",\n \"def index_condensed_matrix(n, i, j):\\n if i == j:\\n main_warning(\\\"Diagonal elements (i=j) are not stored in condensed matrices.\\\")\\n return None\\n elif i > j:\\n i, j = j, i\\n return int(i * (n - (i + 3) * 0.5) + j - 1)\",\n \"def nearest_neighbors(self):\\n neighbor_distances_and_indices = []\\n for idx, data_point in enumerate(self.data):\\n distance = self.euclidean_dis(data_point[:-1], self.query) # Calculate the distance between the query\\n # example and the current example from the data.\\n\\n neighbor_distances_and_indices.append((distance, idx)) # Add the distance and the index of the example\\n # to an ordered collection\\n\\n sorted_neighbor_distances_and_indices = sorted(neighbor_distances_and_indices, key=lambda x: x[0]) #\\n # Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by\\n # the distances\\n\\n k_nearest_distances_and_indices = sorted_neighbor_distances_and_indices[:self.k] # Pick the first K\\n # entries from the sorted collection\\n\\n k_nearest_labels = [self.data[i][1] for distance, i in k_nearest_distances_and_indices] # Get the labels of\\n # the selected K entries\\n\\n return k_nearest_labels, self.mode(k_nearest_labels)\",\n \"def closest_centroid(x,centroids):\\n\\tK =len(centroids)\\n\\tN = len(x)\\n\\tDistance = np.zeros((N,K))\\n\\tfor j in range(K):\\n\\t\\tmu = centroids[j]\\n\\t\\tDistance[:,j] = np.linalg.norm(x-mu,axis=1)\\n\\tout = np.argmin(Distance,axis=1) \\n\\treturn out\",\n \"def medoid_ft(ftvectors,pairwise_dist_matrix):\\r\\n N = len(ftvectors)\\r\\n if N == 1 : \\r\\n return 0 \\r\\n # pairwise_dist_matrix = calculate_dist_matrix_ft(ftvectors) \\r\\n sum_dist = np.sum(pairwise_dist_matrix, axis = 0)\\r\\n min_idx = np.argmin(sum_dist)\\r\\n return min_idx\",\n \"def nn_dists(coords, spherical=True):\\n\\n full_dist_matrix = all_dists(coords, spherical)\\n\\n # find all minimum distances\\n # apply min over ranges of the dist array\\n min_dists = np.min(full_dist_matrix, axis=1)\\n min_ix = np.argmin(full_dist_matrix, axis=1)\\n\\n return min_dists, min_ix\",\n \"def find_indices(colorhs, centres):\\n\\n indices = np.zeros(colorhs.shape[0], dtype=np.uint8)\\n i = 0\\n\\n for hs in colorhs:\\n # Past Euclidian distance\\n past_ed = float(\\\"inf\\\")\\n for cluster in range(centres.shape[0]):\\n # Current Euclidian distance\\n curr_ed = (sum((hs - centres[cluster, :]) ** 2)) ** 1/2\\n # A frame belongs to the cluster with the minimum ed value.\\n if curr_ed <= past_ed:\\n past_ed = curr_ed\\n indices[i] = cluster\\n i += 1\\n return indices\",\n \"def minesweeper(matrix):\\n \\n num_rows = len(matrix)\\n num_cols = len(matrix[0])\\n \\n adj_mines = []\\n \\n adj_row = [0]*num_cols\\n \\n for i in range(num_rows):\\n adj_mines.append(adj_row[:])\\n \\n for r in range(num_rows):\\n for c in range(num_cols):\\n if matrix[r][c] == True:\\n if (r-1) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r-1][c-1] += 1\\n if (r-1) in range(num_rows) and (c) in range(num_cols):\\n adj_mines[r-1][c] += 1\\n if (r-1) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r-1][c+1] += 1\\n if (r) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r][c-1] += 1\\n if (r) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r][c+1] += 1\\n if (r+1) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r+1][c-1] += 1\\n if (r+1) in range(num_rows) and (c) in range(num_cols):\\n adj_mines[r+1][c] += 1\\n if (r+1) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r+1][c+1] += 1\\n\\n \\n return adj_mines\",\n \"def findClosetCentroids(X, centroids):\\n\\tm, n = X.shape\\n\\tK = centroids.shape[0]\\n\\tidx = np.zeros(m) # m\\n\\n\\tfor i in range(m):\\n\\t\\ttemp = np.tile(X[i, :], K).reshape(centroids.shape)\\n\\t\\tidx[i] = np.argmin(np.sum((centroids - temp) ** 2, axis=1))\\n\\treturn idx\",\n \"def find_nearest(ref_array,target_array):\\n ref_tree = scipy.spatial.cKDTree(ref_array)\\n dist, indices = ref_tree.query(target_array, k=1)\\n return indices\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7677283","0.7389674","0.7215832","0.65018827","0.64853066","0.6480093","0.64236856","0.6417016","0.64140975","0.6329937","0.63131666","0.63109416","0.63027555","0.62869954","0.6206853","0.6192381","0.6176218","0.61489695","0.6099502","0.60955817","0.60761005","0.60444117","0.60416424","0.60360634","0.59951365","0.5976779","0.597576","0.593282","0.59230864","0.59188306","0.5918303","0.5917492","0.5910718","0.59076756","0.59005046","0.587552","0.5851686","0.5837209","0.5800773","0.5792634","0.5790263","0.57880366","0.5767146","0.57514036","0.57398194","0.5733956","0.5733956","0.57217693","0.5700117","0.5699564","0.5695812","0.5682173","0.56744385","0.56577855","0.56558686","0.56555545","0.5653263","0.5648361","0.56477726","0.5647009","0.563409","0.56335485","0.5629854","0.5622456","0.5620252","0.5609443","0.5603943","0.5598129","0.5596091","0.55842704","0.55797595","0.55738246","0.55701625","0.5558328","0.55554163","0.55529463","0.55517614","0.55496764","0.5549648","0.55457914","0.55318844","0.551883","0.55082893","0.55014145","0.5501182","0.5492334","0.54880804","0.5487481","0.5485643","0.54840255","0.54826933","0.5478958","0.54749215","0.5461066","0.5455318","0.54510504","0.5439225","0.54364276","0.54334986","0.5426398"],"string":"[\n \"0.7677283\",\n \"0.7389674\",\n \"0.7215832\",\n \"0.65018827\",\n \"0.64853066\",\n \"0.6480093\",\n \"0.64236856\",\n \"0.6417016\",\n \"0.64140975\",\n \"0.6329937\",\n \"0.63131666\",\n \"0.63109416\",\n \"0.63027555\",\n \"0.62869954\",\n \"0.6206853\",\n \"0.6192381\",\n \"0.6176218\",\n \"0.61489695\",\n \"0.6099502\",\n \"0.60955817\",\n \"0.60761005\",\n \"0.60444117\",\n \"0.60416424\",\n \"0.60360634\",\n \"0.59951365\",\n \"0.5976779\",\n \"0.597576\",\n \"0.593282\",\n \"0.59230864\",\n \"0.59188306\",\n \"0.5918303\",\n \"0.5917492\",\n \"0.5910718\",\n \"0.59076756\",\n \"0.59005046\",\n \"0.587552\",\n \"0.5851686\",\n \"0.5837209\",\n \"0.5800773\",\n \"0.5792634\",\n \"0.5790263\",\n \"0.57880366\",\n \"0.5767146\",\n \"0.57514036\",\n \"0.57398194\",\n \"0.5733956\",\n \"0.5733956\",\n \"0.57217693\",\n \"0.5700117\",\n \"0.5699564\",\n \"0.5695812\",\n \"0.5682173\",\n \"0.56744385\",\n \"0.56577855\",\n \"0.56558686\",\n \"0.56555545\",\n \"0.5653263\",\n \"0.5648361\",\n \"0.56477726\",\n \"0.5647009\",\n \"0.563409\",\n \"0.56335485\",\n \"0.5629854\",\n \"0.5622456\",\n \"0.5620252\",\n \"0.5609443\",\n \"0.5603943\",\n \"0.5598129\",\n \"0.5596091\",\n \"0.55842704\",\n \"0.55797595\",\n \"0.55738246\",\n \"0.55701625\",\n \"0.5558328\",\n \"0.55554163\",\n \"0.55529463\",\n \"0.55517614\",\n \"0.55496764\",\n \"0.5549648\",\n \"0.55457914\",\n \"0.55318844\",\n \"0.551883\",\n \"0.55082893\",\n \"0.55014145\",\n \"0.5501182\",\n \"0.5492334\",\n \"0.54880804\",\n \"0.5487481\",\n \"0.5485643\",\n \"0.54840255\",\n \"0.54826933\",\n \"0.5478958\",\n \"0.54749215\",\n \"0.5461066\",\n \"0.5455318\",\n \"0.54510504\",\n \"0.5439225\",\n \"0.54364276\",\n \"0.54334986\",\n \"0.5426398\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62730277"},"document_rank":{"kind":"string","value":"14"}}},{"rowIdx":887,"cells":{"query":{"kind":"string","value":"Each row of the matrix, let's say the jth row, represents the distance between the other data point from the jth point. This function returns the indexes for the points with the smallest distances with respect to each point represented by that specified row. By row, I mean the 0th dimension. Also notice that this function includes the target particle, i.e. the diagonal element along the matrix is set to 1."},"document":{"kind":"string","value":"def nearest_points_values_with_self(matrix, num_to_keep):\n\n # Set the diagonal to 1\n np.fill_diagonal(matrix, 1)\n # Get the position for the resulted values\n sort = np.sort(matrix, axis=1)\n\n return sort[:, : num_to_keep]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def nearest_min(dist_matrix):\n # much faster than np.where\n i, j = np.unravel_index(\n np.argmin(dist_matrix), \n dims=dist_matrix.shape\n )\n return i, j","def nearest_points_indexes_with_self(matrix, num_to_keep):\n\n # Set the diagonal to 1\n np.fill_diagonal(matrix, 1)\n # Get the position for the resulted values\n sort_arg = np.argsort(matrix, axis=1)\n\n return sort_arg[:, : num_to_keep]","def nearest_points_indexes_without_self(matrix, num_to_keep):\n\n # Set the diagonal to 0\n np.fill_diagonal(matrix, 0)\n # Get the position for the resulted values\n sort_arg = np.argsort(matrix, axis=1)\n\n return sort_arg[:, : num_to_keep]","def find_min_distance():\n return np.argmin(d)","def nearest_neighbour(matrix, start=0):\n path = [start]\n while len(matrix) != len(path):\n matrix[:, start] = numpy.inf\n start = numpy.argmin(matrix[start])\n path.append(start)\n return path","def nearest_vertex_to(self, point):\n distances = self.distances_to(point)\n idx = np.argmin(distances)\n return idx","def smallest_distance(self, clusters):\n i, j = numpy.unravel_index(numpy.argmin(clusters), clusters.shape)\n return clusters[i, j], i, j","def nearest_cluster(X,c):\n K = np.size(c,0)\n idx = np.zeros((np.size(X,0),1))\n arr = np.empty((np.size(X,0),1))\n for i in range(0,K):\n y = c[i]\n temp = np.ones((np.size(X,0),1))*y\n b = np.power(np.subtract(X,temp),2)\n a = np.sum(b,axis = 1)\n a.resize((np.size(X,0),1))\n arr = np.append(arr, a, axis=1)\n arr = np.delete(arr,0,axis=1)\n idx = np.argmin(arr, axis=1)\n return idx","def find_min(self):\n\n\n min_x = 1000\n min_y = 1000\n k = len(self.__col_lista)\n for i in range(k):\n x, y = self.__col_lista[i]\n if x < min_x:\n min_x = x\n if y < min_y:\n min_y = y\n return min_x, min_y","def find_closest_index(traj, point):\n\n\t#TODO: vectorise function to receive any length of points.\n\n\tdistances = np.subtract(np.array(point),traj) \n\tdistances = distances.reshape(-1,2)\n\t#distances = distances[~np.isnan(distances)].reshape(-1,2)\n\n\t#print(\"distances\")\n\t#pprint(distances)\n\tdist_array = np.linalg.norm(distances, axis = 1)\n\t#pprint(dist_array)\n\t#dist_array = np.sqrt((distances[:,0]**2)+(distances[:,1]**2)) #array of distances from trajectory to gaze landing point in world. \n\tidx = np.nanargmin(abs(dist_array)) #find smallest difference in pythag distance from 0,0 to get closest point. \n\tdists = distances[idx, :]\n\tdist = dist_array[idx]\n\n\treturn idx#, dists, dist\n\t#return idx","def getclosest_ij(lats,lons,latpt,lonpt):\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\n dist_lon = (lons-lonpt)**2\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\n minindex_lon = dist_lon.argmin()\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index","def getclosest_ij(lats,lons,latpt,lonpt):\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\n dist_lon = (lons-lonpt)**2\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\n minindex_lon = dist_lon.argmin()\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index","def _find_min_pair(pandas_matrix):\n numpy_matrix = pandas_matrix.values\n mins = np.where(numpy_matrix == np.nanmin(numpy_matrix))\n min_col_idx = mins[0][0]\n min_row_idx = mins[1][0]\n (min_col, min_row) = (pandas_matrix.index[min_col_idx], \n pandas_matrix.columns[min_row_idx])\n\n return (min_col, min_row)","def find_closest(distances, threshold):\n n = len(distances)\n person_1 = []\n person_2 = []\n d = []\n\n for i in range(n):\n for j in range(i+1, n):\n if distances[i][j] <= threshold:\n person_1.append(i)\n person_2.append(j)\n d.append(distances[i][j])\n\n return person_1, person_2, d","def nearest_points_values_without_self(matrix, num_to_keep):\n\n # Set the diagonal to 0\n np.fill_diagonal(matrix, 0)\n # Get the position for the resulted values\n sort = np.sort(matrix, axis=1)\n\n return sort[:, : num_to_keep]","def getRowHeuristics(matrix):\n row, col = matrix.shape\n rHeuristic = np.zeros((row,2)) # Dos columnas. La primera para indicar la columna la segunda para la Heuristica\n for i in range(0,row):\n rHeuristic[i,0] = int(i)\n #print (i,sum(matrix[:,i]), pesos[i], float(pesos[i]/sum(matrix[:,i])))\n rHeuristic[i,1] = 1/sum(matrix[i,:])\n return rHeuristic[rHeuristic[:,1].argsort()]","def nearestNeighbor(self, coords, my_index, blacklist):\n min_index, min_distance = 0, 999999999\n my_col, my_row = coords[my_index]\n for i, (col, row) in enumerate(coords):\n if i in blacklist:\n continue\n distance = math.sqrt((my_col-col)**2 + (my_row-row)**2)\n if distance < min_distance:\n min_index, min_distance = i, distance\n return min_index","def _nearest_cluster_distance(distances_row, labels, i):\n label = labels[i]\n b = np.min([np.mean(distances_row[labels == cur_label])\n for cur_label in set(labels) if not cur_label == label])\n return b","def getNearestSampleIndex(test, trainX):\n dist_matrix = test - trainX\n dist_square = dist_matrix ** 2\n dist_sums = dist_square.sum(axis=1)\n distance_vector = np.sqrt(dist_sums)\n return (distance_vector).argmin()","def find_closest_trajectory_pose(self):\n np_state = numpy.array([[self.x], [self.y]])\n temp_distance = numpy.sum(\n (self.np_trajectory[0:2, :] - np_state) ** 2,\n axis=0)\n best_idx = numpy.argmin(temp_distance)\n return best_idx","def findknn(xTr,xTe,k):\n\n # YOUR CODE HERE\n if k > len(xTr):\n k = len(xTr)\n \n D=l2distance(xTe, xTr)\n (m,n) = D.shape\n \n indices = []\n dists = []\n for i in range(m):\n smallest_indices = np.argsort(D[i])\n ind = smallest_indices[:k]\n dis = D[i,smallest_indices[:k]]\n indices.append(ind)\n dists.append(dis)\n \n indices = np.transpose(np.array(indices))\n dists = np.transpose(np.array(dists))\n return indices, dists","def _compute_euclidean_neigh_matrix(src, d_matrix, radius):\n\n n_max = 100\n n_min = 3\n reached_points = np.array([0])\n counter = 0\n n_neigh = []\n list_neigh = []\n\n while counter < reached_points.shape[0] < src.shape[0]:\n P = reached_points[counter]\n aux = np.array(sorted(\n np.where(d_matrix[P] <= radius)[0],\n key=lambda k: d_matrix[P, k]))\n n_neigh.append(aux.shape[0])\n\n # Check the number of neighbours\n if n_neigh[-1] < n_min:\n raise ValueError('Computation of neighbours aborted since '\n 'their minimum number is too small.\\n'\n 'Please choose a higher radius.')\n elif n_neigh[-1] > n_max:\n raise ValueError('Computation of neighbours aborted since'\n 'their maximum number is too big.\\n'\n 'Please choose a lower radius.')\n list_neigh.append(aux)\n reached_points = np.append(reached_points,\n aux[~np.in1d(aux, reached_points)])\n counter += 1\n\n if counter >= reached_points.shape[0]:\n raise ValueError('Too small value of the radius:'\n 'the neighbour-matrix is not connected')\n elif src.shape[0] == reached_points.shape[0]:\n while counter < src.shape[0]:\n P = reached_points[counter]\n aux = np.array(sorted(\n np.where(d_matrix[P] <= radius)[0],\n key=lambda k: d_matrix[P, k]))\n n_neigh.append(aux.shape[0])\n\n if n_neigh[-1] < n_min:\n raise ValueError('Computation of neighbours aborted since '\n 'their minimum number is too small.\\n'\n 'Please choose a higher radius.')\n elif n_neigh[-1] > n_max:\n raise ValueError('Computation of neighbours aborted since'\n 'their maximum number is too big.\\n'\n 'Please choose a lower radius.')\n\n list_neigh.append(aux)\n counter += 1\n\n n_neigh_max = max(n_neigh)\n n_matrix = np.zeros([src.shape[0],\n n_neigh_max], dtype=int) - 1\n for i in range(src.shape[0]):\n n_matrix[i, 0:list_neigh[i].shape[0]] = list_neigh[i]\n index_ord = np.argsort(n_matrix[:, 0])\n n_matrix = n_matrix[index_ord]\n return n_matrix\n else:\n raise RuntimeError(\"Some problems during\"\n \"computation of neighbours.\")","def saddle_points(matrix):\n if not all(len(row) == len(matrix[0]) for row in matrix[1:]):\n raise ValueError('Provided matrix is irregular.')\n columns = [col for col in zip(*matrix)]\n points = set()\n for ridx, row in enumerate(matrix):\n for cidx, element in enumerate(row):\n if element == max(row) and element == min(columns[cidx]):\n points.add((ridx, cidx))\n return points","def _first_index_with_smaller_neighbour(P):\n i = len(P) - 1\n while i > 0 and P[i-1] <= P[i]:\n i -= 1\n return i","def min_distance_vertex(distance, visited):\n vertices = len(visited)\n min_distance = INF\n min_index = None\n for v in range(vertices):\n if not visited[v] and distance[v] <= min_distance:\n min_distance = distance[v]\n min_index = v\n return min_index","def find_nearest_neighbors_idx(X, x, k):\n ## homework:start\n result = \n ## homework:end\n return result","def closest_points_naive(self, x, y):\r\n # Running time: O(n ** 2)\r\n\r\n dist = []\r\n for i in range(len(x)):\r\n for j in range(i+1, len(x)):\r\n d = self.get_euclidean_distance(x[i], x[j], y[i], y[j])\r\n dist.append(d)\r\n \r\n return min(dist)","def shortestDistance(self, grid):\n # return self.house_oriented_TLE(grid)\n # One axis\n row_count = [sum(row) for row in grid]\n col_count = [0]* len(grid[0])\n row_dist = [0]* len(grid)\n col_dist = [0]* len(grid[0])\n output = sys.maxsize\n for i in range(len(grid)): \n for j in range(len(grid[0])):\n col_count[j] += grid[i][j]\n \n for index_p in range(len(row_count)):\n for index_h in range(len(row_count)):\n row_dist[index_p] += abs(index_h - index_p) * row_count[index_h]\n \n for index_p in range(len(col_count)):\n for index_h in range(len(col_count)):\n col_dist[index_p] += abs(index_h - index_p) * col_count[index_h]\n \n # print(row_count)\n # print(col_count)\n # print(row_dist)\n # print(col_dist)\n for i in range(len(grid)):\n for j in range(len(grid[0])):\n if grid[i][j] == 1:\n continue\n output = min(output, row_dist[i] + col_dist[j])\n return output","def find_min_point(points):\r\n smallest_point_index = 0\r\n for i in range(1, len(points)):\r\n if points[i][1] < points[smallest_point_index][1]:\r\n smallest_point_index = i\r\n elif points[i][0] > points[smallest_point_index][0] and points[i][1] == points[smallest_point_index][1]:\r\n smallest_point_index = i\r\n return smallest_point_index","def dist(dm, sm, neighbors):\n\n # Initialize list of possible distances\n distances = []\n\n # loop over all neighbors of the cell\n for neighbor in neighbors:\n # If the neighbor is valid\n if dm[neighbor[0], neighbor[1]] != -1:\n # add neighbor distance + 1 to possible distances\n distances.append(dm[neighbor[0], neighbor[1]] + 1)\n\n # return minimal distance\n return np.min(distances)","def get_min_dist(x0, y0, arr):\n dist = np.hypot(arr.T[0] - x0, arr.T[1] - y0)\n min_dist = np.min(dist)\n val = np.argmin(dist)\n return min_dist, arr[val]","def nodes_min_energy_index(self, node):\n idx = -1\n curr_energy = np.inf\n for i in range(self.cost_matrix.shape[1]):\n new_energy = self.cost_matrix[node][i]\n if new_energy < curr_energy:\n curr_energy = new_energy\n idx = i\n return idx","def get_maxmin_index_from_row(\n distance_matrix: np.ndarray,\n row: int,\n previous_indexes: List,\n type: str,\n )-> int:\n distance_matrix = distance_matrix.copy()\n arr = distance_matrix[row].astype(float)\n \n aux_list = range(arr.shape[0])\n aux_list_2 = []\n for i in aux_list:\n if i in previous_indexes:\n aux_list_2.append(True)\n else:\n aux_list_2.append(False)\n previous_indexes_bool = aux_list_2\n \n if type == 'max':\n arr[previous_indexes_bool] = -1\n target_index = np.argmax(arr)\n if type == 'min':\n arr[previous_indexes_bool] = np.Inf\n target_index = np.argmin(arr)\n \n return target_index","def get_idx(lons, lats, lon, lat):\n dist = ((lons - lon) ** 2 + (lats - lat) ** 2) ** 0.5\n return np.unravel_index(dist.argmin(), dist.shape)","def pair_idx(rows, comm=None):\n raise Exception(\"Not implemented\")\n \n if comm == None:\n comm = MPI.COMM_WORLD\n \n total = comb(rows,2,exact=True)\n size = comm.Get_size()\n \n size = 1000\n \n print(total / size)\n \n target = total / size\n \n current_row = 0\n calc_list = []\n row_list = [[] for x in range(size)]\n for rank in range(size):\n row_list[rank].append(current_row)\n \n current_calcs = 0\n \n for value in range(current_row, rows):\n current_calcs += value\n if current_calcs > target:\n if rank == size-1:\n pass\n else:\n break\n \n calc_list.append(current_calcs)\n row_list[rank].append(value)\n current_row = value\n \n return row_list,calc_list","def _findMin(p, A):\n\n m=(-1, (0,0))\n for p0 in A:\n dist = np.linalg.norm(p0-np.array(p))\n if m[0]==-1 or m[0]>dist:\n m = (dist, p0)\n \n return tuple(m[1])","def distance_matrix(n_row, n_col):\n\n n_pop = int(n_row * n_col)\n center = int(n_row/2*(n_col+1))\n\n pop_idx = np.arange(n_pop)\n pop_idx_col = np.remainder(pop_idx, n_col)\n pop_idx_row = pop_idx // n_row\n\n pos = np.vstack((pop_idx_col,pop_idx_row)).T\n distance = spa.distance.cdist([pos[center]], pos)[0]\n\n return distance","def min(weightData , dataSetVector ):\r\n # weightData: pass the whole weightData array.\r\n # dataSetVector: pass the a data vector to compare with weightdata array, to find its closest match\r\n winnerIndex = 0 #flag for initalizing the winner index\r\n minValue = EcuDist(dataSetVector,weightData[0]) # initalize the minValue\r\n # iterate through all weighdata rows to find the closest match, depending on ecu. distance,\r\n #and then return the index of the closest match(winner)\r\n for i in range(weightData.shape[0]):\r\n if(EcuDist(dataSetVector,weightData[i]) < minValue):\r\n minValue = EcuDist(dataSetVector,weightData[i])\r\n winnerIndex = i\r\n return winnerIndex","def find_min(self, A, w):\n import numpy as np\n\n vcost = self.INFINITY\n vto = vfrom = -1\n for v in w:\n # Get array offset of minimum of this vertex\n i = np.argmin(A[v,:])\n if A[v,i] < vcost:\n vcost = A[v,i]\n vto = i\n vfrom = v\n return (vfrom, vto, vcost)","def get_nearest_atom_inds_per_mol(self):\n self.closest_at_per_mol = np.zeros((self.nmol,\n self.at_per_mol,\n self.at_per_mol-1), dtype=int)\n\n # Get and sort distances\n all_at_inds = np.arange(self.at_per_mol)\n for imol in range(self.nmol):\n for iat in range(self.at_per_mol):\n at_inds = all_at_inds[all_at_inds != iat]\n dist = self.all_dist_per_mol[imol, iat, at_inds]\n\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\n self.closest_at_per_mol[imol, iat] = at_inds","def find_nearest_neighbours_SURF(averageDistance,inst,distanceArray,maxInst): \r\n NN=[]\r\n min_indices=[] \r\n\r\n for j in range(maxInst):\r\n if inst != j:\r\n locator = [inst,j]\r\n locator = sorted(locator, reverse=True) #Access corect half of table (result of removed table redundancy)\r\n d = distanceArray[locator[0]][locator[1]]\r\n if d<averageDistance:\r\n min_indices.append(j)\r\n \r\n for j in range(len(min_indices)):\r\n NN.append(min_indices[j])\r\n \r\n return NN","def find_nearest_neighbors(p, points, k=5):\n dist = np.zeros(points.shape[0])\n for i in range(len(dist)):\n dist[i] = distance(p, points[i])\n ind = np.argsort(dist)\n return ind[0:k]","def update_distmatrix(min_idx, dist_matrix):\n i, j = min_idx\n new_cluster_dist = np.minimum(dist_matrix[i, :], dist_matrix[j, :])\n new_cluster_dist[i] = np.inf\n \n dist_matrix[i, :] = new_cluster_dist\n dist_matrix[:, i] = new_cluster_dist\n \n dist_matrix[j, :] = np.inf\n dist_matrix[:, j] = np.inf\n \n return dist_matrix","def get_nearest_node_index(node_list, random_node):\n\n dist_list = [\n (node.x - random_node.x) ** 2 + (node.y - random_node.y) ** 2\n for node in node_list\n ]\n minind = dist_list.index(min(dist_list))\n\n return minind","def getNeighbors(training_data, test_row, k):\n\n distances = list()\n for training_row in training_data:\n dist = euclidianDistance(training_row, test_row)\n distances.append([training_row, dist])\n \n #Sort on the basis of dist\n distances.sort(key=lambda row:row[1])\n\n neighbors = list()\n\n for i in range(int(k)):\n neighbors.append(distances[i][0])\n\n return neighbors","def eeg_findnearest(x,X):\t\n\t#x array or vector and X a scalar\n\tabsdif = np.abs(x-X)\n\tval = np.min(absdif)\n\tidx = absdif.argmin()\n\treturn val,idx","def get_nearest_atom_inds(self):\n # Create empty data structure\n self.closest_ats = np.zeros((self.natom, self.natom-1), dtype=int)\n\n # Get and sort distances\n all_at_inds = np.arange(self.natom)\n for iat in range(self.natom):\n at_inds = all_at_inds[all_at_inds != iat]\n dist = self.all_dist[iat, at_inds]\n\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\n self.closest_ats[iat] = at_inds","def closestCluster(p, centers):\n\tbestIndex = 0\n closest = float(\"+inf\")\n for i in range(len(centers)):\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\n if distance < closest:\n closest = distance\n bestIndex = i\n\treturn bestIndex","def get_min_shannon_entropy(grid):\r\n curr_min = math.inf\r\n curr_best = []\r\n for i in range(len(grid[0])):\r\n for j in range(len(grid)):\r\n if not grid[j][i].collapsed:\r\n w = grid[j][i].block_weights\r\n shannon_entropy = sum([-math.log(el) for el in w] )\r\n if shannon_entropy < curr_min:\r\n curr_min = shannon_entropy\r\n curr_best = [(i,j)]\r\n elif shannon_entropy == curr_min:\r\n curr_best.append((i,j))\r\n idx = np.random.choice(range(len(curr_best))) #choose randomly if theres a tie\r\n return curr_best[idx] #x,y\r","def findSmallest(distancesWithNames):\n smallest = distancesWithNames[0][2]\n smallestIndex = -1\n for i in range(len(distancesWithNames)):\n if smallest >= distancesWithNames[i][2]:\n smallest = distancesWithNames[i][2]\n smallestIndex = i\n return smallestIndex","def get_index_under_point(self, event):\r\n xy = np.asarray(list(zip(self.xs, self.ys)))\r\n xyt = self.line.get_transform().transform(xy)\r\n xt, yt = xyt[:, 0], xyt[:, 1]\r\n d = np.sqrt((xt - event.x) ** 2 + (yt - event.y) ** 2)\r\n pt_idx = np.argmin(d)\r\n if d[pt_idx] >= self.max_pixels_from_vertex:\r\n pt_idx = None\r\n return pt_idx","def find_nearest_neighbour_from_point(point_cloud:np.ndarray, point:int) -> int:\n pass","def find_min_path(s, t, dist):\n\n rows = len(dist) - 1\n cols = len(dist[0]) - 1\n col = cols\n row = rows\n pos_str = \"Position: (row={} col={}) -> (row={} col={})\"\n cst_str = \"Cost: {}\"\n prev_row = row\n prev_col = col\n\n # init sparse path matrix\n sparse_path = [[\" \" for x in range(cols + 1)] for x in range(rows + 1)]\n sparse_path[0][0] = \"0\"\n\n # start with operation at (rows, cols) and work backwards\n sparse_path[rows][cols] = dist[rows][cols]\n\n if verbose == 2:\n print()\n print(\"Initial Minimum Path Matrix:\")\n print_matrix(s, t, sparse_path)\n\n while True:\n\n # bail out if we are in the corner\n if row == 0 and col == 0:\n break\n\n # if we are not at a matrix boundary\n if row != 0 and col != 0: # if at left edge or top row, cannot move diagonally\n\n # diagonal\n if (dist[row - 1][col - 1] == min(dist[row - 1][col],\n dist[row][col - 1],\n dist[row - 1][col - 1])) and (dist[row - 1][col - 1] == dist[row][col] or dist[row - 1][col - 1] == dist[row][col] - 1):\n sparse_path[row - 1][col - 1] = dist[row - 1][col - 1]\n temp_cost = dist[row - 1][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # left\n elif dist[row][col - 1] <= dist[row][col]:\n sparse_path[row][col - 1] = dist[row][col - 1]\n temp_cost = dist[row][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # above\n else:\n sparse_path[row - 1][col] = dist[row - 1][col]\n temp_cost = dist[row - 1][col]\n\n # move current cell\n prev_row = row\n prev_col = col\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # if at matrix edge, can only move up\n elif col == 0:\n # above\n sparse_path[row - 1][col] = dist[row - 1][col]\n temp_cost = dist[row - 1][col]\n\n # move current cell\n prev_row = row\n prev_col = col\n if row > 0:\n row -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # must be at row boundary, can only move left\n else:\n # left\n if dist[row][col - 1] <= dist[row][col]:\n sparse_path[row][col - 1] = dist[row][col - 1]\n temp_cost = dist[row][col - 1]\n\n # move current cell\n prev_row = row\n prev_col = col\n if col > 0:\n col -= 1\n\n if verbose == 2:\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n print(cst_str.format(temp_cost))\n print()\n\n # print matrix\n if verbose == 2:\n print_matrix(s, t, sparse_path)\n\n return sparse_path","def closestClusterAndDistance(p, centers):\n\tbestIndex = 0\n closest = float(\"+inf\")\n for i in range(len(centers)):\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\n if distance < closest:\n closest = distance\n bestIndex = i\n return (bestIndex, closest)","def get_nearest_indices(vector, matrix, num=5):\n if len(matrix) < num:\n num = len(matrix)\n\n m, n = matrix.shape\n diff_matrix = np.tile(vector, (m, 1)) - matrix\n diff_matrix = abs(diff_matrix)\n\n distance = diff_matrix.sum(axis=1)\n sortIndices = np.argsort(distance)\n sortIndices = sortIndices[0:num]\n\n return sortIndices[random.randint(0, num - 1)]","def detec_mfo_dist(betas):\n k = len(betas)\n min_dist = np.inf\n for i in range(k-1):\n for j in range(i+1,k):\n distance = np.sum((betas[i,:] - betas[j,:])**2)\n if distance < min_dist:\n MFO_index = [i,j]\n min_dist = distance\n return MFO_index","def get_nearest_row(self):\n return (self.rect.top - (self.screen.get_height() // 12)) // self.maze.block_size","def smallest_cert(pts):\n # 1. project the points\n def projected_flattened_pt(pt):\n mag = distance(pt)\n distance_wanted = rect_dist\n return distance_wanted/mag*pt[1], \\\n distance_wanted/mag*pt[2]\n\n flat_pts = np.array([[projected_flattened_pt(pt), pt] for pt in pts])\n\n # 2. identify rows of data\n data = {} \n # maps height (from ground) to the \"row\" (scan) list of\n # horizontal-only (1D) data\n for pt, origpt in flat_pts:\n for row_height in data:\n if abs(pt[1]-row_height) < dist_bt_rows/2:\n data[row_height].append([pt[0], origpt])\n break\n else:\n data[pt[1]] = [[pt[0], origpt]]\n\n #3. build up final_data so each row has enough point x-density\n def get_left_i(pts, left_edge):\n # get last index <= left_edge\n for i, pt in enumerate(pts):\n if pt[0] > left_edge:\n return i - 1\n\n def get_right_i(pts, right_edge):\n # get first index >= right_edge\n i = len(pts)-1\n while True:\n pt = pts[i]\n if pt[0] < right_edge:\n return i + 1\n i -= 1\n\n final_data = []\n for _, subdata in data.items():\n subdata = sorted(subdata, key=lambda t: t[0])\n i = get_left_i(subdata, lane_left)\n end = get_right_i(subdata, lane_right)\n\n final_data = [subdata[i][1]]\n while i < end:\n next_i = get_left_i(subdata[i:], \n subdata[i][0]+max_xpt_separation)\n if not next_i:\n return False\n i += next_i\n final_data.append(subdata[i][1])\n\n return final_data","def _lowestDistanceToCluster(self, clusters: ndarray, sequenceIdx: int) -> Tuple[int, float]:\n lowestClusterIdx = -1\n lowestDistance = np.inf\n for cIdx in range(self.numClusters):\n distance = self._distanceToCluster(clusters[cIdx], sequenceIdx)\n if distance < lowestDistance:\n lowestClusterIdx = cIdx\n lowestDistance = distance\n return lowestClusterIdx, lowestDistance","def find_closest_atom(coords1, coords2):\n\n coords1 = np.array(coords1)\n coords2 = np.array(coords2)\n diff = coords2[:, np.newaxis] - coords1[np.newaxis, :]\n dist = np.einsum('ijk->ij', diff**2)**0.5\n index = np.argmin(dist)\n return index","def closestCentroids(self, points , centroids ):\n dists = scipy.spatial.distance.cdist(points,centroids)\n # 1 is dimension\n minIds = numpy.argmin(dists, 1)\n return minIds","def find_neighbor_indices(atoms, probe, k):\r\n coords_all = atoms.getCoords()\r\n neighbor_indices = []\r\n atom_k = atoms[k]\r\n radius = atom_k.getRadius() + probe + probe\r\n\r\n indices = range(k)\r\n indices.extend(range(k+1, len(atoms)))\r\n\r\n for i in indices:\r\n\tdist = pos_distance(coords_all[k], coords_all[i])\r\n\t#dist = np.linalg.norm(coords_all[k] - coords_all[i])\r\n\tif dist < radius + atoms[i].getRadius():\r\n neighbor_indices.append(i)\r\n\r\n return neighbor_indices","def exhaustive_search(X, z):\n #initialize shortest path and distance to the first row\n d_star = la.norm(z - X[0])\n x_star = X[0]\n #check the distance from each row of X and if shortest thus far, save it\n for i in range(X.shape[0]):\n #calculate distance from current row to target z\n x = X[i,:]\n cur_dist = la.norm(x - z)\n #if the distance is smallest thus far, save the row and distance\n if cur_dist < d_star:\n x_star = x\n d_star = cur_dist\n return x_star, d_star","def _retrieve_neighbors(df, i_point, point, eps, column):\n neigborhood = []\n for index, row in df.iterrows():\n if index != i_point:\n a = np.array(point[column])\n b = np.array([row[column]])\n distance = np.linalg.norm(a - b)\n if distance <= eps:\n neigborhood.append(index)\n\n return neigborhood","def nearest_neighbor(data):\n features = set([i for i, x in enumerate(data[0][1])])\n return leave_one_out_cross_validation(data, features)","def closest_node(node, nodes):\n nodes = np.asarray(nodes)\n deltas = nodes - node\n dist_2 = np.einsum(\"ij,ij->i\", deltas, deltas)\n return np.argmin(dist_2), np.min(dist_2)","def find_nearest_neighbors(p, points, k):\n\timport numpy as np\n\tdistances = np.zeros(points.shape[0])\n\tfor i in range(len(distances)):\n\t\tdistances[i] = distance(p,points[i])\n\tind = np.argsort(distances)\n\treturn ind[0:k]","def djikstre(connection_mat):\n n = connection_mat.shape[0]\n dist, prev = {}, {}\n Q = list(range(n))\n \n for i in Q:\n dist[i] = np.inf\n dist[n-2] = 0.0\n \n while(len(Q)>0):\n\n min_dist = min([dist[key] for key in Q])\n u = [key for key in Q if dist[key] == min_dist][0]\n Q.remove(u)\n\n for v in np.nonzero(connection_mat[:, u])[0]:\n \n alt = dist[u]+connection_mat[v, u]\n \n if alt < dist[v]:\n dist[v] = alt\n prev[v] = u\n \n return dist, prev","def neirest_neighbour(business, cells):\n array = cells.get_neighbours(business, num=1)\n neighbours = pd.DataFrame(array).set_index('index')\n index = neighbours['distance'].idxmin()\n return neighbours.loc[index]","def closest(self, x, y):\n if self.direction == 'horizontal':\n p_pts = np.array([\n self.ax.transData.transform((p, 0))[0] for p in self.positions\n ])\n dist = abs(p_pts - x)\n else:\n p_pts = np.array([\n self.ax.transData.transform((0, p))[1] for p in self.positions\n ])\n dist = abs(p_pts - y)\n index = np.argmin(dist)\n return index, dist[index]","def _nearest_point_index(points, point):\n distance = sys.float_info.max\n index = None\n for i, p in enumerate(points):\n temp = _vec_distance(p, point)\n if temp < distance:\n distance = temp\n index = i\n return index, distance","def _kth_nearest_neighbor_dist(\n distance_matrix: Union[np.ndarray, scipy.sparse.csr_matrix], k\n) -> np.ndarray:\n\n if not is_integer(k):\n raise ValueError(f\"parameter 'k={k}' must be a positive integer\")\n else:\n # make sure we deal with Python built-in\n k = int(k)\n\n if not (0 <= k <= distance_matrix.shape[1]):\n raise ValueError(\n \"'k' must be an integer between 1 and \"\n f\"distance_matrix.shape[1]={distance_matrix.shape[1]}\"\n )\n\n if isinstance(distance_matrix, np.ndarray):\n dist_knn = np.partition(distance_matrix, k - 1, axis=1)[:, k - 1]\n elif isinstance(distance_matrix, scipy.sparse.csr_matrix):\n # see mircobenchmark_kth_nn.py for a comparison of implementations for the\n # sparse case\n\n def _get_kth_largest_elements_sparse(\n data: np.ndarray,\n indptr: np.ndarray,\n row_nnz,\n k_neighbor: int,\n ):\n dist_knn = np.zeros(len(row_nnz))\n for i in range(len(row_nnz)):\n start_row = indptr[i]\n dist_knn[i] = np.partition(\n data[start_row : start_row + row_nnz[i]], k_neighbor - 1\n )[k_neighbor - 1]\n\n return dist_knn\n\n row_nnz = distance_matrix.getnnz(axis=1)\n\n if (row_nnz < k).any():\n raise ValueError(\n f\"There are {(row_nnz < k).sum()} points that \"\n f\"do not have at least k_neighbor={k}.\"\n )\n\n dist_knn = _get_kth_largest_elements_sparse(\n distance_matrix.data,\n distance_matrix.indptr,\n row_nnz,\n k,\n )\n else:\n raise TypeError(f\"type {type(distance_matrix)} not supported\")\n\n return dist_knn","def _first_index_with_bigger_neighbour(P):\n i = len(P) - 1\n while i > 0 and P[i-1] >= P[i]:\n i -= 1\n return i","def getNearestLineIndex(row, tagLineNumbers):\n # DOC {{{\n # }}}\n\n # CODE {{{\n # initialize local auxiliary variables {{{\n nearestLineNumber = -1\n nearestLineIndex = -1\n # }}}\n\n # go through all tag line numbers and find the one nearest to the specified row {{{\n for lineIndex, lineNumber in enumerate(tagLineNumbers):\n # if the current line is nearer the current cursor position, take it {{{\n if (nearestLineNumber < lineNumber <= row):\n nearestLineNumber = lineNumber\n nearestLineIndex = lineIndex\n # }}}\n\n # if we've got past the current cursor position, let's end the search {{{\n if (lineNumber >= row):\n break\n # }}}\n # }}}\n\n # return index of the line with the nearest tag\n return nearestLineIndex\n # }}}","def shortestpathij(self, i, j):\n pathlist = []\n self.pathij(i, j, pathlist)\n distance = []\n \n for i in range(len(pathlist)):\n Temp = 0\n for j in range(len(pathlist[i]) - 1):\n Temp += self.Dismatrix[pathlist[i][j], pathlist[i][j+1]]\n distance.append(Temp)\n \n if(len(distance) == 0):\n return None\n else:\n return min(distance)","def min_horizontal_dist_meters(coords, targets, is_geo=False):\n xe = coords[:, 0]\n ye = coords[:, 1]\n n = len(xe)\n d = np.zeros(n)\n for j in range(n):\n d1 = dist_in_meters(targets, [xe[j], ye[j]], is_geo=is_geo)\n d[j] = d1.min()\n return d","def findNearset(x,y,lon,lat):\n dist = np.sqrt( (lon - x)**2 + (lat - y)**2)\n\n return np.argwhere(dist==dist.min())[0][0]","def get_nearest_neighbours(self, cell):\n\t\tneighs = self.get_neighbours(cell)\n\t\ti, j = cell.find_id()\n\t\tneighbours = []\n\t\tfor neigh in neighs:\n\t\t\tx, y = neigh.find_id()\n\t\t\tif abs(x-i)+abs(y-j) <= 1: \n\t\t\t\tneighbours.append(self.space[y,x])\n\t\treturn neighbours","def _low_tri_indices(rowCount):\n for col in range(rowCount):\n for row in range(col, rowCount):\n yield (row, col)","def min_distance(s1, s2):\n n = len(s1)\n m = len(s2)\n matrix = [([0]*(m+1)) for i in xrange(n+1)]\n for i in xrange(m+1):\n matrix[0][i] = i\n for i in xrange(n+1):\n matrix[i][0] = i\n for i in xrange(1,n+1):\n for j in xrange(1,m+1):\n temp = min(matrix[i-1][j]+1, matrix[i][j-1]+1)\n d = 0 if s1[i-1]==s2[j-1] else 1\n matrix[i][j] = min(temp, matrix[i-1][j-1]+d)\n return matrix[n][m]","def extract_min(H, ds):\n minDist = approxInf\n u = None # min vertex unknown\n i = 0\n for v in H:\n if ds[v] <= minDist:\n minDist = ds[v]\n u = v # note that u is unused (instead returned by pop)\n imin = i\n i += 1\n return(H.pop(imin)) # return [u, d]","def findminpath(tab, gxtab, gytab, pixtab):\n\n pathdist = 2 # the number of points each points on a ray can related to on the previous ray\n pathdist_penalty = 0.3 # penalty of the difference of the pathdist\n pathpix_penalty = 2 # penalty of the difference of pixel values between the point and the previous point\n nray = tab.shape[1]\n\n #tab = np.hstack((tab,tab[:, 0].reshape(tab.shape[0], 1)))\n #pixtab = np.hstack((pixtab,pixtab[:, 0].reshape(pixtab.shape[0], 1)))\n #gxtab = np.hstack((gxtab,gxtab[:, 0].reshape(gxtab.shape[0], 1)))\n #gytab = np.hstack((gytab,gytab[:, 0].reshape(gytab.shape[0], 1)))\n\n tab = np.hstack((tab,tab,tab)) # horizontally stack the tab matrix to prepare for the filtering on the result\n pixtab = np.hstack((pixtab,pixtab,pixtab))\n gxtab = np.hstack((gxtab,gxtab,gxtab))\n gytab = np.hstack((gytab,gytab,gytab))\n\n tab = (tab - tab.min()) / (tab.max() - tab.min()) # noralize the tab matrix\n pixtab = (pixtab - pixtab.min()) / (pixtab.max() - pixtab.min()) * -1 # for we want to find the white contour of the cell so we multipy -1 on the pixtab\n # tab = tab / np.median(tab)\n # pixtab = pixtab / np.median(pixtab)\n path = np.zeros(tab.shape)\n path[:, 0] = np.array(range(0, tab.shape[0]))\n score = np.zeros(tab.shape)\n score[:, 1] = tab[:, 1]\n\n for i in range(1, tab.shape[1]):\n for j in range(tab.shape[0]):\n mins = np.Inf # record the min value of the ray\n minat = 0\n for k in range(-pathdist, pathdist+1):\n if(0 <= (j+k) and (j+k) < tab.shape[0]):\n s = pixtab[j, i]\n pixdiff = abs(pixtab[j, i] - pixtab[j+k, i-1])\n s += pixdiff * pathpix_penalty # two kinds of penalty\n s += abs(k) * pathdist_penalty\n s += score[j+k, i-1]\n\n if(s < mins):\n mins = s\n minat = j + k\n path[j, i] = minat\n score[j, i]= mins\n\n start = int(np.argmin(score[:, -1]))\n path = path.astype(np.int32)\n minpath = [start]\n for i in range(tab.shape[1]-1, 0, -1):\n minpath.append(path[minpath[-1], i])\n minpath = minpath[::-1]\n # print(len(minpath))\n minpath = savgol_filter(minpath, 15, 3) # apply a Savitzky-Golay filter to the raw minpath signal\n minpath = minpath[nray:nray*2] # cut the middle part of minpath whose length is nray\n return np.array(minpath).astype(np.int32)","def find_all_nearest_neighbours(point_cloud:np.ndarray) -> np.ndarray:\n pass","def numNeighbors(minesSet, row_index, cols_index, num_cols, num_rows):\n mines = 0\n for j in np.arange(max(0, cols_index-1), min(num_cols-1, cols_index+1)+1):\n for i in np.arange(max(0, row_index-1), min(num_rows-1, row_index+1)+1):\n if ((i, j) in minesSet):\n mines+=1\n return mines","def index_condensed_matrix(n, i, j):\n if i == j:\n main_warning(\"Diagonal elements (i=j) are not stored in condensed matrices.\")\n return None\n elif i > j:\n i, j = j, i\n return int(i * (n - (i + 3) * 0.5) + j - 1)","def nearest_sparse(self, query):\n self.best_dist = float(\"inf\")\n self.best_element = None\n self._register_best_element = self._register_best_element_single \n self._nearest_sparse_recursive(self._sparse2seq(query), self.root, 0.0)\n return self.best_element,self.best_dist","def find_closest_pt(ref_lon, ref_lat, tlon, tlat):\n\n # compute great circle distance from location to model grid points\n dist = gc_dist(ref_lon, ref_lat, tlon, tlat)\n\n # find j index of closest grid point\n work = N.take(dist,N.argmin(dist,0),0).diagonal()\n jj = N.argsort(work)[0]\n\n # find i index of closest grid point\n work = N.take(dist,N.argmin(dist,1),1).diagonal()\n ii = N.argsort(work)[0]\n\n return ii, jj","def find_nearest(ref_array,target_array):\n ref_tree = scipy.spatial.cKDTree(ref_array)\n dist, indices = ref_tree.query(target_array, k=1)\n return indices","def locate_source(p,d):\n # M = sensors, n = dimensions\n M, n = p.shape\n p = np.matrix( p ).T\n\n # pick closest receiver\n c = np.argmin(d)\n #sensors delta time relative to sensor c\n d = d - min(d)\n\n indices = list(range(M))\n del indices[c]\n\n A = np.zeros([M-2,n])\n b = np.zeros([M-2,1])\n\n i = indices[0]\n for row,j in enumerate(indices[1:]):\n A[row,:] = 2*( (d[j])*(p[:,i]-p[:,c]).T - \\\n (d[i])*(p[:,j]-p[:,c]).T )\n b[row,0] = (d[i])*((d[j])**2-p[:,j].T*p[:,j]) + \\\n ((d[i])-(d[j]))*p[:,c].T*p[:,c] + \\\n (d[j])*(p[:,i].T*p[:,i]-(d[i])**2)\n\n\n x = np.asarray( np.linalg.lstsq(A,b)[0] )[:,0]\n return x","def _intra_cluster_distance(distances_row, labels, i):\n mask = labels == labels[i]\n mask[i] = False\n if not np.any(mask):\n # cluster of size 1\n return 0\n a = np.mean(distances_row[mask])\n return a","def matrix_idx(n_hist, n_req, n_rows):\n\n flat_idx = []\n for i in range(n_rows):\n flat_idx.extend(range(i * n_req, (i + 1) * n_req + n_hist))\n # idx = np.unravel_index(flat_idx, (n_rows, n_hist + n_req))\n\n idx_matrix = np.reshape(flat_idx, (n_rows, n_hist + n_req))\n idxX = idx_matrix[:, n_req:]\n idxY = idx_matrix[:, :n_req]\n\n return idxX, idxY","def minesweeper(matrix):\n \n num_rows = len(matrix)\n num_cols = len(matrix[0])\n \n adj_mines = []\n \n adj_row = [0]*num_cols\n \n for i in range(num_rows):\n adj_mines.append(adj_row[:])\n \n for r in range(num_rows):\n for c in range(num_cols):\n if matrix[r][c] == True:\n if (r-1) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r-1][c-1] += 1\n if (r-1) in range(num_rows) and (c) in range(num_cols):\n adj_mines[r-1][c] += 1\n if (r-1) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r-1][c+1] += 1\n if (r) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r][c-1] += 1\n if (r) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r][c+1] += 1\n if (r+1) in range(num_rows) and (c-1) in range(num_cols):\n adj_mines[r+1][c-1] += 1\n if (r+1) in range(num_rows) and (c) in range(num_cols):\n adj_mines[r+1][c] += 1\n if (r+1) in range(num_rows) and (c+1) in range(num_cols): \n adj_mines[r+1][c+1] += 1\n\n \n return adj_mines","def nearest_neighbors(self):\n neighbor_distances_and_indices = []\n for idx, data_point in enumerate(self.data):\n distance = self.euclidean_dis(data_point[:-1], self.query) # Calculate the distance between the query\n # example and the current example from the data.\n\n neighbor_distances_and_indices.append((distance, idx)) # Add the distance and the index of the example\n # to an ordered collection\n\n sorted_neighbor_distances_and_indices = sorted(neighbor_distances_and_indices, key=lambda x: x[0]) #\n # Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by\n # the distances\n\n k_nearest_distances_and_indices = sorted_neighbor_distances_and_indices[:self.k] # Pick the first K\n # entries from the sorted collection\n\n k_nearest_labels = [self.data[i][1] for distance, i in k_nearest_distances_and_indices] # Get the labels of\n # the selected K entries\n\n return k_nearest_labels, self.mode(k_nearest_labels)","def __find_immediate_predecessors(storm_object_table, target_row):\n\n error_checking.assert_is_integer(target_row)\n error_checking.assert_is_geq(target_row, 0)\n error_checking.assert_is_less_than(\n target_row, len(storm_object_table.index)\n )\n\n predecessor_sec_id_strings = [\n storm_object_table[c].values[target_row]\n for c in PREV_SECONDARY_ID_COLUMNS\n if storm_object_table[c].values[target_row] != ''\n ]\n\n num_predecessors = len(predecessor_sec_id_strings)\n if num_predecessors == 0:\n return numpy.array([], dtype=int)\n\n target_time_unix_sec = storm_object_table[\n tracking_utils.VALID_TIME_COLUMN].values[target_row]\n\n predecessor_rows = numpy.full(num_predecessors, -1, dtype=int)\n\n for i in range(num_predecessors):\n these_rows = numpy.where(numpy.logical_and(\n storm_object_table[tracking_utils.SECONDARY_ID_COLUMN].values ==\n predecessor_sec_id_strings[i],\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values <\n target_time_unix_sec\n ))[0]\n\n if len(these_rows) == 0:\n continue\n\n this_subrow = numpy.argmax(\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values[\n these_rows]\n )\n\n predecessor_rows[i] = these_rows[this_subrow]\n\n return predecessor_rows[predecessor_rows >= 0]","def closest_waypoint(self, location: pylot.utils.Location):\n min_dist = np.infty\n min_index = 0\n for index, waypoint in enumerate(self.waypoints):\n dist = waypoint.location.distance(location)\n if dist < min_dist:\n min_dist = dist\n min_index = index\n return min_index","def medoid_ft(ftvectors,pairwise_dist_matrix):\r\n N = len(ftvectors)\r\n if N == 1 : \r\n return 0 \r\n # pairwise_dist_matrix = calculate_dist_matrix_ft(ftvectors) \r\n sum_dist = np.sum(pairwise_dist_matrix, axis = 0)\r\n min_idx = np.argmin(sum_dist)\r\n return min_idx","def _partition_nearest(self, medoids, dists, only_these=set()):\n if len(only_these) == 0:\n allowed_inds = self._not_ignored_inds\n else:\n allowed_inds = self._not_ignored_inds & only_these\n closest_medoid_ind = np.argmin(dists[:,medoids], 1) # If len(medoids)==3, would look like [2,1,1,0,1,2,...].\n clusts = [[] for i in medoids]\n for node_ind, med_ind in enumerate(closest_medoid_ind):\n if node_ind in allowed_inds:\n clusts[med_ind].append(node_ind)\n return clusts","def search_matrix(to_search: int, a_matrix: list) -> (int, int):\n row = 0\n column = len(a_matrix[0]) - 1\n while row < len(a_matrix) and column >= 0:\n if to_search < a_matrix[row][column]:\n column -= 1\n elif to_search > a_matrix[row][column]:\n row += 1\n else:\n return row, column\n return -1, -1","def closest(self, x, y):\n pts = np.column_stack([self.x, self.y])\n # Transform data coordinates to pixel coordinates.\n pts = self.ax.transData.transform(pts)\n diff = pts - [x, y]\n dist = np.hypot(*diff.T)\n min_index = np.argmin(dist)\n return min_index, dist[min_index]","def closest_centroid(x,centroids):\n\tK =len(centroids)\n\tN = len(x)\n\tDistance = np.zeros((N,K))\n\tfor j in range(K):\n\t\tmu = centroids[j]\n\t\tDistance[:,j] = np.linalg.norm(x-mu,axis=1)\n\tout = np.argmin(Distance,axis=1) \n\treturn out"],"string":"[\n \"def nearest_min(dist_matrix):\\n # much faster than np.where\\n i, j = np.unravel_index(\\n np.argmin(dist_matrix), \\n dims=dist_matrix.shape\\n )\\n return i, j\",\n \"def nearest_points_indexes_with_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 1\\n np.fill_diagonal(matrix, 1)\\n # Get the position for the resulted values\\n sort_arg = np.argsort(matrix, axis=1)\\n\\n return sort_arg[:, : num_to_keep]\",\n \"def nearest_points_indexes_without_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 0\\n np.fill_diagonal(matrix, 0)\\n # Get the position for the resulted values\\n sort_arg = np.argsort(matrix, axis=1)\\n\\n return sort_arg[:, : num_to_keep]\",\n \"def find_min_distance():\\n return np.argmin(d)\",\n \"def nearest_neighbour(matrix, start=0):\\n path = [start]\\n while len(matrix) != len(path):\\n matrix[:, start] = numpy.inf\\n start = numpy.argmin(matrix[start])\\n path.append(start)\\n return path\",\n \"def nearest_vertex_to(self, point):\\n distances = self.distances_to(point)\\n idx = np.argmin(distances)\\n return idx\",\n \"def smallest_distance(self, clusters):\\n i, j = numpy.unravel_index(numpy.argmin(clusters), clusters.shape)\\n return clusters[i, j], i, j\",\n \"def nearest_cluster(X,c):\\n K = np.size(c,0)\\n idx = np.zeros((np.size(X,0),1))\\n arr = np.empty((np.size(X,0),1))\\n for i in range(0,K):\\n y = c[i]\\n temp = np.ones((np.size(X,0),1))*y\\n b = np.power(np.subtract(X,temp),2)\\n a = np.sum(b,axis = 1)\\n a.resize((np.size(X,0),1))\\n arr = np.append(arr, a, axis=1)\\n arr = np.delete(arr,0,axis=1)\\n idx = np.argmin(arr, axis=1)\\n return idx\",\n \"def find_min(self):\\n\\n\\n min_x = 1000\\n min_y = 1000\\n k = len(self.__col_lista)\\n for i in range(k):\\n x, y = self.__col_lista[i]\\n if x < min_x:\\n min_x = x\\n if y < min_y:\\n min_y = y\\n return min_x, min_y\",\n \"def find_closest_index(traj, point):\\n\\n\\t#TODO: vectorise function to receive any length of points.\\n\\n\\tdistances = np.subtract(np.array(point),traj) \\n\\tdistances = distances.reshape(-1,2)\\n\\t#distances = distances[~np.isnan(distances)].reshape(-1,2)\\n\\n\\t#print(\\\"distances\\\")\\n\\t#pprint(distances)\\n\\tdist_array = np.linalg.norm(distances, axis = 1)\\n\\t#pprint(dist_array)\\n\\t#dist_array = np.sqrt((distances[:,0]**2)+(distances[:,1]**2)) #array of distances from trajectory to gaze landing point in world. \\n\\tidx = np.nanargmin(abs(dist_array)) #find smallest difference in pythag distance from 0,0 to get closest point. \\n\\tdists = distances[idx, :]\\n\\tdist = dist_array[idx]\\n\\n\\treturn idx#, dists, dist\\n\\t#return idx\",\n \"def getclosest_ij(lats,lons,latpt,lonpt):\\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\\n dist_lon = (lons-lonpt)**2\\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\\n minindex_lon = dist_lon.argmin()\\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index\",\n \"def getclosest_ij(lats,lons,latpt,lonpt):\\n dist_lat = (lats-latpt)**2 # find squared distance of every point on grid\\n dist_lon = (lons-lonpt)**2\\n minindex_lat = dist_lat.argmin() # 1D index of minimum dist_sq element\\n minindex_lon = dist_lon.argmin()\\n return minindex_lat, minindex_lon # Get 2D index for latvals and lonvals arrays from 1D index\",\n \"def _find_min_pair(pandas_matrix):\\n numpy_matrix = pandas_matrix.values\\n mins = np.where(numpy_matrix == np.nanmin(numpy_matrix))\\n min_col_idx = mins[0][0]\\n min_row_idx = mins[1][0]\\n (min_col, min_row) = (pandas_matrix.index[min_col_idx], \\n pandas_matrix.columns[min_row_idx])\\n\\n return (min_col, min_row)\",\n \"def find_closest(distances, threshold):\\n n = len(distances)\\n person_1 = []\\n person_2 = []\\n d = []\\n\\n for i in range(n):\\n for j in range(i+1, n):\\n if distances[i][j] <= threshold:\\n person_1.append(i)\\n person_2.append(j)\\n d.append(distances[i][j])\\n\\n return person_1, person_2, d\",\n \"def nearest_points_values_without_self(matrix, num_to_keep):\\n\\n # Set the diagonal to 0\\n np.fill_diagonal(matrix, 0)\\n # Get the position for the resulted values\\n sort = np.sort(matrix, axis=1)\\n\\n return sort[:, : num_to_keep]\",\n \"def getRowHeuristics(matrix):\\n row, col = matrix.shape\\n rHeuristic = np.zeros((row,2)) # Dos columnas. La primera para indicar la columna la segunda para la Heuristica\\n for i in range(0,row):\\n rHeuristic[i,0] = int(i)\\n #print (i,sum(matrix[:,i]), pesos[i], float(pesos[i]/sum(matrix[:,i])))\\n rHeuristic[i,1] = 1/sum(matrix[i,:])\\n return rHeuristic[rHeuristic[:,1].argsort()]\",\n \"def nearestNeighbor(self, coords, my_index, blacklist):\\n min_index, min_distance = 0, 999999999\\n my_col, my_row = coords[my_index]\\n for i, (col, row) in enumerate(coords):\\n if i in blacklist:\\n continue\\n distance = math.sqrt((my_col-col)**2 + (my_row-row)**2)\\n if distance < min_distance:\\n min_index, min_distance = i, distance\\n return min_index\",\n \"def _nearest_cluster_distance(distances_row, labels, i):\\n label = labels[i]\\n b = np.min([np.mean(distances_row[labels == cur_label])\\n for cur_label in set(labels) if not cur_label == label])\\n return b\",\n \"def getNearestSampleIndex(test, trainX):\\n dist_matrix = test - trainX\\n dist_square = dist_matrix ** 2\\n dist_sums = dist_square.sum(axis=1)\\n distance_vector = np.sqrt(dist_sums)\\n return (distance_vector).argmin()\",\n \"def find_closest_trajectory_pose(self):\\n np_state = numpy.array([[self.x], [self.y]])\\n temp_distance = numpy.sum(\\n (self.np_trajectory[0:2, :] - np_state) ** 2,\\n axis=0)\\n best_idx = numpy.argmin(temp_distance)\\n return best_idx\",\n \"def findknn(xTr,xTe,k):\\n\\n # YOUR CODE HERE\\n if k > len(xTr):\\n k = len(xTr)\\n \\n D=l2distance(xTe, xTr)\\n (m,n) = D.shape\\n \\n indices = []\\n dists = []\\n for i in range(m):\\n smallest_indices = np.argsort(D[i])\\n ind = smallest_indices[:k]\\n dis = D[i,smallest_indices[:k]]\\n indices.append(ind)\\n dists.append(dis)\\n \\n indices = np.transpose(np.array(indices))\\n dists = np.transpose(np.array(dists))\\n return indices, dists\",\n \"def _compute_euclidean_neigh_matrix(src, d_matrix, radius):\\n\\n n_max = 100\\n n_min = 3\\n reached_points = np.array([0])\\n counter = 0\\n n_neigh = []\\n list_neigh = []\\n\\n while counter < reached_points.shape[0] < src.shape[0]:\\n P = reached_points[counter]\\n aux = np.array(sorted(\\n np.where(d_matrix[P] <= radius)[0],\\n key=lambda k: d_matrix[P, k]))\\n n_neigh.append(aux.shape[0])\\n\\n # Check the number of neighbours\\n if n_neigh[-1] < n_min:\\n raise ValueError('Computation of neighbours aborted since '\\n 'their minimum number is too small.\\\\n'\\n 'Please choose a higher radius.')\\n elif n_neigh[-1] > n_max:\\n raise ValueError('Computation of neighbours aborted since'\\n 'their maximum number is too big.\\\\n'\\n 'Please choose a lower radius.')\\n list_neigh.append(aux)\\n reached_points = np.append(reached_points,\\n aux[~np.in1d(aux, reached_points)])\\n counter += 1\\n\\n if counter >= reached_points.shape[0]:\\n raise ValueError('Too small value of the radius:'\\n 'the neighbour-matrix is not connected')\\n elif src.shape[0] == reached_points.shape[0]:\\n while counter < src.shape[0]:\\n P = reached_points[counter]\\n aux = np.array(sorted(\\n np.where(d_matrix[P] <= radius)[0],\\n key=lambda k: d_matrix[P, k]))\\n n_neigh.append(aux.shape[0])\\n\\n if n_neigh[-1] < n_min:\\n raise ValueError('Computation of neighbours aborted since '\\n 'their minimum number is too small.\\\\n'\\n 'Please choose a higher radius.')\\n elif n_neigh[-1] > n_max:\\n raise ValueError('Computation of neighbours aborted since'\\n 'their maximum number is too big.\\\\n'\\n 'Please choose a lower radius.')\\n\\n list_neigh.append(aux)\\n counter += 1\\n\\n n_neigh_max = max(n_neigh)\\n n_matrix = np.zeros([src.shape[0],\\n n_neigh_max], dtype=int) - 1\\n for i in range(src.shape[0]):\\n n_matrix[i, 0:list_neigh[i].shape[0]] = list_neigh[i]\\n index_ord = np.argsort(n_matrix[:, 0])\\n n_matrix = n_matrix[index_ord]\\n return n_matrix\\n else:\\n raise RuntimeError(\\\"Some problems during\\\"\\n \\\"computation of neighbours.\\\")\",\n \"def saddle_points(matrix):\\n if not all(len(row) == len(matrix[0]) for row in matrix[1:]):\\n raise ValueError('Provided matrix is irregular.')\\n columns = [col for col in zip(*matrix)]\\n points = set()\\n for ridx, row in enumerate(matrix):\\n for cidx, element in enumerate(row):\\n if element == max(row) and element == min(columns[cidx]):\\n points.add((ridx, cidx))\\n return points\",\n \"def _first_index_with_smaller_neighbour(P):\\n i = len(P) - 1\\n while i > 0 and P[i-1] <= P[i]:\\n i -= 1\\n return i\",\n \"def min_distance_vertex(distance, visited):\\n vertices = len(visited)\\n min_distance = INF\\n min_index = None\\n for v in range(vertices):\\n if not visited[v] and distance[v] <= min_distance:\\n min_distance = distance[v]\\n min_index = v\\n return min_index\",\n \"def find_nearest_neighbors_idx(X, x, k):\\n ## homework:start\\n result = \\n ## homework:end\\n return result\",\n \"def closest_points_naive(self, x, y):\\r\\n # Running time: O(n ** 2)\\r\\n\\r\\n dist = []\\r\\n for i in range(len(x)):\\r\\n for j in range(i+1, len(x)):\\r\\n d = self.get_euclidean_distance(x[i], x[j], y[i], y[j])\\r\\n dist.append(d)\\r\\n \\r\\n return min(dist)\",\n \"def shortestDistance(self, grid):\\n # return self.house_oriented_TLE(grid)\\n # One axis\\n row_count = [sum(row) for row in grid]\\n col_count = [0]* len(grid[0])\\n row_dist = [0]* len(grid)\\n col_dist = [0]* len(grid[0])\\n output = sys.maxsize\\n for i in range(len(grid)): \\n for j in range(len(grid[0])):\\n col_count[j] += grid[i][j]\\n \\n for index_p in range(len(row_count)):\\n for index_h in range(len(row_count)):\\n row_dist[index_p] += abs(index_h - index_p) * row_count[index_h]\\n \\n for index_p in range(len(col_count)):\\n for index_h in range(len(col_count)):\\n col_dist[index_p] += abs(index_h - index_p) * col_count[index_h]\\n \\n # print(row_count)\\n # print(col_count)\\n # print(row_dist)\\n # print(col_dist)\\n for i in range(len(grid)):\\n for j in range(len(grid[0])):\\n if grid[i][j] == 1:\\n continue\\n output = min(output, row_dist[i] + col_dist[j])\\n return output\",\n \"def find_min_point(points):\\r\\n smallest_point_index = 0\\r\\n for i in range(1, len(points)):\\r\\n if points[i][1] < points[smallest_point_index][1]:\\r\\n smallest_point_index = i\\r\\n elif points[i][0] > points[smallest_point_index][0] and points[i][1] == points[smallest_point_index][1]:\\r\\n smallest_point_index = i\\r\\n return smallest_point_index\",\n \"def dist(dm, sm, neighbors):\\n\\n # Initialize list of possible distances\\n distances = []\\n\\n # loop over all neighbors of the cell\\n for neighbor in neighbors:\\n # If the neighbor is valid\\n if dm[neighbor[0], neighbor[1]] != -1:\\n # add neighbor distance + 1 to possible distances\\n distances.append(dm[neighbor[0], neighbor[1]] + 1)\\n\\n # return minimal distance\\n return np.min(distances)\",\n \"def get_min_dist(x0, y0, arr):\\n dist = np.hypot(arr.T[0] - x0, arr.T[1] - y0)\\n min_dist = np.min(dist)\\n val = np.argmin(dist)\\n return min_dist, arr[val]\",\n \"def nodes_min_energy_index(self, node):\\n idx = -1\\n curr_energy = np.inf\\n for i in range(self.cost_matrix.shape[1]):\\n new_energy = self.cost_matrix[node][i]\\n if new_energy < curr_energy:\\n curr_energy = new_energy\\n idx = i\\n return idx\",\n \"def get_maxmin_index_from_row(\\n distance_matrix: np.ndarray,\\n row: int,\\n previous_indexes: List,\\n type: str,\\n )-> int:\\n distance_matrix = distance_matrix.copy()\\n arr = distance_matrix[row].astype(float)\\n \\n aux_list = range(arr.shape[0])\\n aux_list_2 = []\\n for i in aux_list:\\n if i in previous_indexes:\\n aux_list_2.append(True)\\n else:\\n aux_list_2.append(False)\\n previous_indexes_bool = aux_list_2\\n \\n if type == 'max':\\n arr[previous_indexes_bool] = -1\\n target_index = np.argmax(arr)\\n if type == 'min':\\n arr[previous_indexes_bool] = np.Inf\\n target_index = np.argmin(arr)\\n \\n return target_index\",\n \"def get_idx(lons, lats, lon, lat):\\n dist = ((lons - lon) ** 2 + (lats - lat) ** 2) ** 0.5\\n return np.unravel_index(dist.argmin(), dist.shape)\",\n \"def pair_idx(rows, comm=None):\\n raise Exception(\\\"Not implemented\\\")\\n \\n if comm == None:\\n comm = MPI.COMM_WORLD\\n \\n total = comb(rows,2,exact=True)\\n size = comm.Get_size()\\n \\n size = 1000\\n \\n print(total / size)\\n \\n target = total / size\\n \\n current_row = 0\\n calc_list = []\\n row_list = [[] for x in range(size)]\\n for rank in range(size):\\n row_list[rank].append(current_row)\\n \\n current_calcs = 0\\n \\n for value in range(current_row, rows):\\n current_calcs += value\\n if current_calcs > target:\\n if rank == size-1:\\n pass\\n else:\\n break\\n \\n calc_list.append(current_calcs)\\n row_list[rank].append(value)\\n current_row = value\\n \\n return row_list,calc_list\",\n \"def _findMin(p, A):\\n\\n m=(-1, (0,0))\\n for p0 in A:\\n dist = np.linalg.norm(p0-np.array(p))\\n if m[0]==-1 or m[0]>dist:\\n m = (dist, p0)\\n \\n return tuple(m[1])\",\n \"def distance_matrix(n_row, n_col):\\n\\n n_pop = int(n_row * n_col)\\n center = int(n_row/2*(n_col+1))\\n\\n pop_idx = np.arange(n_pop)\\n pop_idx_col = np.remainder(pop_idx, n_col)\\n pop_idx_row = pop_idx // n_row\\n\\n pos = np.vstack((pop_idx_col,pop_idx_row)).T\\n distance = spa.distance.cdist([pos[center]], pos)[0]\\n\\n return distance\",\n \"def min(weightData , dataSetVector ):\\r\\n # weightData: pass the whole weightData array.\\r\\n # dataSetVector: pass the a data vector to compare with weightdata array, to find its closest match\\r\\n winnerIndex = 0 #flag for initalizing the winner index\\r\\n minValue = EcuDist(dataSetVector,weightData[0]) # initalize the minValue\\r\\n # iterate through all weighdata rows to find the closest match, depending on ecu. distance,\\r\\n #and then return the index of the closest match(winner)\\r\\n for i in range(weightData.shape[0]):\\r\\n if(EcuDist(dataSetVector,weightData[i]) < minValue):\\r\\n minValue = EcuDist(dataSetVector,weightData[i])\\r\\n winnerIndex = i\\r\\n return winnerIndex\",\n \"def find_min(self, A, w):\\n import numpy as np\\n\\n vcost = self.INFINITY\\n vto = vfrom = -1\\n for v in w:\\n # Get array offset of minimum of this vertex\\n i = np.argmin(A[v,:])\\n if A[v,i] < vcost:\\n vcost = A[v,i]\\n vto = i\\n vfrom = v\\n return (vfrom, vto, vcost)\",\n \"def get_nearest_atom_inds_per_mol(self):\\n self.closest_at_per_mol = np.zeros((self.nmol,\\n self.at_per_mol,\\n self.at_per_mol-1), dtype=int)\\n\\n # Get and sort distances\\n all_at_inds = np.arange(self.at_per_mol)\\n for imol in range(self.nmol):\\n for iat in range(self.at_per_mol):\\n at_inds = all_at_inds[all_at_inds != iat]\\n dist = self.all_dist_per_mol[imol, iat, at_inds]\\n\\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\\n self.closest_at_per_mol[imol, iat] = at_inds\",\n \"def find_nearest_neighbours_SURF(averageDistance,inst,distanceArray,maxInst): \\r\\n NN=[]\\r\\n min_indices=[] \\r\\n\\r\\n for j in range(maxInst):\\r\\n if inst != j:\\r\\n locator = [inst,j]\\r\\n locator = sorted(locator, reverse=True) #Access corect half of table (result of removed table redundancy)\\r\\n d = distanceArray[locator[0]][locator[1]]\\r\\n if d<averageDistance:\\r\\n min_indices.append(j)\\r\\n \\r\\n for j in range(len(min_indices)):\\r\\n NN.append(min_indices[j])\\r\\n \\r\\n return NN\",\n \"def find_nearest_neighbors(p, points, k=5):\\n dist = np.zeros(points.shape[0])\\n for i in range(len(dist)):\\n dist[i] = distance(p, points[i])\\n ind = np.argsort(dist)\\n return ind[0:k]\",\n \"def update_distmatrix(min_idx, dist_matrix):\\n i, j = min_idx\\n new_cluster_dist = np.minimum(dist_matrix[i, :], dist_matrix[j, :])\\n new_cluster_dist[i] = np.inf\\n \\n dist_matrix[i, :] = new_cluster_dist\\n dist_matrix[:, i] = new_cluster_dist\\n \\n dist_matrix[j, :] = np.inf\\n dist_matrix[:, j] = np.inf\\n \\n return dist_matrix\",\n \"def get_nearest_node_index(node_list, random_node):\\n\\n dist_list = [\\n (node.x - random_node.x) ** 2 + (node.y - random_node.y) ** 2\\n for node in node_list\\n ]\\n minind = dist_list.index(min(dist_list))\\n\\n return minind\",\n \"def getNeighbors(training_data, test_row, k):\\n\\n distances = list()\\n for training_row in training_data:\\n dist = euclidianDistance(training_row, test_row)\\n distances.append([training_row, dist])\\n \\n #Sort on the basis of dist\\n distances.sort(key=lambda row:row[1])\\n\\n neighbors = list()\\n\\n for i in range(int(k)):\\n neighbors.append(distances[i][0])\\n\\n return neighbors\",\n \"def eeg_findnearest(x,X):\\t\\n\\t#x array or vector and X a scalar\\n\\tabsdif = np.abs(x-X)\\n\\tval = np.min(absdif)\\n\\tidx = absdif.argmin()\\n\\treturn val,idx\",\n \"def get_nearest_atom_inds(self):\\n # Create empty data structure\\n self.closest_ats = np.zeros((self.natom, self.natom-1), dtype=int)\\n\\n # Get and sort distances\\n all_at_inds = np.arange(self.natom)\\n for iat in range(self.natom):\\n at_inds = all_at_inds[all_at_inds != iat]\\n dist = self.all_dist[iat, at_inds]\\n\\n at_inds = [i[1] for i in sorted(zip(dist, at_inds))]\\n self.closest_ats[iat] = at_inds\",\n \"def closestCluster(p, centers):\\n\\tbestIndex = 0\\n closest = float(\\\"+inf\\\")\\n for i in range(len(centers)):\\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\\n if distance < closest:\\n closest = distance\\n bestIndex = i\\n\\treturn bestIndex\",\n \"def get_min_shannon_entropy(grid):\\r\\n curr_min = math.inf\\r\\n curr_best = []\\r\\n for i in range(len(grid[0])):\\r\\n for j in range(len(grid)):\\r\\n if not grid[j][i].collapsed:\\r\\n w = grid[j][i].block_weights\\r\\n shannon_entropy = sum([-math.log(el) for el in w] )\\r\\n if shannon_entropy < curr_min:\\r\\n curr_min = shannon_entropy\\r\\n curr_best = [(i,j)]\\r\\n elif shannon_entropy == curr_min:\\r\\n curr_best.append((i,j))\\r\\n idx = np.random.choice(range(len(curr_best))) #choose randomly if theres a tie\\r\\n return curr_best[idx] #x,y\\r\",\n \"def findSmallest(distancesWithNames):\\n smallest = distancesWithNames[0][2]\\n smallestIndex = -1\\n for i in range(len(distancesWithNames)):\\n if smallest >= distancesWithNames[i][2]:\\n smallest = distancesWithNames[i][2]\\n smallestIndex = i\\n return smallestIndex\",\n \"def get_index_under_point(self, event):\\r\\n xy = np.asarray(list(zip(self.xs, self.ys)))\\r\\n xyt = self.line.get_transform().transform(xy)\\r\\n xt, yt = xyt[:, 0], xyt[:, 1]\\r\\n d = np.sqrt((xt - event.x) ** 2 + (yt - event.y) ** 2)\\r\\n pt_idx = np.argmin(d)\\r\\n if d[pt_idx] >= self.max_pixels_from_vertex:\\r\\n pt_idx = None\\r\\n return pt_idx\",\n \"def find_nearest_neighbour_from_point(point_cloud:np.ndarray, point:int) -> int:\\n pass\",\n \"def find_min_path(s, t, dist):\\n\\n rows = len(dist) - 1\\n cols = len(dist[0]) - 1\\n col = cols\\n row = rows\\n pos_str = \\\"Position: (row={} col={}) -> (row={} col={})\\\"\\n cst_str = \\\"Cost: {}\\\"\\n prev_row = row\\n prev_col = col\\n\\n # init sparse path matrix\\n sparse_path = [[\\\" \\\" for x in range(cols + 1)] for x in range(rows + 1)]\\n sparse_path[0][0] = \\\"0\\\"\\n\\n # start with operation at (rows, cols) and work backwards\\n sparse_path[rows][cols] = dist[rows][cols]\\n\\n if verbose == 2:\\n print()\\n print(\\\"Initial Minimum Path Matrix:\\\")\\n print_matrix(s, t, sparse_path)\\n\\n while True:\\n\\n # bail out if we are in the corner\\n if row == 0 and col == 0:\\n break\\n\\n # if we are not at a matrix boundary\\n if row != 0 and col != 0: # if at left edge or top row, cannot move diagonally\\n\\n # diagonal\\n if (dist[row - 1][col - 1] == min(dist[row - 1][col],\\n dist[row][col - 1],\\n dist[row - 1][col - 1])) and (dist[row - 1][col - 1] == dist[row][col] or dist[row - 1][col - 1] == dist[row][col] - 1):\\n sparse_path[row - 1][col - 1] = dist[row - 1][col - 1]\\n temp_cost = dist[row - 1][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # left\\n elif dist[row][col - 1] <= dist[row][col]:\\n sparse_path[row][col - 1] = dist[row][col - 1]\\n temp_cost = dist[row][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # above\\n else:\\n sparse_path[row - 1][col] = dist[row - 1][col]\\n temp_cost = dist[row - 1][col]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # if at matrix edge, can only move up\\n elif col == 0:\\n # above\\n sparse_path[row - 1][col] = dist[row - 1][col]\\n temp_cost = dist[row - 1][col]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if row > 0:\\n row -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # must be at row boundary, can only move left\\n else:\\n # left\\n if dist[row][col - 1] <= dist[row][col]:\\n sparse_path[row][col - 1] = dist[row][col - 1]\\n temp_cost = dist[row][col - 1]\\n\\n # move current cell\\n prev_row = row\\n prev_col = col\\n if col > 0:\\n col -= 1\\n\\n if verbose == 2:\\n print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\\n print(cst_str.format(temp_cost))\\n print()\\n\\n # print matrix\\n if verbose == 2:\\n print_matrix(s, t, sparse_path)\\n\\n return sparse_path\",\n \"def closestClusterAndDistance(p, centers):\\n\\tbestIndex = 0\\n closest = float(\\\"+inf\\\")\\n for i in range(len(centers)):\\n distance = np.sqrt(np.sum((np.array(p) - centers[i]) ** 2))\\n if distance < closest:\\n closest = distance\\n bestIndex = i\\n return (bestIndex, closest)\",\n \"def get_nearest_indices(vector, matrix, num=5):\\n if len(matrix) < num:\\n num = len(matrix)\\n\\n m, n = matrix.shape\\n diff_matrix = np.tile(vector, (m, 1)) - matrix\\n diff_matrix = abs(diff_matrix)\\n\\n distance = diff_matrix.sum(axis=1)\\n sortIndices = np.argsort(distance)\\n sortIndices = sortIndices[0:num]\\n\\n return sortIndices[random.randint(0, num - 1)]\",\n \"def detec_mfo_dist(betas):\\n k = len(betas)\\n min_dist = np.inf\\n for i in range(k-1):\\n for j in range(i+1,k):\\n distance = np.sum((betas[i,:] - betas[j,:])**2)\\n if distance < min_dist:\\n MFO_index = [i,j]\\n min_dist = distance\\n return MFO_index\",\n \"def get_nearest_row(self):\\n return (self.rect.top - (self.screen.get_height() // 12)) // self.maze.block_size\",\n \"def smallest_cert(pts):\\n # 1. project the points\\n def projected_flattened_pt(pt):\\n mag = distance(pt)\\n distance_wanted = rect_dist\\n return distance_wanted/mag*pt[1], \\\\\\n distance_wanted/mag*pt[2]\\n\\n flat_pts = np.array([[projected_flattened_pt(pt), pt] for pt in pts])\\n\\n # 2. identify rows of data\\n data = {} \\n # maps height (from ground) to the \\\"row\\\" (scan) list of\\n # horizontal-only (1D) data\\n for pt, origpt in flat_pts:\\n for row_height in data:\\n if abs(pt[1]-row_height) < dist_bt_rows/2:\\n data[row_height].append([pt[0], origpt])\\n break\\n else:\\n data[pt[1]] = [[pt[0], origpt]]\\n\\n #3. build up final_data so each row has enough point x-density\\n def get_left_i(pts, left_edge):\\n # get last index <= left_edge\\n for i, pt in enumerate(pts):\\n if pt[0] > left_edge:\\n return i - 1\\n\\n def get_right_i(pts, right_edge):\\n # get first index >= right_edge\\n i = len(pts)-1\\n while True:\\n pt = pts[i]\\n if pt[0] < right_edge:\\n return i + 1\\n i -= 1\\n\\n final_data = []\\n for _, subdata in data.items():\\n subdata = sorted(subdata, key=lambda t: t[0])\\n i = get_left_i(subdata, lane_left)\\n end = get_right_i(subdata, lane_right)\\n\\n final_data = [subdata[i][1]]\\n while i < end:\\n next_i = get_left_i(subdata[i:], \\n subdata[i][0]+max_xpt_separation)\\n if not next_i:\\n return False\\n i += next_i\\n final_data.append(subdata[i][1])\\n\\n return final_data\",\n \"def _lowestDistanceToCluster(self, clusters: ndarray, sequenceIdx: int) -> Tuple[int, float]:\\n lowestClusterIdx = -1\\n lowestDistance = np.inf\\n for cIdx in range(self.numClusters):\\n distance = self._distanceToCluster(clusters[cIdx], sequenceIdx)\\n if distance < lowestDistance:\\n lowestClusterIdx = cIdx\\n lowestDistance = distance\\n return lowestClusterIdx, lowestDistance\",\n \"def find_closest_atom(coords1, coords2):\\n\\n coords1 = np.array(coords1)\\n coords2 = np.array(coords2)\\n diff = coords2[:, np.newaxis] - coords1[np.newaxis, :]\\n dist = np.einsum('ijk->ij', diff**2)**0.5\\n index = np.argmin(dist)\\n return index\",\n \"def closestCentroids(self, points , centroids ):\\n dists = scipy.spatial.distance.cdist(points,centroids)\\n # 1 is dimension\\n minIds = numpy.argmin(dists, 1)\\n return minIds\",\n \"def find_neighbor_indices(atoms, probe, k):\\r\\n coords_all = atoms.getCoords()\\r\\n neighbor_indices = []\\r\\n atom_k = atoms[k]\\r\\n radius = atom_k.getRadius() + probe + probe\\r\\n\\r\\n indices = range(k)\\r\\n indices.extend(range(k+1, len(atoms)))\\r\\n\\r\\n for i in indices:\\r\\n\\tdist = pos_distance(coords_all[k], coords_all[i])\\r\\n\\t#dist = np.linalg.norm(coords_all[k] - coords_all[i])\\r\\n\\tif dist < radius + atoms[i].getRadius():\\r\\n neighbor_indices.append(i)\\r\\n\\r\\n return neighbor_indices\",\n \"def exhaustive_search(X, z):\\n #initialize shortest path and distance to the first row\\n d_star = la.norm(z - X[0])\\n x_star = X[0]\\n #check the distance from each row of X and if shortest thus far, save it\\n for i in range(X.shape[0]):\\n #calculate distance from current row to target z\\n x = X[i,:]\\n cur_dist = la.norm(x - z)\\n #if the distance is smallest thus far, save the row and distance\\n if cur_dist < d_star:\\n x_star = x\\n d_star = cur_dist\\n return x_star, d_star\",\n \"def _retrieve_neighbors(df, i_point, point, eps, column):\\n neigborhood = []\\n for index, row in df.iterrows():\\n if index != i_point:\\n a = np.array(point[column])\\n b = np.array([row[column]])\\n distance = np.linalg.norm(a - b)\\n if distance <= eps:\\n neigborhood.append(index)\\n\\n return neigborhood\",\n \"def nearest_neighbor(data):\\n features = set([i for i, x in enumerate(data[0][1])])\\n return leave_one_out_cross_validation(data, features)\",\n \"def closest_node(node, nodes):\\n nodes = np.asarray(nodes)\\n deltas = nodes - node\\n dist_2 = np.einsum(\\\"ij,ij->i\\\", deltas, deltas)\\n return np.argmin(dist_2), np.min(dist_2)\",\n \"def find_nearest_neighbors(p, points, k):\\n\\timport numpy as np\\n\\tdistances = np.zeros(points.shape[0])\\n\\tfor i in range(len(distances)):\\n\\t\\tdistances[i] = distance(p,points[i])\\n\\tind = np.argsort(distances)\\n\\treturn ind[0:k]\",\n \"def djikstre(connection_mat):\\n n = connection_mat.shape[0]\\n dist, prev = {}, {}\\n Q = list(range(n))\\n \\n for i in Q:\\n dist[i] = np.inf\\n dist[n-2] = 0.0\\n \\n while(len(Q)>0):\\n\\n min_dist = min([dist[key] for key in Q])\\n u = [key for key in Q if dist[key] == min_dist][0]\\n Q.remove(u)\\n\\n for v in np.nonzero(connection_mat[:, u])[0]:\\n \\n alt = dist[u]+connection_mat[v, u]\\n \\n if alt < dist[v]:\\n dist[v] = alt\\n prev[v] = u\\n \\n return dist, prev\",\n \"def neirest_neighbour(business, cells):\\n array = cells.get_neighbours(business, num=1)\\n neighbours = pd.DataFrame(array).set_index('index')\\n index = neighbours['distance'].idxmin()\\n return neighbours.loc[index]\",\n \"def closest(self, x, y):\\n if self.direction == 'horizontal':\\n p_pts = np.array([\\n self.ax.transData.transform((p, 0))[0] for p in self.positions\\n ])\\n dist = abs(p_pts - x)\\n else:\\n p_pts = np.array([\\n self.ax.transData.transform((0, p))[1] for p in self.positions\\n ])\\n dist = abs(p_pts - y)\\n index = np.argmin(dist)\\n return index, dist[index]\",\n \"def _nearest_point_index(points, point):\\n distance = sys.float_info.max\\n index = None\\n for i, p in enumerate(points):\\n temp = _vec_distance(p, point)\\n if temp < distance:\\n distance = temp\\n index = i\\n return index, distance\",\n \"def _kth_nearest_neighbor_dist(\\n distance_matrix: Union[np.ndarray, scipy.sparse.csr_matrix], k\\n) -> np.ndarray:\\n\\n if not is_integer(k):\\n raise ValueError(f\\\"parameter 'k={k}' must be a positive integer\\\")\\n else:\\n # make sure we deal with Python built-in\\n k = int(k)\\n\\n if not (0 <= k <= distance_matrix.shape[1]):\\n raise ValueError(\\n \\\"'k' must be an integer between 1 and \\\"\\n f\\\"distance_matrix.shape[1]={distance_matrix.shape[1]}\\\"\\n )\\n\\n if isinstance(distance_matrix, np.ndarray):\\n dist_knn = np.partition(distance_matrix, k - 1, axis=1)[:, k - 1]\\n elif isinstance(distance_matrix, scipy.sparse.csr_matrix):\\n # see mircobenchmark_kth_nn.py for a comparison of implementations for the\\n # sparse case\\n\\n def _get_kth_largest_elements_sparse(\\n data: np.ndarray,\\n indptr: np.ndarray,\\n row_nnz,\\n k_neighbor: int,\\n ):\\n dist_knn = np.zeros(len(row_nnz))\\n for i in range(len(row_nnz)):\\n start_row = indptr[i]\\n dist_knn[i] = np.partition(\\n data[start_row : start_row + row_nnz[i]], k_neighbor - 1\\n )[k_neighbor - 1]\\n\\n return dist_knn\\n\\n row_nnz = distance_matrix.getnnz(axis=1)\\n\\n if (row_nnz < k).any():\\n raise ValueError(\\n f\\\"There are {(row_nnz < k).sum()} points that \\\"\\n f\\\"do not have at least k_neighbor={k}.\\\"\\n )\\n\\n dist_knn = _get_kth_largest_elements_sparse(\\n distance_matrix.data,\\n distance_matrix.indptr,\\n row_nnz,\\n k,\\n )\\n else:\\n raise TypeError(f\\\"type {type(distance_matrix)} not supported\\\")\\n\\n return dist_knn\",\n \"def _first_index_with_bigger_neighbour(P):\\n i = len(P) - 1\\n while i > 0 and P[i-1] >= P[i]:\\n i -= 1\\n return i\",\n \"def getNearestLineIndex(row, tagLineNumbers):\\n # DOC {{{\\n # }}}\\n\\n # CODE {{{\\n # initialize local auxiliary variables {{{\\n nearestLineNumber = -1\\n nearestLineIndex = -1\\n # }}}\\n\\n # go through all tag line numbers and find the one nearest to the specified row {{{\\n for lineIndex, lineNumber in enumerate(tagLineNumbers):\\n # if the current line is nearer the current cursor position, take it {{{\\n if (nearestLineNumber < lineNumber <= row):\\n nearestLineNumber = lineNumber\\n nearestLineIndex = lineIndex\\n # }}}\\n\\n # if we've got past the current cursor position, let's end the search {{{\\n if (lineNumber >= row):\\n break\\n # }}}\\n # }}}\\n\\n # return index of the line with the nearest tag\\n return nearestLineIndex\\n # }}}\",\n \"def shortestpathij(self, i, j):\\n pathlist = []\\n self.pathij(i, j, pathlist)\\n distance = []\\n \\n for i in range(len(pathlist)):\\n Temp = 0\\n for j in range(len(pathlist[i]) - 1):\\n Temp += self.Dismatrix[pathlist[i][j], pathlist[i][j+1]]\\n distance.append(Temp)\\n \\n if(len(distance) == 0):\\n return None\\n else:\\n return min(distance)\",\n \"def min_horizontal_dist_meters(coords, targets, is_geo=False):\\n xe = coords[:, 0]\\n ye = coords[:, 1]\\n n = len(xe)\\n d = np.zeros(n)\\n for j in range(n):\\n d1 = dist_in_meters(targets, [xe[j], ye[j]], is_geo=is_geo)\\n d[j] = d1.min()\\n return d\",\n \"def findNearset(x,y,lon,lat):\\n dist = np.sqrt( (lon - x)**2 + (lat - y)**2)\\n\\n return np.argwhere(dist==dist.min())[0][0]\",\n \"def get_nearest_neighbours(self, cell):\\n\\t\\tneighs = self.get_neighbours(cell)\\n\\t\\ti, j = cell.find_id()\\n\\t\\tneighbours = []\\n\\t\\tfor neigh in neighs:\\n\\t\\t\\tx, y = neigh.find_id()\\n\\t\\t\\tif abs(x-i)+abs(y-j) <= 1: \\n\\t\\t\\t\\tneighbours.append(self.space[y,x])\\n\\t\\treturn neighbours\",\n \"def _low_tri_indices(rowCount):\\n for col in range(rowCount):\\n for row in range(col, rowCount):\\n yield (row, col)\",\n \"def min_distance(s1, s2):\\n n = len(s1)\\n m = len(s2)\\n matrix = [([0]*(m+1)) for i in xrange(n+1)]\\n for i in xrange(m+1):\\n matrix[0][i] = i\\n for i in xrange(n+1):\\n matrix[i][0] = i\\n for i in xrange(1,n+1):\\n for j in xrange(1,m+1):\\n temp = min(matrix[i-1][j]+1, matrix[i][j-1]+1)\\n d = 0 if s1[i-1]==s2[j-1] else 1\\n matrix[i][j] = min(temp, matrix[i-1][j-1]+d)\\n return matrix[n][m]\",\n \"def extract_min(H, ds):\\n minDist = approxInf\\n u = None # min vertex unknown\\n i = 0\\n for v in H:\\n if ds[v] <= minDist:\\n minDist = ds[v]\\n u = v # note that u is unused (instead returned by pop)\\n imin = i\\n i += 1\\n return(H.pop(imin)) # return [u, d]\",\n \"def findminpath(tab, gxtab, gytab, pixtab):\\n\\n pathdist = 2 # the number of points each points on a ray can related to on the previous ray\\n pathdist_penalty = 0.3 # penalty of the difference of the pathdist\\n pathpix_penalty = 2 # penalty of the difference of pixel values between the point and the previous point\\n nray = tab.shape[1]\\n\\n #tab = np.hstack((tab,tab[:, 0].reshape(tab.shape[0], 1)))\\n #pixtab = np.hstack((pixtab,pixtab[:, 0].reshape(pixtab.shape[0], 1)))\\n #gxtab = np.hstack((gxtab,gxtab[:, 0].reshape(gxtab.shape[0], 1)))\\n #gytab = np.hstack((gytab,gytab[:, 0].reshape(gytab.shape[0], 1)))\\n\\n tab = np.hstack((tab,tab,tab)) # horizontally stack the tab matrix to prepare for the filtering on the result\\n pixtab = np.hstack((pixtab,pixtab,pixtab))\\n gxtab = np.hstack((gxtab,gxtab,gxtab))\\n gytab = np.hstack((gytab,gytab,gytab))\\n\\n tab = (tab - tab.min()) / (tab.max() - tab.min()) # noralize the tab matrix\\n pixtab = (pixtab - pixtab.min()) / (pixtab.max() - pixtab.min()) * -1 # for we want to find the white contour of the cell so we multipy -1 on the pixtab\\n # tab = tab / np.median(tab)\\n # pixtab = pixtab / np.median(pixtab)\\n path = np.zeros(tab.shape)\\n path[:, 0] = np.array(range(0, tab.shape[0]))\\n score = np.zeros(tab.shape)\\n score[:, 1] = tab[:, 1]\\n\\n for i in range(1, tab.shape[1]):\\n for j in range(tab.shape[0]):\\n mins = np.Inf # record the min value of the ray\\n minat = 0\\n for k in range(-pathdist, pathdist+1):\\n if(0 <= (j+k) and (j+k) < tab.shape[0]):\\n s = pixtab[j, i]\\n pixdiff = abs(pixtab[j, i] - pixtab[j+k, i-1])\\n s += pixdiff * pathpix_penalty # two kinds of penalty\\n s += abs(k) * pathdist_penalty\\n s += score[j+k, i-1]\\n\\n if(s < mins):\\n mins = s\\n minat = j + k\\n path[j, i] = minat\\n score[j, i]= mins\\n\\n start = int(np.argmin(score[:, -1]))\\n path = path.astype(np.int32)\\n minpath = [start]\\n for i in range(tab.shape[1]-1, 0, -1):\\n minpath.append(path[minpath[-1], i])\\n minpath = minpath[::-1]\\n # print(len(minpath))\\n minpath = savgol_filter(minpath, 15, 3) # apply a Savitzky-Golay filter to the raw minpath signal\\n minpath = minpath[nray:nray*2] # cut the middle part of minpath whose length is nray\\n return np.array(minpath).astype(np.int32)\",\n \"def find_all_nearest_neighbours(point_cloud:np.ndarray) -> np.ndarray:\\n pass\",\n \"def numNeighbors(minesSet, row_index, cols_index, num_cols, num_rows):\\n mines = 0\\n for j in np.arange(max(0, cols_index-1), min(num_cols-1, cols_index+1)+1):\\n for i in np.arange(max(0, row_index-1), min(num_rows-1, row_index+1)+1):\\n if ((i, j) in minesSet):\\n mines+=1\\n return mines\",\n \"def index_condensed_matrix(n, i, j):\\n if i == j:\\n main_warning(\\\"Diagonal elements (i=j) are not stored in condensed matrices.\\\")\\n return None\\n elif i > j:\\n i, j = j, i\\n return int(i * (n - (i + 3) * 0.5) + j - 1)\",\n \"def nearest_sparse(self, query):\\n self.best_dist = float(\\\"inf\\\")\\n self.best_element = None\\n self._register_best_element = self._register_best_element_single \\n self._nearest_sparse_recursive(self._sparse2seq(query), self.root, 0.0)\\n return self.best_element,self.best_dist\",\n \"def find_closest_pt(ref_lon, ref_lat, tlon, tlat):\\n\\n # compute great circle distance from location to model grid points\\n dist = gc_dist(ref_lon, ref_lat, tlon, tlat)\\n\\n # find j index of closest grid point\\n work = N.take(dist,N.argmin(dist,0),0).diagonal()\\n jj = N.argsort(work)[0]\\n\\n # find i index of closest grid point\\n work = N.take(dist,N.argmin(dist,1),1).diagonal()\\n ii = N.argsort(work)[0]\\n\\n return ii, jj\",\n \"def find_nearest(ref_array,target_array):\\n ref_tree = scipy.spatial.cKDTree(ref_array)\\n dist, indices = ref_tree.query(target_array, k=1)\\n return indices\",\n \"def locate_source(p,d):\\n # M = sensors, n = dimensions\\n M, n = p.shape\\n p = np.matrix( p ).T\\n\\n # pick closest receiver\\n c = np.argmin(d)\\n #sensors delta time relative to sensor c\\n d = d - min(d)\\n\\n indices = list(range(M))\\n del indices[c]\\n\\n A = np.zeros([M-2,n])\\n b = np.zeros([M-2,1])\\n\\n i = indices[0]\\n for row,j in enumerate(indices[1:]):\\n A[row,:] = 2*( (d[j])*(p[:,i]-p[:,c]).T - \\\\\\n (d[i])*(p[:,j]-p[:,c]).T )\\n b[row,0] = (d[i])*((d[j])**2-p[:,j].T*p[:,j]) + \\\\\\n ((d[i])-(d[j]))*p[:,c].T*p[:,c] + \\\\\\n (d[j])*(p[:,i].T*p[:,i]-(d[i])**2)\\n\\n\\n x = np.asarray( np.linalg.lstsq(A,b)[0] )[:,0]\\n return x\",\n \"def _intra_cluster_distance(distances_row, labels, i):\\n mask = labels == labels[i]\\n mask[i] = False\\n if not np.any(mask):\\n # cluster of size 1\\n return 0\\n a = np.mean(distances_row[mask])\\n return a\",\n \"def matrix_idx(n_hist, n_req, n_rows):\\n\\n flat_idx = []\\n for i in range(n_rows):\\n flat_idx.extend(range(i * n_req, (i + 1) * n_req + n_hist))\\n # idx = np.unravel_index(flat_idx, (n_rows, n_hist + n_req))\\n\\n idx_matrix = np.reshape(flat_idx, (n_rows, n_hist + n_req))\\n idxX = idx_matrix[:, n_req:]\\n idxY = idx_matrix[:, :n_req]\\n\\n return idxX, idxY\",\n \"def minesweeper(matrix):\\n \\n num_rows = len(matrix)\\n num_cols = len(matrix[0])\\n \\n adj_mines = []\\n \\n adj_row = [0]*num_cols\\n \\n for i in range(num_rows):\\n adj_mines.append(adj_row[:])\\n \\n for r in range(num_rows):\\n for c in range(num_cols):\\n if matrix[r][c] == True:\\n if (r-1) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r-1][c-1] += 1\\n if (r-1) in range(num_rows) and (c) in range(num_cols):\\n adj_mines[r-1][c] += 1\\n if (r-1) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r-1][c+1] += 1\\n if (r) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r][c-1] += 1\\n if (r) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r][c+1] += 1\\n if (r+1) in range(num_rows) and (c-1) in range(num_cols):\\n adj_mines[r+1][c-1] += 1\\n if (r+1) in range(num_rows) and (c) in range(num_cols):\\n adj_mines[r+1][c] += 1\\n if (r+1) in range(num_rows) and (c+1) in range(num_cols): \\n adj_mines[r+1][c+1] += 1\\n\\n \\n return adj_mines\",\n \"def nearest_neighbors(self):\\n neighbor_distances_and_indices = []\\n for idx, data_point in enumerate(self.data):\\n distance = self.euclidean_dis(data_point[:-1], self.query) # Calculate the distance between the query\\n # example and the current example from the data.\\n\\n neighbor_distances_and_indices.append((distance, idx)) # Add the distance and the index of the example\\n # to an ordered collection\\n\\n sorted_neighbor_distances_and_indices = sorted(neighbor_distances_and_indices, key=lambda x: x[0]) #\\n # Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by\\n # the distances\\n\\n k_nearest_distances_and_indices = sorted_neighbor_distances_and_indices[:self.k] # Pick the first K\\n # entries from the sorted collection\\n\\n k_nearest_labels = [self.data[i][1] for distance, i in k_nearest_distances_and_indices] # Get the labels of\\n # the selected K entries\\n\\n return k_nearest_labels, self.mode(k_nearest_labels)\",\n \"def __find_immediate_predecessors(storm_object_table, target_row):\\n\\n error_checking.assert_is_integer(target_row)\\n error_checking.assert_is_geq(target_row, 0)\\n error_checking.assert_is_less_than(\\n target_row, len(storm_object_table.index)\\n )\\n\\n predecessor_sec_id_strings = [\\n storm_object_table[c].values[target_row]\\n for c in PREV_SECONDARY_ID_COLUMNS\\n if storm_object_table[c].values[target_row] != ''\\n ]\\n\\n num_predecessors = len(predecessor_sec_id_strings)\\n if num_predecessors == 0:\\n return numpy.array([], dtype=int)\\n\\n target_time_unix_sec = storm_object_table[\\n tracking_utils.VALID_TIME_COLUMN].values[target_row]\\n\\n predecessor_rows = numpy.full(num_predecessors, -1, dtype=int)\\n\\n for i in range(num_predecessors):\\n these_rows = numpy.where(numpy.logical_and(\\n storm_object_table[tracking_utils.SECONDARY_ID_COLUMN].values ==\\n predecessor_sec_id_strings[i],\\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values <\\n target_time_unix_sec\\n ))[0]\\n\\n if len(these_rows) == 0:\\n continue\\n\\n this_subrow = numpy.argmax(\\n storm_object_table[tracking_utils.VALID_TIME_COLUMN].values[\\n these_rows]\\n )\\n\\n predecessor_rows[i] = these_rows[this_subrow]\\n\\n return predecessor_rows[predecessor_rows >= 0]\",\n \"def closest_waypoint(self, location: pylot.utils.Location):\\n min_dist = np.infty\\n min_index = 0\\n for index, waypoint in enumerate(self.waypoints):\\n dist = waypoint.location.distance(location)\\n if dist < min_dist:\\n min_dist = dist\\n min_index = index\\n return min_index\",\n \"def medoid_ft(ftvectors,pairwise_dist_matrix):\\r\\n N = len(ftvectors)\\r\\n if N == 1 : \\r\\n return 0 \\r\\n # pairwise_dist_matrix = calculate_dist_matrix_ft(ftvectors) \\r\\n sum_dist = np.sum(pairwise_dist_matrix, axis = 0)\\r\\n min_idx = np.argmin(sum_dist)\\r\\n return min_idx\",\n \"def _partition_nearest(self, medoids, dists, only_these=set()):\\n if len(only_these) == 0:\\n allowed_inds = self._not_ignored_inds\\n else:\\n allowed_inds = self._not_ignored_inds & only_these\\n closest_medoid_ind = np.argmin(dists[:,medoids], 1) # If len(medoids)==3, would look like [2,1,1,0,1,2,...].\\n clusts = [[] for i in medoids]\\n for node_ind, med_ind in enumerate(closest_medoid_ind):\\n if node_ind in allowed_inds:\\n clusts[med_ind].append(node_ind)\\n return clusts\",\n \"def search_matrix(to_search: int, a_matrix: list) -> (int, int):\\n row = 0\\n column = len(a_matrix[0]) - 1\\n while row < len(a_matrix) and column >= 0:\\n if to_search < a_matrix[row][column]:\\n column -= 1\\n elif to_search > a_matrix[row][column]:\\n row += 1\\n else:\\n return row, column\\n return -1, -1\",\n \"def closest(self, x, y):\\n pts = np.column_stack([self.x, self.y])\\n # Transform data coordinates to pixel coordinates.\\n pts = self.ax.transData.transform(pts)\\n diff = pts - [x, y]\\n dist = np.hypot(*diff.T)\\n min_index = np.argmin(dist)\\n return min_index, dist[min_index]\",\n \"def closest_centroid(x,centroids):\\n\\tK =len(centroids)\\n\\tN = len(x)\\n\\tDistance = np.zeros((N,K))\\n\\tfor j in range(K):\\n\\t\\tmu = centroids[j]\\n\\t\\tDistance[:,j] = np.linalg.norm(x-mu,axis=1)\\n\\tout = np.argmin(Distance,axis=1) \\n\\treturn out\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76830524","0.7358047","0.715276","0.6513678","0.64565593","0.645209","0.64473367","0.64161915","0.6344284","0.6331053","0.629967","0.6284017","0.62775195","0.6246845","0.6215876","0.6197371","0.61904216","0.614493","0.6127443","0.61131895","0.6061455","0.6061441","0.6057897","0.6010826","0.59732074","0.59727025","0.59643596","0.5957675","0.5939356","0.5919417","0.5908226","0.5907221","0.5905387","0.5903414","0.5898643","0.5890703","0.58218634","0.58150864","0.5802884","0.57944125","0.5784227","0.5776671","0.57577515","0.57474875","0.57286876","0.5727042","0.5725983","0.57213503","0.57150173","0.5697625","0.5687498","0.5668719","0.56657505","0.5657357","0.5656998","0.5654915","0.56396127","0.56382775","0.5636967","0.5634042","0.5632858","0.56289357","0.56117386","0.5610537","0.5605596","0.56049234","0.5604687","0.5603997","0.55920154","0.5590341","0.5588727","0.55827326","0.5581248","0.5567834","0.55632454","0.55569965","0.5545793","0.5543416","0.55275446","0.55223936","0.5515401","0.5513707","0.5511564","0.5510364","0.5501646","0.5498301","0.54808396","0.54785067","0.54752326","0.5470072","0.5469641","0.546202","0.545913","0.5458002","0.54521465","0.544734","0.54414034","0.5437693","0.5436167","0.54293483"],"string":"[\n \"0.76830524\",\n \"0.7358047\",\n \"0.715276\",\n \"0.6513678\",\n \"0.64565593\",\n \"0.645209\",\n \"0.64473367\",\n \"0.64161915\",\n \"0.6344284\",\n \"0.6331053\",\n \"0.629967\",\n \"0.6284017\",\n \"0.62775195\",\n \"0.6246845\",\n \"0.6215876\",\n \"0.6197371\",\n \"0.61904216\",\n \"0.614493\",\n \"0.6127443\",\n \"0.61131895\",\n \"0.6061455\",\n \"0.6061441\",\n \"0.6057897\",\n \"0.6010826\",\n \"0.59732074\",\n \"0.59727025\",\n \"0.59643596\",\n \"0.5957675\",\n \"0.5939356\",\n \"0.5919417\",\n \"0.5908226\",\n \"0.5907221\",\n \"0.5905387\",\n \"0.5903414\",\n \"0.5898643\",\n \"0.5890703\",\n \"0.58218634\",\n \"0.58150864\",\n \"0.5802884\",\n \"0.57944125\",\n \"0.5784227\",\n \"0.5776671\",\n \"0.57577515\",\n \"0.57474875\",\n \"0.57286876\",\n \"0.5727042\",\n \"0.5725983\",\n \"0.57213503\",\n \"0.57150173\",\n \"0.5697625\",\n \"0.5687498\",\n \"0.5668719\",\n \"0.56657505\",\n \"0.5657357\",\n \"0.5656998\",\n \"0.5654915\",\n \"0.56396127\",\n \"0.56382775\",\n \"0.5636967\",\n \"0.5634042\",\n \"0.5632858\",\n \"0.56289357\",\n \"0.56117386\",\n \"0.5610537\",\n \"0.5605596\",\n \"0.56049234\",\n \"0.5604687\",\n \"0.5603997\",\n \"0.55920154\",\n \"0.5590341\",\n \"0.5588727\",\n \"0.55827326\",\n \"0.5581248\",\n \"0.5567834\",\n \"0.55632454\",\n \"0.55569965\",\n \"0.5545793\",\n \"0.5543416\",\n \"0.55275446\",\n \"0.55223936\",\n \"0.5515401\",\n \"0.5513707\",\n \"0.5511564\",\n \"0.5510364\",\n \"0.5501646\",\n \"0.5498301\",\n \"0.54808396\",\n \"0.54785067\",\n \"0.54752326\",\n \"0.5470072\",\n \"0.5469641\",\n \"0.546202\",\n \"0.545913\",\n \"0.5458002\",\n \"0.54521465\",\n \"0.544734\",\n \"0.54414034\",\n \"0.5437693\",\n \"0.5436167\",\n \"0.54293483\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6456699"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":888,"cells":{"query":{"kind":"string","value":"Convert the degree vector to degree function."},"document":{"kind":"string","value":"def degree_vector_to_matrix(vector):\n return np.diag(vector)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def vecToFunc(vector):\n def f(x):\n f = 0\n for i in range(len(vector)):\n f += vector[i]*x**i\n return f\n return f","def degree_v(self):\n return self._degree_v","def degree(x):\n return x*(180.0/math.pi)","def laplacian(degree_vector, weight_matrix):\n return np.diag(degree_vector) - weight_matrix","def angleToVector(teta):\r\n x=1 #we fix x and we will determine z in consquence\r\n #we want z/x=tan(teta) so:\r\n z=np.tan(teta)*x\r\n return((x,z))","def rad2deg(x):\r\n # see decorator for function body\r","def in_degrees_tf_vector(self):\n return tf.reshape(self.in_degrees_tf, [self.n])","def degrees(x):\n return 0.0","def vector_to_int(a_vec: np.ndarray, characteristic: int, degree: int) -> int:\n a = 0\n factor = 1\n for i in range(degree - 1, -1, -1):\n a += a_vec[i] * factor\n factor *= characteristic\n\n return a","def rad2deg(a):","def out_degrees_tf_vector(self):\n return tf.reshape(self.out_degrees_tf, [self.n])","def rotorconversion(x):\n return cf.MultiVector(layout, val_rotorconversion(x))","def degree(g, x, norm, deg, rates):\n if type(x) == list: \n x = az.transform(x)\n if norm == 1:\n if network_matrix(x, rates).sum() == 0:\n return 1./x.count(\"1\")\n if deg == \"in\":\n return network_matrix(x, rates).sum(1)[az.transform(x).index(g)]/network_matrix(x, rates).sum()\n else:\n return network_matrix(x, rates).sum(0)[az.transform(x).index(g)]/network_matrix(x, rates).sum()\n else:\n if deg == \"in\":\n return network_matrix(x, rates).sum(1)[az.transform(x).index(g)]\n else:\n return network_matrix(x, rates).sum(0)[az.transform(x).index(g)]","def V2lambda(V):\n return(3956/V)","def v_o(A,vd):\n return A*vd","def angle_between_vectors_degrees(u, v):\n a = np.dot(u, v)\n b = np.linalg.norm(u)\n c = np.linalg.norm(v)\n d = a / (b* c)\n if d > 1:\n d = 1\n if d < -1:\n d = -1\n e = acos(d)\n f = np.degrees(e)\n return f","def dV(X):\n return -4 * a * np.power(X, 3) + 2 * b * X","def cos2degrees(x):\n return math.degrees(math.acos(x))","def derFunc(self,x_vec):\n x=x_vec[0]\n y=x_vec[1]\n z=np.array([2*(self.x_0-x),2*(self.y_0-y)])\n return z*self.sfunc(x,y)","def val_rotorconversion(x):\n B_val = val_vec_repr_to_bivector(x)\n R_val = val_exp(B_val)\n return R_val","def degree(self, u, outgoing=False):\n return NotImplementedError()","def deg2rad(a):","def _orthogonal_vector(vector):\n return -1 * vector[1], vector[0]","def degc_to_degf(input_temp):\r\n return (input_temp * 1.8) + 32","def vector_angle(v):\n assert len(v) == 2\n x, y = v\n return np.arctan2(y, x)","def StepsToDegree(self,Steps):\n return int(round(Steps/(self.Reduction/self.StepAngle/2)))","def trans(x):\r\n return np.arccos(1-2*x)","def deg2rad(x):\r\n # see decorator for function body\r","def angles_vectors_degrees(u, v):\n a = angle_smallest_vectors_degrees(u, v)\n return a, 360. - a","def construct_angle_degrees(loader, node):\n value = loader.construct_scalar(node)\n exprvalue = value\n if exprvalue.startswith(\"deg(\"):\n exprvalue = exprvalue.strip()[4:-1]\n try:\n return float(exprvalue) * math.pi / 180.0\n except ValueError:\n raise RosParamException(\"invalid degree value: %s\"%value)","def vec_angle_deg(v1,v2):\r\n \r\n return math.degrees(vec_angle_rad(v1,v2))","def to_celsius(degrees):\n return (degrees - 32) * (5/9)","def _asind(v):\n return math.degrees(math.asin(v))","def poly(x, degree=2):\n x = np.array(x)\n X_trans = np.transpose(np.vstack((x**k for k in range(degree + 1))))\n return np.linalg.qr(X_trans)[0][:, 1:]","def f(x):\n return np.tan(x) - np.sin(x) - (m*g)/(2*k*L)","def z_to_vector(vector):\n norm = np.linalg.norm(vector)\n if norm == 0:\n return np.identity(3)\n v = np.array(vector) / norm\n phi = np.arccos(v[2])\n if any(v[:2]):\n #projection of vector to unit circle\n axis_proj = v[:2] / np.linalg.norm(v[:2])\n theta = np.arccos(axis_proj[0])\n if axis_proj[1] < 0:\n theta = -theta\n else:\n theta = 0\n phi_down = np.array([\n [np.cos(phi), 0, np.sin(phi)],\n [0, 1, 0],\n [-np.sin(phi), 0, np.cos(phi)]\n ])\n return np.dot(rotation_about_z(theta), phi_down)","def vec2angle(vec):\n return round(atan2(vec[1], vec[0]), 3)","def degree_on_basis(self, b):\n return sum(b)","def angle2fD(angle,units='degrees'):\n if units=='degrees':\n angle*=(math.pi/180.0)\n X = 4.0*math.tan(angle/4.0)\n fD = 1.0/X\n return fD","def deg2rad_inplace(a):","def funcv(x):\n f0 = x[0] ** 3.0 + x[1] + 3.0\n f1 = x[1] - 4.0 * x[0]\n return f0, f1","def zzx_degree(f):\n return len(f) - 1","def degree_u(self):\n return self._degree_u","def degree_to_radians(degree):\n return degree * pi / 180","def atan2_vec(vector):\n return -np.arctan2(vector[1], vector[0])","def build_poly(x, degree):\n phi = np.ones(len(x))\n phi = np.vstack((phi, [x**(j+1) for j in range(degree)]))\n \n return phi.T","def rad2deg_inplace(a):","def dir_vect(theta):\n return np.array([np.cos(theta),np.sin(theta)])","def get_angle(self, t_step, degree=True):\n v_vector = np.array([self.s_velocity[t_step], self.d_velocity[t_step]])\n u_v1 = self._get_unit_vector(v_vector)\n rad = np.arccos(np.clip(np.dot(self.ref_vector, u_v1), -1.0, 1.0))\n if degree:\n return np.rad2deg(rad) * np.sign(self.d_velocity[t_step])\n else:\n return rad","def angle_smallest_vectors_degrees(u, v):\n return degrees(angle_smallest_vectors)","def zx_rotation(vector,theta):\r\n R = np.array([[np.cos(theta),0,np.sin(theta)],\r\n [0,1,0],\r\n [-np.sin(theta),0,np.cos(theta)]\r\n ])\r\n return np.dot(R,vector)","def custom_np_function(vector):\n summation = 0\n for item in vector:\n summation += item ** 2\n return summation","def angle_of_vector(vector):\n z = complex(*vector[:2])\n if z == 0:\n return 0\n return np.angle(complex(*vector[:2]))","def __multidegree(f):\n\n mon = f.monomials()\n mon.sort() # Sorts them with the polynomial ring order\n return mon[-1].degrees() # The multidegree is the degree of the biggest monomial","def value_to_angle(value):\n return ...","def rot(vec, angle, degrees=True):\r\n if degrees:\r\n angle = np.radians(angle)\r\n r = np.array([[np.cos(-angle), -np.sin(-angle)],\r\n [np.sin(-angle), np.cos(-angle)]])\r\n return r.dot(vec)","def degree(f, j=0):\n return dmp_degree_in(f.rep, j, f.lev)","def diriv(x, params):\n return np.array([x,1])","def degree_list(f):\n return dmp_degree_list(f.rep, f.lev)","def degree(self):\n return sum(self)","def out_degrees_tf_vertex(self, vertex: int) -> tf.Tensor:\n return tf.gather(self.out_degrees_tf, [vertex])","def d(u, v):\r\n\tdiff = u-v\r\n\treturn diff.dot(diff)","def __getPolynomial(self) -> 'func':\n return lambda x: sum(self.pts[i]*base(x)\n for i, base in enumerate(self.basis))","def _acosd(v):\n return math.degrees(math.acos(v))","def steps_to_angle():\n pass","def proj_tan(self, v, x, c):\n return v","def to_povray_func(self):\n\n func_str = \"{}\".format(self.mth)\n\n for mb in self.mbs:\n func_str += (\" - {} \"\n \"* exp(-(pow(x-{},2) + pow(y-{},2) + pow(z-{},2))\"\n \"/ (2 * pow({},2)))\"\n .format(mb.a, mb.x, mb.y, mb.z, mb.s))\n\n return func_str","def normalize_in_degrees(num_nodes, dict):\n y = [val / num_nodes for key, val in dict.items()]\n \n return y","def polynomial_degree(self):\n return polynomial_degree(self)","def degree(self, k):\n one_if_my_all_k = 1 if self.my_alll(k) else 0\n return (2 * self.get_prp_j(k)[0]) + one_if_my_all_k","def V_phi(self, x):\n\n x = self.featureExtractor.getFeatures(x)\n\n x = torch.tensor(x).float()\n\n x = F.relu(self.linear(x))\n\n v = self.linear_v(x)\n\n return v","def activ_fn_derivative(z):\n return 1 - np.square(np.tanh(z))","def tan(self, a):\n return math.tan(a)","def to_linear(self):\n return inv(quad_hybrid).dot(self.circular)","def f(x):\n return N.sqrt(N.power(N.cos(x),2)+1.0)","def AngleDtoR(degree):\n\trad=degree*math.pi/180\n\treturn rad","def acc(x: float, v: float, t: float) -> float:\n return -k*v - np.sin(x) + c*np.cos(omega*t)","def convertToDiscreteFunction(boolean: bool) -> cern.japc.value.DiscreteFunction:\n ...","def F(x):\n soln = x - (1.0/5.0)*math.cos(10.0*x+1.0) \n return soln","def in_degrees_np(self) -> np.ndarray:\n return self.run_tf(self.in_degrees_tf)","def angle(self, vec, unit='rad'):\n raise NotImplementedError('angle not implemented for VectorArrays')","def Avv_func(f):\n\n def Avv(x, v):\n def F(s):\n return f(x + v * s)\n\n return jacfwd(jacfwd(F))(0.0)\n\n return Avv","def dec(self):\n return gon2dec(self.gon_angle)","def elevation_to_a_degree(numb1, numb2):\r\n return f\"Your result: {numb1**numb2}\"","def transform(val):\n return (100*math.atan(val) + 50*math.pi) / math.pi","def rotate(vector, angle):\n return np.cos(angle) * vector[0] + np.sin(angle) * vector[1], \\\n -np.sin(angle) * vector[0] + np.cos(angle) * vector[1]","def angle_2D(v):\n len_v=(v[0]**2+v[1]**2)**(0.5)\n if len_v==0:\n return 0\n ret = math.acos(v[0]/len_v)\n if v[1]<0:\n ret=6.283185307179586-ret\n return ret","def get_degrees(self):\n return np.arange(self.lmax + 1)","def orthogonal(v):\n return np.array([-v[1], v[0]])","def ret2dva(r_um):\n sign = np.sign(r_um)\n r_mm = 1e-3 * np.abs(r_um)\n r_deg = 3.556 * r_mm + 0.05993 * r_mm ** 2 - 0.007358 * r_mm ** 3\n r_deg += 3.027e-4 * r_mm ** 4\n return sign * r_deg","def degree(self, v):\n self._validateVertex(v)\n return self._adj[v].size()","def ang_fac(ang):\n return np.exp(2 * 1j * ang)","def _angle(u, v, w, d='+'):\n vu = np.arctan2(u[1] - v[1], u[0] - v[0])\n vw = np.arctan2(w[1] - v[1], w[0] - v[0])\n phi = vw - vu\n if phi < 0:\n phi += 2 * np.pi\n if d == '-':\n phi = 2 * np.pi - phi\n return np.round(phi, 6)","def angle(z):","def convertFahrenheitToCelsius(degrees):\n degrees = str(degrees)\n convert = ((decimal.Decimal(degrees) - 32) * 5) / decimal.Decimal('9')\n return float(convert)","def zerodegree_pol(dim):\n\n out = zeros(dim)\n out[0] = 1\n\n return out","def int_to_vector(a: int, characteristic: int, degree: int) -> np.ndarray:\n a_vec = np.zeros(degree, dtype=DTYPE)\n for i in range(degree - 1, -1, -1):\n q, r = divmod(a, characteristic)\n a_vec[i] = r\n a = q\n\n return a_vec","def conv(x):\n return x#-2*(16.41*x + 65.04-95.12) ","def total_degree(f):\n return dmp_total_degree(f.rep, f.lev)","def map_from_eta_to_n_v(eta):\n eta_0, eta_1 = concatenated_vector_to_scalar_matrix(eta)\n n = 2 * eta_0\n v = np.linalg.inv(-2 * eta_1)\n return n, v"],"string":"[\n \"def vecToFunc(vector):\\n def f(x):\\n f = 0\\n for i in range(len(vector)):\\n f += vector[i]*x**i\\n return f\\n return f\",\n \"def degree_v(self):\\n return self._degree_v\",\n \"def degree(x):\\n return x*(180.0/math.pi)\",\n \"def laplacian(degree_vector, weight_matrix):\\n return np.diag(degree_vector) - weight_matrix\",\n \"def angleToVector(teta):\\r\\n x=1 #we fix x and we will determine z in consquence\\r\\n #we want z/x=tan(teta) so:\\r\\n z=np.tan(teta)*x\\r\\n return((x,z))\",\n \"def rad2deg(x):\\r\\n # see decorator for function body\\r\",\n \"def in_degrees_tf_vector(self):\\n return tf.reshape(self.in_degrees_tf, [self.n])\",\n \"def degrees(x):\\n return 0.0\",\n \"def vector_to_int(a_vec: np.ndarray, characteristic: int, degree: int) -> int:\\n a = 0\\n factor = 1\\n for i in range(degree - 1, -1, -1):\\n a += a_vec[i] * factor\\n factor *= characteristic\\n\\n return a\",\n \"def rad2deg(a):\",\n \"def out_degrees_tf_vector(self):\\n return tf.reshape(self.out_degrees_tf, [self.n])\",\n \"def rotorconversion(x):\\n return cf.MultiVector(layout, val_rotorconversion(x))\",\n \"def degree(g, x, norm, deg, rates):\\n if type(x) == list: \\n x = az.transform(x)\\n if norm == 1:\\n if network_matrix(x, rates).sum() == 0:\\n return 1./x.count(\\\"1\\\")\\n if deg == \\\"in\\\":\\n return network_matrix(x, rates).sum(1)[az.transform(x).index(g)]/network_matrix(x, rates).sum()\\n else:\\n return network_matrix(x, rates).sum(0)[az.transform(x).index(g)]/network_matrix(x, rates).sum()\\n else:\\n if deg == \\\"in\\\":\\n return network_matrix(x, rates).sum(1)[az.transform(x).index(g)]\\n else:\\n return network_matrix(x, rates).sum(0)[az.transform(x).index(g)]\",\n \"def V2lambda(V):\\n return(3956/V)\",\n \"def v_o(A,vd):\\n return A*vd\",\n \"def angle_between_vectors_degrees(u, v):\\n a = np.dot(u, v)\\n b = np.linalg.norm(u)\\n c = np.linalg.norm(v)\\n d = a / (b* c)\\n if d > 1:\\n d = 1\\n if d < -1:\\n d = -1\\n e = acos(d)\\n f = np.degrees(e)\\n return f\",\n \"def dV(X):\\n return -4 * a * np.power(X, 3) + 2 * b * X\",\n \"def cos2degrees(x):\\n return math.degrees(math.acos(x))\",\n \"def derFunc(self,x_vec):\\n x=x_vec[0]\\n y=x_vec[1]\\n z=np.array([2*(self.x_0-x),2*(self.y_0-y)])\\n return z*self.sfunc(x,y)\",\n \"def val_rotorconversion(x):\\n B_val = val_vec_repr_to_bivector(x)\\n R_val = val_exp(B_val)\\n return R_val\",\n \"def degree(self, u, outgoing=False):\\n return NotImplementedError()\",\n \"def deg2rad(a):\",\n \"def _orthogonal_vector(vector):\\n return -1 * vector[1], vector[0]\",\n \"def degc_to_degf(input_temp):\\r\\n return (input_temp * 1.8) + 32\",\n \"def vector_angle(v):\\n assert len(v) == 2\\n x, y = v\\n return np.arctan2(y, x)\",\n \"def StepsToDegree(self,Steps):\\n return int(round(Steps/(self.Reduction/self.StepAngle/2)))\",\n \"def trans(x):\\r\\n return np.arccos(1-2*x)\",\n \"def deg2rad(x):\\r\\n # see decorator for function body\\r\",\n \"def angles_vectors_degrees(u, v):\\n a = angle_smallest_vectors_degrees(u, v)\\n return a, 360. - a\",\n \"def construct_angle_degrees(loader, node):\\n value = loader.construct_scalar(node)\\n exprvalue = value\\n if exprvalue.startswith(\\\"deg(\\\"):\\n exprvalue = exprvalue.strip()[4:-1]\\n try:\\n return float(exprvalue) * math.pi / 180.0\\n except ValueError:\\n raise RosParamException(\\\"invalid degree value: %s\\\"%value)\",\n \"def vec_angle_deg(v1,v2):\\r\\n \\r\\n return math.degrees(vec_angle_rad(v1,v2))\",\n \"def to_celsius(degrees):\\n return (degrees - 32) * (5/9)\",\n \"def _asind(v):\\n return math.degrees(math.asin(v))\",\n \"def poly(x, degree=2):\\n x = np.array(x)\\n X_trans = np.transpose(np.vstack((x**k for k in range(degree + 1))))\\n return np.linalg.qr(X_trans)[0][:, 1:]\",\n \"def f(x):\\n return np.tan(x) - np.sin(x) - (m*g)/(2*k*L)\",\n \"def z_to_vector(vector):\\n norm = np.linalg.norm(vector)\\n if norm == 0:\\n return np.identity(3)\\n v = np.array(vector) / norm\\n phi = np.arccos(v[2])\\n if any(v[:2]):\\n #projection of vector to unit circle\\n axis_proj = v[:2] / np.linalg.norm(v[:2])\\n theta = np.arccos(axis_proj[0])\\n if axis_proj[1] < 0:\\n theta = -theta\\n else:\\n theta = 0\\n phi_down = np.array([\\n [np.cos(phi), 0, np.sin(phi)],\\n [0, 1, 0],\\n [-np.sin(phi), 0, np.cos(phi)]\\n ])\\n return np.dot(rotation_about_z(theta), phi_down)\",\n \"def vec2angle(vec):\\n return round(atan2(vec[1], vec[0]), 3)\",\n \"def degree_on_basis(self, b):\\n return sum(b)\",\n \"def angle2fD(angle,units='degrees'):\\n if units=='degrees':\\n angle*=(math.pi/180.0)\\n X = 4.0*math.tan(angle/4.0)\\n fD = 1.0/X\\n return fD\",\n \"def deg2rad_inplace(a):\",\n \"def funcv(x):\\n f0 = x[0] ** 3.0 + x[1] + 3.0\\n f1 = x[1] - 4.0 * x[0]\\n return f0, f1\",\n \"def zzx_degree(f):\\n return len(f) - 1\",\n \"def degree_u(self):\\n return self._degree_u\",\n \"def degree_to_radians(degree):\\n return degree * pi / 180\",\n \"def atan2_vec(vector):\\n return -np.arctan2(vector[1], vector[0])\",\n \"def build_poly(x, degree):\\n phi = np.ones(len(x))\\n phi = np.vstack((phi, [x**(j+1) for j in range(degree)]))\\n \\n return phi.T\",\n \"def rad2deg_inplace(a):\",\n \"def dir_vect(theta):\\n return np.array([np.cos(theta),np.sin(theta)])\",\n \"def get_angle(self, t_step, degree=True):\\n v_vector = np.array([self.s_velocity[t_step], self.d_velocity[t_step]])\\n u_v1 = self._get_unit_vector(v_vector)\\n rad = np.arccos(np.clip(np.dot(self.ref_vector, u_v1), -1.0, 1.0))\\n if degree:\\n return np.rad2deg(rad) * np.sign(self.d_velocity[t_step])\\n else:\\n return rad\",\n \"def angle_smallest_vectors_degrees(u, v):\\n return degrees(angle_smallest_vectors)\",\n \"def zx_rotation(vector,theta):\\r\\n R = np.array([[np.cos(theta),0,np.sin(theta)],\\r\\n [0,1,0],\\r\\n [-np.sin(theta),0,np.cos(theta)]\\r\\n ])\\r\\n return np.dot(R,vector)\",\n \"def custom_np_function(vector):\\n summation = 0\\n for item in vector:\\n summation += item ** 2\\n return summation\",\n \"def angle_of_vector(vector):\\n z = complex(*vector[:2])\\n if z == 0:\\n return 0\\n return np.angle(complex(*vector[:2]))\",\n \"def __multidegree(f):\\n\\n mon = f.monomials()\\n mon.sort() # Sorts them with the polynomial ring order\\n return mon[-1].degrees() # The multidegree is the degree of the biggest monomial\",\n \"def value_to_angle(value):\\n return ...\",\n \"def rot(vec, angle, degrees=True):\\r\\n if degrees:\\r\\n angle = np.radians(angle)\\r\\n r = np.array([[np.cos(-angle), -np.sin(-angle)],\\r\\n [np.sin(-angle), np.cos(-angle)]])\\r\\n return r.dot(vec)\",\n \"def degree(f, j=0):\\n return dmp_degree_in(f.rep, j, f.lev)\",\n \"def diriv(x, params):\\n return np.array([x,1])\",\n \"def degree_list(f):\\n return dmp_degree_list(f.rep, f.lev)\",\n \"def degree(self):\\n return sum(self)\",\n \"def out_degrees_tf_vertex(self, vertex: int) -> tf.Tensor:\\n return tf.gather(self.out_degrees_tf, [vertex])\",\n \"def d(u, v):\\r\\n\\tdiff = u-v\\r\\n\\treturn diff.dot(diff)\",\n \"def __getPolynomial(self) -> 'func':\\n return lambda x: sum(self.pts[i]*base(x)\\n for i, base in enumerate(self.basis))\",\n \"def _acosd(v):\\n return math.degrees(math.acos(v))\",\n \"def steps_to_angle():\\n pass\",\n \"def proj_tan(self, v, x, c):\\n return v\",\n \"def to_povray_func(self):\\n\\n func_str = \\\"{}\\\".format(self.mth)\\n\\n for mb in self.mbs:\\n func_str += (\\\" - {} \\\"\\n \\\"* exp(-(pow(x-{},2) + pow(y-{},2) + pow(z-{},2))\\\"\\n \\\"/ (2 * pow({},2)))\\\"\\n .format(mb.a, mb.x, mb.y, mb.z, mb.s))\\n\\n return func_str\",\n \"def normalize_in_degrees(num_nodes, dict):\\n y = [val / num_nodes for key, val in dict.items()]\\n \\n return y\",\n \"def polynomial_degree(self):\\n return polynomial_degree(self)\",\n \"def degree(self, k):\\n one_if_my_all_k = 1 if self.my_alll(k) else 0\\n return (2 * self.get_prp_j(k)[0]) + one_if_my_all_k\",\n \"def V_phi(self, x):\\n\\n x = self.featureExtractor.getFeatures(x)\\n\\n x = torch.tensor(x).float()\\n\\n x = F.relu(self.linear(x))\\n\\n v = self.linear_v(x)\\n\\n return v\",\n \"def activ_fn_derivative(z):\\n return 1 - np.square(np.tanh(z))\",\n \"def tan(self, a):\\n return math.tan(a)\",\n \"def to_linear(self):\\n return inv(quad_hybrid).dot(self.circular)\",\n \"def f(x):\\n return N.sqrt(N.power(N.cos(x),2)+1.0)\",\n \"def AngleDtoR(degree):\\n\\trad=degree*math.pi/180\\n\\treturn rad\",\n \"def acc(x: float, v: float, t: float) -> float:\\n return -k*v - np.sin(x) + c*np.cos(omega*t)\",\n \"def convertToDiscreteFunction(boolean: bool) -> cern.japc.value.DiscreteFunction:\\n ...\",\n \"def F(x):\\n soln = x - (1.0/5.0)*math.cos(10.0*x+1.0) \\n return soln\",\n \"def in_degrees_np(self) -> np.ndarray:\\n return self.run_tf(self.in_degrees_tf)\",\n \"def angle(self, vec, unit='rad'):\\n raise NotImplementedError('angle not implemented for VectorArrays')\",\n \"def Avv_func(f):\\n\\n def Avv(x, v):\\n def F(s):\\n return f(x + v * s)\\n\\n return jacfwd(jacfwd(F))(0.0)\\n\\n return Avv\",\n \"def dec(self):\\n return gon2dec(self.gon_angle)\",\n \"def elevation_to_a_degree(numb1, numb2):\\r\\n return f\\\"Your result: {numb1**numb2}\\\"\",\n \"def transform(val):\\n return (100*math.atan(val) + 50*math.pi) / math.pi\",\n \"def rotate(vector, angle):\\n return np.cos(angle) * vector[0] + np.sin(angle) * vector[1], \\\\\\n -np.sin(angle) * vector[0] + np.cos(angle) * vector[1]\",\n \"def angle_2D(v):\\n len_v=(v[0]**2+v[1]**2)**(0.5)\\n if len_v==0:\\n return 0\\n ret = math.acos(v[0]/len_v)\\n if v[1]<0:\\n ret=6.283185307179586-ret\\n return ret\",\n \"def get_degrees(self):\\n return np.arange(self.lmax + 1)\",\n \"def orthogonal(v):\\n return np.array([-v[1], v[0]])\",\n \"def ret2dva(r_um):\\n sign = np.sign(r_um)\\n r_mm = 1e-3 * np.abs(r_um)\\n r_deg = 3.556 * r_mm + 0.05993 * r_mm ** 2 - 0.007358 * r_mm ** 3\\n r_deg += 3.027e-4 * r_mm ** 4\\n return sign * r_deg\",\n \"def degree(self, v):\\n self._validateVertex(v)\\n return self._adj[v].size()\",\n \"def ang_fac(ang):\\n return np.exp(2 * 1j * ang)\",\n \"def _angle(u, v, w, d='+'):\\n vu = np.arctan2(u[1] - v[1], u[0] - v[0])\\n vw = np.arctan2(w[1] - v[1], w[0] - v[0])\\n phi = vw - vu\\n if phi < 0:\\n phi += 2 * np.pi\\n if d == '-':\\n phi = 2 * np.pi - phi\\n return np.round(phi, 6)\",\n \"def angle(z):\",\n \"def convertFahrenheitToCelsius(degrees):\\n degrees = str(degrees)\\n convert = ((decimal.Decimal(degrees) - 32) * 5) / decimal.Decimal('9')\\n return float(convert)\",\n \"def zerodegree_pol(dim):\\n\\n out = zeros(dim)\\n out[0] = 1\\n\\n return out\",\n \"def int_to_vector(a: int, characteristic: int, degree: int) -> np.ndarray:\\n a_vec = np.zeros(degree, dtype=DTYPE)\\n for i in range(degree - 1, -1, -1):\\n q, r = divmod(a, characteristic)\\n a_vec[i] = r\\n a = q\\n\\n return a_vec\",\n \"def conv(x):\\n return x#-2*(16.41*x + 65.04-95.12) \",\n \"def total_degree(f):\\n return dmp_total_degree(f.rep, f.lev)\",\n \"def map_from_eta_to_n_v(eta):\\n eta_0, eta_1 = concatenated_vector_to_scalar_matrix(eta)\\n n = 2 * eta_0\\n v = np.linalg.inv(-2 * eta_1)\\n return n, v\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.6630002\",\n \"0.63732994\",\n \"0.61321235\",\n \"0.608381\",\n \"0.6005241\",\n \"0.600304\",\n \"0.5996289\",\n \"0.5975939\",\n \"0.5958366\",\n \"0.5938791\",\n \"0.5896974\",\n \"0.5866529\",\n \"0.58305264\",\n \"0.58234054\",\n \"0.58143467\",\n \"0.5772291\",\n \"0.5767484\",\n \"0.57465386\",\n \"0.5737111\",\n \"0.5736712\",\n \"0.5721935\",\n \"0.5712332\",\n \"0.5710978\",\n \"0.56644505\",\n \"0.56579167\",\n \"0.5646956\",\n \"0.56460786\",\n \"0.5642318\",\n \"0.5640111\",\n \"0.56067693\",\n \"0.55688775\",\n \"0.5564824\",\n \"0.55592465\",\n \"0.5535152\",\n \"0.55256975\",\n \"0.5480999\",\n \"0.5478233\",\n \"0.54646796\",\n \"0.546352\",\n \"0.54448324\",\n \"0.5442682\",\n \"0.5429706\",\n \"0.54236245\",\n \"0.54232883\",\n \"0.54181314\",\n \"0.54176104\",\n \"0.5417287\",\n \"0.5413836\",\n \"0.5408328\",\n \"0.53983355\",\n \"0.53578293\",\n \"0.53519344\",\n \"0.5348229\",\n \"0.534499\",\n \"0.5339496\",\n \"0.53341174\",\n \"0.53330547\",\n \"0.5325454\",\n \"0.5305228\",\n \"0.52987915\",\n \"0.5298618\",\n \"0.5295663\",\n \"0.52848953\",\n \"0.5284588\",\n \"0.5279334\",\n \"0.5257715\",\n \"0.5254694\",\n \"0.5239386\",\n \"0.5226055\",\n \"0.5212542\",\n \"0.5212093\",\n \"0.5210101\",\n \"0.52074707\",\n \"0.5206228\",\n \"0.5189406\",\n \"0.5187718\",\n \"0.51750964\",\n \"0.5174233\",\n \"0.51690143\",\n \"0.5166648\",\n \"0.51663303\",\n \"0.5165636\",\n \"0.51600724\",\n \"0.51596093\",\n \"0.51566917\",\n \"0.5152906\",\n \"0.5151586\",\n \"0.51513857\",\n \"0.5143696\",\n \"0.51405907\",\n \"0.51388437\",\n \"0.51355094\",\n \"0.51348346\",\n \"0.5133335\",\n \"0.51250345\",\n \"0.5122582\",\n \"0.51183724\",\n \"0.51176697\",\n \"0.5115295\",\n \"0.51101404\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58353925"},"document_rank":{"kind":"string","value":"12"}}},{"rowIdx":889,"cells":{"query":{"kind":"string","value":"Construct the fundamental Laplacian matrix."},"document":{"kind":"string","value":"def laplacian(degree_vector, weight_matrix):\n return np.diag(degree_vector) - weight_matrix"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_Laplacian_matrix(self, X):\n self.laplacian_mat, self.laplacian_sym_mat, self.laplacian_weights = self.laplacian.compute_laplacian(\n self.get_Affinity_matrix(X)\n )","def LaplacianMatrix(adjmatrix):\n if adjmatrix.dtype in [np.uint, np.uint0, np.uint8, np.uint16, np.uint32, np.uint64]:\n adjmatrix = adjmatrix.astype(int)\n N = len(adjmatrix)\n\n laplacianmatrix = np.identity(N, dtype=adjmatrix.dtype) * adjmatrix.sum(axis=1)\n laplacianmatrix -= adjmatrix\n\n return laplacianmatrix","def one_dim_sparse_laplacian(m: int):\n return sp.diags([1.0, -2.0, 1.0], [-1, 0, 1], dtype='float64', shape=(m, m), format='lil')","def rebuild_the_laplacians():\n local_matrix = InteractomeInterface()\n local_matrix.full_rebuild()\n\n annot_matrix = AnnotomeInterface()\n annot_matrix.full_rebuild()","def get_laplacian(adjacency: sparse.csr_matrix) -> sparse.csr_matrix:\n weights = adjacency.dot(np.ones(adjacency.shape[0]))\n return sparse.diags(weights) - adjacency","def generate_graph_laplacian(A):\r\n\r\n #Create symmetric matrix\r\n #A=0.5* (A+ A.T)\r\n \r\n #D is just the identity matrix (because sum(P)=1)\r\n Degree=np.sum(A,1)\r\n D=np.diag(Degree)\r\n \r\n #Laplacian matrix\r\n L=D-A\r\n return L","def compute_matrix(self):\n\n fac = self.a / self.dx ** 2\n\n diagonal = np.ones(self.nx) * 2 * fac\n lower = np.ones(self.nx - 1) * -fac\n upper = np.ones(self.nx - 1) * -fac\n\n matrix = sp.diags(\n diagonals=[diagonal, lower, upper],\n offsets=[0, -1, 1], shape=(self.nx, self.nx),\n format='csr')\n\n return matrix","def prepare_laplacian(laplacian):\n\n def estimate_lmax(laplacian, tol=5e-3):\n r\"\"\"Estimate the largest eigenvalue of an operator.\"\"\"\n lmax = sparse.linalg.eigsh(laplacian, k=1, tol=tol,\n ncv=min(laplacian.shape[0], 10),\n return_eigenvectors=False)\n lmax = lmax[0]\n lmax *= 1 + 2 * tol # Be robust to errors.\n return lmax\n\n def scale_operator(L, lmax, scale=1):\n r\"\"\"Scale the eigenvalues from [0, lmax] to [-scale, scale].\"\"\"\n I = sparse.identity(L.shape[0], format=L.format, dtype=L.dtype)\n L *= 2 * scale / lmax\n L -= I\n return L\n\n lmax = estimate_lmax(laplacian)\n laplacian = scale_operator(laplacian, lmax)\n\n laplacian = sparse.coo_matrix(laplacian)\n\n # PyTorch wants a LongTensor (int64) as indices (it'll otherwise convert).\n indices = np.empty((2, laplacian.nnz), dtype=np.int64)\n np.stack((laplacian.row, laplacian.col), axis=0, out=indices)\n indices = torch.from_numpy(indices)\n\n laplacian = torch.sparse_coo_tensor(indices, laplacian.data, laplacian.shape)\n laplacian = laplacian.coalesce() # More efficient subsequent operations.\n return laplacian","def laplacian_mat(n):\n data = [1, -2, 1]*n\n i = flatten([[k,k,k] for k in range(n)])\n j = flatten([[k-1, k, k+1] for k in range(n)])\n return scipy.sparse.coo_matrix((data[1:-1], (i[1:-1], j[1:-1])))","def compute_mesh_laplacian(mesh, weights=None, fem_b=None, lap_type=\"conformal\"):\n print(\" Computing Laplacian\")\n if weights is None:\n (weights, fem_b) = compute_mesh_weights(mesh, weight_type=lap_type)\n\n if lap_type == \"fem\":\n weights.data = weights.data / 2\n\n N = weights.shape[0]\n sB = fem_b.sum(axis=0)\n diaB = sparse.dia_matrix((sB, 0), shape=(N, N))\n B = sparse.lil_matrix(diaB + fem_b)\n s = weights.sum(axis=0)\n dia = sparse.dia_matrix((s, 0), shape=(N, N))\n L = sparse.lil_matrix(dia - weights)\n\n # if symmetrize == 1 & & normalize == 0\n # L = diag(sum(W, 2)) - W;\n # elseif\n # symmetrize == 1 & & normalize == 1\n # L = speye(n) - diag(sum(W, 2). ^ (-1 / 2)) * W * diag(\n # sum(W, 2). ^ (-1 / 2));\n # elseif\n # symmetrize == 0 & & normalize == 1\n # L = speye(n) - diag(sum(W, 2). ^ (-1)) * W;\n\n li = np.hstack(L.data)\n print(\" -nb Nan in Laplacian : \", len(np.where(np.isnan(li))[0]))\n print(\" -nb Inf in Laplacian : \", len(np.where(np.isinf(li))[0]))\n\n return L, B","def uniform_laplacian(image, radius=1):\n height, width = image.shape[:2]\n window_size = 2 * radius + 1\n\n W = sparse_conv_matrix(width, height, np.ones((window_size, window_size)))\n\n return weights_to_laplacian(W)","def nodalLaplacian(self):\n if getattr(self, '_nodalLaplacian', None) is None:\n print('Warning: Laplacian has not been tested rigorously.')\n # The number of cell centers in each direction\n n = self.vnC\n # Compute divergence operator on faces\n if(self.dim == 1):\n D1 = sdiag(1./self.hx) * ddx(self.nCx)\n L = - D1.T*D1\n elif(self.dim == 2):\n D1 = sdiag(1./self.hx) * ddx(n[0])\n D2 = sdiag(1./self.hy) * ddx(n[1])\n L1 = sp.kron(speye(n[1]+1), - D1.T * D1)\n L2 = sp.kron(- D2.T * D2, speye(n[0]+1))\n L = L1 + L2\n elif(self.dim == 3):\n D1 = sdiag(1./self.hx) * ddx(n[0])\n D2 = sdiag(1./self.hy) * ddx(n[1])\n D3 = sdiag(1./self.hz) * ddx(n[2])\n L1 = kron3(speye(n[2]+1), speye(n[1]+1), - D1.T * D1)\n L2 = kron3(speye(n[2]+1), - D2.T * D2, speye(n[0]+1))\n L3 = kron3(- D3.T * D3, speye(n[1]+1), speye(n[0]+1))\n L = L1 + L2 + L3\n self._nodalLaplacian = L\n return self._nodalLaplacian","def build_laplacian_over_ordinal_integer_actions(\n action_spec: types.BoundedTensorSpec,\n) -> types.Tensor:\n num_actions = policy_utilities.get_num_actions_from_tensor_spec(action_spec)\n adjacency_matrix = np.zeros([num_actions, num_actions])\n for i in range(num_actions - 1):\n adjacency_matrix[i, i + 1] = 1.0\n adjacency_matrix[i + 1, i] = 1.0\n laplacian_matrix = (\n np.diag(np.sum(adjacency_matrix, axis=0)) - adjacency_matrix\n )\n return laplacian_matrix","def normalized_laplacian(degree_vector, weight_matrix, length):\n holders = np.zeros((length, 1))\n holders[:, 0] = 1 / degree_vector\n\n return np.eye(length) - holders * weight_matrix","def decomposeLU(self):\n self.check_square()\n\n N = self.rows\n L = make_matrix(N, N)\n U = make_matrix(N, N)\n A = self #for more math friendly notation\n\n\n for j in range(N):\n L[j, j] = 1.0 #Doolittle factorization\n\n #e.g., if you are in column = 5, you go down 6 rows\n for i in range(j+1):\n U[i, j] = A[i, j] - sum(L[i, k] * U[k, j] for k in range(i))\n #e.g., if you are in column = 5,\n # you start at row 5 and go down for the lower triangular matrix\n for i in range(j, N):\n L[i, j] = (A[i, j] - sum(L[i, k] * U[k, j] for k in range(j))) / U[j, j]\n\n self.L = L\n self.U = U\n return L, U","def calculate_normalized_laplacian(adj):\n adj = sp.coo_matrix(adj)\n d = np.array(adj.sum(1))\n d_inv_sqrt = np.power(d, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n return normalized_laplacian","def calculate_normalized_laplacian(adj):\n adj = sp.coo_matrix(adj)\n d = np.array(adj.sum(1))\n d_inv_sqrt = np.power(d, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n return normalized_laplacian","def lap_mat(self):","def L1U(A, d):\n \n\n n, _ = A.shape\n L = np.eye(n, n, dtype=A.dtype)\n U = np.zeros((n, n), dtype=A.dtype)\n\n U[0, 0] = A[0, 0]\n for k in range(1, n):\n km = max(0, k-d)\n L[k, km : k] = np.transpose(rforwardsolve(np.transpose(U[km:k, km:k]),\\\n np.transpose(A[k, km:k]), d))\n U[km:k+1, k] = rforwardsolve(L[km:k+1, km:k+1], A[km:k+1, k], d)\n return L, U","def make_matrix(self):\n self.leftmost_element()\n self.rightmost_element()\n self.interior_element()\n\n #Transforms all sympy symbolic expressions for the lagrange polynomials into callable functions.\n self.psi_funcs = [sym.lambdify([self.x], self.psi[i], modules = \"numpy\") for i in range(3*self.Ne)]","def laplacian(A):\n #calculate D by creating a diagonal matrix with the column sum of A\n D = np.diag(A.sum(axis=0))\n return D - A","def init_needleman_wunsch_matrix(self):\r\n empty_matrix = self.empty_matrix() # Building on the previous definition, this will give you an empty matrix\r\n for i in range(len(self.s2)+1):\r\n for j in range(len(self.s1)+1):\r\n empty_matrix[0][i] = -i\r\n empty_matrix[j][0] = -j\r\n return empty_matrix","def laplacian_matrix(A, normalized=False):\n n, m = A.shape\n D = degree_matrix(A)\n L = D - A\n if normalized:\n degs = _flat(A.sum(axis=1))\n rootD = sps.spdiags(np.power(degs, -1 / 2), [0], n, n, format=\"csr\")\n L = rootD * L * rootD\n return L","def LUdecomp(Ainput):\n\n n, m = np.shape(Ainput)\n \n if n != m:\n return 'Error: Please enter an invertible matrix.'\n \n U = Ainput.copy() # make copies so as not to write over originals\n L = np.zeros((np.shape(Ainput)))\n \n for i in range(0,n):\n L[i,i] = 1\n for i in range(0,n-1): # loop over pivot rows from row 1 to row n-1 (i to n-2)\n for j in range(i+1,n): # loop over row to be zero'ed from row j+1 to n (j+1 to n-1)\n c = U[j,i]/U[i,i] # multiplicative factor to zero point\n L[j,i] = c\n U[j,i] = 0.0 # we know this element goes to zero\n U[j,i+1:n]=U[j,i+1:n]-c*U[i,i+1:n] # do subtraction of two rows\n\n return (L,U) # return lower and upper decompositions","def offDiagPairs(self):\n return np.transpose(np.nonzero(np.triu(self.LaplacianMatrix,k=2)))","def expansion_matrix_dl(self):\n\n row = self._base_nlp._lower_d_map\n nnz = len(self._base_nlp._lower_d_map)\n col = np.arange(nnz, dtype=np.int)\n data = np.ones(nnz)\n return csr_matrix((data, (row, col)), shape=(self.nd, nnz))","def decompose_to_LU(a):\n # create emtpy LU-matrix\n lu_matrix = np.matrix(np.zeros([a.shape[0], a.shape[1]]))\n n = a.shape[0]\n\n for k in range(n):\n # calculate all residual k-row elements\n for j in range(k, n):\n lu_matrix[k, j] = a[k, j] - lu_matrix[k, :k] * lu_matrix[:k, j]\n # calculate all residual k-column elemetns\n for i in range(k + 1, n):\n lu_matrix[i, k] = (a[i, k] - lu_matrix[i, : k] * lu_matrix[: k, k]) / lu_matrix[k, k]\n\n return lu_matrix","def laplacian(A):\n D = np.diag(np.sum(A, axis=1))\n return D - A","def laplacian(mesh):\n faces = np.array(mesh.triangles)\n N = np.array(mesh.vertices).shape[0]\n A = np.zeros((N, N))\n for i in range(3):\n for j in range(3):\n if i == j:\n continue\n A[(faces[:, i], faces[:, j])] = 1.0\n A = A + A.T\n diag = A.dot(np.ones(N))\n L = np.diag(diag) - A\n return L","def getNormLaplacian(W):\n\td=[np.sum(row) for row in W]\n\tD=np.diag(d)\n\tL=D-W\n\t#Dn=D^(-1/2)\n\tDn=np.power(np.linalg.matrix_power(D,-1),0.5)\n\tLbar=np.dot(np.dot(Dn,L),Dn)\n\treturn Lbar","def _set_diag(laplacian, value, norm_laplacian):\n n_nodes = laplacian.shape[0]\n # We need all entries in the diagonal to values\n if not sparse.isspmatrix(laplacian):\n if norm_laplacian:\n laplacian.flat[::n_nodes + 1] = value\n else:\n laplacian = laplacian.tocoo()\n if norm_laplacian:\n diag_idx = (laplacian.row == laplacian.col)\n laplacian.data[diag_idx] = value\n # If the matrix has a small number of diagonals (as in the\n # case of structured matrices coming from images), the\n # dia format might be best suited for matvec products:\n n_diags = np.unique(laplacian.row - laplacian.col).size\n if n_diags <= 7:\n # 3 or less outer diagonals on each side\n laplacian = laplacian.todia()\n else:\n # csr has the fastest matvec and is thus best suited to\n # arpack\n laplacian = laplacian.tocsr()\n return laplacian","def make_mat_cp_le(cons_pot_mesh, lin_geo_mesh):\n pot_faces = cons_pot_mesh.get_faces()\n assert pot_faces.shape[0] == lin_geo_mesh.get_faces().shape[0]\n num_faces = pot_faces.shape[0]\n K = np.zeros((3 * num_faces, 3 * num_faces))\n add_cp_le_DL_terms(K, cons_pot_mesh, lin_geo_mesh)\n add_cp_le_RBM_terms(K, cons_pot_mesh, lin_geo_mesh)\n return K","def get_correct_distance_matrix(L):\n n = len(L)\n D = np.zeros((n,n))\n for i in range(n):\n for j in range(n):\n if i != j:\n D[i][j] = get_minor(L, [i, j], [i, j]) / get_minor(L, [i], [i])\n return D","def _r_matrix_xxz(self, root):\n r_matrix = np.eye(4, dtype=np.complex128)\n if self.delta == 1:\n b = (root - 1j) / (root + 1j)\n c = 2j / (root + 1j)\n\n elif self.delta > 1:\n gamma = np.arccosh(self.delta)\n b = np.sin(gamma / 2 * (root - 1j)) / np.sin(gamma / 2 * (root + 1j))\n c = 1j * np.sinh(gamma) / np.sin(gamma / 2 * (root + 1j))\n else:\n gamma = np.arccos(self.delta)\n b = np.sinh(gamma / 2 * (root - 1j)) / np.sinh(gamma / 2 * (root + 1j))\n c = 1j * np.sin(gamma) / np.sinh(gamma / 2 * (root + 1j))\n r_matrix[1, 1] = r_matrix[2, 2] = c\n r_matrix[1, 2] = r_matrix[2, 1] = b\n return r_matrix","def _init_transformation_matrix(self):\n # Set up basic transformation matrix\n c_transform = np.zeros((self.n_beads, self.n_beads))\n\n # Get auxiliary array with bead indices\n n = np.arange(1, self.n_beads + 1)\n\n # for k = 0\n c_transform[0, :] = 1.0\n\n for k in range(1, self.n_beads // 2 + 1):\n c_transform[k, :] = np.sqrt(2) * np.cos(2 * np.pi * k * n / self.n_beads)\n\n for k in range(self.n_beads // 2 + 1, self.n_beads):\n c_transform[k, :] = np.sqrt(2) * np.sin(2 * np.pi * k * n / self.n_beads)\n\n if self.n_beads % 2 == 0:\n c_transform[self.n_beads // 2, :] = (-1) ** n\n\n # Since matrix is initialized as C(k,n) does not need to be transposed\n c_transform /= np.sqrt(self.n_beads)\n c_transform = torch.from_numpy(c_transform)\n\n return c_transform","def L1U(A, d):\n n = shape(A)[0]\n L = eye(n)\n U = matrix(zeros((n,n))); U[0,0] = A[0,0]\n for k in range(1,n):\n km = array([0, k - d]).max()\n if km < k:\n L[k, km:k] = A[k, km:k]\n rforwardsolve(U[km:k, km:k].T, L[k, km:k].T, d) # L\n U[km:(k + 1), k] = A[km:(k + 1), k]\n rforwardsolve(L[km:(k + 1), km:(k + 1)], U[km:(k + 1), k], d) # U\n return L, U","def laplacian(W, normalized=False):\r\n # Degree matrix.\r\n d = W.sum(axis=0)\r\n # Laplacian matrix.\r\n if not normalized:\r\n D = scipy.sparse.diags(d.A.squeeze(), 0)\r\n L = D - W\r\n else:\r\n # d += np.spacing(np.array(0, W.dtype))\r\n d = 1 / np.sqrt(d)\r\n D = scipy.sparse.diags(d.A.squeeze(), 0)\r\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\r\n L = I - D * W * D\r\n\r\n # assert np.abs(L - L.T).mean() < 1e-9\r\n assert type(L) is scipy.sparse.csr.csr_matrix\r\n return L","def bloch_matrix(self):\n if self.gf_r is None:\n self.gf()\n\n return -self.gf_r.dot(self.lead[1])","def prepotential(G):\n L = nx.laplacian_matrix(G).toarray()\n L_tild = L[1:,1:]\n T_tild = np.linalg.inv(L_tild)\n T = np.zeros(L.shape)\n T[1:,1:] = T_tild\n return T","def _buildMatrix(self, SparseMatrix, Ncells, MaxFaces, coeff):\n return (0, 0)","def CreateMatrix(self) -> BaseMatrix:","def CreateMatrix(self) -> BaseMatrix:","def LU(A):\n m, n = A.shape\n L, U = np.zeros([m, n]), np.zeros([m, n])\n for i in range(n):\n L[i][i] = 1\n\n for i in range(n):\n\n # Upper triangular matrix\n for j in range(i, n):\n summ = 0\n for k in range(0, i):\n summ += L[i][k]*U[k][j]\n U[i][j] = A[i][j] - summ\n\n # Lower triangular matrix\n for j in range(i+1, n):\n summ = 0\n for k in range(0, i):\n summ += L[j][k]*U[k][i]\n L[j][i] = (A[j][i] - summ)/U[i][i]\n return L, U","def construct_linear_system(self):\n N=self.grid.Ncells()\n Nbc = len(self.dirichlet_bcs)\n self.Ncalc=Ncalc = N - Nbc\n\n # map cells to forced values\n dirichlet = dict( [ (c,v) for c,v,xy in self.dirichlet_bcs])\n\n self.is_calc_c = is_calc_c = np.ones(N,np.bool8)\n for c,v,xy in self.dirichlet_bcs:\n is_calc_c[c] = False\n\n # is_calc_c[self.c_mask] = False\n\n # c_map is indexed by real cell indices, and returns the matrix index\n c_map = self.c_map = np.zeros(N,np.int32)\n self.c_map[is_calc_c] = np.arange(Ncalc)\n\n dzc=self.dzc\n dzf=self.dzf\n area_c=self.area_c\n\n meth='coo' # 'dok'\n if meth == 'dok':\n A=sparse.dok_matrix((Ncalc,Ncalc),np.float64)\n else:\n # construct the matrix from a sequence of indices and values\n ij=[]\n values=[] # successive value for the same i.j will be summed\n \n b = np.zeros(Ncalc,np.float64)\n flux_per_gradient_j = -self.K_j * self.l_j * dzf / self.d_j * self.dt\n\n self.grid.edge_to_cells() # makes sure that edges['cells'] exists.\n \n for j in range(self.grid.Nedges()):\n e = self.grid.edges[j]\n ic1,ic2 = e['cells']\n \n if ic1<0 or ic2<0 or e['deleted']:\n continue # boundary edge, or deleted edge\n \n flux_per_gradient=flux_per_gradient_j[j]\n \n # this is the desired operation:\n # Cdiff[ic1] -= flux_per_gradient / (An[ic1]*dzc) * (C[ic2] - C[ic1])\n # Cdiff[ic2] += flux_per_gradient / (An[ic2]*dzc) * (C[ic2] - C[ic1])\n # Where Cdiff is row, C is col\n\n if is_calc_c[ic1] and is_calc_c[ic2]:\n mic2 = c_map[ic2]\n mic1 = c_map[ic1]\n v1=flux_per_gradient / (area_c[ic1]*dzc[ic1])\n v2=flux_per_gradient / (area_c[ic2]*dzc[ic2])\n \n if meth == 'dok':\n A[mic1,mic2] -= v1\n A[mic1,mic1] += v1\n A[mic2,mic2] += v2\n A[mic2,mic1] -= v2\n else:\n ij.append( (mic1,mic2) ) ; values.append(-v1)\n ij.append( (mic1,mic1) ) ; values.append(v1)\n ij.append( (mic2,mic2) ) ; values.append(v1)\n ij.append( (mic2,mic1) ) ; values.append(-v1)\n \n elif not ( is_calc_c[ic1] or is_calc_c[ic2] ):\n # both are dirichlet, so nothing to do\n pass\n elif not is_calc_c[ic2]:\n mic1 = c_map[ic1]\n v=flux_per_gradient / (self.area_c[ic1]*dzc[ic1])\n if meth == 'dok':\n A[mic1,mic1] += v\n else:\n ij.append( (mic1,mic1) )\n values.append(v)\n\n # roughly\n # A[1,1]*x[1] + A[1,2]*x[2] + ... = b[1]\n # but we already know x[2],\n # A[1,1]*x[1] + ... = b[1] - A[1,2]*x[2]\n # so flip the sign, multiply by known dirichlet value, and\n # add to the RHS\n b[mic1] += flux_per_gradient / (area_c[ic1]*dzc[ic1]) * dirichlet[ic2]\n else: # not is_calc_c[c1]\n mic2 = c_map[ic2]\n # A[mic2,mic2] += flux_per_gradient / (area_c[ic2]*dzc[ic2])\n # A[mic2,mic1] -= flux_per_gradient / (area_c[ic2]*dzc[ic2])\n\n # A[mic2,mic2]*x[2] + A[mic2,mic1]*x[1] = b[2]\n # ...\n # A[mic2,mic2]*x[2] - flux_per_gradient / (area_c[ic2]*dzc[ic2])*x[1] = b[2]\n # ...\n # A[mic2,mic2]*x[2] = b[2] + flux_per_gradient / (area_c[ic2]*dzc[ic2])*x[1]\n v=flux_per_gradient / (area_c[ic2]*dzc[ic2])\n if meth == 'dok':\n A[mic2,mic2] += v\n else:\n ij.append( (mic2,mic2) )\n values.append(v)\n b[mic2] += flux_per_gradient / (area_c[ic2]*dzc[ic2]) * dirichlet[ic1]\n\n # Used to test 'is not 0:' but modern python complains\n if isinstance(self.alpha,np.ndarray): \n for c in range(N):\n if self.is_calc_c[c]:\n mic=self.c_map[c]\n v=self.alpha[c]*self.dt\n if meth == 'dok':\n A[mic,mic] -= v\n else:\n ij.append( (mic,mic) )\n values.append(-v)\n\n # Flux boundary conditions:\n for ic,value,xy in self.neumann_bcs:\n mic=c_map[ic]\n # make mass/time into concentration/step\n # arrived at minus sign by trial and error.\n # 2023-08-04: there was a bug here that used ic2 instead of ic.\n b[mic] -= value/(area_c[ic]*dzc[ic]) * self.dt\n\n if meth == 'dok':\n self.A = sparse.coo_matrix(A)\n else:\n ijs=np.array(ij,dtype=np.int32)\n data=np.array(values,dtype=np.float64)\n A=sparse.coo_matrix( (data, (ijs[:,0],ijs[:,1]) ), shape=(Ncalc,Ncalc) )\n self.A=A\n \n # report scale to get a sense of whether dt is too large\n Ascale = A.diagonal().min()\n log.debug(\"Ascale is %s\"%Ascale)\n\n self.b = b","def laplace_matrix(self):\n n = self.number_of_vertices\n laplace_matrix = np.zeros((n, n))\n for i in range(n):\n laplace_matrix[i][i] = 1\n vertice = self.list_of_vertices[i]\n for edge in vertice.edges_list:\n laplace_matrix[i][edge.linked[1].index] = 1\n return laplace_matrix","def cal_L(self):\n # calculate the l matrix\n self.point_matrixs = self.point_matrix.reshape(\n self.point_matrix.shape[0], 1, self.point_matrix.shape[1])\n self.point_matrixs = np.tile(self.point_matrixs,\n (self.attach_points.shape[0], 1))\n self.attach_points_matrix = np.matlib.repmat(\n self.attach_points[:, 0:3], self.point_matrix.shape[0], 1)\n self.attach_points_matrix = self.attach_points_matrix.reshape(\n self.point_matrix.shape[0], self.attach_points.shape[0], 3)\n self.L = np.subtract(self.attach_points_matrix,\n self.point_matrixs)\n # self.L[:,self.attach_points[:,3]==1,:] = \\\n # - self.L[:,self.attach_points[:,3]==1,:]\n # print(self.L)","def relaxation_matrix(self, uphill, downhill):\n world.KK = numpy.zeros((2,2), dtype=numpy.float64)\n Kup = 1.0/float(uphill)\n world.KK[0,0] = -Kup\n world.KK[1,0] = Kup\n Kdn = 1.0/float(downhill)\n world.KK[0,1] = Kdn\n world.KK[1,1] = -Kdn","def lu_decomposition(self):\n if self.rows_count() != self.columns_count():\n raise ValueError(\"Matrix needs to me square for LU decomposition.\")\n for i in range(self.rows_count() - 1):\n for j in range(i + 1, self.rows_count()):\n if self[i, i] == 0: # or abs(self[i, i]) <= 0.000001):\n raise ValueError(\"Can't divide by 0\")\n self[j, i] = self[j, i] / self[i, i]\n for k in range(i + 1, self.rows_count()):\n self[j, k] -= self[j, i] * self[i, k]","def laplacian(W, normalized=True):\n\n # Degree matrix.\n d = W.sum(dim=0)\n\n # Laplacian matrix.\n if not normalized:\n D = scipy.sparse.diags(d.A.squeeze(), 0)\n L = D - W\n else:\n # d += np.spacing(np.array(0, W.dtype))\n d = 1 / torch.sqrt(d)\n D = torch.diags(d.A.squeeze(), 0)\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\n L = I - D * W * D\n\n # assert np.abs(L - L.T).mean() < 1e-9\n assert type(L) is scipy.sparse.csr.csr_matrix\n return L","def construct_matrix_A(n):\n n2 = n**2\n D0 = 4*np.ones(n2) # 0th diagonal\n D1 = - np.ones(n2 - 1) # -1st, 1st diagonals\n D1[n-1::n] = 0 # Setting every k*n-1 entries = 0 for k < n\n DN = - np.ones(n2 - n) # -nth, nth diagonals\n return scipy.sparse.diags((DN, D1, D0, D1, DN), (-n, -1, 0, 1, n),\n shape=(n2, n2), format=\"csr\")","def buildLaplacianPyramid(guassianPyramid):\n pyramid = []\n for i in range(len(guassianPyramid)-1):\n Gi = upsample(guassianPyramid[i+1])\n G = guassianPyramid[i]\n r, c = G.shape[:2]\n L = G - Gi[:r, :c]\n pyramid.append(L)\n\n pyramid.append(guassianPyramid[-1])\n return pyramid","def symmetrized_normalized_laplacian(degree_vector, weight_matrix, length):\n holders = np.zeros((length, 1))\n holders[:, 0] = np.sqrt(1 / degree_vector)\n\n return np.eye(length) - holders * weight_matrix * holders.T","def mounting_matrix(self):\n # fmt: off\n count = 0\n for x in range(self.ntheta):\n self.M[count][count] = 1\n self.f[count][0] = self.p_in\n count = count + self.nz - 1\n self.M[count][count] = 1\n self.f[count][0] = self.p_out\n count = count + 1\n count = 0\n for x in range(self.nz - 2):\n self.M[self.ntotal - self.nz + 1 + count][1 + count] = 1\n self.M[self.ntotal - self.nz + 1 + count][self.ntotal - self.nz + 1 + count] = -1\n count = count + 1\n count = 1\n j = 0\n for i in range(1, self.nz - 1):\n a = (1 / self.dtheta ** 2) * (self.c1[i][self.ntheta - 1])\n self.M[count][self.ntotal - 2 * self.nz + count] = a\n b = (1 / self.dz ** 2) * (self.c2[i - 1, j])\n self.M[count][count - 1] = b\n c = -((1 / self.dtheta ** 2) * ((self.c1[i][j]) + self.c1[i][self.ntheta - 1])\n + (1 / self.dz ** 2) * (self.c2[i][j] + self.c2[i - 1][j]))\n self.M[count, count] = c\n d = (1 / self.dz ** 2) * (self.c2[i][j])\n self.M[count][count + 1] = d\n e = (1 / self.dtheta ** 2) * (self.c1[i][j])\n self.M[count][count + self.nz] = e\n count = count + 1\n count = self.nz + 1\n for j in range(1, self.ntheta - 1):\n for i in range(1, self.nz - 1):\n a = (1 / self.dtheta ** 2) * (self.c1[i, j - 1])\n self.M[count][count - self.nz] = a\n b = (1 / self.dz ** 2) * (self.c2[i - 1][j])\n self.M[count][count - 1] = b\n c = -((1 / self.dtheta ** 2) * ((self.c1[i][j]) + self.c1[i][j - 1])\n + (1 / self.dz ** 2) * (self.c2[i][j] + self.c2[i - 1][j]))\n self.M[count, count] = c\n d = (1 / self.dz ** 2) * (self.c2[i][j])\n self.M[count][count + 1] = d\n e = (1 / self.dtheta ** 2) * (self.c1[i][j])\n self.M[count][count + self.nz] = e\n count = count + 1\n count = count + 2\n count = 1\n for j in range(self.ntheta - 1):\n for i in range(1, self.nz - 1):\n if j == 0:\n self.f[count][0] = (self.c0w[i][j] - self.c0w[i][self.ntheta - 1]) / self.dtheta\n else:\n self.f[count][0] = (self.c0w[i, j] - self.c0w[i, j - 1]) / self.dtheta\n count = count + 1\n count = count + 2\n # fmt: on","def laplacian2(A, laplacian_type='raw'):\r\n\r\n N = A.shape[0]\r\n # TODO: Raise exception if A is not square\r\n\r\n degrees = A.sum(1)\r\n # To deal with loops, must extract diagonal part of A\r\n diagw = np.diag(A)\r\n\r\n # w will consist of non-diagonal entries only\r\n ni2, nj2 = A.nonzero()\r\n w2 = A[ni2, nj2]\r\n ndind = (ni2 != nj2).nonzero() # Non-diagonal indices\r\n ni = ni2[ndind]\r\n nj = nj2[ndind]\r\n w = w2[ndind]\r\n\r\n di = np.arange(N) # diagonal indices\r\n\r\n if laplacian_type == 'raw':\r\n # non-normalized laplaciand L = D - A\r\n L = np.diag(degrees - diagw)\r\n L[ni, nj] = -w\r\n L = lil_matrix(L)\r\n elif laplacian_type == 'normalized':\r\n # TODO: Implement the normalized laplacian case\r\n # % normalized laplacian D^(-1/2)*(D-A)*D^(-1/2)\r\n # % diagonal entries\r\n # dL=(1-diagw./degrees); % will produce NaN for degrees==0 locations\r\n # dL(degrees==0)=0;% which will be fixed here\r\n # % nondiagonal entries\r\n # ndL=-w./vec( sqrt(degrees(ni).*degrees(nj)) );\r\n # L=sparse([ni;di],[nj;di],[ndL;dL],N,N);\r\n print(\"Not implemented\")\r\n else:\r\n # TODO: Raise an exception\r\n print(\"Don't know what to do\")\r\n\r\n return L","def get_l(m):\n L = m.copy()\n for i in range(L.shape[0]):\n L[i, i] = 1\n L[i, i+1:] = 0\n return np.matrix(L)","def lup_decomposition(self):\n p = [i for i in range(self.rows_count())]\n for i in range(self.rows_count() - 1):\n pivot = i\n for j in range(i + 1, self.rows_count()):\n if abs(self[p[j], i]) > abs(self[p[pivot], i]):\n pivot = j\n p[pivot], p[i] = p[i], p[pivot]\n for j in range(i + 1, self.rows_count()):\n if abs(self[p[i], i]) < math.pow(10, -6):\n raise ValueError(\"Can't divide by 0\")\n self[p[j], i] /= self[p[i], i]\n for k in range(i + 1, self.rows_count()):\n self[p[j], k] -= self[p[j], i] * self[p[i], k]\n lst = []\n for i in p:\n lst.append(self.value[i])\n return p, Matrix(lst)","def lizardite():\n\n rho = 2610.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 245.; C[0,1] = 50.; C[0,2] = 31.; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 245.; C[1,2] = 31.; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 23.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 11.6; C[3,4] = 0.; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 11.6; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 97.5\n\n return C, rho","def get_feature_oriented_matrix(self):\n nbr_features = self.hyperparameters.time_series_depth\n matrix = np.ones(shape=(nbr_features, nbr_features), dtype=np.float)\n np.fill_diagonal(matrix, val=0)\n return matrix","def lform(self):\n a, c, d, b = self.to_ccw()\n if b < c:\n a += b\n b -= b\n c -= b\n d += b\n else:\n a += c\n b -= c\n c -= c\n d += c\n return self.__class__.from_ccw(a, c, d, b)","def build_lower_zeros(self):\r\n for row in range(self.SIZE - 1, 0, -1):\r\n self.__obtain_zero(row, 0, 0)\r\n\r\n for col in range(1, self.SIZE - 1):\r\n for row in range(self.SIZE - 1, col, -1):\r\n self.__obtain_zero(row, col, row - 1)","def lawsonite():\n\n rho = 3090.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 214.; C[0,1] = 69.; C[0,2] = 82.; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 226.; C[1,2] = 65.; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 259.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 60.; C[3,4] = 0.; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 65.; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 17.\n\n return C, rho","def lMatrix(a, b, c, alpha, beta, gamma, convention=None):\n lMatrix = np.zeros((3, 3))\n\n cosAlpha = np.cos(alpha)\n cosBeta = np.cos(beta)\n cosGamma = np.cos(gamma)\n\n sinGamma = np.sin(gamma)\n\n lMatrix[0, 0] = a\n lMatrix[0, 1] = b * cosGamma\n lMatrix[0, 2] = c * cosBeta\n\n lMatrix[1, 1] = b * sinGamma\n lMatrix[1, 2] = c * (cosAlpha - cosBeta * cosGamma) / sinGamma\n\n lMatrix[2, 2] = c * np.sqrt(\n 1 + 2 * cosAlpha * cosBeta * cosGamma -\n cosAlpha**2 - cosBeta**2 - cosGamma**2\n ) / sinGamma\n\n # OI/HKL convention - x // [10-10], y // a2 [-12-10]\n # TSL convention - x // a1 [2-1-10], y // [01-10]\n if convention is None:\n convention = defaults['crystal_ortho_conv']\n\n if convention.lower() in ['hkl', 'oi']:\n # Swap 00 with 11 and 01 with 10 due to how OI orthonormalises\n # From Brad Wynne\n t1 = lMatrix[0, 0]\n t2 = lMatrix[1, 0]\n\n lMatrix[0, 0] = lMatrix[1, 1]\n lMatrix[1, 0] = lMatrix[0, 1]\n\n lMatrix[1, 1] = t1\n lMatrix[0, 1] = t2\n\n elif convention.lower() != 'tsl':\n raise ValueError(\n f\"Unknown convention '{convention}' for orthonormalisation of \"\n f\"crystal structure, can be 'hkl' or 'tsl'\"\n )\n\n # Set small components to 0\n lMatrix[np.abs(lMatrix) < 1e-10] = 0\n\n return lMatrix","def matrix(self, full=False, keeppads=True):\n\n v = np.fft.hfft(self._u, n=self.N) / self.N\n idx = sum(np.ogrid[0:self.N, -self.N:0])\n C = v[idx] # follow scipy.linalg.{circulant,toeplitz,hankel}\n\n if keeppads:\n a = self._yfac_.copy()\n b = self._xfac_.copy()\n else:\n a = self.yfac.copy()\n b = self.xfac.copy()\n C = self._unpad(C, 0, True)\n C = self._unpad(C, 1, False)\n a = a.reshape(-1, 1)\n\n if not full:\n return a, b, C\n else:\n return a * C * b","def reconstructFromLaplacianPyramid(pyramid):\n \n nLevels = len(pyramid)\n out = pyramid[-1]\n if len(pyramid) == 1:\n return out\n\n useStack = False\n if pyramid[0].shape[0:2] == pyramid[-1].shape[0:2]:\n useStack = True\n\n dtp = out.dtype\n for i in range(nLevels-2,-1,-1):\n newSz = pyramid[i].shape[0:2]\n if useStack:\n up = out\n else:\n up = cv2.pyrUp(out,dstsize=(newSz[1],newSz[0]))\n if len(up.shape) < 3:\n up.shape += (1,)\n out = up + pyramid[i]\n out = out.astype(dtp)\n\n return out","def _set_ls_matrices(self):\n zz_t = self.z_matrix * self.z_matrix.transpose()\n l, s, l_t = np.linalg.svd(zz_t)\n s[self.p:] = 0\n self.l_matrix = np.matrix(l)\n self.s_matirx = np.matrix(np.diag(s))","def mult_L(self) -> np.ndarray:\n return np.array([\n [self.w, -self.x, -self.y, -self.z],\n [self.x, self.w, -self.z, self.y],\n [self.y, self.z, self.w, -self.x],\n [self.z, -self.y, self.x, self.w]])","def mk_single_diffy():\n # make matrix:\n mat = zeros((M, M), dtype='d')\n for m in range(M):\n for p in range(m+1, M, 2):\n mat[m,p] = 2*p*oneOverC[m]\n\n return mat","def matrices(self):\n # Creating L\n L = scipy.sparse.diags((self.inv_dx2, -2*self.inv_dx2, self.inv_dx2, 1),\n (-(self.N+1), -self.N, -(self.N-1), self.N),\n shape=(2*self.N, 2*self.N), dtype=np.complex128)\n self.L = scipy.sparse.csr_matrix(L)\n self.L[-(self.N+1), 0], self.L[-1, -self.N] = 0, 0\n\n # Computing largest eigenvalue of L explicitely:\n self.mu_max = self.inv_dx*np.sqrt(2*(1 + np.cos(np.pi/(self.N+1))))\n\n # Creating K\n self.K = scipy.sparse.diags((-self.inv_dx2, 2*self.inv_dx2, -self.inv_dx2),\n (-1, 0, 1), # Diagonals\n shape=(self.N, self.N), # Size of matrix\n dtype=np.complex128)","def get_incorrect_distance_matrix(L):\n n = len(L)\n D = np.zeros((n,n))\n for i in range(n):\n for j in range(n):\n if i != j:\n D[i][j] = get_minor(L, [i], [j]) / get_minor(L, [i], [i])\n return D","def get_L(self, tolerance=None):\r\n index = self.data.index\r\n columns = self.data.columns\r\n\r\n # Obtain the eigenvalues and eigenvectors\r\n E, V = sandy.CategoryCov(self.data).get_eig(tolerance=tolerance)\r\n\r\n # need sparse because much faster for large matrices (2kx2k from J33 Pu9)\r\n # with a lot of zero eigs\r\n # this is exactly equivalent to V.values @ np.diag(np.sqrt(E.values))\r\n A = (sparse.csr_matrix(V.values) @ sparse.csr_matrix(np.diag(np.sqrt(E.values)))).todense()\r\n \r\n# u, s, vh = self.get_svd()\r\n# A = (sparse.csr_matrix(u) @ sparse.csr_matrix(np.diag(np.sqrt(s)))).todense()\r\n\r\n # QR decomposition\r\n Q, R = scipy.linalg.qr(A.T)\r\n L = R.T\r\n\r\n return pd.DataFrame(L, index=index, columns=columns)","def lherzolite():\n\n rho = 3270.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 187.4; C[0,1] = 63.71; C[0,2] = 63.87; C[0,3] = 0.78; C[0,4] = 2.02; C[0,5] = -3.2\n C[1,0] = C[0,1]; C[1,1] = 211.25; C[1,2] = 64.5; C[1,3] = -3.07; C[1,4] = 0.87; C[1,5] = -5.78\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 190.; C[2,3] = 0.38; C[2,4] = 2.38; C[2,5] = -0.12\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 67.9; C[3,4] = -2.12; C[3,5] = 1.6\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 63.12; C[4,5] = -0.55\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 66.83\n\n return C, rho","def build_matrix(self, rawdata):\n\t\tN = len(rawdata)\n\t\ts = set()\n\t\tfor d in rawdata:\n\t\t\ts.update(d[7])\n\t\tK = len(s)\n\t\tself.I = dict((j, i) for (i, j) in enumerate(s))\n\t\tself.I_rev = dict((i, j) for (i, j) in enumerate(s))\n\t\tD = sp.dok_matrix((N, K))\n\t\tself.t = np.zeros(N)\n\t\tprint \"Empty %i x %i matrix created\" % (N, K)\n\t\t\n\t\tfor n,d in enumerate(rawdata):\n\t\t\tif len(d[7]) == 1:\n\t\t\t\tD[n,self.I[d[7][0]]] = 1# d[6] - d[5]\n\t\t\telse:\n\t\t\t\tD[n, self.I[d[7][0]]] = 1 # d[5]\n\t\t\t\tD[n, self.I[d[7][-1]]] = 1 # d[6]\n\t\t\t\tfor eid in d[7][1:-1]:\n\t\t\t\t\tD[n, self.I[eid]] = 1.0\n\t\t\tself.t[n] = float(d[2] - d[1])\n\t\tprint \"DOK matrix built.\"\n\t\t\n\t\tself.M = sp.csr_matrix(D)\n\t\tprint \"DOK to CSR conversion done.\"\n\t\tself.N = N\n\t\tself.K = K","def _prepare_outer_matrix(self):\n self._mat_plane = numpy.array([\n self._scaling[0], 0, 0, 0,\n 0, self._scaling[1], 0, 0,\n 0, 0, 1, 0,\n self.i_border[0], -self.i_border[1], 0, 1\n ], dtype=numpy.float32)","def generate_two_dim_step_matrices(self):\n Lap_h = two_dim_sparse_neumann_laplacian(self.M, format='csc')\n I_m = sp.identity(self.M * self.M, dtype='float64', format='csc')\n\n I_minus_Lap = spla.factorized(I_m - (self.r / 2.0) * Lap_h)\n I_plus_Lap = I_m + (self.r / 2.0) * Lap_h\n\n return I_minus_Lap, I_plus_Lap","def delaunay_triangulate(P: np.ndarray):\n n = P.shape[0]\n if n < 3:\n A = np.ones((n, n)) - np.eye(n)\n else:\n try:\n d = Delaunay(P)\n A = np.zeros((n, n))\n for simplex in d.simplices:\n for pair in itertools.permutations(simplex, 2):\n A[pair] = 1\n except QhullError as err:\n print(\"Delaunay triangulation error detected. Return fully-connected graph.\")\n print(\"Traceback:\")\n print(err)\n A = np.ones((n, n)) - np.eye(n)\n return A","def make_lcp(A,F=0.25):\n\n # [x, b] = MAKE_LCP(A): Make LCP\n #\n # INPUT:\n #\n # A -- The coefficient matrix in the LCP\n # F -- The fraction of zero-values in the x-solution.\n #\n # OUTPUT:\n #\n # x -- A solution for the LCP problem.\n # b -- The right hand side vector\n #\n # Port of Kenny Erleben, DIKU 2011 Matlab code to Python\n\n ##### Get of number of variable ##########\n N = np.size(A,0) # Pick a dimension, it should be NxN\n\n ##### Generate a random LCP solution ##########\n x = np.random.uniform(0,1,(N,1))\n x[x < F] = 0\n\n # x = np.real_if_close(x)\n\n ##### Generate a right-hand-side vector that is ##########\n ##### consistent with a random solution ##########\n b = np.zeros((N,1))\n s = np.real_if_close(np.dot(A,x))\n b[x>0] = -s[x>0]\n\n return (x, b)","def _build_laplacian_pyramid(im, max_levels, filter_size):\n filter_vec = _gaussian_kernel_1d(filter_size).reshape(filter_size, 1)\n pyr = []\n im_curr = im\n im_next = _reduce(im, filter_vec)\n while _image_is_large_enough(im_next) and len(pyr) < max_levels - 1:\n pyr.append(im_curr - _expand(im_next, filter_vec))\n im_curr = im_next\n im_next = _reduce(im_curr, filter_vec)\n\n pyr.append(im_curr)\n\n return pyr, filter_vec.reshape(1, filter_size)","def mat_from_diag_triu_tril(diag, tri_upp, tri_low):\n n = diag.shape[-1]\n (i,) = diag_indices(n, ndim=1)\n j, k = triu_indices(n, k=1)\n mat = _torch.zeros((diag.shape + (n,)))\n mat[..., i, i] = diag\n mat[..., j, k] = tri_upp\n mat[..., k, j] = tri_low\n return mat","def cholesky(A):\n n = len(A)\n\n # Create zero matrix for L\n L=np.zeros((n,n))\n\n # Perform the Cholesky decomposition\n for i in range(n):\n for k in range(i+1):\n tmp_sum = sum(L[i][j] * L[k][j] for j in xrange(k))\n \n if (i == k): # Diagonal elements\n\n L[i][k] = math.sqrt(A[i][i] - tmp_sum)\n else:\n\n L[i][k] = (1.0 / L[k][k] * (A[i][k] - tmp_sum))\n return L","def tridiag_matrix(bc_surface_type, upsilon, space_divisions, dx, k, T, h, hc, emissivity, sigma):\n # create tri-diagonal matrix\n A = np.diagflat([-upsilon for i in range(space_divisions - 1)], -1) +\\\n np.diagflat([1 + 2 * upsilon for i in range(space_divisions)]) +\\\n np.diagflat([-upsilon for i in range(space_divisions - 1)], 1)\n\n # adjust matrix depending on the boundary condition at the exposed surface\n if bc_surface_type == \"linear\":\n A[0,0] = 1 + 2*upsilon + 2*upsilon*dx*h/k\n A[0,1] = -2*upsilon\n \n elif bc_surface_type == \"non-linear\":\n A[0,0] = 1 + 2*upsilon + 2*dx*hc*upsilon/k+ 8*emissivity*sigma*dx*upsilon*T[0]**3/k\n A[0,1] = -2*upsilon\n \n # adjust matrix for the back boundary conditions\n A[-1, -2] = - 2 * upsilon\n A[-1, -1] = 1 + 2 * upsilon\n\n return A","def laplacian(self, array_in):\r\n\r\n # Call-through to Laplacian operator, already computed\r\n return self.laplace_op*array_in","def build_linear_diags(self):\n N = self.N\n dx = self.dx\n j = self._j # Index of the mid-point\n\n diags = np.zeros((2*self._j+1, self.N))\n\n # Advection term\n cff1 = -1/(2*dx)\n\n # Need to stagger these diagonals so lower and upper bands are symmetric\n diags[j-1, :-2] += -1*cff1*self.c[2:]\n diags[j+1, :] += 1*cff1*self.c[:]\n\n # Sponge term\n x = np.arange(0,N*dx,dx)\n rdist = x[-1] - x # Distance from right boundary\n spongefac = -np.exp(-6*rdist/self.spongedist)/self.spongetime\n diags[j,:] += spongefac \n\n return diags","def make_mat_lp_le(lin_pot_mesh, lin_geo_mesh):\n num_nodes = lin_pot_mesh.get_nodes().shape[0]\n K = np.zeros((3 * num_nodes, 3 * num_nodes))\n add_lp_le_DL_terms(K, lin_pot_mesh, lin_geo_mesh)\n add_lp_le_RBM_terms(K, lin_pot_mesh, lin_geo_mesh)\n return K","def build_upper_zeros(self):\r\n for row in range(0, self.SIZE - 1):\r\n self.__obtain_zero(row, self.SIZE - 1, self.SIZE - 1)\r\n\r\n for col in range(self.SIZE - 2, 0, -1):\r\n for row in range(0, col):\r\n self.__obtain_zero(row, col, row + 1)","def _build(self):\n ary = np.zeros( (3,3,3), float )\n ary[0,0,0] = ary[1,1,1] = ary[0,1,2] = ary[1,0,2] = 1.\n ary[0,2,0] = ary[0,2,2] = ary[2,0,0] = ary[2,0,2] = 0.5\n ary[1,2,1] = ary[1,2,2] = ary[2,1,1] = ary[2,1,2] = 0.5\n ary[2,2,0] = ary[2,2,1] = 0.25\n ary[2,2,2] = 0.5\n return ary","def calc_Ls(self, x_surface, geom):\n\n return np.zeros((self.n_wl,))","def _build_sparse_matrix(L):\n shape = L.shape\n i = torch.LongTensor(np.vstack((L.row, L.col)).astype(int))\n v = torch.FloatTensor(L.data)\n return torch.sparse.FloatTensor(i, v, torch.Size(shape))","def build_mat(self):\n for row, s in enumerate(self.S):\n for col, t in enumerate(self.T):\n\n if self.symmetric and row > col:\n pass\n\n else:\n self.mat[row, col] = self.kernel(s, t, self.n)\n\n if self.symmetric:\n self.mat = self.symmetrize(self.mat)\n else:\n for idx, s in enumerate(self.S):\n self.test_normalization[idx] = self.kernel(s, s, self.n)","def make_state_appliable(state):\n size = len(state)\n st_appl = np.zeros((size, size), dtype=complex)\n for p1 in range(size):\n for p2 in range(size):\n st_appl[p1, p2] = state[p1, p2] * sqrt(factorial(p1) * factorial(p2))\n return st_appl","def test_LU(self):\n A = np.random.rand(10, 10)\n MA = to_matrix(A)\n ML, MU = MA.decomposeLU()\n self.assertEqual(ML*MU, MA)\n self.assertTrue(ML.is_lower_triangular())\n self.assertTrue(MU.is_upper_triangular())","def _makeDerivativeMatrix(self, index, a):\n num_neurons = a\n jaccob_matrix = np.zeros(shape=(num_neurons, num_neurons)) # ie S=3, shape 3X3\n # dx_func = self.__getDerivative(self.layers[index]['trans_func'])\n dx_func = self._getTransFunc(self.layers[index]['trans_func']).derivative\n for i in range(num_neurons):\n # diagonal matrix\n a_val = self.layers[index]['a_output'][i]\n jaccob_matrix[i][i] = dx_func(a_val)\n return jaccob_matrix","def lcr_matrix(H):\n if H.ndim != 2 or H.shape[0] != H.shape[1]:\n raise ValueError('H should be a square matrix')\n\n leverages = sqrt(1-H.diagonal())\n leverages = leverages[:, None]\n R = (eye(len(H)) - H) / leverages\n return R - R.mean(0)","def finite_difference(n):\n B=sparse.diags([1,-4,1],[-1,0,1],shape=(n,n))\n A=sparse.block_diag([B for i in xrange(0,n)])\n A.setdiag(1,k=-n)\n A.setdiag(1,k=n) \n b=[-100]+[0 for i in xrange(1,n-1)]+[-100]\n b=b*n\n return A,b","def diag_to_matrix(l_and_u, diag):\n l, u = l_and_u\n dim = diag.shape[1]\n matrix = np.zeros((dim, dim))\n for i in range(l+u+1):\n np.fill_diagonal(\n matrix[max(0,i-u):,max(0,u-i):],\n diag[i,max(0,u-i):]\n )\n return matrix","def build_mat(self,diag_entries=False):\n if self.L is None:\n raise ValueError('Must set number of spins (Operator.L) before building PETSc matrix.')\n\n self.destroy_mat()\n\n term_array = self.get_MSC()\n\n if diag_entries and not np.any(term_array['masks'] == 0):\n term_array = np.hstack([np.array([(0,0,0)],dtype=MSC_dtype),term_array])\n\n if not np.any(term_array['masks'] == 0):\n self._diag_entries = False\n else:\n self._diag_entries = True\n\n self._mat = build_mat(self.L,\n np.ascontiguousarray(term_array['masks']),\n np.ascontiguousarray(term_array['signs']),\n np.ascontiguousarray(term_array['coeffs']),\n bool(self.use_shell),\n self.use_shell == 'gpu')","def lu(matrix):\n SIZE = matrix.shape[0]\n BS = np.BLOCKSIZE\n\n if matrix.shape[0] != matrix.shape[0]:\n raise Exception(\"LU only supports squared matricis\")\n if not matrix.dist():\n raise Exception(\"The matrix is not distributed\")\n\n if(SIZE % np.BLOCKSIZE != 0):\n raise Exception(\"The matrix dimensions must be divisible \"\\\n \"with np.BLOCKSIZE(%d)\"%np.BLOCKSIZE)\n\n (prow,pcol) = matrix.pgrid()\n A = np.zeros((SIZE,SIZE), dtype=matrix.dtype, dist=True);A += matrix\n L = np.zeros((SIZE,SIZE), dtype=matrix.dtype, dist=True)\n U = np.zeros((SIZE,SIZE), dtype=matrix.dtype, dist=True)\n tmpL = np.zeros((SIZE,SIZE), dtype=matrix.dtype, dist=True)\n tmpU = np.zeros((SIZE,SIZE), dtype=matrix.dtype, dist=True)\n for k in xrange(0,SIZE,BS):\n bs = min(BS,SIZE - k) #Current block size\n kb = k / BS # k as block index\n\n #Compute vertical multiplier\n slice = ((kb,kb+1),(kb,kb+1))\n for a,l,u in zip(A.blocks(slice), L.blocks(slice), U.blocks(slice)):\n (p,tl,tu) = linalg.lu(a)\n if not (np.diag(p) == 1).all():#We do not support pivoting\n raise Exception(\"Pivoting was needed!\")\n #There seems to be a transpose bug in SciPy's LU\n l[:] = tl.T\n u[:] = tu.T\n\n #Replicate diagonal block horizontal and vertical\n for tk in xrange(k+bs,SIZE,BS):\n tbs = min(BS,SIZE - tk) #Current block size\n L[tk:tk+tbs,k:k+bs] = U[k:k+tbs,k:k+bs]\n U[k:k+bs,tk:tk+tbs] = L[k:k+bs,k:k+tbs]\n\n if k+bs < SIZE:\n #Compute horizontal multiplier\n slice = ((kb,kb+1),(kb+1,SIZE/BS))\n for a,u in zip(A.blocks(slice), U.blocks(slice)):\n u[:] = np.linalg.solve(u.T,a.T).T\n\n #Compute vertical multiplier\n slice = ((kb+1,SIZE/BS),(kb,kb+1))\n for a,l in zip(A.blocks(slice), L.blocks(slice)):\n l[:] = np.linalg.solve(l,a)\n\n #Apply to remaining submatrix\n A -= pyHPC.summa(L[:,:k+bs],U[:k+bs,:], ao=(k+bs,k),\n bo=(k,k+bs), co=(k+bs,k+bs))\n\n return (L, U)","def build_lower_zeros_with_pivoting(self):\r\n self.__apply_pivoting(0, self.SIZE - 1, 0)\r\n for row in range(self.SIZE - 1, 0, -1):\r\n self.__obtain_zero(row, 0, 0)\r\n\r\n for col in range(1, self.SIZE - 1):\r\n self.__apply_pivoting(col, self.SIZE - 1, col)\r\n for row in range(self.SIZE - 1, col, -1):\r\n self.__obtain_zero(row, col, row - 1)","def build_augmented_matrix(self):\r\n for row in range(self.SIZE):\r\n self.matrix[row].append(self.result[row])","def get_initial_condition_euler(self, tol=1e-10):\n Z = zeros((len(self._meshes), len(self._meshes[0].elements)+1))\n for mi, m in enumerate(self._meshes):\n if not m._left_lift:\n raise Exception(\"get_initial_condition_euler() only works if all boundary conditions are given on the left.\")\n\n Z[mi, 0] = m._left_value\n def get_F(Z, t):\n \"\"\"\n Evaluates the RHS for the vector Z and time tau.\n \"\"\"\n Z0 = zeros((len(self._meshes),))\n for mi, m in enumerate(self._meshes):\n Z0[mi] = self._F(mi, Z, t)\n return Z0\n def get_phi(Z, Zprev, tau, t):\n return Z - tau*get_F(Z, t) - Zprev\n def get_J(Z, tau, t):\n mat = eye(len(self._meshes))\n for i in range(len(self._meshes)):\n for j in range(len(self._meshes)):\n mat[i, j] += - tau*self._DFDY(i, j, Z, t)\n return mat\n\n # initial time and initial condition vector:\n tprev = self._meshes[0].elements[0].nodes[0].x\n Zprev = Z[:, 0].copy()\n Znext = Zprev[:].copy()\n for el_i in range(len(self._meshes[0].elements)):\n #print \"doing element:\", el_i\n tau = self._meshes[0].elements[el_i].length\n tnext = tprev + tau\n error = 1e10\n i = 0\n while error > tol:\n J = get_J(Zprev, tau, tprev)\n phi = get_phi(Znext, Zprev, tau, tprev)\n dZ = solve(J, -phi)\n Znext += dZ\n error_dZ = l2_norm(dZ)\n error_phi = l2_norm(get_phi(Znext, Zprev, tau, tnext))\n #print \"it=%d, l2_norm_dZ=%e, l2_norm_phi=%e\" % \\\n # (i, error_dZ, error_phi)\n error = max(error_dZ, error_phi)\n i += 1\n Z[:, el_i+1] = Znext[:].copy()\n Zprev = Znext[:].copy()\n tprev = tnext\n\n\n # now assign the Z to the vertex dofs and leave zeros in the bubbles\n Y = zeros((self.ndofs,))\n for mi, m in enumerate(self._meshes):\n coeffs_one_mesh = Z[mi, 1:]\n Y[m.dof_start:m.dof_start+len(coeffs_one_mesh)] = coeffs_one_mesh\n return Y","def generate_LLL_matrix(self, matrix):\n LLL_matrix = matrix.transpose().LLL().transpose()\n return LLL_matrix","def matrix_L1(l, omega, S, cn):\n zt = omega * S / cn['t']\n L = np.array((dN2(l, zt), dN4(l, zt)))\n return L.T"],"string":"[\n \"def _get_Laplacian_matrix(self, X):\\n self.laplacian_mat, self.laplacian_sym_mat, self.laplacian_weights = self.laplacian.compute_laplacian(\\n self.get_Affinity_matrix(X)\\n )\",\n \"def LaplacianMatrix(adjmatrix):\\n if adjmatrix.dtype in [np.uint, np.uint0, np.uint8, np.uint16, np.uint32, np.uint64]:\\n adjmatrix = adjmatrix.astype(int)\\n N = len(adjmatrix)\\n\\n laplacianmatrix = np.identity(N, dtype=adjmatrix.dtype) * adjmatrix.sum(axis=1)\\n laplacianmatrix -= adjmatrix\\n\\n return laplacianmatrix\",\n \"def one_dim_sparse_laplacian(m: int):\\n return sp.diags([1.0, -2.0, 1.0], [-1, 0, 1], dtype='float64', shape=(m, m), format='lil')\",\n \"def rebuild_the_laplacians():\\n local_matrix = InteractomeInterface()\\n local_matrix.full_rebuild()\\n\\n annot_matrix = AnnotomeInterface()\\n annot_matrix.full_rebuild()\",\n \"def get_laplacian(adjacency: sparse.csr_matrix) -> sparse.csr_matrix:\\n weights = adjacency.dot(np.ones(adjacency.shape[0]))\\n return sparse.diags(weights) - adjacency\",\n \"def generate_graph_laplacian(A):\\r\\n\\r\\n #Create symmetric matrix\\r\\n #A=0.5* (A+ A.T)\\r\\n \\r\\n #D is just the identity matrix (because sum(P)=1)\\r\\n Degree=np.sum(A,1)\\r\\n D=np.diag(Degree)\\r\\n \\r\\n #Laplacian matrix\\r\\n L=D-A\\r\\n return L\",\n \"def compute_matrix(self):\\n\\n fac = self.a / self.dx ** 2\\n\\n diagonal = np.ones(self.nx) * 2 * fac\\n lower = np.ones(self.nx - 1) * -fac\\n upper = np.ones(self.nx - 1) * -fac\\n\\n matrix = sp.diags(\\n diagonals=[diagonal, lower, upper],\\n offsets=[0, -1, 1], shape=(self.nx, self.nx),\\n format='csr')\\n\\n return matrix\",\n \"def prepare_laplacian(laplacian):\\n\\n def estimate_lmax(laplacian, tol=5e-3):\\n r\\\"\\\"\\\"Estimate the largest eigenvalue of an operator.\\\"\\\"\\\"\\n lmax = sparse.linalg.eigsh(laplacian, k=1, tol=tol,\\n ncv=min(laplacian.shape[0], 10),\\n return_eigenvectors=False)\\n lmax = lmax[0]\\n lmax *= 1 + 2 * tol # Be robust to errors.\\n return lmax\\n\\n def scale_operator(L, lmax, scale=1):\\n r\\\"\\\"\\\"Scale the eigenvalues from [0, lmax] to [-scale, scale].\\\"\\\"\\\"\\n I = sparse.identity(L.shape[0], format=L.format, dtype=L.dtype)\\n L *= 2 * scale / lmax\\n L -= I\\n return L\\n\\n lmax = estimate_lmax(laplacian)\\n laplacian = scale_operator(laplacian, lmax)\\n\\n laplacian = sparse.coo_matrix(laplacian)\\n\\n # PyTorch wants a LongTensor (int64) as indices (it'll otherwise convert).\\n indices = np.empty((2, laplacian.nnz), dtype=np.int64)\\n np.stack((laplacian.row, laplacian.col), axis=0, out=indices)\\n indices = torch.from_numpy(indices)\\n\\n laplacian = torch.sparse_coo_tensor(indices, laplacian.data, laplacian.shape)\\n laplacian = laplacian.coalesce() # More efficient subsequent operations.\\n return laplacian\",\n \"def laplacian_mat(n):\\n data = [1, -2, 1]*n\\n i = flatten([[k,k,k] for k in range(n)])\\n j = flatten([[k-1, k, k+1] for k in range(n)])\\n return scipy.sparse.coo_matrix((data[1:-1], (i[1:-1], j[1:-1])))\",\n \"def compute_mesh_laplacian(mesh, weights=None, fem_b=None, lap_type=\\\"conformal\\\"):\\n print(\\\" Computing Laplacian\\\")\\n if weights is None:\\n (weights, fem_b) = compute_mesh_weights(mesh, weight_type=lap_type)\\n\\n if lap_type == \\\"fem\\\":\\n weights.data = weights.data / 2\\n\\n N = weights.shape[0]\\n sB = fem_b.sum(axis=0)\\n diaB = sparse.dia_matrix((sB, 0), shape=(N, N))\\n B = sparse.lil_matrix(diaB + fem_b)\\n s = weights.sum(axis=0)\\n dia = sparse.dia_matrix((s, 0), shape=(N, N))\\n L = sparse.lil_matrix(dia - weights)\\n\\n # if symmetrize == 1 & & normalize == 0\\n # L = diag(sum(W, 2)) - W;\\n # elseif\\n # symmetrize == 1 & & normalize == 1\\n # L = speye(n) - diag(sum(W, 2). ^ (-1 / 2)) * W * diag(\\n # sum(W, 2). ^ (-1 / 2));\\n # elseif\\n # symmetrize == 0 & & normalize == 1\\n # L = speye(n) - diag(sum(W, 2). ^ (-1)) * W;\\n\\n li = np.hstack(L.data)\\n print(\\\" -nb Nan in Laplacian : \\\", len(np.where(np.isnan(li))[0]))\\n print(\\\" -nb Inf in Laplacian : \\\", len(np.where(np.isinf(li))[0]))\\n\\n return L, B\",\n \"def uniform_laplacian(image, radius=1):\\n height, width = image.shape[:2]\\n window_size = 2 * radius + 1\\n\\n W = sparse_conv_matrix(width, height, np.ones((window_size, window_size)))\\n\\n return weights_to_laplacian(W)\",\n \"def nodalLaplacian(self):\\n if getattr(self, '_nodalLaplacian', None) is None:\\n print('Warning: Laplacian has not been tested rigorously.')\\n # The number of cell centers in each direction\\n n = self.vnC\\n # Compute divergence operator on faces\\n if(self.dim == 1):\\n D1 = sdiag(1./self.hx) * ddx(self.nCx)\\n L = - D1.T*D1\\n elif(self.dim == 2):\\n D1 = sdiag(1./self.hx) * ddx(n[0])\\n D2 = sdiag(1./self.hy) * ddx(n[1])\\n L1 = sp.kron(speye(n[1]+1), - D1.T * D1)\\n L2 = sp.kron(- D2.T * D2, speye(n[0]+1))\\n L = L1 + L2\\n elif(self.dim == 3):\\n D1 = sdiag(1./self.hx) * ddx(n[0])\\n D2 = sdiag(1./self.hy) * ddx(n[1])\\n D3 = sdiag(1./self.hz) * ddx(n[2])\\n L1 = kron3(speye(n[2]+1), speye(n[1]+1), - D1.T * D1)\\n L2 = kron3(speye(n[2]+1), - D2.T * D2, speye(n[0]+1))\\n L3 = kron3(- D3.T * D3, speye(n[1]+1), speye(n[0]+1))\\n L = L1 + L2 + L3\\n self._nodalLaplacian = L\\n return self._nodalLaplacian\",\n \"def build_laplacian_over_ordinal_integer_actions(\\n action_spec: types.BoundedTensorSpec,\\n) -> types.Tensor:\\n num_actions = policy_utilities.get_num_actions_from_tensor_spec(action_spec)\\n adjacency_matrix = np.zeros([num_actions, num_actions])\\n for i in range(num_actions - 1):\\n adjacency_matrix[i, i + 1] = 1.0\\n adjacency_matrix[i + 1, i] = 1.0\\n laplacian_matrix = (\\n np.diag(np.sum(adjacency_matrix, axis=0)) - adjacency_matrix\\n )\\n return laplacian_matrix\",\n \"def normalized_laplacian(degree_vector, weight_matrix, length):\\n holders = np.zeros((length, 1))\\n holders[:, 0] = 1 / degree_vector\\n\\n return np.eye(length) - holders * weight_matrix\",\n \"def decomposeLU(self):\\n self.check_square()\\n\\n N = self.rows\\n L = make_matrix(N, N)\\n U = make_matrix(N, N)\\n A = self #for more math friendly notation\\n\\n\\n for j in range(N):\\n L[j, j] = 1.0 #Doolittle factorization\\n\\n #e.g., if you are in column = 5, you go down 6 rows\\n for i in range(j+1):\\n U[i, j] = A[i, j] - sum(L[i, k] * U[k, j] for k in range(i))\\n #e.g., if you are in column = 5,\\n # you start at row 5 and go down for the lower triangular matrix\\n for i in range(j, N):\\n L[i, j] = (A[i, j] - sum(L[i, k] * U[k, j] for k in range(j))) / U[j, j]\\n\\n self.L = L\\n self.U = U\\n return L, U\",\n \"def calculate_normalized_laplacian(adj):\\n adj = sp.coo_matrix(adj)\\n d = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(d, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\\n return normalized_laplacian\",\n \"def calculate_normalized_laplacian(adj):\\n adj = sp.coo_matrix(adj)\\n d = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(d, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\\n return normalized_laplacian\",\n \"def lap_mat(self):\",\n \"def L1U(A, d):\\n \\n\\n n, _ = A.shape\\n L = np.eye(n, n, dtype=A.dtype)\\n U = np.zeros((n, n), dtype=A.dtype)\\n\\n U[0, 0] = A[0, 0]\\n for k in range(1, n):\\n km = max(0, k-d)\\n L[k, km : k] = np.transpose(rforwardsolve(np.transpose(U[km:k, km:k]),\\\\\\n np.transpose(A[k, km:k]), d))\\n U[km:k+1, k] = rforwardsolve(L[km:k+1, km:k+1], A[km:k+1, k], d)\\n return L, U\",\n \"def make_matrix(self):\\n self.leftmost_element()\\n self.rightmost_element()\\n self.interior_element()\\n\\n #Transforms all sympy symbolic expressions for the lagrange polynomials into callable functions.\\n self.psi_funcs = [sym.lambdify([self.x], self.psi[i], modules = \\\"numpy\\\") for i in range(3*self.Ne)]\",\n \"def laplacian(A):\\n #calculate D by creating a diagonal matrix with the column sum of A\\n D = np.diag(A.sum(axis=0))\\n return D - A\",\n \"def init_needleman_wunsch_matrix(self):\\r\\n empty_matrix = self.empty_matrix() # Building on the previous definition, this will give you an empty matrix\\r\\n for i in range(len(self.s2)+1):\\r\\n for j in range(len(self.s1)+1):\\r\\n empty_matrix[0][i] = -i\\r\\n empty_matrix[j][0] = -j\\r\\n return empty_matrix\",\n \"def laplacian_matrix(A, normalized=False):\\n n, m = A.shape\\n D = degree_matrix(A)\\n L = D - A\\n if normalized:\\n degs = _flat(A.sum(axis=1))\\n rootD = sps.spdiags(np.power(degs, -1 / 2), [0], n, n, format=\\\"csr\\\")\\n L = rootD * L * rootD\\n return L\",\n \"def LUdecomp(Ainput):\\n\\n n, m = np.shape(Ainput)\\n \\n if n != m:\\n return 'Error: Please enter an invertible matrix.'\\n \\n U = Ainput.copy() # make copies so as not to write over originals\\n L = np.zeros((np.shape(Ainput)))\\n \\n for i in range(0,n):\\n L[i,i] = 1\\n for i in range(0,n-1): # loop over pivot rows from row 1 to row n-1 (i to n-2)\\n for j in range(i+1,n): # loop over row to be zero'ed from row j+1 to n (j+1 to n-1)\\n c = U[j,i]/U[i,i] # multiplicative factor to zero point\\n L[j,i] = c\\n U[j,i] = 0.0 # we know this element goes to zero\\n U[j,i+1:n]=U[j,i+1:n]-c*U[i,i+1:n] # do subtraction of two rows\\n\\n return (L,U) # return lower and upper decompositions\",\n \"def offDiagPairs(self):\\n return np.transpose(np.nonzero(np.triu(self.LaplacianMatrix,k=2)))\",\n \"def expansion_matrix_dl(self):\\n\\n row = self._base_nlp._lower_d_map\\n nnz = len(self._base_nlp._lower_d_map)\\n col = np.arange(nnz, dtype=np.int)\\n data = np.ones(nnz)\\n return csr_matrix((data, (row, col)), shape=(self.nd, nnz))\",\n \"def decompose_to_LU(a):\\n # create emtpy LU-matrix\\n lu_matrix = np.matrix(np.zeros([a.shape[0], a.shape[1]]))\\n n = a.shape[0]\\n\\n for k in range(n):\\n # calculate all residual k-row elements\\n for j in range(k, n):\\n lu_matrix[k, j] = a[k, j] - lu_matrix[k, :k] * lu_matrix[:k, j]\\n # calculate all residual k-column elemetns\\n for i in range(k + 1, n):\\n lu_matrix[i, k] = (a[i, k] - lu_matrix[i, : k] * lu_matrix[: k, k]) / lu_matrix[k, k]\\n\\n return lu_matrix\",\n \"def laplacian(A):\\n D = np.diag(np.sum(A, axis=1))\\n return D - A\",\n \"def laplacian(mesh):\\n faces = np.array(mesh.triangles)\\n N = np.array(mesh.vertices).shape[0]\\n A = np.zeros((N, N))\\n for i in range(3):\\n for j in range(3):\\n if i == j:\\n continue\\n A[(faces[:, i], faces[:, j])] = 1.0\\n A = A + A.T\\n diag = A.dot(np.ones(N))\\n L = np.diag(diag) - A\\n return L\",\n \"def getNormLaplacian(W):\\n\\td=[np.sum(row) for row in W]\\n\\tD=np.diag(d)\\n\\tL=D-W\\n\\t#Dn=D^(-1/2)\\n\\tDn=np.power(np.linalg.matrix_power(D,-1),0.5)\\n\\tLbar=np.dot(np.dot(Dn,L),Dn)\\n\\treturn Lbar\",\n \"def _set_diag(laplacian, value, norm_laplacian):\\n n_nodes = laplacian.shape[0]\\n # We need all entries in the diagonal to values\\n if not sparse.isspmatrix(laplacian):\\n if norm_laplacian:\\n laplacian.flat[::n_nodes + 1] = value\\n else:\\n laplacian = laplacian.tocoo()\\n if norm_laplacian:\\n diag_idx = (laplacian.row == laplacian.col)\\n laplacian.data[diag_idx] = value\\n # If the matrix has a small number of diagonals (as in the\\n # case of structured matrices coming from images), the\\n # dia format might be best suited for matvec products:\\n n_diags = np.unique(laplacian.row - laplacian.col).size\\n if n_diags <= 7:\\n # 3 or less outer diagonals on each side\\n laplacian = laplacian.todia()\\n else:\\n # csr has the fastest matvec and is thus best suited to\\n # arpack\\n laplacian = laplacian.tocsr()\\n return laplacian\",\n \"def make_mat_cp_le(cons_pot_mesh, lin_geo_mesh):\\n pot_faces = cons_pot_mesh.get_faces()\\n assert pot_faces.shape[0] == lin_geo_mesh.get_faces().shape[0]\\n num_faces = pot_faces.shape[0]\\n K = np.zeros((3 * num_faces, 3 * num_faces))\\n add_cp_le_DL_terms(K, cons_pot_mesh, lin_geo_mesh)\\n add_cp_le_RBM_terms(K, cons_pot_mesh, lin_geo_mesh)\\n return K\",\n \"def get_correct_distance_matrix(L):\\n n = len(L)\\n D = np.zeros((n,n))\\n for i in range(n):\\n for j in range(n):\\n if i != j:\\n D[i][j] = get_minor(L, [i, j], [i, j]) / get_minor(L, [i], [i])\\n return D\",\n \"def _r_matrix_xxz(self, root):\\n r_matrix = np.eye(4, dtype=np.complex128)\\n if self.delta == 1:\\n b = (root - 1j) / (root + 1j)\\n c = 2j / (root + 1j)\\n\\n elif self.delta > 1:\\n gamma = np.arccosh(self.delta)\\n b = np.sin(gamma / 2 * (root - 1j)) / np.sin(gamma / 2 * (root + 1j))\\n c = 1j * np.sinh(gamma) / np.sin(gamma / 2 * (root + 1j))\\n else:\\n gamma = np.arccos(self.delta)\\n b = np.sinh(gamma / 2 * (root - 1j)) / np.sinh(gamma / 2 * (root + 1j))\\n c = 1j * np.sin(gamma) / np.sinh(gamma / 2 * (root + 1j))\\n r_matrix[1, 1] = r_matrix[2, 2] = c\\n r_matrix[1, 2] = r_matrix[2, 1] = b\\n return r_matrix\",\n \"def _init_transformation_matrix(self):\\n # Set up basic transformation matrix\\n c_transform = np.zeros((self.n_beads, self.n_beads))\\n\\n # Get auxiliary array with bead indices\\n n = np.arange(1, self.n_beads + 1)\\n\\n # for k = 0\\n c_transform[0, :] = 1.0\\n\\n for k in range(1, self.n_beads // 2 + 1):\\n c_transform[k, :] = np.sqrt(2) * np.cos(2 * np.pi * k * n / self.n_beads)\\n\\n for k in range(self.n_beads // 2 + 1, self.n_beads):\\n c_transform[k, :] = np.sqrt(2) * np.sin(2 * np.pi * k * n / self.n_beads)\\n\\n if self.n_beads % 2 == 0:\\n c_transform[self.n_beads // 2, :] = (-1) ** n\\n\\n # Since matrix is initialized as C(k,n) does not need to be transposed\\n c_transform /= np.sqrt(self.n_beads)\\n c_transform = torch.from_numpy(c_transform)\\n\\n return c_transform\",\n \"def L1U(A, d):\\n n = shape(A)[0]\\n L = eye(n)\\n U = matrix(zeros((n,n))); U[0,0] = A[0,0]\\n for k in range(1,n):\\n km = array([0, k - d]).max()\\n if km < k:\\n L[k, km:k] = A[k, km:k]\\n rforwardsolve(U[km:k, km:k].T, L[k, km:k].T, d) # L\\n U[km:(k + 1), k] = A[km:(k + 1), k]\\n rforwardsolve(L[km:(k + 1), km:(k + 1)], U[km:(k + 1), k], d) # U\\n return L, U\",\n \"def laplacian(W, normalized=False):\\r\\n # Degree matrix.\\r\\n d = W.sum(axis=0)\\r\\n # Laplacian matrix.\\r\\n if not normalized:\\r\\n D = scipy.sparse.diags(d.A.squeeze(), 0)\\r\\n L = D - W\\r\\n else:\\r\\n # d += np.spacing(np.array(0, W.dtype))\\r\\n d = 1 / np.sqrt(d)\\r\\n D = scipy.sparse.diags(d.A.squeeze(), 0)\\r\\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\\r\\n L = I - D * W * D\\r\\n\\r\\n # assert np.abs(L - L.T).mean() < 1e-9\\r\\n assert type(L) is scipy.sparse.csr.csr_matrix\\r\\n return L\",\n \"def bloch_matrix(self):\\n if self.gf_r is None:\\n self.gf()\\n\\n return -self.gf_r.dot(self.lead[1])\",\n \"def prepotential(G):\\n L = nx.laplacian_matrix(G).toarray()\\n L_tild = L[1:,1:]\\n T_tild = np.linalg.inv(L_tild)\\n T = np.zeros(L.shape)\\n T[1:,1:] = T_tild\\n return T\",\n \"def _buildMatrix(self, SparseMatrix, Ncells, MaxFaces, coeff):\\n return (0, 0)\",\n \"def CreateMatrix(self) -> BaseMatrix:\",\n \"def CreateMatrix(self) -> BaseMatrix:\",\n \"def LU(A):\\n m, n = A.shape\\n L, U = np.zeros([m, n]), np.zeros([m, n])\\n for i in range(n):\\n L[i][i] = 1\\n\\n for i in range(n):\\n\\n # Upper triangular matrix\\n for j in range(i, n):\\n summ = 0\\n for k in range(0, i):\\n summ += L[i][k]*U[k][j]\\n U[i][j] = A[i][j] - summ\\n\\n # Lower triangular matrix\\n for j in range(i+1, n):\\n summ = 0\\n for k in range(0, i):\\n summ += L[j][k]*U[k][i]\\n L[j][i] = (A[j][i] - summ)/U[i][i]\\n return L, U\",\n \"def construct_linear_system(self):\\n N=self.grid.Ncells()\\n Nbc = len(self.dirichlet_bcs)\\n self.Ncalc=Ncalc = N - Nbc\\n\\n # map cells to forced values\\n dirichlet = dict( [ (c,v) for c,v,xy in self.dirichlet_bcs])\\n\\n self.is_calc_c = is_calc_c = np.ones(N,np.bool8)\\n for c,v,xy in self.dirichlet_bcs:\\n is_calc_c[c] = False\\n\\n # is_calc_c[self.c_mask] = False\\n\\n # c_map is indexed by real cell indices, and returns the matrix index\\n c_map = self.c_map = np.zeros(N,np.int32)\\n self.c_map[is_calc_c] = np.arange(Ncalc)\\n\\n dzc=self.dzc\\n dzf=self.dzf\\n area_c=self.area_c\\n\\n meth='coo' # 'dok'\\n if meth == 'dok':\\n A=sparse.dok_matrix((Ncalc,Ncalc),np.float64)\\n else:\\n # construct the matrix from a sequence of indices and values\\n ij=[]\\n values=[] # successive value for the same i.j will be summed\\n \\n b = np.zeros(Ncalc,np.float64)\\n flux_per_gradient_j = -self.K_j * self.l_j * dzf / self.d_j * self.dt\\n\\n self.grid.edge_to_cells() # makes sure that edges['cells'] exists.\\n \\n for j in range(self.grid.Nedges()):\\n e = self.grid.edges[j]\\n ic1,ic2 = e['cells']\\n \\n if ic1<0 or ic2<0 or e['deleted']:\\n continue # boundary edge, or deleted edge\\n \\n flux_per_gradient=flux_per_gradient_j[j]\\n \\n # this is the desired operation:\\n # Cdiff[ic1] -= flux_per_gradient / (An[ic1]*dzc) * (C[ic2] - C[ic1])\\n # Cdiff[ic2] += flux_per_gradient / (An[ic2]*dzc) * (C[ic2] - C[ic1])\\n # Where Cdiff is row, C is col\\n\\n if is_calc_c[ic1] and is_calc_c[ic2]:\\n mic2 = c_map[ic2]\\n mic1 = c_map[ic1]\\n v1=flux_per_gradient / (area_c[ic1]*dzc[ic1])\\n v2=flux_per_gradient / (area_c[ic2]*dzc[ic2])\\n \\n if meth == 'dok':\\n A[mic1,mic2] -= v1\\n A[mic1,mic1] += v1\\n A[mic2,mic2] += v2\\n A[mic2,mic1] -= v2\\n else:\\n ij.append( (mic1,mic2) ) ; values.append(-v1)\\n ij.append( (mic1,mic1) ) ; values.append(v1)\\n ij.append( (mic2,mic2) ) ; values.append(v1)\\n ij.append( (mic2,mic1) ) ; values.append(-v1)\\n \\n elif not ( is_calc_c[ic1] or is_calc_c[ic2] ):\\n # both are dirichlet, so nothing to do\\n pass\\n elif not is_calc_c[ic2]:\\n mic1 = c_map[ic1]\\n v=flux_per_gradient / (self.area_c[ic1]*dzc[ic1])\\n if meth == 'dok':\\n A[mic1,mic1] += v\\n else:\\n ij.append( (mic1,mic1) )\\n values.append(v)\\n\\n # roughly\\n # A[1,1]*x[1] + A[1,2]*x[2] + ... = b[1]\\n # but we already know x[2],\\n # A[1,1]*x[1] + ... = b[1] - A[1,2]*x[2]\\n # so flip the sign, multiply by known dirichlet value, and\\n # add to the RHS\\n b[mic1] += flux_per_gradient / (area_c[ic1]*dzc[ic1]) * dirichlet[ic2]\\n else: # not is_calc_c[c1]\\n mic2 = c_map[ic2]\\n # A[mic2,mic2] += flux_per_gradient / (area_c[ic2]*dzc[ic2])\\n # A[mic2,mic1] -= flux_per_gradient / (area_c[ic2]*dzc[ic2])\\n\\n # A[mic2,mic2]*x[2] + A[mic2,mic1]*x[1] = b[2]\\n # ...\\n # A[mic2,mic2]*x[2] - flux_per_gradient / (area_c[ic2]*dzc[ic2])*x[1] = b[2]\\n # ...\\n # A[mic2,mic2]*x[2] = b[2] + flux_per_gradient / (area_c[ic2]*dzc[ic2])*x[1]\\n v=flux_per_gradient / (area_c[ic2]*dzc[ic2])\\n if meth == 'dok':\\n A[mic2,mic2] += v\\n else:\\n ij.append( (mic2,mic2) )\\n values.append(v)\\n b[mic2] += flux_per_gradient / (area_c[ic2]*dzc[ic2]) * dirichlet[ic1]\\n\\n # Used to test 'is not 0:' but modern python complains\\n if isinstance(self.alpha,np.ndarray): \\n for c in range(N):\\n if self.is_calc_c[c]:\\n mic=self.c_map[c]\\n v=self.alpha[c]*self.dt\\n if meth == 'dok':\\n A[mic,mic] -= v\\n else:\\n ij.append( (mic,mic) )\\n values.append(-v)\\n\\n # Flux boundary conditions:\\n for ic,value,xy in self.neumann_bcs:\\n mic=c_map[ic]\\n # make mass/time into concentration/step\\n # arrived at minus sign by trial and error.\\n # 2023-08-04: there was a bug here that used ic2 instead of ic.\\n b[mic] -= value/(area_c[ic]*dzc[ic]) * self.dt\\n\\n if meth == 'dok':\\n self.A = sparse.coo_matrix(A)\\n else:\\n ijs=np.array(ij,dtype=np.int32)\\n data=np.array(values,dtype=np.float64)\\n A=sparse.coo_matrix( (data, (ijs[:,0],ijs[:,1]) ), shape=(Ncalc,Ncalc) )\\n self.A=A\\n \\n # report scale to get a sense of whether dt is too large\\n Ascale = A.diagonal().min()\\n log.debug(\\\"Ascale is %s\\\"%Ascale)\\n\\n self.b = b\",\n \"def laplace_matrix(self):\\n n = self.number_of_vertices\\n laplace_matrix = np.zeros((n, n))\\n for i in range(n):\\n laplace_matrix[i][i] = 1\\n vertice = self.list_of_vertices[i]\\n for edge in vertice.edges_list:\\n laplace_matrix[i][edge.linked[1].index] = 1\\n return laplace_matrix\",\n \"def cal_L(self):\\n # calculate the l matrix\\n self.point_matrixs = self.point_matrix.reshape(\\n self.point_matrix.shape[0], 1, self.point_matrix.shape[1])\\n self.point_matrixs = np.tile(self.point_matrixs,\\n (self.attach_points.shape[0], 1))\\n self.attach_points_matrix = np.matlib.repmat(\\n self.attach_points[:, 0:3], self.point_matrix.shape[0], 1)\\n self.attach_points_matrix = self.attach_points_matrix.reshape(\\n self.point_matrix.shape[0], self.attach_points.shape[0], 3)\\n self.L = np.subtract(self.attach_points_matrix,\\n self.point_matrixs)\\n # self.L[:,self.attach_points[:,3]==1,:] = \\\\\\n # - self.L[:,self.attach_points[:,3]==1,:]\\n # print(self.L)\",\n \"def relaxation_matrix(self, uphill, downhill):\\n world.KK = numpy.zeros((2,2), dtype=numpy.float64)\\n Kup = 1.0/float(uphill)\\n world.KK[0,0] = -Kup\\n world.KK[1,0] = Kup\\n Kdn = 1.0/float(downhill)\\n world.KK[0,1] = Kdn\\n world.KK[1,1] = -Kdn\",\n \"def lu_decomposition(self):\\n if self.rows_count() != self.columns_count():\\n raise ValueError(\\\"Matrix needs to me square for LU decomposition.\\\")\\n for i in range(self.rows_count() - 1):\\n for j in range(i + 1, self.rows_count()):\\n if self[i, i] == 0: # or abs(self[i, i]) <= 0.000001):\\n raise ValueError(\\\"Can't divide by 0\\\")\\n self[j, i] = self[j, i] / self[i, i]\\n for k in range(i + 1, self.rows_count()):\\n self[j, k] -= self[j, i] * self[i, k]\",\n \"def laplacian(W, normalized=True):\\n\\n # Degree matrix.\\n d = W.sum(dim=0)\\n\\n # Laplacian matrix.\\n if not normalized:\\n D = scipy.sparse.diags(d.A.squeeze(), 0)\\n L = D - W\\n else:\\n # d += np.spacing(np.array(0, W.dtype))\\n d = 1 / torch.sqrt(d)\\n D = torch.diags(d.A.squeeze(), 0)\\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\\n L = I - D * W * D\\n\\n # assert np.abs(L - L.T).mean() < 1e-9\\n assert type(L) is scipy.sparse.csr.csr_matrix\\n return L\",\n \"def construct_matrix_A(n):\\n n2 = n**2\\n D0 = 4*np.ones(n2) # 0th diagonal\\n D1 = - np.ones(n2 - 1) # -1st, 1st diagonals\\n D1[n-1::n] = 0 # Setting every k*n-1 entries = 0 for k < n\\n DN = - np.ones(n2 - n) # -nth, nth diagonals\\n return scipy.sparse.diags((DN, D1, D0, D1, DN), (-n, -1, 0, 1, n),\\n shape=(n2, n2), format=\\\"csr\\\")\",\n \"def buildLaplacianPyramid(guassianPyramid):\\n pyramid = []\\n for i in range(len(guassianPyramid)-1):\\n Gi = upsample(guassianPyramid[i+1])\\n G = guassianPyramid[i]\\n r, c = G.shape[:2]\\n L = G - Gi[:r, :c]\\n pyramid.append(L)\\n\\n pyramid.append(guassianPyramid[-1])\\n return pyramid\",\n \"def symmetrized_normalized_laplacian(degree_vector, weight_matrix, length):\\n holders = np.zeros((length, 1))\\n holders[:, 0] = np.sqrt(1 / degree_vector)\\n\\n return np.eye(length) - holders * weight_matrix * holders.T\",\n \"def mounting_matrix(self):\\n # fmt: off\\n count = 0\\n for x in range(self.ntheta):\\n self.M[count][count] = 1\\n self.f[count][0] = self.p_in\\n count = count + self.nz - 1\\n self.M[count][count] = 1\\n self.f[count][0] = self.p_out\\n count = count + 1\\n count = 0\\n for x in range(self.nz - 2):\\n self.M[self.ntotal - self.nz + 1 + count][1 + count] = 1\\n self.M[self.ntotal - self.nz + 1 + count][self.ntotal - self.nz + 1 + count] = -1\\n count = count + 1\\n count = 1\\n j = 0\\n for i in range(1, self.nz - 1):\\n a = (1 / self.dtheta ** 2) * (self.c1[i][self.ntheta - 1])\\n self.M[count][self.ntotal - 2 * self.nz + count] = a\\n b = (1 / self.dz ** 2) * (self.c2[i - 1, j])\\n self.M[count][count - 1] = b\\n c = -((1 / self.dtheta ** 2) * ((self.c1[i][j]) + self.c1[i][self.ntheta - 1])\\n + (1 / self.dz ** 2) * (self.c2[i][j] + self.c2[i - 1][j]))\\n self.M[count, count] = c\\n d = (1 / self.dz ** 2) * (self.c2[i][j])\\n self.M[count][count + 1] = d\\n e = (1 / self.dtheta ** 2) * (self.c1[i][j])\\n self.M[count][count + self.nz] = e\\n count = count + 1\\n count = self.nz + 1\\n for j in range(1, self.ntheta - 1):\\n for i in range(1, self.nz - 1):\\n a = (1 / self.dtheta ** 2) * (self.c1[i, j - 1])\\n self.M[count][count - self.nz] = a\\n b = (1 / self.dz ** 2) * (self.c2[i - 1][j])\\n self.M[count][count - 1] = b\\n c = -((1 / self.dtheta ** 2) * ((self.c1[i][j]) + self.c1[i][j - 1])\\n + (1 / self.dz ** 2) * (self.c2[i][j] + self.c2[i - 1][j]))\\n self.M[count, count] = c\\n d = (1 / self.dz ** 2) * (self.c2[i][j])\\n self.M[count][count + 1] = d\\n e = (1 / self.dtheta ** 2) * (self.c1[i][j])\\n self.M[count][count + self.nz] = e\\n count = count + 1\\n count = count + 2\\n count = 1\\n for j in range(self.ntheta - 1):\\n for i in range(1, self.nz - 1):\\n if j == 0:\\n self.f[count][0] = (self.c0w[i][j] - self.c0w[i][self.ntheta - 1]) / self.dtheta\\n else:\\n self.f[count][0] = (self.c0w[i, j] - self.c0w[i, j - 1]) / self.dtheta\\n count = count + 1\\n count = count + 2\\n # fmt: on\",\n \"def laplacian2(A, laplacian_type='raw'):\\r\\n\\r\\n N = A.shape[0]\\r\\n # TODO: Raise exception if A is not square\\r\\n\\r\\n degrees = A.sum(1)\\r\\n # To deal with loops, must extract diagonal part of A\\r\\n diagw = np.diag(A)\\r\\n\\r\\n # w will consist of non-diagonal entries only\\r\\n ni2, nj2 = A.nonzero()\\r\\n w2 = A[ni2, nj2]\\r\\n ndind = (ni2 != nj2).nonzero() # Non-diagonal indices\\r\\n ni = ni2[ndind]\\r\\n nj = nj2[ndind]\\r\\n w = w2[ndind]\\r\\n\\r\\n di = np.arange(N) # diagonal indices\\r\\n\\r\\n if laplacian_type == 'raw':\\r\\n # non-normalized laplaciand L = D - A\\r\\n L = np.diag(degrees - diagw)\\r\\n L[ni, nj] = -w\\r\\n L = lil_matrix(L)\\r\\n elif laplacian_type == 'normalized':\\r\\n # TODO: Implement the normalized laplacian case\\r\\n # % normalized laplacian D^(-1/2)*(D-A)*D^(-1/2)\\r\\n # % diagonal entries\\r\\n # dL=(1-diagw./degrees); % will produce NaN for degrees==0 locations\\r\\n # dL(degrees==0)=0;% which will be fixed here\\r\\n # % nondiagonal entries\\r\\n # ndL=-w./vec( sqrt(degrees(ni).*degrees(nj)) );\\r\\n # L=sparse([ni;di],[nj;di],[ndL;dL],N,N);\\r\\n print(\\\"Not implemented\\\")\\r\\n else:\\r\\n # TODO: Raise an exception\\r\\n print(\\\"Don't know what to do\\\")\\r\\n\\r\\n return L\",\n \"def get_l(m):\\n L = m.copy()\\n for i in range(L.shape[0]):\\n L[i, i] = 1\\n L[i, i+1:] = 0\\n return np.matrix(L)\",\n \"def lup_decomposition(self):\\n p = [i for i in range(self.rows_count())]\\n for i in range(self.rows_count() - 1):\\n pivot = i\\n for j in range(i + 1, self.rows_count()):\\n if abs(self[p[j], i]) > abs(self[p[pivot], i]):\\n pivot = j\\n p[pivot], p[i] = p[i], p[pivot]\\n for j in range(i + 1, self.rows_count()):\\n if abs(self[p[i], i]) < math.pow(10, -6):\\n raise ValueError(\\\"Can't divide by 0\\\")\\n self[p[j], i] /= self[p[i], i]\\n for k in range(i + 1, self.rows_count()):\\n self[p[j], k] -= self[p[j], i] * self[p[i], k]\\n lst = []\\n for i in p:\\n lst.append(self.value[i])\\n return p, Matrix(lst)\",\n \"def lizardite():\\n\\n rho = 2610.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 245.; C[0,1] = 50.; C[0,2] = 31.; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 245.; C[1,2] = 31.; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 23.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 11.6; C[3,4] = 0.; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 11.6; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 97.5\\n\\n return C, rho\",\n \"def get_feature_oriented_matrix(self):\\n nbr_features = self.hyperparameters.time_series_depth\\n matrix = np.ones(shape=(nbr_features, nbr_features), dtype=np.float)\\n np.fill_diagonal(matrix, val=0)\\n return matrix\",\n \"def lform(self):\\n a, c, d, b = self.to_ccw()\\n if b < c:\\n a += b\\n b -= b\\n c -= b\\n d += b\\n else:\\n a += c\\n b -= c\\n c -= c\\n d += c\\n return self.__class__.from_ccw(a, c, d, b)\",\n \"def build_lower_zeros(self):\\r\\n for row in range(self.SIZE - 1, 0, -1):\\r\\n self.__obtain_zero(row, 0, 0)\\r\\n\\r\\n for col in range(1, self.SIZE - 1):\\r\\n for row in range(self.SIZE - 1, col, -1):\\r\\n self.__obtain_zero(row, col, row - 1)\",\n \"def lawsonite():\\n\\n rho = 3090.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 214.; C[0,1] = 69.; C[0,2] = 82.; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 226.; C[1,2] = 65.; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 259.; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 60.; C[3,4] = 0.; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 65.; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 17.\\n\\n return C, rho\",\n \"def lMatrix(a, b, c, alpha, beta, gamma, convention=None):\\n lMatrix = np.zeros((3, 3))\\n\\n cosAlpha = np.cos(alpha)\\n cosBeta = np.cos(beta)\\n cosGamma = np.cos(gamma)\\n\\n sinGamma = np.sin(gamma)\\n\\n lMatrix[0, 0] = a\\n lMatrix[0, 1] = b * cosGamma\\n lMatrix[0, 2] = c * cosBeta\\n\\n lMatrix[1, 1] = b * sinGamma\\n lMatrix[1, 2] = c * (cosAlpha - cosBeta * cosGamma) / sinGamma\\n\\n lMatrix[2, 2] = c * np.sqrt(\\n 1 + 2 * cosAlpha * cosBeta * cosGamma -\\n cosAlpha**2 - cosBeta**2 - cosGamma**2\\n ) / sinGamma\\n\\n # OI/HKL convention - x // [10-10], y // a2 [-12-10]\\n # TSL convention - x // a1 [2-1-10], y // [01-10]\\n if convention is None:\\n convention = defaults['crystal_ortho_conv']\\n\\n if convention.lower() in ['hkl', 'oi']:\\n # Swap 00 with 11 and 01 with 10 due to how OI orthonormalises\\n # From Brad Wynne\\n t1 = lMatrix[0, 0]\\n t2 = lMatrix[1, 0]\\n\\n lMatrix[0, 0] = lMatrix[1, 1]\\n lMatrix[1, 0] = lMatrix[0, 1]\\n\\n lMatrix[1, 1] = t1\\n lMatrix[0, 1] = t2\\n\\n elif convention.lower() != 'tsl':\\n raise ValueError(\\n f\\\"Unknown convention '{convention}' for orthonormalisation of \\\"\\n f\\\"crystal structure, can be 'hkl' or 'tsl'\\\"\\n )\\n\\n # Set small components to 0\\n lMatrix[np.abs(lMatrix) < 1e-10] = 0\\n\\n return lMatrix\",\n \"def matrix(self, full=False, keeppads=True):\\n\\n v = np.fft.hfft(self._u, n=self.N) / self.N\\n idx = sum(np.ogrid[0:self.N, -self.N:0])\\n C = v[idx] # follow scipy.linalg.{circulant,toeplitz,hankel}\\n\\n if keeppads:\\n a = self._yfac_.copy()\\n b = self._xfac_.copy()\\n else:\\n a = self.yfac.copy()\\n b = self.xfac.copy()\\n C = self._unpad(C, 0, True)\\n C = self._unpad(C, 1, False)\\n a = a.reshape(-1, 1)\\n\\n if not full:\\n return a, b, C\\n else:\\n return a * C * b\",\n \"def reconstructFromLaplacianPyramid(pyramid):\\n \\n nLevels = len(pyramid)\\n out = pyramid[-1]\\n if len(pyramid) == 1:\\n return out\\n\\n useStack = False\\n if pyramid[0].shape[0:2] == pyramid[-1].shape[0:2]:\\n useStack = True\\n\\n dtp = out.dtype\\n for i in range(nLevels-2,-1,-1):\\n newSz = pyramid[i].shape[0:2]\\n if useStack:\\n up = out\\n else:\\n up = cv2.pyrUp(out,dstsize=(newSz[1],newSz[0]))\\n if len(up.shape) < 3:\\n up.shape += (1,)\\n out = up + pyramid[i]\\n out = out.astype(dtp)\\n\\n return out\",\n \"def _set_ls_matrices(self):\\n zz_t = self.z_matrix * self.z_matrix.transpose()\\n l, s, l_t = np.linalg.svd(zz_t)\\n s[self.p:] = 0\\n self.l_matrix = np.matrix(l)\\n self.s_matirx = np.matrix(np.diag(s))\",\n \"def mult_L(self) -> np.ndarray:\\n return np.array([\\n [self.w, -self.x, -self.y, -self.z],\\n [self.x, self.w, -self.z, self.y],\\n [self.y, self.z, self.w, -self.x],\\n [self.z, -self.y, self.x, self.w]])\",\n \"def mk_single_diffy():\\n # make matrix:\\n mat = zeros((M, M), dtype='d')\\n for m in range(M):\\n for p in range(m+1, M, 2):\\n mat[m,p] = 2*p*oneOverC[m]\\n\\n return mat\",\n \"def matrices(self):\\n # Creating L\\n L = scipy.sparse.diags((self.inv_dx2, -2*self.inv_dx2, self.inv_dx2, 1),\\n (-(self.N+1), -self.N, -(self.N-1), self.N),\\n shape=(2*self.N, 2*self.N), dtype=np.complex128)\\n self.L = scipy.sparse.csr_matrix(L)\\n self.L[-(self.N+1), 0], self.L[-1, -self.N] = 0, 0\\n\\n # Computing largest eigenvalue of L explicitely:\\n self.mu_max = self.inv_dx*np.sqrt(2*(1 + np.cos(np.pi/(self.N+1))))\\n\\n # Creating K\\n self.K = scipy.sparse.diags((-self.inv_dx2, 2*self.inv_dx2, -self.inv_dx2),\\n (-1, 0, 1), # Diagonals\\n shape=(self.N, self.N), # Size of matrix\\n dtype=np.complex128)\",\n \"def get_incorrect_distance_matrix(L):\\n n = len(L)\\n D = np.zeros((n,n))\\n for i in range(n):\\n for j in range(n):\\n if i != j:\\n D[i][j] = get_minor(L, [i], [j]) / get_minor(L, [i], [i])\\n return D\",\n \"def get_L(self, tolerance=None):\\r\\n index = self.data.index\\r\\n columns = self.data.columns\\r\\n\\r\\n # Obtain the eigenvalues and eigenvectors\\r\\n E, V = sandy.CategoryCov(self.data).get_eig(tolerance=tolerance)\\r\\n\\r\\n # need sparse because much faster for large matrices (2kx2k from J33 Pu9)\\r\\n # with a lot of zero eigs\\r\\n # this is exactly equivalent to V.values @ np.diag(np.sqrt(E.values))\\r\\n A = (sparse.csr_matrix(V.values) @ sparse.csr_matrix(np.diag(np.sqrt(E.values)))).todense()\\r\\n \\r\\n# u, s, vh = self.get_svd()\\r\\n# A = (sparse.csr_matrix(u) @ sparse.csr_matrix(np.diag(np.sqrt(s)))).todense()\\r\\n\\r\\n # QR decomposition\\r\\n Q, R = scipy.linalg.qr(A.T)\\r\\n L = R.T\\r\\n\\r\\n return pd.DataFrame(L, index=index, columns=columns)\",\n \"def lherzolite():\\n\\n rho = 3270.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 187.4; C[0,1] = 63.71; C[0,2] = 63.87; C[0,3] = 0.78; C[0,4] = 2.02; C[0,5] = -3.2\\n C[1,0] = C[0,1]; C[1,1] = 211.25; C[1,2] = 64.5; C[1,3] = -3.07; C[1,4] = 0.87; C[1,5] = -5.78\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 190.; C[2,3] = 0.38; C[2,4] = 2.38; C[2,5] = -0.12\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 67.9; C[3,4] = -2.12; C[3,5] = 1.6\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 63.12; C[4,5] = -0.55\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 66.83\\n\\n return C, rho\",\n \"def build_matrix(self, rawdata):\\n\\t\\tN = len(rawdata)\\n\\t\\ts = set()\\n\\t\\tfor d in rawdata:\\n\\t\\t\\ts.update(d[7])\\n\\t\\tK = len(s)\\n\\t\\tself.I = dict((j, i) for (i, j) in enumerate(s))\\n\\t\\tself.I_rev = dict((i, j) for (i, j) in enumerate(s))\\n\\t\\tD = sp.dok_matrix((N, K))\\n\\t\\tself.t = np.zeros(N)\\n\\t\\tprint \\\"Empty %i x %i matrix created\\\" % (N, K)\\n\\t\\t\\n\\t\\tfor n,d in enumerate(rawdata):\\n\\t\\t\\tif len(d[7]) == 1:\\n\\t\\t\\t\\tD[n,self.I[d[7][0]]] = 1# d[6] - d[5]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tD[n, self.I[d[7][0]]] = 1 # d[5]\\n\\t\\t\\t\\tD[n, self.I[d[7][-1]]] = 1 # d[6]\\n\\t\\t\\t\\tfor eid in d[7][1:-1]:\\n\\t\\t\\t\\t\\tD[n, self.I[eid]] = 1.0\\n\\t\\t\\tself.t[n] = float(d[2] - d[1])\\n\\t\\tprint \\\"DOK matrix built.\\\"\\n\\t\\t\\n\\t\\tself.M = sp.csr_matrix(D)\\n\\t\\tprint \\\"DOK to CSR conversion done.\\\"\\n\\t\\tself.N = N\\n\\t\\tself.K = K\",\n \"def _prepare_outer_matrix(self):\\n self._mat_plane = numpy.array([\\n self._scaling[0], 0, 0, 0,\\n 0, self._scaling[1], 0, 0,\\n 0, 0, 1, 0,\\n self.i_border[0], -self.i_border[1], 0, 1\\n ], dtype=numpy.float32)\",\n \"def generate_two_dim_step_matrices(self):\\n Lap_h = two_dim_sparse_neumann_laplacian(self.M, format='csc')\\n I_m = sp.identity(self.M * self.M, dtype='float64', format='csc')\\n\\n I_minus_Lap = spla.factorized(I_m - (self.r / 2.0) * Lap_h)\\n I_plus_Lap = I_m + (self.r / 2.0) * Lap_h\\n\\n return I_minus_Lap, I_plus_Lap\",\n \"def delaunay_triangulate(P: np.ndarray):\\n n = P.shape[0]\\n if n < 3:\\n A = np.ones((n, n)) - np.eye(n)\\n else:\\n try:\\n d = Delaunay(P)\\n A = np.zeros((n, n))\\n for simplex in d.simplices:\\n for pair in itertools.permutations(simplex, 2):\\n A[pair] = 1\\n except QhullError as err:\\n print(\\\"Delaunay triangulation error detected. Return fully-connected graph.\\\")\\n print(\\\"Traceback:\\\")\\n print(err)\\n A = np.ones((n, n)) - np.eye(n)\\n return A\",\n \"def make_lcp(A,F=0.25):\\n\\n # [x, b] = MAKE_LCP(A): Make LCP\\n #\\n # INPUT:\\n #\\n # A -- The coefficient matrix in the LCP\\n # F -- The fraction of zero-values in the x-solution.\\n #\\n # OUTPUT:\\n #\\n # x -- A solution for the LCP problem.\\n # b -- The right hand side vector\\n #\\n # Port of Kenny Erleben, DIKU 2011 Matlab code to Python\\n\\n ##### Get of number of variable ##########\\n N = np.size(A,0) # Pick a dimension, it should be NxN\\n\\n ##### Generate a random LCP solution ##########\\n x = np.random.uniform(0,1,(N,1))\\n x[x < F] = 0\\n\\n # x = np.real_if_close(x)\\n\\n ##### Generate a right-hand-side vector that is ##########\\n ##### consistent with a random solution ##########\\n b = np.zeros((N,1))\\n s = np.real_if_close(np.dot(A,x))\\n b[x>0] = -s[x>0]\\n\\n return (x, b)\",\n \"def _build_laplacian_pyramid(im, max_levels, filter_size):\\n filter_vec = _gaussian_kernel_1d(filter_size).reshape(filter_size, 1)\\n pyr = []\\n im_curr = im\\n im_next = _reduce(im, filter_vec)\\n while _image_is_large_enough(im_next) and len(pyr) < max_levels - 1:\\n pyr.append(im_curr - _expand(im_next, filter_vec))\\n im_curr = im_next\\n im_next = _reduce(im_curr, filter_vec)\\n\\n pyr.append(im_curr)\\n\\n return pyr, filter_vec.reshape(1, filter_size)\",\n \"def mat_from_diag_triu_tril(diag, tri_upp, tri_low):\\n n = diag.shape[-1]\\n (i,) = diag_indices(n, ndim=1)\\n j, k = triu_indices(n, k=1)\\n mat = _torch.zeros((diag.shape + (n,)))\\n mat[..., i, i] = diag\\n mat[..., j, k] = tri_upp\\n mat[..., k, j] = tri_low\\n return mat\",\n \"def cholesky(A):\\n n = len(A)\\n\\n # Create zero matrix for L\\n L=np.zeros((n,n))\\n\\n # Perform the Cholesky decomposition\\n for i in range(n):\\n for k in range(i+1):\\n tmp_sum = sum(L[i][j] * L[k][j] for j in xrange(k))\\n \\n if (i == k): # Diagonal elements\\n\\n L[i][k] = math.sqrt(A[i][i] - tmp_sum)\\n else:\\n\\n L[i][k] = (1.0 / L[k][k] * (A[i][k] - tmp_sum))\\n return L\",\n \"def tridiag_matrix(bc_surface_type, upsilon, space_divisions, dx, k, T, h, hc, emissivity, sigma):\\n # create tri-diagonal matrix\\n A = np.diagflat([-upsilon for i in range(space_divisions - 1)], -1) +\\\\\\n np.diagflat([1 + 2 * upsilon for i in range(space_divisions)]) +\\\\\\n np.diagflat([-upsilon for i in range(space_divisions - 1)], 1)\\n\\n # adjust matrix depending on the boundary condition at the exposed surface\\n if bc_surface_type == \\\"linear\\\":\\n A[0,0] = 1 + 2*upsilon + 2*upsilon*dx*h/k\\n A[0,1] = -2*upsilon\\n \\n elif bc_surface_type == \\\"non-linear\\\":\\n A[0,0] = 1 + 2*upsilon + 2*dx*hc*upsilon/k+ 8*emissivity*sigma*dx*upsilon*T[0]**3/k\\n A[0,1] = -2*upsilon\\n \\n # adjust matrix for the back boundary conditions\\n A[-1, -2] = - 2 * upsilon\\n A[-1, -1] = 1 + 2 * upsilon\\n\\n return A\",\n \"def laplacian(self, array_in):\\r\\n\\r\\n # Call-through to Laplacian operator, already computed\\r\\n return self.laplace_op*array_in\",\n \"def build_linear_diags(self):\\n N = self.N\\n dx = self.dx\\n j = self._j # Index of the mid-point\\n\\n diags = np.zeros((2*self._j+1, self.N))\\n\\n # Advection term\\n cff1 = -1/(2*dx)\\n\\n # Need to stagger these diagonals so lower and upper bands are symmetric\\n diags[j-1, :-2] += -1*cff1*self.c[2:]\\n diags[j+1, :] += 1*cff1*self.c[:]\\n\\n # Sponge term\\n x = np.arange(0,N*dx,dx)\\n rdist = x[-1] - x # Distance from right boundary\\n spongefac = -np.exp(-6*rdist/self.spongedist)/self.spongetime\\n diags[j,:] += spongefac \\n\\n return diags\",\n \"def make_mat_lp_le(lin_pot_mesh, lin_geo_mesh):\\n num_nodes = lin_pot_mesh.get_nodes().shape[0]\\n K = np.zeros((3 * num_nodes, 3 * num_nodes))\\n add_lp_le_DL_terms(K, lin_pot_mesh, lin_geo_mesh)\\n add_lp_le_RBM_terms(K, lin_pot_mesh, lin_geo_mesh)\\n return K\",\n \"def build_upper_zeros(self):\\r\\n for row in range(0, self.SIZE - 1):\\r\\n self.__obtain_zero(row, self.SIZE - 1, self.SIZE - 1)\\r\\n\\r\\n for col in range(self.SIZE - 2, 0, -1):\\r\\n for row in range(0, col):\\r\\n self.__obtain_zero(row, col, row + 1)\",\n \"def _build(self):\\n ary = np.zeros( (3,3,3), float )\\n ary[0,0,0] = ary[1,1,1] = ary[0,1,2] = ary[1,0,2] = 1.\\n ary[0,2,0] = ary[0,2,2] = ary[2,0,0] = ary[2,0,2] = 0.5\\n ary[1,2,1] = ary[1,2,2] = ary[2,1,1] = ary[2,1,2] = 0.5\\n ary[2,2,0] = ary[2,2,1] = 0.25\\n ary[2,2,2] = 0.5\\n return ary\",\n \"def calc_Ls(self, x_surface, geom):\\n\\n return np.zeros((self.n_wl,))\",\n \"def _build_sparse_matrix(L):\\n shape = L.shape\\n i = torch.LongTensor(np.vstack((L.row, L.col)).astype(int))\\n v = torch.FloatTensor(L.data)\\n return torch.sparse.FloatTensor(i, v, torch.Size(shape))\",\n \"def build_mat(self):\\n for row, s in enumerate(self.S):\\n for col, t in enumerate(self.T):\\n\\n if self.symmetric and row > col:\\n pass\\n\\n else:\\n self.mat[row, col] = self.kernel(s, t, self.n)\\n\\n if self.symmetric:\\n self.mat = self.symmetrize(self.mat)\\n else:\\n for idx, s in enumerate(self.S):\\n self.test_normalization[idx] = self.kernel(s, s, self.n)\",\n \"def make_state_appliable(state):\\n size = len(state)\\n st_appl = np.zeros((size, size), dtype=complex)\\n for p1 in range(size):\\n for p2 in range(size):\\n st_appl[p1, p2] = state[p1, p2] * sqrt(factorial(p1) * factorial(p2))\\n return st_appl\",\n \"def test_LU(self):\\n A = np.random.rand(10, 10)\\n MA = to_matrix(A)\\n ML, MU = MA.decomposeLU()\\n self.assertEqual(ML*MU, MA)\\n self.assertTrue(ML.is_lower_triangular())\\n self.assertTrue(MU.is_upper_triangular())\",\n \"def _makeDerivativeMatrix(self, index, a):\\n num_neurons = a\\n jaccob_matrix = np.zeros(shape=(num_neurons, num_neurons)) # ie S=3, shape 3X3\\n # dx_func = self.__getDerivative(self.layers[index]['trans_func'])\\n dx_func = self._getTransFunc(self.layers[index]['trans_func']).derivative\\n for i in range(num_neurons):\\n # diagonal matrix\\n a_val = self.layers[index]['a_output'][i]\\n jaccob_matrix[i][i] = dx_func(a_val)\\n return jaccob_matrix\",\n \"def lcr_matrix(H):\\n if H.ndim != 2 or H.shape[0] != H.shape[1]:\\n raise ValueError('H should be a square matrix')\\n\\n leverages = sqrt(1-H.diagonal())\\n leverages = leverages[:, None]\\n R = (eye(len(H)) - H) / leverages\\n return R - R.mean(0)\",\n \"def finite_difference(n):\\n B=sparse.diags([1,-4,1],[-1,0,1],shape=(n,n))\\n A=sparse.block_diag([B for i in xrange(0,n)])\\n A.setdiag(1,k=-n)\\n A.setdiag(1,k=n) \\n b=[-100]+[0 for i in xrange(1,n-1)]+[-100]\\n b=b*n\\n return A,b\",\n \"def diag_to_matrix(l_and_u, diag):\\n l, u = l_and_u\\n dim = diag.shape[1]\\n matrix = np.zeros((dim, dim))\\n for i in range(l+u+1):\\n np.fill_diagonal(\\n matrix[max(0,i-u):,max(0,u-i):],\\n diag[i,max(0,u-i):]\\n )\\n return matrix\",\n \"def build_mat(self,diag_entries=False):\\n if self.L is None:\\n raise ValueError('Must set number of spins (Operator.L) before building PETSc matrix.')\\n\\n self.destroy_mat()\\n\\n term_array = self.get_MSC()\\n\\n if diag_entries and not np.any(term_array['masks'] == 0):\\n term_array = np.hstack([np.array([(0,0,0)],dtype=MSC_dtype),term_array])\\n\\n if not np.any(term_array['masks'] == 0):\\n self._diag_entries = False\\n else:\\n self._diag_entries = True\\n\\n self._mat = build_mat(self.L,\\n np.ascontiguousarray(term_array['masks']),\\n np.ascontiguousarray(term_array['signs']),\\n np.ascontiguousarray(term_array['coeffs']),\\n bool(self.use_shell),\\n self.use_shell == 'gpu')\",\n \"def lu(matrix):\\n SIZE = matrix.shape[0]\\n BS = np.BLOCKSIZE\\n\\n if matrix.shape[0] != matrix.shape[0]:\\n raise Exception(\\\"LU only supports squared matricis\\\")\\n if not matrix.dist():\\n raise Exception(\\\"The matrix is not distributed\\\")\\n\\n if(SIZE % np.BLOCKSIZE != 0):\\n raise Exception(\\\"The matrix dimensions must be divisible \\\"\\\\\\n \\\"with np.BLOCKSIZE(%d)\\\"%np.BLOCKSIZE)\\n\\n (prow,pcol) = matrix.pgrid()\\n A = np.zeros((SIZE,SIZE), dtype=matrix.dtype, dist=True);A += matrix\\n L = np.zeros((SIZE,SIZE), dtype=matrix.dtype, dist=True)\\n U = np.zeros((SIZE,SIZE), dtype=matrix.dtype, dist=True)\\n tmpL = np.zeros((SIZE,SIZE), dtype=matrix.dtype, dist=True)\\n tmpU = np.zeros((SIZE,SIZE), dtype=matrix.dtype, dist=True)\\n for k in xrange(0,SIZE,BS):\\n bs = min(BS,SIZE - k) #Current block size\\n kb = k / BS # k as block index\\n\\n #Compute vertical multiplier\\n slice = ((kb,kb+1),(kb,kb+1))\\n for a,l,u in zip(A.blocks(slice), L.blocks(slice), U.blocks(slice)):\\n (p,tl,tu) = linalg.lu(a)\\n if not (np.diag(p) == 1).all():#We do not support pivoting\\n raise Exception(\\\"Pivoting was needed!\\\")\\n #There seems to be a transpose bug in SciPy's LU\\n l[:] = tl.T\\n u[:] = tu.T\\n\\n #Replicate diagonal block horizontal and vertical\\n for tk in xrange(k+bs,SIZE,BS):\\n tbs = min(BS,SIZE - tk) #Current block size\\n L[tk:tk+tbs,k:k+bs] = U[k:k+tbs,k:k+bs]\\n U[k:k+bs,tk:tk+tbs] = L[k:k+bs,k:k+tbs]\\n\\n if k+bs < SIZE:\\n #Compute horizontal multiplier\\n slice = ((kb,kb+1),(kb+1,SIZE/BS))\\n for a,u in zip(A.blocks(slice), U.blocks(slice)):\\n u[:] = np.linalg.solve(u.T,a.T).T\\n\\n #Compute vertical multiplier\\n slice = ((kb+1,SIZE/BS),(kb,kb+1))\\n for a,l in zip(A.blocks(slice), L.blocks(slice)):\\n l[:] = np.linalg.solve(l,a)\\n\\n #Apply to remaining submatrix\\n A -= pyHPC.summa(L[:,:k+bs],U[:k+bs,:], ao=(k+bs,k),\\n bo=(k,k+bs), co=(k+bs,k+bs))\\n\\n return (L, U)\",\n \"def build_lower_zeros_with_pivoting(self):\\r\\n self.__apply_pivoting(0, self.SIZE - 1, 0)\\r\\n for row in range(self.SIZE - 1, 0, -1):\\r\\n self.__obtain_zero(row, 0, 0)\\r\\n\\r\\n for col in range(1, self.SIZE - 1):\\r\\n self.__apply_pivoting(col, self.SIZE - 1, col)\\r\\n for row in range(self.SIZE - 1, col, -1):\\r\\n self.__obtain_zero(row, col, row - 1)\",\n \"def build_augmented_matrix(self):\\r\\n for row in range(self.SIZE):\\r\\n self.matrix[row].append(self.result[row])\",\n \"def get_initial_condition_euler(self, tol=1e-10):\\n Z = zeros((len(self._meshes), len(self._meshes[0].elements)+1))\\n for mi, m in enumerate(self._meshes):\\n if not m._left_lift:\\n raise Exception(\\\"get_initial_condition_euler() only works if all boundary conditions are given on the left.\\\")\\n\\n Z[mi, 0] = m._left_value\\n def get_F(Z, t):\\n \\\"\\\"\\\"\\n Evaluates the RHS for the vector Z and time tau.\\n \\\"\\\"\\\"\\n Z0 = zeros((len(self._meshes),))\\n for mi, m in enumerate(self._meshes):\\n Z0[mi] = self._F(mi, Z, t)\\n return Z0\\n def get_phi(Z, Zprev, tau, t):\\n return Z - tau*get_F(Z, t) - Zprev\\n def get_J(Z, tau, t):\\n mat = eye(len(self._meshes))\\n for i in range(len(self._meshes)):\\n for j in range(len(self._meshes)):\\n mat[i, j] += - tau*self._DFDY(i, j, Z, t)\\n return mat\\n\\n # initial time and initial condition vector:\\n tprev = self._meshes[0].elements[0].nodes[0].x\\n Zprev = Z[:, 0].copy()\\n Znext = Zprev[:].copy()\\n for el_i in range(len(self._meshes[0].elements)):\\n #print \\\"doing element:\\\", el_i\\n tau = self._meshes[0].elements[el_i].length\\n tnext = tprev + tau\\n error = 1e10\\n i = 0\\n while error > tol:\\n J = get_J(Zprev, tau, tprev)\\n phi = get_phi(Znext, Zprev, tau, tprev)\\n dZ = solve(J, -phi)\\n Znext += dZ\\n error_dZ = l2_norm(dZ)\\n error_phi = l2_norm(get_phi(Znext, Zprev, tau, tnext))\\n #print \\\"it=%d, l2_norm_dZ=%e, l2_norm_phi=%e\\\" % \\\\\\n # (i, error_dZ, error_phi)\\n error = max(error_dZ, error_phi)\\n i += 1\\n Z[:, el_i+1] = Znext[:].copy()\\n Zprev = Znext[:].copy()\\n tprev = tnext\\n\\n\\n # now assign the Z to the vertex dofs and leave zeros in the bubbles\\n Y = zeros((self.ndofs,))\\n for mi, m in enumerate(self._meshes):\\n coeffs_one_mesh = Z[mi, 1:]\\n Y[m.dof_start:m.dof_start+len(coeffs_one_mesh)] = coeffs_one_mesh\\n return Y\",\n \"def generate_LLL_matrix(self, matrix):\\n LLL_matrix = matrix.transpose().LLL().transpose()\\n return LLL_matrix\",\n \"def matrix_L1(l, omega, S, cn):\\n zt = omega * S / cn['t']\\n L = np.array((dN2(l, zt), dN4(l, zt)))\\n return L.T\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.680923","0.6638274","0.62569284","0.62069863","0.61390823","0.6101195","0.59771115","0.58065456","0.5798872","0.5760791","0.5729921","0.5715687","0.56979316","0.5615754","0.55505806","0.55295897","0.55295897","0.55002135","0.5498201","0.5496341","0.54951996","0.5462513","0.5439882","0.54309034","0.5421717","0.53955626","0.53927463","0.53541285","0.5353893","0.53453684","0.5336856","0.5329614","0.5324905","0.532192","0.5315906","0.531485","0.5314092","0.530122","0.5266889","0.5260048","0.52495944","0.52495944","0.52469003","0.5228986","0.52238166","0.5218845","0.521041","0.52009887","0.5194418","0.5179579","0.5172927","0.5172689","0.5171359","0.5164043","0.51631415","0.51553845","0.51393247","0.51349163","0.513412","0.51298815","0.511768","0.5107466","0.5096872","0.50947136","0.50396925","0.5038918","0.50308394","0.50274426","0.5014812","0.50050044","0.5001785","0.49982417","0.49965602","0.4987348","0.4987147","0.49753383","0.49710608","0.4955494","0.49463782","0.49447247","0.49387616","0.49365103","0.4932719","0.4930884","0.49290997","0.49265394","0.49239963","0.4922822","0.49207994","0.4920447","0.49189836","0.49187276","0.4918369","0.49171412","0.49122605","0.4904937","0.48893908","0.48845422","0.48834416","0.48827538","0.4882623"],"string":"[\n \"0.680923\",\n \"0.6638274\",\n \"0.62569284\",\n \"0.62069863\",\n \"0.61390823\",\n \"0.6101195\",\n \"0.59771115\",\n \"0.58065456\",\n \"0.5798872\",\n \"0.5760791\",\n \"0.5729921\",\n \"0.5715687\",\n \"0.56979316\",\n \"0.5615754\",\n \"0.55505806\",\n \"0.55295897\",\n \"0.55295897\",\n \"0.55002135\",\n \"0.5498201\",\n \"0.5496341\",\n \"0.54951996\",\n \"0.5462513\",\n \"0.5439882\",\n \"0.54309034\",\n \"0.5421717\",\n \"0.53955626\",\n \"0.53927463\",\n \"0.53541285\",\n \"0.5353893\",\n \"0.53453684\",\n \"0.5336856\",\n \"0.5329614\",\n \"0.5324905\",\n \"0.532192\",\n \"0.5315906\",\n \"0.531485\",\n \"0.5314092\",\n \"0.530122\",\n \"0.5266889\",\n \"0.5260048\",\n \"0.52495944\",\n \"0.52495944\",\n \"0.52469003\",\n \"0.5228986\",\n \"0.52238166\",\n \"0.5218845\",\n \"0.521041\",\n \"0.52009887\",\n \"0.5194418\",\n \"0.5179579\",\n \"0.5172927\",\n \"0.5172689\",\n \"0.5171359\",\n \"0.5164043\",\n \"0.51631415\",\n \"0.51553845\",\n \"0.51393247\",\n \"0.51349163\",\n \"0.513412\",\n \"0.51298815\",\n \"0.511768\",\n \"0.5107466\",\n \"0.5096872\",\n \"0.50947136\",\n \"0.50396925\",\n \"0.5038918\",\n \"0.50308394\",\n \"0.50274426\",\n \"0.5014812\",\n \"0.50050044\",\n \"0.5001785\",\n \"0.49982417\",\n \"0.49965602\",\n \"0.4987348\",\n \"0.4987147\",\n \"0.49753383\",\n \"0.49710608\",\n \"0.4955494\",\n \"0.49463782\",\n \"0.49447247\",\n \"0.49387616\",\n \"0.49365103\",\n \"0.4932719\",\n \"0.4930884\",\n \"0.49290997\",\n \"0.49265394\",\n \"0.49239963\",\n \"0.4922822\",\n \"0.49207994\",\n \"0.4920447\",\n \"0.49189836\",\n \"0.49187276\",\n \"0.4918369\",\n \"0.49171412\",\n \"0.49122605\",\n \"0.4904937\",\n \"0.48893908\",\n \"0.48845422\",\n \"0.48834416\",\n \"0.48827538\",\n \"0.4882623\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":890,"cells":{"query":{"kind":"string","value":"Construct the normalized laplacian matrix."},"document":{"kind":"string","value":"def normalized_laplacian(degree_vector, weight_matrix, length):\n holders = np.zeros((length, 1))\n holders[:, 0] = 1 / degree_vector\n\n return np.eye(length) - holders * weight_matrix"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def laplacian_matrix(A, normalized=False):\n n, m = A.shape\n D = degree_matrix(A)\n L = D - A\n if normalized:\n degs = _flat(A.sum(axis=1))\n rootD = sps.spdiags(np.power(degs, -1 / 2), [0], n, n, format=\"csr\")\n L = rootD * L * rootD\n return L","def normalize(self, lam):\n return (lam.T / np.sum(lam, axis=1)).T","def normalize(self):\r\n max = np.amax(self.matrix)\r\n min = np.amin(self.matrix)\r\n\r\n self.matrix = ((self.matrix - min) / (max - min))","def laplacian(W, normalized=True):\n\n # Degree matrix.\n d = W.sum(dim=0)\n\n # Laplacian matrix.\n if not normalized:\n D = scipy.sparse.diags(d.A.squeeze(), 0)\n L = D - W\n else:\n # d += np.spacing(np.array(0, W.dtype))\n d = 1 / torch.sqrt(d)\n D = torch.diags(d.A.squeeze(), 0)\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\n L = I - D * W * D\n\n # assert np.abs(L - L.T).mean() < 1e-9\n assert type(L) is scipy.sparse.csr.csr_matrix\n return L","def build_normalized(self):\n for row, s in enumerate(self.S):\n for col, t in enumerate(self.T):\n\n if self.symmetric and row > col:\n pass\n\n elif self.symmetric and row == col:\n self.normalized_mat[row, col] = 1\n\n else:\n self.normalized_mat[row, col] = self.normalize(row, col)\n\n if self.symmetric:\n self.normalized_mat = self.symmetrize(self.normalized_mat)","def normalize(self):\n det = self._mat[0][0]*self._mat[1][1] - self._mat[0][1]*self._mat[1][0]\n for i in range(2):\n for j in range(2):\n self._mat[i][j] = (self._mat[i][j])/(np.sqrt(det))","def laplacian(W, normalized=False):\r\n # Degree matrix.\r\n d = W.sum(axis=0)\r\n # Laplacian matrix.\r\n if not normalized:\r\n D = scipy.sparse.diags(d.A.squeeze(), 0)\r\n L = D - W\r\n else:\r\n # d += np.spacing(np.array(0, W.dtype))\r\n d = 1 / np.sqrt(d)\r\n D = scipy.sparse.diags(d.A.squeeze(), 0)\r\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\r\n L = I - D * W * D\r\n\r\n # assert np.abs(L - L.T).mean() < 1e-9\r\n assert type(L) is scipy.sparse.csr.csr_matrix\r\n return L","def laplacian(A,normalize=False,randomWalk=False):\n degr = A.sum(axis=1)*1.\n\n if randomWalk:\n degr[degr!=0] = 1./degr[degr!=0]\n Dinv = np.diag(degr)\n return np.eye(A.shape[0]) - np.dot(Dinv,A)\n if normalize:\n degr = A.sum(axis=1)*1.\n degr[degr!=0] = 1./degr[degr!=0]\n Dinv = np.diag(degr)**.5\n return np.eye(A.shape[0]) - np.dot(Dinv,A).dot(Dinv)\n\n return np.diag(degr) - A","def calculate_normalized_laplacian(adj):\n adj = sp.coo_matrix(adj)\n d = np.array(adj.sum(1))\n d_inv_sqrt = np.power(d, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n return normalized_laplacian","def calculate_normalized_laplacian(adj):\n adj = sp.coo_matrix(adj)\n d = np.array(adj.sum(1))\n d_inv_sqrt = np.power(d, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n return normalized_laplacian","def normalize(self,matrix):\n for i in range(self.N):\n matrix[self.N-1][i] = 0\n for i in range(self.n):\n matrix[self.N - 1][self.index(i,i)] = 1\n return matrix","def L2_normalize(xx):\r\n\tZx = compute_L2_normalization(xx)\r\n\treturn xx / np.sqrt(Zx[:, np.newaxis])","def laplacian(points, sigma):\n\n S = similarity_matrix(points, sigma)\n \n (npnts,npnts) = S.shape \n\n D = np.zeros_like(S)\n\n for i in range(npnts):\n #D[i,i] = 1.0 / np.sqrt(S[i,:].sum()) \n D[i,i] = S[i,:].sum()\n\n\n return (D - S) #(np.eye(npnts,npnts) - np.dot(D,np.dot(S,D)))","def lap_mat(self):","def lu_decomposition(self):\n if self.rows_count() != self.columns_count():\n raise ValueError(\"Matrix needs to me square for LU decomposition.\")\n for i in range(self.rows_count() - 1):\n for j in range(i + 1, self.rows_count()):\n if self[i, i] == 0: # or abs(self[i, i]) <= 0.000001):\n raise ValueError(\"Can't divide by 0\")\n self[j, i] = self[j, i] / self[i, i]\n for k in range(i + 1, self.rows_count()):\n self[j, k] -= self[j, i] * self[i, k]","def laplacian(A):\n #calculate D by creating a diagonal matrix with the column sum of A\n D = np.diag(A.sum(axis=0))\n return D - A","def decompose_to_LU(a):\n # create emtpy LU-matrix\n lu_matrix = np.matrix(np.zeros([a.shape[0], a.shape[1]]))\n n = a.shape[0]\n\n for k in range(n):\n # calculate all residual k-row elements\n for j in range(k, n):\n lu_matrix[k, j] = a[k, j] - lu_matrix[k, :k] * lu_matrix[:k, j]\n # calculate all residual k-column elemetns\n for i in range(k + 1, n):\n lu_matrix[i, k] = (a[i, k] - lu_matrix[i, : k] * lu_matrix[: k, k]) / lu_matrix[k, k]\n\n return lu_matrix","def get_normalization_matrix(x):\n # Input: x 3*N\n #\n # Output: T 3x3 transformation matrix of points\n\n # TODO\n # --------------------------------------------------------------\n # Estimate transformation matrix used to normalize\n # the inputs x\n # --------------------------------------------------------------\n x2d = x[:2, :]\n # Get centroid and mean-distance to centroid\n center = np.mean(x2d, 1, keepdims=True)\n mean_dist = np.mean(np.sqrt(np.sum((x2d - center) ** 2, axis=0)))\n center = center.flatten()\n\n T = np.array([[np.sqrt(2) / mean_dist, 0, -center[0] * np.sqrt(2) / mean_dist],\n [0, np.sqrt(2) / mean_dist, -center[1] * np.sqrt(2) / mean_dist],\n [0, 0, 1]])\n\n return T","def laplace_matrix(self):\n n = self.number_of_vertices\n laplace_matrix = np.zeros((n, n))\n for i in range(n):\n laplace_matrix[i][i] = 1\n vertice = self.list_of_vertices[i]\n for edge in vertice.edges_list:\n laplace_matrix[i][edge.linked[1].index] = 1\n return laplace_matrix","def normalize(self, mel_db: np.ndarray) -> np.ndarray:\n mel_norm = ((mel_db - self.ref_level_db) - self.min_level_db) / (\n -self.min_level_db\n )\n if self.symmetric_norm:\n # Symmetric norm\n mel_norm = ((2 * self.max_norm) * mel_norm) - self.max_norm\n if self.clip_norm:\n mel_norm = np.clip(mel_norm, -self.max_norm, self.max_norm)\n else:\n # Asymmetric norm\n mel_norm = self.max_norm * mel_norm\n if self.clip_norm:\n mel_norm = np.clip(mel_norm, 0, self.max_norm)\n\n return mel_norm","def LU(A):\n m, n = A.shape\n L, U = np.zeros([m, n]), np.zeros([m, n])\n for i in range(n):\n L[i][i] = 1\n\n for i in range(n):\n\n # Upper triangular matrix\n for j in range(i, n):\n summ = 0\n for k in range(0, i):\n summ += L[i][k]*U[k][j]\n U[i][j] = A[i][j] - summ\n\n # Lower triangular matrix\n for j in range(i+1, n):\n summ = 0\n for k in range(0, i):\n summ += L[j][k]*U[k][i]\n L[j][i] = (A[j][i] - summ)/U[i][i]\n return L, U","def laplacian2(A, laplacian_type='raw'):\r\n\r\n N = A.shape[0]\r\n # TODO: Raise exception if A is not square\r\n\r\n degrees = A.sum(1)\r\n # To deal with loops, must extract diagonal part of A\r\n diagw = np.diag(A)\r\n\r\n # w will consist of non-diagonal entries only\r\n ni2, nj2 = A.nonzero()\r\n w2 = A[ni2, nj2]\r\n ndind = (ni2 != nj2).nonzero() # Non-diagonal indices\r\n ni = ni2[ndind]\r\n nj = nj2[ndind]\r\n w = w2[ndind]\r\n\r\n di = np.arange(N) # diagonal indices\r\n\r\n if laplacian_type == 'raw':\r\n # non-normalized laplaciand L = D - A\r\n L = np.diag(degrees - diagw)\r\n L[ni, nj] = -w\r\n L = lil_matrix(L)\r\n elif laplacian_type == 'normalized':\r\n # TODO: Implement the normalized laplacian case\r\n # % normalized laplacian D^(-1/2)*(D-A)*D^(-1/2)\r\n # % diagonal entries\r\n # dL=(1-diagw./degrees); % will produce NaN for degrees==0 locations\r\n # dL(degrees==0)=0;% which will be fixed here\r\n # % nondiagonal entries\r\n # ndL=-w./vec( sqrt(degrees(ni).*degrees(nj)) );\r\n # L=sparse([ni;di],[nj;di],[ndL;dL],N,N);\r\n print(\"Not implemented\")\r\n else:\r\n # TODO: Raise an exception\r\n print(\"Don't know what to do\")\r\n\r\n return L","def getNormLaplacian(W):\n\td=[np.sum(row) for row in W]\n\tD=np.diag(d)\n\tL=D-W\n\t#Dn=D^(-1/2)\n\tDn=np.power(np.linalg.matrix_power(D,-1),0.5)\n\tLbar=np.dot(np.dot(Dn,L),Dn)\n\treturn Lbar","def normalise(self):\n return self / self.mean(axis=1).reshape(self.shape[0], 1)","def normalize(self):\n self.number_of_vectors = self.values.shape[0]\n norm_2 = np.linalg.norm(self.values, axis=1)\n norm_1 = np.sum(self.values_planar, axis=1)\n norm_2 = np.repeat(norm_2, self.number_of_objectives).reshape(\n self.number_of_vectors, self.number_of_objectives\n )\n norm_1 = np.repeat(norm_1, self.number_of_objectives).reshape(\n self.number_of_vectors, self.number_of_objectives\n )\n norm_2[norm_2 == 0] = np.finfo(float).eps\n self.values = np.divide(self.values, norm_2)\n self.values_planar = np.divide(self.values_planar, norm_1)","def normc(A):\r\n # return A / LA.norm(A, axis=0)[None,:]\r\n return A/ np.tile(np.sqrt(np.sum(A*A, axis=0)), (A.shape[0], 1))","def _assure_normalized(self):\n for iwann in range(self.nwann):\n norm = np.trace(\n self.wannR[:, :, iwann].conj().T @ self.wannR[:, :, iwann])\n #print(f\"Norm {iwann}: {norm}\")","def _get_Laplacian_matrix(self, X):\n self.laplacian_mat, self.laplacian_sym_mat, self.laplacian_weights = self.laplacian.compute_laplacian(\n self.get_Affinity_matrix(X)\n )","def normalize_l2(x):\n return x / (npla.norm(x))","def laplacian_mat(n):\n data = [1, -2, 1]*n\n i = flatten([[k,k,k] for k in range(n)])\n j = flatten([[k-1, k, k+1] for k in range(n)])\n return scipy.sparse.coo_matrix((data[1:-1], (i[1:-1], j[1:-1])))","def laplacian(A):\n D = np.diag(np.sum(A, axis=1))\n return D - A","def set_lam_nll(self, lam_nll=1.0):\n zero_ary = np.zeros((1,))\n new_lam = zero_ary + lam_nll\n self.lam_nll.set_value(to_fX(new_lam))\n return","def normalize_data(self):\n\t\tfull_matrix = self.balance_clases()\n\t\ttexture_matrix = Normalizer().fit_transform(X=full_matrix[:,range(0,24)])\n\n\t\treturn texture_matrix","def symmetrized_normalized_laplacian(degree_vector, weight_matrix, length):\n holders = np.zeros((length, 1))\n holders[:, 0] = np.sqrt(1 / degree_vector)\n\n return np.eye(length) - holders * weight_matrix * holders.T","def test_normalization(self):\n u = np.array([np.array([0.7, 1.2]), np.array([0.5, 1.6])])\n with tf.Session() as sess:\n n = sess.run(AbstractModel.l2_normalization_layer(u, axis=1))\n magnitude = np.linalg.norm(n, axis=1)\n np.testing.assert_allclose(magnitude, np.array([1.0, 1.0]))","def normalize(a):\n a = np.array(a)\n return a / np.linalg.norm(a)","def normaliza(self):\n return self * (1 / self.norma())","def norm_with_l2(original_mat):\n normed_mat = np.zeros(original_mat.shape, dtype=np.float32)\n if len(original_mat.shape) == 2:\n for ind_r in range(original_mat.shape[0]):\n a = np.square(original_mat[ind_r]*1.0)\n b = np.sum(a)\n c = np.sqrt(b)\n normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / c\n # normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / np.sqrt(np.sum(np.square(original_mat[ind_r])*1.0))\n return normed_mat","def normalize(data):\n norm_matrix = np.int_(np.log10(data)**2)\n norm_matrix = map(lambda x: x if x < BOARD_SIZE else BOARD_SIZE, norm_matrix)\n norm_matrix = map(lambda x: x if x > 0 else 0, norm_matrix)\n return norm_matrix","def LUdecomp(Ainput):\n\n n, m = np.shape(Ainput)\n \n if n != m:\n return 'Error: Please enter an invertible matrix.'\n \n U = Ainput.copy() # make copies so as not to write over originals\n L = np.zeros((np.shape(Ainput)))\n \n for i in range(0,n):\n L[i,i] = 1\n for i in range(0,n-1): # loop over pivot rows from row 1 to row n-1 (i to n-2)\n for j in range(i+1,n): # loop over row to be zero'ed from row j+1 to n (j+1 to n-1)\n c = U[j,i]/U[i,i] # multiplicative factor to zero point\n L[j,i] = c\n U[j,i] = 0.0 # we know this element goes to zero\n U[j,i+1:n]=U[j,i+1:n]-c*U[i,i+1:n] # do subtraction of two rows\n\n return (L,U) # return lower and upper decompositions","def normalize(self):\r\n\r\n nlen = 1.0/math.sqrt(self*self)\r\n return vec4(self.x*nlen, self.y*nlen, self.z*nlen, self.w*nlen)","def l1_normalize(x: np.ndarray) -> np.ndarray: # pylint: disable=invalid-name\n return x / x.sum()","def _set_ls_matrices(self):\n zz_t = self.z_matrix * self.z_matrix.transpose()\n l, s, l_t = np.linalg.svd(zz_t)\n s[self.p:] = 0\n self.l_matrix = np.matrix(l)\n self.s_matirx = np.matrix(np.diag(s))","def normalisation_l2(x):\n res = np.zeros(x.shape)\n print(x.shape)\n for i in range(x.shape[0]):\n res[i] = x[i]/(np.linalg.norm(x[i],2)+1e-5)\n std = res.std()\n mean = res.mean()\n print(\"normalisation done\")\n return(mean,std,res)","def test_normalize(self):\n\n a1 = vectors.Vector(4, 0, 0)\n self.assertEqual(a1.normalize(),\n vectors.Vector(1, 0, 0))\n\n a1 = vectors.Vector(0, 4, 0)\n self.assertEqual(a1.normalize(),\n vectors.Vector(0, 1, 0))\n\n a1 = vectors.Vector(0, 0, 4)\n self.assertEqual(a1.normalize(),\n vectors.Vector(0, 0, 1))","def normalized(self):\n L = self.length\n if L > pygonal.EPSILON:\n v = tuple.__new__(Vec2, (self[0] / L, self[1] / L))\n v.__dict__['length'] = v.__dict__['length2'] = 1.0\n return v\n else:\n return null","def project_Lnuc_ball(X: \"fasta.linalg.Matrix\", t: float) -> \"fasta.linalg.Matrix\":\n U, s, V = la.svd(X)\n\n # Construct the diagonal matrix of singular values, S, as a shrunken version of the original signal values\n S = np.zeros(X.shape)\n S[:len(s),:len(s)] = np.diag(shrink(s, t))\n return U @ S @ V","def normalized(self):\n return self.from_points(\n vector.normalized() for vector in self._vectors)","def _get_normalized_adj_matrix(self):\n\n # find all of the unique layers in the problem (first index of category tuples)\n row_sums = self._frequency_matrix.sum(axis=1)\n normed_matrix = self._frequency_matrix / row_sums[:, np.newaxis]\n\n return normed_matrix","def compute_L2_normalization(xx):\r\n\treturn np.sum(xx ** 2, axis=1)","def normalize(inp):\n\n out = inp / np.linalg.norm(inp, axis=1, keepdims=True)\n\n return out","def cal_L(self):\n # calculate the l matrix\n self.point_matrixs = self.point_matrix.reshape(\n self.point_matrix.shape[0], 1, self.point_matrix.shape[1])\n self.point_matrixs = np.tile(self.point_matrixs,\n (self.attach_points.shape[0], 1))\n self.attach_points_matrix = np.matlib.repmat(\n self.attach_points[:, 0:3], self.point_matrix.shape[0], 1)\n self.attach_points_matrix = self.attach_points_matrix.reshape(\n self.point_matrix.shape[0], self.attach_points.shape[0], 3)\n self.L = np.subtract(self.attach_points_matrix,\n self.point_matrixs)\n # self.L[:,self.attach_points[:,3]==1,:] = \\\n # - self.L[:,self.attach_points[:,3]==1,:]\n # print(self.L)","def normalize_distancematrix(self):\n INF = self.distmat.max().max()\n df = self.distmat.fillna(INF)\n self.distmat = (df - df.min()) / (df.max() - df.min())","def mult_L(self) -> np.ndarray:\n return np.array([\n [self.w, -self.x, -self.y, -self.z],\n [self.x, self.w, -self.z, self.y],\n [self.y, self.z, self.w, -self.x],\n [self.z, -self.y, self.x, self.w]])","def normalize_matrix(mat):\n return (mat + abs(mat.min())) / (mat.max() - mat.min())","def prox_l1_norm(w, lamb):\n\treturn np.sign(w) * np.maximum( np.abs(w) - lamb, 0)","def normalize_lorentz_1d(\n lorentz, old_f_scale, old_v_scale, new_f_scale=(\n 0, 1, 1024), new_v_scale=(\n 0, 1, 1024)):\n A0 = lorentz[0]\n f0 = lorentz[1]\n G0 = lorentz[2]\n A1 = normalize_0d(A0 + old_v_scale[0], old_v_scale, new_v_scale)\n f1 = normalize_0d(f0, old_f_scale, new_f_scale)\n G1 = normalize_0d(G0 + old_f_scale[0], old_f_scale, new_f_scale)\n return np.array([A1, f1, G1, lorentz[3]])","def test_normalize_matrix(self):\n input_matrix = [\n [0, 1.0],\n [1.0, 1.0]\n ]\n\n expected = [\n [0, 1],\n [0.5, 0.5]\n ]\n\n result = self.summarizer.normalize_matrix(input_matrix)\n\n self.assertEqual(expected, result)","def normalizeRows(x):\n\n ### YOUR CODE HERE\n # using l2 norm to normalize\n x = x / np.sqrt(np.sum(x * x, axis=-1, keepdims=True))\n ### END YOUR CODE\n\n return x","def laplacian_(self, grid, i, j):\n l1 = grid[(i+1+self.N) % self.N][j] + grid[(i-1+self.N) % self.N][j]\n l2 = grid[i][(j+1+self.N) % self.N] + grid[i][(j-1+self.N) % self.N]\n l3 = -4*grid[i][j]\n return (l1 + l2 + l3)/self.dx**2","def normalize(self) -> None:\n assert hasattr(self, \"characterized_inventory\"), \"Must do lcia first\"\n if not hasattr(self, \"normalization_matrix\"):\n self.load_normalization_data()\n self.normalization_calculation()","def local_to_normalized(npboxes: np.ndarray, window: Box):\n height, width = window.size\n return npboxes / np.array([[height, width, height, width]])","def laplacian_1d(window_size) -> torch.Tensor:\n\n filter_1d = torch.ones(window_size)\n filter_1d[window_size // 2] = 1 - window_size\n laplacian_1d: torch.Tensor = filter_1d\n return laplacian_1d","def lMatrix(a, b, c, alpha, beta, gamma, convention=None):\n lMatrix = np.zeros((3, 3))\n\n cosAlpha = np.cos(alpha)\n cosBeta = np.cos(beta)\n cosGamma = np.cos(gamma)\n\n sinGamma = np.sin(gamma)\n\n lMatrix[0, 0] = a\n lMatrix[0, 1] = b * cosGamma\n lMatrix[0, 2] = c * cosBeta\n\n lMatrix[1, 1] = b * sinGamma\n lMatrix[1, 2] = c * (cosAlpha - cosBeta * cosGamma) / sinGamma\n\n lMatrix[2, 2] = c * np.sqrt(\n 1 + 2 * cosAlpha * cosBeta * cosGamma -\n cosAlpha**2 - cosBeta**2 - cosGamma**2\n ) / sinGamma\n\n # OI/HKL convention - x // [10-10], y // a2 [-12-10]\n # TSL convention - x // a1 [2-1-10], y // [01-10]\n if convention is None:\n convention = defaults['crystal_ortho_conv']\n\n if convention.lower() in ['hkl', 'oi']:\n # Swap 00 with 11 and 01 with 10 due to how OI orthonormalises\n # From Brad Wynne\n t1 = lMatrix[0, 0]\n t2 = lMatrix[1, 0]\n\n lMatrix[0, 0] = lMatrix[1, 1]\n lMatrix[1, 0] = lMatrix[0, 1]\n\n lMatrix[1, 1] = t1\n lMatrix[0, 1] = t2\n\n elif convention.lower() != 'tsl':\n raise ValueError(\n f\"Unknown convention '{convention}' for orthonormalisation of \"\n f\"crystal structure, can be 'hkl' or 'tsl'\"\n )\n\n # Set small components to 0\n lMatrix[np.abs(lMatrix) < 1e-10] = 0\n\n return lMatrix","def normalize_face_landmarks(face_landmarks):\r\n\tface_landmarks_norm = np.zeros(face_landmarks.shape)\r\n\t\r\n\tfor (i, lm) in enumerate(face_landmarks):\r\n\t\tface_landmarks_norm[i] = lm - lm[nose_center_idx]\r\n\t\t\t\r\n\tstd_x = np.std(face_landmarks_norm[:,:,0].reshape((-1,)))\r\n\tstd_y = np.std(face_landmarks_norm[:,:,1].reshape((-1,)))\r\n\t\r\n\tface_landmarks_norm[:,:,0] = np.multiply(face_landmarks_norm[:,:,0], 1./std_x)\r\n\tface_landmarks_norm[:,:,1] = np.multiply(face_landmarks_norm[:,:,1], 1./std_y)\r\n\t\r\n\treturn face_landmarks_norm","def MillerNormalVectors_111():\r\n k_111 = np.array(\r\n [[1, 1, 1], [-1, 1, 1], [1, -1, 1], [1, 1, -1]])/np.sqrt(3.0)\r\n return np.array([k_111[0, :], k_111[1, :], k_111[2, :], k_111[3, :],\r\n -k_111[0, :], -k_111[1, :], -k_111[2, :], -k_111[3, :]])","def prepare_laplacian(laplacian):\n\n def estimate_lmax(laplacian, tol=5e-3):\n r\"\"\"Estimate the largest eigenvalue of an operator.\"\"\"\n lmax = sparse.linalg.eigsh(laplacian, k=1, tol=tol,\n ncv=min(laplacian.shape[0], 10),\n return_eigenvectors=False)\n lmax = lmax[0]\n lmax *= 1 + 2 * tol # Be robust to errors.\n return lmax\n\n def scale_operator(L, lmax, scale=1):\n r\"\"\"Scale the eigenvalues from [0, lmax] to [-scale, scale].\"\"\"\n I = sparse.identity(L.shape[0], format=L.format, dtype=L.dtype)\n L *= 2 * scale / lmax\n L -= I\n return L\n\n lmax = estimate_lmax(laplacian)\n laplacian = scale_operator(laplacian, lmax)\n\n laplacian = sparse.coo_matrix(laplacian)\n\n # PyTorch wants a LongTensor (int64) as indices (it'll otherwise convert).\n indices = np.empty((2, laplacian.nnz), dtype=np.int64)\n np.stack((laplacian.row, laplacian.col), axis=0, out=indices)\n indices = torch.from_numpy(indices)\n\n laplacian = torch.sparse_coo_tensor(indices, laplacian.data, laplacian.shape)\n laplacian = laplacian.coalesce() # More efficient subsequent operations.\n return laplacian","def normalized(self):\n len = self.length\n return Vector(self.x / len, self.y / len)","def _normalize(X: np.ndarray) -> np.ndarray:\n # return X * np.sqrt(1 / np.sum(X ** 2, axis=1))[:, None]\n return X * np.sqrt(X.shape[1] / np.sum(X ** 2, axis=1))[:, None]","def _prepare_outer_matrix(self):\n self._mat_plane = numpy.array([\n self._scaling[0], 0, 0, 0,\n 0, self._scaling[1], 0, 0,\n 0, 0, 1, 0,\n self.i_border[0], -self.i_border[1], 0, 1\n ], dtype=numpy.float32)","def normalize(data):\n data = numpy.asmatrix(data)\n std_devs = numpy.std(data, axis=1)\n std_devs[std_devs == 0] = 1 # prevent div by 0\n return (data - numpy.mean(data, axis=1)) / std_devs","def _normalize(a: np.ndarray, u: float=0, s: float=1) -> np.ndarray:\n a_norm = (a - np.mean(a)) / (np.std(a) + STABILITY)\n a_rescaled = a_norm * s + u\n\n return a_rescaled","def rownorm(a):\n return sum(array(a)**2,axis=1)**.5","def normalize(self):\n\n if not self.magnitude():\n return Vector(0, 0)\n\n l = 1 / self.magnitude()\n return self.scale(l)","def get_norma(self):\n return self.norma","def normalized(self):\n length = self.length\n if length != 0:\n return self/length\n return Vec2d(self)","def normalisation_l_inf(x):\n res = np.zeros(x.shape)\n for i in range(x.shape[0]):\n for j in range(x.shape[1]):\n res[i,j] = x[i,j]/(np.max(x[i,j])+1e-5)\n return(res)","def normalize(self):\n self.vector /= np.linalg.norm(self.vector)","def normalize_matrix(matrix, min_val, max_val):\n return (max_val - min_val) * (matrix - np.min(matrix)) / (np.max(matrix) - np.min(matrix)) + min_val","def normalize(input_matrix):\n\n row_sums = input_matrix.sum(axis=1)\n try:\n assert (np.count_nonzero(row_sums)==np.shape(row_sums)[0]) # no row should sum to zero\n except Exception:\n raise Exception(\"Error while normalizing. Row(s) sum to zero\")\n new_matrix = input_matrix / row_sums[:, np.newaxis]\n return new_matrix","def L1Norm(X):\n return max(np.sum(X,axis=0))","def test_LU(self):\n A = np.random.rand(10, 10)\n MA = to_matrix(A)\n ML, MU = MA.decomposeLU()\n self.assertEqual(ML*MU, MA)\n self.assertTrue(ML.is_lower_triangular())\n self.assertTrue(MU.is_upper_triangular())","def normalize_data(data=None):\n # Data pre-processing\n n = data.shape[0]\n for i in range(n):\n xx = data[i,:,:]\n xx -= np.mean(xx) # Centering in 0\n xx /= np.linalg.norm(xx) # Normalizing to 1\n data[i] = xx # Affect value\n return data","def normalization_calculation(self) -> None:\n self.normalized_inventory = (\n self.normalization_matrix * self.characterized_inventory\n )","def normalize(self):\n return Vector(self.args + []) / self.magnitude()","def normalize(x):\n\n x_norm = np.linalg.norm(x, axis=1, keepdims=True)\n print(x_norm)\n x = x / x_norm\n ### END\n\n return x","def norm(self):","def normalize(self):\n self.length = np.ones(self.nV)\n return self","def normalizeRows(x):\n\n ### YOUR CODE HERE\n norm2 = np.linalg.norm(x,2,axis = 1).reshape(x.shape[0],-1)\n x = x/norm2\n ### END YOUR CODE\n\n return x","def lap_normalize(img, scale_n=4):\n # img = tf.expand_dims(img, 0)\n # print(\"Inside lap_normalize Function, img shape {}\".format(tf.shape(img)))\n\n tlevels = lap_split_n(img, scale_n)\n tlevels = list(map(normalize_std, tlevels))\n\n out = lap_merge(tlevels)\n\n return out[0, :, :, :]","def L2_norm(self):\n analyticals = self.analytical(self.x_values, self.C, self.D)\n error = analyticals - self.numerical\n self.L2 = np.sqrt((1/self.gp)*np.sum(error**2))","def unnormalize(self, inputs: Tensor) -> Tensor:\n # The normalizing constants are applied to the entire row, so add dummy outputs to the\n # inputs to make a row.\n row = torch.zeros_like(self._row_range)\n row[self._input_column_indices] = inputs\n row *= self._row_range\n row += self._row_min\n return row[self._input_column_indices]","def lup_decomposition(self):\n p = [i for i in range(self.rows_count())]\n for i in range(self.rows_count() - 1):\n pivot = i\n for j in range(i + 1, self.rows_count()):\n if abs(self[p[j], i]) > abs(self[p[pivot], i]):\n pivot = j\n p[pivot], p[i] = p[i], p[pivot]\n for j in range(i + 1, self.rows_count()):\n if abs(self[p[i], i]) < math.pow(10, -6):\n raise ValueError(\"Can't divide by 0\")\n self[p[j], i] /= self[p[i], i]\n for k in range(i + 1, self.rows_count()):\n self[p[j], k] -= self[p[j], i] * self[p[i], k]\n lst = []\n for i in p:\n lst.append(self.value[i])\n return p, Matrix(lst)","def norm(self):\r\n old_origin = np.array(self.origin)\r\n self.origin = [0, 0, 0]\r\n old_origin[0] = old_origin[0] / self.x[0]\r\n old_origin[1] = old_origin[1] / self.y[1]\r\n old_origin[2] = old_origin[2] / self.z[2]\r\n self.data = ndimage.shift(self.data, -old_origin, mode='wrap')","def normalize(self):\n self.desc += \", normalize\"\n self._vecs /= np.linalg.norm(self._vecs, axis=1)[:, np.newaxis]\n self.reindex()","def laplacian(self, array_in):\r\n\r\n # Call-through to Laplacian operator, already computed\r\n return self.laplace_op*array_in","def nll(self, data):\n nll = np.zeros(data.shape[0])\n self.a[0] = self.c\n for i in range(self.Ndim):\n a = self.rhos[i] * self.a[i]\n h = 0.5 * (a + np.abs(a)) # ReLU\n za = np.dot(self.Vs['alpha'][i].T, h) + self.bs['alpha'][i]\n zm = np.dot(self.Vs['mu'][i].T, h) + self.bs['mu'][i]\n zs = np.dot(self.Vs['sigma'][i].T, h) + self.bs['sigma'][i]\n self.alphas[i] = softmax(za)\n self.mus[i] = zm\n self.sigmas[i] = np.exp(zs)\n self.vars = self.sigmas ** 2.\n nll += nll_MOG_1D(data[:, i], self.alphas[i], self.mus[i],\n self.vars[i])[0]\n return nll","def normalize(self):\n self._vectors = [vector.normalized() for vector in self._vectors]","def normalize_lorentz_2d(\n lorentz, old_f_scale, old_v_scale, new_f_scale=(\n 0, 1, 1024), new_v_scale=(\n 0, 1, 1024)):\n lorentz_array = np.empty((0, 4))\n for i in range(0, lorentz.shape[0]):\n l0 = lorentz[i]\n l1 = normalize_lorentz_1d(\n l0, old_f_scale, old_v_scale, new_f_scale, new_v_scale)\n lorentz_array = np.append(lorentz_array, np.array([l1]), axis=0)\n return lorentz_array","def normalise(a):\n return (a - np.nanmin(a)) / np.nanmax(a - np.nanmin(a))"],"string":"[\n \"def laplacian_matrix(A, normalized=False):\\n n, m = A.shape\\n D = degree_matrix(A)\\n L = D - A\\n if normalized:\\n degs = _flat(A.sum(axis=1))\\n rootD = sps.spdiags(np.power(degs, -1 / 2), [0], n, n, format=\\\"csr\\\")\\n L = rootD * L * rootD\\n return L\",\n \"def normalize(self, lam):\\n return (lam.T / np.sum(lam, axis=1)).T\",\n \"def normalize(self):\\r\\n max = np.amax(self.matrix)\\r\\n min = np.amin(self.matrix)\\r\\n\\r\\n self.matrix = ((self.matrix - min) / (max - min))\",\n \"def laplacian(W, normalized=True):\\n\\n # Degree matrix.\\n d = W.sum(dim=0)\\n\\n # Laplacian matrix.\\n if not normalized:\\n D = scipy.sparse.diags(d.A.squeeze(), 0)\\n L = D - W\\n else:\\n # d += np.spacing(np.array(0, W.dtype))\\n d = 1 / torch.sqrt(d)\\n D = torch.diags(d.A.squeeze(), 0)\\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\\n L = I - D * W * D\\n\\n # assert np.abs(L - L.T).mean() < 1e-9\\n assert type(L) is scipy.sparse.csr.csr_matrix\\n return L\",\n \"def build_normalized(self):\\n for row, s in enumerate(self.S):\\n for col, t in enumerate(self.T):\\n\\n if self.symmetric and row > col:\\n pass\\n\\n elif self.symmetric and row == col:\\n self.normalized_mat[row, col] = 1\\n\\n else:\\n self.normalized_mat[row, col] = self.normalize(row, col)\\n\\n if self.symmetric:\\n self.normalized_mat = self.symmetrize(self.normalized_mat)\",\n \"def normalize(self):\\n det = self._mat[0][0]*self._mat[1][1] - self._mat[0][1]*self._mat[1][0]\\n for i in range(2):\\n for j in range(2):\\n self._mat[i][j] = (self._mat[i][j])/(np.sqrt(det))\",\n \"def laplacian(W, normalized=False):\\r\\n # Degree matrix.\\r\\n d = W.sum(axis=0)\\r\\n # Laplacian matrix.\\r\\n if not normalized:\\r\\n D = scipy.sparse.diags(d.A.squeeze(), 0)\\r\\n L = D - W\\r\\n else:\\r\\n # d += np.spacing(np.array(0, W.dtype))\\r\\n d = 1 / np.sqrt(d)\\r\\n D = scipy.sparse.diags(d.A.squeeze(), 0)\\r\\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\\r\\n L = I - D * W * D\\r\\n\\r\\n # assert np.abs(L - L.T).mean() < 1e-9\\r\\n assert type(L) is scipy.sparse.csr.csr_matrix\\r\\n return L\",\n \"def laplacian(A,normalize=False,randomWalk=False):\\n degr = A.sum(axis=1)*1.\\n\\n if randomWalk:\\n degr[degr!=0] = 1./degr[degr!=0]\\n Dinv = np.diag(degr)\\n return np.eye(A.shape[0]) - np.dot(Dinv,A)\\n if normalize:\\n degr = A.sum(axis=1)*1.\\n degr[degr!=0] = 1./degr[degr!=0]\\n Dinv = np.diag(degr)**.5\\n return np.eye(A.shape[0]) - np.dot(Dinv,A).dot(Dinv)\\n\\n return np.diag(degr) - A\",\n \"def calculate_normalized_laplacian(adj):\\n adj = sp.coo_matrix(adj)\\n d = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(d, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\\n return normalized_laplacian\",\n \"def calculate_normalized_laplacian(adj):\\n adj = sp.coo_matrix(adj)\\n d = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(d, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\\n return normalized_laplacian\",\n \"def normalize(self,matrix):\\n for i in range(self.N):\\n matrix[self.N-1][i] = 0\\n for i in range(self.n):\\n matrix[self.N - 1][self.index(i,i)] = 1\\n return matrix\",\n \"def L2_normalize(xx):\\r\\n\\tZx = compute_L2_normalization(xx)\\r\\n\\treturn xx / np.sqrt(Zx[:, np.newaxis])\",\n \"def laplacian(points, sigma):\\n\\n S = similarity_matrix(points, sigma)\\n \\n (npnts,npnts) = S.shape \\n\\n D = np.zeros_like(S)\\n\\n for i in range(npnts):\\n #D[i,i] = 1.0 / np.sqrt(S[i,:].sum()) \\n D[i,i] = S[i,:].sum()\\n\\n\\n return (D - S) #(np.eye(npnts,npnts) - np.dot(D,np.dot(S,D)))\",\n \"def lap_mat(self):\",\n \"def lu_decomposition(self):\\n if self.rows_count() != self.columns_count():\\n raise ValueError(\\\"Matrix needs to me square for LU decomposition.\\\")\\n for i in range(self.rows_count() - 1):\\n for j in range(i + 1, self.rows_count()):\\n if self[i, i] == 0: # or abs(self[i, i]) <= 0.000001):\\n raise ValueError(\\\"Can't divide by 0\\\")\\n self[j, i] = self[j, i] / self[i, i]\\n for k in range(i + 1, self.rows_count()):\\n self[j, k] -= self[j, i] * self[i, k]\",\n \"def laplacian(A):\\n #calculate D by creating a diagonal matrix with the column sum of A\\n D = np.diag(A.sum(axis=0))\\n return D - A\",\n \"def decompose_to_LU(a):\\n # create emtpy LU-matrix\\n lu_matrix = np.matrix(np.zeros([a.shape[0], a.shape[1]]))\\n n = a.shape[0]\\n\\n for k in range(n):\\n # calculate all residual k-row elements\\n for j in range(k, n):\\n lu_matrix[k, j] = a[k, j] - lu_matrix[k, :k] * lu_matrix[:k, j]\\n # calculate all residual k-column elemetns\\n for i in range(k + 1, n):\\n lu_matrix[i, k] = (a[i, k] - lu_matrix[i, : k] * lu_matrix[: k, k]) / lu_matrix[k, k]\\n\\n return lu_matrix\",\n \"def get_normalization_matrix(x):\\n # Input: x 3*N\\n #\\n # Output: T 3x3 transformation matrix of points\\n\\n # TODO\\n # --------------------------------------------------------------\\n # Estimate transformation matrix used to normalize\\n # the inputs x\\n # --------------------------------------------------------------\\n x2d = x[:2, :]\\n # Get centroid and mean-distance to centroid\\n center = np.mean(x2d, 1, keepdims=True)\\n mean_dist = np.mean(np.sqrt(np.sum((x2d - center) ** 2, axis=0)))\\n center = center.flatten()\\n\\n T = np.array([[np.sqrt(2) / mean_dist, 0, -center[0] * np.sqrt(2) / mean_dist],\\n [0, np.sqrt(2) / mean_dist, -center[1] * np.sqrt(2) / mean_dist],\\n [0, 0, 1]])\\n\\n return T\",\n \"def laplace_matrix(self):\\n n = self.number_of_vertices\\n laplace_matrix = np.zeros((n, n))\\n for i in range(n):\\n laplace_matrix[i][i] = 1\\n vertice = self.list_of_vertices[i]\\n for edge in vertice.edges_list:\\n laplace_matrix[i][edge.linked[1].index] = 1\\n return laplace_matrix\",\n \"def normalize(self, mel_db: np.ndarray) -> np.ndarray:\\n mel_norm = ((mel_db - self.ref_level_db) - self.min_level_db) / (\\n -self.min_level_db\\n )\\n if self.symmetric_norm:\\n # Symmetric norm\\n mel_norm = ((2 * self.max_norm) * mel_norm) - self.max_norm\\n if self.clip_norm:\\n mel_norm = np.clip(mel_norm, -self.max_norm, self.max_norm)\\n else:\\n # Asymmetric norm\\n mel_norm = self.max_norm * mel_norm\\n if self.clip_norm:\\n mel_norm = np.clip(mel_norm, 0, self.max_norm)\\n\\n return mel_norm\",\n \"def LU(A):\\n m, n = A.shape\\n L, U = np.zeros([m, n]), np.zeros([m, n])\\n for i in range(n):\\n L[i][i] = 1\\n\\n for i in range(n):\\n\\n # Upper triangular matrix\\n for j in range(i, n):\\n summ = 0\\n for k in range(0, i):\\n summ += L[i][k]*U[k][j]\\n U[i][j] = A[i][j] - summ\\n\\n # Lower triangular matrix\\n for j in range(i+1, n):\\n summ = 0\\n for k in range(0, i):\\n summ += L[j][k]*U[k][i]\\n L[j][i] = (A[j][i] - summ)/U[i][i]\\n return L, U\",\n \"def laplacian2(A, laplacian_type='raw'):\\r\\n\\r\\n N = A.shape[0]\\r\\n # TODO: Raise exception if A is not square\\r\\n\\r\\n degrees = A.sum(1)\\r\\n # To deal with loops, must extract diagonal part of A\\r\\n diagw = np.diag(A)\\r\\n\\r\\n # w will consist of non-diagonal entries only\\r\\n ni2, nj2 = A.nonzero()\\r\\n w2 = A[ni2, nj2]\\r\\n ndind = (ni2 != nj2).nonzero() # Non-diagonal indices\\r\\n ni = ni2[ndind]\\r\\n nj = nj2[ndind]\\r\\n w = w2[ndind]\\r\\n\\r\\n di = np.arange(N) # diagonal indices\\r\\n\\r\\n if laplacian_type == 'raw':\\r\\n # non-normalized laplaciand L = D - A\\r\\n L = np.diag(degrees - diagw)\\r\\n L[ni, nj] = -w\\r\\n L = lil_matrix(L)\\r\\n elif laplacian_type == 'normalized':\\r\\n # TODO: Implement the normalized laplacian case\\r\\n # % normalized laplacian D^(-1/2)*(D-A)*D^(-1/2)\\r\\n # % diagonal entries\\r\\n # dL=(1-diagw./degrees); % will produce NaN for degrees==0 locations\\r\\n # dL(degrees==0)=0;% which will be fixed here\\r\\n # % nondiagonal entries\\r\\n # ndL=-w./vec( sqrt(degrees(ni).*degrees(nj)) );\\r\\n # L=sparse([ni;di],[nj;di],[ndL;dL],N,N);\\r\\n print(\\\"Not implemented\\\")\\r\\n else:\\r\\n # TODO: Raise an exception\\r\\n print(\\\"Don't know what to do\\\")\\r\\n\\r\\n return L\",\n \"def getNormLaplacian(W):\\n\\td=[np.sum(row) for row in W]\\n\\tD=np.diag(d)\\n\\tL=D-W\\n\\t#Dn=D^(-1/2)\\n\\tDn=np.power(np.linalg.matrix_power(D,-1),0.5)\\n\\tLbar=np.dot(np.dot(Dn,L),Dn)\\n\\treturn Lbar\",\n \"def normalise(self):\\n return self / self.mean(axis=1).reshape(self.shape[0], 1)\",\n \"def normalize(self):\\n self.number_of_vectors = self.values.shape[0]\\n norm_2 = np.linalg.norm(self.values, axis=1)\\n norm_1 = np.sum(self.values_planar, axis=1)\\n norm_2 = np.repeat(norm_2, self.number_of_objectives).reshape(\\n self.number_of_vectors, self.number_of_objectives\\n )\\n norm_1 = np.repeat(norm_1, self.number_of_objectives).reshape(\\n self.number_of_vectors, self.number_of_objectives\\n )\\n norm_2[norm_2 == 0] = np.finfo(float).eps\\n self.values = np.divide(self.values, norm_2)\\n self.values_planar = np.divide(self.values_planar, norm_1)\",\n \"def normc(A):\\r\\n # return A / LA.norm(A, axis=0)[None,:]\\r\\n return A/ np.tile(np.sqrt(np.sum(A*A, axis=0)), (A.shape[0], 1))\",\n \"def _assure_normalized(self):\\n for iwann in range(self.nwann):\\n norm = np.trace(\\n self.wannR[:, :, iwann].conj().T @ self.wannR[:, :, iwann])\\n #print(f\\\"Norm {iwann}: {norm}\\\")\",\n \"def _get_Laplacian_matrix(self, X):\\n self.laplacian_mat, self.laplacian_sym_mat, self.laplacian_weights = self.laplacian.compute_laplacian(\\n self.get_Affinity_matrix(X)\\n )\",\n \"def normalize_l2(x):\\n return x / (npla.norm(x))\",\n \"def laplacian_mat(n):\\n data = [1, -2, 1]*n\\n i = flatten([[k,k,k] for k in range(n)])\\n j = flatten([[k-1, k, k+1] for k in range(n)])\\n return scipy.sparse.coo_matrix((data[1:-1], (i[1:-1], j[1:-1])))\",\n \"def laplacian(A):\\n D = np.diag(np.sum(A, axis=1))\\n return D - A\",\n \"def set_lam_nll(self, lam_nll=1.0):\\n zero_ary = np.zeros((1,))\\n new_lam = zero_ary + lam_nll\\n self.lam_nll.set_value(to_fX(new_lam))\\n return\",\n \"def normalize_data(self):\\n\\t\\tfull_matrix = self.balance_clases()\\n\\t\\ttexture_matrix = Normalizer().fit_transform(X=full_matrix[:,range(0,24)])\\n\\n\\t\\treturn texture_matrix\",\n \"def symmetrized_normalized_laplacian(degree_vector, weight_matrix, length):\\n holders = np.zeros((length, 1))\\n holders[:, 0] = np.sqrt(1 / degree_vector)\\n\\n return np.eye(length) - holders * weight_matrix * holders.T\",\n \"def test_normalization(self):\\n u = np.array([np.array([0.7, 1.2]), np.array([0.5, 1.6])])\\n with tf.Session() as sess:\\n n = sess.run(AbstractModel.l2_normalization_layer(u, axis=1))\\n magnitude = np.linalg.norm(n, axis=1)\\n np.testing.assert_allclose(magnitude, np.array([1.0, 1.0]))\",\n \"def normalize(a):\\n a = np.array(a)\\n return a / np.linalg.norm(a)\",\n \"def normaliza(self):\\n return self * (1 / self.norma())\",\n \"def norm_with_l2(original_mat):\\n normed_mat = np.zeros(original_mat.shape, dtype=np.float32)\\n if len(original_mat.shape) == 2:\\n for ind_r in range(original_mat.shape[0]):\\n a = np.square(original_mat[ind_r]*1.0)\\n b = np.sum(a)\\n c = np.sqrt(b)\\n normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / c\\n # normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / np.sqrt(np.sum(np.square(original_mat[ind_r])*1.0))\\n return normed_mat\",\n \"def normalize(data):\\n norm_matrix = np.int_(np.log10(data)**2)\\n norm_matrix = map(lambda x: x if x < BOARD_SIZE else BOARD_SIZE, norm_matrix)\\n norm_matrix = map(lambda x: x if x > 0 else 0, norm_matrix)\\n return norm_matrix\",\n \"def LUdecomp(Ainput):\\n\\n n, m = np.shape(Ainput)\\n \\n if n != m:\\n return 'Error: Please enter an invertible matrix.'\\n \\n U = Ainput.copy() # make copies so as not to write over originals\\n L = np.zeros((np.shape(Ainput)))\\n \\n for i in range(0,n):\\n L[i,i] = 1\\n for i in range(0,n-1): # loop over pivot rows from row 1 to row n-1 (i to n-2)\\n for j in range(i+1,n): # loop over row to be zero'ed from row j+1 to n (j+1 to n-1)\\n c = U[j,i]/U[i,i] # multiplicative factor to zero point\\n L[j,i] = c\\n U[j,i] = 0.0 # we know this element goes to zero\\n U[j,i+1:n]=U[j,i+1:n]-c*U[i,i+1:n] # do subtraction of two rows\\n\\n return (L,U) # return lower and upper decompositions\",\n \"def normalize(self):\\r\\n\\r\\n nlen = 1.0/math.sqrt(self*self)\\r\\n return vec4(self.x*nlen, self.y*nlen, self.z*nlen, self.w*nlen)\",\n \"def l1_normalize(x: np.ndarray) -> np.ndarray: # pylint: disable=invalid-name\\n return x / x.sum()\",\n \"def _set_ls_matrices(self):\\n zz_t = self.z_matrix * self.z_matrix.transpose()\\n l, s, l_t = np.linalg.svd(zz_t)\\n s[self.p:] = 0\\n self.l_matrix = np.matrix(l)\\n self.s_matirx = np.matrix(np.diag(s))\",\n \"def normalisation_l2(x):\\n res = np.zeros(x.shape)\\n print(x.shape)\\n for i in range(x.shape[0]):\\n res[i] = x[i]/(np.linalg.norm(x[i],2)+1e-5)\\n std = res.std()\\n mean = res.mean()\\n print(\\\"normalisation done\\\")\\n return(mean,std,res)\",\n \"def test_normalize(self):\\n\\n a1 = vectors.Vector(4, 0, 0)\\n self.assertEqual(a1.normalize(),\\n vectors.Vector(1, 0, 0))\\n\\n a1 = vectors.Vector(0, 4, 0)\\n self.assertEqual(a1.normalize(),\\n vectors.Vector(0, 1, 0))\\n\\n a1 = vectors.Vector(0, 0, 4)\\n self.assertEqual(a1.normalize(),\\n vectors.Vector(0, 0, 1))\",\n \"def normalized(self):\\n L = self.length\\n if L > pygonal.EPSILON:\\n v = tuple.__new__(Vec2, (self[0] / L, self[1] / L))\\n v.__dict__['length'] = v.__dict__['length2'] = 1.0\\n return v\\n else:\\n return null\",\n \"def project_Lnuc_ball(X: \\\"fasta.linalg.Matrix\\\", t: float) -> \\\"fasta.linalg.Matrix\\\":\\n U, s, V = la.svd(X)\\n\\n # Construct the diagonal matrix of singular values, S, as a shrunken version of the original signal values\\n S = np.zeros(X.shape)\\n S[:len(s),:len(s)] = np.diag(shrink(s, t))\\n return U @ S @ V\",\n \"def normalized(self):\\n return self.from_points(\\n vector.normalized() for vector in self._vectors)\",\n \"def _get_normalized_adj_matrix(self):\\n\\n # find all of the unique layers in the problem (first index of category tuples)\\n row_sums = self._frequency_matrix.sum(axis=1)\\n normed_matrix = self._frequency_matrix / row_sums[:, np.newaxis]\\n\\n return normed_matrix\",\n \"def compute_L2_normalization(xx):\\r\\n\\treturn np.sum(xx ** 2, axis=1)\",\n \"def normalize(inp):\\n\\n out = inp / np.linalg.norm(inp, axis=1, keepdims=True)\\n\\n return out\",\n \"def cal_L(self):\\n # calculate the l matrix\\n self.point_matrixs = self.point_matrix.reshape(\\n self.point_matrix.shape[0], 1, self.point_matrix.shape[1])\\n self.point_matrixs = np.tile(self.point_matrixs,\\n (self.attach_points.shape[0], 1))\\n self.attach_points_matrix = np.matlib.repmat(\\n self.attach_points[:, 0:3], self.point_matrix.shape[0], 1)\\n self.attach_points_matrix = self.attach_points_matrix.reshape(\\n self.point_matrix.shape[0], self.attach_points.shape[0], 3)\\n self.L = np.subtract(self.attach_points_matrix,\\n self.point_matrixs)\\n # self.L[:,self.attach_points[:,3]==1,:] = \\\\\\n # - self.L[:,self.attach_points[:,3]==1,:]\\n # print(self.L)\",\n \"def normalize_distancematrix(self):\\n INF = self.distmat.max().max()\\n df = self.distmat.fillna(INF)\\n self.distmat = (df - df.min()) / (df.max() - df.min())\",\n \"def mult_L(self) -> np.ndarray:\\n return np.array([\\n [self.w, -self.x, -self.y, -self.z],\\n [self.x, self.w, -self.z, self.y],\\n [self.y, self.z, self.w, -self.x],\\n [self.z, -self.y, self.x, self.w]])\",\n \"def normalize_matrix(mat):\\n return (mat + abs(mat.min())) / (mat.max() - mat.min())\",\n \"def prox_l1_norm(w, lamb):\\n\\treturn np.sign(w) * np.maximum( np.abs(w) - lamb, 0)\",\n \"def normalize_lorentz_1d(\\n lorentz, old_f_scale, old_v_scale, new_f_scale=(\\n 0, 1, 1024), new_v_scale=(\\n 0, 1, 1024)):\\n A0 = lorentz[0]\\n f0 = lorentz[1]\\n G0 = lorentz[2]\\n A1 = normalize_0d(A0 + old_v_scale[0], old_v_scale, new_v_scale)\\n f1 = normalize_0d(f0, old_f_scale, new_f_scale)\\n G1 = normalize_0d(G0 + old_f_scale[0], old_f_scale, new_f_scale)\\n return np.array([A1, f1, G1, lorentz[3]])\",\n \"def test_normalize_matrix(self):\\n input_matrix = [\\n [0, 1.0],\\n [1.0, 1.0]\\n ]\\n\\n expected = [\\n [0, 1],\\n [0.5, 0.5]\\n ]\\n\\n result = self.summarizer.normalize_matrix(input_matrix)\\n\\n self.assertEqual(expected, result)\",\n \"def normalizeRows(x):\\n\\n ### YOUR CODE HERE\\n # using l2 norm to normalize\\n x = x / np.sqrt(np.sum(x * x, axis=-1, keepdims=True))\\n ### END YOUR CODE\\n\\n return x\",\n \"def laplacian_(self, grid, i, j):\\n l1 = grid[(i+1+self.N) % self.N][j] + grid[(i-1+self.N) % self.N][j]\\n l2 = grid[i][(j+1+self.N) % self.N] + grid[i][(j-1+self.N) % self.N]\\n l3 = -4*grid[i][j]\\n return (l1 + l2 + l3)/self.dx**2\",\n \"def normalize(self) -> None:\\n assert hasattr(self, \\\"characterized_inventory\\\"), \\\"Must do lcia first\\\"\\n if not hasattr(self, \\\"normalization_matrix\\\"):\\n self.load_normalization_data()\\n self.normalization_calculation()\",\n \"def local_to_normalized(npboxes: np.ndarray, window: Box):\\n height, width = window.size\\n return npboxes / np.array([[height, width, height, width]])\",\n \"def laplacian_1d(window_size) -> torch.Tensor:\\n\\n filter_1d = torch.ones(window_size)\\n filter_1d[window_size // 2] = 1 - window_size\\n laplacian_1d: torch.Tensor = filter_1d\\n return laplacian_1d\",\n \"def lMatrix(a, b, c, alpha, beta, gamma, convention=None):\\n lMatrix = np.zeros((3, 3))\\n\\n cosAlpha = np.cos(alpha)\\n cosBeta = np.cos(beta)\\n cosGamma = np.cos(gamma)\\n\\n sinGamma = np.sin(gamma)\\n\\n lMatrix[0, 0] = a\\n lMatrix[0, 1] = b * cosGamma\\n lMatrix[0, 2] = c * cosBeta\\n\\n lMatrix[1, 1] = b * sinGamma\\n lMatrix[1, 2] = c * (cosAlpha - cosBeta * cosGamma) / sinGamma\\n\\n lMatrix[2, 2] = c * np.sqrt(\\n 1 + 2 * cosAlpha * cosBeta * cosGamma -\\n cosAlpha**2 - cosBeta**2 - cosGamma**2\\n ) / sinGamma\\n\\n # OI/HKL convention - x // [10-10], y // a2 [-12-10]\\n # TSL convention - x // a1 [2-1-10], y // [01-10]\\n if convention is None:\\n convention = defaults['crystal_ortho_conv']\\n\\n if convention.lower() in ['hkl', 'oi']:\\n # Swap 00 with 11 and 01 with 10 due to how OI orthonormalises\\n # From Brad Wynne\\n t1 = lMatrix[0, 0]\\n t2 = lMatrix[1, 0]\\n\\n lMatrix[0, 0] = lMatrix[1, 1]\\n lMatrix[1, 0] = lMatrix[0, 1]\\n\\n lMatrix[1, 1] = t1\\n lMatrix[0, 1] = t2\\n\\n elif convention.lower() != 'tsl':\\n raise ValueError(\\n f\\\"Unknown convention '{convention}' for orthonormalisation of \\\"\\n f\\\"crystal structure, can be 'hkl' or 'tsl'\\\"\\n )\\n\\n # Set small components to 0\\n lMatrix[np.abs(lMatrix) < 1e-10] = 0\\n\\n return lMatrix\",\n \"def normalize_face_landmarks(face_landmarks):\\r\\n\\tface_landmarks_norm = np.zeros(face_landmarks.shape)\\r\\n\\t\\r\\n\\tfor (i, lm) in enumerate(face_landmarks):\\r\\n\\t\\tface_landmarks_norm[i] = lm - lm[nose_center_idx]\\r\\n\\t\\t\\t\\r\\n\\tstd_x = np.std(face_landmarks_norm[:,:,0].reshape((-1,)))\\r\\n\\tstd_y = np.std(face_landmarks_norm[:,:,1].reshape((-1,)))\\r\\n\\t\\r\\n\\tface_landmarks_norm[:,:,0] = np.multiply(face_landmarks_norm[:,:,0], 1./std_x)\\r\\n\\tface_landmarks_norm[:,:,1] = np.multiply(face_landmarks_norm[:,:,1], 1./std_y)\\r\\n\\t\\r\\n\\treturn face_landmarks_norm\",\n \"def MillerNormalVectors_111():\\r\\n k_111 = np.array(\\r\\n [[1, 1, 1], [-1, 1, 1], [1, -1, 1], [1, 1, -1]])/np.sqrt(3.0)\\r\\n return np.array([k_111[0, :], k_111[1, :], k_111[2, :], k_111[3, :],\\r\\n -k_111[0, :], -k_111[1, :], -k_111[2, :], -k_111[3, :]])\",\n \"def prepare_laplacian(laplacian):\\n\\n def estimate_lmax(laplacian, tol=5e-3):\\n r\\\"\\\"\\\"Estimate the largest eigenvalue of an operator.\\\"\\\"\\\"\\n lmax = sparse.linalg.eigsh(laplacian, k=1, tol=tol,\\n ncv=min(laplacian.shape[0], 10),\\n return_eigenvectors=False)\\n lmax = lmax[0]\\n lmax *= 1 + 2 * tol # Be robust to errors.\\n return lmax\\n\\n def scale_operator(L, lmax, scale=1):\\n r\\\"\\\"\\\"Scale the eigenvalues from [0, lmax] to [-scale, scale].\\\"\\\"\\\"\\n I = sparse.identity(L.shape[0], format=L.format, dtype=L.dtype)\\n L *= 2 * scale / lmax\\n L -= I\\n return L\\n\\n lmax = estimate_lmax(laplacian)\\n laplacian = scale_operator(laplacian, lmax)\\n\\n laplacian = sparse.coo_matrix(laplacian)\\n\\n # PyTorch wants a LongTensor (int64) as indices (it'll otherwise convert).\\n indices = np.empty((2, laplacian.nnz), dtype=np.int64)\\n np.stack((laplacian.row, laplacian.col), axis=0, out=indices)\\n indices = torch.from_numpy(indices)\\n\\n laplacian = torch.sparse_coo_tensor(indices, laplacian.data, laplacian.shape)\\n laplacian = laplacian.coalesce() # More efficient subsequent operations.\\n return laplacian\",\n \"def normalized(self):\\n len = self.length\\n return Vector(self.x / len, self.y / len)\",\n \"def _normalize(X: np.ndarray) -> np.ndarray:\\n # return X * np.sqrt(1 / np.sum(X ** 2, axis=1))[:, None]\\n return X * np.sqrt(X.shape[1] / np.sum(X ** 2, axis=1))[:, None]\",\n \"def _prepare_outer_matrix(self):\\n self._mat_plane = numpy.array([\\n self._scaling[0], 0, 0, 0,\\n 0, self._scaling[1], 0, 0,\\n 0, 0, 1, 0,\\n self.i_border[0], -self.i_border[1], 0, 1\\n ], dtype=numpy.float32)\",\n \"def normalize(data):\\n data = numpy.asmatrix(data)\\n std_devs = numpy.std(data, axis=1)\\n std_devs[std_devs == 0] = 1 # prevent div by 0\\n return (data - numpy.mean(data, axis=1)) / std_devs\",\n \"def _normalize(a: np.ndarray, u: float=0, s: float=1) -> np.ndarray:\\n a_norm = (a - np.mean(a)) / (np.std(a) + STABILITY)\\n a_rescaled = a_norm * s + u\\n\\n return a_rescaled\",\n \"def rownorm(a):\\n return sum(array(a)**2,axis=1)**.5\",\n \"def normalize(self):\\n\\n if not self.magnitude():\\n return Vector(0, 0)\\n\\n l = 1 / self.magnitude()\\n return self.scale(l)\",\n \"def get_norma(self):\\n return self.norma\",\n \"def normalized(self):\\n length = self.length\\n if length != 0:\\n return self/length\\n return Vec2d(self)\",\n \"def normalisation_l_inf(x):\\n res = np.zeros(x.shape)\\n for i in range(x.shape[0]):\\n for j in range(x.shape[1]):\\n res[i,j] = x[i,j]/(np.max(x[i,j])+1e-5)\\n return(res)\",\n \"def normalize(self):\\n self.vector /= np.linalg.norm(self.vector)\",\n \"def normalize_matrix(matrix, min_val, max_val):\\n return (max_val - min_val) * (matrix - np.min(matrix)) / (np.max(matrix) - np.min(matrix)) + min_val\",\n \"def normalize(input_matrix):\\n\\n row_sums = input_matrix.sum(axis=1)\\n try:\\n assert (np.count_nonzero(row_sums)==np.shape(row_sums)[0]) # no row should sum to zero\\n except Exception:\\n raise Exception(\\\"Error while normalizing. Row(s) sum to zero\\\")\\n new_matrix = input_matrix / row_sums[:, np.newaxis]\\n return new_matrix\",\n \"def L1Norm(X):\\n return max(np.sum(X,axis=0))\",\n \"def test_LU(self):\\n A = np.random.rand(10, 10)\\n MA = to_matrix(A)\\n ML, MU = MA.decomposeLU()\\n self.assertEqual(ML*MU, MA)\\n self.assertTrue(ML.is_lower_triangular())\\n self.assertTrue(MU.is_upper_triangular())\",\n \"def normalize_data(data=None):\\n # Data pre-processing\\n n = data.shape[0]\\n for i in range(n):\\n xx = data[i,:,:]\\n xx -= np.mean(xx) # Centering in 0\\n xx /= np.linalg.norm(xx) # Normalizing to 1\\n data[i] = xx # Affect value\\n return data\",\n \"def normalization_calculation(self) -> None:\\n self.normalized_inventory = (\\n self.normalization_matrix * self.characterized_inventory\\n )\",\n \"def normalize(self):\\n return Vector(self.args + []) / self.magnitude()\",\n \"def normalize(x):\\n\\n x_norm = np.linalg.norm(x, axis=1, keepdims=True)\\n print(x_norm)\\n x = x / x_norm\\n ### END\\n\\n return x\",\n \"def norm(self):\",\n \"def normalize(self):\\n self.length = np.ones(self.nV)\\n return self\",\n \"def normalizeRows(x):\\n\\n ### YOUR CODE HERE\\n norm2 = np.linalg.norm(x,2,axis = 1).reshape(x.shape[0],-1)\\n x = x/norm2\\n ### END YOUR CODE\\n\\n return x\",\n \"def lap_normalize(img, scale_n=4):\\n # img = tf.expand_dims(img, 0)\\n # print(\\\"Inside lap_normalize Function, img shape {}\\\".format(tf.shape(img)))\\n\\n tlevels = lap_split_n(img, scale_n)\\n tlevels = list(map(normalize_std, tlevels))\\n\\n out = lap_merge(tlevels)\\n\\n return out[0, :, :, :]\",\n \"def L2_norm(self):\\n analyticals = self.analytical(self.x_values, self.C, self.D)\\n error = analyticals - self.numerical\\n self.L2 = np.sqrt((1/self.gp)*np.sum(error**2))\",\n \"def unnormalize(self, inputs: Tensor) -> Tensor:\\n # The normalizing constants are applied to the entire row, so add dummy outputs to the\\n # inputs to make a row.\\n row = torch.zeros_like(self._row_range)\\n row[self._input_column_indices] = inputs\\n row *= self._row_range\\n row += self._row_min\\n return row[self._input_column_indices]\",\n \"def lup_decomposition(self):\\n p = [i for i in range(self.rows_count())]\\n for i in range(self.rows_count() - 1):\\n pivot = i\\n for j in range(i + 1, self.rows_count()):\\n if abs(self[p[j], i]) > abs(self[p[pivot], i]):\\n pivot = j\\n p[pivot], p[i] = p[i], p[pivot]\\n for j in range(i + 1, self.rows_count()):\\n if abs(self[p[i], i]) < math.pow(10, -6):\\n raise ValueError(\\\"Can't divide by 0\\\")\\n self[p[j], i] /= self[p[i], i]\\n for k in range(i + 1, self.rows_count()):\\n self[p[j], k] -= self[p[j], i] * self[p[i], k]\\n lst = []\\n for i in p:\\n lst.append(self.value[i])\\n return p, Matrix(lst)\",\n \"def norm(self):\\r\\n old_origin = np.array(self.origin)\\r\\n self.origin = [0, 0, 0]\\r\\n old_origin[0] = old_origin[0] / self.x[0]\\r\\n old_origin[1] = old_origin[1] / self.y[1]\\r\\n old_origin[2] = old_origin[2] / self.z[2]\\r\\n self.data = ndimage.shift(self.data, -old_origin, mode='wrap')\",\n \"def normalize(self):\\n self.desc += \\\", normalize\\\"\\n self._vecs /= np.linalg.norm(self._vecs, axis=1)[:, np.newaxis]\\n self.reindex()\",\n \"def laplacian(self, array_in):\\r\\n\\r\\n # Call-through to Laplacian operator, already computed\\r\\n return self.laplace_op*array_in\",\n \"def nll(self, data):\\n nll = np.zeros(data.shape[0])\\n self.a[0] = self.c\\n for i in range(self.Ndim):\\n a = self.rhos[i] * self.a[i]\\n h = 0.5 * (a + np.abs(a)) # ReLU\\n za = np.dot(self.Vs['alpha'][i].T, h) + self.bs['alpha'][i]\\n zm = np.dot(self.Vs['mu'][i].T, h) + self.bs['mu'][i]\\n zs = np.dot(self.Vs['sigma'][i].T, h) + self.bs['sigma'][i]\\n self.alphas[i] = softmax(za)\\n self.mus[i] = zm\\n self.sigmas[i] = np.exp(zs)\\n self.vars = self.sigmas ** 2.\\n nll += nll_MOG_1D(data[:, i], self.alphas[i], self.mus[i],\\n self.vars[i])[0]\\n return nll\",\n \"def normalize(self):\\n self._vectors = [vector.normalized() for vector in self._vectors]\",\n \"def normalize_lorentz_2d(\\n lorentz, old_f_scale, old_v_scale, new_f_scale=(\\n 0, 1, 1024), new_v_scale=(\\n 0, 1, 1024)):\\n lorentz_array = np.empty((0, 4))\\n for i in range(0, lorentz.shape[0]):\\n l0 = lorentz[i]\\n l1 = normalize_lorentz_1d(\\n l0, old_f_scale, old_v_scale, new_f_scale, new_v_scale)\\n lorentz_array = np.append(lorentz_array, np.array([l1]), axis=0)\\n return lorentz_array\",\n \"def normalise(a):\\n return (a - np.nanmin(a)) / np.nanmax(a - np.nanmin(a))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6907984","0.6711874","0.62322444","0.6217343","0.620613","0.6196674","0.6158735","0.6132494","0.61195916","0.61195916","0.6036492","0.594732","0.59065795","0.5906229","0.58959603","0.5889874","0.5884998","0.58620787","0.5861111","0.5846933","0.58036464","0.5801993","0.5788728","0.577097","0.5767137","0.57450557","0.573835","0.5735539","0.5723342","0.56960505","0.5691527","0.5675007","0.5665385","0.5650528","0.5626447","0.5625727","0.5621359","0.5615229","0.56072205","0.56044185","0.5593428","0.55729896","0.5563359","0.55265945","0.55002975","0.54988915","0.5484774","0.5480021","0.5475302","0.5464984","0.546461","0.54483265","0.5446641","0.54449135","0.5430567","0.5427673","0.5417368","0.5412706","0.541223","0.54117167","0.5411509","0.54114646","0.54078287","0.5398319","0.53982574","0.5393246","0.5390396","0.5373508","0.53556174","0.53553236","0.53543246","0.5349406","0.5349223","0.5345089","0.5344909","0.534365","0.53436196","0.5342364","0.53417313","0.534133","0.5335337","0.53315496","0.5324727","0.5322699","0.5321293","0.53199786","0.53199357","0.53152794","0.5314903","0.5311885","0.5302912","0.5301873","0.53007627","0.52982205","0.52981406","0.5289843","0.52867776","0.52819157","0.5280653","0.5270947"],"string":"[\n \"0.6907984\",\n \"0.6711874\",\n \"0.62322444\",\n \"0.6217343\",\n \"0.620613\",\n \"0.6196674\",\n \"0.6158735\",\n \"0.6132494\",\n \"0.61195916\",\n \"0.61195916\",\n \"0.6036492\",\n \"0.594732\",\n \"0.59065795\",\n \"0.5906229\",\n \"0.58959603\",\n \"0.5889874\",\n \"0.5884998\",\n \"0.58620787\",\n \"0.5861111\",\n \"0.5846933\",\n \"0.58036464\",\n \"0.5801993\",\n \"0.5788728\",\n \"0.577097\",\n \"0.5767137\",\n \"0.57450557\",\n \"0.573835\",\n \"0.5735539\",\n \"0.5723342\",\n \"0.56960505\",\n \"0.5691527\",\n \"0.5675007\",\n \"0.5665385\",\n \"0.5650528\",\n \"0.5626447\",\n \"0.5625727\",\n \"0.5621359\",\n \"0.5615229\",\n \"0.56072205\",\n \"0.56044185\",\n \"0.5593428\",\n \"0.55729896\",\n \"0.5563359\",\n \"0.55265945\",\n \"0.55002975\",\n \"0.54988915\",\n \"0.5484774\",\n \"0.5480021\",\n \"0.5475302\",\n \"0.5464984\",\n \"0.546461\",\n \"0.54483265\",\n \"0.5446641\",\n \"0.54449135\",\n \"0.5430567\",\n \"0.5427673\",\n \"0.5417368\",\n \"0.5412706\",\n \"0.541223\",\n \"0.54117167\",\n \"0.5411509\",\n \"0.54114646\",\n \"0.54078287\",\n \"0.5398319\",\n \"0.53982574\",\n \"0.5393246\",\n \"0.5390396\",\n \"0.5373508\",\n \"0.53556174\",\n \"0.53553236\",\n \"0.53543246\",\n \"0.5349406\",\n \"0.5349223\",\n \"0.5345089\",\n \"0.5344909\",\n \"0.534365\",\n \"0.53436196\",\n \"0.5342364\",\n \"0.53417313\",\n \"0.534133\",\n \"0.5335337\",\n \"0.53315496\",\n \"0.5324727\",\n \"0.5322699\",\n \"0.5321293\",\n \"0.53199786\",\n \"0.53199357\",\n \"0.53152794\",\n \"0.5314903\",\n \"0.5311885\",\n \"0.5302912\",\n \"0.5301873\",\n \"0.53007627\",\n \"0.52982205\",\n \"0.52981406\",\n \"0.5289843\",\n \"0.52867776\",\n \"0.52819157\",\n \"0.5280653\",\n \"0.5270947\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6199283"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":891,"cells":{"query":{"kind":"string","value":"Construct the normalized laplacian matrix."},"document":{"kind":"string","value":"def symmetrized_normalized_laplacian(degree_vector, weight_matrix, length):\n holders = np.zeros((length, 1))\n holders[:, 0] = np.sqrt(1 / degree_vector)\n\n return np.eye(length) - holders * weight_matrix * holders.T"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def laplacian_matrix(A, normalized=False):\n n, m = A.shape\n D = degree_matrix(A)\n L = D - A\n if normalized:\n degs = _flat(A.sum(axis=1))\n rootD = sps.spdiags(np.power(degs, -1 / 2), [0], n, n, format=\"csr\")\n L = rootD * L * rootD\n return L","def normalize(self, lam):\n return (lam.T / np.sum(lam, axis=1)).T","def normalize(self):\r\n max = np.amax(self.matrix)\r\n min = np.amin(self.matrix)\r\n\r\n self.matrix = ((self.matrix - min) / (max - min))","def laplacian(W, normalized=True):\n\n # Degree matrix.\n d = W.sum(dim=0)\n\n # Laplacian matrix.\n if not normalized:\n D = scipy.sparse.diags(d.A.squeeze(), 0)\n L = D - W\n else:\n # d += np.spacing(np.array(0, W.dtype))\n d = 1 / torch.sqrt(d)\n D = torch.diags(d.A.squeeze(), 0)\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\n L = I - D * W * D\n\n # assert np.abs(L - L.T).mean() < 1e-9\n assert type(L) is scipy.sparse.csr.csr_matrix\n return L","def build_normalized(self):\n for row, s in enumerate(self.S):\n for col, t in enumerate(self.T):\n\n if self.symmetric and row > col:\n pass\n\n elif self.symmetric and row == col:\n self.normalized_mat[row, col] = 1\n\n else:\n self.normalized_mat[row, col] = self.normalize(row, col)\n\n if self.symmetric:\n self.normalized_mat = self.symmetrize(self.normalized_mat)","def normalize(self):\n det = self._mat[0][0]*self._mat[1][1] - self._mat[0][1]*self._mat[1][0]\n for i in range(2):\n for j in range(2):\n self._mat[i][j] = (self._mat[i][j])/(np.sqrt(det))","def normalized_laplacian(degree_vector, weight_matrix, length):\n holders = np.zeros((length, 1))\n holders[:, 0] = 1 / degree_vector\n\n return np.eye(length) - holders * weight_matrix","def laplacian(W, normalized=False):\r\n # Degree matrix.\r\n d = W.sum(axis=0)\r\n # Laplacian matrix.\r\n if not normalized:\r\n D = scipy.sparse.diags(d.A.squeeze(), 0)\r\n L = D - W\r\n else:\r\n # d += np.spacing(np.array(0, W.dtype))\r\n d = 1 / np.sqrt(d)\r\n D = scipy.sparse.diags(d.A.squeeze(), 0)\r\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\r\n L = I - D * W * D\r\n\r\n # assert np.abs(L - L.T).mean() < 1e-9\r\n assert type(L) is scipy.sparse.csr.csr_matrix\r\n return L","def laplacian(A,normalize=False,randomWalk=False):\n degr = A.sum(axis=1)*1.\n\n if randomWalk:\n degr[degr!=0] = 1./degr[degr!=0]\n Dinv = np.diag(degr)\n return np.eye(A.shape[0]) - np.dot(Dinv,A)\n if normalize:\n degr = A.sum(axis=1)*1.\n degr[degr!=0] = 1./degr[degr!=0]\n Dinv = np.diag(degr)**.5\n return np.eye(A.shape[0]) - np.dot(Dinv,A).dot(Dinv)\n\n return np.diag(degr) - A","def calculate_normalized_laplacian(adj):\n adj = sp.coo_matrix(adj)\n d = np.array(adj.sum(1))\n d_inv_sqrt = np.power(d, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n return normalized_laplacian","def calculate_normalized_laplacian(adj):\n adj = sp.coo_matrix(adj)\n d = np.array(adj.sum(1))\n d_inv_sqrt = np.power(d, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n return normalized_laplacian","def normalize(self,matrix):\n for i in range(self.N):\n matrix[self.N-1][i] = 0\n for i in range(self.n):\n matrix[self.N - 1][self.index(i,i)] = 1\n return matrix","def L2_normalize(xx):\r\n\tZx = compute_L2_normalization(xx)\r\n\treturn xx / np.sqrt(Zx[:, np.newaxis])","def laplacian(points, sigma):\n\n S = similarity_matrix(points, sigma)\n \n (npnts,npnts) = S.shape \n\n D = np.zeros_like(S)\n\n for i in range(npnts):\n #D[i,i] = 1.0 / np.sqrt(S[i,:].sum()) \n D[i,i] = S[i,:].sum()\n\n\n return (D - S) #(np.eye(npnts,npnts) - np.dot(D,np.dot(S,D)))","def lap_mat(self):","def lu_decomposition(self):\n if self.rows_count() != self.columns_count():\n raise ValueError(\"Matrix needs to me square for LU decomposition.\")\n for i in range(self.rows_count() - 1):\n for j in range(i + 1, self.rows_count()):\n if self[i, i] == 0: # or abs(self[i, i]) <= 0.000001):\n raise ValueError(\"Can't divide by 0\")\n self[j, i] = self[j, i] / self[i, i]\n for k in range(i + 1, self.rows_count()):\n self[j, k] -= self[j, i] * self[i, k]","def laplacian(A):\n #calculate D by creating a diagonal matrix with the column sum of A\n D = np.diag(A.sum(axis=0))\n return D - A","def decompose_to_LU(a):\n # create emtpy LU-matrix\n lu_matrix = np.matrix(np.zeros([a.shape[0], a.shape[1]]))\n n = a.shape[0]\n\n for k in range(n):\n # calculate all residual k-row elements\n for j in range(k, n):\n lu_matrix[k, j] = a[k, j] - lu_matrix[k, :k] * lu_matrix[:k, j]\n # calculate all residual k-column elemetns\n for i in range(k + 1, n):\n lu_matrix[i, k] = (a[i, k] - lu_matrix[i, : k] * lu_matrix[: k, k]) / lu_matrix[k, k]\n\n return lu_matrix","def get_normalization_matrix(x):\n # Input: x 3*N\n #\n # Output: T 3x3 transformation matrix of points\n\n # TODO\n # --------------------------------------------------------------\n # Estimate transformation matrix used to normalize\n # the inputs x\n # --------------------------------------------------------------\n x2d = x[:2, :]\n # Get centroid and mean-distance to centroid\n center = np.mean(x2d, 1, keepdims=True)\n mean_dist = np.mean(np.sqrt(np.sum((x2d - center) ** 2, axis=0)))\n center = center.flatten()\n\n T = np.array([[np.sqrt(2) / mean_dist, 0, -center[0] * np.sqrt(2) / mean_dist],\n [0, np.sqrt(2) / mean_dist, -center[1] * np.sqrt(2) / mean_dist],\n [0, 0, 1]])\n\n return T","def laplace_matrix(self):\n n = self.number_of_vertices\n laplace_matrix = np.zeros((n, n))\n for i in range(n):\n laplace_matrix[i][i] = 1\n vertice = self.list_of_vertices[i]\n for edge in vertice.edges_list:\n laplace_matrix[i][edge.linked[1].index] = 1\n return laplace_matrix","def normalize(self, mel_db: np.ndarray) -> np.ndarray:\n mel_norm = ((mel_db - self.ref_level_db) - self.min_level_db) / (\n -self.min_level_db\n )\n if self.symmetric_norm:\n # Symmetric norm\n mel_norm = ((2 * self.max_norm) * mel_norm) - self.max_norm\n if self.clip_norm:\n mel_norm = np.clip(mel_norm, -self.max_norm, self.max_norm)\n else:\n # Asymmetric norm\n mel_norm = self.max_norm * mel_norm\n if self.clip_norm:\n mel_norm = np.clip(mel_norm, 0, self.max_norm)\n\n return mel_norm","def LU(A):\n m, n = A.shape\n L, U = np.zeros([m, n]), np.zeros([m, n])\n for i in range(n):\n L[i][i] = 1\n\n for i in range(n):\n\n # Upper triangular matrix\n for j in range(i, n):\n summ = 0\n for k in range(0, i):\n summ += L[i][k]*U[k][j]\n U[i][j] = A[i][j] - summ\n\n # Lower triangular matrix\n for j in range(i+1, n):\n summ = 0\n for k in range(0, i):\n summ += L[j][k]*U[k][i]\n L[j][i] = (A[j][i] - summ)/U[i][i]\n return L, U","def laplacian2(A, laplacian_type='raw'):\r\n\r\n N = A.shape[0]\r\n # TODO: Raise exception if A is not square\r\n\r\n degrees = A.sum(1)\r\n # To deal with loops, must extract diagonal part of A\r\n diagw = np.diag(A)\r\n\r\n # w will consist of non-diagonal entries only\r\n ni2, nj2 = A.nonzero()\r\n w2 = A[ni2, nj2]\r\n ndind = (ni2 != nj2).nonzero() # Non-diagonal indices\r\n ni = ni2[ndind]\r\n nj = nj2[ndind]\r\n w = w2[ndind]\r\n\r\n di = np.arange(N) # diagonal indices\r\n\r\n if laplacian_type == 'raw':\r\n # non-normalized laplaciand L = D - A\r\n L = np.diag(degrees - diagw)\r\n L[ni, nj] = -w\r\n L = lil_matrix(L)\r\n elif laplacian_type == 'normalized':\r\n # TODO: Implement the normalized laplacian case\r\n # % normalized laplacian D^(-1/2)*(D-A)*D^(-1/2)\r\n # % diagonal entries\r\n # dL=(1-diagw./degrees); % will produce NaN for degrees==0 locations\r\n # dL(degrees==0)=0;% which will be fixed here\r\n # % nondiagonal entries\r\n # ndL=-w./vec( sqrt(degrees(ni).*degrees(nj)) );\r\n # L=sparse([ni;di],[nj;di],[ndL;dL],N,N);\r\n print(\"Not implemented\")\r\n else:\r\n # TODO: Raise an exception\r\n print(\"Don't know what to do\")\r\n\r\n return L","def getNormLaplacian(W):\n\td=[np.sum(row) for row in W]\n\tD=np.diag(d)\n\tL=D-W\n\t#Dn=D^(-1/2)\n\tDn=np.power(np.linalg.matrix_power(D,-1),0.5)\n\tLbar=np.dot(np.dot(Dn,L),Dn)\n\treturn Lbar","def normalise(self):\n return self / self.mean(axis=1).reshape(self.shape[0], 1)","def normalize(self):\n self.number_of_vectors = self.values.shape[0]\n norm_2 = np.linalg.norm(self.values, axis=1)\n norm_1 = np.sum(self.values_planar, axis=1)\n norm_2 = np.repeat(norm_2, self.number_of_objectives).reshape(\n self.number_of_vectors, self.number_of_objectives\n )\n norm_1 = np.repeat(norm_1, self.number_of_objectives).reshape(\n self.number_of_vectors, self.number_of_objectives\n )\n norm_2[norm_2 == 0] = np.finfo(float).eps\n self.values = np.divide(self.values, norm_2)\n self.values_planar = np.divide(self.values_planar, norm_1)","def normc(A):\r\n # return A / LA.norm(A, axis=0)[None,:]\r\n return A/ np.tile(np.sqrt(np.sum(A*A, axis=0)), (A.shape[0], 1))","def _assure_normalized(self):\n for iwann in range(self.nwann):\n norm = np.trace(\n self.wannR[:, :, iwann].conj().T @ self.wannR[:, :, iwann])\n #print(f\"Norm {iwann}: {norm}\")","def _get_Laplacian_matrix(self, X):\n self.laplacian_mat, self.laplacian_sym_mat, self.laplacian_weights = self.laplacian.compute_laplacian(\n self.get_Affinity_matrix(X)\n )","def normalize_l2(x):\n return x / (npla.norm(x))","def laplacian_mat(n):\n data = [1, -2, 1]*n\n i = flatten([[k,k,k] for k in range(n)])\n j = flatten([[k-1, k, k+1] for k in range(n)])\n return scipy.sparse.coo_matrix((data[1:-1], (i[1:-1], j[1:-1])))","def laplacian(A):\n D = np.diag(np.sum(A, axis=1))\n return D - A","def set_lam_nll(self, lam_nll=1.0):\n zero_ary = np.zeros((1,))\n new_lam = zero_ary + lam_nll\n self.lam_nll.set_value(to_fX(new_lam))\n return","def normalize_data(self):\n\t\tfull_matrix = self.balance_clases()\n\t\ttexture_matrix = Normalizer().fit_transform(X=full_matrix[:,range(0,24)])\n\n\t\treturn texture_matrix","def test_normalization(self):\n u = np.array([np.array([0.7, 1.2]), np.array([0.5, 1.6])])\n with tf.Session() as sess:\n n = sess.run(AbstractModel.l2_normalization_layer(u, axis=1))\n magnitude = np.linalg.norm(n, axis=1)\n np.testing.assert_allclose(magnitude, np.array([1.0, 1.0]))","def normalize(a):\n a = np.array(a)\n return a / np.linalg.norm(a)","def normaliza(self):\n return self * (1 / self.norma())","def norm_with_l2(original_mat):\n normed_mat = np.zeros(original_mat.shape, dtype=np.float32)\n if len(original_mat.shape) == 2:\n for ind_r in range(original_mat.shape[0]):\n a = np.square(original_mat[ind_r]*1.0)\n b = np.sum(a)\n c = np.sqrt(b)\n normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / c\n # normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / np.sqrt(np.sum(np.square(original_mat[ind_r])*1.0))\n return normed_mat","def normalize(data):\n norm_matrix = np.int_(np.log10(data)**2)\n norm_matrix = map(lambda x: x if x < BOARD_SIZE else BOARD_SIZE, norm_matrix)\n norm_matrix = map(lambda x: x if x > 0 else 0, norm_matrix)\n return norm_matrix","def LUdecomp(Ainput):\n\n n, m = np.shape(Ainput)\n \n if n != m:\n return 'Error: Please enter an invertible matrix.'\n \n U = Ainput.copy() # make copies so as not to write over originals\n L = np.zeros((np.shape(Ainput)))\n \n for i in range(0,n):\n L[i,i] = 1\n for i in range(0,n-1): # loop over pivot rows from row 1 to row n-1 (i to n-2)\n for j in range(i+1,n): # loop over row to be zero'ed from row j+1 to n (j+1 to n-1)\n c = U[j,i]/U[i,i] # multiplicative factor to zero point\n L[j,i] = c\n U[j,i] = 0.0 # we know this element goes to zero\n U[j,i+1:n]=U[j,i+1:n]-c*U[i,i+1:n] # do subtraction of two rows\n\n return (L,U) # return lower and upper decompositions","def normalize(self):\r\n\r\n nlen = 1.0/math.sqrt(self*self)\r\n return vec4(self.x*nlen, self.y*nlen, self.z*nlen, self.w*nlen)","def l1_normalize(x: np.ndarray) -> np.ndarray: # pylint: disable=invalid-name\n return x / x.sum()","def _set_ls_matrices(self):\n zz_t = self.z_matrix * self.z_matrix.transpose()\n l, s, l_t = np.linalg.svd(zz_t)\n s[self.p:] = 0\n self.l_matrix = np.matrix(l)\n self.s_matirx = np.matrix(np.diag(s))","def normalisation_l2(x):\n res = np.zeros(x.shape)\n print(x.shape)\n for i in range(x.shape[0]):\n res[i] = x[i]/(np.linalg.norm(x[i],2)+1e-5)\n std = res.std()\n mean = res.mean()\n print(\"normalisation done\")\n return(mean,std,res)","def test_normalize(self):\n\n a1 = vectors.Vector(4, 0, 0)\n self.assertEqual(a1.normalize(),\n vectors.Vector(1, 0, 0))\n\n a1 = vectors.Vector(0, 4, 0)\n self.assertEqual(a1.normalize(),\n vectors.Vector(0, 1, 0))\n\n a1 = vectors.Vector(0, 0, 4)\n self.assertEqual(a1.normalize(),\n vectors.Vector(0, 0, 1))","def normalized(self):\n L = self.length\n if L > pygonal.EPSILON:\n v = tuple.__new__(Vec2, (self[0] / L, self[1] / L))\n v.__dict__['length'] = v.__dict__['length2'] = 1.0\n return v\n else:\n return null","def project_Lnuc_ball(X: \"fasta.linalg.Matrix\", t: float) -> \"fasta.linalg.Matrix\":\n U, s, V = la.svd(X)\n\n # Construct the diagonal matrix of singular values, S, as a shrunken version of the original signal values\n S = np.zeros(X.shape)\n S[:len(s),:len(s)] = np.diag(shrink(s, t))\n return U @ S @ V","def normalized(self):\n return self.from_points(\n vector.normalized() for vector in self._vectors)","def _get_normalized_adj_matrix(self):\n\n # find all of the unique layers in the problem (first index of category tuples)\n row_sums = self._frequency_matrix.sum(axis=1)\n normed_matrix = self._frequency_matrix / row_sums[:, np.newaxis]\n\n return normed_matrix","def normalize(inp):\n\n out = inp / np.linalg.norm(inp, axis=1, keepdims=True)\n\n return out","def compute_L2_normalization(xx):\r\n\treturn np.sum(xx ** 2, axis=1)","def normalize_distancematrix(self):\n INF = self.distmat.max().max()\n df = self.distmat.fillna(INF)\n self.distmat = (df - df.min()) / (df.max() - df.min())","def cal_L(self):\n # calculate the l matrix\n self.point_matrixs = self.point_matrix.reshape(\n self.point_matrix.shape[0], 1, self.point_matrix.shape[1])\n self.point_matrixs = np.tile(self.point_matrixs,\n (self.attach_points.shape[0], 1))\n self.attach_points_matrix = np.matlib.repmat(\n self.attach_points[:, 0:3], self.point_matrix.shape[0], 1)\n self.attach_points_matrix = self.attach_points_matrix.reshape(\n self.point_matrix.shape[0], self.attach_points.shape[0], 3)\n self.L = np.subtract(self.attach_points_matrix,\n self.point_matrixs)\n # self.L[:,self.attach_points[:,3]==1,:] = \\\n # - self.L[:,self.attach_points[:,3]==1,:]\n # print(self.L)","def mult_L(self) -> np.ndarray:\n return np.array([\n [self.w, -self.x, -self.y, -self.z],\n [self.x, self.w, -self.z, self.y],\n [self.y, self.z, self.w, -self.x],\n [self.z, -self.y, self.x, self.w]])","def normalize_matrix(mat):\n return (mat + abs(mat.min())) / (mat.max() - mat.min())","def prox_l1_norm(w, lamb):\n\treturn np.sign(w) * np.maximum( np.abs(w) - lamb, 0)","def normalize_lorentz_1d(\n lorentz, old_f_scale, old_v_scale, new_f_scale=(\n 0, 1, 1024), new_v_scale=(\n 0, 1, 1024)):\n A0 = lorentz[0]\n f0 = lorentz[1]\n G0 = lorentz[2]\n A1 = normalize_0d(A0 + old_v_scale[0], old_v_scale, new_v_scale)\n f1 = normalize_0d(f0, old_f_scale, new_f_scale)\n G1 = normalize_0d(G0 + old_f_scale[0], old_f_scale, new_f_scale)\n return np.array([A1, f1, G1, lorentz[3]])","def test_normalize_matrix(self):\n input_matrix = [\n [0, 1.0],\n [1.0, 1.0]\n ]\n\n expected = [\n [0, 1],\n [0.5, 0.5]\n ]\n\n result = self.summarizer.normalize_matrix(input_matrix)\n\n self.assertEqual(expected, result)","def normalize(self) -> None:\n assert hasattr(self, \"characterized_inventory\"), \"Must do lcia first\"\n if not hasattr(self, \"normalization_matrix\"):\n self.load_normalization_data()\n self.normalization_calculation()","def normalizeRows(x):\n\n ### YOUR CODE HERE\n # using l2 norm to normalize\n x = x / np.sqrt(np.sum(x * x, axis=-1, keepdims=True))\n ### END YOUR CODE\n\n return x","def local_to_normalized(npboxes: np.ndarray, window: Box):\n height, width = window.size\n return npboxes / np.array([[height, width, height, width]])","def laplacian_(self, grid, i, j):\n l1 = grid[(i+1+self.N) % self.N][j] + grid[(i-1+self.N) % self.N][j]\n l2 = grid[i][(j+1+self.N) % self.N] + grid[i][(j-1+self.N) % self.N]\n l3 = -4*grid[i][j]\n return (l1 + l2 + l3)/self.dx**2","def laplacian_1d(window_size) -> torch.Tensor:\n\n filter_1d = torch.ones(window_size)\n filter_1d[window_size // 2] = 1 - window_size\n laplacian_1d: torch.Tensor = filter_1d\n return laplacian_1d","def normalize_face_landmarks(face_landmarks):\r\n\tface_landmarks_norm = np.zeros(face_landmarks.shape)\r\n\t\r\n\tfor (i, lm) in enumerate(face_landmarks):\r\n\t\tface_landmarks_norm[i] = lm - lm[nose_center_idx]\r\n\t\t\t\r\n\tstd_x = np.std(face_landmarks_norm[:,:,0].reshape((-1,)))\r\n\tstd_y = np.std(face_landmarks_norm[:,:,1].reshape((-1,)))\r\n\t\r\n\tface_landmarks_norm[:,:,0] = np.multiply(face_landmarks_norm[:,:,0], 1./std_x)\r\n\tface_landmarks_norm[:,:,1] = np.multiply(face_landmarks_norm[:,:,1], 1./std_y)\r\n\t\r\n\treturn face_landmarks_norm","def lMatrix(a, b, c, alpha, beta, gamma, convention=None):\n lMatrix = np.zeros((3, 3))\n\n cosAlpha = np.cos(alpha)\n cosBeta = np.cos(beta)\n cosGamma = np.cos(gamma)\n\n sinGamma = np.sin(gamma)\n\n lMatrix[0, 0] = a\n lMatrix[0, 1] = b * cosGamma\n lMatrix[0, 2] = c * cosBeta\n\n lMatrix[1, 1] = b * sinGamma\n lMatrix[1, 2] = c * (cosAlpha - cosBeta * cosGamma) / sinGamma\n\n lMatrix[2, 2] = c * np.sqrt(\n 1 + 2 * cosAlpha * cosBeta * cosGamma -\n cosAlpha**2 - cosBeta**2 - cosGamma**2\n ) / sinGamma\n\n # OI/HKL convention - x // [10-10], y // a2 [-12-10]\n # TSL convention - x // a1 [2-1-10], y // [01-10]\n if convention is None:\n convention = defaults['crystal_ortho_conv']\n\n if convention.lower() in ['hkl', 'oi']:\n # Swap 00 with 11 and 01 with 10 due to how OI orthonormalises\n # From Brad Wynne\n t1 = lMatrix[0, 0]\n t2 = lMatrix[1, 0]\n\n lMatrix[0, 0] = lMatrix[1, 1]\n lMatrix[1, 0] = lMatrix[0, 1]\n\n lMatrix[1, 1] = t1\n lMatrix[0, 1] = t2\n\n elif convention.lower() != 'tsl':\n raise ValueError(\n f\"Unknown convention '{convention}' for orthonormalisation of \"\n f\"crystal structure, can be 'hkl' or 'tsl'\"\n )\n\n # Set small components to 0\n lMatrix[np.abs(lMatrix) < 1e-10] = 0\n\n return lMatrix","def MillerNormalVectors_111():\r\n k_111 = np.array(\r\n [[1, 1, 1], [-1, 1, 1], [1, -1, 1], [1, 1, -1]])/np.sqrt(3.0)\r\n return np.array([k_111[0, :], k_111[1, :], k_111[2, :], k_111[3, :],\r\n -k_111[0, :], -k_111[1, :], -k_111[2, :], -k_111[3, :]])","def prepare_laplacian(laplacian):\n\n def estimate_lmax(laplacian, tol=5e-3):\n r\"\"\"Estimate the largest eigenvalue of an operator.\"\"\"\n lmax = sparse.linalg.eigsh(laplacian, k=1, tol=tol,\n ncv=min(laplacian.shape[0], 10),\n return_eigenvectors=False)\n lmax = lmax[0]\n lmax *= 1 + 2 * tol # Be robust to errors.\n return lmax\n\n def scale_operator(L, lmax, scale=1):\n r\"\"\"Scale the eigenvalues from [0, lmax] to [-scale, scale].\"\"\"\n I = sparse.identity(L.shape[0], format=L.format, dtype=L.dtype)\n L *= 2 * scale / lmax\n L -= I\n return L\n\n lmax = estimate_lmax(laplacian)\n laplacian = scale_operator(laplacian, lmax)\n\n laplacian = sparse.coo_matrix(laplacian)\n\n # PyTorch wants a LongTensor (int64) as indices (it'll otherwise convert).\n indices = np.empty((2, laplacian.nnz), dtype=np.int64)\n np.stack((laplacian.row, laplacian.col), axis=0, out=indices)\n indices = torch.from_numpy(indices)\n\n laplacian = torch.sparse_coo_tensor(indices, laplacian.data, laplacian.shape)\n laplacian = laplacian.coalesce() # More efficient subsequent operations.\n return laplacian","def normalized(self):\n len = self.length\n return Vector(self.x / len, self.y / len)","def normalize(data):\n data = numpy.asmatrix(data)\n std_devs = numpy.std(data, axis=1)\n std_devs[std_devs == 0] = 1 # prevent div by 0\n return (data - numpy.mean(data, axis=1)) / std_devs","def _normalize(X: np.ndarray) -> np.ndarray:\n # return X * np.sqrt(1 / np.sum(X ** 2, axis=1))[:, None]\n return X * np.sqrt(X.shape[1] / np.sum(X ** 2, axis=1))[:, None]","def _prepare_outer_matrix(self):\n self._mat_plane = numpy.array([\n self._scaling[0], 0, 0, 0,\n 0, self._scaling[1], 0, 0,\n 0, 0, 1, 0,\n self.i_border[0], -self.i_border[1], 0, 1\n ], dtype=numpy.float32)","def _normalize(a: np.ndarray, u: float=0, s: float=1) -> np.ndarray:\n a_norm = (a - np.mean(a)) / (np.std(a) + STABILITY)\n a_rescaled = a_norm * s + u\n\n return a_rescaled","def rownorm(a):\n return sum(array(a)**2,axis=1)**.5","def normalize(self):\n\n if not self.magnitude():\n return Vector(0, 0)\n\n l = 1 / self.magnitude()\n return self.scale(l)","def normalized(self):\n length = self.length\n if length != 0:\n return self/length\n return Vec2d(self)","def normalize(self):\n self.vector /= np.linalg.norm(self.vector)","def normalisation_l_inf(x):\n res = np.zeros(x.shape)\n for i in range(x.shape[0]):\n for j in range(x.shape[1]):\n res[i,j] = x[i,j]/(np.max(x[i,j])+1e-5)\n return(res)","def get_norma(self):\n return self.norma","def normalize(input_matrix):\n\n row_sums = input_matrix.sum(axis=1)\n try:\n assert (np.count_nonzero(row_sums)==np.shape(row_sums)[0]) # no row should sum to zero\n except Exception:\n raise Exception(\"Error while normalizing. Row(s) sum to zero\")\n new_matrix = input_matrix / row_sums[:, np.newaxis]\n return new_matrix","def normalize_matrix(matrix, min_val, max_val):\n return (max_val - min_val) * (matrix - np.min(matrix)) / (np.max(matrix) - np.min(matrix)) + min_val","def L1Norm(X):\n return max(np.sum(X,axis=0))","def test_LU(self):\n A = np.random.rand(10, 10)\n MA = to_matrix(A)\n ML, MU = MA.decomposeLU()\n self.assertEqual(ML*MU, MA)\n self.assertTrue(ML.is_lower_triangular())\n self.assertTrue(MU.is_upper_triangular())","def normalize_data(data=None):\n # Data pre-processing\n n = data.shape[0]\n for i in range(n):\n xx = data[i,:,:]\n xx -= np.mean(xx) # Centering in 0\n xx /= np.linalg.norm(xx) # Normalizing to 1\n data[i] = xx # Affect value\n return data","def normalization_calculation(self) -> None:\n self.normalized_inventory = (\n self.normalization_matrix * self.characterized_inventory\n )","def normalize(self):\n return Vector(self.args + []) / self.magnitude()","def normalize(x):\n\n x_norm = np.linalg.norm(x, axis=1, keepdims=True)\n print(x_norm)\n x = x / x_norm\n ### END\n\n return x","def norm(self):","def normalize(self):\n self.length = np.ones(self.nV)\n return self","def normalizeRows(x):\n\n ### YOUR CODE HERE\n norm2 = np.linalg.norm(x,2,axis = 1).reshape(x.shape[0],-1)\n x = x/norm2\n ### END YOUR CODE\n\n return x","def lap_normalize(img, scale_n=4):\n # img = tf.expand_dims(img, 0)\n # print(\"Inside lap_normalize Function, img shape {}\".format(tf.shape(img)))\n\n tlevels = lap_split_n(img, scale_n)\n tlevels = list(map(normalize_std, tlevels))\n\n out = lap_merge(tlevels)\n\n return out[0, :, :, :]","def unnormalize(self, inputs: Tensor) -> Tensor:\n # The normalizing constants are applied to the entire row, so add dummy outputs to the\n # inputs to make a row.\n row = torch.zeros_like(self._row_range)\n row[self._input_column_indices] = inputs\n row *= self._row_range\n row += self._row_min\n return row[self._input_column_indices]","def L2_norm(self):\n analyticals = self.analytical(self.x_values, self.C, self.D)\n error = analyticals - self.numerical\n self.L2 = np.sqrt((1/self.gp)*np.sum(error**2))","def normalize(self):\n self.desc += \", normalize\"\n self._vecs /= np.linalg.norm(self._vecs, axis=1)[:, np.newaxis]\n self.reindex()","def lup_decomposition(self):\n p = [i for i in range(self.rows_count())]\n for i in range(self.rows_count() - 1):\n pivot = i\n for j in range(i + 1, self.rows_count()):\n if abs(self[p[j], i]) > abs(self[p[pivot], i]):\n pivot = j\n p[pivot], p[i] = p[i], p[pivot]\n for j in range(i + 1, self.rows_count()):\n if abs(self[p[i], i]) < math.pow(10, -6):\n raise ValueError(\"Can't divide by 0\")\n self[p[j], i] /= self[p[i], i]\n for k in range(i + 1, self.rows_count()):\n self[p[j], k] -= self[p[j], i] * self[p[i], k]\n lst = []\n for i in p:\n lst.append(self.value[i])\n return p, Matrix(lst)","def norm(self):\r\n old_origin = np.array(self.origin)\r\n self.origin = [0, 0, 0]\r\n old_origin[0] = old_origin[0] / self.x[0]\r\n old_origin[1] = old_origin[1] / self.y[1]\r\n old_origin[2] = old_origin[2] / self.z[2]\r\n self.data = ndimage.shift(self.data, -old_origin, mode='wrap')","def normalize(self):\n self._vectors = [vector.normalized() for vector in self._vectors]","def nll(self, data):\n nll = np.zeros(data.shape[0])\n self.a[0] = self.c\n for i in range(self.Ndim):\n a = self.rhos[i] * self.a[i]\n h = 0.5 * (a + np.abs(a)) # ReLU\n za = np.dot(self.Vs['alpha'][i].T, h) + self.bs['alpha'][i]\n zm = np.dot(self.Vs['mu'][i].T, h) + self.bs['mu'][i]\n zs = np.dot(self.Vs['sigma'][i].T, h) + self.bs['sigma'][i]\n self.alphas[i] = softmax(za)\n self.mus[i] = zm\n self.sigmas[i] = np.exp(zs)\n self.vars = self.sigmas ** 2.\n nll += nll_MOG_1D(data[:, i], self.alphas[i], self.mus[i],\n self.vars[i])[0]\n return nll","def laplacian(self, array_in):\r\n\r\n # Call-through to Laplacian operator, already computed\r\n return self.laplace_op*array_in","def normalize_lorentz_2d(\n lorentz, old_f_scale, old_v_scale, new_f_scale=(\n 0, 1, 1024), new_v_scale=(\n 0, 1, 1024)):\n lorentz_array = np.empty((0, 4))\n for i in range(0, lorentz.shape[0]):\n l0 = lorentz[i]\n l1 = normalize_lorentz_1d(\n l0, old_f_scale, old_v_scale, new_f_scale, new_v_scale)\n lorentz_array = np.append(lorentz_array, np.array([l1]), axis=0)\n return lorentz_array","def normalise(a):\n return (a - np.nanmin(a)) / np.nanmax(a - np.nanmin(a))"],"string":"[\n \"def laplacian_matrix(A, normalized=False):\\n n, m = A.shape\\n D = degree_matrix(A)\\n L = D - A\\n if normalized:\\n degs = _flat(A.sum(axis=1))\\n rootD = sps.spdiags(np.power(degs, -1 / 2), [0], n, n, format=\\\"csr\\\")\\n L = rootD * L * rootD\\n return L\",\n \"def normalize(self, lam):\\n return (lam.T / np.sum(lam, axis=1)).T\",\n \"def normalize(self):\\r\\n max = np.amax(self.matrix)\\r\\n min = np.amin(self.matrix)\\r\\n\\r\\n self.matrix = ((self.matrix - min) / (max - min))\",\n \"def laplacian(W, normalized=True):\\n\\n # Degree matrix.\\n d = W.sum(dim=0)\\n\\n # Laplacian matrix.\\n if not normalized:\\n D = scipy.sparse.diags(d.A.squeeze(), 0)\\n L = D - W\\n else:\\n # d += np.spacing(np.array(0, W.dtype))\\n d = 1 / torch.sqrt(d)\\n D = torch.diags(d.A.squeeze(), 0)\\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\\n L = I - D * W * D\\n\\n # assert np.abs(L - L.T).mean() < 1e-9\\n assert type(L) is scipy.sparse.csr.csr_matrix\\n return L\",\n \"def build_normalized(self):\\n for row, s in enumerate(self.S):\\n for col, t in enumerate(self.T):\\n\\n if self.symmetric and row > col:\\n pass\\n\\n elif self.symmetric and row == col:\\n self.normalized_mat[row, col] = 1\\n\\n else:\\n self.normalized_mat[row, col] = self.normalize(row, col)\\n\\n if self.symmetric:\\n self.normalized_mat = self.symmetrize(self.normalized_mat)\",\n \"def normalize(self):\\n det = self._mat[0][0]*self._mat[1][1] - self._mat[0][1]*self._mat[1][0]\\n for i in range(2):\\n for j in range(2):\\n self._mat[i][j] = (self._mat[i][j])/(np.sqrt(det))\",\n \"def normalized_laplacian(degree_vector, weight_matrix, length):\\n holders = np.zeros((length, 1))\\n holders[:, 0] = 1 / degree_vector\\n\\n return np.eye(length) - holders * weight_matrix\",\n \"def laplacian(W, normalized=False):\\r\\n # Degree matrix.\\r\\n d = W.sum(axis=0)\\r\\n # Laplacian matrix.\\r\\n if not normalized:\\r\\n D = scipy.sparse.diags(d.A.squeeze(), 0)\\r\\n L = D - W\\r\\n else:\\r\\n # d += np.spacing(np.array(0, W.dtype))\\r\\n d = 1 / np.sqrt(d)\\r\\n D = scipy.sparse.diags(d.A.squeeze(), 0)\\r\\n I = scipy.sparse.identity(d.size, dtype=W.dtype)\\r\\n L = I - D * W * D\\r\\n\\r\\n # assert np.abs(L - L.T).mean() < 1e-9\\r\\n assert type(L) is scipy.sparse.csr.csr_matrix\\r\\n return L\",\n \"def laplacian(A,normalize=False,randomWalk=False):\\n degr = A.sum(axis=1)*1.\\n\\n if randomWalk:\\n degr[degr!=0] = 1./degr[degr!=0]\\n Dinv = np.diag(degr)\\n return np.eye(A.shape[0]) - np.dot(Dinv,A)\\n if normalize:\\n degr = A.sum(axis=1)*1.\\n degr[degr!=0] = 1./degr[degr!=0]\\n Dinv = np.diag(degr)**.5\\n return np.eye(A.shape[0]) - np.dot(Dinv,A).dot(Dinv)\\n\\n return np.diag(degr) - A\",\n \"def calculate_normalized_laplacian(adj):\\n adj = sp.coo_matrix(adj)\\n d = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(d, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\\n return normalized_laplacian\",\n \"def calculate_normalized_laplacian(adj):\\n adj = sp.coo_matrix(adj)\\n d = np.array(adj.sum(1))\\n d_inv_sqrt = np.power(d, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\\n normalized_laplacian = sp.eye(adj.shape[0]) - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\\n return normalized_laplacian\",\n \"def normalize(self,matrix):\\n for i in range(self.N):\\n matrix[self.N-1][i] = 0\\n for i in range(self.n):\\n matrix[self.N - 1][self.index(i,i)] = 1\\n return matrix\",\n \"def L2_normalize(xx):\\r\\n\\tZx = compute_L2_normalization(xx)\\r\\n\\treturn xx / np.sqrt(Zx[:, np.newaxis])\",\n \"def laplacian(points, sigma):\\n\\n S = similarity_matrix(points, sigma)\\n \\n (npnts,npnts) = S.shape \\n\\n D = np.zeros_like(S)\\n\\n for i in range(npnts):\\n #D[i,i] = 1.0 / np.sqrt(S[i,:].sum()) \\n D[i,i] = S[i,:].sum()\\n\\n\\n return (D - S) #(np.eye(npnts,npnts) - np.dot(D,np.dot(S,D)))\",\n \"def lap_mat(self):\",\n \"def lu_decomposition(self):\\n if self.rows_count() != self.columns_count():\\n raise ValueError(\\\"Matrix needs to me square for LU decomposition.\\\")\\n for i in range(self.rows_count() - 1):\\n for j in range(i + 1, self.rows_count()):\\n if self[i, i] == 0: # or abs(self[i, i]) <= 0.000001):\\n raise ValueError(\\\"Can't divide by 0\\\")\\n self[j, i] = self[j, i] / self[i, i]\\n for k in range(i + 1, self.rows_count()):\\n self[j, k] -= self[j, i] * self[i, k]\",\n \"def laplacian(A):\\n #calculate D by creating a diagonal matrix with the column sum of A\\n D = np.diag(A.sum(axis=0))\\n return D - A\",\n \"def decompose_to_LU(a):\\n # create emtpy LU-matrix\\n lu_matrix = np.matrix(np.zeros([a.shape[0], a.shape[1]]))\\n n = a.shape[0]\\n\\n for k in range(n):\\n # calculate all residual k-row elements\\n for j in range(k, n):\\n lu_matrix[k, j] = a[k, j] - lu_matrix[k, :k] * lu_matrix[:k, j]\\n # calculate all residual k-column elemetns\\n for i in range(k + 1, n):\\n lu_matrix[i, k] = (a[i, k] - lu_matrix[i, : k] * lu_matrix[: k, k]) / lu_matrix[k, k]\\n\\n return lu_matrix\",\n \"def get_normalization_matrix(x):\\n # Input: x 3*N\\n #\\n # Output: T 3x3 transformation matrix of points\\n\\n # TODO\\n # --------------------------------------------------------------\\n # Estimate transformation matrix used to normalize\\n # the inputs x\\n # --------------------------------------------------------------\\n x2d = x[:2, :]\\n # Get centroid and mean-distance to centroid\\n center = np.mean(x2d, 1, keepdims=True)\\n mean_dist = np.mean(np.sqrt(np.sum((x2d - center) ** 2, axis=0)))\\n center = center.flatten()\\n\\n T = np.array([[np.sqrt(2) / mean_dist, 0, -center[0] * np.sqrt(2) / mean_dist],\\n [0, np.sqrt(2) / mean_dist, -center[1] * np.sqrt(2) / mean_dist],\\n [0, 0, 1]])\\n\\n return T\",\n \"def laplace_matrix(self):\\n n = self.number_of_vertices\\n laplace_matrix = np.zeros((n, n))\\n for i in range(n):\\n laplace_matrix[i][i] = 1\\n vertice = self.list_of_vertices[i]\\n for edge in vertice.edges_list:\\n laplace_matrix[i][edge.linked[1].index] = 1\\n return laplace_matrix\",\n \"def normalize(self, mel_db: np.ndarray) -> np.ndarray:\\n mel_norm = ((mel_db - self.ref_level_db) - self.min_level_db) / (\\n -self.min_level_db\\n )\\n if self.symmetric_norm:\\n # Symmetric norm\\n mel_norm = ((2 * self.max_norm) * mel_norm) - self.max_norm\\n if self.clip_norm:\\n mel_norm = np.clip(mel_norm, -self.max_norm, self.max_norm)\\n else:\\n # Asymmetric norm\\n mel_norm = self.max_norm * mel_norm\\n if self.clip_norm:\\n mel_norm = np.clip(mel_norm, 0, self.max_norm)\\n\\n return mel_norm\",\n \"def LU(A):\\n m, n = A.shape\\n L, U = np.zeros([m, n]), np.zeros([m, n])\\n for i in range(n):\\n L[i][i] = 1\\n\\n for i in range(n):\\n\\n # Upper triangular matrix\\n for j in range(i, n):\\n summ = 0\\n for k in range(0, i):\\n summ += L[i][k]*U[k][j]\\n U[i][j] = A[i][j] - summ\\n\\n # Lower triangular matrix\\n for j in range(i+1, n):\\n summ = 0\\n for k in range(0, i):\\n summ += L[j][k]*U[k][i]\\n L[j][i] = (A[j][i] - summ)/U[i][i]\\n return L, U\",\n \"def laplacian2(A, laplacian_type='raw'):\\r\\n\\r\\n N = A.shape[0]\\r\\n # TODO: Raise exception if A is not square\\r\\n\\r\\n degrees = A.sum(1)\\r\\n # To deal with loops, must extract diagonal part of A\\r\\n diagw = np.diag(A)\\r\\n\\r\\n # w will consist of non-diagonal entries only\\r\\n ni2, nj2 = A.nonzero()\\r\\n w2 = A[ni2, nj2]\\r\\n ndind = (ni2 != nj2).nonzero() # Non-diagonal indices\\r\\n ni = ni2[ndind]\\r\\n nj = nj2[ndind]\\r\\n w = w2[ndind]\\r\\n\\r\\n di = np.arange(N) # diagonal indices\\r\\n\\r\\n if laplacian_type == 'raw':\\r\\n # non-normalized laplaciand L = D - A\\r\\n L = np.diag(degrees - diagw)\\r\\n L[ni, nj] = -w\\r\\n L = lil_matrix(L)\\r\\n elif laplacian_type == 'normalized':\\r\\n # TODO: Implement the normalized laplacian case\\r\\n # % normalized laplacian D^(-1/2)*(D-A)*D^(-1/2)\\r\\n # % diagonal entries\\r\\n # dL=(1-diagw./degrees); % will produce NaN for degrees==0 locations\\r\\n # dL(degrees==0)=0;% which will be fixed here\\r\\n # % nondiagonal entries\\r\\n # ndL=-w./vec( sqrt(degrees(ni).*degrees(nj)) );\\r\\n # L=sparse([ni;di],[nj;di],[ndL;dL],N,N);\\r\\n print(\\\"Not implemented\\\")\\r\\n else:\\r\\n # TODO: Raise an exception\\r\\n print(\\\"Don't know what to do\\\")\\r\\n\\r\\n return L\",\n \"def getNormLaplacian(W):\\n\\td=[np.sum(row) for row in W]\\n\\tD=np.diag(d)\\n\\tL=D-W\\n\\t#Dn=D^(-1/2)\\n\\tDn=np.power(np.linalg.matrix_power(D,-1),0.5)\\n\\tLbar=np.dot(np.dot(Dn,L),Dn)\\n\\treturn Lbar\",\n \"def normalise(self):\\n return self / self.mean(axis=1).reshape(self.shape[0], 1)\",\n \"def normalize(self):\\n self.number_of_vectors = self.values.shape[0]\\n norm_2 = np.linalg.norm(self.values, axis=1)\\n norm_1 = np.sum(self.values_planar, axis=1)\\n norm_2 = np.repeat(norm_2, self.number_of_objectives).reshape(\\n self.number_of_vectors, self.number_of_objectives\\n )\\n norm_1 = np.repeat(norm_1, self.number_of_objectives).reshape(\\n self.number_of_vectors, self.number_of_objectives\\n )\\n norm_2[norm_2 == 0] = np.finfo(float).eps\\n self.values = np.divide(self.values, norm_2)\\n self.values_planar = np.divide(self.values_planar, norm_1)\",\n \"def normc(A):\\r\\n # return A / LA.norm(A, axis=0)[None,:]\\r\\n return A/ np.tile(np.sqrt(np.sum(A*A, axis=0)), (A.shape[0], 1))\",\n \"def _assure_normalized(self):\\n for iwann in range(self.nwann):\\n norm = np.trace(\\n self.wannR[:, :, iwann].conj().T @ self.wannR[:, :, iwann])\\n #print(f\\\"Norm {iwann}: {norm}\\\")\",\n \"def _get_Laplacian_matrix(self, X):\\n self.laplacian_mat, self.laplacian_sym_mat, self.laplacian_weights = self.laplacian.compute_laplacian(\\n self.get_Affinity_matrix(X)\\n )\",\n \"def normalize_l2(x):\\n return x / (npla.norm(x))\",\n \"def laplacian_mat(n):\\n data = [1, -2, 1]*n\\n i = flatten([[k,k,k] for k in range(n)])\\n j = flatten([[k-1, k, k+1] for k in range(n)])\\n return scipy.sparse.coo_matrix((data[1:-1], (i[1:-1], j[1:-1])))\",\n \"def laplacian(A):\\n D = np.diag(np.sum(A, axis=1))\\n return D - A\",\n \"def set_lam_nll(self, lam_nll=1.0):\\n zero_ary = np.zeros((1,))\\n new_lam = zero_ary + lam_nll\\n self.lam_nll.set_value(to_fX(new_lam))\\n return\",\n \"def normalize_data(self):\\n\\t\\tfull_matrix = self.balance_clases()\\n\\t\\ttexture_matrix = Normalizer().fit_transform(X=full_matrix[:,range(0,24)])\\n\\n\\t\\treturn texture_matrix\",\n \"def test_normalization(self):\\n u = np.array([np.array([0.7, 1.2]), np.array([0.5, 1.6])])\\n with tf.Session() as sess:\\n n = sess.run(AbstractModel.l2_normalization_layer(u, axis=1))\\n magnitude = np.linalg.norm(n, axis=1)\\n np.testing.assert_allclose(magnitude, np.array([1.0, 1.0]))\",\n \"def normalize(a):\\n a = np.array(a)\\n return a / np.linalg.norm(a)\",\n \"def normaliza(self):\\n return self * (1 / self.norma())\",\n \"def norm_with_l2(original_mat):\\n normed_mat = np.zeros(original_mat.shape, dtype=np.float32)\\n if len(original_mat.shape) == 2:\\n for ind_r in range(original_mat.shape[0]):\\n a = np.square(original_mat[ind_r]*1.0)\\n b = np.sum(a)\\n c = np.sqrt(b)\\n normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / c\\n # normed_mat[ind_r] = (original_mat[ind_r] * 1.0) / np.sqrt(np.sum(np.square(original_mat[ind_r])*1.0))\\n return normed_mat\",\n \"def normalize(data):\\n norm_matrix = np.int_(np.log10(data)**2)\\n norm_matrix = map(lambda x: x if x < BOARD_SIZE else BOARD_SIZE, norm_matrix)\\n norm_matrix = map(lambda x: x if x > 0 else 0, norm_matrix)\\n return norm_matrix\",\n \"def LUdecomp(Ainput):\\n\\n n, m = np.shape(Ainput)\\n \\n if n != m:\\n return 'Error: Please enter an invertible matrix.'\\n \\n U = Ainput.copy() # make copies so as not to write over originals\\n L = np.zeros((np.shape(Ainput)))\\n \\n for i in range(0,n):\\n L[i,i] = 1\\n for i in range(0,n-1): # loop over pivot rows from row 1 to row n-1 (i to n-2)\\n for j in range(i+1,n): # loop over row to be zero'ed from row j+1 to n (j+1 to n-1)\\n c = U[j,i]/U[i,i] # multiplicative factor to zero point\\n L[j,i] = c\\n U[j,i] = 0.0 # we know this element goes to zero\\n U[j,i+1:n]=U[j,i+1:n]-c*U[i,i+1:n] # do subtraction of two rows\\n\\n return (L,U) # return lower and upper decompositions\",\n \"def normalize(self):\\r\\n\\r\\n nlen = 1.0/math.sqrt(self*self)\\r\\n return vec4(self.x*nlen, self.y*nlen, self.z*nlen, self.w*nlen)\",\n \"def l1_normalize(x: np.ndarray) -> np.ndarray: # pylint: disable=invalid-name\\n return x / x.sum()\",\n \"def _set_ls_matrices(self):\\n zz_t = self.z_matrix * self.z_matrix.transpose()\\n l, s, l_t = np.linalg.svd(zz_t)\\n s[self.p:] = 0\\n self.l_matrix = np.matrix(l)\\n self.s_matirx = np.matrix(np.diag(s))\",\n \"def normalisation_l2(x):\\n res = np.zeros(x.shape)\\n print(x.shape)\\n for i in range(x.shape[0]):\\n res[i] = x[i]/(np.linalg.norm(x[i],2)+1e-5)\\n std = res.std()\\n mean = res.mean()\\n print(\\\"normalisation done\\\")\\n return(mean,std,res)\",\n \"def test_normalize(self):\\n\\n a1 = vectors.Vector(4, 0, 0)\\n self.assertEqual(a1.normalize(),\\n vectors.Vector(1, 0, 0))\\n\\n a1 = vectors.Vector(0, 4, 0)\\n self.assertEqual(a1.normalize(),\\n vectors.Vector(0, 1, 0))\\n\\n a1 = vectors.Vector(0, 0, 4)\\n self.assertEqual(a1.normalize(),\\n vectors.Vector(0, 0, 1))\",\n \"def normalized(self):\\n L = self.length\\n if L > pygonal.EPSILON:\\n v = tuple.__new__(Vec2, (self[0] / L, self[1] / L))\\n v.__dict__['length'] = v.__dict__['length2'] = 1.0\\n return v\\n else:\\n return null\",\n \"def project_Lnuc_ball(X: \\\"fasta.linalg.Matrix\\\", t: float) -> \\\"fasta.linalg.Matrix\\\":\\n U, s, V = la.svd(X)\\n\\n # Construct the diagonal matrix of singular values, S, as a shrunken version of the original signal values\\n S = np.zeros(X.shape)\\n S[:len(s),:len(s)] = np.diag(shrink(s, t))\\n return U @ S @ V\",\n \"def normalized(self):\\n return self.from_points(\\n vector.normalized() for vector in self._vectors)\",\n \"def _get_normalized_adj_matrix(self):\\n\\n # find all of the unique layers in the problem (first index of category tuples)\\n row_sums = self._frequency_matrix.sum(axis=1)\\n normed_matrix = self._frequency_matrix / row_sums[:, np.newaxis]\\n\\n return normed_matrix\",\n \"def normalize(inp):\\n\\n out = inp / np.linalg.norm(inp, axis=1, keepdims=True)\\n\\n return out\",\n \"def compute_L2_normalization(xx):\\r\\n\\treturn np.sum(xx ** 2, axis=1)\",\n \"def normalize_distancematrix(self):\\n INF = self.distmat.max().max()\\n df = self.distmat.fillna(INF)\\n self.distmat = (df - df.min()) / (df.max() - df.min())\",\n \"def cal_L(self):\\n # calculate the l matrix\\n self.point_matrixs = self.point_matrix.reshape(\\n self.point_matrix.shape[0], 1, self.point_matrix.shape[1])\\n self.point_matrixs = np.tile(self.point_matrixs,\\n (self.attach_points.shape[0], 1))\\n self.attach_points_matrix = np.matlib.repmat(\\n self.attach_points[:, 0:3], self.point_matrix.shape[0], 1)\\n self.attach_points_matrix = self.attach_points_matrix.reshape(\\n self.point_matrix.shape[0], self.attach_points.shape[0], 3)\\n self.L = np.subtract(self.attach_points_matrix,\\n self.point_matrixs)\\n # self.L[:,self.attach_points[:,3]==1,:] = \\\\\\n # - self.L[:,self.attach_points[:,3]==1,:]\\n # print(self.L)\",\n \"def mult_L(self) -> np.ndarray:\\n return np.array([\\n [self.w, -self.x, -self.y, -self.z],\\n [self.x, self.w, -self.z, self.y],\\n [self.y, self.z, self.w, -self.x],\\n [self.z, -self.y, self.x, self.w]])\",\n \"def normalize_matrix(mat):\\n return (mat + abs(mat.min())) / (mat.max() - mat.min())\",\n \"def prox_l1_norm(w, lamb):\\n\\treturn np.sign(w) * np.maximum( np.abs(w) - lamb, 0)\",\n \"def normalize_lorentz_1d(\\n lorentz, old_f_scale, old_v_scale, new_f_scale=(\\n 0, 1, 1024), new_v_scale=(\\n 0, 1, 1024)):\\n A0 = lorentz[0]\\n f0 = lorentz[1]\\n G0 = lorentz[2]\\n A1 = normalize_0d(A0 + old_v_scale[0], old_v_scale, new_v_scale)\\n f1 = normalize_0d(f0, old_f_scale, new_f_scale)\\n G1 = normalize_0d(G0 + old_f_scale[0], old_f_scale, new_f_scale)\\n return np.array([A1, f1, G1, lorentz[3]])\",\n \"def test_normalize_matrix(self):\\n input_matrix = [\\n [0, 1.0],\\n [1.0, 1.0]\\n ]\\n\\n expected = [\\n [0, 1],\\n [0.5, 0.5]\\n ]\\n\\n result = self.summarizer.normalize_matrix(input_matrix)\\n\\n self.assertEqual(expected, result)\",\n \"def normalize(self) -> None:\\n assert hasattr(self, \\\"characterized_inventory\\\"), \\\"Must do lcia first\\\"\\n if not hasattr(self, \\\"normalization_matrix\\\"):\\n self.load_normalization_data()\\n self.normalization_calculation()\",\n \"def normalizeRows(x):\\n\\n ### YOUR CODE HERE\\n # using l2 norm to normalize\\n x = x / np.sqrt(np.sum(x * x, axis=-1, keepdims=True))\\n ### END YOUR CODE\\n\\n return x\",\n \"def local_to_normalized(npboxes: np.ndarray, window: Box):\\n height, width = window.size\\n return npboxes / np.array([[height, width, height, width]])\",\n \"def laplacian_(self, grid, i, j):\\n l1 = grid[(i+1+self.N) % self.N][j] + grid[(i-1+self.N) % self.N][j]\\n l2 = grid[i][(j+1+self.N) % self.N] + grid[i][(j-1+self.N) % self.N]\\n l3 = -4*grid[i][j]\\n return (l1 + l2 + l3)/self.dx**2\",\n \"def laplacian_1d(window_size) -> torch.Tensor:\\n\\n filter_1d = torch.ones(window_size)\\n filter_1d[window_size // 2] = 1 - window_size\\n laplacian_1d: torch.Tensor = filter_1d\\n return laplacian_1d\",\n \"def normalize_face_landmarks(face_landmarks):\\r\\n\\tface_landmarks_norm = np.zeros(face_landmarks.shape)\\r\\n\\t\\r\\n\\tfor (i, lm) in enumerate(face_landmarks):\\r\\n\\t\\tface_landmarks_norm[i] = lm - lm[nose_center_idx]\\r\\n\\t\\t\\t\\r\\n\\tstd_x = np.std(face_landmarks_norm[:,:,0].reshape((-1,)))\\r\\n\\tstd_y = np.std(face_landmarks_norm[:,:,1].reshape((-1,)))\\r\\n\\t\\r\\n\\tface_landmarks_norm[:,:,0] = np.multiply(face_landmarks_norm[:,:,0], 1./std_x)\\r\\n\\tface_landmarks_norm[:,:,1] = np.multiply(face_landmarks_norm[:,:,1], 1./std_y)\\r\\n\\t\\r\\n\\treturn face_landmarks_norm\",\n \"def lMatrix(a, b, c, alpha, beta, gamma, convention=None):\\n lMatrix = np.zeros((3, 3))\\n\\n cosAlpha = np.cos(alpha)\\n cosBeta = np.cos(beta)\\n cosGamma = np.cos(gamma)\\n\\n sinGamma = np.sin(gamma)\\n\\n lMatrix[0, 0] = a\\n lMatrix[0, 1] = b * cosGamma\\n lMatrix[0, 2] = c * cosBeta\\n\\n lMatrix[1, 1] = b * sinGamma\\n lMatrix[1, 2] = c * (cosAlpha - cosBeta * cosGamma) / sinGamma\\n\\n lMatrix[2, 2] = c * np.sqrt(\\n 1 + 2 * cosAlpha * cosBeta * cosGamma -\\n cosAlpha**2 - cosBeta**2 - cosGamma**2\\n ) / sinGamma\\n\\n # OI/HKL convention - x // [10-10], y // a2 [-12-10]\\n # TSL convention - x // a1 [2-1-10], y // [01-10]\\n if convention is None:\\n convention = defaults['crystal_ortho_conv']\\n\\n if convention.lower() in ['hkl', 'oi']:\\n # Swap 00 with 11 and 01 with 10 due to how OI orthonormalises\\n # From Brad Wynne\\n t1 = lMatrix[0, 0]\\n t2 = lMatrix[1, 0]\\n\\n lMatrix[0, 0] = lMatrix[1, 1]\\n lMatrix[1, 0] = lMatrix[0, 1]\\n\\n lMatrix[1, 1] = t1\\n lMatrix[0, 1] = t2\\n\\n elif convention.lower() != 'tsl':\\n raise ValueError(\\n f\\\"Unknown convention '{convention}' for orthonormalisation of \\\"\\n f\\\"crystal structure, can be 'hkl' or 'tsl'\\\"\\n )\\n\\n # Set small components to 0\\n lMatrix[np.abs(lMatrix) < 1e-10] = 0\\n\\n return lMatrix\",\n \"def MillerNormalVectors_111():\\r\\n k_111 = np.array(\\r\\n [[1, 1, 1], [-1, 1, 1], [1, -1, 1], [1, 1, -1]])/np.sqrt(3.0)\\r\\n return np.array([k_111[0, :], k_111[1, :], k_111[2, :], k_111[3, :],\\r\\n -k_111[0, :], -k_111[1, :], -k_111[2, :], -k_111[3, :]])\",\n \"def prepare_laplacian(laplacian):\\n\\n def estimate_lmax(laplacian, tol=5e-3):\\n r\\\"\\\"\\\"Estimate the largest eigenvalue of an operator.\\\"\\\"\\\"\\n lmax = sparse.linalg.eigsh(laplacian, k=1, tol=tol,\\n ncv=min(laplacian.shape[0], 10),\\n return_eigenvectors=False)\\n lmax = lmax[0]\\n lmax *= 1 + 2 * tol # Be robust to errors.\\n return lmax\\n\\n def scale_operator(L, lmax, scale=1):\\n r\\\"\\\"\\\"Scale the eigenvalues from [0, lmax] to [-scale, scale].\\\"\\\"\\\"\\n I = sparse.identity(L.shape[0], format=L.format, dtype=L.dtype)\\n L *= 2 * scale / lmax\\n L -= I\\n return L\\n\\n lmax = estimate_lmax(laplacian)\\n laplacian = scale_operator(laplacian, lmax)\\n\\n laplacian = sparse.coo_matrix(laplacian)\\n\\n # PyTorch wants a LongTensor (int64) as indices (it'll otherwise convert).\\n indices = np.empty((2, laplacian.nnz), dtype=np.int64)\\n np.stack((laplacian.row, laplacian.col), axis=0, out=indices)\\n indices = torch.from_numpy(indices)\\n\\n laplacian = torch.sparse_coo_tensor(indices, laplacian.data, laplacian.shape)\\n laplacian = laplacian.coalesce() # More efficient subsequent operations.\\n return laplacian\",\n \"def normalized(self):\\n len = self.length\\n return Vector(self.x / len, self.y / len)\",\n \"def normalize(data):\\n data = numpy.asmatrix(data)\\n std_devs = numpy.std(data, axis=1)\\n std_devs[std_devs == 0] = 1 # prevent div by 0\\n return (data - numpy.mean(data, axis=1)) / std_devs\",\n \"def _normalize(X: np.ndarray) -> np.ndarray:\\n # return X * np.sqrt(1 / np.sum(X ** 2, axis=1))[:, None]\\n return X * np.sqrt(X.shape[1] / np.sum(X ** 2, axis=1))[:, None]\",\n \"def _prepare_outer_matrix(self):\\n self._mat_plane = numpy.array([\\n self._scaling[0], 0, 0, 0,\\n 0, self._scaling[1], 0, 0,\\n 0, 0, 1, 0,\\n self.i_border[0], -self.i_border[1], 0, 1\\n ], dtype=numpy.float32)\",\n \"def _normalize(a: np.ndarray, u: float=0, s: float=1) -> np.ndarray:\\n a_norm = (a - np.mean(a)) / (np.std(a) + STABILITY)\\n a_rescaled = a_norm * s + u\\n\\n return a_rescaled\",\n \"def rownorm(a):\\n return sum(array(a)**2,axis=1)**.5\",\n \"def normalize(self):\\n\\n if not self.magnitude():\\n return Vector(0, 0)\\n\\n l = 1 / self.magnitude()\\n return self.scale(l)\",\n \"def normalized(self):\\n length = self.length\\n if length != 0:\\n return self/length\\n return Vec2d(self)\",\n \"def normalize(self):\\n self.vector /= np.linalg.norm(self.vector)\",\n \"def normalisation_l_inf(x):\\n res = np.zeros(x.shape)\\n for i in range(x.shape[0]):\\n for j in range(x.shape[1]):\\n res[i,j] = x[i,j]/(np.max(x[i,j])+1e-5)\\n return(res)\",\n \"def get_norma(self):\\n return self.norma\",\n \"def normalize(input_matrix):\\n\\n row_sums = input_matrix.sum(axis=1)\\n try:\\n assert (np.count_nonzero(row_sums)==np.shape(row_sums)[0]) # no row should sum to zero\\n except Exception:\\n raise Exception(\\\"Error while normalizing. Row(s) sum to zero\\\")\\n new_matrix = input_matrix / row_sums[:, np.newaxis]\\n return new_matrix\",\n \"def normalize_matrix(matrix, min_val, max_val):\\n return (max_val - min_val) * (matrix - np.min(matrix)) / (np.max(matrix) - np.min(matrix)) + min_val\",\n \"def L1Norm(X):\\n return max(np.sum(X,axis=0))\",\n \"def test_LU(self):\\n A = np.random.rand(10, 10)\\n MA = to_matrix(A)\\n ML, MU = MA.decomposeLU()\\n self.assertEqual(ML*MU, MA)\\n self.assertTrue(ML.is_lower_triangular())\\n self.assertTrue(MU.is_upper_triangular())\",\n \"def normalize_data(data=None):\\n # Data pre-processing\\n n = data.shape[0]\\n for i in range(n):\\n xx = data[i,:,:]\\n xx -= np.mean(xx) # Centering in 0\\n xx /= np.linalg.norm(xx) # Normalizing to 1\\n data[i] = xx # Affect value\\n return data\",\n \"def normalization_calculation(self) -> None:\\n self.normalized_inventory = (\\n self.normalization_matrix * self.characterized_inventory\\n )\",\n \"def normalize(self):\\n return Vector(self.args + []) / self.magnitude()\",\n \"def normalize(x):\\n\\n x_norm = np.linalg.norm(x, axis=1, keepdims=True)\\n print(x_norm)\\n x = x / x_norm\\n ### END\\n\\n return x\",\n \"def norm(self):\",\n \"def normalize(self):\\n self.length = np.ones(self.nV)\\n return self\",\n \"def normalizeRows(x):\\n\\n ### YOUR CODE HERE\\n norm2 = np.linalg.norm(x,2,axis = 1).reshape(x.shape[0],-1)\\n x = x/norm2\\n ### END YOUR CODE\\n\\n return x\",\n \"def lap_normalize(img, scale_n=4):\\n # img = tf.expand_dims(img, 0)\\n # print(\\\"Inside lap_normalize Function, img shape {}\\\".format(tf.shape(img)))\\n\\n tlevels = lap_split_n(img, scale_n)\\n tlevels = list(map(normalize_std, tlevels))\\n\\n out = lap_merge(tlevels)\\n\\n return out[0, :, :, :]\",\n \"def unnormalize(self, inputs: Tensor) -> Tensor:\\n # The normalizing constants are applied to the entire row, so add dummy outputs to the\\n # inputs to make a row.\\n row = torch.zeros_like(self._row_range)\\n row[self._input_column_indices] = inputs\\n row *= self._row_range\\n row += self._row_min\\n return row[self._input_column_indices]\",\n \"def L2_norm(self):\\n analyticals = self.analytical(self.x_values, self.C, self.D)\\n error = analyticals - self.numerical\\n self.L2 = np.sqrt((1/self.gp)*np.sum(error**2))\",\n \"def normalize(self):\\n self.desc += \\\", normalize\\\"\\n self._vecs /= np.linalg.norm(self._vecs, axis=1)[:, np.newaxis]\\n self.reindex()\",\n \"def lup_decomposition(self):\\n p = [i for i in range(self.rows_count())]\\n for i in range(self.rows_count() - 1):\\n pivot = i\\n for j in range(i + 1, self.rows_count()):\\n if abs(self[p[j], i]) > abs(self[p[pivot], i]):\\n pivot = j\\n p[pivot], p[i] = p[i], p[pivot]\\n for j in range(i + 1, self.rows_count()):\\n if abs(self[p[i], i]) < math.pow(10, -6):\\n raise ValueError(\\\"Can't divide by 0\\\")\\n self[p[j], i] /= self[p[i], i]\\n for k in range(i + 1, self.rows_count()):\\n self[p[j], k] -= self[p[j], i] * self[p[i], k]\\n lst = []\\n for i in p:\\n lst.append(self.value[i])\\n return p, Matrix(lst)\",\n \"def norm(self):\\r\\n old_origin = np.array(self.origin)\\r\\n self.origin = [0, 0, 0]\\r\\n old_origin[0] = old_origin[0] / self.x[0]\\r\\n old_origin[1] = old_origin[1] / self.y[1]\\r\\n old_origin[2] = old_origin[2] / self.z[2]\\r\\n self.data = ndimage.shift(self.data, -old_origin, mode='wrap')\",\n \"def normalize(self):\\n self._vectors = [vector.normalized() for vector in self._vectors]\",\n \"def nll(self, data):\\n nll = np.zeros(data.shape[0])\\n self.a[0] = self.c\\n for i in range(self.Ndim):\\n a = self.rhos[i] * self.a[i]\\n h = 0.5 * (a + np.abs(a)) # ReLU\\n za = np.dot(self.Vs['alpha'][i].T, h) + self.bs['alpha'][i]\\n zm = np.dot(self.Vs['mu'][i].T, h) + self.bs['mu'][i]\\n zs = np.dot(self.Vs['sigma'][i].T, h) + self.bs['sigma'][i]\\n self.alphas[i] = softmax(za)\\n self.mus[i] = zm\\n self.sigmas[i] = np.exp(zs)\\n self.vars = self.sigmas ** 2.\\n nll += nll_MOG_1D(data[:, i], self.alphas[i], self.mus[i],\\n self.vars[i])[0]\\n return nll\",\n \"def laplacian(self, array_in):\\r\\n\\r\\n # Call-through to Laplacian operator, already computed\\r\\n return self.laplace_op*array_in\",\n \"def normalize_lorentz_2d(\\n lorentz, old_f_scale, old_v_scale, new_f_scale=(\\n 0, 1, 1024), new_v_scale=(\\n 0, 1, 1024)):\\n lorentz_array = np.empty((0, 4))\\n for i in range(0, lorentz.shape[0]):\\n l0 = lorentz[i]\\n l1 = normalize_lorentz_1d(\\n l0, old_f_scale, old_v_scale, new_f_scale, new_v_scale)\\n lorentz_array = np.append(lorentz_array, np.array([l1]), axis=0)\\n return lorentz_array\",\n \"def normalise(a):\\n return (a - np.nanmin(a)) / np.nanmax(a - np.nanmin(a))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6905368","0.6712484","0.6235044","0.62139124","0.62099594","0.6200239","0.61991894","0.6155274","0.6130805","0.6119615","0.6119615","0.6039203","0.594853","0.5903943","0.5903158","0.58970135","0.5886368","0.58848","0.58638287","0.58587956","0.58482677","0.5803521","0.5797989","0.57870847","0.5773412","0.5770112","0.5744329","0.5739798","0.5731862","0.5724902","0.5692082","0.56882465","0.56730175","0.5665962","0.5628615","0.56279975","0.5624041","0.56159663","0.56096995","0.56046546","0.55955446","0.5574497","0.55629605","0.55278856","0.550388","0.55023104","0.5484754","0.54839694","0.54776424","0.5467214","0.5464804","0.5450003","0.5447569","0.5443797","0.5432788","0.5427415","0.5418077","0.54159373","0.5415215","0.54135424","0.5413237","0.54075545","0.54046065","0.5399458","0.53968114","0.5394406","0.5386516","0.53770787","0.5357393","0.5357184","0.53548867","0.5351279","0.5349227","0.5348093","0.5346608","0.5345797","0.53454036","0.5345001","0.534491","0.5343089","0.5334035","0.5331855","0.53261834","0.5325881","0.5324536","0.5322077","0.53219295","0.53175217","0.5317103","0.53123534","0.5306175","0.5303099","0.5301771","0.5301258","0.5301113","0.528586","0.5285603","0.5284992","0.5280618","0.5273994"],"string":"[\n \"0.6905368\",\n \"0.6712484\",\n \"0.6235044\",\n \"0.62139124\",\n \"0.62099594\",\n \"0.6200239\",\n \"0.61991894\",\n \"0.6155274\",\n \"0.6130805\",\n \"0.6119615\",\n \"0.6119615\",\n \"0.6039203\",\n \"0.594853\",\n \"0.5903943\",\n \"0.5903158\",\n \"0.58970135\",\n \"0.5886368\",\n \"0.58848\",\n \"0.58638287\",\n \"0.58587956\",\n \"0.58482677\",\n \"0.5803521\",\n \"0.5797989\",\n \"0.57870847\",\n \"0.5773412\",\n \"0.5770112\",\n \"0.5744329\",\n \"0.5739798\",\n \"0.5731862\",\n \"0.5724902\",\n \"0.5692082\",\n \"0.56882465\",\n \"0.56730175\",\n \"0.5665962\",\n \"0.5628615\",\n \"0.56279975\",\n \"0.5624041\",\n \"0.56159663\",\n \"0.56096995\",\n \"0.56046546\",\n \"0.55955446\",\n \"0.5574497\",\n \"0.55629605\",\n \"0.55278856\",\n \"0.550388\",\n \"0.55023104\",\n \"0.5484754\",\n \"0.54839694\",\n \"0.54776424\",\n \"0.5467214\",\n \"0.5464804\",\n \"0.5450003\",\n \"0.5447569\",\n \"0.5443797\",\n \"0.5432788\",\n \"0.5427415\",\n \"0.5418077\",\n \"0.54159373\",\n \"0.5415215\",\n \"0.54135424\",\n \"0.5413237\",\n \"0.54075545\",\n \"0.54046065\",\n \"0.5399458\",\n \"0.53968114\",\n \"0.5394406\",\n \"0.5386516\",\n \"0.53770787\",\n \"0.5357393\",\n \"0.5357184\",\n \"0.53548867\",\n \"0.5351279\",\n \"0.5349227\",\n \"0.5348093\",\n \"0.5346608\",\n \"0.5345797\",\n \"0.53454036\",\n \"0.5345001\",\n \"0.534491\",\n \"0.5343089\",\n \"0.5334035\",\n \"0.5331855\",\n \"0.53261834\",\n \"0.5325881\",\n \"0.5324536\",\n \"0.5322077\",\n \"0.53219295\",\n \"0.53175217\",\n \"0.5317103\",\n \"0.53123534\",\n \"0.5306175\",\n \"0.5303099\",\n \"0.5301771\",\n \"0.5301258\",\n \"0.5301113\",\n \"0.528586\",\n \"0.5285603\",\n \"0.5284992\",\n \"0.5280618\",\n \"0.5273994\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56505066"},"document_rank":{"kind":"string","value":"34"}}},{"rowIdx":892,"cells":{"query":{"kind":"string","value":"Solve for several eigenvectors."},"document":{"kind":"string","value":"def solve_for_eigenvectors(matrix, num, mode=\"general\"):\n\n # Construct a sparse matrix\n if mode == \"general\":\n return linalg.eigs(matrix, num)\n\n if mode == \"symmetric\":\n return linalg.eigsh(matrix, num)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def eigsolve(self,**kwargs):\n return eigsolve(self,**kwargs)","def _solve_eigen(self, X, y):\n self.means_, self.neighbor_means_ = _class_means_and_neighbor_means(\n X, y, self.within_between_ratio, self.nearest_neighbor_ratio)\n \n Sw = _class_cov(X, y) # within class cov\n Sb = _local_pairwise_cov(self.means_, self.neighbor_means_)\n \n evals, evecs = linalg.eigh(Sb, Sw)\n evecs = evecs[:, np.argsort(evals)[::-1]] # sort eigenvectors\n self.scalings_ = np.asarray(evecs)","def eigensolve(self, epsilon=0.85):\n raise NotImplementedError(\"eigensolve Incomplete\")","def eig(self,manifold_num):\n num_sites = len(self.energies[manifold_num])\n ham = self.manifold_hamiltonian(manifold_num).toarray()\n eigvals, eigvecs = eigh(ham)\n # Force degenerate eigenvectors to be orthogonal\n if self.qr_flag:\n eigvecs, r = np.linalg.qr(eigvecs,mode='reduced')\n if self.check_eigenvectors:\n HV = ham.dot(eigvecs)\n D = eigvecs.T.dot(HV)\n if np.allclose(D,np.diag(eigvals),rtol=1E-11,atol=1E-11):\n pass\n else:\n # warnings.warn('Eigenvalues altered by QR factorization, max absolute change in diagonal matrix of {}'.format(np.max(D-np.diag(eigvals))))\n warnings.warn('Using eigenvectors to diagonalize hamiltonian does not result in the expected diagonal matrix to tolerance, largest deviation is {}'.format(np.max(np.abs(D - np.diag(eigvals)))))\n \n sort_indices = eigvals.argsort()\n eigvals.sort()\n eigvecs = eigvecs[:,sort_indices]\n if self.qr_flag:\n r = r[:,sort_indices]\n self.r_mats.append(r)\n # I choose to pick the phase of my eigenvectors such that the state which has the\n # largest overlap has a positive overlap. For sufficiently small d, and alpha close\n # to 1, this will be the overlap between the same excited and ground states.\n for i in range(eigvals.size):\n max_index = np.argmax(np.abs(eigvecs[:,i]))\n if eigvecs[max_index,i] < 0:\n eigvecs[:,i] *= -1\n\n return eigvals, eigvecs","def calc_eigenvectors(verts: [[float]]):\n A = np.zeros((3, len(verts)))\n\n A[0] = np.array([x[0] for x in verts]) # First row is all the X_coords\n A[1] = np.array([x[1] for x in verts]) # second row is all the Y_coords\n A[2] = np.array([x[2] for x in verts]) # third row is all the z-coords\n \n A_cov = np.cov(A) # This is returns a 3x3\n eigenvalues, eigenvectors = np.linalg.eigh(A_cov)\n\n return eigenvalues, eigenvectors","def numpy_eigenvectors(A):\n import numpy\n A = numpy.array(A)\n E, V = numpy.linalg.eigenvectors(A)\n import Numeric\n E = Numeric.array(E)\n V = Numeric.array(V)\n return E, V","def plot_eigenvectors(self, **parameter_overrides):\n par, arr_keys, _ = self._parse_parameter_overrides(\n **parameter_overrides)\n states = [r'\\phi', r'\\delta', r'\\dot{\\phi}', r'\\dot{\\delta}']\n\n eval_seq, evec_seq = self.calc_eigen(**parameter_overrides)\n eval_seq, evec_seq = np.atleast_2d(eval_seq), np.atleast_3d(evec_seq)\n\n if eval_seq.shape[0] > 10:\n msg = ('Plots will be too large, use fewer than 11 values in the '\n 'varied parameter.')\n raise ValueError(msg)\n\n # TODO : sort_eigenmodes() is doing something funny and not adding the\n # 4th eigenvalue, so you often end up with duplicates eigenvalues for\n # one eigenval and one missing. Also the algorithm may not work well\n # with spaced out eigenvalues, which is what I've been trying here. You\n # may have to calculate eigenvals/vec across closer spacing, then\n # sample out the ones you want. For now, we don't sort coarse spaced\n # eigenvalues.\n # if arr_keys:\n # eval_seq, evec_seq = sort_eigenmodes(eval_seq, evec_seq)\n\n fig, axes = plt.subplots(*eval_seq.shape,\n subplot_kw={'projection': 'polar'})\n axes = np.atleast_2d(axes)\n fig.set_size_inches(axes.shape[1]*3, axes.shape[0]*3)\n lw = list(range(1, len(states) + 1))\n lw.reverse()\n\n for k, (evals, evecs, par_val) in enumerate(zip(eval_seq, evec_seq,\n par[arr_keys[0]])):\n\n axes[k, 0].set_ylabel('{} = {:1.2f}'.format(arr_keys[0], par_val),\n labelpad=30)\n\n for i, (eigenval, eigenvec) in enumerate(zip(evals, evecs.T)):\n\n max_com = np.abs(eigenvec[:2]).max()\n\n for j, component in enumerate(eigenvec[:2]):\n\n radius = np.abs(component)/max_com\n theta = np.angle(component)\n axes[k, i].plot([0, theta], [0, radius], lw=lw[j])\n\n axes[k, i].set_rmax(1.0)\n msg = r'Eigenvalue: {:1.3f}'\n if eigenval.real >= 0.0:\n fontcolor = 'red' # red indicates unstable\n else:\n fontcolor = 'black'\n axes[k, i].set_title(msg.format(eigenval),\n fontdict={'color': fontcolor})\n\n axes[0, 0].legend(['$' + s + '$' for s in states],\n loc='upper center', bbox_to_anchor=(0.5, 1.05),\n fancybox=True, shadow=True, ncol=4)\n\n fig.tight_layout()\n\n return axes","def calculate_eigens(self):\n covariance_matrix = np.cov(self.predictor_vars_train.T)\n eigenvalues, eigenvectors = np.linalg.eig(covariance_matrix)\n idx = eigenvalues.argsort()[::-1]\n # Create \"All\" version\n self.eigenvalues_all = eigenvalues[idx]\n self.eigenvectors_all = eigenvectors[:, idx]\n # Create selected percentage version with cutoff\n eigenvalues_pct = self.eigenvalues_all / np.sum(self.eigenvalues_all)\n self.pct_var_exp_cumulative_all = np.cumsum(eigenvalues_pct)\n self.pct_var_exp_cumulative = self.pct_var_exp_cumulative_all[\n self.pct_var_exp_cumulative_all <= self.variance_explained_cutoff\n ]\n self.eigenvectors = self.eigenvectors_all[:, : len(self.pct_var_exp_cumulative)]\n self.eigenvalues = self.eigenvalues_all[: len(self.pct_var_exp_cumulative)]","def eigvals(self):\n raise NotImplementedError","def compute_eigvals(*params, **hyperparams):\n phi = params[0]\n d, t = hyperparams[\"dimension\"]\n\n if qml.math.get_interface(phi) == \"tensorflow\":\n phase = qml.math.exp(1j * qml.math.cast_like(phi, 1j))\n minus_phase = qml.math.exp(-1j * qml.math.cast_like(phi, 1j))\n return stack_last([phase if index < d else minus_phase for index in range(t)])\n\n arg = 1j * phi\n prefactors = qml.math.array([1 if index < d else -1 for index in range(t)], like=phi)\n\n if qml.math.ndim(phi) == 0:\n product = arg * prefactors\n else:\n product = qml.math.outer(arg, prefactors)\n return qml.math.exp(product)","def get_eigenvectors(self):\n return self.eigenVectors","def GetEigenvectors(self):\n\t\treturn self.Solver.GetEigenvectors()","def eigen(M):\n values, vectors = np.linalg.eig(M)\n return values, vectors","def eigensys(self, evals_count):\n hamiltonian_mat = self.hamiltonian()\n evals, evecs = hamiltonian_mat.eigenstates(eigvals=evals_count)\n return evals, evecs","def mesh_laplacian_eigenvectors(mesh, nb_vectors=1):\n lap, lap_b = compute_mesh_laplacian(mesh, lap_type=\"fem\")\n w, v = eigsh(lap.tocsr(), nb_vectors + 1, M=lap_b.tocsr(), sigma=solver_tolerance)\n return v[:, 1:]","def eigvals(input):\n\n is_input_dparray = isinstance(input, dparray)\n\n if (not use_origin_backend(input) and is_input_dparray):\n if (input.size > 0):\n return dpnp_eigvals(input)\n\n return call_origin(numpy.linalg.eigvals, input)","def eigs(self,num_eigvals,manifold_num):\n num_sites = len(self.energies[manifold_num])\n ham = self.manifold_hamiltonian(manifold_num)\n eigvals, eigvecs = eigsh(ham,k=num_eigvals*num_sites,which='SM')\n # Force degenerate eigenvectors to be orthogonal\n if self.qr_flag:\n eigvecs, r = np.linalg.qr(eigvecs,mode='reduced')\n if self.check_eigenvectors:\n HV = ham.dot(eigvecs)\n D = eigvecs.T.dot(HV)\n if np.allclose(D,np.diag(eigvals),rtol=1E-11,atol=1E-11):\n pass\n else:\n # warnings.warn('Eigenvalues altered by QR factorization, max absolute change in diagonal matrix of {}'.format(np.max(D-np.diag(eigvals))))\n warnings.warn('Using eigenvectors to diagonalize hamiltonian does not result in the expected diagonal matrix to tolerance, largest deviation is {}'.format(np.max(np.abs(D - np.diag(eigvals)))))\n \n sort_indices = eigvals.argsort()\n eigvals.sort()\n eigvecs = eigvecs[:,sort_indices]\n if self.qr_flag:\n r = r[:,sort_indices]\n self.r_mats.append(r)\n # I choose to pick the phase of my eigenvectors such that the state which has the\n # largest overlap has a positive overlap. For sufficiently small d, and alpha close\n # to 1, this will be the overlap between the same excited and ground states.\n for i in range(num_eigvals):\n max_index = np.argmax(np.abs(eigvecs[:,i]))\n if eigvecs[max_index,i] < 0:\n eigvecs[:,i] *= -1\n\n return eigvals, eigvecs","def test_eigen_multiple_neighborhoods(self):\n # vectorized version\n t0 = time.time()\n extract_vect = EigenValueVectorizeFeatureExtractor()\n eigvals_vect = extract_vect.extract(self.point_cloud, self.neigh, None, None, None)\n print('Timing Vectorize : {}'.format((time.time() - t0)))\n eigvals_vect = np.vstack(eigvals_vect[:3]).T\n\n # serial version\n eigvals = []\n t0 = time.time()\n for n in self.neigh:\n extract = EigenValueSerial()\n eigvals.append(extract.extract(self.point_cloud, n, None, None, None))\n print('Timing Serial : {}'.format((time.time() - t0)))\n eigvals = np.array(eigvals)\n\n np.testing.assert_allclose(eigvals_vect, eigvals)","def get_eigenvectors(self):\n return self._eigenvectors","def MakeEigenVectors( self ): \n sqrt2 = np.sqrt(2)\n Isqrt2 = 1.0 / sqrt2\n EVectors = np.asarray( [ [ Isqrt2 , Isqrt2 , 0 ] ,\n [ Isqrt2 ,-Isqrt2 , 0 ] , \n [ 0 , 0 , 1 ] ] )\n for i in range( self.NQ ):\n for j in range( self.Nbranches ):\n self.EigenVectors[ i , j , 0 , : ] = EVectors[ j , : ]","def eigenpairs(mesh, nb_eig):\n lap, lap_b = sdg.compute_mesh_laplacian(mesh, lap_type='fem')\n eig_val, eig_vec = eigsh(lap.tocsr(), nb_eig, M=lap_b.tocsr(),\n sigma=1e-6, which='LM')\n return eig_val, eig_vec, lap_b.tocsr()","def calculate_eigenvalues(H):\n eigenvalues, eigenvectors = np.linalg.eigh(H)\n return eigenvalues, eigenvectors","def calculate_eigenvalues(self):\n self.__eigenvalues = []\n dictionary = np.linalg.eig(np.array(self.__A))\n indicator = True\n sum1 = 0\n for i in range(self.__A.shape[0]):\n if all(self.__A[i, j] == 0 for j in range(self.__A.shape[1])):\n indicator = all(self.__B[i,j] for j in range(self.__B.shape[1]))\n if (indicator):\n sum1 += 1\n \n for val in dictionary[0]:\n if (val != 0):\n self.__eigenvalues.append(complex(val))\n elif (indicator) and (sum1 > 0):\n sum1 -= 1\n self.__eigenvalues.append(complex(val))","def project_to_eigenvectors(X, vecs):\n\n return (X-np.mean(X, axis=0)).dot(np.transpose(vecs)) #PCA assumes that the data is centered, so we need to do that before doing the calculations","def run_exact(self):\n self.operator, var_form, opt = self.generate_VQE_args()\n\n exact_eigensolver = ExactEigensolver(self.operator, k=1)\n self.result = exact_eigensolver.run()\n\n solution = self.extract_solution(self.result, True)\n return solution","def solve(self, sparse_args=None):\n Hmat = self.Hamiltonian()\n if sparse_args is not None: self.sparse_args = sparse_args\n if self.sparse_args is None:\n en, ev = eig(Hmat.todense())\n else:\n en, ev = eigsh(Hmat, **self.sparse_args)\n ev = transpose(array(ev))[argsort(en)]\n en = sort(en)\n self.en = en\n self.ev = ev\n self.solved = True\n return self.en, self.ev","def eigen_decomposition(self):\n w, V = linalg.eigh(self.K)\n c = w[::-1]\n if isinstance(self.num_xi, float):\n percent_energy = np.cumsum(c) / np.sum(c)\n self.num_xi = np.arange(c.shape[0])[percent_energy < self.num_xi][-1] # num_xi changes\n self.Lambda = w[::-1][:self.num_xi]\n self.V = V[:, ::-1][:, :self.num_xi]","def compute_eigendecomp(self, atol=1e-13, rtol=None):\n self.eigvals, self.eigvecs = parallel.call_and_bcast(\n util.eigh, self.correlation_array, atol=atol, rtol=rtol,\n is_positive_definite=True)","def calc_eigen(self, left=False, **parameter_overrides):\n A, B = self.form_state_space_matrices(**parameter_overrides)\n\n if len(A.shape) == 3: # array version\n evals = np.zeros(A.shape[:2], dtype='complex128')\n evecs = np.zeros(A.shape, dtype='complex128')\n for i, Ai in enumerate(A):\n if left:\n Ai = np.transpose(Ai)\n evals[i], evecs[i] = np.linalg.eig(Ai)\n return evals, evecs\n else:\n if left:\n A = np.transpose(A)\n return np.linalg.eig(A)","def eigen_decomp(matrix):\n w = None\n v = None\n ### YOUR CODE HERE\n w,v=np.linalg.eig(matrix)\n ### END YOUR CODE\n return w, v","def eig_vals_vects(matrix, sort='imag', not_hermitian=True, verbose=False):\n # if len(matrix) < 10:\n # print '\\nFinding eigvals, matrix = ', matrix\n\n # check if hermitian:\n if not_hermitian:\n eigval, eigvect = np.linalg.eig(matrix)\n else:\n if (matrix == matrix.conj().T).all():\n if verbose:\n print 'Shortcut eigvect/vals since matrix is hermitian...'\n eigval, eigvect = np.linalg.eigh(matrix)\n else:\n if verbose:\n print 'matrix is not hermitian...'\n eigval, eigvect = np.linalg.eig(matrix)\n\n # use imaginary part to get ascending order of eigvals\n if sort == 'imag':\n si = np.argsort(np.imag(eigval))\n elif sort == 'real':\n si = np.argsort(np.real(eigval))\n else:\n si = np.arange(len(eigval))\n\n eigvect = np.array(eigvect)\n eigvect_out = eigvect.T[si]\n eigval_out = eigval[si]\n\n # if len(eigval_out) < 10:\n # print 'eigvals return as =', eigval_out\n\n return eigval_out, eigvect_out","def get_eigvals_eigvects(\n num_layers,\n numeric_matrices_eV_over_angsquared,\n layer_mass_amu,\n use_banded_algorithm=False,\n):\n # Based on the units in input, and indicating with:\n # - [hbar omega] the numeric value for the frequency in meV => hbar omega = [hbar omega] * meV\n # - [K] the numeric value of K in eV/ang^2\n # - [m] the layer mass in amu\n # we have (we omit the sign, and for units considerations we 'drop' U):\n # omega^2 = K / m =>\n # (hbar omega)^2 = hbar^2 * K / m =>\n # [hbar omega]^2 * meV^2 = hbar^2 * [K] / [m] * eV/ang^2 / amu = [K] / [m] * hbar^2 * eV/ang^2 / amu =>\n # [hbar omega]^2 = = [K] / [m] * ( hbar^2 * eV/ang^2 / amu / meV^2 )\n # so that the conversion factor is the last bracketed term:\n # conversion_factor = hbar^2 * eV / (angstrom^2 * amu * meV^2)\n conversion_factor = 4180.15925\n # NOTE: for simplicity, the conversion is applied at the very end\n\n if use_banded_algorithm:\n # 3 blocks (below, same layer, and above) of size 3 => total width of 9\n # Since we only store the upper part, we only need a width of 4 (diagonal + 3 superdiagonals)\n K_matrix = np.zeros((4, num_layers * 3))\n else:\n K_matrix = np.zeros((num_layers * 3, num_layers * 3))\n\n # Note: I construct -K, actually\n for block_idx in range(num_layers):\n # Interaction with upper layer\n if block_idx < num_layers - 1: # Not in the last layer\n current_block = np.array(\n numeric_matrices_eV_over_angsquared[\n block_idx % len(numeric_matrices_eV_over_angsquared)\n ]\n )\n add_block(\n matrix=K_matrix,\n block=current_block,\n block_i=block_idx,\n block_j=block_idx,\n factor=+1,\n banded=use_banded_algorithm,\n )\n add_block(\n matrix=K_matrix,\n block=current_block,\n block_i=block_idx + 1,\n block_j=block_idx,\n factor=-1,\n banded=use_banded_algorithm,\n )\n # Interaction with lower layer\n if block_idx > 0: # Not in the first layer\n previous_block = np.array(\n numeric_matrices_eV_over_angsquared[\n (block_idx - 1) % len(numeric_matrices_eV_over_angsquared)\n ]\n )\n add_block(\n matrix=K_matrix,\n block=previous_block,\n block_i=block_idx,\n block_j=block_idx,\n factor=+1,\n banded=use_banded_algorithm,\n )\n add_block(\n matrix=K_matrix,\n block=previous_block,\n block_i=block_idx - 1,\n block_j=block_idx,\n factor=-1,\n banded=use_banded_algorithm,\n )\n\n # We want to get the eigenvalues of omega^2 U = - 1/M_layer K U\n K_matrix /= layer_mass_amu\n\n # Get frequencies (eigvals) and eigenvectors (for mode analysis)\n if use_banded_algorithm:\n eigvals, eigvects = scipy.linalg.eig_banded(K_matrix, lower=False)\n else:\n eigvals, eigvects = np.linalg.eigh(K_matrix)\n\n eigvals *= conversion_factor\n\n ## The first three should be acoustic i.e. almost zero; the rest should be positive\n ## I don't check as depending on the units it's hard to define a correct absolute energy\n # assert np.sum(np.abs(eigvals[:3])) < 1.0e-8\n\n # Remove the first three acoustic modes\n return eigvals[3:], eigvects[:, 3:]","def eigenvalue_analysis(*, dates, obs_data, model_data, residuals,\n proxy_number=1):\n # Create a masked version of the residuals array so that we can perform the\n # PCA ignoring all nan values\n masked_residuals = np.ma.array(residuals, mask=np.isnan(residuals))\n\n # Calculate the covariance matrix of the masked residuals array\n covariance_matrix = np.ma.cov(masked_residuals, rowvar=False,\n allow_masked=True)\n # Calculate the eigenvalues and eigenvectors of the covariance matrix\n eig_values, eig_vectors = np.linalg.eigh(covariance_matrix)\n # Sort the eigenvalues in decreasing order\n idx = np.argsort(np.abs(eig_values))[::-1]\n eig_values = eig_values[idx]\n # Sort the eigenvectors according to the same index\n eig_vectors = eig_vectors[:, idx]\n\n # Project the residuals onto the eigenvectors\n projected_residuals = np.ma.dot(masked_residuals, eig_vectors)\n\n # Use the method of Cox et al (2018) to remove unmodelled external\n # signal in the SV residuals. The variable 'proxy' contains the sum\n # of the SV residuals projected into the number of dominant PCs\n # specified by proxy_number\n\n corrected_residuals = []\n\n if proxy_number == 1:\n noisy_direction = eig_vectors[:, 0]\n proxy = projected_residuals[:, 0]\n for idx in range(len(proxy)):\n corrected_residuals.append(\n masked_residuals.data[idx, :] - proxy[idx] * noisy_direction)\n # Apply the denoising algorithm to each of the PCs specified\n elif proxy_number > 1:\n noisy_direction = eig_vectors[:, 0:proxy_number]\n proxy = np.sum(projected_residuals[:, 0:proxy_number], axis=1)\n for idx in range(len(projected_residuals[:, 0])):\n corrected = masked_residuals.data[idx, :]\n for direction in range(proxy_number):\n corrected = corrected - projected_residuals[idx, direction] \\\n * noisy_direction[:, direction]\n corrected_residuals.append(corrected)\n\n corrected_residuals = pd.DataFrame(corrected_residuals,\n columns=obs_data.columns)\n # Re-form the SV from the denoised residuals\n denoised_sv = pd.DataFrame(\n corrected_residuals.values + model_data.values,\n columns=obs_data.columns)\n\n denoised_sv.insert(0, 'date', dates)\n\n return denoised_sv, proxy, np.abs(eig_values), eig_vectors,\\\n projected_residuals, corrected_residuals.astype('float'),\\\n covariance_matrix","def computemodes(self):\n evals, modes = np.linalg.eig(self.A)\n return evals, modes","def run_vqd(\n self,\n backend=Aer.get_backend(\"statevector_simulator\"),\n var_form=None,\n optimizer=None,\n reps=2,\n # reps=5,\n ):\n tmp = HermitianSolver(self.mat)\n max_eigval, vqe_result, vqe = tmp.run_vqe(\n backend=backend,\n var_form=var_form,\n optimizer=optimizer,\n reps=reps,\n mode=\"max_val\",\n )\n eigvals = [max_eigval]\n eigstates = [vqe_result.eigenstate]\n for r in range(len(tmp.mat) - 1):\n val, vqe_result, vqe = tmp.run_vqe(\n backend=backend,\n var_form=var_form,\n optimizer=optimizer,\n reps=reps,\n )\n outer_prod = np.outer(\n vqe_result.eigenstate, np.conj(vqe_result.eigenstate).T\n )\n tmp.mat = tmp.mat - (val - max_eigval) * outer_prod\n eigvals.append(val)\n eigstates.append(vqe_result.eigenstate)\n tmp = HermitianSolver(tmp.mat)\n\n eigvals = np.array(eigvals)\n eigstates = np.array(eigstates)\n order = np.argsort(eigvals)\n eigvals = eigvals[order]\n eigstates = eigstates[order]\n return eigvals, eigstates","def eigen_vector_i_all(self):\n return self._eig_vec","def eigen(X):\n\n symmetric = np.alltrue(np.isclose(X - X.T, np.zeros(n)))\n small = max(X.shape) <= 11\n\n if symmetric:\n return jacobi(X)\n elif small:\n maxiter = 10 ** max(*X.shape, 4)\n return qrm3(X, maxiter=maxiter)\n else:\n maxiter = 10 ** max(*X.shape, 4)\n return qrm2(X, maxiter=maxiter)","def FindEigenstates(**args):\n\tprop = SetupProblem(**args)\n\n\t#use custom initial residual if provided\n\tinitialResidual = args.get(\"initialResidual\")\n\tif initialResidual != None:\n\t\tprop.psi.GetData()[:] = initialResidual.GetData()\n\n\t#find eigenstates\n\t#solver = pyprop.ArpackSolver(prop)\n\tsolver = pyprop.PiramSolver(prop)\n\tsolver.Solve()\n\treturn solver","def _solve_principal_eig(a):\n w, v = np.linalg.eig(a)\n idx = np.argmax(w)\n eig_val = w[idx]\n eig_vec = v[:, idx]\n\n # Let eig_vec non-negative\n sign = 0\n i = 0\n while sign == 0 and i < len(eig_vec):\n sign = np.sign(eig_vec[i])\n i += 1\n if sign < 0:\n eig_vec *= -1\n\n return eig_val, eig_vec","def eigen_decomposition(X, features):\n # Center to average\n Xctr = X - X.mean(0)\n # covariance matrix\n Xcov = np.cov(Xctr.T)\n\n # Compute eigenvalues and eigenvectors\n eigen_values, eigen_vectors = sp.linalg.eigh(Xcov)\n\n # Sort the eigenvalues and the eigenvectors descending\n sortedindex = np.argsort(eigen_values)[::-1]\n eigen_values = eigen_values[sortedindex]\n eigen_vectors = eigen_vectors[:, sortedindex]\n\n ###########\n y_pos = np.arange(len(features))\n weight = eigen_vectors[0]\n\n figure, axis = plt.subplots(2, 1)\n\n axis[0].bar(features, eigen_vectors[0])\n plt.setp(axis[0], title=\"First and Second Component's Eigenvectors \", ylabel='Weight')\n axis[0].set_xticks(features, features)\n axis[1].bar(features, eigen_vectors[1])\n axis[1].set_xticks(features, features)\n plt.setp(axis[1], ylabel='Weight')\n # axis[0].bar(y_pos, weight, align='center', alpha=0.5)\n # axis[0].xticks(y_pos, features)\n # axis[0].ylabel('Weight')\n # axis[0].title('Features')\n #\n # axis[1].bar(y_pos, weight, align='center', alpha=0.5)\n # axis[1].xticks(y_pos, features)\n # axis[1].ylabel('Weight')\n # axis[1].title('Features')\n\n plt.show()\n # return eigen_values, eigen_vectors","def eig_vals_vects(self, matrix=None, attribute=False, attribute_matrix=False):\n if matrix is None:\n matrix = self.get_matrix(attribute=attribute_matrix)\n eigval, eigvect = np.linalg.eig(matrix)\n si = np.argsort(np.imag(eigval))\n eigvect = np.array(eigvect)\n eigvect = eigvect.T[si]\n eigval = eigval[si]\n\n if attribute:\n self.eigvect = eigvect\n self.eigval = eigval\n\n # print 'np.shape(eigvect) = ', np.shape(eigvect)\n # sys.exit()\n return eigval, eigvect","def posdef_eig_svd(mat):\n evals, evecs, _ = tf.svd(mat)\n\n return evals, evecs","def get_eigen_values_and_vectors(matrix, num_values):\n w, v = eigen_decomp(matrix)\n eigen_values = []\n eigen_vectors = []\n ### YOUR CODE HERE\n pass\n ### END YOUR CODE\n return eigen_values, eigen_vectors","def scipy_eigsolver(\n kernel_matrix: Union[np.ndarray, scipy.sparse.csr_matrix],\n n_eigenpairs: int,\n is_symmetric: bool,\n is_stochastic: bool,\n):\n\n n_samples, n_features = kernel_matrix.shape\n\n # check only for n_eigenpairs == n_features and n_eigenpairs < n_features\n # wrong parametrized n_eigenpairs are catched in scipy functions\n if n_eigenpairs == n_features:\n if is_symmetric:\n scipy_eigvec_solver = scipy.linalg.eigh\n else:\n scipy_eigvec_solver = scipy.linalg.eig\n\n solver_kwargs: Dict[str, object] = {\n \"check_finite\": False\n } # should be already checked\n\n else: # n_eigenpairs < matrix.shape[1]\n if is_symmetric:\n scipy_eigvec_solver = scipy.sparse.linalg.eigsh\n else:\n scipy_eigvec_solver = scipy.sparse.linalg.eigs\n\n solver_kwargs = {\n \"k\": n_eigenpairs,\n \"which\": \"LM\",\n \"v0\": np.ones(n_samples),\n \"tol\": 1e-14,\n }\n\n # The selection of sigma is a result of a microbenchmark\n if is_symmetric and is_stochastic:\n # NOTE: it turned out that for self.kernel_.is_symmetric=False (-> eigs),\n # setting sigma=1 resulted into a slower computation.\n NUMERICAL_EXACT_BREAKER = 0.1\n solver_kwargs[\"sigma\"] = 1.0 + NUMERICAL_EXACT_BREAKER\n solver_kwargs[\"mode\"] = \"normal\"\n else:\n solver_kwargs[\"sigma\"] = None\n\n # the scipy solvers only work on floating points\n if scipy.sparse.issparse(\n kernel_matrix\n ) and kernel_matrix.data.dtype.kind not in [\"fdFD\"]:\n kernel_matrix = kernel_matrix.asfptype()\n elif isinstance(kernel_matrix, np.ndarray) and kernel_matrix.dtype != \"f\":\n kernel_matrix = kernel_matrix.astype(float)\n\n eigvals, eigvects = scipy_eigvec_solver(kernel_matrix, **solver_kwargs)\n\n return eigvals, eigvects","def fit(self, X):\n mean_vec = X.mean(axis=0)\n cov = (X - mean_vec).T.dot((X - mean_vec)) / (X.shape[0]-1)\n eigenvalues, eigenvectors = np.linalg.eig(cov)\n idx = np.abs(eigenvalues)\n self.best_eigenvalues = idx[:self.n_components]\n self.best_eigenvalues = eigenvectors[:, :self.n_components]\n eigenvectors = np.atleast_1d(eigenvectors)[:, :self.n_components]","def getEigenVectors(X_new, elambda):\n X_cov = X_new.T.dot(X_new)\n eigenvalues, eigenvectors = np.linalg.eig(X_cov)\n eigen_sorted_ind = eigenvalues.argsort()[::-1]\n eigenvalues = eigenvalues / eigenvalues.sum()\n eigenvalues_sum = 0\n eigenvectors_selected = eigenvectors\n for i in xrange(len(eigen_sorted_ind)):\n eigenvalues_sum += eigenvalues[eigen_sorted_ind[i]]\n if eigenvalues_sum >= elambda:\n eigenvectors_selected = eigenvectors[:, eigen_sorted_ind[0:i]]\n break\n return eigenvectors_selected","def compute_eigvals(theta, pauli_word): # pylint: disable=arguments-differ\n if qml.math.get_interface(theta) == \"tensorflow\":\n theta = qml.math.cast_like(theta, 1j)\n\n # Identity must be treated specially because its eigenvalues are all the same\n if set(pauli_word) == {\"I\"}:\n exp = qml.math.exp(-0.5j * theta)\n ones = qml.math.ones(2 ** len(pauli_word), like=theta)\n if qml.math.get_interface(theta) == \"tensorflow\":\n ones = qml.math.cast_like(ones, 1j)\n\n if qml.math.ndim(theta) == 0:\n return exp * ones\n\n return qml.math.tensordot(exp, ones, axes=0)\n\n return MultiRZ.compute_eigvals(theta, len(pauli_word))","def get_eigen_values_and_vectors(matrix, num_values):\n (w, v) = eigen_decomp(matrix)\n eigen_values = []\n eigen_vectors = []\n ### YOUR CODE HERE\n max_indexs=np.argpartition(w, -num_values)\n max_indexs=max_indexs[-num_values:]\n ids=np.argsort(w[max_indexs])\n sort_index=max_indexs[ids]\n eigen_values=w[sort_index]\n eigen_vectors=v[:,sort_index]\n ### END YOUR CODE\n return eigen_values, eigen_vectors","def right_eigenvectors(matrix, nvals=None):\n matrix = np.asarray(matrix)\n return _eigenvectors(matrix, nvals)","def fit_evd(self):\n\n # EVD only work on square matrices as we need to compute the eigenvalues and eigenvectors\n # For this we compute the covariance matrix K\n # K should be n x n matrix (pixels x pixels)\n\n # The covariance matrix is nxn\n self.cov_matrix = np.zeros(shape=[self.n_features, self.n_features], dtype='uint8')\n\n self.cov_matrix = np.cov(self.norm_matrix, rowvar=False)\n # C is a symmetric matrix and so it can be diagonalized:\n eig_val, eig_vec = linalg.eig(self.cov_matrix)\n\n # Sorting the eigenvectors by decreasing eigenvalues\n # [Start : stop : stepcount] stepcount is reversed\n idx = eig_val.argsort()[::-1]\n eig_val, eig_vec = eig_val[idx], eig_vec[:, idx]\n\n # Explained_variance tell us how much of the variance in the data each eigen value explains\n explained_variance = eig_val / (self.n_samples - 1)\n # total_var is the total variance in the data\n total_var = explained_variance.sum()\n explained_variance_ratio = explained_variance / total_var\n # The cumulative sum of all ratios\n ratio_cumsum = np.cumsum(explained_variance_ratio)\n\n # We search in the cumsum for the index of the value which, when added, corresponds to the quality_percent\n # The index of the cumsum gives us the components we need to add to explain X quality percent of our data\n n_components = np.searchsorted(ratio_cumsum, self.quality_percent, side='right') + 1\n\n self.components = eig_vec[:n_components]\n print(\"The principal components have been calculated using eigendecomposition\", self.components.shape)\n\n return self.components","def test_svd(self):\n eigenvectors, eigenvalues = self.svd.run(self.test_matrix)\n\n self.assertEqual(eigenvectors.shape, (100, 100))\n self.assertEqual(eigenvalues.shape, (100,))","def analytical_eig(A):\n n = len(A)\n h = 1/float(n)\n d = 2/float(h)**2\n a = -1/float(h)**2\n eigenval = np.empty(n)\n for j in range(1,n+1):\n eigenval[j-1] = d + 2*a*np.cos((j*np.pi)/(float(n)+1)) # Analytic solution\n \n return eigenval","def readEigenvectors(self,phys,eigname):\r\n EigenvectorReader.EigenvectorReader(self.checkPath(eigname)).read(phys.myEig)","def eigenvects(mat):\n # Check if symbols are present\n if hasSymbols(mat):\n return mat.eigenvects()\n # Purely numeric matrix\n newMat = recursiveEvaluate(mat.as_mutable())\n return newMat.eigenvects()","def eig_vals_vects_hermitian(matrix, sort='imag'):\n # if len(matrix) < 10:\n # print '\\nFinding eigvals, matrix = ', matrix\n eigval, eigvect = np.linalg.eig(matrix)\n # use imaginary part to get ascending order of eigvals\n if sort == 'imag':\n si = np.argsort(np.imag(eigval))\n elif sort == 'real':\n si = np.argsort(np.real(eigval))\n else:\n si = np.arange(len(eigval))\n\n eigvect = np.array(eigvect)\n eigvect_out = eigvect.T[si]\n eigval_out = eigval[si]\n if len(eigval_out) < 10:\n print 'eigvals return as =', eigval_out\n return eigval_out, eigvect_out","def explore_eigenvalues(self, X_train, X_test) :\r\n\r\n # scaler = StandardScaler()\r\n # X_train = scaler.fit_transform(X_train)\r\n # X_test = scaler.transform(X_test)\r\n\r\n print (\"Train feature shape:\", X_train.shape)\r\n print(\"Train feature shape:\", X_test.shape)\r\n \r\n\r\n mean_vec = np.mean(X_test, axis=0)\r\n cov_mat = (X_test - mean_vec).T.dot((X_test - mean_vec)) / (X_test.shape[0]-1)\r\n print('Covariance matrix \\n%s' %cov_mat)\r\n print('NumPy covariance matrix: \\n%s' %np.cov(X_test.T))\r\n eig_vals, eig_vecs = np.linalg.eig(cov_mat)\r\n print('Eigenvectors \\n%s' %eig_vecs)\r\n print(\"---------------------------------\")\r\n print('\\nEigenvalues \\n%s' %eig_vals)\r\n # Make a list of (eigenvalue, eigenvector) tuples\r\n eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]\r\n\r\n # Sort the (eigenvalue, eigenvector) tuples from high to low\r\n eig_pairs.sort(key=lambda x: x[0], reverse=True)\r\n\r\n # Visually confirm that the list is correctly sorted by decreasing eigenvalues\r\n print(\"---------------------------------\")\r\n print('Eigenvalues in descending order:')\r\n for i in eig_pairs:\r\n print(i[0])\r\n # tot = sum(eig_vals)\r\n # var_exp = [(i / tot)*100 for i in sorted(eig_vals, reverse=True)]\r\n # print(\"Explained variance : \\n{}\".format(var_exp))\r\n \r\n\r\n return X_train, X_test","def eigsh(A, M = None, k = 6, sigma = None, which = 'LM', v0=None,\n ncv = None, maxiter = None, tol = 0., return_eigenvectors = True,\n Minv = None, OPinv = None, mode = 'normal'):\n if M is not None:\n raise NotImplementedError(\"M is not currently supported!\")\n if v0 is not None:\n raise NotImplementedError(\"v0 is not currently supported!\")\n if ncv is not None:\n raise NotImplementedError(\"ncv is not currently supported!\")\n if Minv is not None:\n raise NotImplementedError(\"Minv is not currently supported!\")\n if OPinv is not None:\n raise NotImplementedError(\"OPinv is not currently supported!\")\n inp_data = FrovedisFeatureData(A, dense_kind='rowmajor')\n X = inp_data.get()\n x_dtype = inp_data.get_dtype()\n x_itype = inp_data.get_itype()\n dense = inp_data.is_dense()\n nrows = inp_data.numRows()\n ncols = inp_data.numCols()\n\n if nrows != ncols:\n raise ValueError('expected squared symmetric matrix (shape=%s)' % (inp_data.shape,))\n if k <= 0:\n raise ValueError('k must be greater than 0.')\n if k >= nrows:\n raise ValueError('k must be less than or equal to N for N * N square matrix.')\n if sigma is not None and not dense:\n raise ValueError('currently sigma is only supported for dense matrices.')\n if sigma is None:\n sigma = np.finfo(np.float32).max\n\n if which not in ['LM', 'SM', 'LA', 'SA', 'BE']:\n raise ValueError('which must be one of LM, SM, LA, SA, or BE')\n if mode in ['buckling', 'cayley']:\n raise ValueError('currenly normal mode is only supported!')\n if maxiter is None:\n maxiter = 10 * nrows\n wantEv = return_eigenvectors\n (host, port) = FrovedisServer.getServerInstance()\n res = rpclib.compute_eigsh(host, port, X.get(),\n k, which.encode('ascii'),\n sigma, maxiter, wantEv,\n tol, x_dtype,\n x_itype, dense)\n excpt = rpclib.check_server_exception()\n if excpt[\"status\"]:\n raise RuntimeError(excpt[\"info\"])\n sptr = res[\"eigenval\"]\n uptr = res[\"eigenvec\"]\n m_m = res['m']\n k_k = res['k']\n eigval = FrovedisVector({'dptr' : sptr, 'size' : k_k},\n dtype = TypeUtil.to_numpy_dtype(x_dtype)).to_numpy_array()\n if wantEv:\n eigvec = FrovedisDenseMatrix('C', {'dptr' : uptr, 'nrow' : m_m, 'ncol' : k_k},\n dtype = TypeUtil.to_numpy_dtype(x_dtype)).to_numpy_array()\n return eigval, eigvec\n else:\n return eigval","def compute_eigvals(phi): # pylint: disable=arguments-differ\n if qml.math.get_interface(phi) == \"tensorflow\":\n phi = qml.math.cast_like(phi, 1j)\n\n signs = np.array([1, -1, 0, 0])\n if qml.math.ndim(phi) == 0:\n return qml.math.exp(0.5j * phi * signs)\n\n return qml.math.exp(qml.math.tensordot(0.5j * phi, signs, axes=0))","def eigensystem(mat):\n e, v = numpy.linalg.eig(mat)\n\n # `eig` returns complex results but we know all of the\n # eigenstates have real energy.\n e = numpy.real(e)\n\n items = zip(e, v.T)\n items = sorted(items, key = operator.itemgetter(0))\n e, v = zip(*items)\n\n return (e, v)","def test_inverse_eigenvectors_non_interacting(self, size):\n t_nn = 1.2\n idx = np.arange(size)\n g0_inv_full = np.zeros((size, size), dtype=complex)\n g0_inv_full[idx[:-1], idx[1:]] = g0_inv_full[idx[1:], idx[:-1]] = t_nn\n for g0 in self.g0_loc_inv:\n g0_inv_full[idx, idx] = g0\n rv, h, rv_inv = gt.matrix.decompose_gf(g0_inv_full)\n assert_allclose(rv.dot(rv_inv), np.identity(*h.shape), atol=1e-14)","def eigenvalue_decomposition (a_t_a_matrix ):\r\n # get eigenvalues and -vectors from ATA matrix\r\n eigenvalues = np.zeros (a_t_a_matrix.shape[0] )\r\n eigenvectors = np.zeros ((a_t_a_matrix.shape[0], a_t_a_matrix.shape[0] ))\r\n evals, evecs = np.linalg.eig (a_t_a_matrix )\r\n\r\n # sort them\r\n indices = np.argsort (-evals ) # reverse sort: greatest numbers first\r\n for loop_count, index in enumerate(indices ):\r\n eigenvalues[loop_count] = evals[index]\r\n eigenvectors[:, loop_count] = evecs[:, index]\r\n\r\n # get the normal vector, normalize it and if it's turned to the ground, turn it around\r\n normal_vector = normalize_vector (eigenvectors[:, -1] ) # the last (smallest) vector is the normal vector\r\n if (normal_vector[2] < 0):\r\n normal_vector = normal_vector * -1\r\n\r\n return normal_vector, eigenvalues[-1]","def eig(C):\r\n\r\n # class eig(object):\r\n # def __call__(self, C):\r\n\r\n # Householder transformation of a symmetric matrix V into tridiagonal form.\r\n # -> n : dimension\r\n # -> V : symmetric nxn-matrix\r\n # <- V : orthogonal transformation matrix:\r\n # tridiag matrix == V * V_in * V^t\r\n # <- d : diagonal\r\n # <- e[0..n-1] : off diagonal (elements 1..n-1)\r\n\r\n # Symmetric tridiagonal QL algorithm, iterative\r\n # Computes the eigensystem from a tridiagonal matrix in roughtly 3N^3 operations\r\n # -> n : Dimension.\r\n # -> d : Diagonale of tridiagonal matrix.\r\n # -> e[1..n-1] : off-diagonal, output from Householder\r\n # -> V : matrix output von Householder\r\n # <- d : eigenvalues\r\n # <- e : garbage?\r\n # <- V : basis of eigenvectors, according to d\r\n\r\n\r\n # tred2(N, B, diagD, offdiag); B=C on input\r\n # tql2(N, diagD, offdiag, B);\r\n\r\n # private void tred2 (int n, double V[][], double d[], double e[]) {\r\n def tred2 (n, V, d, e):\r\n # This is derived from the Algol procedures tred2 by\r\n # Bowdler, Martin, Reinsch, and Wilkinson, Handbook for\r\n # Auto. Comp., Vol.ii-Linear Algebra, and the corresponding\r\n # Fortran subroutine in EISPACK.\r\n\r\n num_opt = False # factor 1.5 in 30-D\r\n\r\n for j in range(n):\r\n d[j] = V[n-1][j] # d is output argument\r\n\r\n # Householder reduction to tridiagonal form.\r\n\r\n for i in range(n-1,0,-1):\r\n # Scale to avoid under/overflow.\r\n h = 0.0\r\n if not num_opt:\r\n scale = 0.0\r\n for k in range(i):\r\n scale = scale + abs(d[k])\r\n else:\r\n scale = sum(abs(d[0:i]))\r\n\r\n if scale == 0.0:\r\n e[i] = d[i-1]\r\n for j in range(i):\r\n d[j] = V[i-1][j]\r\n V[i][j] = 0.0\r\n V[j][i] = 0.0\r\n else:\r\n\r\n # Generate Householder vector.\r\n if not num_opt:\r\n for k in range(i):\r\n d[k] /= scale\r\n h += d[k] * d[k]\r\n else:\r\n d[:i] /= scale\r\n h = np.dot(d[:i],d[:i])\r\n\r\n f = d[i-1]\r\n g = h**0.5\r\n\r\n if f > 0:\r\n g = -g\r\n\r\n e[i] = scale * g\r\n h = h - f * g\r\n d[i-1] = f - g\r\n if not num_opt:\r\n for j in range(i):\r\n e[j] = 0.0\r\n else:\r\n e[:i] = 0.0\r\n\r\n # Apply similarity transformation to remaining columns.\r\n\r\n for j in range(i):\r\n f = d[j]\r\n V[j][i] = f\r\n g = e[j] + V[j][j] * f\r\n if not num_opt:\r\n for k in range(j+1, i):\r\n g += V[k][j] * d[k]\r\n e[k] += V[k][j] * f\r\n e[j] = g\r\n else:\r\n e[j+1:i] += V.T[j][j+1:i] * f\r\n e[j] = g + np.dot(V.T[j][j+1:i],d[j+1:i])\r\n\r\n f = 0.0\r\n if not num_opt:\r\n for j in range(i):\r\n e[j] /= h\r\n f += e[j] * d[j]\r\n else:\r\n e[:i] /= h\r\n f += np.dot(e[:i],d[:i])\r\n\r\n hh = f / (h + h)\r\n if not num_opt:\r\n for j in range(i):\r\n e[j] -= hh * d[j]\r\n else:\r\n e[:i] -= hh * d[:i]\r\n\r\n for j in range(i):\r\n f = d[j]\r\n g = e[j]\r\n if not num_opt:\r\n for k in range(j, i):\r\n V[k][j] -= (f * e[k] + g * d[k])\r\n else:\r\n V.T[j][j:i] -= (f * e[j:i] + g * d[j:i])\r\n\r\n d[j] = V[i-1][j]\r\n V[i][j] = 0.0\r\n\r\n d[i] = h\r\n # end for i--\r\n\r\n # Accumulate transformations.\r\n\r\n for i in range(n-1):\r\n V[n-1][i] = V[i][i]\r\n V[i][i] = 1.0\r\n h = d[i+1]\r\n if h != 0.0:\r\n if not num_opt:\r\n for k in range(i+1):\r\n d[k] = V[k][i+1] / h\r\n else:\r\n d[:i+1] = V.T[i+1][:i+1] / h\r\n\r\n for j in range(i+1):\r\n if not num_opt:\r\n g = 0.0\r\n for k in range(i+1):\r\n g += V[k][i+1] * V[k][j]\r\n for k in range(i+1):\r\n V[k][j] -= g * d[k]\r\n else:\r\n g = np.dot(V.T[i+1][0:i+1], V.T[j][0:i+1])\r\n V.T[j][:i+1] -= g * d[:i+1]\r\n\r\n if not num_opt:\r\n for k in range(i+1):\r\n V[k][i+1] = 0.0\r\n else:\r\n V.T[i+1][:i+1] = 0.0\r\n\r\n\r\n if not num_opt:\r\n for j in range(n):\r\n d[j] = V[n-1][j]\r\n V[n-1][j] = 0.0\r\n else:\r\n d[:n] = V[n-1][:n]\r\n V[n-1][:n] = 0.0\r\n\r\n V[n-1][n-1] = 1.0\r\n e[0] = 0.0\r\n\r\n\r\n # Symmetric tridiagonal QL algorithm, taken from JAMA package.\r\n # private void tql2 (int n, double d[], double e[], double V[][]) {\r\n # needs roughly 3N^3 operations\r\n def tql2 (n, d, e, V):\r\n\r\n # This is derived from the Algol procedures tql2, by\r\n # Bowdler, Martin, Reinsch, and Wilkinson, Handbook for\r\n # Auto. Comp., Vol.ii-Linear Algebra, and the corresponding\r\n # Fortran subroutine in EISPACK.\r\n\r\n num_opt = False # using vectors from numpy makes it faster\r\n\r\n if not num_opt:\r\n for i in range(1,n): # (int i = 1; i < n; i++):\r\n e[i-1] = e[i]\r\n else:\r\n e[0:n-1] = e[1:n]\r\n e[n-1] = 0.0\r\n\r\n f = 0.0\r\n tst1 = 0.0\r\n eps = 2.0**-52.0\r\n for l in range(n): # (int l = 0; l < n; l++) {\r\n\r\n # Find small subdiagonal element\r\n\r\n tst1 = max(tst1, abs(d[l]) + abs(e[l]))\r\n m = l\r\n while m < n:\r\n if abs(e[m]) <= eps*tst1:\r\n break\r\n m += 1\r\n\r\n # If m == l, d[l] is an eigenvalue,\r\n # otherwise, iterate.\r\n\r\n if m > l:\r\n iiter = 0\r\n while 1: # do {\r\n iiter += 1 # (Could check iteration count here.)\r\n\r\n # Compute implicit shift\r\n\r\n g = d[l]\r\n p = (d[l+1] - g) / (2.0 * e[l])\r\n r = (p**2 + 1)**0.5 # hypot(p,1.0)\r\n if p < 0:\r\n r = -r\r\n\r\n d[l] = e[l] / (p + r)\r\n d[l+1] = e[l] * (p + r)\r\n dl1 = d[l+1]\r\n h = g - d[l]\r\n if not num_opt:\r\n for i in range(l+2, n):\r\n d[i] -= h\r\n else:\r\n d[l+2:n] -= h\r\n\r\n f = f + h\r\n\r\n # Implicit QL transformation.\r\n\r\n p = d[m]\r\n c = 1.0\r\n c2 = c\r\n c3 = c\r\n el1 = e[l+1]\r\n s = 0.0\r\n s2 = 0.0\r\n\r\n # hh = V.T[0].copy() # only with num_opt\r\n for i in range(m-1, l-1, -1): # (int i = m-1; i >= l; i--) {\r\n c3 = c2\r\n c2 = c\r\n s2 = s\r\n g = c * e[i]\r\n h = c * p\r\n r = (p**2 + e[i]**2)**0.5 # hypot(p,e[i])\r\n e[i+1] = s * r\r\n s = e[i] / r\r\n c = p / r\r\n p = c * d[i] - s * g\r\n d[i+1] = h + s * (c * g + s * d[i])\r\n\r\n # Accumulate transformation.\r\n\r\n if not num_opt: # overall factor 3 in 30-D\r\n for k in range(n): # (int k = 0; k < n; k++) {\r\n h = V[k][i+1]\r\n V[k][i+1] = s * V[k][i] + c * h\r\n V[k][i] = c * V[k][i] - s * h\r\n else: # about 20% faster in 10-D\r\n hh = V.T[i+1].copy()\r\n # hh[:] = V.T[i+1][:]\r\n V.T[i+1] = s * V.T[i] + c * hh\r\n V.T[i] = c * V.T[i] - s * hh\r\n # V.T[i] *= c\r\n # V.T[i] -= s * hh\r\n\r\n p = -s * s2 * c3 * el1 * e[l] / dl1\r\n e[l] = s * p\r\n d[l] = c * p\r\n\r\n # Check for convergence.\r\n if abs(e[l]) <= eps*tst1:\r\n break\r\n # } while (Math.abs(e[l]) > eps*tst1);\r\n\r\n d[l] = d[l] + f\r\n e[l] = 0.0\r\n\r\n\r\n # Sort eigenvalues and corresponding vectors.\r\n if 11 < 3:\r\n for i in range(n-1): # (int i = 0; i < n-1; i++) {\r\n k = i\r\n p = d[i]\r\n for j in range(i+1, n): # (int j = i+1; j < n; j++) {\r\n if d[j] < p: # NH find smallest k>i\r\n k = j\r\n p = d[j]\r\n\r\n if k != i:\r\n d[k] = d[i] # swap k and i\r\n d[i] = p\r\n for j in range(n): # (int j = 0; j < n; j++) {\r\n p = V[j][i]\r\n V[j][i] = V[j][k]\r\n V[j][k] = p\r\n # tql2\r\n\r\n N = len(C[0])\r\n if 11 < 3:\r\n V = np.array([x[:] for x in C]) # copy each \"row\"\r\n N = V[0].size\r\n d = np.zeros(N)\r\n e = np.zeros(N)\r\n else:\r\n V = [[x[i] for i in xrange(N)] for x in C] # copy each \"row\"\r\n d = N * [0.]\r\n e = N * [0.]\r\n\r\n tred2(N, V, d, e)\r\n tql2(N, d, e, V)\r\n return (array(d), array(V))","def eigenalgo(self, accuracy: float = 0, cap: int = 50000, version: str = \"Givens\", not_skip: bool = True):\n j, temps, verify_accuracy = 0, 0, np.ones((self.N, self.N), dtype=bool) ^ np.eye(self.N, dtype=bool)\n if version == \"Gram-Schmidt\":\n temps = time()\n while np.any(abs(self.vap[verify_accuracy]) > accuracy) and j < cap:\n j += 1\n q, r = self.gram_schmidt_qr()\n self.vap, self.vep = r @ q, self.vep @ q\n\n elif version == \"Givens\":\n verify_accuracy = np.ones((self.N, self.N), dtype=bool) ^ np.eye(self.N, dtype=bool)\n temps = time()\n while np.any(abs(self.vap[verify_accuracy]) > accuracy) and j < cap:\n j += 1\n q, r = self.givens_qr()\n self.vap, self.vep = r @ q, self.vep @ q\n\n elif version == \"Rayleigh\":\n not_sing, diff, cond, j = True, accuracy + 1, True, 0\n temps = time()\n while cond: # Stop condition, all eigenvalues must be different\n while diff > accuracy and j < cap and not_sing:\n j += 1\n self.rvap, self.vep, diff, not_sing = self.rayleigh_iteration(self.rvap, self.vep)\n\n cond = False\n if j < cap:\n self.calc, first, not_sing = np.zeros(self.N, dtype=bool), True, True\n for i in range(self.N):\n if np.sum(np.less(np.abs(self.rvap - self.rvap[i]), 10 ** -6)) != 1:\n self.rvap[i + 1:] += self.memorize[i]\n if first:\n self.memorize[i] += 0.5\n self.vep[i + 1:, i + 1:] = np.eye(self.N - i - 1)\n first, cond, diff = False, True, accuracy + 1\n self.calc[i + 1:] = 1\n temps = time() - temps\n return self.rvap, self.vep, diff, j, temps\n\n else:\n print(\"Please select an appropriate value for the version parameter\")\n\n temps = time() - temps\n diff = np.max(abs(self.vap[verify_accuracy]))\n return np.diag(self.vap), self.vep, diff, j, temps","def compute_eigvals(phi): # pylint: disable=arguments-differ\n if qml.math.get_interface(phi) == \"tensorflow\":\n phase = qml.math.exp(-0.5j * qml.math.cast_like(phi, 1j))\n return stack_last([phase, qml.math.conj(phase), qml.math.conj(phase), phase])\n\n prefactors = qml.math.array([-0.5j, 0.5j, 0.5j, -0.5j], like=phi)\n if qml.math.ndim(phi) == 0:\n product = phi * prefactors\n else:\n product = qml.math.outer(phi, prefactors)\n return qml.math.exp(product)","def solve_elas(self,x,E_p=None):\n \n if x['Crystal_Structure'] == \"Cubic\":\n self.estf = self.Ccubic( x['Stiffness'][0], x['Stiffness'][1], x['Stiffness'][2] )\n\n elif x['Crystal_Structure'] == \"HCP\":\n self.estf = self.Chcp( x['Stiffness'][0], x['Stiffness'][1], x['Stiffness'][2], x['Stiffness'][3], x['Stiffness'][4] )\n\n # Update orientation\n for n in range(9):\n cell_num_list = list((9*self.cell_num)+n)\n self.orient.vector()[cell_num_list] = self.rots[self.subdomain_num,n]\n \n self.a = inner(self.sigs3x3(self.u), sym(grad(self.v)))*dx\n \n if E_p:\n # Note use of sym(), assuming E_p to be the \\chi field\n L_elas_rhs = self.L_elas + inner(self.sigs_e(sym(E_p)), sym(grad(self.v)))*dx\n else:\n L_elas_rhs = self.L_elas \n\n self.A_elas, self.b_elas = assemble_system(self.a, L_elas_rhs, self.bc_elas) \n \n # Attach near nullspace to matrix\n as_backend_type(self.A_elas).set_near_nullspace(self.null_space)\n\n # Set matrix operator\n self.elasticity_solver.set_operator(self.A_elas);\n\n # Compute solution\n self.elasticity_solver.solve(self.ue.vector(), self.b_elas);\n \n if E_p:\n self.Ue_sym = project( sym(grad(self.ue) - E_p), self.TFS, solver_type=\"cg\", preconditioner_type=\"ilu\")\n else:\n self.Ue_sym = project( sym(grad(self.ue)), self.TFS, solver_type=\"cg\", preconditioner_type=\"ilu\")\n \n self.sim_strn = np.reshape(self.Ue_sym.vector().get_local(),(len(self.grains.array()),9))\n\n for grain_no in range(self.grains.array().max()):\n # Grain numbering is 1 index origin\n cell_subset = self.grains.array()==(grain_no+1)\n if np.any(cell_subset):\n self.sim_avg[grain_no,:] = np.average(self.sim_strn[cell_subset,:],\n axis=0,weights=self.dVol[cell_subset]) \n \n deps = self.exp_strn - self.sim_avg\n resid = np.linalg.norm(deps.ravel())\n print(resid) #,self.its)\n return resid","def compute_eigvals(phi): # pylint: disable=arguments-differ\n if qml.math.get_interface(phi) == \"tensorflow\":\n phi = qml.math.cast_like(phi, 1j)\n\n return qml.math.stack([1, 1, -qml.math.exp(1j * phi), qml.math.exp(1j * phi)])","def eigen_operator(shape, e, v, **kargs):\n def matvec(x):\n k = [np.dot(x.T, vi) for vi in v]\n return np.sum([ki * ei * vi for ki, ei, vi in zip(k, e, v)], axis=0)\n return LinearOperator(shape, matvec=matvec, rmatvec=matvec, **kargs)","def eigenvals(self, evals_count=6):\n hamiltonian_mat = self.hamiltonian()\n return hamiltonian_mat.eigenenergies(eigvals=evals_count)","def hessian_matrix_eigvals(H_elems):\n return _symmetric_compute_eigenvalues(H_elems)","def solve_gevp_gen(a, t_0, algorithm, sort_by_vectors=15, **kwargs):\n B = np.matrix(a[t_0])\n try:\n f = algorithm(B=B, **kwargs)\n except TypeError:\n # If the function doesn't do currying, implement that here\n f = lambda A: algorithm(B=B, A=A)\n except LinAlgError:\n return\n\n eigenvectors = None\n count = 0\n\n for j in range(t_0 + 1, 32):\n try:\n eigenvalues, new_eigenvectors = f(np.matrix(a[j]))\n \n if eigenvectors is None:\n eigenvectors = np.zeros_like(new_eigenvectors)\n\n if j < sort_by_vectors:\n # TODO Sortieren nach Eigenwert\n perm = permutation_indices(eigenvalues)\n else:\n perm = reorder_by_ev(new_eigenvectors, eigenvectors, B)\n\n eigenvectors = new_eigenvectors[:,perm]\n eigenvalues = eigenvalues[:,perm]\n \n count += 1\n\n yield eigenvalues, eigenvectors\n\n except (LinAlgError, TypeError) as e:\n #import traceback\n #traceback.print_exc()\n return","def put_eigvecs(self, dest):\n if parallel.is_rank_zero():\n self.put_array(self.eigvecs, dest)\n parallel.barrier()","def calcEVals(self):\n self.eVals,self.eVecs = np.linalg.eigh(self.rhoOp)","def set_eigenvalue_problem(self, *args, ncc_cutoff=1e-10, tolerance=1e-10, **kwargs):\n # should be set EVP for consistency with set IVP. Why do we have P_problem. Why not IVP, EVP.\n self.problem_type = 'EVP'\n self.problem = de.EVP(self.domain, variables=self.variables, eigenvalue='omega', ncc_cutoff=ncc_cutoff, tolerance=tolerance)\n self.problem.substitutions['dt(f)'] = \"omega*f\"\n self.set_equations(*args, **kwargs)","def compute_eigvals(theta, num_wires): # pylint: disable=arguments-differ\n eigs = qml.math.convert_like(pauli_eigs(num_wires), theta)\n\n if qml.math.get_interface(theta) == \"tensorflow\":\n theta = qml.math.cast_like(theta, 1j)\n eigs = qml.math.cast_like(eigs, 1j)\n\n if qml.math.ndim(theta) == 0:\n return qml.math.exp(-0.5j * theta * eigs)\n\n return qml.math.exp(qml.math.outer(-0.5j * theta, eigs))","def plot_eigenvectors(*, obs_names, eigenvecs, fig_size=(8, 6), font_size=12,\n label_size=16, save_fig=False, write_path=None):\n # Loop over directions and plot each eigenvector on a separate figure\n for direction in range(eigenvecs.shape[1]):\n plt.figure(figsize=fig_size)\n plt.plot(np.abs(eigenvecs[::3, direction]), 'bx',\n np.abs(eigenvecs[1::3, direction]), 'rx',\n np.abs(eigenvecs[2::3, direction]), 'cx',\n markersize=10, mew=3)\n plt.ylim(0, 1)\n plt.grid()\n plt.yticks(fontsize=font_size)\n plt.xticks(range(len(obs_names)), obs_names, fontsize=font_size)\n plt.xticks(rotation=60)\n plt.ylabel(r'$\\mathbf{{v}}_{%03d}$' % (direction), fontsize=label_size)\n plt.legend(['$r$ direction', r'$\\theta$ direction',\n '$\\phi$ direction'], loc='upper right', frameon=False,\n fontsize=label_size)\n plt.xlabel('Location', fontsize=label_size)\n\n if save_fig is True:\n # Create the output directory if it does not exist\n if not os.path.exists(write_path):\n os.makedirs(write_path)\n fpath = os.path.join(write_path,\n 'eigendirection%03d.png' % direction)\n plt.savefig(fpath, bbox_inches='tight')\n plt.close()","def eig(x1):\n\n is_x1_dparray = isinstance(x1, dparray)\n\n if (not use_origin_backend(x1) and is_x1_dparray):\n if (x1.size > 0):\n return dpnp_eig(x1)\n\n return call_origin(numpy.linalg.eig, x1)","def compute_mesh_eigenfunctions(self, mesh, star0, star1, bdry=False):\n nb = len(mesh)\n\n inputs = []\n for m, s0, s1 in zip(mesh, star0, star1):\n d = m['int_d01']\n if bdry:\n d = scipy.sparse.vstack([d, m['bdry_d01']])\n inputs.extend([s0, s1, d])\n\n eigenvalues, eigenvectors = [], []\n outputs = self.hodgefunc(nb, self.num_eigenvectors,\n self.num_extra_eigenvectors, *inputs)\n for i in range(nb):\n eigenvalues.append(outputs[2*i])\n eigenvectors.append(outputs[2*i+1])\n\n return eigenvalues, eigenvectors","def solve_eigenproblem(self, A, M=None, num=None, tol=None):\n def eigenproblem_is_hermitian():\n return is_hermitian(A) and (M == None or is_hermitian(M))\n\n if self.is_hermitian() and not eigenproblem_is_hermitian():\n raise ValueError(\"Eigenproblem matrices are non-Hermitian but solver \"\n \"assumes Hermitian matrices. Aborting.\")\n logger.info(\"Solving eigenproblem. This may take a while...\")\n df.tic()\n omegas, ws = self._solve_eigenproblem(A, M=M, num=num, tol=tol)\n logger.info(\"Computing the eigenvalues and eigenvectors \"\n \"took {}\".format(format_time(df.toc())))\n\n # XXX TODO: Remove this conversion to numpy.arrays once we\n # have better support for different kinds of\n # matrices (but the conversion would happen in\n # compute_relative_error() anyway, so by doing it\n # here we avoid doing it multiple times.\n if not isinstance(A, np.ndarray):\n logger.warning(\n \"Converting sparse matrix A to dense array to check whether it is \"\n \"Hermitian. This might consume a lot of memory if A is big!.\")\n A = as_dense_array(A)\n if not isinstance(M, (np.ndarray, NoneType)):\n logger.warning(\n \"Converting sparse matrix M to dense array to check whether it is \"\n \"Hermitian. This might consume a lot of memory if M is big!.\")\n M = as_dense_array(M)\n\n rel_errors = np.array(\n [compute_relative_error(A, M, omega, w) for omega, w in zip(omegas, ws)])\n return omegas, ws, rel_errors","def matrix_eig(\n self,\n chis=None,\n eps=0,\n print_errors=\"deprecated\",\n hermitian=False,\n break_degenerate=False,\n degeneracy_eps=1e-6,\n sparse=False,\n trunc_err_func=None,\n evenTrunc = False,\n ):\n if print_errors != \"deprecated\":\n msg = (\n \"The `print_errors` keyword argument has been deprecated, \"\n \"and has no effect. Rely instead on getting the error as a \"\n \"return value, and print it yourself.\"\n )\n warnings.warn(msg)\n # If chis is not specfied, there is no even truncation scheme; else, we\n # keep track of the chi we specfied\n if chis is None:\n evenTrunc = False\n else:\n try:\n chis = list(chis)\n except TypeError:\n chis = [chis]\n chiSpec = max(chis)\n chis = self._matrix_decomp_format_chis(chis, eps)\n maxchi = max(chis)\n assert self.defval == 0\n assert self.invar\n assert self.charge == 0\n assert self.dirs[0] + self.dirs[1] == 0\n assert set(zip(self.qhape[0], self.shape[0])) == set(\n zip(self.qhape[1], self.shape[1])\n )\n\n S_dtype = np.float_ if hermitian else np.complex_\n U_dtype = self.dtype if hermitian else np.complex_\n\n # Eigenvalue decompose each sector at a time.\n # While doing so, also keep track of a list of all eigenvalues, as well\n # as a heap that gives the negative of the absolute value of the\n # largest eigenvalue in each sector. These will be needed later when\n # deciding how to truncate the eigenvalues.\n eigdecomps = {}\n dims = {}\n minusabs_next_eigs = []\n all_eigs = []\n for k, v in self.sects.items():\n if 0 in v.shape:\n # This matrix is empty and trivial.\n shp = v.shape\n m = min(shp)\n u = np.empty((shp[0], m), dtype=U_dtype)\n s = np.empty((m,), dtype=S_dtype)\n eigdecomp = (s, u)\n else:\n if sparse and maxchi < min(v.shape) - 1:\n if hermitian:\n s, u = spsla.eighs(\n v, k=maxchi, return_eigenvectors=True\n )\n else:\n s, u = spsla.eigs(\n v, k=maxchi, return_eigenvectors=True\n )\n else:\n if hermitian:\n s, u = np.linalg.eigh(v)\n else:\n s, u = np.linalg.eig(v)\n order = np.argsort(-np.abs(s))\n s = s[order]\n u = u[:, order]\n s = s.astype(S_dtype)\n u = u.astype(U_dtype)\n eigdecomp = (s, u)\n eigdecomps[k] = eigdecomp\n dims[k] = 0\n all_eigs.append(s)\n if 0 not in s.shape:\n heapq.heappush(minusabs_next_eigs, (-np.abs(s[0]), k))\n try:\n all_eigs = np.concatenate(all_eigs)\n except ValueError:\n all_eigs = np.array((0,))\n\n if sparse:\n norm_sq = self.norm_sq()\n else:\n norm_sq = None\n\n # Figure out what bond dimension to truncate to, how this bond\n # dimension is distributed over the different sectors, and what the\n # truncation error is.\n chi, dims, rel_err = type(self)._find_trunc_dim(\n all_eigs,\n eigdecomps,\n minusabs_next_eigs,\n dims,\n chis=chis,\n eps=eps,\n break_degenerate=break_degenerate,\n degeneracy_eps=degeneracy_eps,\n trunc_err_func=trunc_err_func,\n norm_sq=norm_sq,\n )\n\n # truncate in both sectors evenly\n if evenTrunc and chiSpec == chi:\n # This piece of codes is only designed\n # with Z2 symmetry tensor in mind\n errmeg = \"The matrix should have two sectors (0,0) and (1,1).\"\n assert len(dims) == 2, errmeg\n if chiSpec % 2 == 0:\n dims[(0, 0)] = int(chiSpec / 2)\n dims[(1, 1)] = int(chiSpec / 2)\n else:\n dims[(0, 0)] = int((chiSpec + 1) / 2)\n dims[(1, 1)] = int((chiSpec - 1) / 2)\n\n # Truncate each block and create the dim for the new index.\n new_dim = []\n new_qim = []\n eigdecomps = {k: v for k, v in eigdecomps.items() if dims[k] > 0}\n for k, v in eigdecomps.items():\n d = dims[k]\n if d > 0:\n new_dim.append(d)\n new_qim.append(k[0])\n eigdecomps[k] = (v[0][:d], v[1][:, :d])\n else:\n del eigdecomps[k]\n\n # Initialize S and U.\n d = self.dirs[0]\n S = type(self)(\n [new_dim],\n qhape=[new_qim],\n dirs=[d],\n qodulus=self.qodulus,\n dtype=S_dtype,\n invar=False,\n charge=0,\n )\n U = type(self)(\n [self.shape[0], new_dim],\n qhape=[self.qhape[0], new_qim],\n dirs=[d, -d],\n qodulus=self.qodulus,\n dtype=U_dtype,\n charge=0,\n )\n\n # Set the blocks of U, S and V.\n for k, v in eigdecomps.items():\n S[(k[0],)] = v[0]\n k_U = (k[0], k[0])\n U[k_U] = v[1]\n\n return S, U, rel_err","def run_vqe(\n self,\n backend=Aer.get_backend(\"statevector_simulator\"),\n var_form=None,\n optimizer=None,\n reps=None,\n mode=\"min_val\",\n ):\n # N=int(np.ceil(np.log2(len(self.mat))))\n # hk = np.zeros((2**N,2**N),dtype='complex')\n # hk[:self.mat.shape[0], :self.mat.shape[1]] = self.mat\n N = self.n_qubits()\n if mode == \"max_val\":\n Hamil_mat = aqua.operators.MatrixOperator(-1 * self.mat)\n # Hamil_mat = MatrixOperator(-1 * self.mat)\n else:\n Hamil_mat = aqua.operators.MatrixOperator(self.mat)\n # Hamil_mat = MatrixOperator(self.mat)\n Hamil_qop = aqua.operators.op_converter.to_weighted_pauli_operator(\n Hamil_mat\n )\n if var_form is None:\n if reps is None:\n reps = 2\n # reps=5\n from qiskit.circuit.library import EfficientSU2\n\n var_form = EfficientSU2(N, reps=reps)\n if optimizer is None:\n vqe = aqua.algorithms.VQE(Hamil_qop, var_form)\n # vqe = VQE(Hamil_qop, var_form)\n else:\n vqe = aqua.algorithms.VQE(Hamil_qop, var_form, optimizer)\n # vqe = VQE(Hamil_qop, var_form, optimizer)\n vqe_result = vqe.run(backend)\n en = np.real(vqe_result[\"eigenvalue\"])\n # params=vqe.optimal_params\n # circuit=vqe.construct_circuit(params)\n if mode == \"max_val\":\n en = -1 * en\n # states = np.sort(\n # np.real(\n # vqe.expectation.convert(\n # StateFn(vqe.operator, is_measurement=True)\n # ).to_matrix()\n # )\n # )\n return en, vqe_result, vqe","def reflect_eigenvectors(x):\n # random reference vector\n xnew = x.copy()\n for v in range(x.shape[-1]):\n cum_sum = x[0, :, v]\n cum_sum /= np.linalg.norm(cum_sum)\n for i in np.arange(1, x.shape[0]): \n if np.any(np.isnan(x[i, :, v])):\n xnew[i, :, v] = x[i, :, v]\n else:\n cos = cum_sum.dot(x[i, :, v])\n if cos > 0:\n cum_sum += x[i, :, v]\n cum_sum /= np.linalg.norm(cum_sum)\n\n else:\n cum_sum += np.negative(x[i, :, v])\n cum_sum /= np.linalg.norm(cum_sum)\n xnew[i, :, v] = np.negative(x[i, :, v])\n \n return xnew","def eigen_decomp(matrix):\n w = None\n v = None\n ### YOUR CODE HERE\n pass\n ### END YOUR CODE\n return w, v","def eig(A):\n lambdas, vs = linalg.eig(A)\n lvs = [(lambdas[i], vs[i]) for i in range(len(vs))]\n\n def lv_cmp(lv1, lv2):\n lambda1 = lv1[0]\n lambda2 = lv2[0]\n\n r1 = numpy.real(lambda1)\n i1 = numpy.real(lambda1)\n r2 = numpy.real(lambda2)\n i2 = numpy.real(lambda2)\n\n if r1 > r2:\n return 1\n elif r1 < r2:\n return -1\n elif i1 > i2:\n return 1\n elif i1 < i2:\n return -1\n return 0\n\n lvs.sort(cmp = lv_cmp)\n\n lambdas = [lv[0] for lv in lvs]\n vs = [lv[1] for lv in lvs ]\n return lambdas, vs","def left_eigenvectors(matrix, nvals=None):\n matrix = np.asarray(matrix)\n return _eigenvectors(matrix.transpose(), nvals)","def run_numpy(self):\n return np.linalg.eigh(self.mat)","def geneiv(A, B):\n Li = np.linalg.inv(linalg.cholesky(B).T)\n C = Li*A*(Li.T)\n C = np.asmatrix((C + C.T)*0.5, np.float32)\n\n eivs, V = np.linalg.eig(C)\n return eivs, Li.T*V","def eigen_vector_i(self, i):\n return self._eig_vec[:,i]","def pde_eigv(self, u):\n u0, u1, u2 = u.T\n c = np.sqrt(9.81*u0)\n vel = np.sqrt((u1/u0)**2 + (u2/u0)**2)\n return np.array([vel-c, vel, vel+c])","def put_eigvals(self, dest):\n if parallel.is_rank_zero():\n self.put_array(self.eigvals, dest)\n parallel.barrier()","def get_decomp(self, eigvals_src, eigvecs_src):\n self.eigvals = np.squeeze(np.array(parallel.call_and_bcast(\n self.get_array, eigvals_src)))\n self.eigvecs = parallel.call_and_bcast(self.get_array, eigvecs_src)","def test_eigenvalues_in_cylinders(self):\n random.seed(102938482634)\n point_cloud = load(os.path.join('testdata', 'AHN3.las'))\n num_all_pc_points = len(point_cloud[keys.point][\"x\"][\"data\"])\n rand_indices = [random.randint(0, num_all_pc_points) for _ in range(20)]\n target_point_cloud = utils.copy_point_cloud(point_cloud, rand_indices)\n radius = 2.5\n neighbors = compute_neighbors.compute_cylinder_neighborhood(point_cloud, target_point_cloud, radius)\n\n compute_features(point_cloud, neighbors, target_point_cloud,\n [\"eigenv_1\", \"eigenv_2\", \"eigenv_3\"], InfiniteCylinder(5))\n\n self.assertEqual(\"laserchicken.feature_extractor.eigenvals_feature_extractor\",\n target_point_cloud[keys.provenance][-1][\"module\"])","def eig(self, q):\n q1, q2 = q.T\n c = np.sqrt(9.81*q1)\n lambda1 = q2/q1 - c\n lambda2 = q2/q1 + c\n return np.array([lambda1, lambda2])","def compute_residualsNEW(eigenvectors, eps_scale=3, progressBar=True, skipFirst=True, bandwidth_type='median'):\n #from multiprocessing import Pool\n # Check for more than two eigenvectors.\n assert eigenvectors.shape[1] > 2, 'There must be more than two eigenvectors to compute residuals.'\n\n # Set up the residuals and define the second as one.\n residual = np.zeros(eigenvectors.shape[1])\n if skipFirst:\n residual[1] = 1\n firstIndex = 2\n else:\n residual[0] = 1\n firstIndex = 1\n\n ivals = range(firstIndex, eigenvectors.shape[1])\n jvals = np.arange(eigenvectors.shape[0])\n \n # Run the residual calculation for each eigenvector. \n for i in ivals:\n num, den = 0, 0\n\n if bandwidth_type == 'median':\n bandwidth = np.power((np.median(scipy.spatial.distance.squareform(scipy.spatial.distance.pdist(eigenvectors[:,:i]))) / eps_scale), 2)\n elif bandwidth_type == 'mean':\n bandwidth = np.power((np.mean(scipy.spatial.distance.squareform(scipy.spatial.distance.pdist(eigenvectors[:,:i]))) / eps_scale), 2)\n else:\n raise ValueError('bandwidth_type must be either median or mean')\n\n if bandwidth == 0:\n bandwidth = np.finfo(float).eps\n \n regression = partial(local_linear_regression, eigenvectors[:, :i], eigenvectors[:, i], bandwidth=bandwidth, bandwidth_type=bandwidth_type)\n\n fit = np.array(list(map(regression, jvals)))[jvals, jvals]\n num = np.sum(np.power(eigenvectors[:, i] - fit, 2))\n den = np.sum(np.power(eigenvectors[:, i], 2))\n \n # Compute a residual for eigenvector i.\n residual[i] = np.sqrt(num / den)\n \n result = dict([('Residuals', residual), ('Eps Scale', eps_scale), ('Bandwidth Type', bandwidth_type)])\n \n return result","def test_solvers():\n # With P1 elements we have an error E-15 with Krylov solver\n # tolerances of 1E-12, but with P2 elements the error is E-6.\n # P3 elements drive the tolerance down to E-3.\n # For higher mesh resolution we also need reduced tolerances.\n # The tol dict maps degree to expected tolerance for the coarse\n # meshes in the test.\n tol = {'direct': {1: 1E-11, 2: 1E-11, 3: 1E-11},\n 'Krylov': {1: 1E-14, 2: 1E-05, 3: 1E-03}}\n u_D = Expression('1 + x[0]*x[0] + 2*x[1]*x[1]')\n kappa = Expression('x[0] + x[1]')\n f = Expression('-8*x[0] - 10*x[1]')\n for Nx, Ny in [(3,3), (3,5), (5,3)]:\n for degree in 1, 2, 3:\n for linear_solver in 'direct', 'Krylov':\n for solver_func in solver, solver_objects:\n print('solving on 2(%dx%dx) mesh with P%d elements'\n % (Nx, Ny, degree)),\n print(' %s solver, %s function' %\n (linear_solver, solver_func.__name__))\n # Important: Krylov solver error must be smaller\n # than tol!\n u = solver_func(\n kappa, f, u_D, Nx, Ny, degree,\n linear_solver=linear_solver,\n abs_tol=0.1*tol[linear_solver][degree],\n rel_tol=0.1*tol[linear_solver][degree])\n # Make a finite element function of the exact u_D\n V = u.function_space()\n u_D_Function = interpolate(u_D, V) # exact solution\n # Check that dof arrays are equal\n u_D_array = u_D_Function.vector().array() # dof values\n max_error = (u_D_array - u.vector().array()).max()\n msg = 'max error: %g for 2(%dx%d) mesh, degree=%d,'\\\n ' %s solver, %s' % \\\n (max_error, Nx, Ny, degree, linear_solver,\n solver_func.__name__)\n print(msg)\n assert max_error < tol[linear_solver][degree], msg","def power_iteration_two_components(X):\n evector1, evalue1=power_iteration(X)\n X=X-np.outer(evector1.dot(X.T), evector1) #project each point(aka each row of X) on the first euginevector and then substract that from each row\n evector2, evalue2=power_iteration(X)\n\n eigenvectors=np.vstack([evector1,evector2])\n eigenvalues=np.array([evalue1,evalue2])\n\n return eigenvectors, eigenvalues","def heavy_fixCM_eigvals(NP, b, c, params):\n l = params['l']\n k = params['k']\n I3 = params['I3']\n # Here, omega_3 is just the MAGNITUDE, not signed\n w3 = np.abs(params['w3'][0])\n gn = params['Mm'] * params['g']\n\n # Check output if small system\n print 'gn = ', gn\n print 'b = ', b\n print 'c = ', c\n\n if NP == 1:\n pass\n elif NP == 2:\n matrix = -np.array([[0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0.],\n [(-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.,\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.],\n [0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3)],\n [(-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\n (-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.]\n ])\n print 'exact matrix = ', matrix\n eigvals = np.array([\n 1j * l * gn / (I3 * w3),\n -1j * l * gn / (I3 * w3),\n l * np.sqrt(gn) * np.sqrt(0j - 2. * l * k * (-1) ** (b) - gn) / (I3 * w3),\n -l * np.sqrt(gn) * np.sqrt(0j - 2. * l * k * (-1) ** (b) - gn) / (I3 * w3)\n ])\n print 'exact_eigvals are =', eigvals\n return eigvals\n elif NP == 3:\n matrix = -np.array([[0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0., 0., 0.],\n [(-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.,\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0., 0., 0.],\n [0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0.],\n [(-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\n (-1) ** (1 + c) * (-l * gn - 2. * (-1) ** (b) * l ** 2 * k) / (I3 * w3), 0., \\\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.],\n [0., 0., 0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3)],\n [0., 0., (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\n (-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.]\n ])\n print 'exact matrix = ', matrix\n\n eigvals = np.array([\n 1j * l * gn / (I3 * w3),\n # -1j*l*gn/(I3*w3),\n l * np.sqrt(gn) * np.sqrt(0j - 3. * l * k * (-1) ** (b) - gn) / (I3 * w3),\n # -l*np.sqrt(gn)*np.sqrt(0j-3.*l*k*(-1)**(b) - gn)/(I3*w3),\n l * np.sqrt(gn) * np.sqrt(0j - l * k * (-1) ** (b) - gn) / (I3 * w3),\n # -l*np.sqrt(gn)*np.sqrt(0j - l*k*(-1)**(b) - gn)/(I3*w3)\n ])\n return eigvals\n else:\n return np.array([])","def build(self, data: np.ndarray):\n ret = data.dot(self.eigenvectors)\n self.pca_predictor_vars = ret\n return ret","def check(mat, otp):\n prd = mat*otp\n eigval = prd[0]/otp[0]\n print 'computed eigenvalue :' , eigval\n [eigs, vecs] = np.linalg.eig(mat)\n abseigs = list(abs(eigs))\n ind = abseigs.index(max(abseigs))\n print ' largest eigenvalue :', eigs[ind]","def get_eig(self, tolerance=None):\r\n E, V = scipy.linalg.eig(self.data)\r\n E = pd.Series(E.real, name=\"EIG\")\r\n V = pd.DataFrame(V.real)\r\n if tolerance is not None:\r\n E[E/E.max() < tolerance] = 0\r\n return E, V","def load_eig(self, **kwargs):\r\n eig = kwargs['eig']\r\n\r\n self.eig = EIG(eig)"],"string":"[\n \"def eigsolve(self,**kwargs):\\n return eigsolve(self,**kwargs)\",\n \"def _solve_eigen(self, X, y):\\n self.means_, self.neighbor_means_ = _class_means_and_neighbor_means(\\n X, y, self.within_between_ratio, self.nearest_neighbor_ratio)\\n \\n Sw = _class_cov(X, y) # within class cov\\n Sb = _local_pairwise_cov(self.means_, self.neighbor_means_)\\n \\n evals, evecs = linalg.eigh(Sb, Sw)\\n evecs = evecs[:, np.argsort(evals)[::-1]] # sort eigenvectors\\n self.scalings_ = np.asarray(evecs)\",\n \"def eigensolve(self, epsilon=0.85):\\n raise NotImplementedError(\\\"eigensolve Incomplete\\\")\",\n \"def eig(self,manifold_num):\\n num_sites = len(self.energies[manifold_num])\\n ham = self.manifold_hamiltonian(manifold_num).toarray()\\n eigvals, eigvecs = eigh(ham)\\n # Force degenerate eigenvectors to be orthogonal\\n if self.qr_flag:\\n eigvecs, r = np.linalg.qr(eigvecs,mode='reduced')\\n if self.check_eigenvectors:\\n HV = ham.dot(eigvecs)\\n D = eigvecs.T.dot(HV)\\n if np.allclose(D,np.diag(eigvals),rtol=1E-11,atol=1E-11):\\n pass\\n else:\\n # warnings.warn('Eigenvalues altered by QR factorization, max absolute change in diagonal matrix of {}'.format(np.max(D-np.diag(eigvals))))\\n warnings.warn('Using eigenvectors to diagonalize hamiltonian does not result in the expected diagonal matrix to tolerance, largest deviation is {}'.format(np.max(np.abs(D - np.diag(eigvals)))))\\n \\n sort_indices = eigvals.argsort()\\n eigvals.sort()\\n eigvecs = eigvecs[:,sort_indices]\\n if self.qr_flag:\\n r = r[:,sort_indices]\\n self.r_mats.append(r)\\n # I choose to pick the phase of my eigenvectors such that the state which has the\\n # largest overlap has a positive overlap. For sufficiently small d, and alpha close\\n # to 1, this will be the overlap between the same excited and ground states.\\n for i in range(eigvals.size):\\n max_index = np.argmax(np.abs(eigvecs[:,i]))\\n if eigvecs[max_index,i] < 0:\\n eigvecs[:,i] *= -1\\n\\n return eigvals, eigvecs\",\n \"def calc_eigenvectors(verts: [[float]]):\\n A = np.zeros((3, len(verts)))\\n\\n A[0] = np.array([x[0] for x in verts]) # First row is all the X_coords\\n A[1] = np.array([x[1] for x in verts]) # second row is all the Y_coords\\n A[2] = np.array([x[2] for x in verts]) # third row is all the z-coords\\n \\n A_cov = np.cov(A) # This is returns a 3x3\\n eigenvalues, eigenvectors = np.linalg.eigh(A_cov)\\n\\n return eigenvalues, eigenvectors\",\n \"def numpy_eigenvectors(A):\\n import numpy\\n A = numpy.array(A)\\n E, V = numpy.linalg.eigenvectors(A)\\n import Numeric\\n E = Numeric.array(E)\\n V = Numeric.array(V)\\n return E, V\",\n \"def plot_eigenvectors(self, **parameter_overrides):\\n par, arr_keys, _ = self._parse_parameter_overrides(\\n **parameter_overrides)\\n states = [r'\\\\phi', r'\\\\delta', r'\\\\dot{\\\\phi}', r'\\\\dot{\\\\delta}']\\n\\n eval_seq, evec_seq = self.calc_eigen(**parameter_overrides)\\n eval_seq, evec_seq = np.atleast_2d(eval_seq), np.atleast_3d(evec_seq)\\n\\n if eval_seq.shape[0] > 10:\\n msg = ('Plots will be too large, use fewer than 11 values in the '\\n 'varied parameter.')\\n raise ValueError(msg)\\n\\n # TODO : sort_eigenmodes() is doing something funny and not adding the\\n # 4th eigenvalue, so you often end up with duplicates eigenvalues for\\n # one eigenval and one missing. Also the algorithm may not work well\\n # with spaced out eigenvalues, which is what I've been trying here. You\\n # may have to calculate eigenvals/vec across closer spacing, then\\n # sample out the ones you want. For now, we don't sort coarse spaced\\n # eigenvalues.\\n # if arr_keys:\\n # eval_seq, evec_seq = sort_eigenmodes(eval_seq, evec_seq)\\n\\n fig, axes = plt.subplots(*eval_seq.shape,\\n subplot_kw={'projection': 'polar'})\\n axes = np.atleast_2d(axes)\\n fig.set_size_inches(axes.shape[1]*3, axes.shape[0]*3)\\n lw = list(range(1, len(states) + 1))\\n lw.reverse()\\n\\n for k, (evals, evecs, par_val) in enumerate(zip(eval_seq, evec_seq,\\n par[arr_keys[0]])):\\n\\n axes[k, 0].set_ylabel('{} = {:1.2f}'.format(arr_keys[0], par_val),\\n labelpad=30)\\n\\n for i, (eigenval, eigenvec) in enumerate(zip(evals, evecs.T)):\\n\\n max_com = np.abs(eigenvec[:2]).max()\\n\\n for j, component in enumerate(eigenvec[:2]):\\n\\n radius = np.abs(component)/max_com\\n theta = np.angle(component)\\n axes[k, i].plot([0, theta], [0, radius], lw=lw[j])\\n\\n axes[k, i].set_rmax(1.0)\\n msg = r'Eigenvalue: {:1.3f}'\\n if eigenval.real >= 0.0:\\n fontcolor = 'red' # red indicates unstable\\n else:\\n fontcolor = 'black'\\n axes[k, i].set_title(msg.format(eigenval),\\n fontdict={'color': fontcolor})\\n\\n axes[0, 0].legend(['$' + s + '$' for s in states],\\n loc='upper center', bbox_to_anchor=(0.5, 1.05),\\n fancybox=True, shadow=True, ncol=4)\\n\\n fig.tight_layout()\\n\\n return axes\",\n \"def calculate_eigens(self):\\n covariance_matrix = np.cov(self.predictor_vars_train.T)\\n eigenvalues, eigenvectors = np.linalg.eig(covariance_matrix)\\n idx = eigenvalues.argsort()[::-1]\\n # Create \\\"All\\\" version\\n self.eigenvalues_all = eigenvalues[idx]\\n self.eigenvectors_all = eigenvectors[:, idx]\\n # Create selected percentage version with cutoff\\n eigenvalues_pct = self.eigenvalues_all / np.sum(self.eigenvalues_all)\\n self.pct_var_exp_cumulative_all = np.cumsum(eigenvalues_pct)\\n self.pct_var_exp_cumulative = self.pct_var_exp_cumulative_all[\\n self.pct_var_exp_cumulative_all <= self.variance_explained_cutoff\\n ]\\n self.eigenvectors = self.eigenvectors_all[:, : len(self.pct_var_exp_cumulative)]\\n self.eigenvalues = self.eigenvalues_all[: len(self.pct_var_exp_cumulative)]\",\n \"def eigvals(self):\\n raise NotImplementedError\",\n \"def compute_eigvals(*params, **hyperparams):\\n phi = params[0]\\n d, t = hyperparams[\\\"dimension\\\"]\\n\\n if qml.math.get_interface(phi) == \\\"tensorflow\\\":\\n phase = qml.math.exp(1j * qml.math.cast_like(phi, 1j))\\n minus_phase = qml.math.exp(-1j * qml.math.cast_like(phi, 1j))\\n return stack_last([phase if index < d else minus_phase for index in range(t)])\\n\\n arg = 1j * phi\\n prefactors = qml.math.array([1 if index < d else -1 for index in range(t)], like=phi)\\n\\n if qml.math.ndim(phi) == 0:\\n product = arg * prefactors\\n else:\\n product = qml.math.outer(arg, prefactors)\\n return qml.math.exp(product)\",\n \"def get_eigenvectors(self):\\n return self.eigenVectors\",\n \"def GetEigenvectors(self):\\n\\t\\treturn self.Solver.GetEigenvectors()\",\n \"def eigen(M):\\n values, vectors = np.linalg.eig(M)\\n return values, vectors\",\n \"def eigensys(self, evals_count):\\n hamiltonian_mat = self.hamiltonian()\\n evals, evecs = hamiltonian_mat.eigenstates(eigvals=evals_count)\\n return evals, evecs\",\n \"def mesh_laplacian_eigenvectors(mesh, nb_vectors=1):\\n lap, lap_b = compute_mesh_laplacian(mesh, lap_type=\\\"fem\\\")\\n w, v = eigsh(lap.tocsr(), nb_vectors + 1, M=lap_b.tocsr(), sigma=solver_tolerance)\\n return v[:, 1:]\",\n \"def eigvals(input):\\n\\n is_input_dparray = isinstance(input, dparray)\\n\\n if (not use_origin_backend(input) and is_input_dparray):\\n if (input.size > 0):\\n return dpnp_eigvals(input)\\n\\n return call_origin(numpy.linalg.eigvals, input)\",\n \"def eigs(self,num_eigvals,manifold_num):\\n num_sites = len(self.energies[manifold_num])\\n ham = self.manifold_hamiltonian(manifold_num)\\n eigvals, eigvecs = eigsh(ham,k=num_eigvals*num_sites,which='SM')\\n # Force degenerate eigenvectors to be orthogonal\\n if self.qr_flag:\\n eigvecs, r = np.linalg.qr(eigvecs,mode='reduced')\\n if self.check_eigenvectors:\\n HV = ham.dot(eigvecs)\\n D = eigvecs.T.dot(HV)\\n if np.allclose(D,np.diag(eigvals),rtol=1E-11,atol=1E-11):\\n pass\\n else:\\n # warnings.warn('Eigenvalues altered by QR factorization, max absolute change in diagonal matrix of {}'.format(np.max(D-np.diag(eigvals))))\\n warnings.warn('Using eigenvectors to diagonalize hamiltonian does not result in the expected diagonal matrix to tolerance, largest deviation is {}'.format(np.max(np.abs(D - np.diag(eigvals)))))\\n \\n sort_indices = eigvals.argsort()\\n eigvals.sort()\\n eigvecs = eigvecs[:,sort_indices]\\n if self.qr_flag:\\n r = r[:,sort_indices]\\n self.r_mats.append(r)\\n # I choose to pick the phase of my eigenvectors such that the state which has the\\n # largest overlap has a positive overlap. For sufficiently small d, and alpha close\\n # to 1, this will be the overlap between the same excited and ground states.\\n for i in range(num_eigvals):\\n max_index = np.argmax(np.abs(eigvecs[:,i]))\\n if eigvecs[max_index,i] < 0:\\n eigvecs[:,i] *= -1\\n\\n return eigvals, eigvecs\",\n \"def test_eigen_multiple_neighborhoods(self):\\n # vectorized version\\n t0 = time.time()\\n extract_vect = EigenValueVectorizeFeatureExtractor()\\n eigvals_vect = extract_vect.extract(self.point_cloud, self.neigh, None, None, None)\\n print('Timing Vectorize : {}'.format((time.time() - t0)))\\n eigvals_vect = np.vstack(eigvals_vect[:3]).T\\n\\n # serial version\\n eigvals = []\\n t0 = time.time()\\n for n in self.neigh:\\n extract = EigenValueSerial()\\n eigvals.append(extract.extract(self.point_cloud, n, None, None, None))\\n print('Timing Serial : {}'.format((time.time() - t0)))\\n eigvals = np.array(eigvals)\\n\\n np.testing.assert_allclose(eigvals_vect, eigvals)\",\n \"def get_eigenvectors(self):\\n return self._eigenvectors\",\n \"def MakeEigenVectors( self ): \\n sqrt2 = np.sqrt(2)\\n Isqrt2 = 1.0 / sqrt2\\n EVectors = np.asarray( [ [ Isqrt2 , Isqrt2 , 0 ] ,\\n [ Isqrt2 ,-Isqrt2 , 0 ] , \\n [ 0 , 0 , 1 ] ] )\\n for i in range( self.NQ ):\\n for j in range( self.Nbranches ):\\n self.EigenVectors[ i , j , 0 , : ] = EVectors[ j , : ]\",\n \"def eigenpairs(mesh, nb_eig):\\n lap, lap_b = sdg.compute_mesh_laplacian(mesh, lap_type='fem')\\n eig_val, eig_vec = eigsh(lap.tocsr(), nb_eig, M=lap_b.tocsr(),\\n sigma=1e-6, which='LM')\\n return eig_val, eig_vec, lap_b.tocsr()\",\n \"def calculate_eigenvalues(H):\\n eigenvalues, eigenvectors = np.linalg.eigh(H)\\n return eigenvalues, eigenvectors\",\n \"def calculate_eigenvalues(self):\\n self.__eigenvalues = []\\n dictionary = np.linalg.eig(np.array(self.__A))\\n indicator = True\\n sum1 = 0\\n for i in range(self.__A.shape[0]):\\n if all(self.__A[i, j] == 0 for j in range(self.__A.shape[1])):\\n indicator = all(self.__B[i,j] for j in range(self.__B.shape[1]))\\n if (indicator):\\n sum1 += 1\\n \\n for val in dictionary[0]:\\n if (val != 0):\\n self.__eigenvalues.append(complex(val))\\n elif (indicator) and (sum1 > 0):\\n sum1 -= 1\\n self.__eigenvalues.append(complex(val))\",\n \"def project_to_eigenvectors(X, vecs):\\n\\n return (X-np.mean(X, axis=0)).dot(np.transpose(vecs)) #PCA assumes that the data is centered, so we need to do that before doing the calculations\",\n \"def run_exact(self):\\n self.operator, var_form, opt = self.generate_VQE_args()\\n\\n exact_eigensolver = ExactEigensolver(self.operator, k=1)\\n self.result = exact_eigensolver.run()\\n\\n solution = self.extract_solution(self.result, True)\\n return solution\",\n \"def solve(self, sparse_args=None):\\n Hmat = self.Hamiltonian()\\n if sparse_args is not None: self.sparse_args = sparse_args\\n if self.sparse_args is None:\\n en, ev = eig(Hmat.todense())\\n else:\\n en, ev = eigsh(Hmat, **self.sparse_args)\\n ev = transpose(array(ev))[argsort(en)]\\n en = sort(en)\\n self.en = en\\n self.ev = ev\\n self.solved = True\\n return self.en, self.ev\",\n \"def eigen_decomposition(self):\\n w, V = linalg.eigh(self.K)\\n c = w[::-1]\\n if isinstance(self.num_xi, float):\\n percent_energy = np.cumsum(c) / np.sum(c)\\n self.num_xi = np.arange(c.shape[0])[percent_energy < self.num_xi][-1] # num_xi changes\\n self.Lambda = w[::-1][:self.num_xi]\\n self.V = V[:, ::-1][:, :self.num_xi]\",\n \"def compute_eigendecomp(self, atol=1e-13, rtol=None):\\n self.eigvals, self.eigvecs = parallel.call_and_bcast(\\n util.eigh, self.correlation_array, atol=atol, rtol=rtol,\\n is_positive_definite=True)\",\n \"def calc_eigen(self, left=False, **parameter_overrides):\\n A, B = self.form_state_space_matrices(**parameter_overrides)\\n\\n if len(A.shape) == 3: # array version\\n evals = np.zeros(A.shape[:2], dtype='complex128')\\n evecs = np.zeros(A.shape, dtype='complex128')\\n for i, Ai in enumerate(A):\\n if left:\\n Ai = np.transpose(Ai)\\n evals[i], evecs[i] = np.linalg.eig(Ai)\\n return evals, evecs\\n else:\\n if left:\\n A = np.transpose(A)\\n return np.linalg.eig(A)\",\n \"def eigen_decomp(matrix):\\n w = None\\n v = None\\n ### YOUR CODE HERE\\n w,v=np.linalg.eig(matrix)\\n ### END YOUR CODE\\n return w, v\",\n \"def eig_vals_vects(matrix, sort='imag', not_hermitian=True, verbose=False):\\n # if len(matrix) < 10:\\n # print '\\\\nFinding eigvals, matrix = ', matrix\\n\\n # check if hermitian:\\n if not_hermitian:\\n eigval, eigvect = np.linalg.eig(matrix)\\n else:\\n if (matrix == matrix.conj().T).all():\\n if verbose:\\n print 'Shortcut eigvect/vals since matrix is hermitian...'\\n eigval, eigvect = np.linalg.eigh(matrix)\\n else:\\n if verbose:\\n print 'matrix is not hermitian...'\\n eigval, eigvect = np.linalg.eig(matrix)\\n\\n # use imaginary part to get ascending order of eigvals\\n if sort == 'imag':\\n si = np.argsort(np.imag(eigval))\\n elif sort == 'real':\\n si = np.argsort(np.real(eigval))\\n else:\\n si = np.arange(len(eigval))\\n\\n eigvect = np.array(eigvect)\\n eigvect_out = eigvect.T[si]\\n eigval_out = eigval[si]\\n\\n # if len(eigval_out) < 10:\\n # print 'eigvals return as =', eigval_out\\n\\n return eigval_out, eigvect_out\",\n \"def get_eigvals_eigvects(\\n num_layers,\\n numeric_matrices_eV_over_angsquared,\\n layer_mass_amu,\\n use_banded_algorithm=False,\\n):\\n # Based on the units in input, and indicating with:\\n # - [hbar omega] the numeric value for the frequency in meV => hbar omega = [hbar omega] * meV\\n # - [K] the numeric value of K in eV/ang^2\\n # - [m] the layer mass in amu\\n # we have (we omit the sign, and for units considerations we 'drop' U):\\n # omega^2 = K / m =>\\n # (hbar omega)^2 = hbar^2 * K / m =>\\n # [hbar omega]^2 * meV^2 = hbar^2 * [K] / [m] * eV/ang^2 / amu = [K] / [m] * hbar^2 * eV/ang^2 / amu =>\\n # [hbar omega]^2 = = [K] / [m] * ( hbar^2 * eV/ang^2 / amu / meV^2 )\\n # so that the conversion factor is the last bracketed term:\\n # conversion_factor = hbar^2 * eV / (angstrom^2 * amu * meV^2)\\n conversion_factor = 4180.15925\\n # NOTE: for simplicity, the conversion is applied at the very end\\n\\n if use_banded_algorithm:\\n # 3 blocks (below, same layer, and above) of size 3 => total width of 9\\n # Since we only store the upper part, we only need a width of 4 (diagonal + 3 superdiagonals)\\n K_matrix = np.zeros((4, num_layers * 3))\\n else:\\n K_matrix = np.zeros((num_layers * 3, num_layers * 3))\\n\\n # Note: I construct -K, actually\\n for block_idx in range(num_layers):\\n # Interaction with upper layer\\n if block_idx < num_layers - 1: # Not in the last layer\\n current_block = np.array(\\n numeric_matrices_eV_over_angsquared[\\n block_idx % len(numeric_matrices_eV_over_angsquared)\\n ]\\n )\\n add_block(\\n matrix=K_matrix,\\n block=current_block,\\n block_i=block_idx,\\n block_j=block_idx,\\n factor=+1,\\n banded=use_banded_algorithm,\\n )\\n add_block(\\n matrix=K_matrix,\\n block=current_block,\\n block_i=block_idx + 1,\\n block_j=block_idx,\\n factor=-1,\\n banded=use_banded_algorithm,\\n )\\n # Interaction with lower layer\\n if block_idx > 0: # Not in the first layer\\n previous_block = np.array(\\n numeric_matrices_eV_over_angsquared[\\n (block_idx - 1) % len(numeric_matrices_eV_over_angsquared)\\n ]\\n )\\n add_block(\\n matrix=K_matrix,\\n block=previous_block,\\n block_i=block_idx,\\n block_j=block_idx,\\n factor=+1,\\n banded=use_banded_algorithm,\\n )\\n add_block(\\n matrix=K_matrix,\\n block=previous_block,\\n block_i=block_idx - 1,\\n block_j=block_idx,\\n factor=-1,\\n banded=use_banded_algorithm,\\n )\\n\\n # We want to get the eigenvalues of omega^2 U = - 1/M_layer K U\\n K_matrix /= layer_mass_amu\\n\\n # Get frequencies (eigvals) and eigenvectors (for mode analysis)\\n if use_banded_algorithm:\\n eigvals, eigvects = scipy.linalg.eig_banded(K_matrix, lower=False)\\n else:\\n eigvals, eigvects = np.linalg.eigh(K_matrix)\\n\\n eigvals *= conversion_factor\\n\\n ## The first three should be acoustic i.e. almost zero; the rest should be positive\\n ## I don't check as depending on the units it's hard to define a correct absolute energy\\n # assert np.sum(np.abs(eigvals[:3])) < 1.0e-8\\n\\n # Remove the first three acoustic modes\\n return eigvals[3:], eigvects[:, 3:]\",\n \"def eigenvalue_analysis(*, dates, obs_data, model_data, residuals,\\n proxy_number=1):\\n # Create a masked version of the residuals array so that we can perform the\\n # PCA ignoring all nan values\\n masked_residuals = np.ma.array(residuals, mask=np.isnan(residuals))\\n\\n # Calculate the covariance matrix of the masked residuals array\\n covariance_matrix = np.ma.cov(masked_residuals, rowvar=False,\\n allow_masked=True)\\n # Calculate the eigenvalues and eigenvectors of the covariance matrix\\n eig_values, eig_vectors = np.linalg.eigh(covariance_matrix)\\n # Sort the eigenvalues in decreasing order\\n idx = np.argsort(np.abs(eig_values))[::-1]\\n eig_values = eig_values[idx]\\n # Sort the eigenvectors according to the same index\\n eig_vectors = eig_vectors[:, idx]\\n\\n # Project the residuals onto the eigenvectors\\n projected_residuals = np.ma.dot(masked_residuals, eig_vectors)\\n\\n # Use the method of Cox et al (2018) to remove unmodelled external\\n # signal in the SV residuals. The variable 'proxy' contains the sum\\n # of the SV residuals projected into the number of dominant PCs\\n # specified by proxy_number\\n\\n corrected_residuals = []\\n\\n if proxy_number == 1:\\n noisy_direction = eig_vectors[:, 0]\\n proxy = projected_residuals[:, 0]\\n for idx in range(len(proxy)):\\n corrected_residuals.append(\\n masked_residuals.data[idx, :] - proxy[idx] * noisy_direction)\\n # Apply the denoising algorithm to each of the PCs specified\\n elif proxy_number > 1:\\n noisy_direction = eig_vectors[:, 0:proxy_number]\\n proxy = np.sum(projected_residuals[:, 0:proxy_number], axis=1)\\n for idx in range(len(projected_residuals[:, 0])):\\n corrected = masked_residuals.data[idx, :]\\n for direction in range(proxy_number):\\n corrected = corrected - projected_residuals[idx, direction] \\\\\\n * noisy_direction[:, direction]\\n corrected_residuals.append(corrected)\\n\\n corrected_residuals = pd.DataFrame(corrected_residuals,\\n columns=obs_data.columns)\\n # Re-form the SV from the denoised residuals\\n denoised_sv = pd.DataFrame(\\n corrected_residuals.values + model_data.values,\\n columns=obs_data.columns)\\n\\n denoised_sv.insert(0, 'date', dates)\\n\\n return denoised_sv, proxy, np.abs(eig_values), eig_vectors,\\\\\\n projected_residuals, corrected_residuals.astype('float'),\\\\\\n covariance_matrix\",\n \"def computemodes(self):\\n evals, modes = np.linalg.eig(self.A)\\n return evals, modes\",\n \"def run_vqd(\\n self,\\n backend=Aer.get_backend(\\\"statevector_simulator\\\"),\\n var_form=None,\\n optimizer=None,\\n reps=2,\\n # reps=5,\\n ):\\n tmp = HermitianSolver(self.mat)\\n max_eigval, vqe_result, vqe = tmp.run_vqe(\\n backend=backend,\\n var_form=var_form,\\n optimizer=optimizer,\\n reps=reps,\\n mode=\\\"max_val\\\",\\n )\\n eigvals = [max_eigval]\\n eigstates = [vqe_result.eigenstate]\\n for r in range(len(tmp.mat) - 1):\\n val, vqe_result, vqe = tmp.run_vqe(\\n backend=backend,\\n var_form=var_form,\\n optimizer=optimizer,\\n reps=reps,\\n )\\n outer_prod = np.outer(\\n vqe_result.eigenstate, np.conj(vqe_result.eigenstate).T\\n )\\n tmp.mat = tmp.mat - (val - max_eigval) * outer_prod\\n eigvals.append(val)\\n eigstates.append(vqe_result.eigenstate)\\n tmp = HermitianSolver(tmp.mat)\\n\\n eigvals = np.array(eigvals)\\n eigstates = np.array(eigstates)\\n order = np.argsort(eigvals)\\n eigvals = eigvals[order]\\n eigstates = eigstates[order]\\n return eigvals, eigstates\",\n \"def eigen_vector_i_all(self):\\n return self._eig_vec\",\n \"def eigen(X):\\n\\n symmetric = np.alltrue(np.isclose(X - X.T, np.zeros(n)))\\n small = max(X.shape) <= 11\\n\\n if symmetric:\\n return jacobi(X)\\n elif small:\\n maxiter = 10 ** max(*X.shape, 4)\\n return qrm3(X, maxiter=maxiter)\\n else:\\n maxiter = 10 ** max(*X.shape, 4)\\n return qrm2(X, maxiter=maxiter)\",\n \"def FindEigenstates(**args):\\n\\tprop = SetupProblem(**args)\\n\\n\\t#use custom initial residual if provided\\n\\tinitialResidual = args.get(\\\"initialResidual\\\")\\n\\tif initialResidual != None:\\n\\t\\tprop.psi.GetData()[:] = initialResidual.GetData()\\n\\n\\t#find eigenstates\\n\\t#solver = pyprop.ArpackSolver(prop)\\n\\tsolver = pyprop.PiramSolver(prop)\\n\\tsolver.Solve()\\n\\treturn solver\",\n \"def _solve_principal_eig(a):\\n w, v = np.linalg.eig(a)\\n idx = np.argmax(w)\\n eig_val = w[idx]\\n eig_vec = v[:, idx]\\n\\n # Let eig_vec non-negative\\n sign = 0\\n i = 0\\n while sign == 0 and i < len(eig_vec):\\n sign = np.sign(eig_vec[i])\\n i += 1\\n if sign < 0:\\n eig_vec *= -1\\n\\n return eig_val, eig_vec\",\n \"def eigen_decomposition(X, features):\\n # Center to average\\n Xctr = X - X.mean(0)\\n # covariance matrix\\n Xcov = np.cov(Xctr.T)\\n\\n # Compute eigenvalues and eigenvectors\\n eigen_values, eigen_vectors = sp.linalg.eigh(Xcov)\\n\\n # Sort the eigenvalues and the eigenvectors descending\\n sortedindex = np.argsort(eigen_values)[::-1]\\n eigen_values = eigen_values[sortedindex]\\n eigen_vectors = eigen_vectors[:, sortedindex]\\n\\n ###########\\n y_pos = np.arange(len(features))\\n weight = eigen_vectors[0]\\n\\n figure, axis = plt.subplots(2, 1)\\n\\n axis[0].bar(features, eigen_vectors[0])\\n plt.setp(axis[0], title=\\\"First and Second Component's Eigenvectors \\\", ylabel='Weight')\\n axis[0].set_xticks(features, features)\\n axis[1].bar(features, eigen_vectors[1])\\n axis[1].set_xticks(features, features)\\n plt.setp(axis[1], ylabel='Weight')\\n # axis[0].bar(y_pos, weight, align='center', alpha=0.5)\\n # axis[0].xticks(y_pos, features)\\n # axis[0].ylabel('Weight')\\n # axis[0].title('Features')\\n #\\n # axis[1].bar(y_pos, weight, align='center', alpha=0.5)\\n # axis[1].xticks(y_pos, features)\\n # axis[1].ylabel('Weight')\\n # axis[1].title('Features')\\n\\n plt.show()\\n # return eigen_values, eigen_vectors\",\n \"def eig_vals_vects(self, matrix=None, attribute=False, attribute_matrix=False):\\n if matrix is None:\\n matrix = self.get_matrix(attribute=attribute_matrix)\\n eigval, eigvect = np.linalg.eig(matrix)\\n si = np.argsort(np.imag(eigval))\\n eigvect = np.array(eigvect)\\n eigvect = eigvect.T[si]\\n eigval = eigval[si]\\n\\n if attribute:\\n self.eigvect = eigvect\\n self.eigval = eigval\\n\\n # print 'np.shape(eigvect) = ', np.shape(eigvect)\\n # sys.exit()\\n return eigval, eigvect\",\n \"def posdef_eig_svd(mat):\\n evals, evecs, _ = tf.svd(mat)\\n\\n return evals, evecs\",\n \"def get_eigen_values_and_vectors(matrix, num_values):\\n w, v = eigen_decomp(matrix)\\n eigen_values = []\\n eigen_vectors = []\\n ### YOUR CODE HERE\\n pass\\n ### END YOUR CODE\\n return eigen_values, eigen_vectors\",\n \"def scipy_eigsolver(\\n kernel_matrix: Union[np.ndarray, scipy.sparse.csr_matrix],\\n n_eigenpairs: int,\\n is_symmetric: bool,\\n is_stochastic: bool,\\n):\\n\\n n_samples, n_features = kernel_matrix.shape\\n\\n # check only for n_eigenpairs == n_features and n_eigenpairs < n_features\\n # wrong parametrized n_eigenpairs are catched in scipy functions\\n if n_eigenpairs == n_features:\\n if is_symmetric:\\n scipy_eigvec_solver = scipy.linalg.eigh\\n else:\\n scipy_eigvec_solver = scipy.linalg.eig\\n\\n solver_kwargs: Dict[str, object] = {\\n \\\"check_finite\\\": False\\n } # should be already checked\\n\\n else: # n_eigenpairs < matrix.shape[1]\\n if is_symmetric:\\n scipy_eigvec_solver = scipy.sparse.linalg.eigsh\\n else:\\n scipy_eigvec_solver = scipy.sparse.linalg.eigs\\n\\n solver_kwargs = {\\n \\\"k\\\": n_eigenpairs,\\n \\\"which\\\": \\\"LM\\\",\\n \\\"v0\\\": np.ones(n_samples),\\n \\\"tol\\\": 1e-14,\\n }\\n\\n # The selection of sigma is a result of a microbenchmark\\n if is_symmetric and is_stochastic:\\n # NOTE: it turned out that for self.kernel_.is_symmetric=False (-> eigs),\\n # setting sigma=1 resulted into a slower computation.\\n NUMERICAL_EXACT_BREAKER = 0.1\\n solver_kwargs[\\\"sigma\\\"] = 1.0 + NUMERICAL_EXACT_BREAKER\\n solver_kwargs[\\\"mode\\\"] = \\\"normal\\\"\\n else:\\n solver_kwargs[\\\"sigma\\\"] = None\\n\\n # the scipy solvers only work on floating points\\n if scipy.sparse.issparse(\\n kernel_matrix\\n ) and kernel_matrix.data.dtype.kind not in [\\\"fdFD\\\"]:\\n kernel_matrix = kernel_matrix.asfptype()\\n elif isinstance(kernel_matrix, np.ndarray) and kernel_matrix.dtype != \\\"f\\\":\\n kernel_matrix = kernel_matrix.astype(float)\\n\\n eigvals, eigvects = scipy_eigvec_solver(kernel_matrix, **solver_kwargs)\\n\\n return eigvals, eigvects\",\n \"def fit(self, X):\\n mean_vec = X.mean(axis=0)\\n cov = (X - mean_vec).T.dot((X - mean_vec)) / (X.shape[0]-1)\\n eigenvalues, eigenvectors = np.linalg.eig(cov)\\n idx = np.abs(eigenvalues)\\n self.best_eigenvalues = idx[:self.n_components]\\n self.best_eigenvalues = eigenvectors[:, :self.n_components]\\n eigenvectors = np.atleast_1d(eigenvectors)[:, :self.n_components]\",\n \"def getEigenVectors(X_new, elambda):\\n X_cov = X_new.T.dot(X_new)\\n eigenvalues, eigenvectors = np.linalg.eig(X_cov)\\n eigen_sorted_ind = eigenvalues.argsort()[::-1]\\n eigenvalues = eigenvalues / eigenvalues.sum()\\n eigenvalues_sum = 0\\n eigenvectors_selected = eigenvectors\\n for i in xrange(len(eigen_sorted_ind)):\\n eigenvalues_sum += eigenvalues[eigen_sorted_ind[i]]\\n if eigenvalues_sum >= elambda:\\n eigenvectors_selected = eigenvectors[:, eigen_sorted_ind[0:i]]\\n break\\n return eigenvectors_selected\",\n \"def compute_eigvals(theta, pauli_word): # pylint: disable=arguments-differ\\n if qml.math.get_interface(theta) == \\\"tensorflow\\\":\\n theta = qml.math.cast_like(theta, 1j)\\n\\n # Identity must be treated specially because its eigenvalues are all the same\\n if set(pauli_word) == {\\\"I\\\"}:\\n exp = qml.math.exp(-0.5j * theta)\\n ones = qml.math.ones(2 ** len(pauli_word), like=theta)\\n if qml.math.get_interface(theta) == \\\"tensorflow\\\":\\n ones = qml.math.cast_like(ones, 1j)\\n\\n if qml.math.ndim(theta) == 0:\\n return exp * ones\\n\\n return qml.math.tensordot(exp, ones, axes=0)\\n\\n return MultiRZ.compute_eigvals(theta, len(pauli_word))\",\n \"def get_eigen_values_and_vectors(matrix, num_values):\\n (w, v) = eigen_decomp(matrix)\\n eigen_values = []\\n eigen_vectors = []\\n ### YOUR CODE HERE\\n max_indexs=np.argpartition(w, -num_values)\\n max_indexs=max_indexs[-num_values:]\\n ids=np.argsort(w[max_indexs])\\n sort_index=max_indexs[ids]\\n eigen_values=w[sort_index]\\n eigen_vectors=v[:,sort_index]\\n ### END YOUR CODE\\n return eigen_values, eigen_vectors\",\n \"def right_eigenvectors(matrix, nvals=None):\\n matrix = np.asarray(matrix)\\n return _eigenvectors(matrix, nvals)\",\n \"def fit_evd(self):\\n\\n # EVD only work on square matrices as we need to compute the eigenvalues and eigenvectors\\n # For this we compute the covariance matrix K\\n # K should be n x n matrix (pixels x pixels)\\n\\n # The covariance matrix is nxn\\n self.cov_matrix = np.zeros(shape=[self.n_features, self.n_features], dtype='uint8')\\n\\n self.cov_matrix = np.cov(self.norm_matrix, rowvar=False)\\n # C is a symmetric matrix and so it can be diagonalized:\\n eig_val, eig_vec = linalg.eig(self.cov_matrix)\\n\\n # Sorting the eigenvectors by decreasing eigenvalues\\n # [Start : stop : stepcount] stepcount is reversed\\n idx = eig_val.argsort()[::-1]\\n eig_val, eig_vec = eig_val[idx], eig_vec[:, idx]\\n\\n # Explained_variance tell us how much of the variance in the data each eigen value explains\\n explained_variance = eig_val / (self.n_samples - 1)\\n # total_var is the total variance in the data\\n total_var = explained_variance.sum()\\n explained_variance_ratio = explained_variance / total_var\\n # The cumulative sum of all ratios\\n ratio_cumsum = np.cumsum(explained_variance_ratio)\\n\\n # We search in the cumsum for the index of the value which, when added, corresponds to the quality_percent\\n # The index of the cumsum gives us the components we need to add to explain X quality percent of our data\\n n_components = np.searchsorted(ratio_cumsum, self.quality_percent, side='right') + 1\\n\\n self.components = eig_vec[:n_components]\\n print(\\\"The principal components have been calculated using eigendecomposition\\\", self.components.shape)\\n\\n return self.components\",\n \"def test_svd(self):\\n eigenvectors, eigenvalues = self.svd.run(self.test_matrix)\\n\\n self.assertEqual(eigenvectors.shape, (100, 100))\\n self.assertEqual(eigenvalues.shape, (100,))\",\n \"def analytical_eig(A):\\n n = len(A)\\n h = 1/float(n)\\n d = 2/float(h)**2\\n a = -1/float(h)**2\\n eigenval = np.empty(n)\\n for j in range(1,n+1):\\n eigenval[j-1] = d + 2*a*np.cos((j*np.pi)/(float(n)+1)) # Analytic solution\\n \\n return eigenval\",\n \"def readEigenvectors(self,phys,eigname):\\r\\n EigenvectorReader.EigenvectorReader(self.checkPath(eigname)).read(phys.myEig)\",\n \"def eigenvects(mat):\\n # Check if symbols are present\\n if hasSymbols(mat):\\n return mat.eigenvects()\\n # Purely numeric matrix\\n newMat = recursiveEvaluate(mat.as_mutable())\\n return newMat.eigenvects()\",\n \"def eig_vals_vects_hermitian(matrix, sort='imag'):\\n # if len(matrix) < 10:\\n # print '\\\\nFinding eigvals, matrix = ', matrix\\n eigval, eigvect = np.linalg.eig(matrix)\\n # use imaginary part to get ascending order of eigvals\\n if sort == 'imag':\\n si = np.argsort(np.imag(eigval))\\n elif sort == 'real':\\n si = np.argsort(np.real(eigval))\\n else:\\n si = np.arange(len(eigval))\\n\\n eigvect = np.array(eigvect)\\n eigvect_out = eigvect.T[si]\\n eigval_out = eigval[si]\\n if len(eigval_out) < 10:\\n print 'eigvals return as =', eigval_out\\n return eigval_out, eigvect_out\",\n \"def explore_eigenvalues(self, X_train, X_test) :\\r\\n\\r\\n # scaler = StandardScaler()\\r\\n # X_train = scaler.fit_transform(X_train)\\r\\n # X_test = scaler.transform(X_test)\\r\\n\\r\\n print (\\\"Train feature shape:\\\", X_train.shape)\\r\\n print(\\\"Train feature shape:\\\", X_test.shape)\\r\\n \\r\\n\\r\\n mean_vec = np.mean(X_test, axis=0)\\r\\n cov_mat = (X_test - mean_vec).T.dot((X_test - mean_vec)) / (X_test.shape[0]-1)\\r\\n print('Covariance matrix \\\\n%s' %cov_mat)\\r\\n print('NumPy covariance matrix: \\\\n%s' %np.cov(X_test.T))\\r\\n eig_vals, eig_vecs = np.linalg.eig(cov_mat)\\r\\n print('Eigenvectors \\\\n%s' %eig_vecs)\\r\\n print(\\\"---------------------------------\\\")\\r\\n print('\\\\nEigenvalues \\\\n%s' %eig_vals)\\r\\n # Make a list of (eigenvalue, eigenvector) tuples\\r\\n eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]\\r\\n\\r\\n # Sort the (eigenvalue, eigenvector) tuples from high to low\\r\\n eig_pairs.sort(key=lambda x: x[0], reverse=True)\\r\\n\\r\\n # Visually confirm that the list is correctly sorted by decreasing eigenvalues\\r\\n print(\\\"---------------------------------\\\")\\r\\n print('Eigenvalues in descending order:')\\r\\n for i in eig_pairs:\\r\\n print(i[0])\\r\\n # tot = sum(eig_vals)\\r\\n # var_exp = [(i / tot)*100 for i in sorted(eig_vals, reverse=True)]\\r\\n # print(\\\"Explained variance : \\\\n{}\\\".format(var_exp))\\r\\n \\r\\n\\r\\n return X_train, X_test\",\n \"def eigsh(A, M = None, k = 6, sigma = None, which = 'LM', v0=None,\\n ncv = None, maxiter = None, tol = 0., return_eigenvectors = True,\\n Minv = None, OPinv = None, mode = 'normal'):\\n if M is not None:\\n raise NotImplementedError(\\\"M is not currently supported!\\\")\\n if v0 is not None:\\n raise NotImplementedError(\\\"v0 is not currently supported!\\\")\\n if ncv is not None:\\n raise NotImplementedError(\\\"ncv is not currently supported!\\\")\\n if Minv is not None:\\n raise NotImplementedError(\\\"Minv is not currently supported!\\\")\\n if OPinv is not None:\\n raise NotImplementedError(\\\"OPinv is not currently supported!\\\")\\n inp_data = FrovedisFeatureData(A, dense_kind='rowmajor')\\n X = inp_data.get()\\n x_dtype = inp_data.get_dtype()\\n x_itype = inp_data.get_itype()\\n dense = inp_data.is_dense()\\n nrows = inp_data.numRows()\\n ncols = inp_data.numCols()\\n\\n if nrows != ncols:\\n raise ValueError('expected squared symmetric matrix (shape=%s)' % (inp_data.shape,))\\n if k <= 0:\\n raise ValueError('k must be greater than 0.')\\n if k >= nrows:\\n raise ValueError('k must be less than or equal to N for N * N square matrix.')\\n if sigma is not None and not dense:\\n raise ValueError('currently sigma is only supported for dense matrices.')\\n if sigma is None:\\n sigma = np.finfo(np.float32).max\\n\\n if which not in ['LM', 'SM', 'LA', 'SA', 'BE']:\\n raise ValueError('which must be one of LM, SM, LA, SA, or BE')\\n if mode in ['buckling', 'cayley']:\\n raise ValueError('currenly normal mode is only supported!')\\n if maxiter is None:\\n maxiter = 10 * nrows\\n wantEv = return_eigenvectors\\n (host, port) = FrovedisServer.getServerInstance()\\n res = rpclib.compute_eigsh(host, port, X.get(),\\n k, which.encode('ascii'),\\n sigma, maxiter, wantEv,\\n tol, x_dtype,\\n x_itype, dense)\\n excpt = rpclib.check_server_exception()\\n if excpt[\\\"status\\\"]:\\n raise RuntimeError(excpt[\\\"info\\\"])\\n sptr = res[\\\"eigenval\\\"]\\n uptr = res[\\\"eigenvec\\\"]\\n m_m = res['m']\\n k_k = res['k']\\n eigval = FrovedisVector({'dptr' : sptr, 'size' : k_k},\\n dtype = TypeUtil.to_numpy_dtype(x_dtype)).to_numpy_array()\\n if wantEv:\\n eigvec = FrovedisDenseMatrix('C', {'dptr' : uptr, 'nrow' : m_m, 'ncol' : k_k},\\n dtype = TypeUtil.to_numpy_dtype(x_dtype)).to_numpy_array()\\n return eigval, eigvec\\n else:\\n return eigval\",\n \"def compute_eigvals(phi): # pylint: disable=arguments-differ\\n if qml.math.get_interface(phi) == \\\"tensorflow\\\":\\n phi = qml.math.cast_like(phi, 1j)\\n\\n signs = np.array([1, -1, 0, 0])\\n if qml.math.ndim(phi) == 0:\\n return qml.math.exp(0.5j * phi * signs)\\n\\n return qml.math.exp(qml.math.tensordot(0.5j * phi, signs, axes=0))\",\n \"def eigensystem(mat):\\n e, v = numpy.linalg.eig(mat)\\n\\n # `eig` returns complex results but we know all of the\\n # eigenstates have real energy.\\n e = numpy.real(e)\\n\\n items = zip(e, v.T)\\n items = sorted(items, key = operator.itemgetter(0))\\n e, v = zip(*items)\\n\\n return (e, v)\",\n \"def test_inverse_eigenvectors_non_interacting(self, size):\\n t_nn = 1.2\\n idx = np.arange(size)\\n g0_inv_full = np.zeros((size, size), dtype=complex)\\n g0_inv_full[idx[:-1], idx[1:]] = g0_inv_full[idx[1:], idx[:-1]] = t_nn\\n for g0 in self.g0_loc_inv:\\n g0_inv_full[idx, idx] = g0\\n rv, h, rv_inv = gt.matrix.decompose_gf(g0_inv_full)\\n assert_allclose(rv.dot(rv_inv), np.identity(*h.shape), atol=1e-14)\",\n \"def eigenvalue_decomposition (a_t_a_matrix ):\\r\\n # get eigenvalues and -vectors from ATA matrix\\r\\n eigenvalues = np.zeros (a_t_a_matrix.shape[0] )\\r\\n eigenvectors = np.zeros ((a_t_a_matrix.shape[0], a_t_a_matrix.shape[0] ))\\r\\n evals, evecs = np.linalg.eig (a_t_a_matrix )\\r\\n\\r\\n # sort them\\r\\n indices = np.argsort (-evals ) # reverse sort: greatest numbers first\\r\\n for loop_count, index in enumerate(indices ):\\r\\n eigenvalues[loop_count] = evals[index]\\r\\n eigenvectors[:, loop_count] = evecs[:, index]\\r\\n\\r\\n # get the normal vector, normalize it and if it's turned to the ground, turn it around\\r\\n normal_vector = normalize_vector (eigenvectors[:, -1] ) # the last (smallest) vector is the normal vector\\r\\n if (normal_vector[2] < 0):\\r\\n normal_vector = normal_vector * -1\\r\\n\\r\\n return normal_vector, eigenvalues[-1]\",\n \"def eig(C):\\r\\n\\r\\n # class eig(object):\\r\\n # def __call__(self, C):\\r\\n\\r\\n # Householder transformation of a symmetric matrix V into tridiagonal form.\\r\\n # -> n : dimension\\r\\n # -> V : symmetric nxn-matrix\\r\\n # <- V : orthogonal transformation matrix:\\r\\n # tridiag matrix == V * V_in * V^t\\r\\n # <- d : diagonal\\r\\n # <- e[0..n-1] : off diagonal (elements 1..n-1)\\r\\n\\r\\n # Symmetric tridiagonal QL algorithm, iterative\\r\\n # Computes the eigensystem from a tridiagonal matrix in roughtly 3N^3 operations\\r\\n # -> n : Dimension.\\r\\n # -> d : Diagonale of tridiagonal matrix.\\r\\n # -> e[1..n-1] : off-diagonal, output from Householder\\r\\n # -> V : matrix output von Householder\\r\\n # <- d : eigenvalues\\r\\n # <- e : garbage?\\r\\n # <- V : basis of eigenvectors, according to d\\r\\n\\r\\n\\r\\n # tred2(N, B, diagD, offdiag); B=C on input\\r\\n # tql2(N, diagD, offdiag, B);\\r\\n\\r\\n # private void tred2 (int n, double V[][], double d[], double e[]) {\\r\\n def tred2 (n, V, d, e):\\r\\n # This is derived from the Algol procedures tred2 by\\r\\n # Bowdler, Martin, Reinsch, and Wilkinson, Handbook for\\r\\n # Auto. Comp., Vol.ii-Linear Algebra, and the corresponding\\r\\n # Fortran subroutine in EISPACK.\\r\\n\\r\\n num_opt = False # factor 1.5 in 30-D\\r\\n\\r\\n for j in range(n):\\r\\n d[j] = V[n-1][j] # d is output argument\\r\\n\\r\\n # Householder reduction to tridiagonal form.\\r\\n\\r\\n for i in range(n-1,0,-1):\\r\\n # Scale to avoid under/overflow.\\r\\n h = 0.0\\r\\n if not num_opt:\\r\\n scale = 0.0\\r\\n for k in range(i):\\r\\n scale = scale + abs(d[k])\\r\\n else:\\r\\n scale = sum(abs(d[0:i]))\\r\\n\\r\\n if scale == 0.0:\\r\\n e[i] = d[i-1]\\r\\n for j in range(i):\\r\\n d[j] = V[i-1][j]\\r\\n V[i][j] = 0.0\\r\\n V[j][i] = 0.0\\r\\n else:\\r\\n\\r\\n # Generate Householder vector.\\r\\n if not num_opt:\\r\\n for k in range(i):\\r\\n d[k] /= scale\\r\\n h += d[k] * d[k]\\r\\n else:\\r\\n d[:i] /= scale\\r\\n h = np.dot(d[:i],d[:i])\\r\\n\\r\\n f = d[i-1]\\r\\n g = h**0.5\\r\\n\\r\\n if f > 0:\\r\\n g = -g\\r\\n\\r\\n e[i] = scale * g\\r\\n h = h - f * g\\r\\n d[i-1] = f - g\\r\\n if not num_opt:\\r\\n for j in range(i):\\r\\n e[j] = 0.0\\r\\n else:\\r\\n e[:i] = 0.0\\r\\n\\r\\n # Apply similarity transformation to remaining columns.\\r\\n\\r\\n for j in range(i):\\r\\n f = d[j]\\r\\n V[j][i] = f\\r\\n g = e[j] + V[j][j] * f\\r\\n if not num_opt:\\r\\n for k in range(j+1, i):\\r\\n g += V[k][j] * d[k]\\r\\n e[k] += V[k][j] * f\\r\\n e[j] = g\\r\\n else:\\r\\n e[j+1:i] += V.T[j][j+1:i] * f\\r\\n e[j] = g + np.dot(V.T[j][j+1:i],d[j+1:i])\\r\\n\\r\\n f = 0.0\\r\\n if not num_opt:\\r\\n for j in range(i):\\r\\n e[j] /= h\\r\\n f += e[j] * d[j]\\r\\n else:\\r\\n e[:i] /= h\\r\\n f += np.dot(e[:i],d[:i])\\r\\n\\r\\n hh = f / (h + h)\\r\\n if not num_opt:\\r\\n for j in range(i):\\r\\n e[j] -= hh * d[j]\\r\\n else:\\r\\n e[:i] -= hh * d[:i]\\r\\n\\r\\n for j in range(i):\\r\\n f = d[j]\\r\\n g = e[j]\\r\\n if not num_opt:\\r\\n for k in range(j, i):\\r\\n V[k][j] -= (f * e[k] + g * d[k])\\r\\n else:\\r\\n V.T[j][j:i] -= (f * e[j:i] + g * d[j:i])\\r\\n\\r\\n d[j] = V[i-1][j]\\r\\n V[i][j] = 0.0\\r\\n\\r\\n d[i] = h\\r\\n # end for i--\\r\\n\\r\\n # Accumulate transformations.\\r\\n\\r\\n for i in range(n-1):\\r\\n V[n-1][i] = V[i][i]\\r\\n V[i][i] = 1.0\\r\\n h = d[i+1]\\r\\n if h != 0.0:\\r\\n if not num_opt:\\r\\n for k in range(i+1):\\r\\n d[k] = V[k][i+1] / h\\r\\n else:\\r\\n d[:i+1] = V.T[i+1][:i+1] / h\\r\\n\\r\\n for j in range(i+1):\\r\\n if not num_opt:\\r\\n g = 0.0\\r\\n for k in range(i+1):\\r\\n g += V[k][i+1] * V[k][j]\\r\\n for k in range(i+1):\\r\\n V[k][j] -= g * d[k]\\r\\n else:\\r\\n g = np.dot(V.T[i+1][0:i+1], V.T[j][0:i+1])\\r\\n V.T[j][:i+1] -= g * d[:i+1]\\r\\n\\r\\n if not num_opt:\\r\\n for k in range(i+1):\\r\\n V[k][i+1] = 0.0\\r\\n else:\\r\\n V.T[i+1][:i+1] = 0.0\\r\\n\\r\\n\\r\\n if not num_opt:\\r\\n for j in range(n):\\r\\n d[j] = V[n-1][j]\\r\\n V[n-1][j] = 0.0\\r\\n else:\\r\\n d[:n] = V[n-1][:n]\\r\\n V[n-1][:n] = 0.0\\r\\n\\r\\n V[n-1][n-1] = 1.0\\r\\n e[0] = 0.0\\r\\n\\r\\n\\r\\n # Symmetric tridiagonal QL algorithm, taken from JAMA package.\\r\\n # private void tql2 (int n, double d[], double e[], double V[][]) {\\r\\n # needs roughly 3N^3 operations\\r\\n def tql2 (n, d, e, V):\\r\\n\\r\\n # This is derived from the Algol procedures tql2, by\\r\\n # Bowdler, Martin, Reinsch, and Wilkinson, Handbook for\\r\\n # Auto. Comp., Vol.ii-Linear Algebra, and the corresponding\\r\\n # Fortran subroutine in EISPACK.\\r\\n\\r\\n num_opt = False # using vectors from numpy makes it faster\\r\\n\\r\\n if not num_opt:\\r\\n for i in range(1,n): # (int i = 1; i < n; i++):\\r\\n e[i-1] = e[i]\\r\\n else:\\r\\n e[0:n-1] = e[1:n]\\r\\n e[n-1] = 0.0\\r\\n\\r\\n f = 0.0\\r\\n tst1 = 0.0\\r\\n eps = 2.0**-52.0\\r\\n for l in range(n): # (int l = 0; l < n; l++) {\\r\\n\\r\\n # Find small subdiagonal element\\r\\n\\r\\n tst1 = max(tst1, abs(d[l]) + abs(e[l]))\\r\\n m = l\\r\\n while m < n:\\r\\n if abs(e[m]) <= eps*tst1:\\r\\n break\\r\\n m += 1\\r\\n\\r\\n # If m == l, d[l] is an eigenvalue,\\r\\n # otherwise, iterate.\\r\\n\\r\\n if m > l:\\r\\n iiter = 0\\r\\n while 1: # do {\\r\\n iiter += 1 # (Could check iteration count here.)\\r\\n\\r\\n # Compute implicit shift\\r\\n\\r\\n g = d[l]\\r\\n p = (d[l+1] - g) / (2.0 * e[l])\\r\\n r = (p**2 + 1)**0.5 # hypot(p,1.0)\\r\\n if p < 0:\\r\\n r = -r\\r\\n\\r\\n d[l] = e[l] / (p + r)\\r\\n d[l+1] = e[l] * (p + r)\\r\\n dl1 = d[l+1]\\r\\n h = g - d[l]\\r\\n if not num_opt:\\r\\n for i in range(l+2, n):\\r\\n d[i] -= h\\r\\n else:\\r\\n d[l+2:n] -= h\\r\\n\\r\\n f = f + h\\r\\n\\r\\n # Implicit QL transformation.\\r\\n\\r\\n p = d[m]\\r\\n c = 1.0\\r\\n c2 = c\\r\\n c3 = c\\r\\n el1 = e[l+1]\\r\\n s = 0.0\\r\\n s2 = 0.0\\r\\n\\r\\n # hh = V.T[0].copy() # only with num_opt\\r\\n for i in range(m-1, l-1, -1): # (int i = m-1; i >= l; i--) {\\r\\n c3 = c2\\r\\n c2 = c\\r\\n s2 = s\\r\\n g = c * e[i]\\r\\n h = c * p\\r\\n r = (p**2 + e[i]**2)**0.5 # hypot(p,e[i])\\r\\n e[i+1] = s * r\\r\\n s = e[i] / r\\r\\n c = p / r\\r\\n p = c * d[i] - s * g\\r\\n d[i+1] = h + s * (c * g + s * d[i])\\r\\n\\r\\n # Accumulate transformation.\\r\\n\\r\\n if not num_opt: # overall factor 3 in 30-D\\r\\n for k in range(n): # (int k = 0; k < n; k++) {\\r\\n h = V[k][i+1]\\r\\n V[k][i+1] = s * V[k][i] + c * h\\r\\n V[k][i] = c * V[k][i] - s * h\\r\\n else: # about 20% faster in 10-D\\r\\n hh = V.T[i+1].copy()\\r\\n # hh[:] = V.T[i+1][:]\\r\\n V.T[i+1] = s * V.T[i] + c * hh\\r\\n V.T[i] = c * V.T[i] - s * hh\\r\\n # V.T[i] *= c\\r\\n # V.T[i] -= s * hh\\r\\n\\r\\n p = -s * s2 * c3 * el1 * e[l] / dl1\\r\\n e[l] = s * p\\r\\n d[l] = c * p\\r\\n\\r\\n # Check for convergence.\\r\\n if abs(e[l]) <= eps*tst1:\\r\\n break\\r\\n # } while (Math.abs(e[l]) > eps*tst1);\\r\\n\\r\\n d[l] = d[l] + f\\r\\n e[l] = 0.0\\r\\n\\r\\n\\r\\n # Sort eigenvalues and corresponding vectors.\\r\\n if 11 < 3:\\r\\n for i in range(n-1): # (int i = 0; i < n-1; i++) {\\r\\n k = i\\r\\n p = d[i]\\r\\n for j in range(i+1, n): # (int j = i+1; j < n; j++) {\\r\\n if d[j] < p: # NH find smallest k>i\\r\\n k = j\\r\\n p = d[j]\\r\\n\\r\\n if k != i:\\r\\n d[k] = d[i] # swap k and i\\r\\n d[i] = p\\r\\n for j in range(n): # (int j = 0; j < n; j++) {\\r\\n p = V[j][i]\\r\\n V[j][i] = V[j][k]\\r\\n V[j][k] = p\\r\\n # tql2\\r\\n\\r\\n N = len(C[0])\\r\\n if 11 < 3:\\r\\n V = np.array([x[:] for x in C]) # copy each \\\"row\\\"\\r\\n N = V[0].size\\r\\n d = np.zeros(N)\\r\\n e = np.zeros(N)\\r\\n else:\\r\\n V = [[x[i] for i in xrange(N)] for x in C] # copy each \\\"row\\\"\\r\\n d = N * [0.]\\r\\n e = N * [0.]\\r\\n\\r\\n tred2(N, V, d, e)\\r\\n tql2(N, d, e, V)\\r\\n return (array(d), array(V))\",\n \"def eigenalgo(self, accuracy: float = 0, cap: int = 50000, version: str = \\\"Givens\\\", not_skip: bool = True):\\n j, temps, verify_accuracy = 0, 0, np.ones((self.N, self.N), dtype=bool) ^ np.eye(self.N, dtype=bool)\\n if version == \\\"Gram-Schmidt\\\":\\n temps = time()\\n while np.any(abs(self.vap[verify_accuracy]) > accuracy) and j < cap:\\n j += 1\\n q, r = self.gram_schmidt_qr()\\n self.vap, self.vep = r @ q, self.vep @ q\\n\\n elif version == \\\"Givens\\\":\\n verify_accuracy = np.ones((self.N, self.N), dtype=bool) ^ np.eye(self.N, dtype=bool)\\n temps = time()\\n while np.any(abs(self.vap[verify_accuracy]) > accuracy) and j < cap:\\n j += 1\\n q, r = self.givens_qr()\\n self.vap, self.vep = r @ q, self.vep @ q\\n\\n elif version == \\\"Rayleigh\\\":\\n not_sing, diff, cond, j = True, accuracy + 1, True, 0\\n temps = time()\\n while cond: # Stop condition, all eigenvalues must be different\\n while diff > accuracy and j < cap and not_sing:\\n j += 1\\n self.rvap, self.vep, diff, not_sing = self.rayleigh_iteration(self.rvap, self.vep)\\n\\n cond = False\\n if j < cap:\\n self.calc, first, not_sing = np.zeros(self.N, dtype=bool), True, True\\n for i in range(self.N):\\n if np.sum(np.less(np.abs(self.rvap - self.rvap[i]), 10 ** -6)) != 1:\\n self.rvap[i + 1:] += self.memorize[i]\\n if first:\\n self.memorize[i] += 0.5\\n self.vep[i + 1:, i + 1:] = np.eye(self.N - i - 1)\\n first, cond, diff = False, True, accuracy + 1\\n self.calc[i + 1:] = 1\\n temps = time() - temps\\n return self.rvap, self.vep, diff, j, temps\\n\\n else:\\n print(\\\"Please select an appropriate value for the version parameter\\\")\\n\\n temps = time() - temps\\n diff = np.max(abs(self.vap[verify_accuracy]))\\n return np.diag(self.vap), self.vep, diff, j, temps\",\n \"def compute_eigvals(phi): # pylint: disable=arguments-differ\\n if qml.math.get_interface(phi) == \\\"tensorflow\\\":\\n phase = qml.math.exp(-0.5j * qml.math.cast_like(phi, 1j))\\n return stack_last([phase, qml.math.conj(phase), qml.math.conj(phase), phase])\\n\\n prefactors = qml.math.array([-0.5j, 0.5j, 0.5j, -0.5j], like=phi)\\n if qml.math.ndim(phi) == 0:\\n product = phi * prefactors\\n else:\\n product = qml.math.outer(phi, prefactors)\\n return qml.math.exp(product)\",\n \"def solve_elas(self,x,E_p=None):\\n \\n if x['Crystal_Structure'] == \\\"Cubic\\\":\\n self.estf = self.Ccubic( x['Stiffness'][0], x['Stiffness'][1], x['Stiffness'][2] )\\n\\n elif x['Crystal_Structure'] == \\\"HCP\\\":\\n self.estf = self.Chcp( x['Stiffness'][0], x['Stiffness'][1], x['Stiffness'][2], x['Stiffness'][3], x['Stiffness'][4] )\\n\\n # Update orientation\\n for n in range(9):\\n cell_num_list = list((9*self.cell_num)+n)\\n self.orient.vector()[cell_num_list] = self.rots[self.subdomain_num,n]\\n \\n self.a = inner(self.sigs3x3(self.u), sym(grad(self.v)))*dx\\n \\n if E_p:\\n # Note use of sym(), assuming E_p to be the \\\\chi field\\n L_elas_rhs = self.L_elas + inner(self.sigs_e(sym(E_p)), sym(grad(self.v)))*dx\\n else:\\n L_elas_rhs = self.L_elas \\n\\n self.A_elas, self.b_elas = assemble_system(self.a, L_elas_rhs, self.bc_elas) \\n \\n # Attach near nullspace to matrix\\n as_backend_type(self.A_elas).set_near_nullspace(self.null_space)\\n\\n # Set matrix operator\\n self.elasticity_solver.set_operator(self.A_elas);\\n\\n # Compute solution\\n self.elasticity_solver.solve(self.ue.vector(), self.b_elas);\\n \\n if E_p:\\n self.Ue_sym = project( sym(grad(self.ue) - E_p), self.TFS, solver_type=\\\"cg\\\", preconditioner_type=\\\"ilu\\\")\\n else:\\n self.Ue_sym = project( sym(grad(self.ue)), self.TFS, solver_type=\\\"cg\\\", preconditioner_type=\\\"ilu\\\")\\n \\n self.sim_strn = np.reshape(self.Ue_sym.vector().get_local(),(len(self.grains.array()),9))\\n\\n for grain_no in range(self.grains.array().max()):\\n # Grain numbering is 1 index origin\\n cell_subset = self.grains.array()==(grain_no+1)\\n if np.any(cell_subset):\\n self.sim_avg[grain_no,:] = np.average(self.sim_strn[cell_subset,:],\\n axis=0,weights=self.dVol[cell_subset]) \\n \\n deps = self.exp_strn - self.sim_avg\\n resid = np.linalg.norm(deps.ravel())\\n print(resid) #,self.its)\\n return resid\",\n \"def compute_eigvals(phi): # pylint: disable=arguments-differ\\n if qml.math.get_interface(phi) == \\\"tensorflow\\\":\\n phi = qml.math.cast_like(phi, 1j)\\n\\n return qml.math.stack([1, 1, -qml.math.exp(1j * phi), qml.math.exp(1j * phi)])\",\n \"def eigen_operator(shape, e, v, **kargs):\\n def matvec(x):\\n k = [np.dot(x.T, vi) for vi in v]\\n return np.sum([ki * ei * vi for ki, ei, vi in zip(k, e, v)], axis=0)\\n return LinearOperator(shape, matvec=matvec, rmatvec=matvec, **kargs)\",\n \"def eigenvals(self, evals_count=6):\\n hamiltonian_mat = self.hamiltonian()\\n return hamiltonian_mat.eigenenergies(eigvals=evals_count)\",\n \"def hessian_matrix_eigvals(H_elems):\\n return _symmetric_compute_eigenvalues(H_elems)\",\n \"def solve_gevp_gen(a, t_0, algorithm, sort_by_vectors=15, **kwargs):\\n B = np.matrix(a[t_0])\\n try:\\n f = algorithm(B=B, **kwargs)\\n except TypeError:\\n # If the function doesn't do currying, implement that here\\n f = lambda A: algorithm(B=B, A=A)\\n except LinAlgError:\\n return\\n\\n eigenvectors = None\\n count = 0\\n\\n for j in range(t_0 + 1, 32):\\n try:\\n eigenvalues, new_eigenvectors = f(np.matrix(a[j]))\\n \\n if eigenvectors is None:\\n eigenvectors = np.zeros_like(new_eigenvectors)\\n\\n if j < sort_by_vectors:\\n # TODO Sortieren nach Eigenwert\\n perm = permutation_indices(eigenvalues)\\n else:\\n perm = reorder_by_ev(new_eigenvectors, eigenvectors, B)\\n\\n eigenvectors = new_eigenvectors[:,perm]\\n eigenvalues = eigenvalues[:,perm]\\n \\n count += 1\\n\\n yield eigenvalues, eigenvectors\\n\\n except (LinAlgError, TypeError) as e:\\n #import traceback\\n #traceback.print_exc()\\n return\",\n \"def put_eigvecs(self, dest):\\n if parallel.is_rank_zero():\\n self.put_array(self.eigvecs, dest)\\n parallel.barrier()\",\n \"def calcEVals(self):\\n self.eVals,self.eVecs = np.linalg.eigh(self.rhoOp)\",\n \"def set_eigenvalue_problem(self, *args, ncc_cutoff=1e-10, tolerance=1e-10, **kwargs):\\n # should be set EVP for consistency with set IVP. Why do we have P_problem. Why not IVP, EVP.\\n self.problem_type = 'EVP'\\n self.problem = de.EVP(self.domain, variables=self.variables, eigenvalue='omega', ncc_cutoff=ncc_cutoff, tolerance=tolerance)\\n self.problem.substitutions['dt(f)'] = \\\"omega*f\\\"\\n self.set_equations(*args, **kwargs)\",\n \"def compute_eigvals(theta, num_wires): # pylint: disable=arguments-differ\\n eigs = qml.math.convert_like(pauli_eigs(num_wires), theta)\\n\\n if qml.math.get_interface(theta) == \\\"tensorflow\\\":\\n theta = qml.math.cast_like(theta, 1j)\\n eigs = qml.math.cast_like(eigs, 1j)\\n\\n if qml.math.ndim(theta) == 0:\\n return qml.math.exp(-0.5j * theta * eigs)\\n\\n return qml.math.exp(qml.math.outer(-0.5j * theta, eigs))\",\n \"def plot_eigenvectors(*, obs_names, eigenvecs, fig_size=(8, 6), font_size=12,\\n label_size=16, save_fig=False, write_path=None):\\n # Loop over directions and plot each eigenvector on a separate figure\\n for direction in range(eigenvecs.shape[1]):\\n plt.figure(figsize=fig_size)\\n plt.plot(np.abs(eigenvecs[::3, direction]), 'bx',\\n np.abs(eigenvecs[1::3, direction]), 'rx',\\n np.abs(eigenvecs[2::3, direction]), 'cx',\\n markersize=10, mew=3)\\n plt.ylim(0, 1)\\n plt.grid()\\n plt.yticks(fontsize=font_size)\\n plt.xticks(range(len(obs_names)), obs_names, fontsize=font_size)\\n plt.xticks(rotation=60)\\n plt.ylabel(r'$\\\\mathbf{{v}}_{%03d}$' % (direction), fontsize=label_size)\\n plt.legend(['$r$ direction', r'$\\\\theta$ direction',\\n '$\\\\phi$ direction'], loc='upper right', frameon=False,\\n fontsize=label_size)\\n plt.xlabel('Location', fontsize=label_size)\\n\\n if save_fig is True:\\n # Create the output directory if it does not exist\\n if not os.path.exists(write_path):\\n os.makedirs(write_path)\\n fpath = os.path.join(write_path,\\n 'eigendirection%03d.png' % direction)\\n plt.savefig(fpath, bbox_inches='tight')\\n plt.close()\",\n \"def eig(x1):\\n\\n is_x1_dparray = isinstance(x1, dparray)\\n\\n if (not use_origin_backend(x1) and is_x1_dparray):\\n if (x1.size > 0):\\n return dpnp_eig(x1)\\n\\n return call_origin(numpy.linalg.eig, x1)\",\n \"def compute_mesh_eigenfunctions(self, mesh, star0, star1, bdry=False):\\n nb = len(mesh)\\n\\n inputs = []\\n for m, s0, s1 in zip(mesh, star0, star1):\\n d = m['int_d01']\\n if bdry:\\n d = scipy.sparse.vstack([d, m['bdry_d01']])\\n inputs.extend([s0, s1, d])\\n\\n eigenvalues, eigenvectors = [], []\\n outputs = self.hodgefunc(nb, self.num_eigenvectors,\\n self.num_extra_eigenvectors, *inputs)\\n for i in range(nb):\\n eigenvalues.append(outputs[2*i])\\n eigenvectors.append(outputs[2*i+1])\\n\\n return eigenvalues, eigenvectors\",\n \"def solve_eigenproblem(self, A, M=None, num=None, tol=None):\\n def eigenproblem_is_hermitian():\\n return is_hermitian(A) and (M == None or is_hermitian(M))\\n\\n if self.is_hermitian() and not eigenproblem_is_hermitian():\\n raise ValueError(\\\"Eigenproblem matrices are non-Hermitian but solver \\\"\\n \\\"assumes Hermitian matrices. Aborting.\\\")\\n logger.info(\\\"Solving eigenproblem. This may take a while...\\\")\\n df.tic()\\n omegas, ws = self._solve_eigenproblem(A, M=M, num=num, tol=tol)\\n logger.info(\\\"Computing the eigenvalues and eigenvectors \\\"\\n \\\"took {}\\\".format(format_time(df.toc())))\\n\\n # XXX TODO: Remove this conversion to numpy.arrays once we\\n # have better support for different kinds of\\n # matrices (but the conversion would happen in\\n # compute_relative_error() anyway, so by doing it\\n # here we avoid doing it multiple times.\\n if not isinstance(A, np.ndarray):\\n logger.warning(\\n \\\"Converting sparse matrix A to dense array to check whether it is \\\"\\n \\\"Hermitian. This might consume a lot of memory if A is big!.\\\")\\n A = as_dense_array(A)\\n if not isinstance(M, (np.ndarray, NoneType)):\\n logger.warning(\\n \\\"Converting sparse matrix M to dense array to check whether it is \\\"\\n \\\"Hermitian. This might consume a lot of memory if M is big!.\\\")\\n M = as_dense_array(M)\\n\\n rel_errors = np.array(\\n [compute_relative_error(A, M, omega, w) for omega, w in zip(omegas, ws)])\\n return omegas, ws, rel_errors\",\n \"def matrix_eig(\\n self,\\n chis=None,\\n eps=0,\\n print_errors=\\\"deprecated\\\",\\n hermitian=False,\\n break_degenerate=False,\\n degeneracy_eps=1e-6,\\n sparse=False,\\n trunc_err_func=None,\\n evenTrunc = False,\\n ):\\n if print_errors != \\\"deprecated\\\":\\n msg = (\\n \\\"The `print_errors` keyword argument has been deprecated, \\\"\\n \\\"and has no effect. Rely instead on getting the error as a \\\"\\n \\\"return value, and print it yourself.\\\"\\n )\\n warnings.warn(msg)\\n # If chis is not specfied, there is no even truncation scheme; else, we\\n # keep track of the chi we specfied\\n if chis is None:\\n evenTrunc = False\\n else:\\n try:\\n chis = list(chis)\\n except TypeError:\\n chis = [chis]\\n chiSpec = max(chis)\\n chis = self._matrix_decomp_format_chis(chis, eps)\\n maxchi = max(chis)\\n assert self.defval == 0\\n assert self.invar\\n assert self.charge == 0\\n assert self.dirs[0] + self.dirs[1] == 0\\n assert set(zip(self.qhape[0], self.shape[0])) == set(\\n zip(self.qhape[1], self.shape[1])\\n )\\n\\n S_dtype = np.float_ if hermitian else np.complex_\\n U_dtype = self.dtype if hermitian else np.complex_\\n\\n # Eigenvalue decompose each sector at a time.\\n # While doing so, also keep track of a list of all eigenvalues, as well\\n # as a heap that gives the negative of the absolute value of the\\n # largest eigenvalue in each sector. These will be needed later when\\n # deciding how to truncate the eigenvalues.\\n eigdecomps = {}\\n dims = {}\\n minusabs_next_eigs = []\\n all_eigs = []\\n for k, v in self.sects.items():\\n if 0 in v.shape:\\n # This matrix is empty and trivial.\\n shp = v.shape\\n m = min(shp)\\n u = np.empty((shp[0], m), dtype=U_dtype)\\n s = np.empty((m,), dtype=S_dtype)\\n eigdecomp = (s, u)\\n else:\\n if sparse and maxchi < min(v.shape) - 1:\\n if hermitian:\\n s, u = spsla.eighs(\\n v, k=maxchi, return_eigenvectors=True\\n )\\n else:\\n s, u = spsla.eigs(\\n v, k=maxchi, return_eigenvectors=True\\n )\\n else:\\n if hermitian:\\n s, u = np.linalg.eigh(v)\\n else:\\n s, u = np.linalg.eig(v)\\n order = np.argsort(-np.abs(s))\\n s = s[order]\\n u = u[:, order]\\n s = s.astype(S_dtype)\\n u = u.astype(U_dtype)\\n eigdecomp = (s, u)\\n eigdecomps[k] = eigdecomp\\n dims[k] = 0\\n all_eigs.append(s)\\n if 0 not in s.shape:\\n heapq.heappush(minusabs_next_eigs, (-np.abs(s[0]), k))\\n try:\\n all_eigs = np.concatenate(all_eigs)\\n except ValueError:\\n all_eigs = np.array((0,))\\n\\n if sparse:\\n norm_sq = self.norm_sq()\\n else:\\n norm_sq = None\\n\\n # Figure out what bond dimension to truncate to, how this bond\\n # dimension is distributed over the different sectors, and what the\\n # truncation error is.\\n chi, dims, rel_err = type(self)._find_trunc_dim(\\n all_eigs,\\n eigdecomps,\\n minusabs_next_eigs,\\n dims,\\n chis=chis,\\n eps=eps,\\n break_degenerate=break_degenerate,\\n degeneracy_eps=degeneracy_eps,\\n trunc_err_func=trunc_err_func,\\n norm_sq=norm_sq,\\n )\\n\\n # truncate in both sectors evenly\\n if evenTrunc and chiSpec == chi:\\n # This piece of codes is only designed\\n # with Z2 symmetry tensor in mind\\n errmeg = \\\"The matrix should have two sectors (0,0) and (1,1).\\\"\\n assert len(dims) == 2, errmeg\\n if chiSpec % 2 == 0:\\n dims[(0, 0)] = int(chiSpec / 2)\\n dims[(1, 1)] = int(chiSpec / 2)\\n else:\\n dims[(0, 0)] = int((chiSpec + 1) / 2)\\n dims[(1, 1)] = int((chiSpec - 1) / 2)\\n\\n # Truncate each block and create the dim for the new index.\\n new_dim = []\\n new_qim = []\\n eigdecomps = {k: v for k, v in eigdecomps.items() if dims[k] > 0}\\n for k, v in eigdecomps.items():\\n d = dims[k]\\n if d > 0:\\n new_dim.append(d)\\n new_qim.append(k[0])\\n eigdecomps[k] = (v[0][:d], v[1][:, :d])\\n else:\\n del eigdecomps[k]\\n\\n # Initialize S and U.\\n d = self.dirs[0]\\n S = type(self)(\\n [new_dim],\\n qhape=[new_qim],\\n dirs=[d],\\n qodulus=self.qodulus,\\n dtype=S_dtype,\\n invar=False,\\n charge=0,\\n )\\n U = type(self)(\\n [self.shape[0], new_dim],\\n qhape=[self.qhape[0], new_qim],\\n dirs=[d, -d],\\n qodulus=self.qodulus,\\n dtype=U_dtype,\\n charge=0,\\n )\\n\\n # Set the blocks of U, S and V.\\n for k, v in eigdecomps.items():\\n S[(k[0],)] = v[0]\\n k_U = (k[0], k[0])\\n U[k_U] = v[1]\\n\\n return S, U, rel_err\",\n \"def run_vqe(\\n self,\\n backend=Aer.get_backend(\\\"statevector_simulator\\\"),\\n var_form=None,\\n optimizer=None,\\n reps=None,\\n mode=\\\"min_val\\\",\\n ):\\n # N=int(np.ceil(np.log2(len(self.mat))))\\n # hk = np.zeros((2**N,2**N),dtype='complex')\\n # hk[:self.mat.shape[0], :self.mat.shape[1]] = self.mat\\n N = self.n_qubits()\\n if mode == \\\"max_val\\\":\\n Hamil_mat = aqua.operators.MatrixOperator(-1 * self.mat)\\n # Hamil_mat = MatrixOperator(-1 * self.mat)\\n else:\\n Hamil_mat = aqua.operators.MatrixOperator(self.mat)\\n # Hamil_mat = MatrixOperator(self.mat)\\n Hamil_qop = aqua.operators.op_converter.to_weighted_pauli_operator(\\n Hamil_mat\\n )\\n if var_form is None:\\n if reps is None:\\n reps = 2\\n # reps=5\\n from qiskit.circuit.library import EfficientSU2\\n\\n var_form = EfficientSU2(N, reps=reps)\\n if optimizer is None:\\n vqe = aqua.algorithms.VQE(Hamil_qop, var_form)\\n # vqe = VQE(Hamil_qop, var_form)\\n else:\\n vqe = aqua.algorithms.VQE(Hamil_qop, var_form, optimizer)\\n # vqe = VQE(Hamil_qop, var_form, optimizer)\\n vqe_result = vqe.run(backend)\\n en = np.real(vqe_result[\\\"eigenvalue\\\"])\\n # params=vqe.optimal_params\\n # circuit=vqe.construct_circuit(params)\\n if mode == \\\"max_val\\\":\\n en = -1 * en\\n # states = np.sort(\\n # np.real(\\n # vqe.expectation.convert(\\n # StateFn(vqe.operator, is_measurement=True)\\n # ).to_matrix()\\n # )\\n # )\\n return en, vqe_result, vqe\",\n \"def reflect_eigenvectors(x):\\n # random reference vector\\n xnew = x.copy()\\n for v in range(x.shape[-1]):\\n cum_sum = x[0, :, v]\\n cum_sum /= np.linalg.norm(cum_sum)\\n for i in np.arange(1, x.shape[0]): \\n if np.any(np.isnan(x[i, :, v])):\\n xnew[i, :, v] = x[i, :, v]\\n else:\\n cos = cum_sum.dot(x[i, :, v])\\n if cos > 0:\\n cum_sum += x[i, :, v]\\n cum_sum /= np.linalg.norm(cum_sum)\\n\\n else:\\n cum_sum += np.negative(x[i, :, v])\\n cum_sum /= np.linalg.norm(cum_sum)\\n xnew[i, :, v] = np.negative(x[i, :, v])\\n \\n return xnew\",\n \"def eigen_decomp(matrix):\\n w = None\\n v = None\\n ### YOUR CODE HERE\\n pass\\n ### END YOUR CODE\\n return w, v\",\n \"def eig(A):\\n lambdas, vs = linalg.eig(A)\\n lvs = [(lambdas[i], vs[i]) for i in range(len(vs))]\\n\\n def lv_cmp(lv1, lv2):\\n lambda1 = lv1[0]\\n lambda2 = lv2[0]\\n\\n r1 = numpy.real(lambda1)\\n i1 = numpy.real(lambda1)\\n r2 = numpy.real(lambda2)\\n i2 = numpy.real(lambda2)\\n\\n if r1 > r2:\\n return 1\\n elif r1 < r2:\\n return -1\\n elif i1 > i2:\\n return 1\\n elif i1 < i2:\\n return -1\\n return 0\\n\\n lvs.sort(cmp = lv_cmp)\\n\\n lambdas = [lv[0] for lv in lvs]\\n vs = [lv[1] for lv in lvs ]\\n return lambdas, vs\",\n \"def left_eigenvectors(matrix, nvals=None):\\n matrix = np.asarray(matrix)\\n return _eigenvectors(matrix.transpose(), nvals)\",\n \"def run_numpy(self):\\n return np.linalg.eigh(self.mat)\",\n \"def geneiv(A, B):\\n Li = np.linalg.inv(linalg.cholesky(B).T)\\n C = Li*A*(Li.T)\\n C = np.asmatrix((C + C.T)*0.5, np.float32)\\n\\n eivs, V = np.linalg.eig(C)\\n return eivs, Li.T*V\",\n \"def eigen_vector_i(self, i):\\n return self._eig_vec[:,i]\",\n \"def pde_eigv(self, u):\\n u0, u1, u2 = u.T\\n c = np.sqrt(9.81*u0)\\n vel = np.sqrt((u1/u0)**2 + (u2/u0)**2)\\n return np.array([vel-c, vel, vel+c])\",\n \"def put_eigvals(self, dest):\\n if parallel.is_rank_zero():\\n self.put_array(self.eigvals, dest)\\n parallel.barrier()\",\n \"def get_decomp(self, eigvals_src, eigvecs_src):\\n self.eigvals = np.squeeze(np.array(parallel.call_and_bcast(\\n self.get_array, eigvals_src)))\\n self.eigvecs = parallel.call_and_bcast(self.get_array, eigvecs_src)\",\n \"def test_eigenvalues_in_cylinders(self):\\n random.seed(102938482634)\\n point_cloud = load(os.path.join('testdata', 'AHN3.las'))\\n num_all_pc_points = len(point_cloud[keys.point][\\\"x\\\"][\\\"data\\\"])\\n rand_indices = [random.randint(0, num_all_pc_points) for _ in range(20)]\\n target_point_cloud = utils.copy_point_cloud(point_cloud, rand_indices)\\n radius = 2.5\\n neighbors = compute_neighbors.compute_cylinder_neighborhood(point_cloud, target_point_cloud, radius)\\n\\n compute_features(point_cloud, neighbors, target_point_cloud,\\n [\\\"eigenv_1\\\", \\\"eigenv_2\\\", \\\"eigenv_3\\\"], InfiniteCylinder(5))\\n\\n self.assertEqual(\\\"laserchicken.feature_extractor.eigenvals_feature_extractor\\\",\\n target_point_cloud[keys.provenance][-1][\\\"module\\\"])\",\n \"def eig(self, q):\\n q1, q2 = q.T\\n c = np.sqrt(9.81*q1)\\n lambda1 = q2/q1 - c\\n lambda2 = q2/q1 + c\\n return np.array([lambda1, lambda2])\",\n \"def compute_residualsNEW(eigenvectors, eps_scale=3, progressBar=True, skipFirst=True, bandwidth_type='median'):\\n #from multiprocessing import Pool\\n # Check for more than two eigenvectors.\\n assert eigenvectors.shape[1] > 2, 'There must be more than two eigenvectors to compute residuals.'\\n\\n # Set up the residuals and define the second as one.\\n residual = np.zeros(eigenvectors.shape[1])\\n if skipFirst:\\n residual[1] = 1\\n firstIndex = 2\\n else:\\n residual[0] = 1\\n firstIndex = 1\\n\\n ivals = range(firstIndex, eigenvectors.shape[1])\\n jvals = np.arange(eigenvectors.shape[0])\\n \\n # Run the residual calculation for each eigenvector. \\n for i in ivals:\\n num, den = 0, 0\\n\\n if bandwidth_type == 'median':\\n bandwidth = np.power((np.median(scipy.spatial.distance.squareform(scipy.spatial.distance.pdist(eigenvectors[:,:i]))) / eps_scale), 2)\\n elif bandwidth_type == 'mean':\\n bandwidth = np.power((np.mean(scipy.spatial.distance.squareform(scipy.spatial.distance.pdist(eigenvectors[:,:i]))) / eps_scale), 2)\\n else:\\n raise ValueError('bandwidth_type must be either median or mean')\\n\\n if bandwidth == 0:\\n bandwidth = np.finfo(float).eps\\n \\n regression = partial(local_linear_regression, eigenvectors[:, :i], eigenvectors[:, i], bandwidth=bandwidth, bandwidth_type=bandwidth_type)\\n\\n fit = np.array(list(map(regression, jvals)))[jvals, jvals]\\n num = np.sum(np.power(eigenvectors[:, i] - fit, 2))\\n den = np.sum(np.power(eigenvectors[:, i], 2))\\n \\n # Compute a residual for eigenvector i.\\n residual[i] = np.sqrt(num / den)\\n \\n result = dict([('Residuals', residual), ('Eps Scale', eps_scale), ('Bandwidth Type', bandwidth_type)])\\n \\n return result\",\n \"def test_solvers():\\n # With P1 elements we have an error E-15 with Krylov solver\\n # tolerances of 1E-12, but with P2 elements the error is E-6.\\n # P3 elements drive the tolerance down to E-3.\\n # For higher mesh resolution we also need reduced tolerances.\\n # The tol dict maps degree to expected tolerance for the coarse\\n # meshes in the test.\\n tol = {'direct': {1: 1E-11, 2: 1E-11, 3: 1E-11},\\n 'Krylov': {1: 1E-14, 2: 1E-05, 3: 1E-03}}\\n u_D = Expression('1 + x[0]*x[0] + 2*x[1]*x[1]')\\n kappa = Expression('x[0] + x[1]')\\n f = Expression('-8*x[0] - 10*x[1]')\\n for Nx, Ny in [(3,3), (3,5), (5,3)]:\\n for degree in 1, 2, 3:\\n for linear_solver in 'direct', 'Krylov':\\n for solver_func in solver, solver_objects:\\n print('solving on 2(%dx%dx) mesh with P%d elements'\\n % (Nx, Ny, degree)),\\n print(' %s solver, %s function' %\\n (linear_solver, solver_func.__name__))\\n # Important: Krylov solver error must be smaller\\n # than tol!\\n u = solver_func(\\n kappa, f, u_D, Nx, Ny, degree,\\n linear_solver=linear_solver,\\n abs_tol=0.1*tol[linear_solver][degree],\\n rel_tol=0.1*tol[linear_solver][degree])\\n # Make a finite element function of the exact u_D\\n V = u.function_space()\\n u_D_Function = interpolate(u_D, V) # exact solution\\n # Check that dof arrays are equal\\n u_D_array = u_D_Function.vector().array() # dof values\\n max_error = (u_D_array - u.vector().array()).max()\\n msg = 'max error: %g for 2(%dx%d) mesh, degree=%d,'\\\\\\n ' %s solver, %s' % \\\\\\n (max_error, Nx, Ny, degree, linear_solver,\\n solver_func.__name__)\\n print(msg)\\n assert max_error < tol[linear_solver][degree], msg\",\n \"def power_iteration_two_components(X):\\n evector1, evalue1=power_iteration(X)\\n X=X-np.outer(evector1.dot(X.T), evector1) #project each point(aka each row of X) on the first euginevector and then substract that from each row\\n evector2, evalue2=power_iteration(X)\\n\\n eigenvectors=np.vstack([evector1,evector2])\\n eigenvalues=np.array([evalue1,evalue2])\\n\\n return eigenvectors, eigenvalues\",\n \"def heavy_fixCM_eigvals(NP, b, c, params):\\n l = params['l']\\n k = params['k']\\n I3 = params['I3']\\n # Here, omega_3 is just the MAGNITUDE, not signed\\n w3 = np.abs(params['w3'][0])\\n gn = params['Mm'] * params['g']\\n\\n # Check output if small system\\n print 'gn = ', gn\\n print 'b = ', b\\n print 'c = ', c\\n\\n if NP == 1:\\n pass\\n elif NP == 2:\\n matrix = -np.array([[0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0.],\\n [(-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.,\\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.],\\n [0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3)],\\n [(-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\\n (-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.]\\n ])\\n print 'exact matrix = ', matrix\\n eigvals = np.array([\\n 1j * l * gn / (I3 * w3),\\n -1j * l * gn / (I3 * w3),\\n l * np.sqrt(gn) * np.sqrt(0j - 2. * l * k * (-1) ** (b) - gn) / (I3 * w3),\\n -l * np.sqrt(gn) * np.sqrt(0j - 2. * l * k * (-1) ** (b) - gn) / (I3 * w3)\\n ])\\n print 'exact_eigvals are =', eigvals\\n return eigvals\\n elif NP == 3:\\n matrix = -np.array([[0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0., 0., 0.],\\n [(-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.,\\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0., 0., 0.],\\n [0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0.],\\n [(-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\\n (-1) ** (1 + c) * (-l * gn - 2. * (-1) ** (b) * l ** 2 * k) / (I3 * w3), 0., \\\\\\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.],\\n [0., 0., 0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3)],\\n [0., 0., (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\\n (-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.]\\n ])\\n print 'exact matrix = ', matrix\\n\\n eigvals = np.array([\\n 1j * l * gn / (I3 * w3),\\n # -1j*l*gn/(I3*w3),\\n l * np.sqrt(gn) * np.sqrt(0j - 3. * l * k * (-1) ** (b) - gn) / (I3 * w3),\\n # -l*np.sqrt(gn)*np.sqrt(0j-3.*l*k*(-1)**(b) - gn)/(I3*w3),\\n l * np.sqrt(gn) * np.sqrt(0j - l * k * (-1) ** (b) - gn) / (I3 * w3),\\n # -l*np.sqrt(gn)*np.sqrt(0j - l*k*(-1)**(b) - gn)/(I3*w3)\\n ])\\n return eigvals\\n else:\\n return np.array([])\",\n \"def build(self, data: np.ndarray):\\n ret = data.dot(self.eigenvectors)\\n self.pca_predictor_vars = ret\\n return ret\",\n \"def check(mat, otp):\\n prd = mat*otp\\n eigval = prd[0]/otp[0]\\n print 'computed eigenvalue :' , eigval\\n [eigs, vecs] = np.linalg.eig(mat)\\n abseigs = list(abs(eigs))\\n ind = abseigs.index(max(abseigs))\\n print ' largest eigenvalue :', eigs[ind]\",\n \"def get_eig(self, tolerance=None):\\r\\n E, V = scipy.linalg.eig(self.data)\\r\\n E = pd.Series(E.real, name=\\\"EIG\\\")\\r\\n V = pd.DataFrame(V.real)\\r\\n if tolerance is not None:\\r\\n E[E/E.max() < tolerance] = 0\\r\\n return E, V\",\n \"def load_eig(self, **kwargs):\\r\\n eig = kwargs['eig']\\r\\n\\r\\n self.eig = EIG(eig)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72855383","0.6691216","0.66405857","0.661532","0.6592952","0.6520123","0.6501074","0.6423186","0.64160115","0.6396994","0.63655776","0.6349273","0.63411045","0.633906","0.63198537","0.62777245","0.62247896","0.6177474","0.61190665","0.6048089","0.6035364","0.6035216","0.6025096","0.5983382","0.59536195","0.59436613","0.5936795","0.5936643","0.5924191","0.5913852","0.5819113","0.58102685","0.58079034","0.5803273","0.57847077","0.5771842","0.5760612","0.5749367","0.5725966","0.57205045","0.5717227","0.56911206","0.5688461","0.5688453","0.5671726","0.5656796","0.56439185","0.5641463","0.5636974","0.5636009","0.56317985","0.56004167","0.55731887","0.5546995","0.55451417","0.5543868","0.5531172","0.54995495","0.54908574","0.5489857","0.54854876","0.5476947","0.54764116","0.54743","0.5473137","0.5461834","0.54556674","0.5450146","0.5447554","0.5442264","0.54384804","0.54375523","0.54230493","0.5411108","0.5411037","0.54091144","0.5384037","0.5364029","0.5357763","0.53563213","0.5354231","0.53502697","0.5346515","0.5333148","0.532113","0.53189623","0.53155226","0.5298988","0.5295415","0.5286532","0.52846235","0.5281251","0.527359","0.5260705","0.5251012","0.5247143","0.5235704","0.5231239","0.5224415","0.52154183"],"string":"[\n \"0.72855383\",\n \"0.6691216\",\n \"0.66405857\",\n \"0.661532\",\n \"0.6592952\",\n \"0.6520123\",\n \"0.6501074\",\n \"0.6423186\",\n \"0.64160115\",\n \"0.6396994\",\n \"0.63655776\",\n \"0.6349273\",\n \"0.63411045\",\n \"0.633906\",\n \"0.63198537\",\n \"0.62777245\",\n \"0.62247896\",\n \"0.6177474\",\n \"0.61190665\",\n \"0.6048089\",\n \"0.6035364\",\n \"0.6035216\",\n \"0.6025096\",\n \"0.5983382\",\n \"0.59536195\",\n \"0.59436613\",\n \"0.5936795\",\n \"0.5936643\",\n \"0.5924191\",\n \"0.5913852\",\n \"0.5819113\",\n \"0.58102685\",\n \"0.58079034\",\n \"0.5803273\",\n \"0.57847077\",\n \"0.5771842\",\n \"0.5760612\",\n \"0.5749367\",\n \"0.5725966\",\n \"0.57205045\",\n \"0.5717227\",\n \"0.56911206\",\n \"0.5688461\",\n \"0.5688453\",\n \"0.5671726\",\n \"0.5656796\",\n \"0.56439185\",\n \"0.5641463\",\n \"0.5636974\",\n \"0.5636009\",\n \"0.56317985\",\n \"0.56004167\",\n \"0.55731887\",\n \"0.5546995\",\n \"0.55451417\",\n \"0.5543868\",\n \"0.5531172\",\n \"0.54995495\",\n \"0.54908574\",\n \"0.5489857\",\n \"0.54854876\",\n \"0.5476947\",\n \"0.54764116\",\n \"0.54743\",\n \"0.5473137\",\n \"0.5461834\",\n \"0.54556674\",\n \"0.5450146\",\n \"0.5447554\",\n \"0.5442264\",\n \"0.54384804\",\n \"0.54375523\",\n \"0.54230493\",\n \"0.5411108\",\n \"0.5411037\",\n \"0.54091144\",\n \"0.5384037\",\n \"0.5364029\",\n \"0.5357763\",\n \"0.53563213\",\n \"0.5354231\",\n \"0.53502697\",\n \"0.5346515\",\n \"0.5333148\",\n \"0.532113\",\n \"0.53189623\",\n \"0.53155226\",\n \"0.5298988\",\n \"0.5295415\",\n \"0.5286532\",\n \"0.52846235\",\n \"0.5281251\",\n \"0.527359\",\n \"0.5260705\",\n \"0.5251012\",\n \"0.5247143\",\n \"0.5235704\",\n \"0.5231239\",\n \"0.5224415\",\n \"0.52154183\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6650587"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":893,"cells":{"query":{"kind":"string","value":"Returns weight and bias tensors for each layer of the RNN. These tensors are views on the underlying weight buffer allocated by CuDNN."},"document":{"kind":"string","value":"def get_parameters(fn, handle, weight_buf):\n\n cudnn_methods = [\n cudnn.lib.cudnnGetRNNLinLayerMatrixParams,\n cudnn.lib.cudnnGetRNNLinLayerBiasParams\n ]\n\n params = []\n num_linear_layers = _num_linear_layers(fn)\n num_layers = fn.num_directions * fn.num_layers\n for layer in range(num_layers):\n layer_params = []\n for cudnn_method in cudnn_methods:\n for linear_id in range(num_linear_layers):\n lin_layer_mat_desc = cudnn.FilterDescriptor()\n matrix_pointer = ctypes.c_void_p()\n check_error(cudnn_method(\n handle,\n fn.rnn_desc,\n layer,\n fn.x_descs[0],\n fn.w_desc,\n ctypes.c_void_p(weight_buf.data_ptr()),\n linear_id,\n lin_layer_mat_desc,\n ctypes.byref(matrix_pointer)))\n\n data_type = ctypes.c_int()\n format = ctypes.c_int()\n nb_dims = ctypes.c_int()\n min_dim = 3\n filter_dim_a = torch.IntTensor(min_dim)\n check_error(cudnn.lib.cudnnGetFilterNdDescriptor(\n lin_layer_mat_desc,\n min_dim,\n ctypes.byref(data_type),\n ctypes.byref(format),\n ctypes.byref(nb_dims),\n ctypes.c_void_p(filter_dim_a.data_ptr())))\n\n filter_dim_a.resize_(nb_dims.value)\n elem_size = cudnn._sizeofmap[fn.datatype]\n offset_bytes = (matrix_pointer.value - weight_buf.data_ptr())\n assert(offset_bytes % elem_size == 0)\n offset = offset_bytes // elem_size\n\n # for all the RNN types provided by CUDNN, all the ih weights\n # are the same size and are allocated in a contiguous chunk\n # (same for the hh weights, and the ih and hh biases).\n # Since we're storing all the weights in a single tensor anyway,\n # might as well merge the CUDNN ones into a single tensor as well\n if linear_id == 0 or linear_id == num_linear_layers / 2:\n assert(filter_dim_a.prod() == filter_dim_a[0])\n param = fn.weight_buf.new().set_(\n weight_buf.storage(), offset,\n filter_dim_a[0] * num_linear_layers // 2, filter_dim_a[2])\n layer_params.append(param)\n else:\n assert(cur_offset == offset)\n\n cur_offset = offset + filter_dim_a[0]\n\n\n params.append(layer_params)\n\n return params"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def process_weights_and_bias(self, rnn_weights, rnn_props):\n # from code of tensorflow GRU cell, it can be known that shape of hidden_kernel(or candidate_kernel)\n # is (input_size+hidden_unit, hidden_unit)\n hidden_size = rnn_weights[\"hidden_kernel\"].value.shape[1]\n input_size = rnn_weights[\"hidden_kernel\"].value.shape[0] - hidden_size\n weight_dtype = rnn_weights[\"hidden_kernel\"].dtype\n bias_dtype = rnn_weights[\"hidden_bias\"].dtype\n # below code will use same notation as ONNX document\n # z means update gate, r means reset gate, h means hidden gate;\n # at this time weights of gate include input and state, will split it next\n r_kernel, z_kernel = np.split(rnn_weights[\"gate_kernel\"].value, [hidden_size], axis=1)\n h_kernel = rnn_weights[\"hidden_kernel\"].value\n r_bias, z_bias = np.split(rnn_weights[\"gate_bias\"].value, [hidden_size], axis=0)\n h_bias = rnn_weights[\"hidden_bias\"].value\n # ONNX GRU split weights of input and state, so have to split *_kernel\n input_r_kernel, state_r_kernel = np.split(r_kernel, [input_size], axis=0)\n input_z_kernel, state_z_kernel = np.split(z_kernel, [input_size], axis=0)\n input_h_kernel, state_h_kernel = np.split(h_kernel, [input_size], axis=0)\n W_zrh = np.concatenate((input_z_kernel, input_r_kernel, input_h_kernel), axis=1)\n R_zrh = np.concatenate((state_z_kernel, state_r_kernel, state_h_kernel), axis=1)\n # transpose weight matrix\n W_zrh = np.transpose(np.expand_dims(W_zrh, axis=0), axes=(0, 2, 1))\n R_zrh = np.transpose(np.expand_dims(R_zrh, axis=0), axes=(0, 2, 1))\n W_zrh = W_zrh.astype(weight_dtype)\n R_zrh = R_zrh.astype(weight_dtype)\n assert W_zrh.shape == (1, 3*hidden_size, input_size)\n assert R_zrh.shape == (1, 3*hidden_size, hidden_size)\n Wb_zrh = np.concatenate((z_bias, r_bias, h_bias), axis=0)\n # tf don't have bias for state, so use 0 instead\n zero = np.zeros_like(z_bias)\n Rb_zrh = np.concatenate((zero, zero, zero), axis=0)\n B_zrh = np.concatenate((Wb_zrh, Rb_zrh), axis=0)\n B_zrh = np.expand_dims(B_zrh, axis=0)\n B_zrh = B_zrh.astype(bias_dtype)\n assert B_zrh.shape == (1, 6*hidden_size)\n # create const ONNX node\n w_name = utils.make_name(\"W\")\n w_node = self.g.make_const(w_name, W_zrh, skip_conversion=True)\n\n r_name = utils.make_name(\"R\")\n r_node = self.g.make_const(r_name, R_zrh, skip_conversion=True)\n\n b_name = utils.make_name(\"B\")\n b_node = self.g.make_const(b_name, B_zrh, skip_conversion=True)\n\n rnn_props.input_size = input_size\n rnn_props.hidden_size = hidden_size\n rnn_props.onnx_input_ids[\"W\"] = w_node.output[0]\n rnn_props.onnx_input_ids[\"R\"] = r_node.output[0]\n rnn_props.onnx_input_ids[\"B\"] = b_node.output[0]","def get_weights(self):\n weights = []\n for layer in self.layers:\n weights += layer.weights\n return K.batch_get_value(weights)","def get_weights(self):\n # First part is iterating over hidden weights. Then append the output weight.\n return [self.hidden_layers[i].get_weight().cpu().detach().numpy() for i in range(self.depth)] + \\\n [self.output_weight.cpu().detach().numpy()]","def get_weights(self):\n # First part is iterating over hidden weights. Then append the output weight.\n return [self.hidden_layers[i].get_weight() for i in range(self.depth)] + \\\n [self.output_weight.cpu().detach().numpy()]","def get_weights_tensor(self):\n return [self.hidden_layers[i].get_weight_tensor() for i in range(self.depth)] + \\\n [self.output_weight.clone()]","def _create_weights(self):\n\n self.mu_W = tf.get_variable(\n name=\"mu_W\", initializer=tf.random_normal_initializer(),\n shape=[self.rnn_dim, self.z_dim])\n\n self.mu_b = tf.get_variable(\n name=\"mu_b\", initializer=tf.random_normal_initializer(),\n shape=[self.z_dim])\n\n self.log_sig_sq_W = tf.get_variable(\n name=\"log_sig_sq_W\", initializer=tf.random_normal_initializer(),\n shape=[self.rnn_dim, self.z_dim])\n\n self.log_sig_sq_b = tf.get_variable(\n name=\"log_sig_sq_b\", initializer=tf.random_normal_initializer(),\n shape=[self.z_dim])\n \n self.y_W = tf.get_variable(\n name=\"y_W\", initializer=tf.random_normal_initializer(),\n shape=[self.z_dim, self.rnn_dim])\n\n self.y_b = tf.get_variable(\n name=\"y_b\", initializer=tf.random_normal_initializer(),\n shape=[self.rnn_dim])\n \n self.softmax_W = tf.get_variable(\n name=\"softmax_W\", initializer=tf.random_normal_initializer(),\n shape=[self.rnn_dim, self.vocabulary_size])\n \n self.softmax_b = tf.get_variable(\n name=\"softmax_b\", initializer=tf.random_normal_initializer(),\n shape=[self.vocabulary_size])","def _extract_weights(self,W):\n wl1_size = self._D*self._hidden_layer_size\n bl1_size = self._hidden_layer_size\n \n wl2_size = self._hidden_layer_size*self._output_size\n bl2_size = self._output_size\n\n \n weights_L1 = W[0:wl1_size].reshape((self._D,self._hidden_layer_size))\n bias_L1 = W[wl1_size:wl1_size+bl1_size]\n \n start_l2 = wl1_size+bl1_size\n\n weights_L2 = W[start_l2: start_l2 + wl2_size].reshape((self._hidden_layer_size,self._output_size))\n bias_L2 = W[start_l2 + wl2_size : start_l2 + wl2_size + bl2_size]\n \n \n \n return weights_L1,bias_L1,weights_L2,bias_L2","def get_weights(self):\n return [self.w, self.b]","def get_weights(self):\n\n return self._cnn_model.get_weights()","def get_weights(self):\n return self.nn.get_param_values()","def GetWeights(self) -> numpy.ndarray:\n return numpy.concatenate(list(\n weight_layer.GetWeights() for weight_layer in self._weight_layers))","def _generate_weights(self):\n weights = []\n for i in range(1, len(self.layers) - 1):\n weights.append(2 * np.random.random(\n (self.layers[i - 1] + 1, self.layers[i] + 1)) - 1)\n weights.append(2 * np.random.random(\n (self.layers[i] + 1, self.layers[i + 1])) - 1)\n return weights","def get_weights(self):\n params = self.weights\n return K.batch_get_value(params)","def return_weights(self):\n w0 = self.comparator.weight.data.numpy()\n b0 = self.comparator.bias.data.numpy()\n\n w1 = self.matcher.weight.data.numpy()\n b1 = self.matcher.bias.data.numpy()\n\n w2 = self.head.weight.data.numpy()\n b2 = self.head.bias.data.numpy()\n\n return w0, b0, w1, b1, w2, b2","def get_weights(self) -> List[np.matrix]:\n to_ret = []\n for layer in self.__layers:\n to_ret.append(layer.get_coefficients())\n return to_ret","def get_weights(self):\n return [self.W]","def get_weights(self):\n return [self.W]","def weights_lst(self):\n assert self.sess is not None, \"Model has not been fitted yet!\"\n return self.sess.run(self.W_lst)","def get_weights(self):","def GetWeights(self) -> numpy.ndarray:\n return numpy.concatenate(list(\n variable_ndarray.flatten() for variable_ndarray in\n self._layer.get_weights()))","def _create_weights(self):\n gate_size = self._hidden_size * self._num_gates\n # Compute the shape of weight and bias.\n matrix_shapes, bias_shapes = [], []\n for layer in range(self._num_layers):\n for direction in range(self._num_directions):\n layer_input_size = self._input_size if layer == 0 \\\n else self._hidden_size * self._num_directions\n w_ih_shape = [gate_size, layer_input_size]\n w_hh_shape = [gate_size, self._hidden_size]\n b_ih_shape, b_hh_shape = [gate_size], [gate_size]\n matrix_shapes.extend([w_ih_shape, w_hh_shape])\n bias_shapes.extend([b_ih_shape, b_hh_shape])\n # Create single float32 weights.\n weights_count = 0\n self._weights_shapes = matrix_shapes + bias_shapes\n for shape in self._weights_shapes:\n weights_count += math_util.prod(shape)\n self._weights = Tensor([weights_count])\n self._weights.requires_grad = True","def get_weights(self):\n weights = {}\n for idx, layer in enumerate(self.model.layers):\n if len(layer.get_weights())>0:\n weights[idx] = layer.get_weights()[0]\n else:\n weights[idx] = [] \n return weights","def get_weights_from_dna(self):\n\n W1_size = self.n_input*self.n_hidden_1\n W2_size = self.n_hidden_1*self.n_hidden_2\n W3_size = self.n_hidden_2*self.n_output\n\n start_W1, end_W1 = 0, W1_size\n start_B1, end_B1 = end_W1, end_W1 + self.n_hidden_1\n start_W2, end_W2 = end_B1, end_B1 + W2_size\n start_B2, end_B2 = end_W2, end_W2 + self.n_hidden_2\n start_W3, end_W3 = end_B2, end_B2 + W3_size\n start_B3, end_B3 = end_W3, end_W3 + self.n_output\n\n W1 = self.dna[:, start_W1:end_W1].reshape(self.n_hidden_1, self.n_input)\n B1 = self.dna[:, start_B1:end_B1].reshape(self.n_hidden_1, 1)\n W2 = self.dna[:, start_W2:end_W2].reshape(self.n_hidden_2, self.n_hidden_1)\n B2 = self.dna[:, start_B2:end_B2].reshape(self.n_hidden_2, 1)\n W3 = self.dna[:, start_W3:end_W3].reshape(self.n_output, self.n_hidden_2)\n B3 = self.dna[:, start_B3:end_B3].reshape(self.n_output, 1)\n\n return W1, W2, W3, B1, B2, B3","def get_weights_and_biases_from_Onnx(self):\n\t\t\n\t\tpass","def get_weights(self):\n return self.weights\n #print(W)","def layer_weights(self):\n return [l.W for l in self.children if isinstance(l, G.layers.DenseLayer)]","def get_weights(self):\n return self.w","def get_weights(self):\n return self.w","def get_weights(self):\n return self.w","def get_weights(self):\n return self.w","def instantiate_weights(self):\n with tf.name_scope(\"decoder_init_state\"):\n self.W_initial_state = tf.get_variable(\"W_initial_state\", shape=[self.hidden_size, self.hidden_size*2], initializer=self.initializer)\n self.b_initial_state = tf.get_variable(\"b_initial_state\", shape=[self.hidden_size*2])\n with tf.name_scope(\"embedding_projection\"): # embedding matrix\n self.Embedding = tf.get_variable(\"Embedding\", shape=[self.vocab_size, self.embed_size],initializer=self.initializer) # [vocab_size,embed_size] tf.random_uniform([self.vocab_size, self.embed_size],-1.0,1.0)\n self.Embedding_label = tf.get_variable(\"Embedding_label\", shape=[self.num_classes, self.embed_size*2],dtype=tf.float32) #,initializer=self.initializer\n self.W_projection = tf.get_variable(\"W_projection\", shape=[self.hidden_size*2, self.num_classes],\n initializer=self.initializer) # [embed_size,label_size]\n self.b_projection = tf.get_variable(\"b_projection\", shape=[self.num_classes])\n\n # GRU parameters:update gate related\n with tf.name_scope(\"gru_weights_encoder\"):\n self.W_z = tf.get_variable(\"W_z\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\n self.U_z = tf.get_variable(\"U_z\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\n self.b_z = tf.get_variable(\"b_z\", shape=[self.hidden_size])\n # GRU parameters:reset gate related\n self.W_r = tf.get_variable(\"W_r\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\n self.U_r = tf.get_variable(\"U_r\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\n self.b_r = tf.get_variable(\"b_r\", shape=[self.hidden_size])\n\n self.W_h = tf.get_variable(\"W_h\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\n self.U_h = tf.get_variable(\"U_h\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\n self.b_h = tf.get_variable(\"b_h\", shape=[self.hidden_size])\n\n with tf.name_scope(\"gru_weights_decoder\"):\n self.W_z_decoder = tf.get_variable(\"W_z_decoder\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer)\n self.U_z_decoder = tf.get_variable(\"U_z_decoder\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer)\n self.C_z_decoder = tf.get_variable(\"C_z_decoder\", shape=[self.embed_size * 2, self.hidden_size * 2],initializer=self.initializer) #TODO\n self.b_z_decoder = tf.get_variable(\"b_z_decoder\", shape=[self.hidden_size*2])\n # GRU parameters:reset gate related\n self.W_r_decoder = tf.get_variable(\"W_r_decoder\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer)\n self.U_r_decoder = tf.get_variable(\"U_r_decoder\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer)\n self.C_r_decoder = tf.get_variable(\"C_r_decoder\", shape=[self.embed_size * 2, self.hidden_size * 2],initializer=self.initializer) #TODO\n self.b_r_decoder = tf.get_variable(\"b_r_decoder\", shape=[self.hidden_size*2])\n\n self.W_h_decoder = tf.get_variable(\"W_h_decoder\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer)\n self.U_h_decoder = tf.get_variable(\"U_h_decoder\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer) #TODO\n self.C_h_decoder = tf.get_variable(\"C_h_decoder\", shape=[self.embed_size * 2, self.hidden_size * 2],initializer=self.initializer)\n self.b_h_decoder = tf.get_variable(\"b_h_decoder\", shape=[self.hidden_size*2])\n\n with tf.name_scope(\"full_connected\"):\n self.W_fc=tf.get_variable(\"W_fc\",shape=[self.hidden_size*2,self.hidden_size])\n self.a_fc=tf.get_variable(\"a_fc\",shape=[self.hidden_size])","def get_weights(self):\n return self.model.get_weights()","def get_weights(self):\n return self.model.get_weights()","def build_layer(self) :\n inputsWithBias = self.input_count + 1\n self.weights = np.random.rand(inputsWithBias, self.node_count)\n self.weights_and_activations = (self.weights, self.activations)","def targets_weights_fn(self):\n return common_layers.weights_all","def weights(self):\n return [x.numpy() for x in self.core.w]","def get_forward_parameters(self):\n if self.bias is not None:\n return [self.weights, self.bias]\n else:\n return [self.weights]","def instantiate_weights(self):\n self.product_embeddings = tf.get_variable(\n name='product_embeddings',\n shape=[50000, 300],\n dtype=tf.float32\n )\n self.aisle_embeddings = tf.get_variable(\n name='aisle_embeddings',\n shape=[250, 50],\n dtype=tf.float32\n )\n self.department_embeddings = tf.get_variable(\n name='department_embeddings',\n shape=[50, 10],\n dtype=tf.float32\n )\n self.W_relu = tf.get_variable(\"W_relu\",shape=[670, 30]) #这个参数后续需要自适应\n self.b_relu = tf.get_variable(\"bias_relu\",shape=[30]) \n self.W_projection = tf.get_variable(\"W_projection\",shape=[30, 1]) \n self.b_projection = tf.get_variable(\"bias_projection\",shape=[1])","def get_weights(layers):\n get_layer_weights = lambda layer: layer.get_weights()[0]\n return list(map(get_layer_weights, layers))","def get_weights(self):\r\n return self.weights","def get_weights(self):\r\n return self.weights","def init_gru(rnn):\n \n def _concat_init(tensor, init_funcs):\n (length, fan_out) = tensor.shape\n fan_in = length // len(init_funcs)\n \n for (i, init_func) in enumerate(init_funcs):\n init_func(tensor[i * fan_in : (i + 1) * fan_in, :])\n \n def _inner_uniform(tensor):\n fan_in = nn.init._calculate_correct_fan(tensor, 'fan_in')\n nn.init.uniform_(tensor, -math.sqrt(3 / fan_in), math.sqrt(3 / fan_in))\n \n for i in range(rnn.num_layers):\n _concat_init(\n getattr(rnn, 'weight_ih_l{}'.format(i)),\n [_inner_uniform, _inner_uniform, _inner_uniform]\n )\n torch.nn.init.constant_(getattr(rnn, 'bias_ih_l{}'.format(i)), 0)\n\n _concat_init(\n getattr(rnn, 'weight_hh_l{}'.format(i)),\n [_inner_uniform, _inner_uniform, nn.init.orthogonal_]\n )\n torch.nn.init.constant_(getattr(rnn, 'bias_hh_l{}'.format(i)), 0)","def _weights(self, layer_idx, expected_layer_name):\n # wb = self.vgg_layers[0][layer_idx][0][0][2] # based on old vgg-model shape\n wb = self.vgg_layers[layer_idx][0][0][0]\n W = wb[0][0]\n b = wb[0][1]\n # layer_name = self.vgg_layers[0][layer_idx][0][0][0][0] # based on old vgg-model shape\n layer_name = self.vgg_layers[layer_idx][0][0][3][0]\n\n # assert layer_name == expected_layer_name\n\n return W, b","def get_weights(net):\n return [p.data for p in net.parameters()]","def inputs_weights_init(self):\n input_user, input_item, input_rating = self.inputs_init()\n user_embeddings, item_embeddings = self.embeddings_layers_init()\n\n return input_user, input_item, input_rating, user_embeddings, item_embeddings","def get_weights(self):\n return self.__weights","def u_weights(self):\n for i in range(self.n_inputs):\n self._q_neuron.cx(self._weights[i], self.inputs[i])","def get_weights(self):\n return self.weights","def get_weights(self):\n return self.weights","def get_model_and_tile_weights(model):\n weight = model.weight.data.detach().cpu().numpy()\n bias = model.bias.data.detach().cpu().numpy()\n analog_weight, analog_bias = model.analog_tile.get_weights()\n analog_weight = analog_weight.detach().cpu().numpy().reshape(weight.shape)\n analog_bias = analog_bias.detach().cpu().numpy()\n return weight, bias, analog_weight, analog_bias","def getWeights(self):\n return self.W1, self.W2","def init_gru(rnn):\n\n def _concat_init(tensor, init_funcs):\n (length, fan_out) = tensor.shape\n fan_in = length // len(init_funcs)\n\n for (i, init_func) in enumerate(init_funcs):\n init_func(tensor[i * fan_in: (i + 1) * fan_in, :])\n\n def _inner_uniform(tensor):\n fan_in = nn.init._calculate_correct_fan(tensor, 'fan_in')\n nn.init.uniform_(tensor, -math.sqrt(3 / fan_in), math.sqrt(3 / fan_in))\n\n for i in range(rnn.num_layers):\n _concat_init(\n getattr(rnn, 'weight_ih_l{}'.format(i)),\n [_inner_uniform, _inner_uniform, _inner_uniform]\n )\n torch.nn.init.constant_(getattr(rnn, 'bias_ih_l{}'.format(i)), 0)\n\n _concat_init(\n getattr(rnn, 'weight_hh_l{}'.format(i)),\n [_inner_uniform, _inner_uniform, nn.init.orthogonal_]\n )\n torch.nn.init.constant_(getattr(rnn, 'bias_hh_l{}'.format(i)), 0)","def initWeights(self):\n self.weights = []\n self.bias = []\n for i, dim in enumerate(self.dimensions[1:]):\n self.weights.append(np.random.uniform(-1,1,(self.dimensions[i],dim)))\n self.bias.append(np.random.uniform(-1,1,dim))","def get_parameters(self):\n if self.add_bias:\n params = np.concatenate((self.bias, self.W), 0)\n else:\n params = self.W\n return params","def get_weights(self):\n return self._weights","def get_weights(self):\n return self._weights","def get_weights(self):\n return self._weights","def weights(self):\n return self._weights","def weights(self):\n return self._weights","def weights(self):\n return self._weights","def weights(self):\n return self._weights","def weights_(self):\n return self.get_tensor_value('logistic_regression/weights:0')","def weights(self):\n return self.__weights","def weights(self):\n return self.__weights","def weights(self):\n return self.__weights","def weights(self):\n return self.__weights","def weights(self):\n return self.__weights","def get_variables(self) -> typing.List:\n parts = (self.neural_net.encoder, self.neural_net.predictor, self.neural_net.dynamics)\n return [v for v_list in map(lambda n: n.weights, parts) for v in v_list]","def init_weights(self):\n\n params = torch.load(self.resnet_weight)\n\n self.fc1.weight.data = params['state_dict']['module.fc.weight'].clone()\n self.fc1.bias.data = params['state_dict']['module.fc.bias'].clone()\n\n\n r = np.sqrt(1.) / np.sqrt(self.fc3.in_features +\n self.fc3.out_features)\n self.fc3.weight.data.uniform_(-r, r)\n self.fc3.bias.data.fill_(0)\n r = np.sqrt(1.) / np.sqrt(self.fc2.in_features +\n self.fc2.out_features)\n self.fc2.weight.data.uniform_(-r, r)\n self.fc2.bias.data.fill_(0)\n r = np.sqrt(1.) / np.sqrt(self.fc4.in_features +\n self.fc4.out_features)\n self.fc4.weight.data.uniform_(-r, r)\n self.fc4.bias.data.fill_(0)","def get_weights(model) -> Weights:\n return [val.cpu().numpy() for _, val in model.state_dict().items()]","def get_weights(self):\n return []","def initialize_weights_and_bias(self, X_train):\n n_samples, n_features = np.shape(X_train)\n n_output = 1 \n \n # This is the numeber of gridcells and we want to make one prediction pr cell. \n # It this doesn't work calculate the number of griddcells.\n\n self.b_h = [] #np.ones((self.n_hidden_layers, self.n_hidden[0]))\n self.W_h = []\n\n for i in range(len(self.n_hidden)):\n if (i == 0):\n self.W_h.append(self.random.normal(loc=0.0, scale=0.1, size=(n_features, self.n_hidden[0])))\n self.b_h.append(np.ones(self.n_hidden[0]))\n else:\n self.W_h.append(self.random.normal(loc=0.0, scale=0.1, size=(self.n_hidden[i-1], self.n_hidden[i])))\n self.b_h.append(np.ones(self.n_hidden[i])) \n \n self.b_out = [1]\n self.W_out = self.random.normal(loc=0.0, scale=0.1, size=(self.n_hidden[-1], n_output))","def build(self):\n\n self.W = self.init([self.n_atom_input_feat, self.n_output])\n self.b = model_ops.zeros(shape=[\n self.n_output,\n ])\n\n self.trainable_weights = self.W + self.b","def init(InputUnits, OutputUnits, numHiddenLayer, HiddenUnits=None):\n global HiddenUnit\n all_weights = []\n if HiddenUnits is None:\n HiddenUnits = []\n elif isinstance(HiddenUnits, int):\n HiddenUnits = [HiddenUnits]\n\n # for InputLayer\n\n parameters = generate(HiddenUnits[0], InputUnits)\n allWeights = mat.r_[parameters.flatten()]\n\n if numHiddenLayer > 1:\n for i in range(numHiddenLayer):\n if i < numHiddenLayer-1:\n parameters = generate(HiddenUnits[i+1], HiddenUnits[i])\n allWeights = mat.r_[allWeights, parameters.flatten()]\n else:\n parameters = generate(OutputUnits, HiddenUnits[i])\n allWeights = mat.r_[allWeights, parameters.flatten()]\n\n else:\n # for output layer\n parameters = generate( OutputUnits, HiddenUnits[0])\n allWeights = mat.r_[allWeights, parameters.flatten()]\n\n return allWeights","def _build_bdrnn_graph(self, hparams):\n\n sample = self.iterator.get_next()\n\n inputs, tgt_outputs, seq_len = sample\n\n # linear projection to state size\n #with tf.variable_scope(\"bdrnn_in\", dtype=tf.float32):\n # inputs = tf.layers.dense(inputs=inputs,\n # units=hparams.input_proj_size,\n # kernel_initializer=tf.glorot_uniform_initializer())\n\n lm_fw_cell = []\n lm_bw_cell = []\n lm_init_state_fw = []\n lm_init_state_bw = []\n if hparams.pretrained:\n with tf.variable_scope(\"lm_rnn\", dtype=tf.float32):\n # create lm\n with tf.variable_scope(\"fw\", dtype=tf.float32):\n lm_fw_cell = _create_rnn_cell(num_units=hparams.num_units,\n num_layers=1,\n mode=self.mode)\n # build the cell so it is in the correct scope\n # NOTE: this is hard coded\n lm_fw_cell[0].build([None, hparams.num_features])#hparams.input_proj_size])\n lm_init_state_fw = _get_initial_state([lm_fw_cell[0].state_size], tf.shape(inputs)[0], \"lm\")\n with tf.variable_scope(\"bw\", dtype=tf.float32):\n lm_bw_cell = _create_rnn_cell(num_units=hparams.num_units,\n num_layers=1,\n mode=self.mode)\n # NOTE: this is hard coded\n lm_bw_cell[0].build([None, hparams.num_features])#hparams.input_proj_size])\n lm_init_state_bw = _get_initial_state([lm_bw_cell[0].state_size], tf.shape(inputs)[0], \"lm\")\n\n lm_outputs, lm_states = tf.nn.bidirectional_dynamic_rnn(lm_fw_cell[0],\n lm_bw_cell[0],\n inputs,\n sequence_length=seq_len,\n initial_state_fw=lm_init_state_fw[0],\n initial_state_bw=lm_init_state_bw[0],\n dtype=tf.float32)\n # optionally fix the LM weights\n if hparams.fixed_lm:\n print(\"Fixing pretrained language models.\")\n lm_outputs = tf.stop_gradient(lm_outputs)\n lm_outputs = tf.concat([lm_outputs[0], lm_outputs[1]], axis=-1)\n lm_outputs = tf.layers.dense(lm_outputs,\n 20,\n kernel_initializer=tf.glorot_uniform_initializer())\n lm_outputs = tf.concat([lm_outputs, inputs], axis=-1)\n\n\n #lm_outputs = tf.concat([lm_outputs[0], lm_outputs[1], inputs], axis=-1)\n else:\n lm_outputs = tf.concat(lm_outputs, axis=-1)\n\n\n\n with tf.variable_scope(\"bdrnn\", dtype=tf.float32) as bdrnn_scope:\n # create bdrnn\n with tf.variable_scope(\"fw\", dtype=tf.float32):\n fw_cells = _create_rnn_cell(num_units=hparams.num_units,\n num_layers=hparams.num_layers,\n mode=self.mode\n )\n init_state_fw = _get_initial_state([cell.state_size for cell in fw_cells],\n tf.shape(inputs)[0], \"initial_state_fw\")\n\n with tf.variable_scope(\"bw\", dtype=tf.float32):\n bw_cells = _create_rnn_cell(num_units=hparams.num_units,\n num_layers=hparams.num_layers,\n mode=self.mode,\n )\n\n init_state_bw = _get_initial_state([cell.state_size for cell in bw_cells],\n tf.shape(inputs)[0], \"initial_state_bw\")\n # NOTE: this is commented because the lm cells and states are separated now\n #fw_cells = lm_fw_cell + fw_cells\n #bw_cells = lm_bw_cell + bw_cells\n #init_state_fw = lm_init_state_fw + init_state_fw\n #init_state_bw = lm_init_state_bw + init_state_bw\n\n # run bdrnn\n combined_outputs, output_state_fw, output_state_bw = \\\n tf.contrib.rnn.stack_bidirectional_dynamic_rnn(cells_fw=fw_cells,\n cells_bw=bw_cells,\n inputs=lm_outputs,\n sequence_length=seq_len,\n initial_states_fw=init_state_fw,\n initial_states_bw=init_state_bw,\n dtype=tf.float32,\n scope=bdrnn_scope)\n # outputs is a tuple (output_fw, output_bw)\n # output_fw/output_bw are tensors [batch_size, max_time, cell.output_size]\n # outputs_states is a tuple (output_state_fw, output_state_bw) containing final states for\n # forward and backward rnn\n\n # concatenate the outputs of each direction\n #combined_outputs = tf.concat([outputs[0], outputs[1]], axis=-1)\n\n with tf.variable_scope(\"bdrnn_out\", dtype=tf.float32):\n # dense output layers\n dense1 = tf.layers.dense(inputs=combined_outputs,\n units=hparams.num_dense_units,\n kernel_initializer=tf.glorot_uniform_initializer(),\n activation=tf.nn.relu,\n use_bias=True)\n drop1 = tf.layers.dropout(inputs=dense1,\n rate=hparams.dropout,\n training=self.mode==tf.contrib.learn.ModeKeys.TRAIN)\n dense2 = tf.layers.dense(inputs=drop1,\n units=hparams.num_dense_units,\n kernel_initializer=tf.glorot_uniform_initializer(),\n activation=tf.nn.relu,\n use_bias=True)\n drop2 = tf.layers.dropout(inputs=dense2,\n rate=hparams.dropout,\n training=self.mode==tf.contrib.learn.ModeKeys.TRAIN)\n\n logits = tf.layers.dense(inputs=drop2,\n units=hparams.num_labels,\n use_bias=False)\n\n # mask out entries longer than target sequence length\n mask = tf.sequence_mask(seq_len, dtype=tf.float32)\n\n crossent = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,\n labels=tgt_outputs,\n name=\"crossent\")\n\n # divide loss by batch_size * mean(seq_len)\n loss = tf.reduce_sum(crossent*mask)/tf.cast(hparams.batch_size, tf.float32)\n\n metrics = []\n update_ops = []\n if self.mode == tf.contrib.learn.ModeKeys.EVAL:\n # mean eval loss\n loss, loss_update = tf.metrics.mean(values=loss)\n\n predictions = tf.argmax(input=logits, axis=-1)\n tgt_labels = tf.argmax(input=tgt_outputs, axis=-1)\n acc, acc_update = tf.metrics.accuracy(predictions=predictions,\n labels=tgt_labels,\n weights=mask)\n # confusion matrix\n targets_flat = tf.reshape(tgt_labels, [-1])\n predictions_flat = tf.reshape(predictions, [-1])\n mask_flat = tf.reshape(mask, [-1])\n cm, cm_update = streaming_confusion_matrix(labels=targets_flat,\n predictions=predictions_flat,\n num_classes=hparams.num_labels,\n weights=mask_flat)\n tf.add_to_collection(\"eval\", cm_summary(cm, hparams.num_labels))\n metrics = [acc, cm]\n update_ops = [loss_update, acc_update, cm_update]\n\n return logits, loss, metrics, update_ops","def get_weights_and_biases_from_pyTorch(self):\n\t\t\n\t\tilayer_id = 28\n\n\t\tmodel = models.vgg11(weights='VGG11_Weights.IMAGENET1K_V1')\n\t\twatcher = ww.WeightWatcher(model = model, log_level=logging.WARNING)\n\t\t\n\t\tdetails = watcher.describe(layers=self.fclayers)\n\t\tprint(details)\n\t\t\n\t\tN = details.N.to_numpy()[0]\n\t\tM = details.M.to_numpy()[0]\n\t\t\n\t\tparams = ww.DEFAULT_PARAMS.copy()\n\t\tparams[ww.ADD_BIASES] = True\n\t\t\n\t\tweights = watcher.get_Weights(layer=ilayer_id, params=params)\n\t\tself.assertEqual(len(weights),1)\n\t\t\n\t\tW = weights[0]\n\t\tself.assertEqual(np.max(W.shape),N)\n\t\tself.assertEqual(np.min(W.shape),M)\n\n\t\tpass","def getWeights(self,squeeze=False):\n weights = self.weights\n if squeeze:\n weights = weights.squeeze()\n return np.copy(weights)","def generator(real_init): #-1, 5, 3\n with tf.variable_scope('rnn_gen', reuse = tf.AUTO_REUSE, initializer=tf.contrib.layers.xavier_initializer()) as scope:\n x_init = real_init[:,:,0] #-1, 8\n y_init = real_init[:,:,1] #-1, 8\n z_init = real_init[:,:,2] #-1, 8\n \n with tf.variable_scope('x_rw', reuse = tf.AUTO_REUSE, initializer=tf.contrib.layers.xavier_initializer()) as scope:\n x_w1 = tf.get_variable('x_w1', [6, 3])\n x_b1 = tf.get_variable('x_b1', [3])\n x_w2 = tf.get_variable('x_w2', [9, 3])\n x_b2 = tf.get_variable('x_b2', [3])\n x_w3 = tf.get_variable('x_w3', [3, 1])\n x_b3 = tf.get_variable('x_b3', [1])\n x_w4 = tf.get_variable('x_w4', [3, 1])\n x_b4 = tf.get_variable('x_b4', [1])\n \n x_full = x_init#-1, 8\n for i in range(104):\n x_layer1 = tf.nn.elu(tf.matmul(x_init, x_w1) + x_b1)#-1, 4\n x_layer2 = tf.concat([x_init, x_layer1], 1)#-1, 12\n x_layer3 = tf.nn.elu(tf.matmul(x_layer2, x_w2) + x_b2)#-1, 4\n \n x_mean = tf.sigmoid(tf.matmul(x_layer3, x_w3) + x_b3)#-1, 1\n x_gamma = tf.sigmoid(tf.matmul(x_layer3, x_w4) + x_b4)#-1, 1\n x_delta = x_mean + x_gamma * tf.random_normal(tf.shape(x_gamma), dtype = tf.float32)#-1, 1\n x_delta_denorm = (x_delta * 2 - 1) / 80\n x_output = tf.expand_dims(x_full[:,-1], 1) + x_delta_denorm\n x_full = tf.concat([x_full, x_output], axis = 1)#-1, 10 + 1\n x_init = x_full[:,-6:]\n \n with tf.variable_scope('y_rw', reuse = tf.AUTO_REUSE, initializer=tf.contrib.layers.xavier_initializer()) as scope:\n y_w1 = tf.get_variable('y_w1', [6, 3])\n y_b1 = tf.get_variable('y_b1', [3])\n y_w2 = tf.get_variable('y_w2', [9, 3])\n y_b2 = tf.get_variable('y_b2', [3])\n y_w3 = tf.get_variable('y_w3', [3, 1])\n y_b3 = tf.get_variable('y_b3', [1])\n y_w4 = tf.get_variable('y_w4', [3, 1])\n y_b4 = tf.get_variable('y_b4', [1])\n \n y_full = y_init#-1, 8\n for i in range(104):\n y_layer1 = tf.nn.elu(tf.matmul(y_init, y_w1) + y_b1)\n y_layer2 = tf.concat([y_init, y_layer1], 1)#-1, 12\n y_layer3 = tf.nn.elu(tf.matmul(y_layer2, y_w2) + y_b2)#-1, 4\n \n y_mean = tf.sigmoid(tf.matmul(y_layer3, y_w3) + y_b3) #-1, 1\n y_gamma = tf.sigmoid(tf.matmul(y_layer3, y_w4) + y_b4) #-1, 1\n y_delta = y_mean + y_gamma * tf.random_normal(tf.shape(y_gamma), dtype = tf.float32)#-1, 1\n y_delta_denorm = (y_delta * 5 - 2.7) / 1700\n y_output = tf.expand_dims(y_full[:,-1], 1) + y_delta_denorm\n y_full = tf.concat([y_full, y_output], axis = 1)#-1, 10 + 1, 1\n y_init = y_full[:,-6:]\n \n with tf.variable_scope('z_rw', reuse = tf.AUTO_REUSE, initializer=tf.contrib.layers.xavier_initializer()) as scope:\n z_w1 = tf.get_variable('z_w1', [6, 3])\n z_b1 = tf.get_variable('z_b1', [3])\n z_w2 = tf.get_variable('z_w2', [9, 3])\n z_b2 = tf.get_variable('z_b2', [3])\n z_w3 = tf.get_variable('z_w3', [3, 1])\n z_b3 = tf.get_variable('z_b3', [1])\n z_w4 = tf.get_variable('z_w4', [3, 1])\n z_b4 = tf.get_variable('z_b4', [1])\n \n z_full = z_init#-1, 8\n for i in range(104):\n z_layer1 = tf.nn.elu(tf.matmul(z_init, z_w1) + z_b1)\n z_layer2 = tf.concat([z_init, z_layer1], 1)#-1, 12\n z_layer3 = tf.nn.elu(tf.matmul(z_layer2, z_w2) + z_b2)#-1, 4\n \n z_mean = tf.sigmoid(tf.matmul(z_layer3, z_w3) + z_b3) #-1, 1\n z_gamma = tf.sigmoid(tf.matmul(z_layer3, z_w4) + z_b4) #-1, 1\n z_delta = z_mean + z_gamma * tf.random_normal(tf.shape(z_gamma), dtype = tf.float32)#-1, 1\n z_delta_denorm = (z_delta * 3 - 2.2) / 300\n z_output = tf.expand_dims(z_full[:,-1], 1) + z_delta_denorm\n z_full = tf.concat([z_full, z_output], axis = 1)#-1, 10 + 1, 1\n z_init = z_full[:,-6:]\n \n rand_layer1 = tf.concat([tf.expand_dims(x_full, 2), tf.expand_dims(y_full, 2), tf.expand_dims(z_full, 2)], axis = 2)#-1, 200, 3\n rand_layer1_reshape = tf.reshape(rand_layer1, shape = (-1, 110, 3, 1))\n output_filt = tf.ones(shape = (11, 1, 1, 1)) / 11\n \n rand_layer2 = tf.nn.conv2d(rand_layer1_reshape, output_filt, strides = [1,1,1,1], padding = 'VALID')\n \n return tf.reshape(rand_layer2, shape = (-1, 100, 3)) #-1, 110, 3","def sample(self) -> Tuple[torch.Tensor, ...]:\n assert len(self) >= self.batch_size\n\n indices = np.random.choice(\n len(self.buffer), size=self.batch_size, replace=False\n )\n\n states, actions, rewards, next_states, dones = [], [], [], [], []\n\n for i in np.nditer(indices):\n s, a, r, n_s, d = self.buffer[i]\n states.append(np.array(s, copy=False))\n actions.append(np.array(a, copy=False))\n rewards.append(np.array(r, copy=False))\n next_states.append(np.array(n_s, copy=False))\n dones.append(np.array(float(d), copy=False))\n\n states_ = torch.FloatTensor(np.array(states)).to(device)\n actions_ = torch.FloatTensor(np.array(actions)).to(device)\n rewards_ = torch.FloatTensor(np.array(rewards).reshape(-1, 1)).to(device)\n next_states_ = torch.FloatTensor(np.array(next_states)).to(device)\n dones_ = torch.FloatTensor(np.array(dones).reshape(-1, 1)).to(device)\n\n if torch.cuda.is_available():\n states_ = states_.cuda(non_blocking=True)\n actions_ = actions_.cuda(non_blocking=True)\n rewards_ = rewards_.cuda(non_blocking=True)\n next_states_ = next_states_.cuda(non_blocking=True)\n dones_ = dones_.cuda(non_blocking=True)\n\n return states_, actions_, rewards_, next_states_, dones_","def get_weights(self):\n if self.W_tickers is not None:\n return [w for w in [self.W_fwd_conc, self.W_bwd_conc, self.W_red_rec] + list(self.W_tickers.values())\n if w is not None]\n else:\n return [w for w in [self.W_fwd_conc, self.W_bwd_conc, self.W_red_rec] if w is not None]","def initialize_weights(self):\n for layer in self._cnn_layers:\n weights_initializer.WeightsInitializer.initialize_layer_or_model(layer)","def encode_weights(self):\n weights = []\n for param in self.global_policy.parameters():\n shape = list(param.shape)\n param_list = torch.flatten(param.data).tolist()\n weights.append(Tensor(shape, param_list))\n\n return weights","def get_weights(self):\n\n # Check if fit had been called\n check_is_fitted(self, 'weights_')\n\n w = self.weights_.reshape(self.X_.shape[1], self.n_classes_, order='F')\n\n return w","def n_step_birnn(\n n_layers, dropout_ratio, hx, ws, bs, xs, activation='tanh', **kwargs):\n return n_step_rnn_base(n_layers, dropout_ratio, hx, ws, bs, xs,\n activation, use_bi_direction=True)","def get_weights(self):\n return self._weight","def _setup(self) -> None:\n #TODO: type\n self.activation = self.params['activation']\n\n self.batchsize: int = self.params['batchsize']\n\n self.input_shape: Tuple[int,int,int] = self.params['input_shape']\n\n self.d: int = self.input_shape[1]\n assert(not self.d == 0)\n\n self.n: int = int(sqrt(self.input_shape[2]))\n assert(not self.n == 0)\n\n self.dtype: type = self.params['dtype']\n\n # initialize weights\n self.W: List[tf.Tensor] = []\n \n for i in range(3):\n #TODO: type\n w_init = self.params['initializer_w']\n if self.params['initializer_w' + str(i)] is not None:\n w_init = self.params['initializer_w' + str(i)]\n\n w_stddev: float = self.params['stddev_w']\n if self.params['stddev_w' + str(i)] is not None:\n w_stddev = self.params['stddev_w' + str(i)]\n\n self.W.append(tf.get_variable(\"weights_\" + str(i),\n shape = (self.d, (self.d if i < 2 else 2 * self.d)),\n dtype = self.dtype,\n initializer = w_init(stddev=w_stddev)))\n\n #TODO: type\n b_init = self.params['initializer_b']\n b_stddev = self.params['stddev_b']\n self.B: tf.Tensor = tf.get_variable(\"biases\", shape = (1, self.d, 1),\n dtype = self.dtype,\n initializer = b_init(stddev=b_stddev))\n\n # create/load expand matrix\n with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n self.expand: tf.Tensor = tf.get_variable(\n \"expand\" + str(self.n),\n shape = (self.n, self.n * self.n),\n dtype = self.dtype,\n initializer = tf.constant_initializer(make_expand(self.n)))\n\n # create/load tile matrix\n tile: np.ndarray = np.array([([1] + [0]*(self.n-1))*self.n])\n for i in range(1, self.n):\n tile = np.append(tile, [([0]*i + [1] + [0]*(self.n-1-i))*self.n], 0)\n\n self.tile: tf.Tensor = tf.constant(tile, self.dtype)","def initialize_weights(self):\n w1 = np.random.uniform(-1.0, 1.0, size = self.n_hidden * (self.n_features + 1)).reshape(self.n_hidden, (self.n_features + 1))/(self.n_features + 1)\n w2 = np.random.uniform(-1.0, 1.0, size=self.n_output*(self.n_hidden+1)).reshape(self.n_output, self.n_hidden+ 1)/(self.n_hidden + 1)\n return w1, w2","def get_weights(t_vars, sess):\n if TfUtils._is_op_defined(t_vars):\n num_of_layers = len(TfUtils.get_layers(t_vars))\n return [sess.run(t_var) for t_var in t_vars[ : num_of_layers]]\n return [sess.run(t_var) for t_var in t_vars if \"kernel\" in t_var.name]","def get_weights(self):\n \n w = torch.exp(self._weight) * self.mask_d + self._weight * self.mask_o\n\n w_squared_norm = (w ** 2).sum(-1, keepdim=True)\n \n w = self._diag_weight.exp() * w / w_squared_norm.sqrt()\n \n wpl = self._diag_weight + self._weight - 0.5 * torch.log(w_squared_norm) \n\n return w.t(), wpl.t()[self.mask_d.bool().t()].view(\n self.dim, self.in_features // self.dim, self.out_features // self.dim)","def generate_weights(n_features, hidden_layer_sizes, n_out):\n\n weights = []\n biases = []\n\n # Weights from input layer to first hidden layer\n weights.append(init_weight(hidden_layer_sizes[0], n_features))\n biases.append(init_bias(hidden_layer_sizes[0]))\n\n # Weights from one hidden layer to the next\n for i in range(1, hidden_layer_sizes.size):\n weights.append(\n init_weight(hidden_layer_sizes[i],hidden_layer_sizes[i - 1]))\n biases.append(init_bias(hidden_layer_sizes[i]))\n\n # Weights from last hidden layer to output layer\n weights.append(init_weight(n_out, hidden_layer_sizes[-1]))\n biases.append(init_bias(n_out))\n\n return weights, biases","def _compute_weights(self, beta=1):\n N = self.__len__()\n # Allocate memory\n W = np.zeros((N,N), dtype='float')\n \n for i in range(N):\n for j in range(N):\n if i > j:\n W[i,j] = self._kernel(self.flat_image[i],\n self.flat_image[j], \n beta)\n W += W.T \n for i in range(N):\n W[i,i] = 1\n \n return W","def n_step_rnn_base(n_layers, dropout_ratio, hx, ws, bs, xs,\n activation, use_bi_direction, **kwargs): # NOQA\n\n argument.check_unexpected_kwargs(\n kwargs, train='train argument is not supported anymore. '\n 'Use chainer.using_config',\n use_cudnn='use_cudnn argument is not supported anymore. '\n 'Use chainer.using_config')\n argument.assert_kwargs_empty(kwargs)\n\n activation_list = ['tanh', 'relu']\n if activation not in activation_list:\n candidate = ','.join(activation_list)\n raise ValueError('Invalid activation: \"%s\". Please select from [%s]'\n % (activation, candidate))\n\n xp = cuda.get_array_module(hx)\n\n if xp is not numpy and chainer.should_use_cudnn('>=auto', 5000):\n states = get_random_state().create_dropout_states(dropout_ratio)\n lengths = [len(x) for x in xs]\n xs = chainer.functions.concat(xs, axis=0)\n\n rnn_mode = 'rnn_%s' % activation\n w = cudnn_rnn_weight_concat(\n n_layers, states, use_bi_direction, rnn_mode, ws, bs)\n\n if use_bi_direction:\n # Bi-directional RNN\n if activation == 'tanh':\n rnn = NStepBiRNNTanh\n elif activation == 'relu':\n rnn = NStepBiRNNReLU\n else:\n # Uni-directional RNN\n if activation == 'tanh':\n rnn = NStepRNNTanh\n elif activation == 'relu':\n rnn = NStepRNNReLU\n\n hy, ys = rnn(n_layers, states, lengths)(hx, w, xs)\n sections = numpy.cumsum(lengths[:-1])\n ys = chainer.functions.split_axis(ys, sections, 0)\n return hy, ys\n\n else:\n\n def f(x, h, c, w, b):\n xw, hw = w\n xb, hb = b\n rnn_in = linear.linear(x, xw, xb) + linear.linear(h, hw, hb)\n if activation == 'tanh':\n return tanh.tanh(rnn_in), None\n elif activation == 'relu':\n return relu.relu(rnn_in), None\n\n hy, _, ys = n_step_rnn_impl(\n f, n_layers, dropout_ratio, hx, None, ws, bs, xs, use_bi_direction)\n return hy, ys","def pull_weights(self, learning_rate):\n for w in self.weights:\n w.value += learning_rate * w.gradient\n # Reset all the weights' gradient to 0\n # We will not reset all other units' gradient, because all other units should be initialized in next training\n # round, and the init value of gradient is 0\n for w in self.weights:\n w.gradient = 0","def get_flat_weights(model):\n return torch.cat([p.data.view(-1, 1) for p in model.parameters()], 0)","def feed_forward(inputs, output_dim, name='', initializer=None):\n _dim = inputs.get_shape().as_list()[1]\n weights = tf.get_variable('weights_{}'.format(name),\n [_dim, output_dim],\n initializer=initializer)\n zero_init = tf.zeros_initializer()\n bias = tf.get_variable('bias_{}'.format(name), shape=output_dim,\n dtype=tf.float32,\n initializer=zero_init)\n output = tf.nn.xw_plus_b(inputs, weights, bias)\n return output","def influence_weights(sess, n_neighbours, data_size):\n\n # To see the names of the different layers of the neural network\n tf.global_variables()\n\n # Get the weights for a specified layer\n layer_number = 0\n weights = tf.get_default_graph().get_tensor_by_name('layers/fc-layer{:d}/kernel:0'.format(layer_number))\n weights = sess.run(tf.nn.top_k(weights))\n # nn.top_k has as output [[weights][indexes to next node]], so the weights are located at position 0\n weights_numpy = np.array(weights[0])\n\n # Get the working directory of this script\n working_dir = os.path.dirname(os.path.abspath(__file__))\n # Get the file name for saving the numpy array\n file_name = working_dir + \"/output_ANN/HDF5_files/normalized_ANN/{}_norm_ANNweights_{}\".format(\n n_neighbours, data_size)\n # Get an unique file name\n file_name = saving(file_name, save=False, file_type='h5')\n # Save the numpy array as HDF5 file\n h5f = h5py.File(file_name, 'w')\n h5f.create_dataset('dataset_{}'.format(data_size), data=weights_numpy)\n\n return weights_numpy","def get_conv_weights(self, conv_layer_i=0):\n conv_layer = self.get_conv_layer(conv_layer_i)\n weights = conv_layer.weights[0].numpy()\n weights = np.transpose(weights, [2,1,0])\n return weights","def build(self, input_layer, trainable=True):\n\n with tf.variable_scope(self.name):\n\n # Create a weight matrix\n input_size = input_layer.get_shape()[-1].value\n\n self.weights = weight_variable([input_size, self.output_size], 'weights', trainable, False)\n self.bias = bias_variable([self.output_size], 'bias', trainable)\n\n # Create the ReLU layer\n self.layer = tf.matmul(input_layer, self.weights) + self.bias\n\n if self.activation_function:\n self.layer = self.activation_function(self.layer)\n\n return self.layer, self.weights, self.bias","def lecun_ntk_wb(n_hid_layers=5, n_units=50):\n N = [1] + [n_units] * n_hid_layers + [1]\n \n W = []\n B = []\n for i in range(1, len(N)):\n W.append(np.random.randn(N[i], N[i - 1]) / N[i - 1] ** 0.5)\n B.append(np.zeros(N[i]))\n \n return {'N': N, 'W': W, 'B': B}","def forward(self, inputs, mask=None):\n N = inputs.size(0)\n out = inputs.view(N, 1, -1, self.n_mel_channels) # [N, 1, Ty, n_mels]\n for conv, bn in zip(self.convs, self.bns):\n out = conv(out)\n out = bn(out)\n out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]\n\n out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]\n T = out.size(1)\n N = out.size(0)\n out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]\n if mask is not None:\n out = out.masked_fill(mask.unsqueeze(-1), 0)\n\n self.gru.flatten_parameters()\n memory, out = self.gru(out) # memory --- [N, Ty, E//2], out --- [1, N, E//2]\n\n return memory, out.squeeze(0)"],"string":"[\n \"def process_weights_and_bias(self, rnn_weights, rnn_props):\\n # from code of tensorflow GRU cell, it can be known that shape of hidden_kernel(or candidate_kernel)\\n # is (input_size+hidden_unit, hidden_unit)\\n hidden_size = rnn_weights[\\\"hidden_kernel\\\"].value.shape[1]\\n input_size = rnn_weights[\\\"hidden_kernel\\\"].value.shape[0] - hidden_size\\n weight_dtype = rnn_weights[\\\"hidden_kernel\\\"].dtype\\n bias_dtype = rnn_weights[\\\"hidden_bias\\\"].dtype\\n # below code will use same notation as ONNX document\\n # z means update gate, r means reset gate, h means hidden gate;\\n # at this time weights of gate include input and state, will split it next\\n r_kernel, z_kernel = np.split(rnn_weights[\\\"gate_kernel\\\"].value, [hidden_size], axis=1)\\n h_kernel = rnn_weights[\\\"hidden_kernel\\\"].value\\n r_bias, z_bias = np.split(rnn_weights[\\\"gate_bias\\\"].value, [hidden_size], axis=0)\\n h_bias = rnn_weights[\\\"hidden_bias\\\"].value\\n # ONNX GRU split weights of input and state, so have to split *_kernel\\n input_r_kernel, state_r_kernel = np.split(r_kernel, [input_size], axis=0)\\n input_z_kernel, state_z_kernel = np.split(z_kernel, [input_size], axis=0)\\n input_h_kernel, state_h_kernel = np.split(h_kernel, [input_size], axis=0)\\n W_zrh = np.concatenate((input_z_kernel, input_r_kernel, input_h_kernel), axis=1)\\n R_zrh = np.concatenate((state_z_kernel, state_r_kernel, state_h_kernel), axis=1)\\n # transpose weight matrix\\n W_zrh = np.transpose(np.expand_dims(W_zrh, axis=0), axes=(0, 2, 1))\\n R_zrh = np.transpose(np.expand_dims(R_zrh, axis=0), axes=(0, 2, 1))\\n W_zrh = W_zrh.astype(weight_dtype)\\n R_zrh = R_zrh.astype(weight_dtype)\\n assert W_zrh.shape == (1, 3*hidden_size, input_size)\\n assert R_zrh.shape == (1, 3*hidden_size, hidden_size)\\n Wb_zrh = np.concatenate((z_bias, r_bias, h_bias), axis=0)\\n # tf don't have bias for state, so use 0 instead\\n zero = np.zeros_like(z_bias)\\n Rb_zrh = np.concatenate((zero, zero, zero), axis=0)\\n B_zrh = np.concatenate((Wb_zrh, Rb_zrh), axis=0)\\n B_zrh = np.expand_dims(B_zrh, axis=0)\\n B_zrh = B_zrh.astype(bias_dtype)\\n assert B_zrh.shape == (1, 6*hidden_size)\\n # create const ONNX node\\n w_name = utils.make_name(\\\"W\\\")\\n w_node = self.g.make_const(w_name, W_zrh, skip_conversion=True)\\n\\n r_name = utils.make_name(\\\"R\\\")\\n r_node = self.g.make_const(r_name, R_zrh, skip_conversion=True)\\n\\n b_name = utils.make_name(\\\"B\\\")\\n b_node = self.g.make_const(b_name, B_zrh, skip_conversion=True)\\n\\n rnn_props.input_size = input_size\\n rnn_props.hidden_size = hidden_size\\n rnn_props.onnx_input_ids[\\\"W\\\"] = w_node.output[0]\\n rnn_props.onnx_input_ids[\\\"R\\\"] = r_node.output[0]\\n rnn_props.onnx_input_ids[\\\"B\\\"] = b_node.output[0]\",\n \"def get_weights(self):\\n weights = []\\n for layer in self.layers:\\n weights += layer.weights\\n return K.batch_get_value(weights)\",\n \"def get_weights(self):\\n # First part is iterating over hidden weights. Then append the output weight.\\n return [self.hidden_layers[i].get_weight().cpu().detach().numpy() for i in range(self.depth)] + \\\\\\n [self.output_weight.cpu().detach().numpy()]\",\n \"def get_weights(self):\\n # First part is iterating over hidden weights. Then append the output weight.\\n return [self.hidden_layers[i].get_weight() for i in range(self.depth)] + \\\\\\n [self.output_weight.cpu().detach().numpy()]\",\n \"def get_weights_tensor(self):\\n return [self.hidden_layers[i].get_weight_tensor() for i in range(self.depth)] + \\\\\\n [self.output_weight.clone()]\",\n \"def _create_weights(self):\\n\\n self.mu_W = tf.get_variable(\\n name=\\\"mu_W\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.rnn_dim, self.z_dim])\\n\\n self.mu_b = tf.get_variable(\\n name=\\\"mu_b\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.z_dim])\\n\\n self.log_sig_sq_W = tf.get_variable(\\n name=\\\"log_sig_sq_W\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.rnn_dim, self.z_dim])\\n\\n self.log_sig_sq_b = tf.get_variable(\\n name=\\\"log_sig_sq_b\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.z_dim])\\n \\n self.y_W = tf.get_variable(\\n name=\\\"y_W\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.z_dim, self.rnn_dim])\\n\\n self.y_b = tf.get_variable(\\n name=\\\"y_b\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.rnn_dim])\\n \\n self.softmax_W = tf.get_variable(\\n name=\\\"softmax_W\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.rnn_dim, self.vocabulary_size])\\n \\n self.softmax_b = tf.get_variable(\\n name=\\\"softmax_b\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.vocabulary_size])\",\n \"def _extract_weights(self,W):\\n wl1_size = self._D*self._hidden_layer_size\\n bl1_size = self._hidden_layer_size\\n \\n wl2_size = self._hidden_layer_size*self._output_size\\n bl2_size = self._output_size\\n\\n \\n weights_L1 = W[0:wl1_size].reshape((self._D,self._hidden_layer_size))\\n bias_L1 = W[wl1_size:wl1_size+bl1_size]\\n \\n start_l2 = wl1_size+bl1_size\\n\\n weights_L2 = W[start_l2: start_l2 + wl2_size].reshape((self._hidden_layer_size,self._output_size))\\n bias_L2 = W[start_l2 + wl2_size : start_l2 + wl2_size + bl2_size]\\n \\n \\n \\n return weights_L1,bias_L1,weights_L2,bias_L2\",\n \"def get_weights(self):\\n return [self.w, self.b]\",\n \"def get_weights(self):\\n\\n return self._cnn_model.get_weights()\",\n \"def get_weights(self):\\n return self.nn.get_param_values()\",\n \"def GetWeights(self) -> numpy.ndarray:\\n return numpy.concatenate(list(\\n weight_layer.GetWeights() for weight_layer in self._weight_layers))\",\n \"def _generate_weights(self):\\n weights = []\\n for i in range(1, len(self.layers) - 1):\\n weights.append(2 * np.random.random(\\n (self.layers[i - 1] + 1, self.layers[i] + 1)) - 1)\\n weights.append(2 * np.random.random(\\n (self.layers[i] + 1, self.layers[i + 1])) - 1)\\n return weights\",\n \"def get_weights(self):\\n params = self.weights\\n return K.batch_get_value(params)\",\n \"def return_weights(self):\\n w0 = self.comparator.weight.data.numpy()\\n b0 = self.comparator.bias.data.numpy()\\n\\n w1 = self.matcher.weight.data.numpy()\\n b1 = self.matcher.bias.data.numpy()\\n\\n w2 = self.head.weight.data.numpy()\\n b2 = self.head.bias.data.numpy()\\n\\n return w0, b0, w1, b1, w2, b2\",\n \"def get_weights(self) -> List[np.matrix]:\\n to_ret = []\\n for layer in self.__layers:\\n to_ret.append(layer.get_coefficients())\\n return to_ret\",\n \"def get_weights(self):\\n return [self.W]\",\n \"def get_weights(self):\\n return [self.W]\",\n \"def weights_lst(self):\\n assert self.sess is not None, \\\"Model has not been fitted yet!\\\"\\n return self.sess.run(self.W_lst)\",\n \"def get_weights(self):\",\n \"def GetWeights(self) -> numpy.ndarray:\\n return numpy.concatenate(list(\\n variable_ndarray.flatten() for variable_ndarray in\\n self._layer.get_weights()))\",\n \"def _create_weights(self):\\n gate_size = self._hidden_size * self._num_gates\\n # Compute the shape of weight and bias.\\n matrix_shapes, bias_shapes = [], []\\n for layer in range(self._num_layers):\\n for direction in range(self._num_directions):\\n layer_input_size = self._input_size if layer == 0 \\\\\\n else self._hidden_size * self._num_directions\\n w_ih_shape = [gate_size, layer_input_size]\\n w_hh_shape = [gate_size, self._hidden_size]\\n b_ih_shape, b_hh_shape = [gate_size], [gate_size]\\n matrix_shapes.extend([w_ih_shape, w_hh_shape])\\n bias_shapes.extend([b_ih_shape, b_hh_shape])\\n # Create single float32 weights.\\n weights_count = 0\\n self._weights_shapes = matrix_shapes + bias_shapes\\n for shape in self._weights_shapes:\\n weights_count += math_util.prod(shape)\\n self._weights = Tensor([weights_count])\\n self._weights.requires_grad = True\",\n \"def get_weights(self):\\n weights = {}\\n for idx, layer in enumerate(self.model.layers):\\n if len(layer.get_weights())>0:\\n weights[idx] = layer.get_weights()[0]\\n else:\\n weights[idx] = [] \\n return weights\",\n \"def get_weights_from_dna(self):\\n\\n W1_size = self.n_input*self.n_hidden_1\\n W2_size = self.n_hidden_1*self.n_hidden_2\\n W3_size = self.n_hidden_2*self.n_output\\n\\n start_W1, end_W1 = 0, W1_size\\n start_B1, end_B1 = end_W1, end_W1 + self.n_hidden_1\\n start_W2, end_W2 = end_B1, end_B1 + W2_size\\n start_B2, end_B2 = end_W2, end_W2 + self.n_hidden_2\\n start_W3, end_W3 = end_B2, end_B2 + W3_size\\n start_B3, end_B3 = end_W3, end_W3 + self.n_output\\n\\n W1 = self.dna[:, start_W1:end_W1].reshape(self.n_hidden_1, self.n_input)\\n B1 = self.dna[:, start_B1:end_B1].reshape(self.n_hidden_1, 1)\\n W2 = self.dna[:, start_W2:end_W2].reshape(self.n_hidden_2, self.n_hidden_1)\\n B2 = self.dna[:, start_B2:end_B2].reshape(self.n_hidden_2, 1)\\n W3 = self.dna[:, start_W3:end_W3].reshape(self.n_output, self.n_hidden_2)\\n B3 = self.dna[:, start_B3:end_B3].reshape(self.n_output, 1)\\n\\n return W1, W2, W3, B1, B2, B3\",\n \"def get_weights_and_biases_from_Onnx(self):\\n\\t\\t\\n\\t\\tpass\",\n \"def get_weights(self):\\n return self.weights\\n #print(W)\",\n \"def layer_weights(self):\\n return [l.W for l in self.children if isinstance(l, G.layers.DenseLayer)]\",\n \"def get_weights(self):\\n return self.w\",\n \"def get_weights(self):\\n return self.w\",\n \"def get_weights(self):\\n return self.w\",\n \"def get_weights(self):\\n return self.w\",\n \"def instantiate_weights(self):\\n with tf.name_scope(\\\"decoder_init_state\\\"):\\n self.W_initial_state = tf.get_variable(\\\"W_initial_state\\\", shape=[self.hidden_size, self.hidden_size*2], initializer=self.initializer)\\n self.b_initial_state = tf.get_variable(\\\"b_initial_state\\\", shape=[self.hidden_size*2])\\n with tf.name_scope(\\\"embedding_projection\\\"): # embedding matrix\\n self.Embedding = tf.get_variable(\\\"Embedding\\\", shape=[self.vocab_size, self.embed_size],initializer=self.initializer) # [vocab_size,embed_size] tf.random_uniform([self.vocab_size, self.embed_size],-1.0,1.0)\\n self.Embedding_label = tf.get_variable(\\\"Embedding_label\\\", shape=[self.num_classes, self.embed_size*2],dtype=tf.float32) #,initializer=self.initializer\\n self.W_projection = tf.get_variable(\\\"W_projection\\\", shape=[self.hidden_size*2, self.num_classes],\\n initializer=self.initializer) # [embed_size,label_size]\\n self.b_projection = tf.get_variable(\\\"b_projection\\\", shape=[self.num_classes])\\n\\n # GRU parameters:update gate related\\n with tf.name_scope(\\\"gru_weights_encoder\\\"):\\n self.W_z = tf.get_variable(\\\"W_z\\\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\\n self.U_z = tf.get_variable(\\\"U_z\\\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\\n self.b_z = tf.get_variable(\\\"b_z\\\", shape=[self.hidden_size])\\n # GRU parameters:reset gate related\\n self.W_r = tf.get_variable(\\\"W_r\\\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\\n self.U_r = tf.get_variable(\\\"U_r\\\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\\n self.b_r = tf.get_variable(\\\"b_r\\\", shape=[self.hidden_size])\\n\\n self.W_h = tf.get_variable(\\\"W_h\\\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\\n self.U_h = tf.get_variable(\\\"U_h\\\", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)\\n self.b_h = tf.get_variable(\\\"b_h\\\", shape=[self.hidden_size])\\n\\n with tf.name_scope(\\\"gru_weights_decoder\\\"):\\n self.W_z_decoder = tf.get_variable(\\\"W_z_decoder\\\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer)\\n self.U_z_decoder = tf.get_variable(\\\"U_z_decoder\\\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer)\\n self.C_z_decoder = tf.get_variable(\\\"C_z_decoder\\\", shape=[self.embed_size * 2, self.hidden_size * 2],initializer=self.initializer) #TODO\\n self.b_z_decoder = tf.get_variable(\\\"b_z_decoder\\\", shape=[self.hidden_size*2])\\n # GRU parameters:reset gate related\\n self.W_r_decoder = tf.get_variable(\\\"W_r_decoder\\\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer)\\n self.U_r_decoder = tf.get_variable(\\\"U_r_decoder\\\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer)\\n self.C_r_decoder = tf.get_variable(\\\"C_r_decoder\\\", shape=[self.embed_size * 2, self.hidden_size * 2],initializer=self.initializer) #TODO\\n self.b_r_decoder = tf.get_variable(\\\"b_r_decoder\\\", shape=[self.hidden_size*2])\\n\\n self.W_h_decoder = tf.get_variable(\\\"W_h_decoder\\\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer)\\n self.U_h_decoder = tf.get_variable(\\\"U_h_decoder\\\", shape=[self.embed_size*2, self.hidden_size*2], initializer=self.initializer) #TODO\\n self.C_h_decoder = tf.get_variable(\\\"C_h_decoder\\\", shape=[self.embed_size * 2, self.hidden_size * 2],initializer=self.initializer)\\n self.b_h_decoder = tf.get_variable(\\\"b_h_decoder\\\", shape=[self.hidden_size*2])\\n\\n with tf.name_scope(\\\"full_connected\\\"):\\n self.W_fc=tf.get_variable(\\\"W_fc\\\",shape=[self.hidden_size*2,self.hidden_size])\\n self.a_fc=tf.get_variable(\\\"a_fc\\\",shape=[self.hidden_size])\",\n \"def get_weights(self):\\n return self.model.get_weights()\",\n \"def get_weights(self):\\n return self.model.get_weights()\",\n \"def build_layer(self) :\\n inputsWithBias = self.input_count + 1\\n self.weights = np.random.rand(inputsWithBias, self.node_count)\\n self.weights_and_activations = (self.weights, self.activations)\",\n \"def targets_weights_fn(self):\\n return common_layers.weights_all\",\n \"def weights(self):\\n return [x.numpy() for x in self.core.w]\",\n \"def get_forward_parameters(self):\\n if self.bias is not None:\\n return [self.weights, self.bias]\\n else:\\n return [self.weights]\",\n \"def instantiate_weights(self):\\n self.product_embeddings = tf.get_variable(\\n name='product_embeddings',\\n shape=[50000, 300],\\n dtype=tf.float32\\n )\\n self.aisle_embeddings = tf.get_variable(\\n name='aisle_embeddings',\\n shape=[250, 50],\\n dtype=tf.float32\\n )\\n self.department_embeddings = tf.get_variable(\\n name='department_embeddings',\\n shape=[50, 10],\\n dtype=tf.float32\\n )\\n self.W_relu = tf.get_variable(\\\"W_relu\\\",shape=[670, 30]) #这个参数后续需要自适应\\n self.b_relu = tf.get_variable(\\\"bias_relu\\\",shape=[30]) \\n self.W_projection = tf.get_variable(\\\"W_projection\\\",shape=[30, 1]) \\n self.b_projection = tf.get_variable(\\\"bias_projection\\\",shape=[1])\",\n \"def get_weights(layers):\\n get_layer_weights = lambda layer: layer.get_weights()[0]\\n return list(map(get_layer_weights, layers))\",\n \"def get_weights(self):\\r\\n return self.weights\",\n \"def get_weights(self):\\r\\n return self.weights\",\n \"def init_gru(rnn):\\n \\n def _concat_init(tensor, init_funcs):\\n (length, fan_out) = tensor.shape\\n fan_in = length // len(init_funcs)\\n \\n for (i, init_func) in enumerate(init_funcs):\\n init_func(tensor[i * fan_in : (i + 1) * fan_in, :])\\n \\n def _inner_uniform(tensor):\\n fan_in = nn.init._calculate_correct_fan(tensor, 'fan_in')\\n nn.init.uniform_(tensor, -math.sqrt(3 / fan_in), math.sqrt(3 / fan_in))\\n \\n for i in range(rnn.num_layers):\\n _concat_init(\\n getattr(rnn, 'weight_ih_l{}'.format(i)),\\n [_inner_uniform, _inner_uniform, _inner_uniform]\\n )\\n torch.nn.init.constant_(getattr(rnn, 'bias_ih_l{}'.format(i)), 0)\\n\\n _concat_init(\\n getattr(rnn, 'weight_hh_l{}'.format(i)),\\n [_inner_uniform, _inner_uniform, nn.init.orthogonal_]\\n )\\n torch.nn.init.constant_(getattr(rnn, 'bias_hh_l{}'.format(i)), 0)\",\n \"def _weights(self, layer_idx, expected_layer_name):\\n # wb = self.vgg_layers[0][layer_idx][0][0][2] # based on old vgg-model shape\\n wb = self.vgg_layers[layer_idx][0][0][0]\\n W = wb[0][0]\\n b = wb[0][1]\\n # layer_name = self.vgg_layers[0][layer_idx][0][0][0][0] # based on old vgg-model shape\\n layer_name = self.vgg_layers[layer_idx][0][0][3][0]\\n\\n # assert layer_name == expected_layer_name\\n\\n return W, b\",\n \"def get_weights(net):\\n return [p.data for p in net.parameters()]\",\n \"def inputs_weights_init(self):\\n input_user, input_item, input_rating = self.inputs_init()\\n user_embeddings, item_embeddings = self.embeddings_layers_init()\\n\\n return input_user, input_item, input_rating, user_embeddings, item_embeddings\",\n \"def get_weights(self):\\n return self.__weights\",\n \"def u_weights(self):\\n for i in range(self.n_inputs):\\n self._q_neuron.cx(self._weights[i], self.inputs[i])\",\n \"def get_weights(self):\\n return self.weights\",\n \"def get_weights(self):\\n return self.weights\",\n \"def get_model_and_tile_weights(model):\\n weight = model.weight.data.detach().cpu().numpy()\\n bias = model.bias.data.detach().cpu().numpy()\\n analog_weight, analog_bias = model.analog_tile.get_weights()\\n analog_weight = analog_weight.detach().cpu().numpy().reshape(weight.shape)\\n analog_bias = analog_bias.detach().cpu().numpy()\\n return weight, bias, analog_weight, analog_bias\",\n \"def getWeights(self):\\n return self.W1, self.W2\",\n \"def init_gru(rnn):\\n\\n def _concat_init(tensor, init_funcs):\\n (length, fan_out) = tensor.shape\\n fan_in = length // len(init_funcs)\\n\\n for (i, init_func) in enumerate(init_funcs):\\n init_func(tensor[i * fan_in: (i + 1) * fan_in, :])\\n\\n def _inner_uniform(tensor):\\n fan_in = nn.init._calculate_correct_fan(tensor, 'fan_in')\\n nn.init.uniform_(tensor, -math.sqrt(3 / fan_in), math.sqrt(3 / fan_in))\\n\\n for i in range(rnn.num_layers):\\n _concat_init(\\n getattr(rnn, 'weight_ih_l{}'.format(i)),\\n [_inner_uniform, _inner_uniform, _inner_uniform]\\n )\\n torch.nn.init.constant_(getattr(rnn, 'bias_ih_l{}'.format(i)), 0)\\n\\n _concat_init(\\n getattr(rnn, 'weight_hh_l{}'.format(i)),\\n [_inner_uniform, _inner_uniform, nn.init.orthogonal_]\\n )\\n torch.nn.init.constant_(getattr(rnn, 'bias_hh_l{}'.format(i)), 0)\",\n \"def initWeights(self):\\n self.weights = []\\n self.bias = []\\n for i, dim in enumerate(self.dimensions[1:]):\\n self.weights.append(np.random.uniform(-1,1,(self.dimensions[i],dim)))\\n self.bias.append(np.random.uniform(-1,1,dim))\",\n \"def get_parameters(self):\\n if self.add_bias:\\n params = np.concatenate((self.bias, self.W), 0)\\n else:\\n params = self.W\\n return params\",\n \"def get_weights(self):\\n return self._weights\",\n \"def get_weights(self):\\n return self._weights\",\n \"def get_weights(self):\\n return self._weights\",\n \"def weights(self):\\n return self._weights\",\n \"def weights(self):\\n return self._weights\",\n \"def weights(self):\\n return self._weights\",\n \"def weights(self):\\n return self._weights\",\n \"def weights_(self):\\n return self.get_tensor_value('logistic_regression/weights:0')\",\n \"def weights(self):\\n return self.__weights\",\n \"def weights(self):\\n return self.__weights\",\n \"def weights(self):\\n return self.__weights\",\n \"def weights(self):\\n return self.__weights\",\n \"def weights(self):\\n return self.__weights\",\n \"def get_variables(self) -> typing.List:\\n parts = (self.neural_net.encoder, self.neural_net.predictor, self.neural_net.dynamics)\\n return [v for v_list in map(lambda n: n.weights, parts) for v in v_list]\",\n \"def init_weights(self):\\n\\n params = torch.load(self.resnet_weight)\\n\\n self.fc1.weight.data = params['state_dict']['module.fc.weight'].clone()\\n self.fc1.bias.data = params['state_dict']['module.fc.bias'].clone()\\n\\n\\n r = np.sqrt(1.) / np.sqrt(self.fc3.in_features +\\n self.fc3.out_features)\\n self.fc3.weight.data.uniform_(-r, r)\\n self.fc3.bias.data.fill_(0)\\n r = np.sqrt(1.) / np.sqrt(self.fc2.in_features +\\n self.fc2.out_features)\\n self.fc2.weight.data.uniform_(-r, r)\\n self.fc2.bias.data.fill_(0)\\n r = np.sqrt(1.) / np.sqrt(self.fc4.in_features +\\n self.fc4.out_features)\\n self.fc4.weight.data.uniform_(-r, r)\\n self.fc4.bias.data.fill_(0)\",\n \"def get_weights(model) -> Weights:\\n return [val.cpu().numpy() for _, val in model.state_dict().items()]\",\n \"def get_weights(self):\\n return []\",\n \"def initialize_weights_and_bias(self, X_train):\\n n_samples, n_features = np.shape(X_train)\\n n_output = 1 \\n \\n # This is the numeber of gridcells and we want to make one prediction pr cell. \\n # It this doesn't work calculate the number of griddcells.\\n\\n self.b_h = [] #np.ones((self.n_hidden_layers, self.n_hidden[0]))\\n self.W_h = []\\n\\n for i in range(len(self.n_hidden)):\\n if (i == 0):\\n self.W_h.append(self.random.normal(loc=0.0, scale=0.1, size=(n_features, self.n_hidden[0])))\\n self.b_h.append(np.ones(self.n_hidden[0]))\\n else:\\n self.W_h.append(self.random.normal(loc=0.0, scale=0.1, size=(self.n_hidden[i-1], self.n_hidden[i])))\\n self.b_h.append(np.ones(self.n_hidden[i])) \\n \\n self.b_out = [1]\\n self.W_out = self.random.normal(loc=0.0, scale=0.1, size=(self.n_hidden[-1], n_output))\",\n \"def build(self):\\n\\n self.W = self.init([self.n_atom_input_feat, self.n_output])\\n self.b = model_ops.zeros(shape=[\\n self.n_output,\\n ])\\n\\n self.trainable_weights = self.W + self.b\",\n \"def init(InputUnits, OutputUnits, numHiddenLayer, HiddenUnits=None):\\n global HiddenUnit\\n all_weights = []\\n if HiddenUnits is None:\\n HiddenUnits = []\\n elif isinstance(HiddenUnits, int):\\n HiddenUnits = [HiddenUnits]\\n\\n # for InputLayer\\n\\n parameters = generate(HiddenUnits[0], InputUnits)\\n allWeights = mat.r_[parameters.flatten()]\\n\\n if numHiddenLayer > 1:\\n for i in range(numHiddenLayer):\\n if i < numHiddenLayer-1:\\n parameters = generate(HiddenUnits[i+1], HiddenUnits[i])\\n allWeights = mat.r_[allWeights, parameters.flatten()]\\n else:\\n parameters = generate(OutputUnits, HiddenUnits[i])\\n allWeights = mat.r_[allWeights, parameters.flatten()]\\n\\n else:\\n # for output layer\\n parameters = generate( OutputUnits, HiddenUnits[0])\\n allWeights = mat.r_[allWeights, parameters.flatten()]\\n\\n return allWeights\",\n \"def _build_bdrnn_graph(self, hparams):\\n\\n sample = self.iterator.get_next()\\n\\n inputs, tgt_outputs, seq_len = sample\\n\\n # linear projection to state size\\n #with tf.variable_scope(\\\"bdrnn_in\\\", dtype=tf.float32):\\n # inputs = tf.layers.dense(inputs=inputs,\\n # units=hparams.input_proj_size,\\n # kernel_initializer=tf.glorot_uniform_initializer())\\n\\n lm_fw_cell = []\\n lm_bw_cell = []\\n lm_init_state_fw = []\\n lm_init_state_bw = []\\n if hparams.pretrained:\\n with tf.variable_scope(\\\"lm_rnn\\\", dtype=tf.float32):\\n # create lm\\n with tf.variable_scope(\\\"fw\\\", dtype=tf.float32):\\n lm_fw_cell = _create_rnn_cell(num_units=hparams.num_units,\\n num_layers=1,\\n mode=self.mode)\\n # build the cell so it is in the correct scope\\n # NOTE: this is hard coded\\n lm_fw_cell[0].build([None, hparams.num_features])#hparams.input_proj_size])\\n lm_init_state_fw = _get_initial_state([lm_fw_cell[0].state_size], tf.shape(inputs)[0], \\\"lm\\\")\\n with tf.variable_scope(\\\"bw\\\", dtype=tf.float32):\\n lm_bw_cell = _create_rnn_cell(num_units=hparams.num_units,\\n num_layers=1,\\n mode=self.mode)\\n # NOTE: this is hard coded\\n lm_bw_cell[0].build([None, hparams.num_features])#hparams.input_proj_size])\\n lm_init_state_bw = _get_initial_state([lm_bw_cell[0].state_size], tf.shape(inputs)[0], \\\"lm\\\")\\n\\n lm_outputs, lm_states = tf.nn.bidirectional_dynamic_rnn(lm_fw_cell[0],\\n lm_bw_cell[0],\\n inputs,\\n sequence_length=seq_len,\\n initial_state_fw=lm_init_state_fw[0],\\n initial_state_bw=lm_init_state_bw[0],\\n dtype=tf.float32)\\n # optionally fix the LM weights\\n if hparams.fixed_lm:\\n print(\\\"Fixing pretrained language models.\\\")\\n lm_outputs = tf.stop_gradient(lm_outputs)\\n lm_outputs = tf.concat([lm_outputs[0], lm_outputs[1]], axis=-1)\\n lm_outputs = tf.layers.dense(lm_outputs,\\n 20,\\n kernel_initializer=tf.glorot_uniform_initializer())\\n lm_outputs = tf.concat([lm_outputs, inputs], axis=-1)\\n\\n\\n #lm_outputs = tf.concat([lm_outputs[0], lm_outputs[1], inputs], axis=-1)\\n else:\\n lm_outputs = tf.concat(lm_outputs, axis=-1)\\n\\n\\n\\n with tf.variable_scope(\\\"bdrnn\\\", dtype=tf.float32) as bdrnn_scope:\\n # create bdrnn\\n with tf.variable_scope(\\\"fw\\\", dtype=tf.float32):\\n fw_cells = _create_rnn_cell(num_units=hparams.num_units,\\n num_layers=hparams.num_layers,\\n mode=self.mode\\n )\\n init_state_fw = _get_initial_state([cell.state_size for cell in fw_cells],\\n tf.shape(inputs)[0], \\\"initial_state_fw\\\")\\n\\n with tf.variable_scope(\\\"bw\\\", dtype=tf.float32):\\n bw_cells = _create_rnn_cell(num_units=hparams.num_units,\\n num_layers=hparams.num_layers,\\n mode=self.mode,\\n )\\n\\n init_state_bw = _get_initial_state([cell.state_size for cell in bw_cells],\\n tf.shape(inputs)[0], \\\"initial_state_bw\\\")\\n # NOTE: this is commented because the lm cells and states are separated now\\n #fw_cells = lm_fw_cell + fw_cells\\n #bw_cells = lm_bw_cell + bw_cells\\n #init_state_fw = lm_init_state_fw + init_state_fw\\n #init_state_bw = lm_init_state_bw + init_state_bw\\n\\n # run bdrnn\\n combined_outputs, output_state_fw, output_state_bw = \\\\\\n tf.contrib.rnn.stack_bidirectional_dynamic_rnn(cells_fw=fw_cells,\\n cells_bw=bw_cells,\\n inputs=lm_outputs,\\n sequence_length=seq_len,\\n initial_states_fw=init_state_fw,\\n initial_states_bw=init_state_bw,\\n dtype=tf.float32,\\n scope=bdrnn_scope)\\n # outputs is a tuple (output_fw, output_bw)\\n # output_fw/output_bw are tensors [batch_size, max_time, cell.output_size]\\n # outputs_states is a tuple (output_state_fw, output_state_bw) containing final states for\\n # forward and backward rnn\\n\\n # concatenate the outputs of each direction\\n #combined_outputs = tf.concat([outputs[0], outputs[1]], axis=-1)\\n\\n with tf.variable_scope(\\\"bdrnn_out\\\", dtype=tf.float32):\\n # dense output layers\\n dense1 = tf.layers.dense(inputs=combined_outputs,\\n units=hparams.num_dense_units,\\n kernel_initializer=tf.glorot_uniform_initializer(),\\n activation=tf.nn.relu,\\n use_bias=True)\\n drop1 = tf.layers.dropout(inputs=dense1,\\n rate=hparams.dropout,\\n training=self.mode==tf.contrib.learn.ModeKeys.TRAIN)\\n dense2 = tf.layers.dense(inputs=drop1,\\n units=hparams.num_dense_units,\\n kernel_initializer=tf.glorot_uniform_initializer(),\\n activation=tf.nn.relu,\\n use_bias=True)\\n drop2 = tf.layers.dropout(inputs=dense2,\\n rate=hparams.dropout,\\n training=self.mode==tf.contrib.learn.ModeKeys.TRAIN)\\n\\n logits = tf.layers.dense(inputs=drop2,\\n units=hparams.num_labels,\\n use_bias=False)\\n\\n # mask out entries longer than target sequence length\\n mask = tf.sequence_mask(seq_len, dtype=tf.float32)\\n\\n crossent = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,\\n labels=tgt_outputs,\\n name=\\\"crossent\\\")\\n\\n # divide loss by batch_size * mean(seq_len)\\n loss = tf.reduce_sum(crossent*mask)/tf.cast(hparams.batch_size, tf.float32)\\n\\n metrics = []\\n update_ops = []\\n if self.mode == tf.contrib.learn.ModeKeys.EVAL:\\n # mean eval loss\\n loss, loss_update = tf.metrics.mean(values=loss)\\n\\n predictions = tf.argmax(input=logits, axis=-1)\\n tgt_labels = tf.argmax(input=tgt_outputs, axis=-1)\\n acc, acc_update = tf.metrics.accuracy(predictions=predictions,\\n labels=tgt_labels,\\n weights=mask)\\n # confusion matrix\\n targets_flat = tf.reshape(tgt_labels, [-1])\\n predictions_flat = tf.reshape(predictions, [-1])\\n mask_flat = tf.reshape(mask, [-1])\\n cm, cm_update = streaming_confusion_matrix(labels=targets_flat,\\n predictions=predictions_flat,\\n num_classes=hparams.num_labels,\\n weights=mask_flat)\\n tf.add_to_collection(\\\"eval\\\", cm_summary(cm, hparams.num_labels))\\n metrics = [acc, cm]\\n update_ops = [loss_update, acc_update, cm_update]\\n\\n return logits, loss, metrics, update_ops\",\n \"def get_weights_and_biases_from_pyTorch(self):\\n\\t\\t\\n\\t\\tilayer_id = 28\\n\\n\\t\\tmodel = models.vgg11(weights='VGG11_Weights.IMAGENET1K_V1')\\n\\t\\twatcher = ww.WeightWatcher(model = model, log_level=logging.WARNING)\\n\\t\\t\\n\\t\\tdetails = watcher.describe(layers=self.fclayers)\\n\\t\\tprint(details)\\n\\t\\t\\n\\t\\tN = details.N.to_numpy()[0]\\n\\t\\tM = details.M.to_numpy()[0]\\n\\t\\t\\n\\t\\tparams = ww.DEFAULT_PARAMS.copy()\\n\\t\\tparams[ww.ADD_BIASES] = True\\n\\t\\t\\n\\t\\tweights = watcher.get_Weights(layer=ilayer_id, params=params)\\n\\t\\tself.assertEqual(len(weights),1)\\n\\t\\t\\n\\t\\tW = weights[0]\\n\\t\\tself.assertEqual(np.max(W.shape),N)\\n\\t\\tself.assertEqual(np.min(W.shape),M)\\n\\n\\t\\tpass\",\n \"def getWeights(self,squeeze=False):\\n weights = self.weights\\n if squeeze:\\n weights = weights.squeeze()\\n return np.copy(weights)\",\n \"def generator(real_init): #-1, 5, 3\\n with tf.variable_scope('rnn_gen', reuse = tf.AUTO_REUSE, initializer=tf.contrib.layers.xavier_initializer()) as scope:\\n x_init = real_init[:,:,0] #-1, 8\\n y_init = real_init[:,:,1] #-1, 8\\n z_init = real_init[:,:,2] #-1, 8\\n \\n with tf.variable_scope('x_rw', reuse = tf.AUTO_REUSE, initializer=tf.contrib.layers.xavier_initializer()) as scope:\\n x_w1 = tf.get_variable('x_w1', [6, 3])\\n x_b1 = tf.get_variable('x_b1', [3])\\n x_w2 = tf.get_variable('x_w2', [9, 3])\\n x_b2 = tf.get_variable('x_b2', [3])\\n x_w3 = tf.get_variable('x_w3', [3, 1])\\n x_b3 = tf.get_variable('x_b3', [1])\\n x_w4 = tf.get_variable('x_w4', [3, 1])\\n x_b4 = tf.get_variable('x_b4', [1])\\n \\n x_full = x_init#-1, 8\\n for i in range(104):\\n x_layer1 = tf.nn.elu(tf.matmul(x_init, x_w1) + x_b1)#-1, 4\\n x_layer2 = tf.concat([x_init, x_layer1], 1)#-1, 12\\n x_layer3 = tf.nn.elu(tf.matmul(x_layer2, x_w2) + x_b2)#-1, 4\\n \\n x_mean = tf.sigmoid(tf.matmul(x_layer3, x_w3) + x_b3)#-1, 1\\n x_gamma = tf.sigmoid(tf.matmul(x_layer3, x_w4) + x_b4)#-1, 1\\n x_delta = x_mean + x_gamma * tf.random_normal(tf.shape(x_gamma), dtype = tf.float32)#-1, 1\\n x_delta_denorm = (x_delta * 2 - 1) / 80\\n x_output = tf.expand_dims(x_full[:,-1], 1) + x_delta_denorm\\n x_full = tf.concat([x_full, x_output], axis = 1)#-1, 10 + 1\\n x_init = x_full[:,-6:]\\n \\n with tf.variable_scope('y_rw', reuse = tf.AUTO_REUSE, initializer=tf.contrib.layers.xavier_initializer()) as scope:\\n y_w1 = tf.get_variable('y_w1', [6, 3])\\n y_b1 = tf.get_variable('y_b1', [3])\\n y_w2 = tf.get_variable('y_w2', [9, 3])\\n y_b2 = tf.get_variable('y_b2', [3])\\n y_w3 = tf.get_variable('y_w3', [3, 1])\\n y_b3 = tf.get_variable('y_b3', [1])\\n y_w4 = tf.get_variable('y_w4', [3, 1])\\n y_b4 = tf.get_variable('y_b4', [1])\\n \\n y_full = y_init#-1, 8\\n for i in range(104):\\n y_layer1 = tf.nn.elu(tf.matmul(y_init, y_w1) + y_b1)\\n y_layer2 = tf.concat([y_init, y_layer1], 1)#-1, 12\\n y_layer3 = tf.nn.elu(tf.matmul(y_layer2, y_w2) + y_b2)#-1, 4\\n \\n y_mean = tf.sigmoid(tf.matmul(y_layer3, y_w3) + y_b3) #-1, 1\\n y_gamma = tf.sigmoid(tf.matmul(y_layer3, y_w4) + y_b4) #-1, 1\\n y_delta = y_mean + y_gamma * tf.random_normal(tf.shape(y_gamma), dtype = tf.float32)#-1, 1\\n y_delta_denorm = (y_delta * 5 - 2.7) / 1700\\n y_output = tf.expand_dims(y_full[:,-1], 1) + y_delta_denorm\\n y_full = tf.concat([y_full, y_output], axis = 1)#-1, 10 + 1, 1\\n y_init = y_full[:,-6:]\\n \\n with tf.variable_scope('z_rw', reuse = tf.AUTO_REUSE, initializer=tf.contrib.layers.xavier_initializer()) as scope:\\n z_w1 = tf.get_variable('z_w1', [6, 3])\\n z_b1 = tf.get_variable('z_b1', [3])\\n z_w2 = tf.get_variable('z_w2', [9, 3])\\n z_b2 = tf.get_variable('z_b2', [3])\\n z_w3 = tf.get_variable('z_w3', [3, 1])\\n z_b3 = tf.get_variable('z_b3', [1])\\n z_w4 = tf.get_variable('z_w4', [3, 1])\\n z_b4 = tf.get_variable('z_b4', [1])\\n \\n z_full = z_init#-1, 8\\n for i in range(104):\\n z_layer1 = tf.nn.elu(tf.matmul(z_init, z_w1) + z_b1)\\n z_layer2 = tf.concat([z_init, z_layer1], 1)#-1, 12\\n z_layer3 = tf.nn.elu(tf.matmul(z_layer2, z_w2) + z_b2)#-1, 4\\n \\n z_mean = tf.sigmoid(tf.matmul(z_layer3, z_w3) + z_b3) #-1, 1\\n z_gamma = tf.sigmoid(tf.matmul(z_layer3, z_w4) + z_b4) #-1, 1\\n z_delta = z_mean + z_gamma * tf.random_normal(tf.shape(z_gamma), dtype = tf.float32)#-1, 1\\n z_delta_denorm = (z_delta * 3 - 2.2) / 300\\n z_output = tf.expand_dims(z_full[:,-1], 1) + z_delta_denorm\\n z_full = tf.concat([z_full, z_output], axis = 1)#-1, 10 + 1, 1\\n z_init = z_full[:,-6:]\\n \\n rand_layer1 = tf.concat([tf.expand_dims(x_full, 2), tf.expand_dims(y_full, 2), tf.expand_dims(z_full, 2)], axis = 2)#-1, 200, 3\\n rand_layer1_reshape = tf.reshape(rand_layer1, shape = (-1, 110, 3, 1))\\n output_filt = tf.ones(shape = (11, 1, 1, 1)) / 11\\n \\n rand_layer2 = tf.nn.conv2d(rand_layer1_reshape, output_filt, strides = [1,1,1,1], padding = 'VALID')\\n \\n return tf.reshape(rand_layer2, shape = (-1, 100, 3)) #-1, 110, 3\",\n \"def sample(self) -> Tuple[torch.Tensor, ...]:\\n assert len(self) >= self.batch_size\\n\\n indices = np.random.choice(\\n len(self.buffer), size=self.batch_size, replace=False\\n )\\n\\n states, actions, rewards, next_states, dones = [], [], [], [], []\\n\\n for i in np.nditer(indices):\\n s, a, r, n_s, d = self.buffer[i]\\n states.append(np.array(s, copy=False))\\n actions.append(np.array(a, copy=False))\\n rewards.append(np.array(r, copy=False))\\n next_states.append(np.array(n_s, copy=False))\\n dones.append(np.array(float(d), copy=False))\\n\\n states_ = torch.FloatTensor(np.array(states)).to(device)\\n actions_ = torch.FloatTensor(np.array(actions)).to(device)\\n rewards_ = torch.FloatTensor(np.array(rewards).reshape(-1, 1)).to(device)\\n next_states_ = torch.FloatTensor(np.array(next_states)).to(device)\\n dones_ = torch.FloatTensor(np.array(dones).reshape(-1, 1)).to(device)\\n\\n if torch.cuda.is_available():\\n states_ = states_.cuda(non_blocking=True)\\n actions_ = actions_.cuda(non_blocking=True)\\n rewards_ = rewards_.cuda(non_blocking=True)\\n next_states_ = next_states_.cuda(non_blocking=True)\\n dones_ = dones_.cuda(non_blocking=True)\\n\\n return states_, actions_, rewards_, next_states_, dones_\",\n \"def get_weights(self):\\n if self.W_tickers is not None:\\n return [w for w in [self.W_fwd_conc, self.W_bwd_conc, self.W_red_rec] + list(self.W_tickers.values())\\n if w is not None]\\n else:\\n return [w for w in [self.W_fwd_conc, self.W_bwd_conc, self.W_red_rec] if w is not None]\",\n \"def initialize_weights(self):\\n for layer in self._cnn_layers:\\n weights_initializer.WeightsInitializer.initialize_layer_or_model(layer)\",\n \"def encode_weights(self):\\n weights = []\\n for param in self.global_policy.parameters():\\n shape = list(param.shape)\\n param_list = torch.flatten(param.data).tolist()\\n weights.append(Tensor(shape, param_list))\\n\\n return weights\",\n \"def get_weights(self):\\n\\n # Check if fit had been called\\n check_is_fitted(self, 'weights_')\\n\\n w = self.weights_.reshape(self.X_.shape[1], self.n_classes_, order='F')\\n\\n return w\",\n \"def n_step_birnn(\\n n_layers, dropout_ratio, hx, ws, bs, xs, activation='tanh', **kwargs):\\n return n_step_rnn_base(n_layers, dropout_ratio, hx, ws, bs, xs,\\n activation, use_bi_direction=True)\",\n \"def get_weights(self):\\n return self._weight\",\n \"def _setup(self) -> None:\\n #TODO: type\\n self.activation = self.params['activation']\\n\\n self.batchsize: int = self.params['batchsize']\\n\\n self.input_shape: Tuple[int,int,int] = self.params['input_shape']\\n\\n self.d: int = self.input_shape[1]\\n assert(not self.d == 0)\\n\\n self.n: int = int(sqrt(self.input_shape[2]))\\n assert(not self.n == 0)\\n\\n self.dtype: type = self.params['dtype']\\n\\n # initialize weights\\n self.W: List[tf.Tensor] = []\\n \\n for i in range(3):\\n #TODO: type\\n w_init = self.params['initializer_w']\\n if self.params['initializer_w' + str(i)] is not None:\\n w_init = self.params['initializer_w' + str(i)]\\n\\n w_stddev: float = self.params['stddev_w']\\n if self.params['stddev_w' + str(i)] is not None:\\n w_stddev = self.params['stddev_w' + str(i)]\\n\\n self.W.append(tf.get_variable(\\\"weights_\\\" + str(i),\\n shape = (self.d, (self.d if i < 2 else 2 * self.d)),\\n dtype = self.dtype,\\n initializer = w_init(stddev=w_stddev)))\\n\\n #TODO: type\\n b_init = self.params['initializer_b']\\n b_stddev = self.params['stddev_b']\\n self.B: tf.Tensor = tf.get_variable(\\\"biases\\\", shape = (1, self.d, 1),\\n dtype = self.dtype,\\n initializer = b_init(stddev=b_stddev))\\n\\n # create/load expand matrix\\n with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\\n self.expand: tf.Tensor = tf.get_variable(\\n \\\"expand\\\" + str(self.n),\\n shape = (self.n, self.n * self.n),\\n dtype = self.dtype,\\n initializer = tf.constant_initializer(make_expand(self.n)))\\n\\n # create/load tile matrix\\n tile: np.ndarray = np.array([([1] + [0]*(self.n-1))*self.n])\\n for i in range(1, self.n):\\n tile = np.append(tile, [([0]*i + [1] + [0]*(self.n-1-i))*self.n], 0)\\n\\n self.tile: tf.Tensor = tf.constant(tile, self.dtype)\",\n \"def initialize_weights(self):\\n w1 = np.random.uniform(-1.0, 1.0, size = self.n_hidden * (self.n_features + 1)).reshape(self.n_hidden, (self.n_features + 1))/(self.n_features + 1)\\n w2 = np.random.uniform(-1.0, 1.0, size=self.n_output*(self.n_hidden+1)).reshape(self.n_output, self.n_hidden+ 1)/(self.n_hidden + 1)\\n return w1, w2\",\n \"def get_weights(t_vars, sess):\\n if TfUtils._is_op_defined(t_vars):\\n num_of_layers = len(TfUtils.get_layers(t_vars))\\n return [sess.run(t_var) for t_var in t_vars[ : num_of_layers]]\\n return [sess.run(t_var) for t_var in t_vars if \\\"kernel\\\" in t_var.name]\",\n \"def get_weights(self):\\n \\n w = torch.exp(self._weight) * self.mask_d + self._weight * self.mask_o\\n\\n w_squared_norm = (w ** 2).sum(-1, keepdim=True)\\n \\n w = self._diag_weight.exp() * w / w_squared_norm.sqrt()\\n \\n wpl = self._diag_weight + self._weight - 0.5 * torch.log(w_squared_norm) \\n\\n return w.t(), wpl.t()[self.mask_d.bool().t()].view(\\n self.dim, self.in_features // self.dim, self.out_features // self.dim)\",\n \"def generate_weights(n_features, hidden_layer_sizes, n_out):\\n\\n weights = []\\n biases = []\\n\\n # Weights from input layer to first hidden layer\\n weights.append(init_weight(hidden_layer_sizes[0], n_features))\\n biases.append(init_bias(hidden_layer_sizes[0]))\\n\\n # Weights from one hidden layer to the next\\n for i in range(1, hidden_layer_sizes.size):\\n weights.append(\\n init_weight(hidden_layer_sizes[i],hidden_layer_sizes[i - 1]))\\n biases.append(init_bias(hidden_layer_sizes[i]))\\n\\n # Weights from last hidden layer to output layer\\n weights.append(init_weight(n_out, hidden_layer_sizes[-1]))\\n biases.append(init_bias(n_out))\\n\\n return weights, biases\",\n \"def _compute_weights(self, beta=1):\\n N = self.__len__()\\n # Allocate memory\\n W = np.zeros((N,N), dtype='float')\\n \\n for i in range(N):\\n for j in range(N):\\n if i > j:\\n W[i,j] = self._kernel(self.flat_image[i],\\n self.flat_image[j], \\n beta)\\n W += W.T \\n for i in range(N):\\n W[i,i] = 1\\n \\n return W\",\n \"def n_step_rnn_base(n_layers, dropout_ratio, hx, ws, bs, xs,\\n activation, use_bi_direction, **kwargs): # NOQA\\n\\n argument.check_unexpected_kwargs(\\n kwargs, train='train argument is not supported anymore. '\\n 'Use chainer.using_config',\\n use_cudnn='use_cudnn argument is not supported anymore. '\\n 'Use chainer.using_config')\\n argument.assert_kwargs_empty(kwargs)\\n\\n activation_list = ['tanh', 'relu']\\n if activation not in activation_list:\\n candidate = ','.join(activation_list)\\n raise ValueError('Invalid activation: \\\"%s\\\". Please select from [%s]'\\n % (activation, candidate))\\n\\n xp = cuda.get_array_module(hx)\\n\\n if xp is not numpy and chainer.should_use_cudnn('>=auto', 5000):\\n states = get_random_state().create_dropout_states(dropout_ratio)\\n lengths = [len(x) for x in xs]\\n xs = chainer.functions.concat(xs, axis=0)\\n\\n rnn_mode = 'rnn_%s' % activation\\n w = cudnn_rnn_weight_concat(\\n n_layers, states, use_bi_direction, rnn_mode, ws, bs)\\n\\n if use_bi_direction:\\n # Bi-directional RNN\\n if activation == 'tanh':\\n rnn = NStepBiRNNTanh\\n elif activation == 'relu':\\n rnn = NStepBiRNNReLU\\n else:\\n # Uni-directional RNN\\n if activation == 'tanh':\\n rnn = NStepRNNTanh\\n elif activation == 'relu':\\n rnn = NStepRNNReLU\\n\\n hy, ys = rnn(n_layers, states, lengths)(hx, w, xs)\\n sections = numpy.cumsum(lengths[:-1])\\n ys = chainer.functions.split_axis(ys, sections, 0)\\n return hy, ys\\n\\n else:\\n\\n def f(x, h, c, w, b):\\n xw, hw = w\\n xb, hb = b\\n rnn_in = linear.linear(x, xw, xb) + linear.linear(h, hw, hb)\\n if activation == 'tanh':\\n return tanh.tanh(rnn_in), None\\n elif activation == 'relu':\\n return relu.relu(rnn_in), None\\n\\n hy, _, ys = n_step_rnn_impl(\\n f, n_layers, dropout_ratio, hx, None, ws, bs, xs, use_bi_direction)\\n return hy, ys\",\n \"def pull_weights(self, learning_rate):\\n for w in self.weights:\\n w.value += learning_rate * w.gradient\\n # Reset all the weights' gradient to 0\\n # We will not reset all other units' gradient, because all other units should be initialized in next training\\n # round, and the init value of gradient is 0\\n for w in self.weights:\\n w.gradient = 0\",\n \"def get_flat_weights(model):\\n return torch.cat([p.data.view(-1, 1) for p in model.parameters()], 0)\",\n \"def feed_forward(inputs, output_dim, name='', initializer=None):\\n _dim = inputs.get_shape().as_list()[1]\\n weights = tf.get_variable('weights_{}'.format(name),\\n [_dim, output_dim],\\n initializer=initializer)\\n zero_init = tf.zeros_initializer()\\n bias = tf.get_variable('bias_{}'.format(name), shape=output_dim,\\n dtype=tf.float32,\\n initializer=zero_init)\\n output = tf.nn.xw_plus_b(inputs, weights, bias)\\n return output\",\n \"def influence_weights(sess, n_neighbours, data_size):\\n\\n # To see the names of the different layers of the neural network\\n tf.global_variables()\\n\\n # Get the weights for a specified layer\\n layer_number = 0\\n weights = tf.get_default_graph().get_tensor_by_name('layers/fc-layer{:d}/kernel:0'.format(layer_number))\\n weights = sess.run(tf.nn.top_k(weights))\\n # nn.top_k has as output [[weights][indexes to next node]], so the weights are located at position 0\\n weights_numpy = np.array(weights[0])\\n\\n # Get the working directory of this script\\n working_dir = os.path.dirname(os.path.abspath(__file__))\\n # Get the file name for saving the numpy array\\n file_name = working_dir + \\\"/output_ANN/HDF5_files/normalized_ANN/{}_norm_ANNweights_{}\\\".format(\\n n_neighbours, data_size)\\n # Get an unique file name\\n file_name = saving(file_name, save=False, file_type='h5')\\n # Save the numpy array as HDF5 file\\n h5f = h5py.File(file_name, 'w')\\n h5f.create_dataset('dataset_{}'.format(data_size), data=weights_numpy)\\n\\n return weights_numpy\",\n \"def get_conv_weights(self, conv_layer_i=0):\\n conv_layer = self.get_conv_layer(conv_layer_i)\\n weights = conv_layer.weights[0].numpy()\\n weights = np.transpose(weights, [2,1,0])\\n return weights\",\n \"def build(self, input_layer, trainable=True):\\n\\n with tf.variable_scope(self.name):\\n\\n # Create a weight matrix\\n input_size = input_layer.get_shape()[-1].value\\n\\n self.weights = weight_variable([input_size, self.output_size], 'weights', trainable, False)\\n self.bias = bias_variable([self.output_size], 'bias', trainable)\\n\\n # Create the ReLU layer\\n self.layer = tf.matmul(input_layer, self.weights) + self.bias\\n\\n if self.activation_function:\\n self.layer = self.activation_function(self.layer)\\n\\n return self.layer, self.weights, self.bias\",\n \"def lecun_ntk_wb(n_hid_layers=5, n_units=50):\\n N = [1] + [n_units] * n_hid_layers + [1]\\n \\n W = []\\n B = []\\n for i in range(1, len(N)):\\n W.append(np.random.randn(N[i], N[i - 1]) / N[i - 1] ** 0.5)\\n B.append(np.zeros(N[i]))\\n \\n return {'N': N, 'W': W, 'B': B}\",\n \"def forward(self, inputs, mask=None):\\n N = inputs.size(0)\\n out = inputs.view(N, 1, -1, self.n_mel_channels) # [N, 1, Ty, n_mels]\\n for conv, bn in zip(self.convs, self.bns):\\n out = conv(out)\\n out = bn(out)\\n out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]\\n\\n out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]\\n T = out.size(1)\\n N = out.size(0)\\n out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]\\n if mask is not None:\\n out = out.masked_fill(mask.unsqueeze(-1), 0)\\n\\n self.gru.flatten_parameters()\\n memory, out = self.gru(out) # memory --- [N, Ty, E//2], out --- [1, N, E//2]\\n\\n return memory, out.squeeze(0)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7037132","0.6992914","0.695443","0.69183373","0.68530166","0.6803836","0.6534791","0.64984447","0.64839584","0.64703405","0.6404171","0.6397448","0.63523","0.6348087","0.6256905","0.62384343","0.62384343","0.62112504","0.62057334","0.6160526","0.6132221","0.6121456","0.6092795","0.6090453","0.6088111","0.60871804","0.60852444","0.60852444","0.60852444","0.60852444","0.6075956","0.607401","0.607401","0.6040154","0.60285926","0.6020391","0.5996703","0.5991002","0.59800553","0.59720314","0.59720314","0.5969568","0.5964763","0.5956581","0.59439266","0.5936918","0.5927366","0.5925697","0.5925697","0.5910837","0.58979493","0.58946043","0.5893263","0.58868253","0.5881538","0.5881538","0.5881538","0.5842957","0.5842957","0.5842957","0.5842957","0.583861","0.58280253","0.58280253","0.58280253","0.58280253","0.58280253","0.58016163","0.57979035","0.5797783","0.5779588","0.57650375","0.5763204","0.5760754","0.5759614","0.5752538","0.5741973","0.5734295","0.57292473","0.57263285","0.571934","0.5715057","0.5714445","0.57118744","0.5711244","0.57108885","0.5683686","0.5680514","0.5665428","0.5656644","0.5646503","0.5645532","0.5639338","0.56388366","0.56266993","0.56259394","0.5622293","0.5601045","0.55995935","0.55984795"],"string":"[\n \"0.7037132\",\n \"0.6992914\",\n \"0.695443\",\n \"0.69183373\",\n \"0.68530166\",\n \"0.6803836\",\n \"0.6534791\",\n \"0.64984447\",\n \"0.64839584\",\n \"0.64703405\",\n \"0.6404171\",\n \"0.6397448\",\n \"0.63523\",\n \"0.6348087\",\n \"0.6256905\",\n \"0.62384343\",\n \"0.62384343\",\n \"0.62112504\",\n \"0.62057334\",\n \"0.6160526\",\n \"0.6132221\",\n \"0.6121456\",\n \"0.6092795\",\n \"0.6090453\",\n \"0.6088111\",\n \"0.60871804\",\n \"0.60852444\",\n \"0.60852444\",\n \"0.60852444\",\n \"0.60852444\",\n \"0.6075956\",\n \"0.607401\",\n \"0.607401\",\n \"0.6040154\",\n \"0.60285926\",\n \"0.6020391\",\n \"0.5996703\",\n \"0.5991002\",\n \"0.59800553\",\n \"0.59720314\",\n \"0.59720314\",\n \"0.5969568\",\n \"0.5964763\",\n \"0.5956581\",\n \"0.59439266\",\n \"0.5936918\",\n \"0.5927366\",\n \"0.5925697\",\n \"0.5925697\",\n \"0.5910837\",\n \"0.58979493\",\n \"0.58946043\",\n \"0.5893263\",\n \"0.58868253\",\n \"0.5881538\",\n \"0.5881538\",\n \"0.5881538\",\n \"0.5842957\",\n \"0.5842957\",\n \"0.5842957\",\n \"0.5842957\",\n \"0.583861\",\n \"0.58280253\",\n \"0.58280253\",\n \"0.58280253\",\n \"0.58280253\",\n \"0.58280253\",\n \"0.58016163\",\n \"0.57979035\",\n \"0.5797783\",\n \"0.5779588\",\n \"0.57650375\",\n \"0.5763204\",\n \"0.5760754\",\n \"0.5759614\",\n \"0.5752538\",\n \"0.5741973\",\n \"0.5734295\",\n \"0.57292473\",\n \"0.57263285\",\n \"0.571934\",\n \"0.5715057\",\n \"0.5714445\",\n \"0.57118744\",\n \"0.5711244\",\n \"0.57108885\",\n \"0.5683686\",\n \"0.5680514\",\n \"0.5665428\",\n \"0.5656644\",\n \"0.5646503\",\n \"0.5645532\",\n \"0.5639338\",\n \"0.56388366\",\n \"0.56266993\",\n \"0.56259394\",\n \"0.5622293\",\n \"0.5601045\",\n \"0.55995935\",\n \"0.55984795\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60313946"},"document_rank":{"kind":"string","value":"34"}}},{"rowIdx":894,"cells":{"query":{"kind":"string","value":"Redirect to home page on url /"},"document":{"kind":"string","value":"def index():\n return redirect(url_for(\"home\"))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def home_page():\n return redirect(url_for(_DEFAULT_ROUTE, _external=True))","def homepage():\n return redirect('index.html')","def homepage():\n if g.user:\n return redirect(f\"/user/{g.user.id}\")\n else:\n return redirect(\"/landing\")","def start_page():\n if not _home:\n abort(404)\n return redirect(_home)","def start_page():\n if not _home:\n abort(404)\n return redirect(_home)","def homepage():\n return redirect(\"/posts\")","def home_page():\n return redirect('/users')","def go_home(request):\n\n url = request.route_url('home', _app_url=get_app_url(request))\n return HTTPFound(location=url)","def home(request):\n return redirect('commprod/')","def get(self, request):\n return redirect('start:home')","def get(self, request):\n return redirect('start:home')","def get(self, request):\n return redirect('start:home')","def root_redirect():\r\n return redirect(url_for(\"display_top\"))","def homepage( request ):\n if \"email\" in request.session:\n return redirect( '/home' )\n return render_to_response( 'index.html' )","def toLanding():\n return redirect(url_for('landingurl'))","def idx(_request):\n return HttpResponseRedirect('/home')","def home(request):\n if request.user.is_authenticated:\n return redirect('/start')\n return render(request, 'home/home.html')","def go_home(self):\r\n if self.home_url is not None:\r\n self.set_url(self.home_url)","def homepage_redirect():\n return redirect('/upload_file')","def home(request):\n default_url = reverse(root_nodes)\n\n if request.user.moderator_profile != None:\n home_url = request.user.moderator_profile.get_home_url()\n if home_url != None:\n return HttpResponseRedirect(home_url)\n\n return HttpResponseRedirect(default_url)","def home():\n # if session.get('username'):\n # return redirect(url_for('categories'))\n # else:\n return render_template('home.html')","def welcome_page():\n return redirect(\"/static/welcome.html\")","def home_page():\n if not g.user:\n flash(\"Please login to view.\", \"warning\")\n return redirect('/login')\n return render_template('index.html')","def home(request):\n # if request.user.is_authenticated():\n # return redirect('/fastapp')\n return context()","def home(request):\n assert isinstance(request, HttpRequest)\n return redirect('/departments')","def home(request):\n if request.user.is_authenticated():\n return HttpResponseRedirect('done')\n else:\n return render_to_response('home.html', RequestContext(request))","def home():\n\n if not current_user.is_authenticated:\n return redirect(url_for('login'))\n else:\n return redirect(url_for('show_registrations'))","def redirect_view(request):\n path = request.GET.get(\"to\") or \"/\"\n return redirect(path if path.startswith(\"/\") else f\"/{path}\", permanent=True)","def home():\n if not session.get('logged_in'):\n return redirect(url_for('welcome'))\n return render_template('home.html', filename=\"yarg.jpg\")","def index_file():\n return redirect(\"/\")","def home_page():\n return redirect('/register')","def home(request):\n if request.user.is_authenticated():\n return HttpResponseRedirect('logged-in')\n else:\n home_view = 1\n return render_to_response('content/home.html', {'version': version, \"home_view\":home_view},\n RequestContext(request))","def home_view(request):\n return HttpResponseRedirect('/schedule/')","def root1(request):\n\ttemplate = 'main'\n\treturn redirect(template)","def redirect(url):","def home(request):\n #print (\"home\")\n if request.user.is_authenticated():\n return redirect('done')\n return context()","def index():\n if 'name' in session:\n return render_template('home.html')\n return redirect(url_for('log_in'))","def home_redirect(request):\n if request.user.is_authenticated() and request.user.is_staff:\n return redirect(\"volunteers\")\n elif request.user.is_authenticated() and not request.user.is_superuser:\n related_volunteer = get_object_or_404(Volunteer, user_id=request.user.pk)\n return redirect(\"edit-volunteer-profile\", volunteer_id=related_volunteer.pk)\n else:\n return redirect(\"new-volunteer\")","def index():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n return render_template('index.html')","def test_home_page_redirect_when_loggin_attempt_successful(self):\n\t\tpass","def home():\n\n form = HomePageForm()\n if form.validate_on_submit():\n return redirect(url_for('character'))\n return render_template('index.html', form=form)","def index(request):\n\n\tif request.user.is_authenticated:\n\t\treturn HttpResponseRedirect('home')\n\treturn HttpResponseRedirect('login')","def root(request):\n\ttemplate = 'bfbot/main'\n\treturn redirect(template)","def entry_page():\n return redirect(url_for('index'))","def redir_index():\n return redirect(url_for(\"index\"), code=301)","def index():\n return redirect('/client/index.html')","def view_home(self):\n with self.client.get(\"/home\", catch_response=True) as response:\n for r_hist in response.history:\n if r_hist.status_code > 200 and r_hist.status_code < 400:\n response.failure(\"Not logged on: Got redirect to /login\")","def home_view(request):\n if request.authenticated_userid:\n return HTTPFound(location=request.route_url('app_view')) # pragma no cover\n return {} # pragma no cover","def handle_forbidden_for_homepage(self, request):\n\n login_url = request.link(Auth.from_request_path(request), name='login')\n\n if URL(request.url).path() == '/':\n return morepath.redirect(login_url)\n\n return handle_forbidden(self, request)","def root():\n if request.headers['Accept'] == 'application/json':\n return \"Welcome\\n\\n\", 200\n else:\n return redirect(url_for('index'))","def get(self, request):\n return redirect('http://localhost:3000/')","def index():\n return redirect(url_for('second_page'))","def second_page():\n return redirect(url_for('index'))","def landing():\n if g.user:\n return render_template('landing.html', user=g.user)\n return redirect(url_for('login'))","def first_request():\n heroku_url: str = 'https://justice-ndou.herokuapp.com/'\n registered_domain: str = 'https://justice-ndou.herokuapp.com/'\n\n if request.host_url.lower().startswith(heroku_url):\n return redirect(request.host_url.lower().replace(heroku_url, registered_domain)), 301","def index() -> str:\n return redirect('/students')","def home(request):\n return render_to_response('index.html')","def redirect_old_featured(page):\r\n return redirect(url_for('.index', page=page), 301)","def home():\n logging.info('Entering route: HOME')\n\n logging.info('Rendering template: main.html')\n return render_template('main.html')","def me():\n if g.USER:\n return redirect(url_for(\"profile\", username=g.USER.username))\n return redirect(url_for(\"home\"))","def home(request):\n return render_to_response('home.html', {}, RequestContext(request))","def home(request):\n today = datetime.date.today()\n return HttpResponseRedirect(\"%s/newsletter/%d/%d/%d/\" % (SUBSITE, today.year, today.month, today.day))","def redirectPage() -> Response:\n # pass in the function name\n return redirect(url_for('view.loadMainPage'))","def show_index():\r\n if 'username' in flask.session:\r\n return flask.redirect(flask.url_for('home')) # Need to fix redirect\r\n\r\n return flask.render_template(\"index.html\")","def home():\n if request.method == \"GET\":\n return render_template(\"index.html\", result=False)\n\n # if there is post data, we have the form and need to encode the SQL\n # to pass to the results route.\n #\n # this encoding dance is to protect against the possibility of getting a very\n # long SQL string that breaks something in HTTP get.\n sql = request.form[\"sql\"]\n dialect = request.form[\"dialect\"]\n return redirect(\n url_for(\"routes.fluff_results\", sql=sql_encode(sql), dialect=dialect)\n )","def home(request):\n if 'member_id' not in request.session:\n return redirect(\"/login/\")\n return render(request, 'esihapp/index1.html')","def redirector(status, start_response, exc_info=None):\n session['login.pre_uri'] = environ['PATH_INFO']\n session.save()\n start_response('302 Found',[(\"Location\",\"/login\"),(\"Content-type\",\"text\")])\n return []","def index(request):\n return redirect('polls:index')","def redirect_dest(fallback):\n dest = request.args.get('next')\n try:\n if dest.startswith('/') or dest.startswith(request.host_url):\n return redirect(dest)\n dest_url = url_for(dest)\n except:\n return redirect(fallback)\n return redirect(dest_url)","def login_redirect(request):\n return HttpResponseRedirect('/login')","def _home(self, op, context):\n self.page = \"HOME\"\n return {'FINISHED'}","def get(self):\n self.redirect('/admin')","def get(self):\n self.redirect('/admin')","def home(request):\n\treturn render(request, \"compta/home.html\")","def redirect(self, path):\n self.get_controller().redirect(path)","def catch_all(path):\n return redirect('/', code=302)","def home(request):\r\n return render(request, 'home.html')","def home():\n return response(\"OK\")","def test_index_redirect(self):\n response = self.app.get(\"/\")\n self.assertEqual(response.status_code, 302,\n \"/ did not redirect to login when user is not logged in\")\n self.assertTrue(\n response.location.endswith(\"/accounts/login/\"),\n \"Redirect location did not end with /accounts/login/\"\n )","def home():\n return render_template('home.html',\n face=session.get(app.config['SESSION_KEY'], None))","async def index(request: Request, user: UserInfo) -> HTTPResponse:\n return redirect('home')","def home(request):\n return render(request, 'home/index.html')","def defaultlanding():\n #send user to description page if not logged in\n if not g.user:\n return redirect(url_for('description'))\n #display leaderboard for competition if logged in\n return redirect(url_for('leaderboard'))","def home(request):\n if request.user.is_authenticated():\n return HttpResponse(\"{0} <a href='/accounts/logout'>exit</a>\".format(request.user))\n else:\n return HttpResponse(\"<a href='/login/vk-oauth2/'>login with VK</a>\")","def goHome():\n\t#Go to pod home\n\tif screen.lastScreen in screen.protectedScreens:\n\t\tpodScreen.show()\n\telse:\n\t\tsplashScreen.show()","def home():\n\n return render_template('home_page.html')","def get(self):\n self.logout()\n self.redirect('/')","def index():\n redirect(URL('form'))","def home(result=None):\n print(inspect.stack()[1][3])\n\n if not session.get('logged_in') and not result:\n return render_template('login.html')\n else:\n # Based on the user_id passed, print Details, URLS and all.\n # return render_template('dashboard.html', username=result.name, user_id=result.user_type)\n return render_template('webpage/index1.html', username=result.name, user_id=result.user_type)","def redirect(self, url):\n raise RequestRedirect(url)","def index_page():\n\n return redirect(\"/application-form\")\n\n # Alternately, we could make this a Jinja template in `templates/`\n # and return that result of rendering this, like:\n #\n # return render_template(\"index.html\")","def home(self, *args, **kwargs):\n pass","def desktop_or_mobile(request):\n url_name = 'home.mobile' if request.MOBILE else 'home'\n return redirect_to(request, url_name, permanent=False)","def redirect(to):\r\n def _redirect(environ, start_response):\r\n args, kwargs = environ['wsgiorg.routing_args']\r\n start_response('301 MOVED PERMANENTLY',\r\n [('Location', to.format(*args, **kwargs))])\r\n return []\r\n return _redirect","def get(self):\n self.logout()\n self.redirect('/blog/login')","def news0_redirect(request):\n return redirect('news:news', start_id='0')","def home_page(request):\r\n return render(request, 'ez_main/home_page.html')","def get_redirect_url(self):\n return reverse('accounts:home')","def home():\n\n this_dir = os.path.dirname(__file__)\n home_file = app.config['HOME_PAGE_FOLDER'] + 'homepage.html'\n try:\n homepage = open(os.path.join(this_dir, home_file), \"r\").read()\n # Store configuration file values\n except FileNotFoundError:\n homepage = ''\n # Keep preset values\n\n resp = make_response(render_template(\"home.html\", page=\"index\",\n homedoc=homepage, err=request.args.get('err', default=None)))\n if request.cookies.get('lang') is None:\n lang = get_locale()\n resp.set_cookie('lang', lang)\n return resp","def home(self):\n self.goto(0, 0)"],"string":"[\n \"def home_page():\\n return redirect(url_for(_DEFAULT_ROUTE, _external=True))\",\n \"def homepage():\\n return redirect('index.html')\",\n \"def homepage():\\n if g.user:\\n return redirect(f\\\"/user/{g.user.id}\\\")\\n else:\\n return redirect(\\\"/landing\\\")\",\n \"def start_page():\\n if not _home:\\n abort(404)\\n return redirect(_home)\",\n \"def start_page():\\n if not _home:\\n abort(404)\\n return redirect(_home)\",\n \"def homepage():\\n return redirect(\\\"/posts\\\")\",\n \"def home_page():\\n return redirect('/users')\",\n \"def go_home(request):\\n\\n url = request.route_url('home', _app_url=get_app_url(request))\\n return HTTPFound(location=url)\",\n \"def home(request):\\n return redirect('commprod/')\",\n \"def get(self, request):\\n return redirect('start:home')\",\n \"def get(self, request):\\n return redirect('start:home')\",\n \"def get(self, request):\\n return redirect('start:home')\",\n \"def root_redirect():\\r\\n return redirect(url_for(\\\"display_top\\\"))\",\n \"def homepage( request ):\\n if \\\"email\\\" in request.session:\\n return redirect( '/home' )\\n return render_to_response( 'index.html' )\",\n \"def toLanding():\\n return redirect(url_for('landingurl'))\",\n \"def idx(_request):\\n return HttpResponseRedirect('/home')\",\n \"def home(request):\\n if request.user.is_authenticated:\\n return redirect('/start')\\n return render(request, 'home/home.html')\",\n \"def go_home(self):\\r\\n if self.home_url is not None:\\r\\n self.set_url(self.home_url)\",\n \"def homepage_redirect():\\n return redirect('/upload_file')\",\n \"def home(request):\\n default_url = reverse(root_nodes)\\n\\n if request.user.moderator_profile != None:\\n home_url = request.user.moderator_profile.get_home_url()\\n if home_url != None:\\n return HttpResponseRedirect(home_url)\\n\\n return HttpResponseRedirect(default_url)\",\n \"def home():\\n # if session.get('username'):\\n # return redirect(url_for('categories'))\\n # else:\\n return render_template('home.html')\",\n \"def welcome_page():\\n return redirect(\\\"/static/welcome.html\\\")\",\n \"def home_page():\\n if not g.user:\\n flash(\\\"Please login to view.\\\", \\\"warning\\\")\\n return redirect('/login')\\n return render_template('index.html')\",\n \"def home(request):\\n # if request.user.is_authenticated():\\n # return redirect('/fastapp')\\n return context()\",\n \"def home(request):\\n assert isinstance(request, HttpRequest)\\n return redirect('/departments')\",\n \"def home(request):\\n if request.user.is_authenticated():\\n return HttpResponseRedirect('done')\\n else:\\n return render_to_response('home.html', RequestContext(request))\",\n \"def home():\\n\\n if not current_user.is_authenticated:\\n return redirect(url_for('login'))\\n else:\\n return redirect(url_for('show_registrations'))\",\n \"def redirect_view(request):\\n path = request.GET.get(\\\"to\\\") or \\\"/\\\"\\n return redirect(path if path.startswith(\\\"/\\\") else f\\\"/{path}\\\", permanent=True)\",\n \"def home():\\n if not session.get('logged_in'):\\n return redirect(url_for('welcome'))\\n return render_template('home.html', filename=\\\"yarg.jpg\\\")\",\n \"def index_file():\\n return redirect(\\\"/\\\")\",\n \"def home_page():\\n return redirect('/register')\",\n \"def home(request):\\n if request.user.is_authenticated():\\n return HttpResponseRedirect('logged-in')\\n else:\\n home_view = 1\\n return render_to_response('content/home.html', {'version': version, \\\"home_view\\\":home_view},\\n RequestContext(request))\",\n \"def home_view(request):\\n return HttpResponseRedirect('/schedule/')\",\n \"def root1(request):\\n\\ttemplate = 'main'\\n\\treturn redirect(template)\",\n \"def redirect(url):\",\n \"def home(request):\\n #print (\\\"home\\\")\\n if request.user.is_authenticated():\\n return redirect('done')\\n return context()\",\n \"def index():\\n if 'name' in session:\\n return render_template('home.html')\\n return redirect(url_for('log_in'))\",\n \"def home_redirect(request):\\n if request.user.is_authenticated() and request.user.is_staff:\\n return redirect(\\\"volunteers\\\")\\n elif request.user.is_authenticated() and not request.user.is_superuser:\\n related_volunteer = get_object_or_404(Volunteer, user_id=request.user.pk)\\n return redirect(\\\"edit-volunteer-profile\\\", volunteer_id=related_volunteer.pk)\\n else:\\n return redirect(\\\"new-volunteer\\\")\",\n \"def index():\\n if current_user.is_authenticated:\\n return redirect(url_for('home'))\\n return render_template('index.html')\",\n \"def test_home_page_redirect_when_loggin_attempt_successful(self):\\n\\t\\tpass\",\n \"def home():\\n\\n form = HomePageForm()\\n if form.validate_on_submit():\\n return redirect(url_for('character'))\\n return render_template('index.html', form=form)\",\n \"def index(request):\\n\\n\\tif request.user.is_authenticated:\\n\\t\\treturn HttpResponseRedirect('home')\\n\\treturn HttpResponseRedirect('login')\",\n \"def root(request):\\n\\ttemplate = 'bfbot/main'\\n\\treturn redirect(template)\",\n \"def entry_page():\\n return redirect(url_for('index'))\",\n \"def redir_index():\\n return redirect(url_for(\\\"index\\\"), code=301)\",\n \"def index():\\n return redirect('/client/index.html')\",\n \"def view_home(self):\\n with self.client.get(\\\"/home\\\", catch_response=True) as response:\\n for r_hist in response.history:\\n if r_hist.status_code > 200 and r_hist.status_code < 400:\\n response.failure(\\\"Not logged on: Got redirect to /login\\\")\",\n \"def home_view(request):\\n if request.authenticated_userid:\\n return HTTPFound(location=request.route_url('app_view')) # pragma no cover\\n return {} # pragma no cover\",\n \"def handle_forbidden_for_homepage(self, request):\\n\\n login_url = request.link(Auth.from_request_path(request), name='login')\\n\\n if URL(request.url).path() == '/':\\n return morepath.redirect(login_url)\\n\\n return handle_forbidden(self, request)\",\n \"def root():\\n if request.headers['Accept'] == 'application/json':\\n return \\\"Welcome\\\\n\\\\n\\\", 200\\n else:\\n return redirect(url_for('index'))\",\n \"def get(self, request):\\n return redirect('http://localhost:3000/')\",\n \"def index():\\n return redirect(url_for('second_page'))\",\n \"def second_page():\\n return redirect(url_for('index'))\",\n \"def landing():\\n if g.user:\\n return render_template('landing.html', user=g.user)\\n return redirect(url_for('login'))\",\n \"def first_request():\\n heroku_url: str = 'https://justice-ndou.herokuapp.com/'\\n registered_domain: str = 'https://justice-ndou.herokuapp.com/'\\n\\n if request.host_url.lower().startswith(heroku_url):\\n return redirect(request.host_url.lower().replace(heroku_url, registered_domain)), 301\",\n \"def index() -> str:\\n return redirect('/students')\",\n \"def home(request):\\n return render_to_response('index.html')\",\n \"def redirect_old_featured(page):\\r\\n return redirect(url_for('.index', page=page), 301)\",\n \"def home():\\n logging.info('Entering route: HOME')\\n\\n logging.info('Rendering template: main.html')\\n return render_template('main.html')\",\n \"def me():\\n if g.USER:\\n return redirect(url_for(\\\"profile\\\", username=g.USER.username))\\n return redirect(url_for(\\\"home\\\"))\",\n \"def home(request):\\n return render_to_response('home.html', {}, RequestContext(request))\",\n \"def home(request):\\n today = datetime.date.today()\\n return HttpResponseRedirect(\\\"%s/newsletter/%d/%d/%d/\\\" % (SUBSITE, today.year, today.month, today.day))\",\n \"def redirectPage() -> Response:\\n # pass in the function name\\n return redirect(url_for('view.loadMainPage'))\",\n \"def show_index():\\r\\n if 'username' in flask.session:\\r\\n return flask.redirect(flask.url_for('home')) # Need to fix redirect\\r\\n\\r\\n return flask.render_template(\\\"index.html\\\")\",\n \"def home():\\n if request.method == \\\"GET\\\":\\n return render_template(\\\"index.html\\\", result=False)\\n\\n # if there is post data, we have the form and need to encode the SQL\\n # to pass to the results route.\\n #\\n # this encoding dance is to protect against the possibility of getting a very\\n # long SQL string that breaks something in HTTP get.\\n sql = request.form[\\\"sql\\\"]\\n dialect = request.form[\\\"dialect\\\"]\\n return redirect(\\n url_for(\\\"routes.fluff_results\\\", sql=sql_encode(sql), dialect=dialect)\\n )\",\n \"def home(request):\\n if 'member_id' not in request.session:\\n return redirect(\\\"/login/\\\")\\n return render(request, 'esihapp/index1.html')\",\n \"def redirector(status, start_response, exc_info=None):\\n session['login.pre_uri'] = environ['PATH_INFO']\\n session.save()\\n start_response('302 Found',[(\\\"Location\\\",\\\"/login\\\"),(\\\"Content-type\\\",\\\"text\\\")])\\n return []\",\n \"def index(request):\\n return redirect('polls:index')\",\n \"def redirect_dest(fallback):\\n dest = request.args.get('next')\\n try:\\n if dest.startswith('/') or dest.startswith(request.host_url):\\n return redirect(dest)\\n dest_url = url_for(dest)\\n except:\\n return redirect(fallback)\\n return redirect(dest_url)\",\n \"def login_redirect(request):\\n return HttpResponseRedirect('/login')\",\n \"def _home(self, op, context):\\n self.page = \\\"HOME\\\"\\n return {'FINISHED'}\",\n \"def get(self):\\n self.redirect('/admin')\",\n \"def get(self):\\n self.redirect('/admin')\",\n \"def home(request):\\n\\treturn render(request, \\\"compta/home.html\\\")\",\n \"def redirect(self, path):\\n self.get_controller().redirect(path)\",\n \"def catch_all(path):\\n return redirect('/', code=302)\",\n \"def home(request):\\r\\n return render(request, 'home.html')\",\n \"def home():\\n return response(\\\"OK\\\")\",\n \"def test_index_redirect(self):\\n response = self.app.get(\\\"/\\\")\\n self.assertEqual(response.status_code, 302,\\n \\\"/ did not redirect to login when user is not logged in\\\")\\n self.assertTrue(\\n response.location.endswith(\\\"/accounts/login/\\\"),\\n \\\"Redirect location did not end with /accounts/login/\\\"\\n )\",\n \"def home():\\n return render_template('home.html',\\n face=session.get(app.config['SESSION_KEY'], None))\",\n \"async def index(request: Request, user: UserInfo) -> HTTPResponse:\\n return redirect('home')\",\n \"def home(request):\\n return render(request, 'home/index.html')\",\n \"def defaultlanding():\\n #send user to description page if not logged in\\n if not g.user:\\n return redirect(url_for('description'))\\n #display leaderboard for competition if logged in\\n return redirect(url_for('leaderboard'))\",\n \"def home(request):\\n if request.user.is_authenticated():\\n return HttpResponse(\\\"{0} <a href='/accounts/logout'>exit</a>\\\".format(request.user))\\n else:\\n return HttpResponse(\\\"<a href='/login/vk-oauth2/'>login with VK</a>\\\")\",\n \"def goHome():\\n\\t#Go to pod home\\n\\tif screen.lastScreen in screen.protectedScreens:\\n\\t\\tpodScreen.show()\\n\\telse:\\n\\t\\tsplashScreen.show()\",\n \"def home():\\n\\n return render_template('home_page.html')\",\n \"def get(self):\\n self.logout()\\n self.redirect('/')\",\n \"def index():\\n redirect(URL('form'))\",\n \"def home(result=None):\\n print(inspect.stack()[1][3])\\n\\n if not session.get('logged_in') and not result:\\n return render_template('login.html')\\n else:\\n # Based on the user_id passed, print Details, URLS and all.\\n # return render_template('dashboard.html', username=result.name, user_id=result.user_type)\\n return render_template('webpage/index1.html', username=result.name, user_id=result.user_type)\",\n \"def redirect(self, url):\\n raise RequestRedirect(url)\",\n \"def index_page():\\n\\n return redirect(\\\"/application-form\\\")\\n\\n # Alternately, we could make this a Jinja template in `templates/`\\n # and return that result of rendering this, like:\\n #\\n # return render_template(\\\"index.html\\\")\",\n \"def home(self, *args, **kwargs):\\n pass\",\n \"def desktop_or_mobile(request):\\n url_name = 'home.mobile' if request.MOBILE else 'home'\\n return redirect_to(request, url_name, permanent=False)\",\n \"def redirect(to):\\r\\n def _redirect(environ, start_response):\\r\\n args, kwargs = environ['wsgiorg.routing_args']\\r\\n start_response('301 MOVED PERMANENTLY',\\r\\n [('Location', to.format(*args, **kwargs))])\\r\\n return []\\r\\n return _redirect\",\n \"def get(self):\\n self.logout()\\n self.redirect('/blog/login')\",\n \"def news0_redirect(request):\\n return redirect('news:news', start_id='0')\",\n \"def home_page(request):\\r\\n return render(request, 'ez_main/home_page.html')\",\n \"def get_redirect_url(self):\\n return reverse('accounts:home')\",\n \"def home():\\n\\n this_dir = os.path.dirname(__file__)\\n home_file = app.config['HOME_PAGE_FOLDER'] + 'homepage.html'\\n try:\\n homepage = open(os.path.join(this_dir, home_file), \\\"r\\\").read()\\n # Store configuration file values\\n except FileNotFoundError:\\n homepage = ''\\n # Keep preset values\\n\\n resp = make_response(render_template(\\\"home.html\\\", page=\\\"index\\\",\\n homedoc=homepage, err=request.args.get('err', default=None)))\\n if request.cookies.get('lang') is None:\\n lang = get_locale()\\n resp.set_cookie('lang', lang)\\n return resp\",\n \"def home(self):\\n self.goto(0, 0)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8391641","0.80959","0.8045699","0.8035703","0.8035703","0.78215","0.7814849","0.7806786","0.75715935","0.755005","0.755005","0.755005","0.745654","0.7446087","0.73788244","0.7322582","0.73209256","0.7203898","0.71697754","0.71356803","0.7109369","0.7109197","0.7078182","0.7071943","0.7051265","0.7048573","0.69983816","0.69805485","0.691623","0.6914428","0.69098914","0.6878491","0.6875465","0.6859419","0.68276894","0.68254775","0.67906827","0.6789351","0.67627335","0.67348355","0.67242616","0.670553","0.6696331","0.66711575","0.6667568","0.664376","0.66424817","0.66147786","0.6609697","0.6568546","0.6562735","0.65593284","0.6520643","0.6512826","0.6507448","0.6503651","0.6502355","0.6442668","0.6420771","0.64037657","0.6385458","0.63704425","0.636354","0.63415796","0.6326086","0.6314376","0.6293907","0.629073","0.6283342","0.627734","0.6276999","0.62721324","0.62721324","0.62719357","0.62595683","0.62586975","0.6248872","0.62421864","0.62096876","0.62063026","0.620317","0.61864406","0.6183989","0.617825","0.61740994","0.6172349","0.61690885","0.61630625","0.6161938","0.61366916","0.61341584","0.61209047","0.61110336","0.61059684","0.6104767","0.6099633","0.6096979","0.6089853","0.6084587","0.60762095"],"string":"[\n \"0.8391641\",\n \"0.80959\",\n \"0.8045699\",\n \"0.8035703\",\n \"0.8035703\",\n \"0.78215\",\n \"0.7814849\",\n \"0.7806786\",\n \"0.75715935\",\n \"0.755005\",\n \"0.755005\",\n \"0.755005\",\n \"0.745654\",\n \"0.7446087\",\n \"0.73788244\",\n \"0.7322582\",\n \"0.73209256\",\n \"0.7203898\",\n \"0.71697754\",\n \"0.71356803\",\n \"0.7109369\",\n \"0.7109197\",\n \"0.7078182\",\n \"0.7071943\",\n \"0.7051265\",\n \"0.7048573\",\n \"0.69983816\",\n \"0.69805485\",\n \"0.691623\",\n \"0.6914428\",\n \"0.69098914\",\n \"0.6878491\",\n \"0.6875465\",\n \"0.6859419\",\n \"0.68276894\",\n \"0.68254775\",\n \"0.67906827\",\n \"0.6789351\",\n \"0.67627335\",\n \"0.67348355\",\n \"0.67242616\",\n \"0.670553\",\n \"0.6696331\",\n \"0.66711575\",\n \"0.6667568\",\n \"0.664376\",\n \"0.66424817\",\n \"0.66147786\",\n \"0.6609697\",\n \"0.6568546\",\n \"0.6562735\",\n \"0.65593284\",\n \"0.6520643\",\n \"0.6512826\",\n \"0.6507448\",\n \"0.6503651\",\n \"0.6502355\",\n \"0.6442668\",\n \"0.6420771\",\n \"0.64037657\",\n \"0.6385458\",\n \"0.63704425\",\n \"0.636354\",\n \"0.63415796\",\n \"0.6326086\",\n \"0.6314376\",\n \"0.6293907\",\n \"0.629073\",\n \"0.6283342\",\n \"0.627734\",\n \"0.6276999\",\n \"0.62721324\",\n \"0.62721324\",\n \"0.62719357\",\n \"0.62595683\",\n \"0.62586975\",\n \"0.6248872\",\n \"0.62421864\",\n \"0.62096876\",\n \"0.62063026\",\n \"0.620317\",\n \"0.61864406\",\n \"0.6183989\",\n \"0.617825\",\n \"0.61740994\",\n \"0.6172349\",\n \"0.61690885\",\n \"0.61630625\",\n \"0.6161938\",\n \"0.61366916\",\n \"0.61341584\",\n \"0.61209047\",\n \"0.61110336\",\n \"0.61059684\",\n \"0.6104767\",\n \"0.6099633\",\n \"0.6096979\",\n \"0.6089853\",\n \"0.6084587\",\n \"0.60762095\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77726823"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":895,"cells":{"query":{"kind":"string","value":"Renders the index template"},"document":{"kind":"string","value":"def home():\n\n this_dir = os.path.dirname(__file__)\n home_file = app.config['HOME_PAGE_FOLDER'] + 'homepage.html'\n try:\n homepage = open(os.path.join(this_dir, home_file), \"r\").read()\n # Store configuration file values\n except FileNotFoundError:\n homepage = ''\n # Keep preset values\n\n resp = make_response(render_template(\"home.html\", page=\"index\",\n homedoc=homepage, err=request.args.get('err', default=None)))\n if request.cookies.get('lang') is None:\n lang = get_locale()\n resp.set_cookie('lang', lang)\n return resp"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def index(self):\n\t\treturn render_template('index.html')","def index():\r\n return render_template('index.html')","def index(self):\n return render_template('main/index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('0-index.html')","def index(self) -> HTMLBody:\n\t\treturn render_template(\"index.jinja2\")","def index():\n return render_template(\"index.html\",\n title='Index')","def index():\r\n return render_template('index.html')","def index():\r\n return render_template('index.html')","def index():\n\n\treturn(render_template('index.html'))","def index():\n # Render template\n return render_template('index.html')","def index():\n return render_template(\"index.html\")","def index():\n return render_template(\"index.html\")","def index():\n return render_template(\"index.html\")","def index():\n return render_template(\"index.html\")","def index():\n return render_template(\"index.html\")","def index():\n return render_template(\"index.html\")","def index():\n return render_template(\"index.html\")","def index():\n return render_template(\"index.html\")","def index():\n 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render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template('index.html')","def index():\n return render_template(\"index.html\")","def index():\n \n \n return render_template('index.html')","def get_index():\n return render_template('index.html')","def index():\n\n return render_template(\"index.html\"), 200","def index():\n return render_template(\"index.html\", **locals())","def index(request):\r\n\treturn render(request, 'templates/index.html')","def index_page():\n\n return render_template(\"index.html\")","def index_page():\n\n return render_template(\"index.html\")","def index_page():\n\n return render_template(\"index.html\")","def index():\n return render_template('index.html'), 200","def index():\n\treturn render_template(\"index.html\", title=\"Home\")","def index():\n return render_template('index.html', title='Home')","def index():\n return render_template(\"index.html\", page_title=\"Home\")","def index_page():\n \n return render_template(\"index.html\")","def show_index_page():\n\n return render_template('index.html')","def index():\n return render_template('pages/index.html', isNav=True)","def index(self):\n\n return render_template(\n 'home_page/index.html',\n **locals()\n )","def index(self):\n return self.render(\"admin/index.html\")","def index():\n return render_template('index.html', getName=ASK_NAME)","def index():\n # Renders the template (see the index.html template file for details). The\n # additional defines at the end (table, header, username) are the variables\n # handed to Jinja while it is processing the template.\n return render_template('index.html', table=table, header=header,\n username=username())","def index():\n\treturn render_template('public/index.html', title='Home')","def index(self):\n return render(\"/derived/rock/index.mako\")","def index(request):\n\treturn render(request, 'Toeic/index.html')","def todos_index_page():\n return render_template(\n template_name_or_list='index.html',\n todos=Todo.index(mongo.db))","def get(self):\n return render_template(\"index.html\")","def index():\n return render_template(\"index.html\", content=songs, titles=titles, contentO=songsO, titlesO=titlesO)","def index() -> Any:\n return render_template(\"index.html\")","def index(request):\n # Render the HTML template index.html\n return render(\n request,\n 'index.html',\n )","def index():\n today = datetime.today()\n return render_template(\"index.html.j2\", today=today)","def index():\n return render_template('index.html', name=urlpath)","def get(self):\n self.render(\"index.html\")","def index():\n return render_template(\n 'index.html',\n nav=nav,\n title='Home Page',\n year=datetime.now().year\n )","def index():\n return render_template(\n 'index_t.html',\n call_counter=str(get_model().call_counter),\n app_version=str(app.config.get('GIT_HASH', None))\n )","def index():\n return render_template(\n 'main/index.html',\n title='Main page'\n )","def index_template(self):\n return '{}/{}.html'.format(self.object_name, self.index_endpoint)","def get(self):\n self.render('index.html')\n return","def index(self):\n \n return self.view.render('index.html', {\"posts\"=posts})","def creerIndex(request):\n context = {}\n return render(request, 'index.html', context)","def index_view(self) -> str:\n return render_template(\n \"index.html\",\n challenge_groups=self._challenge_groups,\n ctff=current_app,\n )","def index():\n return render_template(\"main.html\")","def index():\n return render_template('main.html')","def index():\n return render_template('main.html')","def index(self):\n return render_template(\"{0}/index.html\".format(self.__APP_DIR__))"],"string":"[\n \"def index(self):\\n\\t\\treturn render_template('index.html')\",\n \"def index():\\r\\n return render_template('index.html')\",\n \"def index(self):\\n return render_template('main/index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('0-index.html')\",\n \"def index(self) -> HTMLBody:\\n\\t\\treturn render_template(\\\"index.jinja2\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\",\\n title='Index')\",\n \"def index():\\r\\n return render_template('index.html')\",\n \"def index():\\r\\n return render_template('index.html')\",\n \"def index():\\n\\n\\treturn(render_template('index.html'))\",\n \"def index():\\n # Render template\\n return render_template('index.html')\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template('index.html')\",\n \"def index():\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n \\n \\n return render_template('index.html')\",\n \"def get_index():\\n return render_template('index.html')\",\n \"def index():\\n\\n return render_template(\\\"index.html\\\"), 200\",\n \"def index():\\n return render_template(\\\"index.html\\\", **locals())\",\n \"def index(request):\\r\\n\\treturn render(request, 'templates/index.html')\",\n \"def index_page():\\n\\n return render_template(\\\"index.html\\\")\",\n \"def index_page():\\n\\n return render_template(\\\"index.html\\\")\",\n \"def index_page():\\n\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template('index.html'), 200\",\n \"def index():\\n\\treturn render_template(\\\"index.html\\\", title=\\\"Home\\\")\",\n \"def index():\\n return render_template('index.html', title='Home')\",\n \"def index():\\n return render_template(\\\"index.html\\\", page_title=\\\"Home\\\")\",\n \"def index_page():\\n \\n return render_template(\\\"index.html\\\")\",\n \"def show_index_page():\\n\\n return render_template('index.html')\",\n \"def index():\\n return render_template('pages/index.html', isNav=True)\",\n \"def index(self):\\n\\n return render_template(\\n 'home_page/index.html',\\n **locals()\\n )\",\n \"def index(self):\\n return self.render(\\\"admin/index.html\\\")\",\n \"def index():\\n return render_template('index.html', getName=ASK_NAME)\",\n \"def index():\\n # Renders the template (see the index.html template file for details). The\\n # additional defines at the end (table, header, username) are the variables\\n # handed to Jinja while it is processing the template.\\n return render_template('index.html', table=table, header=header,\\n username=username())\",\n \"def index():\\n\\treturn render_template('public/index.html', title='Home')\",\n \"def index(self):\\n return render(\\\"/derived/rock/index.mako\\\")\",\n \"def index(request):\\n\\treturn render(request, 'Toeic/index.html')\",\n \"def todos_index_page():\\n return render_template(\\n template_name_or_list='index.html',\\n todos=Todo.index(mongo.db))\",\n \"def get(self):\\n return render_template(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\\"index.html\\\", content=songs, titles=titles, contentO=songsO, titlesO=titlesO)\",\n \"def index() -> Any:\\n return render_template(\\\"index.html\\\")\",\n \"def index(request):\\n # Render the HTML template index.html\\n return render(\\n request,\\n 'index.html',\\n )\",\n \"def index():\\n today = datetime.today()\\n return render_template(\\\"index.html.j2\\\", today=today)\",\n \"def index():\\n return render_template('index.html', name=urlpath)\",\n \"def get(self):\\n self.render(\\\"index.html\\\")\",\n \"def index():\\n return render_template(\\n 'index.html',\\n nav=nav,\\n title='Home Page',\\n year=datetime.now().year\\n )\",\n \"def index():\\n return render_template(\\n 'index_t.html',\\n call_counter=str(get_model().call_counter),\\n app_version=str(app.config.get('GIT_HASH', None))\\n )\",\n \"def index():\\n return render_template(\\n 'main/index.html',\\n title='Main page'\\n )\",\n \"def index_template(self):\\n return '{}/{}.html'.format(self.object_name, self.index_endpoint)\",\n \"def get(self):\\n self.render('index.html')\\n return\",\n \"def index(self):\\n \\n return self.view.render('index.html', {\\\"posts\\\"=posts})\",\n \"def creerIndex(request):\\n context = {}\\n return render(request, 'index.html', context)\",\n \"def index_view(self) -> str:\\n return render_template(\\n \\\"index.html\\\",\\n challenge_groups=self._challenge_groups,\\n ctff=current_app,\\n )\",\n \"def index():\\n return render_template(\\\"main.html\\\")\",\n \"def index():\\n return render_template('main.html')\",\n \"def index():\\n return render_template('main.html')\",\n \"def index(self):\\n return render_template(\\\"{0}/index.html\\\".format(self.__APP_DIR__))\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.8663074\",\n \"0.8416551\",\n \"0.8395232\",\n \"0.8383464\",\n \"0.83140826\",\n \"0.83140826\",\n \"0.8239054\",\n \"0.82325274\",\n \"0.8220168\",\n \"0.82151866\",\n \"0.82151866\",\n \"0.8183771\",\n \"0.8143425\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.808803\",\n \"0.8078401\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8066346\",\n \"0.8036096\",\n \"0.8018531\",\n \"0.79757094\",\n \"0.79545426\",\n \"0.79398835\",\n \"0.7898768\",\n \"0.78937066\",\n \"0.78937066\",\n \"0.78937066\",\n \"0.7884277\",\n \"0.7883415\",\n \"0.7868813\",\n \"0.7788855\",\n \"0.7773321\",\n \"0.77644485\",\n \"0.7761943\",\n \"0.7729839\",\n \"0.7709832\",\n \"0.76929057\",\n \"0.7666915\",\n \"0.76645356\",\n \"0.7662625\",\n \"0.7655997\",\n \"0.764621\",\n \"0.7639892\",\n \"0.7603384\",\n \"0.7601745\",\n \"0.7579957\",\n \"0.7555739\",\n \"0.7524336\",\n \"0.75177985\",\n \"0.75128245\",\n \"0.7512606\",\n \"0.7506056\",\n \"0.74971163\",\n \"0.7495231\",\n \"0.74629784\",\n \"0.7454057\",\n \"0.7445256\",\n \"0.7440252\",\n \"0.7429397\",\n \"0.7429397\",\n \"0.73960966\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":896,"cells":{"query":{"kind":"string","value":"Function to modify the homepage"},"document":{"kind":"string","value":"def modify_homepage():\n this_dir = os.path.dirname(__file__)\n home_file = app.config['HOME_PAGE_FOLDER'] + 'homepage.html'\n value = \"\"\n if request.form.get(\"newHome\"):\n value = request.form.get(\"newHome\")\n\n if not os.path.isdir(os.path.join(this_dir, app.config['HOME_PAGE_FOLDER'])):\n os.mkdir(os.path.join(this_dir, app.config['HOME_PAGE_FOLDER']))\n\n homeFile= open(os.path.join(this_dir, home_file), \"w+\")\n homeFile.write(value)\n homeFile.flush()\n\n files = request.files.getlist(\"Attachments\")\n\n for file in files:\n print(file.filename, file=sys.stdout)\n nameFile = secure_filename(file.filename)\n file.save(os.path.join(this_dir, app.config['HOME_PAGE_FOLDER'], nameFile))\n\n return jsonify(result=True)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def homepage():\n\treturn render_template(\"home/a_homepage.html\", title=\"Welcome\")","def homepage():\n\n return render_template('rosemead_homepage.html')","def homepage():\n return render_template('home/index.html', \n title=\"Bem vindo!\")","def a_homepage():\n\n\treturn render_template('home/a_homepage.html', title=\"Homepage Admin\")","def homepage():\n return render_template('home/index.html', title=\"Home\")","def homepage():\n return render_template('homepage.html')","def home() -> Any:\n return home_page()","def homepage(request):\n\treturn render(request, 'core/homepage.html')","def homepage():\n return render_template('home/index.html', title=\"Welcome\")","def homepage():\n return render_template('home/index.html', title=\"Welcome\")","def homepage():\n return {'sample': 'ADAL'}","def home(self, *args, **kwargs):\n pass","def homepage():\n return render_template(\"home/index.html\", title=\"Welcome\")","def homepage():\n return render_template(\"home/index.html\")","def home():\n return render_template('homepage.html')","def homepage(): \n\n return render_template(\"home_map.html\")","def home():\n\n\treturn render_template('solai.html')","def homepage():\n\n general_news=get_sources('general')\n business_news=get_sources('business')\n sports_news=get_sources('sports')\n return render_template('sources.html',general=general_news, business=business_news, sports=sports_news)","def homepage(request):\n return render_to_response('h1ds_core/homepage.html', \n context_instance=RequestContext(request))","def home():\n\n\treturn render_template('index.html', title='Home Page',\n\t\t\t\t\t\t year=datetime.now().year)","def homepage():\r\n print(__name__ + \" invoked\")","def home():\n\n return render_template('home_page.html')","def home_page(request):\r\n return render(request, 'ez_main/home_page.html')","def home():\n return render_template('home.html', title=\"Home\")","def home():\n return render_template(\n 'home.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\r\n return render_template(\r\n 'index.html',\r\n title='Home Page',\r\n year=datetime.now().year,\r\n )","def home():\n return render_template(\n 'index.html',\n title='Sairam sai baba',\n year=datetime.now().year,\n )","def home():\n return render_template('Main_Page.html')","def homepage(request):\n \n return render(request, 'homepage.html')","def home_page() -> str:\r\n return render_template(\"home.html\")","def home(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'AscensionESports_Baseline/index.html',\n {\n 'background': getSiteBackground(),\n 'color': getSiteColor(),\n 'title':'Home Page',\n 'year':datetime.now().year,\n }\n )","def home():\n\n # sets the page to load depending on the type of user\n # if none specified the login screen will be displayed\n pageName = ''\n userType = session.get('UserType', None)\n if userType == None:\n pageName = 'anonHome.jade'\n elif userType == 'Seeker':\n pageName = 'indexJob.jade'\n elif userType == 'Manager':\n pageName = 'indexManager.jade'\n\n frogHop = url_for('static', filename='loop frog.gif')\n uName = session.get('UserName', 'Unknown') # load a default value if retrieval fails\n return render_template(\n pageName,\n title='Home',\n name=uName,\n getFrog=frogHop,\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year\n )","def homepage():\n return redirect('index.html')","def home():\n return \"<h1>Not Much Going On Here</h1>\"","def home():\n template_stories = [\"silly_story\", \"excited_story\"]\n\n return render_template(\"homepage.html\", story_list = template_stories)","def homepage(): \n return render_template('home/index.html',title='Welcome to SYCLIQ')\n #return render_template_string(\"Welcome to SYCLIQ\")","def home():\n return render_template('index.html',\n title='主页',\n year=datetime.now().year,)","def homepage():\n \n return render_template(\"coursePage.html\",courseName = \"Welcome\", Courses = COURSES, blank = 1)","def _home(self, op, context):\n self.page = \"HOME\"\n return {'FINISHED'}","def show_homepage():\n\n return render_template(\"blank-slate.html\")","def home():\n return render_template('main.html')","def menu_spe_homepage(self, event=None):\n self.link('http://pythonide.stani.be')","def home():\n tc = totalclicks()\n tl1, tl2, tl3 = topthreelinks()\n bl1, bl2, bl3 = topblomoedlinks()\n return flask.render_template('home.html', tc=tc, tl1=tl1, tl2=tl2, tl3=tl3, bl1=bl1, bl2=bl2, bl3=bl3)","def home():\n\n return render_template(\"home.html\")","def home(request):\n\treturn render(request, \"compta/home.html\")","def get_homepage():\n\n projects = hackbright.get_projects()\n students = hackbright.get_students()\n\n return render_template(\"homepage.html\",\n students=students,\n projects=projects)","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def get_home():\n return render_template(\"home.html\")","def main_nav():\n return render_template('home.html')","def home():\n return render_template(\"home.html\")","def home():\n return render_template(\"home.html\")","def home_page(wagtail_site):\n return wagtail_site.root_page","def render_home():\r\n\treturn render_template(\"index.html\")","def home_page():\n\n return render_template('index.html')","def homepage():\n return (\n f\"Welcome to Hawaii - Climate Page<br/>\"\n f\"<br/>\"\n f\"This site has data from 01-01-2010 to 08-23-2017<br/>\"\n f\"<br/>\"\n f\"Available Pages:<br/>\"\n f\"<br/>\"\n f\"<br/>\"\n f\" Station Information<br/>\"\n f\" /api/v1.0/stations<br/>\"\n f\"<br/>\"\n f\" Percipitation Information<br/>\"\n f\" /api/v1.0/percipitation<br/>\"\n f\"<br/>\"\n f\" Temperature Observations<br/>\"\n f\" /api/v1.0/tobs<br/>\"\n f\"<br/>\"\n f\" Start Date information - complete url is '/api/v1.0//yyyy-mm-dd'<br/>\"\n f\" /api/v1.0/start<br/>\"\n f\"<br/>\"\n f\" Start and End Date information - complete url is '/api/v1.0/yyyy-mm-dd/yyyy-mm-dd'<br/>\"\n f\" /api/v1.0/start/end\"\n )","def home(request):\n return render_template('core/home.html')","def home_page():\n return \"<h4>Welcome !</h4><br><a href='/fetch'>View Results</a>\"","def homepage():\n return redirect(\"/posts\")","def initialPage():\n\treturn header() + footer()","def make_navbar_for_homepage(self):\n links = [\n \"home\", [\"Result Pages\", self._result_page_links()], \"Version\"\n ]\n if len(self.samples) > 1:\n links[1][1] += [\"Comparison\"]\n if self.publication:\n links.insert(2, \"Publication\")\n if self.gwdata is not None:\n links.append([\"Detchar\", [i for i in self.gwdata.keys()]])\n if self.notes is not None:\n links.append(\"Notes\")\n return links","def homepage():\n if g.user:\n return redirect(f\"/user/{g.user.id}\")\n else:\n return redirect(\"/landing\")","def home():\n return render_template('index.html', year=datetime.now().year)","def home_page():\n return (\n f\"Available Routes:<br/>\"\n f\"/api/v1.0/precipitation<br/>\"\n f\"/api/v1.0/stations<br/>\"\n f\"/api/v1.0/tobs<br/>\"\n f\"/api/v1.0/start<br/>\"\n f\"/api/v1.0/start/end<br/>\"\n )","def menu_python_homepage(self, event=None):\n self.link('http://www.python.org')","def home_page():\n\n return render_template('index.html', stories=stories.values())","def go_home(self):\r\n if self.home_url is not None:\r\n self.set_url(self.home_url)","def menu_authors_homepage(self, event=None):\n self.link('http://www.stani.be')","def _set_homepage_channel(self):\n Channel = get_model('channels', 'Channel')\n\n channel = Channel.objects.all()[0]\n channel.homepage = True\n channel.save()","def show_homepage():\n\n pets = Pet.query.all()\n rando_pet = get_info_random_pet()\n\n name = rando_pet['petfinder']['pet']['name']['$t']\n age = rando_pet['petfinder']['pet']['age']['$t']\n image = rando_pet['petfinder']['pet']['media']['photos']['photo'][0]['$t']\n\n return render_template('homepage.html', pets=pets,\n name=name, age=age, image=image)","def home():\n\n return render_template('index.html',\n date = d,\n mood = mood,\n header_color = color,\n day = day,\n week = week,\n month = month,\n year = year)","def home():\n\n return render_template('index.html')","def home():\n return render_template(\n 'index.html',\n year=datetime.now().year,\n )","def home():\n payload = manager.get_payload()\n return render_template('index.html', payload=payload)","def visit_homepage(self) -> None:\n if self.home_page is not None:\n webbrowser.open(self.home_page)","def homepage():\r\n words = story.prompts\r\n # i didn't realize you could access class variables like this\r\n\r\n return render_template(\"homepage.html\", words = words)"],"string":"[\n \"def homepage():\\n\\treturn render_template(\\\"home/a_homepage.html\\\", title=\\\"Welcome\\\")\",\n \"def homepage():\\n\\n return render_template('rosemead_homepage.html')\",\n \"def homepage():\\n return render_template('home/index.html', \\n title=\\\"Bem vindo!\\\")\",\n \"def a_homepage():\\n\\n\\treturn render_template('home/a_homepage.html', title=\\\"Homepage Admin\\\")\",\n \"def homepage():\\n return render_template('home/index.html', title=\\\"Home\\\")\",\n \"def homepage():\\n return render_template('homepage.html')\",\n \"def home() -> Any:\\n return home_page()\",\n \"def homepage(request):\\n\\treturn render(request, 'core/homepage.html')\",\n \"def homepage():\\n return render_template('home/index.html', title=\\\"Welcome\\\")\",\n \"def homepage():\\n return render_template('home/index.html', title=\\\"Welcome\\\")\",\n \"def homepage():\\n return {'sample': 'ADAL'}\",\n \"def home(self, *args, **kwargs):\\n pass\",\n \"def homepage():\\n return render_template(\\\"home/index.html\\\", title=\\\"Welcome\\\")\",\n \"def homepage():\\n return render_template(\\\"home/index.html\\\")\",\n \"def home():\\n return render_template('homepage.html')\",\n \"def homepage(): \\n\\n return render_template(\\\"home_map.html\\\")\",\n \"def home():\\n\\n\\treturn render_template('solai.html')\",\n \"def homepage():\\n\\n general_news=get_sources('general')\\n business_news=get_sources('business')\\n sports_news=get_sources('sports')\\n return render_template('sources.html',general=general_news, business=business_news, sports=sports_news)\",\n \"def homepage(request):\\n return render_to_response('h1ds_core/homepage.html', \\n context_instance=RequestContext(request))\",\n \"def home():\\n\\n\\treturn render_template('index.html', title='Home Page',\\n\\t\\t\\t\\t\\t\\t year=datetime.now().year)\",\n \"def homepage():\\r\\n print(__name__ + \\\" invoked\\\")\",\n \"def home():\\n\\n return render_template('home_page.html')\",\n \"def home_page(request):\\r\\n return render(request, 'ez_main/home_page.html')\",\n \"def home():\\n return render_template('home.html', title=\\\"Home\\\")\",\n \"def home():\\n return render_template(\\n 'home.html',\\n title='Home Page',\\n year=datetime.now().year,\\n )\",\n \"def home():\\r\\n return render_template(\\r\\n 'index.html',\\r\\n title='Home Page',\\r\\n year=datetime.now().year,\\r\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Sairam sai baba',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template('Main_Page.html')\",\n \"def homepage(request):\\n \\n return render(request, 'homepage.html')\",\n \"def home_page() -> str:\\r\\n return render_template(\\\"home.html\\\")\",\n \"def home(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'AscensionESports_Baseline/index.html',\\n {\\n 'background': getSiteBackground(),\\n 'color': getSiteColor(),\\n 'title':'Home Page',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def home():\\n\\n # sets the page to load depending on the type of user\\n # if none specified the login screen will be displayed\\n pageName = ''\\n userType = session.get('UserType', None)\\n if userType == None:\\n pageName = 'anonHome.jade'\\n elif userType == 'Seeker':\\n pageName = 'indexJob.jade'\\n elif userType == 'Manager':\\n pageName = 'indexManager.jade'\\n\\n frogHop = url_for('static', filename='loop frog.gif')\\n uName = session.get('UserName', 'Unknown') # load a default value if retrieval fails\\n return render_template(\\n pageName,\\n title='Home',\\n name=uName,\\n getFrog=frogHop,\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Home Page',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Home Page',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Home Page',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Home Page',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Home Page',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Home Page',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Home Page',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Home Page',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Home Page',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Home Page',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n return render_template(\\n 'index.html',\\n title='Home Page',\\n year=datetime.now().year\\n )\",\n \"def homepage():\\n return redirect('index.html')\",\n \"def home():\\n return \\\"<h1>Not Much Going On Here</h1>\\\"\",\n \"def home():\\n template_stories = [\\\"silly_story\\\", \\\"excited_story\\\"]\\n\\n return render_template(\\\"homepage.html\\\", story_list = template_stories)\",\n \"def homepage(): \\n return render_template('home/index.html',title='Welcome to SYCLIQ')\\n #return render_template_string(\\\"Welcome to SYCLIQ\\\")\",\n \"def home():\\n return render_template('index.html',\\n title='主页',\\n year=datetime.now().year,)\",\n \"def homepage():\\n \\n return render_template(\\\"coursePage.html\\\",courseName = \\\"Welcome\\\", Courses = COURSES, blank = 1)\",\n \"def _home(self, op, context):\\n self.page = \\\"HOME\\\"\\n return {'FINISHED'}\",\n \"def show_homepage():\\n\\n return render_template(\\\"blank-slate.html\\\")\",\n \"def home():\\n return render_template('main.html')\",\n \"def menu_spe_homepage(self, event=None):\\n self.link('http://pythonide.stani.be')\",\n \"def home():\\n tc = totalclicks()\\n tl1, tl2, tl3 = topthreelinks()\\n bl1, bl2, bl3 = topblomoedlinks()\\n return flask.render_template('home.html', tc=tc, tl1=tl1, tl2=tl2, tl3=tl3, bl1=bl1, bl2=bl2, bl3=bl3)\",\n \"def home():\\n\\n return render_template(\\\"home.html\\\")\",\n \"def home(request):\\n\\treturn render(request, \\\"compta/home.html\\\")\",\n \"def get_homepage():\\n\\n projects = hackbright.get_projects()\\n students = hackbright.get_students()\\n\\n return render_template(\\\"homepage.html\\\",\\n students=students,\\n projects=projects)\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def home():\\n return render_template('home.html')\",\n \"def get_home():\\n return render_template(\\\"home.html\\\")\",\n \"def main_nav():\\n return render_template('home.html')\",\n \"def home():\\n return render_template(\\\"home.html\\\")\",\n \"def home():\\n return render_template(\\\"home.html\\\")\",\n \"def home_page(wagtail_site):\\n return wagtail_site.root_page\",\n \"def render_home():\\r\\n\\treturn render_template(\\\"index.html\\\")\",\n \"def home_page():\\n\\n return render_template('index.html')\",\n \"def homepage():\\n return (\\n f\\\"Welcome to Hawaii - Climate Page<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\"This site has data from 01-01-2010 to 08-23-2017<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\"Available Pages:<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\" Station Information<br/>\\\"\\n f\\\" /api/v1.0/stations<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\" Percipitation Information<br/>\\\"\\n f\\\" /api/v1.0/percipitation<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\" Temperature Observations<br/>\\\"\\n f\\\" /api/v1.0/tobs<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\" Start Date information - complete url is '/api/v1.0//yyyy-mm-dd'<br/>\\\"\\n f\\\" /api/v1.0/start<br/>\\\"\\n f\\\"<br/>\\\"\\n f\\\" Start and End Date information - complete url is '/api/v1.0/yyyy-mm-dd/yyyy-mm-dd'<br/>\\\"\\n f\\\" /api/v1.0/start/end\\\"\\n )\",\n \"def home(request):\\n return render_template('core/home.html')\",\n \"def home_page():\\n return \\\"<h4>Welcome !</h4><br><a href='/fetch'>View Results</a>\\\"\",\n \"def homepage():\\n return redirect(\\\"/posts\\\")\",\n \"def initialPage():\\n\\treturn header() + footer()\",\n \"def make_navbar_for_homepage(self):\\n links = [\\n \\\"home\\\", [\\\"Result Pages\\\", self._result_page_links()], \\\"Version\\\"\\n ]\\n if len(self.samples) > 1:\\n links[1][1] += [\\\"Comparison\\\"]\\n if self.publication:\\n links.insert(2, \\\"Publication\\\")\\n if self.gwdata is not None:\\n links.append([\\\"Detchar\\\", [i for i in self.gwdata.keys()]])\\n if self.notes is not None:\\n links.append(\\\"Notes\\\")\\n return links\",\n \"def homepage():\\n if g.user:\\n return redirect(f\\\"/user/{g.user.id}\\\")\\n else:\\n return redirect(\\\"/landing\\\")\",\n \"def home():\\n return render_template('index.html', year=datetime.now().year)\",\n \"def home_page():\\n return (\\n f\\\"Available Routes:<br/>\\\"\\n f\\\"/api/v1.0/precipitation<br/>\\\"\\n f\\\"/api/v1.0/stations<br/>\\\"\\n f\\\"/api/v1.0/tobs<br/>\\\"\\n f\\\"/api/v1.0/start<br/>\\\"\\n f\\\"/api/v1.0/start/end<br/>\\\"\\n )\",\n \"def menu_python_homepage(self, event=None):\\n self.link('http://www.python.org')\",\n \"def home_page():\\n\\n return render_template('index.html', stories=stories.values())\",\n \"def go_home(self):\\r\\n if self.home_url is not None:\\r\\n self.set_url(self.home_url)\",\n \"def menu_authors_homepage(self, event=None):\\n self.link('http://www.stani.be')\",\n \"def _set_homepage_channel(self):\\n Channel = get_model('channels', 'Channel')\\n\\n channel = Channel.objects.all()[0]\\n channel.homepage = True\\n channel.save()\",\n \"def show_homepage():\\n\\n pets = Pet.query.all()\\n rando_pet = get_info_random_pet()\\n\\n name = rando_pet['petfinder']['pet']['name']['$t']\\n age = rando_pet['petfinder']['pet']['age']['$t']\\n image = rando_pet['petfinder']['pet']['media']['photos']['photo'][0]['$t']\\n\\n return render_template('homepage.html', pets=pets,\\n name=name, age=age, image=image)\",\n \"def home():\\n\\n return render_template('index.html',\\n date = d,\\n mood = mood,\\n header_color = color,\\n day = day,\\n week = week,\\n month = month,\\n year = year)\",\n \"def home():\\n\\n return render_template('index.html')\",\n \"def home():\\n return render_template(\\n 'index.html',\\n year=datetime.now().year,\\n )\",\n \"def home():\\n payload = manager.get_payload()\\n return render_template('index.html', payload=payload)\",\n \"def visit_homepage(self) -> None:\\n if self.home_page is not None:\\n webbrowser.open(self.home_page)\",\n \"def homepage():\\r\\n words = story.prompts\\r\\n # i didn't realize you could access class variables like this\\r\\n\\r\\n return render_template(\\\"homepage.html\\\", words = words)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7403166","0.7383433","0.7320676","0.730273","0.722838","0.722588","0.7190989","0.71861947","0.7157216","0.7157216","0.71296996","0.71152425","0.7102618","0.7062718","0.7043081","0.6962566","0.69548315","0.6911093","0.69107646","0.6907873","0.6875526","0.68727916","0.68413585","0.68218935","0.6816311","0.6785617","0.67731136","0.67669773","0.6755017","0.67434895","0.6737884","0.6731376","0.67027783","0.67027783","0.67027783","0.67027783","0.67027783","0.67027783","0.67027783","0.67027783","0.67027783","0.67027783","0.6697248","0.6686785","0.668297","0.6679709","0.66755235","0.6661096","0.6654625","0.6649813","0.66496956","0.66454643","0.6640424","0.6637764","0.66321385","0.66299945","0.6624038","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6622609","0.6615179","0.6615046","0.6613337","0.6613337","0.66125184","0.6607648","0.66014564","0.6580962","0.65666336","0.6566127","0.65566796","0.65520644","0.65440536","0.6538731","0.65202475","0.6515344","0.65102","0.64924586","0.64886963","0.6464244","0.6443004","0.6425637","0.64253145","0.64212465","0.6415917","0.64131427","0.640401","0.6400053"],"string":"[\n \"0.7403166\",\n \"0.7383433\",\n \"0.7320676\",\n \"0.730273\",\n \"0.722838\",\n \"0.722588\",\n \"0.7190989\",\n \"0.71861947\",\n \"0.7157216\",\n \"0.7157216\",\n \"0.71296996\",\n \"0.71152425\",\n \"0.7102618\",\n \"0.7062718\",\n \"0.7043081\",\n \"0.6962566\",\n \"0.69548315\",\n \"0.6911093\",\n \"0.69107646\",\n \"0.6907873\",\n \"0.6875526\",\n \"0.68727916\",\n \"0.68413585\",\n \"0.68218935\",\n \"0.6816311\",\n \"0.6785617\",\n \"0.67731136\",\n \"0.67669773\",\n \"0.6755017\",\n \"0.67434895\",\n \"0.6737884\",\n \"0.6731376\",\n \"0.67027783\",\n \"0.67027783\",\n \"0.67027783\",\n \"0.67027783\",\n \"0.67027783\",\n \"0.67027783\",\n \"0.67027783\",\n \"0.67027783\",\n \"0.67027783\",\n \"0.67027783\",\n \"0.6697248\",\n \"0.6686785\",\n \"0.668297\",\n \"0.6679709\",\n \"0.66755235\",\n \"0.6661096\",\n \"0.6654625\",\n \"0.6649813\",\n \"0.66496956\",\n \"0.66454643\",\n \"0.6640424\",\n \"0.6637764\",\n \"0.66321385\",\n \"0.66299945\",\n \"0.6624038\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6622609\",\n \"0.6615179\",\n \"0.6615046\",\n \"0.6613337\",\n \"0.6613337\",\n \"0.66125184\",\n \"0.6607648\",\n \"0.66014564\",\n \"0.6580962\",\n \"0.65666336\",\n \"0.6566127\",\n \"0.65566796\",\n \"0.65520644\",\n \"0.65440536\",\n \"0.6538731\",\n \"0.65202475\",\n \"0.6515344\",\n \"0.65102\",\n \"0.64924586\",\n \"0.64886963\",\n \"0.6464244\",\n \"0.6443004\",\n \"0.6425637\",\n \"0.64253145\",\n \"0.64212465\",\n \"0.6415917\",\n \"0.64131427\",\n \"0.640401\",\n \"0.6400053\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":897,"cells":{"query":{"kind":"string","value":"Generate an identity key pair. Clients should only do this once, at install time. the generated IdentityKeyPair."},"document":{"kind":"string","value":"def generateIdentityKeyPair():\n keyPair = Curve.generateKeyPair()\n publicKey = IdentityKey(keyPair.getPublicKey())\n serialized = '0a21056e8936e8367f768a7bba008ade7cf58407bdc7a6aae293e2c' \\\n 'b7c06668dcd7d5e12205011524f0c15467100dd603e0d6020f4d293' \\\n 'edfbcd82129b14a88791ac81365c'\n serialized = binascii.unhexlify(serialized.encode())\n identityKeyPair = IdentityKeyPair(publicKey, keyPair.getPrivateKey())\n return identityKeyPair\n # return IdentityKeyPair(serialized=serialized)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def gen_key_pair(self, keysize, cb):\n\n def gen_key_pair_pub_cb(data, ctx):\n if not data:\n warning('keymanagement: Could not generate a key pair\\n')\n cb(None, None)\n else:\n cb(ctx, data)\n\n def gen_key_pair_priv_cb(data, ctx):\n if not data:\n warning('keymanagement: Could not generate a key pair\\n')\n cb(None, None)\n else:\n xrun([self.sslname, 'rsa', '-pubout'], gen_key_pair_pub_cb,\n data, data)\n\n return xrun([self.sslname, 'genrsa', str(keysize)],\n gen_key_pair_priv_cb, None)","def gen_key_pair():\n sk = gen_secret_key(BITCOIN.gen.n)\n pk = PublicKey.from_sk(sk)\n return sk, pk","def generate(self):\n if self.curvetype == KeyType.ECDSA_P256v1:\n self.private_key_obj = ec.generate_private_key(ec.SECP256R1(), default_backend())\n elif self.curvetype == KeyType.ECDSA_SECP256k1:\n self.private_key_obj = ec.generate_private_key(ec.SECP256K1(), default_backend())\n self.public_key_obj = self.private_key_obj.public_key()\n self._get_naive_private_key_bytes()\n self._get_naive_public_key_bytes()","def create_keypair(self):\n # NOTE: currently we rely on zmq for convenience, but we may use libnacl directly\n # if we want to isolate this module from zmq dependency.\n public_key, private_key = zmq.curve_keypair()\n return public_key, private_key","def generate_symmetric_key():\n return Fernet.generate_key()","def generate_rsa_key_pair(self):\n\t\tprint \"Started rsa key generation\"\n\t\tkey = RSA.generate(self.key_size, randfunc=self.random_number_generator)\n\t\t\t\n\t\tpub_key = key.publickey().exportKey()\n\t\tprint pub_key\n\t\t\n\n\t\tpriv_key = key.exportKey()\n\t\tprint \"Private key\", priv_key \n\t\tprint \"Note: Normally, the private key should be protected. For the purposes of this demo, I'm printing it to terminal.\"","def generate_key(self, **options):\n\n return security_utils_services.generate_rsa_key(**options)","def create_key_pair(self) -> Keypair:\n res = self.context.post(\n \"/dsum/create_key_pair\", None, None, \"DSum: failed creating a Curve 25519 Keypair\")\n return Keypair(res['private_key_id'], res['public_key_id'])","def create_keypair(self, username):\n msg = \"create_keypair not implemented\"\n raise NotImplementedError(msg)","def generate_signing_keys():\n return SigningKey.generate(curve=SECP256k1)","def create_key_pair(self, keypair, **kwargs):\n\n if not isinstance(keypair, models.CreateKeyPairReq):\n raise HuaweiCloudSDKException(\n message=\"The datatype of parameter(keypair) \"\n \"is not CreateKeyPairReq\")\n body_params = keypair.serialize()\n\n header_params = {}\n header_params['Accept'] = util.select_header_accept(\n ['application/xml', 'application/json'])\n\n header_params['Content-Type'] = util.select_header_content_type(\n ['application/json', 'application/xml'])\n\n return_code, return_data, _ = self.api_client.handle_raw_request(\n 'compute', 'POST',\n '/os-keypairs',\n headers=header_params,\n body=body_params,\n timeout=kwargs.get('_request_timeout', None),\n _preload_content=kwargs.get('_preload_content', True))\n\n if return_data is not None:\n return_data = json.loads(return_data)\n else:\n return_data = {}\n if return_code not in [200, 201]:\n raise HuaweiCloudSDKException(\n return_code,\n \"Run create_key_pair failed, \"\n \"message=%s\" % return_data.get(\"message\"))\n return models.CreateKeyPairResp().deserialize(return_data)","def generate_key(self):\n key = rsa.generate_private_key(\n public_exponent=self.settings['key_public_exponent_size'],\n key_size=self.settings['key_size'],\n backend=default_backend()\n )\n return key","def gen_key(self):\n\n if not self.private_key:\n self._gen_key()\n else:\n raise CryptoError(\"Private Key already existing\")","def generate_key():\n key = crypto.Key.generate_key()\n click.echo('Private Key (len {}):: \\n{}'.format(\n len(key.get_privkey()),\n hexlify(key.get_privkey())))\n click.echo('Public Key (len {})::\\n{}'.format(\n len(key.get_pubkey()),\n hexlify(key.get_pubkey())))","def test_generate_key_pair(self):\n with patch('iceit.crypto.gnupg.GPG') as mock_gpg:\n mock_key = Mock()\n mock_key.fingerprint = 'fake-fingerprint'\n mock_gpg.gen_key.return_value = mock_key\n\n mock_gpg.return_value = mock_gpg\n encryptor = self.test_init()\n fake_key = encryptor.generate_key_pair(key_type=\"RSA\", length=4096, options={\n 'name_real': 'Fake Name', 'name_email': 'fake@example.com', 'name_comment': 'Fake comment'})\n\n self.assertEqual(mock_gpg.gen_key_input.call_count, 1)\n self.assertEqual(fake_key, mock_key.fingerprint)","def generate_key(self):\n\n self.key = Fernet.generate_key()\n self.cryptor = Fernet(self.key)","def _gen_key(self):\n\n input_data = self._gpg.gen_key_input(key_type=\"RSA\",\n key_length=self.key_length, name_real=self.name,\n name_comment=self.comment, name_email=self.email)\n\n log.info(\"Generating key: (%s)\" % input_data)\n\n self.key = self._gpg.gen_key(input_data)","def generate(self, force=False):\n if not self.check_force_generate(force):\n return False\n\n mkdirs(self.path)\n\n command = [openssl, 'ecparam', '-genkey', '-name', self.asn1_oid, '-out', self.key_file]\n\n self.log.info('Generating EC key')\n # Generate the keyfile with no password\n if not run_command(command):\n raise RuntimeError('EC key generation failed', self)\n\n # Now encrypt the key with a password, overwriting the original\n # passwordless key.\n if self.password:\n command = [\n openssl, 'ec',\n '-in', self.key_file,\n '-out', self.key_file,\n '-des3', '-passout', 'pass:{}'.format(self.password)\n ]\n self.log.info('Encrypting key with password')\n\n if not run_command(command):\n raise RuntimeError('EC key file password encryption failed')\n\n if not self.exists():\n raise RuntimeError(\n 'Key generation succeeded but key file does not exist. '\n 'This should not happen', self\n )","def createKeyPair(type, bits):\n pkey = crypto.PKey()\n pkey.generate_key(type, bits)\n return pkey","def create_key ():","def create_keypair(address_type, addresses_path, address_prefix, name):\n vkey_file = get_vkey_file(addresses_path, address_prefix, name)\n skey_file = get_skey_file(addresses_path, address_prefix, name)\n\n if(path.exists(vkey_file)) :\n print(address_prefix, \"key pair already exists for\", name)\n return\n \n makedirs(path.dirname(vkey_file), mode=0o777, exist_ok=True)\n\n run_params = ['cardano-cli', address_type, 'key-gen', '--verification-key-file', vkey_file, '--signing-key-file', skey_file]\n subprocess_run(run_params, capture_output=False, text=True)\n return","def genKeys():\r\n (pub, priv) = rsa.newkeys(256)\r\n context = {\r\n 'pub': pub,\r\n 'priv': priv\r\n }\r\n return context","def new_key_pair(self):\n from plonevotecryptolib.KeyPair import KeyPair # avoids circular imports\n return KeyPair(self)","def generate_keys(self):\n\n # TODO: Store keys encrypted\n rsa1 = RsaPrivateKey.Generate()\n self.sign_private = str(rsa1)\n self.sign_public = str(rsa1.public_key)\n\n rsa2 = RsaPrivateKey.Generate()\n self.crypt_private = str(rsa2)\n self.crypt_public = str(rsa2.public_key)","def generate_keys() -> tuple:\n private_key = ecdsa.SigningKey.generate(curve=curve)\n public_key = private_key.get_verifying_key()\n\n private_key = encode_private_key(private_key)\n public_key = encode_public_key(public_key)\n\n return public_key, private_key","def generate_ecdh_key_pair() -> tuple[X25519PrivateKey, bytes]:\n private_key = X25519PrivateKey.generate()\n public_key_raw = private_key.public_key().public_bytes(\n serialization.Encoding.Raw, serialization.PublicFormat.Raw\n )\n return private_key, public_key_raw","def get_key_pair() -> typing.Tuple[bytes, bytes]: \n return _get_key_pair_from_sk(ecdsa.SigningKey.generate(curve=CURVE))","def generate_key(self):\n self.key = Fernet.generate_key()\n with open(\"secret.key\", \"wb\") as key_file:\n key_file.write(self.key)","def create_keypair(econfig_file=None, region=None, keyname=\"bcbio\"):\n import boto\n import boto.ec2\n if econfig_file:\n keypair_dir = os.path.dirname(econfig_file).replace(\"elasticluster\", \"aws_keypairs\")\n else:\n keypair_dir = os.path.join(os.getcwd(), \"aws_keypairs\")\n if not os.path.exists(keypair_dir):\n os.makedirs(keypair_dir)\n private_key = os.path.join(os.path.join(keypair_dir, keyname))\n new_key = not os.path.exists(private_key)\n if new_key:\n cmd = [\"ssh-keygen\", \"-t\", \"rsa\", \"-N\", \"\", \"-f\", private_key, \"-C\", \"bcbio_aws_keypair\"]\n subprocess.check_call(cmd)\n public_key = private_key + \".pub\"\n if region:\n ec2 = boto.ec2.connect_to_region(region)\n else:\n ec2 = boto.connect_ec2()\n key = ec2.get_key_pair(keyname)\n if key and new_key:\n print(\"Non matching key %s found in AWS, removing.\" % keyname)\n ec2.delete_key_pair(keyname)\n key = None\n if not key:\n print(\"Key %s not found in AWS, importing created key\" % keyname)\n with open(public_key) as in_handle:\n body = in_handle.read()\n try:\n ec2.import_key_pair(keyname, body)\n except TypeError as e:\n body = body.encode('utf-8')\n ec2.import_key_pair(keyname, body)\n return {\"user_key_name\": keyname, \"user_key_private\": private_key,\n \"user_key_public\": public_key}","def generate_keypair(self, key_length: int = 2048) -> Tuple[bytes, bytes]:\n\n return None","def generate_new_key(self, index):\n new_key = self.chain_key.subkey(index)\n self._key_generated(new_key, index)","def create_key_pair(self, key_name):\r\n params = {'KeyName':key_name}\r\n return self.get_object('CreateKeyPair', params, KeyPair, verb='POST')","def keygen():\n pk, pub = generate_signing_key()\n t = PrettyTable([\"Private (install on your witness node)\",\n \"Public (publish with 'conductor enable' command)\"])\n t.align = \"l\"\n t.add_row([pk, pub])\n\n output(t, '')","def gen_temp_key(self, keysize=1024):\n self.temp_session_key = [None, None]\n self.key_exchange_gui.generating_temp_key()\n return self.gen_key_pair(keysize, self.gen_temp_key_cb)","def create(self):\n self.initialize()\n\n if not self.__keypair:\n logger.info('Creating keypair %s...' % self.keypair_settings.name)\n\n if self.keypair_settings.public_filepath and os.path.isfile(\n self.keypair_settings.public_filepath):\n logger.info(\"Uploading existing keypair\")\n self.__keypair = nova_utils.upload_keypair_file(\n self._nova, self.keypair_settings.name,\n self.keypair_settings.public_filepath)\n\n if self.keypair_settings.delete_on_clean is not None:\n delete_on_clean = self.keypair_settings.delete_on_clean\n self.__delete_keys_on_clean = delete_on_clean\n else:\n self.__delete_keys_on_clean = False\n else:\n logger.info(\"Creating new keypair\")\n keys = nova_utils.create_keys(self.keypair_settings.key_size)\n self.__keypair = nova_utils.upload_keypair(\n self._nova, self.keypair_settings.name,\n nova_utils.public_key_openssh(keys))\n file_utils.save_keys_to_files(\n keys, self.keypair_settings.public_filepath,\n self.keypair_settings.private_filepath)\n\n if self.keypair_settings.delete_on_clean is not None:\n delete_on_clean = self.keypair_settings.delete_on_clean\n self.__delete_keys_on_clean = delete_on_clean\n else:\n self.__delete_keys_on_clean = True\n elif self.__keypair and not os.path.isfile(\n self.keypair_settings.private_filepath):\n logger.warn(\"The public key already exist in OpenStack \\\n but the private key file is not found ..\")\n\n return self.__keypair","def CreateKeyFile():\n keyfile = tempfile.mkstemp()[1]\n cmd = [\n 'openssl',\n 'genrsa',\n '-out', keyfile,\n '2048'\n ]\n _RunCommand(cmd)\n return keyfile","def generate_key():\n return get_token_generator().generate_token()","def get_keypair(ec2):\n # call the boto ec2 function to create a key pair\n key_pair = ec2.create_key_pair(KeyName=KEY_PAIR_NAME)\n print(\"\\n===Created a new key pair in AWS.\")\n\n # capture the key and store it in a file\n KeyPairOut = str(key_pair.key_material)\n\n # create a file to store the key locally\n print(\"Saving the keypair.\")\n key_pair_path = KEY_PAIR_NAME + \".pem\"\n with open(key_pair_path, \"w\") as f:\n f.write(KeyPairOut)\n os.chmod(key_pair_path, 0o600)\n print(\"===Changed access permission to read-only.\")","def GenerateKey(self):\n self.key_name = self.key_name or str(uuid.uuid4())\n if self.key is None or not self.key.id():\n self.key = ndb.Key(self._get_kind(), self.key_name)\n return True\n return False","def generate_key_image(\n self, output: OutputInfo, private_view_key: bytes, private_spend_key: bytes\n ) -> bytes:","def generate_key_pair(G):\r\n\r\n global random\r\n\r\n if random == None:\r\n random = hash_drbg.HashDRBG()\r\n\r\n if G.order == None:\r\n raise RuntimeError(\"Base point must have order.\")\r\n\r\n key_size = log(ec.leftmost_bit(G.order)) / log(2)\r\n key_size = int(ceil(key_size) / 2)\r\n private_key = 1\r\n\r\n while private_key <= 1:\r\n private_key = random(key_size) #generates a random number\r\n #with twice the required bits\r\n private_key %= G.order\r\n\r\n return (private_key, G * private_key)","def generate(cls, params = None, quiet = False):\n\n if params is None:\n if not quiet:\n logger.debug(\"Generating new ECDSA key parameters\")\n params = KeyParams.generateEC()\n\n assert isinstance(params, KeyParams)\n\n if not quiet:\n logger.debug(\"Generating new ECDSA key\")\n\n return cls(POW = rpki.POW.Asymmetric.generateFromParams(params.get_POW()))","def genKey(self, privateKey,otherKey):\n\t\tself.sharedSecret = self.genSecret(privateKey, otherKey)\n\n\t\t# Convert the shared secret (int) to an array of bytes in network order\n\t\t# Otherwise hashlib can't hash it.\n\t\ttry:\n\t\t\t_sharedSecretBytes = self.sharedSecret.to_bytes(\n\t\t\t\tself.sharedSecret.bit_length() // 8 + 1, byteorder=\"big\")\n\t\texcept AttributeError:\n\t\t\t_sharedSecretBytes = str(self.sharedSecret)\n\n\t\ts = hashlib.sha256()\n\t\ts.update(bytes(_sharedSecretBytes))\n\t\tself.key = s.digest()","def generate(self, force=False):\n raise NotImplementedError(\n 'Cannot generate Key of unknown algorithm type. Use a subclass.', self\n )","def generate_key(self):\n return str(uuid4())","def get_identity_shared_key(self, identity, curve, their_pubkey, index=0):\n params = {'identity': identity, 'curve': curve, 'index': index,\n 'their_pubkey': their_pubkey}\n return self._jadeRpc('get_identity_shared_key', params)","def generate_keys(self, password):\n\n # TODO: Store keys encrypted\n rsa1 = RsaPrivateKey.Generate()\n self.sign_private = str(rsa1)\n self.sign_public = str(rsa1.public_key)\n\n rsa2 = RsaPrivateKey.Generate()\n self.crypt_private = str(rsa2)\n self.crypt_public = str(rsa2.public_key)","def create_key_pair(self, key_name):\n response = key_pair.create_key_pair(self.url, self.verb, self.headers,\n self.version, key_name)\n if response is not None :\n res = CreateKeyPairResponse.CreateKeyPairResponse()\n parseString(str(response.text), res)\n return res\n else :\n return None","def genKey(self, otherKey):\n self.sharedSecret = self.genSecret(self.privateKey, otherKey)\n #print(\"Shared secret:\")\n #print(self.sharedSecret)\n s = hashlib.sha256()\n s.update(bytes(str(self.sharedSecret).encode()))\n self.key = s.digest()","def generate_input_key(\n self, output: OutputInfo, private_view_key: bytes, private_spend_key: bytes\n ) -> bytes:","def ex_create_keypair(self, name):\n params = {\n 'Action': 'CreateKeyPair',\n 'KeyName': name,\n }\n response = self.connection.request(self.path, params=params).object\n key_material = self._findtext(response, 'keyMaterial')\n key_fingerprint = self._findtext(response, 'keyFingerprint')\n return {\n 'keyMaterial': key_material,\n 'keyFingerprint': key_fingerprint,\n }","def create_keys(self):\n crypto_tool = CryptoTools()\n # creating RSA keys for the signer user\n public_key, private_key = crypto_tool.create_key_with_entropy()\n self.priv_key = crypto_tool.get_pem_format(private_key).decode(\"utf-8\")\n self.pub_key = crypto_tool.get_pem_format(public_key).decode(\"utf-8\")","def gen_private_key():\n return DH.b2i(Random.new().read(DH_SIZE))","def create(self, name, public_key=None):\n data = {\n \"keypair\": {\n \"name\": name\n }\n }\n if public_key is not None:\n data['keypair']['public_key'] = public_key\n \n path = '/os-keypairs'\n res = self.client.call(path, 'POST', data=json.dumps(data), \n token=self.manager.identity.token)\n self.logger.debug('Create/import openstack keypair: %s' % truncate(res))\n return res[0]['keypair']","def generate_key():\r\n # generating key\r\n key = Fernet.generate_key()\r\n\r\n key_dir = os.path.join(os.path.dirname(__file__), \"resources/key\")\r\n\r\n # writing key in file\r\n with open(key_dir, \"wb\") as keyFile:\r\n keyFile.write(key)","def test_aws_service_api_keypair_generate_post(self):\n pass","def sym_key_gen(pairing_group=None, sym_key_size=None, debug=0):\n\n # If sym_key_size is not defined, set a default value\n if sym_key_size is None:\n sym_key_size = SYM_KEY_DEFAULT_SIZE\n\n # Clamp the size between SYM_KEY_MIN_SIZE and the system maximum possible value\n size = clamp(sym_key_size, SYM_KEY_MIN_SIZE, sys.maxsize)\n\n # Check if an error occurred during clamping\n if size is None:\n logging.error('sym_key_gen clamp size exception')\n if debug: # ONLY USE FOR DEBUG\n print('EXCEPTION in sym_key_gen clamp size')\n raise Exception\n\n # Check if size is a power of 2\n if not math.log2(size).is_integer():\n logging.error('sym_key_gen size exception')\n if debug: # ONLY USE FOR DEBUG\n print('EXCEPTION in sym_key_gen size')\n raise Exception\n\n # Generate and return a random symmetric key with the given size\n return random_string_gen(pairing_group, sym_key_size)","def gen_public_key(n, e):\n\n # Assign key parameters\n key_params = (n, e)\n # Construct private key\n key = RSA.construct(key_params)\n\n return key.exportKey()","def generate_key():\n return str(uuid.uuid4())","def vsce_uokms_server_generate_server_key_pair(self, ctx, server_private_key, server_public_key):\n vsce_uokms_server_generate_server_key_pair = self._lib.vsce_uokms_server_generate_server_key_pair\n vsce_uokms_server_generate_server_key_pair.argtypes = [POINTER(vsce_uokms_server_t), POINTER(vsc_buffer_t), POINTER(vsc_buffer_t)]\n vsce_uokms_server_generate_server_key_pair.restype = c_int\n return vsce_uokms_server_generate_server_key_pair(ctx, server_private_key, server_public_key)","def generate_keypair(bits):\n p = generate_prime(bits // 2)\n #print(p)\n q = generate_prime(bits // 2)\n #print(q)\n n = p * q\n return PrivateKey(p, q, n), PublicKey(n)","def generate_keystream(self):","def gen_key(app):\n\tos.system('lxc-attach -n %s -- ssh-keygen -t rsa -N \"\" -f key' % app)","def generate(cls, keylength = 2048, quiet = False):\n\n if not quiet:\n logger.debug(\"Generating new %d-bit RSA key\", keylength)\n if generate_insecure_debug_only_rsa_key is not None:\n return cls(POW = generate_insecure_debug_only_rsa_key())\n else:\n return cls(POW = rpki.POW.Asymmetric.generateRSA(keylength))","def generate_key(seed):\n private_key = sha256(seed)\n public_key = privtopub(private_key)\n return {\"private\": private_key, \"public\": public_key}","def test_create_keypair_only(self):\n self.keypair_creator = create_keypairs.OpenStackKeypair(self.os_creds,\n create_keypairs.KeypairSettings(name=keypair_name))\n self.keypair_creator.create()\n\n keypair = nova_utils.keypair_exists(self.keypair_creator.nova, self.keypair_creator.keypair)\n self.assertEquals(self.keypair_creator.keypair, keypair)","def generate_key():\n key = ''.join([chr(random.randint(0, 0x10)) for _ in range(block_size)])\n return AES.new(second_key, AES.MODE_ECB).encrypt(pad((key.encode('ascii')), block_size))","def create_pem_keys(self):\n self.random_rsa()\n\n return self.keys","def generate_key():\n return get_random_bytes(KEY_SIZE)","def create_handshake_key_pair(cls) -> Tuple[bytes, bytes]:\n ...","def generate_private_key(self):\n if not self.curve:\n raise NoCurveError(\"Curve must be set prior to key generation.\")\n return self.load_private_key(SigningKey.generate(curve=self.curve))","def get_new_key() -> rsa.RSAPrivateKeyWithSerialization:\n\n return rsa.generate_private_key(\n backend=default_backend(), public_exponent=65537, key_size=2048\n )","def generate_keys(attributes: List[AttributeName]) -> Tuple[SecretKey, PublicKey]:\n # Pick uniformly random variables\n x = G1.order().random()\n y = {a: G1.order().random() for a in attributes}\n\n # take generators of G1 and G2\n g1 = G1.generator()\n g2 = G2.generator()\n\n # Compute Xs and Ys\n X1 = g1 ** x\n X2 = g2 ** x\n Y1 = {a: g1 ** y_i for a, y_i in y.items()}\n Y2 = {a: g2 ** y_i for a, y_i in y.items()}\n\n # Output public and secret keys\n pk = PublicKey(attributes, g1, Y1, g2, X2, Y2) # type:ignore\n sk = SecretKey(x, X1, y)\n return sk, pk","def generate_key(self):\n cmd = self.generate_key_cmd()\n self.show(cmd)\n if self.dryrun:\n return None\n s, _, _ = self.as_user(cmd)\n assert s == 0, ('failed to generate key', cmd)\n keyname = self.extract_key_name()\n return keyname","def generate_key():\n key = Fernet.generate_key()\n with open(\"secret.key\", \"wb\") as key_file:\n key_file.write(key)","def generate_key_and_cert():\n signing_key = rsa.generate_private_key(backend=crypto_default_backend(), public_exponent=65537, key_size=2048)\n subject = issuer = x509.Name(\n [\n x509.NameAttribute(NameOID.COUNTRY_NAME, 'NO'),\n x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, 'OSLO'),\n x509.NameAttribute(NameOID.LOCALITY_NAME, 'OSLO'),\n x509.NameAttribute(NameOID.ORGANIZATION_NAME, 'Intility AS'),\n x509.NameAttribute(NameOID.COMMON_NAME, 'intility.no'),\n ]\n )\n signing_cert = (\n x509.CertificateBuilder()\n .subject_name(subject)\n .issuer_name(issuer)\n .public_key(signing_key.public_key())\n .serial_number(x509.random_serial_number())\n .not_valid_before(datetime.utcnow())\n .not_valid_after(\n # Our certificate will be valid for 10 days\n datetime.utcnow()\n + timedelta(days=10)\n # Sign our certificate with our private key\n )\n .sign(signing_key, hashes.SHA256(), crypto_default_backend())\n .public_bytes(crypto_serialization.Encoding.DER)\n )\n return signing_key, signing_cert","def create_rsa_key_pair() -> Tuple[str, str]:\n key = RSA.generate(RSA_KEY_STRENGTH)\n public_key = key.publickey().export_key().decode()\n private_key = key.export_key().decode()\n return public_key, private_key","def generate_key():\n key = Fernet.generate_key()\n with open(\"Secret.key\",\"wb\")as key_file:\n key_file.write(key)","def create_keypair(self, name=None, public_key=None):\n body = {}\n if name is not None:\n body.update({\"name\": name})\n if public_key is not None:\n body.update({\"public_key\": public_key})\n return self._create(_keypair.Keypair, **body)","def generate_rsa_auxiliary_key_pair() -> AuxiliaryKeyPair:\n rsa_key_pair = rsa_keypair()\n return AuxiliaryKeyPair(rsa_key_pair.private_key, rsa_key_pair.public_key)","def gnupg_keypair(\n gnupg_gen_key_conf: Path, gnupg_trust_store: GnuPGTrustStore\n) -> GnuPGKeypair:\n\n LOGGER.debug(\"Initializing GPG keypair ...\")\n environment = {\"HOME\": \"/dev/null\"}\n result = subprocess.run(\n [\n \"gpg\",\n \"--batch\",\n \"--homedir\",\n str(gnupg_trust_store.gnupg_home),\n \"--gen-key\",\n \"--keyid-format\",\n \"long\",\n str(gnupg_gen_key_conf),\n ],\n capture_output=True,\n check=True,\n env=environment,\n )\n keyid = re.findall(\n r\"gpg: key (\\w+) marked as ultimately trusted\", result.stderr.decode(\"utf-8\")\n )[0]\n # LOGGER.debug(\" keyid : %s\", keyid)\n\n result = subprocess.run(\n [\n \"gpg\",\n \"--fingerprint\",\n \"--fingerprint\", # Double --fingerprint needed for subkeys\n \"--homedir\",\n str(gnupg_trust_store.gnupg_home),\n \"--with-colons\",\n str(keyid),\n ],\n capture_output=True,\n check=True,\n env=environment,\n )\n # Fingerprint order: pubkey [, subkey ]...\n fingerprints = re.findall(r\"fpr:{9}(\\w+):\", result.stdout.decode(\"utf-8\"))\n LOGGER.debug(\" Fingerprints:\")\n for fingerprint in fingerprints:\n LOGGER.debug(\" %s\", fingerprint)\n\n yield GnuPGKeypair(\n fingerprints=fingerprints,\n gen_key_conf=gnupg_gen_key_conf,\n gnupg_home=gnupg_trust_store.gnupg_home,\n keyid=keyid,\n )","def generate_key():\n key = Fernet.generate_key()\n with open(\"pass.key\", \"wb\") as key_file:\n key_file.write(key)","def _create_pkey(self, commonname, serial):\n pkey = PKey()\n pkey.generate_key(crypto.TYPE_RSA, self.key_bits)\n private = crypto.dump_privatekey(crypto.FILETYPE_PEM,\n pkey).decode()\n key_path = self._get_key_path(commonname, serial)\n if os.path.exists(key_path):\n raise FileExistsError(key_path)\n with open(key_path, 'w') as private_file:\n private_file.writelines(private)\n\n key_link = self._get_key_link(commonname)\n if os.path.exists(key_link):\n os.unlink(key_link)\n os.symlink(os.path.basename(key_path), key_link)\n\n return pkey","def keygen(\n args: argparse.Namespace,\n config: KSKMConfig,\n p11modules: KSKM_P11,\n logger: logging.Logger,\n) -> bool:\n logger.info(\"Generate key\")\n flags = FlagsDNSKEY.ZONE.value | FlagsDNSKEY.SEP.value\n dnssec_alg = AlgorithmDNSSEC[args.key_alg]\n if is_algorithm_rsa(dnssec_alg):\n if args.key_size is None:\n raise argparse.ArgumentError(\n args.key_size, \"RSA key generation requires key size\"\n )\n p11key = generate_rsa_key(\n flags, args.key_size, p11modules, label=args.key_label\n )\n elif is_algorithm_ecdsa(dnssec_alg):\n crv = algorithm_to_curve(dnssec_alg)\n p11key = generate_ec_key(flags, crv, p11modules, label=args.key_label)\n else:\n raise ValueError(f\"Unknown key algorithm {repr(args.key_alg)}\")\n\n if not p11key or not p11key.public_key:\n raise RuntimeError(\"No public key returned by key generation\")\n\n # Calculate the DNSSEC key tag of the new key and look for a collision in the configuration\n key_tags: List[int] = []\n _key = public_key_to_dnssec_key(\n key=p11key.public_key,\n key_identifier=p11key.label,\n algorithm=AlgorithmDNSSEC[args.key_alg],\n flags=FlagsDNSKEY.SEP.value | FlagsDNSKEY.ZONE.value,\n ttl=config.ksk_policy.ttl,\n )\n logger.info(\n f\"Generated key {p11key.label} has key tag {_key.key_tag} for algorithm={_key.algorithm}, \"\n f\"flags=0x{_key.flags:x}\"\n )\n key_tags += [_key.key_tag]\n _revoked_key = public_key_to_dnssec_key(\n key=p11key.public_key,\n key_identifier=p11key.label,\n algorithm=AlgorithmDNSSEC[args.key_alg],\n flags=FlagsDNSKEY.SEP.value | FlagsDNSKEY.ZONE.value | FlagsDNSKEY.REVOKE.value,\n ttl=config.ksk_policy.ttl,\n )\n logger.info(\n f\"Generated key {p11key.label} has key tag {_revoked_key.key_tag} with the REVOKE bit set \"\n f\"(flags 0x{_revoked_key.flags:x})\"\n )\n key_tags += [_revoked_key.key_tag]\n\n for _name, ksk in config.ksk_keys.items():\n if ksk.key_tag in key_tags:\n logger.error(\n f\"Generated key {p11key.label} has key tags {key_tags} matching \"\n f\"KSK key in configuration: {ksk}\"\n )\n raise RuntimeError(\"Key tag collision detected\")\n\n _now = datetime.utcnow()\n # create_trustanchor_keydigest wants an KSKKey, but it is not used in the digest calculation\n _temp_ksk = KSKKey(\n description=\"Newly generated key\",\n label=_now.isoformat(),\n key_tag=_key.key_tag,\n algorithm=_key.algorithm,\n valid_from=_now,\n valid_until=_now,\n )\n _domain = \".\"\n _ds = create_trustanchor_keydigest(_temp_ksk, _key, domain=_domain)\n digest = binascii.hexlify(_ds.digest).decode(\"UTF-8\").upper()\n _digest_type = \"2\" # create_trustanchor_keydigest always does SHA256\n logger.info(\n f\"DS record for generated key:\\n\"\n f\"{_domain} IN DS {_key.key_tag} {_key.algorithm.value} {_digest_type} {digest}\\n\"\n f\">> {' '.join(pgp_wordlist(_ds.digest))}\"\n )\n\n return True","def generateKeys(self, keys_path, minion_id):\n #Change directory to keys path\n os.chdir(keys_path)\n #Give permission to the salt user\n self.console_manager.printRed(\"Giving permission to the salt user\")\n command = ['sudo', 'chmod', 'a+rwx', '.']\n self.console_manager.runCommandFromShell(command)\n #Generate keys\n self.console_manager.printRed(''.join([\"Generating keys for minion id: \", minion_id]))\n command = ['sudo', 'salt-key', ''.join(['--gen-keys=', minion_id])]\n self.console_manager.runCommandFromShell(command)\n #Give permission to the salt user\n self.console_manager.printRed(\"Allowing vagrant to handle private keys\")\n command = ['sudo', 'chmod', 'a+rwx', ''.join([minion_id, '.pub']), ''.join([minion_id, '.pem'])]\n self.console_manager.runCommandFromShell(command)\n #Add public key to the accepted minion folder\n self.console_manager.printRed(\"Copying the minion public key to the salt master public keys folder\")\n command = ['sudo', 'cp', ''.join([minion_id, '.pub']), ''.join(['/var/lib/salt/pki/master/minions/', minion_id])]\n self.console_manager.runCommandFromShell(command)\n command = ['sudo', 'cp', ''.join([minion_id, '.pub']), ''.join(['/etc/salt/pki/master/minions/', minion_id])]\n self.console_manager.runCommandFromShell(command)\n return","def generate(self, force=False):\n if not self.check_force_generate(force):\n return False\n\n mkdirs(self.path)\n\n command = [openssl, 'genrsa', '-out', self.key_file]\n if self.password:\n command += ['-passout', 'pass:{}'.format(self.password)]\n command += [str(self.key_size)]\n\n self.log.info('Generating RSA key')\n if not run_command(command):\n raise RuntimeError('RSA key generation failed')\n\n if not self.exists():\n raise RuntimeError(\n 'Key generation succeeded but key file does not exist. '\n 'This should not happen', self\n )","def generate_key(self, filename, size):\n if size != 16 and size != 24 and size != 32:\n raise ValueError(\"AES key size not valid.\")\n key = os.urandom(size)\n self.export_key(filename, key)\n return key","def gen_key():\n key = os.urandom(32) # 256 bit\n return base64.b64encode(key).rstrip('=') # strip off padding","def gen_pgp_key(name, email, comment=\"generated by sdata\"):\n\n # we can start by generating a primary key. For this example, we'll use RSA, but it could be DSA or ECDSA as well\n key = pgpy.PGPKey.new(PubKeyAlgorithm.RSAEncryptOrSign, 4096)\n\n # we now have some key material, but our new key doesn't have a user ID yet, and therefore is not yet usable!\n uid = pgpy.PGPUID.new(name, comment=comment, email=email)\n\n # now we must add the new user id to the key. We'll need to specify all of our preferences at this point\n # because PGPy doesn't have any built-in key preference defaults at this time\n # this example is similar to GnuPG 2.1.x defaults, with no expiration or preferred keyserver\n key.add_uid(uid, usage={KeyFlags.Sign, KeyFlags.EncryptCommunications, KeyFlags.EncryptStorage},\n hashes=[HashAlgorithm.SHA256, HashAlgorithm.SHA384, HashAlgorithm.SHA512, HashAlgorithm.SHA224],\n ciphers=[SymmetricKeyAlgorithm.AES256, SymmetricKeyAlgorithm.AES192, SymmetricKeyAlgorithm.AES128],\n compression=[CompressionAlgorithm.ZLIB, CompressionAlgorithm.BZ2, CompressionAlgorithm.ZIP,\n CompressionAlgorithm.Uncompressed])\n return key","def gen_key(self, key):\n b_key = self._hash_digest(key)\n return self._hash_val(b_key, lambda x: x)","def create_key() -> RSA.RsaKey:\n\n return RSA.generate(1024, Crypto.Random.new().read)","def create_keypair(key_name):\n if os.path.isfile(SSH_FOLDER + key_name + \".pem\"):\n return # Key already created\n ec2 = boto.ec2.connect_to_region(AWS_REGION)\n key = ec2.create_key_pair(key_name)\n key.save(SSH_FOLDER)","def create_rsa_keypair( config_path = config.CONFIG_PATH(), prefix = RSA_KEYPAIR_PREFIX(), keysize = RSA_KEYPAIR_SIZE(), force = False ) :\n\n public_key_path, private_key_path = valid_rsa_keypair_paths( config_path, prefix, force )\n utils.create_directory( config_path, 0700 )\n public_key, private_key = rsa.newkeys( keysize )\n with open( public_key_path, \"w\" ) as stream :\n stream.write( public_key.save_pkcs1() )\n os.chmod( public_key_path, 0644 )\n with open( private_key_path, \"w\" ) as stream :\n stream.write( private_key.save_pkcs1() )\n os.chmod( private_key_path, 0600 )\n return public_key_path, private_key_path","def generate_key(self)->bytes:\n return os.urandom(32)","def generate_key():\n # generate random key\n key = get_random_string()\n\n # if it's already taken, generate another\n if EmailManager.objects.filter(key=key).exists():\n return EmailManager.generate_key()\n\n # return it\n return key","def generate_private_key():\n\treturn binascii.hexlify(os.urandom(32)).decode('utf-8').upper()","def Generate(size=keyinfo.AES.default_size):\n key_bytes = util.RandBytes(size // 8)\n key_string = util.Base64WSEncode(key_bytes)\n hmac_key = HmacKey.Generate() # use default HMAC-SHA1 key size\n return AesKey(key_string, hmac_key, size)","def _create_rsa_key_pair(self, length, public_exponent=65537):\n self.logger.info(\n \"Generating an RSA key pair with length: {0}, and \"\n \"public_exponent: {1}\".format(\n length, public_exponent\n )\n )\n try:\n private_key = rsa.generate_private_key(\n public_exponent=public_exponent,\n key_size=length,\n backend=default_backend())\n public_key = private_key.public_key()\n\n private_bytes = private_key.private_bytes(\n serialization.Encoding.DER,\n serialization.PrivateFormat.PKCS8,\n serialization.NoEncryption())\n public_bytes = public_key.public_bytes(\n serialization.Encoding.DER,\n serialization.PublicFormat.PKCS1)\n except Exception as e:\n self.logger.exception(e)\n raise exceptions.CryptographicFailure(\n \"An error occurred while generating the RSA key pair. \"\n \"See the server log for more information.\"\n )\n\n public_key = {\n 'value': public_bytes,\n 'format': enums.KeyFormatType.PKCS_1,\n 'public_exponent': public_exponent\n }\n private_key = {\n 'value': private_bytes,\n 'format': enums.KeyFormatType.PKCS_8,\n 'public_exponent': public_exponent\n }\n\n return public_key, private_key","def generate_key_cmd(self, cfg_path=None):\n # TODO: use tempfile\n if cfg_path is None:\n cfg_path = '/tmp/gen-key.cfg'\n self.create_gen_key_cfg_file(cfg_path)\n return '/usr/bin/gpg --batch --gen-key {cfg_path}'.format(cfg_path=cfg_path)","def generate_key(self, rand = random.SystemRandom()):\n k = rand.randrange(0, self.n - 1)\n return k, self.base_mul(k)"],"string":"[\n \"def gen_key_pair(self, keysize, cb):\\n\\n def gen_key_pair_pub_cb(data, ctx):\\n if not data:\\n warning('keymanagement: Could not generate a key pair\\\\n')\\n cb(None, None)\\n else:\\n cb(ctx, data)\\n\\n def gen_key_pair_priv_cb(data, ctx):\\n if not data:\\n warning('keymanagement: Could not generate a key pair\\\\n')\\n cb(None, None)\\n else:\\n xrun([self.sslname, 'rsa', '-pubout'], gen_key_pair_pub_cb,\\n data, data)\\n\\n return xrun([self.sslname, 'genrsa', str(keysize)],\\n gen_key_pair_priv_cb, None)\",\n \"def gen_key_pair():\\n sk = gen_secret_key(BITCOIN.gen.n)\\n pk = PublicKey.from_sk(sk)\\n return sk, pk\",\n \"def generate(self):\\n if self.curvetype == KeyType.ECDSA_P256v1:\\n self.private_key_obj = ec.generate_private_key(ec.SECP256R1(), default_backend())\\n elif self.curvetype == KeyType.ECDSA_SECP256k1:\\n self.private_key_obj = ec.generate_private_key(ec.SECP256K1(), default_backend())\\n self.public_key_obj = self.private_key_obj.public_key()\\n self._get_naive_private_key_bytes()\\n self._get_naive_public_key_bytes()\",\n \"def create_keypair(self):\\n # NOTE: currently we rely on zmq for convenience, but we may use libnacl directly\\n # if we want to isolate this module from zmq dependency.\\n public_key, private_key = zmq.curve_keypair()\\n return public_key, private_key\",\n \"def generate_symmetric_key():\\n return Fernet.generate_key()\",\n \"def generate_rsa_key_pair(self):\\n\\t\\tprint \\\"Started rsa key generation\\\"\\n\\t\\tkey = RSA.generate(self.key_size, randfunc=self.random_number_generator)\\n\\t\\t\\t\\n\\t\\tpub_key = key.publickey().exportKey()\\n\\t\\tprint pub_key\\n\\t\\t\\n\\n\\t\\tpriv_key = key.exportKey()\\n\\t\\tprint \\\"Private key\\\", priv_key \\n\\t\\tprint \\\"Note: Normally, the private key should be protected. For the purposes of this demo, I'm printing it to terminal.\\\"\",\n \"def generate_key(self, **options):\\n\\n return security_utils_services.generate_rsa_key(**options)\",\n \"def create_key_pair(self) -> Keypair:\\n res = self.context.post(\\n \\\"/dsum/create_key_pair\\\", None, None, \\\"DSum: failed creating a Curve 25519 Keypair\\\")\\n return Keypair(res['private_key_id'], res['public_key_id'])\",\n \"def create_keypair(self, username):\\n msg = \\\"create_keypair not implemented\\\"\\n raise NotImplementedError(msg)\",\n \"def generate_signing_keys():\\n return SigningKey.generate(curve=SECP256k1)\",\n \"def create_key_pair(self, keypair, **kwargs):\\n\\n if not isinstance(keypair, models.CreateKeyPairReq):\\n raise HuaweiCloudSDKException(\\n message=\\\"The datatype of parameter(keypair) \\\"\\n \\\"is not CreateKeyPairReq\\\")\\n body_params = keypair.serialize()\\n\\n header_params = {}\\n header_params['Accept'] = util.select_header_accept(\\n ['application/xml', 'application/json'])\\n\\n header_params['Content-Type'] = util.select_header_content_type(\\n ['application/json', 'application/xml'])\\n\\n return_code, return_data, _ = self.api_client.handle_raw_request(\\n 'compute', 'POST',\\n '/os-keypairs',\\n headers=header_params,\\n body=body_params,\\n timeout=kwargs.get('_request_timeout', None),\\n _preload_content=kwargs.get('_preload_content', True))\\n\\n if return_data is not None:\\n return_data = json.loads(return_data)\\n else:\\n return_data = {}\\n if return_code not in [200, 201]:\\n raise HuaweiCloudSDKException(\\n return_code,\\n \\\"Run create_key_pair failed, \\\"\\n \\\"message=%s\\\" % return_data.get(\\\"message\\\"))\\n return models.CreateKeyPairResp().deserialize(return_data)\",\n \"def generate_key(self):\\n key = rsa.generate_private_key(\\n public_exponent=self.settings['key_public_exponent_size'],\\n key_size=self.settings['key_size'],\\n backend=default_backend()\\n )\\n return key\",\n \"def gen_key(self):\\n\\n if not self.private_key:\\n self._gen_key()\\n else:\\n raise CryptoError(\\\"Private Key already existing\\\")\",\n \"def generate_key():\\n key = crypto.Key.generate_key()\\n click.echo('Private Key (len {}):: \\\\n{}'.format(\\n len(key.get_privkey()),\\n hexlify(key.get_privkey())))\\n click.echo('Public Key (len {})::\\\\n{}'.format(\\n len(key.get_pubkey()),\\n hexlify(key.get_pubkey())))\",\n \"def test_generate_key_pair(self):\\n with patch('iceit.crypto.gnupg.GPG') as mock_gpg:\\n mock_key = Mock()\\n mock_key.fingerprint = 'fake-fingerprint'\\n mock_gpg.gen_key.return_value = mock_key\\n\\n mock_gpg.return_value = mock_gpg\\n encryptor = self.test_init()\\n fake_key = encryptor.generate_key_pair(key_type=\\\"RSA\\\", length=4096, options={\\n 'name_real': 'Fake Name', 'name_email': 'fake@example.com', 'name_comment': 'Fake comment'})\\n\\n self.assertEqual(mock_gpg.gen_key_input.call_count, 1)\\n self.assertEqual(fake_key, mock_key.fingerprint)\",\n \"def generate_key(self):\\n\\n self.key = Fernet.generate_key()\\n self.cryptor = Fernet(self.key)\",\n \"def _gen_key(self):\\n\\n input_data = self._gpg.gen_key_input(key_type=\\\"RSA\\\",\\n key_length=self.key_length, name_real=self.name,\\n name_comment=self.comment, name_email=self.email)\\n\\n log.info(\\\"Generating key: (%s)\\\" % input_data)\\n\\n self.key = self._gpg.gen_key(input_data)\",\n \"def generate(self, force=False):\\n if not self.check_force_generate(force):\\n return False\\n\\n mkdirs(self.path)\\n\\n command = [openssl, 'ecparam', '-genkey', '-name', self.asn1_oid, '-out', self.key_file]\\n\\n self.log.info('Generating EC key')\\n # Generate the keyfile with no password\\n if not run_command(command):\\n raise RuntimeError('EC key generation failed', self)\\n\\n # Now encrypt the key with a password, overwriting the original\\n # passwordless key.\\n if self.password:\\n command = [\\n openssl, 'ec',\\n '-in', self.key_file,\\n '-out', self.key_file,\\n '-des3', '-passout', 'pass:{}'.format(self.password)\\n ]\\n self.log.info('Encrypting key with password')\\n\\n if not run_command(command):\\n raise RuntimeError('EC key file password encryption failed')\\n\\n if not self.exists():\\n raise RuntimeError(\\n 'Key generation succeeded but key file does not exist. '\\n 'This should not happen', self\\n )\",\n \"def createKeyPair(type, bits):\\n pkey = crypto.PKey()\\n pkey.generate_key(type, bits)\\n return pkey\",\n \"def create_key ():\",\n \"def create_keypair(address_type, addresses_path, address_prefix, name):\\n vkey_file = get_vkey_file(addresses_path, address_prefix, name)\\n skey_file = get_skey_file(addresses_path, address_prefix, name)\\n\\n if(path.exists(vkey_file)) :\\n print(address_prefix, \\\"key pair already exists for\\\", name)\\n return\\n \\n makedirs(path.dirname(vkey_file), mode=0o777, exist_ok=True)\\n\\n run_params = ['cardano-cli', address_type, 'key-gen', '--verification-key-file', vkey_file, '--signing-key-file', skey_file]\\n subprocess_run(run_params, capture_output=False, text=True)\\n return\",\n \"def genKeys():\\r\\n (pub, priv) = rsa.newkeys(256)\\r\\n context = {\\r\\n 'pub': pub,\\r\\n 'priv': priv\\r\\n }\\r\\n return context\",\n \"def new_key_pair(self):\\n from plonevotecryptolib.KeyPair import KeyPair # avoids circular imports\\n return KeyPair(self)\",\n \"def generate_keys(self):\\n\\n # TODO: Store keys encrypted\\n rsa1 = RsaPrivateKey.Generate()\\n self.sign_private = str(rsa1)\\n self.sign_public = str(rsa1.public_key)\\n\\n rsa2 = RsaPrivateKey.Generate()\\n self.crypt_private = str(rsa2)\\n self.crypt_public = str(rsa2.public_key)\",\n \"def generate_keys() -> tuple:\\n private_key = ecdsa.SigningKey.generate(curve=curve)\\n public_key = private_key.get_verifying_key()\\n\\n private_key = encode_private_key(private_key)\\n public_key = encode_public_key(public_key)\\n\\n return public_key, private_key\",\n \"def generate_ecdh_key_pair() -> tuple[X25519PrivateKey, bytes]:\\n private_key = X25519PrivateKey.generate()\\n public_key_raw = private_key.public_key().public_bytes(\\n serialization.Encoding.Raw, serialization.PublicFormat.Raw\\n )\\n return private_key, public_key_raw\",\n \"def get_key_pair() -> typing.Tuple[bytes, bytes]: \\n return _get_key_pair_from_sk(ecdsa.SigningKey.generate(curve=CURVE))\",\n \"def generate_key(self):\\n self.key = Fernet.generate_key()\\n with open(\\\"secret.key\\\", \\\"wb\\\") as key_file:\\n key_file.write(self.key)\",\n \"def create_keypair(econfig_file=None, region=None, keyname=\\\"bcbio\\\"):\\n import boto\\n import boto.ec2\\n if econfig_file:\\n keypair_dir = os.path.dirname(econfig_file).replace(\\\"elasticluster\\\", \\\"aws_keypairs\\\")\\n else:\\n keypair_dir = os.path.join(os.getcwd(), \\\"aws_keypairs\\\")\\n if not os.path.exists(keypair_dir):\\n os.makedirs(keypair_dir)\\n private_key = os.path.join(os.path.join(keypair_dir, keyname))\\n new_key = not os.path.exists(private_key)\\n if new_key:\\n cmd = [\\\"ssh-keygen\\\", \\\"-t\\\", \\\"rsa\\\", \\\"-N\\\", \\\"\\\", \\\"-f\\\", private_key, \\\"-C\\\", \\\"bcbio_aws_keypair\\\"]\\n subprocess.check_call(cmd)\\n public_key = private_key + \\\".pub\\\"\\n if region:\\n ec2 = boto.ec2.connect_to_region(region)\\n else:\\n ec2 = boto.connect_ec2()\\n key = ec2.get_key_pair(keyname)\\n if key and new_key:\\n print(\\\"Non matching key %s found in AWS, removing.\\\" % keyname)\\n ec2.delete_key_pair(keyname)\\n key = None\\n if not key:\\n print(\\\"Key %s not found in AWS, importing created key\\\" % keyname)\\n with open(public_key) as in_handle:\\n body = in_handle.read()\\n try:\\n ec2.import_key_pair(keyname, body)\\n except TypeError as e:\\n body = body.encode('utf-8')\\n ec2.import_key_pair(keyname, body)\\n return {\\\"user_key_name\\\": keyname, \\\"user_key_private\\\": private_key,\\n \\\"user_key_public\\\": public_key}\",\n \"def generate_keypair(self, key_length: int = 2048) -> Tuple[bytes, bytes]:\\n\\n return None\",\n \"def generate_new_key(self, index):\\n new_key = self.chain_key.subkey(index)\\n self._key_generated(new_key, index)\",\n \"def create_key_pair(self, key_name):\\r\\n params = {'KeyName':key_name}\\r\\n return self.get_object('CreateKeyPair', params, KeyPair, verb='POST')\",\n \"def keygen():\\n pk, pub = generate_signing_key()\\n t = PrettyTable([\\\"Private (install on your witness node)\\\",\\n \\\"Public (publish with 'conductor enable' command)\\\"])\\n t.align = \\\"l\\\"\\n t.add_row([pk, pub])\\n\\n output(t, '')\",\n \"def gen_temp_key(self, keysize=1024):\\n self.temp_session_key = [None, None]\\n self.key_exchange_gui.generating_temp_key()\\n return self.gen_key_pair(keysize, self.gen_temp_key_cb)\",\n \"def create(self):\\n self.initialize()\\n\\n if not self.__keypair:\\n logger.info('Creating keypair %s...' % self.keypair_settings.name)\\n\\n if self.keypair_settings.public_filepath and os.path.isfile(\\n self.keypair_settings.public_filepath):\\n logger.info(\\\"Uploading existing keypair\\\")\\n self.__keypair = nova_utils.upload_keypair_file(\\n self._nova, self.keypair_settings.name,\\n self.keypair_settings.public_filepath)\\n\\n if self.keypair_settings.delete_on_clean is not None:\\n delete_on_clean = self.keypair_settings.delete_on_clean\\n self.__delete_keys_on_clean = delete_on_clean\\n else:\\n self.__delete_keys_on_clean = False\\n else:\\n logger.info(\\\"Creating new keypair\\\")\\n keys = nova_utils.create_keys(self.keypair_settings.key_size)\\n self.__keypair = nova_utils.upload_keypair(\\n self._nova, self.keypair_settings.name,\\n nova_utils.public_key_openssh(keys))\\n file_utils.save_keys_to_files(\\n keys, self.keypair_settings.public_filepath,\\n self.keypair_settings.private_filepath)\\n\\n if self.keypair_settings.delete_on_clean is not None:\\n delete_on_clean = self.keypair_settings.delete_on_clean\\n self.__delete_keys_on_clean = delete_on_clean\\n else:\\n self.__delete_keys_on_clean = True\\n elif self.__keypair and not os.path.isfile(\\n self.keypair_settings.private_filepath):\\n logger.warn(\\\"The public key already exist in OpenStack \\\\\\n but the private key file is not found ..\\\")\\n\\n return self.__keypair\",\n \"def CreateKeyFile():\\n keyfile = tempfile.mkstemp()[1]\\n cmd = [\\n 'openssl',\\n 'genrsa',\\n '-out', keyfile,\\n '2048'\\n ]\\n _RunCommand(cmd)\\n return keyfile\",\n \"def generate_key():\\n return get_token_generator().generate_token()\",\n \"def get_keypair(ec2):\\n # call the boto ec2 function to create a key pair\\n key_pair = ec2.create_key_pair(KeyName=KEY_PAIR_NAME)\\n print(\\\"\\\\n===Created a new key pair in AWS.\\\")\\n\\n # capture the key and store it in a file\\n KeyPairOut = str(key_pair.key_material)\\n\\n # create a file to store the key locally\\n print(\\\"Saving the keypair.\\\")\\n key_pair_path = KEY_PAIR_NAME + \\\".pem\\\"\\n with open(key_pair_path, \\\"w\\\") as f:\\n f.write(KeyPairOut)\\n os.chmod(key_pair_path, 0o600)\\n print(\\\"===Changed access permission to read-only.\\\")\",\n \"def GenerateKey(self):\\n self.key_name = self.key_name or str(uuid.uuid4())\\n if self.key is None or not self.key.id():\\n self.key = ndb.Key(self._get_kind(), self.key_name)\\n return True\\n return False\",\n \"def generate_key_image(\\n self, output: OutputInfo, private_view_key: bytes, private_spend_key: bytes\\n ) -> bytes:\",\n \"def generate_key_pair(G):\\r\\n\\r\\n global random\\r\\n\\r\\n if random == None:\\r\\n random = hash_drbg.HashDRBG()\\r\\n\\r\\n if G.order == None:\\r\\n raise RuntimeError(\\\"Base point must have order.\\\")\\r\\n\\r\\n key_size = log(ec.leftmost_bit(G.order)) / log(2)\\r\\n key_size = int(ceil(key_size) / 2)\\r\\n private_key = 1\\r\\n\\r\\n while private_key <= 1:\\r\\n private_key = random(key_size) #generates a random number\\r\\n #with twice the required bits\\r\\n private_key %= G.order\\r\\n\\r\\n return (private_key, G * private_key)\",\n \"def generate(cls, params = None, quiet = False):\\n\\n if params is None:\\n if not quiet:\\n logger.debug(\\\"Generating new ECDSA key parameters\\\")\\n params = KeyParams.generateEC()\\n\\n assert isinstance(params, KeyParams)\\n\\n if not quiet:\\n logger.debug(\\\"Generating new ECDSA key\\\")\\n\\n return cls(POW = rpki.POW.Asymmetric.generateFromParams(params.get_POW()))\",\n \"def genKey(self, privateKey,otherKey):\\n\\t\\tself.sharedSecret = self.genSecret(privateKey, otherKey)\\n\\n\\t\\t# Convert the shared secret (int) to an array of bytes in network order\\n\\t\\t# Otherwise hashlib can't hash it.\\n\\t\\ttry:\\n\\t\\t\\t_sharedSecretBytes = self.sharedSecret.to_bytes(\\n\\t\\t\\t\\tself.sharedSecret.bit_length() // 8 + 1, byteorder=\\\"big\\\")\\n\\t\\texcept AttributeError:\\n\\t\\t\\t_sharedSecretBytes = str(self.sharedSecret)\\n\\n\\t\\ts = hashlib.sha256()\\n\\t\\ts.update(bytes(_sharedSecretBytes))\\n\\t\\tself.key = s.digest()\",\n \"def generate(self, force=False):\\n raise NotImplementedError(\\n 'Cannot generate Key of unknown algorithm type. Use a subclass.', self\\n )\",\n \"def generate_key(self):\\n return str(uuid4())\",\n \"def get_identity_shared_key(self, identity, curve, their_pubkey, index=0):\\n params = {'identity': identity, 'curve': curve, 'index': index,\\n 'their_pubkey': their_pubkey}\\n return self._jadeRpc('get_identity_shared_key', params)\",\n \"def generate_keys(self, password):\\n\\n # TODO: Store keys encrypted\\n rsa1 = RsaPrivateKey.Generate()\\n self.sign_private = str(rsa1)\\n self.sign_public = str(rsa1.public_key)\\n\\n rsa2 = RsaPrivateKey.Generate()\\n self.crypt_private = str(rsa2)\\n self.crypt_public = str(rsa2.public_key)\",\n \"def create_key_pair(self, key_name):\\n response = key_pair.create_key_pair(self.url, self.verb, self.headers,\\n self.version, key_name)\\n if response is not None :\\n res = CreateKeyPairResponse.CreateKeyPairResponse()\\n parseString(str(response.text), res)\\n return res\\n else :\\n return None\",\n \"def genKey(self, otherKey):\\n self.sharedSecret = self.genSecret(self.privateKey, otherKey)\\n #print(\\\"Shared secret:\\\")\\n #print(self.sharedSecret)\\n s = hashlib.sha256()\\n s.update(bytes(str(self.sharedSecret).encode()))\\n self.key = s.digest()\",\n \"def generate_input_key(\\n self, output: OutputInfo, private_view_key: bytes, private_spend_key: bytes\\n ) -> bytes:\",\n \"def ex_create_keypair(self, name):\\n params = {\\n 'Action': 'CreateKeyPair',\\n 'KeyName': name,\\n }\\n response = self.connection.request(self.path, params=params).object\\n key_material = self._findtext(response, 'keyMaterial')\\n key_fingerprint = self._findtext(response, 'keyFingerprint')\\n return {\\n 'keyMaterial': key_material,\\n 'keyFingerprint': key_fingerprint,\\n }\",\n \"def create_keys(self):\\n crypto_tool = CryptoTools()\\n # creating RSA keys for the signer user\\n public_key, private_key = crypto_tool.create_key_with_entropy()\\n self.priv_key = crypto_tool.get_pem_format(private_key).decode(\\\"utf-8\\\")\\n self.pub_key = crypto_tool.get_pem_format(public_key).decode(\\\"utf-8\\\")\",\n \"def gen_private_key():\\n return DH.b2i(Random.new().read(DH_SIZE))\",\n \"def create(self, name, public_key=None):\\n data = {\\n \\\"keypair\\\": {\\n \\\"name\\\": name\\n }\\n }\\n if public_key is not None:\\n data['keypair']['public_key'] = public_key\\n \\n path = '/os-keypairs'\\n res = self.client.call(path, 'POST', data=json.dumps(data), \\n token=self.manager.identity.token)\\n self.logger.debug('Create/import openstack keypair: %s' % truncate(res))\\n return res[0]['keypair']\",\n \"def generate_key():\\r\\n # generating key\\r\\n key = Fernet.generate_key()\\r\\n\\r\\n key_dir = os.path.join(os.path.dirname(__file__), \\\"resources/key\\\")\\r\\n\\r\\n # writing key in file\\r\\n with open(key_dir, \\\"wb\\\") as keyFile:\\r\\n keyFile.write(key)\",\n \"def test_aws_service_api_keypair_generate_post(self):\\n pass\",\n \"def sym_key_gen(pairing_group=None, sym_key_size=None, debug=0):\\n\\n # If sym_key_size is not defined, set a default value\\n if sym_key_size is None:\\n sym_key_size = SYM_KEY_DEFAULT_SIZE\\n\\n # Clamp the size between SYM_KEY_MIN_SIZE and the system maximum possible value\\n size = clamp(sym_key_size, SYM_KEY_MIN_SIZE, sys.maxsize)\\n\\n # Check if an error occurred during clamping\\n if size is None:\\n logging.error('sym_key_gen clamp size exception')\\n if debug: # ONLY USE FOR DEBUG\\n print('EXCEPTION in sym_key_gen clamp size')\\n raise Exception\\n\\n # Check if size is a power of 2\\n if not math.log2(size).is_integer():\\n logging.error('sym_key_gen size exception')\\n if debug: # ONLY USE FOR DEBUG\\n print('EXCEPTION in sym_key_gen size')\\n raise Exception\\n\\n # Generate and return a random symmetric key with the given size\\n return random_string_gen(pairing_group, sym_key_size)\",\n \"def gen_public_key(n, e):\\n\\n # Assign key parameters\\n key_params = (n, e)\\n # Construct private key\\n key = RSA.construct(key_params)\\n\\n return key.exportKey()\",\n \"def generate_key():\\n return str(uuid.uuid4())\",\n \"def vsce_uokms_server_generate_server_key_pair(self, ctx, server_private_key, server_public_key):\\n vsce_uokms_server_generate_server_key_pair = self._lib.vsce_uokms_server_generate_server_key_pair\\n vsce_uokms_server_generate_server_key_pair.argtypes = [POINTER(vsce_uokms_server_t), POINTER(vsc_buffer_t), POINTER(vsc_buffer_t)]\\n vsce_uokms_server_generate_server_key_pair.restype = c_int\\n return vsce_uokms_server_generate_server_key_pair(ctx, server_private_key, server_public_key)\",\n \"def generate_keypair(bits):\\n p = generate_prime(bits // 2)\\n #print(p)\\n q = generate_prime(bits // 2)\\n #print(q)\\n n = p * q\\n return PrivateKey(p, q, n), PublicKey(n)\",\n \"def generate_keystream(self):\",\n \"def gen_key(app):\\n\\tos.system('lxc-attach -n %s -- ssh-keygen -t rsa -N \\\"\\\" -f key' % app)\",\n \"def generate(cls, keylength = 2048, quiet = False):\\n\\n if not quiet:\\n logger.debug(\\\"Generating new %d-bit RSA key\\\", keylength)\\n if generate_insecure_debug_only_rsa_key is not None:\\n return cls(POW = generate_insecure_debug_only_rsa_key())\\n else:\\n return cls(POW = rpki.POW.Asymmetric.generateRSA(keylength))\",\n \"def generate_key(seed):\\n private_key = sha256(seed)\\n public_key = privtopub(private_key)\\n return {\\\"private\\\": private_key, \\\"public\\\": public_key}\",\n \"def test_create_keypair_only(self):\\n self.keypair_creator = create_keypairs.OpenStackKeypair(self.os_creds,\\n create_keypairs.KeypairSettings(name=keypair_name))\\n self.keypair_creator.create()\\n\\n keypair = nova_utils.keypair_exists(self.keypair_creator.nova, self.keypair_creator.keypair)\\n self.assertEquals(self.keypair_creator.keypair, keypair)\",\n \"def generate_key():\\n key = ''.join([chr(random.randint(0, 0x10)) for _ in range(block_size)])\\n return AES.new(second_key, AES.MODE_ECB).encrypt(pad((key.encode('ascii')), block_size))\",\n \"def create_pem_keys(self):\\n self.random_rsa()\\n\\n return self.keys\",\n \"def generate_key():\\n return get_random_bytes(KEY_SIZE)\",\n \"def create_handshake_key_pair(cls) -> Tuple[bytes, bytes]:\\n ...\",\n \"def generate_private_key(self):\\n if not self.curve:\\n raise NoCurveError(\\\"Curve must be set prior to key generation.\\\")\\n return self.load_private_key(SigningKey.generate(curve=self.curve))\",\n \"def get_new_key() -> rsa.RSAPrivateKeyWithSerialization:\\n\\n return rsa.generate_private_key(\\n backend=default_backend(), public_exponent=65537, key_size=2048\\n )\",\n \"def generate_keys(attributes: List[AttributeName]) -> Tuple[SecretKey, PublicKey]:\\n # Pick uniformly random variables\\n x = G1.order().random()\\n y = {a: G1.order().random() for a in attributes}\\n\\n # take generators of G1 and G2\\n g1 = G1.generator()\\n g2 = G2.generator()\\n\\n # Compute Xs and Ys\\n X1 = g1 ** x\\n X2 = g2 ** x\\n Y1 = {a: g1 ** y_i for a, y_i in y.items()}\\n Y2 = {a: g2 ** y_i for a, y_i in y.items()}\\n\\n # Output public and secret keys\\n pk = PublicKey(attributes, g1, Y1, g2, X2, Y2) # type:ignore\\n sk = SecretKey(x, X1, y)\\n return sk, pk\",\n \"def generate_key(self):\\n cmd = self.generate_key_cmd()\\n self.show(cmd)\\n if self.dryrun:\\n return None\\n s, _, _ = self.as_user(cmd)\\n assert s == 0, ('failed to generate key', cmd)\\n keyname = self.extract_key_name()\\n return keyname\",\n \"def generate_key():\\n key = Fernet.generate_key()\\n with open(\\\"secret.key\\\", \\\"wb\\\") as key_file:\\n key_file.write(key)\",\n \"def generate_key_and_cert():\\n signing_key = rsa.generate_private_key(backend=crypto_default_backend(), public_exponent=65537, key_size=2048)\\n subject = issuer = x509.Name(\\n [\\n x509.NameAttribute(NameOID.COUNTRY_NAME, 'NO'),\\n x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, 'OSLO'),\\n x509.NameAttribute(NameOID.LOCALITY_NAME, 'OSLO'),\\n x509.NameAttribute(NameOID.ORGANIZATION_NAME, 'Intility AS'),\\n x509.NameAttribute(NameOID.COMMON_NAME, 'intility.no'),\\n ]\\n )\\n signing_cert = (\\n x509.CertificateBuilder()\\n .subject_name(subject)\\n .issuer_name(issuer)\\n .public_key(signing_key.public_key())\\n .serial_number(x509.random_serial_number())\\n .not_valid_before(datetime.utcnow())\\n .not_valid_after(\\n # Our certificate will be valid for 10 days\\n datetime.utcnow()\\n + timedelta(days=10)\\n # Sign our certificate with our private key\\n )\\n .sign(signing_key, hashes.SHA256(), crypto_default_backend())\\n .public_bytes(crypto_serialization.Encoding.DER)\\n )\\n return signing_key, signing_cert\",\n \"def create_rsa_key_pair() -> Tuple[str, str]:\\n key = RSA.generate(RSA_KEY_STRENGTH)\\n public_key = key.publickey().export_key().decode()\\n private_key = key.export_key().decode()\\n return public_key, private_key\",\n \"def generate_key():\\n key = Fernet.generate_key()\\n with open(\\\"Secret.key\\\",\\\"wb\\\")as key_file:\\n key_file.write(key)\",\n \"def create_keypair(self, name=None, public_key=None):\\n body = {}\\n if name is not None:\\n body.update({\\\"name\\\": name})\\n if public_key is not None:\\n body.update({\\\"public_key\\\": public_key})\\n return self._create(_keypair.Keypair, **body)\",\n \"def generate_rsa_auxiliary_key_pair() -> AuxiliaryKeyPair:\\n rsa_key_pair = rsa_keypair()\\n return AuxiliaryKeyPair(rsa_key_pair.private_key, rsa_key_pair.public_key)\",\n \"def gnupg_keypair(\\n gnupg_gen_key_conf: Path, gnupg_trust_store: GnuPGTrustStore\\n) -> GnuPGKeypair:\\n\\n LOGGER.debug(\\\"Initializing GPG keypair ...\\\")\\n environment = {\\\"HOME\\\": \\\"/dev/null\\\"}\\n result = subprocess.run(\\n [\\n \\\"gpg\\\",\\n \\\"--batch\\\",\\n \\\"--homedir\\\",\\n str(gnupg_trust_store.gnupg_home),\\n \\\"--gen-key\\\",\\n \\\"--keyid-format\\\",\\n \\\"long\\\",\\n str(gnupg_gen_key_conf),\\n ],\\n capture_output=True,\\n check=True,\\n env=environment,\\n )\\n keyid = re.findall(\\n r\\\"gpg: key (\\\\w+) marked as ultimately trusted\\\", result.stderr.decode(\\\"utf-8\\\")\\n )[0]\\n # LOGGER.debug(\\\" keyid : %s\\\", keyid)\\n\\n result = subprocess.run(\\n [\\n \\\"gpg\\\",\\n \\\"--fingerprint\\\",\\n \\\"--fingerprint\\\", # Double --fingerprint needed for subkeys\\n \\\"--homedir\\\",\\n str(gnupg_trust_store.gnupg_home),\\n \\\"--with-colons\\\",\\n str(keyid),\\n ],\\n capture_output=True,\\n check=True,\\n env=environment,\\n )\\n # Fingerprint order: pubkey [, subkey ]...\\n fingerprints = re.findall(r\\\"fpr:{9}(\\\\w+):\\\", result.stdout.decode(\\\"utf-8\\\"))\\n LOGGER.debug(\\\" Fingerprints:\\\")\\n for fingerprint in fingerprints:\\n LOGGER.debug(\\\" %s\\\", fingerprint)\\n\\n yield GnuPGKeypair(\\n fingerprints=fingerprints,\\n gen_key_conf=gnupg_gen_key_conf,\\n gnupg_home=gnupg_trust_store.gnupg_home,\\n keyid=keyid,\\n )\",\n \"def generate_key():\\n key = Fernet.generate_key()\\n with open(\\\"pass.key\\\", \\\"wb\\\") as key_file:\\n key_file.write(key)\",\n \"def _create_pkey(self, commonname, serial):\\n pkey = PKey()\\n pkey.generate_key(crypto.TYPE_RSA, self.key_bits)\\n private = crypto.dump_privatekey(crypto.FILETYPE_PEM,\\n pkey).decode()\\n key_path = self._get_key_path(commonname, serial)\\n if os.path.exists(key_path):\\n raise FileExistsError(key_path)\\n with open(key_path, 'w') as private_file:\\n private_file.writelines(private)\\n\\n key_link = self._get_key_link(commonname)\\n if os.path.exists(key_link):\\n os.unlink(key_link)\\n os.symlink(os.path.basename(key_path), key_link)\\n\\n return pkey\",\n \"def keygen(\\n args: argparse.Namespace,\\n config: KSKMConfig,\\n p11modules: KSKM_P11,\\n logger: logging.Logger,\\n) -> bool:\\n logger.info(\\\"Generate key\\\")\\n flags = FlagsDNSKEY.ZONE.value | FlagsDNSKEY.SEP.value\\n dnssec_alg = AlgorithmDNSSEC[args.key_alg]\\n if is_algorithm_rsa(dnssec_alg):\\n if args.key_size is None:\\n raise argparse.ArgumentError(\\n args.key_size, \\\"RSA key generation requires key size\\\"\\n )\\n p11key = generate_rsa_key(\\n flags, args.key_size, p11modules, label=args.key_label\\n )\\n elif is_algorithm_ecdsa(dnssec_alg):\\n crv = algorithm_to_curve(dnssec_alg)\\n p11key = generate_ec_key(flags, crv, p11modules, label=args.key_label)\\n else:\\n raise ValueError(f\\\"Unknown key algorithm {repr(args.key_alg)}\\\")\\n\\n if not p11key or not p11key.public_key:\\n raise RuntimeError(\\\"No public key returned by key generation\\\")\\n\\n # Calculate the DNSSEC key tag of the new key and look for a collision in the configuration\\n key_tags: List[int] = []\\n _key = public_key_to_dnssec_key(\\n key=p11key.public_key,\\n key_identifier=p11key.label,\\n algorithm=AlgorithmDNSSEC[args.key_alg],\\n flags=FlagsDNSKEY.SEP.value | FlagsDNSKEY.ZONE.value,\\n ttl=config.ksk_policy.ttl,\\n )\\n logger.info(\\n f\\\"Generated key {p11key.label} has key tag {_key.key_tag} for algorithm={_key.algorithm}, \\\"\\n f\\\"flags=0x{_key.flags:x}\\\"\\n )\\n key_tags += [_key.key_tag]\\n _revoked_key = public_key_to_dnssec_key(\\n key=p11key.public_key,\\n key_identifier=p11key.label,\\n algorithm=AlgorithmDNSSEC[args.key_alg],\\n flags=FlagsDNSKEY.SEP.value | FlagsDNSKEY.ZONE.value | FlagsDNSKEY.REVOKE.value,\\n ttl=config.ksk_policy.ttl,\\n )\\n logger.info(\\n f\\\"Generated key {p11key.label} has key tag {_revoked_key.key_tag} with the REVOKE bit set \\\"\\n f\\\"(flags 0x{_revoked_key.flags:x})\\\"\\n )\\n key_tags += [_revoked_key.key_tag]\\n\\n for _name, ksk in config.ksk_keys.items():\\n if ksk.key_tag in key_tags:\\n logger.error(\\n f\\\"Generated key {p11key.label} has key tags {key_tags} matching \\\"\\n f\\\"KSK key in configuration: {ksk}\\\"\\n )\\n raise RuntimeError(\\\"Key tag collision detected\\\")\\n\\n _now = datetime.utcnow()\\n # create_trustanchor_keydigest wants an KSKKey, but it is not used in the digest calculation\\n _temp_ksk = KSKKey(\\n description=\\\"Newly generated key\\\",\\n label=_now.isoformat(),\\n key_tag=_key.key_tag,\\n algorithm=_key.algorithm,\\n valid_from=_now,\\n valid_until=_now,\\n )\\n _domain = \\\".\\\"\\n _ds = create_trustanchor_keydigest(_temp_ksk, _key, domain=_domain)\\n digest = binascii.hexlify(_ds.digest).decode(\\\"UTF-8\\\").upper()\\n _digest_type = \\\"2\\\" # create_trustanchor_keydigest always does SHA256\\n logger.info(\\n f\\\"DS record for generated key:\\\\n\\\"\\n f\\\"{_domain} IN DS {_key.key_tag} {_key.algorithm.value} {_digest_type} {digest}\\\\n\\\"\\n f\\\">> {' '.join(pgp_wordlist(_ds.digest))}\\\"\\n )\\n\\n return True\",\n \"def generateKeys(self, keys_path, minion_id):\\n #Change directory to keys path\\n os.chdir(keys_path)\\n #Give permission to the salt user\\n self.console_manager.printRed(\\\"Giving permission to the salt user\\\")\\n command = ['sudo', 'chmod', 'a+rwx', '.']\\n self.console_manager.runCommandFromShell(command)\\n #Generate keys\\n self.console_manager.printRed(''.join([\\\"Generating keys for minion id: \\\", minion_id]))\\n command = ['sudo', 'salt-key', ''.join(['--gen-keys=', minion_id])]\\n self.console_manager.runCommandFromShell(command)\\n #Give permission to the salt user\\n self.console_manager.printRed(\\\"Allowing vagrant to handle private keys\\\")\\n command = ['sudo', 'chmod', 'a+rwx', ''.join([minion_id, '.pub']), ''.join([minion_id, '.pem'])]\\n self.console_manager.runCommandFromShell(command)\\n #Add public key to the accepted minion folder\\n self.console_manager.printRed(\\\"Copying the minion public key to the salt master public keys folder\\\")\\n command = ['sudo', 'cp', ''.join([minion_id, '.pub']), ''.join(['/var/lib/salt/pki/master/minions/', minion_id])]\\n self.console_manager.runCommandFromShell(command)\\n command = ['sudo', 'cp', ''.join([minion_id, '.pub']), ''.join(['/etc/salt/pki/master/minions/', minion_id])]\\n self.console_manager.runCommandFromShell(command)\\n return\",\n \"def generate(self, force=False):\\n if not self.check_force_generate(force):\\n return False\\n\\n mkdirs(self.path)\\n\\n command = [openssl, 'genrsa', '-out', self.key_file]\\n if self.password:\\n command += ['-passout', 'pass:{}'.format(self.password)]\\n command += [str(self.key_size)]\\n\\n self.log.info('Generating RSA key')\\n if not run_command(command):\\n raise RuntimeError('RSA key generation failed')\\n\\n if not self.exists():\\n raise RuntimeError(\\n 'Key generation succeeded but key file does not exist. '\\n 'This should not happen', self\\n )\",\n \"def generate_key(self, filename, size):\\n if size != 16 and size != 24 and size != 32:\\n raise ValueError(\\\"AES key size not valid.\\\")\\n key = os.urandom(size)\\n self.export_key(filename, key)\\n return key\",\n \"def gen_key():\\n key = os.urandom(32) # 256 bit\\n return base64.b64encode(key).rstrip('=') # strip off padding\",\n \"def gen_pgp_key(name, email, comment=\\\"generated by sdata\\\"):\\n\\n # we can start by generating a primary key. For this example, we'll use RSA, but it could be DSA or ECDSA as well\\n key = pgpy.PGPKey.new(PubKeyAlgorithm.RSAEncryptOrSign, 4096)\\n\\n # we now have some key material, but our new key doesn't have a user ID yet, and therefore is not yet usable!\\n uid = pgpy.PGPUID.new(name, comment=comment, email=email)\\n\\n # now we must add the new user id to the key. We'll need to specify all of our preferences at this point\\n # because PGPy doesn't have any built-in key preference defaults at this time\\n # this example is similar to GnuPG 2.1.x defaults, with no expiration or preferred keyserver\\n key.add_uid(uid, usage={KeyFlags.Sign, KeyFlags.EncryptCommunications, KeyFlags.EncryptStorage},\\n hashes=[HashAlgorithm.SHA256, HashAlgorithm.SHA384, HashAlgorithm.SHA512, HashAlgorithm.SHA224],\\n ciphers=[SymmetricKeyAlgorithm.AES256, SymmetricKeyAlgorithm.AES192, SymmetricKeyAlgorithm.AES128],\\n compression=[CompressionAlgorithm.ZLIB, CompressionAlgorithm.BZ2, CompressionAlgorithm.ZIP,\\n CompressionAlgorithm.Uncompressed])\\n return key\",\n \"def gen_key(self, key):\\n b_key = self._hash_digest(key)\\n return self._hash_val(b_key, lambda x: x)\",\n \"def create_key() -> RSA.RsaKey:\\n\\n return RSA.generate(1024, Crypto.Random.new().read)\",\n \"def create_keypair(key_name):\\n if os.path.isfile(SSH_FOLDER + key_name + \\\".pem\\\"):\\n return # Key already created\\n ec2 = boto.ec2.connect_to_region(AWS_REGION)\\n key = ec2.create_key_pair(key_name)\\n key.save(SSH_FOLDER)\",\n \"def create_rsa_keypair( config_path = config.CONFIG_PATH(), prefix = RSA_KEYPAIR_PREFIX(), keysize = RSA_KEYPAIR_SIZE(), force = False ) :\\n\\n public_key_path, private_key_path = valid_rsa_keypair_paths( config_path, prefix, force )\\n utils.create_directory( config_path, 0700 )\\n public_key, private_key = rsa.newkeys( keysize )\\n with open( public_key_path, \\\"w\\\" ) as stream :\\n stream.write( public_key.save_pkcs1() )\\n os.chmod( public_key_path, 0644 )\\n with open( private_key_path, \\\"w\\\" ) as stream :\\n stream.write( private_key.save_pkcs1() )\\n os.chmod( private_key_path, 0600 )\\n return public_key_path, private_key_path\",\n \"def generate_key(self)->bytes:\\n return os.urandom(32)\",\n \"def generate_key():\\n # generate random key\\n key = get_random_string()\\n\\n # if it's already taken, generate another\\n if EmailManager.objects.filter(key=key).exists():\\n return EmailManager.generate_key()\\n\\n # return it\\n return key\",\n \"def generate_private_key():\\n\\treturn binascii.hexlify(os.urandom(32)).decode('utf-8').upper()\",\n \"def Generate(size=keyinfo.AES.default_size):\\n key_bytes = util.RandBytes(size // 8)\\n key_string = util.Base64WSEncode(key_bytes)\\n hmac_key = HmacKey.Generate() # use default HMAC-SHA1 key size\\n return AesKey(key_string, hmac_key, size)\",\n \"def _create_rsa_key_pair(self, length, public_exponent=65537):\\n self.logger.info(\\n \\\"Generating an RSA key pair with length: {0}, and \\\"\\n \\\"public_exponent: {1}\\\".format(\\n length, public_exponent\\n )\\n )\\n try:\\n private_key = rsa.generate_private_key(\\n public_exponent=public_exponent,\\n key_size=length,\\n backend=default_backend())\\n public_key = private_key.public_key()\\n\\n private_bytes = private_key.private_bytes(\\n serialization.Encoding.DER,\\n serialization.PrivateFormat.PKCS8,\\n serialization.NoEncryption())\\n public_bytes = public_key.public_bytes(\\n serialization.Encoding.DER,\\n serialization.PublicFormat.PKCS1)\\n except Exception as e:\\n self.logger.exception(e)\\n raise exceptions.CryptographicFailure(\\n \\\"An error occurred while generating the RSA key pair. \\\"\\n \\\"See the server log for more information.\\\"\\n )\\n\\n public_key = {\\n 'value': public_bytes,\\n 'format': enums.KeyFormatType.PKCS_1,\\n 'public_exponent': public_exponent\\n }\\n private_key = {\\n 'value': private_bytes,\\n 'format': enums.KeyFormatType.PKCS_8,\\n 'public_exponent': public_exponent\\n }\\n\\n return public_key, private_key\",\n \"def generate_key_cmd(self, cfg_path=None):\\n # TODO: use tempfile\\n if cfg_path is None:\\n cfg_path = '/tmp/gen-key.cfg'\\n self.create_gen_key_cfg_file(cfg_path)\\n return '/usr/bin/gpg --batch --gen-key {cfg_path}'.format(cfg_path=cfg_path)\",\n \"def generate_key(self, rand = random.SystemRandom()):\\n k = rand.randrange(0, self.n - 1)\\n return k, self.base_mul(k)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71120954","0.68204904","0.65850353","0.6553852","0.65218014","0.6443924","0.6378423","0.63755655","0.6358998","0.6324435","0.63147116","0.6309187","0.62952316","0.6251726","0.61868006","0.61828184","0.61474425","0.6133282","0.61287004","0.60888094","0.6040706","0.60231555","0.60195357","0.6008617","0.5979798","0.597343","0.5961485","0.5926425","0.5888555","0.5860322","0.5832551","0.58271146","0.5826103","0.5816542","0.5814549","0.5814375","0.5811282","0.580594","0.5791124","0.5787104","0.5783938","0.5780605","0.5772091","0.577154","0.57712805","0.5761656","0.5755106","0.57416207","0.5735575","0.5733245","0.5733074","0.57320446","0.5718721","0.5714307","0.5711257","0.5708222","0.5707401","0.5701368","0.57010907","0.5681143","0.5656023","0.56471545","0.56464636","0.56459415","0.56458044","0.5638617","0.5627469","0.56126213","0.5609052","0.559526","0.5586959","0.5567825","0.5563508","0.5558474","0.5553827","0.5553125","0.5548605","0.5540845","0.5537478","0.5534268","0.5534091","0.55316025","0.55031395","0.5498549","0.548971","0.5485279","0.547969","0.5466015","0.5443588","0.5441804","0.5425906","0.54223436","0.5415646","0.54140556","0.5407447","0.5395988","0.53800106","0.5367588","0.5365692","0.5363728"],"string":"[\n \"0.71120954\",\n \"0.68204904\",\n \"0.65850353\",\n \"0.6553852\",\n \"0.65218014\",\n \"0.6443924\",\n \"0.6378423\",\n \"0.63755655\",\n \"0.6358998\",\n \"0.6324435\",\n \"0.63147116\",\n \"0.6309187\",\n \"0.62952316\",\n \"0.6251726\",\n \"0.61868006\",\n \"0.61828184\",\n \"0.61474425\",\n \"0.6133282\",\n \"0.61287004\",\n \"0.60888094\",\n \"0.6040706\",\n \"0.60231555\",\n \"0.60195357\",\n \"0.6008617\",\n \"0.5979798\",\n \"0.597343\",\n \"0.5961485\",\n \"0.5926425\",\n \"0.5888555\",\n \"0.5860322\",\n \"0.5832551\",\n \"0.58271146\",\n \"0.5826103\",\n \"0.5816542\",\n \"0.5814549\",\n \"0.5814375\",\n \"0.5811282\",\n \"0.580594\",\n \"0.5791124\",\n \"0.5787104\",\n \"0.5783938\",\n \"0.5780605\",\n \"0.5772091\",\n \"0.577154\",\n \"0.57712805\",\n \"0.5761656\",\n \"0.5755106\",\n \"0.57416207\",\n \"0.5735575\",\n \"0.5733245\",\n \"0.5733074\",\n \"0.57320446\",\n \"0.5718721\",\n \"0.5714307\",\n \"0.5711257\",\n \"0.5708222\",\n \"0.5707401\",\n \"0.5701368\",\n \"0.57010907\",\n \"0.5681143\",\n \"0.5656023\",\n \"0.56471545\",\n \"0.56464636\",\n \"0.56459415\",\n \"0.56458044\",\n \"0.5638617\",\n \"0.5627469\",\n \"0.56126213\",\n \"0.5609052\",\n \"0.559526\",\n \"0.5586959\",\n \"0.5567825\",\n \"0.5563508\",\n \"0.5558474\",\n \"0.5553827\",\n \"0.5553125\",\n \"0.5548605\",\n \"0.5540845\",\n \"0.5537478\",\n \"0.5534268\",\n \"0.5534091\",\n \"0.55316025\",\n \"0.55031395\",\n \"0.5498549\",\n \"0.548971\",\n \"0.5485279\",\n \"0.547969\",\n \"0.5466015\",\n \"0.5443588\",\n \"0.5441804\",\n \"0.5425906\",\n \"0.54223436\",\n \"0.5415646\",\n \"0.54140556\",\n \"0.5407447\",\n \"0.5395988\",\n \"0.53800106\",\n \"0.5367588\",\n \"0.5365692\",\n \"0.5363728\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8180875"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":898,"cells":{"query":{"kind":"string","value":"Generate a registration ID. Clients should only do this once, at install time."},"document":{"kind":"string","value":"def generateRegistrationId():\n regId = KeyHelper.getRandomSequence()\n return regId"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate_id():\n return uuid4().get_hex()","def generateID(self):\n\n return str(uuid.uuid1())","def get_id(self) -> str:\n return self._register_id","def makeid(cls):\n return str(uuid.uuid4().hex)","def generate_id():\n\treturn \"%s-%s\" % (str(uuid.uuid4())[:4],random.choice(funnames).lower())","def id_generator():\r\n new_id = uuid.uuid4()\r\n return new_id.hex","def gen_uuid():\n return str( uuid.uuid4() )","def gen_uuid():\n return str( uuid.uuid4() )","def generate_id():\n return \"%s-%s\" % (str(uuid.uuid4())[:4],random.choice(funnames).lower())","def _generate_uuid(self):\n\n return uuid.uuid4()","def generate_uuid():\n return uuid.uuid4()","def gen_uuid():\n return str(uuid.uuid4())","def unique_id() -> str:","def req_id_generator() -> str:\n # 8 chars long should be long enough, add the 'Generated' prefix to know not to search for this id in the elb logs\n return f'Generated-{str(uuid.uuid4())[:8]}'","def generateId( self ):\n # try to use the uuid module\n try:\n import uuid\n return uuid.uuid1()\n \n # otherwise, use the random module\n except ImportError:\n import random\n return random.randint(-1000000000000, 1000000000000)","def userIDGen() :\n\treturn __randomString(8)","def gen_uuid() -> str:\n return str(uuid4())","def new_uid():\n return str(uuid.uuid1())[:30]","def generate_user_id() -> str:\n return 'u' + str((uuid.getnode()))","def _generate_tracking_number(self):\n return uuid.uuid4().hex.upper()","def generate_id():\n return str(uuid.uuid4())[:5].replace('e','a')","def _generate_uuid():\n return str(uuid.uuid4())","def generate_uuid():\n return uuid.uuid4()","def generate_uuid():\n return uuid.uuid4().hex","def generate_wallet_id(cls) -> str:\n return str(uuid.uuid4())","def generate_uuid():\n return f'{uuid.uuid1()}'","def gen_id(self) -> str:\n self._id += 1\n return str(self._id)","def create_uid():\n return random_string(5, string.hexdigits.lower())\n # return (\"%x\" % (int(time.time()) * 0x10 % 0x1000000000)\n # + random_string(7, string.hexdigits.lower()))","def generate_key(self):\n return str(uuid4())","def _generate_id() -> str:\n return \"\".join(sample(\"abcdefghjkmopqrstuvqxyz\", 16))","def generate_key():\n return str(uuid.uuid4())","def generate_device_id():\n\n # TODO this is awful, makes me sad, but for now also makes demoing\n # easier We might want to look into an auto-configuration feature for\n # devices, such that ids are not input manually on devices\n\n _attempts = 0\n generated_id = ''\n while _attempts < 10 and len(generated_id) == 0:\n _attempts += 1\n new_id = create_id()\n if Device.query.filter_by(id=new_id).first() is None:\n LOGGER.debug(f\" Generated a new device id {new_id}\")\n return new_id\n\n LOGGER.error(f\" Failed to generate unique device_id\")\n raise HTTPRequestError(500, \"Failed to generate unique device_id\")","def generate_product_number():\n return str(uuid.uuid4())","def _uniq_id():\n return random.getrandbits(64)","def get_unique_id():\n global unique_id_increment\n if unique_id_increment is None:\n unique_id_increment = 0\n unique_id_increment += 1\n return '%d%d' % (int(time.time()), unique_id_increment)","def get_or_create_unique_id(self):\n if not self.unique_id:\n self.unique_id = uuid.uuid4().hex\n return self.unique_id","def generate_token():\n return uuid4()","def get_unique_id():\n global unique_id_increment\n if unique_id_increment is None:\n unique_id_increment = 0\n unique_id_increment += 1\n return \"%d%d\" % (int(time.time()), unique_id_increment)","def get_unique_id(self):\n if not self.unique_id:\n self.unique_id = uuid.uuid4().hex\n return self.unique_id","def generate_uid(length=10):\n uid = uuid.uuid4().hex[0:length]\n return uid","def generateUID(self):\n global previous_id\n \n id = previous_id\n previous_id += 1\n \n return id","def get_generator_id() -> str:\n res = os.name + str(os.getpid()) + str(random.randint(-1000, 1000))\n res = hashlib.sha224(res.encode('utf-8')).digest()\n res = base64.b64encode(res).decode('utf-8')\n return res","def unique_id() -> bytes:","def generateUUID(): # pylint: disable=C0103\r\n return str(uuid.uuid4())","def _generate_id(self, context):\n tmp = datetime.datetime.now()\n tmp = tmp.strftime('%Y%m%d%H%M%S%f')\n tmp += context.peer()\n m = hashlib.md5()\n m.update(tmp.encode('utf-8'))\n return str(m.hexdigest())","def get_unique_id():\n\n return int.from_bytes(os.urandom(3), byteorder='big')","def unique_id(self):\n return (\n \"a80f3d5b-df3d-4e38-bbb7-1025276830cd\"\n )","def generate_trackerid():\n\n trackerid = None\n while trackerid is None or \\\n Profile.objects.filter(trackerid=trackerid).exists():\n trackerid = uuid.uuid4().hex\n return trackerid","def unique_id(self):\n id = \"{}{}{}\".format(\n DOMAIN, self._account, self.sensorName.lower().replace(\" \", \"\")\n )\n return id","def generate_trackerid():\r\n\r\n trackerid = None\r\n while trackerid is None or \\\r\n Profile.objects.filter(trackerid=trackerid).exists():\r\n trackerid = uuid.uuid4().hex\r\n return trackerid","def make_id():\n global _simple_id\n\n import uuid\n from ..settings import settings\n\n if settings.simple_ids(False):\n _simple_id += 1\n new_id = _simple_id\n else:\n new_id = uuid.uuid4()\n return str(new_id)","def build_id():\n return \"test123\"","def generate_user(self):\n token = str(uuid.uuid4())\n return self.generate_subid(token=token, return_user=True)","def generate_message_control_id():\n d = datetime.datetime.utcnow()\n # Strip off the decade, ID only has to be unique for 3 years.\n # So now we have a 16 char timestamp.\n timestamp = d.strftime(\"%y%j%H%M%S%f\")[1:]\n # Add 4 chars of uniqueness\n unique = \"\".join(random.sample(alphanumerics, 4))\n return timestamp + unique","def registration_definition_id(self) -> str:\n return pulumi.get(self, \"registration_definition_id\")","def generate_project_key():\n return shortuuid.ShortUUID().random(length=32)","def generate_session_id():\n return utils.get_32bit_random_num()","def unique_id(self) -> str:\n return pulumi.get(self, \"unique_id\")","def create_id():\n unique_id = UniqueId()\n unique_id.put()\n return unique_id.key().id()","def create_id():\n unique_id = UniqueId()\n unique_id.put()\n return unique_id.key().id()","def unique_identifier(self) -> str:\n return pulumi.get(self, \"unique_identifier\")","def _unique_id():\n id = \"\"\n for i in xrange(0,8):\n id += choice(ascii_letters)\n return id","def genShareID(store):\n return unicode(os.urandom(16).encode('hex'), 'ascii')","def generate_request_id():\n return 'req-%s' % uuid.uuid4()","def generate_request_id():\n return 'req-%s' % uuid.uuid4()","def generate_id(urandom=os.urandom, encode=base64.b64encode):\n return encode(urandom(12)).strip()","def uuid():\n from dallinger.experiment import Experiment\n\n click.echo(Experiment.make_uuid())","def generate_message_id():\n return str(uuid.uuid1())","def unique_id(self):\n return f\"{self.device.id}-{self.key}\"","def _get_unique_id(self):\n now = datetime.now()\n\n u_id = now.second + 60*(now.minute + 60*(now.hour + 24*(now.day + 31*(now.month + 366*(now.year)))))\n return \"instance\" + str(u_id)","def unique_id(self) -> str:\n return self.get_unique_id(wallet=self.wallet_id, nonce=self.nonce)","def guid():\n base_uuid = uuid.uuid4()\n number = base_uuid.int & ((2 ** 20) - 1)\n return base62_encode(number)","def generate_transaction_id():\r\n return str(int(time.time() * 1000))","def generate_random_UID(self):\n\t\tUID = random.randint(0, 0xFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF)\n\t\tprint(f\"Generated random UID {UID}\")\n\t\treturn UUID(int=UID)","def unique_id(self) -> str:\n return f\"{self._host}_{self._name}_{self._unique_id}\"","def unique_id() -> bytes:\n ...","def new_case_id():\n return uuid.uuid4().hex","def new_case_id():\n return uuid.uuid4().hex","def create_id_number(self):\n id_number = get_random_string(8).lower()\n if User.objects.filter(id_number=id_number).first():\n self.create_id_number()\n\n return id_number","def generate_unique_name():\n return 'titanic-' + str(get_mac())","def _generate(self, hashed = True):\r\n\r\n identifier = str(uuid.uuid4())\r\n identifier = identifier.upper()\r\n if not hashed: return identifier\r\n identifier = legacy.bytes(identifier)\r\n hash = hashlib.sha256(identifier)\r\n identifier = hash.hexdigest()\r\n identifier = identifier.upper()\r\n return identifier","def createUniqueRatingId():\n #connector = appEngine.connect()\n ratingID = 'r' + str(ceil(time.time()))\n return ratingID","def get_uuid():\n return str(uuid4())","def __create_periodic_id() -> str:\n now = datetime.now()\n return now.strftime(\"%m%d%Y%H%M%S%f\")","def unique_id(self) -> str:\n return \"{}-{}-{}\".format(*self._id)","def unique_name():\n return \"unique-{0}\".format(uuid.uuid4())","def generate_id(cls):\n cls._index += 1\n return 'fp_%s' % cls._index","def generate_fwan_process_id() -> str:\n return str(uuid.uuid4())","def _generate_id(self, result):\n validation = result.validation\n\n #this is still not optimal - validations may mutate between creation\n #and here\n message = str(type(validation)) + str(validation.__dict__)\n hasher = hashlib.md5()\n hasher.update(message.encode('utf-8'))\n pagerduty_id = hasher.hexdigest()\n\n logger.debug(\"Generated id {} for {}\".format(pagerduty_id, result))\n return pagerduty_id","def unique_id(self) -> str:\n return f\"{self.wallet_id}{self.WALLET_KEY_POSTFIX}\"","def unique_id(self) -> str:\n return '{0}_{1}'.format(self._mac.replace(':', ''), self.entity_id)","def unique_id(self) -> str:\n return f\"{self._mac}_tracker\"","def unique_identifier(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"unique_identifier\")","def _generate_order_id():\n current_milli_time = str(int(round(time.time())))\n rand_str = random_string_generator()\n\n return '%s%s' % (rand_str, current_milli_time)","def create_job_id() -> str:\n return str(uuid.uuid1())","def generate_random_uid():\n choice = string.ascii_uppercase + string.digits\n return ''.join([random.choice(choice) for _ in range(36)])","def generate_timer_id():\n\treturn base64.b64encode(os.urandom(30), b\"Qx\").decode(\"ascii\")","def unique_id(self) -> str:\n return f\"{self._inst.lower()}-{self._sid_data['sid']}_switch-{self._data[self._sid_data['sid_ref']]}\"","def create_tag_id():\n return uuid.uuid1().int","def get_uuid():\n\n return str(uuid.uuid4())"],"string":"[\n \"def generate_id():\\n return uuid4().get_hex()\",\n \"def generateID(self):\\n\\n return str(uuid.uuid1())\",\n \"def get_id(self) -> str:\\n return self._register_id\",\n \"def makeid(cls):\\n return str(uuid.uuid4().hex)\",\n \"def generate_id():\\n\\treturn \\\"%s-%s\\\" % (str(uuid.uuid4())[:4],random.choice(funnames).lower())\",\n \"def id_generator():\\r\\n new_id = uuid.uuid4()\\r\\n return new_id.hex\",\n \"def gen_uuid():\\n return str( uuid.uuid4() )\",\n \"def gen_uuid():\\n return str( uuid.uuid4() )\",\n \"def generate_id():\\n return \\\"%s-%s\\\" % (str(uuid.uuid4())[:4],random.choice(funnames).lower())\",\n \"def _generate_uuid(self):\\n\\n return uuid.uuid4()\",\n \"def generate_uuid():\\n return uuid.uuid4()\",\n \"def gen_uuid():\\n return str(uuid.uuid4())\",\n \"def unique_id() -> str:\",\n \"def req_id_generator() -> str:\\n # 8 chars long should be long enough, add the 'Generated' prefix to know not to search for this id in the elb logs\\n return f'Generated-{str(uuid.uuid4())[:8]}'\",\n \"def generateId( self ):\\n # try to use the uuid module\\n try:\\n import uuid\\n return uuid.uuid1()\\n \\n # otherwise, use the random module\\n except ImportError:\\n import random\\n return random.randint(-1000000000000, 1000000000000)\",\n \"def userIDGen() :\\n\\treturn __randomString(8)\",\n \"def gen_uuid() -> str:\\n return str(uuid4())\",\n \"def new_uid():\\n return str(uuid.uuid1())[:30]\",\n \"def generate_user_id() -> str:\\n return 'u' + str((uuid.getnode()))\",\n \"def _generate_tracking_number(self):\\n return uuid.uuid4().hex.upper()\",\n \"def generate_id():\\n return str(uuid.uuid4())[:5].replace('e','a')\",\n \"def _generate_uuid():\\n return str(uuid.uuid4())\",\n \"def generate_uuid():\\n return uuid.uuid4()\",\n \"def generate_uuid():\\n return uuid.uuid4().hex\",\n \"def generate_wallet_id(cls) -> str:\\n return str(uuid.uuid4())\",\n \"def generate_uuid():\\n return f'{uuid.uuid1()}'\",\n \"def gen_id(self) -> str:\\n self._id += 1\\n return str(self._id)\",\n \"def create_uid():\\n return random_string(5, string.hexdigits.lower())\\n # return (\\\"%x\\\" % (int(time.time()) * 0x10 % 0x1000000000)\\n # + random_string(7, string.hexdigits.lower()))\",\n \"def generate_key(self):\\n return str(uuid4())\",\n \"def _generate_id() -> str:\\n return \\\"\\\".join(sample(\\\"abcdefghjkmopqrstuvqxyz\\\", 16))\",\n \"def generate_key():\\n return str(uuid.uuid4())\",\n \"def generate_device_id():\\n\\n # TODO this is awful, makes me sad, but for now also makes demoing\\n # easier We might want to look into an auto-configuration feature for\\n # devices, such that ids are not input manually on devices\\n\\n _attempts = 0\\n generated_id = ''\\n while _attempts < 10 and len(generated_id) == 0:\\n _attempts += 1\\n new_id = create_id()\\n if Device.query.filter_by(id=new_id).first() is None:\\n LOGGER.debug(f\\\" Generated a new device id {new_id}\\\")\\n return new_id\\n\\n LOGGER.error(f\\\" Failed to generate unique device_id\\\")\\n raise HTTPRequestError(500, \\\"Failed to generate unique device_id\\\")\",\n \"def generate_product_number():\\n return str(uuid.uuid4())\",\n \"def _uniq_id():\\n return random.getrandbits(64)\",\n \"def get_unique_id():\\n global unique_id_increment\\n if unique_id_increment is None:\\n unique_id_increment = 0\\n unique_id_increment += 1\\n return '%d%d' % (int(time.time()), unique_id_increment)\",\n \"def get_or_create_unique_id(self):\\n if not self.unique_id:\\n self.unique_id = uuid.uuid4().hex\\n return self.unique_id\",\n \"def generate_token():\\n return uuid4()\",\n \"def get_unique_id():\\n global unique_id_increment\\n if unique_id_increment is None:\\n unique_id_increment = 0\\n unique_id_increment += 1\\n return \\\"%d%d\\\" % (int(time.time()), unique_id_increment)\",\n \"def get_unique_id(self):\\n if not self.unique_id:\\n self.unique_id = uuid.uuid4().hex\\n return self.unique_id\",\n \"def generate_uid(length=10):\\n uid = uuid.uuid4().hex[0:length]\\n return uid\",\n \"def generateUID(self):\\n global previous_id\\n \\n id = previous_id\\n previous_id += 1\\n \\n return id\",\n \"def get_generator_id() -> str:\\n res = os.name + str(os.getpid()) + str(random.randint(-1000, 1000))\\n res = hashlib.sha224(res.encode('utf-8')).digest()\\n res = base64.b64encode(res).decode('utf-8')\\n return res\",\n \"def unique_id() -> bytes:\",\n \"def generateUUID(): # pylint: disable=C0103\\r\\n return str(uuid.uuid4())\",\n \"def _generate_id(self, context):\\n tmp = datetime.datetime.now()\\n tmp = tmp.strftime('%Y%m%d%H%M%S%f')\\n tmp += context.peer()\\n m = hashlib.md5()\\n m.update(tmp.encode('utf-8'))\\n return str(m.hexdigest())\",\n \"def get_unique_id():\\n\\n return int.from_bytes(os.urandom(3), byteorder='big')\",\n \"def unique_id(self):\\n return (\\n \\\"a80f3d5b-df3d-4e38-bbb7-1025276830cd\\\"\\n )\",\n \"def generate_trackerid():\\n\\n trackerid = None\\n while trackerid is None or \\\\\\n Profile.objects.filter(trackerid=trackerid).exists():\\n trackerid = uuid.uuid4().hex\\n return trackerid\",\n \"def unique_id(self):\\n id = \\\"{}{}{}\\\".format(\\n DOMAIN, self._account, self.sensorName.lower().replace(\\\" \\\", \\\"\\\")\\n )\\n return id\",\n \"def generate_trackerid():\\r\\n\\r\\n trackerid = None\\r\\n while trackerid is None or \\\\\\r\\n Profile.objects.filter(trackerid=trackerid).exists():\\r\\n trackerid = uuid.uuid4().hex\\r\\n return trackerid\",\n \"def make_id():\\n global _simple_id\\n\\n import uuid\\n from ..settings import settings\\n\\n if settings.simple_ids(False):\\n _simple_id += 1\\n new_id = _simple_id\\n else:\\n new_id = uuid.uuid4()\\n return str(new_id)\",\n \"def build_id():\\n return \\\"test123\\\"\",\n \"def generate_user(self):\\n token = str(uuid.uuid4())\\n return self.generate_subid(token=token, return_user=True)\",\n \"def generate_message_control_id():\\n d = datetime.datetime.utcnow()\\n # Strip off the decade, ID only has to be unique for 3 years.\\n # So now we have a 16 char timestamp.\\n timestamp = d.strftime(\\\"%y%j%H%M%S%f\\\")[1:]\\n # Add 4 chars of uniqueness\\n unique = \\\"\\\".join(random.sample(alphanumerics, 4))\\n return timestamp + unique\",\n \"def registration_definition_id(self) -> str:\\n return pulumi.get(self, \\\"registration_definition_id\\\")\",\n \"def generate_project_key():\\n return shortuuid.ShortUUID().random(length=32)\",\n \"def generate_session_id():\\n return utils.get_32bit_random_num()\",\n \"def unique_id(self) -> str:\\n return pulumi.get(self, \\\"unique_id\\\")\",\n \"def create_id():\\n unique_id = UniqueId()\\n unique_id.put()\\n return unique_id.key().id()\",\n \"def create_id():\\n unique_id = UniqueId()\\n unique_id.put()\\n return unique_id.key().id()\",\n \"def unique_identifier(self) -> str:\\n return pulumi.get(self, \\\"unique_identifier\\\")\",\n \"def _unique_id():\\n id = \\\"\\\"\\n for i in xrange(0,8):\\n id += choice(ascii_letters)\\n return id\",\n \"def genShareID(store):\\n return unicode(os.urandom(16).encode('hex'), 'ascii')\",\n \"def generate_request_id():\\n return 'req-%s' % uuid.uuid4()\",\n \"def generate_request_id():\\n return 'req-%s' % uuid.uuid4()\",\n \"def generate_id(urandom=os.urandom, encode=base64.b64encode):\\n return encode(urandom(12)).strip()\",\n \"def uuid():\\n from dallinger.experiment import Experiment\\n\\n click.echo(Experiment.make_uuid())\",\n \"def generate_message_id():\\n return str(uuid.uuid1())\",\n \"def unique_id(self):\\n return f\\\"{self.device.id}-{self.key}\\\"\",\n \"def _get_unique_id(self):\\n now = datetime.now()\\n\\n u_id = now.second + 60*(now.minute + 60*(now.hour + 24*(now.day + 31*(now.month + 366*(now.year)))))\\n return \\\"instance\\\" + str(u_id)\",\n \"def unique_id(self) -> str:\\n return self.get_unique_id(wallet=self.wallet_id, nonce=self.nonce)\",\n \"def guid():\\n base_uuid = uuid.uuid4()\\n number = base_uuid.int & ((2 ** 20) - 1)\\n return base62_encode(number)\",\n \"def generate_transaction_id():\\r\\n return str(int(time.time() * 1000))\",\n \"def generate_random_UID(self):\\n\\t\\tUID = random.randint(0, 0xFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF)\\n\\t\\tprint(f\\\"Generated random UID {UID}\\\")\\n\\t\\treturn UUID(int=UID)\",\n \"def unique_id(self) -> str:\\n return f\\\"{self._host}_{self._name}_{self._unique_id}\\\"\",\n \"def unique_id() -> bytes:\\n ...\",\n \"def new_case_id():\\n return uuid.uuid4().hex\",\n \"def new_case_id():\\n return uuid.uuid4().hex\",\n \"def create_id_number(self):\\n id_number = get_random_string(8).lower()\\n if User.objects.filter(id_number=id_number).first():\\n self.create_id_number()\\n\\n return id_number\",\n \"def generate_unique_name():\\n return 'titanic-' + str(get_mac())\",\n \"def _generate(self, hashed = True):\\r\\n\\r\\n identifier = str(uuid.uuid4())\\r\\n identifier = identifier.upper()\\r\\n if not hashed: return identifier\\r\\n identifier = legacy.bytes(identifier)\\r\\n hash = hashlib.sha256(identifier)\\r\\n identifier = hash.hexdigest()\\r\\n identifier = identifier.upper()\\r\\n return identifier\",\n \"def createUniqueRatingId():\\n #connector = appEngine.connect()\\n ratingID = 'r' + str(ceil(time.time()))\\n return ratingID\",\n \"def get_uuid():\\n return str(uuid4())\",\n \"def __create_periodic_id() -> str:\\n now = datetime.now()\\n return now.strftime(\\\"%m%d%Y%H%M%S%f\\\")\",\n \"def unique_id(self) -> str:\\n return \\\"{}-{}-{}\\\".format(*self._id)\",\n \"def unique_name():\\n return \\\"unique-{0}\\\".format(uuid.uuid4())\",\n \"def generate_id(cls):\\n cls._index += 1\\n return 'fp_%s' % cls._index\",\n \"def generate_fwan_process_id() -> str:\\n return str(uuid.uuid4())\",\n \"def _generate_id(self, result):\\n validation = result.validation\\n\\n #this is still not optimal - validations may mutate between creation\\n #and here\\n message = str(type(validation)) + str(validation.__dict__)\\n hasher = hashlib.md5()\\n hasher.update(message.encode('utf-8'))\\n pagerduty_id = hasher.hexdigest()\\n\\n logger.debug(\\\"Generated id {} for {}\\\".format(pagerduty_id, result))\\n return pagerduty_id\",\n \"def unique_id(self) -> str:\\n return f\\\"{self.wallet_id}{self.WALLET_KEY_POSTFIX}\\\"\",\n \"def unique_id(self) -> str:\\n return '{0}_{1}'.format(self._mac.replace(':', ''), self.entity_id)\",\n \"def unique_id(self) -> str:\\n return f\\\"{self._mac}_tracker\\\"\",\n \"def unique_identifier(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"unique_identifier\\\")\",\n \"def _generate_order_id():\\n current_milli_time = str(int(round(time.time())))\\n rand_str = random_string_generator()\\n\\n return '%s%s' % (rand_str, current_milli_time)\",\n \"def create_job_id() -> str:\\n return str(uuid.uuid1())\",\n \"def generate_random_uid():\\n choice = string.ascii_uppercase + string.digits\\n return ''.join([random.choice(choice) for _ in range(36)])\",\n \"def generate_timer_id():\\n\\treturn base64.b64encode(os.urandom(30), b\\\"Qx\\\").decode(\\\"ascii\\\")\",\n \"def unique_id(self) -> str:\\n return f\\\"{self._inst.lower()}-{self._sid_data['sid']}_switch-{self._data[self._sid_data['sid_ref']]}\\\"\",\n \"def create_tag_id():\\n return uuid.uuid1().int\",\n \"def get_uuid():\\n\\n return str(uuid.uuid4())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7785231","0.7583207","0.74686027","0.74049723","0.7362628","0.72884035","0.72758955","0.72758955","0.7264104","0.7262016","0.72611034","0.72470003","0.7220589","0.721949","0.7148431","0.71448946","0.7102137","0.7092246","0.70777285","0.7075378","0.7058048","0.70572597","0.70404285","0.7021929","0.70185554","0.7004354","0.6991372","0.6982666","0.6977142","0.6976874","0.69265956","0.6922818","0.6920532","0.6920021","0.69151175","0.6894725","0.6881062","0.6872324","0.68658227","0.6851346","0.6846814","0.6846124","0.68284935","0.6819174","0.68134946","0.68121684","0.6811017","0.68078136","0.68037605","0.68033564","0.6787717","0.6780371","0.67772377","0.67704326","0.67666054","0.6738121","0.6736554","0.67172754","0.6714752","0.6714752","0.6711141","0.6695959","0.66789913","0.66786873","0.66786873","0.6672443","0.6662801","0.6655348","0.6636832","0.6636472","0.6617228","0.6610122","0.66025835","0.65934443","0.65799636","0.65629315","0.6561396","0.6561396","0.6545792","0.654485","0.65413284","0.6536594","0.6531077","0.6527765","0.65219384","0.6517499","0.65172976","0.65166265","0.6502527","0.6494201","0.64862734","0.64835685","0.6476718","0.64696026","0.6468187","0.64579165","0.6456893","0.6456785","0.64560705","0.6451179"],"string":"[\n \"0.7785231\",\n \"0.7583207\",\n \"0.74686027\",\n \"0.74049723\",\n \"0.7362628\",\n \"0.72884035\",\n \"0.72758955\",\n \"0.72758955\",\n \"0.7264104\",\n \"0.7262016\",\n \"0.72611034\",\n \"0.72470003\",\n \"0.7220589\",\n \"0.721949\",\n \"0.7148431\",\n \"0.71448946\",\n \"0.7102137\",\n \"0.7092246\",\n \"0.70777285\",\n \"0.7075378\",\n \"0.7058048\",\n \"0.70572597\",\n \"0.70404285\",\n \"0.7021929\",\n \"0.70185554\",\n \"0.7004354\",\n \"0.6991372\",\n \"0.6982666\",\n \"0.6977142\",\n \"0.6976874\",\n \"0.69265956\",\n \"0.6922818\",\n \"0.6920532\",\n \"0.6920021\",\n \"0.69151175\",\n \"0.6894725\",\n \"0.6881062\",\n \"0.6872324\",\n \"0.68658227\",\n \"0.6851346\",\n \"0.6846814\",\n \"0.6846124\",\n \"0.68284935\",\n \"0.6819174\",\n \"0.68134946\",\n \"0.68121684\",\n \"0.6811017\",\n \"0.68078136\",\n \"0.68037605\",\n \"0.68033564\",\n \"0.6787717\",\n \"0.6780371\",\n \"0.67772377\",\n \"0.67704326\",\n \"0.67666054\",\n \"0.6738121\",\n \"0.6736554\",\n \"0.67172754\",\n \"0.6714752\",\n \"0.6714752\",\n \"0.6711141\",\n \"0.6695959\",\n \"0.66789913\",\n \"0.66786873\",\n \"0.66786873\",\n \"0.6672443\",\n \"0.6662801\",\n \"0.6655348\",\n \"0.6636832\",\n \"0.6636472\",\n \"0.6617228\",\n \"0.6610122\",\n \"0.66025835\",\n \"0.65934443\",\n \"0.65799636\",\n \"0.65629315\",\n \"0.6561396\",\n \"0.6561396\",\n \"0.6545792\",\n \"0.654485\",\n \"0.65413284\",\n \"0.6536594\",\n \"0.6531077\",\n \"0.6527765\",\n \"0.65219384\",\n \"0.6517499\",\n \"0.65172976\",\n \"0.65166265\",\n \"0.6502527\",\n \"0.6494201\",\n \"0.64862734\",\n \"0.64835685\",\n \"0.6476718\",\n \"0.64696026\",\n \"0.6468187\",\n \"0.64579165\",\n \"0.6456893\",\n \"0.6456785\",\n \"0.64560705\",\n \"0.6451179\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8603011"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":899,"cells":{"query":{"kind":"string","value":"Generate a list of PreKeys. Clients should do this at install time, and subsequently any time the list of PreKeys stored on the server runs low. PreKey IDs are shorts, so they will eventually be repeated. Clients should store PreKeys in a circular buffer, so that they are repeated as infrequently as possible. start The starting PreKey ID, inclusive. count The number of PreKeys to generate. the list of generated PreKeyRecords."},"document":{"kind":"string","value":"def generatePreKeys(start, count):\n results = []\n start -= 1\n for i in range(0, count):\n preKeyId = ((start + i) % (Medium.MAX_VALUE - 1)) + 1\n results.append(PreKeyRecord(preKeyId, Curve.generateKeyPair()))\n\n return results"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate_keys(self):\n self.keys = []\n key = string_to_bit_array(self.passwd)\n key = self.permutation(key, CP_1) # Perform initial permutation on the key\n g, d = split_into_n(key, 28) # Split into g (LEFT) & d (RIGHT)\n for i in range(16): # Apply the 16 rounds\n g, d = self.shift(g, d, ROUND_KEY_SHIFT[i]) # Shift the key according to the round\n tmp = g + d # Merge them\n self.keys.append(self.permutation(tmp, CP_2)) # Perform the permutation to get the Ki","def gen_keys():","def create(self, record_count, start_id, lock=None):\n\n message_reference_beginning = self.create_random_string(10)\n\n records = []\n\n for i in range(start_id, record_count + start_id):\n record = self.__create_record(i, message_reference_beginning)\n records.append(record)\n\n return records","def __initialSigningKeys(self) -> None:\n seedStr = '0' * 31\n seedNum = ['1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f']\n seedList = []\n for i in range(15):\n seed = seedStr + seedNum[i]\n seedList.append(seed.encode('utf-8'))\n\n for seed in seedList:\n self.signingKeysList.append(SigningKey(seed))\n log.info(\"15 signing keys have been generated successfully\")","def get_generator(strains, reference_id, start, end):\n primary_keys = []\n vals = range(start, end+1)\n for val in vals:\n for strain in strains:\n primary_keys.append(strain+\"_\"+reference_id+\"_\"+str(val))\n return primary_keys","def import_to_reids(record_count=8):\n\ttry:\n\t\tconn = redis.Redis(host=HOST,port=PORT,password=PASSWD)\n\texcept:\n\t\tprint 'connection error'\n\t\tsys.exit(0)\n\n\t# add to a set,transaction with pipeline\n\ttrans = conn.pipeline(transaction=True) \n\tset_name = 'activation_code'\n\ttry:\n\t\tfor i in xrange(record_count):\n\t\t\tcode = activation_code_generaor()\n\t\t\ttrans.sadd(set_name,code)\n\t\ttrans.execute() #commit all commands at a time\n\t\t# show the code\n\t\tprint'success,number of keys in a set:',conn.scard(set_name)\n\texcept:\n\t\tprint 'error,rollback'\n\t\tsys.exit(0)","def create_keys(i):\n sk = elgamal.create_sk()\n secret_keys.append(sk)\n\n keys = [0, 0]\n\n keys[x[i]] = elgamal.gen(sk)\n keys[1 - x[i]] = elgamal.o_gen()\n\n public_keys.append(keys)","def keys_fetch(self):\n with self.env.begin(write=False) as txn:\n cursor = txn.cursor()\n tot = txn.stat()['entries']\n i = 0\n\n path = self.db_path\n base_name = self.base_path\n cache_file_path = os.path.join(path, '_cache_' + base_name + '.pkl')\n print('cache_file_path = ', cache_file_path) # DEBUG\n\n if os.path.isfile(cache_file_path):\n self.keys = pickle.load(open(cache_file_path, 'rb'))\n self._num_examples = tot\n else:\n keys = []\n for key, _ in cursor:\n i += 1\n if i % 1000 == 0 or i == tot:\n print('Fetching {:>8d} /{:>8d} keys'.format(i, tot),\n end='\\r')\n keys.append(key)\n print('\\nDone.')\n self._num_examples = tot\n self.keys = np.asarray(keys)\n pickle.dump(self.keys, open(cache_file_path, 'wb'))","def keys(self):\n key_index = 0x4\n\n for _ in range(0, self._keys_len()):\n key_offset = self.abs_offset_from_hbin_offset(self.unpack_dword(key_index))\n\n d = HBINCell(self._buf, key_offset, self)\n yield NKRecord(self._buf, d.data_offset(), self)\n key_index += 4","def keys(self):\n key_index = 0x4\n\n for _ in range(0, self._keys_len()):\n key_offset = self.abs_offset_from_hbin_offset(self.unpack_dword(key_index))\n\n d = HBINCell(self._buf, key_offset, self)\n yield NKRecord(self._buf, d.data_offset(), self)\n key_index += 8","def generate_begin(self):\n\t\tlist = []\n\t\tlist += self.ks['begin']\n\t\treturn list","def get_key_set():\n keys = [0] * n_families\n for i in range(n_families):\n keys[i] = get_key(i)\n\n return keys","def random_key_gen(self, number):\n if (number > len(self.vocables)):\n raise IndexError('Liste enthaelt nicht genuegend Elemente')\n else:\n k_list = list(self.vocables.keys())\n random.shuffle(k_list)\n for k in k_list[:number]:\n yield k","def made_key(self):\n \n # select a random number from 1 to infinity \n ran_number = random.randint(1,99)\n\n # create a random set based on the first number you chose \n set = xrange(ran_number,28*ran_number,ran_number)\n\n # increase the value of every number in the set \n for item in set:\n item += 3\n Code_Fouad_Teniou.my_key.append(item)\n\n #return a random key \n return Code_Fouad_Teniou.my_key","def __generateUserIDs(self,_count):\n return map(lambda x:self.__getNewUserID(),range(_count))","def _get_primary_keys(self, table_name, num_rows):\n primary_key = self.metadata.get_primary_key(table_name)\n primary_key_values = None\n\n if primary_key:\n field = self.metadata.get_fields(table_name)[primary_key]\n\n generator = self.primary_key.get(table_name)\n\n if generator is None:\n if field['type'] != 'id':\n raise ValueError('Only columns with type `id` can be primary keys')\n\n subtype = field.get('subtype', 'integer')\n if subtype == 'integer':\n generator = itertools.count()\n remaining = np.inf\n elif subtype == 'string':\n regex = field.get('regex', r'^[a-zA-Z]+$')\n generator = exrex.generate(regex)\n remaining = exrex.count(regex)\n elif subtype == 'datetime':\n raise NotImplementedError('Datetime ids are not yet supported')\n else:\n raise ValueError('Only `integer` or `string` id columns are supported.')\n\n self.primary_key[table_name] = generator\n self.remaining_primary_key[table_name] = remaining\n\n else:\n remaining = self.remaining_primary_key[table_name]\n\n if remaining < num_rows:\n raise ValueError(\n 'Not enough unique values for primary key of table {}'\n ' to generate {} samples.'.format(table_name, num_rows)\n )\n\n self.remaining_primary_key[table_name] -= num_rows\n primary_key_values = pd.Series([x for i, x in zip(range(num_rows), generator)])\n\n return primary_key, primary_key_values","def generate_keys(cls, des_key: str) -> List[list]:\n\n keys = []\n des_key = cls.string_to_bit_array(des_key)\n # Apply the initial Permutation on the key\n des_key = cls.permutation_expand(des_key, Tables.PC_1_TABLE)\n # Split it in to LEFT,RIGHT\n left, right = cls.n_split(des_key, 28)\n # Apply the 16 rounds\n for i in range(16):\n # Apply the shift associated with the round (not always 1)\n left, right = cls.shift(left, right, Tables.SHIFT_ARRAY[i])\n # Merge them\n tmp = left + right\n # Apply the Permutation to get the Ki\n keys.append(cls.permutation_expand(tmp, Tables.PC_2_TABLE))\n return keys","def get_next_keys(self):\n P_List = []\n for key in self.Poss_Tree:\n for choice in self.Poss_Tree[key]:\n P_List.append(int(construct_pass(key, choice)))\n return P_List","def create_keypairs(self,\n names=None,\n count=1,\n public_key=None,\n check=True):\n names = names or utils.generate_ids(count=count)\n\n keypairs = []\n for name in names:\n keypair = self._client.create(name, public_key=public_key)\n keypairs.append(keypair)\n\n if check:\n self.check_keypairs_presence(keypairs)\n\n for keypair in keypairs:\n if public_key is not None:\n assert_that(keypair.public_key, equal_to(public_key))\n\n return keypairs","def GetInputFileKeys(version):\n \n if version == 7:\n inputfile_keys = ['DynBrkFi','PtfmFile',\n 'TwrFile','FurlFile','BldFile(1)',\n 'BldFile(2)','BldFile(3)','NoiseFile','ADAMSFile',\n 'LinFile']\n \n elif version == 8:\n errStr = 'Keys for FAST 8 have not been coded yet.'\n ValueError(errStr)\n \n else:\n errStr = 'Uncoded version \\\"{:d}\\\".'.format(version)\n ValueError(errStr)\n \n return inputfile_keys","def _get_keys(self, listOfKeys):\n return self._keys","def generateDictKeys(string, n,step=1):\n if type(string) != str or type(n) != int:\n raise ValueError('Please input string and integer for first and second argument')\n elif step == 1:\n keylist = [string+str(i) for i in range(n)]\n return keylist\n else:\n keylist = [string+str(i) for i in range(0, n*step, step)]\n return keylist","def genKeys():\r\n (pub, priv) = rsa.newkeys(256)\r\n context = {\r\n 'pub': pub,\r\n 'priv': priv\r\n }\r\n return context","def generate_keys(self):\n\n\t\tmin_ext = 1 << self.size_ext - 1\n\t\tmax_ext = 1 << self.size_ext\n\t\t\n\t\t\t\n\t\t# step 1 : chose random primary numbers p and q\n\t\tn = generate_prime(self.min_bound,self.max_bound)\n\t\tself._p = n\n\t\tn = generate_prime(self.min_bound,self.max_bound)\n\t\twhile(n == self._p):\n\t\t\tn = generate_prime(self.min_bound,self.max_bound)\n\t\tself._q = n\n\n\t\t#step 2 : compute n = pq\n\t\tself.n = self._p * self._q\n\n\t\t#step 3 : compute phi(n)\n\t\tself._phi = (self._p - 1) * (self._q - 1)\n\n\t\t#step 4 : chose the exponent\n\t\textension = randint(min_ext,max_ext) << math.ceil(math.log2(self.D))\n\t\textension = extension + self.D\n\t\twhile (gcd(self._phi,n) != 1):\n\t\t\textension = randint(min_ext,max_ext) << math.ceil(math.log2(self.D))\n\t\tself._d = extension\n\n\t\t#step 5 : compute d (private key)\n\t\tself.e = euclide_algorithm(self._d, self._phi)[\"U\"] % self._phi\n\n\t\tprint(\"p = \", self._p)\n\t\tprint(\"q = \", self._q)\n\t\tprint(\"d = \", self._d)","def __gen_keys__(self):\n if self.seed == b'':\n self.seed = urandom(self.seed_size)\n\n n_prev = Node(hash=hash_factory(data=bytes(self.seed)).digest())\n self.keys.insert(0, n_prev)\n\n for i in range(1, self.l + 1):\n n = Node(hash=hash_factory(data=bytes(n_prev.hash)).digest())\n self.keys.insert(0, n)\n n_prev = n\n\n # Add the decoy nodes as parents of pair nodes.\n # The pair nodes will _always_ be the right child of the decoy nodes.\n for i in range(2, self.l + 1, 2):\n n_pair = self.keys[i] # type: Node\n n_impair_prev = self.keys[i-1] # type: Node\n n_pair.parent = Node(hash=bytes(n_impair_prev.hash))\n n_pair.parent.right_child = n_pair","def __generate_key_from_list_of(self, list_of_keys):\r\n list_of_keys = list(list_of_keys)\r\n list_of_keys.sort()\r\n return \",\".join(list_of_keys)","def makePartition(self, count):\n\t\tvalidPart = (nr.random_sample(count) < self.proportion).astype(\"int32\")\n\t\ttrainPart = n.ones(count, dtype=\"int32\") - validPart\n\n\t\treturn self.toIndexes(trainPart), self.toIndexes(validPart)","def iterkeys(self):\n\n for i in xrange(0, self._limit):\n try:\n self[i]\n yield i\n except KeyError:\n pass","def prefix_keys(self, prefix, maxkeys=None):\n # TODO: write better documentation: describe purpose, provide example code\n if maxkeys is None:\n maxkeys = len(self)\n\n return wait(self.proto.fwmkeys(prefix, maxkeys))","def generate_keys(self):\n\n\t\tcondition = False\n\t\t\n\t\t\t\n\t\twhile (not condition) :\n\t\t\t# step 1 : chose random primary numbers p and q\n\t\t\tn = generate_prime(self.min_bound,self.max_bound)\n\t\t\tself._p = n\n\t\t\tn = generate_prime(self.min_bound,self.max_bound)\n\t\t\twhile(n == self._p):\n\t\t\t\tn = generate_prime(self.min_bound,self.max_bound)\n\t\t\tself._q = n\n\n\t\t\t#step 2 : compute n = pq\n\t\t\tself.n = self._p * self._q\n\t\t\t\n\t\t\ta = find_invpow(self.n,4) // 3\n\t\t\tcondition = (self._p > self._q) and (self._p < 2 * self._q)\n\t\t\tif (not condition) :\n\t\t\t\tcontinue\n\n\t\t\tprint(\"step one OK\")\n\n\t\t\t#step 3 : compute phi(n)\n\t\t\tself._phi = (self._p - 1) * (self._q - 1)\n\n\t\t\t#step 4 : chose the exponent\n\t\t\tn = randint(100,a)\n\t\t\twhile (gcd(self._phi,n) != 1):\n\t\t\t\tn = randint(100,self._phi)\n\t\t\tself._d = n\n\n\t\t\t#step 5 : compute d (private key)\n\t\t\tself.e = euclide_algorithm(self._d, self._phi)[\"U\"] % self._phi\n\n\t\t\tcondition = (self._d < a)\n\n\t\tprint(\"p = \", self._p)\n\t\tprint(\"q = \", self._q)\n\t\tprint(\"d = \", self._d)","def getNoOfKeys(self):\n return len(self.__keyList)","def generate_keys(self, p, q, e):\n d = EucAlg(p, q)\n for i in d:\n if i == 0:\n raise Exception(\"p and q are not relatively prime.\")\n\n n = p*q\n phi_n = (p-1)*(q-1)\n d = EucAlg(e, phi_n)\n\n self._private_key = (d[0],n)\n self.public_key = (e,n)","def gen_records(self, count=None):\n if not count:\n count = self.num_rec\n tt = time.localtime(time.time())\n addr = None\n for i in range(count):\n logdbg(\"reading record %d of %d\" % (i+1, count))\n addr, record = self.get_record(addr, tt.tm_year, tt.tm_mon)\n yield addr, record","def nextPrimaryKey( self, table_key=None ):\n\n # Make sure the dictionary key exists and, if not, create with zero as starting value.\n if not table_key in self.primaryKeys:\n self.primaryKeys[ table_key ] = 0\n\n\n # Increment the id.\n self.primaryKeys[ table_key ] += 1\n\n return self.primaryKeys[ table_key ]","def get_keys(self):\r\n\t\tlogger.debug(\"Getting the keys\")\r\n\t\t\r\n\t\treturn db.get_items('keys')","def keys(self, *args, **kwargs):\n return self._list(*args, **kwargs)","def keys(self):\n return DeviceKeyCollection(client=self)","def get_n_keys_params(filepaths):\n params = []\n for f in filepaths:\n with open(f) as json_file:\n data = json.load(json_file)\n params.append(int(data['n_keys']))\n return sorted(params)","def _update_prepend_key(self):\n self.prepend_key -= 1","def get_capture_keys(pen_id: int, start_date: str, end_date: str, inbound_bucket=INBOUND_BUCKET) -> List:\n\n site_id = PEN_SITE_MAPPING[pen_id]\n dates = get_dates_in_range(start_date, end_date)\n capture_keys = []\n for date in dates:\n print('Getting capture keys for pen_id={}, date={}...'.format(pen_id, date))\n for hour in DAYTIME_HOURS_GMT:\n hr = str(hour).zfill(2)\n s3_prefix = 'environment=production/site-id={}/pen-id={}/date={}/hour={}'.format(site_id, pen_id,\n date, hr)\n\n\n generator = s3.get_matching_s3_keys(inbound_bucket, prefix=s3_prefix,\n subsample=1.0,\n suffixes=['capture.json'])\n\n these_capture_keys = [key for key in generator]\n capture_keys.extend(these_capture_keys)\n\n return capture_keys","def generate_new_key(self, index):\n new_key = self.chain_key.subkey(index)\n self._key_generated(new_key, index)","def generate_key(self, rand = random.SystemRandom()):\n k = rand.randrange(0, self.n - 1)\n return k, self.base_mul(k)","def get_required_parkeys(self, include_reffile_switch=True):\n parkeys = []\n for key in self.parkey:\n if isinstance(key, tuple):\n parkeys += list(key)\n else:\n parkeys.append(key)\n if include_reffile_switch and self._reffile_switch != \"NONE\":\n parkeys.append(self._reffile_switch)\n parkeys.extend(list(self.extra_keys))\n parkeys.extend(getattr(self.obs_package, \"extra_keys\", []))\n return parkeys","def create_data_set():\n data_set = {}\n for index in range(1024):\n size = random.randint(1, 100) #nosec\n key = str(index).encode(\"utf-8\")\n data_set[key] = get_random_bytes(size)\n return data_set","def keyGen(key):\n def leftShift(keyBitList):\n \"\"\"Perform a circular left shift on the first and second five bits\"\"\"\n shiftedKey = [None] * KeyLength\n shiftedKey[0:9] = keyBitList[1:10]\n shiftedKey[4] = keyBitList[0]\n shiftedKey[9] = keyBitList[5]\n return shiftedKey\n\n # Converts input key (integer) into a list of binary digits\n keyList = [(key & 1 << i) >> i for i in reversed(range(KeyLength))]\n permKeyList = [None] * KeyLength\n for index, elem in enumerate(P10table):\n permKeyList[index] = keyList[elem - 1]\n shiftedOnceKey = leftShift(permKeyList)\n shiftedTwiceKey = leftShift(leftShift(shiftedOnceKey))\n subKey1 = subKey2 = 0\n for index, elem in enumerate(P8table):\n subKey1 += (128 >> index) * shiftedOnceKey[elem - 1]\n subKey2 += (128 >> index) * shiftedTwiceKey[elem - 1]\n return (subKey1, subKey2)","def create_pem_keys(self):\n self.random_rsa()\n\n return self.keys","def partition_ids(n_objs: int, table_names: List[str]) -> List[DBObject]:\n\n rev_map: Dict[str, int] = {name: 1 for name in table_names}\n while sum(rev_map.values()) < n_objs:\n rev_map[random.choice(table_names)] += 1\n\n obj_list: List[DBObject] = list()\n for table, n in rev_map.items():\n for _ in range(n):\n obj_list.append(DBObject(len(obj_list), table))\n\n return obj_list","def get_keys(self):\n bucket = self.resource.Bucket(self.bucketname)\n return [key.key for key in bucket.objects.all()]","async def get_keys(self, collection):\n raise NotImplementedError","def _iter(self, key, count, increment=1):\n key %= self.size\n while count > 0:\n try:\n yield self.db[key]\n except KeyError:\n # This shouldn't happen, but there's really nothing we can do if it does.\n # Skip over the damaged part of our database, ignoring the missing item.\n pass\n key = (key + increment) % self.size\n count -= 1","def get_random_inchikeys(inchikey_list, train_val_test_split_fractions):\n random.shuffle(inchikey_list)\n\n train_num = int(train_val_test_split_fractions.train * len(inchikey_list))\n val_num = int(train_val_test_split_fractions.validation * len(inchikey_list))\n\n return TrainValTestInchikeys(inchikey_list[:train_num],\n inchikey_list[train_num:train_num + val_num],\n inchikey_list[train_num + val_num:])","def allocate_ids(self, project_id, keys):\n request_pb = _datastore_pb2.AllocateIdsRequest(keys=keys)\n return _rpc(\n self.client._http,\n project_id,\n \"allocateIds\",\n self.client._base_url,\n self.client._client_info,\n request_pb,\n _datastore_pb2.AllocateIdsResponse,\n )","def __iter__(self):\n with SessionContext(self.SessionClass) as session:\n keys = session.query(PAW2_DBObject.key)\n keys = [c[0] for c in keys]\n random.shuffle(keys)\n return keys.__iter__()","def assign_nice_keys(self):\n\n print \"Assigning nice_key values to new documents on {0}...\".format(self.source_client)\n\n empty_nice_keys = self.source_client.find({\"nice_key\": {\"$exists\": False}}, {\"nice_key\": 1})\n\n total_empty_nice_keys = empty_nice_keys.count()\n\n if total_empty_nice_keys:\n\n print \"{0} empty nice key docs found\".format(total_empty_nice_keys)\n progress_report = \"PROCESSED {0}/{1}\".format(\"{0}\", total_empty_nice_keys)\n\n for ct, doc in enumerate(empty_nice_keys):\n\n nice_key = self.generate_nice_key()\n\n if nice_key:\n\n self.update_document_nice_key(doc, nice_key)\n\n elif nice_key is None:\n\n raise Exception(\"FAILED TO GENERATE KEY on doc {0} with ObjectId {1}\".format(ct, doc[\"_id\"]))\n\n if (ct % 10000 == 0):\n\n print progress_report.format(ct + 1)\n\n print progress_report.format(empty_nice_keys.count())","def iterkeys(self):\n self.proto.iterinit()\n try:\n while True:\n yield wait(self.proto.iternext())\n except TyrantError:\n pass","def input_to_hash(self, keys):\n basic_keys = []\n for i, key in enumerate(keys):\n s = ''\n #print(max(key), min(key))\n for val in key:\n s += \"{:04x}\".format(val)\n basic_keys.append(s)\n return basic_keys","def keys(self) -> List:\n pass","def iter_keys(self, search, itersize=None, client=None, version=None):\r\n\r\n if client is None:\r\n client = self.get_client(write=False)\r\n\r\n pattern = self.make_key(search, version=version)\r\n cursor = b\"0\"\r\n\r\n while True:\r\n cursor, data = client.scan(cursor, match=pattern, count=itersize)\r\n\r\n for item in data:\r\n item = smart_text(item)\r\n yield self.reverse_key(item)\r\n\r\n if cursor == b\"0\":\r\n break","def get_init_import_records(self) -> list[ImportRecord]:\n import_records: set[ImportRecord] = set()\n import_records.add(\n ImportRecord(\n ImportString.parent() + ImportString(ServiceModuleName.client.name),\n self.client.name,\n )\n )\n if self.service_resource:\n import_records.add(\n ImportRecord(\n ImportString.parent() + ImportString(ServiceModuleName.service_resource.name),\n self.service_resource.name,\n )\n )\n for waiter in self.waiters:\n import_records.add(\n ImportRecord(\n ImportString.parent() + ImportString(ServiceModuleName.waiter.name),\n waiter.name,\n )\n )\n for paginator in self.paginators:\n import_records.add(\n ImportRecord(\n ImportString.parent() + ImportString(ServiceModuleName.paginator.name),\n paginator.name,\n )\n )\n\n return sorted(import_records)","def get_partition_keys(\n self,\n current_time: Optional[datetime] = None,\n dynamic_partitions_store: Optional[DynamicPartitionsStore] = None,\n ) -> Sequence[T_str]:\n ...","def Keys(self) -> _n_1_t_4:","def ingest(self, secrecies, ncount=1, ncode=coring.MtrDex.Ed25519_Seed,\n dcode=coring.MtrDex.Blake3_256,\n algo=Algos.salty, salt=None, stem=None, tier=None,\n rooted=True, transferable=True, temp=False):\n pidx, rootSalt, rootTier = self.setup() # pidx, salt, tier for ingested sequence\n\n # configure parameters for creating new keys after ingested sequence\n if rooted and salt is None: # use root salt instead of random salt\n salt = rootSalt\n\n if rooted and tier is None: # use root tier as default\n tier = rootTier\n\n creator = Creatory(algo=algo).make(salt=salt, stem=stem, tier=tier)\n\n dt = \"\"\n pubs = []\n oridx = 0\n okidx = 0\n cridx = 0\n ckidx = 0\n ridx = 0\n kidx = 0\n verferies = [] # list of lists of verfers\n first = True\n secrecies = deque(secrecies)\n while secrecies:\n csecrets = secrecies.popleft() # current\n csigners = [coring.Signer(qb64=secret, transferable=transferable)\n for secret in csecrets]\n csize = len(csigners)\n verferies.append([signer.verfer for signer in csigners])\n\n if first:\n pp = PrePrm(pidx=pidx,\n algo=algo,\n salt=creator.salt,\n stem=creator.stem,\n tier=creator.tier)\n pre = csigners[0].verfer.qb64b\n result = self.keeper.putPre(key=pre, val=pre)\n if not result:\n raise ValueError(\"Already incepted pre={}.\".format(pre.decode(\"utf-8\")))\n\n result = self.keeper.putPrm(key=pre, val=json.dumps(asdict(pp)).encode(\"utf-8\"))\n if not result:\n raise ValueError(\"Already incepted prm for pre={}.\".format(pre.decode(\"utf-8\")))\n\n self.setPidx(pidx + 1) # increment for next inception\n first = False\n\n for signer in csigners: # store secrets (private key val keyed by public key)\n self.keeper.putPri(key=signer.verfer.qb64b, val=signer.qb64b)\n\n self.keeper.putPubs(key=riKey(pre, ri=ridx),\n val=json.dumps([signer.verfer.qb64 for signer in csigners]).encode(\"utf-8\"))\n\n odt = dt\n dt = helping.nowIso8601()\n opubs = pubs\n pubs = [signer.verfer.qb64 for signer in csigners]\n okidx = ckidx # old kidx\n oridx = cridx # old ridx\n ckidx = kidx # current kidx\n cridx = ridx # currrent ridx\n ridx += 1 # next ridx\n kidx += csize # next kidx\n\n\n # create nxt signers after ingested signers\n nsigners = creator.create(count=ncount, code=ncode,\n pidx=pidx, ridx=ridx, kidx=kidx,\n transferable=transferable, temp=temp)\n\n digers = [coring.Diger(ser=signer.verfer.qb64b, code=dcode) for signer in nsigners]\n\n for signer in nsigners: # store secrets (private key val keyed by public key)\n self.keeper.putPri(key=signer.verfer.qb64b, val=signer.qb64b)\n\n self.keeper.putPubs(key=riKey(pre, ri=ridx),\n val=json.dumps([signer.verfer.qb64 for signer in nsigners]).encode(\"utf-8\"))\n\n csith = \"{:x}\".format(max(1, math.ceil(len(csigners) / 2)))\n cst = coring.Tholder(sith=csith).sith\n\n nsith = \"{:x}\".format(max(0, math.ceil(len(nsigners) / 2)))\n nst = coring.Tholder(sith=nsith).sith\n\n dt = helping.nowIso8601()\n old=PubLot(pubs=opubs, ridx=oridx, kidx=okidx, st='0', dt=odt)\n new=PubLot(pubs=[signer.verfer.qb64 for signer in csigners],\n ridx=cridx, kidx=ckidx, st=cst, dt=dt)\n nxt=PubLot(pubs=[signer.verfer.qb64 for signer in nsigners],\n ridx=ridx, kidx=kidx, st=nst, dt=dt)\n\n ps = PreSit(old=old, new=new, nxt=nxt)\n result = self.keeper.setSit(key=pre, val=json.dumps(asdict(ps)).encode(\"utf-8\"))\n if not result:\n raise ValueError(\"Problem updating pubsit db for pre={}.\".format(pre))\n\n return (verferies, digers)","def keys_sorted_by_frequency(self, cutoff=100):\n return [key for key, _ in self.counter.most_common()][:cutoff]","def create_DES_subkeys(key):\r\n # permutation with the original key\r\n fullKey = bit_permutation(key, PC1)\r\n\r\n #we split the key into right and left part\r\n lprev, rprev = fullKey[:28], fullKey[28:]\r\n\r\n nFinalBlock = []\r\n #We now proceed through 16 iterations, for 1<=n<=16\r\n for n in range (16):\r\n lPart, rPart = left_shift_rot(lprev, key_shifts[n]), left_shift_rot(rprev, key_shifts[n])\r\n lprev, rprev = lPart, rPart\r\n nFinalBlock += [\"\".join([lPart, rPart])]\r\n\r\n #We now form the keys Kn, for 1<=n<=16, by applying the following permutation table to each of the concatenated pairs rPart, lPart\r\n nPrimeFinalBlock = []\r\n for n in range(16):\r\n nPrimeFinalBlock += [bit_permutation(nFinalBlock[n], PC2)]\r\n\r\n return nPrimeFinalBlock","def __create_beginning_of_counterpoint(self) -> List[LineElement]:\n start_note = self.counterpoint_specifications['start_note']\n start_element = LineElement(\n self.scale.get_element_by_note(start_note),\n self.counterpoint_specifications['start_pause_in_eighths'],\n N_EIGHTHS_PER_MEASURE\n )\n counterpoint = [start_element]\n return counterpoint","def _gpg_keys(self) -> ListKeys:\n return self.gpg.list_keys()","def keys(self):\n return self._sequence","def generate_valid_keys():\n valid_keys = []\n for minimum, maximum in RANGES:\n for i in range(ord(minimum), ord(maximum) + 1):\n valid_keys.append(chr(i))\n return valid_keys","def keys(self):\n return self.get_list(self.cloudman.list_keypairs(),\n kind=\"key\")","def _Next(self, count=None):\n if count is not None and (not isinstance(count, (int, long)) or count <= 0):\n raise datastore_errors.BadArgumentError(\n 'Argument to _Next must be an int greater than 0; received %s (a %s)' %\n (count, typename(count)))\n\n if self.__buffer:\n if count is None:\n entity_list = self.__buffer\n self.__buffer = []\n return entity_list\n elif count <= len(self.__buffer):\n entity_list = self.__buffer[:count]\n del self.__buffer[:count]\n return entity_list\n else:\n entity_list = self.__buffer\n self.__buffer = []\n count -= len(entity_list)\n else:\n entity_list = []\n\n\n if not self.__more_results:\n return entity_list\n\n req = datastore_pb.NextRequest()\n if count is not None:\n req.set_count(count)\n req.mutable_cursor().CopyFrom(self.__cursor)\n result = datastore_pb.QueryResult()\n try:\n apiproxy_stub_map.MakeSyncCall('datastore_v3', 'Next', req, result)\n except apiproxy_errors.ApplicationError, err:\n raise _ToDatastoreError(err)\n\n return entity_list + self._ProcessQueryResult(result)","def initialize(self, keys: List[str]):","def keys(self, args=None, lo=None, hi=None, reverse=None, max=None,\n include=False, txn=None):\n return itertools.imap(ITEMGETTER_1,\n self.pairs(args, lo, hi, reverse, max, include, txn))","def _make_key(self, record_dict: Dict[str, Any]) -> int:\n return self._keys.setdefault(frozenset(record_dict.keys()), len(self._keys))","def keys(self, pattern=\"*\"):\n lenOfPrefix = len(self.appendKeys(\"\"))\n return [key[lenOfPrefix:] for key in\n self.redis.keys(self.appendKeys(pattern))]","def _id_seq(self):\n return list(self.keys())","def return_least_keys(self,\r\n keyset,\r\n numberof=0,\r\n override=False,\r\n no_allcaps=True,\r\n add_number=False):\r\n\r\n if override:\r\n return keyset\r\n if numberof == 0:\r\n numberof = self.default_dict['numberof']\r\n if not keyset:\r\n return []\r\n freq_list = self.order_keys(keyset)\r\n freq_list = [a_temp[0]+self.show_key_freq*add_number\r\n *(a_temp[1]>0)*(' ('+str(a_temp[1])+')')\r\n for a_temp in freq_list][0 : numberof]\r\n if no_allcaps and len(freq_list) > 3:\r\n freq_list = [a_temp for a_temp\r\n in freq_list\r\n if not a_temp.isupper() and not '@' in a_temp]+[a_temp for a_temp\r\n in freq_list if a_temp.isupper() or not '@' in a_temp]\r\n## freq_list = sorted(freq_list, key=lambda x_temp: len(x_temp))\r\n## freq_list.reverse()\r\n\r\n return freq_list","def PCTSignatures_generateInitPoints(initPoints, count, pointDistribution): # real signature unknown; restored from __doc__\n pass","def create_ssh_keys(self):\n self.random_ssh()\n\n return self.keys","def keys(self):\n sql = u\"\"\"\n SELECT `key` FROM `{table}` WHERE 1\n \"\"\".format(table=self.name)\n\n for row in self.conn.execute(sql):\n yield row['key']","def get_pubkey_ids(self, addr):\n\n if len(self.pubkeys) > 0 and self.pubkeys[-1].has_key('fingerprint') \\\n and self.pubkeys[-1].has_key('uids'):\n\n pubkey_ids = []\n # compile pattern before use for better performance\n RCPT_RE = re.compile(addr)\n for k in self.pubkeys:\n for uid in k['uids']:\n match = RCPT_RE.search(uid)\n if match is not None:\n # check for key expiration\n if k['expires'] == '':\n pubkey_ids.append(k['fingerprint'][-16:])\n elif (time()+60) < float(k['expires']):\n pubkey_ids.append(k['fingerprint'][-16:])\n break\n return pubkey_ids","def _getKeyList(self):\n return LinkedList(InternalRack(self, 1))","def _reset_primary_keys_generators(self):\n self.primary_key = dict()\n self.remaining_primary_key = dict()","def generate_rows(n):\n for i in range(n):\n yield [\n # seq\n i,\n # guid-like id\n hashlib.sha224(bytes(i)).hexdigest(),\n # seq\n i,\n # seq\n i,\n # cc_number \n fake.credit_card_number(card_type=None),\n # expire_date\n fake.date_between('-6y', '+0y').strftime(\"%m/%d/%Y\"),\n # billing_address\n fake.address(),\n ]","def keys(self, search, version=None, client=None):\r\n\r\n if client is None:\r\n client = self.get_client(write=False)\r\n\r\n pattern = self.make_key(search, version=version)\r\n try:\r\n encoding_map = [smart_text(k) for k in client.keys(pattern)]\r\n return [self.reverse_key(k) for k in encoding_map]\r\n except ConnectionError:\r\n raise ConnectionInterrupted(connection=client)","def iterkeys(self, essid):\n with SessionContext(self.SessionClass) as session:\n q = session.query(PAW2_DBObject.key)\n q = q.join(PYR2_DBObject).join(ESSID_DBObject)\n q = q.filter(ESSID_DBObject.essid == essid)\n keys = q.all()\n return (c[0] for c in keys)","def get_partition_keys(\n self,\n current_time: Optional[datetime] = None,\n dynamic_partitions_store: Optional[DynamicPartitionsStore] = None,\n ) -> Sequence[str]:\n return self._partition_keys","def __init__(self, path, number_keys=1):\n\n self.path = path\n self.keyring = []\n if os.path.exists(path):\n self.keyring = read_keys(path)\n else:\n for n in range(number_keys):\n key = generate_key(generate_random())\n self.keyring.append(key)\n write_keys(path, self.keyring)","def generate_key(self, size):\n key = bytearray()\n for i in range(0,size):\n random_byte = ord(os.urandom(1))\n key.append(random_byte)\n return key","def get_key_list(self, email=\"\"):\n\t\tif email:\n\t\t\twhere_clause = \" where email = '%s'\" % email\n\t\telse:\n\t\t\twhere_clause = \"\"\n\n\t\treturn self.app.db.query(\n\t\t\t\"\"\"\n\t\t\tselect\n\t\t\t\tapi_key,\n\t\t\t\towner,\n\t\t\t\tapp_name,\n\t\t\t\temail,\n\t\t\t\turl,\n\t\t\t\tcreated\n\t\t\tfrom\n\t\t\t\tapi_keys\n\t\t\t%s\n\t\t\t\"\"\" % where_clause)","def list(self, path, filename=None, start=None, stop=None, recursive=False, directories=False):\n storageScheme, keys = self.getkeys(\n path, filename=filename, directories=directories, recursive=recursive)\n keys = [storageScheme + \":///\" + key.bucket.name + \"/\" + key.name for key in keys]\n keys.sort()\n keys = select(keys, start, stop)\n return keys","def Keys(self) -> NameObjectCollectionBase.KeysCollection:","def get_rowkeys(\n table_instance, rowkeys: List[str], sep: str = \"#\",\n) -> List[models.RowModelOdd]:\n row_model = [_get_single_row(table_instance, rowkey, sep=\":\") for rowkey in rowkeys]\n\n return row_model","def keys(self):\n key_index = 0x4\n\n for _ in range(0, self._keys_len()):\n key_offset = self.abs_offset_from_hbin_offset(self.unpack_dword(key_index))\n d = HBINCell(self._buf, key_offset, self)\n\n try:\n for k in d.child().keys():\n yield k\n except RegistryStructureDoesNotExist:\n raise ParseException(\"Unsupported subkey list encountered.\")\n\n key_index += 4","def keys():","def getNumberOfKeys(self) -> int:\n ...","def generate_brainpool_curves(count: int, p: ZZ, initial_seed: str) -> SimulatedCurves:\n simulated_curves = SimulatedCurves(\"brainpool\", p.nbits(), initial_seed, count)\n curve = Brainpool(initial_seed, p)\n b_seed = None\n for _ in range(count):\n if curve.not_defined():\n curve.set_a()\n if not curve.check_a():\n curve.seed_update()\n curve.clear()\n continue\n b_seed = increment_seed(curve.seed())\n curve.set_b(b_seed)\n if not curve.check_b():\n b_seed = increment_seed(b_seed)\n continue\n if not curve.secure():\n curve.set_seed(increment_seed(b_seed))\n curve.clear()\n continue\n curve.generate_generator(b_seed)\n curve.compute_properties()\n simulated_curves.add_curve(curve)\n curve = Brainpool(curve.seed(), p)\n curve.seed_update()\n\n return simulated_curves","def process_preqs(need_to_load):\n\n n_to_load = 0\n while n_to_load!=len(need_to_load): # i.e. unchanged\n n_to_load=len(need_to_load)\n new_need_to_load = need_to_load.copy()\n for to_load in need_to_load:\n if to_load in preqs_dict:\n all_to_add = preqs_dict[to_load]\n if isinstance(all_to_add,list):\n new_need_to_load.update(all_to_add)\n else:\n new_need_to_load.add(all_to_add)\n need_to_load = new_need_to_load\n\n return need_to_load","def get_all( # type: ignore\n self,\n limit: t.Optional[int] = None, # dead: disable\n offset: t.Optional[int] = None, # dead: disable\n ) -> t.List[KeyValueEntity]:\n return [KeyValueEntity(uuid=k, val=v) for k, v in self._data.items()]","def fetch(self, n: int) -> t.List[Record]:\n self._buffer(n)\n return [\n self._record_buffer.popleft()\n for _ in range(min(n, len(self._record_buffer)))\n ]","def get_next_conf_keys(self):\n C_List = []\n for key in self.Poss_Tree:\n key_c = int(str(key)[-1])\n for choice in self.Poss_Tree[key]:\n if choice == key_c:\n C_List.append(int(construct_pass(key, choice)))\n return C_List"],"string":"[\n \"def generate_keys(self):\\n self.keys = []\\n key = string_to_bit_array(self.passwd)\\n key = self.permutation(key, CP_1) # Perform initial permutation on the key\\n g, d = split_into_n(key, 28) # Split into g (LEFT) & d (RIGHT)\\n for i in range(16): # Apply the 16 rounds\\n g, d = self.shift(g, d, ROUND_KEY_SHIFT[i]) # Shift the key according to the round\\n tmp = g + d # Merge them\\n self.keys.append(self.permutation(tmp, CP_2)) # Perform the permutation to get the Ki\",\n \"def gen_keys():\",\n \"def create(self, record_count, start_id, lock=None):\\n\\n message_reference_beginning = self.create_random_string(10)\\n\\n records = []\\n\\n for i in range(start_id, record_count + start_id):\\n record = self.__create_record(i, message_reference_beginning)\\n records.append(record)\\n\\n return records\",\n \"def __initialSigningKeys(self) -> None:\\n seedStr = '0' * 31\\n seedNum = ['1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f']\\n seedList = []\\n for i in range(15):\\n seed = seedStr + seedNum[i]\\n seedList.append(seed.encode('utf-8'))\\n\\n for seed in seedList:\\n self.signingKeysList.append(SigningKey(seed))\\n log.info(\\\"15 signing keys have been generated successfully\\\")\",\n \"def get_generator(strains, reference_id, start, end):\\n primary_keys = []\\n vals = range(start, end+1)\\n for val in vals:\\n for strain in strains:\\n primary_keys.append(strain+\\\"_\\\"+reference_id+\\\"_\\\"+str(val))\\n return primary_keys\",\n \"def import_to_reids(record_count=8):\\n\\ttry:\\n\\t\\tconn = redis.Redis(host=HOST,port=PORT,password=PASSWD)\\n\\texcept:\\n\\t\\tprint 'connection error'\\n\\t\\tsys.exit(0)\\n\\n\\t# add to a set,transaction with pipeline\\n\\ttrans = conn.pipeline(transaction=True) \\n\\tset_name = 'activation_code'\\n\\ttry:\\n\\t\\tfor i in xrange(record_count):\\n\\t\\t\\tcode = activation_code_generaor()\\n\\t\\t\\ttrans.sadd(set_name,code)\\n\\t\\ttrans.execute() #commit all commands at a time\\n\\t\\t# show the code\\n\\t\\tprint'success,number of keys in a set:',conn.scard(set_name)\\n\\texcept:\\n\\t\\tprint 'error,rollback'\\n\\t\\tsys.exit(0)\",\n \"def create_keys(i):\\n sk = elgamal.create_sk()\\n secret_keys.append(sk)\\n\\n keys = [0, 0]\\n\\n keys[x[i]] = elgamal.gen(sk)\\n keys[1 - x[i]] = elgamal.o_gen()\\n\\n public_keys.append(keys)\",\n \"def keys_fetch(self):\\n with self.env.begin(write=False) as txn:\\n cursor = txn.cursor()\\n tot = txn.stat()['entries']\\n i = 0\\n\\n path = self.db_path\\n base_name = self.base_path\\n cache_file_path = os.path.join(path, '_cache_' + base_name + '.pkl')\\n print('cache_file_path = ', cache_file_path) # DEBUG\\n\\n if os.path.isfile(cache_file_path):\\n self.keys = pickle.load(open(cache_file_path, 'rb'))\\n self._num_examples = tot\\n else:\\n keys = []\\n for key, _ in cursor:\\n i += 1\\n if i % 1000 == 0 or i == tot:\\n print('Fetching {:>8d} /{:>8d} keys'.format(i, tot),\\n end='\\\\r')\\n keys.append(key)\\n print('\\\\nDone.')\\n self._num_examples = tot\\n self.keys = np.asarray(keys)\\n pickle.dump(self.keys, open(cache_file_path, 'wb'))\",\n \"def keys(self):\\n key_index = 0x4\\n\\n for _ in range(0, self._keys_len()):\\n key_offset = self.abs_offset_from_hbin_offset(self.unpack_dword(key_index))\\n\\n d = HBINCell(self._buf, key_offset, self)\\n yield NKRecord(self._buf, d.data_offset(), self)\\n key_index += 4\",\n \"def keys(self):\\n key_index = 0x4\\n\\n for _ in range(0, self._keys_len()):\\n key_offset = self.abs_offset_from_hbin_offset(self.unpack_dword(key_index))\\n\\n d = HBINCell(self._buf, key_offset, self)\\n yield NKRecord(self._buf, d.data_offset(), self)\\n key_index += 8\",\n \"def generate_begin(self):\\n\\t\\tlist = []\\n\\t\\tlist += self.ks['begin']\\n\\t\\treturn list\",\n \"def get_key_set():\\n keys = [0] * n_families\\n for i in range(n_families):\\n keys[i] = get_key(i)\\n\\n return keys\",\n \"def random_key_gen(self, number):\\n if (number > len(self.vocables)):\\n raise IndexError('Liste enthaelt nicht genuegend Elemente')\\n else:\\n k_list = list(self.vocables.keys())\\n random.shuffle(k_list)\\n for k in k_list[:number]:\\n yield k\",\n \"def made_key(self):\\n \\n # select a random number from 1 to infinity \\n ran_number = random.randint(1,99)\\n\\n # create a random set based on the first number you chose \\n set = xrange(ran_number,28*ran_number,ran_number)\\n\\n # increase the value of every number in the set \\n for item in set:\\n item += 3\\n Code_Fouad_Teniou.my_key.append(item)\\n\\n #return a random key \\n return Code_Fouad_Teniou.my_key\",\n \"def __generateUserIDs(self,_count):\\n return map(lambda x:self.__getNewUserID(),range(_count))\",\n \"def _get_primary_keys(self, table_name, num_rows):\\n primary_key = self.metadata.get_primary_key(table_name)\\n primary_key_values = None\\n\\n if primary_key:\\n field = self.metadata.get_fields(table_name)[primary_key]\\n\\n generator = self.primary_key.get(table_name)\\n\\n if generator is None:\\n if field['type'] != 'id':\\n raise ValueError('Only columns with type `id` can be primary keys')\\n\\n subtype = field.get('subtype', 'integer')\\n if subtype == 'integer':\\n generator = itertools.count()\\n remaining = np.inf\\n elif subtype == 'string':\\n regex = field.get('regex', r'^[a-zA-Z]+$')\\n generator = exrex.generate(regex)\\n remaining = exrex.count(regex)\\n elif subtype == 'datetime':\\n raise NotImplementedError('Datetime ids are not yet supported')\\n else:\\n raise ValueError('Only `integer` or `string` id columns are supported.')\\n\\n self.primary_key[table_name] = generator\\n self.remaining_primary_key[table_name] = remaining\\n\\n else:\\n remaining = self.remaining_primary_key[table_name]\\n\\n if remaining < num_rows:\\n raise ValueError(\\n 'Not enough unique values for primary key of table {}'\\n ' to generate {} samples.'.format(table_name, num_rows)\\n )\\n\\n self.remaining_primary_key[table_name] -= num_rows\\n primary_key_values = pd.Series([x for i, x in zip(range(num_rows), generator)])\\n\\n return primary_key, primary_key_values\",\n \"def generate_keys(cls, des_key: str) -> List[list]:\\n\\n keys = []\\n des_key = cls.string_to_bit_array(des_key)\\n # Apply the initial Permutation on the key\\n des_key = cls.permutation_expand(des_key, Tables.PC_1_TABLE)\\n # Split it in to LEFT,RIGHT\\n left, right = cls.n_split(des_key, 28)\\n # Apply the 16 rounds\\n for i in range(16):\\n # Apply the shift associated with the round (not always 1)\\n left, right = cls.shift(left, right, Tables.SHIFT_ARRAY[i])\\n # Merge them\\n tmp = left + right\\n # Apply the Permutation to get the Ki\\n keys.append(cls.permutation_expand(tmp, Tables.PC_2_TABLE))\\n return keys\",\n \"def get_next_keys(self):\\n P_List = []\\n for key in self.Poss_Tree:\\n for choice in self.Poss_Tree[key]:\\n P_List.append(int(construct_pass(key, choice)))\\n return P_List\",\n \"def create_keypairs(self,\\n names=None,\\n count=1,\\n public_key=None,\\n check=True):\\n names = names or utils.generate_ids(count=count)\\n\\n keypairs = []\\n for name in names:\\n keypair = self._client.create(name, public_key=public_key)\\n keypairs.append(keypair)\\n\\n if check:\\n self.check_keypairs_presence(keypairs)\\n\\n for keypair in keypairs:\\n if public_key is not None:\\n assert_that(keypair.public_key, equal_to(public_key))\\n\\n return keypairs\",\n \"def GetInputFileKeys(version):\\n \\n if version == 7:\\n inputfile_keys = ['DynBrkFi','PtfmFile',\\n 'TwrFile','FurlFile','BldFile(1)',\\n 'BldFile(2)','BldFile(3)','NoiseFile','ADAMSFile',\\n 'LinFile']\\n \\n elif version == 8:\\n errStr = 'Keys for FAST 8 have not been coded yet.'\\n ValueError(errStr)\\n \\n else:\\n errStr = 'Uncoded version \\\\\\\"{:d}\\\\\\\".'.format(version)\\n ValueError(errStr)\\n \\n return inputfile_keys\",\n \"def _get_keys(self, listOfKeys):\\n return self._keys\",\n \"def generateDictKeys(string, n,step=1):\\n if type(string) != str or type(n) != int:\\n raise ValueError('Please input string and integer for first and second argument')\\n elif step == 1:\\n keylist = [string+str(i) for i in range(n)]\\n return keylist\\n else:\\n keylist = [string+str(i) for i in range(0, n*step, step)]\\n return keylist\",\n \"def genKeys():\\r\\n (pub, priv) = rsa.newkeys(256)\\r\\n context = {\\r\\n 'pub': pub,\\r\\n 'priv': priv\\r\\n }\\r\\n return context\",\n \"def generate_keys(self):\\n\\n\\t\\tmin_ext = 1 << self.size_ext - 1\\n\\t\\tmax_ext = 1 << self.size_ext\\n\\t\\t\\n\\t\\t\\t\\n\\t\\t# step 1 : chose random primary numbers p and q\\n\\t\\tn = generate_prime(self.min_bound,self.max_bound)\\n\\t\\tself._p = n\\n\\t\\tn = generate_prime(self.min_bound,self.max_bound)\\n\\t\\twhile(n == self._p):\\n\\t\\t\\tn = generate_prime(self.min_bound,self.max_bound)\\n\\t\\tself._q = n\\n\\n\\t\\t#step 2 : compute n = pq\\n\\t\\tself.n = self._p * self._q\\n\\n\\t\\t#step 3 : compute phi(n)\\n\\t\\tself._phi = (self._p - 1) * (self._q - 1)\\n\\n\\t\\t#step 4 : chose the exponent\\n\\t\\textension = randint(min_ext,max_ext) << math.ceil(math.log2(self.D))\\n\\t\\textension = extension + self.D\\n\\t\\twhile (gcd(self._phi,n) != 1):\\n\\t\\t\\textension = randint(min_ext,max_ext) << math.ceil(math.log2(self.D))\\n\\t\\tself._d = extension\\n\\n\\t\\t#step 5 : compute d (private key)\\n\\t\\tself.e = euclide_algorithm(self._d, self._phi)[\\\"U\\\"] % self._phi\\n\\n\\t\\tprint(\\\"p = \\\", self._p)\\n\\t\\tprint(\\\"q = \\\", self._q)\\n\\t\\tprint(\\\"d = \\\", self._d)\",\n \"def __gen_keys__(self):\\n if self.seed == b'':\\n self.seed = urandom(self.seed_size)\\n\\n n_prev = Node(hash=hash_factory(data=bytes(self.seed)).digest())\\n self.keys.insert(0, n_prev)\\n\\n for i in range(1, self.l + 1):\\n n = Node(hash=hash_factory(data=bytes(n_prev.hash)).digest())\\n self.keys.insert(0, n)\\n n_prev = n\\n\\n # Add the decoy nodes as parents of pair nodes.\\n # The pair nodes will _always_ be the right child of the decoy nodes.\\n for i in range(2, self.l + 1, 2):\\n n_pair = self.keys[i] # type: Node\\n n_impair_prev = self.keys[i-1] # type: Node\\n n_pair.parent = Node(hash=bytes(n_impair_prev.hash))\\n n_pair.parent.right_child = n_pair\",\n \"def __generate_key_from_list_of(self, list_of_keys):\\r\\n list_of_keys = list(list_of_keys)\\r\\n list_of_keys.sort()\\r\\n return \\\",\\\".join(list_of_keys)\",\n \"def makePartition(self, count):\\n\\t\\tvalidPart = (nr.random_sample(count) < self.proportion).astype(\\\"int32\\\")\\n\\t\\ttrainPart = n.ones(count, dtype=\\\"int32\\\") - validPart\\n\\n\\t\\treturn self.toIndexes(trainPart), self.toIndexes(validPart)\",\n \"def iterkeys(self):\\n\\n for i in xrange(0, self._limit):\\n try:\\n self[i]\\n yield i\\n except KeyError:\\n pass\",\n \"def prefix_keys(self, prefix, maxkeys=None):\\n # TODO: write better documentation: describe purpose, provide example code\\n if maxkeys is None:\\n maxkeys = len(self)\\n\\n return wait(self.proto.fwmkeys(prefix, maxkeys))\",\n \"def generate_keys(self):\\n\\n\\t\\tcondition = False\\n\\t\\t\\n\\t\\t\\t\\n\\t\\twhile (not condition) :\\n\\t\\t\\t# step 1 : chose random primary numbers p and q\\n\\t\\t\\tn = generate_prime(self.min_bound,self.max_bound)\\n\\t\\t\\tself._p = n\\n\\t\\t\\tn = generate_prime(self.min_bound,self.max_bound)\\n\\t\\t\\twhile(n == self._p):\\n\\t\\t\\t\\tn = generate_prime(self.min_bound,self.max_bound)\\n\\t\\t\\tself._q = n\\n\\n\\t\\t\\t#step 2 : compute n = pq\\n\\t\\t\\tself.n = self._p * self._q\\n\\t\\t\\t\\n\\t\\t\\ta = find_invpow(self.n,4) // 3\\n\\t\\t\\tcondition = (self._p > self._q) and (self._p < 2 * self._q)\\n\\t\\t\\tif (not condition) :\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tprint(\\\"step one OK\\\")\\n\\n\\t\\t\\t#step 3 : compute phi(n)\\n\\t\\t\\tself._phi = (self._p - 1) * (self._q - 1)\\n\\n\\t\\t\\t#step 4 : chose the exponent\\n\\t\\t\\tn = randint(100,a)\\n\\t\\t\\twhile (gcd(self._phi,n) != 1):\\n\\t\\t\\t\\tn = randint(100,self._phi)\\n\\t\\t\\tself._d = n\\n\\n\\t\\t\\t#step 5 : compute d (private key)\\n\\t\\t\\tself.e = euclide_algorithm(self._d, self._phi)[\\\"U\\\"] % self._phi\\n\\n\\t\\t\\tcondition = (self._d < a)\\n\\n\\t\\tprint(\\\"p = \\\", self._p)\\n\\t\\tprint(\\\"q = \\\", self._q)\\n\\t\\tprint(\\\"d = \\\", self._d)\",\n \"def getNoOfKeys(self):\\n return len(self.__keyList)\",\n \"def generate_keys(self, p, q, e):\\n d = EucAlg(p, q)\\n for i in d:\\n if i == 0:\\n raise Exception(\\\"p and q are not relatively prime.\\\")\\n\\n n = p*q\\n phi_n = (p-1)*(q-1)\\n d = EucAlg(e, phi_n)\\n\\n self._private_key = (d[0],n)\\n self.public_key = (e,n)\",\n \"def gen_records(self, count=None):\\n if not count:\\n count = self.num_rec\\n tt = time.localtime(time.time())\\n addr = None\\n for i in range(count):\\n logdbg(\\\"reading record %d of %d\\\" % (i+1, count))\\n addr, record = self.get_record(addr, tt.tm_year, tt.tm_mon)\\n yield addr, record\",\n \"def nextPrimaryKey( self, table_key=None ):\\n\\n # Make sure the dictionary key exists and, if not, create with zero as starting value.\\n if not table_key in self.primaryKeys:\\n self.primaryKeys[ table_key ] = 0\\n\\n\\n # Increment the id.\\n self.primaryKeys[ table_key ] += 1\\n\\n return self.primaryKeys[ table_key ]\",\n \"def get_keys(self):\\r\\n\\t\\tlogger.debug(\\\"Getting the keys\\\")\\r\\n\\t\\t\\r\\n\\t\\treturn db.get_items('keys')\",\n \"def keys(self, *args, **kwargs):\\n return self._list(*args, **kwargs)\",\n \"def keys(self):\\n return DeviceKeyCollection(client=self)\",\n \"def get_n_keys_params(filepaths):\\n params = []\\n for f in filepaths:\\n with open(f) as json_file:\\n data = json.load(json_file)\\n params.append(int(data['n_keys']))\\n return sorted(params)\",\n \"def _update_prepend_key(self):\\n self.prepend_key -= 1\",\n \"def get_capture_keys(pen_id: int, start_date: str, end_date: str, inbound_bucket=INBOUND_BUCKET) -> List:\\n\\n site_id = PEN_SITE_MAPPING[pen_id]\\n dates = get_dates_in_range(start_date, end_date)\\n capture_keys = []\\n for date in dates:\\n print('Getting capture keys for pen_id={}, date={}...'.format(pen_id, date))\\n for hour in DAYTIME_HOURS_GMT:\\n hr = str(hour).zfill(2)\\n s3_prefix = 'environment=production/site-id={}/pen-id={}/date={}/hour={}'.format(site_id, pen_id,\\n date, hr)\\n\\n\\n generator = s3.get_matching_s3_keys(inbound_bucket, prefix=s3_prefix,\\n subsample=1.0,\\n suffixes=['capture.json'])\\n\\n these_capture_keys = [key for key in generator]\\n capture_keys.extend(these_capture_keys)\\n\\n return capture_keys\",\n \"def generate_new_key(self, index):\\n new_key = self.chain_key.subkey(index)\\n self._key_generated(new_key, index)\",\n \"def generate_key(self, rand = random.SystemRandom()):\\n k = rand.randrange(0, self.n - 1)\\n return k, self.base_mul(k)\",\n \"def get_required_parkeys(self, include_reffile_switch=True):\\n parkeys = []\\n for key in self.parkey:\\n if isinstance(key, tuple):\\n parkeys += list(key)\\n else:\\n parkeys.append(key)\\n if include_reffile_switch and self._reffile_switch != \\\"NONE\\\":\\n parkeys.append(self._reffile_switch)\\n parkeys.extend(list(self.extra_keys))\\n parkeys.extend(getattr(self.obs_package, \\\"extra_keys\\\", []))\\n return parkeys\",\n \"def create_data_set():\\n data_set = {}\\n for index in range(1024):\\n size = random.randint(1, 100) #nosec\\n key = str(index).encode(\\\"utf-8\\\")\\n data_set[key] = get_random_bytes(size)\\n return data_set\",\n \"def keyGen(key):\\n def leftShift(keyBitList):\\n \\\"\\\"\\\"Perform a circular left shift on the first and second five bits\\\"\\\"\\\"\\n shiftedKey = [None] * KeyLength\\n shiftedKey[0:9] = keyBitList[1:10]\\n shiftedKey[4] = keyBitList[0]\\n shiftedKey[9] = keyBitList[5]\\n return shiftedKey\\n\\n # Converts input key (integer) into a list of binary digits\\n keyList = [(key & 1 << i) >> i for i in reversed(range(KeyLength))]\\n permKeyList = [None] * KeyLength\\n for index, elem in enumerate(P10table):\\n permKeyList[index] = keyList[elem - 1]\\n shiftedOnceKey = leftShift(permKeyList)\\n shiftedTwiceKey = leftShift(leftShift(shiftedOnceKey))\\n subKey1 = subKey2 = 0\\n for index, elem in enumerate(P8table):\\n subKey1 += (128 >> index) * shiftedOnceKey[elem - 1]\\n subKey2 += (128 >> index) * shiftedTwiceKey[elem - 1]\\n return (subKey1, subKey2)\",\n \"def create_pem_keys(self):\\n self.random_rsa()\\n\\n return self.keys\",\n \"def partition_ids(n_objs: int, table_names: List[str]) -> List[DBObject]:\\n\\n rev_map: Dict[str, int] = {name: 1 for name in table_names}\\n while sum(rev_map.values()) < n_objs:\\n rev_map[random.choice(table_names)] += 1\\n\\n obj_list: List[DBObject] = list()\\n for table, n in rev_map.items():\\n for _ in range(n):\\n obj_list.append(DBObject(len(obj_list), table))\\n\\n return obj_list\",\n \"def get_keys(self):\\n bucket = self.resource.Bucket(self.bucketname)\\n return [key.key for key in bucket.objects.all()]\",\n \"async def get_keys(self, collection):\\n raise NotImplementedError\",\n \"def _iter(self, key, count, increment=1):\\n key %= self.size\\n while count > 0:\\n try:\\n yield self.db[key]\\n except KeyError:\\n # This shouldn't happen, but there's really nothing we can do if it does.\\n # Skip over the damaged part of our database, ignoring the missing item.\\n pass\\n key = (key + increment) % self.size\\n count -= 1\",\n \"def get_random_inchikeys(inchikey_list, train_val_test_split_fractions):\\n random.shuffle(inchikey_list)\\n\\n train_num = int(train_val_test_split_fractions.train * len(inchikey_list))\\n val_num = int(train_val_test_split_fractions.validation * len(inchikey_list))\\n\\n return TrainValTestInchikeys(inchikey_list[:train_num],\\n inchikey_list[train_num:train_num + val_num],\\n inchikey_list[train_num + val_num:])\",\n \"def allocate_ids(self, project_id, keys):\\n request_pb = _datastore_pb2.AllocateIdsRequest(keys=keys)\\n return _rpc(\\n self.client._http,\\n project_id,\\n \\\"allocateIds\\\",\\n self.client._base_url,\\n self.client._client_info,\\n request_pb,\\n _datastore_pb2.AllocateIdsResponse,\\n )\",\n \"def __iter__(self):\\n with SessionContext(self.SessionClass) as session:\\n keys = session.query(PAW2_DBObject.key)\\n keys = [c[0] for c in keys]\\n random.shuffle(keys)\\n return keys.__iter__()\",\n \"def assign_nice_keys(self):\\n\\n print \\\"Assigning nice_key values to new documents on {0}...\\\".format(self.source_client)\\n\\n empty_nice_keys = self.source_client.find({\\\"nice_key\\\": {\\\"$exists\\\": False}}, {\\\"nice_key\\\": 1})\\n\\n total_empty_nice_keys = empty_nice_keys.count()\\n\\n if total_empty_nice_keys:\\n\\n print \\\"{0} empty nice key docs found\\\".format(total_empty_nice_keys)\\n progress_report = \\\"PROCESSED {0}/{1}\\\".format(\\\"{0}\\\", total_empty_nice_keys)\\n\\n for ct, doc in enumerate(empty_nice_keys):\\n\\n nice_key = self.generate_nice_key()\\n\\n if nice_key:\\n\\n self.update_document_nice_key(doc, nice_key)\\n\\n elif nice_key is None:\\n\\n raise Exception(\\\"FAILED TO GENERATE KEY on doc {0} with ObjectId {1}\\\".format(ct, doc[\\\"_id\\\"]))\\n\\n if (ct % 10000 == 0):\\n\\n print progress_report.format(ct + 1)\\n\\n print progress_report.format(empty_nice_keys.count())\",\n \"def iterkeys(self):\\n self.proto.iterinit()\\n try:\\n while True:\\n yield wait(self.proto.iternext())\\n except TyrantError:\\n pass\",\n \"def input_to_hash(self, keys):\\n basic_keys = []\\n for i, key in enumerate(keys):\\n s = ''\\n #print(max(key), min(key))\\n for val in key:\\n s += \\\"{:04x}\\\".format(val)\\n basic_keys.append(s)\\n return basic_keys\",\n \"def keys(self) -> List:\\n pass\",\n \"def iter_keys(self, search, itersize=None, client=None, version=None):\\r\\n\\r\\n if client is None:\\r\\n client = self.get_client(write=False)\\r\\n\\r\\n pattern = self.make_key(search, version=version)\\r\\n cursor = b\\\"0\\\"\\r\\n\\r\\n while True:\\r\\n cursor, data = client.scan(cursor, match=pattern, count=itersize)\\r\\n\\r\\n for item in data:\\r\\n item = smart_text(item)\\r\\n yield self.reverse_key(item)\\r\\n\\r\\n if cursor == b\\\"0\\\":\\r\\n break\",\n \"def get_init_import_records(self) -> list[ImportRecord]:\\n import_records: set[ImportRecord] = set()\\n import_records.add(\\n ImportRecord(\\n ImportString.parent() + ImportString(ServiceModuleName.client.name),\\n self.client.name,\\n )\\n )\\n if self.service_resource:\\n import_records.add(\\n ImportRecord(\\n ImportString.parent() + ImportString(ServiceModuleName.service_resource.name),\\n self.service_resource.name,\\n )\\n )\\n for waiter in self.waiters:\\n import_records.add(\\n ImportRecord(\\n ImportString.parent() + ImportString(ServiceModuleName.waiter.name),\\n waiter.name,\\n )\\n )\\n for paginator in self.paginators:\\n import_records.add(\\n ImportRecord(\\n ImportString.parent() + ImportString(ServiceModuleName.paginator.name),\\n paginator.name,\\n )\\n )\\n\\n return sorted(import_records)\",\n \"def get_partition_keys(\\n self,\\n current_time: Optional[datetime] = None,\\n dynamic_partitions_store: Optional[DynamicPartitionsStore] = None,\\n ) -> Sequence[T_str]:\\n ...\",\n \"def Keys(self) -> _n_1_t_4:\",\n \"def ingest(self, secrecies, ncount=1, ncode=coring.MtrDex.Ed25519_Seed,\\n dcode=coring.MtrDex.Blake3_256,\\n algo=Algos.salty, salt=None, stem=None, tier=None,\\n rooted=True, transferable=True, temp=False):\\n pidx, rootSalt, rootTier = self.setup() # pidx, salt, tier for ingested sequence\\n\\n # configure parameters for creating new keys after ingested sequence\\n if rooted and salt is None: # use root salt instead of random salt\\n salt = rootSalt\\n\\n if rooted and tier is None: # use root tier as default\\n tier = rootTier\\n\\n creator = Creatory(algo=algo).make(salt=salt, stem=stem, tier=tier)\\n\\n dt = \\\"\\\"\\n pubs = []\\n oridx = 0\\n okidx = 0\\n cridx = 0\\n ckidx = 0\\n ridx = 0\\n kidx = 0\\n verferies = [] # list of lists of verfers\\n first = True\\n secrecies = deque(secrecies)\\n while secrecies:\\n csecrets = secrecies.popleft() # current\\n csigners = [coring.Signer(qb64=secret, transferable=transferable)\\n for secret in csecrets]\\n csize = len(csigners)\\n verferies.append([signer.verfer for signer in csigners])\\n\\n if first:\\n pp = PrePrm(pidx=pidx,\\n algo=algo,\\n salt=creator.salt,\\n stem=creator.stem,\\n tier=creator.tier)\\n pre = csigners[0].verfer.qb64b\\n result = self.keeper.putPre(key=pre, val=pre)\\n if not result:\\n raise ValueError(\\\"Already incepted pre={}.\\\".format(pre.decode(\\\"utf-8\\\")))\\n\\n result = self.keeper.putPrm(key=pre, val=json.dumps(asdict(pp)).encode(\\\"utf-8\\\"))\\n if not result:\\n raise ValueError(\\\"Already incepted prm for pre={}.\\\".format(pre.decode(\\\"utf-8\\\")))\\n\\n self.setPidx(pidx + 1) # increment for next inception\\n first = False\\n\\n for signer in csigners: # store secrets (private key val keyed by public key)\\n self.keeper.putPri(key=signer.verfer.qb64b, val=signer.qb64b)\\n\\n self.keeper.putPubs(key=riKey(pre, ri=ridx),\\n val=json.dumps([signer.verfer.qb64 for signer in csigners]).encode(\\\"utf-8\\\"))\\n\\n odt = dt\\n dt = helping.nowIso8601()\\n opubs = pubs\\n pubs = [signer.verfer.qb64 for signer in csigners]\\n okidx = ckidx # old kidx\\n oridx = cridx # old ridx\\n ckidx = kidx # current kidx\\n cridx = ridx # currrent ridx\\n ridx += 1 # next ridx\\n kidx += csize # next kidx\\n\\n\\n # create nxt signers after ingested signers\\n nsigners = creator.create(count=ncount, code=ncode,\\n pidx=pidx, ridx=ridx, kidx=kidx,\\n transferable=transferable, temp=temp)\\n\\n digers = [coring.Diger(ser=signer.verfer.qb64b, code=dcode) for signer in nsigners]\\n\\n for signer in nsigners: # store secrets (private key val keyed by public key)\\n self.keeper.putPri(key=signer.verfer.qb64b, val=signer.qb64b)\\n\\n self.keeper.putPubs(key=riKey(pre, ri=ridx),\\n val=json.dumps([signer.verfer.qb64 for signer in nsigners]).encode(\\\"utf-8\\\"))\\n\\n csith = \\\"{:x}\\\".format(max(1, math.ceil(len(csigners) / 2)))\\n cst = coring.Tholder(sith=csith).sith\\n\\n nsith = \\\"{:x}\\\".format(max(0, math.ceil(len(nsigners) / 2)))\\n nst = coring.Tholder(sith=nsith).sith\\n\\n dt = helping.nowIso8601()\\n old=PubLot(pubs=opubs, ridx=oridx, kidx=okidx, st='0', dt=odt)\\n new=PubLot(pubs=[signer.verfer.qb64 for signer in csigners],\\n ridx=cridx, kidx=ckidx, st=cst, dt=dt)\\n nxt=PubLot(pubs=[signer.verfer.qb64 for signer in nsigners],\\n ridx=ridx, kidx=kidx, st=nst, dt=dt)\\n\\n ps = PreSit(old=old, new=new, nxt=nxt)\\n result = self.keeper.setSit(key=pre, val=json.dumps(asdict(ps)).encode(\\\"utf-8\\\"))\\n if not result:\\n raise ValueError(\\\"Problem updating pubsit db for pre={}.\\\".format(pre))\\n\\n return (verferies, digers)\",\n \"def keys_sorted_by_frequency(self, cutoff=100):\\n return [key for key, _ in self.counter.most_common()][:cutoff]\",\n \"def create_DES_subkeys(key):\\r\\n # permutation with the original key\\r\\n fullKey = bit_permutation(key, PC1)\\r\\n\\r\\n #we split the key into right and left part\\r\\n lprev, rprev = fullKey[:28], fullKey[28:]\\r\\n\\r\\n nFinalBlock = []\\r\\n #We now proceed through 16 iterations, for 1<=n<=16\\r\\n for n in range (16):\\r\\n lPart, rPart = left_shift_rot(lprev, key_shifts[n]), left_shift_rot(rprev, key_shifts[n])\\r\\n lprev, rprev = lPart, rPart\\r\\n nFinalBlock += [\\\"\\\".join([lPart, rPart])]\\r\\n\\r\\n #We now form the keys Kn, for 1<=n<=16, by applying the following permutation table to each of the concatenated pairs rPart, lPart\\r\\n nPrimeFinalBlock = []\\r\\n for n in range(16):\\r\\n nPrimeFinalBlock += [bit_permutation(nFinalBlock[n], PC2)]\\r\\n\\r\\n return nPrimeFinalBlock\",\n \"def __create_beginning_of_counterpoint(self) -> List[LineElement]:\\n start_note = self.counterpoint_specifications['start_note']\\n start_element = LineElement(\\n self.scale.get_element_by_note(start_note),\\n self.counterpoint_specifications['start_pause_in_eighths'],\\n N_EIGHTHS_PER_MEASURE\\n )\\n counterpoint = [start_element]\\n return counterpoint\",\n \"def _gpg_keys(self) -> ListKeys:\\n return self.gpg.list_keys()\",\n \"def keys(self):\\n return self._sequence\",\n \"def generate_valid_keys():\\n valid_keys = []\\n for minimum, maximum in RANGES:\\n for i in range(ord(minimum), ord(maximum) + 1):\\n valid_keys.append(chr(i))\\n return valid_keys\",\n \"def keys(self):\\n return self.get_list(self.cloudman.list_keypairs(),\\n kind=\\\"key\\\")\",\n \"def _Next(self, count=None):\\n if count is not None and (not isinstance(count, (int, long)) or count <= 0):\\n raise datastore_errors.BadArgumentError(\\n 'Argument to _Next must be an int greater than 0; received %s (a %s)' %\\n (count, typename(count)))\\n\\n if self.__buffer:\\n if count is None:\\n entity_list = self.__buffer\\n self.__buffer = []\\n return entity_list\\n elif count <= len(self.__buffer):\\n entity_list = self.__buffer[:count]\\n del self.__buffer[:count]\\n return entity_list\\n else:\\n entity_list = self.__buffer\\n self.__buffer = []\\n count -= len(entity_list)\\n else:\\n entity_list = []\\n\\n\\n if not self.__more_results:\\n return entity_list\\n\\n req = datastore_pb.NextRequest()\\n if count is not None:\\n req.set_count(count)\\n req.mutable_cursor().CopyFrom(self.__cursor)\\n result = datastore_pb.QueryResult()\\n try:\\n apiproxy_stub_map.MakeSyncCall('datastore_v3', 'Next', req, result)\\n except apiproxy_errors.ApplicationError, err:\\n raise _ToDatastoreError(err)\\n\\n return entity_list + self._ProcessQueryResult(result)\",\n \"def initialize(self, keys: List[str]):\",\n \"def keys(self, args=None, lo=None, hi=None, reverse=None, max=None,\\n include=False, txn=None):\\n return itertools.imap(ITEMGETTER_1,\\n self.pairs(args, lo, hi, reverse, max, include, txn))\",\n \"def _make_key(self, record_dict: Dict[str, Any]) -> int:\\n return self._keys.setdefault(frozenset(record_dict.keys()), len(self._keys))\",\n \"def keys(self, pattern=\\\"*\\\"):\\n lenOfPrefix = len(self.appendKeys(\\\"\\\"))\\n return [key[lenOfPrefix:] for key in\\n self.redis.keys(self.appendKeys(pattern))]\",\n \"def _id_seq(self):\\n return list(self.keys())\",\n \"def return_least_keys(self,\\r\\n keyset,\\r\\n numberof=0,\\r\\n override=False,\\r\\n no_allcaps=True,\\r\\n add_number=False):\\r\\n\\r\\n if override:\\r\\n return keyset\\r\\n if numberof == 0:\\r\\n numberof = self.default_dict['numberof']\\r\\n if not keyset:\\r\\n return []\\r\\n freq_list = self.order_keys(keyset)\\r\\n freq_list = [a_temp[0]+self.show_key_freq*add_number\\r\\n *(a_temp[1]>0)*(' ('+str(a_temp[1])+')')\\r\\n for a_temp in freq_list][0 : numberof]\\r\\n if no_allcaps and len(freq_list) > 3:\\r\\n freq_list = [a_temp for a_temp\\r\\n in freq_list\\r\\n if not a_temp.isupper() and not '@' in a_temp]+[a_temp for a_temp\\r\\n in freq_list if a_temp.isupper() or not '@' in a_temp]\\r\\n## freq_list = sorted(freq_list, key=lambda x_temp: len(x_temp))\\r\\n## freq_list.reverse()\\r\\n\\r\\n return freq_list\",\n \"def PCTSignatures_generateInitPoints(initPoints, count, pointDistribution): # real signature unknown; restored from __doc__\\n pass\",\n \"def create_ssh_keys(self):\\n self.random_ssh()\\n\\n return self.keys\",\n \"def keys(self):\\n sql = u\\\"\\\"\\\"\\n SELECT `key` FROM `{table}` WHERE 1\\n \\\"\\\"\\\".format(table=self.name)\\n\\n for row in self.conn.execute(sql):\\n yield row['key']\",\n \"def get_pubkey_ids(self, addr):\\n\\n if len(self.pubkeys) > 0 and self.pubkeys[-1].has_key('fingerprint') \\\\\\n and self.pubkeys[-1].has_key('uids'):\\n\\n pubkey_ids = []\\n # compile pattern before use for better performance\\n RCPT_RE = re.compile(addr)\\n for k in self.pubkeys:\\n for uid in k['uids']:\\n match = RCPT_RE.search(uid)\\n if match is not None:\\n # check for key expiration\\n if k['expires'] == '':\\n pubkey_ids.append(k['fingerprint'][-16:])\\n elif (time()+60) < float(k['expires']):\\n pubkey_ids.append(k['fingerprint'][-16:])\\n break\\n return pubkey_ids\",\n \"def _getKeyList(self):\\n return LinkedList(InternalRack(self, 1))\",\n \"def _reset_primary_keys_generators(self):\\n self.primary_key = dict()\\n self.remaining_primary_key = dict()\",\n \"def generate_rows(n):\\n for i in range(n):\\n yield [\\n # seq\\n i,\\n # guid-like id\\n hashlib.sha224(bytes(i)).hexdigest(),\\n # seq\\n i,\\n # seq\\n i,\\n # cc_number \\n fake.credit_card_number(card_type=None),\\n # expire_date\\n fake.date_between('-6y', '+0y').strftime(\\\"%m/%d/%Y\\\"),\\n # billing_address\\n fake.address(),\\n ]\",\n \"def keys(self, search, version=None, client=None):\\r\\n\\r\\n if client is None:\\r\\n client = self.get_client(write=False)\\r\\n\\r\\n pattern = self.make_key(search, version=version)\\r\\n try:\\r\\n encoding_map = [smart_text(k) for k in client.keys(pattern)]\\r\\n return [self.reverse_key(k) for k in encoding_map]\\r\\n except ConnectionError:\\r\\n raise ConnectionInterrupted(connection=client)\",\n \"def iterkeys(self, essid):\\n with SessionContext(self.SessionClass) as session:\\n q = session.query(PAW2_DBObject.key)\\n q = q.join(PYR2_DBObject).join(ESSID_DBObject)\\n q = q.filter(ESSID_DBObject.essid == essid)\\n keys = q.all()\\n return (c[0] for c in keys)\",\n \"def get_partition_keys(\\n self,\\n current_time: Optional[datetime] = None,\\n dynamic_partitions_store: Optional[DynamicPartitionsStore] = None,\\n ) -> Sequence[str]:\\n return self._partition_keys\",\n \"def __init__(self, path, number_keys=1):\\n\\n self.path = path\\n self.keyring = []\\n if os.path.exists(path):\\n self.keyring = read_keys(path)\\n else:\\n for n in range(number_keys):\\n key = generate_key(generate_random())\\n self.keyring.append(key)\\n write_keys(path, self.keyring)\",\n \"def generate_key(self, size):\\n key = bytearray()\\n for i in range(0,size):\\n random_byte = ord(os.urandom(1))\\n key.append(random_byte)\\n return key\",\n \"def get_key_list(self, email=\\\"\\\"):\\n\\t\\tif email:\\n\\t\\t\\twhere_clause = \\\" where email = '%s'\\\" % email\\n\\t\\telse:\\n\\t\\t\\twhere_clause = \\\"\\\"\\n\\n\\t\\treturn self.app.db.query(\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tselect\\n\\t\\t\\t\\tapi_key,\\n\\t\\t\\t\\towner,\\n\\t\\t\\t\\tapp_name,\\n\\t\\t\\t\\temail,\\n\\t\\t\\t\\turl,\\n\\t\\t\\t\\tcreated\\n\\t\\t\\tfrom\\n\\t\\t\\t\\tapi_keys\\n\\t\\t\\t%s\\n\\t\\t\\t\\\"\\\"\\\" % where_clause)\",\n \"def list(self, path, filename=None, start=None, stop=None, recursive=False, directories=False):\\n storageScheme, keys = self.getkeys(\\n path, filename=filename, directories=directories, recursive=recursive)\\n keys = [storageScheme + \\\":///\\\" + key.bucket.name + \\\"/\\\" + key.name for key in keys]\\n keys.sort()\\n keys = select(keys, start, stop)\\n return keys\",\n \"def Keys(self) -> NameObjectCollectionBase.KeysCollection:\",\n \"def get_rowkeys(\\n table_instance, rowkeys: List[str], sep: str = \\\"#\\\",\\n) -> List[models.RowModelOdd]:\\n row_model = [_get_single_row(table_instance, rowkey, sep=\\\":\\\") for rowkey in rowkeys]\\n\\n return row_model\",\n \"def keys(self):\\n key_index = 0x4\\n\\n for _ in range(0, self._keys_len()):\\n key_offset = self.abs_offset_from_hbin_offset(self.unpack_dword(key_index))\\n d = HBINCell(self._buf, key_offset, self)\\n\\n try:\\n for k in d.child().keys():\\n yield k\\n except RegistryStructureDoesNotExist:\\n raise ParseException(\\\"Unsupported subkey list encountered.\\\")\\n\\n key_index += 4\",\n \"def keys():\",\n \"def getNumberOfKeys(self) -> int:\\n ...\",\n \"def generate_brainpool_curves(count: int, p: ZZ, initial_seed: str) -> SimulatedCurves:\\n simulated_curves = SimulatedCurves(\\\"brainpool\\\", p.nbits(), initial_seed, count)\\n curve = Brainpool(initial_seed, p)\\n b_seed = None\\n for _ in range(count):\\n if curve.not_defined():\\n curve.set_a()\\n if not curve.check_a():\\n curve.seed_update()\\n curve.clear()\\n continue\\n b_seed = increment_seed(curve.seed())\\n curve.set_b(b_seed)\\n if not curve.check_b():\\n b_seed = increment_seed(b_seed)\\n continue\\n if not curve.secure():\\n curve.set_seed(increment_seed(b_seed))\\n curve.clear()\\n continue\\n curve.generate_generator(b_seed)\\n curve.compute_properties()\\n simulated_curves.add_curve(curve)\\n curve = Brainpool(curve.seed(), p)\\n curve.seed_update()\\n\\n return simulated_curves\",\n \"def process_preqs(need_to_load):\\n\\n n_to_load = 0\\n while n_to_load!=len(need_to_load): # i.e. unchanged\\n n_to_load=len(need_to_load)\\n new_need_to_load = need_to_load.copy()\\n for to_load in need_to_load:\\n if to_load in preqs_dict:\\n all_to_add = preqs_dict[to_load]\\n if isinstance(all_to_add,list):\\n new_need_to_load.update(all_to_add)\\n else:\\n new_need_to_load.add(all_to_add)\\n need_to_load = new_need_to_load\\n\\n return need_to_load\",\n \"def get_all( # type: ignore\\n self,\\n limit: t.Optional[int] = None, # dead: disable\\n offset: t.Optional[int] = None, # dead: disable\\n ) -> t.List[KeyValueEntity]:\\n return [KeyValueEntity(uuid=k, val=v) for k, v in self._data.items()]\",\n \"def fetch(self, n: int) -> t.List[Record]:\\n self._buffer(n)\\n return [\\n self._record_buffer.popleft()\\n for _ in range(min(n, len(self._record_buffer)))\\n ]\",\n \"def get_next_conf_keys(self):\\n C_List = []\\n for key in self.Poss_Tree:\\n key_c = int(str(key)[-1])\\n for choice in self.Poss_Tree[key]:\\n if choice == key_c:\\n C_List.append(int(construct_pass(key, choice)))\\n return C_List\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6100436","0.57589746","0.5668012","0.56462705","0.5632386","0.55703026","0.5561076","0.5519232","0.5507431","0.5470912","0.546842","0.54056185","0.52409744","0.5236303","0.5193606","0.5192819","0.51398957","0.50818324","0.5079338","0.5055663","0.5039773","0.50224555","0.49965414","0.4986866","0.4963776","0.49413583","0.49408898","0.49352106","0.4926059","0.49246648","0.49122408","0.48962703","0.48962343","0.48765165","0.48655427","0.4851234","0.48471648","0.48468205","0.48165303","0.48132","0.48066506","0.47986263","0.47919255","0.4777471","0.47654858","0.47607404","0.47584006","0.47546527","0.47506744","0.47386247","0.4731208","0.47271734","0.4719986","0.47132283","0.47030202","0.46975565","0.46954826","0.4695053","0.46929777","0.46882325","0.46867502","0.46772876","0.46766642","0.4675927","0.46743065","0.46676078","0.46478742","0.46414292","0.4639307","0.4635114","0.46342966","0.46319056","0.4631828","0.4627521","0.46257076","0.46244994","0.46222025","0.4618213","0.4615191","0.46117735","0.46077874","0.46055046","0.46008974","0.45989573","0.45963585","0.4591236","0.45825478","0.45822784","0.45775297","0.4576862","0.45732227","0.45677522","0.45663857","0.45610926","0.45577574","0.4557564","0.45518163","0.45466468","0.45463493","0.45458618"],"string":"[\n \"0.6100436\",\n \"0.57589746\",\n \"0.5668012\",\n \"0.56462705\",\n \"0.5632386\",\n \"0.55703026\",\n \"0.5561076\",\n \"0.5519232\",\n \"0.5507431\",\n \"0.5470912\",\n \"0.546842\",\n \"0.54056185\",\n \"0.52409744\",\n \"0.5236303\",\n \"0.5193606\",\n \"0.5192819\",\n \"0.51398957\",\n \"0.50818324\",\n \"0.5079338\",\n \"0.5055663\",\n \"0.5039773\",\n \"0.50224555\",\n \"0.49965414\",\n \"0.4986866\",\n \"0.4963776\",\n \"0.49413583\",\n \"0.49408898\",\n \"0.49352106\",\n \"0.4926059\",\n \"0.49246648\",\n \"0.49122408\",\n \"0.48962703\",\n \"0.48962343\",\n \"0.48765165\",\n \"0.48655427\",\n \"0.4851234\",\n \"0.48471648\",\n \"0.48468205\",\n \"0.48165303\",\n \"0.48132\",\n \"0.48066506\",\n \"0.47986263\",\n \"0.47919255\",\n \"0.4777471\",\n \"0.47654858\",\n \"0.47607404\",\n \"0.47584006\",\n \"0.47546527\",\n \"0.47506744\",\n \"0.47386247\",\n \"0.4731208\",\n \"0.47271734\",\n \"0.4719986\",\n \"0.47132283\",\n \"0.47030202\",\n \"0.46975565\",\n \"0.46954826\",\n \"0.4695053\",\n \"0.46929777\",\n \"0.46882325\",\n \"0.46867502\",\n \"0.46772876\",\n \"0.46766642\",\n \"0.4675927\",\n \"0.46743065\",\n \"0.46676078\",\n \"0.46478742\",\n \"0.46414292\",\n \"0.4639307\",\n \"0.4635114\",\n \"0.46342966\",\n \"0.46319056\",\n \"0.4631828\",\n \"0.4627521\",\n \"0.46257076\",\n \"0.46244994\",\n \"0.46222025\",\n \"0.4618213\",\n \"0.4615191\",\n \"0.46117735\",\n \"0.46077874\",\n \"0.46055046\",\n \"0.46008974\",\n \"0.45989573\",\n \"0.45963585\",\n \"0.4591236\",\n \"0.45825478\",\n \"0.45822784\",\n \"0.45775297\",\n \"0.4576862\",\n \"0.45732227\",\n \"0.45677522\",\n \"0.45663857\",\n \"0.45610926\",\n \"0.45577574\",\n \"0.4557564\",\n \"0.45518163\",\n \"0.45466468\",\n \"0.45463493\",\n \"0.45458618\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.87624663"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":8,"numItemsPerPage":100,"numTotalItems":95772,"offset":800,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MzU5MDkwNSwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzM1OTQ1MDUsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.3a-b42dPh-Lp52x2wjJDWt2lsylFIAMFjLXJhJwlIPfrhQDgLqHDq2fUwzd3OgKlvuBCnBLq-f4g714vPil1CA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102 values |
|---|---|---|---|---|---|---|
Print the data in slice iz, row ix of an image to standard out. | def print_image_col(input, ix=0, iz=0):
image=get_image(input)
nx = image.get_xsize()
ny = image.get_ysize()
nz = image.get_zsize()
print "(z = %d slice, x = %d row)" % (iz, ix)
line = []
for iy in xrange(ny):
line.append("%12.5g " % (image.get_value_at(ix,iy,iz)))
if ((iy + 1) % 5 == 0): line.append("\n ")
line.append("\n")
print "".join(line) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_row(input, ix=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, x = %d row)\" % (iz, ix)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((iy + 1) % 5 ==... | [
"0.76172984",
"0.75990057",
"0.7544907",
"0.74450433",
"0.73937047",
"0.7347187",
"0.7033798",
"0.68403655",
"0.6811467",
"0.66571254",
"0.6630757",
"0.6279358",
"0.6275334",
"0.61232245",
"0.60401046",
"0.5973948",
"0.5973784",
"0.5965042",
"0.59583265",
"0.59162855",
"0.589... | 0.7349031 | 5 |
Print the data in slice iz, column iy of an image to standard out. | def print_image_row(input, iy=0, iz=0):
image=get_image(input)
nx = image.get_xsize()
ny = image.get_ysize()
nz = image.get_zsize()
print "(z = %d slice, y = %d col)" % (iz, iy)
line = []
for ix in xrange(nx):
line.append("%12.5g " % (image.get_value_at(ix,iy,iz)))
if ((ix + 1) % 5 == 0): line.append("\n ")
line.append("\n")
print "".join(line) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_image_col(input, ix=0, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice, x = %d row)\" % (iz, ix)\n\tline = []\n\tfor iy in xrange(ny):\n\t\tline.append(\"%12.5g \" % (image.get_value_at(ix,iy,iz)))\n\t\tif ((iy + 1) ... | [
"0.77487314",
"0.75519806",
"0.750461",
"0.73919225",
"0.7150529",
"0.7141496",
"0.7027633",
"0.6667874",
"0.66633767",
"0.6654244",
"0.6348636",
"0.621092",
"0.6179944",
"0.60063565",
"0.59464717",
"0.59386533",
"0.5911687",
"0.58938134",
"0.5851181",
"0.58338124",
"0.580308... | 0.7496404 | 3 |
Print the data in slice iz of an image to standard out in a format that agrees with v2 | def print_image_slice(input, iz=0):
image=get_image(input)
nx = image.get_xsize()
ny = image.get_ysize()
nz = image.get_zsize()
print "(z = %d slice)" % (iz)
line = []
for iy in xrange(ny-1,-1,-1):
line.append("Row ")
line.append("%4i " % iy)
for ix in xrange(nx):
line.append("%12.5g " % (image.get_value_at(ix,iy,iz)))
if ((ix + 1) % 5 == 0):
line.append("\n ")
line.append(" ")
line.append("\n")
if(nx%5 != 0): line.append("\n")
print "".join(line) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_image(input):\n\timage=get_image(input)\n\tnz = image.get_zsize()\n\tfor iz in xrange(nz): print_slice(input, iz)",
"def print_slice(input, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice)\" % (iz)\n\tline = []\n\tf... | [
"0.7314874",
"0.72975045",
"0.7103524",
"0.7012286",
"0.6931774",
"0.67332554",
"0.6709795",
"0.6601998",
"0.6534724",
"0.6502416",
"0.6426161",
"0.6281741",
"0.6222216",
"0.6221346",
"0.6040175",
"0.60378903",
"0.59890413",
"0.594744",
"0.5930997",
"0.5928868",
"0.5903941",
... | 0.7488905 | 0 |
Print the data in slice iz of an image to standard out in a format that agrees with v2 | def print_image_slice_3d(input, num=0,direction="z"):
#print "print slice at 3 directions"
image=get_image(input)
nx = image.get_xsize()
ny = image.get_ysize()
nz = image.get_zsize()
if(direction=="x"):
#print "xxxxx"
ix=num
print "(x = %d slice)" % (ix)
line = []
for iz in xrange(nz-1,-1,-1):
line.append("Z ")
line.append("%4i " % iz)
for iy in xrange(ny):
line.append("%12.5g " % (image.get_value_at(ix,iy,iz)))
if ((iy + 1) % 5 == 0):
line.append("\n ")
line.append(" ")
line.append("\n")
if(ny%5 != 0): line.append("\n")
print "".join(line)
elif(direction=="y"):
#print "yyy"
iy=num
print "(y = %d slice)" % (iy)
line = []
for iz in xrange(nz-1,-1,-1):
line.append("Z ")
line.append("%4i " % iz)
for ix in xrange(nx):
line.append("%12.5g " % (image.get_value_at(ix,iy,iz)))
if ((ix + 1) % 5 == 0):
line.append("\n ")
line.append(" ")
line.append("\n")
if(nx%5 != 0): line.append("\n")
print "".join(line)
else:
#print "zzzz"
iz=num
print "(z = %d slice)" % (iz)
line = []
for iy in xrange(ny-1,-1,-1):
line.append("Row ")
line.append("%4i " % iy)
for ix in xrange(nx):
line.append("%12.5g " % (image.get_value_at(ix,iy,iz)))
if ((ix + 1) % 5 == 0):
line.append("\n ")
line.append(" ")
line.append("\n")
if(nx%5 != 0): line.append("\n")
print "".join(line) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_image_slice(input, iz=0):\n\timage=get_image(input)\n\tnx = image.get_xsize()\n\tny = image.get_ysize()\n\tnz = image.get_zsize()\n\tprint \"(z = %d slice)\" % (iz)\n\tline = []\n\tfor iy in xrange(ny-1,-1,-1):\n\t\tline.append(\"Row \")\n\t\tline.append(\"%4i \" % iy)\n\t\tfor ix in xrange(nx):\n\t\t\tl... | [
"0.7489223",
"0.7313441",
"0.72980666",
"0.7102297",
"0.70125145",
"0.6931557",
"0.6733379",
"0.67101216",
"0.6534789",
"0.65026045",
"0.6426267",
"0.62820506",
"0.6222731",
"0.6221045",
"0.6040338",
"0.6038878",
"0.59879136",
"0.594753",
"0.593031",
"0.59290165",
"0.5902861"... | 0.6602657 | 8 |
Read a columnlisted txt file. | def read_text_row(fnam, format="", skip=";"):
from string import split
inf = file(fnam, "r")
strg = inf.readline()
x = []
data = []
while (len(strg) > 0):
com_line = False
for j in xrange(len(strg)):
if(strg[j] == skip): com_line = True
if com_line == False:
word=split(strg)
if format == "s" :
key = int(word[1])
if key != len(word) - 2:
del word
word = []
word.append(strg[0 : 5])
word.append(strg[6 : 7])
for k in xrange(key):
k_start = 7 + k*13
k_stop = k_start + 13
word.append(strg[k_start : k_stop])
line=[]
for i in xrange(len(word)):
line.append(float(word[i]))
data.append(line)
strg=inf.readline()
inf.close
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_text_file(file_name, ncol = 0):\n\t\n\tfrom string import split\n\tinf = file(file_name, \"r\")\n\tline = inf.readline()\n\tdata = []\n\twhile len(line) > 0:\n\t\tif ncol == -1:\n\t\t\tvdata = split(line)\n\t\t\tif data == []:\n\t\t\t\tfor i in xrange(len(vdata)):\n\t\t\t\t\tdata.append([float(vdata[i])])... | [
"0.7060434",
"0.6964561",
"0.69441533",
"0.67769",
"0.6769207",
"0.67402285",
"0.6630263",
"0.6448201",
"0.6428578",
"0.6414579",
"0.628006",
"0.6256943",
"0.61892265",
"0.6181183",
"0.6084818",
"0.6000709",
"0.59918183",
"0.5988072",
"0.59837323",
"0.5958952",
"0.59168094",
... | 0.0 | -1 |
Write to an ASCII file a list of lists containing floats. | def write_text_row(data, file_name):
import types
outf = open(file_name, "w")
if (type(data[0]) == types.ListType):
# It is a list of lists
for i in xrange(len(data)):
for j in xrange(len(data[i])):
outf.write(" %12.5g"%data[i][j])
outf.write("\n")
else:
# Single list
for j in xrange(len(data)):
outf.write(" %12.5g"%data[j])
outf.write(" \n")
outf.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def writeFloatListToFile(ldata, prec, filePath):\n\twith open(filePath, \"w\") as fh:\n\t\tfor d in ldata:\n\t\t\tfh.write(formatFloat(prec, d) + \"\\n\")",
"def write_float32_list(self, float_list: List[float]) -> None:\n self.write_int32(len(float_list))\n for f in float_list:\n self.w... | [
"0.7385591",
"0.6873358",
"0.6700241",
"0.6473418",
"0.6448024",
"0.64338905",
"0.6403743",
"0.62480605",
"0.6234932",
"0.6223964",
"0.61342627",
"0.61157817",
"0.60960627",
"0.6086933",
"0.607191",
"0.59942085",
"0.5985232",
"0.5915316",
"0.59132415",
"0.5909576",
"0.5845459... | 0.5709758 | 30 |
Read data from text file, if ncol = 1, read all columns if ncol >= 0, just read the (ncol+1)th column. | def read_text_file(file_name, ncol = 0):
from string import split
inf = file(file_name, "r")
line = inf.readline()
data = []
while len(line) > 0:
if ncol == -1:
vdata = split(line)
if data == []:
for i in xrange(len(vdata)):
data.append([float(vdata[i])])
else:
for i in xrange(len(vdata)):
data[i].append(float(vdata[i]))
else:
vdata = float(split(line)[ncol])
data.append(vdata)
line = inf.readline()
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readOFColumnData(dataFile,nCol):\n fileCheck(dataFile) # does the file exists ? Stop if not.\n #\n # Init list\n data = []\n #\n for line in fileinput.input(dataFile):\n # remove parenthesis if any\n line = line.replace('(', '')\n line = line.replace(')', '') \n ... | [
"0.69048285",
"0.6310644",
"0.6242134",
"0.61760336",
"0.6040689",
"0.60290545",
"0.6026688",
"0.60042155",
"0.587215",
"0.58236593",
"0.5762683",
"0.5731496",
"0.5718888",
"0.5706086",
"0.5696076",
"0.5676739",
"0.56515324",
"0.56390595",
"0.56192374",
"0.55811644",
"0.55722... | 0.73864484 | 0 |
Write to an ASCII file a list of lists containing floats. | def write_text_file(data, file_name):
import types
outf = open(file_name, "w")
if (type(data[0]) == types.ListType):
# It is a list of lists
for i in xrange(len(data[0])):
for j in xrange(len(data)):
if type(data[j][i]) == type(0):
outf.write(" %12d"%data[j][i])
else:
outf.write(" %12.5g"%data[j][i])
outf.write("\n")
else:
# Single list
for j in xrange(len(data)):
if type(data[j]) == type(0):
outf.write(" %12d\n"%data[j])
else:
outf.write(" %12.5g\n"%data[j])
outf.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def writeFloatListToFile(ldata, prec, filePath):\n\twith open(filePath, \"w\") as fh:\n\t\tfor d in ldata:\n\t\t\tfh.write(formatFloat(prec, d) + \"\\n\")",
"def write_float32_list(self, float_list: List[float]) -> None:\n self.write_int32(len(float_list))\n for f in float_list:\n self.w... | [
"0.73861384",
"0.6875439",
"0.6696524",
"0.64751726",
"0.64447385",
"0.64314556",
"0.62464565",
"0.62336665",
"0.62258744",
"0.6136174",
"0.61130786",
"0.60936034",
"0.6083807",
"0.6069135",
"0.5993641",
"0.59823793",
"0.59138167",
"0.5911231",
"0.59085566",
"0.584276",
"0.58... | 0.6402331 | 6 |
Rotate about a different center | def rotate_about_center(alpha, cx, cy):
cmp1 = compose_transform2(0, -cx, -cy, 1, alpha, 0, 0, 1)
cmp2 = compose_transform2(cmp1[0], cmp1[1], cmp1[2], cmp1[3], 0, cx, cy, 1)
# return compalpha, comptrans.at(0),comptrans.at(1), compscale
return cmp2[0], cmp2[1], cmp2[2], cmp2[3] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotation_center(self, *args, **kwargs) -> Any:\n pass",
"def rotate(self,center, angle):\n \n self.coord = [x-np.repeat([[center[0],center[1]]],[x.shape[0]],axis = 0) for x in self.coord]\n\n alpha = angle\n R = np.array([[np.cos(alpha),-np.sin(alpha)],[np.sin(alpha),np.cos... | [
"0.75949824",
"0.74933934",
"0.7462114",
"0.74131936",
"0.738581",
"0.7327082",
"0.7053461",
"0.70344794",
"0.69995296",
"0.69260097",
"0.69119567",
"0.69119567",
"0.68725055",
"0.676936",
"0.67615163",
"0.6755245",
"0.6707772",
"0.6677421",
"0.6677066",
"0.667549",
"0.667108... | 0.6676087 | 19 |
linearly interpolate a 1D power spectrum to required length with required Pixel size input_object a 1D list with a 1D curve to be interpolated length_current half size of the image size (in case of power spectrum, it can be different from the length of the input_object) length_interpolated length of the interpolated 1D curve Pixel_size_current pixel size of the input 1D list Pixel_size_interpolated pixel size of the target 1D list One can either input the two lengths or two respective pixel sizes | def reshape_1d(input_object, length_current=0, length_interpolated=0, Pixel_size_current = 0., Pixel_size_interpolated = 0.):
interpolated = []
if length_current == 0: length_current = len(input_object)
lt = len(input_object) - 2
if length_interpolated == 0:
if( Pixel_size_interpolated != Pixel_size_current):
length_interpolated = int(length_current*Pixel_size_current/Pixel_size_interpolated + 0.5)
else:
ERROR("Incorrect input parameters","reshape_1d",1)
return []
if Pixel_size_current == 0.:
Pixel_size_current = 1.
Pixel_size_interpolated = Pixel_size_current*float(length_current)/float(length_interpolated)
qt =Pixel_size_interpolated/Pixel_size_current
for i in xrange(length_interpolated):
xi = float(i)*qt
ix = min(int(xi),lt)
df = xi -ix
xval = (1.0-df)*input_object[ix] + df*input_object[ix+1]
interpolated.append(xval)
return interpolated | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def interpolate(signal, new_length):\n assert len(signal) > 1 and len(signal[0]) > 1\n current_length = len(signal)\n signal = np.array(signal).T\n new_signal = []\n x_array = get_x_array(current_length, new_length)\n\n for l in range(len(signal)):\n fp = signal[l]\n xp = list(range... | [
"0.59042335",
"0.5510684",
"0.5495997",
"0.543274",
"0.54308456",
"0.5368978",
"0.53469396",
"0.5295205",
"0.5252446",
"0.5242712",
"0.51369417",
"0.5123754",
"0.5104273",
"0.5101531",
"0.5047771",
"0.50370836",
"0.5025036",
"0.50159943",
"0.49947384",
"0.49876678",
"0.497830... | 0.6795765 | 0 |
Calculate 1D rotationally averaged power spectra from image stack listed in a directory | def rops_dir(indir, output_dir = "1dpw2_dir"):
from EMAN2 import periodogram
import os
flist = os.listdir(indir)
print flist
if os.path.exists(output_dir) is False: os.mkdir(output_dir)
for i, v in enumerate(flist):
(filename, filextension) = os.path.splitext(v)
nima = EMUtil.get_image_count(os.path.join(indir,v))
print nima
for im in xrange(nima):
e = EMData()
file_name = os.path.join(indir,v)
e.read_image(file_name, im)
tmp1 = periodogram(e)
tmp = tmp1.rotavg()
if im == 0:
sum_ima = model_blank(tmp.get_xsize())
sum_ima += tmp
else : sum_ima += tmp
table = []
nr = sum_ima.get_xsize()
for ir in xrange(nr): table.append([sum_ima.get_value_at(ir)])
drop_spider_doc(os.path.join(output_dir, "1dpw2_"+filename+".txt"), table) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def spectra_stacker(file_name):\n file_data = read_file(file_name)\n image_data = file_data[1]\n\n data_shape = np.shape(image_data)\n ra_axis = data_shape[2]\n dec_axis = data_shape[1]\n wl_axis = data_shape[0]\n\n pxl_total = ra_axis * dec_axis\n \n data_unwrap = [] \n... | [
"0.61825204",
"0.5834751",
"0.5748448",
"0.5740321",
"0.5725463",
"0.57223636",
"0.56904304",
"0.5681795",
"0.5607029",
"0.5605107",
"0.55935085",
"0.55795133",
"0.5555331",
"0.55550176",
"0.5546603",
"0.55278903",
"0.5524576",
"0.546944",
"0.54588705",
"0.54191047",
"0.54079... | 0.0 | -1 |
Gather the a list of EMData on all nodes to the main node, we assume the list has the same length on each node. | def gather_EMData(data, number_of_proc, myid, main_node):
from mpi import MPI_COMM_WORLD, MPI_INT, MPI_TAG_UB
from mpi import mpi_send, mpi_recv
l = len(data)
gathered_data = []
inc = 1 # A temp measure
if myid == main_node:
for i in xrange(0, number_of_proc*inc, inc):
if i == main_node:
for k in xrange(l):
gathered_data.append(data[k])
else:
for k in xrange(l):
im = recv_EMData(i, i*l+k)
mem_len = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
members = mpi_recv(int(mem_len[0]), MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
members = map(int, members)
im.set_attr('members', members)
gathered_data.append(im)
else:
for k in xrange(l):
send_EMData(data[k], main_node, myid*l+k)
mem = data[k].get_attr('members')
mpi_send(len(mem), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
mpi_send(mem, len(mem), MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
return gathered_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gather(self, node):\n\n return []",
"def _data_parallel_master(self, intermediates):\n\n # Always using same \"device order\" makes the ReduceAdd operation faster.\n # Thanks to:: Tete Xiao (http://tetexiao.com/)\n intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_d... | [
"0.6460555",
"0.5887683",
"0.56105876",
"0.56016797",
"0.5551757",
"0.5506774",
"0.5503824",
"0.5480824",
"0.54083604",
"0.5394268",
"0.5391553",
"0.53643715",
"0.5339283",
"0.5328205",
"0.5325319",
"0.5300631",
"0.52970463",
"0.52842844",
"0.52776074",
"0.526741",
"0.5261743... | 0.67186177 | 0 |
number_to_send has to be predefined in each node | def bcast_number_to_all(number_to_send, source_node = 0):
from mpi import mpi_bcast, MPI_INT, MPI_COMM_WORLD, MPI_FLOAT
import types
if type(number_to_send) is types.IntType:
TMP = mpi_bcast(number_to_send, 1, MPI_INT, source_node, MPI_COMM_WORLD)
return int(TMP[0])
elif type(number_to_send) is types.FloatType:
TMP = mpi_bcast(number_to_send, 1, MPI_FLOAT, source_node, MPI_COMM_WORLD)
return float(TMP[0])
else:
print " ERROR in bcast_number_to_all" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sendnum(self, n):\n self.sendline(str(n))",
"def send(self, x):\n print x",
"def send(value):\r\n return value",
"def numeric(self, source, numeric, target, *args):\n\n if numeric == ERR_NICKNAMEINUSE:\n self.fire(NICK(\"{0:s}_{1:d}\".format(args[0], randint(0, 32768))))\... | [
"0.6436164",
"0.5802865",
"0.55402374",
"0.5511573",
"0.5425965",
"0.5417966",
"0.5417966",
"0.5406537",
"0.5385631",
"0.5377031",
"0.5364401",
"0.5363881",
"0.53416944",
"0.53416944",
"0.53416944",
"0.53416944",
"0.53416944",
"0.53416944",
"0.5333438",
"0.5325405",
"0.532461... | 0.53139603 | 21 |
write headers from files in data into a disk file called filename. The filename has to be either hdf or bdb. lima list with positions in the disk files into which headers will be written, i.e., header from data[k] will be written into file number lima[k] | def write_headers(filename, data, lima):
from utilities import file_type
from EMAN2db import db_open_dict
ftp = file_type(filename)
if ftp == "bdb":
# For unknown reasons this does not work on Linux, but works on Mac ??? Really?
DB = db_open_dict(filename)
for i in range(len(lima)):
DB.set_header(lima[i], data[i])
DB.close()
#for i in range(len(lima)):
# data[i].write_image(filename, lima[i])
elif ftp == "hdf":
for i in range(len(lima)):
data[i].write_image(filename, lima[i], EMUtil.ImageType.IMAGE_HDF, True)
else:
ERROR("Unacceptable file format","write_headers",1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_header(filename, data, lima):\n\tfrom utilities import file_type\n\tfrom EMAN2db import db_open_dict\n\n\tftp = file_type(filename)\n\tif ftp == \"bdb\":\n\t\tDB = db_open_dict(filename)\n\t\tDB.set_header(lima, data)\n\telif ftp == \"hdf\":\n\t\tdata.write_image(filename, lima, EMUtil.ImageType.IMAGE_HD... | [
"0.81443864",
"0.65365857",
"0.6521095",
"0.642603",
"0.6422746",
"0.6345552",
"0.63141286",
"0.622469",
"0.6156558",
"0.6150772",
"0.6104141",
"0.60294604",
"0.5993755",
"0.5916143",
"0.58797914",
"0.58024967",
"0.57776046",
"0.57631105",
"0.5731549",
"0.57286596",
"0.571765... | 0.8396805 | 0 |
write header from a single file data into a disk file called filename. The filename has to be either hdf or bdb. lima position in the disk files into which header will be written, i.e., header from data will be written into file number lima | def write_header(filename, data, lima):
from utilities import file_type
from EMAN2db import db_open_dict
ftp = file_type(filename)
if ftp == "bdb":
DB = db_open_dict(filename)
DB.set_header(lima, data)
elif ftp == "hdf":
data.write_image(filename, lima, EMUtil.ImageType.IMAGE_HDF, True)
else:
ERROR("Unacceptable file format","write_headers",1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_headers(filename, data, lima):\n\tfrom utilities import file_type\n\tfrom EMAN2db import db_open_dict\n\n\tftp = file_type(filename)\n\tif ftp == \"bdb\":\n\t\t# For unknown reasons this does not work on Linux, but works on Mac ??? Really?\n\t\tDB = db_open_dict(filename)\n\t\tfor i in range(len(lima)):... | [
"0.7826132",
"0.70860624",
"0.6762291",
"0.6738056",
"0.67234135",
"0.67085034",
"0.66346943",
"0.6610399",
"0.6607992",
"0.6557405",
"0.6481333",
"0.6448737",
"0.64438635",
"0.6427536",
"0.64213383",
"0.6419421",
"0.6363067",
"0.63381046",
"0.631573",
"0.63024944",
"0.629878... | 0.8375335 | 0 |
retrieve 2D alignment parameters from the header alpha tx ty mirror scale | def get_params2D(ima, xform = "xform.align2d"):
t = ima.get_attr(xform)
d = t.get_params("2D")
return d["alpha"],d["tx"],d["ty"],d["mirror"],d["scale"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_alignment_params(self, s, w):\n\n\n X1 = s.__get_X(w)\n X2 = self.__get_X(w)\n Y1 = s.__get_Y(w)\n Y2 = self.__get_Y(w)\n Z = self.__get_Z(w)\n W = sum(w)\n C1 = self.__get_C1(w, s)\n C2 = self.__get_C2(w, s)\n\n a = np.array([[ X2, -Y2, W, ... | [
"0.6123718",
"0.6082262",
"0.5826465",
"0.56437796",
"0.5509969",
"0.5504308",
"0.5343178",
"0.5336521",
"0.5331882",
"0.5322334",
"0.5322334",
"0.5299092",
"0.524463",
"0.524463",
"0.524463",
"0.5240063",
"0.51976395",
"0.5196707",
"0.5196707",
"0.5196707",
"0.5195092",
"0... | 0.5867003 | 2 |
set 2D alignment parameters in the header alpha tx ty mirror scale | def set_params2D(ima, p, xform = "xform.align2d"):
t = Transform({"type":"2D","alpha":p[0],"tx":p[1],"ty":p[2],"mirror":p[3],"scale":p[4]})
ima.set_attr(xform, t) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def align(self):\n ...",
"def parameters_ui(layout, params):\n\n r = layout.row()\n r.prop(params, \"rotation_axis\")\n\n if 'auto' not in params.rotation_axis.lower():\n r = layout.row()\n text = \"Auto align Foot\"\n r.prop(params, \"auto_align_extremity\", text=text)\n\n ... | [
"0.5652911",
"0.5642781",
"0.5243032",
"0.51255065",
"0.5104375",
"0.5091596",
"0.5088672",
"0.5071364",
"0.50606203",
"0.5045389",
"0.5044001",
"0.50383675",
"0.50192225",
"0.5016494",
"0.5012836",
"0.50028354",
"0.49517918",
"0.49404538",
"0.4933246",
"0.49027255",
"0.48972... | 0.55857396 | 2 |
retrieve 3D alignment parameters from the header phi theta psi tx ty tz mirror scale | def get_params3D(ima, xform = "xform.align3d"):
t = ima.get_attr(xform)
d = t.get_params("spider")
return d["phi"],d["theta"],d["psi"],d["tx"],d["ty"],d["tz"],d["mirror"],d["scale"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def params_3D_2D(phi, theta, psi, s2x, s2y):\n\tif theta > 90.0:\n\t\tmirror = 1\n\t\talpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 540.0-psi, 0, 0, 1.0)\n\telse:\n\t\tmirror = 0\n\t\talpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 360.0-psi, 0, 0, 1.0)\n\treturn alpha, sx, sy, mirror"... | [
"0.6369768",
"0.63076174",
"0.57975394",
"0.5729281",
"0.5533436",
"0.551851",
"0.5500369",
"0.5439547",
"0.5422399",
"0.54033196",
"0.5388639",
"0.5361121",
"0.53491545",
"0.53419423",
"0.531873",
"0.53108954",
"0.53095436",
"0.53085315",
"0.5303664",
"0.5303664",
"0.5302264... | 0.7157893 | 0 |
set 3D alignment parameters in the header phi theta psi tx ty tz mirror scale | def set_params3D(ima, p, xform = "xform.align3d"):
t = Transform({"type":"spider","phi":p[0],"theta":p[1],"psi":p[2],"tx":p[3],"ty":p[4],"tz":p[5],"mirror":p[6],"scale":p[7]})
ima.set_attr(xform, t) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_params3D(ima, xform = \"xform.align3d\"):\n\tt = ima.get_attr(xform)\n\td = t.get_params(\"spider\")\n\treturn d[\"phi\"],d[\"theta\"],d[\"psi\"],d[\"tx\"],d[\"ty\"],d[\"tz\"],d[\"mirror\"],d[\"scale\"]",
"def setCameraRotation3D(ang):\n dislin.vup3d(ang)",
"def params_3D_2D(phi, theta, psi, s2x, s... | [
"0.59650105",
"0.593235",
"0.5929709",
"0.58860916",
"0.5739548",
"0.5691223",
"0.56045157",
"0.5573526",
"0.55701107",
"0.5569729",
"0.55564326",
"0.55041516",
"0.5470599",
"0.5299311",
"0.5288693",
"0.5281004",
"0.5277822",
"0.52361757",
"0.5231874",
"0.5215359",
"0.5197185... | 0.65861595 | 0 |
retrieve projection alignment parameters from the header phi theta psi s2x s2y | def get_params_proj(ima, xform = "xform.projection"):
t = ima.get_attr(xform)
d = t.get_params("spider")
return d["phi"],d["theta"],d["psi"],-d["tx"],-d["ty"] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def params_3D_2D(phi, theta, psi, s2x, s2y):\n\tif theta > 90.0:\n\t\tmirror = 1\n\t\talpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 540.0-psi, 0, 0, 1.0)\n\telse:\n\t\tmirror = 0\n\t\talpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 360.0-psi, 0, 0, 1.0)\n\treturn alpha, sx, sy, mirror"... | [
"0.61034876",
"0.60535675",
"0.6002323",
"0.55047",
"0.53491384",
"0.53456676",
"0.5342712",
"0.5333783",
"0.5266437",
"0.52358764",
"0.52249706",
"0.5206061",
"0.5192297",
"0.51854604",
"0.5182782",
"0.51370823",
"0.51352656",
"0.512739",
"0.5105829",
"0.50973487",
"0.508592... | 0.6081615 | 1 |
set projection alignment parameters in the header phi theta psi s2x s2y | def set_params_proj(ima, p, xform = "xform.projection"):
from EMAN2 import Vec2f
t = Transform({"type":"spider","phi":p[0],"theta":p[1],"psi":p[2]})
t.set_trans(Vec2f(-p[3], -p[4]))
ima.set_attr(xform, t) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def params_3D_2D(phi, theta, psi, s2x, s2y):\n\tif theta > 90.0:\n\t\tmirror = 1\n\t\talpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 540.0-psi, 0, 0, 1.0)\n\telse:\n\t\tmirror = 0\n\t\talpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 360.0-psi, 0, 0, 1.0)\n\treturn alpha, sx, sy, mirror"... | [
"0.59060866",
"0.5602356",
"0.54695225",
"0.531587",
"0.5250702",
"0.51489925",
"0.512252",
"0.51054573",
"0.50826424",
"0.50823313",
"0.50728947",
"0.5068519",
"0.5064257",
"0.50619185",
"0.504671",
"0.5043761",
"0.5035546",
"0.5006031",
"0.49904823",
"0.49792513",
"0.496036... | 0.5937973 | 0 |
recover numerical values of CTF parameters from EMAN2 CTF object stored in a header of the input image | def get_ctf(ima):
from EMAN2 import EMAN2Ctf
ctf_params = ima.get_attr("ctf")
return ctf_params.defocus, ctf_params.cs, ctf_params.voltage, ctf_params.apix, ctf_params.bfactor, ctf_params.ampcont, ctf_params.dfdiff, ctf_params.dfang | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_aperture_coeffs_in_header(head):\n\n coeffs = {}\n for key, value in head.items():\n exp = '^GAMSE TRACE CHANNEL [A-Z] APERTURE \\d+ COEFF \\d+$'\n if re.match(exp, key) is not None:\n g = key.split()\n channel = g[3]\n aperture = int(g[5])\n ... | [
"0.56780773",
"0.56284475",
"0.55668586",
"0.5510405",
"0.5448828",
"0.5425909",
"0.5414353",
"0.54138607",
"0.541011",
"0.54011285",
"0.5374928",
"0.5361909",
"0.5342145",
"0.533246",
"0.5314134",
"0.5313142",
"0.525259",
"0.52504194",
"0.5208928",
"0.520613",
"0.5202701",
... | 0.60774076 | 0 |
generate EMAN2 CTF object using values of CTF parameters given in the list p | def generate_ctf(p):
from EMAN2 import EMAN2Ctf
defocus = p[0]
cs = p[1]
voltage = p[2]
pixel_size = p[3]
bfactor = p[4]
amp_contrast = p[5]
if defocus > 100: # which means it is very likely in Angstrom, therefore we are using the old convention
defocus *= 1e-4
if amp_contrast < 1.0:
from math import sqrt
amp_contrast = amp_contrast*100/sqrt(2*amp_contrast**2-2*amp_contrast+1)
ctf = EMAN2Ctf()
if(len(p) == 6):
ctf.from_dict({"defocus":defocus, "cs":cs, "voltage":voltage, "apix":pixel_size, "bfactor":bfactor, "ampcont":amp_contrast})
else:
ctf.from_dict({"defocus":defocus, "cs":cs, "voltage":voltage, "apix":pixel_size, "bfactor":bfactor, "ampcont":amp_contrast,'dfdiff':p[6],'dfang':p[7]})
return ctf | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_thermo(T_C, p):\n\ty = y_from_p(p)\n\tx = x_from_Tp(T_C+C_to_K, p)\n\treturn x, y",
"def construct_param_dict(params,K_RC,K_CP,m_P):\n ###scaling constants\n w=params['w']\n pd=params['pd'] # in 3D and 0.21 in 2D\n pv=params['pv']\n Er=params['Er'] ;Ek=params['Ek']\n ER=params['ER'];EC... | [
"0.5515874",
"0.5445426",
"0.5421652",
"0.54088384",
"0.5321685",
"0.5316981",
"0.5300015",
"0.52771187",
"0.52574724",
"0.5255685",
"0.52163506",
"0.5180207",
"0.5175576",
"0.51701",
"0.5159775",
"0.51574296",
"0.51267034",
"0.51238847",
"0.5120029",
"0.5111619",
"0.5101273"... | 0.77108574 | 0 |
set EMAN2 CTF object in the header of input image using values of CTF parameters given in the list p | def set_ctf(ima, p):
from utilities import generate_ctf
ctf = generate_ctf( p )
ima.set_attr( "ctf", ctf ) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_ctf(p):\n\tfrom EMAN2 import EMAN2Ctf\n\n\tdefocus = p[0]\n\tcs = p[1]\n\tvoltage = p[2]\n\tpixel_size = p[3]\n\tbfactor = p[4]\n\tamp_contrast = p[5]\n\t\n\tif defocus > 100: # which means it is very likely in Angstrom, therefore we are using the old convention\n\t\tdefocus *= 1e-4\n\t\n\tif amp_con... | [
"0.6119822",
"0.52715033",
"0.52555937",
"0.521904",
"0.5153852",
"0.5145439",
"0.50969875",
"0.5046172",
"0.5035334",
"0.5006989",
"0.49636608",
"0.49189067",
"0.49184573",
"0.4891116",
"0.4880313",
"0.4876546",
"0.48572096",
"0.4852319",
"0.48459044",
"0.4828242",
"0.482119... | 0.60989493 | 1 |
Find all occurences of val on list lo Returns a list of indices of val on lo. | def findall(lo,val):
u = []
i = -1
while( i < len(lo)-1):
try:
i = lo.index(val,i+1)
u.append(i)
except:
i += 1
return u | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def findall(l, o):\n return [i for i, u in enumerate(l) if u==o]",
"def getIndexes(self, val):\n # Find where this value is listed. \n valNdx = (self.values == val).nonzero()[0]\n \n # If this value is not actually in those listed, then we \n # must return empty indexes\n ... | [
"0.70793176",
"0.7071109",
"0.66156113",
"0.6319743",
"0.6250533",
"0.620535",
"0.62024206",
"0.619781",
"0.6169729",
"0.60985184",
"0.6077737",
"0.6067603",
"0.5907215",
"0.5842827",
"0.58379203",
"0.5817054",
"0.58101517",
"0.5786621",
"0.577902",
"0.5734879",
"0.5654707",
... | 0.8087934 | 0 |
Find overall phi angle and z shift difference between two sets of projection parameters for helical structure. The two sets have to be of the same length and it is assume that k'th element on the first list corresponds to the k'th element on the second list. | def helical_consistency(p2i, p1):
from pixel_error import angle_diff
from math import cos,pi
from utilities import getvec
from pixel_error import angle_error
from EMAN2 import Vec2f
n =len(p1[0])
print n
qtm = -1.0e10
for lf in xrange(0,181,180):
p2 = []
p2.extend(p2i)
if( lf == 180):
tflip = Transform({"type":"spider","theta":180.0})
for j in xrange(n):
t2 = Transform({"type":"spider","phi":p2[0][j],"theta":p2[1][j],"psi":p2[2][j]})
t2.set_trans( Vec2f( -p2[3][j], -p2[4][j] ) )
t2 = t2*tflip
d = t2.get_params("spider")
p2[0][j] = d["phi"]
p2[1][j] = d["theta"]
p2[2][j] = d["psi"]
p2[3][j] = -d["tx"]
p2[4][j] = -d["ty"]
tt1 = [0.0]*n
tt2 = [0.0]*n
mirror = [False]*n
ln = 0
for j in xrange( n ):
t1 = getvec(p1[0][j],p1[1][j])
t2 = getvec(p2[0][j],p2[1][j])
tm = getvec(180.0+p2[0][j],180.0-p2[1][j])
tt1[j] = t1[0]*t2[0]+t1[1]*t2[1]+t1[2]*t2[2]
tt2[j] = t1[0]*tm[0]+t1[1]*tm[1]+t1[2]*tm[2]
if(abs(tt1[j])<1.0e-7): tt1[j] = 0.0
if(abs(tt2[j])<1.0e-7): tt2[j] = 0.0
if(tt1[j]>tt2[j]):
mirror[j] = True
ln+=1
print " FLIP ",lf
if(ln < n//2):
print "mirror ",ln
for j in xrange( n ):
p2[0][j] += 180.0
p2[1][j] = 180.0-p2[1][j]
p2[2][j] = -p2[2][j]
p2[4][j] = -p2[4][j]
mirror[j] = not(mirror[j])
else:
print " straight", ln
phi1 = []
phi2 = []
agree = []
for j in xrange(n):
if(mirror[j]):
phi1.append(p1[0][j])
phi2.append(p2[0][j])
agree.append(j)
print len(phi1)
delta_phi = angle_diff( phi2, phi1 )
print "close form diff===", delta_phi
phi1 = []
phi2 = []
errorm = []
for j in xrange( len( p1[0]) ):
p2[0][j] = (p2[0][j] + delta_phi + 360)%360.0
if(mirror[j]):
phi1.append(p1[0][j])
phi2.append(p2[0][j])
errorm.append(angle_error( [ p2[0][j] ], [ p1[0][j] ]))
qt = sum(errorm)/len(errorm)
print len(errorm),qt
if(qt > qtm):
qtm = qt
p2o = []
p2o.extend(p2)
errormo = []
phi1o = []
phi2o = []
errormo.extend(errorm)
phi1o.extend(phi1)
phi2o.extend(phi2)
return p2o, errormo, agree, delta_phi, phi1o, phi2o | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotation_between_anglesets(agls1, agls2):\n\tfrom math import sin, cos, pi, sqrt, atan2, acos, atan\n\tfrom numpy import array, linalg, matrix\n\timport types\n\n\tdeg2rad = pi/180.0\n\n\tdef ori2xyz(ori):\n\t\tif(type(ori) == types.ListType):\n\t\t\tphi, theta, psi = ori[:3]\n\t\telse:\n\t\t\t# it has to be ... | [
"0.614044",
"0.5826733",
"0.5789848",
"0.5688521",
"0.5658761",
"0.56020504",
"0.5570326",
"0.5457091",
"0.54170704",
"0.54091537",
"0.5399414",
"0.5383706",
"0.53525215",
"0.5337892",
"0.53332806",
"0.5322254",
"0.5320831",
"0.53138745",
"0.53113425",
"0.5300493",
"0.5291562... | 0.5451437 | 8 |
Find overall 3D rotation (phi theta psi) between two sets of Eulerian angles. The two sets have to be of the same length and it is assume that k'th element on the first list corresponds to the k'th element on the second list. | def rotation_between_anglesets(agls1, agls2):
from math import sin, cos, pi, sqrt, atan2, acos, atan
from numpy import array, linalg, matrix
import types
deg2rad = pi/180.0
def ori2xyz(ori):
if(type(ori) == types.ListType):
phi, theta, psi = ori[:3]
else:
# it has to be Transformation object
d = ori.get_params("spider")
phi = d["phi"]
theta = d["theta"]
psi = d["psi"]
"""
# This makes no sense here! PAP 09/2011
if theta > 90.0:
phi += 180.0
theta = 180.0-theta
"""
phi *= deg2rad
theta *= deg2rad
x = sin(theta) * sin(phi)
y = sin(theta) * cos(phi)
z = cos(theta)
return [x, y, z]
N = len(agls1)
if N != len(agls2):
print 'Both lists must have the same length'
return -1
if N < 2:
print 'At least two orientations are required in each list'
return -1
U1, U2 = [], []
for n in xrange(N):
p1 = ori2xyz(agls1[n])
p2 = ori2xyz(agls2[n])
U1.append(p1)
U2.append(p2)
# compute all Suv with uv = {xx, xy, xz, yx, ..., zz}
Suv = [0] * 9
c = 0
nbori = len(U1)
for i in xrange(3):
for j in xrange(3):
for s in xrange(nbori):
Suv[c] += (U2[s][i] * U1[s][j])
c += 1
# create matrix N
N = array([[Suv[0]+Suv[4]+Suv[8], Suv[5]-Suv[7], Suv[6]-Suv[2], Suv[1]-Suv[3]],
[Suv[5]-Suv[7], Suv[0]-Suv[4]-Suv[8], Suv[1]+Suv[3], Suv[6]+Suv[2]],
[Suv[6]-Suv[2], Suv[1]+Suv[3], -Suv[0]+Suv[4]-Suv[8], Suv[5]+Suv[7]],
[Suv[1]-Suv[3], Suv[6]+Suv[2], Suv[5]+Suv[7], -Suv[0]-Suv[4]+Suv[8]]])
# eigenvector corresponding to the most positive eigenvalue
val, vec = linalg.eig(N)
q0, qx, qy, qz = vec[:, val.argmax()]
# create quaternion Rot matrix
r = [q0*q0-qx*qx+qy*qy-qz*qz, 2*(qy*qx+q0*qz), 2*(qy*qz-q0*qx), 0.0,
2*(qx*qy-q0*qz), q0*q0+qx*qx-qy*qy-qz*qz, 2*(qx*qz+q0*qy), 0.0,
2*(qz*qy+q0*qx), 2*(qz*qx-q0*qy), q0*q0-qx*qx-qy*qy+qz*qz, 0.0]
R = Transform(r)
dictR = R.get_rotation('SPIDER')
return dictR['phi'], dictR['theta'], dictR['psi'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_rotation(a, b):\n a.shape = (3,)\n b.shape = (3,)\n\n a /= np.linalg.norm(a)\n b /= np.linalg.norm(b)\n \n v = np.cross(a, b)\n \n angle_AB = -1*vector_angle(a, b) \n \n print(angle_AB)\n s = np.linalg.norm(v) * np.sin(angle_AB)\n \n c = np.dot(a, b) * np.cos(angle_A... | [
"0.61534816",
"0.6057964",
"0.6041564",
"0.6022013",
"0.5908641",
"0.5882892",
"0.5800431",
"0.57707256",
"0.5706791",
"0.56642485",
"0.56599987",
"0.5649005",
"0.5628651",
"0.5623927",
"0.56015086",
"0.5588204",
"0.55626535",
"0.5548194",
"0.55238026",
"0.5522772",
"0.551259... | 0.7362805 | 0 |
Retrieve pixel size from the header. We check attribute Pixel_size and also pixel size from ctf object, if exisits. If the two are different or if the pixel size is not set, return 1.0 and print a warning. | def get_pixel_size(img):
p1 = img.get_attr_default("apix_x", -1.0)
cc = img.get_attr_default("ctf", None)
if cc == None:
p2 = -1.0
else:
p2 = round(cc.apix, 3)
if p1 == -1.0 and p2 == -1.0:
ERROR("Pixel size not set", "get_pixel_size", 0)
return -1.0
elif p1 > -1.0 and p2 > -1.0:
if abs(p1-p2) >= 0.001:
ERROR("Conflict between pixel size in attribute and in ctf object", "get_pixel_size", 0)
# pixel size is positive, so what follows omits -1 problem
return max(p1, p2)
else:
return max(p1, p2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pixelsize(self):\n if hasattr(self, \"_pixelsize\"):\n return self._pixelsize\n\n try:\n return self.header[\"PixSize\"] # [arcsec]\n except KeyError:\n try:\n return abs(self.header[\"CDELT1\"]) * 3600 # [deg] -> [arcsec]\n exce... | [
"0.72802687",
"0.68242306",
"0.68130434",
"0.677174",
"0.6668118",
"0.6537701",
"0.6535796",
"0.6514242",
"0.6482996",
"0.64115053",
"0.63932556",
"0.6321486",
"0.6316444",
"0.6295255",
"0.62676024",
"0.62676024",
"0.62564975",
"0.6233684",
"0.6221311",
"0.6203486",
"0.61531"... | 0.69274837 | 1 |
Set pixel size in the header. Set attribute Pixel_size and also pixel size in ctf object, if exists. | def set_pixel_size(img, pixel_size):
nz = img.get_zsize()
img.set_attr("apix_x", round(pixel_size, 3))
img.set_attr("apix_y", round(pixel_size, 3))
img.set_attr("apix_z", round(pixel_size, 3))
cc = img.get_attr_default("ctf", None)
if(cc):
cc.apix = pixel_size
img.set_attr("ctf", cc) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_pixel_size(self, pixel_size):\n raise NotImplementedError",
"def set_size(self, w, h):\n\t\tpass",
"def set_size(self, value='S'):\n upper = value.upper()\n\n if upper == 'M': # Medium: double height\n # size = 0x01\n # charHeight = 48\n # maxColum... | [
"0.6878347",
"0.6856726",
"0.6558465",
"0.6309463",
"0.6261243",
"0.6204648",
"0.61168814",
"0.6115968",
"0.61134034",
"0.60657334",
"0.60597295",
"0.60385007",
"0.60327333",
"0.6032628",
"0.6013194",
"0.59783244",
"0.5973719",
"0.5945691",
"0.5938708",
"0.59118575",
"0.59118... | 0.67625564 | 2 |
For an input directory, create a dictionary mapping output names to input ROOT files. | def build_groupings(idir: str) -> dict:
bkg_group = {key: [ifile for ifile in glob(f'{idir}/*_{key}_*.root')] for key in bkgs}
pw_group = {key: [ifile for ifile in glob(f'{idir}/{key}*.root')] for key in powhegs}
wh_pw_group = [ifile for name in wh_powhegs for ifile in glob(f'{idir}/{name}*.root')]
ungrouped = [ifile for ifile in glob(f'{idir}/*.root') if 'madgraph' in ifile or 'JHU' in ifile]
group = {}
for key, files in bkg_group.items():
if len(files) > 0:
group[key] = files
for key, files in pw_group.items():
if len(files) > 0:
group[key] = files
for ifile in ungrouped:
name = ifile.split('/')[-1].replace('.root', '')
name = name.split('_SYST')[0].replace('-', '_')
name = name.replace('_ggH125', '').replace('_VBF125', '').replace('_WH125', '').replace('_ZH125', '')
group[name] = [ifile]
if len(wh_pw_group) > 0:
group['wh125_powheg'] = wh_pw_group
return group | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _output_paths(outputs: Dict[str, str]) -> Dict[str, Path]:\n out_paths = dict()\n for name in outputs:\n target_dir = _BASE_PATH / name\n target_dir.mkdir()\n out_paths[name] = target_dir / 'data.json'\n return out_paths",
"def treat(input, output):\n files = find(input)\n ... | [
"0.6456172",
"0.64561534",
"0.63940656",
"0.63858587",
"0.63171786",
"0.62454695",
"0.61177707",
"0.6060311",
"0.60519314",
"0.6003057",
"0.5944946",
"0.5938714",
"0.59148794",
"0.5861261",
"0.5858004",
"0.5832426",
"0.5809002",
"0.5801422",
"0.5792834",
"0.57921237",
"0.5737... | 0.53595054 | 76 |
Build the dictionary of all files to be processed in appropriate groupings. | def build_filelist(input_dir: str, syst: bool = False) -> dict:
filedict = {
idir.split('SYST_')[-1].split('/')[0]: {}
for idir in glob('{}/*'.format(input_dir)) if 'SYST_' in idir
}
filedict['nominal'] = build_groupings(f'{input_dir}/NOMINAL')
if syst:
for idir in filedict.keys():
if idir == 'nominal':
continue
elif 'Rivet' in idir:
continue
filedict[idir] = build_groupings(f'{input_dir}/SYST_{idir}')
else:
filedict = {'nominal': filedict['nominal']}
pprint(filedict, width=150)
return filedict | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_groupings(idir: str) -> dict:\n bkg_group = {key: [ifile for ifile in glob(f'{idir}/*_{key}_*.root')] for key in bkgs}\n pw_group = {key: [ifile for ifile in glob(f'{idir}/{key}*.root')] for key in powhegs}\n wh_pw_group = [ifile for name in wh_powhegs for ifile in glob(f'{idir}/{name}*.root')]\... | [
"0.7275469",
"0.71176326",
"0.7100193",
"0.70664155",
"0.7045975",
"0.70392615",
"0.6858053",
"0.6840117",
"0.6648288",
"0.66272867",
"0.64521015",
"0.6448374",
"0.6421026",
"0.6416618",
"0.63611877",
"0.6361177",
"0.63366175",
"0.6319295",
"0.6307999",
"0.62980103",
"0.62935... | 0.66944414 | 8 |
For the given grouping, convert ROOT files into DataFrames merging groups together. Return a dictionary mapping file names to DataFrames. | def process_group(directory: str, files: dict, channel: str, year: str) -> dict:
if len(files) == 0:
raise Exception('empty file list for directory {}'.format(directory)) + 1
dataframes = {}
for name, ifile in files.items():
# equivalent of hadding
update_dfs = uproot.pandas.iterate(ifile, f'{channel}_tree')
current_dfs = []
for update_df in update_dfs:
update_df.fillna(-999, inplace=True)
current_dfs.append(update_df)
if len(current_dfs) > 0:
dataframes[name] = pd.concat(current_dfs)
dataframes['metadata'] = pd.DataFrame({'channel': [channel], 'year': [year]})
return dataframes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_processed_data(self, group_directory):\n processed_dir = [x for x in group_directory.iterdir()\n if x.is_dir() and 'processed' in x.name][0]\n\n task_dirs = [x for x in processed_dir.iterdir()\n if x.is_dir() and 'task' in x.name]\n\n files =... | [
"0.6541141",
"0.60830426",
"0.6062187",
"0.59850407",
"0.5894021",
"0.5629577",
"0.56196886",
"0.5457541",
"0.5436698",
"0.5417699",
"0.5383131",
"0.53697276",
"0.5362166",
"0.5328199",
"0.5324665",
"0.5262586",
"0.52602506",
"0.5259905",
"0.52521104",
"0.5234652",
"0.5222199... | 0.64353156 | 1 |
Reweight JHU and Madgraph signals to different coupling scenarios. | def ac_reweighting(dataframes: dict, reweight: bool, config: dict) -> dict:
vbf = pd.concat([df for key, df in dataframes.items() if 'vbf125_JHU' in key])
wh = pd.concat([df for key, df in dataframes.items() if 'wh125_JHU' in key])
zh = pd.concat([df for key, df in dataframes.items() if 'zh125_JHU' in key])
# scale evtwt with appropriate reweighting factor and give a new name
for weight, name in config['jhu_ac_reweighting_map']['vbf']:
df = vbf.copy(deep=True)
df['evtwt'] *= df[weight]
dataframes[name] = df
for weight, name in config['jhu_ac_reweighting_map']['wh']:
df = wh.copy(deep=True)
df['evtwt'] *= df[weight]
dataframes[name] = df
for weight, name in config['jhu_ac_reweighting_map']['zh']:
df = zh.copy(deep=True)
df['evtwt'] *= df[weight]
dataframes[name] = df
if reweight:
# add couplings together then apply weights
ggh = pd.concat([df for key, df in dataframes.items() if 'ggh125_madgraph' in key])
for weight, name in config['mg_ac_reweighting_map']['ggh']:
df = ggh.copy(deep=True)
df['evtwt'] *= df[weight]
dataframes[name] = df
else:
# just add couplings without weighting
dataframes['reweighted_ggH_htt_0PM125'] = pd.concat([
df for key, df in dataframes.items() if 'ggh125_madgraph' in key and 'a1_filtered' in key
])
dataframes['reweighted_ggH_htt_0M125'] = pd.concat([
df for key, df in dataframes.items() if 'ggh125_madgraph' in key and 'a3_filtered' in key
])
dataframes['reweighted_ggH_htt_0Mf05ph0125'] = pd.concat([
df for key, df in dataframes.items() if 'ggh125_madgraph' in key and 'a3int_filtered' in key
])
return dataframes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def backward_shimmey(self):\n for x in range(6):\n self.right(primary=-70, counter=-30)\n time.sleep(.5)\n self.left(primary=-70, counter=-30)\n time.sleep(.5)\n self.stop()",
"def backward_shimmey(self):\n for x in range(6):\n self.righ... | [
"0.5252793",
"0.5252793",
"0.52396435",
"0.51686776",
"0.50035083",
"0.49548638",
"0.49380127",
"0.49380127",
"0.49380127",
"0.49363765",
"0.4932626",
"0.49048656",
"0.4897503",
"0.48733047",
"0.48726597",
"0.48689917",
"0.48130462",
"0.47945935",
"0.47870296",
"0.47869524",
... | 0.0 | -1 |
Store information for NN training (signal labels, scaled evtwts, and info for standardization. | def nn_preprocess(dataframes: dict) -> dict:
from sklearn.preprocessing import MinMaxScaler
for name, df in dataframes.items():
if name == 'metadata':
continue
# store signal label for NN training
if 'ggh125' in name or 'vbf125' in name:
df['signalLabel'] = np.ones(len(df))
else:
df['signalLabel'] = np.zeros(len(df))
# normalize sample weights
df['scaled_evtwt'] = MinMaxScaler(feature_range=(1., 2.)).fit_transform(df.evtwt.values.reshape(-1, 1))
return dataframes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def training_info(self):\n pass",
"def _training_vars(self):\n self.vars = dict()\n # Temperature params\n self.vars['TInit'] = -1\n self.vars['TDecayRate'] = 0.05\n # Bowl params\n self.vars['q_init'] = 16.58 # initial strength for the bowl\n self.vars['q... | [
"0.6854838",
"0.6442424",
"0.61368126",
"0.607955",
"0.5979329",
"0.596864",
"0.5946029",
"0.5927915",
"0.5915242",
"0.59119564",
"0.5895071",
"0.58685416",
"0.5860887",
"0.5857673",
"0.5857673",
"0.5851298",
"0.5837819",
"0.5815968",
"0.5808178",
"0.5806393",
"0.5806393",
... | 0.0 | -1 |
Write JSON in a consistent, humanreadable way. | def json_dumps(o: Any) -> bytes:
return json.dumps(
o, indent=4, sort_keys=True, ensure_ascii=True, separators=(",", ": "), cls=NumberEncoder
).encode("ascii") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write(self):\n self.json_o.write()",
"def _write_json(self):\n with open(self._file_path, 'w') as f:\n json.dump(self._content, f, indent=4, separators=None,\n encoding='utf-8', sort_keys=False)",
"def write(self, _filepath=None):\n if _filepath is None:... | [
"0.73995405",
"0.73701173",
"0.7319848",
"0.7299197",
"0.7268811",
"0.71151686",
"0.7094368",
"0.6901286",
"0.6872474",
"0.6859761",
"0.68393916",
"0.67960197",
"0.67669374",
"0.6747494",
"0.6735324",
"0.67135006",
"0.6681034",
"0.6619973",
"0.66117257",
"0.66088766",
"0.6588... | 0.0 | -1 |
Read JSON in a consistent way. | def json_loads(s: Union[bytes, str]) -> Dict[str, Any]:
return json.loads(ensure_text(s, "utf-8")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_json(self):\n self._fopen.seek(self._json_start, 0)\n return json.loads(self._fopen.read().decode('utf-8'))",
"def read_json(self, *args, **kwargs):\n with self.open('rb') as f:\n return json.load(f, *args, **kwargs)",
"def reading_json(json_file):\n with open(json_f... | [
"0.72466516",
"0.71838474",
"0.7122567",
"0.70307904",
"0.70307904",
"0.7005705",
"0.7005705",
"0.69405353",
"0.6842769",
"0.6829329",
"0.6786115",
"0.6777155",
"0.67250484",
"0.6718409",
"0.6713313",
"0.6706307",
"0.6697732",
"0.6672447",
"0.6669862",
"0.6629288",
"0.6623848... | 0.0 | -1 |
Convenience function to normalize the `shape` argument. | def normalize_shape(shape: Union[int, Tuple[int, ...], None]) -> Tuple[int, ...]:
if shape is None:
raise TypeError("shape is None")
# handle 1D convenience form
if isinstance(shape, numbers.Integral):
shape = (int(shape),)
# normalize
shape = cast(Tuple[int, ...], shape)
shape = tuple(int(s) for s in shape)
return shape | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalize(shape):\n s = shape\n matrix = Shape.get_matrix(s.get_vector())\n norm_x = math.sqrt(sum(matrix[:, 0] ** 2))\n norm_y = math.sqrt(sum(matrix[:, 1] ** 2))\n for pt in s.pts:\n pt.x /= norm_x\n pt.y /= norm_y\n return s",
"def _normalize_shape(shape):\n\n if isinsta... | [
"0.7858332",
"0.7624819",
"0.69473106",
"0.6248372",
"0.61092657",
"0.60909945",
"0.6038357",
"0.60316885",
"0.6010704",
"0.587052",
"0.5844497",
"0.5780181",
"0.57371706",
"0.5733882",
"0.5694286",
"0.56934756",
"0.5677857",
"0.5620248",
"0.56186056",
"0.55923927",
"0.558957... | 0.73496735 | 2 |
Guess an appropriate chunk layout for an array, given its shape and the size of each element in bytes. Will allocate chunks only as large as MAX_SIZE. Chunks are generally close to some powerof2 fraction of each axis, slightly favoring bigger values for the last index. Undocumented and subject to change without warning. | def guess_chunks(shape: Tuple[int, ...], typesize: int) -> Tuple[int, ...]:
ndims = len(shape)
# require chunks to have non-zero length for all dimensions
chunks = np.maximum(np.array(shape, dtype="=f8"), 1)
# Determine the optimal chunk size in bytes using a PyTables expression.
# This is kept as a float.
dset_size = np.prod(chunks) * typesize
target_size = CHUNK_BASE * (2 ** np.log10(dset_size / (1024.0 * 1024)))
if target_size > CHUNK_MAX:
target_size = CHUNK_MAX
elif target_size < CHUNK_MIN:
target_size = CHUNK_MIN
idx = 0
while True:
# Repeatedly loop over the axes, dividing them by 2. Stop when:
# 1a. We're smaller than the target chunk size, OR
# 1b. We're within 50% of the target chunk size, AND
# 2. The chunk is smaller than the maximum chunk size
chunk_bytes = np.prod(chunks) * typesize
if (
chunk_bytes < target_size or abs(chunk_bytes - target_size) / target_size < 0.5
) and chunk_bytes < CHUNK_MAX:
break
if np.prod(chunks) == 1:
break # Element size larger than CHUNK_MAX
chunks[idx % ndims] = math.ceil(chunks[idx % ndims] / 2.0)
idx += 1
return tuple(int(x) for x in chunks) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_chunk(chunk, sizes, max_iter=1000, rng=None):\n assert len(chunk) == sum(sizes), f\"{len(chunk)} != {sum(sizes)}\"\n if not isinstance(rng, random.Random):\n rng = random\n # Precompute neighbors for each cube in the chunk\n neighbors = dict()\n for c in chunk:\n neighbors[c]... | [
"0.644044",
"0.629798",
"0.61731964",
"0.60461324",
"0.598301",
"0.5948597",
"0.59229267",
"0.5806321",
"0.57971877",
"0.5785244",
"0.57549477",
"0.56843406",
"0.5641695",
"0.5637497",
"0.56076527",
"0.5582129",
"0.55698544",
"0.556121",
"0.55578834",
"0.5544379",
"0.5536151"... | 0.7806975 | 0 |
Convenience function to normalize the `chunks` argument for an array with the given `shape`. | def normalize_chunks(chunks: Any, shape: Tuple[int, ...], typesize: int) -> Tuple[int, ...]:
# N.B., expect shape already normalized
# handle auto-chunking
if chunks is None or chunks is True:
return guess_chunks(shape, typesize)
# handle no chunking
if chunks is False:
return shape
# handle 1D convenience form
if isinstance(chunks, numbers.Integral):
chunks = tuple(int(chunks) for _ in shape)
# handle bad dimensionality
if len(chunks) > len(shape):
raise ValueError("too many dimensions in chunks")
# handle underspecified chunks
if len(chunks) < len(shape):
# assume chunks across remaining dimensions
chunks += shape[len(chunks) :]
# handle None or -1 in chunks
if -1 in chunks or None in chunks:
chunks = tuple(s if c == -1 or c is None else int(c) for s, c in zip(shape, chunks))
chunks = tuple(int(c) for c in chunks)
return chunks | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _normalize_shape(shape):\n\n if isinstance(shape, (np.integer, int)):\n if shape < 1:\n raise ValueError(\"shape value must be greater than 0: %d\"\n % shape)\n shape = (shape,) # N is a shorthand for (N,)\n try:\n shape = tuple(shape)\n exc... | [
"0.61362606",
"0.60389656",
"0.58118457",
"0.5734301",
"0.5696832",
"0.5670947",
"0.5670829",
"0.5670006",
"0.56443065",
"0.5637183",
"0.56079644",
"0.55675006",
"0.5559034",
"0.55089456",
"0.5506106",
"0.5494193",
"0.54827696",
"0.5472908",
"0.54428035",
"0.54304826",
"0.542... | 0.7882431 | 0 |
Determine whether `item` specifies a complete slice of array with the given `shape`. Used to optimize __setitem__ operations on the Chunk class. | def is_total_slice(item, shape: Tuple[int]) -> bool:
# N.B., assume shape is normalized
if item == Ellipsis:
return True
if item == slice(None):
return True
if isinstance(item, slice):
item = (item,)
if isinstance(item, tuple):
return all(
(
isinstance(it, slice)
and ((it == slice(None)) or ((it.stop - it.start == sh) and (it.step in [1, None])))
)
for it, sh in zip(item, shape)
)
else:
raise TypeError("expected slice or tuple of slices, found %r" % item) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __contains__(self, item):\n if len(item) != len(self.sizes):\n raise ValueError('Point dimension does not match grid dimension')\n for i in range(len(self.sizes)):\n if not 1 <= item[i] < self.sizes[i] - 1:\n return False\n return True",
"def roi_is_f... | [
"0.5930391",
"0.5894886",
"0.5701367",
"0.56653273",
"0.5539224",
"0.54814374",
"0.54136163",
"0.54108244",
"0.5387186",
"0.5382678",
"0.5344155",
"0.5341929",
"0.53009504",
"0.5299111",
"0.52924687",
"0.5279965",
"0.52784073",
"0.52331346",
"0.5224626",
"0.51978904",
"0.5183... | 0.780188 | 0 |
Make several attempts to invoke the callable. If one of the given exceptions is raised, wait the given period of time and retry up to the given number of retries. | def retry_call(
callabl: Callable,
args=None,
kwargs=None,
exceptions: Tuple[Any, ...] = (),
retries: int = 10,
wait: float = 0.1,
) -> Any:
if args is None:
args = ()
if kwargs is None:
kwargs = {}
for attempt in range(1, retries + 1):
try:
return callabl(*args, **kwargs)
except exceptions:
if attempt < retries:
time.sleep(wait)
else:
raise | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def call_with_retries(function, max_retries=10,\n exception_types=(Exception),\n _args=(), _kwargs={}):\n assert max_retries >= 0\n\n retries = 0\n last_exc = Exception('Unknown exception')\n while retries <= max_retries:\n try:\n return funct... | [
"0.790463",
"0.7765928",
"0.7565526",
"0.7538731",
"0.75145954",
"0.7512866",
"0.7507025",
"0.7504739",
"0.74817264",
"0.7449125",
"0.7426998",
"0.73940426",
"0.7380012",
"0.73608816",
"0.7356993",
"0.73083615",
"0.7303313",
"0.72958124",
"0.7114819",
"0.70131445",
"0.7012791... | 0.7604029 | 2 |
Test if all the elements of an array are equivalent to a value. If `value` is None, then this function does not do any comparison and returns False. | def all_equal(value: Any, array: Any):
if value is None:
return False
if not value:
# if `value` is falsey, then just 1 truthy value in `array`
# is sufficient to return False. We assume here that np.any is
# optimized to return on the first truthy value in `array`.
try:
return not np.any(array)
except (TypeError, ValueError): # pragma: no cover
pass
if np.issubdtype(array.dtype, np.object_):
# we have to flatten the result of np.equal to handle outputs like
# [np.array([True,True]), True, True]
return all(flatten(np.equal(value, array, dtype=array.dtype)))
else:
# Numpy errors if you call np.isnan on custom dtypes, so ensure
# we are working with floats before calling isnan
if np.issubdtype(array.dtype, np.floating) and np.isnan(value):
return np.all(np.isnan(array))
else:
# using == raises warnings from numpy deprecated pattern, but
# using np.equal() raises type errors for structured dtypes...
return np.all(value == array) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def all_equal(array):\n if not array:\n raise ValueError(\"Array is empty\")\n\n first_item = array[0]\n\n if any(item != first_item for item in array):\n return False\n\n return True",
"def has_equal_values_vec(x):\n return jnp.all(x == x[0])",
"def check_array(self, array: ArrayD... | [
"0.6699362",
"0.6486526",
"0.6385027",
"0.6280364",
"0.61277896",
"0.612132",
"0.6043027",
"0.60365415",
"0.5891498",
"0.5891476",
"0.5875021",
"0.5753058",
"0.5749066",
"0.57388574",
"0.5702565",
"0.56945324",
"0.5661426",
"0.56569785",
"0.56512713",
"0.56494707",
"0.5626120... | 0.7799601 | 0 |
Convenience function to coerce `buf` to ndarraylike array or bytes. First check if `buf` can be zerocopy converted to a contiguous array. If not, `buf` will be copied to a newly allocated `bytes` object. | def ensure_contiguous_ndarray_or_bytes(buf) -> Union[NDArrayLike, bytes]:
try:
return ensure_contiguous_ndarray_like(buf)
except TypeError:
# An error is raised if `buf` couldn't be zero-copy converted
return ensure_bytes(buf) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buffer_to_bytes(buf):\n if not isinstance(buf, bytes):\n buf = bytes(buf)\n return buf",
"def test_array_as_buffer(parser):\n doc = parser.parse(b'''{\n \"d\": [1.2, 2.3, 3.4],\n \"i\": [-1, 2, -3, 4],\n \"u\": [1, 2, 3, 4, 5],\n \"x\": [1, 2, 3, \"not valid\"]\n ... | [
"0.6886174",
"0.6035865",
"0.5953554",
"0.55916303",
"0.5561849",
"0.54923177",
"0.5430941",
"0.53742135",
"0.51953775",
"0.5164276",
"0.51263016",
"0.5078041",
"0.507584",
"0.5072293",
"0.50700045",
"0.5063474",
"0.50496",
"0.5039605",
"0.5037979",
"0.50145006",
"0.49787346"... | 0.8558664 | 0 |
This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. | def plot_confusion_matrix(cm,classes,normalize=False,title='Confusion Matrix', cmap=plt.cm.Blues):
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
cm=np.around(cm,decimals=2)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=90)
plt.yticks(tick_marks, classes)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],horizontalalignment="center",color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_confusion_matrix(cm, classes=[0,1], normalize=False, title='Confusion matrix', print_matrix=False):\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n... | [
"0.81925154",
"0.8093376",
"0.8027219",
"0.80169857",
"0.7991475",
"0.7988959",
"0.79785",
"0.7973596",
"0.7959107",
"0.7956459",
"0.793519",
"0.79322356",
"0.79320693",
"0.79289055",
"0.79240024",
"0.79221827",
"0.792161",
"0.79209834",
"0.7920566",
"0.7919846",
"0.791543",
... | 0.0 | -1 |
Decompose transformation matrix into parts | def trans_matrix_inv(m:numpy.ndarray):
was2d = False
if m.shape[1] == 3:
was2d = True
m = numpy.asarray([
[1.0, 0.0, 0.0, 0.0],
[0.0, m[0,0], m[0,1], m[0,2]],
[0.0, m[1,0], m[1,1], m[1,2]],
[0.0, 0.0, 0.0, 1.0]], numpy.float64)
trans = m[0:3,3]
rotate = numpy.zeros(3, numpy.float64)
r = m[0:3,0:3]
rc = numpy.linalg.cholesky(numpy.matmul(r.T, r)).T
scale = numpy.diagonal(rc)
if numpy.linalg.det(r) < 0.0:
scale[0] *= -1.0
rcd = rc * numpy.eye(3, dtype=numpy.float64)
rc = numpy.linalg.solve(rcd, rc)
shear = numpy.asarray([rc[0,1], rc[0,2], rc[1,2]], numpy.float64)
r0 = trans_matrix({'rotate': rotate, 'scale': scale, 'shear': shear})[0:3,0:3]
r0 = numpy.linalg.solve(numpy.linalg.inv(r), numpy.linalg.inv(r0))
rotate[1] = numpy.arcsin(_frone(r0[0,2]))
if numpy.abs((numpy.abs(rotate[1]) - (numpy.pi / 2.0))) < 1.0e-6:
rotate[0] = 0.0
rotate[2] = numpy.arctan2(-_frone(r0[1,0]), _frone(-r0[2,0] / r0[0,2]))
else:
rc = numpy.cos(rotate[1])
rotate[0] = numpy.arctan2(_frone(r0[1,2] / rc), _frone(r0[2,2] / rc))
rotate[2] = numpy.arctan2(_frone(r0[0,1] / rc), _frone(r0[0,0] / rc))
if was2d:
trans = trans[1:]
rotate = rotate[0:1]
scale = scale[1:]
shear = shear[2:3]
return (trans, rotate, scale, shear) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def xform_from_transformation_matrix(transformation_matrix):\n transform = Transform(1.0)\n for i in range(0, 4):\n for j in range(0, 4):\n transform[i, j] = transformation_matrix[i][j]\n return transform",
"def _derive_transformation_matrices(self):\n\n if hasattr(self, '_prima... | [
"0.6168927",
"0.6069243",
"0.6052449",
"0.6051412",
"0.5990401",
"0.5962151",
"0.59120136",
"0.58908314",
"0.58383477",
"0.57767695",
"0.57475996",
"0.57253575",
"0.572462",
"0.57190764",
"0.56800544",
"0.56527287",
"0.56141245",
"0.55945146",
"0.55739355",
"0.55634975",
"0.5... | 0.0 | -1 |
Ask a yes no question | def ask_yes_no(question):
answer = None
while answer not in ("y","n"):
answer = input(question).lower()
return answer | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ask_Yes_no(question, **kwargs):\n return ask_yes_no(question, default=\"y\", **kwargs)",
"def ask_yes_No(question, **kwargs):\n return ask_yes_no(question, default=\"n\", **kwargs)",
"def ask_yes_no(question):\r\n\tresponse = None\r\n\twhile response not in (\"y\", \"n\"):\r\n\t\tresponse = input(que... | [
"0.8196788",
"0.8141998",
"0.8027091",
"0.79333156",
"0.78017837",
"0.7636633",
"0.75908566",
"0.75773454",
"0.7574332",
"0.7567365",
"0.75321203",
"0.7493373",
"0.7484439",
"0.74384576",
"0.7435376",
"0.7426852",
"0.7407709",
"0.73929524",
"0.7383109",
"0.7374345",
"0.735202... | 0.7771797 | 5 |
Clears the terminal screen. | def clear_screen():
# Clear command as function of OS
command = "cls" if system_name().lower()=="windows" else "clear"
# Action
system_call(command) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear_screen() -> None:\n os.system(\"cls\" if os.name == \"nt\" else \"clear\")",
"def clear_screen():\n os.system('cls')",
"def clear_screen():\n\tprint(\"\\033[H\\033[J\")",
"def clear_screen(self):\n os.system('cls' if os.name == 'nt' else 'clear')",
"def clear_screen(self):\n o... | [
"0.89045364",
"0.8903247",
"0.8894391",
"0.8885713",
"0.8885713",
"0.88519347",
"0.88519347",
"0.88374126",
"0.8820877",
"0.8814398",
"0.8805836",
"0.87480396",
"0.874027",
"0.86738855",
"0.866956",
"0.8638429",
"0.85816944",
"0.84902024",
"0.83856225",
"0.8375647",
"0.836616... | 0.87332195 | 15 |
Read and preprocess an image with data augmentation (random transform). | def read_for_training(p, augmentation=False):
img = imread(TRAIN + p, mode='RGB')
msk = img
if mode == 'background':
data = {'image': img}
elif mode == 'instance' or mode == 'code':
msk = imread(TRAIN_MASK + p.replace('.jpg', '.png'))
data = {'image': img, 'mask': msk}
if augmentation:
data_aug = strong_aug()(**data)
img = data_aug['image']
if 'mask' in data_aug:
msk = data_aug['mask']
if mode == 'instance' or mode == 'code':
img[~msk.astype(np.bool)] = 0
img, msk = size_normalization(img, msk)
if mode == 'code':
img = encode(img, msk)
return img, msk | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess(self, img):\n img_ = image.load_img(img, target_size=(299, 299))\n img_ = image.img_to_array(img_)\n img_ = np.expand_dims(img_, axis=0)\n img_ = preprocess_input(img_)\n return img_",
"def data_augmentation(image, aug):\n if (aug == \"random_crop\") and (rand... | [
"0.6944267",
"0.6885695",
"0.6801366",
"0.6746409",
"0.6730426",
"0.6700084",
"0.6674317",
"0.6642256",
"0.6604827",
"0.65799797",
"0.65796673",
"0.6575271",
"0.6572347",
"0.6542871",
"0.6535678",
"0.6521868",
"0.64719284",
"0.64391285",
"0.6430673",
"0.6425564",
"0.6418553",... | 0.70998 | 0 |
Helper function to visualize mask on the top of the car | def mask_overlay(image, mask, color=(0, 255, 0)):
mask = np.dstack((mask, mask, mask)) * np.array(color)
mask = mask.astype(np.uint8)
weighted_sum = cv2.addWeighted(mask, 0.5, image, 0.5, 0.)
img = image.copy()
ind = mask[:, :, 1] > 0
img[ind] = weighted_sum[ind]
return img | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_mask(image, mask): \n plt.subplot(1,2,1)\n plt.title('image')\n plt.imshow(image)\n plt.subplot(1,2,2)\n plt.title('mask')\n plt.imshow(mask)\n plt.show()",
"def mask_show(image, mask, groups, name=\"image\"):\n img = cv2.addWeighted(image, 0.4, mask, 0.6, 0)\n img = sg.mar... | [
"0.71640295",
"0.67468154",
"0.6488188",
"0.63977045",
"0.62672323",
"0.6256299",
"0.623426",
"0.62271696",
"0.6222103",
"0.6217956",
"0.6209555",
"0.6123184",
"0.6110045",
"0.6090729",
"0.6049397",
"0.6028649",
"0.59504765",
"0.5926747",
"0.5888969",
"0.5847702",
"0.5817907"... | 0.0 | -1 |
Parse the first YAML document in a stream and produce the corresponding Python object. | def load(stream, schema=None):
stream_type = str(type(stream))
if stream_type not in ("<type 'unicode'>", "<type 'str'>", "<class 'str'>"):
raise TypeError("StrictYAML can only read a string of valid YAML.")
document = ruamelyaml.load(stream, Loader=ruamelyaml.RoundTripLoader)
# Document is single item (string, int, etc.)
if type(document) not in (CommentedMap, CommentedSeq):
document = stream
for token in ruamelyaml.scan(stream):
if type(token) == ruamelyaml.tokens.TagToken:
raise exceptions.TagTokenDisallowed(
document,
token.start_mark.line + 1,
token.end_mark.line + 1
)
if type(token) == ruamelyaml.tokens.FlowMappingStartToken:
raise exceptions.FlowMappingDisallowed(
document,
token.start_mark.line + 1,
token.end_mark.line + 1
)
if type(token) == ruamelyaml.tokens.AnchorToken:
raise exceptions.AnchorTokenDisallowed(
document,
token.start_mark.line + 1,
token.end_mark.line + 1
)
if schema is None:
schema = Any()
return schema(document) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read(self, stream):\n ret = yaml.load(stream)\n self.validate(ret)\n return (ret, self.make_order(ret))",
"def construct_yaml_stream(in_stream):\n\n global _yaml_initialized\n\n logger.info('Request to construct yaml')\n\n if not _yaml_initialized:\n def _object_creator(loader, node,... | [
"0.7405631",
"0.6758382",
"0.64881027",
"0.6360592",
"0.63516474",
"0.63164014",
"0.6199864",
"0.606954",
"0.604542",
"0.60424024",
"0.60077196",
"0.5979247",
"0.594493",
"0.5858309",
"0.5854879",
"0.584637",
"0.584421",
"0.5775051",
"0.5773475",
"0.5768689",
"0.57418305",
... | 0.6677856 | 2 |
Attention based temporal attention forward pass | def forward(self, src, src_t, seq, seq_t, seq_e, mask, vars_dict):
src_ext = torch.unsqueeze(src, dim=1) # src [B, 1, D]
src_e_ph = torch.zeros_like(src_ext)
q = torch.cat([src_ext, src_e_ph, src_t], dim=2) # [B, 1, D + De + Dt] -> [B, 1, D]
k = torch.cat([seq, seq_e, seq_t], dim=2) # [B, 1, D + De + Dt] -> [B, 1, D]
mask = torch.unsqueeze(mask, dim=2) # mask [B, N, 1]
mask = mask.permute([0, 2, 1]) # mask [B, 1, N]
# # target-attention
output, attn = self.multi_head_target(q=q, k=k, v=k, vars_dict=vars_dict, mask=mask) # output: [B, 1, D + Dt], attn: [B, 1, N]
# print('src.shape', src.shape)
output = output.squeeze(1)
# print('output.shape', output.shape)
# print('output', output)
# print('output.squeeze().shape', output.shape)
attn = attn.squeeze()
# output = self.merger(output, src)
x = torch.cat([output, src], dim=1)
# x = self.layer_norm(x)
x = F.relu(F.linear(x, vars_dict['w1_agg_fc']))
output = F.linear(x, vars_dict['w2_agg_fc'])
return output, attn | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward(self, encoder_state, context, context_lens):\n attn = self.attention(context, encoder_state.squeeze(0), context_lens)\n return attn",
"def _Attention(self, name, is_causal=True):\n p = self.params\n tr_atten_p = TransformerAttentionLayer.Params().Set(\n name='transformer_at... | [
"0.6917411",
"0.67771304",
"0.67337334",
"0.64585733",
"0.6420011",
"0.63664645",
"0.63517004",
"0.63190645",
"0.6295559",
"0.62826264",
"0.6252782",
"0.6198423",
"0.61942375",
"0.6190378",
"0.6135776",
"0.61074287",
"0.6046608",
"0.598789",
"0.59875965",
"0.59709185",
"0.596... | 0.5559464 | 78 |
Actions before each test. | def setUp(self):
self.brow = webdriver.Firefox()
staging_server = os.environ.get('STAGING_SERVER')
if staging_server:
self.live_server_url = "http://" + staging_server | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def before_run_tests(cls):\n pass",
"def before_test(self, func, *args, **kwargs):\n pass",
"def do_before(self):\r\n pass",
"def beforeTest(self, test):\n self.setupLoghandler()",
"def before(self) -> None:\n pass",
"def setUp(self):\r\n # nothing to do, all tes... | [
"0.7848565",
"0.7659674",
"0.7591663",
"0.7281655",
"0.7241673",
"0.7141255",
"0.70754164",
"0.7027952",
"0.7027952",
"0.7026638",
"0.70223314",
"0.6992696",
"0.69685715",
"0.6968439",
"0.6968439",
"0.6968439",
"0.6968439",
"0.6968439",
"0.6968439",
"0.6968439",
"0.6968439",
... | 0.0 | -1 |
Actions after each test. | def tearDown(self):
self.brow.quit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def after_test(self, test_results):\n pass",
"def after_test(self, func, *args, **kwargs):\n pass",
"def after(self):\n pass",
"def after(self):\n pass",
"def after_all(self) -> None:",
"def finished_tests(self):\n self.testing = 0",
"def tearDown(self):\n\t\tprint(\"... | [
"0.83760184",
"0.79627854",
"0.76740867",
"0.76740867",
"0.764812",
"0.75801444",
"0.7479539",
"0.7362819",
"0.73584396",
"0.7329822",
"0.72882223",
"0.7255437",
"0.7153491",
"0.70499015",
"0.7021705",
"0.6966801",
"0.6941674",
"0.6791313",
"0.677205",
"0.6720164",
"0.6713851... | 0.0 | -1 |
Get servers local IP address so it can be accessed inside this network. | def print_local_ip():
spacer = '-' * 50
local_ip = gethostbyname(gethostname())
print('\n{}\nLocal IP address is: {}\n{}'.format(spacer, local_ip, spacer)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def localip(self) :\n\t\ttry :\n\t\t\treturn self._localip\n\t\texcept Exception as e:\n\t\t\traise e",
"def get_local_host_ip(self) -> str:",
"def get_local_ip():\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n sock.connect((\"8.8.8.8\", 80))\n local_ip = sock.getsockname()[0]\n sock.c... | [
"0.8273781",
"0.8249747",
"0.8185857",
"0.8137731",
"0.80874467",
"0.80454546",
"0.80384547",
"0.8009976",
"0.7990402",
"0.7969245",
"0.7930696",
"0.7782699",
"0.77280563",
"0.7713387",
"0.7654462",
"0.75452614",
"0.75112414",
"0.75089806",
"0.7462935",
"0.7451744",
"0.744139... | 0.7378908 | 22 |
Amount which will increase x until it's divisible evenly by 64 | def padlen_64(x: int):
return (64 - (x % 64)) % 64 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def modReduce(self, x):\n\n assert 0 <= x < pow(self.mod, 2), 'out of range.'\n q = (x * self.u) >> (2 * self.M_bit)\n r = x - q * self.mod\n while r >= self.mod:\n r -= self.mod\n return r",
"def taille(x):\n n = 0\n \n while (2**n) -1 < x :\n n+=1\n... | [
"0.64682055",
"0.6353813",
"0.63322943",
"0.6308356",
"0.6298304",
"0.62941134",
"0.61767954",
"0.61767954",
"0.61669123",
"0.61343306",
"0.61135375",
"0.60509366",
"0.59431607",
"0.5926209",
"0.5924908",
"0.5908529",
"0.5902806",
"0.5896249",
"0.58734715",
"0.5857224",
"0.58... | 0.6562799 | 0 |
Simulate an SHA256 ending pad (1 bit, zero bad, 64bit length) | def end_shastream(length: int):
padlen = padlen_64(length + 1 + 8)
return bytes([0x80]) + bytes(padlen) + int.to_bytes(length * 8, 8, 'big') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SHA256(self) -> _n_0_t_3[_n_0_t_9]:",
"def _sha256(sha256):\n if not sha256:\n sha256 = \"0\" * 64\n\n return sha256",
"def shake256(data=None, digest_size=512):\n return SpongeHash(512, digest_size, data, \"SHAKE256\", KeccakSponge, PAD_SHAKE)",
"def sha256(message: bytes):\n # conver... | [
"0.7570556",
"0.7199887",
"0.6765323",
"0.64624476",
"0.6452416",
"0.6417613",
"0.63762766",
"0.634967",
"0.6248739",
"0.62149316",
"0.6214463",
"0.61732584",
"0.61626583",
"0.61307865",
"0.61083645",
"0.6072602",
"0.60649854",
"0.6037192",
"0.5975247",
"0.5962065",
"0.593303... | 0.0 | -1 |
Returns None on success, otherwise an explanation string | def test(self, values: Dict[str, Any]) -> Optional[str]:
# This is always True
if self.cond == '#':
return None
def why(cond, field, explanation) -> Optional[str]:
if cond:
return None
return '{}: {}'.format(field, explanation)
# If it's missing, it's only True if it's a missing test.
if self.field not in values:
# Default to ignoring id field as long as no version.
if self.field == '':
return why('-' not in self.value, 'id', 'unknown version {}'.format(self.value))
return why(self.cond == '!', self.field, 'is missing')
# If they supply a function, hand it to them.
if callable(values[self.field]):
return values[self.field](self)
val = str(values[self.field])
if self.cond == '!':
return why(False, self.field, 'is present')
elif self.cond == '=':
return why(val == self.value,
self.field,
'!= {}'.format(self.value))
elif self.cond == '/':
return why(val != self.value,
self.field,
'= {}'.format(self.value))
elif self.cond == '^':
return why(val.startswith(self.value),
self.field,
'does not start with {}'.format(self.value))
elif self.cond == '$':
return why(val.endswith(self.value),
self.field,
'does not end with {}'.format(self.value))
elif self.cond == '~':
return why(self.value in val,
self.field,
'does not contain {}'.format(self.value))
elif self.cond == '<':
try:
actual_int = int(val)
except ValueError:
return why(False, self.field, "not an integer field")
try:
restriction_val = int(self.value)
except ValueError:
return why(False, self.field, "not a valid integer")
return why(actual_int < restriction_val,
self.field,
">= {}".format(restriction_val))
elif self.cond == '>':
try:
actual_int = int(val)
except ValueError:
return why(False, self.field, "not an integer field")
try:
restriction_val = int(self.value)
except ValueError:
return why(False, self.field, "not a valid integer")
return why(actual_int > restriction_val,
self.field,
"<= {}".format(restriction_val))
elif self.cond == '{':
return why(val < self.value,
self.field,
'is the same or ordered after {}'.format(self.value))
elif self.cond == '}':
return why(val > self.value,
self.field,
'is the same or ordered before {}'.format(self.value))
else:
# We checked this in init!
assert False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def description():",
"def describe(self):\n return ''",
"def help(self):\n res = \"\"",
"def _get_problem_str(self):\n return ''",
"def summary_str(self):\n if not self.results:\n return self.summary.empty() or ''\n elif self.state == Ok:\n return self.s... | [
"0.6749599",
"0.6595771",
"0.65909255",
"0.6580674",
"0.6552211",
"0.65474534",
"0.65056944",
"0.6464271",
"0.6464271",
"0.6463483",
"0.6435495",
"0.6418199",
"0.6409962",
"0.63947475",
"0.6379907",
"0.6308825",
"0.62577033",
"0.62099546",
"0.6208194",
"0.6198969",
"0.6187488... | 0.0 | -1 |
Pull an Alternative from encoded string, return remainder | def decode(cls, encstr: str) -> Tuple['Alternative', str]:
cond = None
end_off = 0
# Swallow field up to conditiona
while end_off < len(encstr):
if encstr[end_off] in string.punctuation:
cond = encstr[end_off]
break
end_off += 1
if cond is None:
raise ValueError('{} does not contain any operator'
.format(encstr))
field = encstr[:end_off]
end_off += 1
value = ''
while end_off < len(encstr):
if encstr[end_off] == '|':
# We swallow this
end_off += 1
break
if encstr[end_off] == '&':
break
if encstr[end_off] == '\\':
end_off += 1
value += encstr[end_off]
end_off += 1
return cls(field, cond, value), encstr[end_off:] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decode(cls, encstr: str) -> Tuple['Restriction', str]:\n alts = []\n while len(encstr) != 0:\n if encstr.startswith('&'):\n encstr = encstr[1:]\n break\n alt, encstr = Alternative.decode(encstr)\n alts.append(alt)\n return cls(... | [
"0.66574126",
"0.6132067",
"0.6132067",
"0.5771838",
"0.5631769",
"0.5482572",
"0.5471471",
"0.54456496",
"0.54365635",
"0.537955",
"0.5371491",
"0.53414893",
"0.5331267",
"0.52930796",
"0.52915156",
"0.52808076",
"0.527762",
"0.5274057",
"0.5274057",
"0.5270972",
"0.5267768"... | 0.59058076 | 3 |
Turns this userreadable string into an Alternative (no escaping) | def from_str(cls, encstr: str) -> 'Alternative':
encstr = re.sub(r'\s+', '', encstr)
return cls(*re.split('([' + string.punctuation + '])', encstr, maxsplit=1)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def T(value):\n return (value if isinstance(value, basestring) else\n str(value) if isinstance(value, AltText) else \"\")",
"def decode(cls, encstr: str) -> Tuple['Alternative', str]:\n cond = None\n end_off = 0\n\n # Swallow field up to conditiona\n while end_off < len(encs... | [
"0.5630695",
"0.5217001",
"0.5198397",
"0.5186945",
"0.5184185",
"0.51392734",
"0.51312214",
"0.50683737",
"0.50631917",
"0.5061903",
"0.5048459",
"0.50467837",
"0.50248337",
"0.49875286",
"0.4931769",
"0.49110556",
"0.48986942",
"0.48867592",
"0.48742884",
"0.4862726",
"0.48... | 0.5572365 | 1 |
Returns None on success, otherwise a string of all the failures | def test(self, values: Dict[str, Any]) -> Optional[str]:
reasons = []
for alt in self.alternatives:
reason = alt.test(values)
if reason is None:
return None
reasons.append(reason)
return " AND ".join(reasons) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_error_message(self):\n msg = 'Test case: ' + self.benchmark + '.yaml + ' + self.producer + '.yaml failed. '\n info = ''\n if not self.directory:\n info = 'No results directory found. The benchmark probably failed'\n elif not self.reports:\n info = 'No resul... | [
"0.7311255",
"0.6805091",
"0.6787462",
"0.67572325",
"0.66535556",
"0.66410094",
"0.66374403",
"0.653866",
"0.6504157",
"0.64584297",
"0.64362466",
"0.6418307",
"0.63620204",
"0.6343835",
"0.62825376",
"0.62660265",
"0.62512654",
"0.6249964",
"0.6241896",
"0.6228709",
"0.6224... | 0.0 | -1 |
Pull a Restriction from encoded string, return remainder | def decode(cls, encstr: str) -> Tuple['Restriction', str]:
alts = []
while len(encstr) != 0:
if encstr.startswith('&'):
encstr = encstr[1:]
break
alt, encstr = Alternative.decode(encstr)
alts.append(alt)
return cls(alts), encstr | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_str(cls, encstr: str) -> 'Restriction':\n encstr = re.sub(r'\\s+', '', encstr)\n ret, remainder = cls.decode(encstr)\n if len(remainder) != 0:\n raise ValueError(\"Restriction had extrs characters at end: {}\"\n .format(remainder))\n retur... | [
"0.6682689",
"0.5536616",
"0.54848987",
"0.5409332",
"0.5409332",
"0.5249636",
"0.52139443",
"0.51220363",
"0.5092601",
"0.5083486",
"0.50243723",
"0.49601397",
"0.4911395",
"0.48801553",
"0.48710787",
"0.48674506",
"0.48674506",
"0.48620152",
"0.4848582",
"0.4832647",
"0.479... | 0.61234236 | 1 |
Returns a Restriction from an escaped string (ignoring whitespace) | def from_str(cls, encstr: str) -> 'Restriction':
encstr = re.sub(r'\s+', '', encstr)
ret, remainder = cls.decode(encstr)
if len(remainder) != 0:
raise ValueError("Restriction had extrs characters at end: {}"
.format(remainder))
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _assign_regex(literal, regex):\n if regex:\n return regex.lower().strip()\n else:\n return r'\\b%s\\b'%literal.lower().strip()",
"def interpret_requirement(string):\n string_list = split(string, sep=' ')\n \n requirement = Requirement(points, degree, majors, levels, max_non_degre... | [
"0.5168612",
"0.5026671",
"0.49126112",
"0.48980483",
"0.4877988",
"0.485538",
"0.48332566",
"0.48306048",
"0.4796996",
"0.47644973",
"0.4762547",
"0.47521722",
"0.4743869",
"0.47309223",
"0.47302634",
"0.4718924",
"0.47140232",
"0.47117367",
"0.4668038",
"0.46525672",
"0.463... | 0.6378927 | 0 |
Helper to produce an id 'restriction' | def unique_id(cls,
unique_id: Union[int, str],
version: Optional[Union[int, str]] = None) -> 'Restriction':
idstr = str(unique_id)
if '-' in idstr:
raise ValueError('Hyphen not allowed in unique_id {}'.format(idstr))
if version:
idstr += '-{}'.format(version)
# We use the empty field for this, since it's always present.
return cls([Alternative('', '=', idstr)]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def friendly_id(self):\n id = f\"{self.annotator_id}_{self.document_title.split('_')[0]}\"\n\n try: # try making an sentence identifier if there is an in_sentence attrib\n sen_id = \",\".join(str(se.element_id + 1) for se in self.in_sentence)\n id += f\"_s{sen_id}\"\n ex... | [
"0.6390797",
"0.59261435",
"0.5828868",
"0.58210576",
"0.57121724",
"0.56987214",
"0.56987214",
"0.5654812",
"0.56331956",
"0.5582573",
"0.5568012",
"0.55624306",
"0.55531055",
"0.5549231",
"0.55474275",
"0.5540417",
"0.5539157",
"0.5539157",
"0.5516462",
"0.55037725",
"0.549... | 0.5554285 | 12 |
Tests the restrictions against the values dict given. Normally values are treated strings, but conditions only work if they're actually integers. Returns (True, '') if everything is good. Otherwise, returns (False, reasonstring) | def are_restrictions_met(self, values: Dict[str, Any]) -> Tuple[bool, str]:,
for r in self.restrictions:
reasons = r.test(values)
if reasons is not None:
return False, reasons
return True, '' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(self, values: Dict[str, Any]) -> Optional[str]:\n # This is always True\n if self.cond == '#':\n return None\n\n def why(cond, field, explanation) -> Optional[str]:\n if cond:\n return None\n return '{}: {}'.format(field, explanation)\n\... | [
"0.67283416",
"0.66373456",
"0.63170063",
"0.6068614",
"0.60020655",
"0.5946913",
"0.57890636",
"0.5788082",
"0.57537967",
"0.57021195",
"0.56713057",
"0.5626302",
"0.5622262",
"0.5609995",
"0.5586753",
"0.55860853",
"0.5585117",
"0.5569001",
"0.5539537",
"0.5532167",
"0.5508... | 0.76134247 | 0 |
Perform a shallow copy | def copy(self) -> 'Rune':
return self.__copy__() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shallow_copy(self):\n # TODO: Rename this to __copy__()?\n raise NotImplementedError(\"shallow_copy is not implemented\")",
"def copy(self):\n import copy as pcopy\n return pcopy.deepcopy(self)",
"def copy(self):",
"def _copy_(self):\n return copy.copy(self)",
"def copy(self)... | [
"0.87317777",
"0.80810803",
"0.79437435",
"0.79085433",
"0.7848419",
"0.78046805",
"0.7762016",
"0.7748258",
"0.77443546",
"0.774237",
"0.774237",
"0.774237",
"0.772365",
"0.7720509",
"0.76456845",
"0.76456845",
"0.76456845",
"0.76456845",
"0.76456845",
"0.76456845",
"0.76456... | 0.0 | -1 |
sha256.sha256 doesn't implement pickle | def __deepcopy__(self, memo=None) -> 'Rune':
return Rune(self.shaobj.state[0], copy.deepcopy(self.restrictions)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SHA256(self) -> _n_0_t_3[_n_0_t_9]:",
"def hash(obj):\n \n import hashlib\n import pickle\n \n sha = hashlib.sha256()\n sha.update(pickle.dumps(obj))\n \n return sha.hexdigest()",
"def pickle_and_hash(obj: Any) -> str:\n try:\n s = dill.dumps(obj)\n except:\n rai... | [
"0.68457127",
"0.6561299",
"0.6373584",
"0.62677026",
"0.62606",
"0.603708",
"0.60351026",
"0.60149497",
"0.5985192",
"0.5962576",
"0.5938484",
"0.59240323",
"0.58729625",
"0.5839949",
"0.582465",
"0.5806277",
"0.57929254",
"0.57929254",
"0.57687783",
"0.5719801",
"0.57143664... | 0.0 | -1 |
Perform a shallow copy | def copy(self) -> 'Rune':
return self.__copy__() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shallow_copy(self):\n # TODO: Rename this to __copy__()?\n raise NotImplementedError(\"shallow_copy is not implemented\")",
"def copy(self):\n import copy as pcopy\n return pcopy.deepcopy(self)",
"def copy(self):",
"def _copy_(self):\n return copy.copy(self)",
"def copy(self)... | [
"0.8731979",
"0.80809754",
"0.79425305",
"0.79085165",
"0.7848546",
"0.7804359",
"0.7762183",
"0.77476215",
"0.7744951",
"0.77417994",
"0.77417994",
"0.77417994",
"0.77240235",
"0.7720835",
"0.76460147",
"0.76460147",
"0.76460147",
"0.76460147",
"0.76460147",
"0.76460147",
"0... | 0.0 | -1 |
sha256.sha256 doesn't implement pickle | def __deepcopy__(self, memo=None) -> 'MasterRune':
ret = MasterRune(bytes())
ret.restrictions = copy.deepcopy(self.restrictions)
ret.shaobj.state = self.shaobj.state
ret.shabase = self.shabase
ret.seclen = self.seclen
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SHA256(self) -> _n_0_t_3[_n_0_t_9]:",
"def hash(obj):\n \n import hashlib\n import pickle\n \n sha = hashlib.sha256()\n sha.update(pickle.dumps(obj))\n \n return sha.hexdigest()",
"def pickle_and_hash(obj: Any) -> str:\n try:\n s = dill.dumps(obj)\n except:\n rai... | [
"0.68457127",
"0.6561299",
"0.6373584",
"0.62677026",
"0.62606",
"0.603708",
"0.60351026",
"0.60149497",
"0.5985192",
"0.5962576",
"0.5938484",
"0.59240323",
"0.58729625",
"0.5839949",
"0.582465",
"0.5806277",
"0.57929254",
"0.57929254",
"0.57687783",
"0.5719801",
"0.57143664... | 0.0 | -1 |
This is faster than adding the restrictions onebyone and checking the final authcode (but equivalent) | def is_rune_authorized(self, other: Rune) -> bool:
# Make copy, as we're going to update state.
sha = self.shabase.copy()
totlen = self.seclen
for r in other.restrictions:
pad = end_shastream(totlen)
sha.update(pad)
totlen += len(pad)
enc = bytes(r.encode(), encoding='utf8')
sha.update(enc)
totlen += len(enc)
return other.authcode() == sha.digest() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_code(request):\n user_id = api.keystone.get_user_id(request)\n print \"USER CHECK\"\n print user_id\n user = api.keystone.user_get(request, user_id)\n user_auth_code = request.GET.get('auth_code', None)\n secret = request.GET.get('secret', None)\n\n #Generate ... | [
"0.6526628",
"0.6414991",
"0.60478854",
"0.5907109",
"0.5845477",
"0.57069147",
"0.5651448",
"0.55606484",
"0.5527734",
"0.546124",
"0.54302317",
"0.54030454",
"0.53964144",
"0.5381553",
"0.53508997",
"0.5312594",
"0.52762026",
"0.52541846",
"0.52534354",
"0.5245636",
"0.5223... | 0.5466636 | 9 |
Allinone check that a runestring is valid, derives from this MasterRune and passes all its conditions against the given dictionary of values or callables | def check_with_reason(self, b64str: str, values: Dict[str, Any]) -> Tuple[bool, str]:
try:
rune = Rune.from_base64(b64str)
except: # noqa: E722
return False, "runestring invalid"
if not self.is_rune_authorized(rune):
return False, "rune authcode invalid"
return rune.are_restrictions_met(values) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_with_reason(secret: bytes, b64str: str, values: Dict[str, Any]) -> Tuple[bool, str]:\n return MasterRune(secret).check_with_reason(b64str, values)",
"def test_any_rune(self):\n rule = 'alert (name:\"rune\"; regex:\".{64}\";)'\n\n tests = {\n \"A\"*64: [\"proxying connection ... | [
"0.6080785",
"0.5750079",
"0.5702773",
"0.5655834",
"0.54935133",
"0.5481613",
"0.5304172",
"0.52878016",
"0.5247526",
"0.5234183",
"0.523374",
"0.52119046",
"0.5198196",
"0.51774484",
"0.5176261",
"0.517611",
"0.51729107",
"0.5167192",
"0.51573974",
"0.51418495",
"0.5130349"... | 0.64300054 | 0 |
Convenience function that the b64str runestring is valid, derives from our secret, and passes against these values. If you want to check many runes, it's more efficient to create the MasterRune first then check them, but this is fine if you're only checking one. | def check_with_reason(secret: bytes, b64str: str, values: Dict[str, Any]) -> Tuple[bool, str]:
return MasterRune(secret).check_with_reason(b64str, values) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_with_reason(self, b64str: str, values: Dict[str, Any]) -> Tuple[bool, str]:\n try:\n rune = Rune.from_base64(b64str)\n except: # noqa: E722\n return False, \"runestring invalid\"\n if not self.is_rune_authorized(rune):\n return False, \"rune authcode... | [
"0.67954326",
"0.6076462",
"0.56105393",
"0.55704355",
"0.5488138",
"0.54298264",
"0.5401579",
"0.5401579",
"0.5336335",
"0.5291665",
"0.5288441",
"0.5265383",
"0.522915",
"0.5190011",
"0.51790816",
"0.51719004",
"0.51499397",
"0.51450264",
"0.51145905",
"0.50659394",
"0.5063... | 0.7233146 | 0 |
Convenience function that the b64str runestring is valid, derives from our secret, and passes against these values. If you want to check many runes, it's more efficient to create the MasterRune first then check them, but this is fine if you're only checking one. Unlike check_with_reason(), this discards the reason and returns a simple True or False. | def check(secret: bytes, b64str: str, values: Dict[str, Any]) -> bool:
return check_with_reason(secret, b64str, values)[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_with_reason(secret: bytes, b64str: str, values: Dict[str, Any]) -> Tuple[bool, str]:\n return MasterRune(secret).check_with_reason(b64str, values)",
"def check_with_reason(self, b64str: str, values: Dict[str, Any]) -> Tuple[bool, str]:\n try:\n rune = Rune.from_base64(b64str)\n ... | [
"0.8024349",
"0.75399673",
"0.5762778",
"0.5715149",
"0.56168044",
"0.56168044",
"0.543279",
"0.54015696",
"0.52972245",
"0.5295794",
"0.52827865",
"0.5269401",
"0.52549756",
"0.5223828",
"0.5214541",
"0.5210419",
"0.5160051",
"0.5159024",
"0.5144563",
"0.5132561",
"0.5131473... | 0.6641542 | 2 |
Determines whether fileToCheck is an appropriately formatted FASTA file. An appropriately formatted FASTA file is returned in correctFormatting. | def main(fileToCheck, minLength=-1, maxLength=-1):
# Initialise variables.
lineCount = 1 # The number of the line being examined. Used for displaying error messages.
protDescription = True # Whether or not we are currently expecting a line starting with >.
firstLine = True # Whether or not we are currently examining the first line of the file.
proteinsInFile = {} # A dictionary indexed by the protein description line of the FASTA file.
# The value of each entry is the correctly formatted protein sequence corresponding to the index.
# Strip off all excess whitespace, and split the string into the individual lines of the file.
checking = fileToCheck.rstrip()
checking = checking.lstrip()
checking = checking.split('\n')
for line in checking:
line = line.rstrip()
if firstLine:
# True if we have just started parsing the file string, and haven;t yet examined any lines.
if line[0] == '>':
currentProt = line # Record the description line of the protein which is about to have its sequence inspected.
currentSeq = '' # Initialise the sequence of the protein.
protDescription = False # We are now expecting a protein sequence, not a protein description.
firstLine = False
else:
# The first line of the file MUST be a protein description line (i.e. start with '>'). If the line was not
# the beginning of a protein record, terminate the program.
errorMessage = "Expected line " + str(lineCount) + " to start with a >, but instead got: " + line
return 1, errorMessage
elif protDescription:
# This is true only if a line beginning with a '>' is expected.
if line[0] == '>':
# Expected a protein description line, and found a protein description line. This means that the entire sequence
# of the currentProt protein has been found (i.e. we have finished inspecting the sequence of a protein, and
# have found the protein to be valid). Now determine if the length of the sequence is within the user
# specified bounds.
if minLength == -1:
if maxLength == -1:
# If there are no restrictions on the protein sequence length, then record the protein and its sequence.
proteinsInFile[currentProt] = currentSeq
elif len(currentSeq) <= maxLength:
# If there is no minimum length restriction, and the protein sequence is not longer than the maximum
# sequence length permitted, then record the protein and its sequence.
proteinsInFile[currentProt] = currentSeq
elif len(currentSeq) >= minLength:
if maxLength == -1:
# If there is no maximum length restriction, and the protein sequence is not shorter than the minimum
# sequence length permitted, then record the protein and its sequence.
proteinsInFile[currentProt] = currentSeq
elif len(currentSeq) <= maxLength:
# If the protein sequence is not shorter than the minimum sequence length permitted and not longer
# than the maximum length permitted, then record the protein and its sequence.
proteinsInFile[currentProt] = currentSeq
currentProt = line # Record the description line of the protein which is about to have its sequence inspected.
currentSeq = '' # Initialise the sequence of the protein.
protDescription = False # We are now expecting a protein sequence, not a protein description.
else:
# If the line does not begin with a '>', and it is expected to, it is possible that the amino acid sequence
# is split over multiple lines.
if line.isalpha():
# If every character on the line is a letter, then the line contains a valid portion of the sequence.
# Add the uppercase version of the sequence portion to the sequence currently being recorded.
currentSeq += line.upper()
else:
# If the line did not contain only letters, terminate the program.
errorMessage = "Expected line " + str(lineCount) + " to start with a >, but instead got: " + line
return 1, errorMessage
else:
# If an amino acid sequence is expected.
if line.isalpha():
# If the line is all alphabetic characters, write the line out and indicate that we are expecting a
# protein description line next (i.e. one beginning with a '>').
currentSeq += line.upper()
protDescription = True
else:
# If the line did not contain only letters, terminate the program.
errorMessage = "Expected line " + str(lineCount) + " to contain only letters, but instead got: " + line
return 2, errorMessage
lineCount += 1
# Catch the final protein from the file, and determine whether it should be recorded.
if minLength == -1:
if maxLength == -1:
proteinsInFile[currentProt] = currentSeq
elif len(currentSeq) <= maxLength:
proteinsInFile[currentProt] = currentSeq
elif len(currentSeq) >= minLength:
if maxLength == -1:
proteinsInFile[currentProt] = currentSeq
elif len(currentSeq) <= maxLength:
proteinsInFile[currentProt] = currentSeq
if len(proteinsInFile.keys()) < 2:
# There are too few protein sequences entered
errorMessage = ("Not enough unique protein sequences have been entered." +
" This is possibly caused by not enough sequences of the required minimum and maximum length being provided."
)
return 3, errorMessage
elif protDescription:
# Return an indication that the FASTA file is correctly formatted.
outputString = ''
for i in proteinsInFile.keys():
outputString += i + '\n' + proteinsInFile[i] + '\n'
return 0, outputString[:-1]
else:
# The file did not end with a protein sequence.
errorMessage = "Reached the end of the file, but no protein sequence found for the final protein."
return 3, errorMessage | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_style(file_path, style_config):\n\n with open(file_path, 'r') as f:\n content = f.read()\n is_valid_header = (len(content) == 0 or content.startswith(\n PythonFormatter.standard_header))\n\n _, _, changed = yapf.yapflib.yapf_api.FormatFile(\n ... | [
"0.62929124",
"0.6227019",
"0.5895204",
"0.56863064",
"0.56821287",
"0.5661317",
"0.56600463",
"0.5612253",
"0.55521023",
"0.55268717",
"0.5512988",
"0.5505371",
"0.54996324",
"0.5447883",
"0.5413568",
"0.5376837",
"0.5368521",
"0.53608316",
"0.5308955",
"0.52947026",
"0.5244... | 0.55602217 | 8 |
Parse read and quality strings from a FASTQ file with sequencing reads. | def readFastq(filename):
sequences = []
qualities = []
with open(filename) as fh:
while True:
fh.readline() # skip name line
seq = fh.readline().rstrip() #read base sequence
fh.readline() # skip placeholder line
qual = fh.readline().rstrip() # base quality line
if len(seq) == 0:
break
sequences.append(seq)
qualities.append(qual)
return sequences, qualities | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readFastq(filename):\n\tsequences = []\n\tqualities = []\n\twith open(filename, 'r') as f:\n\t\twhile True: \n\t\t\tf.readline() # skip name line\n\t\t\tseq = f.readline().rstrip()\n\t\t\tf.readline() # skip place holder line \n\t\t\tq = f.readline().rstrip()\n\t\t\tif len(seq) ==0:\n\t\t\t\tbreak \n\t\t\tsequ... | [
"0.72407734",
"0.7033901",
"0.69433504",
"0.67568797",
"0.6737888",
"0.66820616",
"0.65858674",
"0.653524",
"0.652288",
"0.6501695",
"0.6497522",
"0.64846104",
"0.64839154",
"0.646596",
"0.63651484",
"0.63603467",
"0.63447005",
"0.6319669",
"0.63027084",
"0.6278591",
"0.62545... | 0.74991745 | 0 |
Create a hash map between kmers and readings. | def kmerHashMap(reads, k):
kmers_dict = {}
# loop through all reads
for i in range(len(reads)):
# loop read's bases, except for the last k, to obtain its kmers
for j in range(1+len(reads[i])-k):
kmer = reads[i][j:k+j]
if kmers_dict.has_key(kmer):
kmers_dict[kmer].add(i)
else:
kmers_dict[kmer] = set([i])
return kmers_dict | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_kmers_observed(read, k):\n counts = {}\n num_kmers = len(read) - k + 1\n for i in range (num_kmers):\n kmer= read[i:i+k]\n if kmer not in counts:\n counts[kmer] = 0\n counts[kmer] +=1\n return len(counts)",
"def count_kmers_possible(read, k):\n num_kmers = {}\n num_kmers1 ... | [
"0.59770435",
"0.5825306",
"0.5625832",
"0.5522428",
"0.5472376",
"0.5450063",
"0.54420954",
"0.5432603",
"0.5386902",
"0.5372055",
"0.5325172",
"0.5304393",
"0.53000337",
"0.5292728",
"0.5285071",
"0.5272437",
"0.52629334",
"0.5260887",
"0.5257762",
"0.5255245",
"0.5242287",... | 0.7121498 | 0 |
Loads MNIST files into 3D numpy arrays | def load_mnist(dataset="training", digits=np.arange(10), path="."):
if dataset == "training":
fname_img = os.path.join(path, 'train-images-idx3-ubyte')
fname_lbl = os.path.join(path, 'train-labels-idx1-ubyte')
elif dataset == "testing":
fname_img = os.path.join(path, 't10k-images-idx3-ubyte')
fname_lbl = os.path.join(path, 't10k-labels-idx1-ubyte')
else:
raise ValueError("dataset must be 'testing' or 'training'")
flbl = open(fname_lbl, 'rb')
magic_nr, size = struct.unpack(">II", flbl.read(8))
lbl = pyarray("b", flbl.read())
flbl.close()
fimg = open(fname_img, 'rb')
magic_nr, size, rows, cols = struct.unpack(">IIII", fimg.read(16))
img = pyarray("B", fimg.read())
fimg.close()
ind = [ k for k in range(size) if lbl[k] in digits ]
N = len(ind)
images = zeros((N, rows, cols), dtype=uint8)
labels = zeros((N, 1), dtype=int8)
for i in range(len(ind)):
images[i] = array(img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]).reshape((rows, cols))
labels[i] = lbl[ind[i]]
return images, labels | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_mnist(kind='train'):\r\n with open('%s-labels.idx1-ubyte' % kind, 'rb') as lbpath:\r\n magic, n = struct.unpack('>II', lbpath.read(8))\r\n labels = np.fromfile(lbpath, dtype=np.uint8)\r\n\r\n with open('%s-images.idx3-ubyte' % kind, 'rb') as imgpath:\r\n magic, num, rows, cols =... | [
"0.6996989",
"0.69236135",
"0.68585235",
"0.6848802",
"0.68018305",
"0.6801796",
"0.6785937",
"0.67511636",
"0.6737762",
"0.6649627",
"0.6620649",
"0.6572148",
"0.65664387",
"0.6564884",
"0.6544595",
"0.65435225",
"0.6539307",
"0.65093267",
"0.6489088",
"0.6486471",
"0.646639... | 0.64559424 | 21 |
Calculate the squared L2 norm of the pattern. | def l2_norm(pattern):
return np.linalg.norm(pattern) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def L2_norm(self):\n analyticals = self.analytical(self.x_values, self.C, self.D)\n error = analyticals - self.numerical\n self.L2 = np.sqrt((1/self.gp)*np.sum(error**2))",
"def L2norm(m):\n return np.sqrt(np.sum(m**2))",
"def squared_norm(self) -> float:\n return self.__real**2 ... | [
"0.7689054",
"0.7469105",
"0.7411847",
"0.7393104",
"0.73873013",
"0.73019034",
"0.7256206",
"0.72420824",
"0.71961755",
"0.7140405",
"0.71325195",
"0.71182483",
"0.71150076",
"0.7069896",
"0.7034822",
"0.7023806",
"0.7019462",
"0.69990593",
"0.69920164",
"0.69680566",
"0.695... | 0.8329601 | 0 |
Calculate the l2 norm of a stack of patterns. | def l2_norm_batch(pattern_stack):
return np.linalg.norm(pattern_stack, axis=0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def l2_norm(pattern):\n return np.linalg.norm(pattern)",
"def l2(weights):\n\treturn np.sqrt(np.sum(weights * weights))",
"def l2_norm(params):\n flattened, _ = flatten(params)\n return np.dot(flattened, flattened)",
"def L2norm(m):\n return np.sqrt(np.sum(m**2))",
"def L2_norm(self):\n ... | [
"0.74098885",
"0.6808609",
"0.67808706",
"0.67259216",
"0.65520793",
"0.65451896",
"0.6460118",
"0.64029825",
"0.6392266",
"0.62797666",
"0.627647",
"0.626877",
"0.62292206",
"0.6221982",
"0.6214867",
"0.62103754",
"0.61965305",
"0.61522037",
"0.6128999",
"0.6110056",
"0.6074... | 0.83394605 | 0 |
Calculate the inner product of the two patterns as a vecter. | def inner_product(pattern_one, pattern_two):
return np.sum(np.multiply(pattern_one, pattern_two)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inner_product_batch(pattern_stack_one, pattern_num_one, pattern_stack_two, pattern_num_two):\n\n \"\"\"\n Notice that the two stacks can be different. So we can not deduce the lower triangular pattern from the \n other half.\n \"\"\"\n holder = np.zeros((pattern_num_one, pattern_num_two))\n f... | [
"0.6906152",
"0.6847398",
"0.68069786",
"0.67931485",
"0.6750435",
"0.6661231",
"0.665229",
"0.6584153",
"0.6543558",
"0.6477087",
"0.645507",
"0.64223343",
"0.64149445",
"0.64062977",
"0.64052427",
"0.6394551",
"0.6389129",
"0.6387153",
"0.6369647",
"0.6364393",
"0.6364216",... | 0.83498394 | 0 |
Calculate the inner product pair of each pattern in batch one and batch two. Notice that the pattern_stack_one variable represent the pattern along the zero dimension while the pattern_stack_two variable represent patterns along dimension one in the final distance matrix. | def inner_product_batch(pattern_stack_one, pattern_num_one, pattern_stack_two, pattern_num_two):
"""
Notice that the two stacks can be different. So we can not deduce the lower triangular pattern from the
other half.
"""
holder = np.zeros((pattern_num_one, pattern_num_two))
for l in range(pattern_num_one):
for m in range(pattern_num_two):
holder[l, m] = np.sum(np.multiply(pattern_stack_one[l], pattern_stack_two[m]))
return holder | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inner_product(pattern_one, pattern_two):\n\n return np.sum(np.multiply(pattern_one, pattern_two))",
"def l2_norm_batch(pattern_stack):\n\n return np.linalg.norm(pattern_stack, axis=0)",
"def generate_pattern_grid(words1, words2):\n # Convert word lists to integer arrays\n w1, w2 = (\n np... | [
"0.773317",
"0.5649336",
"0.5526998",
"0.54341394",
"0.53960305",
"0.5378274",
"0.5336186",
"0.5309336",
"0.529776",
"0.5289525",
"0.5283448",
"0.52754015",
"0.52056116",
"0.5135578",
"0.50863296",
"0.5050334",
"0.5024294",
"0.49954486",
"0.4992397",
"0.49735647",
"0.49717188... | 0.87116134 | 0 |
This function extract the squared l2 norm from the inner product matrix. | def l2_square_from_inner_product(matrix):
return np.diag(matrix) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inner_product_to_normalized_L2_square(matrix):\n\n length = matrix.shape[0]\n norm = np.divide(1, np.sqrt(l2_square_from_inner_product(matrix)))\n\n normalized_inner_product = np.multiply(np.multiply(np.reshape(norm, [length, 1]), matrix),\n np.reshape(norm, [... | [
"0.8038655",
"0.79935986",
"0.7481027",
"0.7333756",
"0.71862626",
"0.7168722",
"0.71422154",
"0.71272767",
"0.7005889",
"0.6986283",
"0.6936558",
"0.689807",
"0.6859919",
"0.68299115",
"0.6818152",
"0.678622",
"0.67799747",
"0.6730021",
"0.67066455",
"0.6701476",
"0.6701306"... | 0.7382337 | 3 |
Turns the inner product matrix into the |pattern1 pattern2|_{2}^2 matrix. The matrix has to be a square matrix | def inner_product_to_L2_square(matrix):
length = matrix.shape[0]
squared_norm = np.reshape(np.diag(matrix), (length, 1))
return squared_norm + np.transpose(squared_norm) - 2 * matrix | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inner_product(pattern_one, pattern_two):\n\n return np.sum(np.multiply(pattern_one, pattern_two))",
"def inner_product_batch(pattern_stack_one, pattern_num_one, pattern_stack_two, pattern_num_two):\n\n \"\"\"\n Notice that the two stacks can be different. So we can not deduce the lower triangular pa... | [
"0.74826276",
"0.7294018",
"0.6634914",
"0.6595926",
"0.62142396",
"0.5985415",
"0.59326464",
"0.58786714",
"0.5865172",
"0.5857749",
"0.58491534",
"0.5798571",
"0.5797253",
"0.57930493",
"0.57865286",
"0.57813066",
"0.57709336",
"0.57322204",
"0.5704064",
"0.5663638",
"0.565... | 0.6709533 | 2 |
Turns the inner product matrix into the |pattern1 pattern2|_{2}^2 matrix. Here the pattern is normalized. Therefore, it can be reformulated into |pattern1 pattern2|_{2}^2 = 2 2 / |pattern1||pattern2| | def inner_product_to_normalized_L2_square(matrix):
length = matrix.shape[0]
norm = np.divide(1, np.sqrt(l2_square_from_inner_product(matrix)))
normalized_inner_product = np.multiply(np.multiply(np.reshape(norm, [length, 1]), matrix),
np.reshape(norm, [1, length]))
return 2 - 2 * normalized_inner_product | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inner_product(pattern_one, pattern_two):\n\n return np.sum(np.multiply(pattern_one, pattern_two))",
"def inner_product_batch(pattern_stack_one, pattern_num_one, pattern_stack_two, pattern_num_two):\n\n \"\"\"\n Notice that the two stacks can be different. So we can not deduce the lower triangular pa... | [
"0.7459909",
"0.69026655",
"0.62501264",
"0.59727603",
"0.58457905",
"0.56935406",
"0.56712896",
"0.5659509",
"0.5638323",
"0.562021",
"0.55866134",
"0.5575207",
"0.5560609",
"0.55025274",
"0.5491025",
"0.5439844",
"0.5381539",
"0.53541124",
"0.5343363",
"0.53310347",
"0.5323... | 0.5993504 | 3 |
Apply np.exp( matrix/two_sigma_square) elementwise. | def gaussian_dense(matrix, two_sigma_square):
return np.exp(- matrix / two_sigma_square) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exp(tensor):\n return _elementary_op(tensor, np.exp, np.exp)",
"def expval(op, dm):\n return np.tensordot(op, dm, ([0, 1], [0, 1]))",
"def Exp(A, B):\n return A.dot(expm(B))",
"def kernel_sqExp(a,b, ls=1, sv=1):\n a = a.T/ls\n b = b.T/ls\n D, n = np.shape(a)\n d, m = np.shape(b)\n s... | [
"0.60777706",
"0.6070397",
"0.6037772",
"0.60048586",
"0.59793174",
"0.58501387",
"0.58492404",
"0.58091897",
"0.57963014",
"0.57812047",
"0.57540333",
"0.57262975",
"0.57174045",
"0.5708458",
"0.5661374",
"0.5589162",
"0.55674833",
"0.5548229",
"0.5532804",
"0.5519702",
"0.5... | 0.7608249 | 0 |
Each row of the matrix, let's say the jth row, represents the distance between the other data point from the jth point. This function returns the indexes for the points with the smallest distances with respect to each point represented by that specified row. By row, I mean the 0th dimension. Also notice that this function does not include the target particle, i.e. the diagonal element along the matrix is set to zero. | def nearest_points_indexes_without_self(matrix, num_to_keep):
# Set the diagonal to 0
np.fill_diagonal(matrix, 0)
# Get the position for the resulted values
sort_arg = np.argsort(matrix, axis=1)
return sort_arg[:, : num_to_keep] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nearest_min(dist_matrix):\n # much faster than np.where\n i, j = np.unravel_index(\n np.argmin(dist_matrix), \n dims=dist_matrix.shape\n )\n return i, j",
"def nearest_points_indexes_with_self(matrix, num_to_keep):\n\n # Set the diagonal to 1\n np.fill_diagonal(matrix, 1)\n ... | [
"0.76758415",
"0.73902124",
"0.64999825",
"0.6483356",
"0.6479489",
"0.64227754",
"0.6417629",
"0.6413701",
"0.6329621",
"0.63124496",
"0.6312059",
"0.6303333",
"0.6287571",
"0.62725973",
"0.6206513",
"0.6193971",
"0.61772275",
"0.61472964",
"0.6100118",
"0.60979205",
"0.6076... | 0.72165185 | 2 |
Each row of the matrix, let's say the jth row, represents the distance between the other data point from the jth point. This function returns the indexes for the points with the smallest distances with respect to each point represented by that specified row. By row, I mean the 0th dimension. Also notice that this function includes the target particle, i.e. the diagonal element along the matrix is set to 1. | def nearest_points_indexes_with_self(matrix, num_to_keep):
# Set the diagonal to 1
np.fill_diagonal(matrix, 1)
# Get the position for the resulted values
sort_arg = np.argsort(matrix, axis=1)
return sort_arg[:, : num_to_keep] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nearest_min(dist_matrix):\n # much faster than np.where\n i, j = np.unravel_index(\n np.argmin(dist_matrix), \n dims=dist_matrix.shape\n )\n return i, j",
"def nearest_points_indexes_without_self(matrix, num_to_keep):\n\n # Set the diagonal to 0\n np.fill_diagonal(matrix, 0)\n... | [
"0.76846826",
"0.7152678",
"0.65159774",
"0.6458269",
"0.64570606",
"0.6453487",
"0.64491487",
"0.6418172",
"0.6344188",
"0.6331945",
"0.62991154",
"0.62834746",
"0.62771547",
"0.62474954",
"0.6215967",
"0.6195537",
"0.6191709",
"0.6147389",
"0.6129545",
"0.6113139",
"0.60623... | 0.7358082 | 1 |
Each row of the matrix, let's say the jth row, represents the distance between the other data point from the jth point. This function returns the indexes for the points with the smallest distances with respect to each point represented by that specified row. By row, I mean the 0th dimension. Also notice that this function does not include the target particle, i.e. the diagonal element along the matrix is set to zero. | def nearest_points_values_without_self(matrix, num_to_keep):
# Set the diagonal to 0
np.fill_diagonal(matrix, 0)
# Get the position for the resulted values
sort = np.sort(matrix, axis=1)
return sort[:, : num_to_keep] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nearest_min(dist_matrix):\n # much faster than np.where\n i, j = np.unravel_index(\n np.argmin(dist_matrix), \n dims=dist_matrix.shape\n )\n return i, j",
"def nearest_points_indexes_with_self(matrix, num_to_keep):\n\n # Set the diagonal to 1\n np.fill_diagonal(matrix, 1)\n ... | [
"0.7677283",
"0.7389674",
"0.7215832",
"0.65018827",
"0.64853066",
"0.6480093",
"0.64236856",
"0.6417016",
"0.64140975",
"0.6329937",
"0.63131666",
"0.63109416",
"0.63027555",
"0.62869954",
"0.6206853",
"0.6192381",
"0.6176218",
"0.61489695",
"0.6099502",
"0.60955817",
"0.607... | 0.62730277 | 14 |
Each row of the matrix, let's say the jth row, represents the distance between the other data point from the jth point. This function returns the indexes for the points with the smallest distances with respect to each point represented by that specified row. By row, I mean the 0th dimension. Also notice that this function includes the target particle, i.e. the diagonal element along the matrix is set to 1. | def nearest_points_values_with_self(matrix, num_to_keep):
# Set the diagonal to 1
np.fill_diagonal(matrix, 1)
# Get the position for the resulted values
sort = np.sort(matrix, axis=1)
return sort[:, : num_to_keep] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nearest_min(dist_matrix):\n # much faster than np.where\n i, j = np.unravel_index(\n np.argmin(dist_matrix), \n dims=dist_matrix.shape\n )\n return i, j",
"def nearest_points_indexes_with_self(matrix, num_to_keep):\n\n # Set the diagonal to 1\n np.fill_diagonal(matrix, 1)\n ... | [
"0.76830524",
"0.7358047",
"0.715276",
"0.6513678",
"0.64565593",
"0.645209",
"0.64473367",
"0.64161915",
"0.6344284",
"0.6331053",
"0.629967",
"0.6284017",
"0.62775195",
"0.6246845",
"0.6215876",
"0.6197371",
"0.61904216",
"0.614493",
"0.6127443",
"0.61131895",
"0.6061455",
... | 0.6456699 | 4 |
Convert the degree vector to degree function. | def degree_vector_to_matrix(vector):
return np.diag(vector) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vecToFunc(vector):\n def f(x):\n f = 0\n for i in range(len(vector)):\n f += vector[i]*x**i\n return f\n return f",
"def degree_v(self):\n return self._degree_v",
"def degree(x):\n return x*(180.0/math.pi)",
"def laplacian(degree_vector, weight_matrix):\n ... | [
"0.6630002",
"0.63732994",
"0.61321235",
"0.608381",
"0.6005241",
"0.600304",
"0.5996289",
"0.5975939",
"0.5958366",
"0.5938791",
"0.5896974",
"0.5866529",
"0.58305264",
"0.58234054",
"0.58143467",
"0.5772291",
"0.5767484",
"0.57465386",
"0.5737111",
"0.5736712",
"0.5721935",... | 0.58353925 | 12 |
Construct the fundamental Laplacian matrix. | def laplacian(degree_vector, weight_matrix):
return np.diag(degree_vector) - weight_matrix | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_Laplacian_matrix(self, X):\n self.laplacian_mat, self.laplacian_sym_mat, self.laplacian_weights = self.laplacian.compute_laplacian(\n self.get_Affinity_matrix(X)\n )",
"def LaplacianMatrix(adjmatrix):\n if adjmatrix.dtype in [np.uint, np.uint0, np.uint8, np.u... | [
"0.680923",
"0.6638274",
"0.62569284",
"0.62069863",
"0.61390823",
"0.6101195",
"0.59771115",
"0.58065456",
"0.5798872",
"0.5760791",
"0.5729921",
"0.5715687",
"0.56979316",
"0.5615754",
"0.55505806",
"0.55295897",
"0.55295897",
"0.55002135",
"0.5498201",
"0.5496341",
"0.5495... | 0.0 | -1 |
Construct the normalized laplacian matrix. | def normalized_laplacian(degree_vector, weight_matrix, length):
holders = np.zeros((length, 1))
holders[:, 0] = 1 / degree_vector
return np.eye(length) - holders * weight_matrix | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def laplacian_matrix(A, normalized=False):\n n, m = A.shape\n D = degree_matrix(A)\n L = D - A\n if normalized:\n degs = _flat(A.sum(axis=1))\n rootD = sps.spdiags(np.power(degs, -1 / 2), [0], n, n, format=\"csr\")\n L = rootD * L * rootD\n return L",
"def normalize(self, lam)... | [
"0.6907984",
"0.6711874",
"0.62322444",
"0.6217343",
"0.620613",
"0.6196674",
"0.6158735",
"0.6132494",
"0.61195916",
"0.61195916",
"0.6036492",
"0.594732",
"0.59065795",
"0.5906229",
"0.58959603",
"0.5889874",
"0.5884998",
"0.58620787",
"0.5861111",
"0.5846933",
"0.58036464"... | 0.6199283 | 5 |
Construct the normalized laplacian matrix. | def symmetrized_normalized_laplacian(degree_vector, weight_matrix, length):
holders = np.zeros((length, 1))
holders[:, 0] = np.sqrt(1 / degree_vector)
return np.eye(length) - holders * weight_matrix * holders.T | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def laplacian_matrix(A, normalized=False):\n n, m = A.shape\n D = degree_matrix(A)\n L = D - A\n if normalized:\n degs = _flat(A.sum(axis=1))\n rootD = sps.spdiags(np.power(degs, -1 / 2), [0], n, n, format=\"csr\")\n L = rootD * L * rootD\n return L",
"def normalize(self, lam)... | [
"0.6905368",
"0.6712484",
"0.6235044",
"0.62139124",
"0.62099594",
"0.6200239",
"0.61991894",
"0.6155274",
"0.6130805",
"0.6119615",
"0.6119615",
"0.6039203",
"0.594853",
"0.5903943",
"0.5903158",
"0.58970135",
"0.5886368",
"0.58848",
"0.58638287",
"0.58587956",
"0.58482677",... | 0.56505066 | 34 |
Solve for several eigenvectors. | def solve_for_eigenvectors(matrix, num, mode="general"):
# Construct a sparse matrix
if mode == "general":
return linalg.eigs(matrix, num)
if mode == "symmetric":
return linalg.eigsh(matrix, num) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eigsolve(self,**kwargs):\n return eigsolve(self,**kwargs)",
"def _solve_eigen(self, X, y):\n self.means_, self.neighbor_means_ = _class_means_and_neighbor_means(\n X, y, self.within_between_ratio, self.nearest_neighbor_ratio)\n \n Sw = _class_cov(X, y) # within class co... | [
"0.72855383",
"0.6691216",
"0.66405857",
"0.661532",
"0.6592952",
"0.6520123",
"0.6501074",
"0.6423186",
"0.64160115",
"0.6396994",
"0.63655776",
"0.6349273",
"0.63411045",
"0.633906",
"0.63198537",
"0.62777245",
"0.62247896",
"0.6177474",
"0.61190665",
"0.6048089",
"0.603536... | 0.6650587 | 2 |
Returns weight and bias tensors for each layer of the RNN. These tensors are views on the underlying weight buffer allocated by CuDNN. | def get_parameters(fn, handle, weight_buf):
cudnn_methods = [
cudnn.lib.cudnnGetRNNLinLayerMatrixParams,
cudnn.lib.cudnnGetRNNLinLayerBiasParams
]
params = []
num_linear_layers = _num_linear_layers(fn)
num_layers = fn.num_directions * fn.num_layers
for layer in range(num_layers):
layer_params = []
for cudnn_method in cudnn_methods:
for linear_id in range(num_linear_layers):
lin_layer_mat_desc = cudnn.FilterDescriptor()
matrix_pointer = ctypes.c_void_p()
check_error(cudnn_method(
handle,
fn.rnn_desc,
layer,
fn.x_descs[0],
fn.w_desc,
ctypes.c_void_p(weight_buf.data_ptr()),
linear_id,
lin_layer_mat_desc,
ctypes.byref(matrix_pointer)))
data_type = ctypes.c_int()
format = ctypes.c_int()
nb_dims = ctypes.c_int()
min_dim = 3
filter_dim_a = torch.IntTensor(min_dim)
check_error(cudnn.lib.cudnnGetFilterNdDescriptor(
lin_layer_mat_desc,
min_dim,
ctypes.byref(data_type),
ctypes.byref(format),
ctypes.byref(nb_dims),
ctypes.c_void_p(filter_dim_a.data_ptr())))
filter_dim_a.resize_(nb_dims.value)
elem_size = cudnn._sizeofmap[fn.datatype]
offset_bytes = (matrix_pointer.value - weight_buf.data_ptr())
assert(offset_bytes % elem_size == 0)
offset = offset_bytes // elem_size
# for all the RNN types provided by CUDNN, all the ih weights
# are the same size and are allocated in a contiguous chunk
# (same for the hh weights, and the ih and hh biases).
# Since we're storing all the weights in a single tensor anyway,
# might as well merge the CUDNN ones into a single tensor as well
if linear_id == 0 or linear_id == num_linear_layers / 2:
assert(filter_dim_a.prod() == filter_dim_a[0])
param = fn.weight_buf.new().set_(
weight_buf.storage(), offset,
filter_dim_a[0] * num_linear_layers // 2, filter_dim_a[2])
layer_params.append(param)
else:
assert(cur_offset == offset)
cur_offset = offset + filter_dim_a[0]
params.append(layer_params)
return params | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_weights_and_bias(self, rnn_weights, rnn_props):\n # from code of tensorflow GRU cell, it can be known that shape of hidden_kernel(or candidate_kernel)\n # is (input_size+hidden_unit, hidden_unit)\n hidden_size = rnn_weights[\"hidden_kernel\"].value.shape[1]\n input_size = rn... | [
"0.7037132",
"0.6992914",
"0.695443",
"0.69183373",
"0.68530166",
"0.6803836",
"0.6534791",
"0.64984447",
"0.64839584",
"0.64703405",
"0.6404171",
"0.6397448",
"0.63523",
"0.6348087",
"0.6256905",
"0.62384343",
"0.62384343",
"0.62112504",
"0.62057334",
"0.6160526",
"0.6132221... | 0.60313946 | 34 |
Redirect to home page on url / | def index():
return redirect(url_for("home")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def home_page():\n return redirect(url_for(_DEFAULT_ROUTE, _external=True))",
"def homepage():\n return redirect('index.html')",
"def homepage():\n if g.user:\n return redirect(f\"/user/{g.user.id}\")\n else:\n return redirect(\"/landing\")",
"def start_page():\n if not _home:\n ... | [
"0.8391641",
"0.80959",
"0.8045699",
"0.8035703",
"0.8035703",
"0.78215",
"0.7814849",
"0.7806786",
"0.75715935",
"0.755005",
"0.755005",
"0.755005",
"0.745654",
"0.7446087",
"0.73788244",
"0.7322582",
"0.73209256",
"0.7203898",
"0.71697754",
"0.71356803",
"0.7109369",
"0.7... | 0.77726823 | 8 |
Renders the index template | def home():
this_dir = os.path.dirname(__file__)
home_file = app.config['HOME_PAGE_FOLDER'] + 'homepage.html'
try:
homepage = open(os.path.join(this_dir, home_file), "r").read()
# Store configuration file values
except FileNotFoundError:
homepage = ''
# Keep preset values
resp = make_response(render_template("home.html", page="index",
homedoc=homepage, err=request.args.get('err', default=None)))
if request.cookies.get('lang') is None:
lang = get_locale()
resp.set_cookie('lang', lang)
return resp | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def index(self):\n\t\treturn render_template('index.html')",
"def index():\r\n return render_template('index.html')",
"def index(self):\n return render_template('main/index.html')",
"def index():\n return render_template('index.html')",
"def index():\n return render_temp... | [
"0.8663074",
"0.8416551",
"0.8395232",
"0.8383464",
"0.83140826",
"0.83140826",
"0.8239054",
"0.82325274",
"0.8220168",
"0.82151866",
"0.82151866",
"0.8183771",
"0.8143425",
"0.808803",
"0.808803",
"0.808803",
"0.808803",
"0.808803",
"0.808803",
"0.808803",
"0.808803",
"0.8... | 0.0 | -1 |
Function to modify the homepage | def modify_homepage():
this_dir = os.path.dirname(__file__)
home_file = app.config['HOME_PAGE_FOLDER'] + 'homepage.html'
value = ""
if request.form.get("newHome"):
value = request.form.get("newHome")
if not os.path.isdir(os.path.join(this_dir, app.config['HOME_PAGE_FOLDER'])):
os.mkdir(os.path.join(this_dir, app.config['HOME_PAGE_FOLDER']))
homeFile= open(os.path.join(this_dir, home_file), "w+")
homeFile.write(value)
homeFile.flush()
files = request.files.getlist("Attachments")
for file in files:
print(file.filename, file=sys.stdout)
nameFile = secure_filename(file.filename)
file.save(os.path.join(this_dir, app.config['HOME_PAGE_FOLDER'], nameFile))
return jsonify(result=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def homepage():\n\treturn render_template(\"home/a_homepage.html\", title=\"Welcome\")",
"def homepage():\n\n return render_template('rosemead_homepage.html')",
"def homepage():\n return render_template('home/index.html', \n title=\"Bem vindo!\")",
"def a_homepage():\n\n\tret... | [
"0.7403166",
"0.7383433",
"0.7320676",
"0.730273",
"0.722838",
"0.722588",
"0.7190989",
"0.71861947",
"0.7157216",
"0.7157216",
"0.71296996",
"0.71152425",
"0.7102618",
"0.7062718",
"0.7043081",
"0.6962566",
"0.69548315",
"0.6911093",
"0.69107646",
"0.6907873",
"0.6875526",
... | 0.0 | -1 |
Generate an identity key pair. Clients should only do this once, at install time. the generated IdentityKeyPair. | def generateIdentityKeyPair():
keyPair = Curve.generateKeyPair()
publicKey = IdentityKey(keyPair.getPublicKey())
serialized = '0a21056e8936e8367f768a7bba008ade7cf58407bdc7a6aae293e2c' \
'b7c06668dcd7d5e12205011524f0c15467100dd603e0d6020f4d293' \
'edfbcd82129b14a88791ac81365c'
serialized = binascii.unhexlify(serialized.encode())
identityKeyPair = IdentityKeyPair(publicKey, keyPair.getPrivateKey())
return identityKeyPair
# return IdentityKeyPair(serialized=serialized) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gen_key_pair(self, keysize, cb):\n\n def gen_key_pair_pub_cb(data, ctx):\n if not data:\n warning('keymanagement: Could not generate a key pair\\n')\n cb(None, None)\n else:\n cb(ctx, data)\n\n def gen_key_pair_priv_cb(data, ctx):... | [
"0.71120954",
"0.68204904",
"0.65850353",
"0.6553852",
"0.65218014",
"0.6443924",
"0.6378423",
"0.63755655",
"0.6358998",
"0.6324435",
"0.63147116",
"0.6309187",
"0.62952316",
"0.6251726",
"0.61868006",
"0.61828184",
"0.61474425",
"0.6133282",
"0.61287004",
"0.60888094",
"0.6... | 0.8180875 | 0 |
Generate a registration ID. Clients should only do this once, at install time. | def generateRegistrationId():
regId = KeyHelper.getRandomSequence()
return regId | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_id():\n return uuid4().get_hex()",
"def generateID(self):\n\n return str(uuid.uuid1())",
"def get_id(self) -> str:\n return self._register_id",
"def makeid(cls):\n return str(uuid.uuid4().hex)",
"def generate_id():\n\treturn \"%s-%s\" % (str(uuid.uuid4())[:4],random... | [
"0.7785231",
"0.7583207",
"0.74686027",
"0.74049723",
"0.7362628",
"0.72884035",
"0.72758955",
"0.72758955",
"0.7264104",
"0.7262016",
"0.72611034",
"0.72470003",
"0.7220589",
"0.721949",
"0.7148431",
"0.71448946",
"0.7102137",
"0.7092246",
"0.70777285",
"0.7075378",
"0.70580... | 0.8603011 | 0 |
Generate a list of PreKeys. Clients should do this at install time, and subsequently any time the list of PreKeys stored on the server runs low. PreKey IDs are shorts, so they will eventually be repeated. Clients should store PreKeys in a circular buffer, so that they are repeated as infrequently as possible. start The starting PreKey ID, inclusive. count The number of PreKeys to generate. the list of generated PreKeyRecords. | def generatePreKeys(start, count):
results = []
start -= 1
for i in range(0, count):
preKeyId = ((start + i) % (Medium.MAX_VALUE - 1)) + 1
results.append(PreKeyRecord(preKeyId, Curve.generateKeyPair()))
return results | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_keys(self):\n self.keys = []\n key = string_to_bit_array(self.passwd)\n key = self.permutation(key, CP_1) # Perform initial permutation on the key\n g, d = split_into_n(key, 28) # Split into g (LEFT) & d (RIGHT)\n for i in range(16): # Apply the 16 rounds\n ... | [
"0.6100436",
"0.57589746",
"0.5668012",
"0.56462705",
"0.5632386",
"0.55703026",
"0.5561076",
"0.5519232",
"0.5507431",
"0.5470912",
"0.546842",
"0.54056185",
"0.52409744",
"0.5236303",
"0.5193606",
"0.5192819",
"0.51398957",
"0.50818324",
"0.5079338",
"0.5055663",
"0.5039773... | 0.87624663 | 0 |
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