repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
aeroc7/KittyClass | [
"4f463a398df8bf6dd4fb5aace414de46b9630c17"
] | [
"nn/train.py"
] | [
"import torch\nimport hparams\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision.models as models\n\nfrom data import PetData\nfrom torch.utils.data.dataset import random_split\nfrom torch.utils.data.dataloader import DataLoader\nfrom torchvision import transforms\n\n\nclass TrainPetClass():\n ... | [
[
"torch.Generator",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.nn.Linear",
"torch.no_grad",
"torch.cuda.is_available",
"torch.utils.data.dataloader.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pwessels-uhh/sarkas | [
"78fb9f8106ed6b15fb67c22afea09593fed01730"
] | [
"sarkas/potentials/yukawa.py"
] | [
"\"\"\"\nModule for handling Yukawa interaction\n\"\"\"\nimport numpy as np\nfrom numba import njit\nimport math as mt\n\n\n@njit\ndef yukawa_force_pppm(r, pot_matrix):\n \"\"\"\n Calculates Potential and Force between two particles when the P3M algorithm is chosen.\n\n Parameters\n ----------\n r : ... | [
[
"numpy.exp",
"numpy.zeros",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
smartin015/shop_robotics | [
"163cde9af8caf35b776e03a5612107cc327e5f63"
] | [
"sim/rvl/depth_segmentation.py"
] | [
"from numba import cuda\nfrom math import sqrt\nimport numpy as np\nimport cv2\nimport pyrealsense2 as rs\n\nimport jetson.inference\nimport jetson.utils\n\n# Note that column order is height-first\nPX_B = 2\ninput_dim_realsense_reg = (480, 848)\ndim = input_dim_realsense_reg\n\nfrom datetime import datetime\n\ntim... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liudaizong/IA-Net | [
"f19295d13d1468eb582521131cde3de83dfd18f6"
] | [
"code/modules_/cross_gate.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass CrossGate(nn.Module):\n def __init__(self, d_model):\n super().__init__()\n self.fc_gate1 = nn.Linear(d_model, d_model, bias=False)\n self.fc_gate2 = nn.Linear(d_model, d_model, bias=False)\n\n def forward(se... | [
[
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
viktorErasmusHogeschool/artificial-intelligence | [
"84d97bac83f074b2c16d531e4fc9d917a16cd0ce"
] | [
"cah/python-server/bert_api.py"
] | [
"# Model definition\nimport os\nimport pandas as pd\nimport requests\nimport json\nimport numpy as np\nfrom transformers import *\nfrom transformers import BertTokenizer\nfrom tqdm.notebook import tqdm\nimport nltk\n#nltk.download('punkt')\nfrom nltk.tokenize import sent_tokenize\n\n### Docker command\n# sudo docke... | [
[
"numpy.asarray",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Biancolinaa/tf-pose-estimation | [
"58988e0da69af9fe32ecc0c39152b49f0fbe4261"
] | [
"tf_pose/estimator.py"
] | [
"import logging\nimport math\n\nimport slidingwindow as sw\n\nimport cv2\nimport numpy as np\nimport tensorflow as tf\nimport time\n\nfrom tf_pose import common\nfrom tf_pose.common import CocoPart\nfrom tf_pose.tensblur.smoother import Smoother\n# import tensorflow.contrib.tensorrt as trt\n\ntry:\n from tf_pose... | [
[
"tensorflow.import_graph_def",
"tensorflow.nn.pool",
"tensorflow.gfile.GFile",
"tensorflow.equal",
"tensorflow.global_variables",
"tensorflow.placeholder",
"numpy.ndarray",
"tensorflow.report_uninitialized_variables",
"tensorflow.zeros_like",
"numpy.copy",
"tensorflow.S... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
wshien90/Federated-Learning-PyTorch | [
"0065c50408eddc82a6051199c1910eea420dadbd"
] | [
"src/federated_main.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Python version: 3.6\n\n\nimport os\nimport copy\nimport time\nimport pickle\nimport numpy as np\nfrom tqdm import tqdm\n\nimport torch\nfrom tensorboardX import SummaryWriter\n\nfrom options import args_parser\nfrom update import LocalUpdate, test_inference\nfrom m... | [
[
"numpy.array",
"torch.cuda.set_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Baejian/openpilot | [
"5996befaa1493be4155ad03a5caab15ee503e2ef"
] | [
"selfdrive/controls/lib/lateral_planner.py"
] | [
"import math\nimport numpy as np\nfrom common.realtime import sec_since_boot, DT_MDL\nfrom common.numpy_fast import interp\nfrom selfdrive.swaglog import cloudlog\nfrom selfdrive.controls.lib.lateral_mpc_lib.lat_mpc import LateralMpc\nfrom selfdrive.controls.lib.drive_helpers import CONTROL_N, MPC_COST_LAT, LAT_MPC... | [
[
"numpy.abs",
"numpy.gradient",
"numpy.isnan",
"numpy.arange",
"numpy.linalg.norm",
"numpy.ones",
"numpy.mean",
"numpy.column_stack",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marcpaterno/cluster_toolkit | [
"ee5352d799aa1048cc1d5a1b4e01890be429f94b"
] | [
"tests/test_deltasigma.py"
] | [
"import pytest\nfrom cluster_toolkit import deltasigma as ds\nfrom cluster_toolkit import xi\nfrom os.path import dirname, join\nimport numpy as np\nimport numpy.testing as npt\n\n#Need some test data to use first\nM = 1e14\nc = 5\nOm = 0.3\ntry:\n here = dirname(__file__)\n Rxi = np.loadtxt(here+\"/data_for_... | [
[
"numpy.ones_like",
"numpy.logspace",
"numpy.testing.assert_array_less",
"numpy.copy",
"numpy.where",
"numpy.loadtxt",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
isabella232/TensorNetwork | [
"44bbdb47dc7ec4b80b74641e736acc75335ffe2d"
] | [
"tensornetwork/backends/pytorch/pytorch_backend_test.py"
] | [
"import numpy as np\nfrom tensornetwork.backends.pytorch import pytorch_backend\nimport torch\nimport pytest\nfrom unittest.mock import Mock\n\ntorch_dtypes = [torch.float32, torch.float64, torch.int32]\ntorch_eye_dtypes = [torch.float32, torch.float64, torch.int32, torch.int64]\ntorch_randn_dtypes = [torch.float32... | [
[
"numpy.diag",
"torch.abs",
"torch.ge",
"torch.sign",
"torch.zeros",
"torch.diag_embed",
"torch.sum",
"torch.le",
"torch.allclose",
"numpy.trace",
"torch.ones",
"numpy.reshape",
"torch.reshape",
"numpy.eye",
"numpy.matmul",
"torch.eye",
"numpy.zer... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AUT-Data-Group/GTS | [
"5d258643233cd810988b128a0ee7e4f876504641"
] | [
"model/pytorch/cell.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom lib import utils\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\nclass SpatialAttention(nn.Module):\n def __init__(self, num_of_timesteps, num_of_features, num_of_vertices):\n su... | [
[
"torch.max",
"torch.cat",
"torch.zeros",
"torch.sum",
"torch.sparse_coo_tensor",
"torch.cuda.is_available",
"torch.split",
"torch.mm",
"torch.randn",
"torch.reshape",
"numpy.lexsort",
"torch.nn.functional.sigmoid",
"numpy.column_stack",
"torch.isinf",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
diego1q2w/lregret | [
"823c7f609559d1012ed52f619b1aa1297d5f2517"
] | [
"datasets/linear/__init__.py"
] | [
"import os\n\nimport pandas as pd\n\n\nfrom regresion.linear.feature import PolFeatures\nfrom regresion.linear.linear import LinearRegression\n\nfrom matplotlib import pyplot as plt\n\n\nclass LinearProblem:\n def __init__(self,\n samples: pd.DataFrame,\n target: pd.Series,\n ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
alexarnimueller/smiles-transformer | [
"4584a0bd043d6659a941589677951b2c6823cd2a"
] | [
"torch_transformer.py"
] | [
"import math\nimport time\nimport torch\nimport torch.nn as nn\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\nclass TransformerModel(nn.Module):\n def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.2):\n super(TransformerModel, self).__init__()\n fro... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.Dropout",
"torch.ones",
"torch.zeros",
"torch.sin",
"torch.arange",
"torch.nn.Embedding",
"torch.tensor",
"torch.nn.TransformerEncoderLayer",
"torch.nn.Linear",
"torch.no_grad",
"torch.nn.TransformerEncoder",
"torch.cuda.is... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Spencerfar/djin-aging | [
"f6513226e879e6061996d819b4de0e2873860fbc"
] | [
"Plotting_code/plot_network.py"
] | [
"import torch\nimport numpy as np\nimport argparse\nfrom scipy.stats import laplace\n\nfrom pathlib import Path\nimport sys\nfile = Path(__file__). resolve() \npackage_root_directory = file.parents [1] \nsys.path.append(str(package_root_directory))\n\nfrom Model.model import Model\n\nfrom scipy.cluster.hierarchy ... | [
[
"scipy.stats.laplace",
"matplotlib.pyplot.tight_layout",
"numpy.abs",
"torch.load",
"numpy.min",
"numpy.eye",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.get_cmap",
"numpy.ones",
"matplotlib.pyplot.savefig",
"numpy.save",
"numpy.max",
"matplotlib.pyplot.sub... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
klieret/uproot4 | [
"0cce6990b89db0ef7d47fc2857616ab2933c5d03"
] | [
"src/uproot/model.py"
] | [
"# BSD 3-Clause License; see https://github.com/scikit-hep/uproot4/blob/main/LICENSE\n\n\"\"\"\nThis module defines utilities for modeling C++ objects as Python objects and the\n:doc:`uproot.model.Model` class, which is the superclass of all objects that\nare read from ROOT files.\n\nThe :doc:`uproot.model.Versione... | [
[
"numpy.uint32",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AndresPenuela/SAFE-Notebooks | [
"aea3d07651e464d7cba8356f88387d808e01624b"
] | [
"util/flu/flu_model.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nA simple mathematical description of the spread of a flu in a company is the \nso-called the flu model, which divides the (fixed) population of N individuals \ninto three \"compartments\" which may vary as a function of time, t:\n\nV(t) are those vulnerable but not yet infected wit... | [
[
"numpy.min",
"numpy.arange",
"scipy.integrate.odeint",
"numpy.max",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
zjiayao/ms-2dpnts | [
"986e674ae8945943e927fa61b343ec704f6bc4a6"
] | [
"cluster/plot.py"
] | [
"import matplotlib.pyplot as plt\nimport os\nimport glob\n\nFWD = os.path.dirname(os.path.realpath(__file__))\nOUTPUT = os.path.join(FWD, 'oup')\nMODES = os.path.join(FWD, 'ms_modes')\n\n\ndef plot_data(files, xlabel=r'$x_1$', ylabel=r'$x_2$', headers=None, seps=None, block=False):\n\tfig = plt.figure()\n\tfor i, d... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Sharpiless/Face-recognition-for-classroom-sign-in | [
"fd5d98de07be6646527dfd5e50dddd92b547272e"
] | [
"func/facenet.py"
] | [
"import numpy as np\nimport sys\nimport cv2\nimport os\nimport FaceDetection.TestFace as face_recognition\nfrom FaceDetection.TestFace import face_encodings\n\nfrom PIL import ImageFont\nfrom PIL import Image\nfrom PIL import ImageDraw\nfrom time import localtime\nfontC = ImageFont.truetype('platech.ttf', 16, 0)\n\... | [
[
"numpy.max",
"numpy.array",
"numpy.argmin",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NodLabs/FBGEMM-MLIR | [
"bf13ee1d9b7e2ffd9431c54ed3ae6efd20c9ac8f"
] | [
"fbgemm_gpu/bench/histogram_binning_calibration_benchmark.py"
] | [
"# Copyright (c) Meta Platforms, Inc. and its affiliates.\n# All rights reserved.\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\nimport time\nfrom typing import Callable, Tuple\n\nimport click\nimport torch\nfrom... | [
[
"torch.cuda.synchronize",
"torch.ops.load_library",
"torch.randint",
"torch.empty",
"torch.ops.fbgemm.histogram_binning_calibration_by_feature",
"torch.cuda.Event",
"torch.sum",
"torch.rand",
"torch.cuda.is_available",
"torch.arange",
"torch.ops.fbgemm.generic_histogram... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liaison/cs231n | [
"2d48724d94a06006f067ce72d4a2e7881f7194d8"
] | [
"spring1617_assignment1/cs231n/classifiers/linear_classifier.py"
] | [
"from __future__ import print_function\n\nimport numpy as np\nfrom cs231n.classifiers.linear_svm import *\nfrom cs231n.classifiers.softmax import *\nfrom past.builtins import xrange\n\n\nclass LinearClassifier(object):\n \"\"\"\n Reference: http://cs231n.github.io/neural-networks-3/\n \"\"\"\n\n def __init_... | [
[
"numpy.max",
"numpy.dot",
"numpy.zeros",
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kwinkunks/welleng | [
"d0669b9b5164671ff4861a4efd33666c3fc9758f"
] | [
"examples/connect_two_random_points.py"
] | [
"import welleng as we\nimport numpy as np\nfrom vedo import Arrows, Lines\nimport random\n\n# Some code for testing the connector module.\n\n# Generate some random pairs of points\npos1 = [0,0,0]\nmd1 = 0\n\npos2 = np.random.random(3) * 1000\n\nvec1 = np.random.random(3)\nvec1 /= np.linalg.norm(vec1)\ninc1, azi1 = ... | [
[
"numpy.concatenate",
"numpy.array",
"numpy.random.random",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
BastianZim/thewalrus | [
"28cba83b457fc068861c542f0e86d8c9d198b60b"
] | [
"thewalrus/tests/test_hermite_multidimensional.py"
] | [
"# Copyright 2019 Xanadu Quantum Technologies Inc.\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by applica... | [
[
"numpy.allclose",
"numpy.sqrt",
"numpy.arange",
"scipy.special.eval_hermitenorm",
"numpy.ones",
"scipy.special.eval_hermite",
"numpy.block",
"numpy.random.rand",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SilvioGiancola/SoccerNetv2-DevK | [
"fb078911573409f48b85ee854dc8f09f432ee3a6",
"fb078911573409f48b85ee854dc8f09f432ee3a6",
"fb078911573409f48b85ee854dc8f09f432ee3a6",
"fb078911573409f48b85ee854dc8f09f432ee3a6"
] | [
"Task2-CameraShotSegmentation/CALF-detection/src/main.py",
"Task1-ActionSpotting/CALF/src/dataset.py",
"Task1-ActionSpotting/Pooling/src/train.py",
"Task1-ActionSpotting/TemporallyAwarePooling/src/dataset.py"
] | [
"import os\nimport logging\nfrom datetime import datetime\nimport time\nimport numpy as np\nfrom argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\n\nimport torch\n\nfrom dataset import SoccerNet, SoccerNetClips, SoccerNetClipsTesting\nfrom model import Model\nfrom train import trainer, test\nfrom loss ... | [
[
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader"
],
[
"numpy.arange",
"torch.from_numpy",
"numpy.ceil",
"torch.arange",
"torch.stack",
"numpy.array",
"numpy.zeros",
"numpy.ran... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
yiyin/neurodriver | [
"34e6874a1cf35633cda1191920cbaeac5d25dc9b",
"34e6874a1cf35633cda1191920cbaeac5d25dc9b",
"34e6874a1cf35633cda1191920cbaeac5d25dc9b"
] | [
"neurokernel/LPU/LPU.py",
"neurokernel/LPU/NDComponents/SynapseModels/BaseSynapseModel.py",
"neurokernel/LPU/utils/visualizer.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nLocal Processing Unit (LPU) with plugin support for various neuron/synapse models.\n\"\"\"\nimport time\nimport collections\nimport numbers\nimport copy\nimport itertools\n\nfrom future.utils import iteritems\nfrom past.builtins import long\nfrom builtins import zip\n\n\nimport pyc... | [
[
"numpy.argsort",
"numpy.array",
"numpy.cumsum"
],
[
"numpy.double",
"numpy.dtype"
],
[
"numpy.sqrt",
"numpy.linspace",
"numpy.asarray",
"matplotlib.colors.hsv_to_rgb",
"numpy.arctan2",
"numpy.max",
"scipy.interpolate.griddata",
"matplotlib.pyplot.tight_l... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0... |
cyqian97/FlirCamBeamFitter | [
"5ff7c2e0ad42b72f358c69b052b649017b8d7e5b"
] | [
"fitgauss.py"
] | [
"import numpy as np\nimport time\nfrom scipy import optimize\n\n\nclass Parameter:\n def __init__(self, value):\n self.value = value\n\n def set(self, value):\n self.value = value\n\n def __call__(self):\n return self.value\n\n\ndef fit(function, parameters, y, x=None):\n def f(para... | [
[
"numpy.sqrt",
"numpy.array_equal",
"numpy.arange",
"numpy.cos",
"numpy.sin",
"scipy.optimize.leastsq",
"numpy.random.rand",
"numpy.exp",
"matplotlib.pyplot.show",
"numpy.sum",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
datarobot-community/mlops-pipeline | [
"dbb717f9b805dda6933912daa80d427db350e1a5"
] | [
"model.py"
] | [
"import pickle\nfrom sklearn import datasets\niris=datasets.load_iris()\nx=iris.data\ny=iris.target\n\n#labels for iris dataset\nlabels ={\n 0: \"setosa\",\n 1: \"versicolor\",\n 2: \"virginica\"\n}\n\n#split the data set\nfrom sklearn.model_selection import train_test_split\nx_train,x_test,y_train,y_test=trai... | [
[
"sklearn.tree.DecisionTreeClassifier",
"sklearn.datasets.load_iris",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xihuiwu/ProbabilisticRRT | [
"a8e9329a1669e94b0e6ac9990b765ad4ee249309"
] | [
"car.py"
] | [
"import numpy as np\r\nimport math\r\n\r\n# Number of circle centers are odd numbers, i.e., 1 or 3\r\n# state is 4x1 vector\r\n# centers is nx2 matrix\r\nclass Car():\r\n\tdef __init__(self,state,n,radius,dist,u_prev):\r\n\t\tif state.shape[0] == 1:\r\n\t\t\tself.state = np.squeeze(np.transpose(state))\r\n\t\telse:... | [
[
"numpy.squeeze",
"numpy.array",
"numpy.empty",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JiaXiu01/AlgoGraphs_Dev | [
"12c394119779baa84da432cfaffa900e96126d48"
] | [
"backend/bipartite.py"
] | [
"import networkx as nx\nimport matplotlib.pyplot as plt\nimport random\n\ndef bipartite(numNodes):\n\n odds=[]\n evens=[]\n colours=[]\n\n for i in range(1,numNodes+1,2):\n odds.append(i)\n colours.append('red')\n \n \n for i in range(2,numNodes+1,2):\n evens.ap... | [
[
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZijianDu/Neuromophic-classifier-design | [
"293933bfce397013c76e2e2d0ec1a9f6f9ebd752"
] | [
"ClaveClassification.py"
] | [
"from __future__ import print_function\r\n\r\nimport numpy as np\r\nimport csv\r\n\r\n# specifying the gpu to use\r\n# import theano.sandbox.cuda\r\n# theano.sandbox.cuda.use('gpu1')\r\n\r\nimport pickle\r\nimport lasagne\r\n\r\nimport theano\r\nimport theano.tensor as T\r\nimport binary_net, Q2b_net, Q3b_net, Q4b_... | [
[
"numpy.hstack",
"numpy.random.seed",
"numpy.eye",
"numpy.savetxt",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YJiangcm/Chinese-sentence-pair-modeling | [
"90adbc5c121832ce3e4a4057e30417a6ec5e7ebc"
] | [
"Models/BERTs/run_Roberta_model.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 25 00:19:30 2020\n\n@author: 31906\n\"\"\"\nimport os\nimport math\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader\nfrom transformers import get_linear_schedule_with_warmup\nfrom transformers.optimization import AdamW\nfrom sys import ... | [
[
"torch.device",
"torch.utils.data.DataLoader",
"pandas.DataFrame",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
J-L-O/DTC | [
"6851a38f296ea1689689aa510f5b03106acacc51"
] | [
"modules/prototypical_loss.py"
] | [
"# coding=utf-8\nimport torch\nfrom torch.nn import functional as F\nfrom torch.nn.modules import Module\n\n\nclass PrototypicalLoss(Module):\n '''\n Loss class deriving from Module for the prototypical loss function defined below\n '''\n\n def __init__(self, n_support):\n super(PrototypicalLoss,... | [
[
"torch.nn.functional.normalize",
"torch.Tensor",
"torch.nn.functional.log_softmax",
"torch.randn",
"numpy.eye",
"torch.from_numpy",
"numpy.ones",
"torch.unique",
"numpy.argmin",
"torch.arange",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vladiant/ObjectDetectSamples | [
"ac82d0d8284f6fdbcebf1ff429d2aec18cf03fb0",
"ac82d0d8284f6fdbcebf1ff429d2aec18cf03fb0",
"ac82d0d8284f6fdbcebf1ff429d2aec18cf03fb0"
] | [
"BagOfWords/Python/Detection/bag_of_words_evaluate.py",
"HistogramOfGradients/Basic/Python/hog_evaluate.py",
"CategoricalRCNN/predict_airplanes_categorical.py"
] | [
"# https://github.com/1297rohit/RCNN\n# https://github.com/bikz05/bag-of-words\n# https://docs.opencv.org/4.4.0/d1/d73/tutorial_introduction_to_svm.html\n# https://docs.opencv.org/4.4.0/d1/d5c/tutorial_py_kmeans_opencv.html\n\n\nimport os\nimport cv2\nimport joblib\n\nimport pandas as pd\n\nimport numpy as np\n\nan... | [
[
"numpy.vstack",
"numpy.array",
"numpy.zeros",
"numpy.random.seed"
],
[
"numpy.hstack",
"numpy.random.seed",
"numpy.int32",
"numpy.float32",
"numpy.array"
],
[
"tensorflow.keras.models.load_model",
"tensorflow.nn.softmax",
"numpy.expand_dims",
"numpy.maxi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"... |
slacgismo/pge-baseline-study | [
"a521b091a23d25742a4e29c43fbfc8d2492e2b43"
] | [
"baseline/deliverable_create.py"
] | [
"import global_vars\nglobal_vars.init()\nif global_vars.GRAPHFLAG > 0:\n from graph_functions import *\n from error_graphs import *\n\nimport mysql.connector\nfrom deliverable_functions import *\nfrom data_get import *\nimport pandas as pd \nimport sqlalchemy\nimport numpy as np\nimport time\nfrom ran... | [
[
"numpy.add",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
k-dominik/plant-seg | [
"2d77e067e0092e57527f279e83e9d4a7dbdbaf2a"
] | [
"plantseg/predictions/predict.py"
] | [
"import importlib\nimport os\nimport time\n\nimport h5py\nimport torch\nfrom pytorch3dunet.datasets.utils import get_test_loaders\nfrom pytorch3dunet.unet3d import utils\nfrom pytorch3dunet.unet3d.model import get_model\nfrom plantseg.pipeline import gui_logger\nfrom plantseg.predictions.utils import create_predict... | [
[
"torch.cuda.empty_cache",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SteshinSS/serotonin-affinity | [
"accc2046b51259254513b0180c382ca431bb1c24"
] | [
"utils/utils.py"
] | [
"import os\nfrom pathlib import Path\n\nimport numpy as np\nimport pytorch_lightning as pl\n\n\ndef change_directory_to_repo():\n \"\"\"Changes working directory to the repository root folder.\"\"\"\n current_dir = Path.cwd()\n for parent in current_dir.parents:\n # Repository is the first folder wi... | [
[
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
camilo7100/cosas_de_github | [
"5c2bcc1b69569edb0117e6b4bb0a77f347d364a6"
] | [
"hw09/grafico.py"
] | [
"import matplotlib\nimport matplotlib.pyplot as plt\n\nlist_t = []\nlist_h = []\n\n#Se tiene que separar la coma.\nwith open(\"datos.txt\", \"rt\") as data:\n for i in data:\n value = i.split(',')\n \n list_t.append(float(value[0]))\n list_h.append(float(value[1].rstrip('\\n')))\n ... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PraveenVenugopal/autokeras | [
"3ee8737fa400647b93f6203dce63eb2bb310d10b"
] | [
"autokeras/supervised.py"
] | [
"import os\nfrom abc import ABC, abstractmethod\nfrom sklearn.model_selection import train_test_split\nimport torch\nimport numpy as np\nfrom functools import reduce\n\nfrom autokeras.constant import Constant\nfrom autokeras.net_module import CnnModule\nfrom autokeras.search import BayesianSearcher, train\nfrom aut... | [
[
"numpy.concatenate",
"torch.no_grad",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
poodarchu/sscls | [
"8b1bd94b1ef4f0cef3ec6ecbb48be9dab129687b"
] | [
"sscls/datasets/imagenet.py"
] | [
"#!/usr/bin/env python3\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\n\"\"\"ImageNet dataset.\"\"\"\n\nimport cv2\nimport numpy as np\nimport os\nimport re\nimport torch\nimpor... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sparks-baird/matbench | [
"4424609454286e32fff2bcc724379b2a316c5a76",
"7d11a2d63766339ec00e610e2255be29b81544d3"
] | [
"scripts/mvb01_generate_validation.py",
"matbench/tests/test_task.py"
] | [
"import pandas as pd\nfrom monty.serialization import dumpfn\nfrom sklearn.model_selection import KFold, StratifiedKFold\n\nfrom matbench.constants import (\n CLF_KEY,\n REG_KEY,\n TEST_KEY,\n TRAIN_KEY,\n VALIDATION_METADATA_KEY,\n VALIDATION_SPLIT_KEY,\n)\nfrom matbench.data_ops import load\nfro... | [
[
"sklearn.model_selection.StratifiedKFold",
"pandas.set_option",
"sklearn.model_selection.KFold"
],
[
"numpy.floor",
"pandas.concat",
"pandas.DataFrame",
"numpy.ceil"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"... |
ArturOle/TypingTrainerGUI | [
"8dcfc451ffe3aceae6760cafd4a1040f088c7c8a"
] | [
"HighscoresPanel.py"
] | [
"import wx\nimport pandas as pd\n\n\nclass HighscoresPanel(wx.Panel):\n def __init__(self, parent):\n super().__init__(parent=parent)\n self.parent = parent\n self.SetClientSize(self.parent.Size)\n self.v_box = wx.BoxSizer(wx.VERTICAL)\n self.grid_sizer = wx.GridSizer(6, 4, 3, ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Loquats/hyperopt | [
"6c5b33be93a71a4ae6a38f04a29b18020b78b702"
] | [
"hyperopt/tests/unit/test_tpe.py"
] | [
"from past.utils import old_div\nfrom functools import partial\nimport os\nimport unittest\n\nimport nose\n\nimport numpy as np\n\ntry:\n import matplotlib.pyplot as plt\nexcept ImportError:\n pass\n\nfrom hyperopt import pyll\nfrom hyperopt.pyll import scope\n\nfrom hyperopt import Trials\n\nfrom hyperopt.ba... | [
[
"matplotlib.pyplot.legend",
"numpy.sqrt",
"numpy.all",
"numpy.seterr",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.mean",
"numpy.var",
"numpy.exp",
"numpy.random.default_rng",
"numpy.arange",
"numpy.std",
"matplotlib.pyplot.subplot",
"numpy.random.PCG64",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lx10077/dqnpy | [
"2aee374bb5945ebd31b2e7792f41c6cf03e3ff42"
] | [
"config/median_dqn/util.py"
] | [
"import torch\nimport os\nimport numpy as np\n\n\nUSE_CUDA = torch.cuda.is_available()\nFLOAT = torch.cuda.FloatTensor if USE_CUDA else torch.FloatTensor\nDOUBLE = torch.cuda.DoubleTensor if USE_CUDA else torch.DoubleTensor\nLONG = torch.cuda.LongTensor if USE_CUDA else torch.LongTensor\n\nTYPE_LIST = {\"FLOAT\": (... | [
[
"torch.from_numpy",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Aliossandro/WDOntoHistory | [
"d9b9abd73a037abab25e36a990bf1d2be8e54ed5"
] | [
"userframes_last_b.py"
] | [
"import pandas as pd\nimport psycopg2\nimport pickle\nimport numpy as np\n# counterS = 0\n# global counterS\n# global valGlob\n# from sqlalchemy import create_engine\n\n# -*- coding: utf-8 -*-\nimport os\nimport sys\nimport copy\n\n# fileName = '/Users/alessandro/Documents/PhD/OntoHistory/WDTaxo_October2014.csv'\n\... | [
[
"pandas.read_sql",
"numpy.load",
"pandas.to_datetime",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
upura/vivid | [
"6139697d60656d4774aceae880f5a07d929124a8"
] | [
"samples/ensumble.py"
] | [
"import pandas as pd\nfrom sklearn.datasets import load_boston\n\nfrom vivid.core import AbstractFeature\nfrom vivid.metrics import regression_metrics\nfrom vivid.out_of_fold.base import EnsembleFeature\nfrom vivid.out_of_fold.boosting import XGBoostRegressorOutOfFold, LGBMRegressorOutOfFold\nfrom vivid.out_of_fold... | [
[
"pandas.concat",
"pandas.DataFrame",
"sklearn.datasets.load_boston"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
admdev8/probnum | [
"792b6299bac247cf8b1b5056756f0f078855d83a",
"792b6299bac247cf8b1b5056756f0f078855d83a"
] | [
"src/probnum/utils/arrayutils.py",
"src/probnum/filtsmooth/gaussfiltsmooth/kalman.py"
] | [
"\"\"\"Utility functions for arrays and the like.\"\"\"\n\nimport numpy as np\nimport scipy.sparse\n\nimport probnum\n\n__all__ = [\n \"atleast_1d\",\n \"atleast_2d\",\n \"as_colvec\",\n \"assert_is_1d_ndarray\",\n \"assert_is_2d_ndarray\",\n]\n\n\ndef atleast_1d(*rvs):\n \"\"\"\n Convert array... | [
[
"numpy.atleast_1d",
"numpy.atleast_2d"
],
[
"numpy.eye",
"numpy.isscalar",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
filibp/thesis | [
"d970385bcb645b58e678cb3594a2e0cb461187e9"
] | [
"mnist/model.py"
] | [
"import numpy as np\nimport torch.nn as nn\n\n\n\"\"\"\nThe code is:\nCopyright (c) 2018 Erik Linder-Norén\nLicensed under MIT\n(https://github.com/eriklindernoren/PyTorch-GAN/blob/master/LICENSE)\n\"\"\"\n\n\nclass Generator(nn.Module):\n def __init__(self, opt):\n super().__init__()\n self.img_sh... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.nn.LeakyReLU",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Roboy/ss18_hand | [
"3a4b3524fe653478c8a31e62b593708fdeebef70"
] | [
"src/roboy_hand/gesture_recognition/old/real_dataset/data_utils.py"
] | [
"import numpy as np\nfrom skimage import io\nimport matplotlib.pyplot as plt\nfrom scipy.ndimage.interpolation import rotate\n\n'''Data normalization with substracting mean\n and dividing by standard deviation'''\ndef normalize(data):\n mean = np.mean(data)\n std = np.std(data)\n normalized_data = (data ... | [
[
"numpy.min",
"numpy.fliplr",
"numpy.flipud",
"numpy.max",
"numpy.std",
"scipy.ndimage.interpolation.rotate",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
2362696606/pynvme | [
"5cc958bcda63e7468187f11413ba5f787565d628"
] | [
"scripts/test_examples.py"
] | [
"import time\nimport pytest\nimport logging\n\nimport nvme as d\n\n\n# intuitive, spec, qpair, vscode, debug, cmdlog, assert\ndef test_hello_world(nvme0, nvme0n1, qpair):\n # prepare data buffer and IO queue\n read_buf = d.Buffer(512)\n write_buf = d.Buffer(512)\n write_buf[10:21] = b'hello world'\n\n ... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xscale",
"matplotlib.pyplot.yscale"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PacktPublishing/Practical-Machine-Learning | [
"7f33295766aa494a48e235db10af9897c7851fde"
] | [
"python-sckit-learn/Chapter09/naivebayesexample/feature-selection.py"
] | [
"# Practical Machine learning\n# Bayesian learning - Naive Bayes example \n# Chapter 9\n\nfrom datatypes import Dataset\n\nfrom sklearn.feature_selection import SelectKBest, f_classif\nfrom sklearn.lda import LDA\nfrom sklearn.qda import QDA\nfrom sklearn.decomposition import PCA\n\ndef univariate_feature_selection... | [
[
"sklearn.feature_selection.SelectKBest",
"sklearn.lda.LDA",
"sklearn.decomposition.PCA"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ololobus/DeepPavlov | [
"14615e65001b401354a43d780cb0b29e071515b6"
] | [
"deeppavlov/metrics/fmeasure.py"
] | [
"# Copyright 2017 Neural Networks and Deep Learning lab, MIPT\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requi... | [
[
"sklearn.metrics.f1_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lxrobot/object_detection_task | [
"bc52c11a31b2691cebb61a0b98d9b884d0920b99"
] | [
"cv_examples/main.py"
] | [
"# -*- coding:utf-8 -*-\n\nimport cv2\nimport os\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\nimg_name = os.path.join(\"imgs\",\"liu_1.png\")\nimg = cv2.imread(img_name, cv2.IMREAD_COLOR)\nimg_blue = img[:,:,0]\nimg_green = img[:,:,1]\nimg_red= img[:,:,2]\ncv2.imshow(\"img_blue\",img_blue)\ncv2.wait... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
usnistgov/STVMResearch | [
"fd8cc147174c15ac85b3e25e911cda02bf7b8ac7"
] | [
"models/model_{3,4}/bert_helpers/bert_functions.py"
] | [
"import numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport tensorflow_hub as hub\nfrom tensorflow.keras.layers import Input, Dense, Dropout\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import Adam\nimport bert\nfrom tqdm import tqdm\nimport sys\n\nfrom sklearn.model_s... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.keras.models.Model",
"numpy.asarray",
"tensorflow.keras.layers.Dense",
"sklearn.model_selection.KFold",
"tensorflow.keras.optimizers.Adam",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.layers.Input"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
BigHat-Biosciences/AbNumber | [
"86f2033b4e7d9a2e9ad0e3a7cff9d59d83259bcd"
] | [
"abnumber/chain.py"
] | [
"from collections import OrderedDict\nfrom typing import Union, List, Generator, Tuple\nfrom Bio import SeqIO\nfrom Bio.SeqRecord import SeqRecord\nimport pandas as pd\n\nfrom abnumber.alignment import Alignment\nfrom abnumber.common import (\n _anarci_align,\n _validate_chain_type,\n SUPPORTED_SCHEMES,\n ... | [
[
"pandas.isna",
"pandas.read_csv",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ShengmiaoJ/uwo-pa-python-course | [
"7890701b63cbdd56deb2fbb7845724ad9809a220"
] | [
"Lecture 9/L9_lecture.py"
] | [
"from __future__ import print_function, division\n\n### Multiprocessing and parallelization\n# See file: PyDomanParallelizer.py\n\n###\n\n\n#########################################\n\n### Exceptions ###\n\n\n# Let's cause some errors just for fun, and see what happens:\n\n# # Zero division:\n# print(5/0)\n\n# # A... | [
[
"numpy.random.random",
"numpy.sqrt",
"numpy.hypot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
taesung89/deeplab-pytorch | [
"25db353d10a256f1f9e89675a21f6e59af9407e6"
] | [
"eval.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n#\n# Author: Kazuto Nakashima\n# URL: http://kazuto1011.github.io\n# Created: 2017-11-03\n\n\n\nimport json\nimport os.path as osp\n\nimport click\nimport cv2\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport yaml\nfrom... | [
[
"torch.nn.functional.upsample",
"torch.nn.functional.softmax",
"torch.cuda.current_device",
"torch.load",
"torch.utils.data.DataLoader",
"numpy.argmax",
"torch.cuda.is_available",
"torch.cuda.get_device_name",
"torch.nn.DataParallel",
"numpy.zeros",
"torch.autograd.Vari... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abhinav4192/sparce-subspace-clustering-python | [
"2c0c4b8bbc87e8b856fc993d0aefd63ada106840"
] | [
"BuildAdjacency.py"
] | [
"# This function takes a NxN coefficient matrix and returns a NxN adjacency\n# matrix by choosing only the K strongest connections in the similarity graph\n# CMat: NxN coefficient matrix\n# K: number of strongest edges to keep; if K=0 use all the coefficients\n# CKSym: NxN symmetric adjacency matrix\n\n\nimport num... | [
[
"numpy.argsort",
"numpy.add",
"numpy.absolute",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nemodleo/HCFlow | [
"10deed6f8f719e72cf1c3cad486198b8a506c805"
] | [
"codes/data/GTLQ_dataset.py"
] | [
"import random\nimport numpy as np\nimport cv2\nimport lmdb\nimport torch\nimport torch.utils.data as data\nimport data.util as util\nimport sys\nimport os\n\ntry:\n sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n from data.util import imresize_np\n from utils import util as ... | [
[
"numpy.pad",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sepehrsaryazdi/cpsvis | [
"fe57797091a9bf5c116da9827a19cf7e48b45e98"
] | [
"visualise/surface_vis.py"
] | [
"from mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection\nimport matplotlib.pyplot as plt\n\nclass SurfaceVisual:\n def __init__(self, surface):\n self.surface = surface\n self.fig = None\n self.ax = None\n def show_vis_3d(self)... | [
[
"numpy.array",
"matplotlib.pyplot.show",
"numpy.linalg.norm",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
kylemin/wtalc-pytorch | [
"f54a0064962faaa54662347f9f0f312ebd12b863"
] | [
"main.py"
] | [
"from __future__ import print_function\nimport argparse\nimport os\nimport torch\nfrom model import Model\nfrom video_dataset import Dataset\nfrom test import test\nfrom train import train\n#from logger import Logger\nfrom torch.utils.tensorboard import SummaryWriter as Logger\nimport options\ntorch.set_default_ten... | [
[
"torch.set_default_tensor_type",
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"torch.utils.tensorboard.SummaryWriter",
"torch.cuda.is_available",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CisneirosRaphael/ross_c | [
"0c2176522d8cd4c36013c2bb02466a8139a3a513"
] | [
"ross/bearing_seal_element.py"
] | [
"\"\"\"Bearing Element module.\n\nThis module defines the BearingElement classes which will be used to represent the rotor\nbearings and seals. There are 7 different classes to represent bearings options,\nand 2 element options with 8 or 12 degrees of freedom.\n\"\"\"\n# fmt: off\nimport os\nimport warnings\n\nimpo... | [
[
"numpy.hstack",
"scipy.interpolate.UnivariateSpline",
"numpy.allclose",
"numpy.linspace",
"numpy.cos",
"scipy.interpolate.interp1d",
"numpy.insert",
"numpy.isscalar",
"numpy.array",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
ziatdinovmax/e2cnn | [
"e486a0d2cec71f2bde2d61f2f1315922f2883cee",
"e486a0d2cec71f2bde2d61f2f1315922f2883cee"
] | [
"e2cnn/nn/modules/r2_conv/basisexpansion_blocks.py",
"e2cnn/nn/modules/nonlinearities/norm.py"
] | [
"from e2cnn.kernels import KernelBasis, EmptyBasisException\nfrom e2cnn.gspaces import *\nfrom e2cnn.nn import FieldType\nfrom .. import utils\n\nfrom .basisexpansion import BasisExpansion\nfrom .basisexpansion_singleblock import block_basisexpansion\n\nfrom collections import defaultdict\n\nfrom typing import Call... | [
[
"torch.LongTensor",
"torch.meshgrid",
"torch.zeros"
],
[
"torch.LongTensor",
"torch.empty_like",
"torch.max",
"numpy.abs",
"torch.randn",
"torch.tensor",
"torch.exp",
"torch.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DPaletti/dovado | [
"1627f78056ba6fd0b663581b2dea46f96d393418"
] | [
"src/dovado_rtl/genetic_algorithm.py"
] | [
"from typing import List, Tuple, Optional\nfrom collections import OrderedDict\n\nfrom pymoo.model.callback import Callback\nfrom pymoo.model.problem import Problem\nfrom pymoo.algorithms.nsga2 import NSGA2\nfrom pymoo.factory import (\n get_sampling,\n get_crossover,\n get_mutation,\n get_termination,\... | [
[
"numpy.savetxt",
"numpy.random.random",
"numpy.column_stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
opemipoVRB/MeterTracker | [
"3c52e704844628db31b72d008983b6c090266775"
] | [
"threemegawatt/monitor/bulk_insert.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←\n←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←←\n↓↓...........................................................................↓↓\n↓↓............... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
aaronbjohnson/Project-Estimator | [
"95b4af6ed123beaeb36274e3e6b89a63557f2eeb"
] | [
"utilities.py"
] | [
"from sklearn.preprocessing import LabelEncoder\nimport os\n\ndef remove_column(dataset, column):\n \"\"\"\n This will delete a column from a dataset.\n Column needs to be a string\n \"\"\"\n del dataset[column]\n return\n\ndef label_encode(dataset, column):\n \"\"\"\n This will encode a bin... | [
[
"sklearn.preprocessing.LabelEncoder"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
daviguima/deep-learning | [
"28f772890edef1738045b0fb125c782e5c22cb8e"
] | [
"satellite/plots/visualize.py"
] | [
"import numpy as np\nimport logging\nimport matplotlib.pyplot as plt\n\nfrom sklearn.metrics import classification_report, confusion_matrix\n\n\nclass Visualize:\n def __init__(self):\n pass\n\n def plot_loss(self, model, epochs):\n \"\"\"\n Plot loss values regargin model and its epochs\... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.subplots",
"sklearn.metrics.confusion_matrix",
"matplotlib.pyplot.plot",
"numpy.argmax",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
felixdivo/tslearn | [
"e78ba4b0594acf3b3a901054e941d2dcea57975c"
] | [
"tslearn/tests/test_serialize_models.py"
] | [
"import os\nfrom glob import glob\nimport numpy\nimport pytest\nfrom sklearn.exceptions import NotFittedError\nfrom tslearn import hdftools\nfrom tslearn.preprocessing import TimeSeriesScalerMeanVariance\n\nfrom tslearn.neighbors import KNeighborsTimeSeries, \\\n KNeighborsTimeSeriesClassifier\nfrom tslearn.shap... | [
[
"numpy.testing.assert_equal",
"numpy.random.seed",
"numpy.dtype",
"numpy.random.rand",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ratishsp/data2text-seq-plan-py | [
"16b5242903371280cae8d23ad5a2472d539ea744"
] | [
"onmt/inputters/dataset_base.py"
] | [
"# coding: utf-8\n\nfrom itertools import chain, starmap\nfrom collections import Counter\n\nimport torch\nfrom torchtext.data import Dataset as TorchtextDataset\nfrom torchtext.data import Example\nfrom torchtext.vocab import Vocab\n\n\ndef _join_dicts(*args):\n \"\"\"\n Args:\n dictionaries with disj... | [
[
"torch.LongTensor",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SkyGraceHa/openpilot | [
"5f249d413918edda51b196d5bad1deb9a474b518"
] | [
"selfdrive/mapd/mapd.py"
] | [
"#!/usr/bin/env python3\nimport threading\nfrom traceback import print_exception\nimport numpy as np\nfrom time import strftime, gmtime\nimport cereal.messaging as messaging\nfrom common.realtime import Ratekeeper\nfrom selfdrive.mapd.lib.osm import OSM\nfrom selfdrive.mapd.lib.geo import distance_to_points\nfrom s... | [
[
"numpy.array",
"numpy.radians"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
slapshin/TalkNet_ASD | [
"343fac5c8d2bef2b98244e3acf20ac322711a4c7"
] | [
"trainTalkNet.py"
] | [
"import time, os, torch, argparse, warnings, glob\r\n\r\nfrom dataLoader import train_loader, val_loader\r\nfrom utils.tools import *\r\nfrom talkNet import talkNet\r\n\r\ndef main():\r\n # The structure of this code is learnt from https://github.com/clovaai/voxceleb_trainer\r\n warnings.filterwarnings(\"igno... | [
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IMAGINE-Paris/dti-clustering | [
"e3b4d29bf745036eadb20b26f86cf1f0ffa09f67",
"e3b4d29bf745036eadb20b26f86cf1f0ffa09f67"
] | [
"src/model/transformer.py",
"src/model/mini_resnet.py"
] | [
"from abc import ABCMeta, abstractmethod\nfrom copy import deepcopy\n\nfrom kornia import homography_warp\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.optim import Adam\n\nfrom .mini_resnet import get_resnet_model as get_mini_resnet_model\nfrom .resnet im... | [
[
"torch.cat",
"torch.zeros",
"numpy.asarray",
"torch.no_grad",
"torch.split",
"torch.einsum",
"torch.eye",
"torch.inverse",
"torch.nn.Sequential",
"torch.linspace",
"torch.sigmoid",
"torch.full",
"torch.nn.ModuleList",
"torch.exp",
"torch.nn.Linear",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gregmacfarlane/populationsim | [
"347e22ea6e264175346029d91770b3d356fba332"
] | [
"populationsim/steps/integerize_final_seed_weights.py"
] | [
"from __future__ import absolute_import\n# PopulationSim\n# See full license in LICENSE.txt.\n\nimport logging\nimport os\n\nimport pandas as pd\n\nfrom activitysim.core import inject\n\nfrom ..integerizer import do_integerizing\nfrom .helper import get_control_table\nfrom .helper import weight_table_name\nfrom .he... | [
[
"pandas.concat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
cr21/Shopee-Product-Matching | [
"e835b456ef4e48b9cf3fc20c1f03ad94074c266c"
] | [
"dataset.py"
] | [
"import torch\nimport cv2\nfrom torch.utils.data import Dataset\nimport os\n\n\nclass ShopeeQueryDataset(Dataset):\n \"\"\"\n Custom Dataset for Pytorch Model for Inference time\n \"\"\"\n\n def __init__(self, imagePath, transform=None):\n self.imagePath = imagePath\n self.transform = tra... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ianrowan/gpt-2-simple | [
"1583a0de42544543f302e448af867ba9fa5e5cdc"
] | [
"get_tweets.py"
] | [
"import twint\nimport os\nimport csv\nimport numpy as np\n\ncontinuous = False\nbase_path = os.path.dirname(os.path.realpath(__file__))\nname = input(\"Twitter User: @\")\nc = twint.Config()\n\nc.Username = name\nc.Store_csv = True\n# CSV Fieldnames\nc.Custom[\"tweet\"] = [\"tweet\"]\n# Name of the directory\nsave_... | [
[
"numpy.asarray",
"numpy.savetxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhoudaxia233/SmarTrip | [
"79106fed4eddda6359be3f3919a1dbfea30d01d0"
] | [
"graphs.py"
] | [
"import pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv('speed_rpm_revised.csv', encoding='utf-8').drop('Unnamed: 0', axis=1).set_index('trip_id')\nx = [i for i in range(len(df.index))]\ny_dict = {'speed.mean': 'mean of speed', 'speed.median': 'median of speed', 'speed.mode': 'mode of speed'}\n\nf... | [
[
"pandas.read_csv",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Anoopkr/impy | [
"3114b8ba269111da34ff6c325fdad7729277e652"
] | [
"GeometricAugmenters.py"
] | [
"\"\"\"\npackage: Images2Dataset\nclass: DataAugmentation\nEmail: lozuwaucb@gmail.com\nAuthor: Rodrigo Loza\nDescription: Common data augmentation operations \nfor an image.\nLog:\n\tNovemeber, 2017 -> Re-estructured class.\n\tDecember, 2017 -> Researched most used data augmentation techniques.\n\tMarch, 2018 -> Co... | [
[
"numpy.random.rand",
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
totalgood/knowledgequest | [
"88dd23bcfc8f4deb3f8795b645312a2ee66302cc"
] | [
"src/knowledgequest/models.py"
] | [
"# This is an auto-generated Django model module.\n# You'll have to do the following manually to clean this up:\n# * Rearrange models' order\n# * Make sure each model has one field with primary_key=True\n# * Make sure each ForeignKey has `on_delete` set to the desired behavior.\n# * Remove `managed = False`... | [
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
microprediction/successor | [
"80f61a59c93d45cff2851f8048fda5378bd05c4c",
"80f61a59c93d45cff2851f8048fda5378bd05c4c"
] | [
"tests/test_inventory.py",
"successor/conventions.py"
] | [
"import numpy as np\n\n\ndef test_inventory():\n # Ensure we can load and run from JSON\n from successor.skaters.scalarskaters.remote import SKLEARNED_CHAMPIONS, get_remote_compiled_model\n\n for champ in SKLEARNED_CHAMPIONS:\n model = get_remote_compiled_model(**champ)\n x = np.random.randn(... | [
[
"numpy.shape",
"numpy.random.randn"
],
[
"tensorflow.keras.optimizers.Nadam",
"tensorflow.keras.optimizers.Ftrl",
"tensorflow.keras.optimizers.Adamax",
"tensorflow.keras.optimizers.RMSprop",
"tensorflow.keras.optimizers.Adam",
"tensorflow.keras.optimizers.Adagrad",
"tensorf... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
zimonitrome/polandball_flag_mapping | [
"49fbbceaf807b6d74a5cb0664bde3216fccfed66"
] | [
"training/train_BSM.py"
] | [
"from pathlib import Path\nfrom datetime import datetime\nfrom itertools import count\nimport numpy as np\nfrom copy import deepcopy\nfrom tqdm import tqdm\n\nimport torch\nfrom torch import nn\nfrom torch.utils.data.dataloader import DataLoader\nfrom torch.utils.data.dataset import random_split\nfrom torch.utils.t... | [
[
"torch.nn.MSELoss",
"torch.cat",
"torch.manual_seed",
"torch.cuda.amp.autocast",
"torch.cuda.amp.GradScaler",
"torch.utils.tensorboard.writer.SummaryWriter",
"numpy.mean",
"torch.cuda.is_available",
"torch.utils.data.dataloader.DataLoader",
"torch.nn.L1Loss",
"torch.uti... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WongTusnYan/bolt | [
"4d841aa34cb3be0a3bdc3ebf574a168ec5aa18ac"
] | [
"model-tools/tools/pytorch2caffe/lenet.py"
] | [
"import sys\r\nsys.path.insert(0, '.')\r\nimport torch\r\nfrom torch.autograd import Variable\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\nimport pytorch_to_caffe\r\n\r\n\r\nclass LeNet(nn.Module):\r\n def __init__(self):\r\n super(LeNet, self).__init__()\r\n\r\n self.conv1 = ... | [
[
"torch.nn.Linear",
"torch.nn.Conv2d",
"torch.ones",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Silmathoron/matplotlib-chord-diagram | [
"0e3d2fd15539bf2544b1bc368cd62e3c57df9dfe"
] | [
"chord_diagram.py"
] | [
"\"\"\"\nTools to draw a chord diagram in python\n\"\"\"\n\nfrom collections.abc import Sequence\n\nimport matplotlib.patches as patches\n\nfrom matplotlib.colors import ColorConverter\nfrom matplotlib.path import Path\n\nimport numpy as np\nimport scipy.sparse as ssp\n\nfrom .gradient import gradient\nfrom .utilit... | [
[
"matplotlib.colors.ColorConverter.to_rgb",
"matplotlib.pyplot.tight_layout",
"numpy.maximum",
"scipy.sparse.issparse",
"numpy.linspace",
"numpy.clip",
"matplotlib.patches.PathPatch",
"numpy.meshgrid",
"matplotlib.path.Path",
"matplotlib.pyplot.subplots",
"matplotlib.pyp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
naijoaix/ray | [
"d6096df7428e6b2f5f371b7bafa67d998dd81e13"
] | [
"rllib/agents/ddpg/tests/test_ddpg.py"
] | [
"import numpy as np\nimport re\nimport unittest\nfrom tempfile import TemporaryDirectory\n\nimport ray\nimport ray.rllib.agents.ddpg as ddpg\nfrom ray.rllib.agents.ddpg.ddpg_torch_policy import ddpg_actor_critic_loss as loss_torch\nfrom ray.rllib.agents.sac.tests.test_sac import SimpleEnv\nfrom ray.rllib.policy.sam... | [
[
"numpy.random.random",
"numpy.minimum",
"numpy.random.choice",
"numpy.squeeze",
"numpy.ones",
"numpy.concatenate",
"numpy.std",
"numpy.mean",
"numpy.transpose",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ricardo0621/PytorchZeroToAll | [
"7b98eda196cb3c68e9d6ad9c9305f9e736c84f6e"
] | [
"9_Softmax_Classifier.py"
] | [
"from torch import nn, tensor, max\nimport numpy as np\n\n# Cross entropy example\n# One hot\n# 0: 1 0 0\n# 1: 0 1 0\n# 2: 0 0 1\nY = np.array([1, 0, 0])\nY_pred1 = np.array([0.7, 0.2, 0.1])\nY_pred2 = np.array([0.1, 0.3, 0.6])\nprint(f'Loss1: {np.sum(-Y * np.log(Y_pred1)):.4f}')\nprint(f'Loss2: {np.sum(-Y * np.log... | [
[
"torch.nn.CrossEntropyLoss",
"numpy.log",
"torch.max",
"torch.tensor",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
228922025/botty | [
"a139850cfe06b1b533b8c26a3a767356450c26c3"
] | [
"src/ui/ui_manager.py"
] | [
"from typing import List\nimport keyboard\nimport time\nimport cv2\nimport itertools\nimport os\nimport numpy as np\n\nfrom utils.custom_mouse import mouse\nfrom utils.misc import wait, cut_roi, color_filter\n\nfrom logger import Logger\nfrom config import Config, ItemProps\nfrom screen import Screen\nfrom item imp... | [
[
"numpy.average"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ardiantutomo/robot | [
"41df81b6d5e977c6d72a90bbd364544fc6dc20fb"
] | [
"build/lib/python_imagesearch/imagesearch.py"
] | [
"import cv2\nimport numpy as np\nimport pyautogui\nimport random\nimport time\nimport platform\nimport subprocess\nimport os\n\nis_retina = False\nif platform.system() == \"Darwin\":\n is_retina = subprocess.call(\"system_profiler SPDisplaysDataType | grep 'retina'\", shell=True)\n\n'''\n\ngrabs a region (topx, ... | [
[
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qq237942920/tianchi_detection | [
"92dc3d1aef596ebc9150734e120fdd896cf08bc3"
] | [
"maskrcnn_benchmark/modeling/rpn/retinanet/loss.py"
] | [
"\"\"\"\nThis file contains specific functions for computing losses on the RetinaNet\nfile\n\"\"\"\n\nimport torch\nfrom torch.nn import functional as F\n\nfrom ..utils import concat_box_prediction_layers\n\nfrom maskrcnn_benchmark.layers import smooth_l1_loss\nfrom maskrcnn_benchmark.layers import SigmoidFocalLoss... | [
[
"torch.nonzero",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pascalmi/noise-in-dpsgd-2020 | [
"e20d364ecf987fb0c4d6f1d717ddca4c4b5b1cc5"
] | [
"generate.py"
] | [
"#!/usr/bin/env python\n\nfrom sys import path\npath.insert(0, '.')\nfrom os.path import splitext, basename\nfrom argparse import ArgumentParser\n\nparser = ArgumentParser(description=\"Generate sample images from parameter checkpoint\")\nparser.add_argument('params', type=str, help=\"model parameters\")\nparser.ad... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"torch.load",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.subplot",
"torch.cuda.is_available",
"matplotlib.pyplot.figtext",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jungikim/OpenNMT-tf | [
"a4c1da98f7918d019ee01181243e19691c9abdfe"
] | [
"opennmt/models/sequence_to_sequence.py"
] | [
"\"\"\"Standard sequence-to-sequence model.\"\"\"\n\nimport tensorflow as tf\nimport tensorflow_addons as tfa\n\nfrom opennmt import config as config_util\nfrom opennmt import constants, inputters\nfrom opennmt.data import noise, text, vocab\nfrom opennmt.decoders import decoder as decoder_util\nfrom opennmt.layers... | [
[
"tensorflow.fill",
"tensorflow.transpose",
"tensorflow.math.count_nonzero",
"tensorflow.shape",
"tensorflow.roll",
"tensorflow.equal",
"tensorflow.reshape",
"tensorflow.expand_dims",
"tensorflow.squeeze",
"tensorflow.cast",
"tensorflow.nest.flatten",
"tensorflow.gat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13"
]
}
] |
fishduke/vision | [
"0f7914d09a293d14f5ed91fb75068d5dc521b9c9"
] | [
"classification-multi/whoisit/run.py"
] | [
"import os\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import load_model\nimport matplotlib.pyplot as plt\nimport cv2\nimport numpy as np\n\ndef display_multiple_img(images, rows = 5, cols=5):\n figure, ax = plt.subplots(nrows=rows,ncols=cols,figsize=(10,10))\n for ind,title i... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nikodyulger/datathon-logic | [
"6bbed203064f765c5b8a28f2705cd14e70bcc053"
] | [
"dash/categories.py"
] | [
"import dash_bootstrap_components as dbc\nfrom dash import html, dcc, Input, Output, State, callback\nimport plotly.express as px\nimport pandas as pd\nimport datetime as dt\nimport locale\n\n# Importamos datos y los tranformamos\ndf_items = pd.read_csv(\"../Clean Data/clean_items.csv\", parse_dates=[\"date\"])\ndf... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ShuhaoZhangTony/WalnutDB | [
"9ccc10b23351aa2e6793e0f5c7bd3dd511d7b050"
] | [
"hashing/scripts/latency_figure3.py"
] | [
"import itertools as it\nimport os\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport pylab\nfrom matplotlib.font_manager import FontProperties\nfrom matplotlib.ticker import MaxNLocator, LinearLocator, ScalarFormatter\n\nOPT_FONT_NAME = 'Helvetica'\nTICK_FONT_SIZE = 24\nLABEL_FONT_SIZE = 28\nLEGEND_FONT... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.ylim",
"matplotlib.font_manager.FontProperties",
"matplotlib.pyplot.savefig",
"matplotlib.ticker.LinearLocator",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.ticklabel_format",
"matplotlib.pyplot.ylabel",
"matplotlib.ticker.ScalarF... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
huang-ju-git/fast-reid | [
"ef55d8e3ac2995a7969468ea165e3decb2b3f212"
] | [
"demo/demo.py"
] | [
"# encoding: utf-8\n\"\"\"\n@author: liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport argparse\nimport glob\nimport os\nimport sys\n\nimport torch.nn.functional as F\nimport cv2\nimport numpy as np\nfrom tqdm import tqdm\nfrom torch.backends import cudnn\nimport pickle\nimport os.path as osp\n\nsys... | [
[
"torch.nn.functional.normalize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dsheldon/mechbayes | [
"dc1b857e5bee6429aa18233d4f4890b2892a2e4b"
] | [
"scripts/submit_util.py"
] | [
"import numpy as np\nimport pandas as pd\nimport mechbayes.util as util\nimport mechbayes.jhu as jhu\nfrom pathlib import Path\nimport warnings\n\n\n'''Submission'''\ndef create_submission_file(prefix, forecast_date, model, data, places, submit_args):\n \n print(f\"Creating submission file in {prefix}\")\n ... | [
[
"pandas.to_datetime",
"pandas.read_csv",
"pandas.Timedelta",
"numpy.percentile",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
voidism/Mockingjay-Speech-Representation | [
"e77df17a7f63a983c3757140c7a1e8c199cac614"
] | [
"utils/mam.py"
] | [
"# -*- coding: utf-8 -*- #\n\"\"\"*********************************************************************************************\"\"\"\n# FileName [ utils/mam.py ]\n# Synopsis [ Moasked Acoustic Model data processing for the mockingjay model ]\n# Author [ Andy T. Liu (Andi611) ]\n# Copyright ... | [
[
"torch.ByteTensor",
"numpy.power",
"torch.randperm",
"numpy.cos",
"numpy.sin",
"numpy.ones",
"numpy.zeros_like",
"torch.no_grad",
"torch.FloatTensor",
"torch.rand",
"numpy.repeat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fnaghetini/Mapa-Preditivo | [
"8302a4eaffa348717907011a44b4574fcce8a881"
] | [
"functions/Custom_Cleaning.py"
] | [
"# -----------------------------------------------------------------------------------------------------------\n# Função auxiliar para a etapa de limpeza dos dados\n# -----------------------------------------------------------------------------------------------------------\n\nimport pandas as pd\n\n\"\"\"\n tru... | [
[
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
joshuafuller/openpilot | [
"9d9480832e8ecec0f52743eefb1a9c8981a21998",
"9d9480832e8ecec0f52743eefb1a9c8981a21998"
] | [
"selfdrive/locationd/calibrationd.py",
"panda/tests/safety/test_subaru.py"
] | [
"#!/usr/bin/env python3\n\nimport os\nimport copy\nimport json\nimport numpy as np\nimport cereal.messaging as messaging\nfrom selfdrive.locationd.calibration_helpers import Calibration\nfrom selfdrive.swaglog import cloudlog\nfrom common.params import Params, put_nonblocking\nfrom common.transformations.model impo... | [
[
"numpy.radians",
"numpy.isfinite",
"numpy.clip",
"numpy.isnan",
"numpy.tile",
"numpy.arctan2",
"numpy.mean",
"numpy.array",
"numpy.zeros"
],
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
martin-fabbri/ml-continuous-integration | [
"299fa6ad432201421aec0174fb231c0e9111884f"
] | [
"train.py"
] | [
"import pandas as pd \nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\n# Set random seed\nseed = 42\n\n################################\n########## DATA PREP ###########\n###########... | [
[
"sklearn.ensemble.RandomForestRegressor",
"matplotlib.pyplot.tight_layout",
"pandas.read_csv",
"matplotlib.pyplot.ylim",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
if3chi/if3chi-p2-image-classifier | [
"70a1aa417642c222e882ddbeb864ae7114aaca4d"
] | [
"train.py"
] | [
"import argparse\nimport torch\nfrom collections import OrderedDict\nfrom torch import nn, optim\nfrom torchvision import datasets, transforms, models\nfrom workspace_utils import active_session\nimport time\n\n\ndef get_arguments():\n \n parser = argparse.ArgumentParser(description=\"Train.py\")\n parser.... | [
[
"torch.nn.NLLLoss",
"torch.nn.Dropout",
"torch.nn.LogSoftmax",
"torch.max",
"torch.utils.data.DataLoader",
"torch.exp",
"torch.nn.Linear",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dlmorenob/PINNs-TF2.0 | [
"d07587cd63efd08ee826e759e5c5c0bb31edf610"
] | [
"datagen/1d-burgers/r8vec_print.py"
] | [
"#! /usr/bin/env python\n#\ndef r8vec_print ( n, a, title ):\n\n#*****************************************************************************80\n#\n## R8VEC_PRINT prints an R8VEC.\n#\n# Licensing:\n#\n# This code is distributed under the GNU LGPL license.\n#\n# Modified:\n#\n# 31 August 2014\n#\n# Author:... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dennisfarmer/texas-hospital-hackathon | [
"dabf80a2c3d78d595280d4ff9475176da4848349"
] | [
"orders/vendors.py"
] | [
"from numpy.random import ranf\nfrom numpy import sqrt\n\nvendors = [\"H.E.B.\", \"Whole Foods\", \"Kroger\"]\n\ncity_locations = {'The Woodlands': (30.1658, -95.4613), 'Austin':(30.2672, -97.7431), 'Houston':(29.7604, -95.3698), 'Kingwood':(30.0500, -95.1845), 'Humble':(29.9988, -95.2622), 'Bellaire':(29.7058, -95... | [
[
"numpy.random.ranf",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lonePatient/TorchBlocks | [
"4a65d746cc8a396cb7df73ed4644d97ddf843e29"
] | [
"torchblocks/models/nn/bert_with_mdp.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nfrom torch.nn import CrossEntropyLoss\r\nfrom transformers import BertModel, BertPreTrainedModel\r\nfrom torchblocks.models.layers.dropouts import MultiSampleDropout\r\n\r\n\r\nclass BertWithMDP(BertPreTrainedModel):\r\n '''\r\n 对每一层的[CLS]向量进行weight求和，以及添加multi-sample... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.Dropout",
"torch.nn.Parameter",
"torch.softmax",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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