repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
itsraina/keras | [
"5e9376b5b94b6fb445dd52dbfafbc4e95bff5e35",
"5e9376b5b94b6fb445dd52dbfafbc4e95bff5e35",
"5e9376b5b94b6fb445dd52dbfafbc4e95bff5e35",
"5e9376b5b94b6fb445dd52dbfafbc4e95bff5e35",
"5e9376b5b94b6fb445dd52dbfafbc4e95bff5e35",
"5e9376b5b94b6fb445dd52dbfafbc4e95bff5e35",
"5e9376b5b94b6fb445dd52dbfafbc4e95bff5e3... | [
"keras/feature_column/dense_features_v2.py",
"keras/applications/vgg16.py",
"keras/layers/activation/thresholded_relu_test.py",
"keras/models/sharpness_aware_minimization_test.py",
"keras/utils/dataset_utils.py",
"keras/utils/losses_utils_test.py",
"keras/benchmarks/saved_model_benchmarks/saved_model_be... | [
"# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\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 requ... | [
[
"tensorflow.python.util.tf_export.keras_export",
"tensorflow.compat.v2.name_scope"
],
[
"tensorflow.python.util.tf_export.keras_export",
"tensorflow.compat.v2.io.gfile.exists"
],
[
"tensorflow.compat.v2.test.main"
],
[
"tensorflow.compat.v2.random.uniform",
"tensorflow.comp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
haribharadwaj/statsmodels | [
"8675b890607fe6f116b1186dcba4c387c5e3778a",
"8675b890607fe6f116b1186dcba4c387c5e3778a",
"8675b890607fe6f116b1186dcba4c387c5e3778a",
"8675b890607fe6f116b1186dcba4c387c5e3778a",
"844381797a475a01c05a4e162592a5a6e3a48032",
"8675b890607fe6f116b1186dcba4c387c5e3778a"
] | [
"statsmodels/regression/feasible_gls.py",
"statsmodels/graphics/tests/test_tsaplots.py",
"statsmodels/sandbox/tests/test_predict_functional.py",
"examples/incomplete/dates.py",
"statsmodels/tsa/statespace/tests/test_impulse_responses.py",
"statsmodels/tsa/vector_ar/svar_model.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n\nCreated on Tue Dec 20 20:24:20 2011\n\nAuthor: Josef Perktold\nLicense: BSD-3\n\n\"\"\"\n\nfrom statsmodels.compat.python import range\nimport numpy as np\nimport statsmodels.base.model as base\nfrom statsmodels.regression.linear_model import OLS, GLS, WLS, RegressionResults\n\n\... | [
[
"numpy.asarray",
"numpy.ones"
],
[
"numpy.testing.assert_equal",
"pandas.to_datetime",
"pandas.PeriodIndex",
"numpy.arange",
"pandas.DatetimeIndex",
"pandas.DataFrame",
"numpy.testing.assert_",
"numpy.random.RandomState",
"matplotlib.pyplot.figure"
],
[
"num... | [
{
"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",
"... |
Westlake-AI/openmixup | [
"ea81250819e740dd823e30cb7ce382d14a3c1b91",
"ea81250819e740dd823e30cb7ce382d14a3c1b91",
"ea81250819e740dd823e30cb7ce382d14a3c1b91",
"ea81250819e740dd823e30cb7ce382d14a3c1b91"
] | [
"openmixup/models/heads/mim_head.py",
"benchmarks/classification/svm_voc07/extract.py",
"openmixup/models/selfsup/relative_loc.py",
"openmixup/models/losses/regression_loss.py"
] | [
"import torch\nimport torch.nn as nn\nfrom mmcv.runner import BaseModule\nfrom torch.nn import functional as F\nfrom mmcv.cnn.utils.weight_init import trunc_normal_init\n\nfrom ..builder import build_loss\nfrom ..registry import HEADS\nfrom .cls_head import ClsHead\nfrom openmixup.utils import print_log\n\n\n@HEADS... | [
[
"torch.einsum",
"torch.nn.BatchNorm1d",
"torch.nn.functional.l1_loss",
"torch.fft.fftn"
],
[
"numpy.save",
"torch.cuda.current_device"
],
[
"torch.chunk",
"torch.flatten",
"torch.cat"
],
[
"torch.nn.functional.kl_div",
"torch.abs",
"torch.sigmoid",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
fabiansinz/locker | [
"9ca397d0a9aa747552bc43188b07056b87c6e9f0",
"9ca397d0a9aa747552bc43188b07056b87c6e9f0"
] | [
"scripts/fig3_locking_across_frequencies.py",
"locker/modeling.py"
] | [
"import matplotlib\nmatplotlib.use('Agg')\nfrom matplotlib.collections import PolyCollection\nfrom numpy.fft import fft, fftfreq, fftshift\nfrom locker import mkdir\nfrom locker.analysis import *\nfrom locker.data import *\nfrom scripts.config import params as plot_params, FormatedFigure\n\n\ndef generate_filename(... | [
[
"matplotlib.use"
],
[
"numpy.nanmax",
"numpy.sqrt",
"numpy.linspace",
"numpy.fft.fft",
"numpy.abs",
"numpy.arange",
"numpy.asarray",
"numpy.cos",
"matplotlib.pyplot.subplots",
"numpy.sin",
"numpy.where",
"numpy.diff",
"matplotlib.pyplot.close",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thsis/NIS18 | [
"1f2a7be1ab209fa7c0a25cb8eace744336b07c1f"
] | [
"tests/tests_helpers.py"
] | [
"import numpy as np\nfrom algorithms import helpers\n\n\ndef test_QR(Ntests):\n passed = 0\n critical = 0\n for _ in range(Ntests):\n try:\n n = np.random.randint(2, 11)\n X = np.random.uniform(low=0.0,\n high=100.0,\n ... | [
[
"numpy.random.uniform",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
starasteh/DeepLearning_from_scratch | [
"6ed4685e4da57ad5ea51edf84010f2cc9725a2ba"
] | [
"Layers/LSTM.py"
] | [
"'''\nCreated on January 2020.\n\n@author: Soroosh Tayebi Arasteh <soroosh.arasteh@fau.de>\nhttps://github.com/tayebiarasteh/\n'''\n\nfrom Layers.Base import *\nimport numpy as np\nimport pdb\nfrom Layers import Sigmoid, FullyConnected, TanH\nimport copy\n\n\nclass LSTM(base_layer):\n def __init__(self, input_si... | [
[
"numpy.concatenate",
"numpy.copy",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LarsChrWiik/Comparing-Machine-Learning-Models | [
"050b1bdb40c1d2e9c15f927e9eb257b4b7aaacbe"
] | [
"main.py"
] | [
"\r\nfrom scipy.io import arff\r\nfrom sklearn.pipeline import Pipeline\r\nfrom sklearn.utils import shuffle\r\nfrom ModelScorer import ModelScorer\r\nimport pandas as pd\r\nfrom Plotter import *\r\nimport warnings\r\n#warnings.simplefilter(action='ignore', category=FutureWarning)\r\nwarnings.filterwarnings(\"ignor... | [
[
"sklearn.neural_network.MLPClassifier",
"pandas.concat",
"sklearn.naive_bayes.GaussianNB",
"sklearn.dummy.DummyClassifier",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.linear_model.LogisticRegression",
"pandas.DataFrame",
"sklearn.neighbors.KNeighborsClassifier",
"sklea... | [
{
"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"
... |
rs992214/keanu | [
"c75b2a00571a0da93c6b1d5e9f0cbe09aebdde4d",
"c75b2a00571a0da93c6b1d5e9f0cbe09aebdde4d"
] | [
"keanu-python/keanu/infer_type.py",
"keanu-python/tests/test_proposal_distributions.py"
] | [
"from typing import Callable, Dict, Any, Union\n\nimport numpy as np\n\nfrom keanu.vartypes import (numpy_types, tensor_arg_types, runtime_numpy_types, runtime_pandas_types,\n runtime_primitive_types, runtime_bool_types, runtime_int_types, runtime_float_types,\n ... | [
[
"numpy.issubdtype"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gesa23/ds1hw1 | [
"fe69bcfd311467611a9534bbeaa7705ed95fafdb"
] | [
"main.py"
] | [
"from sklearn.datasets import load_iris\nimport pandas as pd\n\nds = load_iris()\ndf = pd.DataFrame(data= ds[\"data\"], columns=ds[\"feature_names\"])\ntarget_names = [ds.target_names[x] for x in ds.target]\ndf['species'] = target_names\nprint(df)"
] | [
[
"sklearn.datasets.load_iris",
"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": []
}
] |
jacke121/MBMD | [
"2daf5edb4fb40ee652baead4f9332ca00fa111a5",
"2daf5edb4fb40ee652baead4f9332ca00fa111a5",
"2daf5edb4fb40ee652baead4f9332ca00fa111a5"
] | [
"core/target_assigner.py",
"siamese_utils.py",
"lib/object_detection/my_train.py"
] | [
"from object_detection.core.target_assigner import TargetAssigner\nimport tensorflow as tf\nfrom object_detection.core import box_list\n\nclass TargetAssignerExtend(TargetAssigner):\n def assign(self, anchors, groundtruth_boxes, groundtruth_labels=None,\n **params):\n \"\"\"Assign classifica... | [
[
"tensorflow.control_dependencies",
"tensorflow.shape",
"tensorflow.cast",
"tensorflow.expand_dims",
"tensorflow.gather",
"tensorflow.dynamic_stitch"
],
[
"numpy.expand_dims",
"numpy.linspace",
"numpy.asarray",
"numpy.round",
"numpy.concatenate",
"numpy.max",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
marklr/vqgan-clip-app | [
"23edb7ae6234ab177a91865c02be160151fcf566"
] | [
"diffusion_logic.py"
] | [
"import clip\nimport sys\nimport torch\nfrom torchvision import transforms\nfrom torchvision.transforms import functional as TF\nfrom kornia import augmentation, filters\nfrom torch import nn\nfrom torch.nn import functional as F\nimport math\nimport lpips\nfrom PIL import Image\n\nsys.path.append(\"./guided-diffus... | [
[
"torch.nn.functional.normalize",
"torch.randint",
"torch.enable_grad",
"torch.ones",
"torch.cat",
"torch.load",
"torch.manual_seed",
"torch.zeros",
"torch.zeros_like",
"torch.tensor",
"torch.nn.functional.adaptive_avg_pool2d",
"torch.rand",
"torch.cuda.is_availa... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jurreht/cic | [
"95a5e32eeb26da8d18642add2259f164426e1a25",
"95a5e32eeb26da8d18642add2259f164426e1a25"
] | [
"tests/cic_test.py",
"cic/cic.py"
] | [
"import os\n\nimport numpy as np\nfrom numpy.testing import assert_allclose\nimport pytest\nimport scipy.io\nimport scipy.stats\n\nimport cic\n\n\ndef cases():\n \"\"\"\n Loads all filenames of the pre-calculated test cases.\n \"\"\"\n case_dir = os.path.join(\n os.path.dirname(os.path.realpath(_... | [
[
"numpy.ones_like",
"numpy.random.seed",
"numpy.arange",
"numpy.ones",
"numpy.concatenate",
"numpy.full",
"numpy.random.randn",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.zeros",
"numpy.empty"
],
[
"numpy.diag",
"numpy.linalg.matrix_rank",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hannesdm/shap | [
"ae96bef7879f47978c8a436ebf19c2f2747cd887"
] | [
"shap/explainers/_deep/deep_tf.py"
] | [
"import numpy as np\nimport warnings\nfrom .._explainer import Explainer\nfrom packaging import version\nfrom ..tf_utils import _get_session, _get_graph, _get_model_inputs, _get_model_output\nkeras = None\ntf = None\ntf_ops = None\ntf_backprop = None\ntf_execute = None\ntf_gradients_impl = None\n\ndef custom_record... | [
[
"tensorflow.concat",
"tensorflow.reduce_sum",
"tensorflow.minimum",
"tensorflow.cast",
"numpy.concatenate",
"tensorflow.python.eager.backprop.record_gradient",
"tensorflow.keras.backend.learning_phase",
"tensorflow.gradients",
"tensorflow.keras.backend.set_learning_phase",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"2.3",
"2.4",
"2.9",
"2.5",
"2.2",
"2.10"
]
}
] |
HustQBW/Single-Object-Localization | [
"3a6bd87cd75543f55eb3eed12b6d09475f05b8fd",
"3a6bd87cd75543f55eb3eed12b6d09475f05b8fd"
] | [
"train.py",
"resnet50_anchor_net.py"
] | [
"from dataset import tiny_dataset\nfrom bbox_codec import bbox_encode\nfrom resnet50_base import Localization_net2\nfrom torch.utils.data import DataLoader,random_split\nimport torch as t\nimport tqdm\nfrom torch.utils.tensorboard import SummaryWriter\nimport torch.nn as nn\nimport torch.optim as optim\nimport argp... | [
[
"torch.Generator",
"torch.cuda.manual_seed",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.optim.SGD",
"torch.nn.init.zeros_",
"torch.nn.init.kaiming_normal_"
],
[
"torch.nn.Sequential",
"torch.s... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Geodan/building-boundary | [
"d0eb88d99743af917568131e8609f481b10e4520"
] | [
"building_boundary/footprint.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n\n@author: Chris Lucas\n\"\"\"\n\nimport math\n\nimport numpy as np\nfrom shapely.geometry import (\n Polygon, MultiPolygon, LineString, MultiLineString, LinearRing\n)\nfrom shapely import wkt\n\nfrom building_boundary import utils\n\n\ndef line_orientations(lines):\n \"\"\"\... | [
[
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mangoyuan/Unifed-Seg3d | [
"74c82464dbe901cf18e38afb0e1b74cc159a8850",
"74c82464dbe901cf18e38afb0e1b74cc159a8850"
] | [
"nnunet/training/network_training/network_trainer.py",
"nnunet/inference/predict.py"
] | [
"from _warnings import warn\nimport matplotlib\nfrom batchgenerators.utilities.file_and_folder_operations import *\nfrom sklearn.model_selection import KFold\nmatplotlib.use(\"agg\")\nfrom time import time, sleep\nimport torch\nimport numpy as np\nfrom torch.optim import lr_scheduler\nimport matplotlib.pyplot as pl... | [
[
"numpy.random.seed",
"torch.cuda.current_device",
"matplotlib.use",
"torch.manual_seed",
"matplotlib.pyplot.figure",
"torch.cuda.empty_cache",
"sklearn.model_selection.KFold",
"torch.from_numpy",
"matplotlib.pyplot.plot",
"numpy.argmax",
"numpy.mean",
"torch.no_grad... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Pradeep-Gopal/yolo_deer_people_final_project | [
"2337e8cbb88f467a6d19ab9cdb14abbf2ba04bc2",
"2337e8cbb88f467a6d19ab9cdb14abbf2ba04bc2"
] | [
"yolov3_tiny_deer_detection/evaluate_mAP.py",
"yolov3_tiny_deer_detection/yolov3/yolov3.py"
] | [
"\nimport os\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\nimport cv2\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.python.saved_model import tag_constants\nfrom yolov3.dataset import Dataset\nfrom yolov3.yolov4 import Create_Yolo\nfrom yolov3.utils import load_yolo_weights, detect_image, image_preproc... | [
[
"tensorflow.concat",
"tensorflow.saved_model.load",
"tensorflow.constant",
"tensorflow.config.experimental.set_memory_growth",
"tensorflow.shape",
"tensorflow.config.experimental.list_physical_devices",
"numpy.copy",
"numpy.array"
],
[
"tensorflow.concat",
"tensorflow.r... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
DenisSch/svca | [
"bd029c120ca8310f43311253e4d7ce19bc08350c",
"bd029c120ca8310f43311253e4d7ce19bc08350c",
"bd029c120ca8310f43311253e4d7ce19bc08350c"
] | [
"svca_limix/limix/core/mean/mean.py",
"svca_limix/demos/demo_gp2kronSumLR.py",
"svca_limix/limix/core/covar/zkz.py"
] | [
"import sys\nfrom limix.core.old.cobj import *\nfrom limix.utils.preprocess import regressOut\nimport numpy as np\n\nimport scipy.linalg as LA\nimport copy\n\ndef compute_X1KX2(Y, D, X1, X2, A1=None, A2=None):\n\n R,C = Y.shape\n if A1 is None:\n nW_A1 = Y.shape[1]\n #A1 = np.eye(Y.shape[1])\t#f... | [
[
"numpy.dot",
"numpy.reshape",
"numpy.arange",
"numpy.eye",
"numpy.kron",
"numpy.ones",
"numpy.concatenate",
"numpy.outer",
"numpy.array",
"numpy.zeros"
],
[
"scipy.eye",
"scipy.randn",
"scipy.rand"
],
[
"numpy.eye",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
koba35/retinanet | [
"99820cde438a2fc14e38973437766de6fe6a94a3"
] | [
"losses.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\n\n\ndef calc_iou(a, b):\n area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1])\n\n iw = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, 0])\n ih = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[:, 3]) - to... | [
[
"torch.abs",
"torch.ge",
"torch.ones",
"torch.max",
"torch.Tensor",
"torch.zeros",
"torch.eq",
"torch.lt",
"torch.unsqueeze",
"torch.tensor",
"torch.le",
"torch.log",
"torch.stack",
"torch.clamp",
"torch.pow",
"torch.ne"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
googleinterns/protein-embedding-retrieval | [
"388563d3206e1486fe5dbcfd8326be6f1185a00e"
] | [
"contextual_lenses/train_utils.py"
] | [
"\"\"\"Train utils\n\nGeneral tools for instantiating and training models.\n\"\"\"\n\nimport flax\nfrom flax import nn\nfrom flax import optim\nfrom flax.training import checkpoints\nfrom flax.training import common_utils\n\nimport jax\nfrom jax import random\nimport jax.nn\nimport jax.numpy as jnp\nfrom jax.config... | [
[
"tensorflow.data.Dataset.zip",
"tensorflow.data.Dataset.from_tensor_slices"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
sethmnielsen/mavsim_template_files | [
"453ec4f7d38fc2d1162198b554834b5bdb7de96f",
"453ec4f7d38fc2d1162198b554834b5bdb7de96f"
] | [
"mavsim_python/chap3/mav_dynamics.py",
"mavsim_python/tools/rotations.py"
] | [
"\"\"\"\nmav_dynamics\n - this file implements the dynamic equations of motion for MAV\n - use unit quaternion for the attitude state\n\npart of mavsimPy\n - Beard & McLain, PUP, 2012\n - Update history:\n 12/17/2018 - RWB\n 1/14/2019 - RWB\n\"\"\"\nimport sys\nsys.path.append('..')\nimpor... | [
[
"numpy.array",
"numpy.sqrt"
],
[
"numpy.arcsin",
"numpy.set_printoptions",
"numpy.cos",
"numpy.sin",
"numpy.arctan2",
"numpy.copy",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EEmGuzman/orphics | [
"f8f25f9db7c9104dba5cbeaac0b4924bf4f6920e",
"f8f25f9db7c9104dba5cbeaac0b4924bf4f6920e"
] | [
"tests/legacy/test_cross_cov.py",
"orphics/unmerged/theory/cosmology.py"
] | [
"from __future__ import print_function\nfrom orphics import maps,io,cosmology,symcoupling as sc,stats,lensing\nfrom enlib import enmap,bench\nimport numpy as np\nimport os,sys\n\n\n\ncache = True\nhdv = False\ndeg = 5\npx = 1.5\nshape,wcs = maps.rect_geometry(width_deg = deg,px_res_arcmin=px)\nmc = sc.LensingModeCo... | [
[
"numpy.arange",
"numpy.sqrt",
"numpy.nan_to_num"
],
[
"numpy.dot",
"numpy.linspace",
"numpy.asarray",
"numpy.nan_to_num",
"numpy.ones",
"scipy.interpolate.interp1d",
"numpy.array",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
... |
AKSHANSH47/crowdsource-platform2 | [
"a31446d44bc10dca56a0d534cab226947a6bbb4e"
] | [
"fixtures/createJson.py"
] | [
"__author__ = 'Megha'\n# Script to transfer csv containing data about various models to json\n# Input csv file constituting of the model data\n# Output json file representing the csv data as json object\n# Assumes model name to be first line\n# Field names of the model on the second line\n# Data seperated by __DELI... | [
[
"numpy.array",
"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": []
}
] |
convergence-lab/covid19-detection | [
"6a57e87ec1d8688712e6170a4c3aafb6e113ca73"
] | [
"src/train.py"
] | [
"import toml\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, f1_score, roc_auc_score\nfrom logzero import logger\n\nimport torch\nfrom torch import nn, optim\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\n\nfrom model import Model\nfr... | [
[
"sklearn.metrics.roc_auc_score",
"torch.nn.NLLLoss",
"sklearn.metrics.accuracy_score",
"torch.utils.data.DataLoader",
"sklearn.model_selection.train_test_split",
"torch.no_grad",
"torch.cuda.is_available",
"sklearn.metrics.f1_score",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HLTCHKUST/emotion-dialogue | [
"0d58b339134dd9a2f386948ae474b270a77370f9",
"0d58b339134dd9a2f386948ae474b270a77370f9",
"0d58b339134dd9a2f386948ae474b270a77370f9",
"0d58b339134dd9a2f386948ae474b270a77370f9"
] | [
"baseline/baseline_classifier.py",
"baseline/LR/LR_emoji_baseline.py",
"voting_confidence.py",
"models/lstm_model.py"
] | [
"from utils import constant\nfrom sklearn import svm\nfrom sklearn.svm import SVC\nfrom sklearn.linear_model import LogisticRegression\nfrom xgboost import XGBClassifier\n\n\ndef get_classifier(ty=\"LR\", c=1.0, max_depth=5, n_estimators=300, gamma=0):\n if(ty==\"LR\"):\n classifier = LogisticRegression(s... | [
[
"sklearn.linear_model.LogisticRegression",
"sklearn.svm.SVC"
],
[
"numpy.arange",
"numpy.zeros"
],
[
"pandas.read_csv",
"numpy.array",
"numpy.argmax"
],
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.LongTensor",
"torch.nn.LSTM",
"torch.cat",
"torc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",... |
dedsec-9/AutoGL | [
"487f2b2f798b9b1363ad5dc100fb410b12222e06",
"487f2b2f798b9b1363ad5dc100fb410b12222e06",
"487f2b2f798b9b1363ad5dc100fb410b12222e06",
"487f2b2f798b9b1363ad5dc100fb410b12222e06",
"487f2b2f798b9b1363ad5dc100fb410b12222e06"
] | [
"examples/node_classification.py",
"test/performance/graph_classification/pyg/base.py",
"test/performance/heterogeneous/dgl/hgt_main.py",
"autogl/module/ensemble/stacking.py",
"test/performance/graph_classification/dgl/base.py"
] | [
"import yaml\nimport random\nimport torch.backends.cudnn\nimport numpy as np\nfrom autogl.datasets import build_dataset_from_name\nfrom autogl.solver import AutoNodeClassifier\nfrom autogl.module import Acc\nfrom autogl.backend import DependentBackend\n\nif __name__ == \"__main__\":\n\n from argparse import Argu... | [
[
"numpy.random.seed"
],
[
"torch.nn.BatchNorm1d",
"torch.Generator",
"numpy.random.seed",
"torch.nn.functional.dropout",
"torch.nn.functional.log_softmax",
"torch.manual_seed",
"torch.cuda.manual_seed",
"torch.nn.functional.nll_loss",
"torch.nn.Linear",
"numpy.std",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
hekaplex/resnet_dl | [
"fc8d4dcc0adffbe22d01d333e6cf5db955f2f011",
"fc8d4dcc0adffbe22d01d333e6cf5db955f2f011",
"fc8d4dcc0adffbe22d01d333e6cf5db955f2f011",
"2e70203197cd79f9522d65731ee5dc0eb236b005",
"fc8d4dcc0adffbe22d01d333e6cf5db955f2f011",
"fc8d4dcc0adffbe22d01d333e6cf5db955f2f011",
"fc8d4dcc0adffbe22d01d333e6cf5db955f2f01... | [
"benchmarks/image_recognition/tensorflow_serving/inceptionv3/inference/fp32/image_recognition_benchmark.py",
"benchmarks/image_recognition/tensorflow_serving/resnet50v1_5/inference/fp32/util.py",
"benchmarks/image_recognition/tensorflow_serving/resnet50v1_5/inference/fp32/model_graph_to_saved_model.py",
"mode... | [
"#\r\n# -*- coding: utf-8 -*-\r\n#\r\n# Copyright (c) 2019 Intel Corporation\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICE... | [
[
"tensorflow.compat.v1.app.flags.DEFINE_string",
"tensorflow.compat.v1.app.flags.DEFINE_integer",
"tensorflow.make_tensor_proto",
"tensorflow.Session",
"tensorflow.compat.v1.disable_eager_execution",
"numpy.random.rand",
"tensorflow.compat.v1.app.run"
],
[
"tensorflow.compat.v1.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
arlo-lib/ARLO | [
"159669884044686e36e07bd1cc0948884ed7cc8d"
] | [
"experiments/Scripts for creating plots/sac_performance_over_generations.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nif __name__ == '__main__':\n x=np.arange(50)\n \n y=np.array([-59.00138158129509, \n -43.966695525591895, \n -52.5277642686108,\n -32.1793153104166,\n -37.81484603001339,\n -24... | [
[
"matplotlib.pyplot.plot",
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
satishpasumarthi/sagemaker-python-sdk | [
"255a339ae985041ef47e3a80da91b9f54bca17b9"
] | [
"tests/integ/test_ntm.py"
] | [
"# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#\n# or in the \"licens... | [
[
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
johnbachman/deft | [
"3643dd33ba4cb548f7622f24a3b87fbe48e38050"
] | [
"adeft/tests/test_disambiguate.py"
] | [
"import os\nimport uuid\nimport json\nimport shutil\nimport logging\nfrom nose.tools import raises\n\nfrom numpy import array_equal\n\nfrom adeft.modeling.classify import load_model\nfrom adeft.locations import TEST_RESOURCES_PATH\nfrom adeft.disambiguate import AdeftDisambiguator, load_disambiguator\n\nlogger = lo... | [
[
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zedian/esm | [
"9d2b50cd96753e8a703ca810e875c9e887047ed9",
"9d2b50cd96753e8a703ca810e875c9e887047ed9"
] | [
"models.py",
"esm/new_modules.py"
] | [
"from __future__ import print_function\nimport torch\nfrom torch import nn\nimport torch.utils.data as Data\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nimport numpy as np\n\nimport collections\nimport math\nimport copy\n\ntorch.manual_seed(1)\nnp.random.seed(1)\n\n\n\nclass BIN_Interactio... | [
[
"torch.nn.Softmax",
"torch.nn.functional.dropout",
"torch.zeros",
"torch.sum",
"torch.nn.Embedding",
"torch.nn.Dropout",
"torch.ones",
"torch.sqrt",
"torch.nn.functional.relu",
"torch.arange",
"torch.nn.BatchNorm1d",
"torch.nn.Conv2d",
"torch.unsqueeze",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ibme-qubic/oxasl | [
"e583103f3313aed2890b60190b6ca7b265a46e3c"
] | [
"oxasl/mask.py"
] | [
"\"\"\"\nOXASL - Module to generate a suitable mask for ASL data\n\nCopyright (c) 2008-2020 Univerisity of Oxford\n\"\"\"\nimport numpy as np\nimport scipy as sp\n\nimport fsl.wrappers as fsl\nfrom fsl.data.image import Image\n\nfrom oxasl import reg\nfrom oxasl.reporting import LightboxImage\n\ndef generate_mask(w... | [
[
"scipy.ndimage.morphology.binary_fill_holes"
]
] | [
{
"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": [... |
skyf0cker/Statistical_learning_method | [
"8151f3b8595ac086f08d161dc0cb961946f4b7fc"
] | [
"lh/DecisionTree2.py"
] | [
"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# @Date : 2019-02-03 15:17:08\r\n# @Author : Vophan Lee (vophanlee@gmail.com)\r\n# @Link : https://www.jianshu.com/u/3e6114e983ad\r\n\r\nfrom sklearn.datasets import make_classification\r\nimport numpy as np\r\nimport math\r\n\r\n\r\nclass Decision_Tree(ob... | [
[
"numpy.array",
"sklearn.datasets.make_classification",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jun-hyeok/SUP5001-41_Deep-Neural-Networks_2022Spring | [
"95bc0f3a7042debbc388c76d9bd43ad24aba2c88"
] | [
"DNN_HW5/main.py"
] | [
"# %% [markdown]\n# [](https://colab.research.google.com/github/jun-hyeok/SUP5001-41_Deep-Neural-Networks_2022Spring/blob/main/DNN_HW5/main.ipynb)\n\n# %% [markdown]\n# # DNN HW5 : #9\n#\n# 2022.03.23\n# 박준혁\n\n# %%\nimport numpy as np\nimpor... | [
[
"torch.nn.functional.binary_cross_entropy",
"torch.nn.Linear",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hacky-DH/pytorch | [
"80dc4be615854570aa39a7e36495897d8a040ecc",
"80dc4be615854570aa39a7e36495897d8a040ecc",
"80dc4be615854570aa39a7e36495897d8a040ecc",
"80dc4be615854570aa39a7e36495897d8a040ecc",
"80dc4be615854570aa39a7e36495897d8a040ecc",
"80dc4be615854570aa39a7e36495897d8a040ecc",
"80dc4be615854570aa39a7e36495897d8a040ec... | [
"benchmarks/distributed/ddp/compare/compare_ddp.py",
"caffe2/quantization/server/tanh_dnnlowp_op_test.py",
"caffe2/python/lazy_dyndep_test.py",
"test/test_gen_backend_stubs.py",
"torch/jit/__init__.py",
"torch/utils/data/sampler.py",
"torch/package/package_importer.py",
"caffe2/python/operator_test/rm... | [
"\"\"\"\nA simple tool to compare the performance of different impls of\nDistributedDataParallel on resnet50, three flavors:\n\n1. DistributedDataParallel, which has a python wrapper and C++ core to do\n gradient distribution and reduction. It's current production version.\n\n2. PythonDDP with async gradient redu... | [
[
"torch.cuda.synchronize",
"torch.distributed.init_process_group",
"torch.cuda.manual_seed",
"torch.multiprocessing.spawn",
"torch.manual_seed",
"torch.cuda.Event",
"numpy.percentile",
"torch.distributed.distributed_c10d._get_default_group",
"numpy.mean",
"torch.rand",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
mingxiaoh/chainer-v3 | [
"815ff00f5eaf7944d6e8a75662ff64a2fe046a4d",
"815ff00f5eaf7944d6e8a75662ff64a2fe046a4d",
"815ff00f5eaf7944d6e8a75662ff64a2fe046a4d",
"815ff00f5eaf7944d6e8a75662ff64a2fe046a4d",
"815ff00f5eaf7944d6e8a75662ff64a2fe046a4d"
] | [
"tests/chainer_tests/functions_tests/connection_tests/test_n_step_lstm.py",
"chainer/testing/unary_math_function_test.py",
"tests/mkldnnpy_tests/test_relu_bench.py",
"tests/mkldnnpy_tests/test_linear_bench.py",
"chainer/functions/connection/n_step_lstm.py"
] | [
"import unittest\n\nimport mock\nimport numpy\n\nimport chainer\nfrom chainer import cuda\nfrom chainer import functions\nfrom chainer import gradient_check\nfrom chainer import testing\nfrom chainer.testing import attr\n\n\ndef sigmoid(x):\n return numpy.tanh(x * 0.5) * 0.5 + 0.5\n\n\ndef _split(inputs, pos):\n... | [
[
"numpy.random.uniform",
"numpy.tanh"
],
[
"numpy.random.uniform"
],
[
"numpy.asarray",
"numpy.ndarray"
],
[
"numpy.asarray",
"numpy.ndarray",
"numpy.ones"
],
[
"numpy.array",
"numpy.uint64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
WuYff/ggnn.pytorch | [
"795bc7fb51876231406d71610aa5ec7ed29865c0",
"795bc7fb51876231406d71610aa5ec7ed29865c0"
] | [
"main_live.py",
"utils/data/wy_dataset3.py"
] | [
"import argparse\nimport random\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom model_live import GGNN\nfrom utils.train_live import train\nfrom utils.test_live import test\nfrom utils.validation_live import validation\nfrom utils.data.wy_dataset_live import bAbIDataset\nfrom utils.data.... | [
[
"torch.nn.SmoothL1Loss",
"torch.manual_seed",
"torch.nn.L1Loss",
"torch.cuda.manual_seed_all",
"torch.nn.MSELoss"
],
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sbl1996/pytorch-hrvvi-ext | [
"f19abcbedd844a700b2e2596dd817ea80cbb6287",
"f19abcbedd844a700b2e2596dd817ea80cbb6287",
"f19abcbedd844a700b2e2596dd817ea80cbb6287"
] | [
"horch/legacy/models/detection/enhance.py",
"horch/models/cifar/testnet2.py",
"horch/models/nas/cifar/darts.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom horch.common import tuplify\nfrom horch.models.block import mb_conv_block, MBConv\nfrom horch.models.detection.nasfpn import ReLUConvBN\n\nfrom horch.models.modules import upsample_add, Conv2d, Sequential, Pool2d, upsample_concat\nfrom horc... | [
[
"torch.nn.ModuleList",
"torch.nn.functional.adaptive_avg_pool2d"
],
[
"torch.nn.Sequential",
"torch.nn.Identity"
],
[
"torch.nn.ModuleList",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dvorotnev/NNEF-Tools | [
"0219a509c34bb5b291bee497cbd658d6a5922171",
"0219a509c34bb5b291bee497cbd658d6a5922171"
] | [
"nnef_tools/io/tf/graphdef/reader.py",
"nnef_tests/conversion/onnx_test.py"
] | [
"# Copyright (c) 2020 The Khronos Group 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 applicable... | [
[
"tensorflow.Graph",
"tensorflow.import_graph_def",
"numpy.dtype",
"numpy.full",
"numpy.frombuffer",
"numpy.prod",
"tensorflow.get_default_graph",
"numpy.array"
],
[
"numpy.random.random",
"numpy.abs",
"numpy.random.seed",
"numpy.dtype",
"numpy.all",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
... |
dbradf/signal-processing-algorithms | [
"75312e873543f0f89aace75f43ded783395425c5"
] | [
"src/signal_processing_algorithms/gesd.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nGESD based Detect outliers.\n\nGeneralized ESD Test for Outliers\nsee 'GESD<https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h3.htm>'\n\"\"\"\nimport collections\n\nfrom typing import List\n\nimport numpy as np\nimport numpy.ma as ma\nimport structlog\n\nfrom scipy.stats ... | [
[
"numpy.sqrt",
"numpy.abs",
"numpy.greater",
"numpy.arange",
"numpy.ma.median",
"scipy.stats.t.ppf",
"numpy.size",
"numpy.ma.array",
"numpy.fabs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
prjemian/XAnoS | [
"8a70380a88421042feff6f4aa9f5cf1f79ab4efc"
] | [
"Functions/FormFactors/SphericalShell_expDecay.py"
] | [
"####Please do not remove lines below####\nfrom lmfit import Parameters\nimport numpy as np\nimport sys\nimport os\nsys.path.append(os.path.abspath('.'))\nsys.path.append(os.path.abspath('./Functions'))\nsys.path.append(os.path.abspath('./Fortran_rountines'))\n\n####Please do not remove lines above####\n\n####Impor... | [
[
"numpy.absolute",
"numpy.linspace",
"numpy.arange",
"numpy.sin",
"numpy.zeros_like",
"numpy.exp",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexandru-dinu/PRML | [
"acd823e098df67abe0306a70225e7539f8edda40"
] | [
"prml/linear/_bayesian_regression.py"
] | [
"import numpy as np\n\nfrom prml.linear._regression import Regression\n\n\nclass BayesianRegression(Regression):\n \"\"\"Bayesian regression model.\n\n w ~ N(w|0, alpha^(-1)I)\n y = X @ w\n t ~ N(t|X @ w, beta^(-1))\n \"\"\"\n\n def __init__(self, alpha: float = 1.0, beta: float = 1.0):\n \... | [
[
"numpy.linalg.solve",
"numpy.sqrt",
"numpy.linalg.inv",
"numpy.random.multivariate_normal",
"numpy.eye",
"numpy.size",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jnorwood/tensorflow | [
"67ab6c9cebc4cbb2103246a1523d04261bef22d2"
] | [
"tensorflow/python/saved_model/save.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\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 requ... | [
[
"tensorflow.python.training.tracking.object_identity.ObjectIdentityDictionary",
"tensorflow.python.util.compat.as_str_any",
"tensorflow.python.saved_model.utils_impl.get_variables_path",
"tensorflow.core.protobuf.meta_graph_pb2.AssetFileDef",
"tensorflow.python.saved_model.builder_impl.copy_as... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"2.7",
"2.6",
"2.4",
"2.3",
"2.9",
"2.5",
"2.2",
"2.10"
]
}
] |
Jianrong-Lu/MONAI | [
"c319ca8ff31aa980a045f1b913fb2eb22aadb080",
"c319ca8ff31aa980a045f1b913fb2eb22aadb080"
] | [
"monai/transforms/spatial/array.py",
"monai/utils/misc.py"
] | [
"# Copyright (c) MONAI Consortium\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# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in... | [
[
"torch.linspace",
"numpy.linalg.solve",
"numpy.allclose",
"numpy.meshgrid",
"torch.zeros",
"numpy.asarray",
"torch.solve",
"numpy.eye",
"torch.eye",
"numpy.append",
"torch.linalg.solve",
"numpy.argsort",
"torch.flip",
"numpy.array",
"numpy.flip",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seitalab/compass | [
"b08b0b711875e8e049ff07793ffe1446a6c3f144"
] | [
"compass/embedding.py"
] | [
"from sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn.manifold import TSNE\nfrom sklearn.cluster import AgglomerativeClustering, KMeans\nfrom sklearn.preprocessing import MinMaxScaler\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport seaborn as sns\nimport pandas\nimport matplotlib.cm as c... | [
[
"matplotlib.pyplot.tight_layout",
"sklearn.cluster.KMeans",
"numpy.median",
"numpy.subtract",
"numpy.average",
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"numpy.transpose",
"sklearn.manifold.TSNE",
"matplotlib.pyplot.subplot",
"numpy.mean",
"matplotlib.pyplot.... | [
{
"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": []
}
] |
azawalich/flair | [
"f0101ab25381aefa586ecb688d4f412d5fab5de3"
] | [
"flair/trainers/language_model_trainer.py"
] | [
"\nimport time\nimport datetime\nimport random\nimport sys\nimport logging\nfrom pathlib import Path\nfrom typing import Union\nfrom torch import cuda\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.optim.sgd import SGD\ntry:\n from apex import amp\nexcept ImportError:\n amp = None\nimport flair... | [
[
"torch.utils.data.DataLoader",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mecanimatico/codigo_edp | [
"42080a4eb0f604873f9743ff0d0b8afde0735181"
] | [
"17abriledp.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 17 09:57:12 2020\n\n@author: Heber\n\"\"\"\nimport numpy as np\nimport pandas as pd\nimport os\nimport matplotlib.pyplot as plt\n#%% valor exacto d ela derivada\nup = np.cos(1.0)\n\nh = 0.1\nup_aprox = (np.sin(1+h)-np.sin(1))/h\nerror = up - up_aprox\n\nprint (\"... | [
[
"matplotlib.pyplot.loglog",
"numpy.cos",
"numpy.sin",
"matplotlib.pyplot.grid",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zfrenchee/pandas | [
"d28f9326de26882a9b4dc0bee9dec5c598747190",
"d28f9326de26882a9b4dc0bee9dec5c598747190",
"d28f9326de26882a9b4dc0bee9dec5c598747190"
] | [
"pandas/tests/indexes/test_category.py",
"pandas/tests/scalar/test_period.py",
"pandas/tests/generic/test_panel.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport pytest\n\nimport pandas.util.testing as tm\nfrom pandas.core.indexes.api import Index, CategoricalIndex\nfrom pandas.core.dtypes.dtypes import CategoricalDtype\nfrom .common import Base\n\nfrom pandas.compat import range, PY3\n\nimport numpy as np\n\nfrom pandas import Categorical... | [
[
"pandas.Series",
"pandas.util.testing.assert_index_equal",
"numpy.random.randint",
"pandas.IntervalIndex.from_arrays",
"pandas.util.testing.assert_numpy_array_equal",
"pandas.util.testing.assert_categorical_equal",
"pandas.compat.text_type",
"pandas.Index",
"pandas.DatetimeInde... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adammoody/Megatron-DeepSpeed | [
"972211163608818fe9e5ba821246f18d0a5dc264",
"972211163608818fe9e5ba821246f18d0a5dc264",
"972211163608818fe9e5ba821246f18d0a5dc264",
"972211163608818fe9e5ba821246f18d0a5dc264"
] | [
"megatron/checkpointing.py",
"megatron/data/biencoder_dataset_utils.py",
"megatron/optimizer/clip_grads.py",
"megatron/data/data_samplers.py"
] | [
"# coding=utf-8\n# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\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... | [
[
"torch.set_rng_state",
"numpy.random.get_state",
"torch.load",
"torch.distributed.get_rank",
"torch.distributed.is_initialized",
"torch.distributed.barrier",
"torch.get_rng_state",
"numpy.random.set_state",
"torch.cuda.get_rng_state",
"torch.cuda.set_rng_state",
"torch.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
tycallen/fast-reid | [
"66683fa95bc7d7222659e8db3ac04e5b8e366190",
"66683fa95bc7d7222659e8db3ac04e5b8e366190"
] | [
"fastreid/layers/cos_softmax.py",
"tools/deploy/onnx_export.py"
] | [
"# encoding: utf-8\n\"\"\"\n@author: xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.nn import Parameter\n\n\nclass CosSoftmax(nn.Module):\n r\"\"\"Implement of large margin cosine distance:\n Args:\n in_feat: s... | [
[
"torch.nn.functional.one_hot",
"torch.nn.functional.normalize",
"torch.Tensor",
"torch.nn.init.xavier_uniform_"
],
[
"torch.randn",
"torch.onnx.export",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JinYAnGHe/openvino_training_extensions | [
"a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee",
"a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee",
"a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee"
] | [
"pytorch_toolkit/face_recognition/model/blocks/mobilenet_v2_blocks.py",
"pytorch_toolkit/face_recognition/utils/read_tfboard.py",
"pytorch_toolkit/face_recognition/model/cnn6.py"
] | [
"\"\"\"\n Copyright (c) 2018 Intel Corporation\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 http://www.apache.org/licenses/LICENSE-2.0\n Unless required by applicable law or agr... | [
[
"torch.nn.PReLU",
"torch.nn.Conv2d",
"torch.nn.BatchNorm2d"
],
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
],
[
"torch.nn.MaxPool2d",
"torch.nn.Conv2d",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jaipradeesh/sagemaker-python-sdk | [
"ef842108ccaa324d2be15978aa678926dd1c21ea"
] | [
"tests/unit/test_amazon_estimator.py"
] | [
"# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#\n# or in th... | [
[
"numpy.array",
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
UmaTaru/run | [
"be29e4d41a4de3dee27cd6796801bfe51382d294",
"be29e4d41a4de3dee27cd6796801bfe51382d294",
"be29e4d41a4de3dee27cd6796801bfe51382d294",
"be29e4d41a4de3dee27cd6796801bfe51382d294",
"be29e4d41a4de3dee27cd6796801bfe51382d294",
"be29e4d41a4de3dee27cd6796801bfe51382d294",
"be29e4d41a4de3dee27cd6796801bfe51382d29... | [
"torchMoji/torchmoji/model_def.py",
"torch/autograd/_functions/blas.py",
"torch/legacy/nn/BCECriterion.py",
"ParlAI/parlai/agents/fairseq/fairseq.py",
"ParlAI/parlai/mturk/tasks/personachat/personachat_chat/worlds.py",
"ParlAI/parlai/core/image_featurizers.py",
"torch/tensor.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\" Model definition functions and weight loading.\n\"\"\"\n\nfrom __future__ import print_function, division, unicode_literals\n\nfrom os.path import exists\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.nn.utils.rnn import pack_padded_sequence,... | [
[
"torch.nn.Softmax",
"torch.nn.init.uniform",
"torch.nn.Dropout",
"torch.nn.Dropout2d",
"torch.load",
"torch.cat",
"torch.nn.Embedding",
"torch.nn.Tanh",
"torch.nn.Sigmoid",
"torch.nn.utils.rnn.PackedSequence",
"torch.nn.utils.rnn.pad_packed_sequence",
"torch.nn.Line... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
madokast/cctpy | [
"b02c64220ea533a4fc9cad0b882d1be6edadf1c0"
] | [
"cctpy6_r100_wn_change/run.py"
] | [
"# from visdom import Visdom\n\nfrom cctpy import *\nfrom ccpty_cuda import *\nimport time\nimport numpy as np\n\nVIZ_PORT = 8098\n\nga32 = GPU_ACCELERATOR()\n\nmomentum_dispersions = [-0.05, -0.025, 0.0, 0.025, 0.05]\nparticle_number_per_plane_per_dp = 12\n\nparticle_number_per_gantry = len(momentum_dispersions) *... | [
[
"numpy.concatenate",
"numpy.max",
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dl4amc/dds | [
"2d53c74ea1f1452beb2c1c52d3048e4260f22948"
] | [
"subsamplers/cldnn.py"
] | [
"# coding: utf-8\n\n# Import all the things we need ---\n#get_ipython().magic(u'matplotlib inline')\nimport os,random\n#os.environ[\"KERAS_BACKEND\"] = \"theano\"\nos.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n#os.environ[\"THEANO_FLAGS\"] = \"device=gpu%d\"%(1) #disabled because we do not have a hardware GPU\... | [
[
"numpy.random.seed",
"matplotlib.use",
"numpy.append",
"numpy.shape",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ofantomas/rlax | [
"58b3672b2f7ac1a400b3934ae9888c677f39b9e2",
"7bf3bf13d4496f1b708f4ccb5865215a16c618d6"
] | [
"rlax/_src/mpo_ops_test.py",
"examples/simple_dqn.py"
] | [
"# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.\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# Unle... | [
[
"numpy.ones_like",
"numpy.array",
"numpy.zeros_like",
"numpy.testing.assert_allclose"
],
[
"numpy.asarray",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wesmith/CSI-Camera | [
"8bcb7c58f3546dbe8c1c81054185d347056b4ff6"
] | [
"modules/ws_dual_camera.py"
] | [
"# ws_dual_camera.py\n# WSmith 12/23/20\n# utilize modified module ws_csi_camera for the camera class\n\nimport cv2\nimport numpy as np\nimport ws_csi_camera as ws\nfrom importlib import reload\n\nreload(ws) # ws is under development\n\ndef display(sensor_mode=ws.S_MODE_3_1280_720_60, \n dispW=ws.DISP_W... | [
[
"numpy.hstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MitchellAcoustics/MoSQITo | [
"15e45888d08b2932909f50fd6af0ef9d5595a588"
] | [
"mosqito/sq_metrics/tonality/tone_to_noise_ecma/_spectrum_smoothing.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 21 16:44:36 2020\n\n@author: wantysal\n\"\"\"\n# Standard library import\nimport numpy as np\n\n# Local import\nfrom mosqito.sound_level_meter.noct_spectrum._getFrequencies import _getFrequencies\n\n\ndef _spectrum_smoothing(freqs_in, spec, noct, low_freq, high_f... | [
[
"numpy.abs",
"numpy.delete",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maltius/tf_blazeface_training | [
"c4c73590f5084fcac56fa1625d227acf45a918ae",
"c4c73590f5084fcac56fa1625d227acf45a918ae"
] | [
"predictor.py",
"predictor_own.py"
] | [
"import tensorflow as tf\nfrom utils import bbox_utils, data_utils, drawing_utils, io_utils, train_utils, landmark_utils\nimport blazeface\n\nargs = io_utils.handle_args()\nif args.handle_gpu:\n io_utils.handle_gpu_compatibility()\n\nbatch_size = 1\nuse_custom_images = False\ncustom_image_path = \"data/images/\"... | [
[
"tensorflow.clip_by_value",
"tensorflow.reshape",
"tensorflow.cast"
],
[
"tensorflow.clip_by_value",
"tensorflow.cast",
"tensorflow.reshape",
"numpy.linalg.norm",
"numpy.mean",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1... |
rkiman/astropy | [
"99de28bc0dbfe2ee0bef95b67f5619e03d22cc06"
] | [
"astropy/io/misc/asdf/tags/unit/tests/test_quantity.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n# -*- coding: utf-8 -*-\n\nimport io\nimport pytest\n\nfrom astropy import units\n\nasdf = pytest.importorskip('asdf', minversion='2.0.0')\nfrom asdf.tests import helpers\n\n\ndef roundtrip_quantity(yaml, quantity):\n buff = helpers.yaml_to_asdf(y... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YannCabanes/geomstats | [
"ce3f4bab6cd59c2f071371a46e336086771d0493",
"ce3f4bab6cd59c2f071371a46e336086771d0493",
"ce3f4bab6cd59c2f071371a46e336086771d0493",
"ce3f4bab6cd59c2f071371a46e336086771d0493"
] | [
"tests/tests_geomstats/test_estimators.py",
"examples/learning_graph_embedding_and_predicting.py",
"geomstats/_backend/numpy/__init__.py",
"examples/plot_knn_s2.py"
] | [
"\"\"\"Template unit tests for scikit-learn estimators.\"\"\"\n\nimport pytest\nfrom sklearn.datasets import load_iris\n\nimport geomstats.backend as gs\nimport geomstats.tests\nfrom geomstats.learning._template import (\n TemplateClassifier,\n TemplateEstimator,\n TemplateTransformer,\n)\n\nESTIMATORS = (... | [
[
"sklearn.datasets.load_iris"
],
[
"matplotlib.pyplot.legend",
"matplotlib.patches.Patch",
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show",
"matplotlib.pyplot.tick_params"
],
[
"numpy.triu_indices",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
MuhammadEzzatHBK/CyclopeptideSequencing | [
"cd07045169758478b4845a54d5710bd329a836ca"
] | [
"test/testing.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun May 16 09:31:53 2021\n\n@author: Muhammad Ayman Ezzat \n Youmna Magdy Abdullah\n\"\"\"\nfrom algorithms import branch_and_bound\nimport timeit\nimport pandas as pd\n\n''' Accuracy Testing '''\nLabSpectrum = [97, 97, 99, 101, 103, 196, 198, 198, 200, 202, 295,... | [
[
"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": []
}
] |
Vizards8/pytorch-spine-segmentation | [
"588b7e7b09c5a370e337e2f12614df69d177ccaa"
] | [
"utils/metrics.py"
] | [
"import torch\nimport torch.nn as nn\nimport numpy as np\nimport math\nimport scipy.spatial\nimport scipy.ndimage.morphology\n\n\"\"\"\nTrue Positive （真正， TP）预测为正的正样本\nTrue Negative（真负 , TN）预测为负的负样本 \nFalse Positive （假正， FP）预测为正的负样本\nFalse Negative（假负 , FN）预测为负的正样本\n\"\"\"\n\n\ndef metrics(predict, label, out_class... | [
[
"torch.max",
"torch.Tensor",
"numpy.asarray",
"torch.sum",
"numpy.histogram",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AIM3-RUC/VideoIC | [
"ea324938e839a679324f42161d195f5bef3db26f"
] | [
"src/MML-CG/train.py"
] | [
"'''\n Re-organize the MMIG model\n 2021-09-20\n'''\n\nimport os\nimport sys\nimport time\nimport json\nimport logging\nimport argparse\n\nimport torch\nimport torch.optim as Optim\nfrom torch.autograd import Variable\n\nimport utils\nimport modules\nimport dataset\nimport metrics\n\n\n# set gpu\nos.environ[\... | [
[
"torch.mean",
"torch.cuda.manual_seed",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.autograd.Variable",
"torch.no_grad",
"torch.stack",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Caged/splineworks | [
"0fad1e98ba6928f6ffeef0018a4d52696a38cce2"
] | [
"sandworks/generators/splines.py"
] | [
"from numpy import pi\nfrom numpy import array\nfrom numpy import linspace\nfrom numpy import arange\nfrom numpy import zeros\nfrom numpy import column_stack\nfrom numpy import array\nfrom time import time\nfrom math import radians\n\nimport cairocffi as cairo\nfrom sand import Sand\nfrom ..lib.sand_spline import S... | [
[
"numpy.linspace",
"numpy.arange",
"numpy.column_stack",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
devchai123/Paddle-Lite | [
"eea59b66f61bb2acad471010c9526eeec43a15ca",
"442d6996a59c3498eae27610d49a0d5b2c320f24"
] | [
"lite/tests/unittest_py/op/test_layer_norm_op.py",
"lite/tests/unittest_py/op/common/test_elementwise_pow_op_base.py"
] | [
"# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.\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 re... | [
[
"numpy.random.random"
],
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
camponogaraviera/qutip | [
"1b1f6dffcb3ab97f11b8c6114293e09f378d2e8f",
"1b1f6dffcb3ab97f11b8c6114293e09f378d2e8f"
] | [
"qutip/cy/br_codegen.py",
"doc/guide/scripts/ex_steady.py"
] | [
"import os\nimport numpy as np\nimport qutip.settings as qset\nfrom qutip.interpolate import Cubic_Spline\n_cython_path = os.path.dirname(os.path.abspath(__file__)).replace(\"\\\\\", \"/\")\n_include_string = \"'\"+_cython_path+\"/complex_math.pxi'\"\n__all__ = ['BR_Codegen']\n\n\nclass BR_Codegen(object):\n \"\... | [
[
"numpy.array2string"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.axhline",
"numpy.sqrt",
"numpy.linspace",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yarikoptic/fitlins | [
"ee7e06330b9cdd5a9b812d51eb545daa84b0d066"
] | [
"fitlins/interfaces/bids.py"
] | [
"import os\nfrom functools import reduce\nfrom pathlib import Path\nfrom gzip import GzipFile\nimport json\nimport shutil\nimport numpy as np\nimport nibabel as nb\n\nfrom collections import defaultdict\n\nfrom nipype import logging\nfrom nipype.utils.filemanip import makedirs, copyfile\nfrom nipype.interfaces.base... | [
[
"numpy.isnan",
"numpy.nanmean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jtchilders/deephyper | [
"06f9653599757a69fa5720820f4de3a1f154b081",
"06f9653599757a69fa5720820f4de3a1f154b081"
] | [
"deephyper/search/nas/model/space/keras_search_space.py",
"deephyper/search/nas/baselines/her/ddpg.py"
] | [
"from collections.abc import Iterable\nfrom functools import reduce\n\nimport networkx as nx\nfrom tensorflow import keras\nfrom tensorflow.python.keras.utils.vis_utils import model_to_dot\n\nfrom deephyper.core.exceptions.nas.space import (InputShapeOfWrongType,\n No... | [
[
"tensorflow.python.keras.utils.vis_utils.model_to_dot",
"tensorflow.keras.Model",
"tensorflow.keras.layers.Input"
],
[
"numpy.random.randn",
"tensorflow.boolean_mask",
"numpy.clip",
"tensorflow.get_collection",
"tensorflow.stop_gradient",
"tensorflow.square",
"numpy.loa... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
... |
leon-liangwu/PillarsRNN | [
"b6e7d64af4e2819098ae9a87a9dd676ee8288874"
] | [
"display3d/msic.py"
] | [
"from __future__ import division, print_function\nimport numpy as np\n\nfrom shapely.geometry import Polygon\nimport cv2\n\nfrom collections import defaultdict\n\nfrom kitti import Calibration\n\n\ndef camera_to_lidar(points, r_rect, velo2cam):\n points_shape = list(points.shape[0:-1])\n if points.shape[-1] =... | [
[
"numpy.dot",
"numpy.pad",
"numpy.cos",
"numpy.stack",
"numpy.sin",
"numpy.concatenate",
"numpy.ones",
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cregouby/FARM | [
"552bc07acffbce4f1f84d926c040fdd17b4ddeb3"
] | [
"farm/file_utils.py"
] | [
"\"\"\"\nUtilities for working with the local dataset cache.\nThis file is adapted from the AllenNLP library at https://github.com/allenai/allennlp\nCopyright by the AllenNLP authors.\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport fnmatch\nimport json\nimport l... | [
[
"torch.hub._get_torch_home",
"numpy.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rubenrtorrado/NLP | [
"2ba6f153e428227fcf6f27080bdd0183d395ef64"
] | [
"alpha-zero-general_one_step/MCTS_Bleu.py"
] | [
"import math\nimport numpy as np\nEPS = 1e-8\n\nclass MCTS():\n \"\"\"\n This class handles the MCTS tree.\n \"\"\"\n\n def __init__(self, game, nnet, args):\n self.game = game\n self.nnet = nnet\n self.args = args\n self.Qsa = {} # stores Q values for s,a (as defined i... | [
[
"numpy.argmax",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
schr476/EXARL | [
"7f4596bd8b3d7960aaf52bc677ceac4f37029834",
"7f4596bd8b3d7960aaf52bc677ceac4f37029834"
] | [
"exarl/candlelib/uq_utils.py",
"exarl/agents/agent_vault/dqn.py"
] | [
"from __future__ import absolute_import\n\nimport numpy as np\nfrom scipy.stats import pearsonr, spearmanr\nfrom scipy import signal\nfrom scipy.interpolate import InterpolatedUnivariateSpline\n\n\ndef generate_index_distribution(numTrain, numTest, numValidation, params):\n \"\"\" Generates a vector of indices t... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.round",
"numpy.int",
"numpy.max",
"numpy.mean",
"scipy.stats.spearmanr",
"numpy.digitize",
"numpy.exp",
"scipy.signal.savgol_filter",
"scipy.interpolate.InterpolatedUnivariateSpline",
"numpy.arange",
"numpy.std",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.14",
"1.6",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": []
... |
ibenemerito88/openBF_workshop | [
"a63a6fbd1ef8528890fb1072730124e054875008"
] | [
"Workshop/Part3/part3_sol.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import integrate\nimport reslast\n\n\nplt.close(\"all\")\n\n# Symmetric network\nq,a,p,u,c,n,s = reslast.resu(\"network\")\n# Non-symmetric network\nqn,an,pn,un,cn,nn,sn = reslast.resu(\"networknonsym\")\n\n\n\n\n\nplt.show()"
] | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AsaphLightricks/3DDFA | [
"7630986c0286cd2c85b5dfd14ae6e8322e4ba605"
] | [
"utils/cython/setup.py"
] | [
"'''\npython setup.py build_ext -i\nto compile\n'''\n\n# setup.py\nfrom distutils.core import setup, Extension\n# from Cython.Build import cythonize\nfrom Cython.Distutils import build_ext\nimport numpy\n\nsetup(\n name='mesh_core_cython',\n cmdclass={'build_ext': build_ext},\n ext_modules=[Extension(\"mes... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
britta-wstnr/mne-python | [
"b69afd1ff3337ac84f219b26c53537a5c8ceb1b9",
"33146156f2660f122ecc04fa0d5b3fd3c34b549e",
"33146156f2660f122ecc04fa0d5b3fd3c34b549e",
"33146156f2660f122ecc04fa0d5b3fd3c34b549e",
"33146156f2660f122ecc04fa0d5b3fd3c34b549e"
] | [
"mne/io/pick.py",
"tutorials/plot_brainstorm_auditory.py",
"mne/io/proj.py",
"examples/visualization/plot_topo_compare_conditions.py",
"mne/preprocessing/tests/test_maxwell.py"
] | [
"# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>\n# Matti Hamalainen <msh@nmr.mgh.harvard.edu>\n# Martin Luessi <mluessi@nmr.mgh.harvard.edu>\n#\n# License: BSD (3-clause)\n\nfrom copy import deepcopy\nimport re\n\nimport numpy as np\n\nfrom .constants import FIFF\nfrom ..u... | [
[
"numpy.array",
"numpy.zeros",
"numpy.where",
"numpy.unique"
],
[
"pandas.concat",
"pandas.read_csv",
"numpy.abs",
"numpy.arange",
"pandas.DataFrame",
"numpy.std",
"numpy.diff",
"numpy.mean"
],
[
"numpy.dot",
"scipy.linalg.svd",
"numpy.unique",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
michaelyeah7/magics_mbrl | [
"7f1503986fd50c8336b8b9e7bb1d2f4be4e84b08",
"7f1503986fd50c8336b8b9e7bb1d2f4be4e84b08"
] | [
"gym-rbdl/gym_rbdl/envs/real_pendulum.py",
"gym-rbdl/gym_rbdl/envs/cartpole_jbdl.py"
] | [
"import gym\nfrom gym import spaces\nfrom gym.utils import seeding\nimport numpy as np\nfrom os import path\n\n\nclass PendulumEnv(gym.Env):\n metadata = {\"render.modes\": [\"human\", \"rgb_array\"], \"video.frames_per_second\": 30}\n\n def __init__(self, g=10.0):\n self.max_speed = 8\n self.ma... | [
[
"numpy.abs",
"numpy.clip",
"numpy.cos",
"numpy.sin",
"numpy.array"
],
[
"numpy.array",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZeroDesigner/quantum-gan | [
"76b12fe1be25ac2a5e75fdc472947a08d7065c50"
] | [
"utils.py"
] | [
"from sklearn.metrics import classification_report as sk_classification_report\nfrom sklearn.metrics import confusion_matrix\n\nimport pickle\nimport gzip\nfrom rdkit import DataStructs\nfrom rdkit import Chem\nfrom rdkit.Chem import QED\nfrom rdkit.Chem import Crippen\nfrom rdkit.Chem import AllChem\nfrom rdkit.Ch... | [
[
"numpy.ones_like",
"numpy.random.choice",
"numpy.argmax",
"numpy.mean",
"numpy.exp",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
akaszynski/keepa | [
"ffc35edc2f7a4601408b0f0a22a8856be88dcb3e"
] | [
"keepa/interface.py"
] | [
"\"\"\"Interface module to download Amazon product and history data from\nkeepa.com\n\"\"\"\n\nimport requests\nimport asyncio\nimport datetime\nimport json\nimport logging\nimport time\nfrom functools import wraps\n\nimport aiohttp\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\n\nfrom keepa.query... | [
[
"numpy.unique",
"numpy.asarray",
"pandas.DataFrame",
"numpy.datetime64",
"numpy.array"
]
] | [
{
"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": []
}
] |
se-hwan/MIT_Driverless | [
"05e416fb26f968300826f0deb0953be9afb22bfe"
] | [
"mpc/kmpc_casadi/utility/casadi-example_pack-v3.4.4/python/vdp_collocation2.py"
] | [
"#\n# This file is part of CasADi.\n#\n# CasADi -- A symbolic framework for dynamic optimization.\n# Copyright (C) 2010-2014 Joel Andersson, Joris Gillis, Moritz Diehl,\n# K.U. Leuven. All rights reserved.\n# Copyright (C) 2011-2014 Greg Horn\n#\n# CasADi is free soft... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"matplotlib.pyplot.title",
"matplotlib.pyplot.step",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"numpy.array",
"numpy.zeros",
"matplotlib... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ghiffaryr/grplot | [
"43ea08febac4ffecbce0a6a3d679850f5013aa28"
] | [
"grplot/features/plot/treemaps.py"
] | [
"# Squarified Treemap Layout\n# Implements algorithm from Bruls, Huizing, van Wijk, \"Squarified Treemaps\" and Laserson with some modifications\n# (but not using their pseudocode)\n\n\n# INTERNAL FUNCTIONS not meant to be used by the user\n\n\ndef pad_rectangle(rect):\n if rect[\"dx\"] > 2:\n rect[\"x\... | [
[
"matplotlib.pyplot.gca"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bkktimber/gluon-nlp | [
"205acce13a83b30eabd7a638e4773e7a4f91059a"
] | [
"tests/unittest/test_sampler.py"
] | [
"import pytest\nimport numpy as np\nfrom mxnet.gluon import data\nimport gluonnlp as nlp\nfrom gluonnlp.data import sampler as s\n\n\nN = 1000\ndef test_sorted_sampler():\n dataset = data.SimpleDataset([np.random.normal(0, 1, (np.random.randint(10, 100), 1, 1))\n for _ in range(N... | [
[
"numpy.arange",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jun2tong/bnp-anomaly | [
"c7fa106b5bb29ed6688a3d91e3f302a0a130b896",
"c7fa106b5bb29ed6688a3d91e3f302a0a130b896",
"c7fa106b5bb29ed6688a3d91e3f302a0a130b896",
"c7fa106b5bb29ed6688a3d91e3f302a0a130b896",
"c7fa106b5bb29ed6688a3d91e3f302a0a130b896",
"c7fa106b5bb29ed6688a3d91e3f302a0a130b896",
"c7fa106b5bb29ed6688a3d91e3f302a0a130b89... | [
"tests/endtoend/TestRealRandomGroupXData.py",
"tests/zzz_deprecated_unmaintained/allocmodel/hmm/TestHMMMergeConstructGlobals.py",
"tests/suffstats/TestSuffStatBag.py",
"bnpy/datasets/zzz_unsupported/Monks.py",
"bnpy/obsmodel/GaussRegressYFromFixedXObsModel.py",
"bnpy/callbacks/InferHeldoutTopics.py",
"b... | [
"import numpy as np\nimport unittest\nfrom collections import OrderedDict\n\nimport bnpy\nfrom AbstractEndToEndTest import AbstractEndToEndTest\n\n\nclass TestEndToEnd(AbstractEndToEndTest):\n __test__ = True\n\n def setUp(self):\n \"\"\" Create the dataset\n \"\"\"\n rng = np.random.Rand... | [
[
"numpy.random.RandomState"
],
[
"numpy.asarray",
"numpy.all",
"numpy.zeros",
"numpy.allclose"
],
[
"numpy.all",
"numpy.isnan",
"numpy.allclose",
"numpy.ones"
],
[
"matplotlib.pylab.show",
"numpy.asarray",
"matplotlib.pylab.title",
"matplotlib.pylab.i... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
BBN-E/nlplingo | [
"32ff17b1320937faa3d3ebe727032f4b3e7a353d",
"32ff17b1320937faa3d3ebe727032f4b3e7a353d",
"32ff17b1320937faa3d3ebe727032f4b3e7a353d"
] | [
"nlplingo/nn/extractor.py",
"nlplingo/nn/keras_models/model/base_model.py",
"nlplingo/nn/pytorch_models/model/softmax_nn.py"
] | [
"import codecs\nimport json\nimport os\n\nimport numpy as np\nfrom nlplingo.nn.sequence_model import SequenceXLMRBase, SequenceXLMRCustom\nfrom nlplingo.nn.spanpair_model import SpanPairModelEmbedded\nfrom nlplingo.tasks.entitycoref.feature import EntityCorefFeatureGenerator\nfrom nlplingo.tasks.entitycoref.generat... | [
[
"numpy.asarray"
],
[
"numpy.log",
"numpy.asarray",
"numpy.ones",
"numpy.argmax",
"numpy.sum"
],
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.max",
"torch.zeros",
"torch.cat",
"torch.nn.Linear",
"torch.cuda.is_available",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jacobbieker/NUR_Handin2 | [
"6e620b23191edaec4452d29eac90ec37ced0c038"
] | [
"one.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom one_a import one_a\nfrom one_b import one_b\nfrom one_c import one_c\nfrom one_d import one_d\nfrom one_e import one_e\n\n\ndef random_generator(seed, m=2 ** 64 - 1, a=2349543, c=913842, a1=21, a2=35, a3=4, a4=4294957665):\n \"\"\"\n Generates psu... | [
[
"matplotlib.pyplot.cla"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
manish-pra/trcopo | [
"df8730f07ef554970c7a0aa653cc42d4886948ec",
"df8730f07ef554970c7a0aa653cc42d4886948ec"
] | [
"others/maddpg/utils/noise.py",
"trcopo_optim/trcopo.py"
] | [
"import numpy as np\n\n\n# from https://github.com/songrotek/DDPG/blob/master/ou_noise.py\nclass OUNoise:\n def __init__(self, action_dimension, scale=0.1, mu=0, theta=0.15, sigma=1):#sigma=0.2\n self.action_dimension = action_dimension\n self.scale = scale\n self.mu = mu\n self.theta... | [
[
"numpy.ones"
],
[
"torch.mean",
"torch.abs",
"torch.norm",
"torch.cat",
"torch.min",
"torch.FloatTensor",
"torch.device",
"torch.autograd.grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cyc/estimator | [
"742a07296c8f584150bb02f97be7207130ded5fd",
"742a07296c8f584150bb02f97be7207130ded5fd",
"742a07296c8f584150bb02f97be7207130ded5fd",
"742a07296c8f584150bb02f97be7207130ded5fd",
"742a07296c8f584150bb02f97be7207130ded5fd"
] | [
"tensorflow_estimator/python/estimator/tpu/tpu_estimator_signals_test.py",
"tensorflow_estimator/contrib/estimator/python/estimator/rnn_v2_test.py",
"tensorflow_estimator/python/estimator/early_stopping.py",
"tensorflow_estimator/python/estimator/gc_test.py",
"tensorflow_estimator/python/estimator/canned/dn... | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\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 requ... | [
[
"numpy.linspace",
"tensorflow.python.data.ops.dataset_ops.Dataset.from_tensor_slices",
"tensorflow.python.framework.ops.Graph",
"tensorflow.python.data.ops.dataset_ops.make_one_shot_iterator",
"tensorflow.python.data.ops.dataset_ops.Dataset.zip",
"tensorflow.python.client.session.Session",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
... |
kavigupta/program_synthesis | [
"0b04b1d3b63954ba3d404a8d96c4da18667a1b02",
"0b04b1d3b63954ba3d404a8d96c4da18667a1b02"
] | [
"program_synthesis/algolisp/dataset/evaluation.py",
"program_synthesis/algolisp/dataset/dataset.py"
] | [
"import numpy as np\n\nfrom program_synthesis.algolisp.tools import bleu\nfrom program_synthesis.algolisp.dataset import executor\n\n\ndef is_same_code(example, res):\n correct = False\n if hasattr(res, 'code_sequence'):\n if res.code_sequence is not None:\n correct = res.code_sequence == ex... | [
[
"numpy.asscalar"
],
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oliverkinch/dtu_mlops | [
"ce3a1f8f02ee95105b7b907735c39ad082321a4b"
] | [
"s2_organisation_and_version_control/exercise_files/typing_exercise_solution.py"
] | [
"from typing import Callable, Tuple, Union, Optional, List\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\n\nclass Network(nn.Module):\n def __init__(self, input_size: int, output_size: int, hidden_layers: List[int], drop_p: float = 0.5) -> None:\n ''' Builds a feedforward network ... | [
[
"torch.nn.Dropout",
"torch.nn.functional.log_softmax",
"torch.exp",
"torch.nn.Linear",
"torch.no_grad",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yuyiming/mars | [
"5e6990d1ea022444dd646c56697e596ef5d7e747",
"5e6990d1ea022444dd646c56697e596ef5d7e747"
] | [
"mars/services/subtask/tests/test_service.py",
"mars/dataframe/datasource/from_records.py"
] | [
"# Copyright 1999-2021 Alibaba Group Holding Ltd.\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 appl... | [
[
"numpy.testing.assert_array_equal",
"numpy.ones"
],
[
"pandas.RangeIndex",
"pandas.Index",
"numpy.cumsum",
"numpy.dtype",
"pandas.DataFrame.from_records"
]
] | [
{
"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",
"... |
emorynlp/stem-cell-hypothesis | [
"48a628093d93d653865fbac6409d179cddd99293",
"48a628093d93d653865fbac6409d179cddd99293",
"48a628093d93d653865fbac6409d179cddd99293"
] | [
"elit/components/srl/span_rank/span_ranking_srl_model.py",
"stem_cell_hypothesis/en_electra_base/head/vis/srl.py",
"stem_cell_hypothesis/en_bert_base/head/dep.py"
] | [
"from typing import Dict\n\nfrom alnlp.modules.feedforward import FeedForward\nfrom alnlp.modules.time_distributed import TimeDistributed\n\nfrom .highway_variational_lstm import *\nimport torch\nfrom alnlp.modules import util\n\nfrom ...parsers.biaffine.biaffine import Biaffine\n\n\ndef initializer_1d(input_tensor... | [
[
"torch.mean",
"torch.Size",
"torch.ones",
"torch.max",
"torch.cat",
"torch.zeros",
"torch.nn.functional.cross_entropy",
"torch.gather",
"torch.zeros_like",
"torch.arange",
"torch.stack",
"torch.clamp",
"torch.cumsum"
],
[
"matplotlib.pyplot.legend",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
artyompal/kaggle_salt | [
"3c323755730745ac7bbfd106f1f20919cceef0ee",
"3c323755730745ac7bbfd106f1f20919cceef0ee",
"3c323755730745ac7bbfd106f1f20919cceef0ee"
] | [
"code_gazay/lenin/lenin/transforms.py",
"code_artyom/resnet50_classifier_01.py",
"code_florian/scripts/make_preds.py"
] | [
"import numpy as np\n\ndef hwc_to_chw(image):\n return np.einsum('hwc->chw', image) # change to pytorch format\n",
"#!/usr/bin/python3.6\n\n# Input data files are available in the \"../data/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input... | [
[
"numpy.einsum"
],
[
"pandas.read_csv",
"numpy.linspace",
"numpy.unique",
"numpy.fliplr",
"sklearn.model_selection.StratifiedKFold",
"numpy.concatenate",
"numpy.ceil",
"numpy.zeros_like",
"numpy.array"
],
[
"numpy.amax",
"numpy.expand_dims",
"numpy.linspa... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
miniTsl/IC3Net | [
"897ed3bae6ad5f65fb3cc4577d4392af6e456703"
] | [
"ic3net_envs/ic3net_envs/predator_prey_env.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nSimulate a predator prey environment.\nEach agent can just observe itself (it's own identity) i.e. s_j = j and vision sqaure around it.\n\nDesign Decisions:\n - Memory cheaper than time (compute)\n - Using Vocab for class of box:\n -1 out of bou... | [
[
"numpy.unravel_index",
"numpy.pad",
"numpy.arange",
"numpy.stack",
"numpy.full",
"numpy.atleast_1d",
"numpy.all",
"numpy.prod",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
antoyang/TubeDETR | [
"3c32cc92a0fdaa0c770d95a59d8764e0e212424c"
] | [
"util/box_ops.py"
] | [
"# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\nimport torch\nimport numpy as np\nfrom torchvision.ops.boxes impo... | [
[
"numpy.maximum",
"torch.max",
"numpy.minimum",
"torch.zeros",
"torch.min",
"torch.arange",
"torch.stack",
"torch.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kejsitake/sktime | [
"5c608f09ce0f5216677ce9f6ad61d71584211db9"
] | [
"sktime/contrib/vector_classifiers/_rotation_forest.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"RotationForest vector classifier.\n\nRotation Forest, sktime implementation for continuous values only.\n\"\"\"\n\n__author__ = [\"MatthewMiddlehurst\"]\n__all__ = [\"RotationForest\"]\n\nimport time\n\nimport numpy as np\nfrom joblib import Parallel, delayed\nfrom sklearn.base impor... | [
[
"numpy.sum",
"sklearn.utils.check_X_y",
"numpy.unique",
"numpy.reshape",
"numpy.arange",
"numpy.isnan",
"numpy.nan_to_num",
"numpy.ones",
"numpy.all",
"numpy.concatenate",
"sklearn.tree.DecisionTreeClassifier",
"numpy.where",
"numpy.iinfo",
"numpy.errstate",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Benjamin15/shap | [
"4b6472c90c89aad403e00dff0cc8a6416f354fea"
] | [
"shap/plots/dependence.py"
] | [
"from __future__ import division\n\nfrom io import BytesIO\nimport base64\nimport numpy as np\nimport warnings\ntry:\n import matplotlib.pyplot as pl\n import matplotlib\nexcept ImportError:\n warnings.warn(\"matplotlib could not be loaded!\")\n pass\nfrom . import labels\nfrom . import colors\nfrom ..c... | [
[
"numpy.nanmax",
"matplotlib.colors.BoundaryNorm",
"numpy.invert",
"numpy.unique",
"numpy.isnan",
"numpy.arange",
"numpy.nanmin",
"numpy.random.shuffle",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.colorbar",
"numpy.diff",
"matplotlib.pyplot.show",
"matplotli... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Awesomex005/CarND-Vehicle-Detection | [
"e12068887946605d148284aeea0262695d54743f"
] | [
"train_classifier.py"
] | [
"import matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport cv2\nimport glob\nimport time\nfrom sklearn.svm import LinearSVC\nfrom sklearn.preprocessing import StandardScaler\nfrom extract_feature import *\n# NOTE: the next import is only valid for scikit-learn version <= 0.17\n# ... | [
[
"sklearn.cross_validation.train_test_split",
"sklearn.svm.LinearSVC",
"sklearn.preprocessing.StandardScaler",
"numpy.vstack",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DannySalem/chemprop | [
"f99cea2c08f54640ccd8ad3851a93f47badc72dd"
] | [
"chemprop/models/FFNetwork.py"
] | [
"import torch.nn as nn\nfrom chemprop.nn_utils import get_activation_function\nfrom chemprop.args import TrainArgs\n\n\ndef create_ffn(output_size: int, input_size: int, args: TrainArgs):\n \"\"\"\n Creates the feed-forward layers for the model.\n\n :param args: A :class:`~chemprop.args.TrainArgs` object c... | [
[
"torch.nn.Linear",
"torch.nn.Sequential",
"torch.nn.Dropout"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lucasdornelles/2DBESO | [
"b92a42346ed4945a3668a3277d67ef412e200cbb"
] | [
"BESO.py"
] | [
"import numpy as np\r\nfrom FEM import get_element_dof\r\nfrom tqdm import tqdm\r\nfrom scipy.spatial.distance import pdist\r\n\r\n\r\ndef get_elements_sensibilities(local_matrix, minimum_density, elements_density,\r\n displacements, penalty, connectivity, nodes_dof):\r\n\r\n # calc... | [
[
"numpy.asmatrix",
"numpy.array",
"scipy.spatial.distance.pdist"
]
] | [
{
"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"
... |
ZJCV/PyCls | [
"1ef59301646b6134f2ffcc009b4fd76550fa4089",
"1ef59301646b6134f2ffcc009b4fd76550fa4089"
] | [
"tests/test_model/test_recognizer/test_sknet.py",
"zcls/model/layers/split_attention_conv2d.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\n@date: 2020/11/21 下午4:16\n@file: test_resnest.py\n@author: zj\n@description: \n\"\"\"\n\nimport torch\n\nfrom zcls.config import cfg\nfrom zcls.config.key_word import KEY_OUTPUT\nfrom zcls.model.recognizers.resnet.resnet import ResNet\n\n\ndef test_data(model, input_shape, output... | [
[
"torch.randn"
],
[
"torch.nn.functional.softmax",
"torch.sigmoid",
"torch.split",
"torch.nn.Conv2d",
"torch.sum",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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