repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
lefthandedroo/Cosmodels | [
"c355d18021467cf92546cf2fc9cb1d1abe59b8d8"
] | [
"History/Stats/emcee_ex_4_merging.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nchose m, c (parameters) for a straight line\nfrom the line pick N points (N=3, 5, 50, 100, 1000)\npick sigma (size of the noise)\nrandomly deviate(offset) points in y direction by using \nsigma*random number from normal distribution\nsigma the same for all p... | [
[
"matplotlib.pyplot.legend",
"numpy.log",
"numpy.linspace",
"numpy.isfinite",
"numpy.sin",
"matplotlib.pyplot.plot",
"numpy.random.normal",
"numpy.argmax",
"scipy.optimize.minimize",
"matplotlib.pyplot.errorbar",
"numpy.random.rand",
"numpy.mean",
"numpy.random.r... | [
{
"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"
... |
Pascal-Bliem/tox-block | [
"fed3d54553a0911d190e421feafddb11969878cd"
] | [
"tox_block/model/lstm_multi_label.py"
] | [
"\"\"\"A bidirectional LSTM model with multi labels (6 types of toxicity)\"\"\"\n\n# general data handling and computation\nimport pandas as pd\nimport numpy as np\n# TensorFlow / Keras\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dense, Embedding, Input\nfrom tensorflow.keras.lay... | [
[
"tensorflow.keras.callbacks.ModelCheckpoint",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Embedding",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.callbacks.ReduceLROnPlateau",
"tensorflow.keras.optimizers.Adam",
"tensorflow.ke... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
frgfm/torch-zoo | [
"c97beacf3d49eaa34398abf47f378ea6b48a70f3",
"c97beacf3d49eaa34398abf47f378ea6b48a70f3",
"c97beacf3d49eaa34398abf47f378ea6b48a70f3"
] | [
"holocron/nn/modules/conv.py",
"holocron/models/classification/darknetv4.py",
"references/clean_checkpoint.py"
] | [
"# Copyright (C) 2019-2022, François-Guillaume Fernandez.\n\n# This program is licensed under the Apache License version 2.\n# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.\n\nimport math\nfrom typing import Any, List, Optional\n\nimport torch\nimport torch.nn as n... | [
[
"torch.sigmoid",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.Unfold",
"torch.relu",
"torch.nn.AvgPool2d",
"torch.nn.modules.utils._pair",
"torch.nn.functional.pad"
],
[
"torch.nn.Linear",
"torch.nn.Mish",
"torch.nn.LeakyReLU"
],
[
"torch.load",
"torch.save... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chnsh/deep-semantic-code-search | [
"57cf12b90b5ec3a49bd6c04cf2b68888162558b3",
"57cf12b90b5ec3a49bd6c04cf2b68888162558b3"
] | [
"code_summarization_transfer_learning/fastai/courses/dl1/fastai/imports.py",
"code_summarization_transfer_learning/fastai/courses/dl1/fastai/dataloader.py"
] | [
"from IPython.lib.deepreload import reload as dreload\nimport PIL, os, numpy as np, threading, json, bcolz, scipy\nimport pandas as pd, pickle, string, sys, re, time, shutil, copy\nimport seaborn as sns, matplotlib\nfrom abc import abstractmethod\nfrom functools import partial\nfrom pandas_summary import DataFrameS... | [
[
"numpy.set_printoptions",
"matplotlib.rc"
],
[
"torch.utils.data.sampler.SequentialSampler",
"torch.utils.data.sampler.RandomSampler",
"torch.utils.data.sampler.BatchSampler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SCUT-AILab/BPAI-Net | [
"d71c92366222c9e226e15f8263fc2d72361735c3"
] | [
"ops/models.py"
] | [
"# Code for \"TSM: Temporal Shift Module for Efficient Video Understanding\"\n# arXiv:1811.08383\n# Ji Lin*, Chuang Gan, Song Han\n# {jilin, songhan}@mit.edu, ganchuang@csail.mit.edu\n\nfrom torch import nn\nfrom ops.basic_ops import ConsensusModule\nfrom ops.transforms import *\nfrom torch.nn.init import normal_, ... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.init.constant_",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.init.normal_",
"torch.utils.model_zoo.load_url"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
csingh27sewts/rlpyt | [
"4252eb63515c9e68c0674fb010d2c6dbfdac9122"
] | [
"rlpyt/envs/dm_control_env.py"
] | [
"from dm_control import suite\nfrom dm_control.suite.wrappers import pixels\nfrom dm_env.specs import Array, BoundedArray\n\nimport numpy as np\nimport os\nimport atari_py\nimport cv2\nimport copy\nfrom collections import namedtuple, OrderedDict\nfrom rlpyt.utils.collections import namedarraytuple\n\nfrom rlpyt.env... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MUST-AI-Lab/NAS-Projects | [
"1ce3249a5a58af3506b8c9af977008ddf8198445",
"1ce3249a5a58af3506b8c9af977008ddf8198445",
"fcb2aae34a2b3c02877fbdb41cda45e1e73327a6",
"fcb2aae34a2b3c02877fbdb41cda45e1e73327a6"
] | [
"exps/NAS-Bench-201/statistics.py",
"lib/models/cell_infers/tiny_network.py",
"exps/algos/DARTS-V2.py",
"lib/nas_201_api/api.py"
] | [
"##################################################\n# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #\n##################################################\nimport os, sys, time, argparse, collections\nfrom copy import deepcopy\nimport torch\nimport torch.nn as nn\nfrom pathlib import Path\nfrom collections import ... | [
[
"torch.load",
"torch.utils.data.DataLoader",
"torch.utils.data.sampler.SubsetRandomSampler",
"torch.set_num_threads",
"torch.save"
],
[
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
rtachi-lab/Human-Cochlear-Model | [
"6584de225176d8d1b2be96939acb7ef7d3f64774"
] | [
"CochlearModel_2D_Direct.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport tqdm\nimport wavfile\n\nclass CochlearModel:\n \"\"\"\n Two-dimensional cochlear model with two-degree-of-freedom\n (2DOF) micro-structure [1] for human. This program employs \n time domain solution proposed in Ref. [2], and for fast calcurati... | [
[
"numpy.dot",
"numpy.cosh",
"numpy.linspace",
"matplotlib.pyplot.title",
"numpy.linalg.inv",
"numpy.arange",
"numpy.eye",
"numpy.ones",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"numpy.tanh",
"numpy.exp",
"numpy.zeros",
"matplotlib.pyplot.show",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ivanwilliammd/32images_hdf5converter | [
"2956c163b790d1fc1c3248e46d17894dde52eeb9"
] | [
"Upsampled_4x/HDF5_converter_0.5.py"
] | [
"import glob\r\nimport os\r\nimport cv2\r\nimport numpy as np\r\nimport h5py\r\nimport IPython \r\nimport pandas as pd\r\nimport csv \r\n\r\ndf = pd.read_csv('lung_annotation_raw_Final.csv')\r\ndf = df[['ACC','TIPE','Xmin','Ymin','Xmax','Ymax','Zt_minsplitnum','Zt_minsplit_rev','Zt_maxsplitnum','Zt_maxsplit_rev','b... | [
[
"numpy.array",
"pandas.read_csv",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
theendsofinvention/cartoonify | [
"39ea84d96b3e93f0480e6d6158bea506d01278ca",
"39ea84d96b3e93f0480e6d6158bea506d01278ca",
"39ea84d96b3e93f0480e6d6158bea506d01278ca"
] | [
"cartoonify/app/object_detection/core/box_predictor.py",
"cartoonify/app/object_detection/core/region_similarity_calculator.py",
"cartoonify/app/object_detection/core/keypoint_ops_test.py"
] | [
"# 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... | [
[
"tensorflow.image.resize_bilinear",
"tensorflow.transpose",
"tensorflow.range",
"tensorflow.shape",
"tensorflow.reduce_mean",
"tensorflow.stack",
"tensorflow.reshape",
"tensorflow.sigmoid",
"tensorflow.ones",
"tensorflow.squeeze",
"tensorflow.constant_initializer",
... | [
{
"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... |
afeedh/facenet | [
"a70159a7c9850a49acd789824273b9b8933a61e8"
] | [
"facenet/src/train_tripletloss.py"
] | [
"\"\"\"Training a face recognizer with TensorFlow based on the FaceNet paper\nFaceNet: A Unified Embedding for Face Recognition and Clustering: http://arxiv.org/abs/1503.03832\n\"\"\"\n# MIT License\n#\n# Copyright (c) 2016 David Sandberg\n#\n# Permission is hereby granted, free of charge, to any person obtaining a... | [
[
"tensorflow.global_variables",
"numpy.all",
"tensorflow.GPUOptions",
"numpy.mean",
"tensorflow.image.decode_image",
"tensorflow.summary.scalar",
"tensorflow.add_n",
"numpy.where",
"numpy.random.randint",
"numpy.square",
"tensorflow.Graph",
"tensorflow.image.random_f... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
preylol/pbrt-v3 | [
"29661bf4caf9e2df2bf21f2a28ac8e53b2439f9f"
] | [
"evaluation/scripts/python-scripts/exr2png.py"
] | [
"import os\nimport sys\nimport pyexr\nimport numpy as np\nfrom PIL import Image\nimport re\n \ndef exec():\n filepaths = []\n savepaths = []\n images = []\n maxvalues = []\n # Prep variable\n filelist = os.listdir(\"output\")\n for file in filelist:\n if file.endswith(\".exr\"):\n... | [
[
"numpy.max",
"numpy.where",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DVM000/keras-yolo3 | [
"ef0baf2ce19b8b637e5535d04ead48020caa06c5"
] | [
"train.py"
] | [
"#! /usr/bin/env python\n\nimport argparse\nimport os\nimport numpy as np\nimport json\nfrom voc import parse_voc_annotation\nfrom yolo import create_yolov3_model, dummy_loss\nfrom generator import BatchGenerator\nfrom utils.utils import normalize, evaluate, makedirs\nfrom tensorflow.keras.callbacks import EarlySto... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.device",
"tensorflow.compat.v1.GPUOptions",
"numpy.random.seed",
"tensorflow.compat.v1.keras.backend.set_session",
"tensorflow.keras.callbacks.ReduceLROnPlateau",
"tensorflow.compat.v1.keras.backend.get_session",
"numpy.random.shuff... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
KiryanovKD/models | [
"e17080247e3c9b3301680f61b8f4815c22509e7e",
"e17080247e3c9b3301680f61b8f4815c22509e7e",
"e17080247e3c9b3301680f61b8f4815c22509e7e",
"e17080247e3c9b3301680f61b8f4815c22509e7e",
"e17080247e3c9b3301680f61b8f4815c22509e7e",
"e17080247e3c9b3301680f61b8f4815c22509e7e",
"e17080247e3c9b3301680f61b8f4815c22509e7... | [
"official/nlp/modeling/networks/albert_encoder.py",
"official/legacy/image_classification/mnist_test.py",
"official/legacy/detection/modeling/architecture/spinenet.py",
"official/nlp/transformer/transformer_main_test.py",
"official/legacy/detection/evaluation/coco_utils.py",
"official/vision/utils/object_... | [
"# Copyright 2021 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.keras.layers.LayerNormalization",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.activations.serialize",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.initializers.serialize",
"tensorflow.keras.utils.register_keras_serializable",
"tensorflow.keras.layers.Add",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.4",
"2.3",
"2.5",
"2.6"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": []... |
dataiku-research/paper_ial_2021 | [
"f860b6eb2d8471bc23e44d282e50c4deaf0813d9",
"f860b6eb2d8471bc23e44d282e50c4deaf0813d9"
] | [
"exp/experimenter.py",
"exp/run.py"
] | [
"import json\nfrom importlib import import_module\nimport pandas as pd\nfrom pandas.errors import EmptyDataError\nimport numpy as np\nimport pickle\nimport shutil\nimport time\nfrom pathlib import Path\nfrom collections import defaultdict\n\n\nclass CsvValue:\n\n def __init__(self, path):\n self.path = pa... | [
[
"pandas.read_csv",
"pandas.DataFrame"
],
[
"numpy.arange",
"numpy.argmax",
"numpy.cumsum",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seo-dev/cvml_project | [
"7c95ce22db6f31dc4624af9417edffde021b5351"
] | [
"segmentation/eval.py"
] | [
"import os\nfrom segmentation.cityscape_reader import CityscapesDemoDataset\nimport tensorflow as tf\nimport argparse\nimport numpy as np\nimport cv2\n\nfrom segmentation.labels import cityscapes_mask_colors\nfrom segmentation.model import DeeplabV3\n\nparser = argparse.ArgumentParser(description=\"Cityscapes\")\np... | [
[
"tensorflow.train.CheckpointManager",
"tensorflow.argmax",
"tensorflow.squeeze",
"tensorflow.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
berquist/eg | [
"4c368b12eaaffcf0af8032f10348cf8bc1c3957a"
] | [
"python/keras/masking.py"
] | [
"from pprint import pprint\n\nimport numpy as np\n\nfrom keras.models import Model\nfrom keras.layers import Activation, Dense, Input, Masking, TimeDistributed\n\n\nif __name__ == \"__main__\":\n\n inp = Input(shape=(3, 6))\n mask = Masking(mask_value=0.1)(inp)\n out = TimeDistributed(Dense(1, activation=\... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DmitryUlyanov/deeppy | [
"79cc7cb552f30bc70eeea9ee7ff4976b0899ea66"
] | [
"examples/siamese_mnist.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nSiamese networks\n================\n\n\"\"\"\n\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import offsetbox\nimport deeppy as dp\n\n# Fetch MNIST data\ndataset = dp.dataset.MNIST()\nx_train, y_train, x_test, y_test = dataset.data(flat=True, ... | [
[
"matplotlib.offsetbox.OffsetImage",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.title",
"numpy.min",
"numpy.reshape",
"numpy.empty_like",
"numpy.max",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xticks",
"numpy.array",
"numpy.sum",
"numpy.empty",
"matplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lucas-sancere/DRFNS | [
"a35e01d516e9b491c09eaca6701e7e0fe9e56880"
] | [
"src_RealData/Data/CreateTFRecords.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport tensorflow as tf\nfrom DataGenRandomT import DataGenRandomT\nfrom DataGenClass import DataGen3, DataGenMulti, DataGen3reduce\nimport numpy as np\n\ndef _bytes_feature(value):\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\ndef _... | [
[
"tensorflow.io.TFRecordWriter",
"tensorflow.train.BytesList",
"tensorflow.train.Int64List"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shreyasvj25/turicreate | [
"dd210c2563930881abd51fd69cb73007955b33fd",
"dd210c2563930881abd51fd69cb73007955b33fd",
"32e84ca16aef8d04aff3d49ae9984bd49326bffd"
] | [
"src/unity/python/turicreate/test/test_graph.py",
"src/external/xgboost/python-package/xgboost/training.py",
"src/unity/python/turicreate/toolkits/activity_classifier/_activity_classifier.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright © 2017 Apple Inc. All rights reserved.\n#\n# Use of this source code is governed by a BSD-3-clause license that can\n# be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause\nfrom __future__ import print_function as _\nfrom __future__ import divisio... | [
[
"pandas.DataFrame.from_records",
"pandas.util.testing.assert_frame_equal",
"pandas.DataFrame"
],
[
"pandas.Series",
"numpy.random.seed",
"pandas.DataFrame",
"numpy.std",
"numpy.mean",
"numpy.array"
],
[
"numpy.concatenate",
"numpy.argsort",
"numpy.expand_dim... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"... |
anuragreddygv323/raster-vision | [
"14a6495f23bbef0bf7f7c47fb37b856a559b272f",
"db2bc35f21968618a333cee2f5e86f29e7d56483"
] | [
"src/rastervision/semseg/tasks/utils.py",
"src/detection/scripts/aggregate_predictions.py"
] | [
"\"\"\"Utility functions shared across tasks.\"\"\"\nimport numpy as np\nimport matplotlib as mpl\n# For headless environments\nmpl.use('Agg') # NOQA\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\n\nfrom rastervision.common.utils import plot_img_row\n\n\ndef predict_x(x, model):\n batch... | [
[
"matplotlib.pyplot.legend",
"matplotlib.patches.Patch",
"numpy.expand_dims",
"numpy.pad",
"matplotlib.use",
"numpy.squeeze",
"matplotlib.pyplot.savefig",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.close",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.figu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",... |
yuyunli2/faster_rcnn | [
"c8ddaa02fdc8ca36438713f2584d83dbbfae9ed9"
] | [
"vis_tool.py"
] | [
"import time\n\nimport numpy as np\nimport matplotlib\nimport torch as t\nimport visdom\n\nmatplotlib.use('Agg')\nfrom matplotlib import pyplot as plot\n\n\nVOC_BBOX_LABEL_NAMES = (\n 'fly',\n 'bike',\n 'bird',\n 'boat',\n 'pin',\n 'bus',\n 'c',\n 'cat',\n 'chair',\n 'cow',\n 'table... | [
[
"matplotlib.pyplot.Rectangle",
"torch.Tensor",
"matplotlib.use",
"matplotlib.pyplot.close",
"numpy.array",
"numpy.roll",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
deeptimittal12/python-neo | [
"7409f47b5debd4d2a75bbf0e77ac10562446c97a",
"7409f47b5debd4d2a75bbf0e77ac10562446c97a"
] | [
"neo/io/brainwaredamio.py",
"neo/rawio/spike2rawio.py"
] | [
"'''\nClass for reading from Brainware DAM files\n\nDAM files are binary files for holding raw data. They are broken up into\nsequence of Segments, each containing a single raw trace and parameters.\n\nThe DAM file does NOT contain a sampling rate, nor can it be reliably\ncalculated from any of the parameters. Yo... | [
[
"numpy.array",
"numpy.fromfile"
],
[
"numpy.nonzero",
"numpy.unique",
"numpy.arange",
"numpy.memmap",
"numpy.cumsum",
"numpy.dtype",
"numpy.all",
"numpy.concatenate",
"numpy.searchsorted",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LeonardoSirino/FuzzyTableExtractor | [
"114f5b2b1c65bfcaa84cb75c876b68ce1974c821"
] | [
"fuzzy_table_extractor/extractor.py"
] | [
"from collections import deque\nfrom dataclasses import dataclass\nfrom enum import Enum, auto\nfrom typing import Callable, Iterable, List\n\nimport numpy as np\nimport pandas as pd\n\nfrom .handlers.base_handler import BaseHandler, BaseNode, TreeFileHandler\nfrom .util import match_regex_list, str_comparison\n\n\... | [
[
"numpy.max",
"numpy.argmax",
"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": []
}
] |
ccyjw8860/deep-text-recognition-benchmark | [
"96f526e6d58e2d0685a5e062f472a3cb7310b8be"
] | [
"train_original.py"
] | [
"import os\nimport sys\nimport time\nimport random\nimport string\nimport argparse\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.nn.init as init\nimport torch.optim as optim\nimport torch.utils.data\nimport numpy as np\n\nfrom utils import CTCLabelConverter, CTCLabelConverterForBaiduWarpctc, A... | [
[
"torch.optim.Adam",
"torch.nn.CrossEntropyLoss",
"torch.cuda.manual_seed",
"numpy.random.seed",
"torch.nn.init.constant_",
"torch.manual_seed",
"torch.load",
"torch.nn.DataParallel",
"torch.no_grad",
"torch.nn.CTCLoss",
"torch.cuda.is_available",
"torch.cuda.device_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Seondong/talkingdata_kaggle_201608 | [
"b9ddbb343dacbcfdaa4b2732c9ea23bf776c773b"
] | [
"code/xgboost_baseline.py"
] | [
"__author__ = 'ZFTurbo: https://kaggle.com/zfturbo'\n\nimport datetime\nimport pandas as pd\nimport numpy as np\nfrom sklearn.cross_validation import train_test_split\nimport xgboost as xgb\nimport random\nimport zipfile\nimport time\nimport shutil\nfrom sklearn.metrics import log_loss\n\nrandom.seed(2016)\n\ndef r... | [
[
"pandas.merge",
"sklearn.cross_validation.train_test_split",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
shengyushen/training | [
"0db663b86001dfc359da98c1504a7a3cb8e1f617"
] | [
"mnist/mnist_with_summaries_bf16.py"
] | [
"# Copyright 2015 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 requir... | [
[
"tensorflow.gfile.DeleteRecursively",
"tensorflow.gfile.Exists",
"tensorflow.RunMetadata",
"tensorflow.cast",
"tensorflow.gfile.MakeDirs",
"tensorflow.train.AdamOptimizer",
"tensorflow.summary.scalar",
"tensorflow.Graph",
"tensorflow.Variable",
"tensorflow.summary.image",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
laket/ape-x | [
"8ccb4192206d9529b5105e9fffd3cff143f48864"
] | [
"replay_buffer_actor.py"
] | [
"'''\nCopyright (c) 2018 Uber Technologies, Inc.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or a... | [
[
"tensorflow.device",
"tensorflow.transpose",
"tensorflow.concat",
"tensorflow.control_dependencies",
"tensorflow.identity",
"tensorflow.expand_dims",
"tensorflow.image.convert_image_dtype",
"tensorflow.logical_not"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
wahyutirta/CNN-numpy | [
"d66e10a53304a0c72c40f278486866493f573d5e"
] | [
"main-1.1.py"
] | [
"from PyQt5.QtWidgets import *\nimport sys,pickle\nimport os\n\nfrom PyQt5 import uic, QtWidgets ,QtCore, QtGui\nfrom PyQt5 import QtWidgets, uic\nfrom PyQt5.QtCore import QDir, Qt, QSortFilterProxyModel\nfrom PyQt5.QtWidgets import QDialog ,QApplication, QFileDialog, QWidget, QTextEdit, QLabel\nfrom PyQt5.uic impo... | [
[
"matplotlib.use",
"numpy.argmax",
"matplotlib.figure.Figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
huskermiao/MaizeLeafCounting | [
"68d3d8e8bebf2dc74f2aa79a3fc62aca67de1dbb"
] | [
"CountingByDetection_FasterRCNNs/cocoeval.py"
] | [
"__author__ = 'tsungyi'\n\nimport numpy as np\nimport datetime\nimport time\nfrom collections import defaultdict\nimport mask as maskUtils\nimport copy\n\n\nclass COCOeval:\n # Interface for evaluating detection on the Microsoft COCO dataset.\n #\n # The usage for CocoEval is as follows:\n # cocoGt=...... | [
[
"numpy.logical_not",
"numpy.spacing",
"numpy.unique",
"numpy.cumsum",
"numpy.ones",
"numpy.concatenate",
"numpy.round",
"numpy.max",
"numpy.mean",
"numpy.count_nonzero",
"numpy.searchsorted",
"numpy.exp",
"numpy.argsort",
"numpy.repeat",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yalov4uk/ML-labs | [
"ca944610614c182259783449d9ec6e9135d6aaf1"
] | [
"5/download.py"
] | [
"import os\nimport tarfile\nimport email\nimport re\nimport nltk\nimport urlextract\nimport numpy as np\nimport scipy.io as sio\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom nltk.stem import PorterStemmer\nfrom html import unescape\nfrom email import parser\nfrom email.policy import default\nfrom ... | [
[
"numpy.array",
"numpy.zeros",
"scipy.io.savemat"
]
] | [
{
"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"... |
ZhaoJY1/nussl | [
"af7d0c50e01d107f4ef3305b89eb130d95d0a7cd",
"af7d0c50e01d107f4ef3305b89eb130d95d0a7cd",
"af7d0c50e01d107f4ef3305b89eb130d95d0a7cd"
] | [
"tests/ml/test_overfit.py",
"docs/examples/benchmark/ideal_binary_mask.py",
"nussl/separation/deep/deep_clustering.py"
] | [
"from nussl import ml, datasets, evaluation\nimport tempfile\nfrom torch import optim\nimport numpy as np\nimport logging\nimport os\nimport torch\nfrom matplotlib import pyplot as plt\n\nlogging.basicConfig(\n format='%(asctime)s,%(msecs)d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',\n datefmt='%Y... | [
[
"matplotlib.pyplot.title",
"torch.load",
"torch.cuda.is_available",
"numpy.array",
"matplotlib.pyplot.figure"
],
[
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Wason1/Rows-to-columns-consolidator | [
"21ee991ba907ba61708d24acfc6d5b3a3e754677"
] | [
"script.py"
] | [
"#Import Libs\nimport pandas as pd\n\n# Inputs\n#col_name = input('What is the name of the column to convert to columns?: ')\n#keyz = input('what are the names of the columns that uniquely identify a row? (seperate these with pipes \"|\"):')\n#keyz.split('|')\nfile_dir = r'data.xlsx'\noutput_dir = r'data-out.xlsx'\... | [
[
"pandas.read_excel",
"pandas.unique"
]
] | [
{
"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": []
}
] |
ltqusst/lpc_vocoder | [
"baf29d40dcf9f4b80a73146dca939c7841045441"
] | [
"sws.py"
] | [
"## MIT License\n\n# Copyright (c) 2017 John Williamson\n# Copyright (c) 2008 Cournapeau David\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including withou... | [
[
"numpy.imag",
"scipy.signal.correlate",
"numpy.concatenate",
"numpy.max",
"numpy.zeros_like",
"numpy.mean",
"numpy.polynomial.polynomial.polyfromroots",
"numpy.exp",
"scipy.io.wavfile.read",
"numpy.arange",
"numpy.sin",
"numpy.atleast_1d",
"numpy.roots",
"sc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.18",
"1.0",
"0.19"
],
"tensorflow": []
}
] |
mokshagna517/recommendation_sys | [
"bc8ced225dff3c93d619ff5da363f42d0aa0676c",
"bc8ced225dff3c93d619ff5da363f42d0aa0676c",
"bc8ced225dff3c93d619ff5da363f42d0aa0676c"
] | [
"venv/Lib/site-packages/pandas/tests/extension/test_categorical.py",
"venv/Lib/site-packages/sklearn/linear_model/tests/test_sparse_coordinate_descent.py",
"venv/Lib/site-packages/scipy/sparse/linalg/tests/test_matfuncs.py"
] | [
"\"\"\"\nThis file contains a minimal set of tests for compliance with the extension\narray interface test suite, and should contain no other tests.\nThe test suite for the full functionality of the array is located in\n`pandas/tests/arrays/`.\n\nThe tests in this file are inherited from the BaseExtensionTests, and... | [
[
"pandas.Categorical",
"pandas.util.testing.assert_produces_warning",
"pandas.api.types.CategoricalDtype",
"pandas.Series"
],
[
"numpy.dot",
"sklearn.utils.testing.assert_array_almost_equal",
"sklearn.utils.testing.assert_almost_equal",
"sklearn.utils.testing.assert_warns",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
... |
nicpittman/tropical_pacific_carbon_export | [
"eacd3e0382616388f418eb21cad859fe7ae0144a"
] | [
"9z_ENSO_spatial_maps.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 1 10:40:28 2020\n@author: Nic Pittman\n\nThis code will reproduce Figure 4 in Pittman et al., 2021. \n\nTrends and pvalues are calculated on the fly and not saved anywhere, however could be done easily. \nregridded data is required for th... | [
[
"matplotlib.pyplot.tight_layout",
"pandas.read_csv",
"numpy.sqrt",
"numpy.arange",
"pandas.DataFrame",
"numpy.datetime64",
"matplotlib.pyplot.colorbar",
"numpy.meshgrid",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
slowy07/medical-BCDU | [
"dab1ddcacbe093b78e6830d52db2a4e6fabc3d52"
] | [
"lungSegmentation/RFunction.py"
] | [
"from __future__ import division\nimport numpy as np\nfrom scipy.ndimage.morphology import binary_erosion, binary_fill_holes\n\ndef hu_to_grayscale(volume):\n volume = np.clip(volume, -512, 512)\n mxmal = np.max(volume)\n mnval = np.min(volume)\n im_volume = (volume - mnval) / max(mxval - mnval, 1e-3)\n... | [
[
"numpy.min",
"numpy.clip",
"numpy.ones",
"numpy.max",
"numpy.equal",
"numpy.where",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jlsuarezdiaz/pyDML-Stats | [
"495de64dbcda73ce20d8e916bf5e5077a8dae98a"
] | [
"scripts/utils/toy_datasets.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jan 26 12:25:25 2018\n\nToy datasets.\n\n@author: jlsuarezdiaz\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom six.moves import xrange\nfrom sklearn.preprocessing import LabelEncoder\nimport seaborn as sns\nimport matplotlib.pyplot as... | [
[
"numpy.matrix",
"numpy.mean",
"numpy.random.randn",
"sklearn.preprocessing.LabelEncoder",
"numpy.random.randint",
"numpy.sin",
"matplotlib.pyplot.axis",
"numpy.zeros",
"numpy.isin",
"sklearn.datasets.load_iris",
"numpy.random.rand",
"numpy.array",
"matplotlib.py... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ayushk4/tsat | [
"07f9535157e45c4b27dae7d73d199fef7fb9d37a",
"07f9535157e45c4b27dae7d73d199fef7fb9d37a"
] | [
"common/metrics/basic_metrics.py",
"ac/data/build.py"
] | [
"#----------------------------------------\n#--------- Torch Related Imports --------\n#----------------------------------------\nimport torch\nimport torch.distributed as distributed\n\n#----------------------------------------\n#--------- Import Wandb Here ------------\n#----------------------------------------\n... | [
[
"torch.distributed.all_reduce",
"torch.tensor"
],
[
"torch.utils.data.distributed.DistributedSampler",
"torch.utils.data.DataLoader",
"torch.utils.data.sampler.SequentialSampler",
"torch.utils.data.sampler.RandomSampler",
"torch.utils.data.sampler.BatchSampler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iimmortall/QuantLib | [
"29e83dad8738d0fb4efb18d0cb5dd3a7029abd86"
] | [
"losses/loss_factory.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport torch\n\n\ndef cross_entropy_dist_epoch(reduction='mean', **_):\n cross_entropy_fn = torch.nn.CrossEntropyLoss(reduction=reduction)\n l1_fn = torch.nn.L1Loss(reduction=reduction)\n\n de... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.L1Loss",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Payuing/evoDNN | [
"79b727d5062a27d3f8e95f175c509613f52e58aa",
"79b727d5062a27d3f8e95f175c509613f52e58aa",
"79b727d5062a27d3f8e95f175c509613f52e58aa",
"79b727d5062a27d3f8e95f175c509613f52e58aa"
] | [
"legacy/src/EvoNN.py",
"src/dataset/yeast_dataloader.py",
"src/evolver.py",
"src/dataset/abalone_dataloader.py"
] | [
"\"\"\"\r\nA module to implement the evolutionary algorithm for\r\na feedforward neural network.\r\nCrossover and mutation\r\n\"\"\"\r\nfrom __future__ import absolute_import\r\nfrom __future__ import print_function\r\n\r\nimport sys\r\nimport math\r\nimport csv\r\nimport warnings\r\nimport numpy as np\r\nimport ra... | [
[
"numpy.dot",
"numpy.random.seed",
"numpy.tile",
"numpy.copy",
"numpy.mean",
"numpy.exp",
"numpy.random.uniform",
"numpy.tanh",
"numpy.zeros",
"numpy.random.randint"
],
[
"sklearn.preprocessing.LabelEncoder",
"numpy.array",
"pandas.read_csv"
],
[
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
jmarine/ezeeai | [
"091b4ce3bc5794c534084bff3301b15ba8a9be1a"
] | [
"ezeeai/core/explainer.py"
] | [
"from lime import lime_tabular, lime_image\nfrom scipy.misc import imresize\nimport numpy as np\nimport tensorflow as tf\n\n\nclass TabularExplainer:\n\n def __init__(self, dataset, verbose=True):\n\n train_dataset, training_labels = dataset.make_numpy_array(dataset.get_train_file())\n\n mode = dat... | [
[
"numpy.array",
"tensorflow.estimator.inputs.numpy_input_fn",
"numpy.apply_along_axis",
"tensorflow.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
murthyn/composer | [
"2a04cf387dd8558556500f7ef2bc6d3d131043d5",
"2a04cf387dd8558556500f7ef2bc6d3d131043d5"
] | [
"composer/models/resnets.py",
"composer/datasets/coco.py"
] | [
"# Copyright 2021 MosaicML. All Rights Reserved.\n\n\"\"\"The CIFAR ResNet torch module.\n\nSee the :doc:`Model Card </model_cards/resnet>` for more details.\n\"\"\"\n\n# Code below adapted from https://github.com/facebookresearch/open_lth\n# and https://github.com/pytorch/vision\n\nfrom typing import List, Tuple\n... | [
[
"torch.nn.Sequential",
"torch.nn.CrossEntropyLoss",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
],
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
evgeniya-egupova/openvino | [
"a9a583eb42d43322b39b95b164b5b22c4f341111",
"a9a583eb42d43322b39b95b164b5b22c4f341111",
"a9a583eb42d43322b39b95b164b5b22c4f341111"
] | [
"src/bindings/python/tests/test_ngraph/test_core.py",
"model-optimizer/extensions/front/tf/sparse_to_dense_replacer.py",
"tools/pot/openvino/tools/pot/graph/passes.py"
] | [
"# Copyright (C) 2018-2021 Intel Corporation\n# SPDX-License-Identifier: Apache-2.0\n\nimport numpy as np\n\nimport openvino.runtime.opset8 as ov\nfrom openvino.runtime.impl import Dimension, Function, PartialShape, Shape\n\n\ndef test_dimension():\n dim = Dimension()\n assert dim.is_dynamic\n assert not d... | [
[
"numpy.int32"
],
[
"numpy.float32"
],
[
"numpy.array",
"numpy.zeros",
"numpy.moveaxis"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
majkee15/HiddenMarkovJumpProcess-RLEnvironment | [
"730ef636bfa51f6137268ab7760f9a504ba583db"
] | [
"control/base.py"
] | [
"import os\n\nimport logging\nimport datetime\n\n\nimport numpy as np\nimport tensorflow as tf\nfrom gym.spaces import Box, Discrete\nfrom gym.utils import colorize\n\nfrom control.utils.misc import Config\nfrom control.utils.misc import REPO_ROOT, RESOURCE_ROOT\n\nfrom abc import ABC, abstractmethod\n\n\n\nclass T... | [
[
"numpy.mean",
"numpy.random.seed",
"tensorflow.summary.create_file_writer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tenpercent/pytorch | [
"7f996b855c5070ab4a6bea0f451c8a22c0ce2394",
"7f996b855c5070ab4a6bea0f451c8a22c0ce2394",
"7f996b855c5070ab4a6bea0f451c8a22c0ce2394"
] | [
"test/test_ops_jit.py",
"test/mobile/model_test/math_ops.py",
"test/mobile/model_test/nn_ops.py"
] | [
"# Owner(s): [\"module: unknown\"]\n\nfrom functools import partial\n\nimport torch\n\nfrom torch.testing import FileCheck\nfrom torch.testing._internal.common_utils import \\\n (run_tests, IS_SANDCASTLE, clone_input_helper, first_sample)\nfrom torch.testing._internal.common_methods_invocations import op_db\nfro... | [
[
"torch.jit.trace",
"torch.testing._internal.jit_utils.disable_autodiff_subgraph_inlining",
"torch.set_default_dtype",
"torch.testing._internal.common_utils.first_sample",
"torch.testing._internal.common_utils.clone_input_helper",
"torch.testing._internal.jit_metaprogramming_utils.create_tr... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AlexPC23/Python | [
"77689d74c5444faa1aa253a122602307e52ac581"
] | [
"Spyder/Ejercicios/Comparacion medias.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 6 19:54:52 2021\n\n@author: Alex\n\"\"\"\n\nimport os #sistema operativo\nimport pandas as pd #gestionar datframes\nimport numpy as np #numeric python (vectores, matrices,...)\... | [
[
"scipy.stats.f_oneway",
"pandas.crosstab",
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.text",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.yla... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"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... |
happog/PaddleOCR | [
"5ed1e2427b4e1759f0e9278f453e8d497db33b59"
] | [
"deploy/pdserving/ocr_local_server.py"
] | [
"# Copyright (c) 2020 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.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PMARINA/COS-429 | [
"25134e77101279c3f9f16a6738beb6170ba1fd09"
] | [
"Assignment 0/Part 2 - Getting Familiar with Python/SettingPixels.py"
] | [
"import numpy as np\nimport cv2\nimport os\nwindow_title = \"The Input Image\"\ninput_image = \"input.jpg\"\noutput_image = os.path.basename(__file__)[:-len(\".py\")] + \".jpg\"\nHORIZONTAL = 0\nVERTICAL = 1\n\ndef read_image(file_name = input_image):\n img = cv2.imread(file_name)\n return img\n\ndef display_... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
raymondEhlers/uproot4 | [
"b266614eb3e24d02fa5ed2be4a2d95ab71a5e499"
] | [
"tests/test_0017-multi-basket-multi-branch-fetch.py"
] | [
"# BSD 3-Clause License; see https://github.com/scikit-hep/uproot4/blob/master/LICENSE\n\nfrom __future__ import absolute_import\n\nimport sys\nimport json\n\ntry:\n from io import StringIO\nexcept ImportError:\n from StringIO import StringIO\n\nimport numpy\nimport pytest\nimport skhep_testdata\n\nimport upr... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sakshamji/FacemaskDetection | [
"b274285ebaef51c110fab3dc608a2c2ef956ec95"
] | [
"facemask.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 22 15:56:47 2020\n\n@author: Saksham\n\"\"\"\n\n\nimport numpy as np\nimport keras\nimport keras.backend as k\nfrom keras.layers import Conv2D,MaxPooling2D,SpatialDropout2D,Flatten,Dropout,Dense\nfrom keras.models import Sequential,load_model\nfrom keras.optimize... | [
[
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AjayJohnAlex/ANN | [
"236bc4ca4aaa07038610bc6870578b1f0255da49"
] | [
"Improving the ANN.py"
] | [
"\n# coding: utf-8\n\n# In[1]:\n\n\nimport pandas as pd\nimport numpy as np \nimport matplotlib.pyplot as plt\nimport seaborn as sns\nget_ipython().run_line_magic('matplotlib', 'inline')\n\n\n# In[2]:\n\n\ndataset = pd.read_csv('Churn_Modelling.csv')\ndataset.head()\n\n\n# In[3]:\n\n\nX = dataset.iloc[:,3:13].value... | [
[
"pandas.read_csv",
"sklearn.preprocessing.OneHotEncoder",
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.StandardScaler",
"sklearn.preprocessing.LabelEncoder"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
TomLXXVI/pipe-network-sim | [
"49e42621180ec3125afa238d3ca56ae9f3a7662a"
] | [
"lib/nummath/deriv.py"
] | [
"import numpy as np\n\n\nclass Deriv:\n \"\"\"\n Calculate the derivative with given order of the function f(t) at point t.\n \"\"\"\n def __init__(self, f, dt, o=1):\n \"\"\"\n Initialize the differentiation solver.\n Params:\n - f the name of the function object... | [
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
whitews/FlowK | [
"d4e43a0488606ce5479b5110486dc3db128f6a87"
] | [
"flowkit/tests/flowjo_wsp_tests.py"
] | [
"\"\"\"\nTests for FlowJo 10 workspace files\n\"\"\"\nimport copy\nimport unittest\nimport os\nfrom io import BytesIO\nimport numpy as np\nfrom flowkit import Session, gates, transforms\nfrom .session_tests import test_samples_8c_full_set\n\n\nclass FlowJoWSPTestCase(unittest.TestCase):\n def test_load_wsp_singl... | [
[
"numpy.abs",
"numpy.sum",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hushukai/tf-tensor2tensor | [
"6e685f57ed170bb7f887271d7bbd58cf57eb6af7"
] | [
"tensor2tensor/utils/multistep_optimizer.py"
] | [
"# coding=utf-8\n# Copyright 2019 The Tensor2Tensor Authors.\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 requir... | [
[
"tensorflow.compat.v1.get_default_graph",
"tensorflow.compat.v1.equal",
"tensorflow.compat.v1.convert_to_tensor",
"tensorflow.compat.v1.control_dependencies",
"tensorflow.compat.v1.executing_eagerly",
"tensorflow.compat.v1.logging.warning",
"tensorflow.compat.v1.group",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
liulisixin/unsupervised-learning-intrinsic-images | [
"0d4ad151d203885c87122bcc305c787210b28a5c"
] | [
"data/params.py"
] | [
"import copy\nimport json\nimport random\nimport hashlib\nimport numpy as np\n\n\nclass IntrinsicParameters():\n \"\"\" Global parameter values for the algorithm \"\"\"\n\n def __init__(self):\n\n #: if True, print progress to the console\n self.logging = False\n\n #: if True, use a fixed... | [
[
"numpy.random.poisson",
"numpy.random.seed",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wenming2014/tensorflow | [
"a102a6a71844e194f3946f6318768c5367f1f16b",
"a102a6a71844e194f3946f6318768c5367f1f16b",
"a102a6a71844e194f3946f6318768c5367f1f16b",
"a102a6a71844e194f3946f6318768c5367f1f16b",
"a102a6a71844e194f3946f6318768c5367f1f16b",
"a102a6a71844e194f3946f6318768c5367f1f16b",
"a102a6a71844e194f3946f6318768c5367f1f16... | [
"tensorflow/python/training/checkpoint_management.py",
"tensorflow/python/framework/dtypes.py",
"tensorflow/python/kernel_tests/diag_op_test.py",
"tensorflow/python/data/kernel_tests/dataset_constructor_op_test.py",
"tensorflow/python/data/experimental/kernel_tests/sleep_test.py",
"tensorflow/python/distr... | [
"# Copyright 2015 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.platform.tf_logging.warning",
"tensorflow.python.ops.variable_scope.variable_creator_scope",
"tensorflow.python.lib.io.file_io.stat",
"tensorflow.python.platform.tf_logging.error",
"tensorflow.python.platform.tf_logging.info",
"tensorflow.python.util.tf_export.tf_export"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.13",
"2.3",
"2.4",
"2.9",
"1.7",
"2.5",
"2.2",
"2.10"
]
},
{
"matplotlib": [],
"numpy"... |
mantuoluozk/MFC | [
"e296d7a8e345bc2ca404b5f0fb7f5048f9c5f0d3"
] | [
"code/test_util.py"
] | [
"import h5py\nimport math\nimport nibabel as nib\nimport numpy as np\nfrom medpy import metric\nimport torch\nimport torch.nn.functional as F\nfrom tqdm import tqdm\nfrom skimage.measure import label\n\n\ndef getLargestCC(segmentation):\n labels = label(segmentation)\n assert(labels.max() != 0) # assume at l... | [
[
"torch.sigmoid",
"numpy.expand_dims",
"numpy.pad",
"numpy.asarray",
"numpy.eye",
"torch.from_numpy",
"torch.no_grad",
"numpy.bincount",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zs7779/Pytorch_Retinaface | [
"eeb92c28f3217da7439118ed89df8a83c75cc161"
] | [
"retina_models/retinaface.py"
] | [
"import torch\nimport torch.nn as nn\nimport torchvision.models.detection.backbone_utils as backbone_utils\nimport torchvision.models._utils as _utils\nimport torch.nn.functional as F\nfrom collections import OrderedDict\n\nfrom retina_models.net import MobileNetV1 as MobileNetV1\nfrom retina_models.net import FPN ... | [
[
"torch.device",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.functional.softmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ldelebec/asteroid | [
"d6390baca5409634f112ceed554ea66c4054cb54",
"d6390baca5409634f112ceed554ea66c4054cb54",
"d6390baca5409634f112ceed554ea66c4054cb54",
"d6390baca5409634f112ceed554ea66c4054cb54",
"d6390baca5409634f112ceed554ea66c4054cb54",
"d6390baca5409634f112ceed554ea66c4054cb54",
"d6390baca5409634f112ceed554ea66c4054cb5... | [
"asteroid/models/demask.py",
"tests/engine/scheduler_test.py",
"asteroid/losses/pit_wrapper.py",
"asteroid/data/utils.py",
"egs/wham/MixIT/eval.py",
"egs/dns_challenge/baseline/denoise.py",
"tests/losses/mixit_wrapper_test.py",
"asteroid/utils/torch_utils.py",
"egs/avspeech/looking-to-listen/local/l... | [
"from torch import nn\nfrom .base_models import BaseEncoderMaskerDecoder\nfrom asteroid_filterbanks import make_enc_dec\nfrom asteroid_filterbanks.transforms import mag, magreim\nfrom ..masknn import norms, activations\nfrom ..utils.torch_utils import pad_x_to_y\nimport warnings\n\n\nclass DeMask(BaseEncoderMaskerD... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.nn.Conv1d"
],
[
"torch.nn.Linear",
"torch.nn.ReLU",
"torch.utils.data.DataLoader",
"torch.nn.MSELoss"
],
[
"torch.mean",
"torch.einsum",
"torch.min",
"torch.gather",
"torch.unsqueeze",
"torch.arange",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.4",
"0... |
hanfengzhai/DARPA-FFT | [
"61705c1dcbe7a75a54003db5e8f7db3717e3040c",
"61705c1dcbe7a75a54003db5e8f7db3717e3040c"
] | [
"code/id53.py",
"code/id56.py"
] | [
"import time\nfrom scipy import fftpack\nimport book_format\nbook_format.set_style()\nimport kf_book.kf_internal as kf_internal\nfrom kf_book.kf_internal import DogSimulation\nfrom kf_book import book_plots as book_plots\nimport numpy as np\nfrom matplotlib import pyplot\nimport scipy.io\nimport pandas as pd\nimpor... | [
[
"scipy.fftpack.idct",
"numpy.linspace",
"numpy.squeeze",
"numpy.array_split",
"numpy.mean",
"numpy.array",
"scipy.fftpack.dct",
"numpy.zeros"
],
[
"scipy.fftpack.idct",
"numpy.linspace",
"numpy.squeeze",
"numpy.array_split",
"numpy.mean",
"numpy.array",
... | [
{
"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"... |
miyuush/AtCoder | [
"9481f15b69b99f56334a623f5a63dbb5e6359522"
] | [
"contest/20190303_ABC120/D.py"
] | [
"# Union-findを使う\n# 論文検索には(Disjoint Set)\n\n\nfrom scipy.special import comb\n\nn, m = map(int, input().split())\na = []\nb = []\nfor i in range(m):\n a0, b0 = [int(i) for i in input().split()]\n a.append(a0)\n b.append(b0)\n\na.append(b)\nl = len(a)\ncmb = comb(m, 2, exact=True)\nbase = int(cmb / 2) + 1\n... | [
[
"scipy.special.comb"
]
] | [
{
"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": []
... |
peterorum/ashrae | [
"6527eb71b2102565bb71b402db700b561cea138c"
] | [
"src/001-constant.py"
] | [
"# baseline: constant 0\n# local score 4.668\n# kaggle score 4.69\n\nimport sys # pylint: disable=unused-import\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import mean_squared_error\nfrom time import time\n\nimport os\n\nis_kaggle = os.environ['HOME'] == '/tmp'\n\nzipext = '' if is_kaggle else '... | [
[
"numpy.log1p",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Danielhp95/Regym | [
"f0f0be0ad23bf1a3410ecd9ed9b8025947d6080a",
"f0f0be0ad23bf1a3410ecd9ed9b8025947d6080a",
"f0f0be0ad23bf1a3410ecd9ed9b8025947d6080a"
] | [
"regym/rl_algorithms/TQL/repeated_update_q_learning.py",
"regym/training_schemes/psro.py",
"regym/networks/bodies.py"
] | [
"import numpy as np\n\n\nclass RepeatedUpdateQLearningAlgorithm():\n '''\n Repeated Update Q Learning (RUQL) as introduced in:\n \"Addressing the Policy Bias of Q-Learning by Repeating Updates\" - Sherief Abdallah, Michael Kaisers\n '''\n def __init__(self, state_space_size, action_space_size, hashin... | [
[
"numpy.amax",
"numpy.zeros",
"numpy.divide"
],
[
"numpy.array",
"torch.save"
],
[
"torch.nn.Sequential",
"torch.zeros",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.LSTMCell",
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PandoraLS/WG-WaveNet | [
"5f27e61cc4d3554af8c16fa35345831099b703e8"
] | [
"model/loss.py"
] | [
"import torch\nimport librosa\nimport numpy as np\nimport torch.nn.functional as F\nfrom hparams import hparams as hps\nfrom utils.util import to_arr, mode\n\n\nclass Loss(torch.nn.Module):\n\tdef __init__(self):\n\t\tsuper(Loss, self).__init__()\n\t\tself.d = 2*hps.sigma*hps.sigma\n\t\tself.loss = MultiResolutionS... | [
[
"torch.norm",
"torch.Tensor",
"torch.nn.ModuleList",
"torch.sum",
"torch.matmul",
"torch.log",
"torch.clamp",
"torch.nn.L1Loss",
"torch.stft"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kanga333/PyAthena | [
"487baa66ae203c3541d37191600f1f3219a2e1ac"
] | [
"pyathena/util.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals\n\nimport functools\nimport logging\nimport threading\nimport re\nimport uuid\n\nimport tenacity\nfrom past.builtins import xrange\nfrom tenacity import (after_log, retry_if_exception,\n st... | [
[
"pandas._lib.infer_dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
1170300521/StyleGAN-nada | [
"1b6dc2d7dcbc37dd2e29af2f8b59d7635e6a26ec",
"1b6dc2d7dcbc37dd2e29af2f8b59d7635e6a26ec"
] | [
"ZSSGAN/utils/svm.py",
"ZSSGAN/test.py"
] | [
"import numpy as np\nfrom sklearn import svm\n\n\ndef train_boundary(pos_codes, neg_codes, split_ratio=0.7):\n pos_ids = np.arange(len(pos_codes))\n np.random.shuffle(pos_ids)\n train_pos_num = int(len(pos_ids) * split_ratio)\n train_pos_codes = pos_codes[pos_ids[:train_pos_num]]\n val_pos_codes = po... | [
[
"numpy.linalg.norm",
"numpy.save",
"numpy.random.shuffle",
"numpy.concatenate",
"numpy.ones",
"sklearn.svm.SVC",
"numpy.load",
"numpy.zeros",
"numpy.sum"
],
[
"torch.no_grad",
"torch.Tensor",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Giuseppe5/pytorch-ocr | [
"f8e89295e911c7a3eec6e3aa13335c031cd3adfe"
] | [
"main.py"
] | [
"# Copyright (c) 2018, Xilinx, Inc.\n# All rights reserved.\n# \n# Redistribution and use in source and binary forms, with or without \n# modification, are permitted provided that the following conditions are met:\n#\n# 1. Redistributions of source code must retain the above copyright notice, \n# t... | [
[
"torch.set_printoptions",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alsmeirelles/ResRep | [
"abc8d221cfa153de577ca1bbba515cc7abb94378"
] | [
"display_hdf5.py"
] | [
"from utils.misc import read_hdf5\nfrom utils.misc import extract_deps_from_weights_file\nimport sys\nimport numpy as np\n\nwf = sys.argv[1]\ndeps = extract_deps_from_weights_file(wf)\ndi = read_hdf5(wf)\nnum_kernel_params = 0\n\nconv_kernel_cnt = 0\nmatrix_param_cnt = 0\nvec_param_cnt = 0\n\nbias_cnt = 0\nbeta_cnt... | [
[
"numpy.abs",
"numpy.std",
"numpy.mean",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xsppp/gpipe_with_Mnist | [
"5cd8aff375e7f8fc3c6fb065ce3f40854eb6f31a",
"4486e675c7b52c7519a6d39f97e9b22ed5461944"
] | [
"lingvo/tasks/car/input_preprocessors.py",
"lingvo/core/layers.py"
] | [
"# Lint as: python3\n# 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... | [
[
"numpy.linspace",
"numpy.asarray",
"tensorflow.python.ops.inplace_ops.empty",
"numpy.isposinf",
"numpy.isneginf",
"numpy.prod",
"numpy.array"
],
[
"numpy.min",
"tensorflow.python.ops.inplace_ops.alias_inplace_update",
"numpy.tile",
"numpy.max",
"tensorflow.pytho... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.13",
"2.3",
"2.4",
"2.... |
thegalang/lime | [
"a4cd83e20c838c782728c02f07e21ab01d17f3fa"
] | [
"lime/lime_tabular.py"
] | [
"\"\"\"\nFunctions for explaining classifiers that use tabular data (matrices).\n\"\"\"\nimport collections\nimport copy\nfrom functools import partial\nimport json\nimport warnings\n\nimport numpy as np\nimport sklearn\nimport sklearn.preprocessing\nfrom sklearn.utils import check_random_state\n\nfrom lime.discret... | [
[
"numpy.sqrt",
"numpy.transpose",
"numpy.exp",
"numpy.argsort",
"sklearn.preprocessing.StandardScaler",
"numpy.array",
"numpy.zeros",
"sklearn.utils.check_random_state"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OllieBoyne/dog-dynamics | [
"c472f984cb04e6dea932be6a42f4daaf174fb44c"
] | [
"dynamics/dynamics.py"
] | [
"\"\"\"DEFINES THE INVERSEDYNAMICS SOLVER, A Solver for solving the joint based model of a dog.\"\"\"\n\nfrom scipy import optimize, signal\n\nfrom data.data_loader import C3DData, load_force_plate_data, ForcePlateData, SMALData, get_delay_between, DataSources, \\\n\tpath_join\nfrom vis.utils import *\nfrom vis imp... | [
[
"scipy.signal.resample",
"scipy.signal.savgol_filter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.14",
"1.6",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"0.16",
"1.8"
],
"tensorflow": []
... |
gosticks/body-pose-animation | [
"eb1b5876a845f277d43bfc18dcd48c4a9c694c06",
"eb1b5876a845f277d43bfc18dcd48c4a9c694c06"
] | [
"utils/video.py",
"utils/render.py"
] | [
"from dataset import SMPLyDataset\nimport pickle\nfrom typing import Tuple\nfrom model import SMPLyModel\nfrom renderer import DefaultRenderer\nimport cv2\nfrom tqdm import tqdm\nimport numpy as np\nfrom scipy import interpolate\n\n\ndef make_video(images, video_name: str, fps=30, ext: str = \"mp4\", post_process_f... | [
[
"numpy.expand_dims",
"numpy.linspace",
"numpy.arange",
"numpy.concatenate",
"scipy.interpolate.interp1d",
"numpy.array"
],
[
"numpy.eye",
"numpy.ones"
]
] | [
{
"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"
... |
caditi97/exatrkx-ctd2020 | [
"ed090ddfcc9e2e623fb45000fca71d5ad6ccf3b9"
] | [
"GraphLearning/src/distributed/torch.py"
] | [
"\"\"\"Utility code for running native pytorch distributed\"\"\"\n\nimport os\n\nimport torch.distributed as dist\n\ndef init_workers_file():\n rank = int(os.environ['SLURM_PROCID'])\n n_ranks = int(os.environ['SLURM_NTASKS'])\n sync_file = 'file:///tmp/%s_%s_pytorch_sync' % (\n os.environ['USER'], ... | [
[
"torch.distributed.get_rank",
"torch.distributed.get_world_size",
"torch.distributed.init_process_group"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ishandutta2007/incubator-mxnet | [
"54a3c58c49fdfac595a348301b6f0701db09d4ab"
] | [
"tests/python/unittest/test_io.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
flaviofontes29/Machine-Learning-e-Data-Science-com-Python | [
"7b8188b6e7003426ae3a6d46d91d61494135a2b7"
] | [
"Secao 3 - Pre-processamento com Pandas e scikit-learm/template_credit_data.py"
] | [
"import pandas as pd\nimport numpy as np\n\nbase = pd.read_csv('credit_data.csv')\nbase.loc[base.age < 0, 'age'] = 40.92\n \nprevisores = base.iloc[:, 1:4].values\nclasse = base.iloc[:, 4].values\n\nfrom sklearn.impute import SimpleImputer\nimputer = SimpleImputer(missing_values = np.nan, strategy = '... | [
[
"sklearn.preprocessing.StandardScaler",
"pandas.read_csv",
"sklearn.impute.SimpleImputer",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
tomvdon/lidar-bonnetal | [
"0bb78eb9a731e98e6f3b893d735b6c3ca96cb0e8"
] | [
"train_test_split.py"
] | [
"import shutil\nimport os\nimport glob\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nimport shutil\nimport re\n\nclouds = glob.glob('range_images/point_cloud_*.pcd')\ntrain, test = train_test_split(clouds, test_size=0.20, random_state=42)\nfor file in train:\n shutil.copy(file, \"sim... | [
[
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arunraja-hub/transformers | [
"76cadb7943c8492ec481f4f3925e9e8793a32c9d",
"3f51e6a35871fefbdfb705902355d7530a72d1b8",
"eb2e006b35938e7b6476d3bfc55343ebfe5ec501"
] | [
"tests/test_modeling_flax_vit.py",
"src/transformers/data/data_collator.py",
"examples/research_projects/wav2vec2/run_asr.py"
] | [
"# Copyright 2021 The HuggingFace Team. 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 requir... | [
[
"numpy.ones"
],
[
"torch.randint",
"torch.full",
"torch.nn.utils.rnn.pad_sequence",
"torch.tensor",
"torch.bernoulli",
"torch.stack"
],
[
"numpy.argmax",
"torch.cuda.amp.autocast"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yamamon75/PmagPy | [
"fa5b189800a239683fc17c6b312cdfdd839a46c3",
"fa5b189800a239683fc17c6b312cdfdd839a46c3",
"fa5b189800a239683fc17c6b312cdfdd839a46c3"
] | [
"pmagpy/controlled_vocabularies2.py",
"programs/incfish.py",
"programs/zeq_magic.py"
] | [
"#!/usr/bin/env python\nfrom __future__ import print_function\nfrom __future__ import absolute_import\n\nimport json\nimport os\nfrom builtins import object\n\nimport pandas as pd\nfrom pandas import Series\nfrom . import find_pmag_dir\n\npmag_dir = find_pmag_dir.get_pmag_dir()\ndata_model_dir = os.path.join(pmag_d... | [
[
"pandas.Series",
"pandas.read_json"
],
[
"numpy.loadtxt"
],
[
"matplotlib.get_backend",
"matplotlib.use"
]
] | [
{
"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": []
},
{
"matplotlib": [],
"nump... |
halotudio/openPNM-copy2 | [
"d400ec65e9421256a531f6d22a38255b002d5dcb",
"d400ec65e9421256a531f6d22a38255b002d5dcb",
"d400ec65e9421256a531f6d22a38255b002d5dcb",
"d400ec65e9421256a531f6d22a38255b002d5dcb",
"d400ec65e9421256a531f6d22a38255b002d5dcb",
"d400ec65e9421256a531f6d22a38255b002d5dcb",
"d400ec65e9421256a531f6d22a38255b002d5dc... | [
"openpnm/io/CSV.py",
"scripts/example_salome_export.py",
"openpnm/io/PerGeos.py",
"tests/unit/models/geometry/ConduitLengthsTest.py",
"tests/unit/core/BaseTest.py",
"tests/unit/models/geometry/PoreSurfaceAreaTest.py",
"tests/unit/models/physics/FlowShapeFactorsTest.py",
"tests/unit/network/GenericNetw... | [
"import re\nimport numpy as np\nfrom openpnm.io.Pandas import Pandas\nfrom openpnm.io import GenericIO, Dict\nfrom openpnm.utils import logging, Workspace\nlogger = logging.getLogger(__name__)\nws = Workspace()\n\n\nclass CSV(GenericIO):\n r\"\"\"\n Reads and writes CSV (comma-separated-value files) containin... | [
[
"pandas.read_table",
"numpy.vstack"
],
[
"numpy.random.seed"
],
[
"numpy.reshape",
"numpy.set_printoptions",
"numpy.sort",
"numpy.ones",
"numpy.atleast_2d",
"numpy.fromstring",
"numpy.array2string",
"numpy.zeros"
],
[
"numpy.array",
"numpy.testing.as... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
inestm28/si | [
"a82ba37bd628c5ebdc723f5e1a9894832c8f1a76",
"a82ba37bd628c5ebdc723f5e1a9894832c8f1a76"
] | [
"src/si/util/cv.py",
"src/si/data/dataset.py"
] | [
"from .util import train_test_split\nimport numpy as np\nimport itertools\n\n# MODEL SELECTION\n\nclass Cross_Validation:\n #avaliar a performance de um modelo\n def __init__(self, model, dataset,score=None, **kwargs):\n self.model=model #modelo que se quer avaliar\n self.dataset=dataset\n ... | [
[
"numpy.all",
"numpy.ma.apply_along_axis",
"pandas.DataFrame"
],
[
"numpy.min",
"numpy.unique",
"numpy.var",
"pandas.DataFrame",
"numpy.genfromtxt",
"numpy.max",
"numpy.mean",
"numpy.savetxt"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0... |
EmadAlamoudi/libpetab-python-MS | [
"7d21d79e9c02200d361a19c737d61c0e56123ca0"
] | [
"tests/test_visualization.py"
] | [
"import warnings\nfrom os import path\nfrom tempfile import TemporaryDirectory\nimport pytest\nfrom petab.C import *\nfrom petab.visualize import (plot_data_and_simulation,\n plot_measurements_by_observable,\n save_vis_spec)\nimport matplotlib.pyplot as plt\n\... | [
[
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jeffersonHsieh/tapas | [
"a2f1c8c763c08487bed6b91884dac946dd766ab9",
"a2f1c8c763c08487bed6b91884dac946dd766ab9"
] | [
"tapas/utils/tf_example_utils.py",
"tapas/retrieval/tfidf_baseline_utils.py"
] | [
"# coding=utf-8\n# Copyright 2019 The Google AI Language Team Authors.\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# Unl... | [
[
"tensorflow.compat.v1.train.Features",
"tensorflow.compat.v1.io.gfile.GFile"
],
[
"tensorflow.compat.v1.python_io.tf_record_iterator"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gdepalma93/bright-athlete-academy | [
"54ba0cc6633637c1bd6d90120153e04b981244bf",
"54ba0cc6633637c1bd6d90120153e04b981244bf",
"54ba0cc6633637c1bd6d90120153e04b981244bf",
"54ba0cc6633637c1bd6d90120153e04b981244bf"
] | [
"Resources/books/long_short_term_memory_networks_with_python/code/lesson_12/tune_batch_size.py",
"Resources/books/deep_learning_time_series_forecasting/code/chapter_14/03_cnn_forecast_model.py",
"Resources/books/deep_learning_time_series_forecasting/code/chapter_16/05_plots_of_daily_power_consumption.py",
"Re... | [
"from keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import LSTM\nfrom matplotlib import pyplot\nfrom pandas import DataFrame\nfrom numpy import array\n\n# return training data\ndef get_train():\n\tseq = [[0.0, 0.1], [0.1, 0.2], [0.2, 0.3], [0.3, 0.4], [0.4, 0.5]]\n\tseq = array(s... | [
[
"numpy.array",
"matplotlib.pyplot.show",
"pandas.DataFrame"
],
[
"matplotlib.pyplot.boxplot",
"pandas.concat",
"pandas.read_csv",
"pandas.DataFrame",
"sklearn.metrics.mean_squared_error",
"numpy.std",
"numpy.mean",
"numpy.array",
"matplotlib.pyplot.show"
],
... | [
{
"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": []
},
{
"matplotlib": [],
"nump... |
GU-DataLab/fairness-and-missing-values | [
"4b9383d2e383ae49a0cd6c94e3c9cf7c3a584581",
"62ffe7c951d8a60d49a9ea6ac7b04aa4432a3fb7",
"36a900aa235d1d53bd57e11c89e3f73f9a585aca",
"065ac24e7f5ec124f6cfe39ce21f085f4c87a401",
"4b9383d2e383ae49a0cd6c94e3c9cf7c3a584581",
"4b9383d2e383ae49a0cd6c94e3c9cf7c3a584581",
"62ffe7c951d8a60d49a9ea6ac7b04aa4432a3fb... | [
"env/lib/python3.7/site-packages/art/defences/preprocessor/variance_minimization.py",
"env/lib/python3.7/site-packages/art/attacks/poisoning/feature_collision_attack.py",
"models/tot_metrics.py",
"env/lib/python3.7/site-packages/tests/attacks/evasion/test_frame_saliency.py",
"env/lib/python3.7/site-packages... | [
"# MIT License\n#\n# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the \"Software\"), to deal in the Software without restriction, including without limita... | [
[
"numpy.clip",
"numpy.reshape",
"numpy.power",
"numpy.linalg.norm",
"numpy.sign",
"numpy.random.rand",
"numpy.repeat",
"numpy.array",
"numpy.zeros"
],
[
"tensorflow.norm",
"numpy.expand_dims",
"torch.norm",
"numpy.clip",
"numpy.linalg.norm",
"numpy.co... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"1.0",
"1.2"
]
},... |
meta00/vital_sqi | [
"7e64a26c9d56af26bfbd25c3ba30211414f5f845"
] | [
"vital_sqi/sqi/dtw_sqi.py"
] | [
"import numpy as np\nimport sys\nimport os\nif bool(getattr(sys, 'ps1', sys.flags.interactive)):\n old_stdout = sys.stdout\n sys.stdout = open(os.devnull, 'w')\n from dtw import dtw\n sys.stdout = old_stdout\nelse:\n from dtw import dtw\n\nfrom vital_sqi.common.generate_template import (\n ppg... | [
[
"numpy.log",
"numpy.sum",
"scipy.spatial.distance.euclidean"
]
] | [
{
"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"
... |
QianLabUSC/cognitively-enhanced-decision-framework | [
"1797ddd41edcbfbfafca5b599ff7ab70f5fdc37f"
] | [
"rule_based_decision_making.py"
] | [
"# This FILE is part of multi-legged robot field exploration model\r\n# env_wrapper.py - to obtain user interaction data from website\r\n#\r\n# This programm is explained by roboLAND in university of southern california.\r\n# Please notify the source if you use it\r\n# \r\n# Copyright(c) 2021-2025 Ryoma Liu\r\n# Em... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"numpy.asarray",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.mean",
"numpy.argmin",
"scipy.optimize.curve_fit",
"numpy.where",
"numpy.square",
"matplotlib.pylab.rcParams.update",
"numpy.unique",
"numpy.arange",
... | [
{
"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"... |
GT-AcerZhang/yolov3.insects_challenge | [
"1ac6ee5a8a5c534ec11723542f4c10583935a2ad"
] | [
"train.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport time\nimport os\nimport numpy as np\nimport paddle\nimport paddle.fluid as fluid\nfrom paddle.fluid.dygraph.base import to_variable\n\nfrom reader import data_loader, test_data_loader, multithread_loader\nfrom yolov3 import YOLOv3\n\n# train.py\n# 提升点： 可以改变anchor的大小，注意训练和测试时要使用同样的... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShanuDey/tf-slim | [
"19c840abfa6de567d760254c42ea68760cf5d9f0"
] | [
"tf_slim/nets/vgg_test.py"
] | [
"# coding=utf-8\n# Copyright 2016 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... | [
[
"tensorflow.python.ops.math_ops.argmax",
"tensorflow.python.framework.ops.Graph",
"tensorflow.python.ops.variable_scope.get_variable_scope",
"tensorflow.python.ops.math_ops.reduce_mean",
"tensorflow.python.platform.test.main",
"tensorflow.python.ops.variables.global_variables_initializer",... | [
{
"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... |
pedrob37/PhysicsPyTorch | [
"a892dfe89740b6fa75d3de5319f99d41bcf4ca63",
"a892dfe89740b6fa75d3de5319f99d41bcf4ca63",
"a892dfe89740b6fa75d3de5319f99d41bcf4ca63"
] | [
"ponai/data/grid_dataset.py",
"ponai/transforms/post/array.py",
"ponai/transforms/intensity/array.py"
] | [
"# Copyright 2020 ponai 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 i... | [
[
"torch.utils.data.get_worker_info"
],
[
"torch.sigmoid",
"torch.clamp_",
"torch.softmax",
"torch.ones",
"torch.nn.functional.conv3d",
"torch.nn.functional.conv2d",
"torch.zeros_like",
"torch.unsqueeze",
"torch.tensor",
"torch.any",
"torch.squeeze",
"torch.ar... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kylemath/mne-python | [
"586c5d918a673ab5d5c92ffb4479fe57fee5559d"
] | [
"mne/channels/tests/test_montage.py"
] | [
"# Author: Teon Brooks <teon.brooks@gmail.com>\n# Stefan Appelhoff <stefan.appelhoff@mailbox.org>\n#\n# License: BSD (3-clause)\n\nfrom itertools import chain\nimport os\nimport os.path as op\n\nimport pytest\n\nimport numpy as np\nfrom functools import partial\nfrom string import ascii_lowercase\n\nfrom nu... | [
[
"numpy.testing.assert_equal",
"numpy.arange",
"numpy.eye",
"numpy.empty",
"numpy.stack",
"numpy.testing.assert_array_equal",
"numpy.full",
"matplotlib.pyplot.close",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.zeros",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NYC00kie/PhysWikiQuiz | [
"4243fd6fa6f23670b9743b6a2c79339a9f3d32fc"
] | [
"module_unit_tests.py"
] | [
"import unittest\n\nfrom old import module0_formula_and_identifier_retrieval\n\nimport pandas as pd\n\n# Python Tutorial: Unit Testing Your Code with the unittest Module:\n#https://www.youtube.com/watch?v=6tNS--WetLI\n\n# Retrieve sample QIDs\nsample_IDs_filepath = r'evaluation\\sample_IDs.csv'\nQIDs_column_name = ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
bertelschmitt/multistreamYOLO | [
"827a1d2ae11653fe5fde2cee3b52cda8baae9899"
] | [
"2_Training/Train_YOLO.py"
] | [
"\n\"\"\"\nMODIFIED FROM keras-yolo3 PACKAGE, https://github.com/qqwweee/keras-yolo3\nRetrain the YOLO model for your own dataset.\n\n10-26-20 MODIFIED by bertelschmitt to use new repo name if changed to something else than \"TrainYourOwnYOLO\"\n10-31-20 UPDATED by bertelschmitt to reflect TrainYourOwnYOLO version... | [
[
"numpy.random.shuffle",
"numpy.random.seed",
"tensorflow.compat.v1.logging.set_verbosity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
limeng357/Paddle | [
"dbd25805c88c48998eb9dc0f4b2ca1fd46326482",
"dbd25805c88c48998eb9dc0f4b2ca1fd46326482",
"dbd25805c88c48998eb9dc0f4b2ca1fd46326482",
"dbd25805c88c48998eb9dc0f4b2ca1fd46326482",
"dbd25805c88c48998eb9dc0f4b2ca1fd46326482",
"dbd25805c88c48998eb9dc0f4b2ca1fd46326482",
"dbd25805c88c48998eb9dc0f4b2ca1fd4632648... | [
"python/paddle/fluid/tests/unittests/test_bipartite_match_op.py",
"python/paddle/fluid/tests/unittests/test_pool2d_op.py",
"python/paddle/fluid/data_feeder.py",
"python/paddle/fluid/tests/unittests/test_beam_search_decode_op.py",
"python/paddle/fluid/tests/unittests/test_parallel_op.py",
"python/paddle/fl... | [
"# Copyright (c) 2018 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 requir... | [
[
"numpy.random.random",
"numpy.argwhere",
"numpy.ones",
"numpy.argmax",
"numpy.zeros"
],
[
"numpy.random.random",
"numpy.min",
"numpy.max",
"numpy.zeros",
"numpy.sum"
],
[
"numpy.array"
],
[
"numpy.array"
],
[
"numpy.random.random",
"numpy.all... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
Felix-neko/catalyst | [
"df80986f1c12ef6a3776637453a0c04aaef0068c"
] | [
"catalyst/rl/scripts/run_samplers.py"
] | [
"#!/usr/bin/env python\n\nimport os\nos.environ[\"OMP_NUM_THREADS\"] = \"1\"\nos.environ[\"MKL_NUM_THREADS\"] = \"1\"\n\nimport copy # noqa E402\nimport time # noqa E402\nimport atexit # noqa E402\nimport argparse # noqa E402\nimport multiprocessing as mp # noqa E402\n\nimport torch # noqa E402\ntorch.set_num... | [
[
"torch.set_num_threads"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jahau/addons | [
"11b842781b0f022830f35f2e6ee1cc93c80abe50",
"11b842781b0f022830f35f2e6ee1cc93c80abe50",
"11b842781b0f022830f35f2e6ee1cc93c80abe50",
"11b842781b0f022830f35f2e6ee1cc93c80abe50",
"11b842781b0f022830f35f2e6ee1cc93c80abe50"
] | [
"tensorflow_addons/image/interpolate_spline.py",
"tensorflow_addons/optimizers/stochastic_weight_averaging.py",
"tensorflow_addons/seq2seq/loss_test.py",
"tensorflow_addons/layers/poincare_test.py",
"tensorflow_addons/losses/quantiles.py"
] | [
"# 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.convert_to_tensor",
"tensorflow.matmul",
"tensorflow.linalg.diag_part",
"tensorflow.transpose",
"tensorflow.concat",
"tensorflow.shape",
"tensorflow.zeros",
"tensorflow.maximum",
"tensorflow.pow",
"tensorflow.ones_like",
"tensorflow.expand_dims",
"tensor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},... |
awesome-archive/pycorrector | [
"022da83ab794d9f9ddc40caef67b0578e7e3f513"
] | [
"pycorrector/seq2seq/infer.py"
] | [
"# -*- coding: utf-8 -*-\n# Author: XuMing <xuming624@qq.com>\n# Brief: \n\nimport numpy as np\nimport tensorflow as tf\nfrom keras.models import load_model\n\nfrom pycorrector.seq2seq import cged_config as config\nfrom pycorrector.seq2seq.corpus_reader import CGEDReader, load_word_dict\nfrom pycorrector.seq2seq.re... | [
[
"tensorflow.get_default_graph",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
rahulvigneswaran/TailCalibX | [
"0ed18cc8903715c0e31934c54226a53b1bbfc198"
] | [
"libs/models/DotProductClassifier.py"
] | [
"# Imports\nimport torch.nn as nn\nfrom os import path\nimport torch\nimport torch.nn.functional as F\n\nclass DotProduct_Classifier(nn.Module):\n def __init__(self, num_classes=1000, feat_dim=2048, *args):\n super(DotProduct_Classifier, self).__init__()\n self.fc = nn.Linear(feat_dim, num_classes)... | [
[
"torch.nn.Linear",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Pavivenkatesan/TicTacToe-RL-MM- | [
"fbaab6bb9574b82ae0d79c818ba74d049375bfd4"
] | [
"testing.py"
] | [
"import numpy as np\nfrom math import inf as infinity\nfrom itertools import product\nfrom collections import defaultdict\nimport random\nimport time\n\n# Initializing the Tic-Tac-Toe environment\n# Three rows-Three columns, creating an empty list of three empty lists\nstate_space = [[' ', ' ', ' '], [' ', ' ', ' '... | [
[
"numpy.argmax",
"numpy.loadtxt",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Deech08/modspectra | [
"4af177418f9ac3e1ff30bf99968251ac143a96bc"
] | [
"modspectra/tests/test_spectrum_creation.py"
] | [
"import pytest\nfrom numpy.random import randn\nfrom numpy.random import random\nimport numpy as np\n\ndef test_non_detection():\n from ..cube import EmissionCube\n from astropy.coordinates import SkyCoord\n import astropy.units as u\n '''\n Test that an anti-center pointing returns zero emission\n ... | [
[
"numpy.random.randn",
"numpy.zeros_like",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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