repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
opdev1004/tensorflowtts-demo | [
"723b4119c8969e8815d6c87fa0eebf38df5b4a28"
] | [
"app.py"
] | [
"import os\nimport sys\nimport tensorflow as tf\nimport yaml\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport IPython.display as ipd\n\nfrom tensorflow_tts.inference import AutoConfig\nfrom tensorflow_tts.inference import TFAutoModel\nfrom tensorflow_tts.inference import AutoProcessor\nfrom scipy.io.wav... | [
[
"tensorflow.convert_to_tensor",
"numpy.rot90",
"matplotlib.pyplot.tight_layout",
"scipy.io.wavfile.write",
"matplotlib.pyplot.figure",
"tensorflow.reshape",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"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"... |
xunilrj/sandbox | [
"f92c12f83433cac01a885585e41c02bb5826a01f",
"f92c12f83433cac01a885585e41c02bb5826a01f",
"f92c12f83433cac01a885585e41c02bb5826a01f",
"f92c12f83433cac01a885585e41c02bb5826a01f"
] | [
"courses/columbia-ia/linearregression/problem2_3.py",
"courses/MITx/MITx 6.86x Machine Learning with Python-From Linear Models to Deep Learning/project0/neuralnetwork.py",
"courses/MITx/MITx 6.86x Machine Learning with Python-From Linear Models to Deep Learning/MITx 6.86x/homework4.lstm.py",
"courses/MITx/MIT... | [
"\nimport sys\nimport pandas as pd\nimport numpy as np\n#import matplotlib as mpl\n#import matplotlib.pyplot as plt\n#from mpl_toolkits.mplot3d import Axes3D\n\ninputfile = \"input2.csv\"\ntry:\n inputfile = sys.argv[1]\nexcept:\n inputfile = \"input2.csv\"\noutputfile = \"output.csv\"\ntry:\n outputfile =... | [
[
"pandas.read_csv",
"numpy.sum"
],
[
"numpy.tanh"
],
[
"numpy.exp",
"numpy.array",
"numpy.tanh"
],
[
"numpy.isreal",
"numpy.random.random",
"numpy.abs",
"numpy.arange",
"numpy.linalg.norm",
"numpy.ones",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
zgahhblhc/ESAPN | [
"5ae9afdbeb7e2d098bde05e68503814077381d16"
] | [
"baselines/Darts/train_darts.py"
] | [
"# %matplotlib inline\nimport os, time, pickle, argparse\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom scipy.stats import beta\ntorch.set_printoptions(threshold=10000)\nnp.set_printoptions(threshold=np.inf)\n\nparser = argparse.ArgumentParser(description='RSAutoML')\nparser.add... | [
[
"torch.nn.Softmax",
"torch.max",
"torch.cat",
"pandas.DataFrame",
"torch.nn.BCEWithLogitsLoss",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"torch.nn.CrossEntropyLoss",
"torch.from_numpy",
"torch.tensor",
"torch.nn.Sigmoid",
"scipy.stats.beta.cdf... | [
{
"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": []
}
] |
m0n0l0c0/squad-experiments | [
"b7d5e9a73117e0921a0bcb5eda764be10661ebc9"
] | [
"scripts/overlap_plot.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nCross answers from different models, this gets an amount of overlap between\ndifferent models against the same dataset.\n\"\"\"\nfrom matplotlib import pyplot as plt\nfrom collections import defaultdict\n\nimport json, os, sys\nimport pandas as pd\nimport argparse\n\nargs = None\n\... | [
[
"matplotlib.pyplot.grid",
"matplotlib.pyplot.rcParams.update",
"matplotlib.pyplot.show",
"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": []
}
] |
philchang/yt | [
"5fae744569cd0719db61838c0fd2b20fe29c7184",
"5fae744569cd0719db61838c0fd2b20fe29c7184",
"5fae744569cd0719db61838c0fd2b20fe29c7184"
] | [
"yt/frontends/gadget/data_structures.py",
"yt/data_objects/level_sets/clump_handling.py",
"yt/frontends/halo_catalog/tests/test_outputs.py"
] | [
"\"\"\"\nData structures for Gadget frontend\n\n\n\n\n\"\"\"\nfrom __future__ import print_function\n\n#-----------------------------------------------------------------------------\n# Copyright (c) 2014, yt Development Team.\n#\n# Distributed under the terms of the Modified BSD License.\n#\n# The full license is i... | [
[
"numpy.array",
"numpy.zeros",
"numpy.dtype",
"numpy.ones"
],
[
"numpy.array",
"numpy.unique"
],
[
"numpy.random.RandomState",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ravitejasssihl/Artifact_Detection | [
"0c86839a711c92f3fdd6c0459a1b2582e27a2832"
] | [
"artifact_code/common_functions.py"
] | [
"\"\"\"Code which is used for both blockiness and also Ringing and mosquito_noise artifacts.\"\"\"\r\nimport logging\r\nfrom cv2 import cv2\r\nimport matplotlib.pyplot as plt\r\n\r\n#Constant values that can be used in this program.\r\nBLOCK_ROWS = 5\r\nBLOCK_COLS = 5\r\nT_FLAT = 1\r\nT_TEX = 200\r\n\r\n\r\ndef ge... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carsault/ace_pytorch | [
"fbed64990001afdafe44ef7b0d5567f94c81a3c3"
] | [
"createDataset.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 27 15:31:58 2019\n\n@author: carsault\n\"\"\"\n#%%\nimport argparse\nimport os\nimport numpy as np\nimport torch\nimport torch.utils.data as data_utils\nfrom torch.utils.data.dataset import *\nfrom torch.utils import data\nimport pickle\nf... | [
[
"numpy.load",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vdesgrange/autodeeplab-1 | [
"7c4ccf6f7cf7b9d8da75e602c8523771cda8c556"
] | [
"evaluate.py"
] | [
"import os\nimport numpy as np\nimport os.path as osp\n\nimport torch\nimport torch.nn as nn\nimport torch.backends.cudnn\nimport torch.nn.functional as F\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom mypath import Path\nfrom utils.utils import AverageMeter, inter_and_union\nfrom config_utils.evaluate... | [
[
"torch.nn.functional.upsample",
"torch.max",
"torch.load",
"torch.zeros",
"torch.nn.DataParallel",
"torch.no_grad",
"torch.cuda.is_available",
"torch.utils.data.dataloader.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ggear/asystem_archive | [
"b97f67218e8aa60991fba386c9e73d27d20d6c47"
] | [
"asystem-amodel/src/main/script/python/energyforecast_intraday.py"
] | [
"\"\"\"\nPRE-PROCESSED SCRIPT - EDITS WILL BE CLOBBERED BY MAVEN BUILD\n\nThis file is in the SCRIPT pre-processed state with template available by the\nsame package and file name under the modules src/main/template directory.\n\nIf editing as a SCRIPT or LIBRARY, all changes must be merged to the TEMPLATE,\nelse t... | [
[
"sklearn.externals.joblib.dump",
"pandas.concat",
"pandas.to_datetime",
"numpy.arange",
"pandas.DataFrame",
"matplotlib.pyplot.rcParams.update",
"pandas.set_option",
"sklearn.externals.joblib.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alzaia/applied_machine_learning_python | [
"3ddd7e2cda14f42c9f5b2e57a4ecbb334f649aa9",
"3ddd7e2cda14f42c9f5b2e57a4ecbb334f649aa9"
] | [
"knn/knn_regression_synthetic_dataset.py",
"linear_regression/linear_regression_synthetic_dataset.py"
] | [
"# Example of knn regression using a simple synthetic dataset\n\n\nimport numpy as np\nimport pandas as pd\nimport seaborn as sn\nimport matplotlib.pyplot as plt\n\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.datasets import make_regression\nfrom sklearn.model_selection import train_test_split\n... | [
[
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"sklearn.model_selection.train_test_split",
"sklearn.neighbors.KNeighborsRegressor",
"sklearn.datasets.make_regression",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"matplotlib.pyplot.scatter",
"matplotli... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
beammieb/earthpy | [
"92e90934e37a8d07ba594c6f95d13553b0d304dd"
] | [
"earthpy/spatial.py"
] | [
"\"\"\"\nearthpy.spatial\n===============\n\nFunctions to manipulate spatial raster and vector data.\n\n\"\"\"\n\nimport os\nimport sys\nimport contextlib\nimport warnings\nimport numpy as np\nfrom shapely.geometry import mapping, box\nimport geopandas as gpd\nimport rasterio as rio\nfrom rasterio.mask import mask\... | [
[
"numpy.sqrt",
"numpy.gradient",
"numpy.isnan",
"numpy.squeeze",
"numpy.cos",
"numpy.sin",
"numpy.arctan2",
"numpy.ma.masked_equal",
"numpy.ma.masked_invalid",
"numpy.errstate",
"numpy.array",
"numpy.isinf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xinshuwei/models | [
"b39bd997f1621ce9fdf57efe48e1767a9b7c9214",
"7d0b040e730f01e79cb749fa55361b32456c5175"
] | [
"research/slim/nets/dcgan_test.py",
"research/object_detection/trainer_test.py"
] | [
"# 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#\n# Unless requ... | [
[
"tensorflow.zeros",
"tensorflow.test.main",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.reset_default_graph",
"tensorflow.set_random_seed",
"tensorflow.random_uniform",
"tensorflow.random_normal"
],
[
"tensorflow.constant",
"tensorfl... | [
{
"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... |
sevagh/slicqt | [
"883ef261a5bd3d1b3da3f8351ddd61656cffee5d"
] | [
"nsgt/plot.py"
] | [
"import matplotlib.pyplot as plt\nimport torch\nimport numpy\nfrom nsgt.slicq import overlap_add_slicq\n\n\ndef spectrogram(c, fs, coef_factor, transform_name, freqs, frames, sliced=True, flatten=False, fontsize=14, cmap='inferno', slicq_name='', output_file=None):\n # dB\n if not sliced:\n mls = 20.*t... | [
[
"torch.mean",
"torch.abs",
"numpy.linspace",
"matplotlib.pyplot.subplots",
"torch.quantile",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.rcParams.update",
"matplotlib.pyplot.show",
"torch.squeeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
flothesof/pyvista | [
"ea24bc5463c9f07a58387440685fe59048c24b88"
] | [
"pyvista/core/objects.py"
] | [
"\"\"\"This module provides wrappers for vtkDataObjects.\n\nThe data objects does not have any sort of spatial reference.\n\n\"\"\"\n\nimport numpy as np\nimport vtk\n\nimport pyvista\nfrom pyvista.utilities import (ROW_DATA_FIELD, assert_empty_kwargs,\n convert_array, get_array, parse... | [
[
"numpy.nanmax",
"numpy.ascontiguousarray",
"numpy.issubdtype",
"numpy.nanmin",
"pandas.DataFrame",
"numpy.array",
"numpy.flip"
]
] | [
{
"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": []
}
] |
Egiob/stable-baselines3 | [
"4917217f8d7862fb509f60550e44cc251bad26d1"
] | [
"stable_baselines3/diayn/diayn.py"
] | [
"import io\nimport pathlib\nimport sys\nimport time\nfrom collections import deque\nfrom logging import log\nfrom types import FunctionType as function\nfrom typing import Any, Dict, List, Optional, Tuple, Type, Union\n\nimport gym\nimport numpy as np\nimport torch as th\nfrom numpy.core.fromnumeric import mean\nfr... | [
[
"torch.ones",
"numpy.abs",
"torch.Tensor",
"torch.cat",
"numpy.isnan",
"scipy.special.expit",
"torch.min",
"torch.nn.functional.mse_loss",
"torch.no_grad",
"numpy.mean",
"numpy.prod",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gokceneraslan/ProDy | [
"74d40c372c53bd68f5e9f9c47b991b2e2b1b9f27"
] | [
"prody/measure/contacts.py"
] | [
"# -*- coding: utf-8 -*-\n# ProDy: A Python Package for Protein Dynamics Analysis\n# \n# Copyright (C) 2010-2012 Ahmet Bakan\n# \n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either ver... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Wang-Yikai/Final-Project-for-Natural-Language-Processing-FDU | [
"4072f57a57b58736b0ebf9cf6dedfda8a22e8f4d"
] | [
"TextCNN_Siamese/siameseTextCNN_v3.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jan 20 23:56:18 2018\n\n@author: Hendry\n\"\"\"\n\nimport tensorflow as tf\nimport numpy as np\n\nclass siameseTextCNN(object):\n\n # Create model\n def __init__(self,w2v_model, seqLengthDoc, seqLengthTitle, vocabSize,\n embeddingSi... | [
[
"tensorflow.nn.bias_add",
"tensorflow.nn.xw_plus_b",
"tensorflow.matmul",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.concat",
"tensorflow.constant",
"tensorflow.multiply",
"tensorflow.reduce_mean",
"tensorflow.truncated_normal",
"tensorflow.cast",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Peta0228/TC | [
"9b12f7daea2187eeab6539ceded6dab8f322d612"
] | [
"scripts/search.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# ###### Intro\n# - query from twitter api, pipelined to aws RDS\n# - before pipelining, check queries for alert, alert sent by email\n\n# establish twitter api connection\n\n# In[ ]:\n\n\n# import library\nimport pandas as pd\nimport numpy as np\nimport tweepy\nimport csv... | [
[
"numpy.isnan",
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
xgenpanda/keras_extension | [
"99384d96025d9ef29bb6a757fbeda942a3610a11"
] | [
"examples/rbm_example.py"
] | [
"from __future__ import division\n\nimport time\n\nimport numpy as np\nfrom theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\n\nnp.random.seed(1234) # seed random number generator\nsrng_seed = np.random.randint(2**30)\n\nfrom keras.models import Sequential\nfrom keras.optimizers import SGD\n\nfrom k... | [
[
"numpy.ones",
"numpy.zeros",
"numpy.random.seed",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Maxence-Labesse/MLKit | [
"7f8d92b5d3e025dc3719c3bbaf1f2e55afda5107"
] | [
"build/lib/AutoMxL/Preprocessing/Categorical.py"
] | [
"\"\"\" Categorical features processing\n\n - CategoricalEncoder (class) : Encode categorical features\n - dummy_all_var (func) : get one hot encoded vector for each category of a categorical features list\n - get_embedded_cat (func) : get embedding representation with NN\n - mca (func) : to do\n\n\"\"\"\nimport pa... | [
[
"pandas.concat",
"sklearn.preprocessing.LabelEncoder",
"pandas.get_dummies"
]
] | [
{
"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": []
}
] |
IoannisAndrikos/BladderThickness | [
"508cd5f80ccdad1bccf528d14a37e90668a323f7"
] | [
"Unet/sequenceLoader.py"
] | [
"'''\n\tData generator inheritting from the Sequence Keras Generator\n'''\nfrom keras.utils import Sequence\nimport numpy as np\nimport cv2\nfrom augmentation import augment\nfrom glob import glob\nimport os\nfrom matplotlib import pyplot as plt\nfrom math import ceil, floor\nfrom skimage import exposure\n\nfrom co... | [
[
"numpy.intersect1d",
"numpy.array",
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dreamer820/code-snippets | [
"8f7a8ae032138d8fe444357916def887fe0faff5"
] | [
"2019/avro-to-arrow/avro_to_arrow/decoder.py"
] | [
"# Copyright 2019 The Avro-to-Arrow Project\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# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable... | [
[
"numpy.int64",
"numpy.uint64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mishidemudong/my_Knover | [
"818913235a5179d3bc2cc24ae576eb1dafe8e02d"
] | [
"readers/nsp_reader.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 ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lcassano/Logical_Team_Q_Learning_paper | [
"6a7c25bed6a6292032702a11fc2ebcf54518a4d6"
] | [
"catch_prey/experiment.py"
] | [
"# coding=utf-8\n\n# Import all packages\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\n\ndef run(agent,\n environment,\n num_agents: int,\n num_epochs: int,\n nmbr_games: int,\n save_period: int,\n seed: int,\n save_model: bool,\n ... | [
[
"tensorflow.constant",
"numpy.reshape",
"numpy.arange",
"matplotlib.pyplot.subplots",
"numpy.logical_or",
"numpy.ceil",
"numpy.mean",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.close",
"numpy.savetxt",
"matplotlib.pyplot.xlabel",
"numpy.floor",
"numpy.zeros",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.4",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1.2",
"2.... |
PierreMarion23/ML_exercises | [
"a5a0df86311a0ecab27ff9e03d34736a75aa9238"
] | [
"AMAL/week5/tp5_preprocess.py"
] | [
"import array\nimport csv\nimport gzip\nimport logging\nimport re\nimport shutil\nimport subprocess\nimport sys\nfrom collections import namedtuple\nfrom pathlib import Path\nfrom tqdm import tqdm\n\nimport click\nimport sentencepiece as spm\nimport torch\n\nfrom datamaestro import Dataset, prepare_dataset\n\nloggi... | [
[
"torch.LongTensor",
"torch.nn.utils.rnn.pad_sequence",
"torch.load",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wisrovi/imagenesDocker | [
"fcaddf578ea09bcf7070739ead62a0df7610b305"
] | [
"python-tensorflow/test_gpu.py"
] | [
"import tensorflow as tf\n\nprint(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n\nprint(tf.test.is_gpu_available())\n\nprint(tf.test.is_built_with_cuda())\n\nfrom tensorflow.python.client import device_lib\nprint(device_lib.list_local_devices())\n"
] | [
[
"tensorflow.test.is_gpu_available",
"tensorflow.test.is_built_with_cuda",
"tensorflow.config.list_physical_devices",
"tensorflow.python.client.device_lib.list_local_devices"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
bhoeckendorf/serialization | [
"5642031874b7cdafad2438fa35fa431734c83d03"
] | [
"_tests/test_numpy.py"
] | [
"import numpy as np\nimport pickle\nimport serialization\nfrom copy import deepcopy\nimport pytest\n\n\ndef test_numpy():\n arr = np.ones((128, 256, 256), dtype=np.float32)\n arr[8, 8, 8] = 2\n arrpkl = pickle.dumps(arr)\n\n data = [\n 1, \"2\", arr,\n [3, \"4\", deepcopy(arr), {\"arr\": d... | [
[
"numpy.all",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
huxiaoman7/mindspore | [
"930a1fb0a8fa9432025442c4f4732058bb7af592"
] | [
"tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py"
] | [
"# Copyright 2019 Huawei Technologies Co., Ltd\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/LICENSE-2.0\r\n#\r\n# Unless required... | [
[
"numpy.split",
"numpy.allclose",
"numpy.random.seed",
"numpy.reshape",
"numpy.arange",
"numpy.set_printoptions",
"numpy.ones",
"numpy.cumprod",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gluesolutions/glue-genomics-data | [
"0e8b055908727b2c385ef64ce4d116de5cd995a9"
] | [
"glue_genomics_data/bed_factory.py"
] | [
"from glue.config import data_factory\nfrom glue.core import Data\nimport pandas as pd\nfrom pathlib import Path\nfrom qtpy.QtWidgets import QDialog\n#from .preprocessors.peak_correlations import PeakCorrelationsPreprocessor\n\n\n__all__ = ['is_bed', 'read_bed']\n\n\ndef is_bed(filename, **kwargs):\n return file... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Nobregaigor/febio-python | [
"6895b4e2f8959444a66b6f77a000ce193c3357a7"
] | [
"febio_python/feb/FEBio_xml_handler.py"
] | [
"from tkinter import E\nimport xml.etree.ElementTree as ET\nfrom os.path import isfile, join\n# from .. logger import console_log as log\nfrom .enums import *\nfrom pathlib import Path\nimport numpy as np\n\nfrom febio_python.utils.enum_utils import *\n\nclass FEBio_xml_handler():\n def __init__(self, tree, root... | [
[
"numpy.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhoudoao/bayes_vae | [
"8e7477dc1369a9ba17086a9c6982d7cd83f7e94b"
] | [
"bayes_vae/examples/mnist_bayes_nn.py"
] | [
"# Copyright 2019, zhoudoao@gmail.com.\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 law or a... | [
[
"tensorflow.keras.Sequential",
"tensorflow.keras.datasets.mnist.load_data",
"tensorflow.keras.optimizers.Adam",
"tensorflow.one_hot",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
zli117/Evolution | [
"b5be1552338fa57b9a3e4743c8e917e30d2caada"
] | [
"evolution/train/trainer.py"
] | [
"import os\nfrom abc import ABC\nfrom abc import abstractmethod\nfrom dataclasses import dataclass\nfrom multiprocessing import Manager\nfrom multiprocessing import Pool\nfrom multiprocessing import Process\nfrom queue import Queue\nfrom typing import Dict, Any, Tuple, Generator, List\n\nimport numpy as np\nimport ... | [
[
"tensorflow.device",
"tensorflow.compat.v1.ConfigProto",
"tensorflow.compat.v1.GPUOptions",
"tensorflow.python.client.device_lib.list_local_devices",
"tensorflow.keras.Input",
"tensorflow.compat.v1.keras.backend.set_session",
"sklearn.model_selection.KFold",
"tensorflow.keras.Model... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
benmoseley/simple-variational-quantum-eigensolver | [
"917f00860a3fd68fbef14d2ff603d4a6908004ac"
] | [
"HamiltonianFile.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Aug 2 12:47:55 2018\n\n@author: bmoseley\n\"\"\"\n\nfrom projectq.ops import QubitOperator\nimport numpy as np\nfrom helper import isHermitian\n\nclass HamiltonianFile:\n '''Get Hamiltonian operator from text file.\n \n Expects Hamil... | [
[
"numpy.set_printoptions",
"numpy.kron",
"numpy.copy",
"numpy.linalg.eigh",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pablovillabay/geopkg | [
"fcb6950f2ae24cf559b83f9b7aa44ab0ef515482"
] | [
"geopkg/viz/well_viz.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\n\nplt.style.use('seaborn')\n\n\n\ndef well_track(plot_logs, depth, zmin, zmax, zones, zone_colors=[], tracks={}, figsize=(20,10)):\n\n \"\"\"\n\n Display a one-well log section defined with a 2-level dict\n\n Paramete... | [
[
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChenyanWu/3DMPPE_POSENET_RELEASE | [
"6aca594c2ecd611ec6a4657dfac06ab901fb120f"
] | [
"data/MuPoTS/MuPoTS.py"
] | [
"import os\nimport os.path as osp\nimport scipy.io as sio\nimport numpy as np\nfrom pycocotools.coco import COCO\nfrom config import cfg\nimport json\nimport cv2\nimport random\nimport math\nfrom utils.pose_utils import pixel2cam, process_bbox\nfrom utils.vis import vis_keypoints, vis_3d_skeleton\n\nclass MuPoTS:\n... | [
[
"numpy.take",
"numpy.ones",
"numpy.concatenate",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jesterhui/interpretable_ml_description_chemisorption_alloys | [
"42694cc6bf8669601dfd24dde4b458c5cb970d0d"
] | [
"src/models/pt_oh_gam.py"
] | [
"\"\"\"\nGenerate OH model.\n\"\"\"\nimport pickle\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nimport setup\nfrom src.bst_bag_tree import BoostedBaggedTreeGAM\n\nnp.random.seed()\nDATA = np.loadtxt('../../data/processed/pt_oh_data.csv', dtype='float',\n delimiter=',',... | [
[
"sklearn.model_selection.train_test_split",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SaverioVad/HAD_Net | [
"67b2dfb6bae5b971c86b42d747975684d96e36b1"
] | [
"utils/data_preprocessor.py"
] | [
"import os\nimport time\nfrom threading import Timer\nimport multiprocessing\nfrom multiprocessing import Process, JoinableQueue\n\nimport nibabel as nib\nfrom os import listdir, makedirs\nfrom os.path import join, exists, realpath\n\nfrom scipy import ndimage\nimport numpy as np\nimport traceback\nimport argparse\... | [
[
"numpy.amax",
"numpy.absolute",
"numpy.nonzero",
"numpy.multiply",
"numpy.amin",
"numpy.around",
"numpy.subtract",
"numpy.ones",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
slebastard/GeomAbs | [
"39f5fe8add38f7bd234a22b00c666cd9febd5ac9"
] | [
"geom_abs/methods/tests/test_models.py"
] | [
"## Testing libraries imports\nimport pytest\nimport pdb\n\n## Standard library imports \nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\n## Home-cooked methods imports\nimport os, sys\nlib_path = os.path.abspath('./methods')\nsys.path.insert(0, lib_path)\n\n\n# Loading small sample... | [
[
"sklearn.decomposition.PCA"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahclab/reflection | [
"d09fd500e9ff74211e1eb15711d479bb204c7a83"
] | [
"acl_version/src/reflection_based_transfer/evals.py"
] | [
"import numpy\nimport chainer\nfrom chainer import Variable\nfrom tqdm import tqdm\nfrom statistics import mean\nimport random\nimport data_loader\nimport copy\nimport json\n\n\ndef evaluation1_acc(xs, ts, zs, net, word2vec, n_top, dtype):\n results = get_accuracy(xs, ts, zs, net, word2vec, n_top, show=False) # ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hundlab/MAT | [
"d3b9ff0ee7f4a77e3264976d86bb5ac7b70c1312"
] | [
"src/MAT/BDD.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 2 12:07:17 2019\n\n@author: alexj, grat05\n\"\"\"\n\nfrom scipy.ndimage import gaussian_filter\nfrom scipy.ndimage.morphology import binary_erosion\nfrom skimage.morphology import disk\nfrom sklearn.cluster import DBSCAN\nfrom sklearn.pre... | [
[
"scipy.ndimage.gaussian_filter",
"numpy.abs",
"numpy.asarray",
"numpy.less",
"sklearn.cluster.DBSCAN",
"numpy.max",
"numpy.mean",
"numpy.zeros_like",
"numpy.argmin",
"sklearn.preprocessing.StandardScaler",
"numpy.logical_and",
"numpy.where"
]
] | [
{
"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"... |
dingyanna/DepthNets | [
"b0795b97c94bbba1a1e3310670d0868f3eacb479"
] | [
"cyclegan/task_launcher_faceswap.py"
] | [
"import numpy as np\nimport torch\nimport glob\nimport os\nimport pickle\nimport argparse\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.dataset import (TensorDataset,\n ConcatDataset)\nfrom i2i.cyclegan import CycleGAN\nfrom util import (convert_to_rgb,\n ... | [
[
"torch.utils.data.DataLoader",
"numpy.zeros",
"torch.utils.data.dataset.ConcatDataset",
"numpy.min"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jmnel/combinatorial-optimization | [
"c921dee6cb0febc47a8a791f8220b02b35caf0cf"
] | [
"midterm/test/NEL_Jacques-212588109-midterm1/hill_climb.py"
] | [
"from typing import Tuple, Callable\nimport numpy as np\nfrom time import perf_counter\nimport matplotlib\nmatplotlib.use('GTK3Cairo')\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\ndef hill_climb(initial_state: Tuple[int, int],\n grid: np.array,\n max_epo... | [
[
"numpy.linspace",
"numpy.power",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"numpy.argmax",
"numpy.array",
"numpy.meshgrid",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
miyamotok0105/modeldb | [
"6b2b7fb598d90be733e4b68efae3165c24efe2d1",
"6b2b7fb598d90be733e4b68efae3165c24efe2d1"
] | [
"client/python/modeldb/sklearn_native/GridCrossValidation.py",
"client/python/samples/sklearn/Titanic-LogisticRegression.py"
] | [
"import numpy as np\nimport time\nimport warnings\nfrom sklearn.grid_search import GridSearchCV, ParameterGrid, _CVScoreTuple\nfrom sklearn.pipeline import Pipeline\nfrom sklearn import datasets, linear_model, cross_validation, grid_search\nfrom sklearn.cross_validation import _safe_split, _score\nfrom sklearn.cros... | [
[
"sklearn.utils.validation._num_samples",
"sklearn.metrics.scorer.check_scoring",
"sklearn.externals.joblib.delayed",
"sklearn.cross_validation._safe_split",
"sklearn.grid_search.ParameterGrid",
"sklearn.base.clone",
"sklearn.utils.multiclass.type_of_target",
"sklearn.base.is_classi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
minexew/h3explorer | [
"43582e025327bf9eea324e085735703fb55e32af"
] | [
"DefFile.py"
] | [
"import numpy as np\nimport struct\n\nclass DefFile:\n def __init__(self, f):\n self.f = f\n\n unk1, image_width, image_height, num_images = struct.unpack('<IIII', f.read(16))\n #print((unk1, image_width, image_height, num_images))\n\n self.w = image_width\n self.h = image_heig... | [
[
"numpy.frombuffer",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
umarov90/DeepFake | [
"e65c72f255817532e8a8a3afe2138ae270477601"
] | [
"figures/profiles_viz.py"
] | [
"import os\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom scipy import stats\n\nmatplotlib.use(\"Agg\")\nsns.set(font_scale=1.3, style='ticks')\n\n\ndef draw_profiles(test_profile, decoded, closest_profile, pname, input_size, bp, dp, output_file):\n img_dat... | [
[
"matplotlib.pyplot.tight_layout",
"numpy.abs",
"matplotlib.pyplot.scatter",
"numpy.min",
"numpy.asarray",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"numpy.max",
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kangheeyong/PROJECT-personal-recommendation-system-demo | [
"f8c6489f8bc17463fa572dc47e4bcbfc5fdb1397"
] | [
"step_1/demo_user.py"
] | [
"import time\n\nimport json\nimport asyncio\nimport websockets\nimport numpy as np\nfrom fire import Fire\n\nfrom Feynman.cloud import Google_drive\nfrom Feynman.serialize import Pickle_serializer\nfrom Feynman.etc.util import Config, get_logger\n\n\nclass Demo_user():\n def __init__(self):\n self.logger ... | [
[
"numpy.random.poisson",
"numpy.dot",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
karzexcc/EE208-labs | [
"5e6fc8208c0d0bfa5c338d1294e23fc6848d6fc6"
] | [
"lab8/code/utilies.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef compute_gradient(img):\n\t'''\n\t\tcompute the corresponding gradient of a grayscale image.\n\t\tInput:\n\t\t\ta grayscale image, which is represented with a numpy array.\n\t\tOutput:\n\t\t\ta tuple of numpy array, (I_x, I_y), \n\t\t\tthe gradient in the x... | [
[
"numpy.power",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.clf",
"numpy.zeros_like",
"numpy.floor",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"numpy.sum",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Y-Kuro-u/chariot | [
"032f3eecdd55b30c65351e1e636c939c4b20919e"
] | [
"chariot/dataset_preprocessor.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom chariot.transformer.formatter.base import BaseFormatter\nfrom chariot.transformer.generator.base import BaseGenerator\nfrom chariot.preprocessor import Preprocessor\nfrom chariot.base_dataset_preprocessor import BaseDatasetPreprocessor\n\n\nclass ProcessBuilder():\n\n ... | [
[
"numpy.arange",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HodaEb/ViT-pytorch | [
"2643740b1d846ae666635bb0f5a71bceba208675"
] | [
"models/modeling.py"
] | [
"# coding=utf-8\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport copy\nimport logging\nimport math\n\nfrom os.path import join as pjoin\n\nimport torch\nimport torch.nn as nn\nimport numpy as np\n\nfrom torch.nn import CrossEntropyLoss, Dropou... | [
[
"torch.nn.Softmax",
"numpy.sqrt",
"torch.cat",
"torch.zeros",
"numpy.concatenate",
"torch.nn.init.xavier_uniform_",
"torch.no_grad",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"scipy.ndimage.zoom",
"torch.from_numpy",
"torch.sigmoid",
"torch.nn.ModuleList"... | [
{
"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"... |
nickhand/geopandas | [
"9fdeb1474185c8d2edaede428f57d258810d87b7"
] | [
"geopandas/io/tests/test_pickle.py"
] | [
"\"\"\"\nSee generate_legacy_storage_files.py for the creation of the legacy files.\n\n\"\"\"\nfrom distutils.version import LooseVersion\nimport glob\nimport os\nimport pathlib\n\nimport pandas as pd\n\nimport pyproj\n\nimport pytest\nfrom geopandas.testing import assert_geodataframe_equal\nfrom geopandas import _... | [
[
"pandas.read_pickle"
]
] | [
{
"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": []
}
] |
USU-ET-lab/ec-file-processing | [
"6ed2ce2d3bfb31e7be81676815ad833db8d9502c"
] | [
"python_utils/file_convert.py"
] | [
"'''\n\n@author: miksch\n'''\n\nimport pandas as pd\nimport numpy as np\nimport dask.dataframe as dd\nimport dask\nimport os\nimport csv\nimport multiprocessing\nfrom dask.distributed import Client\nfrom . file_utils import read_header\n\n \nclass toa5_convert_dask(object):\n '''\n Class used to read multi... | [
[
"pandas.read_csv",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
marxide/skymapper | [
"536264a8ce16fa63dffaecb64ac3f4a247d66da1"
] | [
"skymapper/projection.py"
] | [
"import numpy as np\nimport scipy.integrate\nimport scipy.optimize\n\nDEG2RAD = np.pi/180\n\ndef _toArray(x):\n \"\"\"Convert x to array if needed\n\n Returns:\n array(x), boolean if x was an array before\n \"\"\"\n if hasattr(x, '__iter__'):\n return np.array(x), True\n return np.array... | [
[
"numpy.maximum",
"numpy.abs",
"numpy.sqrt",
"numpy.arcsin",
"numpy.arctan",
"numpy.unique",
"numpy.median",
"numpy.cos",
"numpy.dstack",
"numpy.sin",
"numpy.sign",
"numpy.arctan2",
"numpy.tan",
"numpy.ones",
"numpy.random.rand",
"numpy.array",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jacky3213/face-recognition-system | [
"4004b85bbfc41a872e81beac1ec9da9f91ff7d2c"
] | [
"FR/sphereface.py"
] | [
"import caffe\r\nimport cv2\r\nimport numpy as np\r\nfrom _argparse import argparse\r\n\r\n\r\nclass Spereface(object):\r\n def __init__(self):\r\n self.args = argparse()\r\n gpu_id = int(self.args.gpu) \r\n if gpu_id<0: \r\n caffe.set_mode_cpu()\r\n else:\r\n ... | [
[
"numpy.concatenate",
"numpy.dot",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
TU-Berlin-DIMA/babelfish | [
"4926384d994ab4ab324a056d69be64d9b52ed7a0"
] | [
"scripts/plots/create_analytica_plot.py"
] | [
"import pandas as pd\n\n\ndef getQueryString(string):\n return string.split(\"_\")[0]\n\n\ndata = pd.read_csv(\"../../results/AnalyticsBenchmark.csv\", index_col=False, names=[\"data\", \"query\", \"language\", \"lazy\",\n \"ts_1_m... | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
imciflam/music-genre-classifier | [
"0c1df26b67a3c9caf86750763fe9aba452b40423"
] | [
"track_preparation.py"
] | [
"import os\nimport glob\nfrom pydub import AudioSegment\nimport os\nimport pandas as pd\nimport numpy as np\nimport librosa\nfrom scipy.io import wavfile\nfrom python_speech_features import mfcc, logfbank\n\nstandart_dir = \"\"\n\n\ndef envelope(y, rate, threshold): # signal envelope\n mask = []\n y = pd.Ser... | [
[
"scipy.io.wavfile.write",
"pandas.Series",
"numpy.fft.rfft",
"numpy.fft.rfftfreq",
"pandas.DataFrame",
"scipy.io.wavfile.read"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.11",
"1.10",
"1.12",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.21",
"1.20",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"... |
cfzd/Automold--Road-Augmentation-Library | [
"ef25b2cb3c46ed95e33aaab8fddd6539a1ecf8ac"
] | [
"Automold.py"
] | [
"\n# import glob\nimport cv2 as cv2\nimport numpy as np\n# import matplotlib.pyplot as plt\nimport random\nimport math\n\n\n###################### HLS #############################\n\ndef hls(image,src='RGB'):\n verify_image(image)\n if(is_list(image)):\n image_HLS=[]\n image_list=image\n ... | [
[
"numpy.linspace",
"numpy.ones",
"numpy.zeros_like",
"numpy.average",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Institute-for-Risk-and-Uncertainty/pba-for-python | [
"c6bd179809692e54f0dd36e0c0b39c7c929dc1d5"
] | [
"pba/pbox.py"
] | [
"import numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\nfrom .interval import Interval\r\nfrom .copula import Copula\r\nfrom .core import env\r\n\r\n__all__ = [\r\n # import class\r\n 'Pbox',\r\n 'mixture',\r\n # import non distribution functions\r\n 'box', 'mmms'\r\n]\r\n\r\nclass Pbox(ob... | [
[
"numpy.minimum",
"numpy.linspace",
"matplotlib.pyplot.plot",
"numpy.all",
"numpy.max",
"numpy.mean",
"numpy.arange",
"numpy.copy",
"numpy.repeat",
"matplotlib.pyplot.title",
"numpy.min",
"numpy.isnan",
"numpy.append",
"numpy.argsort",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
serihiro/blueoil | [
"e538a08cb149c6f630263905819cc8c53a0a6081"
] | [
"dlk/python/dlk/plugins/tf.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright 2018 The Blueoil 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... | [
[
"numpy.asarray",
"numpy.frombuffer",
"tensorflow.core.framework.types_pb2.DataType.items"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
best99317/Deep-RL-Package | [
"8a6fe4d80c3ab12d062d6aeecac5a50ac5144aad",
"8a6fe4d80c3ab12d062d6aeecac5a50ac5144aad"
] | [
"basenets/Conv.py",
"agents/HPG/HPG_Gaussian.py"
] | [
"import torch\nfrom torch import nn\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport numpy as np\n\n\nclass Conv(nn.Module):\n def __init__(self,\n num_input_feats, # input dimension\n num_output_feats, # output dimension\n k_sizes=No... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.init.uniform_",
"numpy.sqrt",
"torch.Tensor",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.init.normal_",
"torch.nn.init.orthogonal_",
"torch.nn.BatchNorm2d",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nick-Singstock/qiskit-aqua | [
"8c2bc57b78dec447faec3adbc966471a3206c2ef"
] | [
"qiskit/aqua/components/uncertainty_problems/univariate_piecewise_linear_objective.py"
] | [
"# -*- coding: utf-8 -*-\n\n# This code is part of Qiskit.\n#\n# (C) Copyright IBM Corp. 2017 and later.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licens... | [
[
"numpy.argsort",
"numpy.append",
"numpy.array",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
muziyongshixin/adv_cross_modal_hashing | [
"cf0f9f33a3dad763ab6dd4232a00a3b0b93c64a1"
] | [
"tools/test.py"
] | [
"import json\r\nfrom tqdm import tqdm\r\nimport os\r\nfrom sklearn import metrics\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.cluster import KMeans\r\nfrom sklearn.datasets.samples_generator import make_blobs\r\nimport numpy as np\r\nimport torch\r\nimport pickle\r\n\r\n\r\ndef get_imgid2object():\r\n inp... | [
[
"sklearn.cluster.KMeans",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
agis85/multimodal_segmentation | [
"a4fa1b39830f6c1bc320ff5b5e3fda82b8382e18",
"a4fa1b39830f6c1bc320ff5b5e3fda82b8382e18"
] | [
"callbacks/image_callback.py",
"model_executors/mmsdnet_executor.py"
] | [
"import logging\nimport os\nfrom abc import abstractmethod\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom keras.callbacks import Callback\n\nfrom costs import dice\nfrom utils.image_utils import save_segmentation, intensity_augmentation\n\nlog = logging.getLogger('BaseSaveImage')\n\n\nclass BaseSaveIm... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.savefig",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"numpy.mean",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xticks",
"numpy.zeros",
"matplotlib.pyplot.f... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CostanzoPablo/audiomate | [
"080402eadaa81f77f64c8680510a2de64bc18e74"
] | [
"tests/tracks/test_file.py"
] | [
"import os\n\nimport pytest\nimport numpy as np\nimport librosa\n\nfrom audiomate import tracks\n\nfrom tests import resources\n\n\n@pytest.fixture()\ndef audio_path():\n return os.path.join(os.path.dirname(resources.__file__), 'audio_formats')\n\n\nclass TestFile:\n\n @pytest.mark.parametrize('name,sampling_... | [
[
"numpy.random.random",
"numpy.allclose",
"numpy.array_equal",
"numpy.pad",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AIRoading/GeneralNewsExtractor | [
"d15a921a5401c66e2604760f1a74c7cf96b3c750"
] | [
"gne/extractor/ContentExtractor.py"
] | [
"import re\nimport numpy as np\nfrom gne.utils import iter_node\nfrom gne.defaults import USELESS_TAG\nfrom lxml.html import etree\nfrom html import unescape\n\n\nclass ContentExtractor:\n def __init__(self, content_tag='p'):\n \"\"\"\n\n :param content_tag: 正文内容在哪个标签里面\n \"\"\"\n sel... | [
[
"numpy.std",
"numpy.log10",
"numpy.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jaelyangChoi/ICO | [
"e816a0d050cc2906caf71d2abfa6a04d438ed5b4"
] | [
"ml/ml_by_pumsa/predict_comment.py"
] | [
"import pickle\r\n\r\nimport numpy as np # 행렬, 대규모 다차원 배열을 쉽게 처리 할 수 있도록 지원하는 파이썬의 라이브러리\r\nfrom konlpy.tag import Okt # Okt(Open Korean Text) 클래스\r\nfrom sklearn.externals import joblib\r\n\r\nML_FILE_PATH = \"./dataset_pumsa_ml/\"\r\n# MODELNAME=\"tensor4\"\r\nMODELNAME = \"linear\"\r\n\r\n\r\nclass CommentPred... | [
[
"numpy.asarray",
"sklearn.externals.joblib.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IntelAI/inference-model-manager | [
"55b1a87bea2eb78b897bbd5cdb34332d3d1755ca"
] | [
"examples/grpc_client/images_2_numpy.py"
] | [
"#\n# Copyright (c) 2018-2019 Intel Corporation\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 applic... | [
[
"numpy.amax",
"numpy.amin",
"numpy.dtype",
"numpy.save",
"numpy.frombuffer",
"numpy.append"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ppddry/UCAS_Course_2019 | [
"85581e84b54ba8ef95307adcd9e770276eb71606"
] | [
"计算机算法设计与分析19-20秋季/Lec8-IPM_affine_scaling1.py"
] | [
"import numpy as np; \n\nm=5;\nn=7; \n\nc = np.array( [2, 1.5, 0, 0, 0, 0, 0] );\nA = np.array( [ [12, 24, -1, 0, 0, 0, 0], \n\t\t[16, 16, 0, -1, 0, 0, 0],\n\t\t[30, 12, 0, 0, -1, 0, 0],\n\t\t[1, 0, 0, 0, 0, 1, 0],\n\t\t[0, 1, 0, 0, 0, 0, 1] ] );\nb = np.array( [ 120, 120, 120, 15, 15] ); \n\nx = np.array( [10... | [
[
"numpy.dot",
"numpy.linalg.solve",
"numpy.transpose",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
czq13/czq_reinforce | [
"5e2cee5743e258d5476cb636217ebc901d4a7fc7"
] | [
"tmp/try_test.py"
] | [
"import tensorflow as tf\n\n'''\nclass SquareTest(tf.test.TestCase):\n def testSquare(self):\n with self.test_session():\n x = tf.square([2, 3])\n self.assertAllEqual(x.eval(), [4, 9])\n\n\nif __name__ == '__main__':\n tf.test.main()\n'''\nwith tf.name_scope('graph') as scope:\n m... | [
[
"tensorflow.matmul",
"tensorflow.constant",
"tensorflow.summary.FileWriter",
"tensorflow.global_variables_initializer",
"tensorflow.name_scope",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
15191572804/miniprograme-yolo | [
"cf2b9efba07fc83e148ef338744d1403359873a4"
] | [
"YOLOV5-5.0/yolov5-5.0/yolov5-5.0/hubconf.py"
] | [
"\"\"\"File for accessing YOLOv5 models via PyTorch Hub https://pytorch.org/hub/ultralytics_yolov5/\n\nUsage:\n import torch\n model = torch.hub.load('ultralytics/yolov5', 'yolov5s')\n\"\"\"\n\nfrom pathlib import Path\n\nimport torch\n\nfrom models.yolo import Model\nfrom utils.general import check_requireme... | [
[
"torch.device",
"numpy.zeros",
"torch.cuda.is_available",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cdown/statsmodels | [
"c99558b287735eb5f0c1e561866e1501e41fecbe",
"c99558b287735eb5f0c1e561866e1501e41fecbe"
] | [
"statsmodels/stats/tests/test_statstools.py",
"statsmodels/tsa/statespace/tests/test_smoothing.py"
] | [
"# TODO: Test robust skewness\n# TODO: Test robust kurtosis\nimport numpy as np\nimport pandas as pd\nfrom numpy.testing import (assert_almost_equal, assert_raises)\nfrom statsmodels.stats.stattools import (omni_normtest, jarque_bera,\n durbin_watson, _medcouple_1d, medcouple... | [
[
"numpy.dot",
"pandas.Series",
"numpy.random.randn",
"numpy.exp",
"numpy.arange",
"numpy.testing.assert_almost_equal",
"numpy.std",
"numpy.diff",
"numpy.column_stack",
"scipy.stats.kurtosistest",
"numpy.zeros",
"numpy.log",
"scipy.stats.skewtest",
"numpy.test... | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
ukenia/quantization-cnn | [
"bc46b0b4f9e711ac893bdf9eaf9a5acf61896816"
] | [
"utils/train_utils.py"
] | [
"import torch\n\ndef get_num_workers():\n # Check if cuda is available\n\n cuda = torch.cuda.is_available()\n num_workers = 4 if cuda else 0\n print(\"Cuda = \"+str(cuda)+\" with num_workers = \"+str(num_workers))\n return num_workers\n\n\ndef get_device():\n if torch.cuda.is_available():\n ... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ldpe2G/oneflow | [
"d2096ae14cf847509394a3b717021e2bd1d72f62"
] | [
"oneflow/python/test/ops/test_batch_normalization.py"
] | [
"\"\"\"\nCopyright 2020 The OneFlow Authors. All rights reserved.\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 ap... | [
[
"tensorflow.nn.batch_normalization",
"numpy.allclose",
"tensorflow.Variable",
"tensorflow.cast",
"numpy.ones",
"tensorflow.keras.layers.BatchNormalization",
"numpy.random.rand",
"numpy.random.uniform",
"tensorflow.GradientTape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
abdullah-zaiter/Semantic-Segmentation | [
"6d48575ce958a13bd653c76c343fb789328ea295"
] | [
"src/deeplab/datasets/build_voc2007_data.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 requi... | [
[
"tensorflow.app.flags.DEFINE_string",
"tensorflow.python_io.TFRecordWriter",
"tensorflow.gfile.FastGFile",
"tensorflow.app.run"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
phiresky/char-rnn-tensorflow | [
"d56dfc92e354708d572d7d91c16c054b017591ad"
] | [
"sample-stdin.py"
] | [
"#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nimport argparse\nimport os\nfrom six.moves import cPickle\nimport sys\nimport json\n\nfrom six import text_type\n\n\nparser = argparse.ArgumentParser(\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\nparser.add_argument('--save_dir', ty... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.global_variables_initializer",
"tensorflow.global_variables",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ffallrain/fpdb | [
"af3989ca705adfdfffc1ba195352608b30398ff0"
] | [
"fpdb/fpdb.py"
] | [
"#!/bin/env python\nimport math\n#import simtk.openmm.app as soa\n#import simtk.unit as su\nimport sys,os\nimport numpy as np\nfrom .fhet import hetnames,cofactors\nimport copy\n\nMASS = {'O':15.999, 'N':14.010,\n 'C':12.010, 'H': 1.008,\n 'F':19.000,\n 'Na':22.99, 'NA':22.99,\n ... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.cross",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nicedi/ML_course_projects | [
"136a18ec8615ae72bb60b4d60e920beb77728115"
] | [
"assignment5/softmax.py"
] | [
"# -*- coding: utf-8 -*-\nimport numpy as np\n\nclass Softmax(object):\n \n def __init__(self):\n pass\n \n \n def forward(self, X):\n shiftX = X - np.max(X, axis=1).reshape((X.shape[0], -1)) # for numerical stability\n # 任务2。利用上文的shiftX计算softmax的输出p\n pass\n ... | [
[
"numpy.max"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
s-okugawa/HDNNP | [
"aa7250219f8bcffdf48a2390f1fef9c89f642e8a"
] | [
"hdnnpy/dataset/descriptor/weighted_symmetry_function_dataset.py"
] | [
"# coding: utf-8\n\n\"\"\"Weighted symmetry function dataset for descriptor of HDNNP.\"\"\"\n\nimport chainer\nimport chainer.functions as F\nimport numpy as np\n\nfrom hdnnpy.dataset.descriptor.descriptor_dataset_base import (\n DescriptorDatasetBase)\n\n\nclass WeightedSymmetryFunctionDataset(DescriptorDataset... | [
[
"numpy.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
benmaier/vaccontrib | [
"1be75e049d3069ba465e7779e850c2e2504daae9"
] | [
"vaccontrib/io.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nLoad data regarding COVID-19 vaccine efficacies.\n\"\"\"\n\nimport csv\nimport numpy as np\n\nfrom vaccontrib.paths import get_data_dir\nfrom pathlib import Path\n\n_POPULATIONS = ('[00;12)','[12;18)','[18;60)','[60;oo)')\n_VACC_STATUSES = ('no','astra','biontech','moderna','jj')\n... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alisure-fork/Video-Swin-Transformer | [
"aa0a31bd4df0ad2cebdcfb2ad53df712fce79809"
] | [
"a_other_video/MCL-Motion-Focused-Contrastive-Learning/evaluate/linear_probe/eval_svm_feature_extract.py"
] | [
"import torch\nimport torch.backends.cudnn as cudnn\nimport torch.nn as nn\nimport torch.distributed as dist\nimport torchvision.transforms as transforms\nimport os\nimport sys\nsys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))\nfrom dataset.video_dataset import Video... | [
[
"torch.mean",
"torch.distributed.init_process_group",
"torch.utils.data.distributed.DistributedSampler",
"torch.load",
"torch.cuda.set_device",
"torch.cat",
"torch.utils.data.DataLoader",
"numpy.stack",
"torch.nn.AdaptiveAvgPool3d",
"torch.no_grad",
"torch.stack",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
resemble-ai/sentiment-discovery | [
"0b05e210ee3019890493478abdc917e197d19780"
] | [
"run_classifier.py"
] | [
"import argparse\r\nimport os\r\nimport time\r\nimport math\r\nimport collections\r\nfrom tqdm import tqdm\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torch.autograd import Variable\r\nimport torch.nn.functional as F\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\nfrom reparameterization import app... | [
[
"torch.nn.functional.softmax",
"torch.cuda.manual_seed",
"torch.load",
"torch.manual_seed",
"numpy.save",
"numpy.concatenate",
"torch.no_grad",
"numpy.zeros_like",
"torch.cuda.is_available",
"torch.clamp",
"numpy.array",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gobber/aima-python | [
"786b93c0c48095d38f93886226152410b1387704"
] | [
"notebook.py"
] | [
"from inspect import getsource\n\nfrom utils import argmax, argmin\nfrom games import TicTacToe, alphabeta_player, random_player, Fig52Extended, infinity\nfrom logic import parse_definite_clause, standardize_variables, unify, subst\nfrom learning import DataSet\nfrom IPython.display import HTML, display\nfrom colle... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.rcParams.update",
"numpy.exp",
"numpy.arange",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.vlines",
"matplotlib.pyplot.text",
"numpy.zeros",
"matplotlib.pyplot.figur... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ariella879/rasa | [
"f96a0f0d903082ca5e6e32b5fa7b5ae4393b84c4"
] | [
"rasa/core/domain.py"
] | [
"import collections\nimport copy\nimport json\nimport logging\nimport os\nimport typing\nfrom pathlib import Path\nfrom typing import Any, Dict, List, NamedTuple, Optional, Set, Text, Tuple, Union\n\nfrom ruamel.yaml import YAMLError\n\nimport rasa.core.constants\nfrom rasa.utils.common import (\n raise_warning,... | [
[
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AndreMaz/transformer-pointer-critic | [
"97cfa1e667514a5651d855d6ffd498ac49339c00",
"97cfa1e667514a5651d855d6ffd498ac49339c00"
] | [
"tests/unit/environment/resource_v3/utils_test.py",
"environment/custom/knapsack_v2/env.py"
] | [
"import sys\nsys.path.append('.')\nimport unittest\nimport numpy as np\n\n\nfrom environment.custom.resource_v3.misc.utils import round_half_up, reshape_into_horizontal_format, reshape_into_vertical_format\n\nclass TestUtils(unittest.TestCase):\n def test_round_ops(self):\n expected_total = 0.41\n ... | [
[
"numpy.array"
],
[
"numpy.expand_dims",
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.cast",
"tensorflow.equal",
"numpy.all",
"numpy.zeros_like",
"numpy.full",
"numpy.zeros",
"tensorflow.tile",
"tensorflow.fill",
"tensorflow.less",
"tensorflow.ran... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
elishatofunmi/TenseFlex | [
"4961a4511571e94c637c090207b41eb0cee66ea8"
] | [
"parameter tuning/.ipynb_checkpoints/TenseFlexRegressor-checkpoint.py"
] | [
"import tensorflow.compat.v1 as tf\ntf.disable_eager_execution()\nfrom tensorflow.keras import layers\nfrom tensorflow.keras import layers\nfrom sklearn.metrics import accuracy_score, precision_score, f1_score\nimport numpy as np\nimport time\nimport pandas as pd\nfrom tqdm import tqdm\nimport warnings\nwarnings.fi... | [
[
"tensorflow.compat.v1.keras.optimizers.RMSprop",
"tensorflow.compat.v1.keras.optimizers.Adam",
"numpy.random.choice",
"tensorflow.compat.v1.keras.optimizers.SGD",
"tensorflow.keras.layers.Dense",
"sklearn.metrics.precision_score",
"pandas.DataFrame",
"tensorflow.compat.v1.Session",... | [
{
"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": []
}
] |
cmonserr/Why_Difficulty | [
"7b34cc3556a1b99ac67cb155fba8d0837c9b7b10"
] | [
"openml/visualization_diff_vs_pert.py"
] | [
"\n\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\nimport pandas as pd\nimport os\nroot_path=\"images\"\ncsv=\"elasticnet_L1_mnist784_ref_data.csv\"\npath=\"./openml/adversarial_images\"\ndata=pd.read_csv(os.path.join(path,csv))\nprefix=csv.split(\"_\")[0]+csv.split(\"_\")[1]\n# print(data.head())\n# a=... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.close",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.suptitle",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hangg7/pytorcg3d | [
"f7f363eeb8efeba0927f674c83ab927ad8ce3e32",
"f7f363eeb8efeba0927f674c83ab927ad8ce3e32"
] | [
"pytorch3d/ops/points_alignment.py",
"pytorch3d/loss/mesh_normal_consistency.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n\nimport warnings\nfrom typing import TYPE_CHECKING, List, NamedTuple, Optional, Union\n\nimport torch\nfrom pytorch3d.ops import knn_points\nfrom pytorch3d.structures import utils as strutil\n\nfrom . import utils as oputil\n\n\nif TYPE_CHEC... | [
[
"torch.Size",
"torch.svd",
"torch.diagonal",
"torch.det",
"torch.eye",
"torch.bmm",
"torch.arange",
"torch.clamp"
],
[
"torch.cosine_similarity",
"torch.no_grad",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
llSourcell/pytorch_geometric | [
"3525bf4efb5d95b0c6e31c2e82ac00c9899232fb"
] | [
"torch_geometric/data/batch.py"
] | [
"import torch\nfrom torch_geometric.data import Data\n\n\nclass Batch(Data):\n def __init__(self, batch=None, **kwargs):\n super(Batch, self).__init__(**kwargs)\n self.batch = batch\n\n @staticmethod\n def from_data_list(data_list):\n keys = [set(data.keys) for data in data_list]\n ... | [
[
"torch.full",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yeeon/sirepo | [
"081595df256d40fbc7959614689d64ad2bc745d4"
] | [
"sirepo/template/webcon.py"
] | [
"# -*- coding: utf-8 -*-\nu\"\"\"Webcon execution template.\n\n:copyright: Copyright (c) 2019 RadiaSoft LLC. All Rights Reserved.\n:license: http://www.apache.org/licenses/LICENSE-2.0.html\n\"\"\"\nfrom __future__ import absolute_import, division, print_function\nfrom pykern import pkcollections\nfrom pykern impor... | [
[
"numpy.abs",
"numpy.unique",
"numpy.alen",
"numpy.around",
"numpy.sort",
"numpy.genfromtxt",
"numpy.finfo",
"numpy.max",
"numpy.append",
"numpy.insert",
"numpy.savetxt",
"numpy.array",
"numpy.where",
"numpy.diagonal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mustafazcn/Python_Requests_Mod-l- | [
"aab9f8797de3cd4ede7e06931383038c38f3a35c"
] | [
"Arabam.com/main.py"
] | [
"import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport numpy as np\n\nimport pyodbc\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.testing import db\n\nurl = \"https://www.arabam.com/ikinci-el/otomobil?take=50\"\nr = requests.get(url)\nr.status_code\nr.content\nsoup = BeautifulSoup(r.c... | [
[
"pandas.read_sql_query",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Polirecyliente/SGConocimiento | [
"560b08984236d7a10f50c6b5e6fb28844193d81b",
"560b08984236d7a10f50c6b5e6fb28844193d81b",
"560b08984236d7a10f50c6b5e6fb28844193d81b"
] | [
"Math/C01_Geometry_basics/Programs/S03/Converse_of_the_bases_angles_of_an_isosceles_trapezoid_theorem_image.py",
"Math/C01_Geometry_basics/Programs/S02_2/Triangle_longer_side_theorem_image.py",
"Math/C01_Geometry_basics/Programs/S01/Triangle_image.py"
] | [
"#T# the following code shows how to draw a trapezoid to show the converse of the bases angles of an isosceles trapezoid theorem\n\n#T# to draw a trapezoid to show the converse of the bases angles of an isosceles trapezoid theorem, the pyplot module of the matplotlib package is used\nimport matplotlib.pyplot as plt... | [
[
"matplotlib.patches.FancyArrowPatch",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show"
],
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
],
[
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.annotate",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shadowgamefly/osf_analysis | [
"e5f22b01220016d7058e76e411a79aff1e912f08"
] | [
"util/plot.py"
] | [
"import matplotlib.pyplot as plt\n\ndef plot(data):\n if 'key' not in data:\n raise KeyError(\"x-axis label needs to be named as key in the data dict.\")\n key = data['key']\n fig, ax1 = plt.subplots()\n ax1.set_xlabel('Threshold of possibility to categorize')\n ax1.set_ylabel('Percentage in t... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sesmond/tf-faster-rcnn | [
"aae16ace641096bc98e63e21b0a4a00e80ee718a"
] | [
"tools/tuning.py"
] | [
"#!/usr/bin/env python\nfrom __future__ import print_function\nimport cv2\nimport numpy as np\nimport time\nimport PIL\nfrom PIL import Image\nfrom math import *\nfrom numpy import (dtype, sin)\n\nclass RotateProcessor(object):\n def __init__(self):\n self.zoom = 0.5\n self.perc = 50\n self.... | [
[
"numpy.amax",
"numpy.linspace",
"numpy.clip",
"numpy.amin",
"numpy.asarray",
"numpy.median",
"numpy.dtype",
"numpy.sort",
"numpy.mean",
"numpy.prod",
"numpy.var",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SleepySoft/StockAnalysisSystem | [
"75f95738831614f7946f85d09118e447f7ac6dc7"
] | [
"StockAnalysisSystem/core/Utility/CollectorUtility.py"
] | [
"import pandas as pd\nfrom os import path\n\nfrom .common import *\nfrom .time_utility import *\n\n\n# Tushare access limit, as fetch times per minute.\n# If fetch data from tushare gets error message like: \"抱歉，您每分钟最多访问该接口x次\"\n# Fill the x to the corresponding entry of this table\n# This config is for 5000 scores... | [
[
"pandas.to_datetime",
"pandas.DataFrame.from_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20"
],
"scipy": [],
"tensorflow": []
}
] |
sarvesh96/xview_ssd | [
"53a182f69790e6401e792916880d9dd8665c945f"
] | [
"frcnn/utils/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# from data.voc_dataset import XVIEW_BBOX_LABEL_NAMES\n\n\nXVIEW_BBOX_LABEL_NAMES = ('__noclass__', 'fixed_wing_aircraft', 'small_aircraft', 'passenger_OR_cargo_... | [
[
"matplotlib.pyplot.Rectangle",
"torch.Tensor",
"matplotlib.use",
"matplotlib.pyplot.close",
"numpy.array",
"numpy.roll",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ijusttyped/it_support_tickets | [
"d105fe5f7990a5be96f077e5636b0a2588bb5b48"
] | [
"src/modeling/evaluation.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\n\ndef rmsle(actuals: pd.DataFrame, predictions: pd.DataFrame) -> float:\n \"\"\"\n Computes the root mean square log error between the actuals and predictions.\n Raises and error if there are multiple predictions for a single work... | [
[
"numpy.log",
"matplotlib.pyplot.subplots",
"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": []
}
] |
northeastern-datalab/domain_net | [
"e15b1ea82fcf6ba1565e4745c786374b5d5da93a",
"e15b1ea82fcf6ba1565e4745c786374b5d5da93a"
] | [
"network_analysis/main.py",
"network_analysis/homograph_injection_evaluation.py"
] | [
"import networkx as nx\nimport networkit as nk\nimport pandas as pd\nimport random\nimport sys\nimport math\n\nimport utils\n\nimport pickle\nimport json\nimport argparse\n\nfrom timeit import default_timer as timer\nfrom pathlib import Path\n\ndef get_source_target_nodes_list(G, source_target_nodes):\n '''\n ... | [
[
"pandas.merge",
"pandas.Series",
"pandas.DataFrame"
],
[
"numpy.average"
]
] | [
{
"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... |
lbito/union-of-shapes | [
"315216ab18e999b916c04b5bac8fdab3aff18de3"
] | [
"src/imageApproximation/ellipse.py"
] | [
"import numpy as np\nfrom shape import Shape\n\n\nclass Ellipse(Shape):\n name = \"ellipse\"\n x = 0\n y = 0\n w = 1\n h = 1\n \n def __init__(self, args):\n self.x, self.y = args[0]\n self.w = args[1]\n self.h = args[2]\n\n def update_params(self):\n atr = np.ran... | [
[
"numpy.random.choice",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rfcx/defunct | [
"bbd49954c14e8ca83439902699d883cbda0e1d42"
] | [
"sound-localization/localization/multivariate_gaussian.py"
] | [
"import numpy as np\nfrom signal_likelihood import SignalLikelihood\nimport unittest\nfrom numpy.testing import assert_array_almost_equal,assert_almost_equal, assert_equal\nimport math\n\n\"\"\"\nModels the ambient audio scenery with a multivariate\nGaussian distributions. Based on that model we can distinguish \nb... | [
[
"numpy.testing.assert_almost_equal",
"numpy.zeros_like",
"numpy.any",
"numpy.prod",
"numpy.exp",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hungpdn/h1st | [
"0b733c7b9b194098577920efd048494ed370b2e8"
] | [
"examples/Ensemble/utils.py"
] | [
"import logging\nfrom sklearn.model_selection import train_test_split\n\nfrom . import config\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\ndef prepare_train_test_data(df):\n X = df[config.DATA_FEATURES].values\n y = df[config.DATA_TARGETS].values\n\n X_train, X_test, ... | [
[
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jt658/BBAVectors-Oriented-Object-Detection | [
"00aeb758f44cdcec674b8d9003f27ef5435527c9"
] | [
"datasets/base.py"
] | [
"import torch.utils.data as data\nimport cv2\nimport torch\nimport numpy as np\nimport math\nfrom .draw_gaussian import draw_umich_gaussian, gaussian_radius\nfrom .transforms import random_flip, load_affine_matrix, random_crop_info, ex_box_jaccard\nfrom . import data_augment\n\nclass BaseDataset(data.Dataset):\n ... | [
[
"numpy.maximum",
"numpy.min",
"numpy.asarray",
"numpy.clip",
"torch.from_numpy",
"numpy.ones",
"numpy.max",
"numpy.argmax",
"numpy.argmin",
"numpy.transpose",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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