repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
clodoaldoBasaglia/personalSoftwareProcess | [
"c60e16d0694a5fa8a4bfa5515fbe95b70cba2091"
] | [
"Programa21- 7/Programa7.py"
] | [
"'''\n Universidade Tecnológica Federal do Paraná\n Aluno: Clodoaldo A. Basaglia da Fonseca\n RA: 968692\n Engenharia de Software 2\n'''\nimport sys, math\nfrom Simpson import Simpson\nfrom Simpson_Inv import SimpsonInv\nimport numpy as np\n\n'''\nIN: vetor de valores\nSplit em virgulas para separar os valores de X... | [
[
"numpy.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
msadowski20/sqlalchemy-challenge | [
"48a959fac9a18e8ceae0ac8c154f4366c89f1ce2"
] | [
"app.py"
] | [
"import numpy as np\n\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\n\nimport datetime as dt\n\nfrom flask import Flask, jsonify\n\n#################################################\n# Database Setup\n#############... | [
[
"numpy.ravel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maxadss/hummingbot | [
"48aea1716dc02c010af230f80d50ef273086ba6d"
] | [
"hummingbot/client/command/history_command.py"
] | [
"from decimal import Decimal\nimport pandas as pd\nimport threading\nimport time\nfrom typing import (\n Set,\n Tuple,\n TYPE_CHECKING,\n List,\n Optional\n)\nfrom datetime import datetime\nfrom hummingbot.client.config.global_config_map import global_config_map\nfrom hummingbot.client.settings impor... | [
[
"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": []
}
] |
Georgitanev/matplotlib | [
"c60367fb6cedafcc8fa4657f4e54e0c5530cce70"
] | [
"02_bars.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Mar 15 14:30:23 2020\r\n\r\n@author: G\r\n\"\"\"\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\n\r\nplt.style.use(\"fivethirtyeight\")\r\n\r\nages_x = [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]\r\nx_indexes = np.arange(len(ages_x))\r\nwidt... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexbrx/generative-features | [
"f0ff783ac4973ec8952764aef622fabe95704fc1"
] | [
"logger.py"
] | [
"import tensorflow as tf\n\n\nclass Logger(object):\n \"\"\"Tensorboard logger.\"\"\"\n\n def __init__(self, log_dir):\n \"\"\"Initialize summary writer.\"\"\"\n self.writer = tf.summary.FileWriter(log_dir)\n\n def scalar_summary(self, tag, value, step):\n \"\"\"Add scalar summary.\"\"... | [
[
"tensorflow.Summary.Value",
"tensorflow.summary.FileWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
sieumap43/GazeML | [
"2b4a15b95b4e1f2e9e8c7359416747fd4d26d4a9"
] | [
"src/core/model.py"
] | [
"\"\"\"Base model class for Tensorflow-based model construction.\"\"\"\nfrom .data_source import BaseDataSource\nimport os\nimport sys\nimport time\nfrom typing import Any, Dict, List\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom .live_tester import LiveTester\nfrom .time_manager import TimeManager\nfrom .... | [
[
"tensorflow.losses.get_regularization_losses",
"tensorflow.Variable",
"tensorflow.control_dependencies",
"tensorflow.get_collection",
"tensorflow.reduce_sum",
"tensorflow.assign",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.variable_scope",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Aalto-Arotor/openTorsion | [
"f8e9c2520206a1f87edfc73a5681dac46fba6e07"
] | [
"opentorsion/induction_motor.py"
] | [
"import numpy as np\nfrom scipy import linalg as LA\n\n# from opentorsion.shaft_element import Shaft\n# from opentorsion.disk_element import Disk\n# from opentorsion.gear_element import Gear\n\n\nclass Induction_motor:\n \"\"\"Induction motor object\n Arguments:\n ----------\n n: int\n Node posit... | [
[
"numpy.complex",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Xunpeng746/ACMo-Angle-Calibrated-Moment-Methods-for-Stochastic-Optimization | [
"77ab7100cf18134ed9e5a805daf8b5c85d08cf7b"
] | [
"pytorch_cifar_task/models/resnet.py"
] | [
"'''ResNet in PyTorch.\r\n\r\nFor Pre-activation ResNet, see 'preact_resnet.py'.\r\n\r\nReference:\r\n[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\r\n Deep Residual Learning for Image Recognition. arXiv:1512.03385\r\n'''\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\n... | [
[
"torch.nn.Sequential",
"torch.randn",
"torch.nn.functional.avg_pool2d",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
W-Qiu/revise_reminder | [
"bc5fbc88ea4da52a42e117d7f44bddd654cf2fc7",
"bc5fbc88ea4da52a42e117d7f44bddd654cf2fc7"
] | [
"tts/utils/audio.py",
"tts/layers/attention.py"
] | [
"import os\nimport librosa\nimport pickle\nimport copy\nimport numpy as np\nfrom pprint import pprint\nfrom scipy import signal, io\n\n\nclass AudioProcessor(object):\n def __init__(self,\n bits=None,\n sample_rate=None,\n num_mels=None,\n min_level... | [
[
"numpy.dot",
"numpy.log",
"numpy.maximum",
"numpy.abs",
"numpy.power",
"numpy.clip",
"numpy.max",
"numpy.random.rand",
"scipy.signal.lfilter"
],
[
"torch.nn.ConstantPad1d",
"torch.softmax",
"torch.nn.init.calculate_gain",
"torch.sigmoid",
"torch.cat",
... | [
{
"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"... |
HiroakiMikami/mlprogram | [
"573e94c567064705fa65267dd83946bf183197de",
"573e94c567064705fa65267dd83946bf183197de"
] | [
"mlprogram/nn/action_sequence/embedding.py",
"test/mlprogram/nn/treegen/test_gating.py"
] | [
"import torch\nfrom torch import nn\n\nfrom mlprogram.nn import EmbeddingWithMask\nfrom mlprogram.nn.utils.rnn import PaddedSequenceWithMask\n\n\nclass PreviousActionsEmbedding(nn.Module):\n def __init__(self, n_rule: int, n_token: int, embedding_size: int):\n super().__init__()\n self.n_token = n_... | [
[
"torch.nn.init.normal_",
"torch.cat",
"torch.split"
],
[
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tbaudier/gaga | [
"87ec65450b9048ddf87cfa22a53ecba12fd019bd",
"87ec65450b9048ddf87cfa22a53ecba12fd019bd"
] | [
"src/gaga.py",
"src/gaga_model.py"
] | [
"import copy\n\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nfrom gaga_functions import *\nfrom gaga_helpers import *\nfrom gaga_model import Discriminator, Generator\n\n# from torch.utils.tensorboard import SummaryWriter\n\n# import numpy as np\n# import torch\n# from to... | [
[
"torch.utils.data.DataLoader",
"torch.nn.BCELoss"
],
[
"torch.sigmoid",
"torch.nn.ModuleList",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AmeyaWagh/CarND-Vehicle-Detection | [
"5173c0384dd8f400adaed31d03b792b714dd7a44"
] | [
"VehicleDetector/pipeline.py"
] | [
"from VehicleDetector import classifier\nfrom VehicleDetector import features\nfrom VehicleDetector import data_handler\nfrom VehicleDetector import visualizer\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.ndimage.measurements import label\n\n\nclass VehicleDetector(object):\n \"\"... | [
[
"scipy.ndimage.measurements.label",
"numpy.int",
"numpy.copy",
"numpy.array",
"numpy.vstack",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.16",
"1.0",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"0.10",
"0.17",
"1.3"
],
"tensorflow": [... |
umma-zannat/ginan | [
"a4d1a3bb8696267f23d26e8c6a2f6080b87bb494"
] | [
"scripts/gn_lib/gn_io/trace.py"
] | [
"'''TRACE file parser. Note the separate functions for values and residuals'''\nimport os as _os\nimport re as _re\nfrom io import BytesIO as _BytesIO\nfrom itertools import product\nfrom multiprocessing import Pool as _Pool\n\nimport numpy as _np\nimport pandas as _pd\n\nfrom ..gn_datetime import gpsweeksec2dateti... | [
[
"pandas.concat",
"numpy.abs",
"pandas.Series",
"numpy.isnan",
"pandas.DataFrame",
"pandas.set_option"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Saran-nns/traja | [
"f2256cc47abd33377b3a87f110f4c8da1cf6765f"
] | [
"traja/trajectory.py"
] | [
"import logging\nimport math\nfrom collections import OrderedDict\nfrom typing import Callable, Optional, Union, Tuple\n\nimport numpy as np\nimport pandas as pd\nfrom pandas.core.dtypes.common import (\n is_datetime_or_timedelta_dtype,\n is_datetime64_any_dtype,\n is_timedelta64_dtype,\n)\nfrom scipy impo... | [
[
"numpy.split",
"pandas.to_datetime",
"numpy.sqrt",
"numpy.linspace",
"numpy.squeeze",
"numpy.cumsum",
"pandas.DataFrame",
"numpy.arctan2",
"numpy.mean",
"numpy.digitize",
"numpy.exp",
"numpy.where",
"scipy.signal.savgol_filter",
"numpy.ones_like",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"0.19... |
qualdocs/qualdocs | [
"1e10e414672203540aeb951ed3a52455dce5972f"
] | [
"qualdocs/core.py"
] | [
"from __future__ import print_function\nimport httplib2\nimport os\n\nfrom googleapiclient import discovery\nfrom oauth2client import client\nfrom oauth2client import tools\nfrom oauth2client.file import Storage\n\nfrom collections import Counter\n\nimport argparse\nimport json\nimport pandas as pd\nimport numpy as... | [
[
"pandas.Series",
"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": []
}
] |
JiaHongZ/DRNet | [
"f2c797238b352af8bcb48200021895a576e4add5"
] | [
"utils/metric.py"
] | [
"\nimport torch\nfrom skimage.measure import compare_psnr,compare_ssim\n\n\n\ndef torch2numpy(tensor, gamma=None):\n tensor = torch.clamp(tensor, 0.0, 1.0)\n # Convert to 0 - 255\n if gamma is not None:\n tensor = torch.pow(tensor, gamma)\n tensor *= 255.0\n while len(tensor.size()) < 4:\n ... | [
[
"torch.clamp",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AntonJorg/02456-yolo | [
"2eb237e951e964e6fde535f890620fe50e58e661"
] | [
"detect.py"
] | [
"import argparse\nimport os\nimport imageio\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nfrom pygifsicle import optimize\n\nfrom model.dataloader import HELMETDataSet, class_dict\nfrom model.dataloader import class_dict\nfrom model.models import Darknet, load_weights, load_darknet_weights\nfrom utils.ut... | [
[
"matplotlib.pyplot.Rectangle",
"matplotlib.pyplot.margins",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.NullLocator",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hyyc116/citation_saturation | [
"6b167f72173ac9626467e4c7984b78d4a99fe6fe"
] | [
"basic_config.py"
] | [
"#coding:utf-8\nimport os\nimport sys\nimport json\nfrom collections import defaultdict\nfrom collections import Counter\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import curve_fit\nfrom sklearn.metrics import r2_score\nimport math\nimport numpy as np\nimport ran... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.plot",
"numpy.exp",
"matplotlib.pyplot.tight_layout",
"numpy.arange",
"matplotlib.patheffects.withStroke",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"numpy.amin",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NYUDataBootcamp/Lab | [
"fd51be264cc2aaf3ae4f3836d9c83866b38c0abe"
] | [
"pisa_xls.py"
] | [
"\"\"\"\nPISA education data\nInternational surveys of student performance\n\nSources\n* https://en.wikipedia.org/wiki/Programme_for_International_Student_Assessment\n* http://www.oecd.org/pisa/\n* https://nces.ed.gov/surveys/pisa/\n* http://www.oecd.org/pisa/keyfindings/pisa-2012-results-volume-I.pdf\n* https://gi... | [
[
"pandas.read_excel"
]
] | [
{
"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": []
}
] |
boltzmannlabs/boltpro | [
"3ddebbeb10191150a29fc81ef4336ec2dbe88092",
"3ddebbeb10191150a29fc81ef4336ec2dbe88092"
] | [
"molpro/docking/PLI.py",
"molpro/site_pred/model.py"
] | [
"from pathlib import Path\nimport time\nfrom tqdm import tqdm\nimport pandas as pd\nimport openbabel\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import colors\nfrom plip.structure.preparation import PDBComplex\nfrom plip.exchange.report import BindingSiteReport\n\ndef retrieve_plip_interac... | [
[
"pandas.DataFrame.from_records"
],
[
"torch.isnan"
]
] | [
{
"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... |
GeoCode-polymtl/Deep_1D_velocity | [
"8f42fc4f5c984d0e11b4c93ae7eee99ba3843b4c"
] | [
"semblance/nmo_correction.py"
] | [
"\"\"\"\nA collection of seismic functions to compute semblance, NMO correction and\nseismic velocities.\n\"\"\"\nimport numpy as np\nfrom scipy.interpolate import CubicSpline\n\n\ndef stack(cmp, times, offsets, velocities):\n \"\"\"\n Compute the stacked trace of a list of CMP gathers\n \n ... | [
[
"numpy.convolve",
"numpy.sqrt",
"numpy.abs",
"numpy.ones",
"numpy.zeros_like",
"scipy.interpolate.CubicSpline",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
Epikem/dev-tips | [
"ed5a258334dd18ef505f51e320f7a9f5ee535cf9"
] | [
"sport/solutions/programmers/kakao-2020-blind/60057 fail.py"
] | [
"# -*- coding: utf-8 -*-\n#!/usr/bin/python\n\n# 풀이는 `solve()` 함수에 있습니다.\n# #NYAN NYAN\n# ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░\n# ░░░░░░░░░░▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄░░░░░░░░░\n# ░░░░░░░░▄▀░░░░░░░░░░░░▄░░░░░░░▀▄░░░░░░░\n# ░░░░░░░░█░░▄░░░░▄░░░░░░░░░░░░░░█░░░░░░░\n# ░░░░░░░░█░░░░░░░░░░░░▄█▄▄░░▄░░░█░▄▄▄░░░\n# ░▄▄▄▄▄░░█░░... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiangycTarheel/Compositional-Auxseq | [
"e4645a92c21c893cd320eb186c19d392bc147b43"
] | [
"models/modeling_utils.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in complianc... | [
[
"torch.nn.functional.softmax",
"torch.zeros",
"torch.cat",
"torch.load",
"torch.nn.Embedding",
"torch.multinomial",
"torch.nn.BCEWithLogitsLoss",
"torch.no_grad",
"torch.topk",
"torch.full_like",
"torch.nn.CrossEntropyLoss",
"torch.nn.Dropout",
"torch.einsum",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Bluefog-Lib/EastCoastTutorial2021 | [
"845aa3f78c0e3e2410a952157a2f57e8d6211d52"
] | [
"tutorial/DecentralizedADMM.py"
] | [
"import os\n\nimport numpy as np\nimport bluefog.torch as bf\nfrom bluefog.common import topology_util\nimport matplotlib.pyplot as plt\nimport torch\nimport scipy.io as sio\n\n# Generate different A and b through different seed.\nbf.init()\nbf.set_topology(topology_util.RingGraph(bf.size()))\ntorch.manual_seed(123... | [
[
"torch.norm",
"torch.zeros",
"torch.randn",
"torch.eye",
"torch.no_grad",
"torch.rand",
"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"... |
awmliu2019/apache-spark | [
"b5241c97b17a1139a4ff719bfce7f68aef094d95"
] | [
"python/pyspark/pandas/indexes/multi.py"
] | [
"#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); yo... | [
[
"pandas.MultiIndex",
"pandas.MultiIndex.from_tuples",
"pandas.MultiIndex.from_arrays",
"pandas.api.types.is_list_like",
"pandas.api.types.is_hashable",
"pandas.MultiIndex.from_product"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.24"
],
"scipy": [],
"tensorflow": []
}
] |
IgoPereiraBarros/Redes-Neurais-Artificiais | [
"4a6551bc1bd7a8b5ae758f565bb3ea180ddbc0f3"
] | [
"Modulo1 - Perceptron/perceptron3.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 28 20:21:00 2018\n\n@author: Igo\n\"\"\"\n\nimport numpy as np\n\n# Aplicando o operador E\nentradas = np.array([[0,0], [0,1], [1,0], [1,1]])\nsaidas = np.array([0, 0, 0, 1])\n\n# Aplicando o operador OU\n#entradas = np.array([[0,0], [0,1], [1,0], [1,1]])\n#saida... | [
[
"numpy.asarray",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OMasa-Thesis/DA-AlgorithmForShinfuri | [
"e47de9b09651c1c9009bfb06d35a801610edebd0"
] | [
"Function/analyzeuniv.py"
] | [
"import pandas as pd\nimport numpy as np\nimport datetime as dt\nimport random\nimport csv\nimport pprint\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport itertools\nmpl.font_manager._rebuild()\n#mpl.rcParameters['font.family'] = 'IPAexGothic'\n\n\n\ndf_univcap = np.loadtxt(... | [
[
"matplotlib.pyplot.legend",
"matplotlib.font_manager._rebuild",
"pandas.read_csv",
"matplotlib.pyplot.title",
"numpy.tile",
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"numpy.concatenate",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.sho... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
gari927/youtube_getcomment | [
"fa6abe1d7c91ef82b01e2b5e0d6cf69d348424b1"
] | [
"main.py"
] | [
"import os\n\nimport pandas as pd\nimport requests\nfrom typing import Dict\nimport requests\nimport json\nfrom dotenv import load_dotenv\n\nload_dotenv()\nURL = 'https://www.googleapis.com/youtube/v3/'\n\nAPI_KEY = os.getenv('API_KEY')\nPLAYLIST_ID = os.getenv('PLAYLIST_ID')\n\n# 1つ目の動画のコメント情報を取得\n# VIDEO_ID = '1v... | [
[
"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": []
}
] |
nmasahiro/zr-obp | [
"dde815dfe75fc6cc3c9ee6479d97db1e5567de6d",
"dde815dfe75fc6cc3c9ee6479d97db1e5567de6d"
] | [
"examples/online/evaluate_off_policy_estimators.py",
"tests/policy/test_linear.py"
] | [
"import argparse\nfrom pathlib import Path\n\nimport numpy as np\nfrom pandas import DataFrame\nfrom joblib import Parallel, delayed\n\nfrom obp.dataset import (\n SyntheticBanditDataset,\n logistic_reward_function,\n)\nfrom obp.policy import (\n BernoulliTS,\n EpsilonGreedy,\n LinEpsilonGreedy,\n ... | [
[
"numpy.arange",
"numpy.random.seed",
"pandas.DataFrame"
],
[
"numpy.copy",
"numpy.array",
"numpy.allclose",
"numpy.ones"
]
] | [
{
"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... |
divija-palleti/Regress_CNN | [
"494d129779f211151e6ed41cfba6ca6dcb126523"
] | [
"data_generator.py"
] | [
"''' Create generators from dataset '''\n\nimport numpy as np\nimport cv2\nimport random\n\nHIGH_DIM = 512\nGLLIM_K = 1\n\n\nBATCH_SIZE = 128\n\n# Mode for the validation set for our mixture model\n\ndef load_data_generator_List(rootpath, imIn, file_test, validation=1.0,subsampling=1.0,processingTarget=None,transfo... | [
[
"numpy.asarray",
"numpy.expand_dims",
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
www516717402/TinyNeuralNetwork | [
"9b4313bbe6fb46d602681b69799e4725eef4d71b",
"9b4313bbe6fb46d602681b69799e4725eef4d71b",
"9b4313bbe6fb46d602681b69799e4725eef4d71b"
] | [
"tinynn/graph/quantization/fake_quantize.py",
"tests/common_utils.py",
"models/rexnetv1.py"
] | [
"import torch\nfrom torch.nn import Module\nfrom torch.quantization.observer import _with_args\n\n\nclass FakeQuantizeBFloat16(Module):\n \"\"\" Simulate the quantize and dequantize operations in training time for bfloat16 \"\"\"\n\n def __init__(self, **observer_kwargs):\n super(FakeQuantizeBFloat16, ... | [
[
"torch.tensor"
],
[
"torch.ones"
],
[
"torch.nn.modules.batchnorm.BatchNorm2d",
"torch.nn.modules.activation.ReLU",
"torch.ones",
"torch.nn.modules.dropout.Dropout",
"torch.nn.modules.activation.Sigmoid",
"torch.nn.modules.pooling.AdaptiveAvgPool2d",
"torch.nn.modules.a... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iamaziz/pandas | [
"43c4dcd45a50de04658dc78c7f2e5c62eaf6b5d2"
] | [
"pandas/core/indexes/datetimes.py"
] | [
"# pylint: disable=E1101\nfrom __future__ import division\n\nfrom datetime import datetime, time, timedelta\nimport operator\nimport warnings\n\nimport numpy as np\n\nfrom pandas._libs import (\n Timestamp, index as libindex, join as libjoin, lib, tslib as libts)\nfrom pandas._libs.tslibs import ccalendar, field... | [
[
"pandas.core.arrays.datetimes.DatetimeArrayMixin._generate_range",
"pandas.tseries.offsets.Hour",
"pandas.tseries.offsets.Day",
"pandas.tseries.frequencies.to_offset",
"pandas.Series",
"numpy.asarray",
"pandas.io.formats.format._is_dates_only",
"pandas.core.accessor.delegate_names"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
nicolargo/mandelbrot | [
"39dfc98e31db35414647331fae2f87551c2c847b"
] | [
"mandelbrot-tensorflow.py"
] | [
"#!/usr/bin/env python\n#\n# Source: https://www.tensorflow.org/tutorials/mandelbrot\n#\n\n# Import libraries for simulation\nimport tensorflow as tf\nimport numpy as np\n\n# Imports for visualization\nimport PIL.Image\nfrom io import BytesIO\n\n\ndef DisplayFractal(a, fmt='jpeg'):\n \"\"\"Display an array of it... | [
[
"tensorflow.InteractiveSession",
"tensorflow.Variable",
"numpy.clip",
"tensorflow.cast",
"numpy.cos",
"numpy.sin",
"tensorflow.zeros_like",
"tensorflow.global_variables_initializer",
"tensorflow.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
alphamatic/amp | [
"5018137097159415c10eaa659a2e0de8c4e403d4",
"5018137097159415c10eaa659a2e0de8c4e403d4",
"5018137097159415c10eaa659a2e0de8c4e403d4",
"5018137097159415c10eaa659a2e0de8c4e403d4",
"5018137097159415c10eaa659a2e0de8c4e403d4"
] | [
"core/feature_analyzer.py",
"dataflow/core/nodes/sarimax_models.py",
"im/ib/test/test_ib_sql_writer_backend.py",
"dataflow/core/nodes/base.py",
"im_v2/cryptodatadownload/data/client/cdd_client.py"
] | [
"\"\"\"\nImport as:\n\nimport core.feature_analyzer as cfeaanal\n\"\"\"\n\nimport collections\nimport logging\nfrom typing import Dict, List, Tuple, Union\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nimport statsmodels.api as sm\n\nimport helpers.hdbg as hdbg\nimport helpers.hprin... | [
[
"matplotlib.pyplot.show",
"pandas.DataFrame"
],
[
"pandas.concat",
"numpy.array",
"numpy.vstack",
"pandas.DataFrame"
],
[
"pandas.to_datetime",
"pandas.DataFrame"
],
[
"pandas.concat",
"pandas.DataFrame"
],
[
"pandas.to_datetime"
]
] | [
{
"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... |
hologerry/coco-a | [
"f6e0145129744785f27b8d4826e687a9aa540a2e"
] | [
"cocoa_beta_2015_demo.py"
] | [
"\n# coding: utf-8\n\n# In[1]:\n\n# general imports\nimport json\nimport time\nimport sys\nimport random\n\n# drawing imports\nimport matplotlib.pyplot as plt\nimport skimage.io as io\n\n\n# In[2]:\n\n# path variables\n# set paths here and you're good to go...\n\n# directory containing coco-a annotations\nCOCOA_DIR... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Pangxiaox/Machine-Learning-Lab | [
"463f705bbeea21e31fe4e9dfbf5f915140344943"
] | [
"2019ML_Lab/Lab4/mtcnn_pytorch/tools/image_reader.py"
] | [
"import numpy as np\nimport cv2\n\nclass TestImageLoader:\n def __init__(self, imdb, batch_size=1, shuffle=False):\n self.imdb = imdb\n self.batch_size = batch_size\n self.shuffle = shuffle\n self.size = len(imdb)\n self.index = np.arange(self.size)\n\n self.cur = 0\n ... | [
[
"numpy.arange",
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sachinkmohan/ssd_keras_git | [
"3a7566109e2d6e902ad24293bd445d5edb8f8632"
] | [
"keras_loss_function/keras_ssd_loss.py"
] | [
"'''\nThe Keras-compatible loss function for the SSD model. Currently supports TensorFlow only.\n\nCopyright (C) 2018 Pierluigi Ferrari\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 ... | [
[
"tensorflow.reduce_max",
"tensorflow.constant",
"tensorflow.count_nonzero",
"tensorflow.less",
"tensorflow.shape",
"tensorflow.reduce_sum",
"tensorflow.maximum",
"tensorflow.zeros",
"tensorflow.reshape",
"tensorflow.expand_dims",
"tensorflow.ones_like",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
ground0state/artlearn | [
"6eb6b4a880b44c3c3911dc1dba3a228538d5be6f"
] | [
"tests/test_art2.py"
] | [
"import numpy as np\nfrom artlearn import ART2\n\n\ndef prepare_data():\n X = np.array([[0.30337076, 0.6262552],\n [0.97010031, 0.],\n [0.41828834, 0.28989424],\n [0.14816287, 0.64219202],\n [0.02348559, 0.94155722],\n [0.375888... | [
[
"numpy.all",
"numpy.hstack",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adamduster/adaptive_md_tools | [
"9753d69747f84dcb453a08ba8d837d969f8e067b"
] | [
"adaptive_md_tools/ap_algorithms.py"
] | [
"#!/usr/bin/env python\n\"\"\" \n\"\"\"\n__author__ = 'Adam Duster'\n__copyright__ = ''\n__credits__ = ['Adam Duster']\n__license__ = 'CC-BY-SA'\n__version__ = '0.1'\n__email__ = 'adam.duster@ucdenver.edu'\n__status__ = 'Development'\n\nfrom scipy.spatial.distance import cdist\nimport sys\nimport adaptive_md_tools.... | [
[
"numpy.asscalar",
"numpy.random.random",
"scipy.spatial.distance.cdist",
"numpy.argwhere"
]
] | [
{
"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"
... |
tianzixie/CAP5415_PA1 | [
"96e675696faba087444b92132479168dc9d791d8"
] | [
"Answers/code/Answer5_2.py"
] | [
"# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\n\r\nCreated on Thu Oct 12 10:21:34 2017\r\n\r\n\r\n\r\n@author: tianzixie\r\n\r\n\"\"\"\r\n\r\n\r\n\r\nimport numpy as np\r\n\r\nfrom PIL import Image\r\nI = Image.open( \"..//images/181079.jpg\")\r\n#I = Image.open( \"C://Users/tianzixie/Desktop/181079.jpg\")\r\n#imgsrc = ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gekho457/ray | [
"bed660b085fa9949bca71160addfc0a69931c64b",
"bed660b085fa9949bca71160addfc0a69931c64b"
] | [
"python/ray/data/tests/test_dataset.py",
"rllib/algorithms/maml/maml.py"
] | [
"import math\nimport os\nimport random\nimport requests\nimport time\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pytest\n\nimport ray\n\nfrom ray.tests.conftest import * # noqa\nfrom ray.data.dataset import Dataset, _sliding_window\nfrom ray.data.datasourc... | [
[
"pandas.testing.assert_series_equal",
"pandas.Series",
"torch.cat",
"numpy.asarray",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.random.randint",
"numpy.testing.assert_equal",
"numpy.arange",
"torch.tensor",
"numpy.testing.assert_array_almost_equal",
"pandas.con... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"1.13",
... |
xpliu16/bmtk | [
"a34dbdc5979985a9b9bfcdf2386c92ac0b6e844f"
] | [
"bmtk/tests/utils/reports/spike_trains/test_spikes_buffer.py"
] | [
"import pytest\nimport tempfile\nimport numpy as np\nfrom six import string_types\n\nfrom bmtk.utils.reports.spike_trains import sort_order\nfrom bmtk.utils.reports.spike_trains.spike_train_buffer import STMemoryBuffer, STCSVBuffer, STMPIBuffer\n\ntry:\n from mpi4py import MPI\n comm = MPI.COMM_WORLD\n MPI... | [
[
"numpy.linspace",
"numpy.arange",
"numpy.sort",
"numpy.full",
"numpy.diff",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JustCal1MeLee/Skin-Type-Prediction-Website-using-Flask | [
"94bd7921d3f2fb4e832607b41eb22f9a6435706e"
] | [
"api.py"
] | [
"import os\nfrom flask import Flask\nfrom flask import request\nfrom flask import render_template\nfrom torch_utils import transform_image, prediction\nimport torch\n\nUPLOAD_FOLDER = 'D:\\learn_flask\\Practice_Flask\\static'\nALLOWED_FILE = ['jpg', 'png', 'jpeg' ]\nDEVICE = 'cuda' if torch.cuda.is_available() else... | [
[
"torch.cuda.is_available",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vineet247/money_in_politics | [
"23417a419887681892d1a4c345dd04defc30790c"
] | [
"project/transform.py"
] | [
"import pandas as pd\n\nfile = pd.read_csv(\"final\", sep = \",\")\ndf = file.groupby(['Cand_ID','ideology','Cand_name','party']).agg({'Amt':sum}).reset_index()\ndf = df.set_index(['Cand_ID','Cand_name', 'party', 'ideology']).unstack(['ideology'])\ndf = df.fillna('0.0')\ndf.to_csv('output_data.csv',sep = ';')\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
weepingwillowben/music_net | [
"ac80332809d8b1af23d85d9ca39a68a4cc80a03f"
] | [
"stateful_predict.py"
] | [
"from lstm import cell_forget_fn,add_barrier_fn,add_cell_fn,to_new_output_fn,full_output_fn,CELL_STATE_LEN,OUT_LEN\nimport numpy as np\nfrom WeightBias import NP_WeightBias\nimport string_processing\n\ndef np_sigmoid(vec):\n return 1.0/(1.0+np.exp(-vec))\n\ndef np_calc_output(inp_vec,cell_state,output_vec,np_cel... | [
[
"numpy.concatenate",
"numpy.tanh",
"numpy.zeros",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xida2020/scikit-fem | [
"2ad37635fe80346326592bc999c3b68ecc34ef83",
"73890816c2142385abf4a9ffcd8d233e2d25e865"
] | [
"skfem/assembly/basis/abstract_basis.py",
"skfem/mesh/mesh.py"
] | [
"import logging\nfrom warnings import warn\nfrom typing import Any, Dict, List, Optional, Tuple, Type, Union\n\nimport numpy as np\nfrom numpy import ndarray\nfrom skfem.assembly.dofs import Dofs, DofsView\nfrom skfem.element import DiscreteField, Element, ElementComposite\nfrom skfem.mapping import Mapping\nfrom s... | [
[
"numpy.einsum",
"numpy.arange",
"numpy.concatenate",
"numpy.array",
"numpy.zeros"
],
[
"numpy.dot",
"numpy.asarray",
"numpy.max",
"numpy.ravel_multi_index",
"numpy.hstack",
"numpy.unique",
"numpy.arange",
"numpy.zeros",
"numpy.isin",
"numpy.nonzero",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iselic/pytorch-retinanet | [
"4175cfe734f3312b1b8ffccb0d945d8a32fb52b0"
] | [
"csv_eval.py"
] | [
"from __future__ import print_function\n\nimport numpy as np\nimport json\nimport os\n\nimport torch\n\n\ndef compute_overlap(a, b):\n \"\"\"\n Parameters\n ----------\n a: (N, 4) ndarray of float\n b: (K, 4) ndarray of float\n Returns\n -------\n overlaps: (N, K) ndarray of overlap between ... | [
[
"numpy.expand_dims",
"numpy.maximum",
"numpy.cumsum",
"numpy.finfo",
"numpy.concatenate",
"numpy.append",
"numpy.argmax",
"torch.no_grad",
"numpy.argsort",
"numpy.where",
"numpy.sum",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kingspp/tsexp | [
"6f2f0ed0b1f6ceecadc04fc8261a5fa6746f7f6b"
] | [
"tsexp/utils.py"
] | [
"import numpy as np\nfrom torch.autograd import Variable\nimport torch\nimport wandb\nfrom PIL import Image\nfrom matplotlib.colors import ListedColormap\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport io\nimport logging\n\nlogger = logging.getLogger(__name__)\n\nSNS_CMAP = ListedColormap(sns.light_... | [
[
"torch.abs",
"matplotlib.pyplot.tight_layout",
"numpy.min",
"torch.from_numpy",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.plot",
"numpy.max",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.clf",
"numpy.argmin",
"numpy.argmax",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vgonzalezd/vgonzalezd.github.io | [
"8066ecfc063871a71a854396fc6d0fb53e42b25b"
] | [
"markdown_generator/talks.py"
] | [
"\n# coding: utf-8\n\n# # Talks markdown generator for academicpages\n# \n# Takes a TSV of talks with metadata and converts them for use with [vgonzalezd.github.io](vgonzalezd.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/wh... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
gitbackspacer/jcvi | [
"6ef8ee8e7160676b3f623d964c9be3e65bf1ed6b"
] | [
"jcvi/graphics/synteny.py"
] | [
"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\n\"\"\"\n%prog mcscan.txt all.bed layout.csv\n\nIllustrate MCscan multiple collinearity alignments. Use layout.csv to indicate\nthe positions of tracks. For example:\n\n#x, y, rotation, ha, va, color, ratio\n0.5, 0.6, 0, left, center, g\n0.25, 0.7, 45, top, center, ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hjbolide/pdf-table-extract | [
"f099d1d6bc368a7d656199210da8246d3555a37a"
] | [
"src/pdftableextract/core.py"
] | [
"import sys\nimport os\nfrom numpy import array, fromstring, ones, zeros, uint8, diff, where, sum, delete\nimport subprocess\nfrom pipes import quote\nfrom .pnm import readPNM, dumpImage\nimport re\nfrom pipes import quote\nfrom xml.dom.minidom import getDOMImplementation\nimport json\nimport csv\n\n#--------------... | [
[
"numpy.ones",
"numpy.delete",
"numpy.where",
"numpy.diff",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chesvectain/i2am | [
"82aee4f617d0cefe5f358d0a0f83b0b2b6a1ef9b"
] | [
"i2am-app/AlgorithmSelectionEngine/PeriodicClassification/CNN.py"
] | [
"\"\"\" Learned classification model \"\"\"\nimport tensorflow as tf\nimport time\nfrom PeriodicClassification import ModelConfig as myConfig\nfrom PeriodicClassification import Preprocess as pre\n\n\ndef _model(X):\n\n # width( size of feature map), input channel, output channel\n W1 = tf.Variable(tf.random_... | [
[
"tensorflow.layers.max_pooling1d",
"tensorflow.matmul",
"tensorflow.nn.softmax",
"tensorflow.train.latest_checkpoint",
"tensorflow.reshape",
"tensorflow.placeholder",
"tensorflow.nn.tanh",
"tensorflow.train.import_meta_graph",
"tensorflow.global_variables_initializer",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
connor-obrien888/tractrix-paper | [
"b111741c63632f6ac01a1709c870c543c4116e0d"
] | [
"SW_pressure.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Sep 18 14:23:25 2020\r\n \r\n A script that pulls all available 1 minute OMNI SW data, calculates thermal and magnetic pressure, and \r\n plots a neat historgam of dynamic, magnetic, and thermal pressure sources. Takes a while to download all\r\n that... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"numpy.min",
"numpy.asarray",
"numpy.arange",
"matplotlib.pyplot.savefig",
"numpy.max",
"matplotlib.pyplot.xlim",
"numpy.append",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xscale",
"matplotlib.pyplot.hist"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AndreiSerbanescu/streamlit | [
"413d87d5fc7901f230526b3d39ebf27de4af0a8b"
] | [
"lib/streamlit/elements/pyplot.py"
] | [
"# Copyright 2018-2020 Streamlit Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or ... | [
[
"matplotlib.pyplot.ioff"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pratikkorat26/cilans_intern_project | [
"9cadf4660b09b5bf0b8fc6a791000b1f4ec20e57"
] | [
"pix2pix/inference/models.py"
] | [
"import torch\r\nimport torch.nn as nn\r\n\r\n\r\nclass Block(nn.Module):\r\n def __init__(self, in_channels, out_channels, down=True, act=\"relu\", use_dropout=False):\r\n super(Block, self).__init__()\r\n self.conv = nn.Sequential(\r\n nn.Conv2d(in_channels, out_channels, 4, 2, 1, bias... | [
[
"torch.nn.Dropout",
"torch.nn.ConvTranspose2d",
"torch.load",
"torch.cat",
"torch.randn",
"torch.nn.Conv2d",
"torch.nn.Tanh",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jwj04ok/ONNX_Convertor | [
"067a17e16dfc8aa80e36f44c4523959daf7359f5"
] | [
"optimizer_scripts/tflite_vs_onnx.py"
] | [
"import argparse\nimport numpy as np\nimport tensorflow as tf\nimport onnx\nimport onnxruntime\n\nfrom tools import helper\n\ndef compare_tflite_and_onnx(tflite_file, onnx_file, total_times=10):\n # Setup onnx session and get meta data\n onnx_session = onnxruntime.InferenceSession(onnx_file, None)\n onnx_o... | [
[
"tensorflow.lite.Interpreter",
"numpy.testing.assert_almost_equal",
"numpy.random.random_sample",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
deeplearningforall/automl | [
"56f326f3d2b093fb6b237eed0bbc81f2320786dc"
] | [
"src/PreProcessText.py"
] | [
"import spacy\nfrom gensim.models.phrases import Phraser\nfrom gensim.models import Phrases\nfrom gensim.models import Word2Vec\nfrom wordcloud import WordCloud\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport plotly.plotly as py\n\nnlp = spacy.load('en')\n\n\nclass PreProcessText():\n def createSequ... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EandrewJones/activelearner | [
"ad706c222a5bdd65d30c1e00b3b9187b82525a59"
] | [
"activelearner/models/svm.py"
] | [
"'''\nSVM\n\nAn interrace for scikit-learn's C-Support Vector Classifier Model\n'''\nimport logging\nLOGGER = logging.getLogger(__name__)\n\nimport numpy as np\n\nimport sklearn.svm\nfrom sklearn.multiclass import OneVsRestClassifier\n\nfrom activelearner.interfaces import ContinuousModel\n\n\nclass SVM(ContinuousM... | [
[
"numpy.shape",
"sklearn.multiclass.OneVsRestClassifier",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
apaleyes/pybnn | [
"1362546662ebdf5d7f507add16a386fa665c51a1"
] | [
"pybnn/sampler/sgld.py"
] | [
"import numpy as np\nimport torch\nfrom torch.optim import Optimizer\n\n\n# def decay(t, a, b , gamma):\n# return a * (b + t) ** (-gamma)\n#\n# def poly(base_lr, t, max_iter, power):\n# return base_lr * (1 - t / max_iter) ** power\n#\n# def const(*args):\n# return 1\n\n\nclass SGLD(Optimizer):\n \"\"... | [
[
"torch.zeros_like",
"torch.ones_like",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brianchiang-tw/Python | [
"dab3e737a5b77ad4582c11dcddce67cb98cc9f25"
] | [
"Matplotlib/coordinate_axis.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.linspace( -50, 50, 100 )\n\ny1 = x + 3\ny2 = x ** 2\n\nplt.figure()\n\nplt.plot(x, y2)\n# plt.plot(x, y1, color='green', linewidth=1.0,linestyle='--')\nplt.plot(x, y1)\n\n# x range\nplt.xlim( (-50,50) )\n\n# y range\nplt.ylim( (-50,50) )\n\nplt.xlabel(\... | [
[
"numpy.linspace",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ychnlgy/TIMIT-diarization | [
"1fbf410cbb643de60201d2d351f1654273885674"
] | [
"src/modules/ShakeBlock.py"
] | [
"import torch\n\nclass ShakeShake(torch.autograd.Function):\n\n @staticmethod\n def forward(ctx, x1, x2, training=True):\n if training:\n alpha = torch.rand_like(x1)\n else:\n alpha = 0.5\n return alpha * x1 + (1 - alpha) * x2\n\n @staticmethod\n def backward(c... | [
[
"torch.rand_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mie-lab/change-detection | [
"53f44d1e39405ab56be4e24da9e40cca31af8908"
] | [
"clusterVisualization.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\nimport os\nimport glob\nimport datetime\n\nfrom matplotlib import pyplot as plt\nimport matplotlib\nfrom itertools import groupby\n\nmatplotlib.rcParams[\"figure.dpi\"] = 300\nmatplotlib.rcParams[\"xtick.labelsize\"] = 13\nmatplotlib.rcParams[\"ytick.... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"pandas.to_datetime",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.xscale",
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"pandas.DataFrame",
"matplotlib.pyplot.xlim",
"numpy.zeros_l... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
amsks/RL-Experiements | [
"9a911775b032e813064ad9162fa96fc75a97ebf3"
] | [
"DDPG/buffer.py"
] | [
"import numpy as np\n\n# Class to implement the replay buffer which will later be used by the agent\nclass ReplayBuffer:\n def __init__(self, max_size, input_shape, n_actions):\n self.mem_size = max_size\n self.mem_ctr = 0 # To keep a track of the first available memory\n self.state_m... | [
[
"numpy.zeros",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
choiking10/mytorch | [
"67140b608b14e2ec6ecca1638705af91d2d71b6b"
] | [
"steps/step26.py"
] | [
"import numpy as np\n\nimport mytorch\nfrom mytorch import utils\nfrom mytorch import as_variable\n\ndef ex1():\n x = as_variable(np.random.randn(2, 3))\n x.name = 'x'\n print(utils._dot_var(x))\n print(utils._dot_var(x, verbose=True))\n\n\ndef ex2():\n x0 = as_variable(np.random.randn(2, 3))\n x1... | [
[
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
louis2889184/pytorch-cifar | [
"bca636bb694989b8f55b5cb582736455174030ed"
] | [
"plot.py"
] | [
"import os\nimport torch\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef jointly_draw_learning_curves():\n folders = [\n \"checkpoints_update1_pretrain0.bin\",\n \"checkpoints_update1_sparsity010.bin\",\n \"checkpoints_update1_sparsity100.bin\",\n # \"checkpoints_update1_p... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gkumarg/neural_prophet | [
"0ce60daeef80955bc5c9ba7e4988a25db07eeea7"
] | [
"tests/test_integration.py"
] | [
"#!/usr/bin/env python3\n\nimport unittest\nimport os\nimport pathlib\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport logging\nimport math\nimport torch\n\nfrom neuralprophet import NeuralProphet, set_random_seed\nfrom neuralprophet import df_utils\n\nlog = logging.getLogger(\"nprophet.test\")\nlog.se... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.to_datetime",
"pandas.DataFrame",
"matplotlib.pyplot.show",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
robust-dl-ai/psa | [
"e2aaf36be00d1b1c2b3a753a9162cb31703a1aff"
] | [
"voc12/make_cls_labels.py"
] | [
"import argparse\n\nimport numpy as np\n\nfrom psa.voc12 import data\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--train_list\", default='train_aug.txt', type=str)\n parser.add_argument(\"--val_list\", default='val.txt', type=str)\n parser.add_argument(\"... | [
[
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stoykostanchev/aipnd-project | [
"da4114caca3060b8058bf1019ee4edeb9135a27d"
] | [
"predict_f.py"
] | [
"global str\nfrom PIL import Image\nimport numpy as np\nimport torch\n\ndef process_image(image):\n ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n returns an Numpy array\n '''\n pil_image = Image.open(image)\n\n # --- Resize to a min of 255px on a side, keep aspect ratio\n ... | [
[
"torch.exp",
"numpy.array",
"torch.no_grad",
"torch.from_numpy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zjc6666/SpeakerProfiling | [
"a1b5474343e1f28c261a62a67fbedd5b20c776fc"
] | [
"TIMIT/lightning_model_spectral.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport pytorch_lightning as pl\nfrom pytorch_lightning.metrics.regression import MeanAbsoluteError as MAE\nfrom pytorch_lightning.metrics.regression import MeanSquaredError as MSE\nfrom pytorch_lightning.metrics.classification import Accuracy... | [
[
"pandas.read_csv",
"torch.tensor",
"torch.nn.BCELoss",
"torch.nonzero",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
zhihao-chen/NLP-experiments | [
"c7512276050f5b8489adb4c745fa970ea8119646"
] | [
"nlp/callback/optimizers/lamb.py"
] | [
"import torch\r\nfrom torch.optim.optimizer import Optimizer\r\n\r\n\r\nclass Lamb(Optimizer):\r\n r\"\"\"Implements Lamb algorithm.\r\n It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.\r\n Arguments:\r\n params (iterable): iterable of parameters to... | [
[
"torch.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
saurabhjha443/Datascience | [
"49ec9d06846dc448af0172d5e9fdb71ca606d7d5"
] | [
"Loan Prediction/loan data.py"
] | [
"import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.linear_model import LogisticRegression\r\n\r\n#read csv file in daatFrame\r\ntrain=pd.read_csv(\"train.csv\")\r\nprint(train.head())\r\n#Description of dataFrame\r\nprint(train.describe())\r\n\r\nprint(train.info())\r\n\r\n#Appy funtion to print sum of null... | [
[
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
wbarfuss/POLD | [
"0c12d3f937831770efa83e20d72c37df60c96882"
] | [
"environments/Env_2StateSingh.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n2 state envrionment according to Singh et al., 1994\n\"\"\"\n\nimport numpy as np\n\nclass TwoStateSingh(object):\n\n def __init__(self, obs_noise=0.0):\n self.N = 1 # starting with one agent, but this could be made adaptive\n self.M = 2 # 2 for now, but eventual... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LZX-0201/L3-Net- | [
"fdb8398e7ff87a0a148f82f320cab391c2c1ff8d"
] | [
"utils/logger/basic_logger.py"
] | [
"import builtins\nimport os\nimport datetime\nimport logging\nimport pickle\nimport shutil\nimport torch\nimport subprocess\nfrom model.model_base import ModelBase\nfrom utils.config.Configuration import Configuration\nfrom tensorboardX import SummaryWriter\n\n# todo: add lock while used in multiprocessing...\n\n\n... | [
[
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cxyth/torch_to_tf | [
"5280fc1ac6cb4179d4a9c9db1d9470d2d59b9c33"
] | [
"test_torch.py"
] | [
"# -*- coding: utf-8 -*-\r\n\r\nimport os\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\r\nimport cv2\r\nimport time\r\nimport numpy as np\r\nimport torch\r\nimport segmentation_models_pytorch as smp\r\nimport albumentations as A\r\nfrom albumentations.pytorch import ToTensorV2\r\n\r\nfrom config import MEAN, STD... | [
[
"numpy.argmax",
"torch.no_grad",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RoryKurek/thermo | [
"985279467faa028234ab422a19b69385e5100149"
] | [
"tests/test_vapor_pressure.py"
] | [
"# -*- coding: utf-8 -*-\n'''Chemical Engineering Design Library (ChEDL). Utilities for process modeling.\nCopyright (C) 2016, Caleb Bell <Caleb.Andrew.Bell@gmail.com>\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software... | [
[
"numpy.log",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nudin/PyKEEN | [
"c750494ae7f9fd8d79ff1775ca4349dca7468f78"
] | [
"src/pykeen/kge_models/trans_r.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"Implementation of TransR.\"\"\"\n\nfrom typing import Dict, Optional\n\nimport numpy as np\nimport torch\nimport torch.autograd\nfrom pykeen.constants import RELATION_EMBEDDING_DIM, SCORING_FUNCTION_NORM, TRANS_R_NAME\nfrom torch import nn\n\nfrom .base import BaseModule\n\n__all__... | [
[
"torch.nn.init.uniform_",
"torch.norm",
"numpy.sqrt",
"torch.einsum",
"torch.nn.Embedding",
"torch.mul",
"torch.clamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jhod0/SIDMpy | [
"e41d75e05af560614c9b1b0f3870a01ae1a35882"
] | [
"sidmpy/CrossSections/yukawa.py"
] | [
"from sidmpy.CrossSections.cross_section import InteractionCrossSection\nimport numpy as np\n\n\nclass AttractiveYukawa(InteractionCrossSection):\n\n \"\"\"\n This implements a velocity-dependent cross section of the form:\n\n sigma_max * (4 pi / 22.7) * b^2 * log(1 + 1/b) for b < 0.1\n sigma_max * (8 p... | [
[
"numpy.log",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
urbrob/ai-pacman | [
"da06ee01e1b01ed3e8f76cb9815d77576734c75c"
] | [
"api/ai_tools/utils.py"
] | [
"from tensorflow.keras.models import load_model\nfrom jsonschema import exceptions, validate\nfrom ai_tools.schema import data_schema\nmodel = None # Model for our instance, we use it as global to do not reload it every time\n\n\ndef validate_if_entry_data_is_valid(data):\n if data:\n try:\n va... | [
[
"tensorflow.keras.models.load_model"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
LandAndLand/ClinicalNotesICU | [
"0665a4ce61da110012e24eaa5500247ad9d0bea0"
] | [
"scripts/preprocess_embeddings.py"
] | [
"from gensim.models import KeyedVectors\nimport pickle\nimport os\nfrom config import Config\n\nargs = Config()\n\nfilename = '~/embeds/BioWordVec_PubMed_MIMICIII_d200.vec.bin'\nmodel = KeyedVectors.load_word2vec_format(filename, binary=True)\n\nprint(model.vectors.shape)\nprint(len(model.index2word))\nprint(model.... | [
[
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tcl326/liegroups-python | [
"4aee2298369005344a97a0e8a55d74d146d6fde2",
"4aee2298369005344a97a0e8a55d74d146d6fde2"
] | [
"src/liegroups/se3_test.py",
"src/liegroups/so3_test.py"
] | [
"from numpy.core.numeric import identity\nfrom numpy.testing import assert_almost_equal\nimport pytest\n\nimport math\n\nimport numpy as np\n\nfrom liegroups.se3 import SE3\nfrom liegroups.util import normalize_range, norm\n\n\ndef test_identity():\n identity = SE3(0.0, 0.0, 0.0, 0.0, 0.0, 0.0)\n assert ident... | [
[
"numpy.testing.assert_almost_equal",
"numpy.identity",
"numpy.array",
"numpy.zeros"
],
[
"numpy.linalg.norm",
"numpy.testing.assert_almost_equal",
"numpy.identity",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NII-Kobayashi/furry-palm-tree | [
"cce746e6596b2daeda33aabf4b503dc18600e4a9"
] | [
"example_cross_validation.py"
] | [
"\"\"\"\nThis code evaluates the linear regression with degree, based on retweets dataset (Data/training/RT*.txt),\nassuming parameters are the same for all tweets.\nPlease replace file paths according to your local directory structure.\n\nInput are\n1) Data file that includes the retweet times and the number of fo... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iphyer/DefectDetection-MaskRCNN | [
"7d1faf99bb2c4ffdbbaaf4544a996b97b2f3a2f9"
] | [
"tools/train_net.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nr\"\"\"\nBasic training script for PyTorch\n\"\"\"\n# Set up custom environment before nearly anything else is imported\n# NOTE: this should be the first import (no not reorder)\nfrom maskrcnn_benchmark.utils.env import setup_environment # n... | [
[
"torch.distributed.init_process_group",
"torch.cuda.set_device",
"torch.manual_seed",
"torch.cuda.empty_cache",
"torch.device",
"torch.nn.parallel.DistributedDataParallel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
majesticfish/CS156B-Covid-Challenge | [
"aebfb03d57171e4a45440e3c77bf5f43b6f3ec2a"
] | [
"exploratory/Sean's Notebook/lib.py"
] | [
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport git\nimport math\nimport os\nimport json\nfrom sklearn import neighbors\nimport statsmodels.api as sm\nfrom sklearn.linear_model import LinearRegression, Ridge,Lasso\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom statsmodels.tsa... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"numpy.unique",
"sklearn.neighbors.NearestNeighbors",
"numpy.transpose",
"numpy.array",
"numpy.where",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
mniverthi/canadarank | [
"ff921a400d4d55715f0d11fad6b9bbbee5ca5341"
] | [
"scripts/errors.py"
] | [
"import language_tool_python\nfrom newspaper import Article\nfrom newspaper import fulltext\nfrom newspaper import build\nimport traceback\nimport sys\nfrom pandas import DataFrame\nimport pandas as pd\nimport numpy as np\nimport praw\nimport csv\ntool = language_tool_python.LanguageTool('en-US')\n\ndata = pd.DataF... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
UtenaTenjo371/COVID-19-data-Visualization | [
"e36a5a1a8c20e3e850f655c78f42127127a1d739"
] | [
"utils/utils.py"
] | [
"import pandas as pd\nimport numpy as np\n\n\ndef filter_data(data, conditions):\n \"\"\"\n Remove elements that do not match the condition provided.\n Takes a data list as input and returns a filtered list.\n Conditions should be a list of strings of the following format:\n '<field> <op> <value>'\... | [
[
"numpy.array",
"pandas.to_datetime",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
jstoppelman/schnetpack | [
"4b22940d924f03ab9e400408004b47d401aa5958"
] | [
"src/schnetpack/data/loader.py"
] | [
"import logging\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\n\nlogger = logging.getLogger(__name__)\n\nfrom schnetpack import Properties\nfrom .stats import StatisticsAccumulator\n\n\n__all__ = [\"AtomsLoader\"]\n\n\ndef _collate_aseatoms(examples):\n \"\"\"\n Build b... | [
[
"torch.sum",
"torch.zeros_like",
"torch.no_grad",
"torch.from_numpy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rcgale/deepSpeech2 | [
"68f37275708a68f764b6b17453e63d60f5d033af"
] | [
"ymdeepspeech2/custom_ops.py"
] | [
"\"\"\"\nCustom RNN Cell definition.\nDefault RNNCell in TensorFlow throws errors when\nvariables are re-used between devices.\n\"\"\"\nimport tensorflow as tf\n\nfrom tensorflow.contrib.rnn import BasicRNNCell\nfrom tensorflow.python.util import nest\nfrom tensorflow.python.training import moving_averages\nfrom te... | [
[
"tensorflow.concat",
"tensorflow.control_dependencies",
"tensorflow.minimum",
"tensorflow.cast",
"tensorflow.nn.bidirectional_dynamic_rnn",
"tensorflow.orthogonal_initializer",
"tensorflow.train.ExponentialMovingAverage",
"tensorflow.python.training.moving_averages.assign_moving_av... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
TurnerAtwood/IR_Project | [
"6c4c5f7295db7c851b6086c13764563c45e3edc8"
] | [
"model.py"
] | [
"from gensim.models import Word2Vec,KeyedVectors\nfrom typing import List\nimport numpy as np\n\ndef convert_model():\n# model = Word2Vec.load('model.bin')\n# model = KeyedVectors.load_word2vec_format('model.bin',binary=True)\n model = Word2VecEmbedding()\n return model\n\nclass Word2VecEmbedding(object... | [
[
"numpy.array",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
m-bizhani/Digital-rock-image-processing | [
"4d3914dcfa1f814b953e6ce7e97a198f861f8e3a"
] | [
"Denoising.py"
] | [
"import tensorflow as tf\r\nimport os\r\n\r\nfrom model.DIDN import get_model\r\nfrom tensorflow.keras.optimizers import Adam\r\nfrom common.losses import *\r\nfrom common.lr_scheduler import *\r\nfrom common.metrics import *\r\nfrom functools import wraps\r\nimport time\r\n\r\ntf.keras.backend.clear_session()\r\n\... | [
[
"tensorflow.keras.callbacks.ModelCheckpoint",
"tensorflow.device",
"tensorflow.test.gpu_device_name",
"tensorflow.keras.optimizers.Adam",
"tensorflow.keras.backend.clear_session",
"tensorflow.keras.losses.mean_absolute_error",
"tensorflow.keras.losses.mean_squared_error",
"tensorfl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
taomo/SOTA-RL-Algorithms | [
"b82ebd8c15296597bf111478fdf25b59a426bb24"
] | [
"model.py"
] | [
"from torch import nn\nfrom TCN.tcn import TemporalConvNet\n\n\nclass TCN(nn.Module):\n def __init__(self, input_size, output_size, num_channels, kernel_size, dropout):\n super(TCN, self).__init__()\n self.tcn = TemporalConvNet(input_size, num_channels, kernel_size=kernel_size, dropout=dropout)\n ... | [
[
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ccongge/vision | [
"1dd5d1f6a581c42f32006014f46fae11ecd96c70",
"1dd5d1f6a581c42f32006014f46fae11ecd96c70"
] | [
"torchvision/models/mnasnet.py",
"torchvision/models/detection/ssdlite.py"
] | [
"import warnings\n\nimport torch\nfrom torch import Tensor\nimport torch.nn as nn\nfrom .._internally_replaced_utils import load_state_dict_from_url\nfrom typing import Any, Dict, List\n\n__all__ = ['MNASNet', 'mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mnasnet1_3']\n\n_MODEL_URLS = {\n \"mnasnet0_5\":\n \"ht... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.nn.init.zeros_",
"torch.nn.Conv2d",
"torch.nn.init.kaiming_uniform_",
"torch.nn.Linear",
"torch.nn.init.ones_",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.init.kaiming_normal_"
],
[
"torch.nn.Sequential",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nju-fuzy/baselines | [
"c97a379944632dcc769167b4b0381f6d61729a4f"
] | [
"acer/acer.py"
] | [
"import time\nimport functools\nimport numpy as np\nimport tensorflow as tf\nfrom baselines import logger\n\nfrom baselines.common import set_global_seeds\nfrom baselines.common.policies import build_policy\nfrom baselines.common.tf_util import get_session, save_variables\nfrom baselines.common.vec_env.vec_frame_st... | [
[
"tensorflow.control_dependencies",
"tensorflow.reduce_sum",
"tensorflow.minimum",
"tensorflow.train.ExponentialMovingAverage",
"tensorflow.group",
"tensorflow.gradients",
"tensorflow.stop_gradient",
"numpy.random.poisson",
"tensorflow.square",
"tensorflow.train.RMSPropOptim... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
FlixCoder/py-es-optimizer | [
"5b025562eeb1bf50fdea6e7c7ee1fbb6cf816b3a"
] | [
"esopt.py"
] | [
"import numpy as np\nimport pickle\n\n\"\"\"\nTODO:\n- parallel?\n- LR scheduling\n\"\"\"\n\n\n\"\"\" Rectified Adam optimizer \"\"\"\n#becomes SGD with beta2 = 0\nclass RAdam:\n \"\"\"\n Attributes:\n lr : float - learning rate\n lam : float - weight decay coefficient\n beta1 : float - exponential m... | [
[
"numpy.square",
"numpy.abs",
"numpy.sqrt",
"numpy.power",
"numpy.random.seed",
"numpy.linalg.norm",
"numpy.random.normal",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hritikbhandari/gradio | [
"3fd74364ee76325fe77a3399ffdd0d49b106a286"
] | [
"demo/reverse_audio.py"
] | [
"import gradio as gr\nimport numpy as np\nimport random\n\ndef reverse_audio(audio):\n sr, data = audio\n return (sr, np.flipud(data))\n\ngr.Interface(reverse_audio, \"microphone\", \"audio\").launch()\n"
] | [
[
"numpy.flipud"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
james-w-balcomb/kaggle--home-credit-default-risk | [
"b8346ba1640c42daf8457588f7fadd81ded74b8f"
] | [
"jb/LogReg-Automatic.py"
] | [
"#%%\n\nimport matplotlib\nimport numpy\nimport os\nimport pandas\nimport random\nimport sklearn\nimport statsmodels.api as sm\n\nfrom patsy import dmatrices\nfrom sklearn import linear_model\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import model_selection\nfrom sklearn.model_selection impo... | [
[
"sklearn.metrics.roc_auc_score",
"matplotlib.pyplot.legend",
"sklearn.metrics.confusion_matrix",
"matplotlib.pyplot.plot",
"sklearn.metrics.classification_report",
"numpy.where",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"sklearn.model... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
wenh06/torch_ecg | [
"a260bac0b4be8cd84c411874af3337358f135442"
] | [
"benchmarks/train_unet_ludb/trainer.py"
] | [
"\"\"\"\nReferences\n----------\n[1] https://github.com/milesial/Pytorch-UNet/blob/master/train.py\n\"\"\"\nimport os\nimport sys\nimport time\nimport logging\nimport argparse\nimport textwrap\nfrom copy import deepcopy\nfrom collections import deque, OrderedDict\nfrom typing import Union, Optional, Tuple, Sequence... | [
[
"torch.set_default_tensor_type",
"torch.cuda.synchronize",
"torch.nn.parallel.DataParallel",
"numpy.set_printoptions",
"torch.utils.data.DataLoader",
"numpy.repeat",
"numpy.concatenate",
"torch.no_grad",
"numpy.mean",
"torch.cuda.is_available",
"torch.cuda.device_count"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mwestera/iterated-char-rnn | [
"f78b617a421e87921e8bdce4973e31269c8c3112"
] | [
"main.py"
] | [
"import copy\nimport os.path\nimport output_utils\nimport sys\nimport torch\nimport numpy as np\nimport argparse\nimport time\nimport data_utils\nimport models\nimport model_utils\nimport config_utils\nimport sklearn.metrics\nimport pandas as pd # Try to avoid?\nfrom matplotlib import pyplot as plt\nfrom collection... | [
[
"matplotlib.pyplot.legend",
"torch.Tensor",
"torch.load",
"matplotlib.pyplot.rc",
"torch.from_numpy",
"matplotlib.pyplot.plot",
"torch.set_num_threads",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JeffT13/SCOTUS_Speaker_Verification | [
"276f52c23fe40d1f55ae77889b202350f3220d1d"
] | [
"SpeechEmbedder/data_load.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 6 20:55:52 2018\n\n@author: harry\n\"\"\"\nimport glob\nimport numpy as np\nimport os\nimport random\nfrom random import shuffle\nimport torch\nfrom torch.utils.data import Dataset\n\nfrom .hparam import hparam as hp\nfrom .utils import m... | [
[
"numpy.transpose",
"torch.Tensor",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pancetta/python-hpc-performance | [
"fc4c0fcd87d5a0fde78a0d6f284d1c89a31fbb03"
] | [
"benchmarks/montecarlo.py"
] | [
"import time\nfrom numba import jit\nimport numpy as np\nimport random\n\nfrom .benchmarks import Benchmarks\n\n\nclass MonteCarlo(Benchmarks):\n # Idea taken directly from https://numba.pydata.org/\n\n def __init__(self, name, params, num_threads, comm):\n super(MonteCarlo, self).__init__(name=name, p... | [
[
"numpy.sum",
"numpy.random.rand",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jedz5/maml | [
"6ea20f8cac0d562a4047358c122f48d2dfd80200"
] | [
"data_generator.py"
] | [
"\"\"\" Code for loading data. \"\"\"\nimport numpy as np\nimport os\nimport random\nimport tensorflow as tf\n\nfrom tensorflow.python.platform import flags\nfrom utils import get_images\n\nFLAGS = flags.FLAGS\n\nclass DataGenerator(object):\n \"\"\"\n Data Generator capable of generating batches of sinusoid ... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.concat",
"numpy.linspace",
"tensorflow.stack",
"tensorflow.image.decode_png",
"tensorflow.cast",
"tensorflow.random_shuffle",
"tensorflow.train.batch",
"tensorflow.WholeFileReader",
"numpy.sin",
"tensorflow.gather",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
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