repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
nicofirst1/gym-extensions | [
"0a5bb74a248484d2a7e5d2cb552e247612650f04"
] | [
"gym_extensions/continuous/mujoco/modified_half_cheetah.py"
] | [
"import numpy as np\nfrom gym import utils\nfrom gym.envs.mujoco import mujoco_env\nfrom gym_extensions.continuous.mujoco.wall_envs import WallEnvFactory\nfrom gym_extensions.continuous.mujoco.gravity_envs import GravityEnvFactory\nfrom gym.envs.mujoco.half_cheetah import HalfCheetahEnv\nfrom gym_extensions.continu... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shitake/ml-sandbox | [
"a23a312251d82b27582df0029c23ff4aa7792d4c"
] | [
"src/examples/matrix-factorization/matrix_factorization.py"
] | [
"import numpy as np\n\n\nclass MatrixFactorization:\n \n @classmethod\n def run(cls, R, P, Q, K, steps=5000, alpha=0.0002, beta=0.02, threshold=0.001):\n \"\"\"\n Args:\n R (numpy.ndarray): Rating\n P (numpy.ndarray): m x K のユーザ行列\n Q (numpy.ndarray): n x K のア... | [
[
"numpy.dot",
"numpy.array",
"numpy.linalg.norm",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MIT-REALM/architect | [
"1b5bbf6ddf08146cd3b8ad5c058539ac140e9ebb",
"1b5bbf6ddf08146cd3b8ad5c058539ac140e9ebb"
] | [
"architect/components/dynamics/linear_satellite.py",
"architect/optimization/variance_regularized_optimizer_ad.py"
] | [
"\"\"\"Dynamics information for a discrete-time satellite, linearized in the CHW frame\"\"\"\nfrom functools import partial\nfrom typing import Tuple\n\nimport jax\nimport jax.numpy as jnp\nimport matplotlib.pyplot as plt\n\n\n# Define constant parameters\nMU = 3.986e14 # Earth's gravitational parameter (m^3 / s^2... | [
[
"matplotlib.pyplot.axes",
"matplotlib.pyplot.show"
],
[
"numpy.array",
"scipy.optimize.minimize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
... |
WesleyClode/Computational-Photography | [
"c7f62ac57ddea780ecb1e17920fac78dd137e16c"
] | [
"cs276/HM1/hw1_5.py"
] | [
"import sys\r\nfrom PyQt5.QtCore import Qt\r\nfrom PyQt5.QtWidgets import *\r\nfrom PyQt5.QtGui import *\r\nfrom PIL import Image as img,ImageTk\r\nimport time\r\nimport math\r\nimport numpy as np\r\nfrom tkinter import *\r\nimport cv2 as cv\r\n\r\n\r\nimages=[]\r\n\r\ndef init():\r\n for i in range(16):\r\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ibraheem-moosa/XLM-Indic | [
"62314aaa6a34efb994eb644d5d6f8ce510bb465b"
] | [
"src/non-transliteration-model-downstream-tasks/midas.py"
] | [
"from datasets import load_from_disk, set_caching_enabled, load_dataset\nfrom transformers import AlbertForSequenceClassification, AlbertTokenizer, DataCollatorWithPadding\nfrom transformers import Trainer, TrainingArguments, set_seed, EvalPrediction\nfrom indicnlp.normalize import indic_normalize\nimport torch\nfr... | [
[
"sklearn.metrics.confusion_matrix",
"numpy.stack",
"sklearn.metrics.precision_recall_fscore_support",
"numpy.std",
"torch.no_grad",
"numpy.mean",
"torch.log",
"matplotlib.pyplot.show",
"scipy.special.softmax",
"sklearn.metrics.ConfusionMatrixDisplay",
"sklearn.metrics.a... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
LingyuMa/kaggle_planet | [
"82fad232b11ad76e7ca49b47ec9da20747dde206"
] | [
"src/data/data_provider.py"
] | [
"import tensorflow as tf\nimport src.settings as settings\nimport matplotlib.pyplot as plt\n\n\ndef read_and_decode(filename_queue):\n reader = tf.TFRecordReader()\n _, serialized_example = reader.read(filename_queue)\n features = tf.parse_single_example(\n serialized_example,\n features={\n ... | [
[
"matplotlib.pyplot.imshow",
"tensorflow.local_variables_initializer",
"tensorflow.image.random_flip_left_right",
"tensorflow.FixedLenFeature",
"tensorflow.train.start_queue_runners",
"tensorflow.decode_raw",
"tensorflow.cast",
"tensorflow.reshape",
"tensorflow.train.Coordinator... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
vadim0x60/perturb-validate | [
"66c8eaf8f98400b167a5e099c16cac882d547724"
] | [
"perturbvalidate/models/perdict_model.py"
] | [
"import torch\nfrom torch import nn\nfrom torch.distributions import Normal\nimport numpy as np\n\nnormal_dist = Normal(0, 1)\n\ndef multi_layer_perceptron(input_size, output_size, hidden_layers, activation=nn.Sigmoid):\n sizes = np.linspace(input_size, output_size, hidden_layers+2)\n sizes = sizes.round().as... | [
[
"torch.nn.Sequential",
"numpy.linspace",
"torch.nn.LSTM",
"torch.cat",
"torch.Tensor",
"torch.nn.GRU",
"torch.nn.Linear",
"torch.distributions.Normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rwoodsrobinson/pymatgen | [
"f9226b79b8f9ccd0b305e557c9e4878e096bfc77"
] | [
"pymatgen/io/abinit/variable.py"
] | [
"import string\nimport collections\nimport numpy as np\n\n\"\"\"\nTODO: MODULE DOC.\n\"\"\"\n\n__all__ = [\n 'InputVariable',\n]\n\n_SPECIAL_DATASET_INDICES = (':', '+', '?')\n\n_DATASET_INDICES = ''.join(list(string.digits) + list(_SPECIAL_DATASET_INDICES))\n\n_INTERNAL_DATASET_INDICES = ('__s', '__i', '__a')\n... | [
[
"numpy.reshape",
"numpy.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sunutf/AR-Net | [
"8da398f40f9c314019b4cd56c1dc24093badf19c"
] | [
"ops/dataset.py"
] | [
"import torch.utils.data as data\nimport torch\n\nfrom PIL import Image\nimport os\nimport numpy as np\nfrom numpy.random import randint\n\n\nclass VideoRecord(object):\n def __init__(self, row):\n self._data = row\n self._labels = torch.tensor([-1, -1, -1])\n labels = sorted(list(set([int(x... | [
[
"numpy.linspace",
"torch.tensor",
"torch.stack",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mmilutinovic013/xGitGuard | [
"da52de5c09831f981a86c9d9af9e5215c40c2ba7"
] | [
"xgitguard/models/model.py"
] | [
"\"\"\"\nCopyright 2021 Comcast Cable Communications Management, LLC\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\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by app... | [
[
"sklearn.ensemble.RandomForestClassifier",
"sklearn.metrics.precision_score",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.f1_score",
"sklearn.metrics.recall_score",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kangyongxin/face_recognition_framework | [
"df7c6f10303e12ad398337a0eadd507191b02afe",
"df7c6f10303e12ad398337a0eadd507191b02afe"
] | [
"tools/ensemble.py",
"models/backbones/densenet.py"
] | [
"import numpy as np\nimport os\n\npath = 'output/committee_ms1m/base/'\nexps = ['base-nasnetasmall-lr0.1', 'base-irv2-lr0.1', 'base-densenet121-lr0.1', 'base-resnet101-lr0.1', 'base-resnet50-lr0.3']\noutput_exp = 'ensemble-top{}'.format(len(exps))\nif not os.path.isdir(path + output_exp):\n os.makedirs(path + ou... | [
[
"numpy.fromfile"
],
[
"torch.nn.init.kaiming_normal",
"torch.nn.Dropout",
"torch.nn.functional.dropout",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.functional.relu",
"torch.nn.MaxPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shirtsgroup/foldamers | [
"b67c164aff31cf7b6ff64d7b121059b75eaaf14c"
] | [
"foldamers/parameters/angle_distributions.py"
] | [
"import os\r\nimport numpy as np\r\nimport mdtraj as md\r\nfrom simtk import unit\r\nfrom foldamers.cg_model.cgmodel import CGModel\r\nfrom foldamers.utilities.plot import plot_distribution\r\n\r\n# These functions calculate and plot bond angle and torsion distributions from a CGModel object and pdb trajectory\r\n\... | [
[
"numpy.reshape",
"numpy.zeros",
"numpy.histogram",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YSHsieh7777/tvm | [
"b51973fb48deb34ff725bf1206f1b683f8bc2773"
] | [
"python/tvm/autotvm/measure/measure_methods.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
blahblahbloopster/2898_2019_vision | [
"dc7b8b98c653cd6f09686ad549fd77c6bd438e2c"
] | [
"contourLib.py"
] | [
"import cv2\nimport numpy as np\n\n\n# ----------------- IMAGE PREPROCESSING ------------------------\ndef preprocess(img, resize=True, bilaterial=True, gaussian=True,\n IMG_WIDTH=640, IMG_HEIGHT=480,\n bilatDiameter=9, bilatSigmaColor=150, bilatSigmaSpace=150,\n gaussKerne... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Surabhivj/BRANet | [
"a5fff9aa7ba58f0ea30aca05555796487db78f0e"
] | [
"BRANet.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Sep 7 17:37:57 2021\n\n@author: jagtaps\n\"\"\"\n\n\nimport numpy as np\nimport networkx as nx\nimport pandas as pd\nimport requests\nimport gzip\nimport shutil\nimport mygene\nimport functions as f\n\n\nmirna_dat_normal = pd.read_csv(\"data//Pancancer_miRNA_Normal... | [
[
"numpy.array",
"pandas.concat",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lmikolajczak/gdal2tiles | [
"758abc98db533945b267a0ab4ad36d54b91525ea"
] | [
"gdal2tiles.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# ******************************************************************************\n# $Id$\n#\n# Project: Google Summer of Code 2007, 2008 (http://code.google.com/soc/)\n# Support: BRGM (http://www.brgm.fr)\n# Purpose: Convert a raster into TMS (Tile Map Service) t... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kavinyao/SKBPR | [
"305aeb846ee89234d8eae3b73452c2fdad2496b4"
] | [
"graphs/visit_sequence_hist.py"
] | [
"import sys\nimport math\nimport config\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# copy to root folder to run this file\n\nSEQ_UPPER_BOUND = 200\n\nif __name__ == '__main__':\n filename = sys.argv[1] if len(sys.argv) > 1 else 'graphs/output/sequence_hist.png'\n\n dbm = config.DatabaseManager(co... | [
[
"numpy.hstack",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.xlabel",
"numpy.repeat",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
salt-die/nurses_2 | [
"29b76c34b9a28bf7c115998f4e81979966c82df0",
"29b76c34b9a28bf7c115998f4e81979966c82df0"
] | [
"nurses_2/widgets/line_plot/_traces.py",
"examples/minesweeper/minesweeper/minesweeper.py"
] | [
"import cv2\nimport numpy as np\n\nfrom ...colors import Color, rainbow_gradient\nfrom ...data_structures import Size\nfrom ...easings import lerp\nfrom .._binary_to_braille import binary_to_braille\nfrom ..text_widget import TextWidget, Anchor\n\nTICK_WIDTH = 11\nTICK_HALF = TICK_WIDTH // 2\nVERTICAL_SPACING = 5\n... | [
[
"numpy.array",
"numpy.zeros",
"numpy.dstack",
"numpy.full"
],
[
"numpy.array",
"numpy.zeros",
"numpy.random.default_rng"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CaitlynCarney/what_causes_heart_disease | [
"a50c422427bce2d3f78c1c9b1f7972cd862263f5"
] | [
"Workbooks/wrangle.py"
] | [
"import openpyxl\nimport pandas as pd\nimport numpy as np\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nimport sklearn.preprocessing\nfrom sklearn.cluster import KMeans\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler, StandardScaler\nfrom sklearn.... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"pandas.cut",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ilvivl/Python_Project | [
"58546d08d618e237e2fe37ebb44cea1827c6e59e"
] | [
"scripts/file_utils.py"
] | [
"import pandas as pd\ndef insert_history_to_file(data, filepath):\n\tdf = pd.DataFrame.from_dict(data)\n\tdf.to_csv(filepath, sep = '|', index=False)\n\t\t# keys_of_data = (list(data[0].keys()))\n\t\t# keys_to_file = str(keys_of_data[0]) + ',' + str(keys_of_data[1]) + '\\n'\n\t\t# file.write(keys_to_file)\n\t\t# fo... | [
[
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
DolorHunter/hfut-exp-archived | [
"c67e26c1f4fba550c8974eaba10dfa302b928868"
] | [
"BigData_exp/exp2/0203/0203.py"
] | [
"'''\nFileName: 0203.py\nAuther: Wang Zixiang\nDate: 04.20.2019\n\nFunction: draw a func\n'''\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef draw():\n x = np.linspace(-5, 5, 1000)\n y = [np.arcsin(i)/np.tan(x) for i in x]\n plt.plot(x, y)\n plt.show()\n\n\ndef main():\n ... | [
[
"numpy.linspace",
"numpy.arcsin",
"matplotlib.pyplot.plot",
"numpy.tan",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vortexlab-uclouvain/multiflap | [
"6de0a9ceabf8c42b72b2a82943fb78e105480636",
"6de0a9ceabf8c42b72b2a82943fb78e105480636",
"6de0a9ceabf8c42b72b2a82943fb78e105480636"
] | [
"multiflap/odes/rossler.py",
"examples/forced_van_der_pol.py",
"multiflap/main_torus.py"
] | [
"'''\nfile rossler.py\n@author Gianmarco Ducci\n@copyright Copyright © UCLouvain 2020\n\nmultiflap is a Python tool for finding periodic orbits and assess their stability via the Floquet multipliers.\n\nCopyright <2020> <Université catholique de Louvain (UCLouvain), Belgique>\n\nList of the contributors to the deve... | [
[
"numpy.array"
],
[
"matplotlib.pyplot.scatter",
"numpy.linspace",
"numpy.linalg.eig",
"scipy.integrate.odeint",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show",
"numpy.linspace",
"matplotlib.pyplot.fig... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
... |
cirosantilli/scipy | [
"b2393d93e511d90064f94b62798c2e554d355a6e"
] | [
"scipy/linalg/decomp_qr.py"
] | [
"\"\"\"QR decomposition functions.\"\"\"\n\nimport numpy\n\n# Local imports\nfrom blas import get_blas_funcs\nfrom lapack import get_lapack_funcs, find_best_lapack_type\nfrom misc import _datacopied\n\n# XXX: what is qr_old, should it be kept?\n__all__ = ['qr', 'qr_multiply', 'rq', 'qr_old']\n\ndef safecall(f, name... | [
[
"numpy.asarray",
"numpy.identity",
"numpy.triu",
"numpy.zeros",
"numpy.empty",
"numpy.asarray_chkfinite"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jbrockmendel/cyberpandas | [
"0db23fcdf91eede631ed603cb321f39d834bd12c"
] | [
"cyberpandas/base.py"
] | [
"import operator\n\nimport numpy as np\n\nfrom pandas.core.arrays import ExtensionArray\n\n\nclass NumPyBackedExtensionArrayMixin(ExtensionArray):\n @property\n def dtype(self):\n \"\"\"The dtype for this extension array, IPType\"\"\"\n return self._dtype\n\n @classmethod\n def _from_seque... | [
[
"numpy.concatenate",
"numpy.sort",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wilson1745/QUANTAXIS | [
"ff105ed17f40753afeea9a742304c59979cc4ade"
] | [
"QUANTAXIS/QAFetch/QATdx.py"
] | [
"# coding:utf-8\n#\n# The MIT License (MIT)\n#\n# Copyright (c) 2016-2021 yutiansut/QUANTAXIS\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including withou... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.DataFrame",
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
megagonlabs/rotom | [
"09a9dd9ab672c6daa315ad0e077ccbe3612fa934"
] | [
"invda/generate.py"
] | [
"import torch\nimport sys\nimport os\nimport jsonlines\nimport argparse\nimport spacy\n\nfrom tqdm import tqdm\nfrom transformers import T5ForConditionalGeneration,T5Tokenizer\n\ndef set_seed(seed):\n \"\"\"set seeds\"\"\"\n torch.manual_seed(seed)\n if torch.cuda.is_available():\n torch.cuda.manual... | [
[
"torch.LongTensor",
"torch.manual_seed",
"torch.no_grad",
"torch.cuda.is_available",
"torch.cuda.manual_seed_all"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MighTy-Weaver/Inefficient-AC-detection | [
"8229f19accd1569ba7b48f77f71783173393d9ed"
] | [
"shapley_cluster.py"
] | [
"import os\nimport pickle\nfrom datetime import datetime\nfrom glob import glob\nfrom shutil import copyfile\n\nimport cv2\nimport numpy as np\nimport pandas as pd\nimport shap\nfrom sklearn.cluster import KMeans, MiniBatchKMeans\nfrom tqdm import tqdm\n\ncategories = 4\navailable_cates = [\"KMeans\", \"MiniKMeans\... | [
[
"sklearn.cluster.MiniBatchKMeans",
"numpy.array",
"pandas.read_csv",
"sklearn.cluster.KMeans"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
abhileshborode/vehicle-detection | [
"34c9991d8bd835b159983eef9b665480709c203b"
] | [
"training.py"
] | [
"\n# Import math and CV libs\nfrom helper_functions import get_hog_features, bin_spatial, color_hist, slide_window, draw_boxes, convert_image ,get_dataset,extract_features\nfrom heatmap import add_heat, apply_threshold, draw_labeled_bboxes, heat_threshold\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as... | [
[
"numpy.hstack",
"sklearn.model_selection.train_test_split",
"numpy.int",
"numpy.copy",
"sklearn.svm.LinearSVC",
"sklearn.preprocessing.StandardScaler",
"numpy.vstack",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kkrish39/realtime-depth-prediction-from-monocular-videos | [
"9cde9c1a6df6c91af1ada80b3aaeebae03fc59dc"
] | [
"src/train_main_posenet.py"
] | [
"import torch\nfrom torch import optim\nimport matplotlib.pyplot as plt\nimport torchvision\nimport numpy as np\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nimport scipy.io as sio\nimport os\n\nfrom SfMLearner import SfMLearner\nfrom KITTIdataset import KITTIdataset\n\nfrom collect... | [
[
"numpy.mod",
"torch.utils.data.DataLoader",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xtkisscs/robosuite | [
"fb9220491607ead5f1cd5eb25d17626c9ae3756c"
] | [
"robosuite/demos/demo_device_control.py"
] | [
"\"\"\"Teleoperate robot with keyboard or SpaceMouse.\n\n***Choose user input option with the --device argument***\n\nKeyboard:\n We use the keyboard to control the end-effector of the robot.\n The keyboard provides 6-DoF control commands through various keys.\n The commands are mapped to joint velocities ... | [
[
"numpy.concatenate",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cocolord/mmsegmentation | [
"45db7269d7aa40f8aac5ddaabf7e1b4b01353ca5"
] | [
"mmseg/models/backbones/vit_mla_fuseconv_resnet18.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision.models as models\n\nfrom functools import partial\nimport math\n\n\nfrom .helpers import load_pretrained\nfrom .layers import DropPath, to_2tuple, trunc_normal_\n\nfrom ..builder import BACKBONES\n\nfrom mmcv.cnn import build_... | [
[
"torch.nn.Dropout",
"torch.linspace",
"torch.cat",
"torch.zeros",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.no_grad",
"torch.nn.functional.interpolate",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RokasRutkauskas/kaggle-working | [
"6b8a3b98fdcc34b87046f9aeed84d9fecfc0e58d"
] | [
"prepare_data.py"
] | [
"#!/usr/bin/env python3\r\n\"\"\"\r\nScript for preparing data for the RSNA Pneumonia Detection Challenge\r\n\"\"\"\r\nimport json\r\nimport glob\r\nimport os\r\nfrom collections import defaultdict\r\nimport pandas as pd\r\nfrom sklearn.model_selection import StratifiedKFold\r\nimport util\r\n\r\nsz = 1000\r\nwith ... | [
[
"pandas.DataFrame.from_records",
"pandas.read_csv",
"sklearn.model_selection.StratifiedKFold"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
janelia-cosem/fibsem-optflow | [
"8047965c7599e19528552b629434f5047d7d8b79"
] | [
"support_scripts/upload_matches.py"
] | [
"#!/usr/bin/env python\n\nimport renderapi\nimport argparse\nimport numpy as np\nimport os\nfrom PIL import Image\nfrom glob import glob\n\n\ndef gen_matches(flow_dir, match_name, n, stack, render_connect_params):\n render = renderapi.connect(**render_connect_params)\n tilespecs = renderapi.tilespec.get_tile_... | [
[
"numpy.array",
"numpy.random.random",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kcc3/hackerrank-solutions | [
"f862b44b840bd447d99dc148f6bb5e2f5bfb8a86",
"f862b44b840bd447d99dc148f6bb5e2f5bfb8a86"
] | [
"python/numpy/sum_and_prod.py",
"python/numpy/transpose_and_flatten.py"
] | [
"\"\"\"Hackerrank Problem: https://www.hackerrank.com/challenges/np-sum-and-prod/problem\n\nTask\n\nYou are given a 2-D array with dimensions N x M.\nYour task is to perform the sum tool over axis 0 and then find the product of that result.\n\"\"\"\nimport numpy\n\nn, m = map(int, input().split(' '))\n\narray = []\... | [
[
"numpy.sum"
],
[
"numpy.array",
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MultiW/nimble-xc | [
"bb9058d0f441b5d382abb97c3bb1753fd9d7c9b7"
] | [
"model/src/data/clean_labels.py"
] | [
"import numpy as np\nimport copy\n\nfrom src.data.imu_util import (\n load_imu_data, Sensor, get_sensor_file, clean_imu_data, fix_epoch, ImuCol\n)\nfrom src.data.labels_util import get_labels_file, load_labels, get_workouts, LabelCol, save_clean_labels\nfrom src.data.data import DataState\nfrom src.data.workout ... | [
[
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FlxB2/rl-course | [
"0db6632fbb2e9f90977b5b3422494279477cb8a8"
] | [
"ex08-nstep/ex08-nstep.py"
] | [
"import gym\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef sarsa(env, alpha=0.1, gamma=0.9, epsilon=0.1, num_ep=int(1e4)):\n # Initalize Q arbitrarily, but make terminal states = 0\n Q = np.random.random((env.observation_space.n, env.action_space.n))\n env_t_states = ((env.desc == b'H') | (e... | [
[
"numpy.random.random",
"numpy.power",
"numpy.arange",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neu-szy/GSTSR | [
"6669ab757f683e6740376cdb962fea2fdfb88cba"
] | [
"basicsr/models/sr_model.py"
] | [
"import torch\nfrom collections import OrderedDict\nfrom os import path as osp\nfrom tqdm import tqdm\n\nfrom basicsr.archs import build_network\nfrom basicsr.losses import build_loss\nfrom basicsr.metrics import calculate_metric\nfrom basicsr.utils import get_root_logger, imwrite, tensor2img\nfrom basicsr.utils.re... | [
[
"torch.no_grad",
"torch.cuda.empty_cache"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bomelino/stupid | [
"6fa1bdb883cfb4ce203c7dc865f8fcb5b6516241"
] | [
"stupid/__init__.py"
] | [
"\"\"\"\nmodule documentation\n\"\"\"\n\nimport numpy as np \nimport imageio\nimport tensorflow as tf\nimport requests # für http\n\nfrom . import checkpoint\nfrom . import layer\nfrom . import graph\nfrom .ops import * # das müsste ok sein, weil operations sehr spezielle namen haben und sich da nichts in die quere... | [
[
"matplotlib.pyplot.imshow",
"tensorflow.cast",
"matplotlib.image.imread",
"tensorflow.image.decode_image",
"numpy.reshape",
"numpy.stack",
"tensorflow.is_tensor",
"tensorflow.shape",
"tensorflow.math.reduce_max",
"numpy.transpose",
"numpy.array",
"matplotlib.pyplot.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
MariamHarmouche/Dysarthrie-dTHS-im-STN | [
"21bc7d6a9c96aa0f512aaad6220ce309b7d38fa3"
] | [
"plots/barchart.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mticker\n\n# 'DRT links', 'CST links', 'DRT rechts', 'CST rechts'\noben = [6.22, 20.81, 2.64, 20.94]\nunten = [6, 25.85, 5.53, 10.05]\n\n\nwidth =0.3\nplt.bar(np.arange(len(oben)), oben, width=width, label=\"oben\")\nplt.bar(np.arange... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.legend",
"matplotlib.ticker.FormatStrFormatter",
"matplotlib.pyplot.text",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hoainam25699/snorkel | [
"dd240003857ee4e95fa49599d708c5ba878b3939"
] | [
"snorkel/labeling/model/label_model.py"
] | [
"import logging\nimport random\nfrom collections import Counter, defaultdict\nfrom itertools import chain\nfrom typing import Any, DefaultDict, Dict, List, NamedTuple, Optional, Set, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom munkres import Munkres # ... | [
[
"numpy.diag",
"torch.optim.lr_scheduler.LambdaLR",
"torch.zeros",
"numpy.around",
"torch.sum",
"numpy.any",
"torch.cuda.is_available",
"numpy.where",
"numpy.random.randint",
"torch.norm",
"torch.ones",
"numpy.clip",
"torch.eye",
"torch.from_numpy",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
reallongnguyen/Optical-Flow-Guided-Feature | [
"f05081d76ced407c7c68013d49d57f0a20c71715"
] | [
"datasets/ucf11.py"
] | [
"\"\"\"Provides data for the ucf11 dataset.\n\nThe dataset scripts used to create the dataset can be found at: convert_ucf11.py\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport tensorflow as tf\n\nfrom datasets import data... | [
[
"tensorflow.FixedLenFeature",
"tensorflow.range",
"tensorflow.zeros",
"tensorflow.decode_raw",
"tensorflow.cast",
"tensorflow.reshape",
"tensorflow.gather",
"tensorflow.gfile.Glob",
"tensorflow.train.string_input_producer",
"tensorflow.TFRecordReader",
"tensorflow.parse... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
groadabike/asteroid | [
"276d98346ab791d904fbfe79b9b8e374392dd128"
] | [
"asteroid/losses/sdr.py"
] | [
"import torch\nfrom torch.nn.modules.loss import _Loss\nfrom ..utils.deprecation_utils import DeprecationMixin\n\n\nclass PairwiseNegSDR(_Loss):\n \"\"\"Base class for pairwise negative SI-SDR, SD-SDR and SNR on a batch.\n\n Args:\n sdr_type (str): choose between \"snr\" for plain SNR, \"sisdr\" for\n ... | [
[
"torch.mean",
"torch.log10",
"torch.sum",
"torch.unsqueeze"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rizavelioglu/AIDO-CITEC | [
"97f4d8564dc6eb743063a7902a8932a429349c04"
] | [
"tutorials/image_processing/process_image.py"
] | [
"\"\"\"\nExplanation on how Canny Edge Detector works:\n https://www.youtube.com/watch?v=5dL7FvL-oy0\n\"\"\"\nimport cv2\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\n# Load an RGB image on a gray scale\nimg = cv2.imread(\"../images/road.png\", 0)\n# One way to get rid of the noise on the image, i... | [
[
"numpy.hstack",
"numpy.median",
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahoyosid/Local_Bures_Wasserstein | [
"53b982197eb51fa5b186411f53d7ae3644bb03ed"
] | [
"gaussian_transport.py"
] | [
"\"\"\"Gaussian transport utils\n\nAuthor: XXX\n2019\n\"\"\"\nimport numpy as np\nimport torch\nfrom torch import nn\n\n\ndef root_p_matrix(X, p=0.5):\n \"\"\" Computes the p-root of a matrix X.\n \"\"\"\n U, S, V = torch.svd(X)\n S_root = S.clamp(min=0).pow(p)\n return U.mm(torch.diag(S_root)).mm(V.... | [
[
"torch.nn.MSELoss",
"torch.svd",
"torch.eye",
"torch.diag",
"torch.trace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chanyani/siamfc-tf | [
"e70b0a1ee60c173313861c5070851a6563d5d4b1"
] | [
"embeddings/convolutional_cbam2.py"
] | [
"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Copyright © 2017 bily Huazhong University of Science and Technology\n#\n# Distributed under terms of the MIT license.\n\n\"\"\"Contains definitions of the network in [1].\n\n [1] Bertinetto, L., et al. (2016).\n \"Fully-Convolutional Siamese Networks ... | [
[
"tensorflow.variable_scope",
"tensorflow.split",
"tensorflow.concat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
zoeheez/CSE257Project | [
"921cae356a8335d90439b4f1843cd9f0b982f1a9"
] | [
"psytrack_learning/simulate_learning.py"
] | [
"import numpy as np\nfrom scipy.special import expit\n\n\ndef reward_max(W, X, y, r, answer, i, base=None):\n pR = expit(X[i-1] @ W[i-1])\n return pR * (1-pR) * X[i-1] * (-1)**(answer[i-1]+1) / (1/2)\n\n\ndef predict_max(W, X, y, r, answer, i, base=None):\n pCorrect = np.abs((1-answer[i-1]) - expit(X[i-1] ... | [
[
"numpy.random.seed",
"scipy.special.expit",
"numpy.random.normal",
"numpy.random.rand",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
imamol555/Machine-Learning | [
"aa0a4914db85ae7e1b38774425f2bcc1468a7e4e"
] | [
"log_regex.py"
] | [
"import numpy as np\r\nfrom log_regex_utils import load_dataset\r\n\r\ndef process_data():\r\n\ttrain_x_orig, train_y, test_x_orig, test_y, classes = load_dataset()\r\n\t#print(train_set_x_orig.shape)\r\n\r\n\ttrain_x_flatten = train_x_orig.reshape(train_x_orig.shape[0],-1).T\r\n\ttest_x_flatten = test_x_orig.resha... | [
[
"numpy.dot",
"numpy.log",
"numpy.abs",
"numpy.exp",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NunoEdgarGFlowHub/torchio | [
"656e96c8863ecff0bb29bf880af054675bbb30fd"
] | [
"examples/example_inference.py"
] | [
"\"\"\"\nExample of segmenting a big image using patch-based dense inference.\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\nfrom torchio import IMAGE, LOCATION\nfrom torchio.data.inference import GridSampler, GridAggregator\n\n\npatch_size = 128, 128... | [
[
"torch.nn.Module",
"torch.no_grad",
"torch.utils.data.DataLoader",
"torch.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ehtnamuh/Smarts-Fork | [
"2cd7e85947e7feb7da46753f664ff5e8cc6ace6c"
] | [
"smarts/core/tests/test_map.py"
] | [
"# MIT License\n#\n# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danilojodas/keras-YOLOv3-model-set | [
"da6d1af57bcea139d548843f0488cf61ab00f965"
] | [
"utils/plots.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 7 22:56:27 2020\n\n@author: DANILO\n\"\"\"\n\nimport os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef loss_function(folder='', save_plot=False):\n plt.rc('axes', labelsize=12)\n plt.rc('xtick', labelsize=11)\n plt.rc... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"numpy.array",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RuiChen96/maskrcnn-benchmark | [
"dfd532ed6847e0686f9b09250c1059e617165730"
] | [
"demo/predictor.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nimport cv2\nimport torch\nfrom torchvision import transforms as T\nfrom torchvision.transforms import functional as F\nfrom maskrcnn_benchmark.modeling.detector import build_detection_model\nfrom maskrcnn_benchmark.utils.checkpoint import Det... | [
[
"torch.zeros",
"torch.cat",
"matplotlib.pyplot.get_cmap",
"torch.tensor",
"numpy.copy",
"torch.no_grad",
"torch.nonzero",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pgmikhael/MLExperiments | [
"d3613a70e537ea5aaa0453ddaa76938c32637c49"
] | [
"commands/dispatcher.py"
] | [
"import sys\nfrom os.path import dirname, realpath\nsys.path.append((dirname(dirname(realpath(__file__)))))\nimport argparse\nimport subprocess\nimport os\nimport multiprocessing\nimport pickle\nimport csv\nimport json\nimport random\nimport helpers.parsing as parsing\nimport hashlib\nfrom helpers.reporting_methods... | [
[
"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": []
}
] |
shibing624/text-classifier | [
"4f8f8a0b3e345e524d4c69516c47a40ce888ab0d"
] | [
"pytextclassifier/textclassifier.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n@author:XuMing(xuming624@qq.com)\n@description: \n\"\"\"\nimport os\nimport torch\nimport json\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport sys\n\nsys.path.append('..')\nfrom pytextclassifier.log import logger\n\ndevice = torch.device('cuda' if... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"torch.cuda.is_available",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Critical-Materials-Institute/LOGKPREDICT | [
"93effdc3276d8ac06f70e38a48e1aed72e67c12f"
] | [
"featurization.py"
] | [
"from typing import List, Tuple, Union\n\nfrom rdkit import Chem\nimport torch\nimport numpy as np\n\n# Atom feature sizes\nMAX_ATOMIC_NUM = 100\nATOM_FEATURES = {\n 'atomic_num': list(range(MAX_ATOMIC_NUM)),\n 'degree': [0, 1, 2, 3, 4, 5],\n 'formal_charge': [-1, -2, 1, 2, 0],\n 'chiral_tag': [0, 1, 2,... | [
[
"torch.LongTensor",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marykthompson/foursu_timecourse | [
"22512fbdd52e374b5aa9d75a91e8af121a08e79b"
] | [
"scripts/get_feature_lengths.py"
] | [
"'''\nMKT 201015\nParse the txt to gene file and the one of the kallisto quantification files to\nproduce a dataframes of transcript -> gene and gene -> exon/intron length\n'''\nimport pandas as pd\n\ntxt_df = pd.read_csv(snakemake.input['txt_2_gene_file'], sep = '\\t',\nnames = ['transcript_ID', 'gene_ID', 'symbol... | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
csruiliu/NSD-Project | [
"d81389b041542a0ca910657e049b9dc374ae1a28"
] | [
"dist-test/tf-dist-bak.py"
] | [
"import datetime\nimport numpy as np\nimport os\nimport argparse\nimport tensorflow as tf\n\n\nepoch = 10\n\n\n\n\ndef main(jtg, tig, is_chief):\n # Create train data\n train_X = np.linspace(-1, 1, 100)\n train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.33 + 10\n learning_rate = 0.01\n sta... | [
[
"tensorflow.multiply",
"tensorflow.train.Server",
"numpy.linspace",
"tensorflow.Variable",
"tensorflow.identity",
"tensorflow.train.ClusterSpec",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.train.GradientDescentOptimizer",
"numpy.random.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
danieldiamond/gitlab-analytics | [
"f99e02c95c3a964b01cb14617a43cd5f64ecd88d",
"f99e02c95c3a964b01cb14617a43cd5f64ecd88d"
] | [
"extract/sheetload/sheetload.py",
"orchestration/ci_helpers.py"
] | [
"import sys\nimport re\nfrom io import StringIO\nimport json\nimport time\nfrom logging import error, info, basicConfig, getLogger, warning\nfrom os import environ as env\nfrom typing import Dict, Tuple, List\nfrom yaml import load, safe_load, YAMLError\n\nimport boto3\nimport gspread\nimport pandas as pd\nfrom fir... | [
[
"pandas.read_csv"
],
[
"pandas.read_table",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1... |
TerryLines/gnssmapper | [
"6a6e051612642e3b131a5c0b0a0041b1e070c35d"
] | [
"gnssmapper/geo.py"
] | [
"\"\"\" \n\nThis module defines geometric methods that work in 3D and allow receiverpoints and observation objects to interact with a map\n\n\"\"\"\n# rays,to_crs used in observations\n# fresnel,to_crs,is_outside,ground_level used in sim\n# map_to_crs is a standalone map method\n\nfrom itertools import chain, compr... | [
[
"numpy.log",
"pandas.Series",
"numpy.nonzero",
"pandas.DataFrame",
"numpy.ones",
"numpy.exp",
"numpy.argsort",
"numpy.array",
"numpy.where",
"numpy.sum"
]
] | [
{
"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": []
}
] |
cld378632668/rocksdb2 | [
"76bdd5c28ef27cec353497597fe50cf5a6804e79"
] | [
"my_log-and-log_parser_python_script/Rocksdb-The_level_where_compaction_is_doing.py"
] | [
"#/usr/bin/python\n#\n\n\nimport re\nimport matplotlib.pyplot as plt\n\n###\n#The_level_where_compaction_is_doing_of_Rocksdb\n###\n\n#log_file_path = 'LOG_of_leveldb_example'\nlog_file_path = r'G:\\1-RocksDB\\Slot实验\\LOG_rocksdb_origin-ycsb_1G_50G_onlyrun_cld'\n\nwith open(log_file_path) as log_file:\n text = lo... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oasiszrz/XAIGen | [
"8c60ace59da854e35c68b056549a9b142425a5dd"
] | [
"tpe_model.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport lime\nimport sklearn\nimport numpy as np\nimport sklearn\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nfrom sklearn.naive_bayes import MultinomialNB\nfrom lime import lime_text\nfrom sklearn.pipeline import make_pi... | [
[
"sklearn.pipeline.make_pipeline",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.f1_score",
"sklearn.preprocessing.LabelEncoder",
"sklearn.feature_extraction.text.TfidfVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seasonsOfTheSun/UMAPanalysis | [
"d7f413af59e01aa5e9b1aa909d1fd5858019aa23"
] | [
"src/data/make_class_list.py"
] | [
"import pandas as pd\nimport sys\ndataset = sys.argv[1]\ndf = pd.read_csv(f\"data/intermediate/{dataset}/labels.csv\", index_col = 0)\n\nfor i,moa in enumerate(df.columns):\n fp = open(f\"data/intermediate/{dataset}/classes/class_{i}\", 'w')\n fp.write(moa)\n fp.close()\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
baycelik/LRModel-Script | [
"0b836f7d642ca7e3c950c7bf9d0d8fbd2dc9bcad"
] | [
"LRModel-Script.py"
] | [
"import numpy as np \r\nimport pandas as pd \r\nimport os\r\nfor dirname, _, filenames in os.walk('/kaggle/input'):\r\n for filename in filenames:\r\n print(os.path.join(dirname, filename))\r\n \r\ndf = pd.DataFrame(d) \r\ndf.head()\r\ndf.info()\r\ndf.describe().T['std']\r\ndf.isnull().sum()\r\ndf.... | [
[
"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": []
}
] |
ammarhakim/ammar-simjournal | [
"85b64ddc9556f01a4fab37977864a7d878eac637"
] | [
"sims/s264/cmp-arcOn-centmassplots.py"
] | [
"from pylab import *\nimport numpy\n\n# load data\ncom_264 = loadtxt(\"../s264/s264-blobs_centerOfMass.txt\")\narcOn_tm = loadtxt('craig-time.txt')\narcOn_xl = loadtxt('craig-com.txt')\narcOn_vl = loadtxt('craig-com-vel.txt')\n\n# plot COM data\nfigure(1)\nplot(com_264[:,0], com_264[:,1]-10, label='Gkeyll')\nplot(a... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ocatak/malware_api_class | [
"446f0c7643e93bda26599524d3cb796d9294cd91"
] | [
"src/other/OtherAnalize_SVM.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Aug 1 14:52:43 2018\r\n\r\n@author: user\r\n\"\"\"\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nfrom keras import preprocessing\r\nimport os\r\n\r\nimport pandas as pd\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.metrics import confusion_matr... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"sklearn.svm.SVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
NicoBrunello/RL_Project | [
"a7306147958c35c030121382341dfac9ac1fc5af"
] | [
"original_sample_agent.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nclass Agent(object):\n def __init__(self, dim_action):\n self.dim_action = dim_action\n\n def act(self, ob, reward, done, vision_on):\n #print(\"ACT!\")\n\n # Get an Observation from the environment.\n # Each observation vecto... | [
[
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hhu-machine-learning/hdc2021-psfnn | [
"275eec198e57c49dcd3eee3e7b09ee36d1655ede"
] | [
"train/dewarp.py"
] | [
"import torch\nimport torch.nn.functional as F\n\ndef get_dewarping_matrix(step):\n if step == 0: return [1.0074838399887085, 0.0007350334199145436, 0.0018522378522902727, 0.0011821923544630408, -9.216999023919925e-05, -2.1575890059466474e-05, -0.0008361316868104041, -2.0978655811632052e-05, 2.024814057222102e-0... | [
[
"torch.linspace",
"torch.eye",
"torch.tensor",
"torch.nn.functional.grid_sample",
"torch.square",
"torch.stack",
"torch.device",
"torch.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bale-go/katrain-bots | [
"9d2c23275b6eeaa529c4a6827567e4e655c0b793"
] | [
"analyze_rank.py"
] | [
"import math, glob, time, os\nimport numpy as np\n\nfrom katrain.core.base_katrain import KaTrainBase\nfrom katrain.core.game import Game, KaTrainSGF\nfrom katrain.core.engine import KataGoEngine\n\n\ndef averagemod(data):\n sorteddata = sorted(data)\n lendata = len(data)\n return sum(sorteddata[int(lendat... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhangruihan1/robust-face-recognition | [
"a7a03c7d260768fed2dfbe5a3af8dd65d6839ca5"
] | [
"glass_attack.py"
] | [
"import pl_models\n\nimport sys\nsys.path.append('eameo-faceswap-generator')\n\nimport faceBlendCommon as fbc\nimport dlib\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport os\nimport numpy as np\nfrom tqdm import tqdm\nfrom PIL import Image\n\n\nimport argparse\n\n\ndetector = dlib.... | [
[
"torch.clamp",
"torch.load",
"torch.cat",
"numpy.rint",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"torch.acos",
"torch.cuda.is_available",
"torch.nn.ZeroPad2d",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eyezenheim/openpilot | [
"55b548cae08b8d8a040f87284fe44bacf9036707"
] | [
"selfdrive/controls/lib/longitudinal_planner.py"
] | [
"#!/usr/bin/env python3\nimport math\nimport numpy as np\nfrom common.params import Params\nfrom common.numpy_fast import interp\n\nimport cereal.messaging as messaging\nfrom common.realtime import sec_since_boot\nfrom selfdrive.swaglog import cloudlog\nfrom selfdrive.config import Conversions as CV\nfrom selfdrive... | [
[
"numpy.arange",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
franklinxzw/byol-pytorch | [
"9667c41cea0cf0f923a195a4f9b707cf2a40742e"
] | [
"transform.py"
] | [
"import math\nimport numpy as np\n\n# import cv2\nimport random\nimport torch\nimport torchvision.transforms.functional as F\nfrom PIL import Image\nfrom torchvision import transforms\n\n_pil_interpolation_to_str = {\n Image.NEAREST: \"PIL.Image.NEAREST\",\n Image.BILINEAR: \"PIL.Image.BILINEAR\",\n Image.... | [
[
"torch.mean",
"torch.linspace",
"numpy.maximum",
"torch.zeros",
"numpy.reshape",
"torch.zeros_like",
"numpy.random.normal",
"torch.nn.functional.interpolate",
"numpy.random.uniform",
"numpy.repeat",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sourcery-ai-bot/sktime | [
"5f618fa0e29f75eda72e4d671de77ee7c88679f8",
"5f618fa0e29f75eda72e4d671de77ee7c88679f8"
] | [
"sktime/classification/frequency_based/_rise.py",
"sktime/transformers/series_as_features/compose.py"
] | [
"\"\"\" Random Interval Spectral Forest (RISE).\nImplementation of Deng's Time Series Forest, with minor changes\n\"\"\"\n__author__ = [\"Tony Bagnall\"]\n__all__ = [\"RandomIntervalSpectralForest\", \"acf\", \"matrix_acf\", \"ps\"]\n\nimport math\n\nimport numpy as np\nfrom numpy import random\nfrom sklearn.base i... | [
[
"numpy.square",
"numpy.isinf",
"numpy.fft.fft",
"numpy.random.seed",
"numpy.isnan",
"numpy.unique",
"numpy.asarray",
"numpy.ones",
"numpy.concatenate",
"sklearn.base.clone",
"sklearn.tree.DecisionTreeClassifier",
"numpy.argmax",
"numpy.corrcoef",
"numpy.arra... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"... |
jackd/graph-tf | [
"7b75182084d515fda16ab7f84ba175fe12d86274"
] | [
"graph_tf/projects/dfnet/layers.py"
] | [
"import tensorflow as tf\n\nfrom graph_tf.projects.dfnet.ops import dfnet_conv\n\nactivations = tf.keras.activations\ninitializers = tf.keras.initializers\nregularizers = tf.keras.regularizers\nconstraints = tf.keras.constraints\n\n\nclass DFNetConv(tf.keras.layers.Layer):\n def __init__(\n self,\n ... | [
[
"tensorflow.nn.bias_add"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
SweetSourPeter/violin | [
"91abf09442c990968650e61f33ffb19e14214968"
] | [
"venv/Lib/site-packages/ttg/ttg.py"
] | [
"#!/usr/bin/env python\n\n# Copyright 2019 Francisco Bustamante\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# ... | [
[
"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": []
}
] |
LSSTDESC/transient-host-sims | [
"a9c02077d2378bd2e3391c52ef2eeecf916cf313"
] | [
"scripts/validutils_ztf/misc.py"
] | [
"\"\"\"\nAdditional utilities\n\"\"\"\nimport pandas\nimport numpy as np\n\nfrom .table import LSST_FILTERS\n\n\ndef dict2df(datadict):\n \"\"\"\n Convert dictionary object to a DataFrame\n \"\"\"\n for idx in datadict:\n sn = datadict[idx]\n for filt in LSST_FILTERS:\n sn['mjd_... | [
[
"numpy.array",
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
siesta-project/aiida_siesta_plugin | [
"6242c2d38a6db445ec9042868fa08263616b2263"
] | [
"aiida_siesta/parsers/stm.py"
] | [
"from aiida.parsers import Parser\nfrom aiida.orm import Dict\nfrom aiida.common import OutputParsingError\nfrom aiida.common import exceptions\n\n# See the LICENSE.txt and AUTHORS.txt files.\n\n\nclass STMOutputParsingError(OutputParsingError):\n pass\n\n\n#---------------------------\n\n\nclass STMParser(Parse... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GlobalFishingWatch/pyseas | [
"7559c4e07db3f7df8d42281029b793c8a30c552c"
] | [
"pyseas/maps/rasters.py"
] | [
"import numpy as np\n\nKM_PER_DEG_LAT = 110.574\nKM_PER_DEG_LON0 = 111.320\n\n# Add test\nclass LonLat2Km2Scaler(object):\n\n def __init__(self, xyscale, scale=1):\n self.xyscale = xyscale\n self.scale = scale\n self._scale0 = KM_PER_DEG_LAT * KM_PER_DEG_LON0 / xyscale ** 2\n\n def __call... | [
[
"numpy.radians",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dongyan007/Pretrained-IPT-main-master | [
"7ed47002373e11bd57b7904f6935acdfba1e44ff"
] | [
"model/ipt.py"
] | [
"# 2021.05.07-Changed for IPT\n# Huawei Technologies Co., Ltd. <foss@huawei.com>\n\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nfrom model import common\n\nimport math\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\n# from einops import rearrang... | [
[
"torch.nn.Dropout",
"torch.nn.MultiheadAttention",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.arange",
"torch.nn.functional.unfold",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sandeshkatakam/flasklearning | [
"f28f44e82ad2e29f2bdde8482e75690065f9011b"
] | [
"app.py"
] | [
"import numpy as np\nfrom flask import Flask, request, jsonify, render_template\nimport pickle\n\napp = Flask(__name__)\nmodel = pickle.load(open('model.pkl', 'rb'))\n\n@app.route('/')\ndef home():\n return render_template('index.html')\n\n@app.route('/predict',methods=['POST'])\ndef predict():\n '''\n For... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ytakzk/n2p | [
"e6c4bb71693842b3f7bc0b3c4898f4d3567d64f7"
] | [
"pix2pix/options/base_options.py"
] | [
"import argparse\nimport os\nfrom pix2pix.util import util\nimport torch\n\n\nclass BaseOptions():\n def __init__(self):\n self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n self.initialized = False\n\n def initialize(self):\n # self.parser.add... | [
[
"torch.cuda.set_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jihoonerd/Robot-Kinematics | [
"1dd15a07b201929e05c25bb15a26f0e50959c171"
] | [
"src/rk/robot_config.py"
] | [
"import numpy as np\nfrom rk.utils import calc_vw_err, rodrigues, find_route\n\n\nclass RobotObject:\n\n def __init__(self, ulink):\n self.ulink = ulink\n \n def print_link_info(self, link_id):\n \n if link_id not in self.ulink.keys():\n return KeyError(\"Does not have match... | [
[
"numpy.diag",
"numpy.linalg.solve",
"numpy.linalg.norm",
"numpy.cross",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ycfang-lab/fasnet | [
"04842d29e6fa8a2d9e3c152f7da9a30fce54c541"
] | [
"common/generator.py"
] | [
"import h5py as hp\nimport numpy as np\n\n\ndef train_gen(dataset, input_num=25):\n assert input_num in (1, 25)\n assert dataset in ('casia', 'replayattack')\n data_file = '-'.join(['h5/' + dataset, 'train', 'lstm.h5'])\n\n scale = (1.8, 2.0, 2.2)\n batch_size = 32\n while True:\n with hp.F... | [
[
"numpy.asarray",
"numpy.random.permutation"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChikaYan/hypernerf | [
"8c5d158908a9938127fd3bf8d13be292fab24191"
] | [
"hypernerf/datasets/nerfies.py"
] | [
"# Copyright 2021 Google LLC\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 agreed ... | [
[
"numpy.asarray",
"numpy.load",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lmmx/emoji-liif | [
"7528dbb25c708cc07a9e7362224809f19a007fcb"
] | [
"osx/catalina/calculate_bg.py"
] | [
"from tqdm import tqdm\nfrom pathlib import Path\nfrom imageio import imread\nimport numpy as np\nimport sqlite3\nimport pandas as pd\nfrom enum import IntEnum\n\ndb_filename = \"emoji_bw_calc.db\"\n\ndef euclidean_dist(v1, v2):\n \"Returns the Euclidean distance between vectors 'v1' and 'v2'.\"\n return np.l... | [
[
"numpy.abs",
"numpy.linalg.norm",
"numpy.sort",
"numpy.all",
"numpy.max",
"numpy.array",
"pandas.read_sql"
]
] | [
{
"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": []
}
] |
suleimank/transformers | [
"a5b249472e3630bf3d133653cb1fff08f33525aa"
] | [
"src/transformers/modeling_electra.py"
] | [
"import logging\nimport os\n\nimport torch\nimport torch.nn as nn\n\nfrom transformers import ElectraConfig, add_start_docstrings\nfrom transformers.activations import get_activation\n\nfrom .file_utils import add_start_docstrings_to_callable\nfrom .modeling_bert import BertEmbeddings, BertEncoder, BertLayerNorm, B... | [
[
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.ones",
"torch.zeros",
"torch.from_numpy",
"torch.nn.Embedding",
"numpy.transpose",
"tensorflow.train.load_variable",
"torch.nn.Linear",
"torch.nn.BCEWithLogitsLoss",
"tensorflow.train.list_variables"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
bcebere/synthetic_data_generation | [
"84d34d00a5859d3db5d160ef8798b865a0c59fe7"
] | [
"src/synthesis/evaluation/efficacy.py"
] | [
"\"\"\"Methods to determine whether the synthetic data performs similar to the original data on specific tasks\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom lifelines import KaplanMeierFitter\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.pipeline im... | [
[
"sklearn.model_selection.GridSearchCV",
"matplotlib.pyplot.tight_layout",
"sklearn.ensemble.RandomForestClassifier",
"matplotlib.pyplot.title",
"sklearn.preprocessing.OneHotEncoder",
"sklearn.impute.SimpleImputer",
"sklearn.pipeline.Pipeline",
"matplotlib.pyplot.subplots",
"skl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
saroudant/sobolev_alignment | [
"beec4074b24544020359550040e2b66707d113b3"
] | [
"sobolev_alignment/krr_approx.py"
] | [
"\"\"\" Encoder approximation by Kernel Ridge Regression\n\n@author: Soufiane Mourragui\n\nThis modules train a Kernel Ridge Regression (KRR) on a pair of samples \n(x_hat) and embeddings (z_hat) using two possible implementations:\n - scikit-learn: deterministic, but limited in memory and time efficiency.\n ... | [
[
"sklearn.kernel_ridge.KernelRidge",
"numpy.arange",
"sklearn.preprocessing.StandardScaler",
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gategill/elliot | [
"113763ba6d595976e14ead2e3d460d9705cd882e",
"113763ba6d595976e14ead2e3d460d9705cd882e"
] | [
"elliot/recommender/neural/DMF/deep_matrix_factorization.py",
"elliot/recommender/latent_factor_models/MF2020/custom_sampler.py"
] | [
"\"\"\"\nModule description:\n\n\"\"\"\n\n\n__version__ = '0.3.1'\n__author__ = 'Vito Walter Anelli, Claudio Pomo'\n__email__ = 'vitowalter.anelli@poliba.it, claudio.pomo@poliba.it'\n\nimport numpy as np\nfrom ast import literal_eval as make_tuple\nfrom tqdm import tqdm\n\nfrom elliot.dataset.samplers import pointw... | [
[
"numpy.max"
],
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hsiaoyi0504/cptac | [
"296978a9a7ea0f298490984d8ffe10fb92b5ab34",
"296978a9a7ea0f298490984d8ffe10fb92b5ab34"
] | [
"cptac/pancan/broadgbm.py",
"cptac/pancan/broaducec.py"
] | [
"# Copyright 2018 Samuel Payne sam_payne@byu.edu\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by app... | [
[
"pandas.read_csv"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
niulinlnc/tensorflow | [
"302ec317f27a1bc3a63c1357baa2e128b10d4024"
] | [
"tensorflow/python/compat/compat.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.util.tf_export.tf_export"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.5",
"1.7",
"1.4"
]
}
] |
bsha6/nltk | [
"b0cd83ded0c3c2394f878d8577d71187fa3f9ae4"
] | [
"nltk/sentiment/util.py"
] | [
"# coding: utf-8\n#\n# Natural Language Toolkit: Sentiment Analyzer\n#\n# Copyright (C) 2001-2020 NLTK Project\n# Author: Pierpaolo Pantone <24alsecondo@gmail.com>\n# URL: <http://nltk.org/>\n# For license information, see LICENSE.TXT\n\n\"\"\"\nUtility methods for Sentiment Analysis.\n\"\"\"\n\nimport codecs\nimpo... | [
[
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.locator_params",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.margins",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.plot",
"sklearn.svm.LinearSVC",
"matplotlib.pyplot.xticks",
"matplotlib.pypl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KWR-Water/hgc | [
"cf6f92c28e3f787a653c3e7f4b58ccc33fe36cbf"
] | [
"hgc/import_default.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nRoutine to import default \nXin Tian, Martin van der Schans\nKWR, April-July 2020\n\"\"\"\nimport pandas as pd\nimport scipy.interpolate\nfrom fuzzywuzzy import fuzz\nfrom fuzzywuzzy import process\n\ndef default_features():\n \"\"\"Generate a default dataframe with features and... | [
[
"pandas.read_excel",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
indrag49/Computational-Stat-Mech | [
"0877f54a0245fce815f03478f4fb219fd6314951"
] | [
"Chapter 1/naive_sphere.py"
] | [
"import random\nimport pylab\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\ndef naive_sphere(d):\n \"\"\" d is the dimension of the unit sphere\"\"\"\n x=[0]*d\n S=0\n for k in range(d):\n x[k]=random.uniform(-1, 1)\n S+=x[k]**2... | [
[
"matplotlib.pyplot.axes",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shicaiwei123/FASR | [
"8b070734a9c9960d8e9e1cce9d4689dc1bc7a5db"
] | [
"lbp/lib/data_process.py"
] | [
"import sys\n\nsys.path.append('..')\nimport cv2\nimport os\nimport numpy as np\nimport sys\nfrom lib.processing_utils import read_txt, get_file_list\nimport shutil\nimport datetime\nimport time\n\n'''\n数据和数据集的一些预处理操作,包括但不限于:从视频中获取图像,人脸检测,利用原始数据集,生成满足条件的数据集.\n'''\n\n\ndef video_to_frames(pathIn='',\n ... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wangjili/PaddleHub | [
"91f3dd3501eca4874f23661c55a4255dc8a2a13f"
] | [
"modules/image/semantic_segmentation/FCN_HRNet_W18_Face_Seg/module.py"
] | [
"import os\r\nimport cv2\r\nimport paddle\r\nimport paddle.nn as nn\r\nimport numpy as np\r\nfrom FCN_HRNet_W18_Face_Seg.model import FCN, HRNet_W18\r\nfrom paddlehub.module.module import moduleinfo\r\n\r\n@moduleinfo(\r\n name=\"FCN_HRNet_W18_Face_Seg\", # 模型名称\r\n type=\"CV\", # 模型类型\r\n author=\"jm12138... | [
[
"numpy.min",
"numpy.concatenate",
"numpy.max",
"numpy.transpose",
"numpy.array_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nilanshrajput/vilbert-multi-task-1 | [
"d967f7ee8958f1be7a3a4b720d631305a7cddd1a"
] | [
"vilbert/datasets/visual_genome_dataset.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport os\nimport json\nimport _pickle as cPickle\nimport logging\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import D... | [
[
"torch.zeros",
"torch.from_numpy",
"torch.tensor",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kittaaron/kittaaron_facenet | [
"706b637c9876a697b3d642d6ad3d6fd308403e66"
] | [
"src/train_softmax_org.py"
] | [
"\"\"\"Training a face recognizer with TensorFlow using softmax cross entropy loss\n\"\"\"\n# MIT License\n#\n# Copyright (c) 2016 David Sandberg\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# i... | [
[
"tensorflow.python.ops.array_ops.shape",
"tensorflow.global_variables",
"tensorflow.contrib.slim.l2_regularizer",
"numpy.cumsum",
"tensorflow.cast",
"numpy.mean",
"numpy.zeros_like",
"tensorflow.GPUOptions",
"numpy.histogram",
"tensorflow.summary.scalar",
"tensorflow.ad... | [
{
"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... |
yonghee12/deepnp | [
"7eec0e391fff21b700831b2e0e78244047b958f8"
] | [
"dataloader/sequence.py"
] | [
"# coding: utf-8\nimport sys\n\nsys.path.append('..')\nimport os\nimport numpy\n\nid_to_char = {}\nchar_to_id = {}\n\n\ndef _update_vocab(txt):\n chars = list(txt)\n\n for i, char in enumerate(chars):\n if char not in char_to_id:\n tmp_id = len(char_to_id)\n char_to_id[char] = tmp... | [
[
"numpy.random.seed",
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
linnarsson-lab/scVI | [
"12c028970e9d8bc6d3d2278ff4e381f0c6180a57"
] | [
"scvi/inference/posterior.py"
] | [
"import copy\n\nimport numpy as np\nimport pandas as pd\nimport scipy\nimport torch\nfrom matplotlib import pyplot as plt\nfrom scipy.stats import kde, entropy, itemfreq\nfrom sklearn.cluster import KMeans\nfrom sklearn.manifold import TSNE\nfrom sklearn.metrics import adjusted_rand_score as ARI\nfrom sklearn.metri... | [
[
"matplotlib.pyplot.legend",
"sklearn.utils.linear_assignment_.linear_assignment",
"numpy.minimum",
"numpy.linspace",
"sklearn.metrics.silhouette_score",
"torch.cat",
"sklearn.cluster.KMeans",
"torch.utils.data.DataLoader",
"pandas.DataFrame",
"numpy.dtype",
"matplotlib.... | [
{
"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": [
"0.13",
"1.6",
"0.14",
"0.15",
"1... |
egpbos/egp | [
"5e82c2de9e6884795b4ee89f2b15ed5dde70388f"
] | [
"egp/field_filtering.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nfield_filtering.py\n/field_filtering/ module in the /egp/ package.\nFilter Fields using convolution kernels.\n\nCreated by Evert Gerardus Patrick Bos.\nCopyright (c) 2012-2018. All rights reserved.\n\"\"\"\nimport numpy as np\nimport numbers\nimport egp.basic_types\nimport egp.to... | [
[
"numpy.sqrt",
"numpy.cos",
"numpy.nan_to_num",
"numpy.sin",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Samad999777/tensorflow-recorder | [
"3797da3e730137d467f812612ac8dc0c1b1f8a6e"
] | [
"tfrecorder/dataset_loader.py"
] | [
"# Lint as: python3\n\n# Copyright 2020 Google LLC.\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 ap... | [
[
"tensorflow.data.TFRecordDataset",
"tensorflow.io.parse_single_example"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.