repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
p-morais/rl | [
"6a39d8cec58fdd471f2de80a9c7c9b2f1879f096"
] | [
"rl/utils/experiment.py"
] | [
"import atexit, os\nimport os.path as osp\nfrom subprocess import Popen\nfrom functools import partial\nimport torch.multiprocessing as mp\nfrom .render import renderloop\nfrom .logging import Logger\nfrom rl.envs import Normalize, Vectorize\n\n\ndef run_experiment(algo, policy, env_fn, args, log=True, monitor=Fals... | [
[
"torch.multiprocessing.Process"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
johannestreutlein/op-tie-breaking | [
"ef9dada6c14efa416ecb4f1fcf48a7e4b344ba27",
"ef9dada6c14efa416ecb4f1fcf48a7e4b344ba27"
] | [
"src/op_with_tie_breaking.py",
"src/modules/critics/pair_coma.py"
] | [
"import argparse\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport datetime\nimport pandas as pd\n\nimport json\n\nimport os\n\nfrom utils.uniquify import uniquify\n\ndef op_tie_breaking_evaluation(hash_lists, args):\n '''This function evaluates our method, other-play with tie-breaking. It applies the... | [
[
"numpy.log2",
"numpy.array",
"pandas.DataFrame"
],
[
"torch.gather",
"torch.nn.Linear",
"torch.eye",
"torch.zeros_like"
]
] | [
{
"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... |
giulio1979/dldt | [
"b2140c083a068a63591e8c2e9b5f6b240790519d"
] | [
"model-optimizer/mo/graph/graph.py"
] | [
"\"\"\"\n Copyright (C) 2018-2020 Intel Corporation\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable... | [
[
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wangdingkang/DiscreteMorse | [
"3e1dcf215d96047f0e6754a34e45057bf1a19ff5"
] | [
"Code/DIPHA/write_dipha_file_3d.py"
] | [
"import sys\nfrom matplotlib import image as mpimg\nimport numpy as np\nimport os\n\nDIPHA_CONST = 8067171840\nDIPHA_IMAGE_TYPE_CONST = 1\nDIM = 3\n\ninput_dir = sys.argv[1]\ndipha_output_filename = sys.argv[2]\nvert_filename = sys.argv[3]\n\ninput_filenames = [name for name in os.listdir(input_dir) if (os.path.isf... | [
[
"numpy.int64",
"matplotlib.image.imread",
"numpy.zeros",
"numpy.float64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PinchukKPI/RGS_parasol_model | [
"4c0848951658d8129a0ed71b2becdeab30bf70e5"
] | [
"main.py"
] | [
"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import minimize\nfrom scipy.io import loadmat\n\n\ndef load_white_noise_data(cell_id):\n data = loadmat('Data/elife-38841-fig4-data1-v2.mat') # loadmat is a function in scipy.io\n # data from Figure 4 can be downloaded from https://d... | [
[
"scipy.io.loadmat",
"numpy.zeros"
]
] | [
{
"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"... |
RacleRay/Fine-grained_TextClassification | [
"ffd43b576ee766dfdfefa17946592a8506eae0de",
"ffd43b576ee766dfdfefa17946592a8506eae0de",
"ffd43b576ee766dfdfefa17946592a8506eae0de"
] | [
"multi_class_base.py",
"Albert/models/multi_class_cnn.py",
"Albert/models/awd_lstm.py"
] | [
"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n'''\n@File : multi_class_base.py\n'''\n\n# multi-task learning implementation of Kim's paper : Convolutional Neural Networks for Sentence Classification.\n\nimport tensorflow as tf\nimport numpy as np\n\n\nclass TextCNN(object):\n \"\"\"\n A CNN for text... | [
[
"tensorflow.device",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.concat",
"tensorflow.nn.max_pool",
"tensorflow.cast",
"tensorflow.nn.l2_loss",
"tensorflow.nn.conv2d",
"tensorflow.name_scope",
"tensorflow.contrib.layers.xavier_initializer",
"tensorflow.ar... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflo... |
GeoDaCenter/accessibility | [
"731ca101ca3744740ea246fd9f57e29f893e8405"
] | [
"access/tests/test_floating_catchment_area.py"
] | [
"import sys\nsys.path.append('../..')\n\nimport math\nimport unittest\n\nimport numpy as np\nimport pandas as pd\nimport geopandas as gpd\nfrom access import access, weights\nimport util as tu\n\n\nclass TestFloatingCatchmentArea(unittest.TestCase):\n\n def setUp(self):\n n = 5\n supply_grid = tu.c... | [
[
"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": []
}
] |
pome-ta/pystaMetalStudy | [
"530248ad8621ec951fcbaf450ebd26ac2752e540"
] | [
"src/everythingAboutTheMetalAPI/chapter09/__main__.py"
] | [
"import pathlib\nimport ctypes\nimport numpy as np\n\nfrom objc_util import c, create_objc_class, ObjCClass, ObjCInstance\nimport ui\n\n#import pdbg\n\n\n\nshader_path = pathlib.Path('./Shaders.metal')\n\n\n# --- load objc classes\nMTKView = ObjCClass('MTKView')\nMTLCompileOptions = ObjCClass('MTLCompileOptions')\n... | [
[
"numpy.dot",
"numpy.ctypeslib.as_array",
"numpy.cos",
"numpy.sin",
"numpy.tan",
"numpy.identity",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AdvAiLab/smart_lab | [
"e99d2e0129e7dba3a8847bf215b2588128fc32b1"
] | [
"webap_tools/webap_tools/captcha_prediction.py"
] | [
"import os\nfrom time import sleep\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.backend import clear_session\nfrom tensorflow.keras.models import load_model\nfrom tensorflow.python.keras.backend import set_session\n\n\ndef rgb2gray(rgb):\n r, ... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.ConfigProto",
"tensorflow.keras.backend.clear_session",
"tensorflow.python.keras.backend.set_session",
"tensorflow.Session",
"tensorflow.get_default_graph",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
sidgan/ETCI-2021-Competition-on-Flood-Detection | [
"dbb73bef7e26f0109870be13ef4d30c15ce15a33"
] | [
"src/etci_dataset.py"
] | [
"\"\"\"\nReferenced from:\nhttps://medium.com/cloud-to-street/jumpstart-your-machine-learning-satellite-competition-submission-2443b40d0a5a\n\"\"\"\n\nimport cv2\nimport numpy as np\n\nfrom torch.utils.data import Dataset\n\n\ndef s1_to_rgb(vv_image, vh_image):\n ratio_image = np.clip(np.nan_to_num(vh_image / vv... | [
[
"numpy.nan_to_num",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CrazyNicolas/PyTorch-1.x-Reinforcement-Learning-Cookbook | [
"1cca7e0218c2683a730b1c4a66681e68023657ef"
] | [
"Chapter03/chapter3/off_policy_mc_control_weighted_importance_sampling.py"
] | [
"'''\nSource codes for PyTorch 1.0 Reinforcement Learning (Packt Publishing)\nChapter 3: Monte Carlo Methods For Making Numerical Estimations\nAuthor: Yuxi (Hayden) Liu\n'''\n\nimport torch\nimport gym\n\nenv = gym.make('Blackjack-v0')\n\n\ndef gen_random_policy(n_action):\n probs = torch.ones(n_action) / n_acti... | [
[
"torch.ones",
"torch.empty",
"torch.multinomial",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
flatironinstitute/inferelator-prior | [
"572a8016b14d922c74f482dcfc24a83dc7efcc83"
] | [
"inferelator_prior/motifs/homer.py"
] | [
"import subprocess\nimport io\nimport pandas as pd\nimport numpy as np\n\nfrom inferelator_prior.motifs import chunk_motifs, homer_motif, SCAN_SCORE_COL, SCORE_PER_BASE\nfrom inferelator_prior.motifs._motif import MotifScanner\nfrom inferelator_prior import HOMER_EXECUTABLE_PATH\n\nHOMER_DATA_SUFFIX = \".homer.tsv\... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
danjia21/dcp | [
"437c6d10f447304277115f021b8888394ef41a31"
] | [
"model.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\nimport os\nimport sys\nimport glob\nimport h5py\nimport copy\nimport math\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom .util import quat2mat\n\n\n# Part of the code is referre... | [
[
"torch.nn.functional.softmax",
"torch.svd",
"torch.cat",
"torch.zeros",
"torch.sum",
"torch.device",
"torch.softmax",
"torch.norm",
"torch.ones",
"torch.eye",
"torch.arange",
"torch.nn.Sequential",
"torch.nn.BatchNorm1d",
"torch.nn.Conv2d",
"torch.nn.Lin... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
claylau/genetic_algorithm | [
"692f995a2ca325ba94ac1656b28b651c9a861f46"
] | [
"test_ga.py"
] | [
"import math\nimport numpy as np\nfrom genetic_algorithm.ga import GA\n\n\ndef objective_1():\n cfg = {}\n cfg[\"name\"] = \"Sphere-2D\"\n cfg[\"dimension\"] = 2\n cfg[\"obj_func\"] = lambda x: np.sum(np.power(x, 2))\n cfg[\"fitness_func\"] = lambda x: np.sum(np.power(x, 2))\n cfg[\"lower_bounds\"... | [
[
"numpy.array",
"numpy.absolute",
"numpy.power"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jiayouff/Paddle | [
"dc76e4b0f1f9abe61c3886382a004c929379e870"
] | [
"python/paddle/fluid/framework.py"
] | [
"# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless ... | [
[
"numpy.array",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qwilka/PDover2t | [
"4387d153228f1af20a8f5f3f368aa49c42cda2cd"
] | [
"pdover2t/pipe/pipe.py"
] | [
"import logging\n\nimport numpy as np\n\nfrom ..utilities.function_tools import func_call_exception_trap\n\nlogger = logging.getLogger(__name__)\n\n#π = np.pi\n\n\ndef WT_from_D(Do, Di):\n \"\"\"Calculate pipe wall thickness from outer diameter and inner diameter.\n \"\"\"\n return (Do - Di) / 2\n\ndef Di_... | [
[
"numpy.power"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Anukriti12/PersonalizedFashionStylist | [
"25c45f79ad96b5b52e5dd986d9ba9d837df2d4dc"
] | [
"Recommender-System/model/mlp_inference.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 20 15:03:38 2019\n\n@author: lee\n\"\"\"\n\nimport numpy as np\nimport keras\nfrom keras import regularizers\nfrom keras.layers import Embedding, Input, Dense, merge, Reshape, Flatten\nfrom time import time\nimport argparse\nimport json\ni... | [
[
"numpy.arange",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
demmojo/curve-dao-contracts | [
"6922cd98c7403cc7c6302f5379194c5418c5cb66"
] | [
"scripts/stats/plot_vecrv.py"
] | [
"from brownie import Contract, web3\n\nimport numpy as np\nimport pylab\n\nSTART_BLOCK = 10647813\n\n\ndef main():\n vecrv = Contract(\"0x5f3b5DfEb7B28CDbD7FAba78963EE202a494e2A2\")\n current_block = web3.eth.blockNumber\n blocks = np.linspace(START_BLOCK, current_block, 100)\n powers = [vecrv.totalSupp... | [
[
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ags3927/frustum-convnet | [
"0ccb4a8e45c9973f902aef5cbb5f776ea634ee32"
] | [
"datasets/temp.py"
] | [
"''' Provider class and helper functions for Frustum PointNets.\n\nAuthor: Charles R. Qi\nDate: September 2017\n\nModified by Zhixin Wang\n'''\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\nimport time\nimport pickle\nimport sys\nimp... | [
[
"numpy.expand_dims",
"torch.zeros",
"numpy.arctan2",
"numpy.round",
"torch.FloatTensor",
"numpy.argmin",
"numpy.random.randn",
"numpy.square",
"numpy.clip",
"numpy.arange",
"numpy.subtract",
"numpy.copy",
"torch.utils.data.dataloader.default_collate",
"torch... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cycleke/FaceRecognition | [
"c7882ca88b5d7d4bb51aa0852c5225f13f20728c"
] | [
"utils/core/recognizer.py"
] | [
"# *-* coding: utf-8 *-*\n\nimport os\n\nimport cv2\nimport dlib\nimport numpy as np\n\n\nclass Recognizer:\n \"\"\"\n Recognise faces\n \"\"\"\n\n def __init__(\n self,\n *,\n threshold=0.6,\n predictor_path=\"static/shape_predictor_68_face_landmarks.dat\",\n... | [
[
"numpy.array",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
ahmedbesbes/character-based-cnn | [
"593197610498bf0b4898b3bdf2e1f6730f954613"
] | [
"src/model.py"
] | [
"import json\nimport torch\nimport torch.nn as nn\n\n\nclass CharacterLevelCNN(nn.Module):\n def __init__(self, args, number_of_classes):\n super(CharacterLevelCNN, self).__init__()\n\n # define conv layers\n\n self.dropout_input = nn.Dropout2d(args.dropout_input)\n\n self.conv1 = nn.... | [
[
"torch.nn.Dropout",
"torch.nn.Dropout2d",
"torch.nn.Linear",
"torch.nn.MaxPool1d",
"torch.rand",
"torch.nn.Conv1d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bryan-flywire/openem | [
"1510ecbbb6b4a43b9f1f9503c87ec66216200677"
] | [
"train/openem_train/classify.py"
] | [
"__copyright__ = \"Copyright (C) 2018 CVision AI.\"\n__license__ = \"GPLv3\"\n# This file is part of OpenEM, released under GPLv3.\n# OpenEM is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version... | [
[
"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": []
}
] |
JonSn0w/advent-of-code | [
"f62636ef975dd89d788cba66578d16e07b70d7e9"
] | [
"2017/day10p2.py"
] | [
"import numpy as np;\nRNG = 256;\n\ndef partOne(seq, num_lst, pos, skip):\n\tlst_len = len(num_lst)\n\tcurr_pos = pos\n\tskip_size = skip\n\tlengths = seq.split(',')\n\n\tfor leng in lengths:\n\t\tleng = int(leng.strip())\n\t\tif leng > lst_len:\n\t\t\tcontinue\n\t\trev_end = (curr_pos + leng) % lst_len\n\t\tif rev... | [
[
"numpy.flipud"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dlvu/vugrad | [
"dabb7fba29f1727c170bc5f37dff5f52adc62536"
] | [
"experiments/train_mlp.py"
] | [
"from _context import vugrad\n\nimport numpy as np\n\n# for running from the command line\nfrom argparse import ArgumentParser\n\nimport vugrad as vg\n\n# Parse command line arguments\nparser = ArgumentParser()\n\nparser.add_argument('-D', '--dataset',\n dest='data',\n help='Which data... | [
[
"numpy.argmax",
"numpy.bincount"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jinjiaodawang/bayesmark | [
"4fbf52c41288ec802cc03c23372e81d2b678ecb9"
] | [
"example_opt_root/bo/models/rf/rf.py"
] | [
"# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.\r\n\r\n# This program is free software; you can redistribute it and/or modify it under\r\n# the terms of the MIT license.\r\n\r\n# This program is distributed in the hope that it will be useful, but WITHOUT ANY\r\n# WARRANTY; without even the... | [
[
"sklearn.ensemble.RandomForestRegressor",
"torch.cat",
"torch.zeros",
"numpy.concatenate",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WillianEsp/RM_with_CV | [
"4c2cd607426c73181dc2b2b3ab5722faa42b4a68"
] | [
"python-codes/tictactoe.py"
] | [
"\"\"\"\r\n:File: tictactoe.py\r\n:Description: | Computer Vision for Tic-Tac-Toe\r\n | Detect board and pieces\r\n | Defines next play\r\n | Sends next play through serial communication following a protocol\r\n\r\n:Author: Willian Beraldi Esperandio\r\n:Email: willian.esperan... | [
[
"numpy.rot90",
"numpy.dot",
"numpy.around",
"numpy.chararray",
"numpy.array2string"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mehrdadj93/handyscripts | [
"5df9a69e17345ca5a3e42dda2424da2da0ab6f12"
] | [
"python/filledlines.py"
] | [
"from collections.abc import Iterable\nimport itertools as it\n\nimport tecplot as tp\nfrom tecplot.constant import AxisMode, Color, PlotType, SurfacesToPlot\n\n\ndef plot_filled_lines_3d(x, *yy, z=(-0.2, 0.2), y0=0, colors=None,\n name='Line Data', page=None):\n \"\"\"Plot a series of li... | [
[
"numpy.asarray",
"numpy.meshgrid",
"numpy.polynomial.legendre.Legendre",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BobAnkh/THUEE_ROBOTS | [
"2a302c847058a8d80d83b70b1670e1ffb6de8c57"
] | [
"exp2/svm_canny.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# @Author : BobAnkh\n# @Github : https://github.com/BobAnkh\n# @Date : 2020-10-22 19:29:56\n# @LastEditTime : 2020-10-31 11:09:44\n# @Description :\n# @Copyright 2020 BobAnkh\n\nimport cv2\nfrom sklearn import svm\nimport os\nimport random\nimp... | [
[
"sklearn.externals.joblib.dump",
"numpy.array",
"sklearn.externals.joblib.load",
"sklearn.svm.SVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ohenriksson/MastersThesisData | [
"c657deb933d2cbbb7fd55e836e424b9b58c84aab"
] | [
"axis-plotter/functions.py"
] | [
"import numpy as np\n\ndef build_zero_array(n_axes,time):\n return list(map(lambda a: list(map(\n lambda t: 0 ,range(time))) ,range(n_axes) ))\n\n\ndef plot_data(axis, data, title, time):\n axis.set_title(title)\n axis.grid()\n if(len(data) > 1):\n for d in data:\n axis.plot(tim... | [
[
"numpy.round",
"numpy.abs",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kad99kev/FGTD-Streamlit | [
"0dc8d2894eadf2260d5e5dcf10ead12ff62f6cd8"
] | [
"app.py"
] | [
"import streamlit as st\nimport torch\nimport gc\n\nfrom utils.toc import Toc\nfrom utils.model_downloader import download_models\nfrom utils.footer import footer\n\nfrom streamlit_utils.loaders import load_face_generators, load_mnist_generators\nfrom streamlit_utils.io import get_sample, read_csv, get_output, face... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MSU-MLSys-Lab/CATE | [
"654c393d7df888d2c3f3b90f9e6752faa061157e"
] | [
"darts/cnn/utils.py"
] | [
"import os\nimport numpy as np\nimport torch\nimport shutil\nimport torchvision.transforms as transforms\n\n\nclass AvgrageMeter(object):\n\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.avg = 0\n self.sum = 0\n self.cnt = 0\n\n def update(self, val, n=1):\n self.sum += val * n\n... | [
[
"numpy.clip",
"torch.load",
"torch.from_numpy",
"numpy.ones",
"torch.save",
"torch.cuda.is_available",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
giuscri/TensorFlow-Tutorials | [
"309f7afc803126e882ad185a32f3b39e18452044"
] | [
"eleven.py"
] | [
"import tensorflow as tf\nimport inception\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nfrom sys import argv, stderr\nfrom PIL import Image\nfrom PIL import ImageFilter\n\n\"\"\"\nexercises:\n Try using some of your own images.\n > ✔\n\n Try other arguments for adversary_example().\... | [
[
"numpy.abs",
"numpy.clip",
"numpy.squeeze",
"tensorflow.gradients",
"tensorflow.placeholder",
"matplotlib.pyplot.subplots",
"numpy.ceil",
"numpy.argmax",
"tensorflow.nn.sparse_softmax_cross_entropy_with_logits",
"tensorflow.Session",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
praveenck06/Hippocampal-Volume-Quantification-in-Alzheimer-s-Progression | [
"6944ebdf681b35e78b84cd676227f0c396c6a770"
] | [
"ModelTraining/src/experiments/UNetExperiment.py"
] | [
"\"\"\"\nThis module represents a UNet experiment and contains a class that handles\nthe experiment lifecycle\n\"\"\"\nimport os\nimport time\n\nimport numpy as np\nimport torch\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard imp... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.functional.softmax",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.load",
"torch.no_grad",
"torch.utils.tensorboard.SummaryWriter",
"torch.cuda.is_available",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
toddrme2178/pyccel | [
"deec37503ab0c5d0bcca1a035f7909f7ce8ef653",
"deec37503ab0c5d0bcca1a035f7909f7ce8ef653"
] | [
"tests/epyccel/modules/loops.py",
"tests/macro/scripts/blas/dswap.py"
] | [
"from pyccel.decorators import types\n\n#==============================================================================\n\n@types( int )\ndef sum_natural_numbers( n ):\n x = 0\n for i in range( 1, n+1 ):\n x += i\n return x\n\n# ...\n@types( int )\ndef factorial( n ):\n x = 1\n for i in range(... | [
[
"numpy.shape"
],
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
linhvannguyen/PhDworks | [
"9336e5257f5ddc3c899a6fb68b1028c905d13ff9"
] | [
"codes/isotropic/regression/funcs/KRR_poly_cv_alpha_degree_sspacing3_tspacing4.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 3 14:20:44 2016\n\n@author: nguyen\n\"\"\"\nimport numpy as np\nfrom netCDF4 import Dataset\n\n# Constants\nNh = 96\nNt = 37\nsspacing = 3\ntspacing = 4\n\nHTLS_sknots = np.arange(0,Nh,sspacing)\nHTHS_sknots = np.arange(0,Nh,1)\nLTHS_tknots = np.arange(0,Nh,tspa... | [
[
"numpy.logspace",
"numpy.arange",
"numpy.reshape",
"sklearn.kernel_ridge.KernelRidge",
"numpy.std",
"numpy.mean",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
activeshadow/HELICS-Examples | [
"750cd111eb11efc681d2575b4919759bdce38e51"
] | [
"python/BLOSEM_tutorial/EVComboFed.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 8/27/2020\n\nThis is a simple EV federate that models a set of EV terminals in an\nEV charging garage. Each terminal can support charging at levels 1, 2,\nand 3 but the EVs that come to charge have a randomly assigned charging\nlevel.\n\nManaging these terminals is a cen... | [
[
"numpy.random.seed",
"numpy.linspace",
"matplotlib.pyplot.title",
"numpy.arange",
"numpy.random.choice",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
landbroken/MyPaper | [
"e77581262aac210e6273c3647d091f7cf53eae4a"
] | [
"src/lib_learning/logistics_learning.py"
] | [
"#!/usr/bin/python3.9\n# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2022 LinYulong. All Rights Reserved \n#\n# @Time : 2022/2/7\n# @Author : LinYulong\n# @Description: 逻辑斯蒂回归模型Logistics regression\n# https://blog.csdn.net/u013421629/article/details/78470020\n\nimport pandas as pd\nimport numpy as np\nfrom sklear... | [
[
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.confusion_matrix",
"sklearn.preprocessing.StandardScaler",
"sklearn.metrics.classification_report",
"sklearn.linear_model.SGDClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
saikrishnarallabandi/Vocoding-Experiments | [
"f2e6c23ea5743ad1d1162669df3c34ccef0541e3"
] | [
"lstmvc.py"
] | [
"import numpy as np\nimport sys, os\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nfrom torch.autograd import Variable\nfrom model import *\n\n# Locations\nsrc_folder = '../feats/VCC2SF1'\ntgt_folder = '../feats/VCC2TF1'\n\nsrc_files = sorted(os.listdir(src_folder))\ntgt_files = sor... | [
[
"torch.utils.data.DataLoader",
"numpy.loadtxt",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
autoih/tensorflow | [
"4a1ae31d56c3c7f40232aace615945c29dcf9c38"
] | [
"tensorflow/python/framework/ops.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.util.function_utils.get_func_code",
"tensorflow.python.pywrap_tensorflow.TF_OperationGetAttrType",
"tensorflow.python.eager.tape.stop_recording",
"tensorflow.python.eager.context.context",
"tensorflow.python.pywrap_tensorflow.TF_GraphCopyFunction",
"tensorflow.python.pyw... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sdressler/s64da-benchmark-toolkit | [
"d69b4151c3615fa064795e174e95d159a12ac4ed"
] | [
"s64da_benchmark_toolkit/netdata.py"
] | [
"\nimport logging\n\nimport requests\nimport pandas\n\n\nLOG = logging.getLogger()\n\nclass Netdata:\n def __init__(self, config):\n self.url = f\"{config['url']}/api/v1/data\"\n self.metrics = config['metrics']\n self.charts = config['charts']\n\n def _get_data(self, timerange):\n ... | [
[
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
jdherman/eci273 | [
"86828b2e075258afdd528e86295170e162cc99e3"
] | [
"L15-reservoir-control-multiobj.py"
] | [
"import numpy as np \nimport matplotlib.pyplot as plt\nfrom cvxpy import *\nimport seaborn as sns\nsns.set_style('whitegrid')\n\nQ = np.loadtxt('data/FOL-monthly-inflow-TAF.csv', delimiter=',', skiprows=1, usecols=[1])\nT = len(Q)\nK = 975 # reservoir capacity\nd = 150*np.ones(T) # target demand (TAF/day)\ndw = 0.1... | [
[
"matplotlib.pyplot.scatter",
"numpy.arange",
"numpy.ones",
"numpy.loadtxt",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EliasVansteenkiste/edge_detection_framework | [
"b9b3d74bba78edce8b1b7382d0822966b80a61a5"
] | [
"configs/pixelnet_pretrained.py"
] | [
"import numpy as np\nimport torch\nimport torchvision\nimport torch.optim as optim\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom collections import namedtuple\nfrom functools import partial\nfrom PIL import Image\n\nimport data_transforms\nimport data_iterators\nimport pathfinder\nimport utils\nimpo... | [
[
"torch.mean",
"torch.max",
"torch.cat",
"numpy.asarray",
"torch.sign",
"torch.sum",
"torch.utils.model_zoo.load_url",
"numpy.swapaxes",
"torch.nn.Dropout",
"torch.nn.functional.sigmoid",
"numpy.asanyarray",
"torch.min",
"torch.nn.Conv2d",
"torch.nn.Linear",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MichaelMonashev/albumentations | [
"86acab98a81754c2c2d0609519791059316ad121"
] | [
"albumentations/augmentations/transforms.py"
] | [
"from __future__ import absolute_import, division\n\nimport math\nimport random\nimport warnings\nfrom enum import Enum\nfrom types import LambdaType\n\nimport cv2\nimport numpy as np\n\nfrom . import functional as F\nfrom .bbox_utils import denormalize_bbox, normalize_bbox, union_of_bboxes\nfrom ..core.transforms_... | [
[
"numpy.isin",
"numpy.rot90",
"numpy.split",
"numpy.take",
"numpy.linspace",
"numpy.clip",
"numpy.arange",
"numpy.indices",
"numpy.stack",
"numpy.dtype",
"numpy.argwhere",
"numpy.all",
"numpy.random.uniform",
"numpy.array",
"numpy.meshgrid",
"numpy.ze... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alx/joliGAN | [
"f6350d78a73a2d705a22f80d97b6565f4372a3db",
"f6350d78a73a2d705a22f80d97b6565f4372a3db"
] | [
"data/online_creation.py",
"models/modules/resnet_architecture/mobile_resnet_generator.py"
] | [
"import math\nimport numpy as np\nimport random\nfrom PIL import Image\nimport torchvision.transforms.functional as F\nfrom torchvision.transforms import InterpolationMode\nfrom tqdm import tqdm\n\ndef crop_image(img_path,bbox_path,mask_delta,crop_delta,mask_square,crop_dim,output_dim):\n\n img = np.array(Image.... | [
[
"numpy.zeros",
"numpy.full"
],
[
"torch.nn.Sequential",
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.ReflectionPad2d",
"torch.nn.ConvTranspose2d",
"torch.nn.Conv2d",
"torch.nn.Tanh",
"torch.nn.InstanceNorm2d",
"torch.nn.ReLU",
"torch.nn.functional.pad",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hzursa/Play-Reader | [
"d3486fd8306fecc92439606736edbda5a4b1e381"
] | [
"genderbyname.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.naive_bayes import MultinomialNB\ndef feat... | [
[
"pandas.read_csv",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.model_selection.train_test_split",
"numpy.vectorize",
"sklearn.feature_extraction.DictVectorizer",
"pandas.to_numeric"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
MasazI/python-r-stan-bayesian-model | [
"05a224958a3f5cbea207001465ac12b6862d9d9f"
] | [
"2-5.py"
] | [
"###############\n#\n# Translate R to Python Copyright (c) 2019 Masahiro Imai Released under the MIT license\n#\n###############\n\nimport os\n\nimport pystan\nimport pandas\nimport pickle\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\n\nimport arviz as az\n\nfile_beer_sales_1 = pandas... | [
[
"pandas.read_csv",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"numpy.arange",
"numpy.median",
"numpy.quantile",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"numpy.mean",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
quic-bharathr/aimet | [
"c6ffd3c31c290fe0913b50831d58534f6df61d76",
"363308217dca3fc52644bdda31e69e356397adaf"
] | [
"NightlyTests/torch/test_quantize_resnet18.py",
"TrainingExtensions/tensorflow/src/python/aimet_tensorflow/examples/test_models.py"
] | [
"# /usr/bin/env python3.5\n# -*- mode: python -*-\n# =============================================================================\n# @@-COPYRIGHT-START-@@\n# \n# Copyright (c) 2017-2018, Qualcomm Innovation Center, Inc. All rights reserved.\n# \n# Redistribution and use in source and binary forms, with or wit... | [
[
"torch.nn.CrossEntropyLoss",
"torch.cuda.empty_cache",
"torch.device",
"torch.cuda.memory_allocated",
"torch.optim.lr_scheduler.StepLR"
],
[
"tensorflow.python.keras.layers.Flatten",
"tensorflow.python.keras.layers.Dense",
"tensorflow.stack",
"tensorflow.equal",
"tensor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
NovemberChopin/GuideLine | [
"d49b3b527a5e54f3ee734c8d5245efb89150d594"
] | [
"BSpline/parameter_selection.py"
] | [
"import numpy as np\n\n# 参数域均匀分布\ndef uniform_spaced(n):\n '''\n Calculate parameters using the uniform spaced method.\n :param n: the number of the data points\n :return: parameters\n '''\n parameters = np.linspace(0, 1, n)\n return parameters\n\n# 根据数据点弦长关系分割参数域\ndef chord_length(n, P):\n ... | [
[
"numpy.zeros",
"numpy.sqrt",
"numpy.linspace",
"numpy.power"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thomasperrot/aes-square-attack | [
"cdc63f4552324a91fa0e7d9bb14cd481bff65740"
] | [
"aes/square.py"
] | [
"import binascii\nimport logging\nfrom concurrent.futures import ProcessPoolExecutor\nfrom functools import partial\nfrom typing import Callable, Iterable, List, Tuple\n\nimport numpy as np\n\nfrom .common import S_BOX, State\nfrom .key_expension import get_first_key\n\nSQUARE_ROUNDS = 4\nREVERSED_S_BOX = {v: k for... | [
[
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ska-sa/mkatsim | [
"94f0e5fb28bf3f9c18f0559f9049636db2abcc27"
] | [
"mkatsim/subarray/telescopearray.py"
] | [
"#! /usr/bin/python\n## Display array antenna locations on EARTH grid using astropy coordinates\n\nimport numpy\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.basemap import Basemap\n\n# Copied library functions\ndef shoot(lon, lat, azimuth, maxdist=None):\n \"\"\"Shooter Function\n Original javascript o... | [
[
"matplotlib.pyplot.legend",
"numpy.sqrt",
"matplotlib.pyplot.title",
"numpy.abs",
"numpy.min",
"numpy.arange",
"numpy.cos",
"matplotlib.pyplot.savefig",
"numpy.sin",
"numpy.arctan2",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.axes",
"numpy.tan",
"numpy.max... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
trangvu/ape-npi | [
"4ae2cd6ed1be773dfe1513458d5e7adae0d46283"
] | [
"translate/translation_model.py"
] | [
"import tensorflow as tf\nimport os\nimport pickle\nimport re\nimport time\nimport numpy as np\nimport sys\nimport math\nimport shutil\nimport itertools\nfrom collections import OrderedDict\nfrom translate import utils, evaluation, import_graph\nfrom translate.seq2seq_model import Seq2SeqModel\nfrom subprocess impo... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.get_default_session",
"tensorflow.device",
"tensorflow.get_variable",
"tensorflow.Variable",
"tensorflow.global_variables",
"tensorflow.trainable_variables",
"tensorflow.global_variables_initializer",
"tensorflow.train.Saver"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Mohamed-hanafy30/Disasters-pipeline-project- | [
"83787c04063f05404eab8f72c0710980588fb3c5"
] | [
"data/process_data.py"
] | [
"import sys\nimport pandas as pd\nfrom sqlalchemy import create_engine\n\ndef load_data(messages_filepath, categories_filepath):\n \"\"\"Load dataframe from filepaths\n\n INPUT\n messages_filepath -- str, link to file\n categories_filepath -- str, link to file\n\n OUTPUT\n df - pandas DataFrame\n ... | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
swarajthakur/deep-q-learning | [
"7856f2f003455c6c4935c902d722fe435bded863"
] | [
"dqn.py"
] | [
"# -*- coding: utf-8 -*-\nimport random\nimport gym\nimport numpy as np\nfrom collections import deque\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.optimizers import Adam\n\nEPISODES = 1000\n\nclass DQNAgent:\n def __init__(self, state_size, action_size):\n self.state_s... | [
[
"numpy.reshape",
"numpy.argmax",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leoorshansky/DeepCTR-Torch-MLET | [
"54e8f947ac677b5c0afe7967a224fb8d9cceb516"
] | [
"deepctr_torch/inputs.py"
] | [
"# -*- coding:utf-8 -*-\n\"\"\"\nAuthor:\n Weichen Shen,wcshen1994@163.com\n\"\"\"\n\nfrom collections import OrderedDict, namedtuple, defaultdict\nfrom itertools import chain\n\nimport torch\nimport torch.nn as nn\nimport numpy as np\n\nfrom .layers.sequence import SequencePoolingLayer\nfrom .layers.utils impor... | [
[
"torch.nn.functional.embedding",
"torch.nn.Parameter",
"torch.Tensor",
"torch.cat",
"torch.nn.Embedding",
"torch.matmul",
"torch.nn.init.normal_",
"torch.no_grad",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gmagannaDevelop/TousAntiCovid | [
"7174845b0614b6a20e48834d5a76579cfbf80bd6"
] | [
"metrics/probability_analysis.py"
] | [
"#!/usr/bin/env python3\n\nimport sys\nimport pandas as pd\n\nif __name__ == \"__main__\":\n x = pd.read_csv(sys.argv[1] , header=None)\n x.columns = [\"lambda\", \"doctor\", \"virus\"]\n print(x.describe())\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
alexlyttle/cpnest | [
"ae620b5f214a7a2a52e16ef5f2fe7354992aff88"
] | [
"tests/test_half_gaussian.py"
] | [
"import sys\nimport unittest\nimport numpy as np\nfrom scipy import integrate,stats\nimport cpnest\nimport cpnest.model\nimport matplotlib as mpl\nmpl.use('Agg')\nfrom matplotlib import pyplot as plt\n\nclass HalfGaussianModel(cpnest.model.Model):\n \"\"\"\n A simple gaussian model with parameters mean and si... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.linspace",
"matplotlib.pyplot.title",
"numpy.abs",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"scipy.stats.norm",
"numpy.zeros",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
trungvdhp/mpsnet | [
"e76979e2f8ecd9ea50a0d864533494af2afbb2d4"
] | [
"Keras_Code/model/nets.py"
] | [
"from model.adacos import AdaCos\nfrom model.blocks import NetBlock\nfrom tensorflow.keras.layers import Input, Reshape, Conv2D, Activation, Flatten, Dropout, add\nfrom tensorflow.keras.models import Model\nimport tensorflow.keras.backend as K\n\nclass Net:\n \n def __init__(self, config):\n self.model... | [
[
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.models.Model",
"tensorflow.keras.backend.int_shape",
"tensorflow.keras.layers.add",
"tensorflow.keras.layers.Reshape",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras.layers.Input"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
neerajbafila/pytorch-CNN | [
"9828166149b73473138ab54ee45bec054eb9e591"
] | [
"src/utils/evaluation_model.py"
] | [
"from unittest import result\nimport torch\nimport os\nimport logging\nfrom src.utils.common import read_yaml, create_directories\nimport argparse\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix, classification_report\nfrom pathlib import Pat... | [
[
"torch.load",
"torch.argmax",
"sklearn.metrics.confusion_matrix",
"matplotlib.pyplot.savefig",
"numpy.concatenate",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dougct/predictability | [
"9dbc905b75900477637f3f90a5c4da27c3c778d9"
] | [
"entropy.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport math\nimport numpy as np\n\n\ndef uniform_entropy(sequence):\n \"\"\"\n Computes the \"random entropy\", that is, the entropy of a uniform distribution.\n\n Equation:\n $H_{uniform} = \\log_{2}(n)$, where n is the number of unique symbols in the input sequence.\n\n... | [
[
"numpy.log2",
"numpy.logical_and",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goesslfabian/unify-eval | [
"ced486e44ca57ed31b552fd20b53cae61015e486"
] | [
"unify_eval/utils/load_data.py"
] | [
"import abc\nimport sys\nfrom abc import ABC\nfrom typing import List, Iterator, Dict, Callable\n\nimport numpy as np\nfrom tqdm import tqdm\n\n\nclass DataLoader(ABC):\n @abc.abstractmethod\n def next_minibatch(self, minibatch_size: int = 16):\n pass\n\n @abc.abstractmethod\n def is_exhausted(se... | [
[
"numpy.random.choice",
"numpy.min",
"numpy.arange",
"numpy.ceil",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dennis-j-lee/AirNet-SNL | [
"c35b84b50b7f1351a450a5970b19d8a8b83053d1"
] | [
"airnetSNL/model/train_loop.py"
] | [
"import errno\nimport numpy as np\nimport os\nimport torch\nfrom torch.nn import functional as F\n\n\ndef train_model(model: torch.nn.Module,\n optimizer: torch.optim.Adam,\n train_loader: torch.utils.data.DataLoader,\n nEpochs: int,\n saveModel: bool = Fa... | [
[
"torch.ones",
"torch.cat",
"torch.load",
"numpy.ones",
"torch.tensor",
"torch.nn.functional.mse_loss",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jayunit100/katib | [
"d6ea2d5b3ce5a2ca454b079e18e0dc0d4f7dfeed"
] | [
"pkg/suggestion/bayesianoptimization/src/algorithm_manager.py"
] | [
"\"\"\" module for algorithm manager \"\"\"\n\nimport numpy as np\n\nfrom pkg.api.python import api_pb2\nimport logging\nfrom logging import getLogger, StreamHandler, INFO, DEBUG\n\ndef deal_with_discrete(feasible_values, current_value):\n \"\"\" function to embed the current values to the feasible discrete spac... | [
[
"numpy.hstack",
"numpy.absolute",
"numpy.subtract",
"numpy.argmin",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
maryamhgf/Heterogeneous-Multiscale- | [
"dd41532a98603d7b75a035b14d28586dd4133baa"
] | [
"examples/Toy_Example_HMM.py"
] | [
"#run export PYTHONPATH=\"${PYTHONPATH}:/home/mhaghifam/Documents/Research/Neural-ODE/Code/torchdiffeq/torchdiffeq\" to be able to import torchdiffeq\n\nimport os\nimport argparse\nimport time\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport matplotlib.pyplot as plt\n\... | [
[
"matplotlib.pyplot.legend",
"torch.sin",
"torch.zeros",
"matplotlib.pyplot.plot",
"torch.no_grad",
"torch.cuda.is_available",
"torch.Size",
"torch.tensor",
"matplotlib.pyplot.figure",
"torch.linspace",
"matplotlib.pyplot.title",
"torch.full",
"torch.nn.init.cons... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DonovanZhu/minimalRL | [
"333d22e226a168e7af327913cd07f6cc4637acb0"
] | [
"a2c.py"
] | [
"import gym\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.distributions import Categorical\nimport torch.multiprocessing as mp\nimport time\nimport numpy as np\n\n# Hyperparameters\nn_train_processes = 3\nlearning_rate = 0.0002\nupdate_interval = 5\ng... | [
[
"torch.nn.functional.softmax",
"torch.from_numpy",
"numpy.stack",
"torch.tensor",
"torch.nn.Linear",
"torch.distributions.Categorical",
"torch.log",
"torch.multiprocessing.Pipe",
"torch.multiprocessing.Process"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MrRubyRed/DLS | [
"9dd92e83c8ce1a7ec9b3954b7c4640f2fb8dd1dd"
] | [
"DeepLS.py"
] | [
"import reachable_computation as Utils\nimport baselines.common.tf_util as U\nimport numpy as np\nimport tensorflow as tf\nimport pickle\nimport cvxpy\nimport os\n\n#Define a new TF session\nsess = U.single_threaded_session()\nsess.__enter__()\n\n#Experiment Directory\ndirectory = \"/home/vrubies/Documents/Research... | [
[
"tensorflow.get_collection",
"numpy.linalg.norm",
"numpy.ones",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
FlyBrainLab/Neuroballad | [
"dc8f3aef60e89183e4d5644a226aaf76addcacd1"
] | [
"neuroballad/neuroballad.py"
] | [
"#!/usr/bin/env python\n\"\"\"\nNeuroballad circuit class and components for simplifying Neurokernel/Neurodriver\nworkflow.\n\"\"\"\nfrom __future__ import absolute_import\nimport os\nimport copy\nimport json\nimport h5py\nimport time\nimport random\nimport pickle\nimport inspect\nimport argparse\nimport itertools\... | [
[
"matplotlib.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
593903762/center | [
"1093f4519422d417b44d5caa4aea12fa7141ba55"
] | [
"src/cocoeval.py"
] | [
"__author__ = 'tsungyi'\n\nimport numpy as np\nimport datetime\nimport time\nfrom collections import defaultdict\nfrom . import mask as maskUtils\nimport copy\n\nclass COCOeval:\n # Interface for evaluating detection on the Microsoft COCO dataset.\n #\n # The usage for CocoEval is as follows:\n # cocoG... | [
[
"numpy.logical_not",
"numpy.spacing",
"numpy.unique",
"numpy.cumsum",
"numpy.ones",
"numpy.concatenate",
"numpy.round",
"numpy.max",
"numpy.mean",
"numpy.count_nonzero",
"numpy.searchsorted",
"numpy.exp",
"numpy.argsort",
"numpy.repeat",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IPUdk/iputemplates | [
"f182305ffbdd95a75cc55ba8a6a570aca6f78c54"
] | [
"tex/latex/ipu/ipucolours.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import print_function, division, absolute_import\nimport matplotlib as mpl\nimport matplotlib.cm as mplcm\nfrom cycler import cycler \nimport numpy as np\nfrom matplotlib.colors import LinearSegmentedColormap\n#import brewer2mpl\nfrom itertools import cycle\nimport platform... | [
[
"numpy.linspace",
"matplotlib.cm._reverse_cmap_spec",
"matplotlib.cm.get_cmap",
"matplotlib._cm.cubehelix",
"matplotlib.cm.register_cmap"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mmjazzar/-python-snippets | [
"fa53a6bc5c2f60b8a19677ae8dc848fdb6197589"
] | [
"open cv_example 1.py"
] | [
"import cv2\r\nimport numpy as np\r\n\r\nimg = cv2.imread('C:\\\\Users\\\\mmjaz\\\\Desktop\\\\OneDrive_3_7-3-2017\\\\Button\\\\images_190.jpeg')\r\n\r\nimg = cv2.imread('C:\\\\Users\\\\mmjaz\\\\Desktop\\\\OneDrive_3_7-3-2017\\\\OneDrive_4_7-3-2017\\\\pop up.jpg')\r\n\r\ngray= cv2.imread('C:\\\\Users\\\\mmjaz\\\\Des... | [
[
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ltthinhtb/image_bit | [
"4f4118355b44d5b2d6e4537334065ebdc8312ad1"
] | [
"main.py"
] | [
"import argparse\nimport logging\nimport random\nimport numpy as np\nimport cv2\n\nlogging.basicConfig(level=logging.DEBUG)\n\n\ndef convert_image_to_bit_planes(img, bit_size):\n \"\"\"\n Convert a color image to separate rgb bit planes\n\n Parameters:\n img: OpenCV image\n bit_size: \n\n Returns\... | [
[
"numpy.packbits",
"numpy.unpackbits"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rahul263-stack/TIGRE | [
"073b9c6d42f71f42451c4cd2db68ba8d67363e4c"
] | [
"Python/tigre/algorithms/krylov_subspace_algorithms.py"
] | [
"from __future__ import division\r\n\r\nimport time\r\n\r\nimport numpy as np\r\nimport tigre\r\nfrom tigre.algorithms.iterative_recon_alg import IterativeReconAlg\r\nfrom tigre.algorithms.iterative_recon_alg import decorator\r\nfrom tigre.utilities.Atb import Atb\r\nfrom tigre.utilities.Ax import Ax\r\n\r\n\r\nif ... | [
[
"numpy.zeros",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
vaesl/LFIP | [
"eb9d934616c508c9a9032f170baa1d97fa792822"
] | [
"models/LFIP_VOC_300.py"
] | [
"import torch\nimport torch.nn as nn\n\n\nclass ConvBlock(nn.Module):\n\n def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):\n super(ConvBlock, self).__init__()\n self.out_channels = out_planes\n self.conv = nn.C... | [
[
"torch.nn.Softmax",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wakeful-sun/vehicle-detector | [
"080eab6acb2fc11dc5ea5a93ee5437347612aab2"
] | [
"code/classifier/tools.py"
] | [
"import numpy as np\r\n\r\n\r\ndef insert_separator(items, separator):\r\n row = []\r\n for index, item in enumerate(items):\r\n if index:\r\n row.append(separator)\r\n row.append(item)\r\n return row\r\n\r\n\r\ndef create_composite_image(bgr_images, h_span=5, v_span=5, n_columns=3... | [
[
"numpy.hstack",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mynameisguy/tensorflow | [
"2803742817755846f847ac506bbe20b4d4a14195"
] | [
"tensorflow/contrib/rnn/python/ops/rnn_cell.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.ops.math_ops.log",
"tensorflow.python.ops.array_ops.constant",
"tensorflow.python.ops.array_ops.shape",
"tensorflow.python.ops.array_ops.split",
"tensorflow.contrib.compiler.jit.experimental_jit_scope",
"tensorflow.python.framework.op_def_registry.get_registered_ops",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"2.7",
"1.4",
"2.6",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1.2",
"2.10"
]
}
... |
sxontheway/milliEye | [
"bfdb041c978a45d7481071e8e9579d226ce523ff",
"bfdb041c978a45d7481071e8e9579d226ce523ff"
] | [
"module3_our_dataset/yolov3/models.py",
"module2_mixed/test_mixed.py"
] | [
"from __future__ import division\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nimport numpy as np\n\nfrom utils.parse_config import *\nfrom utils.utils import build_targets, to_cpu\n\ndef create_modules(module_defs):\n \"\"\"\n Constructs module ... | [
[
"torch.nn.Sequential",
"torch.sigmoid",
"numpy.fromfile",
"torch.cat",
"torch.nn.ModuleList",
"torch.sum",
"torch.from_numpy",
"torch.arange",
"torch.nn.BCELoss",
"torch.exp",
"torch.nn.LeakyReLU",
"torch.nn.functional.interpolate",
"torch.nn.BatchNorm2d",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhangw106/FlamePyrometry | [
"8f0a9473e54bb4a898effc5bb310b8700e6dd9ad"
] | [
"ExaminationTIF.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nA useful tool to examine a series of raw2tif images for the FlamePyrometry code.\r\nFor more information on the FlamePyrometry code, please see [https://doi.org/10.1364/AO.58.002662].\r\n\r\nThe inverse Abel transformation is very sensitive to the symmetry of flame and the unif... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.plot",
"numpy.uint8",
"numpy.std",
"matplotlib.pyplot.subplot",
"numpy.argmax",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.axis",
"numpy.zeros",
"matplotlib.pyplot.figure",
"matpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bankbiz/image-matching-benchmark | [
"c314f067b2d7337b9e7de0875214bdbab9750afc"
] | [
"methods/feature_matching/nn.py"
] | [
"# Copyright 2020 Google LLC, University of Victoria, Czech Technical University\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.... | [
[
"numpy.asarray",
"numpy.zeros",
"numpy.concatenate",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ADMoreau/CapsNet_Keras | [
"9aa34b6da7e5528c50f72ecf102c4788df561eeb"
] | [
"capsulenet-multi-gpu.py"
] | [
"\"\"\"\nKeras implementation of CapsNet in Hinton's paper Dynamic Routing Between Capsules.\nThe current version maybe only works for TensorFlow backend. Actually it will be straightforward to re-write to TF code.\nAdopting to other backends should be easy, but I have not tested this.\n\nUsage:\n python caps... | [
[
"tensorflow.device",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ami-a/MaskDetection | [
"9df329a24a987e63331c17db154319b3ebcaad74"
] | [
"mask_example/keras_layers/keras_layer_AnchorBoxes.py"
] | [
"'''\nA custom Keras layer to generate anchor boxes.\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 http://www.apache.org/licenses/LICENSE-2.... | [
[
"numpy.expand_dims",
"numpy.sqrt",
"numpy.linspace",
"numpy.meshgrid",
"numpy.tile",
"numpy.concatenate",
"numpy.zeros_like",
"numpy.any",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pillera/cta-lstchain | [
"699b8385bc4bdc35b226c14020638f5d2fcf3c07",
"699b8385bc4bdc35b226c14020638f5d2fcf3c07"
] | [
"lstchain/visualization/plot_drs4.py",
"lstchain/calib/camera/r0.py"
] | [
"\nfrom matplotlib import pyplot as plt\nfrom traitlets.config.loader import Config\nimport numpy as np\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom ctapipe.io import event_source\nfrom lstchain.calib.camera.r0 import LSTR0Corrections\nfrom ctapipe.io.containers import PedestalContainer\nfrom ctapipe... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.step",
"matplotlib.pyplot.plot",
"matplotlib.backends.backend_pdf.PdfPages",
"matplotlib.pyplot.tight_layout",
"numpy.arange",
"numpy.std",
"matplotlib.pyplot.subplot",
"matplotlib.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amanyara/Boston-Home-Price-Forecast | [
"600ccb25c40240ffddee8871d77728a782a80498"
] | [
"Paddle_house_predict.py"
] | [
"# 19200300157\r\n# 张宇含\r\n#加载飞桨、Numpy和相关类库\r\nimport paddle\r\nfrom paddle.nn import Linear\r\nimport paddle.nn.functional as F\r\nimport numpy as np\r\n\r\n\r\ndef load_data():\r\n # 从文件导入数据\r\n datafile = 'housing.data'\r\n data = np.fromfile(datafile, sep=' ', dtype=np.float32)\r\n\r\n # 每条数据包括14项，其... | [
[
"numpy.array",
"numpy.fromfile",
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
infobeisel/lightmetrica-v3 | [
"833d74e5ed8a470c33aca100c9494be11ecbf1be"
] | [
"functest/func_render_all.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:light\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.4'\n# jupytext_version: 1.2.4\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# ## ... | [
[
"matplotlib.pyplot.show",
"numpy.power",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jamesthesken/stock-scraper | [
"619e6689a4963deeea2b60c63c869eb43d017f3d"
] | [
"wsb_scraper.py"
] | [
"import config\nimport praw\nfrom praw.models import MoreComments\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\nfrom sqlalchemy import create_engine\n\n# Creates a set of stock tickers in NASDAQ\ndef nasdaq_tickers():\n fin = open(\"n... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"matplotlib.pyplot.savefig"
]
] | [
{
"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": []
}
] |
Lucien-MG/torchreinforce | [
"5ba852bb255c14140d7bc300a44e60e7b4b572ff"
] | [
"torchreinforce/agents/temporal_difference.py"
] | [
"import warnings\nfrom collections import namedtuple, defaultdict\nfrom functools import partial\nfrom typing import Optional, Tuple, List, Callable, Any\n\nimport torch\nfrom torch import Tensor\nfrom torch import distributions\nfrom torch import nn\n\nclass TemporalDifference(nn.Module):\n def __init__(\n ... | [
[
"torch.distributions.binomial.Binomial",
"torch.zeros",
"torch.max",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
frankling2020/Self-learn-Repo | [
"294df18469d6d4ef6d479b1b533f42445cd01ac1"
] | [
"GNN_PRP/prp_3_21/adgcl/test_transfer_finetune_chem.py"
] | [
"import argparse\nimport logging\nimport random\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom sklearn.metrics import roc_auc_score\nfrom torch_geometric.data import DataLoader\nfrom tqdm import tqdm\n\nfrom datasets import MoleculeDataset\nfrom tr... | [
[
"torch.optim.Adam",
"sklearn.metrics.roc_auc_score",
"pandas.read_csv",
"numpy.random.seed",
"torch.cat",
"torch.zeros",
"torch.manual_seed",
"torch.sum",
"torch.nn.BCEWithLogitsLoss",
"torch.no_grad",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ChristophReich1996/ECG_Classification | [
"d281e0d3df85e7917e7a838529d073cf4b0a71a4"
] | [
"predict.py"
] | [
"from typing import List, Tuple, Union, Dict\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nimport torch_optimizer\nimport numpy as np\nimport os\nfrom tqdm import tqdm\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n\nfrom ecg_classification import *\nfrom wettbewerb import load_... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sagar1993/BrainNet_server | [
"70972f4ccd06bb2615afc19a8e077fb9e39470f3"
] | [
"service/feature_extractor.py"
] | [
"import numpy as np\nfrom pyedflib import EdfReader\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.metrics import confusion_matrix\n\n\ndef featureVecs(out, sample_size):\n # sample_size = 120\n fVec = np.zeros((sample_size, 6), dtype=float)\n\n i = 0\n j = 0\n while i < len(out) and j < s... | [
[
"sklearn.naive_bayes.GaussianNB",
"numpy.fft.fft",
"numpy.arange",
"sklearn.metrics.confusion_matrix",
"numpy.ones",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ivanB1975/scipy | [
"8fed46cd7e7b5b63eb101c5d5d521ff7d7bac9b9"
] | [
"setup.py"
] | [
"#!/usr/bin/env python\n\"\"\"SciPy: Scientific Library for Python\n\nSciPy (pronounced \"Sigh Pie\") is open-source software for mathematics,\nscience, and engineering. The SciPy library\ndepends on NumPy, which provides convenient and fast N-dimensional\narray manipulation. The SciPy library is built to work with... | [
[
"numpy.distutils.misc_util.Configuration",
"numpy.distutils.core.setup",
"numpy.distutils.command.build_clib.build_clib.build_a_library",
"scipy._build_utils.system_info.get_info",
"scipy._build_utils.system_info.NotFoundError",
"numpy.distutils.command.build_ext.build_ext.build_extension"... | [
{
"matplotlib": [],
"numpy": [
"1.11",
"1.19",
"1.24",
"1.16",
"1.23",
"1.20",
"1.7",
"1.12",
"1.21",
"1.22",
"1.14",
"1.6",
"1.13",
"1.9",
"1.17",
"1.10",
"1.18",
"1.15",
"1.8"
],
"pand... |
amessadiqi/humorDetection | [
"998a46219b0ef593d7ad416a649f232e6a79c2c2"
] | [
"HumorDetector.py"
] | [
"import pandas as pd\nfrom data_processing.DataProcessor import DataProcessor\nfrom humor_features.HumorFeatures import HumorFeatures\nfrom humor_model.Models import Models\nfrom prediction_server.app import app\n\n\nclass HumorDetector:\n def __init__(self, dataset = None):\n if isinstance(dataset, pd.Da... | [
[
"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": []
}
] |
Jakob-Unfried/jax | [
"bec943cee0234178a9143a0447b224a5faa9fbdc"
] | [
"tests/api_test.py"
] | [
"# Copyright 2018 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# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"numpy.__version__.split",
"numpy.asarray",
"numpy.dtype",
"numpy.all",
"numpy.random.randn",
"numpy.iinfo",
"numpy.exp",
"numpy.arange",
"numpy.eye",
"numpy.float16",
"numpy.sin",
"numpy.float32",
"numpy.zeros",
"numpy.random.rand",
"numpy.testing.asser... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ozgay/MockingBird | [
"b46e7a78667732114c22d4f5774c8481d6b75683"
] | [
"toolbox/__init__.py"
] | [
"from toolbox.ui import UI\nfrom encoder import inference as encoder\nfrom synthesizer.inference import Synthesizer\nfrom vocoder.wavernn import inference as rnn_vocoder\nfrom vocoder.hifigan import inference as gan_vocoder\nfrom pathlib import Path\nfrom time import perf_counter as timer\nfrom toolbox.utterance im... | [
[
"numpy.abs",
"torch.manual_seed",
"numpy.concatenate",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
slocke716/airflow | [
"d5ad0761fd0b33cb89258ff6924c608c3e086680"
] | [
"tests/hooks/test_hive_hook.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# 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... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
netaz/dirty-rl | [
"189b377b09db9c183fac78274ecfc7857bec695b"
] | [
"utils/utils.py"
] | [
"import torch\nimport os\nfrom colorama import Fore, Back, Style\n\n\ndtype = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor\n\n\n\"\"\"Various decorators\"\"\"\n\ndef cached_function(func):\n \"\"\"Use this wrapper for functions that run often, but always return the same result.\n\n ... | [
[
"torch.load",
"torch.cuda.is_available",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wty9391/maximal-revenue-rtb | [
"a0c8cc5e03e2306023e77323c8f0bfc5b4988823"
] | [
"winner.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import quad\n\nepsilon = sys.float_info.epsilon\n\nclass wining_pridictor():\n def __init__(self,d=0.1,max_iter=5000,eta=0.001,step=1,eta_decay=0.99):\n self.d = d\n ... | [
[
"matplotlib.pyplot.legend",
"numpy.arange",
"matplotlib.pyplot.plot",
"numpy.round",
"numpy.zeros_like",
"numpy.shape",
"numpy.tanh"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LucSkyvvalker/TAUS | [
"a804de4386fa31de3fb9a7aebbd686ba2c12a243"
] | [
"scripts/scoreModel.py"
] | [
"import extractSents as es\nimport pandas as pd\nimport numpy as np\nimport cleandatas as cd\nimport learnclassify as lc\nimport score as sc\nimport corpusFuncs as corpf\n\n\n\"\"\"\nscoreModel() is used to get a print of:\n'Precision, Recall\nF1-score\nAccuracy\nCross Entropy'\nThe function will ask for input on w... | [
[
"pandas.read_csv",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dreamflyer/musket_core | [
"1bdf1b4715a3b5c63bf687799d7b977fdf49053f"
] | [
"musket_core/crf.py"
] | [
"from __future__ import absolute_import\r\nfrom __future__ import division\r\n\r\nimport warnings\r\n\r\nfrom keras import backend as K\r\nfrom keras import activations\r\nfrom keras import initializers\r\nfrom keras import regularizers\r\nfrom keras import constraints\r\nfrom keras.layers import Layer\r\nfrom kera... | [
[
"tensorflow.gather_nd",
"tensorflow.range"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
seanpquinn/augerta | [
"43862fd6b5360c9b7c5a7b3502fb7738ea2e8d75"
] | [
"web_monitor/north_daily_events_final_2016_catchup.py"
] | [
"import time\nimport math\nimport datetime\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom matplotlib.dates import DateFormatter, date2num\nimport numpy as np\nimport subprocess as sp\nimport time\nimport sys\nimport os\nfrom itertools import groupby\nfrom sklearn.neighbors import K... | [
[
"numpy.linspace",
"matplotlib.pyplot.step",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.tight_layout",
"numpy.arange",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.close",
"numpy.zeros",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.savefig... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brendankeith/PyMFEM | [
"5ff7c88cae07ca2a0dfe0b1d4224491c31c8c2ed"
] | [
"mfem/_par/sparsemat.py"
] | [
"# This file was automatically generated by SWIG (http://www.swig.org).\n# Version 4.0.2\n#\n# Do not make changes to this file unless you know what you are doing--modify\n# the SWIG interface file instead.\n\nfrom sys import version_info as _swig_python_version_info\nif _swig_python_version_info < (2, 7, 0):\n ... | [
[
"numpy.ascontiguousarray",
"numpy.real"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
escherba/ivis | [
"bbfd8381c0f40f7219585df851ed9a2f4278bee4"
] | [
"ivis/data/sequence/image.py"
] | [
"\"\"\"Custom datasets that load images from disk.\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom .sequence import IndexableDataset\n\n\nclass ImageDataset(IndexableDataset):\n \"\"\"When indexed, loads images from disk, resizes to consistent size, then returns image.\n Since the returned images... | [
[
"tensorflow.image.decode_png",
"tensorflow.cast",
"tensorflow.image.resize",
"numpy.prod",
"tensorflow.io.read_file",
"tensorflow.image.resize_with_pad",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
junekihong/beam-span-parser | [
"206e032409640556ac4765a5d15dc2f72fbddd74"
] | [
"src/main.py"
] | [
"import argparse\nimport itertools\nimport os.path\nimport time, timeit\nimport sys\n\nimport dynet as dy\nimport numpy as np\n\nimport evaluate\nimport parse\nimport trees\nimport vocabulary\nimport gc\nfrom collections import defaultdict\n\ndef format_elapsed(start_time):\n elapsed_time = int(time.time() - sta... | [
[
"numpy.random.shuffle",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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