repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
ProfFan/gtsam | [
"67ce22c039cc0d04b0085203651dd859bbc44934"
] | [
"cython/gtsam/tests/test_JacobianFactor.py"
] | [
"\"\"\"\nGTSAM Copyright 2010-2019, Georgia Tech Research Corporation,\nAtlanta, Georgia 30332-0415\nAll Rights Reserved\n\nSee LICENSE for the license information\n\nJacobianFactor unit tests.\nAuthor: Frank Dellaert & Duy Nguyen Ta (Python)\n\"\"\"\nimport unittest\n\nimport numpy as np\n\nfrom gtsam_py import gt... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mayankiitg/cs224n | [
"c67b7904101c8f19a5a231e4fe521e764470d41b"
] | [
"test_ensemble.py"
] | [
"\"\"\"Test a model and generate submission CSV.\n\nUsage:\n > python test_ensemble.py --split SPLIT --load_path PATH --name NAME\n where\n > SPLIT is either \"dev\" or \"test\"\n > PATH is a path to a checkpoint (e.g., save/train/model-01/best.pth.tar)\n > NAME is a name to identify the test run\n\n... | [
[
"torch.mean",
"torch.max",
"torch.nn.functional.nll_loss",
"torch.sum",
"torch.utils.data.DataLoader",
"torch.tensor",
"torch.no_grad",
"torch.nn.DataParallel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YannickNoelStephanKuhn/PyBaMM | [
"d90636a755b7b77bbc75ae7bc2728c8ee2fa730a",
"a31e2095600bb92e913598ac4d02b2b6b77b31c1"
] | [
"tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py",
"pybamm/expression_tree/operations/evaluate.py"
] | [
"#\n# Tests for the lithium-ion SPM model\n#\nimport pybamm\nimport tests\nimport numpy as np\nimport unittest\nfrom platform import system\n\n\nclass TestSPM(unittest.TestCase):\n def test_basic_processing(self):\n options = {\"thermal\": \"isothermal\"}\n model = pybamm.lithium_ion.SPM(options)\n... | [
[
"numpy.testing.assert_array_almost_equal"
],
[
"numpy.all",
"numpy.array",
"numpy.argwhere"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GOLoDovkA-A/tardis | [
"847b562022ccda2db2486549f739188ba48f172c",
"847b562022ccda2db2486549f739188ba48f172c"
] | [
"tardis/plasma/standard_plasmas.py",
"tardis/gui/datahandler.py"
] | [
"import os\nimport logging\n\nimport numpy as np\nimport pandas as pd\n\nfrom tardis.io.atom_data import AtomData\nfrom tardis.io.config_reader import ConfigurationError\nfrom tardis.util.base import species_string_to_tuple\nfrom tardis.plasma import BasePlasma\nfrom tardis.plasma.properties.property_collections im... | [
[
"pandas.MultiIndex.from_tuples",
"pandas.Series"
],
[
"matplotlib.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
MickaelRigault/snprop | [
"e2505b612240b41e984d79e18355926e0c0462f4"
] | [
"snprop/age.py"
] | [
"\"\"\" Prompt vs. Delayed model of the SN population \"\"\"\nimport pandas\nimport numpy as np\n\nfrom scipy import stats\nfrom .tools import asym_gaussian\n\n\nclass PrompDelayModel(object):\n\n def __init__(self):\n \"\"\" \"\"\"\n\n # ====================== #\n # Methods #\n # ... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.random.choice",
"numpy.asarray",
"matplotlib.pyplot.cm.get_cmap",
"pandas.DataFrame",
"numpy.atleast_1d",
"numpy.concatenate",
"numpy.shape",
"numpy.zeros",
"numpy.sum",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
Pawandeep-prog/hand-gestured-vlc | [
"8760ee6d1d17456ddc96248775031dc2cee30ca6"
] | [
"vlc_main.py"
] | [
"import cv2\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom tensorflow.keras.models import load_model\r\nfrom pynput.keyboard import Controller, Key\r\nimport time\r\ncont= Controller()\r\nflag = True\r\n\r\nmodel = load_model('finger.hdf')\r\n\r\ncap = cv2.VideoCapture(0)\r\n\r\nstart = time.time(... | [
[
"tensorflow.keras.models.load_model",
"numpy.argmax",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
lamto20132223/mealpy | [
"b25bd7548299d490cf2f40d3ecfc5dc87cf60994"
] | [
"mealpy/swarm_based/SSA.py"
] | [
"#!/usr/bin/env python\n# ------------------------------------------------------------------------------------------------------%\n# Created by \"Thieu Nguyen\" at 11:59, 17/03/2020 %\n# ... | [
[
"numpy.log",
"numpy.min",
"numpy.reshape",
"numpy.arange",
"numpy.repeat",
"scipy.spatial.distance.cdist",
"numpy.tile",
"numpy.std",
"numpy.argmax",
"numpy.argmin",
"numpy.random.rand",
"numpy.random.uniform",
"numpy.array",
"numpy.exp",
"numpy.zeros",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
Spiph/GTG | [
"4a45032290d0c1364e4398684582c51094b245f5"
] | [
"torchbeast/monobeast.py"
] | [
"import argparse\nimport logging\nimport os\nimport pprint\nimport threading\nimport time\nimport timeit\nimport traceback\nimport typing\nimport wandb\n\nos.environ[\"OMP_NUM_THREADS\"] = \"1\" # Necessary for multithreading.\n\nimport torch\nfrom torch import multiprocessing as mp\nfrom torch import nn\nfrom tor... | [
[
"torch.mean",
"torch.nn.functional.softmax",
"torch.optim.lr_scheduler.LambdaLR",
"torch.clamp",
"torch.nn.functional.log_softmax",
"torch.load",
"torch.cuda.current_device",
"torch.zeros",
"torch.sum",
"pandas.DataFrame",
"torch.nn.Sigmoid",
"torch.multiprocessing.... | [
{
"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": []
}
] |
Kou-kun42/Fire-Detection-API | [
"8f69c2c4b1ac30b4dbf0b230f3229e9ff779742b"
] | [
"app/main_test.py"
] | [
"import unittest\nfrom fastapi import FastAPI\nfrom fastapi.testclient import TestClient\nimport tensorflow as tf\n\nfrom app.main import app\n\nclient = TestClient(app)\n\n\nclass ClassifierAPITest(unittest.TestCase):\n\n TEST_COLOR_IMG_URL = \"https://storage.googleapis.com/download.tensorflow.org/example_imag... | [
[
"tensorflow.keras.utils.get_file"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
astrolabsoftware/fink-filters | [
"1542b68e7c1bfff6b3747e60d5f2e1eec5b7c9ae"
] | [
"fink_filters/filter_sn_candidates/filter.py"
] | [
"# Copyright 2019-2022 AstroLab Software\n# Author: Julien Peloton\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.set_printoptions"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iliesidaniel/image-classification | [
"138c7fa83e084ccb8f5c2ad8827f1fbb2527c00c"
] | [
"session/test_session.py"
] | [
"from controllers.data_set_controller import DataSetController\nfrom controllers.session_controller import SessionController\n\nfrom utils.train.session_details import SessionDetails\n\n\nimport constants.cnn_constants as cnn_const\nimport neural_networks.cnn as cnn\n\nimport tensorflow as tf\nimport threading\nimp... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.Graph",
"tensorflow.summary.FileWriter",
"tensorflow.get_collection",
"tensorflow.train.Coordinator",
"tensorflow.nn.top_k",
"tensorflow.train.ExponentialMovingAverage",
"tensorflow.summary.merge_all",
"tensorflow.Session",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gdikov/adversarial-variational-bayes | [
"c23da9cc1c914d10646a0c0bc1a2497fe2cbaaca"
] | [
"third_party/ite/cost/meta_i.py"
] | [
"\"\"\" Meta mutual information estimators. \"\"\"\n\nfrom numpy.random import rand\nfrom numpy import ones\n\nfrom ite.cost.x_initialization import InitX, InitAlpha\nfrom ite.cost.x_verification import VerCompSubspaceDims, VerOneDSubspaces,\\\n VerSubspaceNumberIsK\nfrom ite.cost... | [
[
"numpy.random.rand",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hiroyasuakada/CycleGAN-PyTorch | [
"9ad0762b9ed13f3ef9a342ba5b4585fa6a507927"
] | [
"test.py"
] | [
"import os\r\nimport random\r\nimport argparse\r\nimport itertools\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torchvision import utils \r\nfrom tensorboardX import SummaryWriter\r\nimport time\r\n\r\nfrom dataset import UnalignedDataset\r\nfrom model_base import ResNetBlock, Generator, D... | [
[
"numpy.random.seed",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
peendebak/PycQED_py3 | [
"dcc19dbaedd226112a2f98a7985dcf2bab2c9734"
] | [
"pycqed/tests/analysis_v2/test_simple_analysis.py"
] | [
"import unittest\nimport pycqed as pq\nimport os\nimport matplotlib.pyplot as plt\nfrom pycqed.analysis_v2 import measurement_analysis as ma\n\n\nclass Test_SimpleAnalysis(unittest.TestCase):\n\n @classmethod\n def tearDownClass(self):\n plt.close('all')\n\n @classmethod\n def setUpClass(self):\n... | [
[
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
batuozt/transformer-ls | [
"80961f8ad5aa43dda2e938526b1b7c0733a9d097"
] | [
"lra/attention_transformer_ls_sketch.py"
] | [
"# Copyright (c) 2021 NVIDIA CORPORATION. Licensed under the MIT license.\n# Written by Chen Zhu during an internship at NVIDIA, zhuchen.eric@gmail.com\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\n\n\nclass AttentionSketch(nn.Module):\n \"\"\"Sketching Attention adapted f... | [
[
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.softmax",
"torch.cat",
"torch.sum",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.Conv1d",
"torch.nn.init.zeros_",
"torch.as_strided",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pepealza/Curso-ML-A-a-Z | [
"951229ea730e6975f3fa323f734a8310f6aed378"
] | [
"datasets/Part 10 - Model Selection & Boosting/Section 48 - Model Selection/k_fold_cross_validation.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 2 16:47:17 2019\n\n@author: juangabriel\n\"\"\"\n\n# k - Fold Cross Validation\n\n\n# Cómo importar las librerías\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importar el data set\ndataset = pd.read_csv('... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"sklearn.model_selection.cross_val_score",
"matplotlib.pyplot.title",
"numpy.unique",
"sklearn.metrics.confusion_matrix",
"sklearn.model_selection.train_test_split",
"matplotlib.colors.ListedColormap",
"sklearn.svm.SVC",
"matpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
egdaub/seistools | [
"9ec61de03c2231760dba6a42e7fa2018309a99f3"
] | [
"seistools/stz.py"
] | [
"import numpy as np\nimport seistools.stress as stress\nimport seistools.integration as integration\n\nclass stzparams:\n \"class holding parameters for STZ equations\"\n def __init__(self, epsilon = 10, t0 = 1.e-13, E0 = 2.424, T = 293., V = 1.98e-28, sy = 1.,\n c0 = 2.e4, R = 1.e6, beta = 0.... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.array",
"numpy.exp",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
edlectrico/dissertation | [
"eec342383ef4f15968e6417020681a3eb095bf08"
] | [
"graphics/sus_developers_pie.py"
] | [
"from pylab import *\nimport matplotlib.pyplot as plt\n\n# make a square figure and axes\nfigure(1, figsize=(6,6))\nax = axes([0.1, 0.1, 0.8, 0.8])\n\n# The slices will be ordered and plotted counter-clockwise.\nlabels = ['SUS punctuation under 50 points', 'SUS punctuation between 50 and 70 points', 'SUS punctuatio... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.axis"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nicktien007/Nick.Udic.RNN | [
"e6ebabb880a61e8ca41bba4f079cae452197fa79"
] | [
"service/train_service.py"
] | [
"import logging as log\nimport os\n\nimport numpy as np\nimport torch\nfrom torchtext import data\n\nfrom model.RNN import RNN\nfrom service.tokenize_service import tokenize\n\n\ndef build_RNN_model(train_data_path, trained_model_path, device):\n if not os.path.isdir(trained_model_path):\n os.mkdir(traine... | [
[
"torch.sigmoid",
"torch.cuda.manual_seed",
"torch.eq",
"torch.manual_seed",
"torch.nn.BCEWithLogitsLoss",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.array",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
polossk/Peridynamics-Example-Using-HMSolver | [
"cc13a96db0257f9db19ec32131cc72ff43bda3b8"
] | [
"Group-2020-11-17/test_of_correction_config_A5.py"
] | [
"import numpy as np\nimport time\nimport sys\nimport argparse\nimport pickle\n\nimport hmsolver.geometry as geometry\nfrom hmsolver.app import Simulation2d\nfrom hmsolver.app import PdSimulation2d\nfrom hmsolver.basis import Quad4Node\nfrom hmsolver.meshgrid import Zone2d, HybridMesh2d\nfrom hmsolver.material impor... | [
[
"numpy.abs",
"numpy.dot",
"numpy.array",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haoNTT/logisticRegression | [
"4405e50c8320b8c777a1f4bb6453b4a2a9545768"
] | [
"logisticRegression.py"
] | [
"# Author: Haonan Tian \n# Date: 07/26/2018\n# All Rights Reserved\n\n################################# Description ########################################\n# This is a demo of applying logistic regression to sample data set. The description \n# the data set applied in this program can be found at the following li... | [
[
"numpy.square",
"numpy.dot",
"numpy.log",
"sklearn.linear_model.LogisticRegression",
"numpy.unique",
"numpy.ones",
"numpy.exp",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mvsusp/sagemaker-mxnet-container | [
"90b3e096c1b27109926184780087d9d4192d1020"
] | [
"test/integration/sagemaker/test_mnist_distributed.py"
] | [
"# Copyright 2018 Amazon.com, Inc. or its affiliates. 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# A copy of the License is located at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KyreOn/web | [
"557bdfa0f5608a1f2f203349089adda405ef8a2f"
] | [
"main.py"
] | [
"from flask import Flask\nfrom flask import render_template\nfrom flask import Response\nimport sqlite3\nimport random\nimport io\n\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\nfrom matplotlib.figure import Figure\n\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef cv_index():\n ... | [
[
"matplotlib.backends.backend_agg.FigureCanvasAgg",
"matplotlib.figure.Figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bobbyphilip/basicpython3 | [
"4d2bc87acb13734a8d1f2bbfd589d52983febf6c"
] | [
"code/puzzles/castleescape.py"
] | [
"#!/usr/bin/python3\nimport sys\nimport matplotlib.pyplot as plt\nfrom random import randint\n\ndef main():\n '''\n 4 prisoners isolated in cells in a castle. They are given a fair coin, and the choice to toss it.\n If all tosses attempted are heads, they will all be released.\n If no one attempts it o... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pgniewko/vistools | [
"b4a8d91a9cd73fad1d7e6d83ef98ad82c4d8e305"
] | [
"src/shellswire/shellswire.py"
] | [
"#! /usr/bin/env python\n\nimport sys\nimport numpy as np\nfrom mayavi import mlab\nimport networkx as nx\n\n\ndef draw_bonds_color(coords, bonds, scale_factor=.1, resolution=2):\n '''\n color by coordination number\n '''\n nn = bonds\n G = nx.Graph()\n G.add_edges_from(nn)\n deg = np.array([G.... | [
[
"numpy.random.random",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yifan0330/NiMARE | [
"cb542af17c4f969afd05e4fd381a1200a1e4817c",
"cb542af17c4f969afd05e4fd381a1200a1e4817c"
] | [
"nimare/utils.py",
"examples/02_meta-analyses/05_plot_correctors.py"
] | [
"\"\"\"Utility functions for NiMARE.\"\"\"\nimport contextlib\nimport datetime\nimport inspect\nimport logging\nimport multiprocessing as mp\nimport os\nimport os.path as op\nimport re\nfrom functools import wraps\nfrom tempfile import mkstemp\n\nimport joblib\nimport nibabel as nib\nimport numpy as np\nimport pand... | [
[
"numpy.dot",
"pandas.merge",
"numpy.vstack",
"pandas.DataFrame",
"numpy.round",
"numpy.where",
"numpy.unique",
"numpy.full",
"numpy.ceil",
"numpy.ravel",
"pandas.concat",
"pandas.notnull",
"numpy.linalg.inv",
"numpy.ascontiguousarray",
"numpy.memmap",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
jackred/CW2_GERMAN_SIGN | [
"988a99d6012ae95bec778a91785c76a2ca40ba87"
] | [
"src/preprocess.py"
] | [
"# -*- Mode: Python; tab-width: 8; indent-tabs-mode: nil; python-indent-offset: 4 -*-\n# vim:set et sts=4 ts=4 tw=80:\n# This Source Code Form is subject to the terms of the MIT License.\n# If a copy of the ML was not distributed with this\n# file, You can obtain one at https://opensource.org/licenses/MIT\n\n# auth... | [
[
"sklearn.cluster.KMeans",
"numpy.random.seed",
"numpy.unique",
"sklearn.model_selection.train_test_split",
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cogsys-tuebingen/uninas | [
"06729b9cf517ec416fb798ae387c5bd9c3a278ac"
] | [
"uninas/training/optimizers/common.py"
] | [
"from torch.optim.sgd import SGD\nfrom torch.optim.adam import Adam\nfrom torch.optim.rmsprop import RMSprop\nfrom uninas.utils.args import Argument\nfrom uninas.training.optimizers.abstract import WrappedOptimizer\nfrom uninas.register import Register\n\n\n@Register.optimizer()\nclass SGDOptimizer(WrappedOptimizer... | [
[
"torch.optim.adam.Adam"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CV-YYDS/YOLOv3 | [
"a433064721dfc932509aaed6cb44a785b24bc768"
] | [
"model/layers/activate.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\n\r\nclass Mish(nn.Module):\r\n def __init__(self):\r\n super(Mish).__init__()\r\n\r\n def forward(self, x):\r\n x = x * (torch.tanh(F.softplus(x)))\r\n return x\r\n\r\n\r\nclass Swish(nn.Module):\r\n def _... | [
[
"torch.nn.functional.sigmoid",
"torch.nn.functional.softplus"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
antu3199/cs486-project-hanabi | [
"655152cea4cd2459b2a7ae036b39ad24730364af"
] | [
"agents/rainbow/third_party/dopamine/checkpointer.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Dopamine Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required b... | [
[
"tensorflow.gfile.Exists",
"tensorflow.gfile.GFile",
"tensorflow.gfile.MakeDirs",
"tensorflow.gfile.Glob",
"tensorflow.gfile.Remove"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
muwangshu/AverageDistance | [
"28e22bfb36021607be9d7900282e54b467450263"
] | [
"avgdist/RPTinPolygon/RndPointinPolygon.py"
] | [
"#Fast algorithm to generate random points in a polygon.\nimport shapely\nfrom ..EARCut import earcut\nfrom numpy import array, sqrt, abs, where\nfrom shapely.geometry import Polygon, Point\nfrom random import uniform, random\n\n\ndef getRndPointinPolygon(plg, count):\n \"\"\"[summary]\n\n Args:\n plg ... | [
[
"numpy.abs",
"numpy.where",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
madpilot/flatcam | [
"82169e73225c9ffdb97cc67d5f243eafbe734f1a"
] | [
"PlotCanvas.py"
] | [
"# ###########################################################\n# FlatCAM: 2D Post-processing for Manufacturing #\n# http://caram.cl/software/flatcam #\n# Author: Juan Pablo Caram (c) #\n# Date: 2/5/2014 #\n# MI... | [
[
"matplotlib.backends.backend_agg.FigureCanvasAgg",
"matplotlib.use",
"matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg",
"matplotlib.figure.Figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dioph/Light-Curve | [
"92693d92d74bd2e5ceaa03f3cc8d24c6ceb9a4ca"
] | [
"litecurve/cleanest.py"
] | [
"import numpy as np\nfrom astropy.stats import LombScargle as ls\nfrom matplotlib import pyplot as plt\n\n\ndef cleanest(x, y, n=1):\n '''\n removes sequentially the n first frequencies from y(x) lightcurve\n returns cleanest lightcurve y(x) and its periodogram a2\n '''\n p = np.linspace(0.5, 50.5, 1... | [
[
"numpy.linspace",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"numpy.argmax",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gjacquenot/sktime-dl | [
"e519bf5983f9ed60b04b0d14f4fe3fa049a82f04"
] | [
"sktime_dl/tests/test_classifiers.py"
] | [
"from sktime.datasets import load_basic_motions\nfrom sktime.datasets import load_italy_power_demand\n\nfrom sktime_dl.classification import LSTMFCNClassifier\nfrom sktime_dl.classification import CNTCClassifier\nfrom sktime_dl.utils.model_lists import SMALL_NB_EPOCHS\nfrom sktime_dl.utils.model_lists import constr... | [
[
"sklearn.pipeline.Pipeline",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
haerynkim/bme590final | [
"78ce4c54dc7a2be6fe17cf55471f105dc53547de"
] | [
"server.py"
] | [
"from flask import Flask, jsonify, request\nfrom pymodm import connect\nfrom pymodm import MongoModel, fields\nimport datetime\nimport base64\nimport io as io2\nimport numpy as np\nfrom skimage import io as im\nfrom skimage import io, exposure\nfrom PIL import Image\nimport logging\nlogging.basicConfig(filename='lo... | [
[
"numpy.amax",
"numpy.log10",
"numpy.nditer",
"numpy.percentile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexlokotochek/predictive-alerting | [
"dea92a248653e9002284875e04b2a3c2f0870144"
] | [
"example-app/app_prometheus.py"
] | [
"import time\n\nimport argparse\nfrom scipy.stats import poisson, gamma\nfrom prometheus_client import Summary, start_http_server\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\n 'rps',\n default=100,\n type=int,\n nargs='?',\n)\nparser.add_argument(\n 'response_time_avg',\n default... | [
[
"scipy.stats.gamma.rvs",
"scipy.stats.poisson.rvs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cohenINSA/relative-rotation | [
"e677fd29fccf15314deb8f52dce49da41df80a8e"
] | [
"code/eval.py"
] | [
"#!/usr/bin/env python3\n\n# Import packages\nimport argparse\nimport os\n\nfrom tqdm import tqdm\nimport torch\nimport torch.nn.functional as F\n\nfrom net import ResBase, ResClassifier\nfrom data_loader import DatasetGeneratorMultimodal, MyTransformer\nfrom utils import make_paths, add_base_args, default_paths, m... | [
[
"torch.nn.functional.softmax",
"torch.argmax",
"torch.utils.data.DataLoader",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
carusyte/ADNC | [
"4a5dfa5be1aca9f815794c2c276ec220a1eb591d"
] | [
"adnc/model/memory_units/dnc_cell.py"
] | [
"# Copyright 2018 Jörg Franke\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 t... | [
[
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.nn.top_k",
"tensorflow.dynamic_stitch",
"tensorflow.contrib.layers.xavier_initializer",
"tensorflow.cumprod",
"tensorflow.nn.dropout",
"tensorflow.TensorShape",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
szufix/mapel | [
"9eaacf7963021ea3eab701a14efd82cf1bfc5c5b"
] | [
"mapel/voting/elections/single_peaked.py"
] | [
"import random as rand\nimport numpy as np\nfrom random import *\n\nfrom scipy.special import binom\n\n\ndef generate_sp_party(model=None, num_voters=None, num_candidates=None, params=None) -> np.ndarray:\n candidates = [[] for _ in range(num_candidates)]\n _ids = [i for i in range(num_candidates)]\n\n for... | [
[
"scipy.special.binom",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
scaldas/federated | [
"d46aef1f7d3915977b3a67eb0df709b7e28fca67",
"d46aef1f7d3915977b3a67eb0df709b7e28fca67"
] | [
"optimization/shared/iterative_process_builder_test.py",
"targeted_attack/emnist_with_targeted_attack.py"
] | [
"# Copyright 2019, 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 agree... | [
[
"tensorflow.keras.losses.SparseCategoricalCrossentropy",
"tensorflow.test.main",
"numpy.ones",
"tensorflow.keras.metrics.SparseCategoricalAccuracy",
"tensorflow.TensorSpec"
],
[
"tensorflow.cast",
"tensorflow.compat.v1.enable_eager_execution",
"numpy.concatenate",
"tensorfl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
... |
VJWQ/forecasting | [
"0b540aa8fc073f1f9a7725066c2c4fec59c690c6",
"f24bbd59195280ce8697968c172e5120fb2265d2"
] | [
"Ego4D-Future-Hand-Prediction/slowfast/datasets/utils.py",
"Ego4D-Future-Hand-Prediction/slowfast/visualization/ava_demo_precomputed_boxes.py"
] | [
"#!/usr/bin/env python3\n\nimport logging\nimport numpy as np\nimport os\nimport random\nimport time\nfrom collections import defaultdict\nimport cv2\nimport torch\nfrom iopath.common.file_io import g_pathmgr\nfrom torch.utils.data.distributed import DistributedSampler\n\nfrom . import transform as transform\n\nlog... | [
[
"torch.linspace",
"torch.utils.data.distributed.DistributedSampler",
"numpy.stack",
"torch.tensor",
"numpy.zeros"
],
[
"torch.manual_seed",
"torch.Tensor",
"numpy.array",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WangShengguang/NERE | [
"4b8166aa348b9db207bb9a1e1da6eed5d567ae6f",
"4b8166aa348b9db207bb9a1e1da6eed5d567ae6f"
] | [
"nere/re_models/bert_softmax.py",
"nere/torch_trainer.py"
] | [
"\"\"\"BERT + Softmax for named entity recognition\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom pytorch_pretrained_bert.modeling import BertModel, BertPreTrainedModel, gelu\nfrom torch.nn import CrossEntropyLoss\n\ntry:\n from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm\nexce... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.Dropout",
"torch.ones",
"torch.cat",
"torch.zeros",
"torch.sqrt",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.where",
"torch.ones_like"
],
[
"torch.optim.Adam",
"torch.nn.DataParallel",
"torch.optim.lr_scheduler.Lam... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gsgoncalves/K-NRM | [
"b7bc8c44ddf6c8d0bc14a399beb05c9c1956fe2f"
] | [
"knrm/model/model_base.py"
] | [
"# Copyright (c) 2017, Carnegie Mellon University. All rights reserved.\n#\n# Use of the K-NRM package is subject to the terms of the software license set\n# forth in the LICENSE file included with this software, and also available at\n# https://github.com/AdeDZY/K-NRM/blob/master/LICENSE\n\nimport tensorflow as tf... | [
[
"numpy.sqrt",
"tensorflow.Variable",
"numpy.concatenate",
"numpy.array",
"tensorflow.random_uniform",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
roark-z/Sprite-Lighter | [
"ba6a3f8816406c7911c418fc00be72ec4147e4d4"
] | [
"launch.py"
] | [
"import torch\nimport models\nimport numpy as np\nfrom PIL import Image, ImageFilter\n\n# load model\nmodel = torch.load(\"network.bin\", map_location=torch.device('cpu'))\nmodel.eval()\n\n# open input image\nimg = torch.tensor(np.array(Image.open(\"in.png\").convert(\"RGB\").resize((32, 32))))\nimg = torch.reshape... | [
[
"torch.device",
"torch.reshape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brunoklaus/MercadoLibre_2020 | [
"8349ab473dd26ce6345f0ca8772d1f1711fb4c06"
] | [
"src/nn/domain_string_identifier.py"
] | [
"'''\nCreated on 18 de nov de 2020\n\n@author: klaus\n'''\n\n\n'''\nCreated on 16 de nov de 2020\n\n@author: klaus\n'''\nimport jsonlines\nfrom folders import DATA_DIR, SUBMISSIONS_DIR\nfrom input.read_input import TRAIN_LINES, NUM_DOMS, get_emb, get_emb_Kstr\nimport os\nfrom os import path\nimport pandas as pd\nim... | [
[
"tensorflow.cast",
"numpy.concatenate",
"numpy.max",
"tensorflow.config.list_physical_devices",
"tensorflow.keras.Input",
"tensorflow.config.experimental.set_memory_growth",
"numpy.reshape",
"numpy.argmax",
"numpy.zeros",
"tensorflow.TensorShape",
"tensorflow.shape",
... | [
{
"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": [
"1.10"
]
}
] |
EBosi/BOFdat | [
"30447be0759eac1811d05634bda198cdb192592f"
] | [
"BOFdat/core/maintenance.py"
] | [
"\"\"\"\nMaintenance\n===========\n\nGrowth associated maintenance (GAM) is defined as the energy cost of growth, namely polymerization of macromolecules.\nNon-Growth associated maintenance (NGAM) represent all the extra costs that the cell must overcome to operate.\nThis package offers two options for the user:\n1... | [
[
"numpy.polyfit",
"pandas.read_csv",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.ylim",
"pandas.DataFrame",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.close",
"numpy.corrcoef",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
GnssTao/ginan | [
"b69593b584f75e03238c1c667796e2030391fbed"
] | [
"scripts/gn_lib/gn_const.py"
] | [
"'''Constants to be declared here'''\nfrom numpy import datetime64 as _datetime64\nfrom pandas import CategoricalDtype as _CategoricalDtype, DataFrame as _DataFrame\n\nGPS_ORIGIN = _datetime64('1980-01-06 00:00:00')\nJ2000_ORIGIN = _datetime64('2000-01-01 12:00:00')\n\nSECS_IN_WEEK = 604800\nSEC_IN_DAY = 86400\... | [
[
"pandas.CategoricalDtype",
"pandas.DataFrame",
"numpy.datetime64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
roussel-ryan/turn_key_bayesian_exploration | [
"1ff94c2515f09275fda170f5bdde3a4ffa81d636"
] | [
"demo/constrained.py"
] | [
"import torch\nfrom torch.distributions import Normal\nfrom botorch.acquisition import analytic \nfrom botorch.acquisition import acquisition\n\nfrom botorch.utils.transforms import t_batch_mode_transform\n\n\nclass ConstrainedAcquisitionFunction(acquisition.AcquisitionFunction):\n def __init__(self, model, cons... | [
[
"torch.Size",
"torch.ones",
"torch.tensor",
"torch.distributions.Normal",
"torch.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MitchellAcoustics/Soundscapy | [
"bf450f583c076a9adaeed0b5a3617475d4be5315"
] | [
"soundscapy/ssid/database.py"
] | [
"import os\nimport sys\n\nmyPath = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, myPath + \"/../../\")\n\nimport csv\nfrom datetime import date\nfrom pathlib import Path\nimport warnings\n\nimport numpy as np\nimport pandas as pd\n\n# Constants and Labels\nfrom soundscapy.ssid.parameters import (\n... | [
[
"pandas.read_excel",
"pandas.read_csv",
"numpy.sqrt",
"numpy.setdiff1d",
"numpy.deg2rad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Guest400123064/fed-iris | [
"daf443cbaf315486804ce08cf491eb85dc06b87c"
] | [
"client/src/core/graph.py"
] | [
"import torch\nimport torch.nn as nn\nfrom easydict import EasyDict\n\n\nclass IrisModel(nn.Module):\n\n def __init__(self, config: EasyDict=EasyDict()):\n\n # We know exactly that the iris data set \n # contains 4 factors and 3 classes; therefore\n # the model structure is hard-coded\n ... | [
[
"torch.softmax",
"torch.nn.CrossEntropyLoss",
"torch.nn.Linear",
"torch.no_grad",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
janzkyle23/models | [
"a8ae09e23d309f8893619387a7133bcc4db3d8a9"
] | [
"official/nlp/bert/run_pretraining.py"
] | [
"# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.version.VERSION.startswith",
"tensorflow.reduce_mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.4",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1.2",
"2.... |
hunter-packages/tvm | [
"d68bfa0f3a080a425a7ffd8d054d55022eeb9fc1"
] | [
"nnvm/python/nnvm/frontend/tensorflow.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... | [
[
"tensorflow.python.framework.tensor_util.MakeNdarray",
"tensorflow.python.framework.tensor_util.TensorShapeProtoToList",
"numpy.cumsum",
"numpy.dtype",
"tensorflow.python.framework.dtypes.as_dtype",
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.2",
"1... |
ahmetustun/fairseq | [
"5ec791d95476beaa1513da14efa9035337bf6a15"
] | [
"fairseq/tasks/translation_multi_simple_epoch_with_prefix.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 datetime\nimport logging\nimport time\n\nimport torch\nfrom fairseq.data import (\n FairseqDataset,\n LanguagePairDatas... | [
[
"torch.no_grad",
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dyning/AlexNet-Prod | [
"54de9dfcf540997ff227bd92d0c7a73dc73c45aa"
] | [
"pipeline/Step3/AlexNet_paddle/loss_alexnet.py"
] | [
"import numpy as np\nimport paddle\nimport paddle.nn as nn\nfrom paddlevision.models.alexnet import alexnet\n\nfrom reprod_log import ReprodLogger\n\nif __name__ == \"__main__\":\n paddle.set_device(\"cpu\")\n # load model\n # the model is save into ~/.cache/torch/hub/checkpoints/alexnet-owt-4df8aa71.pth\n... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bonskotti/desdeo-emo | [
"6a607fa96478fd3c76394ac7b2dafdfd3c08eb2a"
] | [
"desdeo_emo/selection/oAPD.py"
] | [
"import numpy as np\nfrom warnings import warn\nfrom typing import List, Callable\nfrom desdeo_emo.selection.SelectionBase import SelectionBase\nfrom desdeo_emo.population.Population import Population\nfrom desdeo_emo.othertools.ReferenceVectors import ReferenceVectors\n\n\nclass Optimistic_APD_Select(SelectionBase... | [
[
"numpy.dot",
"numpy.power",
"numpy.amin",
"numpy.isnan",
"numpy.vstack",
"numpy.nanmin",
"numpy.arccos",
"numpy.linalg.norm",
"numpy.finfo",
"numpy.argmax",
"numpy.transpose",
"numpy.array",
"numpy.where",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
toru-ver4/sample_code | [
"9165b4cb07a3cb1b3b5a7f6b3a329be081bddabe",
"8bda35b674d770da5a0e6c210634a77691527fce",
"8bda35b674d770da5a0e6c210634a77691527fce"
] | [
"2020/020_explain_BT2407/_debug_bt2047_gamut_mapping.py",
"2019/001_make_3dlut/make_3dlut.py",
"2019/011_hdr_to_sdr_tonemapping/tone_mapping_research.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nBT2407 実装用の各種LUTを作成する\n===============================\n\n\"\"\"\n\n# import standard libraries\nimport os\n\n# import third-party libraries\nimport numpy as np\nfrom multiprocessing import Pool, cpu_count, Array\nimport matplotlib.pyplot as plt\nfrom colour.models import BT709_COL... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"numpy.rad2deg",
"numpy.round",
"numpy.max",
"numpy.hstack",
"numpy.ones_like",
"numpy.clip",
"numpy.sin",
"scipy.interpolate.interp1d",
"numpy.argmax",
"matplotlib.pyplot.close",
"numpy.zeros",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
nelimee/qtoolkit | [
"1e99bd7d3a143a327c3bb92595ea88ec12dbdb89",
"1e99bd7d3a143a327c3bb92595ea88ec12dbdb89"
] | [
"tests/maths/matrix/su2/transformations.py",
"qtoolkit/maths/matrix/su2/transformations.py"
] | [
"# ======================================================================\n# Copyright CERFACS (October 2018)\n# Contributor: Adrien Suau (adrien.suau@cerfacs.fr)\n#\n# This software is governed by the CeCILL-B license under French law and\n# abiding by the rules of distribution of free software. You can use,\n#... | [
[
"numpy.array",
"numpy.identity",
"numpy.random.rand",
"scipy.linalg.expm"
],
[
"numpy.imag",
"numpy.linalg.norm",
"numpy.cos",
"numpy.sin",
"numpy.arctan2",
"numpy.arccos",
"numpy.real",
"numpy.identity",
"numpy.lib.scimath.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.11",
"1.19",
"1.24",
"1.16",
"1.23",
"1.20",
"1.7",
"1.12",
... |
mimoralea/king-pong | [
"c0ec61f60de64293d00a48d1b7724a24d2b6602a"
] | [
"agent.py"
] | [
"#!/usr/bin/python\nfrom __future__ import print_function\nimport logging as log\nimport argparse\nimport time\nimport king_pong as env\nimport multicnet as percept\nimport random\nimport numpy as np\nfrom collections import deque\nimport os\n\n\nclass DeepLearningAgent:\n \"\"\"\n Agent class that uses a dee... | [
[
"numpy.max",
"numpy.append",
"numpy.zeros",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
codelibs/recotem | [
"383ccdd6e1e9feb59bc3adb2543c00b08277317a"
] | [
"backend/recotem/recotem/api/tasks.py"
] | [
"import json\nimport pickle\nimport random\nimport re\nimport tempfile\nfrom logging import Logger\nfrom pathlib import Path\nfrom typing import Dict, List, Optional\n\nimport pandas as pd\nimport scipy.sparse as sps\nfrom billiard.connection import Pipe\nfrom billiard.context import Process\nfrom celery import cha... | [
[
"scipy.sparse.vstack"
]
] | [
{
"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"... |
ReyesDeJong/AnomalyDetectionTransformations | [
"c60b1adaf0065b684d76ecacabed1eae39a4e3a9"
] | [
"modules/geometric_transform/transformations_tf.py"
] | [
"import abc\r\nimport itertools\r\n\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\n\"\"\"There is a small discrepancy between original and his transformer, \r\ndue the fact that padding reflects wiithouy copying the edge pixels\"\"\"\r\n\r\n\r\ndef cnn2d_depthwise_tf(image_batch, filters):\r\n features_tf... | [
[
"matplotlib.pyplot.imshow",
"tensorflow.concat",
"tensorflow.stack",
"numpy.concatenate",
"numpy.max",
"tensorflow.image.rot90",
"numpy.exp",
"tensorflow.nn.depthwise_conv2d",
"tensorflow.config.experimental.set_memory_growth",
"numpy.arange",
"tensorflow.name_scope",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
bharats97/ddna-toolbox | [
"1856d130c14bab094297b1371c7bde3d78d057db"
] | [
"examples/plot_sequence_color.py"
] | [
"\"\"\"\n==================\nSequences by Color\n==================\n\nAn example plot of :class:`digitaldna.TwitterDDNASequencer`\n\"\"\"\nfrom digitaldna import TwitterDDNASequencer\nfrom digitaldna import SequencePlots\nimport numpy as np\n\n# Generate DDNA from Twitter\nmodel = TwitterDDNASequencer(input_file='... | [
[
"numpy.repeat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
benbuleong/mmfeat | [
"c8d40f2e0bc5385b6978599fac5e46683336ade8"
] | [
"mmfeat/bow/vw.py"
] | [
"'''\nBag of Visual Words (BoVW)\n'''\n\nfrom ..base import DataObject\n\nfrom .bow import BoW\nfrom .dsift import DsiftExtractor\n\nimport os\n\nimport numpy as np\n\nfrom scipy.misc import imread\nfrom scipy.io import loadmat\n\nclass BoVW(BoW):\n def loadFile(self, fname):\n '''\n fname: fi... | [
[
"numpy.load",
"scipy.io.loadmat",
"scipy.misc.imread",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",
"1.2"
],
"tensorflow": []
}
] |
aaronenyeshi/benchmark | [
"f12d634ef7d86e457131998f7b641d3b046bc040"
] | [
"torchbenchmark/models/resnext50_32x4d/__init__.py"
] | [
"\n# Generated by gen_torchvision_benchmark.py\nimport torch\nimport torch.optim as optim\nimport torchvision.models as models\nfrom ...util.model import BenchmarkModel\nfrom torchbenchmark.tasks import COMPUTER_VISION\n\n#######################################################\n#\n# DO NOT MODIFY THESE FILES ... | [
[
"torch.jit.script",
"torch.nn.CrossEntropyLoss",
"torch.jit.optimize_for_inference",
"torch.empty",
"torch.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
khansamad47/statsmodels | [
"e1ebcacf92aa9c99aaca5b62a2155d7e061772f1"
] | [
"statsmodels/tsa/ardl/model.py"
] | [
"from __future__ import annotations\n\nfrom statsmodels.compat.pandas import Appender, Substitution\nfrom statsmodels.compat.python import Literal\n\nfrom collections import defaultdict\nimport datetime as dt\nfrom itertools import combinations, product\nimport textwrap\nfrom types import SimpleNamespace\nfrom typi... | [
[
"pandas.Series",
"numpy.asarray",
"numpy.squeeze",
"numpy.cumsum",
"pandas.DataFrame",
"numpy.random.default_rng",
"numpy.ix_",
"numpy.arange",
"numpy.matmul",
"numpy.full",
"numpy.diff",
"numpy.column_stack",
"scipy.stats.t",
"numpy.zeros",
"numpy.log",... | [
{
"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": []
}
] |
WorldEditors/Knover | [
"e4664e8eabe598d8203a5ffc8445b79de2fc9191",
"e4664e8eabe598d8203a5ffc8445b79de2fc9191"
] | [
"knover/utils/tensor_utils.py",
"knover/data/dialog_reader.py"
] | [
"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless ... | [
[
"numpy.concatenate",
"numpy.array"
],
[
"numpy.array",
"numpy.random.RandomState",
"numpy.zeros_like",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vinisalazar/vamb | [
"3ba78e0d5a1a30cfdefb20c373c7df5494437a60"
] | [
"vamb/vambtools.py"
] | [
"__doc__ = \"Various classes and functions Vamb uses internally.\"\n\nimport os as _os\nimport gzip as _gzip\nimport bz2 as _bz2\nimport lzma as _lzma\nimport numpy as _np\nfrom vamb._vambtools import _kmercounts, _overwrite_matrix\nimport collections as _collections\nfrom hashlib import md5 as _md5\n\nclass PushAr... | [
[
"numpy.ascontiguousarray",
"numpy.issubdtype",
"numpy.frombuffer",
"numpy.savez_compressed",
"numpy.load",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gghatano/PPMTF-1 | [
"670bff7e58150418f9e7b75e6367f0f069c454b5"
] | [
"python/EvalUtilPriv.py"
] | [
"#!/usr/bin/env python3\nimport numpy as np\nfrom scipy.sparse import lil_matrix\nfrom scipy.stats import wasserstein_distance\nimport math\nimport csv\nimport sys\nimport glob\nimport os\n\n################################# Parameters ##################################\n#sys.argv = [\"EvalUtilPriv.py\", \"PF\", \"... | [
[
"scipy.stats.wasserstein_distance",
"numpy.abs",
"numpy.arange",
"numpy.linalg.norm",
"numpy.percentile",
"numpy.full",
"numpy.ones",
"numpy.max",
"numpy.argmax",
"numpy.argsort",
"numpy.zeros",
"numpy.sum",
"numpy.loadtxt",
"scipy.sparse.lil_matrix"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"1.5",
"1.7",
"1.0",
"1.2",
"1.8"
],
"tensorflow": []
}
] |
oatsu-gh/nnsvs | [
"510f37bc1d1f15282646e4d34435b5d63686cf40"
] | [
"egs/jsut-song/svs-world-conv/local/data_prep.py"
] | [
"# coding: utf-8\nimport os\n\nimport argparse\nfrom glob import glob\nfrom os.path import join, basename, splitext, exists, expanduser\nfrom nnmnkwii.io import hts\nfrom scipy.io import wavfile\nimport librosa\nimport soundfile as sf\nimport sys\nimport numpy as np\n\nfrom nnsvs.io.hts import get_note_indices\n\n\... | [
[
"numpy.asarray",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yaozhang2016/deepwater | [
"861a2dbeffeafab83dd53956deeb4f8193b9cb2e"
] | [
"tensorflow/src/main/resources/deepwater/models/alexnet.py"
] | [
"import tensorflow as tf\n\nfrom deepwater.models import BaseImageClassificationModel\nfrom deepwater.models.nn import fc, conv3x3, conv5x5, conv11x11, max_pool_3x3\n\n\nclass AlexNet(BaseImageClassificationModel):\n def __init__(self, width, height, channels, classes):\n super(AlexNet, self).__init__()\n... | [
[
"tensorflow.nn.relu",
"tensorflow.nn.softmax",
"tensorflow.image.resize_images",
"tensorflow.reshape",
"tensorflow.placeholder",
"tensorflow.nn.lrn",
"tensorflow.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
donghaozhang/Pytorch_Geometric | [
"bbe2f21e5208d30636c22c40f55a1d17e18ca9b4"
] | [
"test/nn/dense/test_dense_gcn_conv.py"
] | [
"import torch\nfrom torch_geometric.nn import GCNConv, DenseGCNConv\n\n\ndef test_dense_gcn_conv():\n channels = 16\n sparse_conv = GCNConv(channels, channels)\n dense_conv = DenseGCNConv(channels, channels)\n assert dense_conv.__repr__() == 'DenseGCNConv(16, 16)'\n\n # Ensure same weights and bias.\... | [
[
"torch.randn",
"torch.allclose",
"torch.Tensor",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wudbug/RoboND-Rover-Project | [
"09dbc392fc75afdfab645be290e9450f64784f11"
] | [
"code/perception.py"
] | [
"import numpy as np\nimport cv2\n\n# Identify pixels above the threshold\n# Threshold of RGB > 160 does a nice job of identifying ground pixels only\ndef color_thresh(img, rgb_thresh=(160, 160, 160)):\n # Create an array of zeros same xy size as img, but single channel\n color_select = np.zeros_like(img[:,:,0... | [
[
"numpy.ones_like",
"numpy.sqrt",
"numpy.cos",
"numpy.int_",
"numpy.arctan2",
"numpy.sin",
"numpy.copy",
"numpy.zeros_like",
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BeatHubmann/19H-AdvNCSE | [
"3979f768da933de82bd6ab29bbf31ea9fc31e501"
] | [
"series2_handout/hyp_sys_1d/plot_euler.py"
] | [
"import json\nimport glob\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom matplotlib.ticker import LogLocator\nfrom matplotlib import rc\nfrom matplotlib import rcParams\nfont = {'family' : 'Dejavu Sans',\n 'weight' : 'normal',\n 'size' : 22}\nrc('font', **font)\nrcParams['lines.linew... | [
[
"matplotlib.pyplot.title",
"matplotlib.rc",
"matplotlib.pyplot.plot",
"numpy.array",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jhartika/audioapinat | [
"f14809f8573a54cd5a2a384f3ca9d75cc754e17b"
] | [
"NN.py"
] | [
"import numpy as np\n\ndef main():\n \n \n train_data_file = \"data/mel/train_data.npy\"\n train_labels_file = \"data/mel/train_labels.npy\"\n test_data_file = \"data/mel/test_data.npy\"\n test_labels_file = \"data/mel/test_labels.npy\"\n \n train_data = np.load(train_data_file)\n train_l... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
smearle/gym-micropolis | [
"42b338ee050fb654e1c74dfe59bf5f03f03d39a4",
"42b338ee050fb654e1c74dfe59bf5f03f03d39a4"
] | [
"poet_distributed/optimizers.py",
"extinction_eval.py"
] | [
"# The following code is modified from openai/evolution-strategies-starter\n# (https://github.com/openai/evolution-strategies-starter)\n# under the MIT License.\n\n# Modifications Copyright (c) 2019 Uber Technologies, Inc.\n\n\nimport numpy as np\n#from algo import A2C_ACKTR_NOREWARD\n\n\nclass Optimizer(object):\n... | [
[
"numpy.sqrt",
"numpy.zeros",
"numpy.linalg.norm"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"torch.zeros",
"matplotlib.pyplot.plot",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabe... | [
{
"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": [],
... |
terraregina/BalancingControl | [
"36330cc0a20ad1f2fbd3a8f87ef8fed98df3fb22"
] | [
"world.py"
] | [
"\"\"\"This module contains the World class that defines interactions between\nthe environment and the agent. It also keeps track of all observations and\nactions generated during a single experiment. To initiate it one needs to\nprovide the environment class and the agent class that will be used for the\nexperimen... | [
[
"numpy.arange",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
francesco-mannella/ArmSim | [
"897d1797e81c4d65f679f4783f2a9b5e0f6f67f7"
] | [
"ArmSim/examples/test.py"
] | [
"import numpy as np\nfrom scipy import interpolate\nimport gym\nimport ArmSim\n\nenv = gym.make(\"ArmSimOneArm-v0\")\n\nstime = 120\nactions = np.pi * np.array(\n [\n [0.00, 0.00, 0.00, 0.00, 0.00],\n [0.20, -0.30, -0.20, 0.50, 0.00],\n [0.20, -0.30, -0.30, 0.50, 0.00],\n [0.10, -0.30... | [
[
"numpy.array",
"numpy.zeros",
"scipy.interpolate.interp1d",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
cybersnus/text-to-text-transfer-transformer | [
"da14d97fa7f4be2da6ebb84e5d20ba6a3fd8add2"
] | [
"t5/scripts/dump_task.py"
] | [
"# Copyright 2020 The T5 Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agr... | [
[
"tensorflow.compat.v1.enable_eager_execution",
"tensorflow.compat.v1.disable_v2_behavior"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TangYuan-Liu/Paddle-Lite | [
"0349642ad19a155f2adb748c5f68a750c786aac2",
"0349642ad19a155f2adb748c5f68a750c786aac2"
] | [
"lite/tests/unittest_py/op/test_gather_nd_op.py",
"lite/tests/unittest_py/op/test_elementwise_add_op.py"
] | [
"# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"numpy.array",
"numpy.random.random",
"numpy.random.randint"
],
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yu-efremov/ViscoIndent | [
"b9658509fdf9ed232cf682cb554f668572735e4d",
"b9658509fdf9ed232cf682cb554f668572735e4d"
] | [
"bottom_effect_correction.py",
"selection_windows_common_gui.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author: Yuri Efremov\r\nbottom effect correction coefficients\r\n\"\"\"\r\nimport numpy as np\r\nfrom numpy import pi\r\n\r\n\r\ndef bottom_effect_correction(Poisson, Probe_dimension, Height, modelprobe,\r\n indentationfull):\r\n BEC = np.ones(le... | [
[
"matplotlib.pyplot.plot",
"numpy.linspace"
],
[
"numpy.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brauliotegui/DEMAND | [
"ffe0180a0a45b6a9aa5b800b8d2c43cf45f31997"
] | [
"linear_model.py"
] | [
"\"\"\"\nRidge model for predicting bike rental demand for Capital Bike Share\nin Washington D.C.\n\"\"\"\nimport pandas as pd\nfrom scipy import stats\nimport numpy as np\n\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import Ridge\n\nD... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"scipy.stats.zscore",
"sklearn.preprocessing.PolynomialFeatures",
"sklearn.linear_model.Ridge",
"numpy.log1p"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2... |
dashmoment/moxa_ai_training | [
"675a6b208ed130e92a0d49be8ad27e4bd1a98813"
] | [
"CNN_HW_solution/utility/utility.py"
] | [
"import numpy as np\nimport random\nimport tensorflow as tf\n\ndef ramdom_batch(data_length, batch_size):\n fid_list = list(range(data_length))\n random.shuffle(fid_list) \n datapool = []\n \n for i in range(data_length//batch_size):\n datapool.append(fid_list[i*batch_size:i*batch_size + b... | [
[
"tensorflow.nn.softmax",
"tensorflow.equal",
"tensorflow.name_scope",
"tensorflow.get_default_graph",
"tensorflow.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
louay-rouabeh/Energy-Management-Live-Dashboard | [
"cf321b99119bef252c6f6dee0f07bb8254a91149"
] | [
"project/GroundFloor.py"
] | [
"import dash_core_components as dcc\nimport dash_html_components as html\nimport pandas as pd\nfrom dash.dependencies import Output, Input\nfrom app import app\nfrom dash_extensions import Download\nfrom dash_extensions.snippets import send_data_frame\n\nground = pd.read_excel(\"consumption.xlsx\", \"Sheet1\")\n\nc... | [
[
"pandas.read_excel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ivallesp/abs | [
"8a250cdab230ec2420e3d22eb3dfae67642430bd"
] | [
"src/metrics.py"
] | [
"import numpy as np\nfrom scipy.special import softmax\n\n\ndef accuracy(y_true, y_pred):\n pred_labels = y_pred.argmax(axis=1)\n hits = y_true == pred_labels\n assert len(hits.shape) == 1\n return hits.mean()\n\n\ndef crossentropy(y_true, y_pred, eps=1e-9):\n y_pred = softmax(y_pred, axis=1)\n ma... | [
[
"numpy.eye",
"scipy.special.softmax",
"numpy.sum",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
nishaq503/polus-plugins-dl | [
"511689e82eb29a84761538144277d1be1af7aa44"
] | [
"polus-cell-nuclei-segmentation/src/dsb2018_topcoders/albu/src/pytorch_zoo/inplace_abn/modules/bn.py"
] | [
"from collections import OrderedDict, Iterable\r\nfrom itertools import repeat\r\n\r\ntry:\r\n # python 3\r\n from queue import Queue\r\nexcept ImportError:\r\n # python 2\r\n from Queue import Queue\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.autograd as autograd\r\n\r\ntry:\r\n fro... | [
[
"torch.ones",
"torch.Tensor",
"torch.zeros",
"torch.cuda.device_count",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vnpavanelli/SimpleITK | [
"63e6dbcf17b0f5233a872cc0d0b778923d9ca365"
] | [
"Utilities/Statistics/download_stats.py"
] | [
"#!/usr/bin/env python\n\n\n# /files/some/path/stats/json\n\nfrom __future__ import print_function\nimport requests\nimport json\nimport datetime\n\nimport pandas as pd\nimport numpy as np\n\nimport cartopy.crs as ccrs\nimport cartopy.feature as cf\nimport cartopy.io.shapereader as shpreader\n\nimport matplotlib... | [
[
"matplotlib.pyplot.legend",
"pandas.to_datetime",
"matplotlib.colors.LogNorm",
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.style.use",
"pandas.Series",
"matplotlib.pyplot.figure",
"pandas.DataFrame",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.show",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
LeandroNog/Resolu-o-de-Sistemas-lineares | [
"477643d4f9ce60632f5ffeddb45497b5193533fb"
] | [
"sistemasLineares.py"
] | [
"#! /usr/bin/env python\n#coding: utf-8\n\nimport numpy as np\nimport timeit\nimport copy\nimport os\n\ndef readFile(fileName):\n data = np.loadtxt(fileName)\n A = np.array(data[:-1])\n b = data[-1]\n return A,b\n\n\ndef substituicoesSucessivas(b,L):\n assert np.allclose(L, np.tril(L)), \"Matriz L não é triang... | [
[
"numpy.ones",
"numpy.loadtxt",
"numpy.triu",
"numpy.array",
"numpy.zeros",
"numpy.tril"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hepynet/hepynet | [
"cab72aa5eb1a2fed26e3d3bd47833d05a5958f32"
] | [
"hepynet/evaluate/evaluate_utils.py"
] | [
"import logging\nimport pathlib\nfrom typing import Tuple\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom hepynet.common import config_utils\nfrom hepynet.data_io import numpy_io\nfrom hepynet.train import hep_model\n\nlogger = logging.getLogger(\"hepynet\")\n\n\ndef create_epoch... | [
[
"numpy.sqrt",
"numpy.power",
"numpy.concatenate",
"matplotlib.pyplot.ioff",
"numpy.minimum.reduce",
"numpy.mean",
"numpy.array",
"numpy.histogram",
"numpy.sum",
"numpy.maximum.reduce"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lhadhazy/sklearn_estimator_model | [
"9d94ed266dbc54f3ea5571bc63bdddc04a644f97"
] | [
"EstimatorModel/svc.py"
] | [
"from sklearn.base import ClassifierMixin, BaseEstimator\nfrom sklearn.svm import SVC\nfrom sklearn.pipeline import make_pipeline\nfrom TransformerModel.StandardScaler import StandardScaler\nfrom TransformerModel.NullColumnCleanse import NullColumnCleanse\nfrom TransformerModel.LDATransformer import LDA\n\n\n# Rand... | [
[
"sklearn.pipeline.make_pipeline",
"sklearn.svm.SVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ruiyangsong/mCNN | [
"889f182245f919fb9c7a8d97965b11576b01a96c",
"889f182245f919fb9c7a8d97965b11576b01a96c"
] | [
"src/Network/test/test3/wild_VS_wild_mutant_3_neighbor60_test01.py",
"src/Network/deepddg/feature118/regressor/SimpleConv1D_BlindTest_Epoch.py"
] | [
"from hyperopt import Trials, STATUS_OK, tpe\nfrom hyperas import optim\nfrom hyperas.distributions import choice, uniform\n\nimport os, sys\nimport numpy as np\nfrom sklearn.utils import class_weight\nimport tensorflow as tf\nfrom keras.backend.tensorflow_backend import set_session\nfrom keras.utils import to_cate... | [
[
"numpy.amax",
"numpy.random.seed",
"numpy.unique",
"numpy.random.shuffle",
"numpy.argwhere",
"tensorflow.ConfigProto",
"tensorflow.Session",
"numpy.load",
"numpy.vstack"
],
[
"numpy.hstack",
"numpy.argwhere",
"tensorflow.ConfigProto",
"numpy.mean",
"tens... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
tomsonsgs/TRAN-MMA-master | [
"91bf927c64a8d813ba60ae12e61e8f44830a82cc"
] | [
"components/evaluator.py"
] | [
"from __future__ import print_function\nfrom asdl.transition_system import GenTokenAction, TransitionSystem, ApplyRuleAction, ReduceAction,score_acts\nimport sys, traceback\nimport numpy as np\nfrom common.registerable import Registrable\nimport tqdm\ncachepredict=[]\ncachetrue=[]\nfrom dependency import nlp\nfrom ... | [
[
"numpy.array",
"numpy.average"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aidoop/CoboMarkerTracking | [
"283195a82076450d00d1f56f15c45895df69f6ee"
] | [
"object_tracker/applications/roi_arucomanager.py"
] | [
"import numpy as np\nimport cv2\n\nfrom pyaidoop.aruco.aruco_detect import ArucoDetect\n\n\nclass ROIAruco2DManager:\n def __init__(self, markerSelectDict, markerSize, mtx, dist):\n # arouco marker id list\n self.arucoRangeList = []\n\n # ROI Region List\n self.ROIRegions = []\n ... | [
[
"numpy.all",
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DavidChoi76/spotpy | [
"850f8f64873f648f3a5f179eee27be71558e25b2"
] | [
"spotpy/examples/tutorial_dream_hymod.py"
] | [
"# -*- coding: utf-8 -*-\n'''\nCopyright 2015 by Tobias Houska\nThis file is part of Statistical Parameter Estimation Tool (SPOTPY).\n\n:author: Tobias Houska\n\nThis class holds example code how to use the dream algorithm\n'''\n\nimport numpy as np\nimport spotpy\n#from spotpy.examples.spot_setup_hymod_exe import ... | [
[
"matplotlib.pyplot.subplots",
"numpy.percentile",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sieniven/self-driving-project | [
"ae135319f0991a940241c71f498865822a4f5b80"
] | [
"transfer-learning/vgg-demo.py"
] | [
"# Load our images first, and we'll check what we have\nfrom glob import glob\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom keras.preprocessing import image\nfrom keras.applications.vgg16 import preprocess_input\n# Note - this will likely need to download a new version... | [
[
"matplotlib.image.imread",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
flyingdutchman23/step-detector | [
"07d3a94917cc646cc3bb6e98c3284bd4725654e7"
] | [
"data_processing/nn_models/mlp.py"
] | [
"import tensorflow as tf\n\n\ndef build_mlp_model_fn(dense_layers=(20, 20), learning_rate=0.001, rho=0.95):\n def mlp_model_fn(features, labels, mode):\n \"\"\"Model function for MLP.\"\"\"\n acc_feat = features[\"accelerometer\"]\n layer = tf.reshape(acc_feat, [-1, acc_feat.shape[-2] * acc_... | [
[
"tensorflow.nn.softmax",
"tensorflow.metrics.accuracy",
"tensorflow.metrics.true_negatives",
"tensorflow.metrics.false_negatives",
"tensorflow.estimator.export.PredictOutput",
"tensorflow.layers.dropout",
"tensorflow.reshape",
"tensorflow.cast",
"tensorflow.layers.dense",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
liuweihust/worldcupodds | [
"72a6768587f54667332a13e910364f68c7a4f0ba"
] | [
"src/datasets/WorldCup.py"
] | [
"import csv\nimport sys\nimport json\nimport numpy as np\n\ndef VecScore2Res90(rec):\n if rec['comment'] != '':\n return [0.,1.,0.]\n elif rec['hscore'] > rec['gscore']:\n return [1.,0.,0.]\n elif rec['hscore'] < rec['gscore']:\n return [0.,0.,1.]\n else:\n return [0.,1.,0.]\... | [
[
"numpy.concatenate",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
soosten/halite-bot | [
"b5ecc20cb8407c6ecfda58c4850ac8017033f34a"
] | [
"src/spawns.py"
] | [
"import numpy as np\n\nfrom convert import working_yards\nfrom settings import (SPAWNING_STEP, STEPS_FINAL, MIN_SHIPS, SPAWNING_OFFSET,\n YARD_SCHEDULE)\n\n\nclass Spawns:\n def __init__(self, state, actions):\n # determine how many ships to build\n self.num_ships(state, action... | [
[
"numpy.ix_",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ktl014/mrec | [
"e3fb987572ab302ad0a17bba437bc9693e467af6"
] | [
"mrec/data/dataset.py"
] | [
"\"\"\"Dataset Operations\n\nDataset operations module currently contains functions for the following:\n- reading datasets\n- loading & processing datasets\n\n\"\"\"\n# Standard Dist\nfrom collections import namedtuple\nimport logging\nimport os\n\n# Third Party Imports\nimport pandas as pd\n\n# Project Level Impor... | [
[
"pandas.read_csv",
"pandas.read_sql"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
LindgeW/BiaffineParser | [
"3671f9f5d4fdbcad67d90ecfdafbeb316e4378db"
] | [
"modules/layer.py"
] | [
"import torch\nimport torch.nn as nn\n\n\nclass NonlinearMLP(nn.Module):\n def __init__(self, in_feature, out_feature, activation=nn.ReLU(), bias=True):\n super(NonlinearMLP, self).__init__()\n\n if activation is None:\n self.activation = lambda x: x\n else:\n assert ca... | [
[
"torch.cat",
"torch.nn.Linear",
"torch.nn.init.xavier_uniform_",
"torch.nn.init.orthogonal_",
"torch.nn.init.zeros_",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stevenstetzler/feets | [
"54804271bc78215f84c0261d0ee1282623274cce"
] | [
"feets/extractors/ext_car.py"
] | [
"#!/usr/bin/env python\n\n# The MIT License (MIT)\n\n# Copyright (c) 2017 Juan Cabral\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 limitat... | [
[
"numpy.size",
"numpy.exp",
"numpy.mean",
"scipy.optimize.minimize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
aksr-aashish/DaisyX-Extra | [
"e1cafbbb4f5a7845ffd70e16e4395e7e797b2cf7"
] | [
"bassboost.py"
] | [
"import asyncio\nimport io\nimport math\nimport os\n\nimport numpy as np\nfrom pydub import AudioSegment\nfrom telethon import types\n\nfrom DaisyX.utils import admin_cmd\n\n\n@bot.on(admin_cmd(pattern=\"bassbost ?(.*)\"))\nasync def __(message):\n v = False\n accentuate_db = 40\n reply = await message.get... | [
[
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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