repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
mylestunglee/supeltex | [
"5a366c39bf793bf23e7549e4df724b75561cad3e"
] | [
"supeltex.py"
] | [
"import argparse\nimport json\nimport os\nimport sys\n\nimport numpy as np\nimport tqdm\nfrom PIL import Image\n\nimport render\nfrom render import BUFFER_RESOLUTION as TILE_RESOLUTION\nimport geometry\n\n\nTEMP_DIRECTORY = 'temp'\n\n\ndef generate(parameters_filename, texture_filename, tile_grid_size, target_resol... | [
[
"numpy.ceil",
"numpy.array",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ManosL/Reinforcement-Learning-Projects | [
"d67332f1785228776854c2c36456fd0aa321c4f0"
] | [
"D.Silver Easy21/vfa.py"
] | [
"from easy21 import Action, State, step\nfrom monte_carlo import MonteCarlo\nfrom utils import zeros_2d, zeros_3d, mse_3d, dot_product, random_vec\n \nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nimport random \n\n# I have it as external vars because its easier\nmc =... | [
[
"numpy.array",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jj-chung/detectron2_testing | [
"f9ee969e6a1d3493750e32e5d9af9d600df3b35c"
] | [
"detectron2/engine/hooks.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\n\nimport datetime\nimport itertools\nimport logging\nimport os\nimport tempfile\nimport time\nfrom collections import Counter\nimport torch\nfrom fvcore.common.checkpoint import PeriodicCheckpointer as _PeriodicCheckpointer\nfrom fvcore.c... | [
[
"torch.autograd.profiler.profile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hsuxu/vnet_attention | [
"6958932f3974d268e93bd6443369a3f43c497ed3"
] | [
"magic_vnet/blocks/mabn/mabn.py"
] | [
"\"\"\"\nmodule from `https://github.com/megvii-model/MABN`,\nand changed to 3d version by Hsu\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n__all__ = ['MABN2d', 'MABN3d']\n\n\nclass BatchNormFunction2d(torch.autograd.Function):\n\n @staticmethod\n def forward(ctx, x, weig... | [
[
"torch.sqrt",
"torch.clamp",
"torch.ones",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kw-corne/openml-pimp | [
"d0a14f3eb480f2a90008889f00041bdccc7b9265"
] | [
"examples/experiments/run_pimp_on_arff.py"
] | [
"import arff\nimport argparse\nimport fanova.fanova\nimport fanova.visualizer\nimport itertools\nimport json\nimport numpy as np\nimport logging\nimport openmlcontrib\nimport openmlpimp\nimport os\nimport pandas as pd\nimport sklearnbot\n\n\n# to plot: <openml_pimp_root>/examples/plot/plot_fanova_aggregates.py\ndef... | [
[
"numpy.array",
"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": []
}
] |
yuegc/PaddleSpeech | [
"dae4b5b365dd872a05757b5ef445a4b80e129763",
"dae4b5b365dd872a05757b5ef445a4b80e129763"
] | [
"paddlespeech/t2s/models/melgan/style_melgan.py",
"paddlespeech/text/models/ernie_linear/dataset.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.prod"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EvolvedSquid/acme | [
"c5c7d074edaf776e346594da9347e8db49af3f46"
] | [
"acme/utils/tf2_savers_test.py"
] | [
"# Lint as: python3\n# Copyright 2018 DeepMind Technologies Limited. 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/LI... | [
[
"numpy.allclose",
"tensorflow.Variable",
"numpy.int32",
"tensorflow.math.reduce_sum",
"tensorflow.GradientTape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
craig-martinson/quadcopter-project | [
"0d5e7bbfa334319477be621ef97849941e74cdea"
] | [
"agents/agent.py"
] | [
"import random\nfrom collections import namedtuple, deque\nfrom keras import layers, models, optimizers\nfrom keras import backend as K\nimport numpy as np\nimport copy\nfrom agents.critic import Critic\nfrom agents.actor import Actor\n\nclass ReplayBuffer:\n \"\"\"Fixed-size buffer to store experience tuples.\"... | [
[
"numpy.reshape",
"numpy.array",
"numpy.vstack",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AalaaNagy88/Data_Extrafilteration | [
"79a3af71ec6e020f5c97b73f48421a58c9d0cbd0"
] | [
"src/visualization/visualize.py"
] | [
"from sklearn.metrics import classification_report\nfrom sklearn import metrics\nfrom sklearn.metrics import ConfusionMatrixDisplay\n\n\"\"\"\n Args:\n clf: model object\n x_test: test_set\n y_test: true labels of the test\n y_pred: prediction of the test\n\n Return:\n classific... | [
[
"sklearn.metrics.ConfusionMatrixDisplay.from_estimator",
"sklearn.metrics.classification_report"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eimrek/cclib | [
"6e8eab4226fd976dfb105d71ae11a8bd01ca12d0"
] | [
"cclib/parser/gaussianparser.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2020, the cclib development team\n#\n# This file is part of cclib (http://cclib.github.io) and is distributed under\n# the terms of the BSD 3-Clause License.\n\n\"\"\"Parser for Gaussian output files\"\"\"\n\n\nfrom __future__ import print_function\n\nimport re\n\nimport... | [
[
"numpy.testing.assert_equal",
"numpy.sqrt",
"numpy.tril_indices",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KitwareMedical/lungair-desktop-application | [
"8bb83024777094ca5cb79f6e594757b6f2b0a0c8"
] | [
"Modules/Scripted/Home/HomeLib/xray.py"
] | [
"from email.mime import image\nimport logging\nimport os\nimport numpy as np\nimport slicer\nimport vtk\nfrom .image_utils import create_segmentation_node_from_numpy_array\n\ndef create_linear_transform_node_from_matrix(matrix, node_name):\n \"\"\"Given a 3D affine transform as a 4x4 matrix, create a vtkMRMLTransf... | [
[
"numpy.ix_",
"numpy.identity",
"numpy.transpose",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JiehangXie/E4_cvxEDA | [
"e6e6c67cfb2f4e802ba5e36fe205552a3165d3f2"
] | [
"E4_cvxEDA.py"
] | [
"import time\r\nimport numpy as np\r\nimport pandas as pd\r\nimport pylab as pl\r\nimport edas\r\nimport cvxEDA\r\nimport os\r\nimport datetime\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\nmatplotlib.rcParams['text.usetex']=False #禁用LaTex\r\n\r\n\r\ndef correct_eda(file,mit_file):\r\n '''\r\n ED... | [
[
"numpy.array",
"pandas.set_option",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
parkinkon1/diverse_nus | [
"156bc2907cb07e2e8e4bf8ac2a55678490b061d5"
] | [
"layers.py"
] | [
"import numpy as np\nimport torch\nfrom torch import nn\nfrom torch.distributions.multivariate_normal import MultivariateNormal\nfrom torchvision.models import mobilenet_v2\nfrom efficientnet_pytorch import EfficientNet\n\n\nclass DifferentionalRasterizerLayer(nn.Module):\n \"\"\"\n Differential trajectory ra... | [
[
"torch.range",
"torch.ones",
"torch.cat",
"torch.nn.LSTM",
"torch.zeros",
"torch.nn.Conv2d",
"torch.eye",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d",
"torch.nn.Conv1d",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
styler00dollar/Colab-mmdetection | [
"da81622dc0686ad7f5081dd01c9e1626666f31ca"
] | [
"mmdet/core/optimizer/MADGRAD.py"
] | [
"\"\"\"\n6-jul-21\nhttps://github.com/facebookresearch/madgrad/blob/master/madgrad/madgrad.py\n\"\"\"\n# 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\nfrom mmcv.runner import OPTIMI... | [
[
"torch.clone",
"torch.zeros_like",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brennane/scikit-kge | [
"29bcd744a92f98c4282056e16f824be0e20fc8a1",
"29bcd744a92f98c4282056e16f824be0e20fc8a1"
] | [
"skge/rescal.py",
"skge/hole.py"
] | [
"import numpy as np\nfrom numpy import dot\nfrom skge.base import Model\nfrom skge.util import grad_sum_matrix, unzip_triples, memoized\nfrom skge.param import normless1\nimport skge.actfun as af\nfrom collections import defaultdict\n\n\nclass RESCAL(Model):\n \"\"\"\n Base class for RESCAL\n\n Use either\... | [
[
"numpy.dot",
"numpy.unique",
"numpy.vstack",
"numpy.outer",
"numpy.where",
"numpy.sum",
"numpy.logaddexp"
],
[
"numpy.vstack",
"numpy.where",
"numpy.logaddexp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
blanclemjk/Hackatown2019 | [
"d8ca67779dc754959a9eeabff5869db007b05b25"
] | [
"camera_receiver/extract_parking.py"
] | [
"import imutils\nimport numpy as np\nimport cv2\nfrom random import randint\n\nfrom extract_car import extract_car\nfrom extract_rectangle import extract_rectangle\n\n\ndef extract_parking(img):\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n ret, thresh = cv2.threshold(gray, 127, 255, 1)\n\n # cv2.imsh... | [
[
"numpy.int0",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
minkyu-choi04/misc | [
"f854187dfa5801fbe18d7d0634cc6f71996921fa"
] | [
"Gaussian2d_mask_generator_v1.py"
] | [
"import torch\nimport math\nimport torch.nn as nn\nimport numpy as np\n\ndef get_gaussian_kernel(attn_p, kernel_size=[192,256], sigma=1, channels=3, norm='max', device='cuda'):\n '''\n This function does not suuport batched sigma.\n For applying different sigmas for each element in a batch, \n see /... | [
[
"torch.ones",
"numpy.abs",
"torch.max",
"torch.sum",
"torch.tensor",
"torch.nn.functional.relu",
"torch.arange",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
svenlr/car-physics-pacejka | [
"bef64a7c3c813419a76f55c2b0553b5fe82f0808"
] | [
"car_sim_gen/plot_fy_of_alpha.py"
] | [
"import sys\n\nimport casadi\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom car_model import calc_wheel_centric_velocities, create_car_model, calc_sigma_xy, calc_wheel_centric_forces\nfrom car_sim_gen.constants import WheelConstants\n\nif __name__ == '__main__':\n model, c, q = create_car_model()\n... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"numpy.linspace",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.rcParams.update",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JiazeWang/Luna16 | [
"5ef7f4b539cc1ca72291e93d17cc18f408a3119d"
] | [
"main/train.py"
] | [
"import random\n\nimport torch\nimport numpy as np\nimport time\nimport os\nfrom model.net import Net\nfrom model.loss import Loss\nfrom torch.autograd import Variable\nimport itertools\nimport pandas as pd\nfrom main.dataset import LunaDataSet\nfrom torch.utils.data import DataLoader\nfrom configs import VAL_PCT, ... | [
[
"pandas.read_csv",
"torch.load",
"numpy.asarray",
"torch.utils.data.DataLoader",
"numpy.mean",
"torch.cuda.is_available",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
vensentzhou/akshare | [
"c74a8747fb017d7b80ccbc2aa70fe7b81d5ddf63"
] | [
"akshare/stock/zh_stock_a_tick_tx_163.py"
] | [
"# -*- coding:utf-8 -*-\n# /usr/bin/env python\n\"\"\"\nDate: 2021/2/1 16:39\nDesc: 腾讯-网易-股票-实时行情-成交明细\n成交明细-每个交易日 16:00 提供当日数据\n港股报价延时 15 分钟\n\"\"\"\nfrom io import StringIO, BytesIO\nfrom tqdm import tqdm\n\nimport pandas as pd\nimport requests\n\n\ndef stock_zh_a_tick_tx_js(code: str = \"sz000001\") -> pd.DataFr... | [
[
"pandas.DataFrame",
"pandas.date_range"
]
] | [
{
"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": []
}
] |
blue0513/gokart | [
"14db6bcb76743da4627bfad34fd10cb28d078e4e"
] | [
"gokart/file_processor.py"
] | [
"import os\nimport pickle\nimport xml.etree.ElementTree as ET\nfrom abc import abstractmethod\nfrom logging import getLogger\n\nimport luigi\nimport luigi.contrib.s3\nimport luigi.format\nimport numpy as np\nimport pandas as pd\nimport pandas.errors\n\nfrom gokart.object_storage import ObjectStorage\n\n\nlogger = g... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"pandas.read_parquet",
"numpy.savez_compressed",
"pandas.read_json",
"pandas.DataFrame.from_dict",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dietmarw/FMPy | [
"47760e9e4c4f435c43e8c14cdd8e12fccf6f7028"
] | [
"fmpy/gui/MainWindow.py"
] | [
"\"\"\" Entry point for the graphical user interface \"\"\"\r\n\r\ntry:\r\n from . import compile_resources\r\n compile_resources()\r\nexcept Exception as e:\r\n print(\"Failed to compiled resources. %s\" % e)\r\n\r\nimport os\r\nimport sys\r\n\r\nfrom PyQt5.QtCore import QCoreApplication, QDir, Qt, pyqtSi... | [
[
"numpy.repeat",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Wwwwan/pytorch-loss | [
"4cc9dc88478dbfdc00cba78014f4fbbb07ca27c8"
] | [
"setup.py"
] | [
"\nfrom setuptools import setup, Extension\nfrom torch.utils import cpp_extension\n\n'''\n python setup.py install\n usage: import torch first, then import this module\n'''\n\nsetup(\n name='pytorch_loss',\n ext_modules=[\n cpp_extension.CUDAExtension(\n 'focal_cpp',\n ['csr... | [
[
"torch.utils.cpp_extension.CUDAExtension"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
o0olele/ray | [
"d9bca63dc342196d0f54e35a86a139d1686c09f1"
] | [
"python/ray/experimental/data/tests/test_dataset.py"
] | [
"import os\nimport random\nimport requests\nimport shutil\nimport time\n\nfrom unittest.mock import patch\nimport math\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pytest\n\nimport ray\n\nfrom ray.tests.conftest import * # noqa\nfrom ray.experimental.data.dat... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.expand_dims",
"numpy.array_equal",
"torch.cat",
"numpy.arange",
"pandas.DataFrame",
"torch.tensor",
"numpy.concatenate",
"numpy.ceil",
"pandas.read_parquet",
"numpy.sort",
"pandas.read_json",
"numpy.array",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
... |
cuill/PyFVCOM | [
"41930776a5a1f4034f750784d4f2c0385025c8a1"
] | [
"PyFVCOM/read_results.py"
] | [
"from __future__ import print_function\n\nimport sys\nimport inspect\n\nimport numpy as np\n\nfrom warnings import warn\nfrom datetime import datetime\nfrom netCDF4 import Dataset, MFDataset, num2date\n\n\nclass ncwrite():\n \"\"\"\n Save data in a dict to a netCDF file.\n\n Notes\n -----\n 1. Unlimi... | [
[
"numpy.hstack",
"numpy.min",
"numpy.genfromtxt",
"numpy.ndim",
"numpy.max",
"numpy.shape",
"numpy.floor",
"numpy.column_stack",
"numpy.argsort",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rahulbordoloi/OpenCV | [
"170eaf07b21773f7243905b1e3000921974671fa"
] | [
"chapter6.py"
] | [
"import cv2\nimport numpy as np\nprint('Package(s) Imported')\n\n# img = cv2.imread('Resources/me.jpg')\n\n# To Mitigate the Problem of Scaling\ndef stackImages(scale, imgArray):\n rows = len(imgArray)\n cols = len(imgArray[0])\n rowsAvailable = isinstance(imgArray[0], list)\n width = imgArray[0][0].sha... | [
[
"numpy.hstack",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kevinmpeterson/COVID-analysis | [
"82fd7d63de6e8224253a49d640a0bc84169cb541"
] | [
"plot_cov.py"
] | [
"import matplotlib.pyplot as plt\nimport datetime as dt\nimport dateutil as du\nimport csv\nimport os\nimport numpy as np\n\nCOVDIR_BASE=\"COVID-19\"\nCOVDIR_SUB=\"/csse_covid_19_data/csse_covid_19_daily_reports/\"\nCOVDIR_FULL=COVDIR_BASE+COVDIR_SUB\n\n# Magic factor to convert from reported to estimated infection... | [
[
"matplotlib.pyplot.legend",
"numpy.amax",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.annotate",
"numpy.cumsum",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tarod13/CLminihack | [
"7f26c77809359aeff060504c58084b491605f394",
"7f26c77809359aeff060504c58084b491605f394"
] | [
"multistep_trainers.py",
"train_DRIMseq.py"
] | [
"from collections import deque\nimport numpy as np\nfrom episode_buffers import (\n EpisodeExperienceBuffer, ExperienceFirstLevel)\nfrom multistep_policy_optimizers import MultiStep_Second_Level_SAC_PolicyOptimizer as PolicyOptimizer\n\nfrom utils import cat_state_task, scale_action\n\nimport wandb\n\nimport cv... | [
[
"numpy.stack",
"numpy.ones",
"numpy.std",
"numpy.mean",
"numpy.array",
"numpy.random.randint"
],
[
"numpy.array",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mondrian-scwgs/vcfutils | [
"673ad48f59a8e263cf7e0c9244623dfba383a822"
] | [
"vcfutils/tests/test_snv_parser.py"
] | [
"from vcfutils.parsers import vcf_snv_parser\r\nimport pytest\r\nimport os\r\nimport yaml\r\nimport pandas as pd\r\nfrom numpy import isnan\r\n\r\n\r\n@pytest.fixture\r\ndef freebayes():\r\n file = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"testdata\", \"freebayes.vcf.gz\")\r\n assert os.path... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vuule/cudf | [
"efebcd1452692ee15f3d30627a9ef3d0cafa85d5",
"efebcd1452692ee15f3d30627a9ef3d0cafa85d5"
] | [
"python/cudf/cudf/tests/test_pickling.py",
"python/cudf/cudf/utils/dtypes.py"
] | [
"# Copyright (c) 2018, NVIDIA CORPORATION.\n\nimport pickle\nimport sys\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom cudf.core import DataFrame, GenericIndex, Series\nfrom cudf.core.buffer import Buffer\nfrom cudf.tests.utils import assert_eq\n\n\ndef check_serialization(df):\n # basic\n ... | [
[
"numpy.arange",
"pandas.Categorical",
"numpy.random.random",
"numpy.random.seed"
],
[
"numpy.datetime_data",
"pandas.api.types.pandas_dtype",
"numpy.issubdtype",
"numpy.promote_types",
"numpy.dtype",
"numpy.result_type",
"pandas.core.dtypes.common.infer_dtype_from_o... | [
{
"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... |
vseib/deep-learning-nano-degree | [
"c21f90641582f892af0239bef8d46368d829008f"
] | [
"02_neural_networks/2-5_project_1_predicting_bike_sharing_patterns/project_1_orig/main.py"
] | [
"'''\nIn this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n'... | [
[
"matplotlib.pyplot.legend",
"pandas.concat",
"pandas.read_csv",
"pandas.to_datetime",
"numpy.random.choice",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"numpy.mean",
"matplotlib.pyplot.show",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Rainxyh/rl-tutorials | [
"194b8e1f10118f0368a25126e35ae4470d6fd5d9"
] | [
"DDPG/task0_train.py"
] | [
"#!/usr/bin/env python\n# coding=utf-8\n'''\n@Author: John\n@Email: johnjim0816@gmail.com\n@Date: 2020-06-11 20:58:21\n@LastEditor: John\nLastEditTime: 2021-09-16 01:31:33\n@Discription: \n@Environment: python 3.7.7\n'''\nimport sys,os\ncurr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径\nparent_pat... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abulimity/Learn-Machine-Learning | [
"24455a473fdfa439e2a2871d21ffa66245557d69"
] | [
"CH02_DecisionTree/DecisionTree.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom math import log\nimport datetime\nfrom functools import wraps\n\n\ndef createDataSet():\n dataSet = np.array(\n [\n [0, 0, 0, 0],\n [0, 0, 0, 1],\n [0, 1, 0, 1],\n [0, 1, 1, 0],\n [0, 0, 0, 0],\n ... | [
[
"numpy.array",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
faradaym/Lantern | [
"f453de532da638c1f467953b32bbe49a3dedfa45"
] | [
"src/out/ICFP18evaluation/evaluationCNN/PyTorch/download_data.py"
] | [
"from __future__ import print_function\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.autograd import Variable\nimport time\n\n# Training settings\nparser = argparse.ArgumentParser(descript... | [
[
"torch.manual_seed",
"torch.set_num_threads",
"torch.cuda.manual_seed",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thomaskamalakis/telecomsystems | [
"476350a121debfd580dced0823b4bcf94f1f896d"
] | [
"Chap2/calcRXXgauss.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 19 12:23:22 2019\n\n@author: thomas\n\"\"\"\nimport matplotlib.pyplot as plt\n\nimport numpy as np\n\nfont = {'family' : 'serif',\n 'weight' : 'normal',\n 'size' : 16}\t\nplt.rc('font', **font)\n\n# time axis\nTmax=3.0\nN=1... | [
[
"matplotlib.pyplot.tight_layout",
"numpy.abs",
"numpy.multiply",
"numpy.arange",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.savefig",
"numpy.fft.fftshift",
"matplotlib.pyplot.plot",
"numpy.asmatrix",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.ylabel",
"numpy.rando... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kousu-1/spinalcordtoolbox | [
"9b1c2179fe31be489dab7f08c43e9bd5902931c0",
"9b1c2179fe31be489dab7f08c43e9bd5902931c0"
] | [
"scripts/sct_process_segmentation.py",
"testing/test_sct_analyze_texture.py"
] | [
"#!/usr/bin/env python\n#########################################################################################\n#\n# Perform various types of processing from the spinal cord segmentation (e.g. extract centerline, compute CSA, etc.).\n# (extract_centerline) extract the spinal cord centerline from the segmentation... | [
[
"matplotlib.figure.Figure",
"matplotlib.backends.backend_agg.FigureCanvasAgg",
"matplotlib.ticker.MaxNLocator",
"numpy.argsort",
"numpy.array"
],
[
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
... |
Clark1216/Nvidia_Jetson-TensorRT_Inference_Basics | [
"e45a6972ea9e231f1baa371ac4ec2e49e9b0067f"
] | [
"my-detection-python/camera_based_person_counter_API2_original.py"
] | [
"#!/usr/bin/python3\n\nimport cv2\nimport jetson.inference\nimport jetson.utils\n\nimport os\n#os.system(\"apt update\")\n#os.system(\"apt install -y fswebcam\")\n#os.system(\"pip3 install flask\")\n\nimport json\nimport time\nimport numpy as np\n\nfrom datetime import datetime\nfrom flask import Flask, render_temp... | [
[
"numpy.reshape",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jerryzcn/autogluon | [
"778cfa23e5695b44fc3c7a5da0cbc764917d80a2"
] | [
"autogluon/utils/tabular/features/abstract_feature_generator.py"
] | [
"import copy\nimport logging\nimport re\nimport warnings\nfrom collections import defaultdict\n\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame, Series\nfrom pandas.api.types import CategoricalDtype\n\nfrom ..utils.decorators import calculate_time\nfrom ..utils.savers import save_pkl\n\nlogge... | [
[
"pandas.api.types.CategoricalDtype",
"numpy.issubdtype",
"pandas.IntervalIndex.from_tuples",
"pandas.cut",
"numpy.floor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
vinbigdata-medical/endocv2020-seg | [
"91675391911a3d70a09c51edb0eeb73b1081b037"
] | [
"lib/modeling/backbone/efficientnet.py"
] | [
"from collections import OrderedDict\r\nimport timm\r\nfrom timm.models.layers import SelectAdaptivePool2d\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\nfrom .build import BACKBONE_REGISTRY\r\n\r\n\r\n__all__ = [\"EfficientNet\", \"build_efficientnet_backbone\"]\r\n\r\n\r\nclass... | [
[
"torch.nn.Linear",
"torch.nn.functional.dropout",
"torch.flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DemonFlexCouncil/ddsp | [
"ea9111de50b89acc2deb4c2b4376b2f253162281"
] | [
"ddsp/core.py"
] | [
"# Copyright 2020 The DDSP 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 a... | [
[
"tensorflow.compat.v2.cumsum",
"tensorflow.compat.v2.executing_eagerly",
"tensorflow.compat.v2.transpose",
"numpy.linspace",
"tensorflow.compat.v2.compat.v1.image.resize",
"tensorflow.compat.v2.clip_by_value",
"tensorflow.compat.v2.range",
"tensorflow.compat.v2.convert_to_tensor",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.4",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"1.3"
],
"tensorflow": []
}
] |
supsi-dacd-isaac/mbtr_experiments | [
"276e011b303435513961923ad1fb74e1b76cf5b7"
] | [
"analysis/quantiles.py"
] | [
"import numpy as np\nfrom utils.cross_val import get_cv_results\nfrom analysis.plot_utils import plot_reliability, plot_reliability_tilted, plot_QS, plot_crsp, plot_reliability_diff, plot_QS_diff\nfont_scale = 0.7\nsize = (4.5,3)\n\n\n# ----------------------------------- compare normal, refitted and squared-refitt... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
grofit/ESRGAN | [
"7c2bb4ce67ebb10f41e0ba84a1f3efb6d0c04c0d"
] | [
"infrastructure/utils/architecture.py"
] | [
"import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom . import block as B\n\n\nclass Get_gradient_nopadding(nn.Module):\n def __init__(self):\n super(Get_gradient_nopadding, self).__init__()\n kernel_v = [[0, -1, 0],\n [0, 0, 0],\n ... | [
[
"torch.nn.Parameter",
"torch.FloatTensor",
"torch.pow",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ReLRail/project-touhou | [
"fbfbdb81c40aa9b87143797c32af43d4e9d7c1e9"
] | [
"models/cnn.py"
] | [
"import torch\nimport torch.nn as nn\nfrom typing import Any\n\n\n__all__ = ['AlexNet', 'alexnet']\n\n\nmodel_urls = {\n 'alexnet': 'https://download.pytorch.org/models/alexnet-owt-7be5be79.pth',\n}\n\n\nclass AlexNet(nn.Module):\n\n def __init__(self, num_classes: int = 1000) -> None:\n super(AlexNet,... | [
[
"torch.nn.Dropout",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.flatten",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nhutnamhcmus/google-research | [
"5b645f0005fb5a8d96c58d8d84017d97662d564d"
] | [
"gfsa/linear_solvers_test.py"
] | [
"# coding=utf-8\n# Copyright 2022 The Google Research 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 requ... | [
[
"numpy.testing.assert_allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ismailsunni/scripts | [
"723678f5db37b1e32756e8877d146bb06efce1a2"
] | [
"love-to-wkt.py"
] | [
"# Love equation: x^{2}+\\left(y-\\sqrt{\\left|x\\right|}\\right)^{2}=1\n# Positive love: y=\\sqrt{\\left(1-x^{2}\\right)}\\ +\\ \\sqrt{\\left|x\\right|}\n# Negative love: y=-\\sqrt{\\left(1-x^{2}\\right)}\\ +\\ \\sqrt{\\left|x\\right|}\n# Reference: https://www.desmos.com/calculator/9vwjltfkwg\n\nimport math\nimpo... | [
[
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oleg-fiksel/rasa | [
"1aa1b6c93ba2603064737814f9fcdce764af40d9"
] | [
"rasa/core/test.py"
] | [
"import logging\nimport os\nimport warnings\nimport typing\nfrom collections import defaultdict, namedtuple\nfrom typing import Any, Dict, List, Optional, Text, Tuple\n\nfrom rasa import telemetry\nfrom rasa.core.policies.policy import PolicyPrediction\nfrom rasa.nlu.test import EntityEvaluationResult, evaluate_ent... | [
[
"sklearn.utils.multiclass.unique_labels",
"sklearn.metrics.confusion_matrix"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mari-linhares/deep-python-scratch | [
"b447ed20c981db5ffef810b6f80d1638cf7d2ccd"
] | [
"examples/regression.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\n\nfrom deepscratch.models.neural_network import NeuralNetwork\nfrom deepscratch.learning.trainer import Trainer\nimport deepscratch.models.layers as layers\nimport deepscratch.dataloader as dataloader\n\n\ndef main():\n # Generating fake data: 7 * X + 15\n ... | [
[
"numpy.reshape",
"matplotlib.pyplot.plot",
"numpy.random.normal",
"numpy.random.rand",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adrn/hq | [
"5fd56401dc704a280d9fafb970d50b23b41cd9a2"
] | [
"hq/cli/run_thejoker.py"
] | [
"# Standard library\nimport atexit\nimport glob\nimport os\nimport shutil\nimport socket\nimport time\n\n# Third-party\nfrom astropy.utils import iers\niers.conf.auto_download = False\n\nimport theano\ntheano.config.optimizer = 'None'\ntheano.config.mode = 'FAST_COMPILE'\ntheano.config.reoptimize_unpickled_function... | [
[
"numpy.isin",
"numpy.random.SeedSequence",
"numpy.random.default_rng",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
djjh/reinforcement-learning-labs | [
"22706dab9e7f16e364ee4ed79c0bd67a343e5b08"
] | [
"src/practice/20_framework/rl/common/__init__.py"
] | [
"import numpy as np\n\nfrom rl.types import Episode\n\nclass Sampler:\n def sample(self, probabilities):\n raise NotImplementedError()\n\nclass DeterminisiticSampler(Sampler):\n def sample(self, probabilities):\n return np.argmax(probabilities)\n\nclass StochasticSampler(Sampler):\n def sampl... | [
[
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sisudata/chromatic-encoding | [
"3b4b021d648e78ee5bfa03819b4c7d53f7a9596a"
] | [
"run_torchfm.py"
] | [
"#!/usr/bin/env python3\n#\n# script for evaluating torch fm routines\n#\n# python run_torchfm.py budget dataset compress quiet(yes/n) nthreads device modeltype\n#\n# device can be 'cpu' 'cuda'\n# on multigpu systems you can specify the devicenum as well\n#\n# note ffm, fnfm will not run, essentially, due to quadra... | [
[
"torch.device",
"torch.set_num_threads"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Honkl/general-ai | [
"dfbdb18dec64985b6210051b3fa6810522e94438"
] | [
"Game-interfaces/Game2048/game_2048.py"
] | [
"\"\"\"\nOriginal license of game 2048, made by G. Cirulli in 2014:\n\n==================================\nThe MIT License (MIT)\n\nCopyright (c) 2014 Gabriele Cirulli\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software... | [
[
"numpy.log2",
"numpy.random.RandomState",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nusdbsystem/ARM-Net | [
"cafda2654b67ec51aeb8834d1995711f918db811"
] | [
"models/pnn.py"
] | [
"import torch\nfrom models.layers import Embedding, MLP, get_triu_indices\n\nclass InnnerProduct(torch.nn.Module):\n\n def forward(self, x):\n \"\"\"\n :param x: FloatTensor B*F*E\n :return: FloatTensor B*(Fx(F-1))\n \"\"\"\n nfield = x.size(1)\n vi_indices, vj_indi... | [
[
"torch.einsum",
"torch.sum",
"torch.ones",
"torch.nn.init.xavier_uniform_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
osu-xai/sc2env | [
"5935e7125e5937f649e94fd0b7ff1e1eae318713"
] | [
"sc2env/environments/tug_of_war_2L_self_play_4grid.py"
] | [
"import numpy as np\nimport torch\nimport pysc2\nfrom pysc2.agents import base_agent\nfrom pysc2.env import sc2_env\nfrom pysc2.lib import actions, features, units\nfrom pysc2 import maps, lib\nfrom s2clientprotocol import sc2api_pb2 as sc_pb\nfrom sc2env.pysc2_util import register_map\nfrom sc2env.utility import g... | [
[
"torch.load",
"numpy.reshape",
"numpy.unique",
"numpy.set_printoptions",
"numpy.append",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
schmidtchristoph/medical_image_segmentation | [
"0b4be1710e01ef9271bf295e9830747dd9232d96"
] | [
"code/baseline.py"
] | [
"# part of this script was taken from https://github.com/jocicmarko/ultrasound-nerve-segmentation\nimport argparse\nfrom glob import glob\n\nimport numpy as np\nfrom PIL import Image\nfrom keras import backend as K\nfrom keras import losses\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlat... | [
[
"tensorflow.zeros_like",
"numpy.array",
"tensorflow.equal",
"tensorflow.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
joannhsiao/ChatBot | [
"d518a2524aa55a0d304703e14053a435b1abad32"
] | [
"chatbot_seq2seq_lstm.py"
] | [
"# -*- coding: utf-8 -*-\nimport os\nimport re\nimport yaml\n\nimport numpy as np\nfrom tensorflow.keras.layers import Input, Embedding, LSTM, Dense\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras import preprocessing, utils\nfrom tensorflow.keras.utils import plot_model\n\n\ndef tokenize(sentence... | [
[
"tensorflow.keras.preprocessing.text.Tokenizer",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Embedding",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.utils.plot_model",
"tensorflow.keras.layers.LSTM",
"numpy.array",
"tensorflow.keras.utils.to_categorical",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
omron-sinicx/diabolo | [
"a0258fdf634d27c7cf185b2e40c6b12699417d36"
] | [
"diabolo_play/scripts/p_controller.py"
] | [
"#!/usr/bin/env python\n\nimport time\nimport math\nimport numpy as np\nimport signal, sys\n\nimport rospy\nimport geometry_msgs.msg\nfrom geometry_msgs.msg import Point, Pose, PoseArray\nfrom visualization_msgs.msg import Marker, MarkerArray\n\nfrom collections import deque\n\ntry:\n import tf\n import tf_co... | [
[
"numpy.dot",
"numpy.array",
"numpy.mat",
"numpy.cross"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marchcarax/DeepLearningFC | [
"7529ae410a07b6ee8fe9226900dcb04352361b42"
] | [
"data.py"
] | [
"#Prepare data to feed the Neural Network\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import MinMaxScaler\nfrom pandas_datareader import data as web\n\n\nclass data_process:\n\n def __init__(self, ticker, start_date, end_date):\n \n self.df = self.get_data(ticker, start_d... | [
[
"numpy.reshape",
"numpy.array",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Evensgn/MNIST-learning | [
"b751d875d59570ba3efa2b4cc4d4cf4b6a0d6cfe"
] | [
"mnist_cnn.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nGRAY_SCALE_RANGE = 255\n\nimport pickle\n\ndata_filename = 'data_deskewed.pkl'\nprint('Loading data from file \\'' + data_filename + '\\' ...')\nwith open(data_filename, 'rb') as f:\n train_labels = pickle.load(f)\n train_images = pickle.load(f)\n tes... | [
[
"tensorflow.nn.max_pool",
"tensorflow.cast",
"tensorflow.nn.l2_loss",
"numpy.mean",
"tensorflow.nn.conv2d",
"tensorflow.Variable",
"tensorflow.Session",
"tensorflow.argmax",
"numpy.zeros",
"tensorflow.nn.dropout",
"tensorflow.matmul",
"tensorflow.truncated_normal",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
XH-B/attention-feature-distillation | [
"aaab9f63da1b27fd25a1b75b8844b3b66cbc9d82"
] | [
"dataset.py"
] | [
"# Attention-based Feature-level Distillation\r\n# Original Source : https://github.com/HobbitLong/RepDistiller\r\n\r\nimport os\r\nfrom torchvision import transforms, datasets\r\nimport torch.utils.data as data\r\nimport torch\r\n\r\n\r\ndef create_loader(batch_size, data_dir, data):\r\n data_dir = os.path.join... | [
[
"torch.utils.data.DataLoader",
"torch.utils.data.lower"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dylanashley/outlier-based-exploration | [
"1376304a07a8803a06d5277138d5549327a23585"
] | [
"src/policy_iteration_for_optimal_policy.py"
] | [
"#!/usr/bin/env python -O\n# -*- coding: ascii -*-\n\nimport argparse\nimport numpy as np\nimport signal\nimport sys\n\nfrom grid_world import GridWorld\nfrom tile_coder import TileCoder\n\nLAMBDA = 0.9\nMU = 0.1\nNUMBER_OF_ACTIONS = 4\nNUMBER_OF_EPISODES = 10000\nPI = 0.0\n\nNUMBER_OF_TILINGS = 1\nTILING_CARDINALI... | [
[
"numpy.random.seed",
"numpy.save",
"numpy.seterr",
"numpy.argmax",
"numpy.random.rand",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jbcnrlz/san | [
"1eab20f83d3c7dba5607e22d1c70768905b62b12"
] | [
"FRGC.py"
] | [
"from baseClasses.DatabaseProcessingUtility import *\nfrom helper.functions import outputObj,loadOBJ,minmax\nfrom FRGCTemplate import *\nfrom SymmetricFilling import *\nfrom FixWithAveragedModel import *\nfrom RotateFace import *\nimport os, glob, copy, numpy as np\n\nclass FRGC(DatabaseProcessingUtility):\n\n d... | [
[
"numpy.asarray",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sahngmin/IMK_Keyboard | [
"22375ed5798ec43c9734c8b9d4fe374b8db58f48"
] | [
"models/BiRNN.py"
] | [
"import torch\nimport torch.nn as nn\nfrom data import chars\nimport torch.optim as optim\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\nfrom utils import load_model, evaluate, save_model\nfrom torch.nn import init\nimport time\nfrom test import test_\nimport copy\nfrom torch.nn import D... | [
[
"torch.nn.utils.rnn.pack_padded_sequence",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.utils.rnn.pad_packed_sequence",
"torch.nn.init.xavier_uniform_",
"torch.nn.DataParallel",
"torch.optim.lr_scheduler.StepLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kesegotumisang/or-tools | [
"bed3fa9922646d3283f61bbc052e8c3c01414bdf"
] | [
"examples/python/cvrptw.py"
] | [
"# This Python file uses the following encoding: utf-8\n# Copyright 2015 Tin Arm Engineering AB\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... | [
[
"numpy.ones_like",
"numpy.sqrt",
"matplotlib.pyplot.cm.get_cmap",
"numpy.linspace",
"matplotlib.pyplot.figure",
"numpy.cos",
"numpy.sin",
"numpy.max",
"numpy.size",
"numpy.deg2rad",
"numpy.random.random_integers",
"numpy.random.randn",
"numpy.isscalar",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TNTWEN/yolov3-channel-and-layer-pruning | [
"8864638d3c5f5ddfb7239865c2d9d3984bc4727f"
] | [
"layer_prune.py"
] | [
"from models import *\r\nfrom utils.utils import *\r\nimport torch\r\nimport numpy as np\r\nfrom copy import deepcopy\r\nfrom test import test\r\nfrom terminaltables import AsciiTable\r\nimport time\r\nfrom utils.utils import *\r\nfrom utils.prune_utils import *\r\nimport argparse\r\n\r\n\r\n\r\nif __name__ == '__m... | [
[
"torch.load",
"torch.zeros",
"numpy.ones",
"torch.tensor",
"torch.no_grad",
"torch.sort",
"torch.cuda.is_available",
"torch.rand",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lephong/diffmetric_coref | [
"37543d5000911917f86a3c1747a50aec65b53c2d"
] | [
"src/tmp.py"
] | [
"# import tensorflow as tf\r\nimport numpy as np\r\n\r\nfor _ in range(500):\r\n iwi = np.zeros([350, 350], dtype=np.int32)\r\n for i in range(350):\r\n for j in range(350):\r\n if i == j + 2:\r\n iwi[i, j] = 1\r\n\r\n"
] | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
naotohori/16S-central-folding | [
"c811602cecd374be4d8bebd203e5c8dc2c31b7fc"
] | [
"analysis/Mg_distribution_3D/bestfit_around_nt665.py"
] | [
"#!/usr/bin/env python\n\nfrom cafysis.file_io.dcd import DcdFile\nfrom cafysis.file_io.pdb import PdbFile\nfrom Superimpose_mask import superimpose\nfrom numpy import zeros, asarray, float64\nimport sys\n\nid_begin = []\nid_end = []\nfor iarg in range(4, len(sys.argv)-1, 2) :\n id_begin.append(int(sys.argv[iarg... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tyohei/chainerkfac | [
"99e88396268e8b7d099fdb6bbf54e309e98293c8"
] | [
"examples/imagenet/models/resnet50_d.py"
] | [
"# Original author: yasunorikudo\n# (https://github.com/yasunorikudo/chainer-ResNet)\n\nimport chainer\nfrom chainer.backends import cuda\nfrom chainer import initializers\nfrom chainer import configuration\nimport chainer.functions as F\nimport chainer.links as L\n\nfrom softmax_cross_entropy import softmax_cross_... | [
[
"numpy.arange",
"numpy.random.beta",
"numpy.maximum",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BaryonPasters/diffprof | [
"d8064f65e24df749fb69a04c33de1f2a90157016"
] | [
"diffprof/tests/test_conc_pop_model.py"
] | [
"\"\"\"\n\"\"\"\nimport numpy as np\nfrom ..conc_pop_model import get_u_param_grids\nfrom ..conc_pop_model import DEFAULT_PARAMS, get_pdf_weights_on_grid\nfrom ..nfw_evolution import DEFAULT_CONC_PARAMS\nfrom ..conc_pop_model import lgc_pop_vs_lgt_and_p50\nfrom ..conc_pop_model import lgc_pop_vs_lgt_and_p50, get_pa... | [
[
"numpy.log10",
"numpy.sum",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nishant173/helperpy | [
"7568be2f4a15c06c3dc2f94e9a549d78eb845b49"
] | [
"helperpy/data_wrangler/transform.py"
] | [
"from typing import Any, Callable, Dict, List, Optional, Union\nfrom collections import Counter\nfrom functools import reduce\nimport random\n\nimport numpy as np\nimport pandas as pd\n\nfrom helperpy.core.date_ops import get_current_timestamp_as_integer\nfrom helperpy.core.text_casing import (\n camel_to_pascal... | [
[
"pandas.merge",
"numpy.min",
"numpy.isnan",
"numpy.ptp",
"numpy.floor",
"numpy.array"
]
] | [
{
"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": []
}
] |
vineeths96/Natural-Language-Inference | [
"8ad7d6cd254ea971c9fb65d679be0b7d3e9471e1"
] | [
"deep_model/BERT/model_test.py"
] | [
"# Imports\nimport numpy as np\nfrom transformers import *\nfrom tensorflow.keras.models import load_model\nfrom deep_model.BERT.bert_input import get_inputs\nfrom deep_model.BERT.preprocess import preprocess\nfrom deep_model.BERT.parameters import *\n\n\"\"\"\n# Uncomment for generating plots. Requires some librar... | [
[
"tensorflow.keras.models.load_model",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
profitware/titanic-sandbox | [
"ece190b894d1b0ffe3b677b5419ffe44d465950d"
] | [
"titanic/__init__.py"
] | [
"# -*- coding: utf-8 -*-\n\n__author__ = 'Sergey Sobko'\n\n\nfrom pandas import (\n concat,\n read_csv,\n Series\n)\nfrom sklearn.tree import DecisionTreeClassifier\n\n\nclass Titanic(object):\n titanic_data = None\n\n def __init__(self, titanic_csv):\n self.titanic_data = read_csv(titanic_csv... | [
[
"sklearn.tree.DecisionTreeClassifier",
"pandas.read_csv",
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
samiemostafavi/conditional-density-estimation | [
"a00e51dccd51b673896055c22e09fa4197efabde",
"a00e51dccd51b673896055c22e09fa4197efabde"
] | [
"cde/evaluation/simulation_eval/question6_noise_schedules.py",
"cde/evaluation/simulation_eval/plotting/question3_plots.py"
] | [
"import matplotlib as mpl\n\nmpl.use(\"PS\") # handles X11 server detection (required to run on console)\nimport numpy as np\nimport types\nfrom cde.model_fitting.GoodnessOfFitResults import GoodnessOfFitResults\nfrom cde.evaluation.simulation_eval import base_experiment\nfrom ml_logger import logger\n\nEXP_PREFIX... | [
[
"numpy.logspace",
"matplotlib.use"
],
[
"matplotlib.pyplot.tight_layout",
"numpy.linspace",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.suptitle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
regro-cf-autotick-bot/ncvis-feedstock | [
"c97af7ca146d75d3684ecf134cfcad83e3f33dcd"
] | [
"recipe/test.py"
] | [
"import numpy as np\nimport ncvis\nimport time\n\ndef test_distances():\n np.random.seed(42)\n X = np.random.random((5, 3))\n distances=['euclidean', 'cosine', 'correlation', 'inner_product']\n for distance in distances:\n vis = ncvis.NCVis(n_neighbors=15, M=16, ef_construction=200, random_seed=4... | [
[
"numpy.random.random",
"numpy.abs",
"numpy.isfinite",
"numpy.random.seed",
"numpy.random.normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dokaptur/neural-fanfiction-generator | [
"41b2305f96d97beb6752b306ebd8e05e14ffae12"
] | [
"models/encdec_model.py"
] | [
"import numpy as np\nimport tensorflow as tf\n\nfrom models.utils import batch_same_matmul, scan, softmax_loss\nfrom tensorflow.python.ops.rnn_cell import MultiRNNCell, LSTMCell, InputProjectionWrapper, \\\n EmbeddingWrapper\n\n\ndef stacked_rnn_step(input_vocabulary_size, hidden_size=13, emb_dim=11, n_layers=2,... | [
[
"tensorflow.matmul",
"tensorflow.get_variable",
"tensorflow.nn.softmax",
"tensorflow.python.ops.rnn_cell.InputProjectionWrapper",
"tensorflow.arg_max",
"tensorflow.reduce_mean",
"tensorflow.python.ops.rnn_cell.LSTMCell",
"tensorflow.reduce_sum",
"tensorflow.python.ops.rnn_cell.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
test-v2/alibi | [
"10de673d1c00ef74a023343887e2fd20ebb32c50"
] | [
"alibi/explainers/tests/conftest.py"
] | [
"import pytest\n\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\n\nfrom alibi.explainers import AnchorTabular\nfrom alibi.explainers.tests.utils import predict_fcn, adult_dataset, iris_dataset\nfrom keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D, Input\nfrom keras.models... | [
[
"sklearn.ensemble.RandomForestClassifier",
"sklearn.linear_model.LogisticRegression",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
uiano/abs_placement_via_radio_maps | [
"8d0849fbb194b520c82cff4de71e475fb038a820"
] | [
"common/runner.py"
] | [
"\"\"\"\nChangelog:\n\n2021/08/07: When the invoked function returns a row vector, it is left as a row\nvector, not converted to a column. See x22Dpythonarray\n\n\"\"\"\n\n\nimport os\nfrom contextlib import redirect_stdout, redirect_stderr\nimport numpy as np\nfrom IPython.core.debugger import set_trace\nimport tr... | [
[
"numpy.asarray",
"numpy.array",
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ArlindKadra/DeepLearning | [
"4e9ffe39bbb8722ca658522e6b6d26c6f2291ef6"
] | [
"src/utilities/plot.py"
] | [
"import matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set()\n\nimport os\nimport pickle\nimport json\nimport math\n\nfrom scipy.stats import spearmanr\n\n\ndef load_data(working_dir):\n\n with open(os.path.join(working_dir, \"results.json\"), \"r\") as fp:\n\n ... | [
[
"matplotlib.pyplot.boxplot",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"matplotlib.use",
"scipy.stats.spearmanr",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.fill_between",
... | [
{
"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"
... |
Snackya/DotTrack | [
"7d48b05bbd2c09a9d94a0ddb9b87be5d93907bc2"
] | [
"auto_capture/auto_capture.py"
] | [
"#!/usr/bin/env python3\n\nimport ebb_motion\nimport ebb_serial\nimport time\nimport math\nimport numpy as np\nimport serial\nfrom PIL import Image\nimport re\nimport os\n\n\nclass AutoCapture(object):\n\n \"\"\"Automatically capture camera/sensor frames with help of the AxiDraw.\"\"\"\n\n def __init__(self, ... | [
[
"numpy.geomspace",
"numpy.diff",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vishalbelsare/operalib | [
"9f42cdf8b6df09baec374259b86a4d9a32d9e8cb"
] | [
"examples/plot_ovk_onorma.py"
] | [
"\"\"\"\n======================================================\nOnline Learning with Operator-Valued kernels\n======================================================\n\nAn example to illustrate online learning with operator-valued\nkernels.\n\"\"\"\n\nimport operalib as ovk\nimport numpy as np\nimport matplotlib.py... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"numpy.linspace",
"numpy.arange",
"numpy.eye",
"numpy.linalg.norm",
"numpy.cumsum",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"numpy.empty",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FabianGroeger96/deep-embedded-music | [
"9ca6d5588bf025ae6feb848412261c10ac012e1f"
] | [
"src/models_embedding/conv_gru_net.py"
] | [
"import tensorflow as tf\n\nfrom src.models_embedding.base_model import BaseModel\nfrom src.models_embedding.model_factory import ModelFactory\n\n\n@ModelFactory.register(\"ConvGRUNet\")\nclass ConvGRUNet(BaseModel):\n \"\"\" A 2-dimensional CNN model with an additional GRU layer before the fully connected one. ... | [
[
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.MaxPool2D",
"tensorflow.keras.layers.GRU",
"tensorflow.keras.layers.BatchNormalization",
"tensorflow.keras.layers.Reshape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
t-brandt/acorns-adi | [
"6645fae7878a1801beeda0c6604b01e61f37ca15"
] | [
"destripe_utils/horizontal.py"
] | [
"#!/usr/bin/env python\n# Original filename: horizontal.py\n#\n# Author: Tim Brandt\n# Email: tbrandt@astro.princeton.edu\n# Date: January 2011\n# \n# Summary: Compute the best-fit offset between two stripes, subtract \n# from the first stripe supplied. \n# \n\nimport numpy as np\nimport sys\n\ndef horizontal(flu... | [
[
"numpy.hstack",
"numpy.std",
"numpy.mean",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EricCousineau-TRI/blender_server | [
"ffd0bbe72e5646961ac3f4c08c3d633ec3277d8c"
] | [
"render_main_bsm.py"
] | [
"import bpy\nimport numpy as np\nimport os\nimport sys\n\nimport blender_scripts.blender_scene_management as bsm\n\nif __name__ == '__main__':\n bsm.initialize_scene()\n\n metal26_path = \"./data/test_pbr_mats/Metal26/Metal26\"\n bsm.register_material(\"metal26\",\n material_type=\... | [
[
"numpy.random.uniform",
"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": [],
... |
carljohanhoel/EnsembleQuantileNetworks | [
"7dfbed562f3e0c7552fa98a821db502ea565618c"
] | [
"src/core.py"
] | [
"\"\"\"\nSmall changes to the original core.py from keras-rl (https://github.com/keras-rl/keras-rl).\n- Tensorboard support added\n- Keep test episodes fixed for each evaluation run\n\"\"\"\n\n# -*- coding: utf-8 -*-\nimport warnings\nfrom copy import deepcopy\n\nimport numpy as np\nfrom keras.callbacks import Hist... | [
[
"numpy.random.get_state",
"numpy.random.seed",
"numpy.int16",
"numpy.random.set_state",
"numpy.float32",
"numpy.array",
"numpy.sum",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hezl1592/deeplabv3-_mobilenetv2 | [
"cc3134d54bd84f700531c230353228541a7336f5"
] | [
"src/utils/loss.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\nimport numpy as np\n\n\nclass OhemCELoss(nn.Module):\n def __init__(self, thresh, n_min, ignore_lb=255, *args, **kwargs):\n super(OhemCELoss, self).__init__()\n self.thresh = -torch.log(torch.tensor(thresh, dtype=to... | [
[
"torch.mean",
"torch.nn.CrossEntropyLoss",
"torch.nn.NLLLoss",
"torch.nn.functional.softmax",
"torch.nn.functional.log_softmax",
"torch.cat",
"numpy.arange",
"torch.sum",
"torch.zeros_like",
"torch.tensor",
"torch.sort",
"torch.nn.L1Loss",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
svlandeg/healthCy | [
"c085e4f7577e900c132beca97e5d3c708a0463a0"
] | [
"pipeline/scripts/product/lookup_product.py"
] | [
"import typer\nfrom pathlib import Path\nimport pyodbc\nimport pandas as pd\n\nimport json\nimport time\n\nfrom wasabi import Printer\n\nmsg = Printer()\n\n\ndef main(output: Path):\n # Loading reviews\n conn = pyodbc.connect(\n \"Driver={ODBC Driver 17 for SQL Server};\"\n \"Server=localhost;\"... | [
[
"pandas.read_sql_query",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
abirnb01/birnbaum-etal_2022_LACwaterscarcity | [
"a85360d0d7f5b999aaf35a32411a0b9915c9333d"
] | [
"scripts/query_scripts/processing_queries/query_combine.py"
] | [
"#Abigail Birnbaum\n#script to combine query results from scenarios and unconstrained scenarios into single result\n#for water withdrawals and water price\n\n#import statements\nimport pandas as pd\nimport numpy as np\nimport os\nimport glob\n\n#newpath = '/cluster/tufts/lamontagnelab/abirnb01/GCAM_queries/query_re... | [
[
"pandas.read_pickle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
benjaminleroy/smooth_rf | [
"de166a7e777e8a203656b194d772def9d3c8f06d"
] | [
"main_old/understanding_smoothing_splice.py"
] | [
"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport sklearn.ensemble\nimport sklearn.metrics\nimport sklearn\nimport progressbar\nimport sklearn.model_selection\nfrom plotnine import *\nimport pdb\nimport sys\n\nimport smooth_rf\npath = \"../\"\n\n# function\n\ndef average_depth(random... | [
[
"pandas.read_csv",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.metrics.accuracy_score",
"pandas.factorize",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.subplots",
"sklearn.metrics.confusion_matrix",
"numpy.array",
"numpy.zeros",
"pandas.get_dummie... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
nghiaplt/slowfast_new_train | [
"74fc1a576d1888f325382bcc5513205073a07a56"
] | [
"tools/demo_net.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n\nimport numpy as np\nfrom time import time\nimport cv2\nimport pandas as pd\nimport torch\nimport tqdm\nfrom detectron2 import model_zoo\nfrom detectron2.config import get_cfg\nfrom detectron2.engine import DefaultPre... | [
[
"torch.linspace",
"pandas.read_csv",
"numpy.random.seed",
"numpy.min",
"numpy.nonzero",
"torch.manual_seed",
"torch.tensor",
"numpy.array",
"torch.index_select"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
yullidias/AutomaticIronyDetection | [
"3297ddc4ecc97e840b00df4ba4f9e6b8e710fdb9"
] | [
"src/svm.py"
] | [
"# -*- coding: utf-8 -*-\nimport src.utils.constants as cns\nfrom src.my_cross_validation import CrossValidation\nfrom src.utils.files import write_json\nfrom src.utils.files import write_obj\nfrom src.utils.files import read_excel\nfrom src.utils.files import remove_extension\nfrom src.generate_vocabulary import g... | [
[
"sklearn.metrics.confusion_matrix.tolist",
"numpy.asarray",
"sklearn.metrics.confusion_matrix",
"sklearn.svm.SVC",
"sklearn.metrics.classification_report"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yashMustak/mlpack | [
"354938177a718b58685d2d1f5eda3591d61ad3bc"
] | [
"src/mlpack/bindings/python/tests/test_python_binding.py"
] | [
"#!/usr/bin/env python\n\"\"\"\ntest_python_binding.py\n\nTest that passing types to Python bindings works successfully.\n\nmlpack is free software; you may redistribute it and/or modify it under the\nterms of the 3-clause BSD license. You should have received a copy of the\n3-clause BSD license along with mlpack.... | [
[
"numpy.random.rand",
"pandas.Series",
"numpy.random.randint"
]
] | [
{
"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": []
}
] |
wendywwang/DeepLIIF | [
"d30f481cf052771c2f2b49dd5ba76719e4b6590b"
] | [
"deepliif/util/visualizer.py"
] | [
"import numpy as np\nimport os\nimport sys\nimport ntpath\nimport time\nfrom . import util, html\nfrom subprocess import Popen, PIPE\nimport pickle\n\n\nif sys.version_info[0] == 2:\n VisdomExceptionBase = Exception\nelse:\n VisdomExceptionBase = ConnectionError\n\n\ndef save_images(webpage, visuals, image_pa... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cuiyuhao1996/mmnas | [
"d62e0b3ddc6d15e8f01d0d66367e05fc9691cd3b"
] | [
"mmnas/model/mixed.py"
] | [
"import torch, math\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmnas.utils.ops_adapter import OpsAdapter\nOPS_ADAPTER = OpsAdapter()\n\nclass ArchGradientFunction(torch.autograd.Function):\n\n @staticmethod\n def forward(ctx, s, pre, s_mask, pre_mask, binary_gates, run_func, backward_func):... | [
[
"torch.nn.functional.softmax",
"torch.enable_grad",
"torch.zeros",
"torch.nn.ModuleList",
"torch.zeros_like",
"torch.sum",
"torch.multinomial",
"torch.no_grad",
"torch.stack",
"torch.autograd.grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kyushik/JORLDY | [
"6a24a2195e5e87ade157ee53f631af2221f0a188"
] | [
"jorldy/core/network/dueling.py"
] | [
"import torch\nimport torch.nn.functional as F\n\nfrom .base import BaseNetwork\nfrom .utils import orthogonal_init\n\n\nclass Dueling(BaseNetwork):\n def __init__(self, D_in, D_out, D_hidden=512, head=\"mlp\"):\n D_head_out = super(Dueling, self).__init__(D_in, D_hidden, head)\n\n self.l1_a = torc... | [
[
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
julienbiau/CGP-IP | [
"630af2835152494313becb49ab86558a540fc523"
] | [
"cgpip/function_viewer.py"
] | [
"import matplotlib.pyplot as plt\n\nfrom functions import Functions\nfrom fct_std_uint8 import STD_UINT8\n#from supp_functions import SuppFunctions\n\nfrom skimage import data\nimport time\n\nclass FunctionViewer(object):\n def __init__(self,functions):\n self.function_bundle = functions\n self.num... | [
[
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.axis"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chen1310054465/RE | [
"df1444292aae7f4441fb3b9202bcb88cf555f021"
] | [
"train.py"
] | [
"import tensorflow as tf\n\nimport framework as fw\nimport data_loader as dl\nimport model.model_base as mb\nimport model.model_rl as mr\n\nFLAGS = tf.flags.FLAGS\n\nif __name__ == '__main__':\n fw.init()\n model = mr.model_rl if '_rl' in FLAGS.se else mb.model\n\n framework = fw.framework(dl.json_file_dat... | [
[
"tensorflow.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
tjd2002/pynwb | [
"426109b85c2e683d96f2126286fb3369013abb8b"
] | [
"docs/gallery/general/extensions.py"
] | [
"'''\n.. _tutorial-extending-nwb:\n\nExtensions\n=========================\n\nThe NWB-N format was designed to be easily extendable. Here we will demonstrate how to extend NWB using the\nPyNWB API.\n\n.. note::\n\n A more in-depth discussion of the components and steps for creating and using extensions is\n a... | [
[
"numpy.arange",
"numpy.random.rand",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neuralaudio/hear-preprocess | [
"444c645d0bdc530cfc01897b130d6c0cd9f8b647",
"444c645d0bdc530cfc01897b130d6c0cd9f8b647"
] | [
"hearpreprocess/tfds_pipeline.py",
"hearpreprocess/secrettasks/hearsecrettasks/coughvid.py"
] | [
"#!/usr/bin/env python3\n\"\"\"\nCustom Preprocessing pipeline for tensorflow dataset\n\nTfds audio datasets can be preprocessed with the hear-preprocess pipeline by defining\nthe generic_task_config dict and optionally overriding the extract metadata in this\nfile\nSee example tfds_speech_commands.py for a sample ... | [
[
"numpy.issubdtype",
"numpy.abs"
],
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
liangshb/few-shot-text-classification | [
"3bb2b3e87215ccf0fb6d5b0d436774557ac9ddd0"
] | [
"sysevr_data.py"
] | [
"import os\nimport pickle\nimport copy\nimport random\nfrom random import sample\nfrom omegaconf import OmegaConf\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nfrom fastNLP import Vocabulary\nfrom dataset import Dataset\nfrom dataloader import TrainDataLoader\nfrom utils import paddi... | [
[
"numpy.random.seed",
"torch.cat",
"torch.manual_seed",
"torch.tensor",
"torch.cuda.manual_seed_all"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yxngl/GSEApy | [
"785acd9be989d7900fe89dba889d163c7bd5c1f1"
] | [
"gseapy/parser.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport sys, logging, json, os\nimport requests\nimport pandas as pd\nfrom io import StringIO\nfrom numpy import in1d\nfrom requests.packages.urllib3.util.retry import Retry\nfrom requests.adapters import HTTPAdapter\nfrom bioservices import BioMart, BioServicesError\nfrom gseapy.utils im... | [
[
"pandas.concat",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
treverhines/ModEst | [
"3478c730ff17b27ed3f1a28d2ee31c2c9054802d"
] | [
"modest/weight.py"
] | [
"#!/usr/bin/env python\nimport numpy as np\nimport scipy\n\nclass Weight:\n def __init__(self,cov=None,var=None,std=None,weight=None): \n if cov is not None: \n cov = np.asarray(cov)\n assert axes_no(cov) == 2, 'covariance matrix must be 2 dimensional'\n if isdiagonal(cov):\n ... | [
[
"numpy.diag",
"numpy.sqrt",
"numpy.einsum",
"numpy.asarray",
"numpy.eye",
"numpy.shape",
"numpy.linalg.cholesky",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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