repo_name stringlengths 8 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
adcrn/knest | [
"a274dc9ddb642cc30f837e225f000bf33430eb43"
] | [
"utils/compare.py"
] | [
"# UCF Senior Design 2017-18\n# Group 38\n\nfrom PIL import Image\nimport cv2\nimport imagehash\nimport math\nimport numpy as np\n\nDIFF_THRES = 20\nLIMIT = 2\nRESIZE = 1000\n\n\ndef calc_hash(img):\n \"\"\"\n Calculate the wavelet hash of the image\n img: (ndarray) image file\n \"\"\"\n # resize... | [
[
"numpy.shape"
]
] |
dongmengshi/easylearn | [
"df528aaa69c3cf61f5459a04671642eb49421dfb",
"df528aaa69c3cf61f5459a04671642eb49421dfb"
] | [
"eslearn/utils/lc_featureSelection_variance.py",
"eslearn/machine_learning/test/GCNNCourseCodes/metrics.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 24 14:38:20 2018\ndimension reduction with VarianceThreshold using sklearn.\nFeature selector that removes all low-variance features.\n@author: lenovo\n\"\"\"\nfrom sklearn.feature_selection import VarianceThreshold\nimport numpy as np\n#\nnp.random.seed(1)\nX = ... | [
[
"numpy.random.randn",
"numpy.random.seed",
"numpy.zeros",
"sklearn.feature_selection.VarianceThreshold"
],
[
"tensorflow.nn.sigmoid_cross_entropy_with_logits",
"tensorflow.nn.sigmoid",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.round",
"tensorflow.reduce... |
silent567/examples | [
"e9de12549125ecd93a4924f6b8e2bbf66d7635d9"
] | [
"mnist/my_multi_tune3.py"
] | [
"#!/usr/bin/env python\n# coding=utf-8\n\nfrom my_multi_main3 import main\nimport numpy as np\nimport argparse\nimport time\n\nparser = argparse.ArgumentParser(description='PyTorch MNIST Example')\nparser.add_argument('--batch-size', type=int, default=64, metavar='N',\n help='input batch size for... | [
[
"numpy.arange",
"numpy.savetxt"
]
] |
neonbjb/DL-Art-School | [
"a6f0f854b987ac724e258af8b042ea4459a571bc"
] | [
"codes/data/image_corruptor.py"
] | [
"import functools\nimport random\nfrom math import cos, pi\n\nimport cv2\nimport kornia\nimport numpy as np\nimport torch\nfrom kornia.augmentation import ColorJitter\n\nfrom data.util import read_img\nfrom PIL import Image\nfrom io import BytesIO\n\n\n# Get a rough visualization of the above distribution. (Y-axis ... | [
[
"numpy.ones",
"numpy.sum",
"numpy.zeros",
"torch.from_numpy",
"numpy.clip",
"numpy.random.rand",
"numpy.mean"
]
] |
pclucas14/continuum | [
"09034db1371e9646ca660fd4d4df73e61bf77067"
] | [
"tests/test_background_swap.py"
] | [
"import os\n\nfrom torch.utils.data import DataLoader\nfrom continuum.datasets import CIFAR10, InMemoryDataset\nfrom continuum.datasets import MNIST\nimport torchvision\nfrom continuum.scenarios import TransformationIncremental\nimport pytest\nimport numpy as np\n\nfrom continuum.transforms.bg_swap import Backgroun... | [
[
"numpy.ones",
"torch.utils.data.DataLoader",
"numpy.random.rand",
"numpy.array_equal",
"numpy.random.normal"
]
] |
g-nightingale/tox_examples | [
"d7714375c764580b4b8af9db61332ced4e851def"
] | [
"packaging/squarer/ml_squarer.py"
] | [
"import numpy as np\n\n\ndef train_ml_squarer() -> None:\n print(\"Training!\")\n\n\ndef square() -> int:\n \"\"\"Square a number...maybe\"\"\"\n return np.random.randint(1, 100)\n\n\nif __name__ == '__main__':\n train_ml_squarer()"
] | [
[
"numpy.random.randint"
]
] |
GOOGLE-M/SGC | [
"78ad8d02b80808302e38559e2d0f430f66a809bd"
] | [
"venv/lib/python3.7/site-packages/torch/utils/benchmark/utils/timer.py"
] | [
"\"\"\"Timer class based on the timeit.Timer class, but torch aware.\"\"\"\nimport enum\nimport timeit\nimport textwrap\nfrom typing import Any, Callable, Dict, List, NoReturn, Optional, Type, Union\n\nimport numpy as np\nimport torch\nfrom torch.utils.benchmark.utils import common, cpp_jit\nfrom torch.utils.benchm... | [
[
"torch.utils.benchmark.utils.common.TaskSpec",
"torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.wrapper_singleton",
"torch.utils.benchmark.utils.common.Measurement",
"torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CopyIfCallgrind.unwrap_all",
"torch.cuda.synchronize"... |
mohammedshariqnawaz/Pedestron | [
"9785feb94f00e07ae24a662525b4678f12d0fdc8"
] | [
"mmdet/models/detectors/csp.py"
] | [
"\nfrom .single_stage import SingleStageDetector\nfrom ..registry import DETECTORS\nfrom mmdet.core import bbox2result\nimport torch.nn as nn\nimport torch\nfrom .. import builder\nimport numpy as np\nimport cv2\nfrom mmdet.core import bbox2roi, bbox2result, build_assigner, build_sampler\n\n@DETECTORS.register_modu... | [
[
"torch.tensor",
"numpy.transpose"
]
] |
MichaelAllen1966/stroke_outcome_algorithm | [
"99050bf4e0b19c38c8973fe10234fee4f230a172"
] | [
"clinical_outcome.py"
] | [
"\"\"\"\nClass to hold clinical outcome model.\nPredicts probability of good outcome of patient(s) or group(s) of patients.\n\nCall `calculate_outcome_for_all(args)` from outside of the object\n\nInputs\n======\n\nAll inputs take np arrays (for multiple groups of patients).\n\nmimic: proportion of patients with str... | [
[
"numpy.log",
"pandas.DataFrame",
"numpy.zeros",
"numpy.full"
]
] |
brettelliot/event-study | [
"cffc6a80dbc4b33e68e863488428996af51cc991"
] | [
"examples/earnings_surprises/earnings-converter.py"
] | [
"import pandas as pd\nfrom pandas.compat import StringIO\nimport numpy\nnumpy.set_printoptions(threshold=numpy.nan)\n\n\ndef main():\n df = pd.read_csv(StringIO(earnings), sep=\",\", header=None,\n names=['symbol', 'exchange', 'eps_pct_diff_surp', 'asof_date'])\n df = df.sort_values(by=['a... | [
[
"numpy.set_printoptions",
"pandas.compat.StringIO"
]
] |
SJCosgrove/quantoipian | [
"8beba055aa4211dc2debc5c3083077cbd19d0bbc"
] | [
"zipline/data/history_loader.py"
] | [
"# Copyright 2016 Quantopian, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or ag... | [
[
"pandas.tslib.normalize_date",
"pandas.isnull"
]
] |
taroxd/mindspore | [
"9bb620ff2caaac7f1c53c4b104935f22352cb88f",
"9bb620ff2caaac7f1c53c4b104935f22352cb88f"
] | [
"model_zoo/official/cv/ssd/src/dataset.py",
"model_zoo/official/nlp/lstm/eval.py"
] | [
"# Copyright 2020 Huawei Technologies Co., Ltd\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 l... | [
[
"numpy.random.choice",
"numpy.expand_dims",
"numpy.random.rand",
"numpy.maximum",
"numpy.concatenate",
"numpy.array",
"numpy.minimum"
],
[
"numpy.pad",
"numpy.ceil"
]
] |
doronbehar/lab4 | [
"90af5a8fd562ba6a35b6ba90611122573e7de485"
] | [
"x2.ESR/ESRB.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport pint\n# Use the same registry\nfrom main import ureg\nureg.setup_matplotlib(True)\nfrom uncertainties import ufloat, umath, unumpy\nimport pandas as pd\nfrom scipy.signal import find_peaks\nfrom scipy.integrate import simpson\nfrom scipy.optimize import c... | [
[
"pandas.read_csv",
"matplotlib.pyplot.show",
"numpy.array",
"matplotlib.pyplot.subplots"
]
] |
ayushkarnawat/profit | [
"f3c4d601078b52513af6832c3faf75ddafc59ac5"
] | [
"examples/gb1/train_oracle.py"
] | [
"\"\"\"Train (basic) densely-connected oracle.\"\"\"\n\nimport os\nimport time\nimport multiprocessing as mp\n\nimport pandas as pd\n\nimport torch\nfrom torch import optim\nfrom torch.utils.data import DataLoader, Subset, TensorDataset, WeightedRandomSampler\n\nfrom profit.dataset.splitters import split_method_dic... | [
[
"torch.unsqueeze",
"torch.zeros_like",
"torch.cuda.is_available",
"torch.arange",
"torch.unique",
"torch.zeros"
]
] |
Air-Factories-2-0/af2-hyperledger | [
"7aeeb831cf03fdf7fe64f9500da17c02688a0886"
] | [
"scripts/printingValidation/venv/lib/python3.9/site-packages/skimage/morphology/tests/test_max_tree.py"
] | [
"import numpy as np\nfrom skimage.morphology import max_tree, area_closing, area_opening\nfrom skimage.morphology import max_tree_local_maxima, diameter_opening\nfrom skimage.morphology import diameter_closing\nfrom skimage.util import invert\n\nfrom skimage._shared.testing import assert_array_equal, TestCase\n\nep... | [
[
"numpy.array",
"numpy.max",
"numpy.zeros"
]
] |
hw07216/imaginaire | [
"d82c87aced50afd44fd162491ba5b59056b74034"
] | [
"imaginaire/losses/feature_matching.py"
] | [
"# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n#\n# This work is made available under the Nvidia Source Code License-NC.\n# To view a copy of this license, check out LICENSE.md\nimport torch.nn as nn\n\n\nclass FeatureMatchingLoss(nn.Module):\n r\"\"\"Compute feature matching loss\... | [
[
"torch.nn.L1Loss",
"torch.nn.MSELoss"
]
] |
GZHoffie/analytics-zoo | [
"d0258aa113ffd1a5c4927376fb32b09fb0baf73c",
"d0258aa113ffd1a5c4927376fb32b09fb0baf73c"
] | [
"pyzoo/zoo/zouwu/model/Seq2Seq.py",
"pyzoo/zoo/zouwu/model/MTNet_keras.py"
] | [
"#\n# Copyright 2018 Analytics Zoo 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 ... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.keras.optimizers.RMSprop",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.LSTM",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Input"
],
[
"tensorflow.keras.backend.reshape",
"tensorflow.keras.backend.dot"... |
ILoveRedEd55/AIML_Detection_System | [
"b2fdd8475f069884060f7bb31f41953bae057d7b"
] | [
"lib/src/layers/RNN.py"
] | [
"from src.layers.LayerHelper import *\nfrom settings import LayerSettings as layerSettings\nimport tensorflow as tf\nimport os\nCUDA_VISIBLE_DEVICES=0\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\" # set gpu number\n\ndef LSTM(name_, inputTensor_, numberOfOutputs_, isTraining_, dropoutProb_=None):\n\twith tf.name_sc... | [
[
"tensorflow.placeholder",
"tensorflow.losses.add_loss",
"tensorflow.nn.rnn_cell.LSTMCell",
"tensorflow.trainable_variables",
"tensorflow.nn.dynamic_rnn",
"tensorflow.name_scope",
"tensorflow.nn.rnn_cell.DropoutWrapper",
"tensorflow.cond"
]
] |
tadasdanielius/P5-Vehicle-Detection-And-Tracking | [
"38513e91d863f7fff50703349aacbe5d5bbfae39"
] | [
"sdc/detection/cnn_classifier.py"
] | [
"from keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Convolution2D, MaxPooling2D\nfrom keras.layers import Activation, Dropout, Flatten, Dense, Lambda, ELU\nfrom keras.optimizers import Adam\nfrom sklearn.model_selection import train_test_split\nfr... | [
[
"tensorflow.shape",
"numpy.zeros",
"tensorflow.device",
"tensorflow.name_scope",
"tensorflow.slice",
"tensorflow.concat",
"numpy.concatenate",
"sklearn.model_selection.train_test_split"
]
] |
ManjunathaPatkar/Machine-Learning | [
"f1c6ec1a9f802f6e88ed67c0da6c1e9373790537"
] | [
"Machine Learning A-Z Template Folder/Part 2 - Regression/Section 5 - Multiple Linear Regression/data_preprocessing_template.py"
] | [
"# Data Preprocessing Template\n\n# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the dataset\ndataset = pd.read_csv('50_Startups.csv')\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, 4].values\n\n#encoding independent variable state\n#from sk... | [
[
"numpy.ones",
"pandas.read_csv",
"sklearn.linear_model.LinearRegression",
"numpy.array",
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.OneHotEncoder"
]
] |
marijnfs/onnxruntime | [
"6e1eb4b0efca9644c5f8979fbded9416fdd722dc"
] | [
"tools/ci_build/build.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\nimport argparse\nimport glob\nimport multiprocessing\nimport os\nimport re\nimport shutil\nimport subprocess\nimport sys\nimport hashlib\nfrom logger import log\n\n\nclass BaseError(Exception):... | [
[
"torch.cuda.device_count"
]
] |
insilicomedicine/TRIP | [
"5e7b9da298aa47a71c71e1144ff1d8e538dbccaa"
] | [
"core/learnable_priors/normal_prior.py"
] | [
"import torch\nfrom torch import nn\nfrom torch.distributions import MultivariateNormal\n\nclass Normal(nn.Module):\n def __init__(self, num_vars=100):\n super(Normal, self).__init__()\n\n self.num_vars = num_vars\n\n self.means = nn.Parameter(torch.zeros(num_vars))\n self.std = nn.Pa... | [
[
"torch.zeros",
"torch.eye",
"torch.distributions.MultivariateNormal"
]
] |
Ejjaffe/dit | [
"c9d206f03d1de5a0a298b1d0ea9d79ea5e789ee1"
] | [
"dit/math/ops.py"
] | [
"\"\"\"\nClasses to contextualize math operations in log vs linear space.\n\n\"\"\"\nfrom types import MethodType\n\nimport numpy as np\n\nfrom ..exceptions import InvalidBase\n\n\n__all__ = (\n 'get_ops',\n 'LinearOperations',\n 'LogOperations',\n)\n\n\n# For 2.x, these are ascii strings. For 3.x these ar... | [
[
"numpy.log2",
"numpy.logaddexp2.reduce",
"numpy.isclose",
"numpy.asarray",
"numpy.exp",
"numpy.prod",
"numpy.log",
"numpy.logaddexp2"
]
] |
NingAnMe/voxelmorph | [
"3a1a4c2f456af2dba5552efc1b08c68af38e54dc"
] | [
"scripts/sphere/register.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nExample script to register two volumes with VoxelMorph models.\n\nPlease make sure to use trained models appropriately. Let's say we have a model trained to register \na scan (moving) to an atlas (fixed). To register a scan to the atlas and save the warp field, run:\n\n register... | [
[
"matplotlib.pyplot.imshow",
"numpy.nanmean",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.title",
"torch.from_numpy",
"numpy.linspace",
"numpy.mean",
"numpy.zeros",
"scipy.interpolate.RegularGridInterpolator",
"numpy.max",
"numpy.min",... |
meretp/pymor | [
"01876cd39e04bec6d4299f36b59663cd08beafd3",
"0965a5c3d0725466103efae5190493fceb2bf441"
] | [
"src/pymor/reductors/residual.py",
"src/pymor/bindings/ngsolve.py"
] | [
"# This file is part of the pyMOR project (https://www.pymor.org).\n# Copyright 2013-2021 pyMOR developers and contributors. All rights reserved.\n# License: BSD 2-Clause License (https://opensource.org/licenses/BSD-2-Clause)\n\nimport numpy as np\n\nfrom pymor.algorithms.image import estimate_image_hierarchical\nf... | [
[
"numpy.nan_to_num"
],
[
"numpy.argmax"
]
] |
Mishrasubha/napari | [
"c4d1038fc3ed30dc228949cbdedf12826ec2efc2"
] | [
"napari/_qt/layer_controls/qt_vectors_controls.py"
] | [
"import numpy as np\nfrom qtpy.QtCore import Qt\nfrom qtpy.QtWidgets import QComboBox, QDoubleSpinBox, QLabel\n\nfrom ...layers.utils._color_manager_constants import ColorMode\nfrom ...utils.translations import trans\nfrom ..utils import qt_signals_blocked\nfrom ..widgets.qt_color_swatch import QColorSwatchEdit\nfr... | [
[
"numpy.union1d"
]
] |
XuboGU/neorl | [
"066cdbd9e9cdbfe371278dba3ece116f25edab2d"
] | [
"neorl/tune/runners/estune.py"
] | [
"import numpy as np\nimport pandas as pd\nimport os\nimport random\nimport math\nfrom itertools import repeat\nimport itertools\nimport sys, copy, shutil\nimport subprocess\nfrom multiprocessing.dummy import Pool\nfrom collections import defaultdict\nimport copy\n\nimport random\nimport matplotlib.pyplot as plt\n\n... | [
[
"pandas.read_csv",
"numpy.floor",
"numpy.max",
"numpy.sqrt",
"numpy.mean"
]
] |
zhuriheng/faster-rcnn.pytorch | [
"7536b0f5eee254350fb4dce5c4a077ac6d29db16"
] | [
"test_net.py"
] | [
"# --------------------------------------------------------\n# Tensorflow Faster R-CNN\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick\n# --------------------------------------------------------\nfrom __future__ import absolute_impo... | [
[
"torch.utils.data.DataLoader",
"torch.FloatTensor",
"numpy.tile",
"torch.nonzero",
"torch.load",
"numpy.sort",
"torch.autograd.Variable",
"numpy.random.seed",
"numpy.copy",
"torch.cuda.is_available",
"numpy.array",
"numpy.where",
"torch.LongTensor",
"torch.s... |
hirune924/kaggle-HuBMAP | [
"e4c2008378eb773db551cee52380bfccdf3a10fa"
] | [
"system/system.py"
] | [
"import pytorch_lightning as pl\nfrom loss.loss import get_loss\nfrom optimizer.optimizer import get_optimizer\nfrom scheduler.scheduler import get_scheduler\n\nimport torch\nimport numpy as np\nfrom pytorch_lightning.metrics import Accuracy\nimport segmentation_models_pytorch as smp\n\nfrom utils.utils import load... | [
[
"numpy.random.beta",
"torch.stack"
]
] |
han-kwang/coronatest-scandata | [
"98fd49f4fdcda10561bce41e769bbbb70ecfe94e"
] | [
"coronatest_analyze_csv.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"Analyze CSV file into scores.\n\nCreated on Sat Feb 12 22:15:29 2022 // @hk_nien\n\"\"\"\nfrom pathlib import Path\nimport os\nimport re\nimport sys\nimport pandas as pd\nimport numpy as np\n\nPCODES = dict([\n # Regio Noord\n (1011, 'Amsterdam'),\n ... | [
[
"pandas.read_csv",
"numpy.median",
"pandas.Timedelta",
"pandas.DataFrame.from_records",
"pandas.to_datetime",
"pandas.concat",
"numpy.around",
"pandas.Timestamp",
"pandas.isna"
]
] |
hardywu/vnpy | [
"81ab73dc57d12a3ff7c74c73665513b46fc0f668"
] | [
"vnpy/app/portfolio_strategy/backtesting.py"
] | [
"from collections import defaultdict\nfrom datetime import date, datetime, timedelta\nfrom typing import Dict, List, Set, Tuple\nfrom functools import lru_cache\nfrom copy import copy\nimport traceback\n\nimport numpy as np\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\nfrom pandas i... | [
[
"numpy.sqrt",
"numpy.nan_to_num",
"pandas.DataFrame.from_dict"
]
] |
rohit-konda/markovGames | [
"d6dd1b8a11f1c95658a468f9e471aecfcf0e6839"
] | [
"markovGames/learning/bruteSearch.py"
] | [
"import numpy as np\nfrom itertools import product\nfrom markovGames.gameDefs.mdpDefs import Policy\n\n\ndef getAllDetPol(numStates, numActions):\n detProbs = [np.array([1 if j == i else 0 for j in range(numActions)]) for i in range(numActions)]\n return product(detProbs, repeat=numStates)\n\n\ndef getPolList... | [
[
"numpy.amax",
"numpy.max",
"numpy.argmax",
"numpy.zeros"
]
] |
M155K4R4/Tensorflow | [
"e5e03ef3148303b3dfed89a1492dedf92b45be25",
"e5e03ef3148303b3dfed89a1492dedf92b45be25"
] | [
"tensorflow/contrib/rnn/python/ops/lstm_ops.py",
"tensorflow/contrib/bayesflow/python/ops/hmc_impl.py"
] | [
"# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.ops.array_ops.sequence_mask",
"tensorflow.python.ops.nn_ops.bias_add_grad",
"tensorflow.python.ops.array_ops.expand_dims",
"tensorflow.python.ops.array_ops.reshape",
"tensorflow.python.platform.resource_loader.get_path_to_datafile",
"tensorflow.python.ops.math_ops.matmul... |
DEVESHTARASIA/big-data-tutorial | [
"74e2aa1241c30913c5f12b9667f9d626002b98a2"
] | [
"tutorial/helpers.py"
] | [
"\"\"\"\nSmall helpers for code that is not shown in the notebooks\n\"\"\"\n\nfrom sklearn import neighbors, datasets, linear_model\nimport pylab as pl\nimport numpy as np\nfrom matplotlib.colors import ListedColormap\n\n# Create color maps for 3-class classification problem, as with iris\ncmap_light = ListedColorm... | [
[
"matplotlib.colors.ListedColormap",
"sklearn.linear_model.LinearRegression",
"numpy.random.RandomState",
"sklearn.neighbors.KNeighborsClassifier",
"numpy.linspace",
"sklearn.datasets.load_iris"
]
] |
cosmic-cortex/torchkit | [
"9f44c8a500a4345d81feac14b6b200c5d190283a"
] | [
"torchkit/models/vision/segmentation/unet.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom torch.autograd import Variable\r\n\r\n\r\ndef pad_to_shape(this, shp):\r\n \"\"\"\r\n Not a very safe function.\r\n \"\"\"\r\n return F.pad(this, (0, shp[3] - this.shape[3], 0, shp[2] - this.shape[2]))\r\n\r\n\r\nclass Fir... | [
[
"torch.nn.BatchNorm2d",
"torch.nn.MaxPool2d",
"torch.nn.Dropout2d",
"torch.nn.Softmax2d",
"torch.nn.functional.pad",
"torch.nn.Conv2d",
"torch.nn.Sequential",
"torch.nn.Sigmoid",
"torch.nn.ReLU",
"torch.nn.ConvTranspose2d"
]
] |
romquentin/decod_WM_Selection_and_maintenance | [
"fc1bf2f21959795fbea731f642cc750c2b61bce2"
] | [
"run_decoding/run_decoding_WM_across_epochs_and_conditions.py"
] | [
"\"\"\"Run decoding analyses in sensors space accross memory content and\nvisual perception for the working memory task and save decoding performance\"\"\"\n\n# Authors: Romain Quentin <rom.quentin@gmail.com>\n# Jean-Remi King <jeanremi.king@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport os\nimport os.... | [
[
"sklearn.model_selection.StratifiedKFold",
"sklearn.linear_model.Ridge",
"numpy.where",
"sklearn.metrics.make_scorer",
"sklearn.preprocessing.StandardScaler",
"numpy.array",
"numpy.concatenate",
"numpy.isnan",
"numpy.mean"
]
] |
Pandinosaurus/dsbox | [
"aea56049025ed7e6e66427f8636286f8be1b6e03"
] | [
"dsbox/ml/visualization/metrics.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.metrics import roc_curve, auc\n\n__author__ = \"Aurélien Massiot\"\n__credits__ = \"https://github.com/octo-technology/bdacore\"\n__license__ = \"Apache 2.0\"\n\n\ndef plot_confusion_matrix(confusion_matrix, classes_list, norm... | [
[
"sklearn.metrics.roc_curve",
"matplotlib.pyplot.figure",
"sklearn.metrics.auc",
"pandas.DataFrame",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.matshow",
"matplotlib.pyplot.ylim",
"nump... |
aschueth/MetPy | [
"5e906c0fcfadccdc8514011d15d911243130d405"
] | [
"src/metpy/calc/thermo.py"
] | [
"# Copyright (c) 2008,2015,2016,2017,2018,2019 MetPy Developers.\n# Distributed under the terms of the BSD 3-Clause License.\n# SPDX-License-Identifier: BSD-3-Clause\n\"\"\"Contains a collection of thermodynamic calculations.\"\"\"\nimport warnings\n\nimport numpy as np\nimport scipy.integrate as si\nimport scipy.o... | [
[
"numpy.any",
"scipy.optimize.fixed_point",
"numpy.argsort",
"numpy.insert",
"numpy.copy",
"numpy.isclose",
"numpy.log",
"numpy.trapz",
"numpy.ediff1d",
"numpy.append",
"numpy.isnan",
"numpy.where",
"numpy.searchsorted",
"numpy.argmax",
"numpy.max",
"... |
mrcagney/googlemaps_helpers | [
"75dfcc3e5e788d04c3af3e7608909b349ac83e8d"
] | [
"googlemaps_helpers/main.py"
] | [
"from itertools import product\nimport math\nfrom collections import OrderedDict\nfrom pathlib import Path\nimport logging\n\nimport pandas as pd\nimport numpy as np\nimport geopandas as gpd\nimport shapely.geometry as sg\nimport googlemaps\n\n\n# Configure logging\nlogger = logging.getLogger()\nhandler = logging.S... | [
[
"pandas.Series",
"pandas.DataFrame"
]
] |
quantumalaviya/keras | [
"8d874de12ed2e199d9528bfff891f4f60ee2a636"
] | [
"keras/saving/saved_model/layer_serialization.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.compat.v2.__internal__.tracking.wrap"
]
] |
Pengchengpcx/Neighbor-Sampling-GCN | [
"4b47385bdbfeb5957a56b05c441482e701dd10de"
] | [
"build/lib/pygcn/utils.py"
] | [
"import numpy as np\nimport scipy.sparse as sp\nimport torch\n\n\ndef encode_onehot(labels):\n classes = set(labels)\n classes_dict = {c: np.identity(len(classes))[i, :] for i, c in\n enumerate(classes)}\n labels_onehot = np.array(list(map(classes_dict.get, labels)),\n ... | [
[
"numpy.vstack",
"numpy.ones",
"torch.sum",
"torch.Size",
"torch.randint",
"numpy.dtype",
"numpy.isinf",
"scipy.sparse.csr_matrix",
"torch.zeros",
"scipy.sparse.diags",
"scipy.sparse.eye",
"torch.from_numpy",
"torch.sparse.FloatTensor",
"numpy.power",
"nu... |
EricLina/attn2d | [
"12c3a53887c985ae24199ecef2f7b2335fe214c6"
] | [
"examples/pervasive/modules/archive/expanding_resnet.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the LICENSE file in\n# the root directory of this source tree. An additional grant of patent rights\n# can be found in the PATENTS file in the same directory.\n\nimport torch\nimport t... | [
[
"torch.nn.init.xavier_uniform_",
"torch.nn.Linear",
"torch.nn.init.constant_",
"torch.nn.functional.dropout",
"torch.nn.functional.relu",
"torch.nn.Conv2d",
"torch.nn.ModuleList"
]
] |
watsonjj/gammapy | [
"8d2498c8f63f73d1fbe4ba81ab02d9e72552df67",
"8d2498c8f63f73d1fbe4ba81ab02d9e72552df67"
] | [
"gammapy/utils/fitting/tests/test_iminuit.py",
"gammapy/catalog/tests/test_hess.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\nimport pytest\nfrom numpy.testing import assert_allclose\nfrom .. import Parameter, Parameters, optimize_iminuit\n\npytest.importorskip(\"iminuit\")\n\n\ndef fcn(parameters):\n x = parameters[\"x\"].value\n y = parameters[\"y\"].value\n z = ... | [
[
"numpy.testing.assert_allclose"
],
[
"numpy.meshgrid",
"numpy.linspace",
"numpy.testing.assert_allclose"
]
] |
mbilos/stribor | [
"76082c255653d6bd8d506519223183e5d8395578"
] | [
"stribor/flow.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.distributions as td\r\n\r\nclass Flow(nn.Module):\r\n \"\"\"\r\n Building both normalizing flows and neural flows.\r\n\r\n Example:\r\n >>> import stribor as st\r\n >>> torch.manual_seed(123)\r\n >>> dim = 2\... | [
[
"torch.zeros_like",
"torch.nn.ModuleList"
]
] |
xiaodashuaiya/fairseq | [
"9e3850bd87f4da751671d503406115730b99ea8a"
] | [
"fairseq/utils.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 contextlib\nimport copy\nimport importlib.util\nimport logging\nimport math\nimport os\nimport sys\nimport warnings\nfrom col... | [
[
"torch.empty",
"torch.stack",
"torch.cuda.manual_seed",
"torch.cuda.get_rng_state",
"torch.nn.functional.softmax",
"torch.cumsum",
"torch.cuda.set_rng_state",
"torch.norm",
"torch.arange",
"torch.set_rng_state",
"torch.manual_seed",
"torch.tensor",
"torch.cuda.c... |
MaryZolfaghar/WCSLS | [
"fcb3bfd11c19bb90690ec772f91bbd107832d636"
] | [
"utils/analyze.py"
] | [
"from numpy.core.fromnumeric import reshape\nimport torch \nimport numpy as np\nimport pickle\nfrom itertools import combinations, permutations\nfrom sklearn.decomposition import PCA\nfrom sklearn.manifold import MDS, TSNE\nfrom scipy.stats import pearsonr, ttest_ind\nimport statsmodels.api as sm\nfrom dataset impo... | [
[
"numpy.sum",
"scipy.stats.ttest_ind",
"torch.no_grad",
"numpy.argsort",
"numpy.asarray",
"numpy.any",
"sklearn.manifold.MDS",
"numpy.abs",
"sklearn.manifold.TSNE",
"numpy.expand_dims",
"numpy.mean",
"scipy.stats.pearsonr",
"numpy.zeros",
"numpy.arange",
... |
jirivrany/kagle-statoil | [
"8c70691fc7ca7d8a6a33a3544f76b22f1b508f7a"
] | [
"cnn_2bands.py"
] | [
"\n# coding: utf-8\n\n\"\"\"\n\n\"\"\"\n\n\nimport pandas as pd \nimport numpy as np \nimport cv2 # Used to manipulated the images \nfrom scipy.signal import wiener\n\nnp.random.seed(1207) # The seed I used - pick your own or comment out for a random seed. A constant seed allows for better comparisons though\n\n# I... | [
[
"numpy.concatenate",
"numpy.random.seed",
"pandas.read_json",
"numpy.dstack",
"numpy.array",
"numpy.where",
"sklearn.model_selection.train_test_split"
]
] |
pagun12/predictive-monitoring-benchmark | [
"78a3c2723406dd85aec3b5b01e1ae2edb657f8e2"
] | [
"bucketers/StateBasedBucketer.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom time import time\nimport sys\n\nclass StateBasedBucketer(object):\n \n def __init__(self, encoder):\n self.encoder = encoder\n \n self.dt_states = None\n self.n_states = 0\n \n \n def fit(self, X, y=None):\n \n ... | [
[
"pandas.merge"
]
] |
EkremBayar/bayar | [
"aad1a32044da671d0b4f11908416044753360b39",
"aad1a32044da671d0b4f11908416044753360b39",
"aad1a32044da671d0b4f11908416044753360b39",
"aad1a32044da671d0b4f11908416044753360b39",
"aad1a32044da671d0b4f11908416044753360b39",
"aad1a32044da671d0b4f11908416044753360b39",
"aad1a32044da671d0b4f11908416044753360b3... | [
"venv/Lib/site-packages/pandas/tests/plotting/test_hist_method.py",
"venv/Lib/site-packages/statsmodels/tsa/filters/tests/test_filters.py",
"venv/Lib/site-packages/plotnine/scales/scale_stroke.py",
"venv/Lib/site-packages/scipy/sparse/linalg/isolve/tests/test_gcrotmk.py",
"venv/Lib/site-packages/scipy/spars... | [
"\"\"\" Test cases for .hist method \"\"\"\n\nimport numpy as np\nimport pytest\n\nimport pandas.util._test_decorators as td\n\nfrom pandas import DataFrame, Index, Series, to_datetime\nimport pandas._testing as tm\nfrom pandas.tests.plotting.common import TestPlotBase, _check_plot_works\n\npytestmark = pytest.mark... | [
[
"pandas._testing.assert_numpy_array_equal",
"pandas.tests.plotting.common.TestPlotBase.setup_method",
"matplotlib.pyplot.gcf",
"pandas._testing.assert_produces_warning",
"numpy.random.choice",
"pandas._testing.close",
"pandas._testing.assert_almost_equal",
"numpy.random.rand",
... |
RussellM2020/maml_gps | [
"631560dfd4e23dc2da9bfbbd2e3c5252aa9775c5"
] | [
"rllab/optimizers/conjugate_gradient_optimizer.py"
] | [
"import numpy as np\nimport theano\nimport theano.tensor as TT\n\nfrom rllab.core.serializable import Serializable\nfrom rllab.misc import ext\nfrom rllab.misc import krylov\nfrom rllab.misc import logger\nfrom rllab.misc.ext import sliced_fun\n\n\nclass PerlmutterHvp(Serializable):\n\n def __init__(self, num_sl... | [
[
"numpy.arange",
"numpy.reshape",
"numpy.linalg.norm",
"numpy.isnan"
]
] |
ngwenbin/ExpenseTracker | [
"f50793c9a4c6efc4f58cc7d759b45f2e16b7832e"
] | [
"app.py"
] | [
"from flask import Flask, render_template, redirect, url_for, flash, request, abort\nfrom functions import UserLogin, UserRegistration, NewExpense\nfrom flask_sqlalchemy import SQLAlchemy\nfrom sqlalchemy import func\nfrom datetime import datetime, timedelta, date\nfrom flask_bcrypt import Bcrypt\nfrom flask_login ... | [
[
"matplotlib.backends.backend_agg.FigureCanvasAgg",
"numpy.sign",
"matplotlib.pyplot.subplots",
"numpy.deg2rad"
]
] |
comp5331-Xtimeseries/mWDN | [
"3805f90230b93d04f86201079358ec1f6dd6bb2d"
] | [
"utils.py"
] | [
"import torch\nimport numpy as np;\nfrom torch.autograd import Variable\n\n\ndef normal_std(x):\n return x.std() * np.sqrt((len(x) - 1.)/(len(x)))\n\nclass Data_utility(object):\n # train and valid is the ratio of training set and validation set. test = 1 - train - valid\n def __init__(self, dSet, train, v... | [
[
"numpy.ones",
"numpy.zeros",
"torch.autograd.Variable",
"numpy.abs",
"torch.from_numpy",
"numpy.max",
"torch.randperm",
"torch.zeros",
"torch.mean"
]
] |
timmyzhao/ptstat | [
"0401203e5b6053df6d62b2af9ab4b831f1b41660"
] | [
"ptstat/dist/categorical.py"
] | [
"import torch\nfrom ptstat.core import RandomVariable, _to_v\n\n\nclass Categorical(RandomVariable):\n \"\"\"\n Categorical over 0,...,N-1 with arbitrary probabilities, 1-dimensional rv, long type.\n \"\"\"\n def __init__(self, p=None, p_min=1E-6, size=None, cuda=False):\n super(Categorical, self... | [
[
"torch.sum",
"torch.min",
"torch.log",
"torch.clamp"
]
] |
SX-Aurora/orchespy | [
"6b85a78831c8e3e05df7143101ca3418817fcbbd"
] | [
"tests/device_tests/test_device_args_numpy_module.py"
] | [
"from orchespy import device\nfrom orchespy.devicetype import CUDAGPU, Host, VE\nimport sys\nimport pytest\n\nimport numpy as np\n\nif \"cupy\" in sys.modules:\n import cupy as cp\nif \"nlcpy\" in sys.modules:\n import nlcpy as vp\n\nno_nlcpy = pytest.mark.skipif(\n \"nlcpy\" not in sys.modules, reason... | [
[
"numpy.full"
]
] |
ncfrey/mlmsynth | [
"99fc8fabba511aefd6f0a0be4e85c78c54dd3648"
] | [
"pumml/learners.py"
] | [
"\"\"\"\nDeploy semi-supervised PU machine learning models.\n\nThis module provides classes for training, testing, and deploying a PU\nlearning model for predicting material synthesizability. Utility functions\nfor plotting aid in visualizing and analyzing results.\n\nReferences:\n [1] DOI: 10.1021/acsnano.8b080... | [
[
"numpy.sum",
"numpy.ones",
"numpy.random.seed",
"numpy.asarray",
"sklearn.cluster.KMeans",
"sklearn.tree.DecisionTreeClassifier",
"pandas.read_json",
"numpy.where",
"numpy.unique",
"numpy.mean",
"numpy.corrcoef",
"numpy.zeros",
"sklearn.model_selection.RepeatedK... |
CitrineInformatics/smlb | [
"28a3689bd36aa8d51031b4faf7e2331bbd8148a9"
] | [
"tests/learners/scikit_learn/test_gpr_skl.py"
] | [
"\"\"\"GaussianProcessRegressionSklearn tests.\n\nScientific Machine Learning Benchmark:\nA benchmark of regression models in chem- and materials informatics.\n\"\"\"\n\nimport pytest\n\nimport numpy as np\n\nskl = pytest.importorskip(\"sklearn\")\n\nimport smlb\nfrom smlb.learners.scikit_learn.gaussian_process_reg... | [
[
"numpy.allclose",
"numpy.ones",
"numpy.zeros",
"numpy.sqrt",
"numpy.square",
"numpy.array"
]
] |
lmarti/pandas | [
"fdfd66cdf3f357fb52831eb644897e144a0d7f30"
] | [
"pandas/core/frame.py"
] | [
"\"\"\"\nDataFrame\n---------\nAn efficient 2D container for potentially mixed-type time series or other\nlabeled data series.\n\nSimilar to its R counterpart, data.frame, except providing automatic data\nalignment and a host of useful data manipulation methods having to do with the\nlabeling information\n\"\"\"\nf... | [
[
"pandas.core.generic.NDFrame._set_item",
"pandas.core.internals.create_block_manager_from_arrays",
"numpy.asarray",
"pandas.core.indexing.convert_to_index_sliceable",
"pandas.lib.map_infer",
"pandas.util.decorators.deprecate_kwarg",
"pandas.core.format.DataFrameFormatter",
"pandas.... |
AIDefender/MyMBPO | [
"d75699b65af8eea14acffc1b5738900d1079ad46"
] | [
"mbpo/static/reacher.py"
] | [
"import numpy as np\n\nclass StaticFns:\n\n @staticmethod\n def termination_fn(obs, act, next_obs):\n\n done = np.array([False]).repeat(len(obs))\n done = done[:,None]\n return done\n"
] | [
[
"numpy.array"
]
] |
colizoli/letter_color_mri | [
"f4c4d8a91aa17664bdeb16b0436fc8f8fdac2710"
] | [
"experiment/Behav_Consistency.py"
] | [
"\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nLetter-color Consistency test\nO.Colizoli 2020\nEach letter of the alphabet in random order x 2\nColor wheel opens at a randomized color on each trial (but does not turn)\nPython 2..7\n\"\"\"\n# data saved in ~/LogFiles/sub-XXX\n\n# Import necessary modules... | [
[
"numpy.arange",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.tight_layout",
"pandas.DataFrame"
]
] |
terraPulse/boreal-tcc-analysis | [
"e8a7b4bae727811d03bb57c5738945af7fe2920d"
] | [
"src/bin/create_esta_layers.py"
] | [
"'''\r\nFile: detect_forest_change.py\r\nAuthor: Min Feng\r\nVersion: 0.1\r\nCreate: 2018-04-20 15:42:37\r\nDescription: detect forest changes from foest probility layers and tree cover layers\r\n'''\r\n\r\nimport logging\r\n\r\ndef _load_tcc(f_tcc, msk):\r\n from gio import geo_raster_ex as gx\r\n from gio i... | [
[
"numpy.empty",
"numpy.zeros"
]
] |
DonaldWhyte/high-performance-data-processing-in-python | [
"f7f8076ff67d53be09e1d2f9988976e31b92f8e9"
] | [
"code/rolling_tests.py"
] | [
"import numpy as np\n\n\ndef _main():\n # Inputs\n n = 3\n x = np.arange(20, dtype=np.float64)\n\n # Slow average/std\n avg = np.zeros(len(x) - n + 1)\n std = np.zeros(len(x) - n + 1)\n for i in range(len(avg)):\n avg[i] = np.mean(x[i:i+n])\n std[i] = np.std(x[i:i+n])\n\n print... | [
[
"numpy.ones",
"numpy.arange",
"numpy.sqrt",
"numpy.std",
"numpy.square",
"numpy.mean"
]
] |
INK-USC/RiddleSense | [
"a3d57eaf084da9cf6b77692c608e2cd2870fbd97"
] | [
"methods/transformers/examples/deebert/src/modeling_highway_bert.py"
] | [
"import torch\r\nfrom torch import nn\r\nfrom torch.nn import CrossEntropyLoss, MSELoss\r\n\r\nfrom transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward\r\nfrom transformers.modeling_bert import (\r\n BERT_INPUTS_DOCSTRING,\r\n BERT_START_DOCSTRING,\r\n BertEmbeddings,... | [
[
"torch.sum",
"torch.ones",
"torch.nn.Linear",
"torch.nn.MSELoss",
"torch.exp",
"torch.nn.CrossEntropyLoss",
"torch.log",
"torch.zeros",
"torch.nn.Dropout"
]
] |
pailabteam/pailab | [
"3995b25f105827ae631e6120f380748d7d284c9f",
"3995b25f105827ae631e6120f380748d7d284c9f"
] | [
"pailab/tools/tree.py",
"pailab/analysis/tools_jupyter.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"This module contains all functions and classes for the MLTree. The MLTree buils a tree-like\nstructure of the objects in a given repository. This allows the user to access objects in a\ncomfortable way allowing for autocompletion (i.e. in Jupyter notebooks).\n\nTo use it one can simp... | [
[
"numpy.load"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.rcdefaults",
"pandas.DataFrame",
"matplotlib.dates.DateFormatter",
"matplotlib.pyplot.subplots",
"pandas.set_option",
"matplotlib.pyplot.show",
"pandas.concat",
"matplotlib.... |
liwanjunit/ASRGAN | [
"ac01e546939c435c246fbdce64606464f8fdfc00"
] | [
"loss/loss_new.py"
] | [
"import torch\nfrom torch import nn\nfrom torchvision.models.vgg import vgg16\n\n\nclass GeneratorLoss_NEW(nn.Module):\n def __init__(self):\n super(GeneratorLoss_NEW, self).__init__()\n vgg = vgg16(pretrained=True)\n # loss_network = nn.Sequential(*list(vgg.features)[:31]).eval()\n l... | [
[
"torch.nn.MSELoss",
"torch.add",
"torch.sqrt",
"torch.mean",
"torch.pow"
]
] |
LSanselme/kerod | [
"cb52775ed501cbe4bd5fc0f22ec0359ca1d5f902"
] | [
"src/kerod/core/sampling_ops.py"
] | [
"# Copyright 2017 The TensorFlow Authors and modified by Emilien Garreau. 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/licens... | [
[
"tensorflow.size",
"tensorflow.equal",
"tensorflow.logical_or",
"tensorflow.shape",
"tensorflow.reshape",
"tensorflow.random.shuffle",
"tensorflow.logical_not",
"tensorflow.map_fn",
"tensorflow.logical_and",
"tensorflow.cast",
"tensorflow.where"
]
] |
tapasi-brahma/nobrainer | [
"c46586658d226bc3ca22869fd45a2674fdd52be9"
] | [
"nobrainer/metrics.py"
] | [
"\"\"\"Implementations of metrics for 3D semantic segmentation.\"\"\"\n\nimport tensorflow as tf\n\n\ndef average_volume_difference():\n raise NotImplementedError()\n\n\ndef dice(y_true, y_pred, axis=(1, 2, 3, 4)):\n \"\"\"Calculate Dice similarity between labels and predictions.\n\n Dice similarity is in ... | [
[
"tensorflow.shape",
"tensorflow.zeros_like",
"tensorflow.cast",
"tensorflow.keras.backend.epsilon",
"tensorflow.convert_to_tensor",
"tensorflow.not_equal",
"tensorflow.reduce_sum",
"tensorflow.math.is_finite"
]
] |
huylb314/AVIAD_AVIJST | [
"bf8e0617849b4f8f4b95ea345be1565ea063ee38"
] | [
"avijst/tensorflow/data.py"
] | [
"import numpy as np\nfrom sklearn import metrics\nimport math\nfrom keras.preprocessing import sequence\nfrom keras.preprocessing.text import Tokenizer\nfrom typing import *\n\n# fastai utility\ndef listify(o):\n if o is None: return []\n if isinstance(o, list): return o\n if isinstance(o, str): return [o]... | [
[
"numpy.vstack",
"numpy.zeros"
]
] |
orrinjelo/AedanWallpaper | [
"c5d67c45d7d295d90bc979f2cda645e0b578f10c"
] | [
"scripts/rainbow.py"
] | [
"from PIL import Image\nimport numpy as np\nimport colorsys\nimport os, sys\nimport argparse\nimport matplotlib.pyplot as plt \n\n\nrgb_to_hsv = np.vectorize(colorsys.rgb_to_hsv)\nhsv_to_rgb = np.vectorize(colorsys.hsv_to_rgb)\n\ndef crop(image, box=None):\n if box:\n imageBox = box\n else:\n im... | [
[
"numpy.asarray",
"numpy.dstack",
"numpy.vectorize",
"numpy.rollaxis"
]
] |
leike666666/tensorflow | [
"04f2870814d2773e09dcfa00cbe76a66a2c4de88",
"04f2870814d2773e09dcfa00cbe76a66a2c4de88",
"a3fd0ddfcb716be124e95b51e96e6c1e4507ef64"
] | [
"tensorflow/python/keras/regularizers_test.py",
"tensorflow/python/kernel_tests/cwise_ops_test.py",
"tensorflow/python/data/experimental/kernel_tests/stats_dataset_ops_test.py"
] | [
"# Copyright 2016 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... | [
[
"numpy.ones",
"tensorflow.python.keras.regularizers.l1_l2",
"tensorflow.python.keras.testing_utils.should_run_eagerly",
"tensorflow.python.keras.layers.Average",
"tensorflow.python.keras.models.Sequential",
"tensorflow.python.keras.Model",
"tensorflow.python.keras.Input",
"tensorfl... |
ersilia-os/osm-series4-candidates | [
"2d06ae0a5c26efea70d2a21f06a376625977b8b7"
] | [
"postprocess/_5_1_chemprop.py"
] | [
"from tqdm import tqdm\nimport pandas as pd\nfrom __init__ import FILE\n\ndf = pd.read_csv(FILE)\nsmiles = list(df[\"Smiles\"])\n\nwith open(\"_chemprop.csv\", \"w\") as f:\n f.write(\"smiles\\n\")\n for smi in smiles:\n f.write(\"{0}\\n\".format(smi))\n"
] | [
[
"pandas.read_csv"
]
] |
jaketae/pytorch | [
"e5282c3cb8bf6ad8c5161f9d0cc271edb9abed25",
"5654e6339879e438efb7cf50e88e356472eb0545"
] | [
"torch/distributed/_shard/sharded_tensor/__init__.py",
"test/test_public_bindings.py"
] | [
"# coding=utf-8\n\nimport copy\nimport functools\nfrom typing import List\n\nimport torch\nimport torch.distributed._shard.sharding_spec as shard_spec\n\nfrom .api import (\n _register_sharded_op,\n Shard,\n ShardedTensor,\n ShardedTensorMetadata,\n TensorProperties,\n)\nfrom .metadata import ShardMe... | [
[
"torch.nn.init.uniform_",
"torch.nn.init.constant_"
],
[
"torch.testing._internal.common_utils.run_tests"
]
] |
ofirpress/shortformer | [
"0281f7618fb3833c8ac99f3e8e0512aed95fa2a1"
] | [
"fairseq/data/iterators.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 itertools\nimport logging\nimport math\nimport operator\nimport os\nimport queue\nimport time\nfrom threading import Thread\n... | [
[
"torch.utils.data.DataLoader",
"numpy.random.shuffle"
]
] |
berkott/SpaceInvadersAI | [
"0d1d095f60b06f09b337bd3abf7bb46a08a8ed70"
] | [
"NeuroEvolution/evolution.py"
] | [
"import gym\nimport keras as k\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, Activation, MaxPooling2D, Flatten, Dense, Dropout\nfrom keras.optimizers import Adam\nimport numpy as np\nfrom datetime import datetime\nfrom matplotlib import pyplot as PLT\nimport time\nimport csv\nimport os\n\n#... | [
[
"numpy.random.random_sample",
"numpy.load",
"numpy.sum",
"numpy.zeros",
"numpy.reshape",
"numpy.argmax",
"numpy.copy",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.show",
"numpy.max",
"numpy.array",
"numpy.where",
"numpy.random.randint"
]
] |
BeibinLi/SSD | [
"2cd30f02c21b0a8731a34dca2a89d6e099ca3442"
] | [
"ssd/modeling/backbone/vgg.py"
] | [
"import torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ssd.layers import L2Norm\nfrom ssd.modeling import registry\nfrom ssd.utils.model_zoo import load_state_dict_from_url\n\nmodel_urls = {\n 'vgg': 'https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth',\n}\n\n\n# borrowed from https://github.... | [
[
"torch.nn.MaxPool2d",
"torch.nn.init.xavier_uniform_",
"torch.nn.BatchNorm2d",
"torch.nn.Conv2d",
"torch.nn.init.zeros_",
"torch.nn.ReLU"
]
] |
fedarko/songbird | [
"44827596bc9ca16d8046aeafee24ee1dd74dcc0b"
] | [
"songbird/util.py"
] | [
"import os\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nfrom sklearn.utils import check_random_state\nfrom skbio.stats.composition import clr_inv as softmax\nfrom biom import Table\nfrom patsy import dmatrix\n\n\ndef random_multinomial_model(num_samples, num_features,\n ... | [
[
"pandas.read_table",
"numpy.sum",
"numpy.ones",
"sklearn.utils.check_random_state",
"numpy.zeros",
"pandas.DataFrame",
"numpy.random.seed",
"numpy.argsort",
"numpy.random.random",
"numpy.linspace",
"tensorflow.compat.v1.logging.set_verbosity"
]
] |
lsst-sitcom/spot_motion_monitor | [
"3d0242276198126240667ba13e95b7bdf901d053"
] | [
"tests/models/test_full_frame_model.py"
] | [
"# This file is part of spot_motion_monitor.\n#\n# Developed for LSST System Integration, Test and Commissioning.\n#\n# See the LICENSE file at the top-level directory of this distribution\n# for details of code ownership.\n#\n# Use of this source code is governed by a 3-clause BSD-style\n# license that can be foun... | [
[
"numpy.ones"
]
] |
jmendozais/SDSSDepth | [
"7a4d0c5affef3eda7056876ccb2365ac883c08eb"
] | [
"loss/general_adaptive_loss.py"
] | [
"import sys\nimport math\nimport os\n\nimport torch\nimport torchvision\nimport numpy as np\n\nfrom pkg_resources import resource_stream\n\ndef interpolate1d(x, values, tangents):\n '''\n Returns:\n Returns the interpolated or extrapolated values for each query point,\n depending on whether or n... | [
[
"numpy.load",
"torch.square",
"torch.as_tensor",
"torch.tensor",
"torch.where",
"torch.is_tensor",
"torch.abs"
]
] |
dzungcamlang/noise_adversarial_tacotron | [
"7a7fda49eb8bf82f5139743d55639d48ff204e9e"
] | [
"dataset/cut_chime.py"
] | [
"import hp\nfrom pathlib import Path\nimport numpy as np\nfrom tqdm import tqdm\nimport librosa\nimport torch\nimport librosa.filters\nimport numpy as np\nimport scipy\nfrom random import randint\nfrom os import makedirs\n\n\ndef load_wav(path, sample_rate):\n return librosa.core.load(path, sr=sample_rate)[0]\n\... | [
[
"numpy.abs"
]
] |
JaeyoonSSim/Design-Project | [
"8a0037bec50b44b3f5d92da5254e79964fdaf9cf"
] | [
"Detector_1/fusion_detecting.py"
] | [
"import cv2\r\nimport sys\r\nimport os\r\nimport numpy as np\r\nimport time\r\n\r\n# Initialize the parameters\r\nconfThreshold = 0.5 # Confidence threshold\r\nnmsThreshold = 0.4 # Non-maximum suppression threshold\r\ninpWidth = 416 # Width of network's input image\r\ninpHeight = 416 # Height of netwo... | [
[
"numpy.array",
"numpy.argmax"
]
] |
grisoniFr/virtual_libraries | [
"0aac0ce249f6f3bc529abb3cbdf2d3f49be84388"
] | [
"experiments/do_data_generation.py"
] | [
"import os, sys\nimport time\nimport warnings\nimport argparse\nimport configparser\nimport ast\nimport numpy as np\nfrom math import log\nfrom rdkit import Chem\nfrom rdkit import rdBase\nrdBase.DisableLog('rdApp.*')\nfrom rdkit.Chem import Draw\n\nfrom keras.models import load_model\n\nsys.path.append('../src/')\... | [
[
"numpy.sum",
"numpy.asarray",
"numpy.exp",
"numpy.log",
"numpy.random.multinomial"
]
] |
JosmarSuarez/yolact | [
"43b694603638562ffcdc81df7b04783c9990291c"
] | [
"yolact.py"
] | [
"import torch, torchvision\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.models.resnet import Bottleneck\nimport numpy as np\nfrom itertools import product\nfrom math import sqrt\nfrom typing import List\nfrom collections import defaultdict\n\nfrom data.config import cfg, mask_type\nfrom... | [
[
"torch.nn.init.xavier_uniform_",
"torch.nn.functional.softmax",
"torch.set_default_tensor_type",
"numpy.log",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.cat",
"torch.nn.BatchNorm2d",
"torch.nn.functional.pad",
"torch.cuda.device_count",
"torch.sigmoid",
"torch... |
metabacchi/FuzzyClassificator | [
"f59c10364b872edce342403db6ef26e30d7f69b8"
] | [
"pybrain/tools/functions.py"
] | [
"__author__ = 'Tom Schaul, tom@idsia.ch'\n\nfrom scipy import array, exp, tanh, clip, log, dot, sqrt, power, pi, tan, diag, rand, real_if_close\nfrom scipy.linalg import inv, det, svd, logm, expm2\n\n\ndef semilinear(x):\n \"\"\" This function ensures that the values of the array are always positive. It is\n ... | [
[
"scipy.sqrt",
"scipy.log",
"scipy.dot",
"scipy.exp",
"scipy.linalg.inv",
"scipy.tanh",
"scipy.clip",
"scipy.linalg.logm",
"scipy.tan",
"scipy.diag",
"scipy.power",
"scipy.linalg.det",
"scipy.linalg.svd"
]
] |
RQuispeC/opacus | [
"5c83d59fc169e93667946204f7a6859827a38ace"
] | [
"opacus/tests/ddp_hook_check.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport os\nimport sys\nimport unittest\n\nimport torch\nimport torch.distributed as dist\nimport torch.multiprocessing as mp\nimport torch.nn as nn\nimport torch.optim as optim\nfrom opacus import PrivacyEngine\nfrom op... | [
[
"torch.nn.Linear",
"torch.nn.MSELoss",
"torch.multiprocessing.spawn",
"torch.randn",
"torch.manual_seed",
"torch.distributed.init_process_group",
"torch.cuda.device_count",
"torch.norm",
"torch.nn.parallel.DistributedDataParallel",
"torch.zeros",
"torch.distributed.dest... |
scpepper69/ml | [
"13ad41dd7b22d3fa152cf3665fc4dc7c1c747917"
] | [
"mnist/app/app/mnist.py"
] | [
"from datetime import datetime\nimport cv2\nimport re\nimport base64\nfrom flask import Flask, render_template, request, jsonify\nfrom flask_cors import CORS\nimport numpy as np\n\nfrom io import BytesIO\nfrom PIL import Image, ImageOps\nimport os,sys\nimport requests\nfrom graphpipe import remote\nfrom matplotlib ... | [
[
"numpy.sort",
"numpy.multiply",
"numpy.argsort",
"numpy.argmax"
]
] |
Manish-rai21bit/deep_learning_for_camera_trap_images | [
"f9d9fd50824ece4743b39d5136f67235871cc0ef"
] | [
"phase2_recognition_only/architectures/vgg.py"
] | [
"import tensorflow as tf\nimport common\n\ndef inference(x, num_output, wd, dropout_rate, is_training, transfer_mode= False, model_type= 'A'):\n # Create tables describing VGG configurations A, B, D, E\n if model_type == 'A':\n config = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']\n e... | [
[
"tensorflow.nn.relu",
"tensorflow.variable_scope",
"tensorflow.nn.dropout"
]
] |
innovator-zero/Python | [
"f776eb081c6688c2f5a98b0050b33582c1769391"
] | [
"kmeans/fish.py"
] | [
"import numpy as np\nimport random\nimport matplotlib.pyplot as plt\n\npoints=np.loadtxt('points.txt')\nherring_r = np.loadtxt('distribution.txt')\nherring=np.zeros((802,350))\nfor i in range(350):\n for j in range(802):\n herring[j,349-i]=herring_r[i,j]\n\n# s=np.zeros(10)\n#\n# for i in range(10):\n# ... | [
[
"numpy.zeros",
"numpy.loadtxt"
]
] |
luoyi1hao/ACRN_Chest_X-ray_IA | [
"b2ecaf88e6b1bb59101fd2d611bf9d1e6716367a",
"b2ecaf88e6b1bb59101fd2d611bf9d1e6716367a"
] | [
"acregnet/train_acregnet.py",
"acregnet/data.py"
] | [
"from data import DataHandler\nfrom models import ACRegNet\nimport tensorflow as tf\nfrom utils import get_random_batch, read_config_file, create_dir\n\n\nRUN_IN_GPU = False\n\n\ndef train_acregnet_model(config):\n tf.reset_default_graph()\n tf_config = tf.ConfigProto()\n\n if RUN_IN_GPU:\n tf_confi... | [
[
"tensorflow.reset_default_graph",
"tensorflow.ConfigProto",
"tensorflow.Session"
],
[
"numpy.unique",
"numpy.expand_dims",
"numpy.array_equal",
"numpy.array",
"sklearn.model_selection.train_test_split",
"numpy.loadtxt"
]
] |
NNstorm/MinkowskiEngine | [
"443b37a58c379b2482b5d160d9e874b356b4bf2f"
] | [
"examples/classification_modelnet40.py"
] | [
"# Copyright (c) 2020 NVIDIA CORPORATION.\n# Copyright (c) 2018-2020 Chris Choy (chrischoy@ai.stanford.edu).\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of\n# this software and associated documentation files (the \"Software\"), to deal in\n# the Software without restriction, i... | [
[
"torch.optim.lr_scheduler.CosineAnnealingLR",
"torch.utils.data.DataLoader",
"torch.nn.functional.log_softmax",
"torch.cuda.empty_cache",
"numpy.random.uniform",
"torch.nn.Module.__init__",
"torch.nn.init.constant_",
"torch.argmax",
"torch.zeros_like",
"torch.no_grad",
... |
brunomarct/archetypal | [
"ce8daf4e18ef3ec92967e5d6837b392199caf83b"
] | [
"archetypal/schedule.py"
] | [
"################################################################################\n# Module: schedule.py\n# Description: Functions for handling conversion of EnergyPlus schedule objects\n# License: MIT, see full license in LICENSE.txt\n# Web: https://github.com/samuelduchesne/archetypal\n###########################... | [
[
"numpy.roll",
"pandas.Series",
"pandas.date_range",
"pandas.read_csv",
"numpy.arange",
"pandas.Grouper",
"pandas.concat",
"numpy.array",
"numpy.unique",
"numpy.mean"
]
] |
sleep-yearning/magenta | [
"a03a14ef5a691ee9e3d336aa621281028dc5af32",
"a03a14ef5a691ee9e3d336aa621281028dc5af32"
] | [
"magenta/models/score2perf/music_encoders_test.py",
"magenta/models/drums_rnn/drums_rnn_generate.py"
] | [
"# Copyright 2020 The Magenta 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 o... | [
[
"tensorflow.compat.v1.test.main"
],
[
"tensorflow.compat.v1.logging.set_verbosity",
"tensorflow.compat.v1.app.flags.DEFINE_boolean",
"tensorflow.compat.v1.logging.debug",
"tensorflow.compat.v1.gfile.Exists",
"tensorflow.compat.v1.app.flags.DEFINE_float",
"tensorflow.compat.v1.gfile... |
energyinpython/pre-pyrepo | [
"92e44594e12d1110247f011e51734e5ce1fe0b8e"
] | [
"tests/test_correlations.py"
] | [
"from pyrepo import correlations as corrs\nfrom scipy.stats import pearsonr\nimport unittest\nimport numpy as np\n\n\n# Test for Spearman rank correlation coefficient\nclass Test_Spearman(unittest.TestCase):\n\n def test_spearman(self):\n \"\"\"Test based on paper Sałabun, W., & Urbaniak, K. (2020, June).... | [
[
"numpy.array",
"numpy.round",
"scipy.stats.pearsonr"
]
] |
YifanQie/Deep_Learning_for_Manufacturing | [
"9ba19e41f69c561b04b8573ab9c52c0969f45bfd"
] | [
"core/assembly_system.py"
] | [
"import numpy as np\nimport pandas as pd\n\"\"\" Contains core classes and methods for initializing a Assembly System, the inputs are provided in assemblyconfig file in utilities\"\"\"\n\nclass AssemblySystem:\n\t\"\"\"Assembly System Class\n\n\t\t:param assembly_type: Type of assembly Single-Station/Multi-Station\... | [
[
"pandas.read_csv",
"pandas.read_sql_query"
]
] |
dipdeb/DAT210x | [
"9103844fa7f76052bdcc5a4ec60e8afbc91a9f6b"
] | [
"Module3/assignment2.py"
] | [
"import pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib\n\n# Look pretty...\n# matplotlib.style.use('ggplot')\nplt.style.use('ggplot')\n\n\n#\n# TODO: Load up the Seeds Dataset into a Dataframe\n# It's located at 'Datasets/wheat.data'\n# \nwheat_df = pd.read_csv('/home/dipanjan/DAT210x/Module3/Data... | [
[
"pandas.read_csv",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.show"
]
] |
hongzhouye/sigma-SCF | [
"62e2dce538d1e68c4dc3c72fdf27beb1911e544f"
] | [
"scf/scf_utils.py"
] | [
"import numpy as np\nimport os, sys\nsys.path.append(os.path.dirname(__file__))\nfrom diis_solver import diis_solver, diis_solver_uhf\nsys.path.pop()\nimport jk\nimport xform\n\n\ndef homo_lumo_mix(C, nocc, beta):\n \"\"\"\n Mix a portion of LUMO to HOMO.\n Used when generating spin-unrestricted guess.\n ... | [
[
"numpy.sum",
"numpy.diag",
"numpy.swapaxes",
"numpy.linalg.eigh",
"numpy.einsum",
"numpy.mean"
]
] |
APrigarina/open_model_zoo | [
"b1ff98b64a6222cf6b5f3838dc0271422250de95"
] | [
"tools/accuracy_checker/accuracy_checker/annotation_converters/cvat_multilabel_recognition.py"
] | [
"\"\"\"\nCopyright (c) 2018-2021 Intel Corporation\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law ... | [
[
"numpy.ones"
]
] |
AXATechLab/models | [
"c39ac760cfa6ce2339f5781f2a78d70db3ea5bb2"
] | [
"research/object_detection/meta_architectures/faster_rcnn_meta_arch_override_RPN.py"
] | [
"# Copyright 2017 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.reshape",
"tensorflow.unstack",
"tensorflow.sigmoid",
"tensorflow.ones",
"tensorflow.variable_scope",
"tensorflow.squeeze",
"tensorflow.name_scope",
"tensorflow.concat",
"tensorflow.slice",
"tensorflow.nn.softmax",
"tensorflow.reduce_sum",
"tensorflow.gr... |
Sakura176/PointRCNN | [
"a7fbb25e931609a39c32cb821a7c98a326e8b0c0"
] | [
"tools/train_eval.py"
] | [
"import os\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom lib.utils.bbox_transform import decode_bbox_target\nfrom tools.kitti_object_eval_python.evaluate import evaluate as kitti_evaluate\n\nfrom lib.config import cfg\nimport lib.utils.kitti_utils as kitti_utils\nimport lib.utils.iou3d.io... | [
[
"numpy.arctan2",
"numpy.sign",
"torch.argmax",
"torch.nn.functional.softmax",
"numpy.logical_and",
"numpy.random.seed",
"numpy.clip",
"torch.from_numpy",
"torch.sigmoid",
"torch.clamp"
]
] |
zblanks/parallel_esn | [
"25a979d0863ce54a4a588f4216dc473d4e9c5e8a"
] | [
"parallel_esn/bo.py"
] | [
"from math import log10\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import Matern\nimport numpy as np\nfrom .utils import create_rng\n\n\nclass BO:\n \"\"\"\n Bayesian Optimization framework\n \"\"\"\n\n def __init__(self, k, hidden_dim=(100, 100... | [
[
"numpy.argmin",
"numpy.arange",
"numpy.power",
"sklearn.gaussian_process.kernels.Matern",
"numpy.concatenate"
]
] |
hjonnala/deeplab2 | [
"1868757c4333ec5287cc0bf0a6bbf38fbbe34c2e"
] | [
"model/encoder/model_export_test.py"
] | [
"# coding=utf-8\n# Copyright 2022 The Deeplab2 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... | [
[
"tensorflow.test.main",
"tensorflow.keras.Input"
]
] |
kagemeka/atcoder-submissions | [
"91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e",
"91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e"
] | [
"jp.atcoder/abc012/abc012_4/21865313.py",
"jp.atcoder/abc081/arc086_b/17664033.py"
] | [
"from __future__ import annotations\n\nfrom typing import Generator, NoReturn\n\n\nclass StdReader:\n def __init__(\n self,\n ) -> NoReturn:\n import sys\n\n self.buf = sys.stdin.buffer\n self.lines = self.async_readlines()\n self.chunks: Generator\n\n def async_readlines... | [
[
"numpy.array",
"scipy.sparse.csr_matrix",
"scipy.sparse.csgraph.floyd_warshall"
],
[
"numpy.logical_or",
"numpy.ones",
"numpy.sum",
"numpy.any",
"numpy.argsort",
"numpy.logical_or.at",
"numpy.fill_diagonal",
"numpy.amax",
"numpy.append",
"scipy.optimize",
... |
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