repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
ayush268/RecoEdge | [
"b6554c646bae602704f44ae80571a4c319f90fd2"
] | [
"experiments/regression/net.py"
] | [
"import sys\n\nimport torch\nfrom experiments.regression.data_processor import RegressionPreprocessor\nfrom fedrec.utilities import registry\nfrom torch import nn, sigmoid\n\n### define LogisticRegression in PyTorch ###\n\n\n@registry.load(\"model\", \"regression\")\nclass Regression_Net(nn.Module):\n Preproc = ... | [
[
"torch.sigmoid",
"torch.nn.CrossEntropyLoss",
"torch.nn.Linear",
"torch.clamp",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yoshi-ki/BACHELOR | [
"65d01c62ab2ea4a6d2616a6b6c535bd4f1645630"
] | [
"Numerical_Analysis/6/cg.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom scipy import sparse\n\ndef solve_cg (A,b):\n n = len(A)\n As = sparse.csr_matrix(A)\n x = np.zeros(n)\n r = (b - (As * x.T).T)\n #r = (b - np.dot(A,x.T).T)\n p = r\n count = 0\n count_list = [... | [
[
"numpy.dot",
"numpy.linalg.norm",
"matplotlib.pyplot.savefig",
"scipy.sparse.csr_matrix",
"matplotlib.pyplot.plot",
"numpy.append",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
kyhoolee/shortest-path-problem-using-rl | [
"99bad27efb3d7050260b12bdd1ffdfe80d03fd64"
] | [
"draw_container_state_graph.py"
] | [
"\"\"\"\ndraw graph of container stacked states\nthis file can draw graph using only setting from config file\n\n\"\"\"\n\nimport networkx as nx\nfrom copy import deepcopy\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom config import CFG\n\n\nclass container_env:\n def __init__(self):\n self.ac... | [
[
"numpy.rot90",
"numpy.array_equal",
"matplotlib.pyplot.close"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abraker-osu/osu_analyzer | [
"f930b1e75d1c4c973dfa49fdab2afedb2a432e31"
] | [
"analysis/std/replay_data.py"
] | [
"import numpy as np\nimport pandas as pd\nimport itertools\nimport time\n\nfrom osu_interfaces import IReplay\n\n\nclass StdReplayData():\n \"\"\"\n Class used for navigating, extracting, and operating on standard gamemode replay data.\n \n .. note::\n This is not to be confused with the replay d... | [
[
"numpy.asarray",
"numpy.any",
"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": []
}
] |
dumpmemory/AspDecSSCL | [
"004d73d3248e8fdee1336cfc6490ef4872583665"
] | [
"AspDec/SSCL/model_sscl.py"
] | [
"'''\n@author Tian Shi\nPlease contact tshi@vt.edu\n'''\nimport os\nimport time\n\nimport numpy as np\nimport torch\nfrom torch.autograd import Variable\n\nfrom AspDec.model_sscl_base import modelSSCLBase\nfrom LeafNATS.modules.encoder.encoder_cnn import EncoderCNN\n\n\nclass modelSSCL(modelSSCLBase):\n\n def __... | [
[
"torch.softmax",
"torch.nn.Parameter",
"torch.cat",
"torch.sum",
"torch.nn.Embedding",
"torch.tanh",
"torch.nn.Linear",
"torch.exp",
"torch.no_grad",
"torch.FloatTensor",
"torch.log",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AmirPupko/pandas-to-sql | [
"12cb699d70acd368a284304d78c086fa0b1bc204"
] | [
"pandas_to_sql/testing/tests/test_datetime.py"
] | [
"from datetime import timedelta, datetime\nimport pytest\nfrom pandas_to_sql.testing.utils.asserters import assert_, get_expected_and_actual\nfrom copy import copy\nimport pandas as pd\nimport pandas_to_sql \n\n\ndef test_add_days():\n df = pytest.df1\n df['new_value'] = df.random_datetime + timedelta(days=20... | [
[
"pandas.offsets.DateOffset"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eLTER-RI/deimsPy | [
"30fa46d9954c8b3ca6b12d08a1f1cd5a49878425"
] | [
"deims.py"
] | [
"\"\"\"A module for interacting with the DEIMS-SDR API.\n\nThe four main functions exported are:\n - getListOfSites\n - getSiteById\n - getSitesWithinRadius\n - getSiteBoundaries\n\nA fifth function normaliseDeimsID is provided but should not normally be\nneeded by end users, as other functions already ... | [
[
"pandas.concat"
]
] | [
{
"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": []
}
] |
AK391/mindspore | [
"f5aeaa9172dcd647885774e7f657593c81b79fc6"
] | [
"mindspore/python/mindspore/train/callback/_loss_monitor.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.isnan",
"numpy.isinf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shintarokkk/toppra | [
"1a7be8feb68fec91459d6dc625f0114692dac885"
] | [
"toppra/algorithm/reachabilitybased/reachability_algorithm.py"
] | [
"from ..algorithm import ParameterizationAlgorithm\nfrom ...constants import LARGE, SMALL\nfrom ...constraint import ConstraintType\n\nimport numpy as np\nimport logging\nlogger = logging.getLogger(__name__)\n\n\nclass ReachabilityAlgorithm(ParameterizationAlgorithm):\n \"\"\"Base class for all Reachability Anal... | [
[
"numpy.isnan",
"numpy.copy",
"numpy.zeros",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
daangn/ranking | [
"4ed523746cc473652aba89c731019b505c1acc38"
] | [
"tensorflow_ranking/python/model_test.py"
] | [
"# Copyright 2019 The TensorFlow Ranking 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 appli... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.compat.v1.train.Saver",
"tensorflow.train.load_checkpoint",
"tensorflow.compat.v1.enable_v2_behavior",
"tensorflow.Graph",
"tensorflow.Variable",
"tensorflow.test.main",
"tensorflow.train.list_variables",
"tensorflow.compat.v1.set_ran... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
satwik2663/Machine-Learning-Song-Mood-Classifier | [
"6a05b5031abc65306867139aefcb3e211886dce6"
] | [
"Model/train_fine_tuned_sequence_model.py"
] | [
"\"\"\"Module to train sequence model with fine-tuned pre-trained embeddings.\r\n\r\nVectorizes training and validation texts into sequences and uses that for\r\ntraining a sequence model - a sepCNN model. We use sequence model with\r\npre-trained embeddings that are fine-tuned for text classification when the\r\nr... | [
[
"numpy.asarray",
"tensorflow.keras.optimizers.Adam",
"numpy.zeros",
"tensorflow.keras.callbacks.EarlyStopping"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
ShangyinGao/pytorch-cifar | [
"480e19825bb155e3d0fafae3545faa3a4165bd77"
] | [
"models/senet.py"
] | [
"'''SENet in PyTorch.\n\nSENet is the winner of ImageNet-2017. The paper is not released yet.\n'''\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom utils import get_kernel_op\n\nimport pdb\n\n\nclass BasicBlock(nn.Module):\n def __init__(self, adder2d, in_planes, planes, stride=1):\n... | [
[
"torch.nn.Sequential",
"torch.randn",
"torch.nn.functional.avg_pool2d",
"torch.nn.Conv2d",
"torch.nn.functional.relu",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gagneurlab/gfeat | [
"a5ddb081567253ee2d6046485f81941bebb1f0f9"
] | [
"tests/transcript/test_codon_usage.py"
] | [
"\"\"\"\nTest codon_usage\n\"\"\"\n\nfrom tests.transcript.config import transcript2\nimport pandas.util.testing as pdt\nimport pandas as pd\n\n\n# devide by 1638\n\ndef test_codon_usage(transcript2):\n res = transcript2.codon_usage()\n dict_test = {'AAA': {0: -6.1541576855839955},\n 'AAC': {0... | [
[
"pandas.util.testing.assert_frame_equal",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ionais/SentimentAnalysis | [
"7c35b8c77400544e04901b99def8c1dbc207aca9"
] | [
"20211215_ResearchMethodsProject_SentimentAnalysisCocaCola.py"
] | [
"import re\r\nimport tweepy\r\nfrom tweepy.errors import TweepyException\r\nfrom tweepy import OAuthHandler \r\nimport nltk\r\nfrom nltk.corpus import stopwords\r\nfrom nltk.tokenize import word_tokenize\r\nfrom nltk.stem import PorterStemmer\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport yfina... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.imshow",
"pandas.read_csv",
"pandas.concat",
"scipy.stats.ttest_1samp",
"matplotlib.pyplot.title",
"pandas.DataFrame",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.pie",
"matplotlib.pyplot.show",
"scipy.stats.ttest_ind",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2"... |
hysteriaX/python_file | [
"7052d66f98c3c95a0a5434416efb7838aa280087"
] | [
"Inputation_4_Missing_data/GAN_GRU/ReadTestData.py"
] | [
"# writen by @LorneM 2020.1.2\nimport numpy as np\nimport pandas as pd\nimport copy\nimport random\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom Missing_data import missing_data\n\nimport os,sys\nsys.path.append('../utils')\nsys.path.append('utils')\n... | [
[
"pandas.read_excel",
"numpy.isnan",
"numpy.nan_to_num",
"numpy.array",
"numpy.zeros"
]
] | [
{
"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": []
}
] |
QamarQuqa/Real-time-Traffic-and-Pedestrian-Counting | [
"c53384f496e816bb852afea328d94b9e963fe753",
"c53384f496e816bb852afea328d94b9e963fe753"
] | [
"filterpy/kalman/fading_memory.py",
"filterpy/discrete_bayes/discrete_bayes.py"
] | [
"# -*- coding: utf-8 -*-\n# pylint: disable=invalid-name, too-many-arguments, too-many-instance-attributes\n\n\n\"\"\"Copyright 2015 Roger R Labbe Jr.\n\nFilterPy library.\nhttp://github.com/rlabbe/filterpy\n\nDocumentation at:\nhttps://filterpy.readthedocs.org\n\nSupporting book at:\nhttps://github.com/rlabbe/Kalm... | [
[
"numpy.dot",
"numpy.eye",
"numpy.size",
"numpy.isscalar",
"scipy.linalg.inv",
"numpy.array",
"numpy.zeros"
],
[
"numpy.asarray",
"numpy.roll",
"scipy.ndimage.interpolation.shift"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"0.15",
"1.4",
"0.1... |
nanand2/protein_seq_des | [
"b7599d17cbc82abba644c643929723fffde3a33c"
] | [
"run.py"
] | [
"import numpy as np\nimport os\nimport torch\nimport torch.nn as nn\n\nfrom seq_des import *\nimport seq_des.sampler as sampler\nimport seq_des.models as models\n\nimport common.run_manager\nimport common.atoms\n\nimport sys\nimport pickle\nimport glob\n\nfrom pyrosetta.rosetta.protocols.simple_filters import Burie... | [
[
"torch.nn.DataParallel",
"torch.no_grad",
"torch.cuda.is_available",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ycao5602/SAL | [
"85a363ed1b0d18451daefe11357c565a991f18b5"
] | [
"torchreid/losses/bce_focal_loss.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\n\nimport torch\nimport torch.nn as nn\n\n\nclass FocalLoss(nn.Module):\n \"\"\"Cross entropy loss with label smoothing regularizer.\n \n Reference:\n Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2... | [
[
"torch.tensor",
"torch.nn.Sigmoid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
willfrey/transformers | [
"cabcc75171650f9131a4cf31c62e1f102589014e"
] | [
"src/transformers/training_args.py"
] | [
"# Copyright 2020 The HuggingFace Team. 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 requir... | [
[
"torch.distributed.init_process_group",
"torch.cuda.set_device",
"torch.cuda.device_count",
"torch.distributed.barrier",
"torch.cuda.is_available",
"torch.device",
"torch.distributed.get_rank",
"torch.distributed.get_world_size"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LongerVision/audio | [
"afb6626c440e92423549818401afa54f4addfb39"
] | [
"torchaudio/transforms.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport math\nimport warnings\nfrom typing import Callable, Optional\n\nimport torch\nfrom torch import Tensor\nfrom torchaudio import functional as F\n\nfrom .functional.functional import (\n _get_sinc_resample_kernel,\n _apply_sinc_resample_kernel,\n)\n\n__all__ = [\n 'Spectrog... | [
[
"torch.linspace",
"torch.ones",
"torch.empty",
"torch.sin",
"torch.cat",
"torch.log",
"torch.optim.SGD",
"torch.clamp",
"torch.log10",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BigusD/Game-of-Life | [
"430e032c3be64e284e50a691c408edd3a8281cf7"
] | [
"GameofLife.py"
] | [
"import numpy as np\r\nimport argparse\r\nfrom matplotlib import pyplot as plt\r\nfrom matplotlib import animation\r\nfrom SeedDict import seeds\r\n\r\n\r\ndef initialize_world(dimensions, seed = None):\r\n '''\r\n Places a Seed at the center of a world of 0s. Else, randomly initialize with 0s and 1s.\r\n\r\n... | [
[
"matplotlib.pyplot.imshow",
"numpy.pad",
"matplotlib.animation.ArtistAnimation",
"numpy.ones",
"numpy.ceil",
"numpy.shape",
"numpy.random.randint",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.sum",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ddimmery/softblock | [
"25f1ed61a0ab4c377e0b57546b57287bb90667bf"
] | [
"run_hp_comparison.py"
] | [
"import pandas as pd\nfrom tqdm import tqdm\n\nimport dgp\nimport design\nfrom plan import Plan\nimport estimator as est\nimport evaluator as evalr\nimport numpy as np\n\nNUM_ITERS = 50\n\ndef make_plan(designs):\n plan = Plan()\n\n for name, dgn, estr, kw in designs:\n plan.add_design(name, dgn, estr,... | [
[
"numpy.logspace",
"pandas.concat"
]
] | [
{
"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": []
}
] |
awesome-archive/MaskInsightface | [
"b5cad2b330aa2134a7e4aba28af88a6f0fb7905f"
] | [
"PRNet_Mask/gen_3daug_mesh.py"
] | [
"import numpy as np\nimport os\nfrom glob import glob\nimport scipy.io as sio\nfrom skimage.io import imread, imsave\nfrom skimage.transform import rescale, resize\nfrom time import time\nimport argparse\nimport ast\nimport sys\n\nfrom PRNet_Mask.api import PRN\n\n# from PRNet_Mask.utils.estimate_pose import estima... | [
[
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhaoxin111/mmclassification | [
"a0308064678c69035cf9a02b949c2f7a31dcb18b",
"75b087f27eed3125cb879d2b8bde10f5e371fbb3"
] | [
"tests/test_models/test_heads.py",
"mmcls/models/heads/vision_transformer_head.py"
] | [
"# Copyright (c) OpenMMLab. All rights reserved.\nfrom unittest.mock import patch\n\nimport pytest\nimport torch\n\nfrom mmcls.models.heads import (ClsHead, LinearClsHead, MultiLabelClsHead,\n MultiLabelLinearClsHead, StackedLinearClsHead,\n VisionTransf... | [
[
"torch.Size",
"torch.randint",
"torch.rand"
],
[
"torch.nn.Linear",
"torch.nn.functional.softmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neduchal/io_classification_experiment | [
"f22a0eac84c23dbbcb9eed35c00c37a1e1a1342b"
] | [
"data_creator/gbp.py"
] | [
"import numpy as np\r\nfrom scipy.signal import convolve\r\n\r\n\r\ndef gbp(img):\r\n g1 = np.array([[-1,0,1]])\r\n g2 = np.array([[-1],[0],[1]])\r\n g3 = np.array([[0,0,1],[0,0,0],[-1,0,0]])\r\n g4 = np.array([[-1,0,0],[0,0,0],[0,0,1]]) \r\n\r\n\r\n rg1 = convolve(img, g1, mode=\"same\")\r\n rg2 ... | [
[
"numpy.array",
"scipy.signal.convolve",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
raccoonML/hifigan-demo | [
"ac2f7993b2f203f720ec1e0429ba090ee12a3e43"
] | [
"raccoonML/audiotools/audio.py"
] | [
"# raccoonML audio tools.\n# MIT License\n# Copyright (c) 2021 raccoonML (https://patreon.com/raccoonML)\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\") to deal\n# in the Software without restriction, includ... | [
[
"numpy.diag",
"numpy.dot",
"numpy.log",
"numpy.maximum",
"numpy.abs",
"numpy.power",
"numpy.clip",
"numpy.matmul",
"numpy.random.rand",
"scipy.signal.lfilter",
"numpy.exp",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
keflavich/aplpy | [
"0cce7bdbf850c40ed4e8d7134cabd9e2ac611f17",
"0cce7bdbf850c40ed4e8d7134cabd9e2ac611f17"
] | [
"aplpy/core.py",
"aplpy/tests/test_rgb.py"
] | [
"from __future__ import absolute_import, print_function, division\n\nfrom distutils import version\nimport os\nimport operator\nfrom functools import reduce\n\nimport matplotlib\n\nif version.LooseVersion(matplotlib.__version__) < version.LooseVersion('1.0.0'):\n raise Exception(\"matplotlib 1.0.0 or later is re... | [
[
"numpy.radians",
"numpy.minimum",
"numpy.sqrt",
"numpy.linspace",
"matplotlib.pyplot.rc",
"matplotlib.patches.Polygon",
"numpy.sin",
"matplotlib.pyplot.close",
"numpy.column_stack",
"numpy.zeros",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.cm.get_cmap",
"mat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
uuddlrlrb4/pyopencl | [
"1e850da761797c70aa0b2580845585c2401dd0a0"
] | [
"test/test_wrapper.py"
] | [
"from __future__ import division, absolute_import, print_function\n\n__copyright__ = \"Copyright (C) 2009 Andreas Kloeckner\"\n\n__license__ = \"\"\"\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the... | [
[
"numpy.random.random",
"numpy.array_equal",
"numpy.empty_like",
"numpy.int32",
"numpy.linalg.norm",
"numpy.dtype",
"numpy.random.rand",
"numpy.float32",
"numpy.zeros",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HudsonHuang/aidc-2018-timeseries | [
"7b39fbe0fd4088ecb1fd6dfda681b4050f2361a8"
] | [
"datasets/music.py"
] | [
"import numpy as np\nimport os\nimport urllib.request\nfrom scipy.io import loadmat\nfrom datasets.timeseries import TimeSeries\n\nSOURCE_URL = \"https://github.com/locuslab/TCN/raw/master/TCN/poly_music/mdata/\"\nJSB_FILENAME = \"JSB_Chorales.mat\"\nNott_FILENAME = \"Nottingham.mat\"\n\nclass Music():\n def __i... | [
[
"scipy.io.loadmat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
MahmoudMuhammedAli/faint-rush | [
"2e298597cf9e23424857ad64121dfe21c9456c59"
] | [
"faint_detection_system/object_detection/utils/visualization_utils.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.compat.v1.math.sigmoid",
"tensorflow.compat.v1.concat",
"matplotlib.pyplot.get_cmap",
"numpy.cumsum",
"tensorflow.compat.v1.py_func",
"tensorflow.compat.v1.executing_eagerly",
"numpy.concatenate",
"tensorflow.compat.v1.shape",
"numpy.zeros_like",
"tensorflow.com... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jie-Qiao/disentanglement_lib | [
"8e521c2996a1d3a25e22afcb732a787ea29f1af1"
] | [
"disentanglement_lib/methods/unsupervised/vae.py"
] | [
"# coding=utf-8\n# Copyright 2018 The DisentanglementLib 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/LICE... | [
[
"tensorflow.math.abs",
"tensorflow.train.LoggingTensorHook",
"tensorflow.stack",
"tensorflow.reduce_sum",
"tensorflow.diag",
"tensorflow.random_shuffle",
"tensorflow.group",
"tensorflow.summary.scalar",
"tensorflow.reduce_logsumexp",
"tensorflow.contrib.tpu.TPUEstimatorSpec... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
saputran/my-learning-analytics | [
"474de8d4eb230587f826ad7d72f4a0ec80dbf687"
] | [
"dashboard/views.py"
] | [
"import json\nimport logging\nimport math\nfrom collections import namedtuple\nfrom datetime import timedelta\nfrom json import JSONDecodeError\n\nimport jsonschema\nimport numpy as np\nimport pandas as pd\nfrom django.conf import settings\nfrom django.contrib import auth\nfrom django.core.exceptions import ObjectD... | [
[
"pandas.merge",
"pandas.to_datetime",
"pandas.DataFrame",
"numpy.uint64",
"pandas.set_option",
"pandas.read_sql"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
chrico7/data515_project | [
"c2d87297a9eb8d9e3dbb14fcc44380b45fe8bba7"
] | [
"data515_project/tests/test_organize_county_data.py"
] | [
"import unittest\r\nfrom datetime import datetime\r\nimport pandas as pd\r\nfrom data515_project.kc_real_estate import organize_county_data\r\n\r\n\r\n# Define a class in which the tests will run\r\nclass UnitTests(unittest.TestCase):\r\n\r\n # Each method in the class to execute a test\r\n def setUp(self):\r... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jiahy0825/scikit-multiflow | [
"910fa62605de49dea3e4599bb233c3d9c6f4527b"
] | [
"src/skmultiflow/data/data_stream.py"
] | [
"import pandas as pd\nimport numpy as np\n\nimport warnings\n\nfrom skmultiflow.data.base_stream import Stream\n\n\nclass DataStream(Stream):\n \"\"\" Creates a stream from a data source.\n\n DataStream takes the whole data set containing the `X` (features) and `Y` (targets) or takes `X` and `Y` separately.\n... | [
[
"numpy.issubdtype",
"pandas.DataFrame",
"numpy.unique"
]
] | [
{
"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": []
}
] |
IceMikl/slogert | [
"e214ffbf5e3477af176dfb3f478e11c555bc292c"
] | [
"executable/logpai/logparser/Spell/Spell.py"
] | [
"\"\"\"\nDescription : This file implements the Spell algorithm for log parsing\nAuthor : LogPAI team\nLicense : MIT\n\"\"\"\n\nimport sys\nimport re\nimport os\nimport numpy as np\nimport pandas as pd\nimport hashlib\nfrom datetime import datetime\nimport string\n\n\nclass LCSObject:\n \"\"\" Class obj... | [
[
"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": []
}
] |
lkk688/3DDepth | [
"ee36f3f7e3dee0638be297bc4f01b4576403c2bb"
] | [
"Waymo/extract_merged_pointclouds.py"
] | [
"# adapted from 'example/visualize_pcl.py'\n\"\"\"\n# * Basic Information\nCreates a HDFStore which contains data in this form\n\"frame_{counter}\" -> each frame data\n\"box_metadata_{counter}_{box_counter}\" -> each frame can contain multiple boxes\n\n# * Usage\nYou can update the following variables to customize ... | [
[
"pandas.Series",
"numpy.matmul",
"numpy.cos",
"pandas.DataFrame",
"numpy.sin",
"pandas.HDFStore",
"numpy.array",
"numpy.empty"
]
] | [
{
"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": []
}
] |
leobrowning92/multimeter-measurement | [
"f1354f7bda9b7374fc57d035e615609ebd4a9028"
] | [
"techtronix4040_mesureV_plot.py"
] | [
"import telnetlib\nimport time\nimport datetime\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport re\n###Notes from Felica###\n#b'+9.90000000E+37\\r\\n'\n#+9.90000000E+37\n#This is the open circuit value for any measurements\n#Set multimeter to DHCP and hard reset.\n#Might have to c... | [
[
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.animation.FuncAnimation",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iwatadive28/burger_war_dev | [
"e7c8b8133b769c27922b1a55ed31447f65dd92b0"
] | [
"burger_war_dev/scripts/navirun_enemy_RikaK.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport rospy\nimport random\nimport math\nimport numpy as np\npi = math.pi\nfrom geometry_msgs.msg import Twist\nfrom geometry_msgs.msg import PoseWithCovarianceStamped\nimport tf\nfrom math import radians, degrees, atan2\n\nimport actionlib\nfrom move_base_msgs.msg ... | [
[
"numpy.mod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BaranovArtyom/aiexamples | [
"f0099d55f1a19328b09ab2b9b5968b804c2d5f07"
] | [
"tensorflow/textboxes/utils/training.py"
] | [
"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport keras.backend as K\nimport tensorflow as tf\nimport json\nimport time\n\nfrom keras.callbacks import Callback\nfrom keras.optimizers import Optimizer\nfrom keras.legacy import interfaces\n\n\ndef smooth_l1_loss(y_true, y_pred):\n \"\"\"Compute L1-smo... | [
[
"matplotlib.pyplot.legend",
"numpy.sqrt",
"numpy.linspace",
"tensorflow.reduce_sum",
"tensorflow.equal",
"matplotlib.pyplot.plot",
"numpy.hanning",
"numpy.exp",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.figure",
"numpy.log",
"matplotlib.pyplot.title",
"tensorf... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
RFogarty1/plato_pylib | [
"b0ab65bfe489c4bb1fd321cc102580bef2b6ff68",
"b0ab65bfe489c4bb1fd321cc102580bef2b6ff68"
] | [
"plato_pylib/parseOther/unit_tests/utest_parse_cp2k_files.py",
"plato_pylib/plato/parse_tbint_files.py"
] | [
"#!/usr/bin/python3\n\nimport itertools\nimport os\nimport sys\nimport types\nimport unittest\nimport unittest.mock as mock\n\nimport plato_pylib.shared.ucell_class as uCellHelp\nimport plato_pylib.parseOther.parse_cp2k_files as tCode\nimport plato_pylib.shared.custom_errors as errorHelp\n\nimport numpy as np\n\n\n... | [
[
"numpy.array",
"numpy.allclose"
],
[
"numpy.array",
"numpy.allclose",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vigorfif/keras | [
"f06524c44e5f6926968cb2bb3ddd1e523f5474c5"
] | [
"keras/backend/tensorflow_backend.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nfrom tensorflow.python.framework import ops as tf_ops\nfrom tensorflow.python.training import moving_averages\nfrom tensorflow.python.ops import tensor_array_ops\nfrom tensorfl... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.sign",
"tensorflow.control_dependencies",
"tensorflow.reduce_sum",
"tensorflow.minimum",
"tensorflow.variables_initializer",
"tensorflow.sparse_tensor_dense_matmul",
"tensorflow.nn.sigmoid_cross_entropy_with_logits",
"tensorflow.spars... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
sisl/nns-as-gps | [
"a19e76df31335d9e32fc07a749eb33672e3c9224"
] | [
"main.py"
] | [
"import numpy as np\nimport tensorflow as tf\nimport pdb\n\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\n\nfrom models import *\n\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\n\n\ndef main():\n\n ... | [
[
"matplotlib.pyplot.tight_layout",
"numpy.sqrt",
"numpy.random.seed",
"sklearn.gaussian_process.kernels.RBF",
"numpy.linspace",
"matplotlib.animation.ImageMagickWriter",
"numpy.sin",
"tensorflow.global_variables_initializer",
"sklearn.gaussian_process.GaussianProcessRegressor",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
GitHubChuanYu/T3Project4_SystemIntegration_New | [
"2bc115eb48e88a0c3f397ed306fe160ada969592"
] | [
"ros/src/twist_controller/cte_calculator.py"
] | [
"import numpy as np\n\ndef get_xy_from_waypoints(waypoints):\n \"\"\"\n Given a list of waypoints, returns a list of [x,y] \n coordinates associated with those waypoints\n \"\"\"\n\n return list(map(lambda waypoint: [waypoint.pose.pose.position.x, waypoint.pose.pose.position.y], waypoints))\n... | [
[
"numpy.dot",
"numpy.polyfit",
"numpy.cos",
"numpy.sin",
"numpy.arctan2",
"numpy.array",
"numpy.polyval"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iridia-ulb/INFOH410 | [
"66a4f3e34b59de403ee99033b1e22dce8177d472"
] | [
"TP_NN/src/nn.py"
] | [
"import numpy as np\n\n\nclass NeuralNetwork:\n def __init__(self, shape, learning_rate=1e-2):\n self.size = len(shape)\n self.shape = shape\n self.l_r = learning_rate\n self.biases = []\n self.weights = []\n for prev_layer, layer in zip(self.shape[:-1], self.shape[1:]):... | [
[
"numpy.dot",
"numpy.argmax",
"numpy.random.randn",
"numpy.exp",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WajdiBenSaad/cavaface.pytorch | [
"246567c09354b7132830bac25bc5f0ebc7821abd"
] | [
"backbone/mobilefacenet.py"
] | [
"from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout2d, Dropout, AvgPool2d, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module, Parameter\nimport torch.nn.functional as F\nimport torch\nimport torch.nn as nn\nfrom collections import namedtuple\nimport math\nfrom .common import... | [
[
"torch.nn.Sequential",
"torch.nn.BatchNorm1d",
"torch.norm",
"torch.mean",
"torch.nn.PReLU",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gjeunen/reference_database_creator | [
"0097a2e116335f008ca2fd51527a4749ae0616d3"
] | [
"function/older_versions/reference_database_creator_v2.py"
] | [
"#! /usr/bin/env python3\n\n## import modules\nimport argparse\nfrom Bio import Entrez\nimport time\nfrom urllib.error import HTTPError\nimport http.client\nhttp.client.HTTPConnection._http_vsn = 10\nhttp.client.HTTPConnection._http_vsn_str = 'HTTP/1.0'\nimport subprocess as sp\nimport shutil\nimport re\nimport pan... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"pandas.DataFrame.from_dict",
"matplotlib.rc",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
JOHNKYON/Object_Detection_Track | [
"83a5cdee8a044080d007e473c84c347c1f324c36"
] | [
"script/YOLO/util.py"
] | [
"from __future__ import division\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nimport numpy as np\nimport cv2\n\n\ndef predict_transform(prediction, inp_dim, anchors, num_classes, cuda=False):\n batch_size = prediction.size(0)\n stride = inp_dim ... | [
[
"torch.sigmoid",
"torch.max",
"numpy.unique",
"torch.cat",
"numpy.arange",
"torch.min",
"torch.from_numpy",
"numpy.full",
"torch.exp",
"torch.FloatTensor",
"torch.sort",
"torch.nonzero",
"torch.clamp",
"numpy.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amonod/udvd | [
"a1ccb777d205255ac68c40efb93dd3996f562c45"
] | [
"models/udvd.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom models import register_model\n\nclass crop(nn.Module):\n def __init__(self):\n super().__init__()\n\n def forward(self, x):\n N, C, H, W = x.shape\n x = x[0:N, 0:C, 0:H-1, 0:W]\n return x\n\nclass shift(n... | [
[
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Upsample",
"torch.nn.LeakyReLU",
"torch.chunk",
"torch.nn.ZeroPad2d",
"torch.nn.functional.pad",
"torch.nn.ReplicationPad2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BeardHealth/Combined-Heat-and-Power-System-Economic-Dispatch | [
"378127d6304e9b9979f87ccc20004ddd222de8dc"
] | [
"CHP_MODEL.py"
] | [
"\"\"\"\nEnvironment for Intergrated Energy System.\nYou can customize this script in a way you want.\n\nRequirement:\npyglet >= 1.2.4\nnumpy >= 1.12.1\n\"\"\"\nimport numpy as np\n\n\nclass CHPEnv(object):\n GB_bound = [0.2, 1]\n GT_bound = [0, 1]\n TST_bound = [- 0.1, 0.2]\n Grid_bound = [-1, 1]\n ... | [
[
"numpy.square",
"numpy.hstack",
"numpy.abs",
"numpy.clip",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tshi04/ACCE | [
"56814761171aad3a9264c6f092a2b90e9a0709b7",
"56814761171aad3a9264c6f092a2b90e9a0709b7"
] | [
"ACEclass/RNNAttn/model.py",
"ACEclass/RNNAttnWE/model.py"
] | [
"'''\n@author Tian Shi\nPlease contact tshi@vt.edu\n'''\nimport numpy as np\nimport torch\nfrom ACEclass.model_base import modelClassificationBase\nfrom LeafNATS.modules.attention.attention_self import AttentionSelf\nfrom LeafNATS.modules.encoder.encoder_rnn import EncoderRNN\n\n\nclass modelClassification(modelCla... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.Dropout",
"numpy.around",
"torch.nn.Embedding",
"torch.nn.Linear"
],
[
"torch.nn.CrossEntropyLoss",
"torch.nn.Dropout",
"numpy.around",
"torch.nn.Embedding",
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
42Questions/ml_examples | [
"c4bf4172ba9fad2a5e0f37d22314d10174e6a0c0"
] | [
"assignments/hw7-backprop/code/nodes.py"
] | [
"\"\"\"Computation graph node types\n\nNodes must implement the following methods:\n__init__ - initialize node\nforward - (step 1 of backprop) retrieve output (\"out\") of predecessor nodes (if\n applicable), update own output (\"out\"), and set gradient (\"d_out\") to zero\nbackward - (step 2 of... | [
[
"numpy.dot",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Borda/torchmetrics | [
"6144eb3b248b7fa315bb9afeb96c690a5d747001",
"6144eb3b248b7fa315bb9afeb96c690a5d747001",
"6144eb3b248b7fa315bb9afeb96c690a5d747001"
] | [
"torchmetrics/regression/explained_variance.py",
"tests/wrappers/test_multioutput.py",
"tests/classification/test_f_beta.py"
] | [
"# Copyright The PyTorch Lightning team.\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... | [
[
"torch.tensor"
],
[
"torch.argmax",
"sklearn.metrics.r2_score",
"torch.rand",
"torch.randint"
],
[
"torch.transpose",
"numpy.expand_dims",
"torch.tensor",
"numpy.concatenate",
"numpy.insert",
"torch.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zimiao1025/BioSeq-BLM | [
"8e833d9a767d2585bbf1fbee987a065fc421e8e6"
] | [
"code/MachineLearningAlgorithm/utils/utils_plot.py"
] | [
"import os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy import interp\nfrom sklearn.metrics import roc_curve, auc, precision_recall_curve\n\n\ndef plot_roc_curve(cv_labels, cv_prob, file_path):\n \"\"\"Plot ROC curve.\"\"\"\n # Receiver Operating Characteristic\n tpr_list = []\n au... | [
[
"matplotlib.pyplot.legend",
"numpy.minimum",
"numpy.linspace",
"matplotlib.pyplot.plot",
"numpy.mean",
"matplotlib.pyplot.gca",
"sklearn.metrics.precision_recall_curve",
"numpy.std",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
young666/EDVR | [
"d432fd3dca0b231ba74789067e54bbd6ef2340a1"
] | [
"codes/data/data_sampler.py"
] | [
"\"\"\"\nModified from torch.utils.data.distributed.DistributedSampler\nSupport enlarging the dataset for *iter-oriented* training, for saving time when restart the\ndataloader after each epoch\n\"\"\"\nimport math\nimport torch\nfrom torch.utils.data.sampler import Sampler\nimport torch.distributed as dist\n\n\ncl... | [
[
"torch.Generator",
"torch.randperm",
"torch.distributed.is_available",
"torch.distributed.get_rank",
"torch.distributed.get_world_size"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sealneaward/movement-quadrants | [
"138f1177a1b363352fed7d0ce9cc31bdd2b1a1ca"
] | [
"convert_full_court_to_half_court.py"
] | [
"import pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Circle, Rectangle, Arc\n\n##################################################################################################\n# Plot a series of movement positions:http://savvastjortjoglou.com/nba-play-by-pla... | [
[
"matplotlib.pyplot.gca",
"pandas.read_csv",
"matplotlib.patches.Arc",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.ylim",
"matplotlib.patches.Rectangle",
"matplotlib.patches.Circle",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.xlim",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
rth/xarray | [
"48d55eea052fec204b843babdc81c258f3ed5ce1",
"48d55eea052fec204b843babdc81c258f3ed5ce1"
] | [
"xarray/core/dataset.py",
"xarray/coding/strings.py"
] | [
"from __future__ import absolute_import, division, print_function\n\nimport functools\nimport sys\nimport warnings\nfrom collections import Mapping, defaultdict\nfrom distutils.version import LooseVersion\nfrom numbers import Number\n\nimport numpy as np\nimport pandas as pd\n\nimport xarray as xr\n\nfrom . import ... | [
[
"pandas.MultiIndex",
"numpy.asarray",
"pandas.Categorical",
"numpy.issubdtype",
"pandas.Index",
"pandas.MultiIndex.from_product",
"numpy.prod",
"pandas.Int64Index",
"numpy.zeros"
],
[
"numpy.asarray",
"numpy.array",
"numpy.zeros",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.3",
"0.19",
"1.1",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": ... |
andreasabo-ibbme/mmskeleton | [
"13a46479170b54eb9a7817b396d85a8d226029cb"
] | [
"mmskeleton/processor/recognition_tri.py"
] | [
"from collections import OrderedDict\nimport torch\nimport logging\nimport numpy as np\nfrom mmskeleton.utils import call_obj, import_obj, load_checkpoint\nfrom mmcv.runner import Runner\nfrom mmcv import Config, ProgressBar\nfrom mmcv.parallel import MMDataParallel\nimport os, re, copy\nfrom sklearn.model_selectio... | [
[
"torch.mean",
"numpy.asarray",
"sklearn.metrics.mean_absolute_error",
"torch.utils.data.DataLoader",
"sklearn.metrics.confusion_matrix",
"sklearn.model_selection.KFold",
"numpy.random.random_sample",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"numpy.round",
"torch.F... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ternaus/pytorch-toolbelt | [
"a952882bae1ded4f9a583cbdf87ba6f335ef3abf",
"a952882bae1ded4f9a583cbdf87ba6f335ef3abf"
] | [
"pytorch_toolbelt/modules/backbone/wider_resnet.py",
"pytorch_toolbelt/modules/agn.py"
] | [
"from collections import OrderedDict\nfrom functools import partial\n\nimport torch\nfrom torch import nn\n\nfrom pytorch_toolbelt.modules.abn import ABN\nfrom pytorch_toolbelt.modules.pooling import GlobalAvgPool2d\nfrom pytorch_toolbelt.utils.torch_utils import count_parameters\n\n\nclass IdentityResidualBlock(nn... | [
[
"torch.nn.Linear",
"torch.randn",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d"
],
[
"torch.ones",
"torch.zeros",
"torch.nn.functional.relu6",
"torch.nn.init.ones_",
"torch.nn.functional.relu",
"torch.nn.functional.leaky_relu",
"torch.nn.init.zeros_",
"torch.nn.functi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
visinf/mnvi | [
"654b68888f86e008c9b686950f7f3e493b47c011"
] | [
"models/resnet_mfvi.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport torch.nn as nn\nimport logging\nimport torch\nfrom contrib import varprop\n\n\ndef keep_variance(x, min_variance):\n return x.clamp(min=min_variance)\n\ndef finitialize(modules, small=False):... | [
[
"torch.nn.init.constant_",
"torch.nn.init.kaiming_normal_",
"torch.zeros_like",
"torch.flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Severson-Group/MachEval | [
"dbb7999188133f8744636da53cab475ae538ce80"
] | [
"examples/mach_opt_examples/LegacyCode/IM/machine_design/im_architect.py"
] | [
"import numpy as np\n\nfrom .architect import Architect\nfrom .machines import IM_Machine\nfrom .winding_layout_im import winding_layout_v2\nimport numpy as np\n\nfrom specifications.machine_specs.im1_machine_specs import DesignSpec\n\n__all__ = ['IMArchitectType1']\n\n\nclass IMArchitectType1(Architect):\n '''\... | [
[
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
philipkalinda/StackerPy | [
"4ac62ac6b803caa0743cab35d0e3bcb17046ed01"
] | [
"stackerpy/test.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression, RidgeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import classification_report, accuracy_score, recall_score, f1_score, precision_s... | [
[
"sklearn.datasets.load_breast_cancer",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.linear_model.LogisticRegression",
"sklearn.metrics.precision_score",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"matplotlib.pyplot.savefig",
"sklearn.metrics.classification_report",
... | [
{
"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": []
}
] |
wyq24/suncasa | [
"1f94aaabaf6a3911fa532648ec6676a221553436"
] | [
"suncasa/eovsa/eovsa_diskmodel.py"
] | [
"from tqdm import tqdm\nfrom taskinit import ms, tb, qa\nfrom taskinit import iatool\nfrom taskinit import cltool\nfrom delmod_cli import delmod_cli as delmod\nfrom clearcal_cli import clearcal_cli as clearcal\nfrom suncasa.utils import mstools as mstl\nfrom suncasa.utils import helioimage2fits as hf\nimport shutil... | [
[
"numpy.nanmax",
"matplotlib.pyplot.legend",
"numpy.polyfit",
"numpy.nanmedian",
"numpy.sqrt",
"numpy.linspace",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"numpy.argmin",
"numpy.nanmean",
"numpy.zeros_like",
"numpy.exp",
"numpy.where",
"numpy.ones_like",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nopeless/perfectionist | [
"4024c25b71201ab77eb0f81350fd5d514476f93f"
] | [
"gamev9.1 nhr rewrite.py"
] | [
"import numpy as np\n\n\ndef nHr_next(n, r):\n\tarrange = np.full(r, n-1, dtype=np.uint8, order='C')\n\tarrange[-1] = n\n\n\tdef nHr_recursive():\n\t\tnonlocal arrange\n\n\t\tif arrange[0] == 0:\n\t\t\tprint(\"SOFTWARN: overloop\")\n\t\t\tarrange = np.full(r, n-1, dtype=np.uint8, order='C')\n\t\t\tarrange[-1] = n\n... | [
[
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
birdx0810/tbrain20_AML | [
"3c00753e591e957708564861e1e849b7dfc59f62",
"3c00753e591e957708564861e1e849b7dfc59f62"
] | [
"baseline/preprocessor.py",
"baseline/ner.py"
] | [
"# -*- coding: UTF-8 -*-\nimport ast\nimport os\nimport pickle\n\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd\n\nimport tokenizer\nimport dataset\n\n# Path for loading dataset\nCSV_PATH = \"../data/tbrain_train_final_0610.csv\"\nNEWS_PATH = os.path.abspath(\n f\"../data/news\"\n)\n\... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
baek-jinoo/Horizon | [
"0f9a1b16ddd6e5a8ac98e61acd227aae7c201b57",
"0f9a1b16ddd6e5a8ac98e61acd227aae7c201b57"
] | [
"ml/rl/evaluation/sequential_doubly_robust_estimator.py",
"ml/rl/test/constant_reward/env.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n\nimport logging\nfrom typing import List\n\nimport numpy as np\nimport torch\nfrom ml.rl.evaluation.cpe import CpeEstimate\nfrom ml.rl.evaluation.evaluation_data_page import EvaluationDataPage\n\n\nlogger = logging.ge... | [
[
"numpy.array",
"torch.sum",
"numpy.mean"
],
[
"numpy.logical_not",
"torch.ones",
"numpy.ones",
"torch.tensor",
"numpy.array",
"numpy.zeros",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gengcong940126/Real-ESRGAN | [
"8cc57f962591b0f72dc4c7913b9d50e6af21a3b0"
] | [
"EBM/model.py"
] | [
"import numpy as np\nimport random\nimport torch\nfrom collections import OrderedDict\nfrom basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt\nfrom EBM import loss\nfrom os import path as osp\nfrom tqdm import tqdm\nfrom basicsr.archs import build_network\nfrom basicsr.losse... | [
[
"torch.zeros",
"torch.randperm",
"torch.cuda.empty_cache",
"torch.no_grad",
"torch.nn.functional.interpolate",
"numpy.random.uniform",
"torch.clamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Christian-kam/sagemaker-python-sdk | [
"2af99f14aa0a07c134b71666e77703cfa37adc14"
] | [
"tests/unit/sagemaker/feature_store/test_feature_store.py"
] | [
"# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#\n# or in the \"l... | [
[
"pandas.Series"
]
] | [
{
"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": []
}
] |
necla-ml/ml | [
"7ebd29382326e3958297607da7182c211865e7ff"
] | [
"ml/vis.py"
] | [
"class Keeper(object):\n def __init__(self, name=None, suffix=None, backend='tensorboard', **kwargs):\n from datetime import datetime\n from ml import distributed as dist\n now = datetime.now()\n prefix = f\"{now.month:02d}{now.day:02d}_{now.hour:02d}-{now.minute:02d}-{now.second:02d}... | [
[
"torch.utils.tensorboard.SummaryWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
devitocodes/devitoboundary | [
"a2b4c62015f7bab8b4f53ef2ea181a674c1751a4"
] | [
"tests/test_evaluation.py"
] | [
"import pytest\nimport os\n\nimport numpy as np\nimport pandas as pd\nfrom devitoboundary.stencils.evaluation import (get_data_inc_reciprocals,\n split_types, add_distance_column,\n get_component_weights,\n ... | [
[
"numpy.absolute",
"numpy.linspace",
"numpy.logical_and",
"numpy.arange",
"pandas.DataFrame",
"numpy.full",
"numpy.all",
"numpy.sign",
"numpy.array",
"numpy.zeros",
"numpy.isclose"
]
] | [
{
"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": []
}
] |
fartashf/tensorboard-pytorch | [
"7f0bb15529d789340b8732a394efd7f762ef76c0"
] | [
"demo.py"
] | [
"import torch\nimport torchvision.utils as vutils\nimport numpy as np\nimport torchvision.models as models\nfrom torchvision import datasets\nfrom tensorboardX import SummaryWriter\n#import skimage\n#from skimage import data, io\n\nresnet18 = models.resnet18(False)\nwriter = SummaryWriter()\nsample_rate = 44100\nfr... | [
[
"numpy.arctan",
"torch.zeros",
"torch.cat",
"numpy.cos",
"numpy.sin",
"torch.rand",
"numpy.random.rand",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GasserElAzab/Platooning-of-3-ackermann-vehiles-confrolled-by-Fuzzy-Logic-Control | [
"0c7f1adbe95177d6cefff92e5ee36cb3d9ad94cb"
] | [
"ackermann_vehicle_gazebo/scripts/Follower2.py"
] | [
"#!/usr/bin/env python\n# A basic python code to implement the kinematics model for the differential mobile robot\n# and reflect its behavior on graphs.\n#########################################################################################################\n#Import the required libraries:\nimport rospy\nimport m... | [
[
"numpy.arctan2",
"numpy.square",
"numpy.absolute"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
niasand/experiment_code | [
"b4eaa255667952ab7a412e8217687aab2539edcb"
] | [
"pandas_learn/data_formalize.py"
] | [
"# -*- coding: utf-8 -*-\n# @Author: jerry\n# @Date: 2017-10-18 10:10:04\n# @Last Modified by: jerry\n# @Last Modified time: 2017-10-18 14:56:17\n\nimport pandas as pd\nfrom pandas import DataFrame\n\n\ndef merge():\n\n df1 = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],\n 'data1... | [
[
"pandas.value_counts",
"pandas.merge",
"pandas.cut",
"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": []
}
] |
JKalnins/M-LOOP | [
"e1587a905ae7b1d282037185e2f0d33edaf06f6d"
] | [
"tests/test_examples.py"
] | [
"'''\nUnit test for all of the example scripts provided in the examples folder.\n'''\nfrom __future__ import absolute_import, division, print_function\n\nimport os\nimport unittest\nimport mloop.testing as mlt\nimport mloop.launchers as mll\nimport mloop.utilities as mlu\nimport logging\nimport numpy as np\nimport ... | [
[
"numpy.square"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
azyner/radip | [
"89defb093d7b960edfe1ed10af1803a38fcbcd26"
] | [
"recurrent_batchnorm_tensorflow/BN_LSTMCell.py"
] | [
"# Copyright (C) 2016 by Akira TAMAMORI\n#\n# This program is free software; you can redistribute it and/or modify it under\n# the terms of the GNU General Public License as published by the Free Software\n# Foundation, either version 3 of the License, or (at your option) any later\n# version.\n#\n# This program is... | [
[
"tensorflow.get_variable",
"tensorflow.concat",
"tensorflow.control_dependencies",
"numpy.linalg.svd",
"tensorflow.nn.moments",
"tensorflow.add",
"tensorflow.matmul",
"tensorflow.nn.batch_normalization",
"tensorflow.zeros_initializer",
"tensorflow.variable_scope",
"tens... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
mockinggod/Generatorbot | [
"502ef3b72ad841f2cd2e6ebaf8bb6655ab7c18ef"
] | [
"medievalshipnamegenerator.py"
] | [
"import numpy as np\n\n# Generates a genuine sounding ship name from two tables\n\n\n\nwith open(\"shipnames.txt\", encoding='latin-1') as f:\n shipnames = f.read().splitlines() \n\nwith open(\"shipnamespt1.txt\", encoding='latin-1') as f:\n part1 = f.read().splitlines() \t\n\t\nwith open(\"shipnamespt2.txt\"... | [
[
"numpy.random.random",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
charlessuresh/Abalone_Age_Prediction | [
"db5315f461f2b5619c7cc949d7480347c486e409"
] | [
"src/data_wrangling/pre_process_abalone_data.py"
] | [
"# author: Charles Suresh\n# date: 2020-11-27\n\n\"\"\"Cleans, pre-processes splits the Abalone Data Set (from http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data).\nWrites the training and test data to separate csv files.\n\nUsage: src/data_wrangling/pre_process_abalone_data.py --input=<i... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Nin0n/Lottery-Ticket | [
"92e47319580c52eb16cc4fa8cb9555d2a8c5dac6"
] | [
"masking.py"
] | [
"from __future__ import print_function\nimport torch\nimport torch.nn.utils\nfrom mnist import NNet\nimport math\nimport argparse\nimport pickle\n\nINITIAL_MODEL_FILENAME = 'initial_model.pt'\n\nENTIRE_MODEL_FILENAME = \"mnist_cnn.pt\"\nMODEL_WEIGHTS_FILENAME = \"mnist_weights_cnn.pt\"\n\n# Load the initial model s... | [
[
"torch.Tensor.abs",
"torch.cat",
"torch.sort",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fgolemo/pytorch-a2c-ppo-acktr-old | [
"555e9a0968b1587983f4897a37945ae023c72a36"
] | [
"pytorch_a2c_ppo_acktr/wrappers.py"
] | [
"import cv2\nimport gym\nfrom gym import spaces\nimport numpy as np\n\n\nclass DuckietownRewardWrapper(gym.RewardWrapper):\n\n def __init__(self, env):\n super().__init__(env)\n\n def reward(self, reward):\n if reward == -1000:\n reward = -10\n elif reward > 0:\n rew... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
parthanand-in/Virtual-Mouse-Using-Hand-Gesture | [
"43dfc495c3942fcc97772249896efdaa4f8e6aad"
] | [
"Part1.py"
] | [
"import cv2\r\nimport numpy as np\r\nfrom pynput.mouse import Button,Controller #Contains Mouse Operations\r\nimport wx #WX Lib is used To get the Screen Size Of Monitor\r\n\r\ndef leftClick():\r\n mouse = Controller()\r\n\r\n app = wx.App(False)\r\n (sx,sy) = wx.GetDisplaySize() #sx=screen x co-ordinate ;... | [
[
"numpy.array",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CorentinKervadec/EGG | [
"5ccd49c4a493514b1194699954d41940f5e2a5c6"
] | [
"egg/zoo/sum_game/architectures.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 torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\n# In EGG, the game designer must implement the core func... | [
[
"torch.nn.LogSoftmax",
"torch.cat",
"torch.zeros_like",
"torch.nn.Linear",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neumason/zi2zi | [
"e8ce521ebd260eae569cd5b3e7923f9450b0cca2",
"e8ce521ebd260eae569cd5b3e7923f9450b0cca2"
] | [
"infer.py",
"flow/flow-master/f-VAEs.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nfrom __future__ import absolute_import\n\nimport tensorflow as tf\nimport os\nimport argparse\nfrom model.unet import UNet\nfrom model.utils import compile_frames_to_gif\n\n\"\"\"\nPeople are made to have fun and be 中二 sometimes\n ... | [
[
"tensorflow.ConfigProto",
"tensorflow.Session",
"tensorflow.app.run"
],
[
"scipy.misc.imresize",
"numpy.log",
"numpy.clip",
"numpy.random.choice",
"numpy.random.shuffle",
"numpy.random.randn",
"scipy.misc.imread",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1... |
gabrieljablonski/ReactionTime | [
"769ad9e55166435887f5b4f72f329e59911fe00b"
] | [
"analysis/RTMainWindow.py"
] | [
"# -*- coding: utf-8 -*-\n\n# Form implementation generated from reading ui file 'mainwindow.ui'\n#\n# Created by: PyQt5 UI code generator 5.10\n#\n# WARNING! All changes made in this file will be lost!\n\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom RTFilesWindow import RTFilesWindow\nimport matplotlib\nimport... | [
[
"matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg.__init__",
"numpy.take",
"numpy.abs",
"matplotlib.figure.Figure",
"matplotlib.use",
"matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg.updateGeometry",
"numpy.array",
"matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg.setS... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
agk2000/catalyst_project | [
"4b45307328d94fb7b1eafa318059ddcb86fda21f"
] | [
"cut_RTI.py"
] | [
"# This function serves as a image cutter for RTI images to smaller pieces so that the labelling process is easier\n\nimport numpy as np\nimport pandas as pd\nimport os\nimport rasterio\nfrom data.data_utils import patch_tile_single\nfrom mrs_utils import misc_utils\nimport gc\n\nfrom multiprocessing import Pool\n\... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stephaniebamerico/GRUEN | [
"ace9629a4ba0bac91e38cbdab777d97c5721be9c"
] | [
"Teste.py"
] | [
"import difflib\nimport editdistance\nimport math\nimport numpy as np\nimport re\nimport spacy\nimport string\nimport torch\nfrom nltk.tokenize import sent_tokenize\nfrom tqdm import tqdm\nfrom transformers import BertConfig, BertForSequenceClassification, BertTokenizer, BertForMaskedLM\nfrom transformers import gl... | [
[
"torch.utils.data.TensorDataset",
"torch.utils.data.SequentialSampler",
"torch.tensor",
"torch.no_grad",
"torch.cuda.is_available",
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
y-yu/qwgc | [
"018c4ad7b986a338d54c43f02ad7b1a7b5ab624b"
] | [
"notebook/qwgc_tutorial.py"
] | [
"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.3.3\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# # Quantum... | [
[
"numpy.sqrt",
"numpy.identity",
"numpy.count_nonzero",
"numpy.random.uniform",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vincentzhang/faster-rcnn-fcn | [
"7118d715a430f0ec2697e5f7a9a39c9752b466da"
] | [
"tools/gen_bbox_ac.py"
] | [
"# generated bbox ground truth from pixel-wise segmentation\n# it currently only generate one bbox\nfrom __future__ import print_function\nimport numpy as np\nimport h5py\nimport os\nimport pdb\n\nmask_path = '../data/acce'\nf = h5py.File(os.path.join(mask_path, \"resized_label_ac_2d.h5\"), 'r')\n\nbbox_path = '../... | [
[
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mayscopeland/priceguide | [
"3a8c4ea1e64264675deb4c568dbcaa7aff572b24"
] | [
"priceguide.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nclass League:\n SCORING_ROTO = \"R\"\n SCORING_POINTS = \"P\"\n\n LEAGUE_STANDARD_4x4 = \"4x4\"\n LEAGUE_STANDARD_5x5 = \"5x5\"\n LEAGUE_YAHOO = \"Yahoo\"\n LEAGUE_CBS_ROTO = \"CBS Roto\"\n LEAGUE_CBS_POINTS = \"CBS Points\"\... | [
[
"pandas.merge",
"pandas.read_csv",
"pandas.DataFrame",
"pandas.to_numeric",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Qualcomm-AI-research/BayesianBits | [
"fb04b6fd9680b4269b7b5d6da150d73f4b1973ca"
] | [
"benchmarks/mnist/mnist_data_loader.py"
] | [
"# Copyright (c) 2021 Qualcomm Technologies, Inc.\n# All Rights Reserved.\n\nimport torch.utils.data as torch_data\nimport torchvision\nimport torchvision.transforms as transforms\n\n\nclass MnistDataLoader(object):\n def __init__(self, images_dir: str, batch_size: int):\n train_set = torchvision.datasets... | [
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ilvieira/yaaf | [
"931bde3dbad521bf5fb2744793f54791ca7add11"
] | [
"yaaf/models/ConvolutionalNetwork.py"
] | [
"import torch\nfrom torch.nn import Linear\nfrom yaaf.models import TorchModel\nfrom yaaf.models.feature_extraction import MLPFeatureExtractor, Conv2dFeatureExtractor\nfrom yaaf.models.utils import activations\n\n\nclass ConvolutionalNeuralNetwork(TorchModel):\n\n def __init__(self, num_channels, width, height, ... | [
[
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cjw296/pandas | [
"0e2caede0b211cbd1ed915a71041bc62ca95948a"
] | [
"pandas/tseries/plotting.py"
] | [
"\"\"\"\nPeriod formatters and locators adapted from scikits.timeseries by\nPierre GF Gerard-Marchant & Matt Knox\n\"\"\"\n\n#!!! TODO: Use the fact that axis can have units to simplify the process\nimport datetime as pydt\nfrom datetime import datetime\n\nfrom matplotlib import pylab\nimport matplotlib.units as un... | [
[
"matplotlib.pyplot.gca",
"pandas.tseries.frequencies.is_subperiod",
"pandas.isnull",
"pandas.tseries.frequencies.get_period_alias",
"pandas.tseries.frequencies.is_superperiod",
"pandas.tseries.frequencies.get_base_alias",
"matplotlib.pylab.draw_if_interactive",
"pandas.tseries.conv... | [
{
"matplotlib": [],
"numpy": [
"1.11",
"1.10",
"1.12",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
... |
Kannampuzha/neural-network-from-scratch | [
"a6e208e7d8988173eed64c8fcdaf06922689416a"
] | [
"classify-mnist.py"
] | [
"import sys\n\nimport numpy as np\nimport neuralnetwork as nn\nfrom mnist import MNIST\n\nprint(\"Starting...\")\n\nmndata = MNIST('mnist-dataset')\n\n\nnum_classes = 10\nnet = nn.neural_network(3, [784, 20, 10], [None, \"tanh\", \"softmax\"], cost_function=\"cross_entropy\")\n\ndef train():\n print(\"Training..... | [
[
"numpy.eye",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
emilyreff7/ibis | [
"c2323b8dfd7b56db821426513c379de38203b332"
] | [
"ibis/pandas/execution/selection.py"
] | [
"\"\"\"Dispatching code for Selection operations.\n\"\"\"\n\nfrom __future__ import absolute_import\n\nimport functools\nimport operator\nfrom collections import OrderedDict\nfrom operator import methodcaller\n\nimport numpy as np\nimport pandas as pd\nimport toolz\nfrom multipledispatch import Dispatcher\nfrom too... | [
[
"pandas.concat",
"pandas.Series",
"numpy.isscalar"
]
] | [
{
"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": []
}
] |
Mr-Markovian/SphericalHarmonics_for_StokesFlow | [
"f1577426bcc3805f3b3727b0cb607daa87576b03"
] | [
"codes/Bulkflowcodefinal.py"
] | [
"#We can compute Bulk flow and find the flow field inside at a radius r,given \n#the velocity field at the Surface(radius R),l is a 1-D array \n#%!-L/usr/local/lib -lfftw3 -lm\n#!/usr/bin/python3.7\n\n#l=0,1 mode not possible for Vlm_y and Vlm_psi,l=0 not possible for Vlm_phi.\ndef modify_(a,b,c,l): \n x=np.wh... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.arange",
"numpy.arccos",
"numpy.cos",
"numpy.sin",
"matplotlib.cm.get_cmap",
"numpy.meshgrid",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stroxler/bottleneck | [
"6e91bcb8a21170588ee9a3f2c425a4e307ae05de"
] | [
"bottleneck/tests/input_modifcation_test.py"
] | [
"\"Test functions.\"\n\n# For support of python 2.5\nfrom __future__ import with_statement\n\nimport numpy as np\nfrom numpy.testing import assert_equal\nnan = np.nan\nimport bottleneck as bn\n\n\ndef arrays(dtypes=bn.dtypes, nans=True):\n \"Iterator that yield arrays to use for unit testing.\"\n ss = {}\n ... | [
[
"numpy.errstate",
"numpy.arange",
"numpy.testing.assert_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
regginold/drosophila-courtship | [
"a40249ee538a73f66b25d55c47e06f8505ba10f2"
] | [
"courtship/stats/behaviors.py"
] | [
"# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\n.. module:: statistics\r\n :synopsis: Functions for analyzing and scoring behaviors.\r\n\r\n.. moduleauthor:: Ross McKinney\r\n\"\"\"\r\nfrom copy import deepcopy\r\nimport numpy as np\r\n\r\nfrom ..behavior import Behavior\r\n\r\nimport markov\r\nimport spatial\r\n\r\nde... | [
[
"numpy.pad",
"numpy.asarray",
"numpy.cos",
"numpy.flatnonzero",
"numpy.ones",
"numpy.sin",
"numpy.nansum",
"numpy.mean",
"numpy.diff",
"numpy.where",
"numpy.sum",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jmeyers314/jtrace | [
"9149a5af766fb9a9cd7ebfe6f3f18de0eb8b2e89"
] | [
"batoid/medium.py"
] | [
"import numpy as np\nfrom . import _batoid\n\n\nclass Medium:\n \"\"\"The `Medium` class is used to model a refractive medium.\n\n Media have essentially one function: to return their refractive index given\n a wavelength.\n \"\"\"\n def getN(self, wavelength):\n \"\"\"Return refractive index.... | [
[
"numpy.array_equal",
"numpy.array",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LennartJKlein/chips-circuits | [
"6e33888694e5a04b6f995a6192e8916d0ff8213d",
"6e33888694e5a04b6f995a6192e8916d0ff8213d"
] | [
"experiments/netlist-length/program/run95.py",
"experiments/netlist-length/program/amount95/classes.py"
] | [
"import subprocess\nfrom ast import literal_eval\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\n\n# Set plot\nfig, ax = plt.subplots()\nax.set_xlabel(\"Connections in netlist\")\nax.set_ylabel(\"Average paths drawn (%)\")\n\ntotal_results = []\ntotal_scores = []\nnetl... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
],
[
"numpy.set_printoptions",
"numpy.argwhere",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.empty",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
martin-fabbri/reinforcement-learning-playground | [
"96d7e7177d46b3b7e7d8fc11efe92a96a0e380ba"
] | [
"multi-armed-bandid-problem/random_selection.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\ndataset = pd.read_csv(\"Ads_CTR_Optimisation.csv\")\n\n# implementing random selection\nimport random\nN = 10000\nd = 10\nads_selected = []\ntotal_reward = 0\nfor n in range(0, N):\n ad = random.randrange(d)\n ads_selected.append(ad)... | [
[
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Yiran-Zhu/GCHB | [
"81c23931d9b4ec13e439982322f3b85e7d920751"
] | [
"main.py"
] | [
"#!/usr/bin/env python\r\nfrom __future__ import print_function\r\n\r\nimport argparse\r\nimport inspect\r\nimport os\r\nimport pickle\r\nimport random\r\nimport shutil\r\nimport time\r\nfrom collections import OrderedDict\r\n\r\nimport numpy as np\r\nimport torch\r\nimport torch.backends.cudnn as cudnn\r\nimport t... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.nn.CrossEntropyLoss",
"torch.max",
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"numpy.concatenate",
"numpy.mean",
"torch.no_grad",
"torch.cuda.manual_seed_all",
"torch.nn.DataParallel",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xujunhuii/huobi_Python | [
"958df8b22ce774329c7e15a1ecf2f52eea5f6af8"
] | [
"All_Methods/Geometric.py"
] | [
"import pandas as pd\nfrom utils import (\n calc_bbands,\n generate_records_df,\n get_final_profit,\n calc_RSV,\n dealing_results,\n ALL_FILES,\n)\n\nBOLL_UPPER = \"boll_upper\"\nBOLL_LOWER = \"boll_lower\"\nCLOSE = \"Close\"\nTIMESTAMP = \"TimeStamp\"\n\nBEGINNING_CASH = 100000\nFEE = 0.005\nreco... | [
[
"pandas.read_excel",
"pandas.to_datetime"
]
] | [
{
"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": []
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.