repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
jthestness/catamount | [
"9de3090f1a02a04774f28a0d10f677a76f50446f"
] | [
"catamount/tests/ops/dynamic_stitch.py"
] | [
"import sympy\nimport numpy as np\n\nimport catamount\nfrom catamount.graph import Graph\n\nfrom catamount.tests.utils.helpers import *\n\n\ndef test_dynamic_stitch_op():\n ''' Specify graphs with DynamicStitch operations and make sure they behave\n as desired.\n '''\n\n combos = [ # Speech examples\n ... | [
[
"numpy.array",
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
prucehuang/quickly-start-python | [
"614cb12e6993d99326138c16de5309206dc02bd8"
] | [
"Learning Tensor Flow/04__convolutional_neural_networks/layers.py"
] | [
"import tensorflow as tf\n\n\ndef weight_variable(shape):\n initial = tf.truncated_normal(shape, stddev=0.1)\n return tf.Variable(initial)\n\n\ndef bias_variable(shape):\n initial = tf.constant(0.1, shape=shape)\n return tf.Variable(initial)\n\n\ndef conv2d(x, W):\n return tf.nn.conv2d(x, W, strides=... | [
[
"tensorflow.matmul",
"tensorflow.constant",
"tensorflow.truncated_normal",
"tensorflow.Variable",
"tensorflow.nn.max_pool",
"tensorflow.nn.conv2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
victen18/DVC-CNN-TF-Pipeline-Demo | [
"01e0c400727372cae7557bb8137694e83c35f0a9"
] | [
"src/utils/model.py"
] | [
"import tensorflow as tf\nimport logging\nimport io\nfrom src.utils.common import get_timestamp\nimport os\n\n\ndef __get_model_summary(model):\n with io.StringIO() as stream:\n model.summary(print_fn=lambda x: stream.write(f\"{x}\\n\"))\n summary_str = stream.getvalue()\n return summary_str... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.keras.losses.CategoricalCrossentropy",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.applications.vgg16.VGG16",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras.optimizers.SGD"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
MLH-Fellowship/0.1.2-visualization | [
"cb9968bded9f5d9528599ec4a5194b1f455bfc9e"
] | [
"train.py"
] | [
"import LSTM\nimport data\nimport json\nfrom tensorflow.keras.optimizers import RMSprop\nfrom tensorflow.keras.callbacks import EarlyStopping\n\nVOCAB_SIZE = 5000\nMAX_SEQ_LEN = 100\n\nimdb = data.Dataset(MAX_SEQ_LEN, VOCAB_SIZE)\n\nmodel = LSTM.Network(MAX_SEQ_LEN, VOCAB_SIZE)\nmodel.summary()\nmodel.compile(loss=... | [
[
"tensorflow.keras.optimizers.RMSprop",
"tensorflow.keras.callbacks.EarlyStopping"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
wangyixiaohuihui/spark2-annotation | [
"421979234f03e698d61b2d14010deb0c6d34d890"
] | [
"python/pyspark/mllib/linalg/__init__.py"
] | [
"#\r\n# Licensed to the Apache Software Foundation (ASF) under one or more\r\n# contributor license agreements. See the NOTICE file distributed with\r\n# this work for additional information regarding copyright ownership.\r\n# The ASF licenses this file to You under the Apache License, Version 2.0\r\n# (the \"Lice... | [
[
"numpy.dot",
"numpy.array_equal",
"numpy.nonzero",
"numpy.isnan",
"numpy.asarray",
"numpy.in1d",
"numpy.linalg.norm",
"numpy.frombuffer",
"numpy.bincount",
"numpy.count_nonzero",
"numpy.searchsorted",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BoogalooLi/python_spiders | [
"7b5cd0127fd072ca8346aba4c9338d738cb7f0db"
] | [
"bs4/bs4_combat.py"
] | [
"from bs4 import BeautifulSoup\nimport requests\nimport pandas as pd\n\n'''\nurl = https://www.lmoneky.com/t\ncontent: titles, authors, links, times\ntool: python, requests, bs4, pandas\n'''\n# 1.url & headers\nurl = 'https://www.lmonkey.com/t'\nheaders = {\n 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:71.... | [
[
"pandas.read_hdf",
"pandas.HDFStore",
"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": []
}
] |
michaeldeistler/diffeqtorch | [
"e06cb276ceb4dc6b2b1684e480da08a17cc59c26"
] | [
"diffeqtorch/diffeqtorch.py"
] | [
"from __future__ import annotations\n\nimport os\nimport time\nfrom pathlib import Path\nfrom textwrap import dedent\nfrom typing import Any, Dict, List, Tuple, Union\nfrom warnings import warn\n\nimport torch\nfrom julia.api import Julia\nfrom opt_einsum import contract\n\nfrom diffeqtorch.logging import get_logge... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
avbatchelor/insight-articles-project | [
"852b338b786cb5b9c281fcec2e378aed8d3dc617"
] | [
"src/topic modeling/generate_graph.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun 12 10:41:40 2018\n\n@author: Alex\n\nGenerate distance matrix and directed graph \n\n\"\"\"\n#%% Import packages \nfrom scipy.spatial.distance import pdist, squareform\nimport networkx as nx\nimport numpy as np\nimport os \nos.chdir('C:\\\\Users\\\\Alex\\\\Docume... | [
[
"matplotlib.pyplot.gca",
"sklearn.cluster.KMeans",
"matplotlib.pyplot.subplots",
"numpy.argmax",
"scipy.spatial.distance.pdist",
"numpy.fill_diagonal",
"matplotlib.pyplot.close",
"numpy.count_nonzero",
"numpy.zeros",
"numpy.where",
"scipy.cluster.hierarchy.ward",
"m... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"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",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
aa10402tw/tpu | [
"cac2ccf0176344465327bb19e47a1bede24fd039"
] | [
"models/official/efficientnet/eff_utils.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.v1.assign_sub",
"tensorflow.compat.v1.concat",
"tensorflow.compat.v1.subtract",
"tensorflow.compat.v1.initializers.zeros",
"tensorflow.compat.v1.logging.warn",
"tensorflow.compat.v1.train.ExponentialMovingAverage",
"tensorflow.compat.v1.shape",
"tensorflow.compat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gronki/diskvert | [
"459238a46fc173c9fec3ff609cd42595bbc0c858"
] | [
"python/diskvert/col2python.py"
] | [
"#!/usr/bin/env python\n\n# Dominik Gronkiewicz (c) 2017\n# this source is distributed under MIT License\n# gronki@camk.edu.pl\n\nimport re\nfrom typing import Dict\n\ndef pyminiconf_dict(f):\n buf = f.read() + \"\\n\"\n d = {}\n\n # multiline strings\n rp = r'([a-zA-Z0-9\\_\\-\\:]+)(?:[ ]+|[ ]*\\=[ ]*)... | [
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yuhaoluo/facenet | [
"d3a3087f52ae1a17a77a1dadb81c53911be97b4b",
"d3a3087f52ae1a17a77a1dadb81c53911be97b4b"
] | [
"src/models/inception_resnet_v1.py",
"src/docker_code/docker_face_detect_v0.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 required... | [
[
"tensorflow.concat",
"tensorflow.contrib.slim.dropout",
"tensorflow.contrib.slim.arg_scope",
"tensorflow.contrib.slim.max_pool2d",
"tensorflow.contrib.slim.l2_regularizer",
"tensorflow.contrib.slim.repeat",
"tensorflow.contrib.slim.initializers.xavier_initializer",
"tensorflow.cont... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13",
"1.10",
"1.12"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.0",
"0.19",
"0.18",
"1.... |
gdlmx/DAMASK_GUI | [
"cf463c8e17f2754f40d3588ab8a1e55304bc4e99"
] | [
"damask_gui/plugin/plotdat.py"
] | [
"#Copyright (c) 2015 Mingxuan Lin\n\nfrom ..ui import *\nimport numpy as np\nfrom optparse import OptionParser\nimport re\n\n\ndef Mises(tensor , mtype='stress'):\n tensor = np.array(tensor).reshape([3,3])\n PreFact = {'stress': 3.0/2.0, 'strain': 2.0/3.0}[mtype]\n dev = tensor - np.trace(tensor)/3.0*np.ey... | [
[
"numpy.diag",
"numpy.dot",
"numpy.linalg.svd",
"numpy.log",
"numpy.linalg.inv",
"numpy.linalg.eig",
"numpy.eye",
"numpy.trace",
"numpy.ones",
"numpy.linalg.det",
"numpy.cross",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JiaXiao243/PaddleClas | [
"38bdf3c8ff7ba7206674084443ce1ff9985b0572"
] | [
"ppcls/data/dataloader/multilabel_dataset.py"
] | [
"# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MODAK27/tts-replica | [
"4fef1b2b415c23d74296196f39560f4308f91447"
] | [
"examples_tts/tacotron2/train_tacotron2.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright 2020 Minh Nguyen (@dathudeptrai)\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# Unl... | [
[
"tensorflow.reduce_sum",
"tensorflow.math.not_equal",
"tensorflow.keras.losses.MeanAbsoluteError",
"matplotlib.pyplot.tight_layout",
"tensorflow.keras.losses.BinaryCrossentropy",
"matplotlib.pyplot.close",
"tensorflow.keras.metrics.Mean",
"matplotlib.pyplot.figure",
"numpy.rot9... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12"
]
}
] |
axel-sirota/flambe | [
"0dc2f5b2b286694defe8abf450fe5be9ae12c097"
] | [
"flambe/nn/rnn.py"
] | [
"from typing import Optional, Tuple, cast\nimport warnings\nimport logging\n\nimport torch\nfrom torch import nn\nfrom torch import Tensor\n\nfrom flambe.nn.module import Module\n\nlogger = logging.getLogger(__name__)\n\n\nclass RNNEncoder(Module):\n \"\"\"Implements a multi-layer RNN.\n\n This module can be ... | [
[
"torch.nn.utils.rnn.pad_packed_sequence",
"torch.ones_like",
"torch.nn.utils.rnn.pack_padded_sequence"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WanMotion/recogNum-Pytorch | [
"2ffbad9f1a9f2d9ca0a22d4631bfda2be0319aca"
] | [
"config.py"
] | [
"from torch import nn\n\n# 设定基本参数\ninputDimension = 28 * 28 # 输入层\nhiddenDimension = 100 # 隐藏层\noutputDimension = 10 # 输出层\nepoch = 3000\nbatchSize = 64\nlearningRate = 0.001\nTEST_SIZE = 100 # 测试集大小\nTRAIN_SIZE = 2000 # 训练集大小\nloss = nn.MSELoss(size_average=False, reduce=True) # 损失函数\n\n# 输出位置\noutputDir = \... | [
[
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
StatNLP/ada4asr | [
"3f40fac990afa471153ff6a8a450dfce9712b962"
] | [
"examples/speech_to_text/data_utils.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport csv\nimport os\nimport os.path as op\nimport zipfile\nfrom functools import reduce\nfrom glob import ... | [
[
"pandas.read_csv",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
leochien1110/Patch-NetVLAD | [
"9282217dd2c9bcf0446a05400fd277e651cecf4e"
] | [
"feature_match_read.py"
] | [
"#!/usr/bin/env python\n\n'''\nMIT License\n\nCopyright (c) 2021 Stephen Hausler, Sourav Garg, Ming Xu, Michael Milford and Tobias Fischer\n\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 Software... | [
[
"numpy.argsort",
"torch.transpose",
"torch.load",
"torch.cuda.device_count",
"torch.cuda.empty_cache",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"numpy.load",
"torch.nn.DataParallel",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
beibeiyang/cf-iot-example | [
"0f0454e6311cda96a8f43edbd42a6e0e9651a381"
] | [
"app1_pcfdev/app/main.py"
] | [
"from pymongo import MongoClient, errors\r\nimport os, sys\r\nimport json\r\nimport redis\r\nimport time, datetime\r\nimport calendar\r\nimport numpy as np\r\nfrom bokeh.layouts import layout, widgetbox\r\nfrom bokeh.models.widgets import Select, Slider, Div, Button, Panel, Tabs, CheckboxGroup\r\nfrom bokeh.models.... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
peterhan91/Invertible-Image-Rescaling | [
"b92162f5e9be2cff2f5dba379914fcded4e04f4c"
] | [
"codes/models/modules/Inv_arch.py"
] | [
"import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\nfrom data.fastmri import transforms\n\n\nclass InvBlockExp(nn.Module):\n def __init__(self, subnet_constructor, channel_num, channel_split_num, clamp=1.):\n super(InvBlockExp, self).__init__()\n\n ... | [
[
"torch.nn.functional.conv_transpose2d",
"numpy.log",
"torch.nn.Parameter",
"torch.ones",
"torch.transpose",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.functional.conv2d",
"torch.sum",
"torch.zeros_like",
"torch.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NunoEdgarGFlowHub/image_captioning | [
"35bda7dab986fb3130180b64c86c5845bca8c718"
] | [
"utils/misc.py"
] | [
"\nimport numpy as np\nimport cv2\nimport heapq\n\nclass ImageLoader(object):\n def __init__(self, mean_file):\n self.bgr = True\n self.scale_shape = np.array([224, 224], np.int32)\n self.crop_shape = np.array([224, 224], np.int32)\n self.mean = np.load(mean_file).mean(1).mean(1)\n\n ... | [
[
"numpy.load",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hakatashi/atcoder-diff-predictor | [
"4a329c51ec3e2a13d69a81740d96495f1512de2e"
] | [
"linear_regression.py"
] | [
"import numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndata = np.loadtxt('data/fa10.csv', skiprows=1, delimiter=',')\ndifficulty = data[:, 0]\nscore = data[:, 1]\nelapsed = data[:, 2]\nrating = data[:, 3]\n\nlog_score = np.log(score)\nlog_elapsed = np.log(elapsed)\n\nX = np.column_stack((log_scor... | [
[
"numpy.log",
"sklearn.linear_model.LinearRegression",
"numpy.loadtxt",
"numpy.column_stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andypwyu/Image_Classifier | [
"ea31d81663af4c081f6daba66ab983ff001e9992"
] | [
"predict.py"
] | [
"import argparse\nimport json\nimport torch\nimport numpy as np\nfrom PIL import Image\nfrom torch import nn\nfrom torch import optim\nfrom math import ceil\nfrom torchvision import datasets, models, transforms\n\ndef arg_parse():\n # Define a parser\n parser = argparse.ArgumentParser(description=\"Neural Net... | [
[
"numpy.expand_dims",
"torch.load",
"torch.exp",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.transpose",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danabens/amazon-sagemaker-examples | [
"5133eb81be55564ab34fa6f5302cc84cd9b3988f"
] | [
"reinforcement_learning/rl_deepracer_robomaker_coach_gazebo/src/training_worker.py"
] | [
"import os\nimport shutil\nimport argparse\nimport json\nimport logging\nimport math\nimport numpy as np\nimport subprocess\nimport time\n\nfrom rl_coach.base_parameters import TaskParameters, DistributedCoachSynchronizationType, RunType\nfrom rl_coach import core_types\nfrom rl_coach.data_stores.data_store import ... | [
[
"tensorflow.logging.set_verbosity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Gerryflap/progan_experiments | [
"8da6337bafc416ccd32289a998055def92408d23"
] | [
"train_progan.py"
] | [
"import math\nimport os\nimport time\nfrom datetime import date, datetime\n\nimport torchvision\nfrom torch.utils.data import DataLoader\nimport torchvision.transforms as transforms\nimport torch.nn.functional as F\nimport torch\n\nimport util\nfrom models import ProGANDiscriminator, ProGANGenerator\n\n# Algo\nfrom... | [
[
"torch.normal",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.nn.functional.mse_loss",
"torch.pow",
"torch.rand",
"torch.autograd.grad",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
smarttourist/Number-plate-detection-system-backend | [
"dd0d200e23fa71972395f01ae07d937190cc1aa2"
] | [
"detect.py"
] | [
"\nimport argparse\nimport cv2\nimport numpy as np\nfrom keras.models import Sequential, load_model\nfrom segmentImage import segmentImage\nwidth=608\nheight=608\nconfThreshold = 0.5\nnmsThreshold = 0.4\n\nparser = argparse.ArgumentParser(description='Object Detection using YOLO in OPENCV')\nparser.add_argument('--... | [
[
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hainow/seq2seq | [
"3fb9b56a32acbc0c52384842b0cb7c3c3e64729c"
] | [
"seq2seq/test/models_test.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright 2017 Google 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... | [
[
"numpy.minimum",
"tensorflow.local_variables_initializer",
"numpy.random.choice",
"numpy.isnan",
"tensorflow.test.main",
"numpy.testing.assert_array_equal",
"numpy.max",
"tensorflow.global_variables_initializer",
"tensorflow.contrib.rnn.LSTMCell",
"numpy.random.randn",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"1.0",
"1.2"
]
}
] |
VEXAS-team/VEXAS-DR2 | [
"a10e363f7588354884b5f155e87b2ca40b65d482"
] | [
"classification/models/factory.py"
] | [
"import os\nimport numpy as np\n\nfrom models.models import CatBoost, kNN, ANNClassifier, LogRegression\nfrom log.plot import (plot_confusion_matrix, probability_threshold,\n regression_coefficients)\nfrom settings import MODELS_PATH, CLASSES\n\n\n\nclass ModelFactory:\n _single_models = {\n... | [
[
"numpy.hstack",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
finagle29/DBSP_DRP | [
"d2f869f85e1425507dbc84e4e76fa44a6784f9d1"
] | [
"dbsp_drp/show_spectrum.py"
] | [
"import argparse\nimport os\nfrom typing import List, Optional\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom astropy.io import fits\n\ndef parser(options: Optional[List[str]] = None) -> argparse.Namespace:\n argparser = argparse.ArgumentParser(description=\"Script to plot DBSP spectra\",\n ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.step",
"numpy.percentile",
"numpy.max",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ankandrew/pytorch-cifar | [
"6ff4ff2a7f41ec68bf597578df83bb77ff41c6db"
] | [
"models/shufflenet.py"
] | [
"'''ShuffleNet in PyTorch.\n\nSee the paper \"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices\" for more details.\n'''\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom .settings import CFG\n\n\nclass ShuffleBlock(nn.Module):\n def __init__(self, group... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.randn",
"torch.nn.functional.avg_pool2d",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.functional.relu",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nahmad16/sptrsv_framework | [
"723b4859c3c9c2f53a5468e97b5924708b3c91f3"
] | [
"evaluation.py"
] | [
"import pandas as pd\nimport seaborn as sns\nimport json\nimport matplotlib.pyplot as plt\nimport sys\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import chi2\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nfrom sklearn.model_selection import train_test_split\n... | [
[
"matplotlib.pyplot.legend",
"scipy.stats.norm.ppf",
"numpy.sqrt",
"pandas.DataFrame",
"sklearn.model_selection.KFold",
"numpy.max",
"numpy.mean",
"pandas.read_csv",
"matplotlib.pyplot.tight_layout",
"sklearn.ensemble.RandomForestClassifier",
"numpy.arange",
"numpy.s... | [
{
"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",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2... |
tilacyn/DeepSEED-3D-ConvNets-for-Pulmonary-Nodule-Detection | [
"c56c4b4acaa259d9a0fb22b0aebca69fc3b98331"
] | [
"luna_detector/lidc_dataset.py"
] | [
"import glob\nimport json\nimport os\nimport time\nfrom os.path import join as opjoin\n\nimport torch\nfrom torch.utils.data import Dataset\nimport xml.etree.ElementTree as ET\nimport pydicom as dicom\nimport numpy as np\nfrom data_loader import LabelMapping\nfrom data_loader import Crop\nfrom numpy import array as... | [
[
"numpy.maximum",
"numpy.reshape",
"numpy.uint8",
"torch.from_numpy",
"numpy.save",
"numpy.full",
"numpy.mean",
"numpy.load",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sidhikabalachandar/lig_clash_score | [
"449bac16a7c2b9779e7cd51ff17eb5e41be6ff99"
] | [
"src/models/train_pdbbind.py"
] | [
"\"\"\"\nThe purpose of this code is to train the gnn model\n\nIt can be run on sherlock using\n$ sbatch 1gpu_gnn_score_feat.sbatch /home/groups/rondror/software/sidhikab/miniconda/envs/test_env/bin/python train_pdbbind_score_layer.py /home/users/sidhikab/lig_clash_score/models /oak/stanford/groups/rondror/projects... | [
[
"torch.nn.BatchNorm1d",
"numpy.sqrt",
"numpy.random.seed",
"torch.nn.functional.dropout",
"torch.manual_seed",
"scipy.stats.spearmanr",
"matplotlib.pyplot.savefig",
"torch.unsqueeze",
"torch.nn.Linear",
"torch.nn.functional.mse_loss",
"matplotlib.pyplot.clf",
"torch... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
PipeGen/pipeline-generator | [
"be69df0eab4601259bb0d084e56bf0b60b35f5ea"
] | [
"datawiz/modules/loader.py"
] | [
"import pandas as pd\nfrom var_dict import VarDict\n\nclass Loader():\n def __init__(self, vardict=VarDict()):\n self.vardict = vardict\n \n def load(self, data, data_name):\n df = pd.read_csv(data)\n self.vardict.add(df, data_name, \"df\")"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
aisthesis/opttrack | [
"17e0c7740ea43e0f07166e30d689b106d0319d0b"
] | [
"opttrack/lib/spreads/dgb_finder.py"
] | [
"\"\"\"\n./opttrack/lib/spreads/dgb_finder.py\n\nCopyright (c) 2016 Marshall Farrier\nlicense http://opensource.org/licenses/MIT\n\nFind promising diagonal butterfly spreads.\n\nThe logic for defining promising spreads is encapsulated\nin the function `_meets_criteria()` below.\n\"\"\"\n\nimport locale\n\nimport pa... | [
[
"pandas.Timedelta"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
McMasterAI/RadiologyandAI-MedicalZooPytorch | [
"c6831d8ddebfbc1b33c04f8cec0d01c2ceb828f6",
"c6831d8ddebfbc1b33c04f8cec0d01c2ceb828f6"
] | [
"lib/augment3D/elastic_deform.py",
"lib/medloaders/iseg2017.py"
] | [
"import numpy as np\nfrom scipy.interpolate import RegularGridInterpolator\nfrom scipy.ndimage.filters import gaussian_filter\n\n\"\"\"\n Elastic deformation of images as described in\n Simard, Steinkraus and Platt, \"Best Practices for\n Convolutional Neural Networks applied to Visual\n Document Analysis\", in\n P... | [
[
"numpy.reshape",
"numpy.arange",
"scipy.interpolate.RegularGridInterpolator",
"numpy.random.rand"
],
[
"numpy.load",
"torch.FloatTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.14",
"1.6",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": []
... |
OrchisLloyd/minihack | [
"65fc16f0f321b00552ca37db8e5f850cbd369ae5"
] | [
"minihack/scripts/cached_env_test.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\nimport gym\nimport numpy as np\nimport time\nimport minihack\nimport argparse\nfrom minihack.agent.common.envs.wrapper import CachedEnvWrapper\n\n\ndef compare_speed(env, num_steps, queue_size):\n env_vanila = gym.make(env)\n test_speed(env_vanila, env, num... | [
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kozistr/naver-nlp-challenge-2018 | [
"e8dbdda2e5a58586678cd1494734b3cadbd570c8"
] | [
"missions/srl/bert_model.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team 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# Unl... | [
[
"tensorflow.concat",
"tensorflow.control_dependencies",
"tensorflow.zeros",
"tensorflow.gfile.GFile",
"tensorflow.cast",
"tensorflow.assert_less_equal",
"tensorflow.truncated_normal_initializer",
"tensorflow.squeeze",
"tensorflow.train.list_variables",
"tensorflow.nn.dropou... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
bkoz/mlops | [
"e3cca43ca60fec777708e32131977de4beaf7c3d"
] | [
"src/models/model.py"
] | [
"from torch import nn\nimport torch.nn.functional as F\n\nclass MyAwesomeModel(nn.Module):\n def __init__(self):\n super().__init__()\n self.fc1 = nn.Linear(784, 256)\n self.fc2 = nn.Linear(256, 128)\n self.fc3 = nn.Linear(128, 64)\n self.fc4 = nn.Linear(64, 10)\n \n ... | [
[
"torch.nn.Linear",
"torch.nn.Dropout"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
manashmndl/LawNetworkApp | [
"4bfc4946d9e2f4e917b37e651e7555addf990d78"
] | [
"app/backend/search_v2.py"
] | [
"\"\"\"\r\nInstructions:\r\n\r\n1. Process input query, tokenize it and prepare bigram for it\r\n2. Run the input query through the database and fine the related laws\r\n3. Prepare a dynamic graph using citation details\r\n\r\n\"\"\"\r\nfrom . import Bigram, tfidf_model, tfidf_bigram_model, index_sparse, vocabulary... | [
[
"numpy.argsort",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SaynaEbrahimi/BayesianContinualLearning | [
"850299cefa2b42cc16fef392bfe3407d31e0f86f"
] | [
"src/utils.py"
] | [
"import os\nimport numpy as np\nimport gzip\nimport pickle\nfrom copy import deepcopy\n\n########################################################################################################################\ndef print_arguments(args):\n print('=' * 100)\n print('Arguments =')\n for arg in vars(args):\n ... | [
[
"numpy.diag",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Deepika1108/Facial-Expression-Recognition | [
"7e37c0a5e69d1e8d5af429af4a43c70371c9cecf"
] | [
"base_model.py"
] | [
"import torch\r\nimport os\r\nfrom collections import OrderedDict\r\nimport random\r\n\r\n\r\nclass BaseModel:\r\n \"\"\"docstring for BaseModel\"\"\"\r\n def __init__(self):\r\n super(BaseModel, self).__init__()\r\n\r\n def initialize(self, opt):\r\n self.opt = opt\r\n self.gpu_ids = ... | [
[
"torch.nn.CrossEntropyLoss",
"torch.cuda.is_available",
"torch.device",
"torch.nn.DataParallel",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Griizz/ComputerVisionPraktikum | [
"30276eb0b039ea42728d7433c573414d2dfe1ec2",
"30276eb0b039ea42728d7433c573414d2dfe1ec2"
] | [
"Projekt/BerechneDeskriptoren.py",
"Termin 5/Aufgabe 2.py"
] | [
"\"\"\"\nDer klassische Ansatz bei unserem Fruits Dataset.\n\nLabels:\n0 - Apple Green\n1 - Apple Red\n2 - Banana\n3 - Carambola\n4 - Guava\n5 - Kiwi\n6 - Mango\n7 - Muskmelon\n8 - Orange\n9 - Peach\n10 - Pear\n11 - Persimmon\n12 - Pitaya\n13 - Plum\n14 - Pomegranate\n15 - Tomato\n\"\"\"\n\nimport glob\ni... | [
[
"numpy.hstack",
"numpy.asarray",
"numpy.save",
"numpy.dstack",
"numpy.histogramdd",
"numpy.load",
"numpy.histogram"
],
[
"matplotlib.pyplot.subplots",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ashubertt/statslib | [
"5a35c0d10c3ca44c2d48f329c4f3790c91c385ac"
] | [
"tests/test_transforms.py"
] | [
"import os\nimport numpy as np\nimport pandas as pd\n\nfrom unittest import TestCase\nfrom statslib._pathmap import TEST_FOLDER\nfrom statslib.utils.dframe import to_pd_todatetime\n\n\nclass TransformationsTest(TestCase):\n def setUp(self) -> None:\n path = os.path.join(TEST_FOLDER, '../statslib/datasets'... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Nanda-Kishore-V/AlgosForRobotics | [
"2b261a023bf64668f159ad6d0e02f660b83c7d4a"
] | [
"controllers/LongitudinalPID.py"
] | [
"#!/usr/bin/env python3\nimport numpy as np\n\nclass LongitudinalPID:\n \"\"\"\n PID controller for longitudinal control\n \"\"\"\n def __init__(self, v=0, L=3, Kp=1, Kd=0.01, Ki=0.01,\n integrator_min=None, integrator_max=None):\n # States\n self.v = v\n self.prev_e... | [
[
"numpy.fmax",
"numpy.fmin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Hoclor/CoSADUV-Contextual-Saliency-for-Detecting-Anomalies-in-UAV-Video | [
"674b72af15ba8833317b8daa9d1e614ea63151c1"
] | [
"model/models/CoSADUV.py"
] | [
"\"\"\"Model for Contextual Saliency for Anomaly Detection in UAV Video (CoSADUV)\nBased on the Deep Spatial Contextual Long-term Recurrent Convolutional Network model\n\nThis model with ConvLSTM temporal implementation, Sigmoid activation function\n\"\"\"\nimport numpy as np\nimport torch\nimport torch.nn as nn\ni... | [
[
"torch.nn.functional.upsample",
"torch.nn.LSTM",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.nn.functional.interpolate",
"torch.stack",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chunibyo-wly/SmartCities | [
"0d6a5455a1fd2b5ef2b885ed830489e53c52e2c7"
] | [
"model/yolov4/core/backbone.py"
] | [
"import tensorflow as tf\nfrom . import common\n\n\ndef darknet53(input_data):\n\n input_data = common.convolutional(input_data, (3, 3, 3, 32))\n input_data = common.convolutional(\n input_data, (3, 3, 32, 64), downsample=True\n )\n\n for i in range(1):\n input_data = common.residual_block... | [
[
"tensorflow.nn.max_pool",
"tensorflow.concat",
"tensorflow.keras.layers.MaxPool2D"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
junprog/cc-base | [
"c2670e6250f83be4ec708a80dc4f1fdbce5ce289"
] | [
"models/fusion_model.py"
] | [
"import torch.nn as nn\nimport torch\nimport torch.nn.functional as F\n\nimport sys\nimport os\ncurrent_path = os.getcwd()\nsys.path.append(current_path)\nfrom models.bagnet import BagNet\nfrom models.resnet import ResNet\n\n## bagnet + resnet\n## bagnet + vgg\n\nclass ScaleAdaptiveLayer(nn.Module):\n def __init... | [
[
"torch.nn.Sequential",
"torch.abs",
"torch.cat",
"torch.load",
"torch.nn.Conv2d",
"torch.nn.functional.relu",
"torch.nn.AdaptiveAvgPool2d",
"torch.rand",
"torch.nn.functional.interpolate",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
M87K452b/logistic-map-plot | [
"9878888d010d78413040bb29213bb0bf85b00531"
] | [
"lmap_bifircation_plot.py"
] | [
"'''\r\nThis program computes the logistic map equation.\r\nThe logistic map equation is a second degree polynomial equation often used as an example in the discussions of chaos\r\n\r\nMore information:\r\nwiki: https://en.wikipedia.org/wiki/Logistic_map#Finding_cycles_of_any_length_when_r_=_4\r\n\r\nAuthor: Harivi... | [
[
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.ylim",
"numpy.arange",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
phd-jaybie/spatial-privacy-pointnetvlad | [
"a9857fe7030395eadfa1e9a9567ad7d889bad31f"
] | [
"nn_matchers.py"
] | [
"import numpy as np\nimport sys\nimport matplotlib.pyplot as plt\nimport math\nimport pickle\nimport pandas as pd\nimport scipy.io\nimport time\n\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\n\nfrom numpy import linalg as LA\nfrom scipy.spatial import Delaunay\nf... | [
[
"numpy.amax",
"numpy.asarray",
"sklearn.neighbors.KDTree",
"numpy.mean",
"numpy.where",
"numpy.unique",
"numpy.arange",
"numpy.eye",
"numpy.argmax",
"numpy.insert",
"numpy.count_nonzero",
"numpy.multiply",
"numpy.isnan",
"numpy.argsort",
"numpy.sum",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
w32zhong/FuxiCTR | [
"f9cac1c5b0a977d6a7dfd16c0ffb524811470f97",
"f9cac1c5b0a977d6a7dfd16c0ffb524811470f97"
] | [
"fuxictr/datasets/data_utils.py",
"fuxictr/pytorch/models/HOFM.py"
] | [
"# =========================================================================\r\n# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.\r\n# \r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obt... | [
[
"numpy.arange",
"numpy.random.shuffle"
],
[
"torch.LongTensor",
"torch.index_select"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RubenPants/EvolvableRNN | [
"818a4ce941536611c0f1780f7c4a6238f0e1884e",
"818a4ce941536611c0f1780f7c4a6238f0e1884e"
] | [
"population/utils/visualizing/gru_analysis.py",
"population/utils/gene_util/gru_no_reset.py"
] | [
"\"\"\"\ngru_analysis.py\n\nAnalyse a single-GRU genome of the population.\n\"\"\"\nimport argparse\nimport copy\nimport multiprocessing as mp\nimport os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.interpolate import griddata\nfrom tqdm import tqdm\n\nfrom config import Config\nfrom environme... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"numpy.asarray",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.close",
"scipy.interpolate.griddata",
"numpy.ndenumerate",
"numpy.average",
"numpy.zero... | [
{
"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"... |
Josephbousaleh/MACT21.22_Digital_tools_Big_Data_part_2 | [
"d17a98d5ac1371c04b2616fe594ff867ec575a57"
] | [
"session_4/b_chunk_geodataframe.py"
] | [
"# encoding: utf-8\n\n##################################################\n# This script shows how to read large files using pandas\n# With files exceeding sizes of GB, the chunk alternative is most of the times needed\n# https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html\n\n\n#\n# Note: the project k... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
classicsong/dgl-graphloader | [
"0bc9f8710bfb71f613c3fcb70573ed4c62dfb735"
] | [
"tests/test_csv_loader.py"
] | [
"import os\nfrom pathlib import Path\n\nimport unittest, pytest\nimport numpy as np\nimport torch as th\n\nimport dgl_graphloader\n\ndef create_category_node_feat(tmpdir, file_name, separator='\\t'):\n node_feat_f = open(os.path.join(tmpdir, file_name), \"w\")\n node_feat_f.write(\"node{}feat1{}feat2{}feat3\\... | [
[
"numpy.allclose",
"numpy.array_equal",
"numpy.random.seed",
"torch.nonzero",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
smaiti7/openforcefield | [
"ee0f715e094e6f1749bf60f3a3b48174a9e6a9b7"
] | [
"openforcefield/tests/test_molecule.py"
] | [
"#!/usr/bin/env python\n\n#=============================================================================================\n# MODULE DOCSTRING\n#=============================================================================================\n\n\"\"\"\nTests for molecular topology representations\n\nAt least one support... | [
[
"numpy.array",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kponder/GMM | [
"e9b7d8f72ea253db6ca643a1a56c43763d46e8c2"
] | [
"Car_convergence.py"
] | [
"import numpy as np\nimport Convergence_testing as CT\nimport sys\n\ndata_file = sys.argv[1]\nsamples = np.loadtxt(data_file)\n\nlabels = [ \"$\\Omega_M$\", \"$\\Omega_L$\", \"$w$\", \"$\\sigma_{int}$\",\"$\\mathcal{M}$\"]\n\ny = np.linspace(0, 99, 100)\nCT.plotting(labels, y, samples, savefig = True)\n"
] | [
[
"numpy.loadtxt",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
201419/PersonalCodeRepository | [
"e79ac1489fa424f1334e74aab74ea25d1246b40e"
] | [
"optim/error-feedback-SGD/main.py"
] | [
"\"\"\"\nScript containing the main functions to train and evaluate the models\nwith several optimizers. The results can be easily saved.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.backends.cudnn as cudnn\n\nimport torchvision\n... | [
[
"torch.nn.CrossEntropyLoss",
"torch.load",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.is_available",
"torch.nn.DataParallel",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lucasperin/WOTS | [
"88f09cf070839595190a3a495c4af8b0b8358172"
] | [
"benchmark/gen_plot_bench.py"
] | [
"from math import factorial as fac, sqrt, floor, log\nfrom functools import reduce, lru_cache\nfrom multiprocessing import Pool\nfrom itertools import product, chain\nfrom random import getrandbits\nfrom scipy.special import comb\n\n\nmyfile = \"src/Plot_Encoding_bench.cpp\"\nheader = \"\"\"\n#include <benchmark/be... | [
[
"scipy.special.comb"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.14",
"1.6",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": []
... |
OdincoGaming/Text-Posting | [
"56e4102644f02837d68d13d9e05922c8e559ce57"
] | [
"main.py"
] | [
"'''\n code by TaeHwan Jung(@graykode)\n Original Paper and repository here : https://github.com/openai/gpt-2\n GPT2 Pytorch Model : https://github.com/huggingface/pytorch-pretrained-BERT\n'''\nimport os\nimport sys\nimport torch\nimport random\nimport argparse\nimport numpy as np\nfrom GPT2.model import (... | [
[
"torch.cuda.manual_seed",
"torch.random.manual_seed",
"numpy.random.seed",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XiangLi1999/transformers | [
"3e62aadd98366c975530986c05e1fc605fa8d928"
] | [
"tests/test_tokenization_common.py"
] | [
"# coding=utf-8\n# Copyright 2019 HuggingFace 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 appl... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
whigg/ICESat_data_analysis_tools | [
"46f4132d2b34efe9a21470cdbaddf195301cfcd3"
] | [
"get_columns.py"
] | [
"#get_columns.py\nimport numpy as np\nimport pandas as pd\nimport glob\nimport os\nimport sys\n\ndef save_csv (file, columns):\n # saves selected columns of file into a new csv.\n # file is a string, and columns is a list of integers.\n # returns saved dataframe and new filename\n\n if os.path.getsize(f... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
IrvanDimetrio/Hipertensi-Classification-NearestNeighboard | [
"fa59fcf7dfd4a112e085c160e9a5e00a7a382b0c"
] | [
"Step3_HipertensiPredictionKNN.py"
] | [
"\"\"\"\r\n@author : Muhamad Irvan Dimetrio\r\nNIM : 18360018\r\nTeknik Informatika\r\nInstitut Sains dan Teknologi Nasional\r\n\"\"\"\r\nimport pandas as pd\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\n\r\n# Meload Dataset dari file csv dan mengekstrak fitur dan label classnya\r\nhipertensiDataset ... | [
[
"pandas.read_csv",
"sklearn.neighbors.KNeighborsClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
JonaBecher/spektral | [
"ad2d96549c00f68ce992a7d29e2c3fd025fb529b"
] | [
"spektral/layers/convolutional/gcs_conv.py"
] | [
"from tensorflow.keras import backend as K\n\nfrom spektral.layers import ops\nfrom spektral.layers.convolutional.conv import Conv\nfrom spektral.utils import normalized_adjacency\n\n\nclass GCSConv(Conv):\n r\"\"\"\n A `GraphConv` layer with a trainable skip connection.\n\n **Mode**: single, disjoint, mix... | [
[
"tensorflow.keras.backend.dot",
"tensorflow.keras.backend.bias_add"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
bm2-lab/X-MOL | [
"a64cd4222ab819326767224d91fa8605f52f4fc4"
] | [
"FT_to_generation/pt_build_optfile.py"
] | [
"from rdkit import Chem\nfrom rdkit import DataStructs\nfrom random import shuffle\nimport numpy as np\nimport time\nfrom rdkit.Chem import Descriptors\nfrom tqdm import tqdm\nfrom multiprocessing import Process\nimport os\nimport subprocess\n\ndef get_(similarity_lib, scale, to_file=False, n_dev=10000, show=True, ... | [
[
"numpy.array",
"numpy.mean",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jayliu99/bcbn | [
"6ec0977299c66c224877ec953b64d34f9b74c6f6"
] | [
"learning.py"
] | [
"import sys, csv\nimport random\nimport pandas as pd\nimport numpy as np\n\nimport itertools as it\nimport time\nimport networkx as nx\nfrom matplotlib import pyplot as plt\nimport scipy.special as sc\nimport math\n\nimport scipy.sparse as sps\nfrom collections import defaultdict\n\nfrom pomegranate import *\n\n\nd... | [
[
"pandas.crosstab",
"pandas.read_csv",
"pandas.DataFrame",
"numpy.copy",
"numpy.delete",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
microbial-pangenomes-lab/2021_ecoli_pathogenicity | [
"f25925c21679e2f89692ae3cfa512060c8bc04bf"
] | [
"workflow/scripts/mapped_summary.py"
] | [
"#!/usr/bin/env python\n\n\nimport sys\nimport argparse\nimport numpy as np\nimport pandas as pd\n\n\ndef get_options():\n description = 'Make a summary of mapped unitigs'\n parser = argparse.ArgumentParser(description=description)\n\n parser.add_argument('mapped',\n help='Mapped uni... | [
[
"numpy.isnan",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
akashlevy/NEM-Relay-CGRA | [
"13aa2b7c2a7a583ec35e7ee6af424075d6e8a8f4"
] | [
"gls/results/delayplot.py"
] | [
"import glob, re\nimport altair as alt, numpy as np, matplotlib.pyplot as plt, pandas as pd\n\nCLK_PER = 5\nHEADER = ['Hinst Name', 'Module Name', 'Inst Count', 'Total Area', 'Buffer', 'Inverter', 'Combinational', 'Flop', 'Latch', 'Clock Gate', 'Macro', 'Physical']\nMODULES = ['PE Core', 'CBs', 'SB', 'Other']\nDESI... | [
[
"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": []
}
] |
mahdihosseini/HistoKT | [
"0d23d4210a14f2f227e129cb7deec78d483ea8f5"
] | [
"dataset_processing/preprocessing/BCSSProcessing.py"
] | [
"from pathlib import Path\n\nfrom torchvision.datasets.folder import default_loader\nimport numpy as np\nimport pandas as pd\nimport os\nimport csv\nfrom transforms import ProcessImages\nfrom skimage import io\nimport pickle\nimport random\n\n\nclass BCSSLoader:\n \"\"\"loader to return tuples of pillow images a... | [
[
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OpenXAIProject/Real-time-Financial-Data | [
"f94ddd3bb58e0b4bfdb95b366dddf49d30567741"
] | [
"web_crawler_01_22_2018_github_v1.py"
] | [
"#Copyright 2018 UNIST under XAI Project supported by Ministry of Science and ICT, Korea\n\n#Licensed under the Apache License, Version 2.0 (the \"License\"); \n#you may not use this file except in compliance with the License.\n#You may obtain a copy of the License at\n\n# https://www.apache.org/licenses/LICENSE-... | [
[
"numpy.array",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
mtrazzi/gomoku | [
"5a83da929ebba902ef9992ff0706b1a6f8948026"
] | [
"src/gomoku/utils.py"
] | [
"import numpy as np\n\nSLOPES = np.array([[1, 0], [-1, 1], [0, 1], [1, 1]])\n\n\ndef coordinates(x, y, dx, dy, nb_consecutive=5):\n \"\"\"Coordinates of consecutive intersections from (x,y) directed by (dx,dy).\"\"\"\n return [(x + i * dx, y + i * dy) for i in range(nb_consecutive)]\n\n\ndef boundaries(coord):\n ... | [
[
"numpy.log",
"numpy.unique",
"numpy.subtract",
"numpy.sign",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vishalbelsare/TCN | [
"2f8c2b817050206397458dfd1f5a25ce8a32fe65"
] | [
"TCN/char_cnn/utils.py"
] | [
"import unidecode\nimport torch\nfrom torch.autograd import Variable\nfrom collections import Counter\nimport observations\nimport os\nimport pickle\n\n\ncuda = torch.cuda.is_available()\n\n\ndef data_generator(args):\n file, testfile, valfile = getattr(observations, args.dataset)('data/')\n file_len = len(fi... | [
[
"torch.autograd.Variable",
"torch.cuda.is_available",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DITEP/prescreen-prediction | [
"216870c085db1515e02d62274802921f4d89bba7"
] | [
"prescreen/simbad/load_targets.py"
] | [
"\"\"\"\nscript to load SF patients that are not in VCare into the database\n\n\"\"\"\nimport pandas as pd\n\nfrom clintk.utils.connection import get_engine\n\nimport argparse\n\n\ndef load():\n # patient_id to start iterate\n description = 'Load targets from file into the sql server'\n parser = argparse.A... | [
[
"pandas.read_excel",
"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": []
}
] |
marcospiau/pytrends | [
"6bc4a16acdd1e72a0bd93bee3f6ee85e1446d6f6"
] | [
"pytrends/request.py"
] | [
"import json\nimport sys\nimport time\nfrom datetime import datetime, timedelta\n\nimport pandas as pd\nimport requests\n\nfrom pandas.io.json._normalize import nested_to_record\nfrom requests.adapters import HTTPAdapter\nfrom requests.packages.urllib3.util.retry import Retry\n\nfrom pytrends import exceptions\n\nf... | [
[
"pandas.concat",
"pandas.io.json._normalize.nested_to_record",
"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": []
}
] |
iamwendellbalagot/water-hammer-app | [
"04e54ef34a99da550493c24f84590723b4ce4eda"
] | [
"processSerialData/processSerialData.py"
] | [
"import serial\nimport numpy as np\nimport sqlite3\nimport pandas as pd\nfrom tqdm import tqdm\n\nprocess = True\n\ndef get_dataframe(table='test', path='database/whDB.db'):\n conn = sqlite3.connect(path)\n df = pd.read_sql('SELECT * FROM {}'.format(table), con=conn)\n conn.close()\n ret... | [
[
"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": []
}
] |
rjtsg/StockExchangeAI | [
"3ec6bb415c73018933aa091d26e34fffc3cb3ef8"
] | [
"RoyStatesDev.py"
] | [
"\"\"\" \nSo here I try to build the StockSimPlay.py for-loop into a function, which should give outputs like\ngym.openai.com environments does. Then implement the greedy epsilon deep Q learning from RLtutMLadventuries.py\nto see if anything interesting happens. We still use 1 stock for 1 year.\n\"\"\"\n\n#Import p... | [
[
"pandas.read_excel",
"matplotlib.pylab.show",
"numpy.random.random",
"numpy.ndarray",
"pandas.DataFrame",
"numpy.identity",
"matplotlib.pylab.subplots",
"matplotlib.pylab.ylabel",
"matplotlib.pylab.plot",
"matplotlib.pylab.xlabel",
"numpy.random.randint"
]
] | [
{
"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": []
}
] |
santapresent/plotly.py | [
"96967d7937fed1777f737f8c3302af48252b4e7a"
] | [
"packages/python/plotly/plotly/tests/test_core/test_px/test_px.py"
] | [
"import plotly.express as px\nimport numpy as np\n\n\ndef test_scatter():\n iris = px.data.iris()\n fig = px.scatter(iris, x=\"sepal_width\", y=\"sepal_length\")\n assert fig.data[0].type == \"scatter\"\n assert np.all(fig.data[0].x == iris.sepal_width)\n assert np.all(fig.data[0].y == iris.sepal_len... | [
[
"numpy.all",
"numpy.in1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Vincent-Vercruyssen/anomatools | [
"aef8698fb0d5ad709dc911ea5a4972e2cb98cefc"
] | [
"anomatools/models/ssdo.py"
] | [
"# -*- coding: UTF-8 -*-\n\"\"\"\n\nSemi-Supervised Detection of Anomalies.\n\nReference:\n V. Vercruyssen, W. Meert, G. Verbruggen, K. Maes, R. Baumer, J. Davis.\n Semi-supervised anomaly detection with an application to water analytics.\n In IEEE International Conference on Data Mining, Singapore, 2018, ... | [
[
"numpy.log",
"numpy.meshgrid",
"numpy.power",
"scipy.stats.hmean",
"numpy.percentile",
"numpy.ones",
"sklearn.utils.validation.check_X_y",
"numpy.array",
"numpy.where",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
Jeffrey-Ede/One-Shot | [
"27696c0886b8d6b5f088ff1a93fadf5c3115b856",
"27696c0886b8d6b5f088ff1a93fadf5c3115b856"
] | [
"wavefunctions/defocusor.py",
"wavefunctions/33/one-shot.py"
] | [
"\"\"\"\r\nPropagate exit wavefunction to various focuses.\r\n\r\nBased on C++ code from https://github.com/morawatur/PyEWRecRepo\r\n\"\"\"\r\n\r\nimport tensorflow as tf\r\nimport sonnet as snt\r\n\r\nimport numpy as np\r\n\r\ndef fft_to_diff(self, x):\r\n \"\"\"Change diffraction pattern to fft layout or vice ... | [
[
"tensorflow.sin",
"tensorflow.concat",
"tensorflow.cos",
"tensorflow.ifft2d",
"tensorflow.fft2d",
"tensorflow.meshgrid",
"tensorflow.linspace"
],
[
"tensorflow.device",
"numpy.product",
"tensorflow.concat",
"tensorflow.control_dependencies",
"numpy.bool",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
wuzuowuyou/ogre-magi | [
"8b5da1f43fdfd2a1b3fb1d3ae91d8c551e21b9b4"
] | [
"net/loss/l1_loss.py"
] | [
"import torch\nimport torch.nn as nn\n\nclass MaskL1Loss(nn.Module):\n def __init__(self):\n super(MaskL1Loss, self).__init__()\n\n def forward(self, pred: torch.Tensor, gt, mask):\n mask_sum = mask.sum()\n if mask_sum.item() == 0:\n return mask_sum, dict(l1_loss=mask_sum)\n ... | [
[
"torch.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
uwon0625/prudential | [
"2466495bd07e0774b74d27ec0ca856f22d23b5e6"
] | [
"src/models/test.py"
] | [
"import pandas as pd \n\nfile_path = '../submissions/submission.csv'\ndf = pd.read_csv(file_path)\ndf['Response']+=1\ndf.to_csv(file_path.replace('.csv','_adj.csv'), index=False)\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
simpeg/aurora | [
"1c81c464fb2e01da58e2c6b361b406b117100dcf",
"1c81c464fb2e01da58e2c6b361b406b117100dcf"
] | [
"aurora/transfer_function/regression/helper_functions.py",
"aurora/sandbox/io_helpers/emtf_band_setup.py"
] | [
"import numpy as np\n\n\ndef rme_beta(r0):\n \"\"\"\n This is an RME specific property.\n It represents a bias in the calculation of residual_variance\n which we correct for in TRME and TRME_RR.\n\n The implemented formula is an approximation. This is approximately equal to 1/beta\n where beta is... | [
[
"numpy.exp"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
anshulbshah/MMCL | [
"d6f747ae0e7f3f244a5dc6ceb6034976eacd1dce"
] | [
"CIFAR100/linear.py"
] | [
"import argparse\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nimport os\nimport pickle\n\nimport utils\nfrom model import Model\nfrom warmup_scheduler import GradualWarmupScheduler\nimport wandb\n\ndevice = torch.device(\"cuda... | [
[
"torch.nn.CrossEntropyLoss",
"torch.enable_grad",
"torch.load",
"torch.argsort",
"torch.utils.data.DataLoader",
"torch.nn.Linear",
"torch.no_grad",
"torch.cuda.is_available",
"torch.flatten",
"torch.nn.DataParallel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ds3300/adl_final_project | [
"49e7e6bf9190e6c56bd950ccd9bee7a39606dd13"
] | [
"pdf_text_extraction.py"
] | [
"from pdf2image import convert_from_path, convert_from_bytes\r\nfrom IPython.display import display, Image\r\nimport matplotlib.pyplot as plt\r\nimport os\r\nimport pandas as pd\r\nimport s3fs\r\nimport boto3\r\nimport sys\r\nfrom PIL import Image\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport cv2\r\nfr... | [
[
"numpy.amax",
"pandas.DataFrame",
"numpy.max",
"numpy.mean",
"numpy.argmin",
"numpy.reshape",
"numpy.full",
"numpy.ceil",
"numpy.argmax",
"tensorflow.keras.preprocessing.image.img_to_array",
"pandas.concat",
"numpy.min",
"numpy.amin",
"numpy.median",
"nu... | [
{
"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": []
}
] |
dmitry-s-danilov/kaggle-customer-churn-prediction-2020 | [
"e7ada73a6f413d7de9bfd18fa2af7e74aa9f6bc3"
] | [
"src/models/level_1/transformer.py"
] | [
"from sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n\ntransformer = ColumnTransformer(\n [\n # Numeric columns - minmax scaler.\n (\n 'minmax_scaler',\n MinMaxScaler(),\n [\n 'account_length',... | [
[
"sklearn.preprocessing.OneHotEncoder",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ClaudiuCreanga/kaggle | [
"49ed31cd12040a7996611d9aa5379acfb61c0192"
] | [
"tensorflow/tensorboard/mnist_with_summaries.py"
] | [
"# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the 'License');\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requir... | [
[
"tensorflow.gfile.DeleteRecursively",
"tensorflow.gfile.Exists",
"tensorflow.RunMetadata",
"tensorflow.cast",
"tensorflow.gfile.MakeDirs",
"tensorflow.train.AdamOptimizer",
"tensorflow.summary.scalar",
"tensorflow.Variable",
"tensorflow.summary.image",
"tensorflow.name_scop... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
OmneyaEssam/iti_ds_live | [
"78f004dff6dea6c0b08d4c14733273ae98d69643"
] | [
"scripts/JobPrediction.py"
] | [
"LOG_DATA_PKL = \"data.pkl\"\nLOG_MODEL_PKL = \"model.pkl\"\n\n#-------------------------------------------------------------\n\nimport os \nimport sklearn\nimport pickle\nimport yaml\n\nimport pandas as pd\n\nimport mlflow\nfrom mlflow.tracking import MlflowClient\n\n#---------------------------------------... | [
[
"pandas.concat",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
rfriedman22/selene | [
"a972fbf650664b8d955bb54e1737484131895b09"
] | [
"selene_sdk/interpret/vis.py"
] | [
"\"\"\"\nThis module provides the methods for visualizing different ouputs\nfrom selene analysis methods.\n\n\"\"\"\nfrom collections import defaultdict\nfrom copy import deepcopy\nimport os\nimport re\nimport warnings\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import transforms\nfrom m... | [
[
"numpy.amax",
"numpy.sum",
"numpy.min",
"numpy.arange",
"matplotlib.transforms.Affine2D",
"matplotlib.path.Path",
"matplotlib.pyplot.subplots",
"numpy.percentile",
"numpy.argwhere",
"numpy.max",
"numpy.zeros_like",
"numpy.zeros",
"numpy.argsort",
"matplotlib... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cglacet/robotarium_python_simulator | [
"b58ad6b201f404d0b079dc695f68d916260cc9d1"
] | [
"rps/examples/go_to_pose/uni_go_to_pose_hybrid.py"
] | [
"import rps.robotarium as robotarium\nfrom rps.utilities.transformations import *\nfrom rps.utilities.barrier_certificates import *\nfrom rps.utilities.misc import *\nfrom rps.utilities.controllers import *\n\nimport numpy as np\nimport time\n\n# Instantiate Robotarium object\nN = 5\ninitial_conditions = np.array(n... | [
[
"numpy.arange",
"numpy.mat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ag-gipp/NLPLand | [
"a614f83de50b516a452bced6287a4391a54ffd55"
] | [
"tests/test_dataset.py"
] | [
"import os\nfrom collections import defaultdict\nfrom typing import List\nfrom urllib.error import HTTPError\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom _pytest.capture import CaptureFixture\nfrom _pytest.monkeypatch import MonkeyPatch\nfrom mock import MagicMock\nfrom pytest_mock import MockerF... | [
[
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
chenj209/TensorFlowOnSpark | [
"df468ffba500c2254b2310aa1b30dbad8d5fb162"
] | [
"tensorflowonspark/TFSparkNode.py"
] | [
"# Copyright 2017 Yahoo Inc.\n# Licensed under the terms of the Apache 2.0 license.\n# Please see LICENSE file in the project root for terms.\n\"\"\"This module provides low-level functions for managing the TensorFlowOnSpark cluster.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\n... | [
[
"tensorflow.test.is_built_with_cuda"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WangXinglin/BIT_framework | [
"1484874fcd00d052c7536789dec95050b480b25d"
] | [
"BIT_DL/pytorch/core/cell_wrappers.py"
] | [
"# Copyright 2019 The Texar 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 required... | [
[
"torch.nn.functional.dropout",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.ModuleList",
"torch.nn.LSTMCell",
"torch.nn.Linear",
"torch.no_grad",
"torch.nn.RNNCell",
"torch.nn.GRUCell"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Derek-Xiao/LightGBM | [
"1b7643ba60e0add0d8ddf55d4baaedca1f6362b5"
] | [
"examples/python-guide/advanced_example.py"
] | [
"# coding: utf-8\n# pylint: disable = invalid-name, C0111\nimport lightgbm as lgb\nimport pandas as pd\nimport numpy as np\n\n# load or create your dataset\nprint('Load data...')\ndf_train = pd.read_csv('../binary_classification/binary.train', header=None, sep='\\t')\ndf_test = pd.read_csv('../binary_classification... | [
[
"numpy.exp",
"pandas.read_csv",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
rizwandel/haystack | [
"892ce4a760c6b7d7d108aa01eee985ab6ecf8483"
] | [
"haystack/nodes/reader/table.py"
] | [
"from typing import List, Optional, Tuple, Dict\n\nimport logging\nfrom statistics import mean\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom quantulum3 import parser\nfrom transformers import pipeline, TapasTokenizer, TapasForQuestionAnswering, BatchEncoding\n\nfrom haystack.schema import Document, A... | [
[
"numpy.exp",
"numpy.mean",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
donaldrauscher/etf-portfolio | [
"c85e8d1af376a14f845948754329b1f65fd4d59b"
] | [
"steps/summary.py"
] | [
"import luigi, abc, math\nimport pandas as pd\nimport numpy as np\n\n# calculate summary statistics for portfolios\nclass CalcSummary(luigi.Task):\n\n @abc.abstractproperty\n def requires(self):\n pass\n\n @abc.abstractproperty\n def output(self):\n pass\n\n def run(self):\n # br... | [
[
"numpy.std",
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AntonisCSt/jads_kaggle | [
"a9f67d6779957d30425de8ff16bfd574a4dcd332"
] | [
"earthquakes/engineering.py"
] | [
"import os\nimport gc\nimport numpy as np\nimport pandas as pd\nimport pickle\n\nfrom scipy import signal, stats\nfrom sklearn.linear_model import LinearRegression\nfrom obspy.signal.trigger import classic_sta_lta\n\nfrom common.utils import progress\n\n\ndef find_earthquakes(data, ycol=\"time_to_failure\", chunks=... | [
[
"pandas.Series",
"scipy.signal.stft",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.max",
"numpy.zeros_like",
"numpy.mean",
"numpy.where",
"scipy.signal.hilbert",
"numpy.unique",
"numpy.ceil",
"numpy.std",
"numpy.diff",
"numpy.insert",
"scipy.stats.ske... | [
{
"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": [
"1.0",
"0.19"
],
"tensorflow": []
}
] |
kylevedder/Open3D-ML | [
"87ec50ed81d531377b1bb27e5c16f964201eadb0"
] | [
"ml3d/datasets/customdataset.py"
] | [
"import numpy as np\nimport os, sys, glob, pickle\nfrom pathlib import Path\nfrom os.path import join, exists, dirname, abspath\nimport random\nfrom sklearn.neighbors import KDTree\nfrom tqdm import tqdm\nimport logging\n\nfrom .base_dataset import BaseDataset, BaseDatasetSplit\nfrom ..utils import make_dir, DATASE... | [
[
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tacaswell/mplcairo | [
"d9bd1bee87e403d484b6b9ccef8013e7dcb1fb5d"
] | [
"lib/mplcairo/_backports.py"
] | [
"import functools\nimport re\n\nfrom matplotlib import dviread\nfrom matplotlib.dviread import PsfontsMap\n\n\n@functools.lru_cache()\ndef _parse_enc(path):\n r\"\"\"\n Parses a \\*.enc file referenced from a psfonts.map style file.\n The format this class understands is a very limited subset of PostScript... | [
[
"matplotlib.dviread.find_tex_file"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CMPUT455-Yogo/assignment4 | [
"88ff8eeef6f35beca79e3bc3fa9eaf283b68ea1e"
] | [
"random_player/simple_board.py"
] | [
"\"\"\"\nsimple_board.py\n\nImplements a basic Go board with functions to:\n- initialize to a given board size\n- check if a move is legal\n- play a move\n\nThe board uses a 1-dimensional representation with padding\n\"\"\"\n\nimport numpy as np\nfrom board_util import GoBoardUtil, BLACK, WHITE, EMPTY, BORDER, \\\n... | [
[
"numpy.copy",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dhb2128/pybasicbayes | [
"61f65ad6c781288605ec5f7347efcc5dbd73c4fc"
] | [
"examples/robust_regression.py"
] | [
"# Demo of a robust regression model with multivariate-t distributed noise\n\nimport numpy as np\nimport numpy.random as npr\nnp.random.seed(0)\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set_style(\"white\")\n\nfrom pybasicbayes.util.text import progprint_xrange\nfrom pybasicbayes.distributions ... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"matplotlib.pyplot.plot",
"numpy.random.randn",
"numpy.mean",
"numpy.eye",
"matplotlib.pyplot.subplot",
"numpy.column_stack",
"numpy.zeros",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
duyetdev/api.duyetdev.com | [
"4c33cc2cfb43ad6c4089873230e7b657659bff15"
] | [
"lib/gensim/test/test_logentropy_model.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2010 Radim Rehurek <radimrehurek@seznam.cz>\n# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html\n\n\"\"\"\nAutomated tests for checking transformation algorithms (the models package).\n\"\"\"\n\n\nimport logging\nimport unit... | [
[
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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