repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
andreeaiana/geneg_benchmarking | [
"0b53989c79b8e3771c144c0332fd36587dfe0f4d",
"0b53989c79b8e3771c144c0332fd36587dfe0f4d"
] | [
"src/ripplenet_data_loader.py",
"src/dkn_kg_preprocess.py"
] | [
"# -*- coding: utf-8 -*-\n\n# DISCLAIMER\n# This code file is forked and adapted from https://github.com/tezignlab/RippleNet-TF2/blob/master/tools/load_data.py, which is under an MIT license.\n\n\"\"\" Utilities for data loading for RippleNet. \"\"\"\n\n# import libraries\nimport os\nimport numpy as np\nfrom collec... | [
[
"numpy.load",
"numpy.loadtxt",
"numpy.save"
],
[
"numpy.average"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ankitbhatia/word-mastermind | [
"51529b04e6e1bb150c867e0f6e44f36131c33189"
] | [
"scripts/name_creator.py"
] | [
"import csv\nimport glob\nimport pandas as pd\n\nfiles = glob.glob(\"data/*.txt\")\n\nnames = {}\n\nfor file in files:\n with open(file) as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n for row in reader:\n name = row[0]\n sex = row[1]\n number = row[2]\n... | [
[
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
ChambinLee/Pointnet_Pointnet2_pytorch | [
"c5612493ce3bbdbb18a65eefc0dc8d90e09da74d"
] | [
"data_utils/ModelNetDataLoader.py"
] | [
"'''\n@author: Xu Yan\n@file: ModelNet.py\n@time: 2021/3/19 15:51\n'''\nimport os\nimport numpy as np\nimport warnings\nimport pickle\n\nfrom tqdm import tqdm\nfrom torch.utils.data import Dataset\n\nwarnings.filterwarnings('ignore')\n\n\ndef pc_normalize(pc):\n centroid = np.mean(pc, axis=0)\n pc = pc - cent... | [
[
"torch.utils.data.DataLoader",
"numpy.ones",
"numpy.genfromtxt",
"numpy.argmax",
"numpy.mean",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.loadtxt",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
txya900619/Intern-Training | [
"76cac20ac988609f313765ebeb72d20da9dcc05e",
"76cac20ac988609f313765ebeb72d20da9dcc05e",
"76cac20ac988609f313765ebeb72d20da9dcc05e",
"76cac20ac988609f313765ebeb72d20da9dcc05e"
] | [
"eunice012716/Week2/ch4/4.3/exercise2.py",
"coookie89/Week2/ch5/5.1/example.py",
"eunice012716/Week2/ch4/4.1/exercise3.py",
"coookie89/Week2/ch4/4.1/exercise3.py"
] | [
"import torch\nfrom torch import nn\nfrom d2l import torch as d2l\n\nBATCH_SIZE, LR, NUM_EPOCHS = 256, 0.1, 10\nACTIVATE_FUNCS = [nn.ReLU(), nn.Sigmoid(), nn.Tanh()]\n\n\ndef init_weights(m):\n if type(m) == nn.Linear:\n nn.init.normal_(m.weight, std=0.01)\n\n\nif __name__ == \"__main__\":\n for i in r... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.Flatten",
"torch.nn.Tanh",
"torch.nn.Sigmoid",
"torch.nn.Linear",
"torch.nn.init.normal_",
"torch.nn.ReLU"
],
[
"torch.nn.Linear",
"torch.nn.ReLU",
"torch.mm",
"torch.rand"
],
[
"torch.tanh",
"torch.sigmoid",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
niqbal996/paz | [
"9fbd50b993f37e1e807297a29c6044c09967c9cc",
"9fbd50b993f37e1e807297a29c6044c09967c9cc",
"9fbd50b993f37e1e807297a29c6044c09967c9cc",
"9fbd50b993f37e1e807297a29c6044c09967c9cc"
] | [
"tests/paz/backend/processor.py",
"paz/processors/standard.py",
"paz/backend/quaternion.py",
"paz/processors/geometric.py"
] | [
"from paz.core import Processor\nfrom paz.core import SequentialProcessor\nimport numpy as np\n\n\nclass ProcessorA(Processor):\n def __init__(self):\n super(ProcessorA, self).__init__()\n\n def call(self, image, boxes):\n boxes = boxes - 1.0\n return image, boxes\n\n\nclass ProcessorB(Pr... | [
[
"numpy.allclose",
"numpy.isclose"
],
[
"numpy.expand_dims",
"numpy.squeeze",
"numpy.concatenate",
"numpy.argmax",
"numpy.random.rand"
],
[
"numpy.cos",
"numpy.linalg.norm",
"numpy.sin"
],
[
"numpy.hstack",
"numpy.maximum",
"numpy.minimum",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
pedroMoya/M5_kaggle_uncertainty_share | [
"f1dea9af9ec2e29e9bccb21d9b6e3627dff14c6e"
] | [
"5.2_CUSTOM_LIBRARY/model_analyzer.py"
] | [
"# Model architecture analyzer\nimport os\nimport logging\nimport logging.handlers as handlers\nimport json\nimport numpy as np\nimport tensorflow as tf\nphysical_devices = tf.config.list_physical_devices('GPU')\ntf.config.experimental.set_memory_growth(physical_devices[0], enable=True)\ntf.keras.backend.set_floatx... | [
[
"tensorflow.keras.models.Model",
"tensorflow.config.experimental.set_memory_growth",
"tensorflow.keras.backend.set_floatx",
"tensorflow.keras.utils.plot_model",
"tensorflow.config.list_physical_devices",
"tensorflow.keras.layers.Input"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
luiscarlosgph/easyipc | [
"befe03bd2d1bf9f8378bcdf391dbeac8576bd723"
] | [
"tests/test_easyipc.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @brief This module has unit tests for the classes of EasyIPC.\n# @author Luis C. Garcia-Peraza Herrera (luiscarlos.gph@gmail.com).\n# @date 25 June 2020.\n\nimport unittest\nimport os\nimport sys\nimport numpy as np\n\n# My imports\nimport easyipc\n\nclass TestE... | [
[
"numpy.sum",
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
juangamella/icp | [
"80548610a13b6b76515f46f56e0f7f486cf9c1c7"
] | [
"causalicp/test/tests_icp.py"
] | [
"# Copyright 2021 Juan L. Gamella\n\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n\n# 1. Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disc... | [
[
"numpy.sum",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yqzhangthu/tatk | [
"fafabc45d02ad889f59354acac4e3b1367e7d4bf",
"fafabc45d02ad889f59354acac4e3b1367e7d4bf"
] | [
"tatk/policy/mdrg/multiwoz/model.py",
"tatk/nlg/sclstm/multiwoz/train.py"
] | [
"import json\nimport math\nimport operator\nimport os\nimport random\nfrom io import open\nfrom queue import PriorityQueue\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import optim\n\nimport functools\n\nimport tatk.policy.mdrg.multiwoz.default_policy as po... | [
[
"torch.nn.functional.softmax",
"torch.cat",
"numpy.asarray",
"torch.nn.functional.dropout",
"torch.zeros",
"torch.nn.RNN",
"torch.nn.GRU",
"torch.nn.Embedding",
"torch.tanh",
"torch.nn.utils.rnn.pad_packed_sequence",
"torch.no_grad",
"torch.device",
"torch.topk"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhangxu999/magenta | [
"60b85828cc69cff855fabce78b51ddaddc873a5d",
"60b85828cc69cff855fabce78b51ddaddc873a5d"
] | [
"magenta/models/arbitrary_image_stylization/arbitrary_image_stylization_distill_mobilenet.py",
"magenta/models/music_vae/trained_model.py"
] | [
"# Copyright 2020 The Magenta Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law o... | [
[
"tensorflow.compat.v1.train.AdamOptimizer",
"tensorflow.compat.v1.disable_v2_behavior",
"tensorflow.compat.v1.summary.image",
"tensorflow.compat.v1.trainable_variables",
"tensorflow.compat.v1.losses.mean_squared_error",
"tensorflow.compat.v1.logging.set_verbosity",
"tensorflow.compat.v... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
UKPLab/curriculum-annotation | [
"1d6ca490ea180019bb09d1d3818874f4321d4d0f"
] | [
"experiments/ca/ext.py"
] | [
"from typing import List, Tuple\n\nimport pandas as pd\n\n\n@pd.api.extensions.register_dataframe_accessor(\"tag\")\nclass CaTaggingAccessor:\n def __init__(self, df: pd.DataFrame):\n self._df = df\n\n def group_by_sentences(self):\n yield from (x[1] for x in self._df.groupby(\"sentence_id\"))\n... | [
[
"pandas.api.extensions.register_dataframe_accessor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
gmittal/jax | [
"281816221dea03c64f6d8b61253397c719c55feb",
"281816221dea03c64f6d8b61253397c719c55feb"
] | [
"jax/_src/lax/lax.py",
"jax/interpreters/batching.py"
] | [
"# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"numpy.take",
"numpy.sqrt",
"numpy.asarray",
"numpy.issubdtype",
"numpy.cumsum",
"numpy.dtype",
"numpy.zeros_like",
"numpy.iinfo",
"numpy.negative",
"numpy.swapaxes",
"numpy.greater",
"numpy.arange",
"numpy.less",
"numpy.subtract",
"numpy.finfo",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Seledriac/A-small-python-library-for-deep-learning | [
"c041287b04ba217910f621d34c7739365c36ad48"
] | [
"hd_recognition/GUI.py"
] | [
"\n# -*- coding:utf-8 -*-\n\n\"\"\"Handwritten digits recognition Graphic interface module : training done with the mnist dataset\"\"\"\n\n# Third-party gui/system/plotting Libraries\nimport numpy as np\nimport tkinter as tk\nimport tkinter.font as tkFont\nfrom tkinter import messagebox\nfrom tkinter import filedia... | [
[
"numpy.array",
"matplotlib.backends.backend_tkagg.FigureCanvasTkAgg",
"numpy.argmax",
"matplotlib.figure.Figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tsubedy/web-scraping-challenge | [
"785faf0b9855086dedaef1c1df2ea7b81a0dde4b"
] | [
"scrape_mars.py"
] | [
"\n# Dependencies\nfrom splinter import Browser\nfrom bs4 import BeautifulSoup\nfrom webdriver_manager.chrome import ChromeDriverManager\nimport time \nimport pandas as pd\nfrom pprint import pprint\nfrom urllib.parse import urlsplit\nimport pymongo\n\n# Initialize PyMongo to work with MongoDBs\nconn = 'mongodb://l... | [
[
"pandas.read_html"
]
] | [
{
"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": []
}
] |
danielamassiceti/geneval_visdial | [
"fbbe12b1e4ed7e21a002b16a87bdf42b2af3b35e"
] | [
"clusters/dataset_utils.py"
] | [
"import sys\nimport utils\nimport torch\nfrom datasets import VisualDialogDataset\nimport torchvision.transforms as transforms\n\ndef build_dataset(mode, args, shared_dictionary=None, with_options=True):\n \n normalize = transforms.Normalize(mean=[0.4711, 0.4475, 0.4080], std=[0.1223, 0.1221, 0.1450]) #visdia... | [
[
"torch.sum",
"torch.FloatTensor",
"torch.stack",
"torch.cumsum",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jbeomlee93/BBAM | [
"bebd2358d0497960c9a8415e5dca8de4a25fd899"
] | [
"tools/BBAM/BBAM_utils.py"
] | [
"import torch\nfrom torch.autograd import Variable\nfrom torchvision import models\nimport cv2\nimport sys\nimport numpy as np\nimport os\nimport math\nimport torch.nn.functional as F\n\nidx_to_class = {0 : 'aeroplane', 1 : 'bicycle', 2 : 'bird', 3 : 'boat', 4 : 'bottle', 5 : 'bus', 6 : 'car', 7 : 'cat',\n ... | [
[
"torch.abs",
"torch.nn.functional.softmax",
"numpy.expand_dims",
"torch.zeros",
"torch.sum",
"torch.cuda.is_available",
"torch.nn.functional.interpolate",
"torch.autograd.Variable",
"torch.sqrt",
"torch.from_numpy",
"torch.tensor",
"numpy.float32",
"torch.arange... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rdenaux/acred | [
"ee45840c942ef2fac4f26da8d756b7c47e42847c"
] | [
"scripts/pred_coinfo250.py"
] | [
"#\n# 2020 ExpertSystem\n#\n'''Script for generating predictions for the coinform250 dataset\n using the acred predictor\n\nSee https://github.com/co-inform/Datasets\n\nSee also scripts/fetch-data.sh, which should download the input json file\nand place it in the `data/evaluation/` folder.\n'''\nimport argparse\nim... | [
[
"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": []
}
] |
xcnick/TurboTransformers | [
"48b6ba09af2219616c6b97cc5c09222408e080c2"
] | [
"turbo_transformers/python/tests/bert_model_test.py"
] | [
"# Copyright (C) 2020 THL A29 Limited, a Tencent company.\n# All rights reserved.\n# Licensed under the BSD 3-Clause License (the \"License\"); you may\n# not use this file except in compliance with the License. You may\n# obtain a copy of the License at\n# https://opensource.org/licenses/BSD-3-Clause\n# Unless req... | [
[
"torch.randint",
"torch.set_grad_enabled",
"torch.set_num_threads",
"torch.cuda.is_available",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
evgeny-izutov/open_model_zoo | [
"2cd6145ef342fc9b7ccf32676af73f4a1cb8d9ba",
"2cd6145ef342fc9b7ccf32676af73f4a1cb8d9ba"
] | [
"tools/accuracy_checker/accuracy_checker/annotation_converters/cluttered_mnist.py",
"demos/python_demos/monodepth_demo/monodepth_demo.py"
] | [
"\"\"\"\nCopyright (c) 2018-2020 Intel Corporation\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law ... | [
[
"numpy.argmax"
],
[
"matplotlib.pyplot.imsave",
"numpy.squeeze",
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
esayyari/empress | [
"092044d4444a1569784cd9d336eb2a2a44a92abc"
] | [
"empress/tree.py"
] | [
"# ----------------------------------------------------------------------------\n# Copyright (c) 2016-2020, empress development team.\n#\n# Distributed under the terms of the Modified BSD License.\n#\n# The full license is in the file LICENSE, distributed with this software.\n# -------------------------------------... | [
[
"numpy.zeros",
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gustavoeso/img_manipulation | [
"3d1d8705820cae39d5be956836a94c7884ab490d"
] | [
"auxiliar.py"
] | [
"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n Atenção: usado no notebook da aula. \n Não precisa ser usado diretamente\n\"\"\"\n\nprint(\"Este script não deve ser executado diretamente\")\n\nfrom ipywidgets import widgets, interact, interactive, FloatSlider, IntSlider\nimport numpy as np\nimport cv2... | [
[
"numpy.copy",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
m4xst3r/Udacity-AdvancedLaneLines | [
"c95a1831a418726ad374a1ebd65d4ec5e9900ab9"
] | [
"claib_cam.py"
] | [
"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport glob\nimport pickle\n\n# read in all the images in the calibration folder\ncalib_images = glob.glob(\".\\camera_cal\\*.jpg\")\n\n#define chess board parameters:\nnx = 9\nny = 6\n\n# Arrays to store image point and opbject points\nimgpoints = [... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
savazeb/cosmos-ai | [
"4606e959396ebedca73086601078aa9c0ed77b31",
"4606e959396ebedca73086601078aa9c0ed77b31"
] | [
"sample/pid/wall_follower_pid.py",
"api/control/getitem.py"
] | [
"import sys\nsys.path.append(\"../..\")\n\nfrom api.control.PID import PID\nfrom api.control.sensor import sensor\nfrom api.control.robot import robot\nimport posix_ipc as ipc\nimport time\nimport threading\nimport math\nimport numpy as np\n\ngraphq = ipc.MessageQueue('/graphQueue', ipc.O_CREAT)\nmq = ipc.MessageQu... | [
[
"numpy.asarray",
"numpy.abs"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tensaiz/DyNSimF | [
"6288ff83f1b3f56fa626f741b55ade57b7c1b358"
] | [
"dynsimf/examples/school_segregation.py"
] | [
"import networkx as nx\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\nimport math\n\nfrom dynsimf.models.Model import Model\nfrom dynsimf.models.Model import ModelConfiguration\nfrom dynsimf.models.components.Memory import MemoryConfiguration\nfrom dynsimf.models.components.Memory import Memor... | [
[
"numpy.random.choice",
"numpy.random.shuffle",
"numpy.random.normal",
"numpy.fill_diagonal",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhaoaite/CorrMNN | [
"f88a70a199b462e9f3648da3ffdc5ee80a3e5f02"
] | [
"fusionmodel.py"
] | [
"#-*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 10 12:48:22 2018\n\n@author: Aite Zhao\n\"\"\"\n\nfrom __future__ import print_function\n#import random\nimport tensorflow as tf\n#from tensorflow.python.ops import rnn, rnn_cell\nimport numpy as np\n#import plot_confusion_matrix\nimport rnn_cell_GRU as rnn_cell\n... | [
[
"tensorflow.device",
"sklearn.cross_validation.train_test_split",
"tensorflow.cast",
"numpy.concatenate",
"numpy.mean",
"tensorflow.train.AdamOptimizer",
"numpy.exp",
"tensorflow.Graph",
"tensorflow.reset_default_graph",
"tensorflow.Session",
"tensorflow.train.Saver",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
kking423/digital_library | [
"643c396991bbc9664312826e849d3b9baae98c0d"
] | [
"workflow/workflow_inventory.py"
] | [
"import datetime\nimport shutil\nimport services.inventory\nimport workflow\nimport pandas as pd\nimport os\nimport file_system\nimport file_system.images as images\nimport json\nfrom file_system.file_system_object import FileSystemObject\nfrom services import inventory, library\nfrom tabulate import tabulate\nimpo... | [
[
"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": []
}
] |
toothlessLi/crnn_keras | [
"1179a82a732b83482c40176350062b3aca4fc0ab"
] | [
"testing/test.py"
] | [
"import keras\nimport tensorflow as tf\nimport keras.backend.tensorflow_backend as K\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True\n# config.gpu_options.per_process_gpu_memory_fraction = 0.9\nsess = tf.Session(config=config)\nK.set_session(sess)\n\nimport os\nimport sys\n\nsys.path.insert(0, '.... | [
[
"tensorflow.ConfigProto",
"numpy.expand_dims",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
jiyuanzFB/pytorch | [
"d047e475f830631d8fcc877ea17eac8fb34748d7"
] | [
"torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py"
] | [
"# Copyright 2022 Cruise LLC\nimport warnings\nfrom collections import OrderedDict\nimport logging\n\nimport torch.distributed as dist\nimport torch.distributed.algorithms.model_averaging.utils as utils\n\nlogger = logging.getLogger(__name__)\n\n\nclass HierarchicalModelAverager:\n r\"\"\"\n A group of model ... | [
[
"torch.distributed.new_subgroups",
"torch.distributed.get_world_size"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AnthonyNg404/Deep-Learning | [
"ef1dafaa1d07e9c9b574ba1722a7954c16ef463d",
"ef1dafaa1d07e9c9b574ba1722a7954c16ef463d",
"ef1dafaa1d07e9c9b574ba1722a7954c16ef463d"
] | [
"assignment2/deeplearning/gradient_check.py",
"assignment1/deeplearning/gradient_check.py",
"assignment2/deeplearning/coco_utils.py"
] | [
"import numpy as np\nfrom random import randrange\n\ndef eval_numerical_gradient(f, x, verbose=True, h=0.00001):\n \"\"\"\n a naive implementation of numerical gradient of f at x\n - f should be a function that takes a single argument\n - x is the point (numpy array) to evaluate the gradient at\n \"\... | [
[
"numpy.sum",
"numpy.copy",
"numpy.zeros_like",
"numpy.nditer"
],
[
"numpy.sum",
"numpy.copy",
"numpy.zeros_like",
"numpy.nditer"
],
[
"numpy.asarray",
"numpy.random.randint",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kmaterna/Utility_Code | [
"f713a3ce2a80d2e6dbdc42596451405bb873adbb"
] | [
"Tectonic_Utils/read_write/test/test_conversion_functions.py"
] | [
"# Testing code\n\nimport numpy as np\nimport unittest\nimport subprocess\nfrom .. import netcdf_read_write\n\n\nclass Tests(unittest.TestCase):\n\n def test_pixel_node_writer(self):\n \"\"\"\n See if the writing function for pixel-node files produces a pixel-node file.\n The behavior has be... | [
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
davisidarta/dynamo-release | [
"0dbd769f52ea07f3cdaa8fb31022ceb89938c382",
"0dbd769f52ea07f3cdaa8fb31022ceb89938c382"
] | [
"dynamo/tools/_dynamics_deprecated.py",
"dynamo/tools/dynamo_bk.py"
] | [
"import warnings\nimport numpy as np\nfrom .utils_moments import moments\nfrom .velocity import velocity, ss_estimation\nfrom .utils import (\n get_mapper,\n get_valid_bools,\n get_data_for_kin_params_estimation,\n get_U_S_for_velocity_estimation,\n)\nfrom .utils import set_velocity, set_param_ss, set_p... | [
[
"numpy.ones"
],
[
"numpy.log",
"numpy.min",
"scipy.optimize.least_squares",
"numpy.mean",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
... |
victor-tuda/chatbot | [
"3cadd018759344991c77e2aa86b8965ed0271789"
] | [
"training.py"
] | [
"import random\nimport json\nimport pickle\nimport numpy as np\n\nimport nltk\nnltk.download('punkt')\nnltk.download('wordnet')\nfrom nltk.stem import WordNetLemmatizer\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Activation, Dropout\nfrom tensorflow.keras.optimizers... | [
[
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Dropout",
"numpy.array",
"tensorflow.keras.models.Sequential",
"tensorflow.keras.optimizers.SGD"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
ppplinday/Situation-Awareness-Visualization | [
"13b233a1119b21f55e61d81d7d584d45d57a7385"
] | [
"myapp.py"
] | [
"import os\nimport json\nimport numpy as np\nimport pandas as pd\nimport datetime\n\nimport SAVIZ.situation_awareness_visualization as saviz\n\nwith open(\"tempfile.json\", 'r') as f:\n\n\tjson_file = f.readlines()[0]\n\nhas_type = True\nhas_time = False\ntimeRange = [0, 1]\n\nwith open(\"tempconfig.json\", 'r') as... | [
[
"pandas.to_datetime",
"pandas.DataFrame.from_dict"
]
] | [
{
"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": []
}
] |
MortisHuang/VIFFI-image-analysis | [
"ad144970e9cb53d61119dd96370157251c03cc07",
"ad144970e9cb53d61119dd96370157251c03cc07"
] | [
"SFig11_Whitecell_Cell_Area.py",
"Fig5b_lipid_droplets_area.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Nov 14 11:30:55 2019\r\n\r\n@author: Mortis Huang\r\n\"\"\"\r\n\r\n# import the necessary packages\r\nfrom PIL import Image\r\nimport numpy as np\r\nimport datetime\r\nimport os\r\nimport pandas as pd\r\n#%% Set the output file location\r\nrun_data = datetime.dat... | [
[
"pandas.concat",
"numpy.asarray",
"pandas.DataFrame",
"numpy.max",
"numpy.count_nonzero",
"numpy.array"
],
[
"scipy.ndimage.filters.maximum_filter",
"pandas.DataFrame",
"scipy.ndimage.label",
"numpy.mean",
"scipy.ndimage.find_objects",
"scipy.ndimage.filters.min... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
tchaye59/torchutils | [
"ca7b01bf63b6c3adaa36a4a66dfd87e927ef2460"
] | [
"torchutils/losses/losses.py"
] | [
"import torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom torchutils import to_device\n\n\nclass FocalLoss(nn.Module):\n \"\"\"weighted version of Focal Loss\"\"\"\n\n def __init__(self, alpha=.25, gamma=2, device=None):\n super(FocalLoss, self).__init__()\n self.alpha = ... | [
[
"torch.exp",
"torch.nn.functional.cross_entropy",
"torch.nn.functional.binary_cross_entropy",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kenneth-Schroeder/pytorch_geometric | [
"f7ec9e964bfae1ce5fb21d9b2b30e9e717bf8e24",
"f7ec9e964bfae1ce5fb21d9b2b30e9e717bf8e24",
"f7ec9e964bfae1ce5fb21d9b2b30e9e717bf8e24",
"f7ec9e964bfae1ce5fb21d9b2b30e9e717bf8e24",
"f7ec9e964bfae1ce5fb21d9b2b30e9e717bf8e24",
"f7ec9e964bfae1ce5fb21d9b2b30e9e717bf8e24"
] | [
"test/nn/functional/test_gini.py",
"examples/proteins_mincut_pool.py",
"torch_geometric/nn/models/metapath2vec.py",
"test/nn/conv/test_gcn_conv.py",
"examples/cluster_gcn_reddit.py",
"examples/proteins_topk_pool.py"
] | [
"import torch\n\nfrom torch_geometric.nn.functional import gini\n\n\ndef test_gini():\n w = torch.tensor(\n [\n [0., 0., 0., 0.],\n [0., 0., 0., 1000.0]\n ]\n )\n assert torch.isclose(gini(w), torch.tensor(0.5))\n",
"import os.path as osp\nfrom math import ceil\n\nimpo... | [
[
"torch.tensor"
],
[
"torch.nn.Linear",
"torch.nn.functional.log_softmax",
"torch.no_grad",
"torch.cuda.is_available"
],
[
"torch.sigmoid",
"sklearn.linear_model.LogisticRegression",
"torch.cat",
"torch.zeros_like",
"torch.nn.Embedding",
"torch.tensor",
"torc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
BoyDun/tensorflow-tensorrt | [
"6fa64812fbbc441f59e4c68f174f8c835cac7d05"
] | [
"Tensorflow-JSON.py"
] | [
"import numpy as np\nimport json\n\n\nprefixes = ['softmax', 'fc', 'conv', 'max_pool', 'avg_pool', 'relu'] # TODO: ADD CONCAT\n\n# Validate that every dictionary key is the name of a valid layer format\ndef validate_prefixes(names):\n for name in names:\n index = name.rfind('/')\n if index != -1: ... | [
[
"numpy.transpose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ian-ludden/redist-vis | [
"af8cae8e849b04aa4409a82e11cf5da831d5934b"
] | [
"metrics.py"
] | [
"import pandas as pd\nimport geopandas\nimport json\nimport altair as alt\n\ndef make_metrics_df():\n GEOJSON = 'geojson/wi_map_plan_{}.geojson'\n mm_gaps = []\n sl_indices = []\n efficiency_gaps = []\n plan_number = [i for i in range(1,84)]\n for i in range(1,84):\n plan = geopandas.read_f... | [
[
"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": []
}
] |
sanbeichahegongheguo/pinyinwork | [
"eb32244db90a549aa03866e892ab7507ca49f0df"
] | [
"src/pinyin3.py"
] | [
"import sys, fileinput, json\nimport numpy as np\n\n\nfir_p = {} # 某字符出现在句首的概率对数 {str: float}\ndou_count = {} # 字符的二元出现次数 {(str, str): int}\ntri_count = {} # 字符的三元出现次数 {str: {str: {str: int}}}\nsin_count = {} # 字符出现计数 {str: int}\npch = {} # 拼音到字符的dict {pinyin: [chs]}\nsin_total = 396468407\n\n\ndef preload3():... | [
[
"numpy.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AnkitKumar2698/scikit-learn | [
"589329e5130de48b4a88b707213cf92a3112e236"
] | [
"sklearn/linear_model/_logistic.py"
] | [
"\"\"\"\nLogistic Regression\n\"\"\"\n\n# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>\n# Fabian Pedregosa <f@bianp.net>\n# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>\n# Manoj Kumar <manojkumarsivaraj334@gmail.com>\n# Lars Buitinck\n# Simon Wu <s8w... | [
[
"numpy.dot",
"numpy.expand_dims",
"numpy.asarray",
"numpy.mean",
"scipy.sparse.dia_matrix",
"numpy.exp",
"numpy.hstack",
"numpy.swapaxes",
"scipy.sparse.issparse",
"numpy.unique",
"numpy.reshape",
"numpy.empty_like",
"numpy.linalg.multi_dot",
"numpy.argmax",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shoz/ProtLearn | [
"2c6edac2c3cfdc4aeeb2b55bb3cb5e4407e2065e"
] | [
"tests/test_length.py"
] | [
"import os\nimport sys\npath = os.environ.get('TRAVIS_BUILD_DIR')\nsys.path.insert(0, path+'/protlearn')\nimport numpy as np\n\nfrom preprocessing import txt_to_df\nfrom feature_engineering import length\n\n\ndef test_lengths():\n \"Test sequence lengths\"\n \n # load data\n df = txt_to_df(path+'/tests/... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
harwiltz/bach-robot-suite | [
"1126a665266cc5819d331af79effd03f3efe043f"
] | [
"examples/rocket_lander_test.py"
] | [
"import gym\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport time\n\nimport brs_envs\n\nenv = gym.make('RocketLanderBRSEnv-v0',\n render=True,\n max_lateral_offset=0,\n max_pitch_offset=0,\n max_roll_offset=0,\n max_yaw_offset=0,\n ... | [
[
"numpy.sin",
"matplotlib.pyplot.xlabel",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CarlChenCC/TradingStockWeb | [
"da8ab0163daa980b9506686d25465da2a34c029d"
] | [
"TradingSystemApp/ML.py"
] | [
"from IPython.display import Image\n#%matplotlib inline\n\nfrom distutils.version import LooseVersion as Version\nfrom sklearn import __version__ as sklearn_version\n#Image(filename='./images/10_01.png', width=500)\n\n\nimport pandas as pd\n\ndf = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databas... | [
[
"numpy.dot",
"matplotlib.pyplot.tight_layout",
"pandas.read_csv",
"matplotlib.pyplot.scatter",
"numpy.corrcoef",
"matplotlib.pyplot.xlabel",
"sklearn.preprocessing.StandardScaler",
"matplotlib.pyplot.show",
"numpy.zeros",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
nbechor/SlipperySlope | [
"5a456a9632b73e2f5ff0d90fe080aeec1ec3cc3a"
] | [
"src/features/built_events_w_weather_features_labels0.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom weatherClass import weatherClass\nfrom IdentifierClass import identifierClass\nfrom eventsClass import eventsClass\nimport datetime\n\n### load some data:\n\n#read the ticket+complaint data, combined for location:\n# events fields: date, lat, lng, address, identi... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Tensor46/TensorMONK | [
"1785132b82c685c3b3fc05b00dec46b1fccfc948",
"1785132b82c685c3b3fc05b00dec46b1fccfc948"
] | [
"tensormonk/layers/routingcapsule.py",
"tensormonk/architectures/trees.py"
] | [
"\"\"\" TensorMONK :: layers :: RoutingCapsule \"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom ..activations import Activations\n\n\nclass RoutingCapsule(nn.Module):\n r\"\"\" Routing capsule from Dynamic Routing Between Capsules.\n Implemented -- https... | [
[
"torch.randn",
"torch.nn.functional.softmax",
"numpy.prod",
"torch.zeros"
],
[
"torch.randn",
"torch.nn.functional.softmax",
"numpy.cumsum",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cgrinaldi/pocket_analytics | [
"7a79458de82b186fd4d5078b28d765d3bafe12aa"
] | [
"storage.py"
] | [
"import os\nimport logging\nimport pandas as pd\n\nfrom pathlib import Path\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\nDIR_PATH = Path(os.path.dirname(os.path.abspath(__file__)))\nSINCE_PATH = DIR_PATH / Path('data/since.txt')\nARTICLES_PATH = DIR_PATH / Path('data/articles.... | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
phunc20/dsp | [
"e7c496eb5fd4b8694eab0fc049cf98a5e3dfd886",
"e7c496eb5fd4b8694eab0fc049cf98a5e3dfd886",
"e7c496eb5fd4b8694eab0fc049cf98a5e3dfd886"
] | [
"stanford/sms-tools/lectures/07-Sinusoidal-plus-residual-model/plots-code/hpsModel-sax-phrase.py",
"stanford/sms-tools/lectures/01-Introduction/plots-code/sinewave.py",
"stanford/sms-tools/software/models_interface/dftModel_function.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.signal import hamming, hanning, triang, blackmanharris, resample\nimport math\nimport sys, os, time\nsys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))\nimport utilFunctions as UF\nimport hpsModel a... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.pyplot.autoscale",
"numpy.less",
"numpy.arange",
"numpy.blackman",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.f... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0... |
neilswainston/gae | [
"00ce67b8dd25e79055f55133f7775b20476c8995"
] | [
"gae/chem/sparse/chem_train_single.py"
] | [
"'''\n(c) University of Liverpool 2020\n\nAll rights reserved.\n\n@author: neilswainston\n'''\n# pylint: disable=invalid-name\n# pylint: disable=no-member\n# pylint: disable=wrong-import-order\nfrom rdkit import Chem\nimport scipy\n\nfrom gae.tf import train_single\nimport numpy as np\nimport pandas as pd\n\n\ndef ... | [
[
"pandas.read_csv",
"scipy.sparse.csr_matrix",
"scipy.sparse.lil_matrix"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"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"... |
noegroup/membrane_kinetics | [
"ee2da9e076e402dc83e886aac129144d7c58a69f"
] | [
"diffusion_tensors/surface_integration.py"
] | [
"import numpy as np\n\ndef build_local_integration_grid_circle(n_quad_points, r_c):\n # Guass-Legendre quadrature on the unit disk (by KyoungJoong Kim and ManSuk Song)\n\n if n_quad_points == 1:\n\n w_1 = 3.141592653589793\n x_1 = 0.0\n\n quad_point_x = np.array([x_1]) * r_c\n\n qu... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cutebomb/ta | [
"2d3d292c6513f8dc30b277bffa6246475a8d27b1"
] | [
"ta/volume.py"
] | [
"\"\"\"\n.. module:: volume\n :synopsis: Volume Indicators.\n\n.. moduleauthor:: Dario Lopez Padial (Bukosabino)\n\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\n\nfrom ta.utils import IndicatorMixin, ema\n\n\nclass AccDistIndexIndicator(IndicatorMixin):\n \"\"\"Accumulation/Distribution Index (ADI)\n\n ... | [
[
"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": []
}
] |
kekeblom/Doubly-Stochastic-DGP | [
"64e40a8c22514c9b7917f0c6b79ca11b2bd67ea3"
] | [
"doubly_stochastic_dgp/layers.py"
] | [
"# Copyright 2017 Hugh Salimbeni\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed t... | [
[
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.matrix_diag_part",
"tensorflow.reduce_sum",
"tensorflow.stack",
"tensorflow.cast",
"tensorflow.map_fn",
"tensorflow.cholesky",
"tensorflow.matrix_triangular_solve",
"tensorflow.cholesky_solve",
"numpy.eye",
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
jinsoo9595/LeNet_5-pytorch | [
"188cdeaf81bdd259f8a0d4686ffbaf5210ae5ce2"
] | [
"lenet.py"
] | [
"import torch.nn as nn\r\nfrom collections import OrderedDict\r\n\r\n\r\nclass C1(nn.Module):\r\n def __init__(self):\r\n super(C1, self).__init__()\r\n\r\n self.c1 = nn.Sequential(OrderedDict([\r\n ('c1', nn.Conv2d(1, 6, kernel_size=(5, 5))),\r\n ('relu1', nn.ReLU()),\r\n ... | [
[
"torch.nn.LogSoftmax",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
naisy/donkeycar | [
"11f7598d51c1c085db2a76943c86132bf1eb9e30"
] | [
"donkeycar/templates/complete.py"
] | [
"#!/usr/bin/env python3\n\"\"\"\nScripts to drive a donkey 2 car\n\nUsage:\n manage.py (drive) [--model=<model>] [--js] [--type=(linear|categorical)] [--camera=(single|stereo)] [--meta=<key:value> ...] [--myconfig=<filename>]\n manage.py (train) [--tubs=tubs] (--model=<model>) [--type=(linear|inferred|tensorr... | [
[
"tensorflow.python.keras.models.model_from_json"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.5",
"1.4"
]
}
] |
yongyuwen/mbti_rnn | [
"3f67cd90d76ab82d65afc3c155728e3d349a1107"
] | [
"src/rnn.py"
] | [
"\"\"\"rnn.py\r\n~~~~~~~~~~~~~~\r\nWritten by Yong Yu Wen, 2018\r\n\r\n(Built using tensorflow-gpu 1.6.0)\r\n\r\nA TensorFlow-based many-to-one recurrent neural network specifically\r\nfor the classification of MBTI types based on social media posts.\r\nRaw un-processed dataset used for this task can be found at\r\... | [
[
"tensorflow.nn.dynamic_rnn",
"tensorflow.get_variable",
"tensorflow.cast",
"tensorflow.train.cosine_decay_restarts",
"tensorflow.train.AdamOptimizer",
"tensorflow.Variable",
"tensorflow.name_scope",
"tensorflow.train.Saver",
"tensorflow.argmax",
"tensorflow.nn.rnn_cell.GRUC... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
wzthu/NeuronMotif | [
"0f7f786e4b75916039388824d04d2041747fd299"
] | [
"dcnn/demo/demo2/simu.py"
] | [
"import h5py\nimport matplotlib\nmatplotlib.use('Agg')\nfrom matplotlib import pyplot as plt\n\n\nimport keras\n\n\nimport h5py\nimport numpy as np\nfrom keras.layers import Input, Dense, Conv1D, MaxPooling2D, MaxPooling1D, BatchNormalization\nfrom keras.layers.core import Dropout, Activation, Flatten\nfrom keras.l... | [
[
"numpy.random.choice",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"numpy.ones",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"numpy.random.randint",
"numpy.zeros",
"numpy.loadtxt",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pally2409/viral-diseases-simulator | [
"488168a481c277d99d758d2a9851df6524a9a57b"
] | [
"src/movements.py"
] | [
"'''\nCreated on Nov 29, 2020\n@author: manik\n'''\n'''\nFile with classes and code which control how a particular person\nwill move and to where\n'''\nfrom src.population import Population\nimport numpy as np\nimport src.person_properties_util as idx\n\nclass Movement():\n \"\"\"\n Class providing abstractio... | [
[
"numpy.random.normal",
"numpy.random.random",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hunterowens/pandas | [
"bb468f86d57f4eb0e65d75c3161d9e3209ea2c05"
] | [
"pandas/tests/test_multilevel.py"
] | [
"# -*- coding: utf-8 -*-\n# pylint: disable-msg=W0612,E1101,W0141\nimport datetime\nimport itertools\nimport nose\n\nfrom numpy.random import randn\nimport numpy as np\n\nfrom pandas.core.index import Index, MultiIndex\nfrom pandas import Panel, DataFrame, Series, notnull, isnull, Timestamp\n\nfrom pandas.util.test... | [
[
"pandas.to_datetime",
"pandas.util.testing.assert_isinstance",
"pandas.Series",
"pandas.PeriodIndex",
"pandas.core.common.is_integer_dtype",
"pandas.compat.product",
"pandas.util.testing._skip_if_no_pytz",
"pandas.DataFrame",
"pandas.compat.range",
"pandas.util.testing.asse... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20"
],
"scipy": [],
"tensorflow": []
}
] |
driesvr/The-Photoswitch-Dataset | [
"fbc7858343b56ed8526ed6a3feeed260fac1963c"
] | [
"property_prediction/predict_with_RF.py"
] | [
"# Copyright Ryan-Rhys Griffiths and Aditya Raymond Thawani 2020\n# Author: Ryan-Rhys Griffiths\n\"\"\"\nProperty prediction on the photoswitch dataset using Random Forest.\n\"\"\"\n\nimport argparse\n\nimport numpy as np\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train... | [
[
"sklearn.ensemble.RandomForestRegressor",
"sklearn.metrics.r2_score",
"sklearn.metrics.mean_absolute_error",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.mean_squared_error",
"numpy.std",
"numpy.mean",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zhiquan-Wen/D-VQA | [
"688c4dcc811f49b431daea81406e628ec71a7247"
] | [
"data/utils.py"
] | [
"from __future__ import print_function\r\n\r\nimport errno\r\nimport os\r\nfrom PIL import Image\r\nimport torch\r\nimport torch.nn as nn\r\nimport re\r\n\r\nimport json\r\nimport pickle as cPickle\r\nimport numpy as np\r\nimport utils\r\nimport h5py\r\nimport operator\r\nimport functools\r\nfrom torch._six import... | [
[
"torch.Size",
"torch.LongTensor",
"torch.max",
"numpy.abs",
"torch.load",
"torch.sum",
"torch.from_numpy",
"torch.is_tensor",
"torch.zeros_like",
"numpy.mean",
"torch.utils.data.dataloader.default_collate",
"torch.stack",
"torch.sparse.FloatTensor",
"torch.D... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leo0519/TensorRT | [
"498dcb009fe4c2dedbe9c61044d3de4f3c04a41b",
"498dcb009fe4c2dedbe9c61044d3de4f3c04a41b",
"498dcb009fe4c2dedbe9c61044d3de4f3c04a41b"
] | [
"samples/python/yolov3_onnx/yolov3_to_onnx.py",
"demo/HuggingFace/T5/export.py",
"tools/onnx-graphsurgeon/tests/test_examples.py"
] | [
"#!/usr/bin/env python3\n#\n# Copyright (c) 2021, NVIDIA CORPORATION. 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/L... | [
[
"numpy.array"
],
[
"torch.tensor"
],
[
"numpy.random.random_sample"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DennisHgj/pyela | [
"bbdbf1a55b31cd2ae4dc1877d220ad32589ead4c",
"bbdbf1a55b31cd2ae4dc1877d220ad32589ead4c"
] | [
"pyvista_sample/VisualizeDataProcess.py",
"ela/spatial.py"
] | [
"import os\nimport pickle\nimport PVGeo\nimport pyvista as pv\nimport pandas as pd\nfrom ela.classification import GridInterpolation\n\nfrom ela.spatial import create_meshgrid_cartesian\nfrom ela.visual import *\n\n'''\n@author: Guanjie Huang\n@date: Aug 16th,2019\nThis class is used to process data before generati... | [
[
"pandas.read_pickle"
],
[
"pandas.DataFrame",
"pandas.notna",
"numpy.unique",
"numpy.arange",
"numpy.empty_like",
"numpy.full",
"sklearn.neighbors.KNeighborsClassifier",
"numpy.float32",
"numpy.column_stack",
"numpy.ravel",
"pandas.concat",
"numpy.isnan",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
ashblib/protocell | [
"037c3aa6ab2250eae09889729d512c243518e282"
] | [
"model/protonet.py"
] | [
"import torch.nn as nn\nimport torch\n\nclass ProtoNetBig(nn.Module):\n def __init__(self, x_dim=23433, hid_dim=[2000, 1000, 500, 250], z_dim=100):\n super(ProtoNetBig, self).__init__()\n self.linear0 = nn.Linear(x_dim, hid_dim[0])\n self.bn1 = nn.BatchNorm1d(hid_dim[0])\n self.linear... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.Dropout",
"torch.cat",
"torch.nn.Linear",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XiaoJake/SOLD2 | [
"ddd36788c112136be2975ee29b096df979571bb2",
"ddd36788c112136be2975ee29b096df979571bb2"
] | [
"sold2/model/model_util.py",
"sold2/model/metrics.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.init as init\n\nfrom .nets.backbone import HourglassBackbone, SuperpointBackbone\nfrom .nets.junction_decoder import SuperpointDecoder\nfrom .nets.heatmap_decoder import PixelShuffleDecoder\nfrom .nets.descriptor_decoder import SuperpointDescriptor\n\n\ndef get_... | [
[
"torch.nn.init.constant_",
"torch.nn.init.xavier_normal_",
"torch.nn.init.normal_"
],
[
"torch.nn.functional.normalize",
"numpy.pad",
"numpy.arange",
"numpy.squeeze",
"torch.min",
"torch.sum",
"numpy.vstack",
"torch.tensor",
"numpy.concatenate",
"torch.nn.fu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JohnGriffiths/dipy | [
"5fb38e9b77547cdaf5eb140730444535733ae01d",
"5fb38e9b77547cdaf5eb140730444535733ae01d",
"5fb38e9b77547cdaf5eb140730444535733ae01d",
"5fb38e9b77547cdaf5eb140730444535733ae01d",
"5fb38e9b77547cdaf5eb140730444535733ae01d",
"5fb38e9b77547cdaf5eb140730444535733ae01d",
"5fb38e9b77547cdaf5eb140730444535733ae01... | [
"dipy/reconst/tests/test_peakdf.py",
"dipy/viz/tests/test_fvtk_widgets.py",
"dipy/viz/actor.py",
"dipy/viz/fvtk.py",
"dipy/core/tests/__init__.py",
"doc/examples/simulate_dki.py",
"dipy/denoise/tests/test_noise_estimate.py",
"dipy/tracking/__init__.py"
] | [
"import numpy as np\nimport numpy.testing as npt\n\nfrom dipy.reconst.peaks import default_sphere, peaks_from_model\n\n\ndef test_PeaksAndMetricsDirectionGetter():\n\n class SillyModel(object):\n def fit(self, data, mask=None):\n return SillyFit(self)\n\n class SillyFit(object):\n\n d... | [
[
"numpy.testing.assert_equal",
"numpy.testing.run_module_suite",
"numpy.random.random",
"numpy.array",
"numpy.zeros",
"numpy.testing.assert_array_almost_equal"
],
[
"numpy.testing.dec.skipif",
"numpy.array",
"numpy.testing.run_module_suite",
"numpy.testing.assert_equal"
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
reactivetype/cs234-reinforcement-learning | [
"693a90854d6548157ac8ec1c70a90b08810aec1b"
] | [
"assignment/assignment2/q3_nature.py"
] | [
"import tensorflow as tf\r\nimport tensorflow.contrib.layers as layers\r\n\r\nfrom utils.general import get_logger\r\nfrom utils.test_env import EnvTest\r\nfrom q1_schedule import LinearExploration, LinearSchedule\r\nfrom q2_linear import Linear\r\n\r\n\r\nfrom configs.q3_nature import config\r\n\r\n\r\nclass Natur... | [
[
"tensorflow.variable_scope",
"tensorflow.contrib.layers.conv2d",
"tensorflow.contrib.layers.flatten",
"tensorflow.contrib.layers.fully_connected"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13",
"1.10",
"1.12"
]
}
] |
onodip/OpenMDAO | [
"96a99806fb3a547b881d2ad3da2733bca9978567",
"96a99806fb3a547b881d2ad3da2733bca9978567",
"96a99806fb3a547b881d2ad3da2733bca9978567"
] | [
"openmdao/approximation_schemes/complex_step.py",
"openmdao/components/tests/test_meta_model_unstructured_comp.py",
"openmdao/core/tests/test_distrib_list_vars.py"
] | [
"\"\"\"Complex Step derivative approximations.\"\"\"\nfrom __future__ import division, print_function\n\nfrom itertools import groupby\nfrom six.moves import range\n\nimport numpy as np\n\nfrom openmdao.approximation_schemes.approximation_scheme import ApproximationScheme\nfrom openmdao.utils.name_maps import abs_k... | [
[
"numpy.zeros"
],
[
"numpy.linspace",
"numpy.cos",
"numpy.sin",
"numpy.array",
"numpy.zeros"
],
[
"numpy.arange",
"numpy.zeros",
"numpy.sum",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yarikoptic/NiPy-OLD | [
"8759b598ac72d3b9df7414642c7a662ad9c55ece",
"8759b598ac72d3b9df7414642c7a662ad9c55ece",
"8759b598ac72d3b9df7414642c7a662ad9c55ece",
"8759b598ac72d3b9df7414642c7a662ad9c55ece",
"8759b598ac72d3b9df7414642c7a662ad9c55ece",
"8759b598ac72d3b9df7414642c7a662ad9c55ece"
] | [
"nipy/neurospin/register/__init__.py",
"nipy/neurospin/register/tests/test_iconic_matcher.py",
"examples/neurospin/hierarchical_rois.py",
"nipy/neurospin/register/texture.py",
"examples/create_fmri_model.py",
"nipy/neurospin/group/__init__.py"
] | [
"from iconic_matcher import IconicMatcher\n#from realign4d import TimeSeries, realign4d, resample4d\nimport transform\n\nfrom numpy.testing import Tester\n\ntest = Tester().test\nbench = Tester().bench \n\n",
"#!/usr/bin/env python\n\nfrom nipy.testing import assert_equal, assert_almost_equal, assert_raises\nimpo... | [
[
"numpy.testing.Tester"
],
[
"numpy.asarray",
"numpy.eye",
"numpy.concatenate",
"numpy.random.rand"
],
[
"matplotlib.pylab.show",
"numpy.nonzero",
"numpy.reshape",
"numpy.eye",
"numpy.ones",
"numpy.size",
"numpy.argmax",
"matplotlib.pylab.figure",
"ma... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
bdshieh/cnl-dyna | [
"9013fa11cabb6ad51aaa385b44ef99cc43bf6a2b"
] | [
"cnld/util.py"
] | [
"'''\nUtility functions.\n'''\nimport argparse\nimport functools\nimport itertools\nimport os\nimport sqlite3 as sql\nfrom contextlib import closing\nfrom copy import deepcopy\nfrom itertools import repeat\n\nimport numpy as np\nimport pandas as pd\nimport scipy as sp\nimport scipy.fftpack\nimport scipy.signal\nfro... | [
[
"numpy.sqrt",
"numpy.arctan",
"pandas.DataFrame",
"scipy.fftpack.fft",
"scipy.fftpack.fftfreq",
"scipy.signal.firwin",
"numpy.pad",
"numpy.arange",
"numpy.sin",
"scipy.signal.gausspulse",
"numpy.ceil",
"scipy.signal.butter",
"scipy.signal.lfilter",
"numpy.ze... | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
datanonymous/TFandroid | [
"6aa83398ab03bfae822f36772757097bcb98b6ed",
"6aa83398ab03bfae822f36772757097bcb98b6ed",
"6aa83398ab03bfae822f36772757097bcb98b6ed",
"6aa83398ab03bfae822f36772757097bcb98b6ed",
"89927e863b1ad96184ab09188f62b7e391c896d9",
"6aa83398ab03bfae822f36772757097bcb98b6ed",
"89927e863b1ad96184ab09188f62b7e391c896d... | [
"tensorflow/python/kernel_tests/linalg/linear_operator_adjoint_test.py",
"tensorflow/python/ops/string_ops.py",
"tensorflow/examples/saved_model/integration_tests/use_rnn_cell.py",
"tensorflow/python/ops/linalg/linear_operator_test_util.py",
"tensorflow/python/keras/integration_test.py",
"tensorflow/contr... | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.ops.linalg.linear_operator_test_util.random_positive_definite_matrix",
"tensorflow.python.ops.linalg.linear_operator_test_util.random_normal",
"tensorflow.python.platform.test.main",
"tensorflow.python.ops.linalg.linear_operator_test_util.random_tril_matrix",
"tensorflow.pyt... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"1.5",
"1.7",
"2.5",
"2.8",
"2.10"
]
},
{
"matplotlib": []... |
DanielOrtega94/pytorch-lightning | [
"34e11fab167a7beb78fbe6991ff8721dc9208793",
"1d565e175d98103c2ebd6164e681f76143501da9"
] | [
"pytorch_lightning/callbacks/model_checkpoint.py",
"tests/models/test_grad_norm.py"
] | [
"\"\"\"\nModel Checkpointing\n===================\n\nAutomatically save model checkpoints during training.\n\n\"\"\"\n\nimport os\nimport re\n\nimport numpy as np\nfrom typing import Optional\n\nimport torch\nfrom pytorch_lightning import _logger as log\nfrom pytorch_lightning.callbacks.base import Callback\nfrom p... | [
[
"torch.tensor"
],
[
"numpy.linalg.norm",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sitek/subcortical-auditory-atlas | [
"8218140c457ab97a6d897eb26aae4d6240596033"
] | [
"code/invivo/diffusion/02_analysis/dipy_atlas_target.py"
] | [
"'''\nAfter creating tractography streamlines with dipy_csd.py,\nthis workflow takes an atlas file and finds connections\nbetween each region in the atlas\nKRS 2018.05.04\n'''\nfrom nipype import config\nconfig.set('execution', 'remove_unnecessary_outputs', 'false')\nconfig.set('execution', 'crashfile_format', 'txt... | [
[
"numpy.eye"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HaojieYuan/cleverhans | [
"02a5ac27870ad8318c1e6ef3b210467e3500fdd9"
] | [
"cleverhans/future/tf2/attacks/fast_gradient_method.py"
] | [
"\"\"\"The Fast Gradient Method attack.\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\n\n\ndef fast_gradient_method(model_fn, x, eps, ord, clip_min=None, clip_max=None, y=None,\n targeted=False, sanity_checks=False):\n \"\"\"\n Tensorflow 2.0 implementation of the Fast Gradient Meth... | [
[
"tensorflow.clip_by_value",
"tensorflow.reduce_max",
"tensorflow.sign",
"tensorflow.multiply",
"tensorflow.reduce_sum",
"tensorflow.equal",
"tensorflow.math.less_equal",
"tensorflow.stop_gradient",
"numpy.all",
"tensorflow.math.greater_equal",
"tensorflow.square",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
data301-2021-winter1/project-group25-project | [
"203421ca91c95786de4a2fff5412693493b9371f"
] | [
"notebooks/project_functions.py"
] | [
"import pandas as pd\nimport numpy as np\n\ndef load_and_process_data(path):\n rawData = pd.read_csv(path, sep=\";\")\n rawData = rawData[rawData.columns[:-2]].dropna().rename(columns={\"RH\": \"Relative Humidity\", \"AH\": \"Absolute Humdity\", \"T\": \"Temp\"})\n \n for col in rawData.columns:\n ... | [
[
"pandas.read_csv",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
hietalajulius/clothmanip | [
"ec2ee1177d5cf31ee2367c2576c34b9cf3691501",
"ec2ee1177d5cf31ee2367c2576c34b9cf3691501"
] | [
"experiments/produce_images.py",
"clothmanip/envs/cloth.py"
] | [
"from clothmanip.utils.utils import get_variant, argsparser, get_randomized_env, dump_commit_hashes, get_keys_and_dims, dump_goal\nfrom clothmanip.envs.cloth import ClothEnvPickled as ClothEnv\nimport numpy as np\nfrom rlkit.torch.sac.policies import TanhGaussianPolicy, MakeDeterministic, TanhScriptPolicy, CustomSc... | [
[
"numpy.clip",
"numpy.genfromtxt"
],
[
"numpy.ndarray",
"numpy.all",
"numpy.round",
"numpy.random.randint",
"pandas.read_csv",
"numpy.clip",
"numpy.reshape",
"numpy.eye",
"numpy.float32",
"numpy.zeros",
"numpy.random.choice",
"numpy.isnan",
"numpy.lin... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
... |
stevenkfirth/OBLib | [
"12ab46ca2c24d28d8ed5b14be0978fb5dacae394",
"12ab46ca2c24d28d8ed5b14be0978fb5dacae394"
] | [
"OBLib/Model.py",
"tests/test_ScheduleModel.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport pandas as pd\n\n\n\nclass Model():\n \"\"\"Abstract model class.\n This is the top-level class and should not be used directly.\n Instead this class is inherited by other more specialised model classes.\n \n \"\"\"\n \n def __init__(self):\n \"\"\n ... | [
[
"pandas.Timestamp",
"pandas.DataFrame",
"pandas.date_range"
],
[
"numpy.testing.assert_array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
ZoneTsuyoshi/pyassim | [
"1b40ce914a7b1e4ec6e240a6d67a19a22e431137",
"1b40ce914a7b1e4ec6e240a6d67a19a22e431137"
] | [
"sample/sample_advection.py",
"pyassim/vmpf.py"
] | [
"\"\"\"\nsample code for LLOCK, SLOCK, LSLOCK\napplication the method to advection model (periodic boundary condition)\n\"\"\"\n\nimport os, sys\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nsys.path.append(\"..\")\nfrom pyassim import KalmanFilter, LocalLOCK, SpatiallyUniformLOCK... | [
[
"matplotlib.pyplot.legend",
"numpy.random.seed",
"numpy.arange",
"numpy.eye",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"numpy.zeros",
"matplotlib.pyplot.figure"
],
[
"numpy.repeat",
"numpy.tile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jihunchoi/probability | [
"685c5012eba03a23d1b849d35f5e8efe7fdc402d",
"685c5012eba03a23d1b849d35f5e8efe7fdc402d",
"685c5012eba03a23d1b849d35f5e8efe7fdc402d"
] | [
"tensorflow_probability/python/distributions/mixture.py",
"tensorflow_probability/python/distributions/mvn_tril_test.py",
"tensorflow_probability/python/distributions/uniform_test.py"
] | [
"# Copyright 2018 The TensorFlow Probability 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 a... | [
[
"tensorflow.convert_to_tensor",
"tensorflow.concat",
"tensorflow.control_dependencies",
"tensorflow.stack",
"tensorflow.reduce_sum",
"tensorflow.add_n",
"tensorflow.reduce_logsumexp",
"tensorflow.dynamic_stitch",
"tensorflow.gather",
"tensorflow.python.framework.tensor_util... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},... |
jbampton/dash-table | [
"1e25a1296ccbe0f061cc791e259a3f37ed3fbed9"
] | [
"tests/selenium/test_scrolling.py"
] | [
"import dash\nimport dash.testing.wait as wait\nfrom dash_table import DataTable\n\nimport pandas as pd\nimport pytest\nfrom selenium.webdriver.common.keys import Keys\n\ndf = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/solar.csv\")\n\nbase_props = dict(\n id=\"table\",\n columns=[{... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
connorkerry/RobinhoodBot | [
"6a1e1733d900abfc00a8e6fff1cf48184af4edc3"
] | [
"robinhoodbot/main.py"
] | [
"import robin_stocks as r\nimport pandas as pd\nimport numpy as np\nimport ta as ta\nfrom pandas.plotting import register_matplotlib_converters\nfrom ta import *\nfrom misc import *\nfrom tradingstats import *\n\n#Log in to Robinhood\nlogin = r.login('YOUR_EMAIL','YOUR_PASSWORD')\n\n#Safe divide by zero division fu... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.Series",
"numpy.isnan",
"pandas.Timedelta",
"pandas.plotting.register_matplotlib_converters",
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TheGlobalExpert/protein-stability | [
"b5cc6efaaa6a2f7784729420b746a7ec07bd0d97"
] | [
"old/ROC_foldx.py"
] | [
"import pandas as pd\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\n\ndata = pd.read_csv(\"../results/master.csv\")\ndata = pd.read_csv(\"../data/FoldX_predictions.csv\")\n\nx = list(data[\"ddG\"])\ny = list(data[\"FoldX_dGG\"])\n\n\n#clean # XXX:\n\n\n\n\nimport itertools\n\n#lists = sorted(z... | [
[
"matplotlib.pyplot.legend",
"numpy.polyfit",
"pandas.read_csv",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.figure",
"numpy.arange",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"numpy.corrcoef",
"matpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
d3ft0uch/Tacotron-2 | [
"a508bb842053599697a7c0a20d2b8cbb32e28632"
] | [
"hparams.py"
] | [
"# -*- coding: utf-8 -*-\nimport numpy as np\nimport tensorflow as tf\n\n# Default hyperparameters\nhparams = tf.contrib.training.HParams(\n # Comma-separated list of cleaners to run on text prior to training and eval. For non-English\n # text, you may want to use \"basic_cleaners\" or \"transliteration_clean... | [
[
"numpy.log"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Slavkata/Forecast-Report | [
"3cfeac5ab6b60ad32e1b9433b3281b5336373c30",
"3cfeac5ab6b60ad32e1b9433b3281b5336373c30"
] | [
"PastYearFeatures/setup.py",
"weather-app/vendor/single_date_predictor.py"
] | [
"import pandas as p\nimport numpy as np\nfrom file_setup_helper import FileSetupHelper as fsh\nfrom preprocess_data import PreprocessData as pd\nfrom model_export import ModelExport as me\nimport sys\nfrom sklearn.linear_model import Ridge\n\ndef main():\n #call for file download with given date\n file_name =... | [
[
"sklearn.linear_model.Ridge",
"pandas.read_csv"
],
[
"sklearn.externals.joblib.load",
"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... |
Elscha/MetricsML | [
"2ecbc42ad7bd2465f4f75658f44452ea5c552c3b"
] | [
"metricsML/Normalizator.py"
] | [
"from metricsML.NormalizationType import NormalizationType\nimport numpy as np\nimport math\n\ndef normalization(normalization, train_data, test_data, validation_data=None):\n if not isinstance(normalization, NormalizationType):\n print(\"Unknown normalization specified, use \" + str(NormalizationType.PER... | [
[
"numpy.amax",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
int-brain-lab/ONE | [
"8766cd27308ddc2c247acb56685be3b2ce204390"
] | [
"one/tests/util.py"
] | [
"\"\"\"Utilities functions for setting up test fixtures.\"\"\"\nimport tempfile\nfrom pathlib import Path\nimport shutil\nimport json\nfrom uuid import uuid4\n\nimport pandas as pd\nimport numpy as np\nfrom iblutil.io.parquet import uuid2np, np2str\n\nimport one.params\n\n\ndef set_up_env(use_temp_cache=True) -> te... | [
[
"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": []
}
] |
mduranmustafa/keras | [
"c458024d5c379ef990f72b6f6b738301e1895cff",
"d4a14ee54728ac8ea6c5ffbf41f559662dcfba46"
] | [
"tests/keras/layers/recurrent_test.py",
"keras/backend/theano_backend.py"
] | [
"import pytest\nimport numpy as np\nfrom numpy.testing import assert_allclose\n\nimport keras\nfrom keras.utils.test_utils import layer_test\nfrom keras.utils.test_utils import keras_test\nfrom keras.layers import recurrent\nfrom keras.layers import embeddings\nfrom keras.models import Sequential\nfrom keras.models... | [
[
"numpy.ones",
"numpy.random.random",
"numpy.zeros",
"numpy.testing.assert_allclose"
],
[
"numpy.asarray",
"numpy.eye",
"numpy.int32",
"numpy.ones",
"numpy.random.normal",
"numpy.prod",
"numpy.float32",
"numpy.random.uniform",
"numpy.zeros",
"numpy.random... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pwalczysko/ilastik | [
"e4fa2c3c1ba1f83d3dcc392ccdd29e4391b8dbcf"
] | [
"scripts/pixel_classification_zarr.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n#\n# Copyright (c) 2020 University of Dundee.\n#\n# Redistribution and use in source and binary forms, with or without modification, \n# are permitted provided that the following conditions are met:\n# \n# Redistributions of source code must retain the above... | [
[
"numpy.asarray"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ryancoe/WDRT | [
"039d53b13b8d6ee98bbbab69d6433af4f709e6c0"
] | [
"examples/example_shortTermExtreme_2.py"
] | [
"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport WDRT.shortTermExtreme as ecm\nimport WDRT.fatigue as fatigue\n\nmethod = 1\n\t# 1 - All peaks Weibull\n\t# 2 - Weibull tail fit\n\t# 3 - Peaks over threshold\n\t# 4 - Block maxima GEV\n\t# 5 - Block maxima Gumbel\n\n# load global peaks\nt_peaks = np.loa... | [
[
"matplotlib.pyplot.legend",
"numpy.min",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"numpy.max",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.ticklabel_format",
"matplotlib.pyplot.subplot",
"numpy.mean",
"matplotlib.pyplot.grid",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MaayanLab/maayanlab-bioinformatics | [
"f84bda02a8841a65d4c72e491129cdc339fb73b3"
] | [
"maayanlab_bioinformatics/plotting/upset.py"
] | [
"import itertools\nimport pandas as pd\nfrom typing import Dict, Set, Hashable\n\ndef upset_from_dict_of_sets(inputs: Dict[Hashable, Set[Hashable]]):\n ''' Given a dictionary of sets, produce input ready for `upsetplot` python package\n\n We produce this input by computing set intersections of all relevant combin... | [
[
"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": []
}
] |
ludwigjer/visualsudoku | [
"a5ed257edfda45123ef3779b8181d5f27412ea50"
] | [
"src/OCR_CNN_Trainning.py"
] | [
"import numpy as np\nimport cv2\nimport os\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.utils.np_utils import to_categorical\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom kera... | [
[
"matplotlib.pyplot.legend",
"tensorflow.keras.layers.Activation",
"matplotlib.pyplot.title",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"sklearn.model_selection.train_test_split",
"tensorflow.keras.layers.MaxPooling2D",
"matplotlib.pyplot.plot",
"matplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
mihaid-b/CyberSakura | [
"f60e6b6bfd6898c69b84424b080090ae98f8076c"
] | [
"Gathered CTF writeups/ctf-7867/2020/pbctf/queensarah2/graphic.py"
] | [
"from string import ascii_lowercase\nfrom itertools import product\nimport gizeh\nimport numpy as np\nimport random\n\nrandom.seed(1234)\n\nalphabet = ascii_lowercase + \"_\"\nbigrams = [''.join(bigram) for bigram in product(alphabet, repeat=2)]\nrandom.shuffle(bigrams)\n\nscale = 2\nwidth = 512 * scale\nheight = 5... | [
[
"numpy.cos",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tarepan/mutated_DVC | [
"7fbbf4754285944387ec5d5108ed5f3d473d4f81"
] | [
"nets/block.py"
] | [
"import math\nimport chainer\nimport chainer.functions as F\nimport chainer.links as L\nimport numpy as np\nfrom .sn_convolution_2d import SNConvolution2D, SNDeconvolution2D\nfrom .sn_linear import SNLinear\n\ndef _upsample(x):\n h, w = x.shape[2:]\n return F.unpooling_2d(x, 2, outsize=(h * 2, w * 2))\n\ndef ... | [
[
"numpy.prod"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
autonomousvision/handheld_svbrdf_geometry | [
"41218b0546e7386229b87c94d528cd193127acff"
] | [
"code/main.py"
] | [
"\"\"\"\nCopyright (c) 2020 Autonomous Vision Group (AVG), Max Planck Institute for Intelligent Systems, Tuebingen, Germany\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 without restri... | [
[
"torch.device",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KMarkert/RHEAS | [
"453f24ef635ca5a6338d3e2b19f215835dd1f10d"
] | [
"src/datasets/decorators.py"
] | [
"\"\"\" Definition for RHEAS Datasets decorators.\n\n.. module:: datasets.decorators\n :synopsis: Definition of the Datasets decorators\n\n.. moduleauthor:: Kostas Andreadis <kandread@jpl.nasa.gov>\n\n\"\"\"\n\nfrom functools import wraps\nimport tempfile\nimport shutil\nimport urllib\nfrom datetime import dateti... | [
[
"numpy.argsort",
"numpy.arange",
"numpy.sort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vidurj/allennlp | [
"5b513d4f7c7365ac33b3cbc557506b46a9b50450"
] | [
"tests/training/trainer_test.py"
] | [
"# pylint: disable=invalid-name\nimport glob\nimport os\nimport re\nimport time\n\nimport torch\nimport pytest\n\nfrom allennlp.common.testing import AllenNlpTestCase\nfrom allennlp.training.trainer import Trainer, sparse_clip_norm, is_sparse\nfrom allennlp.data import Vocabulary\nfrom allennlp.common.params import... | [
[
"torch.cuda.device_count",
"torch.cuda.is_available",
"torch.rand",
"torch.nn.Embedding"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
speakupai/ml_deployment | [
"f80735049de8111b2415608046bb2b0af57fcdd3"
] | [
"inference.py"
] | [
"import os\n\nimport librosa\nimport numpy as np\nimport soundfile as sf\nimport torch\nfrom tqdm import tqdm\n\nfrom utils import data, spectrogram, spectrogram_clean\nfrom models.hifi_gan import Generator\nfrom models.wavenet import WaveNet\n\nfrom utils.hparams import hparams as hp\n\ndef inference(audio_clip):\... | [
[
"torch.device",
"numpy.array",
"torch.no_grad",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SvipRepetitionCounting/AlphaPose | [
"0cc38e4c1d6f08ea9c34c720ae188506d3de6eb6",
"0cc38e4c1d6f08ea9c34c720ae188506d3de6eb6"
] | [
"trackers/tracking/utils/io.py",
"detector/efficientdet/effdet/bench.py"
] | [
"import os\nfrom typing import Dict\nimport numpy as np\n\nfrom utils.log import logger\n\n\ndef write_results(filename, results_dict: Dict, data_type: str):\n if not filename:\n return\n path = os.path.dirname(filename)\n if not os.path.exists(path):\n os.makedirs(path)\n\n if data_type i... | [
[
"numpy.asarray"
],
[
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rturrisige/POT | [
"c5039bcafde999114283f7e59fb03e176027d740"
] | [
"test/test_bregman.py"
] | [
"\"\"\"Tests for module bregman on OT with bregman projections \"\"\"\n\n# Author: Remi Flamary <remi.flamary@unice.fr>\n# Kilian Fatras <kilian.fatras@irisa.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport ot\nimport pytest\n\n\ndef test_sinkhorn():\n # test sinkhorn\n n = 100\n rng = ... | [
[
"numpy.arange",
"numpy.median",
"numpy.vstack",
"numpy.random.randn",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
linus87/drl_shape_optimization | [
"39e6b66bd5b70dfce07e145aafe815071bc1b6fe",
"39e6b66bd5b70dfce07e145aafe815071bc1b6fe",
"39e6b66bd5b70dfce07e145aafe815071bc1b6fe"
] | [
"src/tensorforce/tensorforce/core/optimizers/optimizer.py",
"src/tensorforce/tensorforce/core/models/constant_model.py",
"src/tensorforce/tensorforce/core/optimizers/evolutionary.py"
] | [
"# Copyright 2018 Tensorforce 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 required b... | [
[
"tensorflow.assign_add",
"tensorflow.control_dependencies"
],
[
"tensorflow.constant",
"tensorflow.concat",
"tensorflow.zeros"
],
[
"tensorflow.zeros_like",
"tensorflow.sign",
"tensorflow.control_dependencies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
SaronZhou/python | [
"40d73b49b9b17542c73a3c09d28e479d2fefcde3",
"40d73b49b9b17542c73a3c09d28e479d2fefcde3"
] | [
"sjfxjc/foundations-for-analytics-with-python-master/csv/pandas_value_meets_condition.py",
"sjfxjc/foundations-for-analytics-with-python-master/csv/pandas_value_matches_pattern.py"
] | [
"#!/usr/bin/env python3\nimport pandas as pd\nimport sys\n\ninput_file = sys.argv[1]\noutput_file = sys.argv[2]\n\ndata_frame = pd.read_csv(input_file)\n\ndata_frame['Cost'] = data_frame['Cost'].str.strip('$').astype(float)\ndata_frame_value_meets_condition = data_frame.loc[(data_frame['Supplier Name']\\\n.str.cont... | [
[
"pandas.read_csv"
],
[
"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... |
yohasebe/spaCy | [
"3dcb747980303457c662d668d5a0735c9efc9b72"
] | [
"spacy/tests/pipeline/test_spancat.py"
] | [
"from numpy.testing import assert_equal\nfrom spacy.language import Language\nfrom spacy.training import Example\nfrom spacy.util import fix_random_seed, registry\n\n\nSPAN_KEY = \"labeled_spans\"\n\nTRAIN_DATA = [\n (\"Who is Shaka Khan?\", {\"spans\": {SPAN_KEY: [(7, 17, \"PERSON\")]}}),\n (\n \"I li... | [
[
"numpy.testing.assert_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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