repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
MJ10/BioSeq-GFN-AL | [
"d389aeb729ac29578ad825da5b828ff968a1d555"
] | [
"lib/dataset/base.py"
] | [
"import numpy as np\n\nclass Dataset:\n def __init__(self, args, oracle):\n self.oracle = oracle\n self.args = args\n self.rng = np.random.RandomState(142857)\n\n def sample(self, num_samples, ratio=0.5):\n raise NotImplementedError()\n \n def validation_set(self, ratio=None)... | [
[
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
schroedk/sensAI | [
"a2d6d7c6ab7bed9ccd5eac216dd988c49d69aec7",
"a2d6d7c6ab7bed9ccd5eac216dd988c49d69aec7"
] | [
"src/sensai/util/pandas.py",
"src/sensai/evaluation/eval_util.py"
] | [
"import logging\nfrom copy import copy\n\nimport numpy as np\nimport pandas as pd\n\nlog = logging.getLogger(__name__)\n\n\nclass DataFrameColumnChangeTracker:\n \"\"\"\n A simple class for keeping track of changes in columns between an initial data frame and some other data frame\n (usually the result of ... | [
[
"numpy.array"
],
[
"pandas.DataFrame.from_dict",
"pandas.concat",
"pandas.DataFrame",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
VITA-Group/Sandwich-Batch-Normalization | [
"25e7df6e64a67cebd7e70b911f874cfc1bd19df0"
] | [
"Adv/functions.py"
] | [
"# -*- coding: utf-8 -*-\n# @Date : 2/16/21\n# @Author : Xinyu Gong (xinyu.gong@utexas.edu)\n# @Link : None\n# @Version : 0.0\n\nimport torch\n\nfrom attack_algo import PGD\n\n\ndef train(args, train_loader, model, criterion, optimizer, epoch):\n losses = AverageMeter()\n top1 = AverageMeter()\n\n #... | [
[
"torch.no_grad",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JasonD1997/Machine-Learning-Programs | [
"81e54ccaf2ca4bd7e2d79ed0e538f13cb355c0bf",
"81e54ccaf2ca4bd7e2d79ed0e538f13cb355c0bf"
] | [
"p8.py",
"p9.py"
] | [
"from sklearn.cluster import KMeans\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\ndata=pd.read_csv(\"data8.csv\")\ndf1=pd.DataFrame(data)\nprint(df1)\nf1 = df1['Distance_Feature'].values\nf2 = df1['Speeding_Feature'].values\nX=np.matrix(list(zip(f1,f2)))\nplt.plot()\nplt.xlim([0, 100])\... | [
[
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"sklearn.cluster.KMeans",
"matplotlib.pyplot.ylim",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mvtea/sgl | [
"b6307e6fe9db536b392f181ca113128c7f0f1821"
] | [
"not_my_code/bolton/lensdemo_funcs.py"
] | [
"#\n# lensdemo_funcs.py\n#\n# Function module for strong lensing demos\n#\n# Intended for use with lensdemo_script.py\n#\n# Copyright 2009 by Adam S. Bolton\n# Creative Commons Attribution-Noncommercial-ShareAlike 3.0 license applies:\n# http://creativecommons.org/licenses/by-nc-sa/3.0/\n# All redistributions, modi... | [
[
"numpy.arctanh",
"numpy.abs",
"numpy.sqrt",
"numpy.arctan",
"numpy.cos",
"numpy.sin",
"numpy.deg2rad",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Royzon/dl-utils | [
"be95707da9cb812a5f25a11f1469055e0b7abcd2"
] | [
"dlutils/losses/focal.py"
] | [
"from functools import reduce\n\nimport torch\nfrom rising.transforms.functional.channel import one_hot_batch\nfrom torch.nn import functional as F\n\nfrom dlutils.utils.tensor_ops import reduce\n\n__all__ = [\n 'focal_loss',\n 'focal_loss_with_logits',\n 'binary_focal_loss',\n 'binary_focal_loss_logits... | [
[
"torch.sigmoid",
"torch.nn.functional.softmax",
"torch.ones",
"torch.nn.functional.nll_loss",
"torch.eq",
"torch.nn.functional.binary_cross_entropy_with_logits",
"torch.nn.functional.cross_entropy",
"torch.nn.functional.binary_cross_entropy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
raybellwaves/climpred | [
"4ce5e3d30dbaa98fb974b54d82a5403c424a79db"
] | [
"climpred/tests/test_uninitialized.py"
] | [
"import numpy as np\nimport pytest\n\nfrom climpred.comparisons import HINDCAST_COMPARISONS\nfrom climpred.constants import VALID_ALIGNMENTS\nfrom climpred.metrics import DETERMINISTIC_HINDCAST_METRICS\nfrom climpred.reference import compute_uninitialized\n\n# uacc breaks\nDETERMINISTIC_HINDCAST_METRICS = DETERMINI... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
reginaeckert/isofit | [
"30d9ada3915b779e2d92e7d9fd5abc49bb6f512e"
] | [
"isofit/radiative_transfer/look_up_tables.py"
] | [
"#! /usr/bin/env python3\n#\n# Copyright 2018 California Institute of Technology\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/LICENS... | [
[
"numpy.array",
"numpy.random.random",
"numpy.logical_and"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anaxsouza/FBPINNs | [
"fc50f9e69613318353ccff339d25c723cd8dd55c"
] | [
"fbpinns/main.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 15 21:53:56 2021\n\n@author: bmoseley\n\"\"\"\n\n# This module defines trainer classes for FBPINNs and PINNs. It is the main entry point for training FBPINNs and PINNs\n# To train a FBPINN / PINN, use a Constants object to setup the proble... | [
[
"torch.cat",
"numpy.save",
"numpy.prod",
"torch.stack",
"numpy.array",
"torch.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Bhaskers-Blu-Org1/Semantic-Search-for-Sustainable-Development | [
"08b79de5df1f7427e63c8d9806e0bd509ca58fb7"
] | [
"src/undp_experiments_2.py"
] | [
"\"\"\"\r\n Copyright 2018 IBM Corporation\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 obtain a copy of the License at\r\n\r\n http://www.apache.org/licenses/LICENSE-2.0\r\n\r\n Unless requ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"numpy.arange",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CookieNotSession/facial_POC | [
"4c89aeadf76051948406e7a941a8d64ac68d20ec"
] | [
"src/flask_Server.py"
] | [
"import cv2\nfrom flask import Flask, render_template, Response, jsonify, request, redirect, url_for\nfrom web_camera import VideoCamera\nfrom statistics import mode\nfrom keras.models import load_model\nimport numpy as np\nimport tensorflow as tf\nfrom utils.datasets import get_labels\nfrom utils.inference import ... | [
[
"numpy.expand_dims",
"numpy.asarray",
"numpy.max",
"numpy.argmax",
"tensorflow.get_default_graph"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
naojibrainmachine/chinese-tokenization-bert | [
"7ffad6ee89a5e9b570d1fb4ff9f1ef5e2d8ed132"
] | [
"test_bert_tokenize.py"
] | [
"import tensorflow as tf\nimport numpy as np\nimport math\n#from BERT import return_accuracy\nfrom LOADDATA import get_corpus_indices,data_format,get_data,build_vocab,ner_load_data,bild_vocab_token,build_vocab_label\nfrom LOAD_SAVE_PARAMS.LOAD_SAVE_PARAMS import save_weight,load_weight\nfrom Tokenizer import tokeni... | [
[
"numpy.dot",
"tensorflow.nn.softmax",
"tensorflow.concat",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.reshape",
"tensorflow.math.reduce_mean",
"numpy.ones",
"numpy.argmax",
"tensorflow.one_hot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
lcarnevale/gympy | [
"157156f64b6c2567077d9c00ae1645360d0a7514"
] | [
"gympy/envs/blife.py"
] | [
"import gym\nimport time\nimport random\nimport numpy as np\nfrom gym import error, spaces, utils\nfrom gym.utils import seeding\nfrom .sensors import Sensors\nfrom .scenarios import WhiteNoiseScenario\n\nclass BatteryLifetimeEnv(gym.Env):\n \"\"\"\n Description:\n The blife environment simulates the b... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PrabhanshuAttri/BentoML | [
"7253155677a4d683387b9d117881a7fcba9c98a3"
] | [
"tests/integration/test_tensorflow_v2_2_savedmodel_artifact.py"
] | [
"import json\nimport pytest\n\nimport tensorflow as tf\n\nimport bentoml\nfrom tests.bento_service_examples.tensorflow_classifier import Tensorflow2Classifier\nfrom tests.integration.api_server.conftest import (\n run_api_server_docker_container,\n build_api_server_docker_image,\n)\n\ntest_data = [[1, 2, 3, 4... | [
[
"tensorflow.constant",
"tensorflow.keras.initializers.Ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
EduardoMoraesRitter/Alura | [
"c0f5e7e9807e8e1d1dc46e6b847df8a8085783a6",
"c0f5e7e9807e8e1d1dc46e6b847df8a8085783a6"
] | [
"Alura/MLClassificacao2/A4V2_Classificando_email.py",
"Alura/MLClassificacao2/A4V5_Classificando_email.py"
] | [
"#!-*- coding: utf8 -*-\n\nimport pandas as pd\n\nclassificacoes = pd.read_csv('email.csv')\ntextosPuros = classificacoes['email']\ntextosQuebrados = textosPuros.str.lower().str.split(' ')\ndicionario = set()\nfor lista in textosQuebrados:\n dicionario.update(lista)\n#dicionario sem indice - print(dicionario)\n#... | [
[
"pandas.read_csv"
],
[
"pandas.read_csv",
"sklearn.model_selection.cross_val_score",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.ensemble.AdaBoostClassifier",
"numpy.mean",
"sklearn.svm.LinearSVC",
"numpy.array"
]
] | [
{
"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... |
temach/timesketch | [
"3420931f55acab27ca1de6ebe0fd4da1ff249665"
] | [
"timesketch/lib/analyzers/utils.py"
] | [
"# Copyright 2019 Google Inc. 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 by appl... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mindspore-ai/docs | [
"e7cbd69fe2bbd7870aa4591510ed3342ec6a3d41"
] | [
"docs/sample_code/maskrcnn_fine_tune/src/maskrcnn/resnet50.py"
] | [
"# Copyright 2020 Huawei Technologies Co., Ltd\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 obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
StephenGrey/my_api | [
"e43ee8e897a4fa49399757feb785887b8b1c44a6",
"e43ee8e897a4fa49399757feb785887b8b1c44a6"
] | [
"graph/scotland.py",
"graph/phe_fetch.py"
] | [
"import requests,json,csv,pandas,os,logging\nfrom datetime import datetime\nfrom bs4 import BeautifulSoup as BS\nfrom .ons_week import week as ons_week,sunday,stored_names,nation,scotcode\nfrom datetime import timedelta\nfrom .models import CovidWeek, DailyCases, AverageWeek\nfrom .import_csv import URLImporter,Pan... | [
[
"pandas.read_excel",
"pandas.to_datetime",
"pandas.read_csv"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1... |
csavur/biosignalsnotebooks | [
"c99596741a854c58bdefb429906023ac48ddc3b7",
"c99596741a854c58bdefb429906023ac48ddc3b7"
] | [
"biosignalsnotebooks/biosignalsnotebooks/visualise.py",
"biosignalsnotebooks/build/lib/biosignalsnotebooks/detect.py"
] | [
"\n\"\"\"\nList of functions intended to visualise the loaded data (electrophysiological signals).\n\nThis functions are mainly supported by Bokeh package.\n\nAvailable Functions\n-------------------\n[Public]\n\nplot_future !!!!!!!!!!! Untested version for future application !!!!!!!!!!!!!!!!!\n Plotting functi... | [
[
"numpy.max",
"numpy.shape",
"numpy.min"
],
[
"numpy.absolute",
"scipy.signal.filtfilt",
"numpy.power",
"numpy.arange",
"numpy.max",
"numpy.std",
"numpy.append",
"numpy.diff",
"numpy.zeros_like",
"scipy.signal.butter",
"scipy.stats.linregress",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
... |
jlevine18/mirgecom | [
"5089accba9f7954ca426ee5b3bd97c511e4f6861"
] | [
"examples/wave-mpi.py"
] | [
"\"\"\"Demonstrate wave MPI example.\"\"\"\n\n__copyright__ = \"Copyright (C) 2020 University of Illinois Board of Trustees\"\n\n__license__ = \"\"\"\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the... | [
[
"numpy.dot",
"numpy.array",
"numpy.cos"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ClaudeCoulombe/DeepRLBootcamp2017 | [
"83568aae6e4531fb426a981fcea39c0bea60835b"
] | [
"lab3/simpledqn/gridworld_env.py"
] | [
"\"\"\"\nThis project was developed by Rein Houthooft, Rocky Duan, Peter Chen, Pieter Abbeel for the Berkeley Deep RL Bootcamp, August 2017. Bootcamp website with slides and lecture videos: https://sites.google.com/view/deep-rl-bootcamp/.\n\nCode adapted from OpenAI Baselines: https://github.com/openai/baselines\n\... | [
[
"numpy.asarray",
"numpy.array",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pyansys/pydpf-post | [
"8fea9103259786067d3451dc12e7c0ae5a38ea33"
] | [
"tests/test_electricanalysis.py"
] | [
"import numpy as np\nimport pytest\nfrom ansys.dpf import post\nfrom ansys.dpf.post import errors as dpf_errors\nfrom ansys.dpf.post.common import _PhysicsType\nfrom ansys.dpf.post.electric_results import ElectricField, ElectricPotential\n\n\ndef test_electricfield(rth_electric):\n solution = post.load_solution(... | [
[
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CDMCH/ddpg-curiosity-and-multi-criteria-her | [
"49c3aabe73e19aeec06cde0c3e0b6ab239d04467"
] | [
"ddpg_curiosity_mc_her/ddpg/main.py"
] | [
"import argparse\nimport time\nimport os\nfrom ddpg_curiosity_mc_her import logger\nfrom ddpg_curiosity_mc_her.common.misc_util import (\n set_global_seeds,\n)\nimport ddpg_curiosity_mc_her.ddpg.training as training\nimport ddpg_curiosity_mc_her.ddpg.demo as demo\nfrom ddpg_curiosity_mc_her.ddpg.config import DE... | [
[
"tensorflow.train.Saver",
"tensorflow.reset_default_graph"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
PetritIgrishtaj/new_NIH | [
"f1674e806df85d8109220d9b3be73cadb9289499"
] | [
"modules/collate.py"
] | [
"\nimport collections\nimport re\n\nimport numpy as np\nimport torch\n\nnp_str_obj_array_pattern = re.compile(r'[SaUO]')\n\ndef cf(batch):\n r\"\"\"Puts each data field into a tensor with outer dimension batch size\"\"\"\n\n elem = batch[0]\n elem_type = type(elem)\n if isinstance(elem, torch.Tensor):\n... | [
[
"torch.stack",
"torch.utils.data.get_worker_info",
"numpy.stack",
"torch.as_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
duccluong/proReadBook | [
"65ba6ad25902d4920ad96c00a843cbea7d831c52"
] | [
"src/DataLoader.py"
] | [
"from __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport random\nimport numpy as np\nimport cv2\nfrom SamplePreprocessor import preprocess\n\n\nclass Sample:\n\t\"sample from the dataset\"\n\tdef __init__(self, gtText, filePath):\n\t\tself.gtText = gtText\n\t\tself.filePath = fil... | [
[
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anlthms/neon | [
"cba318c9f0a2acf2ab8a3d7725b588b2a8b17cb9"
] | [
"neon/data/image.py"
] | [
"# ----------------------------------------------------------------------------\n# Copyright 2014-2016 Nervana Systems Inc.\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# h... | [
[
"numpy.diag",
"numpy.dot",
"numpy.isnan",
"numpy.eye",
"numpy.cov",
"numpy.mean",
"numpy.array",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shawn233/BLASPoisoning | [
"3b54a1f2b83fe2f6a89f8065d519fd8312a3f7b4"
] | [
"dev/model/imgutils.py"
] | [
"'''\r\nAuthor: shawn233\r\nDate: 2021-01-15 17:54:21\r\nLastEditors: shawn233\r\nLastEditTime: 2021-01-17 18:37:08\r\nDescription: Image reading tools\r\n'''\r\nfrom PIL import Image\r\nimport numpy as np\r\nimport os\r\nimport logging\r\n# import cv2\r\n# import matplotlib.pyplot as plt\r\n# from skimage import i... | [
[
"numpy.asarray",
"numpy.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
e-ucm/countrix-analysis | [
"9da9ef419f996e9ebd3709ecc7ad260cc1f72493"
] | [
"analysis_countrix.py"
] | [
"\"\"\"\r\nCreated on Fri May 06 11:18:38 2016\r\n\r\n@author: crisal24\r\n\r\nDeveloped as part of e-ucm development group.\r\n\r\n\"\"\"\r\n\r\nfrom __future__ import division\r\nimport numpy as np\r\nimport json\r\nimport csv\r\nimport os\r\nimport unicodedata\r\nimport time\r\nimport datetime\r\n\r\n\r\nclass A... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BinWang28/AnomalyHop | [
"8e6b1c597e66d250cd6c154f3ea02a6a060a03a4"
] | [
"src/display.py"
] | [
"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n'''\n@File : display.py\n@Time : 2021/04/21 19:49:59\n@Author : bin.wang\n@Version : 1.0\n'''\n\n# here put the import lib\nimport os\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib\n\nfrom skimage import morphology\nfrom sk... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.colors.Normalize",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.close",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
roks/deepsnap | [
"d5b349b9ee05c0e3d7215a226a436f325093edb1"
] | [
"deepsnap/batch.py"
] | [
"import torch\nfrom deepsnap.graph import Graph\nfrom typing import (\n Callable,\n Dict,\n List,\n)\n\n\nclass Batch(Graph):\n r\"\"\"\n A plain old python object modeling a batch of :class:`deepsnap.graph.Graph` objects\n as one big (disconnected) graph, with :class:`torch_geometric.data.Data` b... | [
[
"torch.stack",
"torch.is_tensor",
"torch.full",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SebaDro/ST-DeepHydro | [
"98cf8c1fabd9098c34f486655cc608383db36eaa"
] | [
"libs/plotutils.py"
] | [
"import matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nimport tensorflow as tf\nimport xarray as xr\n\n\ndef plot_loss(history: tf.keras.callbacks.History):\n \"\"\"\n Visualizes the progress of a trained model by plotting the loss per epoch\n\n Parameters\n ----------\n history: tf.ker... | [
[
"matplotlib.pyplot.legend",
"matplotlib.dates.DateFormatter",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ternaus/retinafacemask | [
"94e00317e9f7ecbb7b5134daaf10093f25694855"
] | [
"retinafacemask/box_utils.py"
] | [
"from typing import List\n\nimport numpy as np\nimport torch\n\n\ndef point_form(boxes: torch.Tensor) -> torch.Tensor:\n \"\"\"Convert prior_boxes to (x_min, y_min, x_max, y_max) representation for comparison to point form ground truth data.\n\n Args:\n boxes: center-size default boxes from priorbox la... | [
[
"numpy.maximum",
"numpy.minimum",
"torch.cat",
"torch.exp",
"torch.log",
"numpy.prod",
"torch.clamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
j-friedrich/neuronalGPR | [
"0a05f0b0a30ac6ecbd5b3ff175d3cbfeb723479b"
] | [
"table2.py"
] | [
"import h5py\nimport GPy\nimport numpy as np\nimport os\nfrom GPnet import NN, KL\n\n\ndef KLs(data_directory):\n _DATA_DIRECTORY_PATH = \"DropoutUncertaintyExps-master/UCI_Datasets/\" + \\\n data_directory + \"/data/\"\n\n data = np.loadtxt(_DATA_DIRECTORY_PATH + \"data.txt\")\n index_features = np... | [
[
"numpy.sqrt",
"numpy.std",
"numpy.mean",
"numpy.load",
"numpy.zeros",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xapple/cbm_runner | [
"ec532819e0a086077475bfd479836a378f187f6f"
] | [
"cbmcfs3_runner/pump/pre_flight.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nWritten by Lucas Sinclair and Paul Rougieux.\n\nJRC biomass Project.\nUnit D1 Bioeconomy.\n\"\"\"\n\n# Built-in modules #\n\n# Third party modules #\nimport pandas\n\n# First party modules #\n\n# Internal modules #\n\n######################################... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
TheGentlemanOctopus/oracle | [
"2857b9c1886548d9aefcb480ce6e77169ee9e7ef"
] | [
"core/layouts/strip.py"
] | [
"from core.devices.pixel import Pixel\nfrom layout import Layout\n\nimport numpy as np\nfrom numpy.linalg import norm\n\nclass Strip(Layout):\n \"\"\"\n Represents a strip of Leds\n start/direction are two element arrays (x,y)\n spacing/num_pixels are scalars\n \"\"\"\n def __init__(se... | [
[
"numpy.array",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
PABTester/quantitativeProcedures | [
"c8ee3eb0dc7e2b792de36283351facf79b505490"
] | [
"job.py"
] | [
"#-*- coding:utf-8 -*-\nimport jqdatasdk as jq\nfrom datetime import datetime, timedelta\nimport time\nimport numpy as np\nimport math\nfrom sendmail import mail\n# 本地化的 api申请权限地址：https://www.joinquant.com/default/index/sdk\n# 聚宽平台运行的代码:https://github.com/Jack-Cherish/quantitative/blob/main/lesson1/quantitive-etf-j... | [
[
"numpy.polyfit",
"numpy.log",
"numpy.arange",
"numpy.std",
"numpy.mean",
"numpy.var"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
microsoft/flin-nl2web | [
"ec8ec0c477df83ae95dde21b0922b91d049ed902"
] | [
"code/train_data_preprocessing/preprocess_util.py"
] | [
"import nltk\nimport random\nimport spacy\nimport string\n\nimport numpy as np\nfrom nltk import ngrams\nfrom nltk.corpus import stopwords\nfrom nltk.stem import PorterStemmer\nfrom nltk.stem import WordNetLemmatizer\nfrom spacy.tokens import Doc\n\nnp.random.seed(1234)\nrandom.seed(1234)\n\nstopWords = set(stopwor... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Re3write/siamdw | [
"f5d7d4bda36cb8c14e93b460fbc77bb225aa8572"
] | [
"lib/models/siamfc.py"
] | [
"# ------------------------------------------------------------------------------\n# Copyright (c) Microsoft\n# Licensed under the MIT License.\n# Written by Houwen Peng and Zhipeng Zhang\n# Email: houwen.peng@microsoft.com\n# Main Results: see readme.md\n# ----------------------------------------------------------... | [
[
"torch.nn.BCEWithLogitsLoss",
"torch.index_select"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
giovana-morais/carat | [
"982af4dde63e8560ccc9120f3767f1420a545b67"
] | [
"docs/source/examples/plot_feature_map_clusters.py"
] | [
"# -*- coding: utf-8 -*-\n# pylint: disable=C0103\n\"\"\"\n=========================\nPlot feature map clusters\n=========================\n\nThis example shows how to cluster rhythmic patterns from a feature map.\n\nThis is based on the rhythmic patterns analysis proposed in [CIM2014]_.\n\"\"\"\n\n# Code source: M... | [
[
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jmhessel/lxmert | [
"4ee7b1a1ec652eb163572e3ae8189ea8b93c957e"
] | [
"src/param.py"
] | [
"# coding=utf-8\n# Copyleft 2019 project LXRT.\n\nimport argparse\nimport random\n\nimport numpy as np\nimport torch\n\n\ndef get_optimizer(optim):\n # Bind the optimizer\n if optim == 'rms':\n print(\"Optimizer: Using RMSProp\")\n optimizer = torch.optim.RMSprop\n elif optim == 'adam':\n ... | [
[
"torch.manual_seed",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
magicknight/event-model | [
"9f65b9d3c452a235781604827b8b32e6db355041"
] | [
"event_model/tests/test_em.py"
] | [
"import copy\nimport json\nimport event_model\nimport numpy\nimport pytest\n\n\ndef test_documents():\n dn = event_model.DocumentNames\n for k in ('stop', 'start', 'descriptor',\n 'event', 'bulk_events', 'datum',\n 'resource', 'bulk_datum', 'event_page', 'datum_page'):\n asser... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
akirasosa/pre-training-mol | [
"2fd65a959eee50e2eea260719633042ae37bb92c"
] | [
"src/run_train.py"
] | [
"from collections import OrderedDict\nfrom typing import Dict, Optional\n\nimport torch\nfrom omegaconf import DictConfig\n\nfrom mol.dimenet.dimenet import DimeNet\nfrom mol.dimenet.loader import AtomsBatch\nfrom mol.logging import configure_logging\nfrom mol.loss import mae_loss\nfrom mol.params import Params\nfr... | [
[
"torch.log",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KenMighell/asdf | [
"aae8d9aeb5ff0bfe7248bfa595f256d4756ade18"
] | [
"asdf/tests/test_reference.py"
] | [
"import io\nimport os\n\nimport numpy as np\nimport pytest\nfrom numpy.testing import assert_array_equal\n\nimport asdf\nfrom asdf import reference, util\nfrom asdf.tags.core import ndarray\n\nfrom .helpers import assert_tree_match\n\n\ndef test_external_reference(tmpdir):\n exttree = {\n \"cool_stuff\": ... | [
[
"numpy.testing.assert_array_equal",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SpyRefused/componist | [
"002cb8d8fef3d5406d1cb3be4a23fe23ec32b2f5"
] | [
"src/tutorial/save_restore_models.py"
] | [
"# 1) Save and restore models\n# Model progress can be saved during—and after—training. This means a model can resume where it left\n# off and avoid long training times. Saving also means you can share your model and others can recreate your work.\n# When publishing research models and techniques, most machine lear... | [
[
"tensorflow.keras.callbacks.ModelCheckpoint",
"tensorflow.keras.models.load_model",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.datasets.mnist.load_data",
"tensorflow.keras.optimizers.Adam",
"tensorflow.keras.layers.Dropout"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
nagrao/CascadeTabNet | [
"2ceb6371d4bef23d32a3be5f61cae0bea86fc469"
] | [
"Table Structure Recognition/Functions/line_detection.py"
] | [
"import cv2\r\nimport numpy as np\r\n\r\n# Input : Image\r\n# Output : hor,ver \r\ndef line_detection(image):\r\n\r\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 15, 1)\r\n bw = cv2.bitwise_not(bw)\r\n ## To visu... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ngocjr7/geneticpython | [
"4b4157523ce13b3da56cef61282cb0a984cd317b"
] | [
"geneticpython/tools/math.py"
] | [
"from geneticpython.utils.typing import SimpleSolution\nfrom geneticpython.utils.validation import check_simple_solution\n\nfrom copy import deepcopy\nimport math\nimport numpy as np\n\ndef is_dominated(a: SimpleSolution, b: SimpleSolution) -> bool:\n \"\"\"\n Return True if a dominate b, otherwise return... | [
[
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
ikekilinc/dnnSuperBinoculars | [
"b0fc584b1d449961bdbab37cf9d72c0b466f197f"
] | [
"serverPythonClient/SRNTT/SRNTT/model.py"
] | [
"import tensorflow as tf\nfrom .tensorlayer import *\nfrom .tensorlayer.layers import *\nfrom os.path import join, exists, split, isfile\nfrom os import makedirs, environ\nfrom shutil import rmtree\nfrom .vgg19 import *\nfrom .swap import *\nfrom glob import glob\nfrom scipy.misc import imread, imresize, imsave, im... | [
[
"tensorflow.get_variable",
"tensorflow.train.AdamOptimizer",
"tensorflow.Variable",
"tensorflow.gradients",
"tensorflow.ConfigProto",
"tensorflow.Session",
"tensorflow.square",
"tensorflow.trainable_variables",
"tensorflow.random_normal_initializer",
"tensorflow.matmul",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",
"1.2"
],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7... |
RodolpheMth/oculus | [
"4057a2f17837876b927d9b38e5480b11a68a4922"
] | [
"app/utils/data_to_viz.py"
] | [
"import csv\nimport pandas as pd\n\ntest = data = pd.read_csv(\"C:/Users/rodol/Documents/2020-2021/Projet 4/oculus_monitoring/data/tweets/TechCrunch_content.csv\", sep=';')\n\n# for raw in test['Company Name']:\n# print(raw.count(\"Lime\"))\n\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jaumecolom/hackeseiaat | [
"fe6777fc95d26ed88949c4b9f2992414a7e54335"
] | [
"POL_code/detectDoorsv1.py"
] | [
"#REFERENCE: http://www.pyimagesearch.com/2015/05/04/target-acquired-finding-targets-in-drone-and-quadcopter-video-streams-using-python-and-opencv/\r\n\r\n# What does this code?\r\n#Finally, the code detects all the structure with green margins or similars,\r\n#with a polygonal shape and a minimum dimensions. Once ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YufengJin/deep-reinforcement-learning | [
"141cf00f169b46aa492c9e7520429bfdaab0117d"
] | [
"ddpg-pendulum/ddpg.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"DDPG.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/github/YufengJin/deep-reinforcement-learning/blob/master/ddpg-pendulum/DDPG.ipynb\n\n# Deep Deterministic Policy Gradients (DDPG)\n---\nIn this notebook, we tr... | [
[
"torch.load",
"matplotlib.pyplot.figure",
"numpy.mean",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ydecastro/lar_testing | [
"26c20a57fe85c889bf0670f8b25b350e21656872"
] | [
"lars/Knockoffs.py"
] | [
"import numpy as np\nfrom cvxopt import solvers\nfrom cvxopt.solvers import conelp\nfrom sklearn import linear_model\nfrom cvxopt import matrix\nimport scipy.cluster.hierarchy\nfrom statsmodels.stats.moment_helpers import cov2corr\n\nclass Knockoffs():\n\n\tdef __init__(self):\n\t\tpass\n\n\tdef fdr_power_knockoffs... | [
[
"numpy.diag",
"numpy.linalg.eigvals",
"numpy.sqrt",
"numpy.where",
"numpy.hstack",
"numpy.linalg.svd",
"numpy.linalg.eig",
"numpy.eye",
"sklearn.linear_model.lars_path",
"numpy.real",
"numpy.zeros",
"numpy.linalg.inv",
"numpy.isnan",
"numpy.array",
"nump... | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
liuhanyao98/nums-1 | [
"4c27262e424ff7911cb9f4500d1df3019945d7fe"
] | [
"nums/core/array/application.py"
] | [
"# coding=utf-8\n# Copyright (C) 2020 NumS Development Team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requir... | [
[
"numpy.__getattribute__",
"numpy.product",
"numpy.ones_like",
"numpy.eye",
"numpy.dtype",
"numpy.finfo",
"numpy.ceil",
"numpy.argmax",
"numpy.zeros_like",
"numpy.iinfo",
"numpy.floor",
"numpy.diff",
"numpy.exp",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LiWen525/ultrasound-nerve-segmentation | [
"918a630108cca29d17aa5126176040dd2915827a"
] | [
"train.py"
] | [
"from __future__ import print_function\n\nimport os\nfrom skimage.transform import resize\nfrom skimage.io import imsave\nimport numpy as np\nfrom keras.models import Model\nfrom keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose\nfrom keras.optimizers import Adam\nfrom keras.callbacks im... | [
[
"numpy.std",
"numpy.mean",
"numpy.ndarray",
"numpy.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
granrothge/multiphonon | [
"486a998eeb6b73b964a58ba0f98fe3ece15bdf6e"
] | [
"tests/data/work-V/round-5/plot_sqe.py"
] | [
"#!/usr/bin/env python\nimport os\ncurdir = os.path.dirname(__file__)\nimport matplotlib as mpl\nmpl.rcParams['figure.figsize'] = 12,9\nfrom multiphonon.backward.plotutils import plot_intermediate_result_sqe as plot\nplot(curdir)\nfrom matplotlib import pyplot as plt\nplt.show()\n"
] | [
[
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
3secondz-lab/Vanshe | [
"535616f70635115521c7888369b4f304512dd9a7"
] | [
"imu_publisher/utils/imu_time_check.py"
] | [
"#!/usr/bin/env python3\n\nimport rospy\nimport rosbag\nfrom sensor_msgs.msg import *\nfrom matplotlib import pyplot as plt\n\nimport argparse\n\nparser = argparse.ArgumentParser(description='Rosbag IMU time checker')\nparser.add_argument('--input', type=str, required=False,\n # default='/home/rn... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Omerdan03/image-caption-dnz | [
"5e3c0ac4ab01cd02d50406d45bf06b0345614779"
] | [
"main.py"
] | [
"import numpy as np\nimport cv2\nimport os\nfrom flask import Flask, request, render_template, send_file\n\nTO_SCALE = 600\n\napp = Flask(__name__)\n\nUPLOAD_FOLDER = os.path.basename('uploads')\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n\n# get the categories names and colors\nf = open(\"coco.names\", \"r\")\nc... | [
[
"numpy.argmax",
"numpy.vstack",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Lukas-Justen/Airbnb-Price-Evaluator | [
"8759e32e94510520984223d18f0f9b09396aa448"
] | [
"scripts/text_analysis.py"
] | [
"import pandas as pd\nimport re\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.wrappers.scikit_learn import KerasRegressor\nfrom sklearn.preprocessing import StandardScaler\nfrom nltk.corpus import stopwords\nimport nltk\nfrom sklearn.feature_extraction.text import TfidfVectorizer\... | [
[
"pandas.read_csv",
"sklearn.feature_extraction.text.TfidfVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
rmkm/handwritten-japanese-ocr | [
"82945a0fa0eb0b9b8f55a98a8892f059373a8970"
] | [
"handwritten-japanese-OCR-touch-panel-demo.py"
] | [
"\"\"\"\nHandwritten Japanese OCR demo program\n Based on a sample program from OpenVINO 2020.2 (handwritten-japanese-recognition-demo.py)\n\"\"\"\n\n\"\"\"\n Copyright (c) 2020 Intel Corporation\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance wit... | [
[
"numpy.reshape",
"numpy.linalg.norm",
"numpy.full",
"numpy.array",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
laure-delisle/cs148-hw2 | [
"dc7c4ba22876370c1b8288c45b9a3be8dbb0bb17"
] | [
"generate_split.py"
] | [
"import numpy as np\nimport os\n\nnp.random.seed(2020) # to ensure you always get the same train/test split\n\ndata_path = '../data/RedLights2011_Medium'\ngts_path = '../data/hw02_annotations'\nsplit_path = '../data/hw02_splits'\nos.makedirs(split_path, exist_ok=True) # create directory if needed\n\nsplit_test = Fa... | [
[
"numpy.random.seed",
"numpy.rint",
"numpy.intersect1d",
"numpy.random.permutation",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rheiland/pc4fury | [
"41ef56afcfdfc7931fd1b82450f36fd33dfc7697"
] | [
"bin/substrates.py"
] | [
"# substrates Tab\n\nimport os, math\nfrom pathlib import Path\nfrom shutil import copyfile\nfrom ipywidgets import Layout, Label, Text, Checkbox, Button, BoundedIntText, HBox, VBox, Box, \\\n FloatText, Dropdown, interactive\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import BoundaryNorm\nfrom mat... | [
[
"matplotlib.pyplot.gca",
"matplotlib.collections.PatchCollection",
"matplotlib.pyplot.sci",
"matplotlib.pyplot.title",
"matplotlib.colors.to_rgb",
"matplotlib.pyplot.ylim",
"matplotlib.patches.Circle",
"numpy.broadcast",
"matplotlib.pyplot.xlim",
"numpy.broadcast_to",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
daniel-s-cunha/probability | [
"4b9bdefc2466dace58f3109c117802ab7ad4929b"
] | [
"tensorflow_probability/python/internal/distribution_util_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... | [
[
"numpy.diag",
"tensorflow.compat.v2.executing_eagerly",
"numpy.asarray",
"tensorflow.compat.v2.shape",
"tensorflow.compat.v2.convert_to_tensor",
"tensorflow.compat.v2.ones",
"tensorflow.compat.v2.identity",
"numpy.random.randn",
"tensorflow.compat.v1.placeholder_with_default",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HieuLsw/blobjob.editor | [
"c33473ffb7836a70ba3a1b2a9dd9452a9d3a1b81"
] | [
"libs/cocos/particle.py"
] | [
"# ----------------------------------------------------------------------------\n# cocos2d\n# Copyright (c) 2008 Daniel Moisset, Ricardo Quesada, Rayentray Tappa, Lucio Torre\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that... | [
[
"numpy.swapaxes",
"numpy.sqrt",
"numpy.ascontiguousarray",
"numpy.select",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stefanvdlugt/markedgraphs | [
"1eee51fdaf4add9064d1e53ecc3a811bcea82f06"
] | [
"markedgraphs/sequences.py"
] | [
"import numpy as np\n\ndef integer_sequences(L, S, nondecr=False, m=None, M=None):\n \"\"\"\n Generate sequences of non-negative integers.\n \n Parameters:\n L: the length of the sequences\n S: the sum of the integers in each sequence\n \n Optional parameters:\n nondecr: ... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
swaldow/pycaret | [
"f46a388ad8db6896a6a4252ac21bfd582b803fa1"
] | [
"pycaret/internal/preprocess.py"
] | [
"# Module: Preprocess\n# Author: Fahad Akbar <m.akbar@queensu.ca>\n# License: MIT\n\nimport pandas as pd\nimport numpy as np\nimport ipywidgets as wg\nfrom IPython.display import display\nfrom ipywidgets import Layout\nfrom sklearn.base import BaseEstimator, TransformerMixin, clone\nfrom sklearn.impute._base import... | [
[
"sklearn.ensemble.RandomForestRegressor",
"pandas.merge",
"pandas.to_datetime",
"sklearn.utils.validation.check_is_fitted",
"sklearn.cluster.KMeans",
"sklearn.metrics.silhouette_score",
"sklearn.decomposition.IncrementalPCA",
"sklearn.preprocessing.QuantileTransformer",
"sklear... | [
{
"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... |
eth-sri/dp-sniper | [
"ee087b2584ca97d1064154db5dd2bda0bb5a8ceb"
] | [
"statdpwrapper/verification.py"
] | [
"from typing import List\n\nimport numpy as np\n\nfrom dpsniper.search.ddconfig import DDConfig\nfrom statdpwrapper.postprocessing import Postprocessing\nfrom dpsniper.probability.estimators import PrEstimator\nfrom dpsniper.attack.attack import Attack\n\nfrom statdpwrapper.postprocessing import the_zero_noise_prng... | [
[
"numpy.logical_and",
"numpy.random.default_rng",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ckvic3/VisDrone | [
"bdba106e4d5081f9d3f07af80e736eb3287ba6e8"
] | [
"training_dataset/VisDrone/par_crop.py"
] | [
"from os.path import join, isdir\nfrom os import listdir, mkdir, makedirs\nimport cv2\nimport numpy as np\nimport glob\nimport xml.etree.ElementTree as ET\nfrom concurrent import futures\nimport sys\nimport time\nsub_sets = ['VisDrone2019-SOT-train','VisDrone2019-SOT-val']\nVisDrone_base_path = '/home/tempuser1/Vis... | [
[
"numpy.array",
"numpy.mean",
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jordanparker6/datascience-starter | [
"3eef1640a45d19431e9fb26adf5e089d3708dab1"
] | [
"ds/dataset/gridsearchcv.py"
] | [
"import itertools\nimport numpy as np\nimport pandas as pd\nfrom typing import Dict, Any, Tuple\nfrom sklearn.model_selection import TimeSeriesSplit\nfrom sklearn.model_selection import ShuffleSplit\nfrom tqdm import tqdm\nfrom abc import ABC\n\nclass GridsearchCVBase(ABC):\n \"\"\"A base class for cross validat... | [
[
"numpy.nanargmin",
"numpy.nanargmax",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
patilli/vqa_benchmarking | [
"53a05d8956e71e99de6d97db5e7a7e400b6cc65f"
] | [
"backend/vqa_benchmarking_backend/datasets/CLEVRDataset.py"
] | [
"import json\nimport os\nimport random\nimport re\nfrom typing import Dict, List, Tuple, Union\n\nimport cv2 as cv\nimport numpy as np\nimport PIL\nimport torch\nfrom tqdm.auto import tqdm\nfrom vqa_benchmarking_backend.datasets.dataset import (DataSample,\n Dia... | [
[
"numpy.load",
"torch.from_numpy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SFU-MARS/optimized_dp | [
"4a3b33f7da29479a3bfdc61905b61c05c76e6795"
] | [
"solver.py"
] | [
"import heterocl as hcl\nimport numpy as np\nimport time\n\nfrom Plots.plotting_utilities import *\nfrom argparse import ArgumentParser\n\n# Backward reachable set computation library\nfrom computeGraphs.graph_3D import *\nfrom computeGraphs.graph_4D import *\nfrom computeGraphs.graph_5D import *\nfrom computeGraph... | [
[
"numpy.reshape",
"numpy.array",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Curly-Mo/tf-sandbox | [
"18d4483dcfb25caa7301f1307362b09ddd7ccb74"
] | [
"audio_gen/audio_gen_complex.py"
] | [
"import datetime\nimport librosa\nimport tflearn\nimport numpy as np\nimport argparse\nimport tensorflow as tf\n\n\ndef features_from_audio(y, sr, n_fft=512, seq_len=400):\n S = librosa.core.stft(y, n_fft=n_fft).astype(np.complex64)\n return features_from_spectrogram(S, sr, seq_len=seq_len)\n\n\ndef features_... | [
[
"tensorflow.ones",
"tensorflow.placeholder",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
mmaaz60/ovr-cnn | [
"28d125ae51f0216575e12b5d5ded878874131b22"
] | [
"maskrcnn_benchmark/engine/bbox_aug.py"
] | [
"import torch\nimport torchvision.transforms as TT\n\nfrom maskrcnn_benchmark.config import cfg\nfrom maskrcnn_benchmark.data import transforms as T\nfrom maskrcnn_benchmark.structures.image_list import to_image_list\nfrom maskrcnn_benchmark.structures.bounding_box import BoxList\nfrom maskrcnn_benchmark.modeling.r... | [
[
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sbruch/rlstudio | [
"ba831f794a6b6ababe18bd154317367206511fdb"
] | [
"rlstudio/stats/point_estimate.py"
] | [
"from rlstudio.experiment import base as exp_base\nfrom rlstudio.typing import TaskId\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom typing import List\nimport warnings\n\n\nclass PointEstimate:\n \"\"\"Records point estimates such as returns.\"\"\"\n def __init__(self,\n id: str,\n ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.tight_layout",
"numpy.array_equal",
"numpy.unique",
"numpy.arange",
"numpy.squeeze",
"matplotlib.pyplot.subplots",
"numpy.nanstd",
"matplotlib.pyplot.fill_between",
"matplotlib.pyplot.close",
"n... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
snehsagarajput/sudoku-solver-app | [
"cf3ac2ae8d413ef88b0c76314ac298c169a0d1c2"
] | [
"server/utility/preprocess.py"
] | [
"try:\n from cv2 import cv2\nexcept:\n import cv2 \nimport numpy as np\n\nfrom utility.preprocessGrid import get_corner_points_of_largest_contour, crop_and_reshape\nfrom utility.preprocessNumberCell import PreprocessNumber\n\n\ndef preprocess_sudoku_board(board_img, dimension):\n # Thicker lines to eas... | [
[
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
omri-haim/IML.HUJI | [
"24584be25b0bdb3f29044c6089c36c38ec613b47"
] | [
"IMLearn/learners/classifiers/perceptron.py"
] | [
"from __future__ import annotations\nfrom typing import Callable\nfrom typing import NoReturn\nfrom ...base import BaseEstimator\nimport numpy as np\n\n\ndef default_callback(fit: Perceptron, x: np.ndarray, y: int):\n pass\n\n\nclass Perceptron(BaseEstimator):\n \"\"\"\n Perceptron half-space classifier\n\... | [
[
"numpy.zeros",
"numpy.column_stack",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amirsarikhani/NEST | [
"2771f6593bca0827489359c4129db9eea439d036",
"2771f6593bca0827489359c4129db9eea439d036"
] | [
"Community_Water_Model/CWATM_analyze/plot_indus5min_precipitation_worldclim.py",
"Community_Water_Model/CWATM_analyze/plot_indus5min_temperature_worldclim.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 14 12:12:11 2018\n\n@author: wangy\n\nCompare the annual mean precipitation in the Indus River Basin in the WorldClim\ndataset and the WFDEI dataset\n\"\"\"\n\nimport xarray as xr\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport o... | [
[
"numpy.array",
"numpy.meshgrid",
"matplotlib.colors.BoundaryNorm",
"matplotlib.pyplot.subplots"
],
[
"numpy.arange",
"numpy.meshgrid",
"matplotlib.colors.BoundaryNorm",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sWizad/TensoRF | [
"f7782140d6493b841cc07cad995ee10d56250fa2"
] | [
"models/hashgrid.py"
] | [
"# hashgrid based model\nimport torch\nimport torch.nn.functional as F\n\nimport tinycudann as tcnn\n\nfrom .tensoRF import TensorVMSplit\n\nclass HashGridDecomposition(TensorVMSplit):\n \"\"\"\n a drop-in replacement for for VM decompositon where we factor into hashgrid instead\n \"\"\"\n def __init__(... | [
[
"torch.all",
"torch.randn",
"torch.round",
"torch.zeros_like",
"torch.nn.functional.relu",
"torch.no_grad",
"torch.nn.ParameterList",
"torch.stack",
"torch.nn.functional.softplus"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tbattz/aws-price-collator | [
"76be52e8af9bedb4171bd805dbf73e1a38c00e14"
] | [
"priceParsing/spotPrices.py"
] | [
"from priceParsing.baseParser import BaseParser\n\nimport os\nimport time\nimport boto3\nimport pandas as pd\n\n\n\nclass SpotPrices(BaseParser):\n\t\"\"\"\n\tParsers current spot prices, or reads existing spot prices from csv file.\n\t\"\"\"\n\tdef __init__(self, apiKeyFilePath=None, csvDir='csvFiles', regionId='a... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
krishna939/Emotion-Recognition | [
"78050f494408989fff0931b9565d12898077f202"
] | [
"python/sahuja_data_augment.py"
] | [
"import cv2\r\nimport numpy as np\r\nimport os\r\nfrom matplotlib import pyplot as plt\r\n\r\nsource_directory = 'Test/'\r\ntarget_directory = 'Test_output/'\r\nfor file_name in os.listdir(source_directory):\r\n source_image =(os.path.join(source_directory, file_name))\r\n \r\n index_of_dot = file_name.ind... | [
[
"numpy.float32",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zealoe/HRNet-Semantic-Segmentation | [
"e5082879d6a46f1eb1127429e9948c80c0e15418"
] | [
"lib/models/sync_bn/inplace_abn/bn.py"
] | [
"import os, sys\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as functional\r\n\r\ntry:\r\n from queue import Queue\r\nexcept ImportError:\r\n from Queue import Queue\r\n\r\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\r\nsys.path.append(BASE_DIR)\r\nsys.path.append(os.path.j... | [
[
"torch.nn.functional.batch_norm",
"torch.ones",
"torch.zeros",
"torch.nn.init.constant_",
"torch.nn.functional.relu",
"torch.nn.functional.leaky_relu",
"torch.nn.functional.elu",
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
helderdaniel/hdlib | [
"ae766db53dc33e3cc6c1bb233073559c0b610a52"
] | [
"tensorflow/annmodel.py"
] | [
"'''\nDefine ANN models and algorithms base class\nDefines also some common network topologies\n\nv0.1 jan 2019\nv0.2 aug 2019\nhdaniel@ualg.pt\n'''\nimport tensorflow as tf\nfrom tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\nimport pandas as pd\nfrom hdlib.time.stopwatch impo... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.keras.callbacks.ModelCheckpoint",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.callbacks.ReduceLROnPlateau",
"pandas.DataFrame",
"tensorflow.keras.callbacks.EarlyStopping",
"tensorflow.keras.models.Sequential"
]
] | [
{
"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": [
"2.7",
"2.6",
"2.4",
... |
1e100/keras-retinanet | [
"38f37d1a3c3c7126b8b2cd1f2c124aa2bf551959"
] | [
"keras_retinanet/preprocessing/coco.py"
] | [
"\"\"\"\nCopyright 2017-2018 Fizyr (https://fizyr.com)\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 la... | [
[
"numpy.concatenate",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dqmis/neuralTPPs-1 | [
"594f86e52faf3895a37301bc8757359afd829227"
] | [
"tpp/models/decoders/hawkes.py"
] | [
"import torch as th\nimport torch.nn as nn\n\nfrom typing import Dict, Optional, Tuple\n\nfrom tpp.models.decoders.base.decoder import Decoder\nfrom tpp.utils.events import Events\nfrom tpp.utils.nnplus import non_neg_param\nfrom tpp.processes.hawkes_fast import decoder_fast as hawkes_decoder\n# from tpp.processes.... | [
[
"torch.nn.init.uniform_",
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
a6802739/tensorflow | [
"a26413ef0a179dd93b5228b031de83dce97a8684",
"ca1f0ce1b9e82e9577a2439c1c40cf38d4694c40"
] | [
"tensorflow/python/autograph/pyct/transformer_test.py",
"tensorflow/python/keras/engine/network.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.autograph.pyct.anno.setanno",
"tensorflow.python.autograph.pyct.anno.hasanno",
"tensorflow.python.autograph.pyct.parser.parse_entity",
"tensorflow.python.autograph.pyct.transformer.EntityInfo",
"tensorflow.python.autograph.pyct.transformer.Context",
"tensorflow.python.pl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"2.7",
"2.6",
"2.4",
"2.3",
"2.9",
"2.5",
"2.2",
"2.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflo... |
nikitazviahin/UniversityProjects | [
"87b23d3528cce814bed78ddf3c9833884f2ec625"
] | [
"Mathematical_Algorithms/lab3/runge2.py"
] | [
"import pandas as pd\nimport numpy as np\nimport pylab\n\n\ndef func(z, x):\n return -z / x\n \ntry:\n a = float(input('Ввежите начальную границу промежутка: '))\n b = float(input('Введите конечную границу промежутка: '))\n if a>=b:\n print('Начальная граница не может быьб больше конечной')\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Masao-Someki/espnet_onnx | [
"a0f44a087329bdcc292a4479ddf29d299edd92e5"
] | [
"espnet_onnx/asr/frontend/logmel.py"
] | [
"from typing import Tuple\nfrom typeguard import check_argument_types\n\nimport numpy as np\nimport librosa\n\nfrom espnet_onnx.utils.function import (\n make_pad_mask,\n mask_fill\n)\nfrom espnet_onnx.utils.config import Config\n\n\nclass LogMel:\n \"\"\"Convert STFT to fbank feats\n The arguments is s... | [
[
"numpy.log",
"numpy.log2",
"numpy.matmul",
"numpy.log10"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tiago939/feature_aligment | [
"423c606b0bed5a677b5fddde9cd1f17af91bdab6",
"423c606b0bed5a677b5fddde9cd1f17af91bdab6"
] | [
"mnist/training.py",
"mnist/networks.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.backends.cudnn as cudnn\nimport torchvision\nimport torchvision.transforms as transforms\nimport os, random, sys, math\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport networks\n\n#random seed\nmanualSeed = 1\nnp.random.seed... | [
[
"torch.sigmoid",
"torch.empty",
"numpy.random.seed",
"torch.load",
"torch.cuda.manual_seed",
"torch.manual_seed",
"torch.zeros",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.exp",
"torch.nn.init.orthogonal_",
"torch.cuda.is_available",
"torch.cuda.manual_s... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gandor26/covid-open | [
"50dcb773160edc16b107785a6bb32ae6f82fc9a7"
] | [
"hosps.py"
] | [
"from typing import Optional, Tuple, List, Dict\nfrom copy import deepcopy\nimport pandas as pd\nimport numpy as np\nimport torch as pt\nfrom torch import Tensor, BoolTensor, nn\nfrom torch.nn import init, functional as F\nfrom torch.optim import Adam\n\nfrom data import *\nfrom expsmooth import ExpSmooth\nfrom att... | [
[
"torch.nn.functional.softmax",
"pandas.to_datetime",
"torch.Tensor",
"torch.cat",
"numpy.arange",
"torch.nn.functional.conv1d",
"torch.zeros_like",
"torch.sum",
"pandas.Timedelta",
"torch.tensor",
"torch.exp",
"torch.nn.functional.relu",
"torch.no_grad",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Turakar/mutation-prediction | [
"2fe9d56dbdba66e1e0ece2776c13e0a9d07ce366"
] | [
"mutation_prediction/cli/__init__.py"
] | [
"import argparse\nimport logging\nimport os\nimport sys\nimport time\nimport warnings\n\nimport hjson\nimport numpy as np\nimport optuna\nfrom optuna.trial import TrialState\nfrom sklearn import metrics\nfrom sklearn.metrics import mean_squared_error, r2_score\nfrom tqdm import tqdm\n\nimport mutation_prediction.cl... | [
[
"numpy.median"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
oj9040/SpectralNet2 | [
"0657116249418fe05fb13097ed32c2264fe40210"
] | [
"src/core/networks.py"
] | [
"'''\nnetworks.py: contains network definitions (for siamese net,\ntriplet siamese net, and spectralnet)\n'''\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom keras import backend as K\nfrom keras.models import Model\nfrom keras.layers import Input, Lambda, Subtract\n\nfrom . import train\nfrom . import costs... | [
[
"tensorflow.concat",
"tensorflow.Variable",
"tensorflow.shape",
"tensorflow.train.RMSPropOptimizer",
"tensorflow.variables_initializer",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
EagleW/Stage-wise-Fine-tuning | [
"6054152aa0f64cab9c6eea47bd3570e6d79eaf26"
] | [
"src/file_utils.py"
] | [
"\"\"\"\nUtilities for working with the local dataset cache.\nThis file is adapted from the AllenNLP library at https://github.com/allenai/allennlp\nCopyright by the AllenNLP authors.\n\"\"\"\n\nimport fnmatch\nimport json\nimport os\nimport re\nimport shutil\nimport sys\nimport tarfile\nimport tempfile\nfrom colle... | [
[
"torch.hub._get_torch_home"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vonlippmann/Topology-optimization-of-structure-with-simp-method | [
"c9e81e2254f01e4babb69a337370a7a86bf31fb8"
] | [
"Python/shorthaircat.py"
] | [
"# -*- coding: utf-8 -*-\r\n\r\n# Standard imports.\r\nimport numpy as np\r\nfrom numpy import e\r\nimport threading\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.patches import Circle\r\nimport matplotlib.ticker as ticker\r\n\r\n#user defined pakages import\r\nfrom optimization_simp import Simp\r\nfrom pos... | [
[
"numpy.e.add_source",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.close",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZongSingHuang/NSGA-II | [
"d7f0d3596b711eadd0c34ce1d741fc60caa37d77"
] | [
"NSGA II.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Aug 24 16:01:41 2021\r\n\r\n@author: zongsing.huang\r\n\"\"\"\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport itertools\r\n\r\n#%% 目標函式\r\ndef fitness(X):\r\n if X.ndim==1:\r\n X = X.reshape(1, -1)\r\n \r\n F1 = X**2 # First... | [
[
"numpy.max",
"numpy.argmin",
"numpy.where",
"numpy.hstack",
"numpy.unique",
"numpy.zeros",
"matplotlib.pyplot.figure",
"numpy.random.choice",
"numpy.min",
"numpy.argsort",
"matplotlib.pyplot.show",
"numpy.sum",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aa1371/pandas | [
"77443dce2734d57484e3f5f38eba6d1897089182"
] | [
"pandas/core/indexes/base.py"
] | [
"from __future__ import annotations\n\nfrom datetime import datetime\nimport functools\nfrom itertools import zip_longest\nimport operator\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n Hashable,\n Literal,\n Sequence,\n TypeVar,\n cast,\n final,\n overload,\n)\nimport war... | [
[
"numpy.all",
"pandas.core.indexes.multi.MultiIndex",
"pandas.core.indexes.range.RangeIndex",
"pandas.util._decorators.deprecate_nonkeyword_arguments",
"pandas.core.dtypes.common.ensure_object",
"numpy.where",
"pandas.core.dtypes.common.is_interval_dtype",
"pandas.core.reshape.merge... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.0",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
cerebis/pygraphistry | [
"53bd7a779b9efc216301fcee94b493de9184cbc2"
] | [
"graphistry/tigeristry.py"
] | [
"import requests\nimport pandas as pd\n\ndef merge_dicts(x, y):\n return dict(list(x.items()) + list(y.items()))\n\nclass Tigeristry(object):\n \"\"\"Tigergraph bindings class\n\n * Initialize with DB cfg\n * Register named stored procedures and graphistry bindings\n * Call stored procedu... | [
[
"pandas.isna",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
aioz-ai/ECCVW20_MILQT | [
"5fa179cb880d8915af96e9f0cd8c742f75fdc8b7",
"5fa179cb880d8915af96e9f0cd8c742f75fdc8b7"
] | [
"evaluate.py",
"tools/adaptive_detection_features_converter.py"
] | [
"\"\"\"\nThis code is modified from Hengyuan Hu's repository.\nhttps://github.com/hengyuan-hu/bottom-up-attention-vqa\n\"\"\"\nimport argparse\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nimport os\n\nfrom dataset_VQA import Dictionary, VQAFeatureDataset\nimport dataset_TDIUC\nimpo... | [
[
"torch.cuda.device_count",
"torch.utils.data.DataLoader",
"torch.load"
],
[
"numpy.concatenate",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
opendilab/DI-smartcross | [
"362c6c6dcfd2e1f59d3e7c955ffe2d9d1b13d8d2"
] | [
"entry/sumo_config/sumo_wj3_rainbow_default_config.py"
] | [
"from easydict import EasyDict\nfrom torch import nn\n\nnstep = 3\nsumo_rainbow_default_config = dict(\n exp_name='sumo_wj3_md_rainbow_dqn',\n env=dict(\n manager=dict(\n # Whether to use shared memory. Only effective if manager type is 'subprocess'\n shared_memory=False,\n ... | [
[
"torch.nn.Tanh"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bnoi/scikit-tracker | [
"8771006a3a682ab299e4446c800c05f36a027df7"
] | [
"sktracker/utils/tests/test_progress.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\nfrom __future__ import division\nfrom __future__ import absolute_import\nfrom __future__ import print_function\n\nimport io\n\nimport pandas as pd\nimport numpy as np\n\nfrom sktracker.utils import progress_apply\nfrom sktracker.utils import print... | [
[
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jaspereb/DM-Count | [
"48c7636223e5244b35ddb222f2075368c03fbb66"
] | [
"losses/ot_loss.py"
] | [
"import torch\nfrom torch.nn import Module\nfrom .bregman_pytorch import sinkhorn\n\nclass OT_Loss(Module):\n def __init__(self, c_size, stride, norm_cood, device, num_of_iter_in_ot=100, reg=10.0):\n super(OT_Loss, self).__init__()\n assert c_size % stride == 0\n\n self.c_size = c_size\n ... | [
[
"torch.sum",
"torch.zeros",
"torch.matmul",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AndreaCeolin/Functionalism_Contrast_Change | [
"1557a4c76c253c7db292e503d6bd5cff5cea2d93"
] | [
"Chapter2/Fig2.12-contr-biginventory/sound_change-contr-biginventory.py"
] | [
"#!/usr/bin/env python3\r\n\r\nimport random\r\nimport sys\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n\r\n\r\n\r\n'''\r\n1. ALPHABET and LEXICON\r\n\r\nThis part defines the alphabet of the language, and maps symbols to indexes.\r\n\r\nIn this version, the vowel dictionary maps 30 different sym... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jbueltemeier/pystiche | [
"0d0707121e63c4355303446e62a4894e86a7b763",
"0d0707121e63c4355303446e62a4894e86a7b763"
] | [
"pystiche/misc/misc.py",
"tests/test_enc.py"
] | [
"import contextlib\nimport hashlib\nimport itertools\nimport random\nimport shutil\nimport tempfile\nimport warnings\nfrom collections import OrderedDict\nfrom functools import reduce as _reduce\nfrom operator import mul\nfrom os import path\nfrom typing import (\n Any,\n Callable,\n Dict,\n Iterable,\n... | [
[
"torch.mean",
"numpy.abs",
"numpy.random.seed",
"torch.rand_like",
"torch.manual_seed",
"torch.hub._get_torch_home",
"torch.backends.cudnn.is_available",
"torch.sum",
"torch.cuda.is_available",
"numpy.mod",
"torch.device",
"torch.save"
],
[
"torch.nn.Sequent... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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