repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
MaricelaM/torchdiffeq | [
"4bd6a7a57fc895fb56ad59c67924ec08baaf221e"
] | [
"tests/gradient_tests.py"
] | [
"import unittest\nimport torch\nimport torchdiffeq\n\nfrom problems import construct_problem, PROBLEMS, DEVICES, METHODS\n\n\ndef max_abs(tensor):\n return torch.max(torch.abs(tensor))\n\n\nclass TestGradient(unittest.TestCase):\n def test_odeint(self):\n for device in DEVICES:\n for method ... | [
[
"torch.abs",
"torch.linspace",
"torch.mm",
"torch.rand_like",
"torch.manual_seed",
"torch.tensor",
"torch.nn.Linear",
"torch.autograd.gradcheck"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ELEKTRONN/elektronn3 | [
"502062995e6c31a99c56f256a3b777a502f2c20c"
] | [
"elektronn3/training/metrics.py"
] | [
"# ELEKTRONN3 - Neural Network Toolkit\n#\n# Copyright (c) 2017 - now\n# Max Planck Institute of Neurobiology, Munich, Germany\n# Authors: Martin Drawitsch\n\n# TODO: Update docs to show Evaluator\n\"\"\"Metrics and tools for evaluating neural network predictions\n\nReferences:\n\n- https://en.wikipedia.org/wiki/Co... | [
[
"torch.device",
"torch.nn.functional.softmax",
"torch.empty",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cvxgrp/extquadcontrol | [
"be3b60e07755f1d0c514c7cafcd8cebeb61580fc"
] | [
"examples/plot_settings.py"
] | [
"import matplotlib.pyplot as plt\n\nplt.rc('font', family='serif', serif='Computer Modern Roman')\nplt.rc('text', usetex=True)\nplt.rc('xtick', labelsize=12)\nplt.rc('ytick', labelsize=12)\nplt.rc('axes', labelsize=12)\nplt.rc('legend', fontsize=12)\n\ndefault_width = 4\ndefault_height = default_width/1.618\n\ndef ... | [
[
"matplotlib.pyplot.rc"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
seqam-lab/MTDA-ITA | [
"5d249aa4a75f804180cbe30db1e0823668f068bd"
] | [
"src/main.py"
] | [
"import argparse\nimport os\nimport scipy.misc\nimport numpy as np\nimport tensorflow as tf\nfrom model import MDA\n\nparser = argparse.ArgumentParser(description='')\nparser.add_argument('--dataset_dir', dest='dataset_dir', default='svhn2mnist', help='path of the dataset')\nparser.add_argument('--epoch', dest='epo... | [
[
"tensorflow.Session",
"tensorflow.app.run"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
dho-IOD/futu_algo | [
"f4bdf5edcc261efbd252e9e9c53a89563b0ed68f"
] | [
"fastquant/network.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 28, 2020\n\n\n@author: jpdeleon\n\"\"\"\n# Import standard library\nimport itertools\nfrom datetime import datetime\nfrom pathlib import Path\n\n# Import modules\nimport matplotlib.pyplot as pl\nimport numpy as np\nimport pandas as pd\nimp... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"pandas.Series",
"numpy.sqrt",
"numpy.triu_indices_from",
"matplotlib.pyplot.subplots",
"numpy.zeros_like",
"matplotlib.pyplot.setp",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Chandrahasd/OKGIT | [
"16f4dbbfca1020809d3bae0445ee564fa8af9193"
] | [
"src/bertutils.py"
] | [
"import sys\nimport os\n\nimport argparse\nimport numpy as np\nfrom lama.modules import build_model_by_name\nfrom lama.modules.base_connector import *\nimport torch\n\nclass BertUtils(object):\n def __init__(self, args):\n self.model = build_model_by_name(args.models, args)\n if args.use_cuda:\n ... | [
[
"torch.stack",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DAB-Team/contracts | [
"ae58f66cff7af90cea188b321b2a9616091674c1"
] | [
"solidity/python/theoretical_reserve.py"
] | [
"import matplotlib.pyplot as plot\nimport cmath\nimport random\n\ndef sigmoid(l, d, a, b, x):\n \"\"\"\n CRR Curve Function\n :param l:\n :param d:\n :param a:\n :param b:\n :param x:\n :return: CRR\n \"\"\"\n return 1/(1+cmath.exp((x-l)/d))*a+b\n\n\n\ndef sigmoid_integral(l, d, a, b, ... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
L-Net-1992/DI-engine | [
"268d77db3cb54401b2cfc83e2bc3ec87c31e7b83"
] | [
"dizoo/procgen/envs/procgen_env.py"
] | [
"from typing import Any, List, Union, Optional\nfrom easydict import EasyDict\nimport time\nimport gym\nimport numpy as np\nfrom ding.envs import BaseEnv, BaseEnvTimestep\nfrom ding.envs.common.env_element import EnvElement, EnvElementInfo\nfrom ding.torch_utils import to_ndarray, to_list\nfrom ding.utils import EN... | [
[
"numpy.random.seed",
"numpy.ones",
"numpy.transpose",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kundajelab/seqxgene | [
"cd25818514ab90fad53c64550eea9f43908f2fbb"
] | [
"preprocess/make_accessibility_joblib.py"
] | [
"import joblib\nimport argparse\nimport gzip\nimport os\nfrom pyfaidx import Fasta\nfrom tqdm import tqdm\nimport numpy as np\n\n\"\"\"\nInput file must be a tab-separated gzipped file formatted as below.\n\nchr start end task1 task2 ... taskM\nchr1 50 1050 0 0 0\nchr1 1000 ... | [
[
"numpy.arange",
"numpy.array",
"numpy.packbits",
"numpy.int8"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jamesdaus/NLPAccessibilitySC2 | [
"759bea30ef3ca9d7f683a72c59b111e3ad0a0c82"
] | [
"Starcraft/PythonSC2/examples/terran/ramp_wall.py"
] | [
"import sys, os\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"../..\"))\n\nimport random, numpy as np\n\nimport sc2\nfrom sc2 import Race, Difficulty\nfrom sc2.constants import *\nfrom sc2.player import Bot, Computer\nfrom sc2.position import Point2, Point3\nfrom sc2.unit import Unit\nfrom sc2.units ... | [
[
"numpy.ndenumerate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mghane/Image-Classifier-with-Tensorflow-Keras | [
"cf6d018a0819e632441ef321b9b6a2c24dc4a309"
] | [
"predict.py"
] | [
"import tensorflow as tf\nfrom PIL import Image\nimport tensorflow_hub as hub\nfrom processing import process_image\n\nimport argparse\nimport numpy as np\nimport json\n\n\ndef predict(image_path, model, top_k, category_names):\n '''\n Image object ==> top k classes predicted by model along with their probabi... | [
[
"numpy.asarray",
"tensorflow.keras.models.load_model",
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
mananmadan/DeepLogo | [
"ebcd5793c270696cd6c6b1a74da603b017f11718"
] | [
"single_inf.py"
] | [
"import numpy as np\nimport os\nimport sys\nimport tarfile\nimport math\nimport tensorflow.compat.v1 as tf\nimport zipfile\nimport cv2\nfrom google.colab.patches import cv2_imshow\nfrom distutils.version import StrictVersion\nfrom collections import defaultdict\nfrom io import StringIO\nfrom matplotlib import pyplo... | [
[
"tensorflow.compat.v1.get_default_graph",
"numpy.expand_dims",
"tensorflow.compat.v1.import_graph_def",
"tensorflow.compat.v1.expand_dims",
"tensorflow.compat.v1.Session",
"tensorflow.compat.v1.slice",
"tensorflow.compat.v1.Graph",
"tensorflow.compat.v1.gfile.GFile",
"tensorflo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
giganticode/bohr | [
"d3317bae621ad9f5629ef0d3f13591e4b8ac3ef5"
] | [
"data-preprocessing/fine-grained-refactorings.py"
] | [
"#!/usr/bin/env python3\n\nimport os\nimport subprocess\nimport tempfile\nfrom pathlib import Path\n\nimport pandas as pd\n\npath_to_file = os.path.realpath(__file__)\nrepo_root = Path(path_to_file).parent.parent\n\nINPUT_ZIP = repo_root / \"downloaded-data\" / \"fine-grained-refactorings.zip\"\nSAVE_TO = repo_root... | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
x5675602/fy4a-forest-fire | [
"155759cdb0abb92fb0c6e09d7d671b9dbb1a02f2"
] | [
"main.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n#\n# Author: Kazuto Nakashima\n# URL: http://kazuto1011.github.io\n# Created: 2017-05-18\n\nfrom __future__ import print_function\n\nimport copy\nimport os.path as osp\n\nimport click\nimport cv2\nimport PIL\nimport matplotlib.cm as cm\nimport numpy as np\nimport tor... | [
[
"torch.nn.functional.softmax",
"torch.cuda.current_device",
"numpy.uint8",
"matplotlib.cm.jet_r",
"matplotlib.cm.bwr_r",
"torch.mul",
"numpy.shape",
"torch.cuda.is_available",
"torch.cuda.get_device_name",
"torch.device",
"torch.nn.DataParallel",
"numpy.array",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Timurff/dlcourse_ai | [
"ab7c0fb5d463f9e963a6624be78520dda1cf39e9"
] | [
"assignments/assignment2/trainer.py"
] | [
"from copy import deepcopy\n\nimport numpy as np\nfrom metrics import multiclass_accuracy\n\n\nclass Dataset:\n \"\"\"\n Utility class to hold training and validation data\n \"\"\"\n\n def __init__(self, train_X, train_y, val_X, val_y):\n self.train_X = train_X\n self.train_y = train_y\n ... | [
[
"numpy.arange",
"numpy.random.shuffle",
"numpy.zeros_like",
"numpy.mean",
"numpy.not_equal",
"numpy.array_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dd-iuonac/vision3d | [
"9ea514c80eb99d265c3247321e59bfc1c2ccd94a"
] | [
"pvrcnn/detector/proposal.py"
] | [
"import torch\nimport math\nfrom torch import nn\nimport torch.nn.functional as F\n\nfrom pvrcnn.ops import sigmoid_focal_loss, batched_nms_rotated\nfrom pvrcnn.core.box_encode import decode\n\n\nclass ProposalLayer(nn.Module):\n \"\"\"\n Use BEV feature map to generate 3D box proposals.\n TODO: Fix long v... | [
[
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.init.normal_",
"torch.nn.functional.smooth_l1_loss",
"torch.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
paulbaumgarten/examinations-timer | [
"686f3ecae255dd873bd85da893a81340a3149efc"
] | [
"examtimer.py"
] | [
"import pygame, time, random\nfrom pygame.locals import *\nimport pandas as pd\nimport json\nfrom datetime import datetime, timedelta\nimport threading, time\n\n\"\"\" Settings \"\"\"\nEXCEL_FILE = \"exams.xlsx\"\nWORKSHEET = \"exams\"\n\n\"\"\" Read excel file into json data \"\"\"\ndef get_excel_data(xlsx_filenam... | [
[
"pandas.read_excel"
]
] | [
{
"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": []
}
] |
zcemctt/MPHY0041_Segmentation | [
"a89638153a44fe030b2ab9abd8e3136306bf2712"
] | [
"UNet_3D_Cy/testing.py"
] | [
"# Load Trained Model\n\nimport tensorflow as tf\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\nfrom keras.optimizers import Adam, SGD\nfrom tqdm import tqdm\nimport random\nfrom metrics import dice_coef, dice_coef_loss\nimport yaml\n\n# Hyperparameters\nconfig = yaml.safe_load(open(\"hyper_parame... | [
[
"tensorflow.keras.models.load_model",
"numpy.squeeze",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.close",
"numpy.zeros",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
rlouf/aehmc | [
"8c27dd1153e4642e8499e202bb967dfc2b190309"
] | [
"tests/test_hmc.py"
] | [
"import aesara\nimport aesara.tensor as at\nimport arviz\nimport numpy as np\nimport pytest\nimport scipy.stats as stats\nfrom aeppl import joint_logprob\n\nfrom aehmc import hmc, nuts\n\n\n@pytest.mark.skip(reason=\"this test is flaky\")\ndef test_hmc():\n \"\"\"Test the HMC kernel on a gaussian target.\"\"\"\n... | [
[
"numpy.diag",
"numpy.expand_dims",
"numpy.sqrt",
"numpy.abs",
"numpy.testing.assert_array_less",
"numpy.std",
"numpy.mean",
"scipy.stats.norm.sf",
"numpy.var",
"numpy.array",
"numpy.random.default_rng"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
arjunakula/vit-pytorch | [
"b786029e18b3b2140f29ec43a50c06c801e1d135"
] | [
"vit_pytorch/vit_pytorch.py"
] | [
"import torch\nimport torch.nn.functional as F\nfrom einops import rearrange, repeat\nfrom torch import nn\n\nMIN_NUM_PATCHES = 16\n\nclass Residual(nn.Module):\n def __init__(self, fn):\n super().__init__()\n self.fn = fn\n def forward(self, x, **kwargs):\n return self.fn(x, **kwargs) + ... | [
[
"torch.nn.Dropout",
"torch.nn.GELU",
"torch.cat",
"torch.einsum",
"torch.nn.ModuleList",
"torch.randn",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.finfo"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BrenoPremoli/Faculdade | [
"dcf5d106b10453224e37af830a53b2214af1d2ee"
] | [
"python/linguagem-de-programacao/arquivo-xlsx-e-csv/exercicio_4.py"
] | [
"# 4. Crie um programa que carregue o arquivo Propaganda.csv, por meio da biblioteca Pandas. Calcule e apresente o valor máximo da coluna do arquivo 'Jornal'.\n\nimport pandas as pd\ndf = pd.read_csv('Propaganda.csv')\nprint('Valor máximo da coluna do arquivo Jornal: R$ {:.2f}'.format(max(df['Jornal'])))\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ankushjain2001/FairPrep | [
"6c87a6d07a5789208b4e060ef996f341908db49f"
] | [
"fp/dataset_experiments.py"
] | [
"from fp.experiments import BinaryClassificationExperiment\nimport pandas as pd\nimport numpy as np\n\n\nclass AdultDatasetWhiteMaleExperiment(BinaryClassificationExperiment):\n\n def __init__(self, fixed_random_seed, train_data_sampler, missing_value_handler, numeric_attribute_scaler,\n learners... | [
[
"pandas.read_csv",
"pandas.to_datetime",
"numpy.float"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
iwbailey/shakemap_oasisloss | [
"91789c9f6ff7e7d4f573cc28d1926f810dab9d36"
] | [
"example/plot_footprintmap.py"
] | [
"\"\"\"Read in the footprint and plot the exceedance probability for a given\nintensity.\n\n\"\"\"\n\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom matplotlib import pylab\nfrom oasis_utils import read_footprint, get_areaperilcoords, read_intensbins\n\n# import pdb\n\n# Parameters ----------------... | [
[
"pandas.concat",
"matplotlib.pylab.show",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.colorbar"
]
] | [
{
"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": []
}
] |
Mhmd-Elsersy/Reinforcement-Learning-Algorithms | [
"9b3aab1cd8d4347dee0baa8eb6557984554d4215"
] | [
"Dynamic Programming/Value-Iteration.py"
] | [
"import numpy as np\nimport pprint\nimport sys\nimport gym.spaces\nif \"../\" not in sys.path:\n sys.path.append(\"../\") \nfrom lib.envs.gridworld import GridworldEnv\n\n\npp = pprint.PrettyPrinter(indent=2)\nenv = GridworldEnv()\n\ndef value_iteration(env, epsilon=0.0001, discount_factor=1.0):\n \"\"\"\n V... | [
[
"numpy.eye",
"numpy.argmax",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vishalbelsare/fbooja | [
"c0a2293f39f3af37cd610eade80fbd61c531337f"
] | [
"ctorch.py"
] | [
"\"\"\"\nTensors with complex-valued entries.\n\nCopyright (c) Facebook, Inc. and its affiliates.\n\nThis source code is licensed under the MIT license found in the LICENSE file in\nthe root directory of this source tree.\n\"\"\"\n\nimport ctypes\nimport functools\n\nimport numpy as np\nimport torch\nfrom torch.aut... | [
[
"torch.cuda.synchronize",
"torch.Tensor",
"torch.cat",
"torch.sqrt",
"torch.from_numpy",
"torch.bmm",
"numpy.prod",
"torch.stack",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
abhishekkrthakur/clickbaits_revisited | [
"de9020635c58a7526a83e8ad002ee33a4597c456"
] | [
"deepnets/LSTM_Title_Content_Numerical_with_GloVe.py"
] | [
"# coding: utf-8\n\"\"\"\nLSTM on title + content text + numerical features with GloVe embeddings\n@author: Abhishek Thakur\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Activation, Dropout\nfrom keras.layers.embe... | [
[
"numpy.asarray",
"sklearn.preprocessing.StandardScaler",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
navchandar/File-Convertor-Utils | [
"4a68653c02cf8d4bb592d91d0ee0239226481fd1"
] | [
"Excel2str.py"
] | [
"#! python3\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nExcel2str extracts string content from Excel files\r\n\"\"\"\r\nimport pandas as pd\r\n\r\n\r\ndef convertExcel(xl_filename, txt_filename):\r\n '''\r\n Input should be a single excel file.\r\n Output will be a single txt file with all content as string.\... | [
[
"pandas.ExcelFile"
]
] | [
{
"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": []
}
] |
amazon-research/network-deconvolution-pp | [
"99e27ecec7d27c7c4c3fb230e96005bdcbf6f2ce"
] | [
"Classification/net_util.py"
] | [
"from __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\n\nimport torchvision\nimport torchvision.transforms as transforms\n\nimport os\nimport argparse\nimport time\nfrom copy import deepcopy\n... | [
[
"torch.no_grad",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bpblakely/safegraph_py | [
"661452c97cc779177a0337d09692cb0fb5c397d1"
] | [
"tests/explode_array_test.py"
] | [
"# content of explode_array_test.py\nfrom safegraph_py_functions.safegraph_py_functions import *\nimport pytest\nimport pandas.util.testing as pdt\n\n\n### Expected DFs\n\nexpected_output_data = {\n 'safegraph_place_id': ['sg:64d0ee4695af4ab4906fe82997ead9ff', 'sg:64d0ee4695af4ab4906fe82997ead9ff', 'sg:64d0... | [
[
"pandas.util.testing.assert_frame_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
AnthonySMaida/RBP_predcode | [
"8714862d891e3df44445d8f6155f5a633f6a16f0"
] | [
"data_utils_RBP.py"
] | [
"#import hickle as hkl\nimport numpy as np\nfrom keras import backend as K\nfrom keras.preprocessing.image import Iterator\nimport matplotlib.pyplot as plt\n\n# Defines one class: SequenceGenerator. a subclass of Iterator\n# ====================================\n\n# Called from kitti_train.py and kitti_evaluate.py.... | [
[
"matplotlib.pyplot.imshow",
"numpy.random.permutation",
"numpy.transpose",
"numpy.load",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kabrapratik28/Stanford_courses | [
"9b02845ad15103d7e7f934319f43298f8cd8a364",
"9b02845ad15103d7e7f934319f43298f8cd8a364"
] | [
"cs224n/assignment3/q2_rnn.py",
"cs20si/tf-stanford-tutorials/assignments/style_transfer/style_transfer_sols.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nQ2: Recurrent neural nets for NER\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\n\nimport argparse\nimport logging\nimport sys\nimport time\nfrom datetime import datetime\n\nimport tensorflow as tf\nimport numpy as np\n\n... | [
[
"tensorflow.zeros",
"tensorflow.stack",
"tensorflow.train.AdamOptimizer",
"tensorflow.boolean_mask",
"tensorflow.Graph",
"tensorflow.Variable",
"tensorflow.contrib.layers.xavier_initializer",
"tensorflow.Session",
"tensorflow.train.Saver",
"tensorflow.argmax",
"tensorfl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
jeffmacinnes/pyneal | [
"750f0ec5a231ccfa77aea960242de9b5019ba493",
"750f0ec5a231ccfa77aea960242de9b5019ba493"
] | [
"tests/pyneal_tests/test_pynealAnalysis.py",
"pyneal_scanner/utils/Siemens_utils.py"
] | [
"import os\nfrom os.path import join\nimport sys\n\nimport numpy as np\nimport nibabel as nib\n\nimport pyneal_helper_tools as helper_tools\n\n# get dictionary with relevant paths for tests within this module\npaths = helper_tools.get_pyneal_test_paths()\nif paths['pynealDir'] not in sys.path:\n sys.path.ins... | [
[
"numpy.testing.assert_almost_equal",
"numpy.array"
],
[
"numpy.matrix",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qusaykaid/pythonic-learning-machine | [
"38cbca7c56720ecc1f9f0dfc89702e9c18c5ae2c"
] | [
"src/algorithms/semantic_learning_machine/algorithm.py"
] | [
"from algorithms.common.neural_network.node import Sensor\nfrom algorithms.common.neural_network.neural_network import NeuralNetwork, create_neuron\nfrom algorithms.common.neural_network.connection import Connection\nfrom algorithms.semantic_learning_machine.solution import Solution\nfrom algorithms.common.algorith... | [
[
"numpy.matrix",
"numpy.array",
"numpy.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Chizuchizu/riadd | [
"c3f55aebc0f582d9fa55dc517b1489963cf0506f"
] | [
"src/loss.py"
] | [
"import torch\nfrom torch import nn\n\n\nclass BCEFocalLoss(nn.Module):\n\n def __init__(self, gamma=2, alpha=None, reduction='elementwise_mean'):\n super().__init__()\n self.gamma = gamma\n self.alpha = alpha\n self.reduction = reduction\n\n def forward(self, _input, target):\n ... | [
[
"torch.mean",
"torch.sigmoid",
"torch.max",
"torch.zeros",
"torch.reshape",
"torch.zeros_like",
"torch.sum",
"torch.no_grad",
"torch.log",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kuadrat/data-slicer | [
"1634929569491579b7fe7ed5d928da790f552337"
] | [
"data_slicer/cmaps.py"
] | [
"\"\"\"\nConvert some of the nicer matplotlib and kustom colormaps to pyqtgraph \ncolormaps.\n\"\"\"\nimport copy\nimport logging\nimport os\nimport pathlib\nimport pickle\nimport pkg_resources\nimport warnings\n\nimport numpy as np\nfrom matplotlib import cm\nfrom matplotlib.colors import LinearSegmentedColormap\n... | [
[
"numpy.linspace",
"numpy.arange",
"matplotlib.pyplot.colormaps",
"numpy.ones",
"matplotlib.cm.get_cmap",
"matplotlib.colors.LinearSegmentedColormap.from_list",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alejoe91/python-neo | [
"7695048371b15b57beaf546b9982f19856c658ea"
] | [
"neo/io/nsdfio.py"
] | [
"\"\"\"\nModule for reading and writing NSDF files\n\nAuthor: Mieszko Grodzicki\n\nThis module support both reading and writing NDSF files.\nNote: Read file must be written using this IO\n\"\"\"\n\n\nimport numpy as np\nimport quantities as pq\n\nfrom uuid import uuid1\nimport pickle\nfrom datetime import datetime\... | [
[
"numpy.swapaxes"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
demdecuong/SEGMENT | [
"629dc55dcbc9629b35fb237e313b95ceacecdc89"
] | [
"layers/decoder.py"
] | [
"\"\"\"FocusSeq2Seq\nCopyright (c) 2019-present NAVER Corp.\nMIT license\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport math\n\nfrom .copy_attention import BahdanauAttention, CopySwitch, PointerGenerator\nfrom .beam_search import Beam\n\n\ndef repeat(tensor, K):\n \"\"\... | [
[
"torch.nn.functional.softmax",
"torch.zeros",
"torch.cat",
"torch.sum",
"torch.nn.Embedding",
"torch.tanh",
"torch.where",
"torch.full_like",
"torch.nn.Dropout",
"torch.ones",
"torch.tensor",
"torch.nn.LSTMCell",
"torch.arange",
"torch.nn.GRUCell",
"torc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YifanLi/SGVCut | [
"8fde53d17876fade2bc561dd04c9acea35334a24"
] | [
"src/Test_plotly.py"
] | [
"import plotly\nfrom plotly.graph_objs import Scatter, Layout\nimport plotly.plotly as py\nimport plotly.graph_objs as go\n# Create random data with numpy\nimport numpy as np\n\n#plotly.offline.init_notebook_mode()\n\n'''\nurl = plotly.offline.plot({\n \"data\": [Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1])],\n \"... | [
[
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JXQI/ModelsGenesis | [
"f961288313a78f03bd3045ac27722f791f365bd8"
] | [
"pytorch/main.py"
] | [
"import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nimport torchvision.transforms as transforms\nimport unet3d\nimport config\nfrom loader import NIIloader\nimport os\nos.environ['KMP_DUPLICATE_LIB_OK']='True'\n\n\n# prepare your own data\npath = '/Users/ji... | [
[
"torch.utils.data.DataLoader",
"torch.nn.BCELoss",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.device",
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qixinbo/imagepy | [
"3c378ebaf72762b94f0826a410897757ebafe689"
] | [
"sciapp/action/plugin/generalio.py"
] | [
"from ..advanced import dataio\nfrom skimage.io import imread, imsave\n\nfor i in ('bmp', 'jpg', 'jpeg', 'tif', 'png', 'gif'):\n\tdataio.ReaderManager.add(i, imread, 'img')\n\tdataio.WriterManager.add(i, imsave, 'img')\n\nclass OpenFile(dataio.Reader):\n title = 'Open'\n\n def load(self):\n self.filt =... | [
[
"pandas.read_excel",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
yatao91/learning_road | [
"e88dc43de98e35922bfc71c222ec71766851e618"
] | [
"coding/learn_numpy/np_sort.py"
] | [
"# -*- coding: UTF-8 -*-\nimport numpy as np\n\na = np.array([[4, 3, 2], [2, 4, 1]])\n\nprint(np.sort(a))\nprint(np.sort(a, axis=None))\nprint(np.sort(a, axis=0))\nprint(np.sort(a, axis=1))\n"
] | [
[
"numpy.array",
"numpy.sort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hologerry/pix2pix-flow | [
"d512e64bf3539140d8f1295db98f793f1b2b912f"
] | [
"data_loaders/get_data.py"
] | [
"import os\nimport tensorflow as tf\nimport numpy as np\nimport glob\n\n_FILES_SHUFFLE = 1024\n_SHUFFLE_FACTOR = 4\n\n\ndef parse_tfrecord_tf(record, res, rnd_crop):\n features = tf.parse_single_example(record, features={\n 'shape': tf.FixedLenFeature([3], tf.int64),\n 'data': tf.FixedLenFeature([]... | [
[
"numpy.log2",
"tensorflow.FixedLenFeature",
"tensorflow.decode_raw",
"tensorflow.reshape",
"tensorflow.contrib.data.parallel_interleave",
"tensorflow.random_crop",
"numpy.concatenate",
"numpy.ceil",
"tensorflow.data.Dataset.list_files"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
liaha/tensorflow-sc-todate | [
"1f1c2eb8f30525ddcb819d6fc6941c7a84f33e4c"
] | [
"tensorflow/python/debug/lib/debug_v2_ops_test.py"
] | [
"# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.framework.ops.enable_eager_execution",
"tensorflow.python.framework.tensor_util.MakeNdarray",
"numpy.sqrt",
"tensorflow.python.ops.math_ops.square",
"tensorflow.python.debug.lib.debug_events_reader.DebugEventsReader",
"tensorflow.python.ops.math_ops.greater",
"tensor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
mmcauliffe/Conch | [
"7668612d7a610d0f5ae3332f990e71b26c5e8b34"
] | [
"conch/analysis/gammatone.py"
] | [
"from numpy import pi,exp,log,abs,sum,sqrt,array, hanning, arange, zeros,cos,ceil,mean\n\nfrom scipy.signal import filtfilt,butter,hilbert\n\nfrom .helper import preproc,make_erb_cfs,nextpow2,fftfilt\n\n\ndef to_gammatone(path,num_bands,freq_lims):\n sr, proc = preproc(path,alpha=0)\n\n proc = proc / 32768 #h... | [
[
"numpy.arange",
"numpy.cos",
"numpy.array",
"numpy.exp",
"scipy.signal.hilbert"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
vibhatha/PytorchExamples | [
"df356f120d6eef69a94586af93bff75af307582d"
] | [
"mnist/mnist_dist_rhel7.py"
] | [
"from __future__ import print_function\n\nimport time\n\nfrom math import ceil\nfrom random import Random\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nimport os\nimport torch\nimport torch.distributed as dist\nfrom torch.multiprocessing import Process\n\nim... | [
[
"torch.nn.Dropout2d",
"torch.nn.functional.nll_loss",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.is_available",
"torch.flatten",
"torch.device",
"torch.distributed.get_rank",
"torch.distributed.init_process_group",
"numpy.reshape",
"torch.tensor",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Brauntt/One-Pixel-New | [
"6017441f2d476f9c6c568dd886da43c6c0fd89bd"
] | [
"networks/capsulenet/capsule_net.py"
] | [
"from keras.layers import (\n Input,\n Conv2D,\n Activation,\n Dense,\n Flatten,\n Reshape,\n Dropout\n)\nfrom keras.layers.merge import add\nfrom keras.regularizers import l2\nfrom keras.models import Model\nfrom keras.layers.normalization import BatchNormalization\nimport keras.backend as K\n... | [
[
"matplotlib.pyplot.imread",
"numpy.concatenate",
"numpy.argmax",
"numpy.prod",
"numpy.exp",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bstee615/ReVeal | [
"ca31b783384b4cdb09b69950e48f79fa0748ef1d",
"ca31b783384b4cdb09b69950e48f79fa0748ef1d"
] | [
"Vuld_SySe/code_data.py",
"Vuld_SySe/representation_learning/trainer.py"
] | [
"import copy\n\nimport sys\n\nimport numpy as np\nimport torch\nfrom gensim.models import Word2Vec\n\n\nclass DataEntry:\n def __init__(self, dataset, sentence, label, meta_data=None, parser=None):\n self.dataset = dataset\n assert isinstance(self.dataset, DataSet)\n self.sentence = sentence... | [
[
"numpy.random.choice",
"numpy.asarray",
"numpy.arange",
"numpy.random.shuffle",
"numpy.random.uniform",
"numpy.zeros"
],
[
"sklearn.metrics.precision_score",
"numpy.argmax",
"torch.no_grad",
"sklearn.metrics.f1_score",
"numpy.array",
"sklearn.metrics.recall_scor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kyri-petrou/windpowerlib | [
"11907880634970fd20ccf403e92f61ebdeae4022"
] | [
"windpowerlib/power_output.py"
] | [
"\"\"\"\nThe ``power_output`` module contains functions to calculate the power output\nof a wind turbine.\n\nSPDX-FileCopyrightText: 2019 oemof developer group <contact@oemof.org>\nSPDX-License-Identifier: MIT\n\"\"\"\nimport numpy as np\nimport pandas as pd\n\n\ndef power_coefficient_curve(\n wind_speed,\n p... | [
[
"numpy.array",
"numpy.interp",
"pandas.Series",
"numpy.power"
]
] | [
{
"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": []
}
] |
SCCH-KVS/NuclearSegmentationPipeline | [
"5b4a37b74890e0a6fb767061c60a9f2a880d370d"
] | [
"DeepLearningArchitectures/MaskRCNN/config.py"
] | [
"\"\"\"\r\nMask R-CNN\r\nBase Configurations class.\r\n\r\nCopyright (c) 2017 Matterport, Inc.\r\nLicensed under the MIT License (see LICENSE for details)\r\nWritten by Waleed Abdulla\r\n\"\"\"\r\n\r\nimport math\r\nimport numpy as np\r\n\r\n\r\n# Base Configuration Class\r\n# Don't use this class directly. Instead... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lianghongzhuo/PointNetGPD | [
"c61ab1111d08007ecb41972142acdb67ed6a496d"
] | [
"PointNetGPD/model/dataset.py"
] | [
"import os\nimport glob\nimport pickle\nimport torch\nimport torch.utils.data\nimport torch.nn as nn\nimport numpy as np\n\n\nclass PointGraspDataset(torch.utils.data.Dataset):\n def __init__(self, obj_points_num, grasp_points_num, pc_file_used_num, grasp_amount_per_file, thresh_good,\n thresh_ba... | [
[
"numpy.hstack",
"numpy.dot",
"numpy.unique",
"numpy.cos",
"numpy.random.shuffle",
"numpy.sin",
"numpy.linalg.norm",
"numpy.dstack",
"numpy.delete",
"numpy.where",
"numpy.floor",
"numpy.cross",
"numpy.load",
"numpy.array",
"numpy.zeros",
"numpy.sum",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jigneshoo7/AlgoBook | [
"8aecc9698447c0ee561a1c90d5c5ab87c4a07b79"
] | [
"ml/Perceptrons/softmax.py"
] | [
"# Softmax function\n\nimport numpy as np\n\ndef Softmax(z): \n # The input for function sofmax is a vector\n \n return np.exp(z)/np.sum(np.exp(z),axis=0)"
] | [
[
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zzzeus/simple-faster-rcnn-pytorch | [
"e0aca1c6105ae6452faa2bff2441c05345be911e"
] | [
"trainer.py"
] | [
"from __future__ import absolute_import\nimport os\nfrom collections import namedtuple\nimport time\nfrom torch.nn import functional as F\nfrom model.utils.creator_tool import AnchorTargetCreator, ProposalTargetCreator\n\nfrom torch import nn\nimport torch as t\nfrom utils import array_tool as at\n# from utils.vis... | [
[
"torch.nn.CrossEntropyLoss",
"torch.zeros",
"torch.load",
"torch.arange",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gedeschaines/CS206_2017_Ludobots | [
"cb0c5a830222def0ca6221a9b78113be0da9b2b9"
] | [
"refactoring/randomSearch.py"
] | [
"#!/usr/bin/env python2\n\"\"\" CS206 Spring 2017 ludobots -- Project Random Search\n https://www.reddit.com/r/ludobots/wiki/pyrosim/randomsearch\n\"\"\"\nfrom individual import INDIVIDUAL\nfrom random import seed\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\n\nplay_blind = True\nsave_best... | [
[
"numpy.amax",
"numpy.amin",
"numpy.ndarray",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kozistr/naver-ai-hackathon-2018 | [
"b38b3a24c25402317d9409c2cab9a9205ab12f4c"
] | [
"kin/b_residual_concept/main.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nCopyright 2018 NAVER Corp.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and\nassociated documentation files (the \"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, ... | [
[
"tensorflow.get_variable",
"tensorflow.nn.sigmoid_cross_entropy_with_logits",
"tensorflow.train.AdamOptimizer",
"tensorflow.layers.batch_normalization",
"tensorflow.contrib.layers.variance_scaling_initializer",
"tensorflow.nn.moments",
"tensorflow.subtract",
"tensorflow.add",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
payal006/Movie-Review-Sentiment-Analysis | [
"2e1c9e2d82fe878bbf4ece7c64f39263f3855423"
] | [
"Sentiment Analysis /classifyUpdateLabelsInDataset.py"
] | [
"import collections\nfrom collections import deque\nimport nltk.classify.util, nltk.metrics\nfrom nltk.classify import NaiveBayesClassifier\nfrom nltk.corpus import CategorizedPlaintextCorpusReader\nfrom nltk.corpus import PlaintextCorpusReader\nfrom sklearn import svm\nfrom sklearn.svm import LinearSVC\n\nimport s... | [
[
"sklearn.svm.LinearSVC"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cranberrymuffin/ASAR-Deep-Loglizer | [
"2472cd971bc5241e72606f63401129532bf74a64"
] | [
"data_preprocess/preprocess_intrepid_ras.py"
] | [
"import os\nimport re\nimport pickle\nimport argparse\nimport pandas as pd\nimport numpy as np\nfrom utils import decision, json_pretty_dump\nfrom collections import OrderedDict, defaultdict\n\n\nseed = 42\nnp.random.seed(seed)\n\nparser = argparse.ArgumentParser()\n# TODO: Not accurate\nparser.add_argument(\"--tra... | [
[
"numpy.random.shuffle",
"pandas.read_csv",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
hendraet/IIC | [
"a5bab915eda133b0ecfd42eaacd60c7b26807cb6"
] | [
"src/scripts/cluster/cluster_sobel.py"
] | [
"from __future__ import print_function\n\nimport argparse\nimport itertools\nimport os\nimport pickle\nimport sys\nfrom datetime import datetime\n\nimport matplotlib\nimport numpy as np\nimport torch\n\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nimport src.archs as archs\nfrom src.utils.cluster.gener... | [
[
"torch.load",
"torch.zeros",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"torch.nn.DataParallel",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hshen14/Paddle | [
"0962be9c800d29e0804fc3135163bdfba1564c61"
] | [
"python/paddle/fluid/tests/unittests/test_imperative_optimizer.py"
] | [
"# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"numpy.array",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
victor-iyiola/deep-learning-with-tensorflow | [
"4d8e9b1054a24c92023f7e1c24c2911d6c30b59a"
] | [
"inception/inception.py"
] | [
"########################################################################\n#\n# The Inception Model v3 for TensorFlow.\n#\n# This is a pre-trained Deep Neural Network for classifying images.\n# You provide an image or filename for a jpeg-file which will be\n# loaded and input to the Inception model, which will then... | [
[
"tensorflow.Graph",
"tensorflow.import_graph_def",
"numpy.squeeze",
"tensorflow.train.SummaryWriter",
"tensorflow.Session",
"tensorflow.GraphDef",
"numpy.array",
"tensorflow.gfile.FastGFile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
lstorchi/molsuperimpose | [
"c6129e676b287040b17a7e5d5680176e2db361b6"
] | [
"modules/xyzutil.py"
] | [
"import re\nimport numpy\nimport math\n\n#####################################################################\n\ndef read_ncxyz (filename, trans = True):\n\n filep = open(filename, \"r\")\n \n filep.readline()\n filep.readline()\n \n xlist = []\n ylist = []\n zlist = []\n atoms = []\n \n for line in fil... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dendisuhubdy/newma-md | [
"723d1e5ae46397c867079e9e615a12d7df0c008b"
] | [
"newma_coronavirus_DESRES.py"
] | [
"# -*- coding: utf-8 -*-\n\n\n\"\"\"\nScript to recover the results for detecting the conformational changes of\nSARS-CoV-2. Is it possible to choose\nbetween real opu (to use it contact lighton at\n https://www.lighton.ai/contact-us/) and its synthetic version.\n\"\"\"\n\nimport argparse\n\nimport numpy as np\nimp... | [
[
"numpy.sqrt",
"numpy.min",
"numpy.arange",
"numpy.linalg.norm",
"numpy.concatenate",
"numpy.max",
"numpy.savez_compressed",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goedge-dev/dropt-example | [
"fbddc6a95edf28de69c303c2e4084367350b5f20"
] | [
"trials/mnist-pytorch/mnist.py"
] | [
"'''\nReference\n---\nhttps://github.com/pytorch/examples/tree/master/mnist\n'''\n\nfrom __future__ import print_function\nimport logging\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom argparse import ArgumentParser\nfrom torchvision import datasets, transfo... | [
[
"torch.nn.Dropout2d",
"torch.nn.functional.log_softmax",
"torch.nn.functional.nll_loss",
"torch.manual_seed",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.no_grad",
"torch.cuda.is_available",
"torch.flatten",
"torch.device",
"torch.nn.fun... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
flaviostutz/datascience-snippets | [
"768083c4eda972bc1f6548baa86751e0405bda9b"
] | [
"kaggle-sea-lion/modules/cnn.py"
] | [
"import h5py\n\nimport tensorflow as tf\nimport tflearn\nfrom sklearn import metrics\nfrom sklearn import preprocessing\nimport itertools\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom modules.logging import logger\nimport modules.logging\nimport modules.utils as utils\nfrom modules.utils import Timer... | [
[
"matplotlib.pyplot.legend",
"numpy.split",
"matplotlib.pyplot.title",
"sklearn.metrics.cohen_kappa_score",
"sklearn.metrics.confusion_matrix",
"matplotlib.pyplot.plot",
"numpy.concatenate",
"matplotlib.pyplot.subplot",
"numpy.argmax",
"numpy.shape",
"sklearn.preprocessi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShubhamAnandJain/MWP-CS229 | [
"ce86233504fdb37e104a3944fd81d4606fbfa621"
] | [
"mwptoolkit/module/Graph/gcn.py"
] | [
"# -*- encoding: utf-8 -*-\n# @Author: Yihuai Lan\n# @Time: 2021/08/29 21:49:49\n# @File: gcn.py\n\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom mwptoolkit.module.Layer.graph_layers import GraphConvolution\n\n\nclass GCN(nn.Module):\n def __init__(self, in_feat_dim, nhid, ou... | [
[
"torch.nn.functional.dropout"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pradeepkr12/text | [
"97e6d1d64b82ff91899b9083350951d8619e5914"
] | [
"torchtext/nn/modules/multiheadattention.py"
] | [
"import torch\nfrom typing import Tuple, Optional\n\n\nclass MultiheadAttentionContainer(torch.nn.Module):\n def __init__(self, nhead, in_proj_container, attention_layer, out_proj, batch_first=False):\n r\"\"\" A multi-head attention container\n\n Args:\n nhead: the number of heads in th... | [
[
"torch.nn.functional.softmax",
"torch.ones",
"torch.cat",
"torch.nn.functional.dropout",
"torch.matmul",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Geunwoo-Jeon/iclr_17_compression | [
"a28746b1f1c518d91125d8f289d9511cde488c77"
] | [
"models/Joint_baseline.py"
] | [
"from compressai.models.priors import JointAutoregressiveHierarchicalPriors as Joint\nimport torch\nimport math\n\nclass JointCompressor(Joint):\n def __init__(self, N=192, M=192, **kwargs):\n super().__init__(N=N, M=M, **kwargs)\n\n def forward(self, x):\n y = self.g_a(x)\n z = self.h_a(... | [
[
"torch.log",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marx-alex/Morphelia | [
"809278b07f1a535789455d54df3cbddc850d609c"
] | [
"morphelia/preprocessing/normalize.py"
] | [
"import numpy as np\nfrom sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\nfrom morphelia.tools import RobustMAD\nfrom morphelia.preprocessing import drop_nan as drop_nan_feats\n\n\ndef normalize(adata,\n by=(\"BatchNumber\", \"PlateNumber\"),\n method=\"standard\",... | [
[
"sklearn.preprocessing.RobustScaler",
"sklearn.preprocessing.StandardScaler",
"sklearn.preprocessing.MinMaxScaler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ipab-rad/softgym | [
"eeee770d8720c2cebaa9c5f72408b3340b07d367"
] | [
"softgym/utils/generate_font_img.py"
] | [
"from PIL import Image, ImageDraw, ImageFont\nimport numpy as np\nimport cv2\nimport pickle\n\ndef find_skeleton(arr, start_point, threshold=45):\n tmp_arr = arr.copy()\n queue = []\n res = []\n queue.append(start_point)\n res.append(start_point)\n head = start_point\n while len(queue) > 0:\n ... | [
[
"numpy.asarray",
"numpy.array",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IngiOrn/pymc | [
"8f3636daf7d9946f6eca4717f3bb0c6d77d9c6e9"
] | [
"pymc/distributions/discrete.py"
] | [
"# Copyright 2020 The PyMC Developers\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appli... | [
[
"numpy.ndim",
"numpy.log",
"scipy.stats.randint.rvs",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
utda/piranesi | [
"28c41334fdc7df44bd1b892542eb0423edd13013"
] | [
"batch/create_print_collection.py"
] | [
"import pandas as pd\nfrom rdflib import URIRef, BNode, Literal, Graph\nfrom rdflib.namespace import RDF, RDFS, FOAF, XSD\nfrom rdflib import Namespace\nimport numpy as np\nimport math\nimport sys\nimport argparse\nimport json\nimport html\n\n\ndef read_excel(path):\n df = pd.read_excel(path, sheet_name=0, heade... | [
[
"pandas.read_excel",
"pandas.isnull"
]
] | [
{
"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": []
}
] |
r7vme/sdsandbox | [
"701646f542ae1f55fa68198b8600e872e7497810"
] | [
"src/donkey_gym/donkey_gym/envs/donkey_sim.py"
] | [
"'''\r\nfile: donkey_sim.py\r\nauthor: Tawn Kramer\r\ndate: 2018-08-31\r\n'''\r\n\r\nimport os\r\nimport json\r\nimport shutil\r\nimport base64\r\nimport random\r\nimport time\r\nfrom io import BytesIO\r\nimport math\r\nfrom threading import Thread\r\n\r\nimport numpy as np\r\nfrom PIL import Image\r\nfrom io impor... | [
[
"numpy.asarray",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LonelyQuantum/SQuADPractice | [
"3c2d3c47bce08079d60596dc76668b2b458f0a48"
] | [
"models.py"
] | [
"\"\"\"Top-level model classes.\n\nAuthor:\n Chris Chute (chute@stanford.edu)\n\"\"\"\n\nimport layers\nimport torch\nimport torch.nn as nn\n\n\nclass BiDAF_CW(nn.Module):\n \"\"\"BiDAF model with Character Embedding for SQuAD.\n\n Based on the paper:\n \"Bidirectional Attention Flow for Machine Compreh... | [
[
"torch.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MatthiasJReisinger/addnn | [
"1d68648e81faf478cfb1f7d9a3f944a014fa3867"
] | [
"src/addnn/dataset.py"
] | [
"import functools\nimport multiprocessing\nimport os\nimport torch\nimport torchvision\nimport torchvision.datasets\nimport torch.utils.data\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\nfrom typing import Callable, Iterable, List, Optional, Tuple\n\n\nclass Dataset:\n def __init_... | [
[
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
praveeenbadimala/flow_unsupervised | [
"07385fd45e9213c06acacfd891e116f07993575e"
] | [
"models/actor_critic_combined.py"
] | [
"import torch\nimport torch.nn as nn\nfrom torch.nn.init import kaiming_normal\nimport torch.nn as nns\n\n# source ref: https://github.com/ClementPinard/FlowNetPytorch\n\ndef conv(batchNorm, in_planes, out_planes, kernel_size=3, stride=1):\n if batchNorm:\n return nn.Sequential(\n nn.Conv2d(in_... | [
[
"torch.nn.functional.upsample",
"torch.nn.init.kaiming_normal",
"torch.nn.ConvTranspose2d",
"torch.cat",
"torch.load",
"torch.nn.Conv2d",
"torch.nn.functional.adaptive_avg_pool2d",
"torch.nn.LeakyReLU",
"torch.nn.BatchNorm2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AdiVarma27/pyAB | [
"4cb3f693ce63ae48b42d3b8353057a00e5dceb6b"
] | [
"pyab/experiments/experiments.py"
] | [
"\"\"\"Main file comprising Frequentist & Bayesian A/B Testing methods.\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport scipy.stats as st\nimport statsmodels.stats.api as sms\nfrom statsmodels.stats.power import tt_ind_solve_power\nfrom pyab.utils import check_valid_input... | [
[
"matplotlib.pyplot.legend",
"scipy.stats.norm.ppf",
"numpy.sqrt",
"scipy.stats.norm.cdf",
"numpy.round",
"numpy.where",
"numpy.arange",
"matplotlib.pyplot.subplot",
"numpy.diff",
"scipy.stats.t",
"scipy.stats.t.cdf",
"matplotlib.pyplot.figure",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dukduque/PortfolioManager | [
"bc30a3ec9062abb5e7abd99d197b6cc043ab9786"
] | [
"source/database_handler.py"
] | [
"\"\"\"\nCreated on Thu May 23 22:32:31 2019\n\n@author: dduque\n\nThis module implements function to manage the prices database and\naccess the information. As of 2019/06/03, the package to access price\ndata is yfinance.\n\"\"\"\n'''\nSetup paths\n'''\n\nimport shutil\nimport yfinance as yf\nimport requests\nimpo... | [
[
"pandas.concat",
"pandas.read_pickle",
"pandas.Series",
"numpy.isnan",
"pandas.DataFrame",
"numpy.random.uniform",
"numpy.array"
]
] | [
{
"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": []
}
] |
Uditsingh7/Fake-news-predictor | [
"e1528b49673edf3b0908630177cbf5dea74fa3ba"
] | [
"FeatureSelection.py"
] | [
"\nimport DataPrep\nimport pandas as pd\nimport numpy as np\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.pipeline import Pipeline\nimport nltk\nimport nltk.corpus ... | [
[
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.feature_extraction.text.TfidfTransformer",
"numpy.zeros",
"sklearn.feature_extraction.text.TfidfVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ubco-mds-2020-labs/dashboard-project-data551_group10 | [
"ff7932cba58e63e1cd55eafb462e614c0670ab04"
] | [
"src/app.py"
] | [
"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\nfrom dash.dependencies import Input, Output\nimport numpy as np\nimport pandas as pd\nimport altair as alt\nfrom vega_datasets import data\nimport plotly.express as px \n\n\n#####\n##### D... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ScienceKot/kydavra | [
"e17f3855b94045953d0df6d13b9d9e0dffa9a8a3"
] | [
"kydavra/GeneticAlgorithmSelector.py"
] | [
"'''\r\nCreated with love by Sigmoid\r\n\r\n@Author - Păpăluță Vasile - vpapaluta06@gmail.com\r\n'''\r\n# Importing all needed libraries\r\nimport random\r\nfrom sklearn.metrics import accuracy_score\r\nfrom sklearn.model_selection import train_test_split\r\nclass GeneticAlgorithmSelector:\r\n def __init__(self,... | [
[
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
js05212/CollaborativeDeepLearning-TensorFlow | [
"f0891a7ce017f0560dca61bb7093d267f6212211"
] | [
"train_cdl.py"
] | [
"from lib.cdl import *\nimport numpy as np\nimport tensorflow as tf\nimport scipy.io\nfrom lib.utils import *\n\nnp.random.seed(0)\ntf.set_random_seed(0)\ninit_logging(\"cdl.log\")\n\ndef load_cdl_data():\n data = {}\n data_dir = \"data/citeulike-a/\"\n variables = scipy.io.loadmat(data_dir + \"mult_nor.mat\")\n... | [
[
"tensorflow.set_random_seed",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
PandasCute/DeepCTR | [
"bd5dc215e1c21bf12e6b5297d10f293c45e16c77"
] | [
"deepctr/models/autoint.py"
] | [
"# -*- coding:utf-8 -*-\n\"\"\"\n\nAuthor:\n Weichen Shen,wcshen1994@163.com\n\nReference:\n [1] Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.(https://arxiv.org/abs/1810.11921)\n\n\"\"\"\n\nimport ten... | [
[
"tensorflow.keras.layers.Concatenate",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras.models.Model"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
djcunningham0/multielo | [
"de91e3caa9906db56ae4f30cf0b3dccefa00af5c"
] | [
"tests/test_score_functions.py"
] | [
"import pytest\nimport numpy as np\nfrom multielo.score_functions import linear_score_function, create_exponential_score_function\nfrom multielo import MultiElo\n\nfrom typing import List\n\n\ndef test_2_player_score_function():\n # should always return [1, 0]\n linear_scores = linear_score_function(2)\n a... | [
[
"numpy.allclose",
"numpy.all",
"numpy.diff",
"numpy.random.uniform",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
trialanderror123/APSPT | [
"1eae0075efd066443037d69d165199eb07e3ad9e"
] | [
"src/data/dataloader.py"
] | [
"import torch\nfrom src.features.build_features import build_features\nfrom src.utils import load_train_data, load_prediction_data\nfrom src.config import *\nfrom torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\nfrom transformers import *\nfrom sklearn.model_selection import trai... | [
[
"torch.utils.data.TensorDataset",
"torch.utils.data.SequentialSampler",
"torch.utils.data.DataLoader",
"torch.utils.data.RandomSampler",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kosehy/TensorNetwork | [
"6478f03bc69e76487b04e69c11f29bbde7ded2e6"
] | [
"tensornetwork/backends/jax/jax_backend_test.py"
] | [
"# pytype: skip-file\n\"\"\"Tests for graphmode_tensornetwork.\"\"\"\nimport tensorflow as tf\nimport numpy as np\nimport jax\nimport pytest\nfrom tensornetwork.backends.jax import jax_backend\n\nnp_randn_dtypes = [np.float32, np.float16, np.float64]\nnp_dtypes = np_randn_dtypes + [np.complex64, np.complex128]\n\n\... | [
[
"numpy.imag",
"numpy.eye",
"tensorflow.ones",
"numpy.ones",
"numpy.random.rand",
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.zeros"
]
] | [
{
"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... |
joenghl/AMC_MPE | [
"2b629e52e2ad8dec546868b6b74035c895368b3f"
] | [
"multiagent/scenarios/evader_v0.py"
] | [
"import numpy as np\r\nfrom multiagent.core import World, Agent, Landmark\r\nfrom multiagent.scenario import BaseScenario\r\n\r\nclass Scenario(BaseScenario):\r\n \"\"\"\r\n chaser and evader\r\n \"\"\"\r\n def make_world(self):\r\n world = World()\r\n # set any world properties\r\n ... | [
[
"numpy.square",
"numpy.concatenate",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
josephsalmon/celer | [
"2bd090dd450afb269054eb718bb49dc1f697bd99"
] | [
"celer/tests/test_mtl.py"
] | [
"import pytest\nimport itertools\nimport numpy as np\nfrom numpy.linalg import norm\n\nfrom sklearn.utils.estimator_checks import check_estimator\nfrom sklearn.linear_model import MultiTaskLassoCV as sklearn_MultiTaskLassoCV\nfrom sklearn.linear_model import MultiTaskLasso as sklearn_MultiTaskLasso\nfrom sklearn.li... | [
[
"numpy.testing.assert_equal",
"sklearn.linear_model.MultiTaskLasso",
"numpy.arange",
"sklearn.linear_model.MultiTaskLassoCV",
"numpy.linalg.norm",
"numpy.testing.assert_array_less",
"sklearn.linear_model.lasso_path",
"sklearn.utils.estimator_checks.check_estimator",
"numpy.test... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Podshot/Amulet-Core | [
"678a722daa5e4487d193a7e947ccceacac325fd2"
] | [
"amulet/world_interface/formats/anvil/anvil_format.py"
] | [
"from __future__ import annotations\n\nimport os\nimport struct\nimport zlib\nimport gzip\nfrom typing import Tuple, Any, Dict, Union, Generator\nimport numpy\nimport time\nimport re\n\nimport amulet_nbt as nbt\n\nfrom amulet.world_interface.formats import Format\nfrom amulet.utils import world_utils\nfrom amulet.u... | [
[
"numpy.fromfile",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hackathonismb/Membrane-Protein-Domains-Motifs-Annotations | [
"0adf8a34af1691986e6e8d14aeb2fd629b6ee276"
] | [
"Consensus_domain_extraction/OPM_parser.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nimport urllib.parse\nimport urllib.request\nimport json\nimport pandas\n\nurl = 'https://www.uniprot.org/uploadlists/'\nGPCR_file = \"uniprot-human-allGPCRs.txt\"\nlymphycyte_file = \"uniprot-t-lymphocyte-activation-antigens.txt\"\n\n\nfor line in GPCR_file:\... | [
[
"pandas.merge",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Spartan-Velanjeri/Virtual-Assistant-Project | [
"f2fa52680900c33db5de0807f0bbc498fa9400ee"
] | [
"extract_embeddings.py"
] | [
"# USAGE\n# python extract_embeddings.py --dataset Dataset --embeddings output/embeddings.pickle \\\n#\t--detector face_detection_model --embedding-model openface_nn4.small2.v1.t7\n\n# import the necessary packages\nfrom imutils import paths\nimport numpy as np\nimport argparse\nimport imutils\nimport pickle\nimpor... | [
[
"numpy.array",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cainmagi/MDNC | [
"ce5e88b4c4880e8ecdbd82ee1da48335bad20b53"
] | [
"mdnc/utils/__main__.py"
] | [
"#!python\n# -*- coding: UTF-8 -*-\n'''\n################################################################\n# Utilities - Compatibility tests\n# @ Modern Deep Network Toolkits for pyTorch\n# Yuchen Jin @ cainmagi@gmail.com\n# Requirements: (Pay attention to version)\n# python 3.5+\n# numpy 1.13+\n# matplotlib ... | [
[
"numpy.square",
"numpy.expand_dims",
"matplotlib.pyplot.title",
"numpy.linspace",
"numpy.power",
"numpy.arange",
"numpy.stack",
"numpy.random.normal",
"numpy.mean",
"numpy.random.rand",
"numpy.exp",
"matplotlib.pyplot.show",
"numpy.random.default_rng"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xgarrido/pspy | [
"8c1c13828ca982a1747ddeed2ee9c35b09fd9f0b"
] | [
"tutorials/tuto_analytic_cov_spin0and2.py"
] | [
"\"\"\"\nThese are tests of analytic estimation of a gaussian covariance matrix for spin0 and 2 field\nIt is done in CAR pixellisation.\nWe also provide an option for monte-carlo verification of the covariance matrix\n\"\"\"\nimport matplotlib\n\nmatplotlib.use(\"Agg\")\nimport os\nimport time\n\nimport healpy as h... | [
[
"matplotlib.use",
"numpy.arange",
"numpy.save",
"numpy.append",
"numpy.mean",
"numpy.outer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
angshine/KP2D | [
"6673d3b93575585c98f451589cd52e7498803fa0"
] | [
"kp2d/networks/keypoint_resnet.py"
] | [
"# Copyright 2020 Toyota Research Institute. All rights reserved.\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.model_zoo as model_zoo\nimport torchvision.models as models\nfrom kp2d.utils.image import image_grid\n\n\ndef upsample(x):\n return F.... | [
[
"torch.nn.ReflectionPad2d",
"torch.nn.Dropout2d",
"torch.ones",
"torch.norm",
"torch.cat",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.unsqueeze",
"torch.nn.Tanh",
"torch.nn.Sigmoid",
"torch.nn.init.xavier_uniform_",
"torch.nn.functional.grid_sample",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thomassutter/MoPoE | [
"477a441ecb6c735a0b8af4d643fe3ac04c58171f",
"477a441ecb6c735a0b8af4d643fe3ac04c58171f"
] | [
"modalities/MNIST.py",
"celeba/networks/ConvNetworkImgClfCelebA.py"
] | [
"\nimport torch\n\nfrom modalities.Modality import Modality\n\nfrom utils import utils\nfrom utils.save_samples import write_samples_img_to_file\n\n\nclass MNIST(Modality):\n def __init__(self, name, enc, dec, class_dim, style_dim, lhood_name):\n super().__init__(name, enc, dec, class_dim, style_dim, lhoo... | [
[
"torch.Size"
],
[
"torch.nn.Linear",
"torch.nn.Dropout",
"torch.nn.Sigmoid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Racemuis/epoch-based-covariance-estimates | [
"9ae2faba05c043031bfdb69c6937492d18c7db1e"
] | [
"figures/figure_2_2.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun May 2 18:16:28 2021\r\n\r\nFigure 2.2: An aggregated ERP response with a soa of 226.\r\nCode partially taken from SpotPilotData (adapted).\r\n\"\"\"\r\n\r\nimport mne\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport yaml\r\nimport glob\r\n\r\ni... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
remram44/d3m-primitives | [
"bc7e609447cc827cd43a84fce348f85f4cd2fcd2"
] | [
"featSelect/Feature_Selector_model.py"
] | [
"# Algorithms\r\nfrom rpi_d3m_primitives.featSelect.tian_STMB_new import tian_STMB_new\r\nfrom rpi_d3m_primitives.featSelect.STMB_G_test import STMB_G_test\r\nfrom rpi_d3m_primitives.featSelect.sSTMB import sSTMBplus\r\nfrom rpi_d3m_primitives.featSelect.Large_Scale_STMB import Large_Scale_STMB\r\nfrom rpi_d3m_prim... | [
[
"numpy.array_equal",
"numpy.linspace",
"numpy.arange",
"sklearn.neighbors.KNeighborsClassifier",
"sklearn.neighbors.KNeighborsRegressor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adt9595/FootballLeagueSim | [
"75cea60ca3f29d32f68894ace82e3f8db3bf7338"
] | [
"LeagueSimulator/leaguesim.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Dec 12 19:41:59 2019\r\nLeague Simulator\r\n - Loads team data from text files\r\n - Simulates matches minute-by-minute \r\n - A goal may be scored each minute, with probability based on previous finishes and recent form\r\n - Leagues can be run throu... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
computational-medicine/BMED360-2021 | [
"2c6052b9affedf1fee23c89d23941bf08eb2614c",
"2c6052b9affedf1fee23c89d23941bf08eb2614c"
] | [
"Prog4comp-SL-HPL-Extra/src/Taylor_exp.py",
"Prog4comp-SL-HPL-Extra/src/file_handling_numpy.py"
] | [
"from math import factorial\r\nfrom numpy import exp, linspace\r\nimport matplotlib.pyplot as plt\r\n\r\ndef f(x):\r\n \"\"\"f(x) and its all its derivatives of higher order\"\"\"\r\n return exp(x)\r\n\r\ndef T(x, c, N):\r\n \"\"\"Builds the T.s. approxim. for f(x) = exp(x) with N + 1 terms\"\"\"\r\n su... | [
[
"numpy.linspace",
"matplotlib.pyplot.hold",
"matplotlib.pyplot.axis",
"numpy.exp",
"matplotlib.pyplot.show"
],
[
"numpy.savetxt",
"numpy.log",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Apoorba-Bibeka/LPGA-VISSIM-DataProcessing | [
"c707dff6c191dc073add594857331382b6e6891e",
"c707dff6c191dc073add594857331382b6e6891e"
] | [
"After-Validation-TT-Processing.py",
"12th_Street_VISSIM_Process/Network_Eval_12th_St.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 27 08:04:07 2019\n\n@author: abibeka\n# Get Travel time on LPGA and I-95 (After Validation)\n\"\"\"\n\n#0.0 Housekeeping. Clear variable space\nfrom IPython import get_ipython #run magic commands\nipython = get_ipython()\nipython.magic(\"reset -f\")\nipython = g... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.Categorical",
"matplotlib.pyplot.subplots",
"pandas.MultiIndex.from_product",
"matplotlib.pyplot.close",
"pandas.ExcelWriter",
"matplotlib.pyplot.figure"
],
[
"pandas.read_csv",
"pandas.ExcelFile",
"pandas.ExcelWriter"
]... | [
{
"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... |
KyawThuHtun/OpenCV-with-Python | [
"2c471060d4f2aa4ec45cda5f544a61923e4ea2ed"
] | [
"coding-exercises-avairds/week2-image-operation/part2-intermediate/mask.py"
] | [
"import cv2 as cv\nimport numpy as np\n\nimg = cv.imread(\"nancy1.jpg\", 1)\ncv.imshow(\"org\", img)\n\nmask = np.zeros(img.shape[:2], dtype=\"uint8\") # black image as like org-image dimension\n(cX, cY) = (img.shape[1] // 2, img.shape[0] // 2) # find center\n\n# create mask\ncv.rectangle(mask, (cX - 60, cY - 60)... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TaylorHere/modin | [
"044534315c95aa45aed8e53b665cec19abb055e2"
] | [
"modin/backends/base/query_compiler.py"
] | [
"# Licensed to Modin Development Team under one or more contributor license agreements.\n# See the NOTICE file distributed with this work for additional information regarding\n# copyright ownership. The Modin Development Team licenses this file to you under the\n# Apache License, Version 2.0 (the \"License\"); you... | [
[
"pandas.concat",
"numpy.conj",
"pandas.core.dtypes.common.is_scalar",
"pandas.get_dummies"
]
] | [
{
"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": []
}
] |
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