repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
wwe1428103707/colab_test | [
"6be33ef43573a737111d34ee1a2c8cc098742bb5"
] | [
"model.py"
] | [
"import os\n#os.environ['CUDA_VISIBLE_DEVICES']='1'\nimport torch\nimport torch.nn as nn\nfrom torch.nn import init\nimport torch.nn.functional as F\nimport numpy as np\n\nfrom pytorch_pretrained_bert import BertModel\nfrom data_load import idx2trigger, argument2idx\nfrom consts import NONE\nfrom utils import find_... | [
[
"torch.LongTensor",
"torch.nn.LSTM",
"torch.nn.functional.dropout",
"torch.cat",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.no_grad",
"torch.device",
"torch.nn.ReLU",
"torch.index_select",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eagle-robot/py-feat | [
"47a34b062dd5e6e07761c73d21285f085dfeaafa"
] | [
"feat/tests/test_detector.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"Tests for `feat` package.\"\"\"\n\nfrom feat.detector import Detector\nfrom feat.data import Fex\nfrom feat.utils import get_resource_path, read_pictures\nfrom feat.tests.utils import get_test_data_path\nimport pandas as pd\nimport feat\nimport os\nimport cv2... | [
[
"numpy.isnan",
"pandas.read_csv",
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jpn--/pine | [
"3980a9f0b09dd36b2fed7e52750847637be5f067"
] | [
"pines/latin_hypercube.py"
] | [
"\nimport numpy\nfrom scipy.optimize import minimize_scalar\nfrom scipy.stats import norm\n\ndef lhs( n_factors, n_samples, genepool=10000, random_in_cell=True ):\n\t\"\"\"\n\n\tParameters\n\t----------\n\tn_factors : int\n\t\tThe number of columns to sample\n\tn_samples : int\n\t\tThe number of Latin hypercube sam... | [
[
"numpy.dot",
"numpy.triu_indices",
"numpy.tril_indices",
"numpy.eye",
"scipy.optimize.minimize_scalar",
"numpy.full",
"scipy.stats.norm",
"numpy.max",
"numpy.random.permutation",
"numpy.argmin",
"numpy.random.rand",
"numpy.linalg.cholesky",
"numpy.corrcoef",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
johnr0/GeDi | [
"cb0b23cd9c9254539626b2a5ed0bf1d45e3fef78"
] | [
"train_GeDi.py"
] | [
"# Adapted from https://github.com/huggingface/transformers/blob/21da895013a95e60df645b7d6b95f4a38f604759/examples/run_glue.py\n# for training GPT-2 medium for sequence classification with GeDi objective\n\n\n# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copy... | [
[
"torch.cat",
"torch.load",
"numpy.squeeze",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.no_grad",
"torch.cuda.manual_seed_all",
"torch.split",
"torch.device",
"torch.distributed.get_rank",
"sklearn.metrics.f1_score",
"torch.cuda.is_available",
"torch.save... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anirbanlahiri2017/Tartarus | [
"13d01266511e5d64a77cdb071960cc58ee8c47a0"
] | [
"src/text-processing/load_w2v.py"
] | [
"from scipy.sparse import csr_matrix\nimport numpy as np\nimport re\nimport itertools\nfrom collections import Counter\nimport sys\nsys.path.insert(0, '../')\nimport common\nfrom gensim.models import word2vec\nfrom os.path import join, exists, split\nfrom nltk import sent_tokenize\nimport os\nimport numpy as np\nim... | [
[
"numpy.savez",
"numpy.save",
"scipy.sparse.csr_matrix",
"numpy.random.uniform",
"numpy.load",
"numpy.array"
]
] | [
{
"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"... |
aimldl/skills | [
"621876d0091fe74d9eca8ef996e03d8d92d6cef6"
] | [
"amazon_web_services/en/transcribe/src/amazon/transcribe.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\ntranscribe.py\n\nCAUTION:\nEnsure to use a new job_name for every single job request. Otherwise an error occurs:\n botocore.errorfactory.ConflictException: An error occurred\n (ConflictException) when calling the StartTranscriptionJob operation:\n T... | [
[
"pandas.DataFrame",
"pandas.DataFrame.from_dict"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
NizarGhandri/GDP | [
"8718fdaab8246c57f40d55754dcc169a7b45e6c4"
] | [
"src/statistical_analysis.py"
] | [
"from itertools import chain, combinations\nfrom typing import List, Tuple\n\nfrom numpy import linalg\nfrom scipy import stats\nfrom scipy.stats import chi2\n\nfrom src.regressions import least_squares, ridge_regression\nfrom src.evaluation_metrics import *\nfrom src.helpers import *\nimport math\n\nimport copy\n\... | [
[
"scipy.stats.t.ppf",
"scipy.stats.chi2.sf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ixdat/LowOverpotentialRegime | [
"4e9872230e44fa189129afa4352b1c9bf556c53f"
] | [
"figures/part_II_main_figs/part_I_fig3.py"
] | [
"\"\"\"This module is from Part I. It is here for use by part_II_fig5.py.\"\"\"\n\nimport re\nfrom pyOER.tof import all_tofs, TurnOverFrequency\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom pyOER.constants import (\n STANDARD_SPECIFIC_CAPACITANCE,\n STANDARD_SITE_DENSITY,\n FARADAY_CONS... | [
[
"matplotlib.pyplot.rc",
"matplotlib.pyplot.subplots",
"numpy.round",
"numpy.std",
"numpy.log10",
"numpy.mean",
"numpy.array",
"matplotlib.pyplot.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jzenn/NeuralStyleTransfer | [
"b0c9506071d29142adbd474d4d0f475ee76e5677"
] | [
"train.py"
] | [
"import os\nimport sys\nimport torch\n\nfrom net import get_vgg_model\nfrom net import get_style_loss_module\nfrom net import get_content_loss_module\nfrom net import get_full_style_model\nfrom net import get_input_optimizer\n\nfrom data_loader import get_images\nfrom data_loader import load_image\n\nfrom utils imp... | [
[
"torch.manual_seed",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hannahbos/deep-rl-hw | [
"335e1cf967b5b6ebf832dbaafd4d6c03fb9987da"
] | [
"hw3/train_ac_f18.py"
] | [
"\"\"\"\nOriginal code from John Schulman for CS294 Deep Reinforcement Learning Spring 2017\nAdapted for CS294-112 Fall 2017 by Abhishek Gupta and Joshua Achiam\nAdapted for CS294-112 Fall 2018 by Soroush Nasiriany, Sid Reddy, and Greg Kahn\n\"\"\"\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow_pro... | [
[
"tensorflow.get_variable",
"tensorflow.reduce_sum",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"tensorflow.train.AdamOptimizer",
"tensorflow.layers.dense",
"tensorflow.ConfigProto",
"numpy.std",
"tensorflow.Session",
"tensorflow.argmax",
"numpy.min",
"tenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
PeterSulcs/mlflow | [
"14c48e7bb1ca6cd6a3c1b249a486cd98bd5e7051",
"14c48e7bb1ca6cd6a3c1b249a486cd98bd5e7051"
] | [
"mlflow/pytorch/__init__.py",
"examples/sklearn_autolog/pipeline.py"
] | [
"\"\"\"\nThe ``mlflow.pytorch`` module provides an API for logging and loading PyTorch models. This module\nexports PyTorch models with the following flavors:\n\nPyTorch (native) format\n This is the main flavor that can be loaded back into PyTorch.\n:py:mod:`mlflow.pyfunc`\n Produced for use by generic pyfun... | [
[
"torch.jit.load",
"torch.load",
"torch.from_numpy",
"torch.no_grad",
"torch.jit.ScriptModule.save",
"torch.save"
],
[
"sklearn.preprocessing.StandardScaler",
"numpy.array",
"sklearn.linear_model.LinearRegression"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
BackPropagators/cs207-FinalProject | [
"6ec7d35f1af3b091d8c93c59bc0e8fe286a5fafc"
] | [
"AutoDiff/optimize.py"
] | [
"from AutoDiff.ForwardAD import Var, MultiFunc\nimport numpy as np\nimport types\n\ndef optimize(func, initial_guess, tolerance = 10e-6, solver = 'BFGS', max_iter = 100,\n gd_lr = 0.1):\n \"\"\"\n Returns the values for the elements in var_list that minimize function func.\n The opt... | [
[
"numpy.linalg.inv",
"numpy.dot",
"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": [],
... |
XJay18/ChineseTextClassification | [
"0920af34f68830b842fd6a246d1ee72183fe23d6"
] | [
"nn.py"
] | [
"import _pickle as pickle\nimport argparse\nimport math\nimport os\nimport random\nimport sys\nimport time\nfrom pprint import pprint\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport yaml\nfrom sklearn.metrics import accuracy_score as accuracy\nfrom sklearn.metrics import f1_score as f1\... | [
[
"matplotlib.pyplot.legend",
"torch.load",
"torch.utils.data.DataLoader",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"torch.no_grad",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available",
"torch.device",
"sklearn.metrics.f1_score",
"sklearn.metrics.precision_recal... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cjswo672/face-mosaic-on-real-time-streaming | [
"37c3f3f53a70bbddfbc2874a94762d421d043469"
] | [
"recognition/recognition.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nimport cv2\nfrom scipy import misc\nimport numpy as np\n\nimport time\nfrom recognition import detection, facenet, utils\nimport os\nimport pickle\n\n\nclass Recognition:\n ... | [
[
"scipy.misc.imresize",
"tensorflow.ConfigProto",
"numpy.max",
"numpy.argmax",
"tensorflow.GPUOptions",
"tensorflow.get_default_graph",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.0",
"0.19",
"0.18",
"1.2",
"0.12",
"0.10",
"0.17",
"0.16"
],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.... |
SFoxGit/reactdemoparse | [
"4153a60ef0911f90e16349d1e2cd45820d790efb"
] | [
"parsedemo.py"
] | [
"import csv\nimport shlex\nimport sys\nimport math\nimport numpy as np\nimport os.path\nimport time\nimport colorama\nfrom statistics import median\n\nfrom data.powers import *\nfrom data.config import *\nfrom data.Player import Player\nfrom data.Target import Target\nimport data.override as override\n\ndef main(ar... | [
[
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DavidPoliakoff/GDBKokkos | [
"7404ce502f5aa3d88d50070c7cd275a6b924831a"
] | [
"tests/test_LayoutRight.py"
] | [
"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n# vim:fenc=utf-8\n#\n# Copyright 2020 Char Aznable <aznable.char.0083@gmail.com>\n# Description: Utilities for pretty-printing Kokkos::View\n#\n# Distributed under terms of the 3-clause BSD license.\nimport re\nimport numpy as np\n\nshape = (3, 4, 5)\nstrShape = \"... | [
[
"numpy.arange",
"numpy.cumprod",
"numpy.array",
"numpy.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sdhilip200/Salary-Analysis--Australia---New-Zealand | [
"cb542024c701b15edd89fb7004e11d85a9f3a5b0"
] | [
"nicoleScrapingCode/cleanCSV.py"
] | [
"import pandas as pd \nimport glob\nimport re\nimport os\n\nos.chdir('../glassDoorReviews')\n\n# #create a new column, location, with the country information of the file name in that column\n# fileNames = [i for i in glob.glob('Data Analyst*.csv')]\n\n# # for each entry in filenames, read the file into a dataframe,... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
asah/meatshields-python-botkit | [
"0ad2fa1fb7806e554f3dd8d5c01b91465f34cf69"
] | [
"basicbot_lib.py"
] | [
"#!/usr/bin/env python3\n# -*- compile-command: \"/usr/local/bin/python3 sim.py\" -*-\n#\n# pylint:disable=locally-disabled,fixme,bad-whitespace,missing-docstring,multiple-imports,global-statement,multiple-statements,no-self-use,too-few-public-methods,\n#\n# basicbot.py\n#\n# TODO list:\n# - damage, healing ==> the... | [
[
"numpy.random.seed",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sckw/pySeaFlux | [
"3ecc2c7bedc121925be4056fa55f0e5a544eae64"
] | [
"tests/test_PyCO2SYS.py"
] | [
"# Tests against PyCO2SYS (originally v1.6.0).\n#\n# Weiss (1974) CO2 solubility cannot be directly tested because PyCO2SYS evaluates this\n# in /kg units while pyseaflux uses /l units.\n\nimport numpy as np\nimport PyCO2SYS as pyco2\n\nimport pyseaflux as sf\n\n\n# Seed random number generator for reproducibility\... | [
[
"numpy.isnan",
"numpy.random.default_rng",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pbujold/macaqueModules | [
"3f55ec45f691972e40cc8bd98071b7934ae24349"
] | [
"macaque/.ipynb_checkpoints/f_choices-checkpoint.py"
] | [
"\"\"\"\nModule of functions that apply to/get choice data for a trials dataframe (monkeys).\n\n\"\"\"\nfrom macaque.f_toolbox import *\nfrom collections import Counter\nimport pandas as pd\nimport numpy as np\ntqdm = ipynb_tqdm()\nfrom macaque.f_trials import add_chosenEV\n\n\n#%%\n#from numba import jit\n#from nu... | [
[
"numpy.hstack",
"pandas.concat",
"numpy.unique",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.diff"
]
] | [
{
"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": []
}
] |
bvraghav/releave | [
"d56598232e21921cd700cfd4eaaccf136a6aeb7e"
] | [
"image_functions.py"
] | [
"import logging as lg\n\nimport cv2\nimport numpy as np\n\ndef open_image(image_name) :\n img = cv2.imread(image_name, cv2.IMREAD_GRAYSCALE)\n if img is None :\n lg.warn('Problem opening: %s', image_name)\n raise Exception('Problem opening: %s' % image_name)\n\n lg.info('Success opening : %s, %s', image_na... | [
[
"numpy.min",
"numpy.max",
"numpy.std",
"numpy.average",
"numpy.array",
"numpy.where",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AndRod1104/Senior_Design_2020 | [
"49d0f9c7569c2071745c67dec635b3111c914e53"
] | [
"Data-recording-UI/Controller.py"
] | [
"# import seatease.spectrometers as s # Emulator to test w/o spectrometer\nimport seabreeze.spectrometers as s\n\nfrom datetime import datetime\nfrom tkinter.messagebox import showerror\n\nimport matplotlib\nimport numpy as np\n\nfrom LogPatient import *\nfrom LoginPage import *\nfrom ResetPW import *\nfrom SignUp... | [
[
"matplotlib.style.use",
"matplotlib.use",
"numpy.median",
"matplotlib.lines.Line2D",
"matplotlib.pyplot.subplots",
"numpy.round",
"matplotlib.animation.FuncAnimation",
"numpy.searchsorted",
"numpy.array",
"matplotlib.backends.backend_tkagg.FigureCanvasTkAgg"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tu-nv/detectron2 | [
"7394ed4eb6a5088ce25772e4a35c78de63c3d1a3"
] | [
"detectron2/modeling/box_regression.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\nimport math\nfrom typing import List, Tuple, Union\nimport torch\nfrom fvcore.nn import giou_loss, smooth_l1_loss\nfrom torch.nn import functional as F\n\nfrom detectron2.layers import cat, ciou_loss, diou_loss, cdiou_loss, aiou_loss\nfrom detectron2.structures i... | [
[
"torch.zeros_like",
"torch.exp",
"torch.nn.functional.relu",
"torch.log",
"torch.unbind",
"torch.stack",
"torch.clamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TamilNeram/nitsm-devtools-python | [
"4436bc630491e243a5dc4282bff9bc895bf9c02a"
] | [
"tests/ts_scope.py"
] | [
"import ctypes\nimport os\nimport time\nimport typing\n\nimport nidevtools.scope as scope\nimport niscope\nimport nitsm.codemoduleapi\nimport pytest\nfrom nitsm.codemoduleapi import SemiconductorModuleContext as SMContext\n\n# modules added for matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport... | [
[
"matplotlib.pyplot.figure",
"numpy.arange",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
niasw/import_mesh_CSV_CoordSurface | [
"27761ea6d98891e5d194777f02600566031e9ad3"
] | [
"Examples/gen_mobius.py"
] | [
"import io_csv\r\nimport math\r\nimport numpy\r\n\r\na=numpy.linspace(0,2*numpy.pi*(7.0/8.0),8)\r\nr=numpy.linspace(-0.2,0.2,3)\r\n\r\nvec_0=numpy.matrix([[0],[1],[0]])\r\nvec_1=numpy.matrix([[0],[0],[1]])\r\n\r\ncoords=[]\r\n\r\nfor ita in a:\r\n\tRz=numpy.matrix([[math.cos(ita),math.sin(ita),0],[-math.sin(ita),ma... | [
[
"numpy.matrix",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sariyanidi/face_alignment_keras | [
"1574ebbde014e7400e2c8626a2008d521ed7f894"
] | [
"FAN.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 23 16:08:04 2020\n\n@author: sariyanidi\n\"\"\"\n\nfrom tensorflow import keras\nfrom functools import partial\n\nDefaultConv2D = partial(keras.layers.Conv2D, kernel_size=3, use_bias=False, activation=None, padding=\"SAME\")\n\nclass ConvB... | [
[
"tensorflow.keras.layers.Concatenate",
"tensorflow.keras.layers.AveragePooling2D",
"tensorflow.keras.layers.ReLU",
"tensorflow.keras.layers.UpSampling2D",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.BatchNormalization"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
ICRA-2018/QuadsManip | [
"445ebdd451bb725dbb32e336fbf9b21ceddacf12"
] | [
"quadsmanip/visual.py"
] | [
"# Description: Plotting utilities using matplotlib\n# Author: Zijian Wang, Stanford University\n# Date: Jul 18, 2017\n# Email: zjwang@stanford.edu\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib.patches import Circle\nimport mpl_toolkits.mplot3d.art3... | [
[
"numpy.matrix",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"numpy.asscalar",
"numpy.linspace",
"numpy.arange",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ioff",
"matplotlib.pyplot.ylabel",
"numpy.copy",
"matplotlib.rcParams.update",
"matplotlib.pyplot.xl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
csarron/MobileAccelerator | [
"5e1b40cb2332073da6cd8a52bbba2712ae30f7bd"
] | [
"common/slim/syn_nets/var_alexnet/alexnet_wd8.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nslim = tf.contrib.slim\nfrom nets import alexnet\nfrom nets.alexnet import trunc_normal\n\nalexnet_wd8_arg_scope = alexnet.alexnet_v2_arg_scope\n\n\ndef alexnet_wd8(inputs,\n... | [
[
"tensorflow.reduce_mean",
"tensorflow.zeros_initializer",
"tensorflow.squeeze",
"tensorflow.constant_initializer",
"tensorflow.variable_scope"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
cwh94/SimpleModelsInference | [
"4be66d4d4d7aebcf104f363776b33d9a62e885f0"
] | [
"core/layers/depthwise.py"
] | [
"import math\n\nimport numpy as np\n\n# https://stackoverflow.com/questions/37674306/what-is-the-difference-between-same-and-valid-padding-in-tf-nn-max-pool-of-t\nfrom core.layers import pad_inputs\n\n\ndef padding(X, pad):\n print(\"====== pad .....: \", (int(pad[0] // 2), pad[0] - int(pad[0] // 2)),\n ... | [
[
"numpy.dot",
"numpy.array",
"numpy.zeros",
"numpy.multiply"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caibolun/torch2trt | [
"36db3d5fdaf7bb5fe6a03dd4752728a418fd8e48"
] | [
"torch2trt/tests/torchvision/retinanet/anchors.py"
] | [
"#!/usr/bin/env python\n# coding=utf-8\n'''\n@Author: ArlenCai\n@Date: 2020-06-19 12:42:12\n@LastEditTime: 2020-06-19 12:42:12\n'''\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\n\nclass Anchors(nn.Module):\n def __init__(self, pyramid_levels=None, strides=None, sizes=None, ratios=None, scales=None)... | [
[
"numpy.expand_dims",
"numpy.meshgrid",
"numpy.arange",
"numpy.tile",
"numpy.append",
"torch.cuda.is_available",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nehamorabagal/File-Structure-Indexing-Project | [
"403566c9b24d9ccfbf075518517b0575ff4df9cf"
] | [
"src/Indexing.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Untitled2.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/drive/1efgI8QTwUK1KBcTtJDwXgjVn2PhsfCQG\n\"\"\"\n\nimport pandas as pd\nimport time\n\n\nclass Index1:\n FILENAME = '1L_sales_records.csv'\n csv_df ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
byamao1/MMSA | [
"1a894d042144c9ac75b3465d38871ce8c2987251",
"1a894d042144c9ac75b3465d38871ce8c2987251"
] | [
"models/multiTask/MLMF.py",
"models/subNets/AlignNets.py"
] | [
"\"\"\"\npaper: Efficient Low-rank Multimodal Fusion with Modality-Specific Factors\nref: https://github.com/Justin1904/Low-rank-Multimodal-Fusion\n\"\"\"\nfrom __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom torch.nn.... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.Dropout",
"torch.ones",
"torch.Tensor",
"torch.nn.LSTM",
"torch.cat",
"torch.nn.init.xavier_normal_",
"torch.nn.Linear",
"torch.matmul"
],
[
"torch.nn.Softmax",
"torch.cat",
"torch.nn.LSTM",
"torch.bmm",
"torch.nn.Conv1d... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vanjulisharma/lightfm | [
"cadaa43d4a63ead08ae43e19e09be2b7ae6e504a"
] | [
"tests/test_datasets.py"
] | [
"import pytest\n\nimport numpy as np\n\nimport scipy.sparse as sp\n\nfrom lightfm.datasets import fetch_movielens, fetch_stackexchange\n\n\ndef test_basic_fetching_movielens():\n\n data = fetch_movielens()\n\n assert isinstance(data[\"train\"], sp.coo_matrix)\n assert isinstance(data[\"test\"], sp.coo_matr... | [
[
"numpy.all"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
impet14/handtrcking | [
"e721985191dbb5294ea4248a63e4250a0981360c"
] | [
"detect_multi_threaded_ori.py"
] | [
"from utils import detector_utils as detector_utils \nimport cv2\nimport tensorflow as tf\nimport multiprocessing\nfrom multiprocessing import Queue, Pool\nimport time\nfrom utils.detector_utils import WebcamVideoStream\nimport datetime\nimport argparse\n\n\nframe_processed = 0\nscore_thresh = 0.2\n\n\n# Create a w... | [
[
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
Takuya-Miyazaki/aws-data-wrangler | [
"9382eb08870f5f79cf5dbed52c36d0a36f6d8a07"
] | [
"awswrangler/_config.py"
] | [
"\"\"\"Configuration file for AWS Data Wrangler.\"\"\"\n\nimport inspect\nimport logging\nimport os\nfrom typing import Any, Callable, Dict, List, NamedTuple, Optional, Tuple, Type, Union, cast\n\nimport pandas as pd\n\nfrom awswrangler import exceptions\n\n_logger: logging.Logger = logging.getLogger(__name__)\n\n\... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
NiteshBharadwaj/pytorch-unsup-pc | [
"76f3baf54c18a4aff0e8a593952dda6e63459a60",
"76f3baf54c18a4aff0e8a593952dda6e63459a60"
] | [
"dpc/render/render_point_cloud.py",
"dpc/run/eval_chamfer_to.py"
] | [
"import os\nimport tempfile\nimport subprocess\n\nfrom easydict import EasyDict as edict\n\nimport numpy as np\nimport imageio\n\nfrom util.common import build_command_line_args\n\n\nscript_dir = os.path.dirname(os.path.realpath(__file__))\n\nblender_exec = f'{script_dir}/../../external/blender'\npython_script = f'... | [
[
"numpy.reshape",
"numpy.savez"
],
[
"numpy.expand_dims",
"numpy.isnan",
"numpy.squeeze",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"numpy.concatenate",
"numpy.mean",
"numpy.any",
"torch.cuda.is_available",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hayitsdavid/spotify-playlist-taste-flask | [
"c0d33113ab619a3b6207c14da2fd123fb916853f"
] | [
"app/generator.py"
] | [
"# Standard Library Imports\nimport base64\nimport io\nimport os\n\n# Third party imports\nfrom dotenv import load_dotenv\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import MinMaxScaler\nimport spotipy\nfrom spotipy.oauth2 import SpotifyClientCredentials\n\n... | [
[
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.fill",
"pandas.DataFrame.from_dict",
"matplotlib.pyplot.close",
"matplotlib.pyplot.polar",
"matplotlib.pyplot.xticks",
"sklearn.preprocessing.MinMaxScaler",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
Lotx-001/Easy | [
"c7f406cc3dfaa2eb825e8ff977072492a96dee3c"
] | [
"selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py"
] | [
"#!/usr/bin/env python3\nimport os\nimport numpy as np\n\nfrom common.realtime import sec_since_boot\nfrom common.numpy_fast import clip, interp\nfrom selfdrive.swaglog import cloudlog\nfrom selfdrive.modeld.constants import index_function\nfrom selfdrive.controls.lib.radar_helpers import _LEAD_ACCEL_TAU\n\nfrom py... | [
[
"numpy.diag",
"numpy.min",
"numpy.arange",
"numpy.tile",
"numpy.cumsum",
"numpy.ones",
"numpy.full",
"numpy.copy",
"numpy.diff",
"numpy.any",
"numpy.argmin",
"numpy.column_stack",
"numpy.exp",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
susht3/webQA_sequence_labelling_pytorch | [
"7a53322b0da1f99dbc90125501daebb866741559"
] | [
"code/online_util.py"
] | [
"import torch\nfrom torch import Tensor\nfrom torch.autograd import Variable\nimport random\nimport numpy as np\nfrom test import get_batch_scores, clean_answer, get_corrected_results,get_tagging_results, get_batch_ques2ans, fuzzy_match\nfrom util import load_vocab, pad_sequence\nfrom collections import Counter\nfr... | [
[
"torch.ByteTensor",
"torch.LongTensor",
"torch.utils.data.DataLoader",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Brontomerus/prince | [
"2f2bb6476445cd8cf4e77c400a84babe9aebe532"
] | [
"prince/mfa.py"
] | [
"\"\"\"Multiple Factor Analysis (MFA)\"\"\"\nimport itertools\nimport numpy as np\nimport pandas as pd\nfrom sklearn import utils\nimport warnings\n\nfrom . import mca\nfrom . import pca\n\n\nclass MFA(pca.PCA):\n\n def __init__(self, groups=None, normalize=True, n_components=2, n_iter=10,\n copy... | [
[
"pandas.concat",
"sklearn.utils.check_array",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"pandas.api.types.is_numeric_dtype",
"matplotlib.markers.MarkerStyle.markers.keys",
"pandas.api.types.is_string_dtype",
"numpy.array",
"pandas.get_dummies"
]
] | [
{
"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": []
}
] |
APMonitor/pds | [
"7cb4087dd8e75cb1e9b2a4283966c798175f61f7"
] | [
"All_Source_Code/GaussianMixtureModel/GaussianMixtureModel_1.py"
] | [
"from sklearn.mixture import GaussianMixture\ngmm = GaussianMixture(n_components=2)\ngmm.fit(XA)\nyP = gmm.predict_proba(XB) # produces probabilities\n# Arbitrary labels with unsupervised clustering may need to be reversed\nif len(XB[np.round(yP[:,0])!=yB]) > n/4: yP = 1 - yP "
] | [
[
"sklearn.mixture.GaussianMixture"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bollwyvl/vak | [
"1876b30a5b72f841e19720cca2c95d7940a5d9a9"
] | [
"src/vak/labels.py"
] | [
"import numpy as np\n\nfrom . import annotation\nfrom .validation import column_or_1d\n\n\ndef has_unlabeled(labels_int,\n onsets_s,\n offsets_s,\n time_bins):\n \"\"\"determine whether there are unlabeled segments in a spectrogram,\n given labels, onsets, an... | [
[
"numpy.abs",
"numpy.unique",
"numpy.asarray",
"numpy.ones",
"numpy.max",
"numpy.diff",
"numpy.insert",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sstcam/sstcam-simulation | [
"75bb863675991f1a36b7d430f9253ae09416f33e"
] | [
"optimisation_studies/pulse_shape/measure_pulse.py"
] | [
"from scipy.signal import find_peaks, peak_widths\n\n\ndef _extract_widths(pulse_y):\n peaks, _ = find_peaks(pulse_y)\n return peak_widths(pulse_y, peaks)\n\n\ndef extract_width(pulse_x, pulse_y):\n sample_width = pulse_x[1] - pulse_x[0]\n pulse_width = _extract_widths(pulse_y)[0][0] * sample_width\n\n ... | [
[
"scipy.signal.find_peaks",
"scipy.signal.peak_widths"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"1.5",
"1.7",
"1.2",
"1.8"
],
"tensorflow": []
}
] |
canerozer/eisen-core | [
"02ab7dc4cc7e3e21fd48da2bc1a91ce474922804"
] | [
"eisen/utils/logging/logs.py"
] | [
"import numpy as np\n\nfrom eisen import EISEN_END_EPOCH_EVENT\n\nfrom pydispatch import dispatcher\nfrom prettytable import PrettyTable\n\n\nclass LoggingHook:\n \"\"\"\n Logging object aiming at printing on the console the progress of model training/validation/testing.\n This logger uses an event based s... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
doriguzzi/lucid | [
"3c20ca2050defeaf22ed87200d1fda62cd1b3bff"
] | [
"lucid_dataset_parser.py"
] | [
"# Copyright (c) 2020 @ FBK - Fondazione Bruno Kessler\n# Author: Roberto Doriguzzi-Corin\n# Project: LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the Li... | [
[
"sklearn.feature_extraction.text.CountVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mottaquikarim/pydev-psets | [
"9749e0d216ee0a5c586d0d3013ef481cc21dee27"
] | [
"pset_pandas1_wine_reviews/selecting_data/p1.py"
] | [
"\"\"\"\nSelecting Data I - Access a Row\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nwine_reviews = pd.read_csv('../winemag-data-130k.csv')\n\n\n# Return the 12th row in the wine_reviews dataframe.\n\n\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
hwaranlee/SUMBT-LaRL | [
"822235a2383631a97c49ed6d731b99093d427d6d"
] | [
"convlab/modules/e2e/multiwoz/SUMBT_LaRL/utils/db_utils.py"
] | [
"\nimport sys, os\nsys.path.insert(0, os.path.abspath('.'))\n\nimport json\nimport sqlite3\nimport numpy as np\nimport copy\nimport os\nimport random\nimport re\nimport math\nimport pprint\n\nfrom nltk.stem.porter import *\n\nstemmer = PorterStemmer()\n\nrequestable_slots = {\n 'restaurant': ['name', 'food', '... | [
[
"numpy.hstack",
"numpy.ones",
"numpy.append",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
joaomonteirof/e2e_antispoofing | [
"dfa31396bb546bd2526d4aae06758126a17e3946"
] | [
"train.py"
] | [
"from __future__ import print_function\nimport argparse\nimport torch\nfrom train_loop import TrainLoop\nimport torch.optim as optim\nimport torch.utils.data\nimport model as model_\nimport numpy as np\nfrom data_load import Loader\nfrom torch.utils.tensorboard import SummaryWriter\nfrom utils import *\n\n# Trainin... | [
[
"torch.cuda.manual_seed",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.cuda.is_available",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GSByeon/studio | [
"782cf484541c6d68e1451ff6a0d3b5dc80172664"
] | [
"function/python/brightics/function/textanalytics/lda.py"
] | [
"from brightics.common.repr import BrtcReprBuilder, strip_margin, pandasDF2MD, dict2MD\nfrom brightics.function.utils import _model_dict\nfrom brightics.common.groupby import _function_by_group\nfrom brightics.common.utils import check_required_parameters\nfrom brightics.function.validation import raise_runtime_err... | [
[
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.decomposition.LatentDirichletAllocation",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
utilForever/2021-JBSH-MakeANN | [
"6a3e7164702e1ea272e9689e44d2624e662054fb"
] | [
"code/nn/average_digits.py"
] | [
"from matplotlib import pyplot as plt\nimport numpy as np\nfrom dlgo.nn.load_mnist import load_data\nfrom dlgo.nn.layers import sigmoid_double\n\n\ndef average_digit(data, digit):\n filtered_data = [x[0] for x in data if np.argmax(x[1]) == digit]\n filtered_array = np.asarray(filtered_data)\n return np.ave... | [
[
"numpy.dot",
"matplotlib.pyplot.imshow",
"numpy.reshape",
"numpy.asarray",
"numpy.average",
"numpy.argmax",
"numpy.transpose",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ebernhardson/LightGBM | [
"7501faa6489d1832ba815ea371897d4eb9be2817"
] | [
"python-package/lightgbm/basic.py"
] | [
"# coding: utf-8\n# pylint: disable = invalid-name, C0111, C0301\n# pylint: disable = R0912, R0913, R0914, W0105, W0201, W0212\n\"\"\"Wrapper c_api of LightGBM\"\"\"\nfrom __future__ import absolute_import\n\nimport copy\nimport ctypes\nimport os\nimport warnings\nfrom tempfile import NamedTemporaryFile\n\nimport n... | [
[
"numpy.all",
"numpy.copy",
"numpy.fromiter",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danirivas/cova-tuner | [
"e7eaf7e75f0c15ce35c449fb67529c9c73386817"
] | [
"src/cova/dnn/eval.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport copy\nimport os\n\nimport tensorflow.compat.v2 as tf\nfrom object_detection import inputs\nfrom object_detection import model_lib_v2\nfrom object_detection.utils import config_util\nfrom object_detection.builders import model_builder\n\n\ndef eval_continuous... | [
[
"tensorflow.compat.v2.train.CheckpointManager",
"tensorflow.compat.v2.compat.v2.Variable",
"tensorflow.compat.v2.compat.v2.distribute.get_strategy",
"tensorflow.compat.v2.config.set_soft_device_placement",
"tensorflow.compat.v2.train.Checkpoint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bizzyvinci/models | [
"af924a4c2168b5804c618e8361c37a6756463b49"
] | [
"official/nlp/data/classifier_data_lib.py"
] | [
"# Copyright 2021 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.io.TFRecordWriter",
"tensorflow.io.gfile.GFile",
"tensorflow.io.gfile.listdir",
"tensorflow.train.Features"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
terragord7/PyDGN | [
"60918b42126c25c8f8d4ab083a0e3d548f7092ca"
] | [
"pydgn/model/readout/node_readout.py"
] | [
"import torch.nn as nn\n\nfrom pydgn.model.interface import ReadoutInterface\n\n\nclass LinearNodeReadout(ReadoutInterface):\n \"\"\"\n Class that implements a simple readout mapping for node prediction\n \"\"\"\n def __init__(self, dim_node_features, dim_edge_features, dim_target, config):\n sup... | [
[
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kaanakan/slamp | [
"7c47e6b561d1f973626061d8e8600e072ed6785e"
] | [
"models/conv_lstms.py"
] | [
"import torch.nn as nn\nimport torch\nfrom torch.autograd import Variable\n\n\nclass ConvLSTMCell(nn.Module):\n\n def __init__(self, input_dim, hidden_dim, kernel_size, bias):\n \"\"\"\n Initialize ConvLSTM cell.\n\n Parameters\n ----------\n input_dim: int\n Number ... | [
[
"torch.sigmoid",
"torch.cat",
"torch.zeros",
"torch.nn.Conv2d",
"torch.nn.Tanh",
"torch.tanh",
"torch.split",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mike968/Matcher | [
"30967b76f4a2ca08cf81ce91ebeedb31009e0c80"
] | [
"matcher/core.py"
] | [
"# =====================\n# Package imports\n# =====================\nimport pandas as pd\nimport random as rd\n# =====================\n# Module imports\n# =====================\nfrom matcher.data import Data\nfrom visiulization.graph import Graph\n# =====================\n# Script\n# =====================\n\nclas... | [
[
"pandas.merge",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Juanlu001/poliastro | [
"38a28e4713986df9098e8948935c5b63e9f1b115"
] | [
"src/poliastro/core/elements.py"
] | [
"\"\"\"This module contains a set of functions that can be used to\nconvert between different elements that define the orbit of a body.\n\"\"\"\n\nimport sys\n\nimport numpy as np\nfrom numba import njit as jit, prange\nfrom numpy import cos, cross, sin, sqrt\n\nfrom poliastro._math.linalg import norm\nfrom poliast... | [
[
"numpy.log",
"numpy.sqrt",
"numpy.cos",
"numpy.sin",
"numpy.arctan2",
"numpy.tan",
"numpy.cross",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kasey-/ArduinoDQNCar | [
"cf1f2a74ea4f79808a3155fe9900c3207534d4e5"
] | [
"Step-4-TrainingOverBLE/training/main.py"
] | [
"import numpy as np\nimport gym\nimport gym_arduino\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation, Flatten\nfrom keras.optimizers import Adam\n\nfrom rl.agents.dqn import DQNAgent\nfrom rl.policy import BoltzmannQPolicy\nfrom rl.memory import SequentialMemory\n\nENV_NAME = 'ardu... | [
[
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Solacex/Content-Consistent-Matching-for-Domain-Adaptive-Semantic-Segmentation-CCM- | [
"2fcd9cb3b1d2070dd403613f00ca55ef872b3104"
] | [
"trainer/source_only_trainer.py"
] | [
"import torch\nfrom utils.optimize import *\nfrom .base_trainer import BaseTrainer\nfrom pytorch_memlab import profile\nfrom easydict import EasyDict as edict\nimport os.path as osp\nfrom dataset import dataset\nimport torch.optim as optim\nfrom tqdm import tqdm\nimport neptune\nimport math\nfrom PIL import Image\... | [
[
"torch.nn.functional.cross_entropy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hkrsnd/dilp-st | [
"54ef5b4a8393bf534493cbb85e8f5da80b51c14c"
] | [
"src/optimizer.py"
] | [
"import random\nimport time\nimport torch\nfrom collections import OrderedDict\nfrom tqdm import tqdm\n\nrandom.seed(a=7014) # PAPER ID 7014\n\nif torch.cuda.is_available():\n device = 'cuda'\nelse:\n device = 'cpu'\n\n\nclass WeightOptimizer():\n \"\"\"\n optimizer of clause weights using gradient des... | [
[
"torch.optim.RMSprop",
"torch.gather",
"torch.cuda.is_available",
"torch.nn.BCELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aTechGuide/machine-learning | [
"3866b32a29e40ef8553469fe0dc0cdd8f98128c1",
"3866b32a29e40ef8553469fe0dc0cdd8f98128c1"
] | [
"algorithms/regression/support_vector_regression/svr.py",
"projects/template/src/dispatcher.py"
] | [
"# SVR\n\n# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the dataset\ndataset = pd.read_csv('Position_Salaries.csv')\nX = dataset.iloc[:, 1:2].values ## Choosing level\ny = dataset.iloc[:, 2:3].values ## Choosing Salary\n\n# We don't have enough d... | [
[
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.pyplot.scatter",
"sklearn.svm.SVR",
"matplotlib.pyplot.xlabel",
"sklearn.preprocessing.StandardScaler",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
],
[
"sklearn.ensemble.ExtraTreesClassifier",
"sklearn.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yk/pyvttbl | [
"af66c1aba410ba5386249cd5b95f2ae0ed01d870"
] | [
"pyvttbl/stats/_stats.py"
] | [
"# Copyright (c) 1999-2007 Gary Strangman; All Rights Reserved.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to ... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.take",
"numpy.asarray",
"numpy.cumsum",
"numpy.concatenate",
"numpy.exp",
"numpy.where",
"numpy.greater",
"numpy.clip",
"numpy.reshape",
"numpy.less",
"numpy.arange",
"numpy.add.reduce",
"numpy.multiply.outer",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ukml2018/Hand-Detection-and-Distance-Estimation | [
"169d32b7f35c58eb1c6563f70d7a6dcd40311089"
] | [
"utils/detector_utils.py"
] | [
"# Utilities for object detector.\n\nimport numpy as np\nimport sys\nimport tensorflow as tf\nimport os\nfrom threading import Thread\nfrom datetime import datetime\nimport cv2\nfrom utils import label_map_util\nfrom collections import defaultdict\n\ndetection_graph = tf.Graph()\n\nTRAINED_MODEL_DIR = 'frozen_graph... | [
[
"tensorflow.Graph",
"numpy.expand_dims",
"tensorflow.import_graph_def",
"tensorflow.gfile.GFile",
"numpy.squeeze",
"tensorflow.Session",
"tensorflow.GraphDef"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pyscioffice/pydatamail_google | [
"c4a235423fcaa0617e895b3c0685fad26eef4075"
] | [
"pydatamail_google/base/archive.py"
] | [
"import os\nimport base64\nimport email\nimport numpy as np\nfrom tqdm import tqdm\nfrom datetime import datetime\nfrom PyPDF3 import PdfFileMerger\nfrom email2pdf2 import (\n get_unique_version,\n get_input_email,\n handle_message_body,\n remove_invalid_urls,\n get_formatted_header_info,\n output... | [
[
"numpy.argsort",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
starsky68/capsnet_blk_prune | [
"9a8b896bfa8fedb287edae5286b65824b3e8cc84"
] | [
"main.py"
] | [
"\"\"\"\nPyTorch implementation of CapsNet in Sabour, Hinton et al.'s paper\nDynamic Routing Between Capsules. NIPS 2017.\nhttps://arxiv.org/abs/1710.09829\n\nUsage:\n python main.py\n python main.py --epochs 30\n python main.py --epochs 30 --num-routing 1\n\nAuthor: Cedric Chee\n\"\"\"\n\nfrom __future__ ... | [
[
"torch.Size",
"torch.cuda.manual_seed",
"torch.manual_seed",
"torch.cuda.device_count",
"torch.cuda.is_available",
"torch.nn.DataParallel",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
snsunx/pyscf | [
"677ced9bd360ac2c8ee07d3a6039a9903e35fb6c",
"677ced9bd360ac2c8ee07d3a6039a9903e35fb6c",
"677ced9bd360ac2c8ee07d3a6039a9903e35fb6c"
] | [
"pyscf/pbc/df/df.py",
"pyscf/cc/rccsd.py",
"pyscf/pbc/scf/krohf.py"
] | [
"#!/usr/bin/env python\n# Copyright 2014-2018 The PySCF Developers. 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/LIC... | [
[
"numpy.hstack",
"numpy.sqrt",
"numpy.einsum",
"numpy.asarray",
"numpy.reshape",
"numpy.vstack",
"numpy.ndarray",
"numpy.ones",
"numpy.all",
"numpy.zeros_like",
"numpy.any",
"numpy.prod",
"numpy.count_nonzero",
"numpy.zeros",
"numpy.where",
"numpy.emp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chang-group/bk | [
"35b0c5738a8a77d3113701d371b9811888f341c9"
] | [
"examples/cdk/util.py"
] | [
"import numpy as np\nimport scipy.interpolate as interp\n\ndef interpolate_path(path, npoints=1000, image_spacing=1.0):\n t = np.cumsum(np.linalg.norm(np.diff(path, axis=0), axis=1))\n t = np.insert(t, 0, 0)\n \n f = interp.interp1d(t, path.T, kind='cubic')\n path_new = f(np.linspace(0, t[-1], npoint... | [
[
"numpy.linspace",
"numpy.arange",
"scipy.interpolate.interp1d",
"numpy.diff",
"numpy.insert"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
yulonglin/imitation | [
"e5479b18f741b1d3591bec553ea84033fbd10ced"
] | [
"src/imitation/algorithms/mce_irl.py"
] | [
"\"\"\"Finite-horizon tabular Maximum Causal Entropy IRL.\n\nFollows the description in chapters 9 and 10 of Brian Ziebart's `PhD thesis`_.\n\n.. _PhD thesis:\n http://www.cs.cmu.edu/~bziebart/publications/thesis-bziebart.pdf\n\"\"\"\n\nfrom typing import Any, Iterable, Mapping, Optional, Tuple, Type, Union\n\ni... | [
[
"numpy.abs",
"numpy.full",
"numpy.all",
"numpy.ones",
"torch.dot",
"numpy.array",
"numpy.exp",
"numpy.zeros",
"torch.squeeze",
"torch.as_tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bolianchen/pytorch_depth_from_videos_in_the_wild | [
"6f013154ba9f3a8de944c788a9370980e4c2b7c8",
"6f013154ba9f3a8de944c788a9370980e4c2b7c8"
] | [
"evaluators/wild_evaluator.py",
"gen_data.py"
] | [
"import os\nimport numpy as np\nimport torch\nfrom torchvision import transforms\n\nfrom trainers import WildTrainer\nfrom options import WildOptions\n\nfrom .base_evaluator import BaseEvaluator\n\nclass WildEvaluator(BaseEvaluator):\n def __init__(self, opt):\n self.opt = opt\n if not hasattr(self... | [
[
"numpy.array",
"torch.no_grad"
],
[
"numpy.hstack",
"numpy.random.random",
"numpy.random.seed",
"torch.utils.data.DataLoader",
"torch.cuda.is_available",
"numpy.array_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dsoliveir/TCV-X21 | [
"784c55adb33417e21a6736e2504a3895a9348dbe"
] | [
"2.simulation_data/GRILLIX_2021/checkpoints_for_1mm/sub_scripts/checkpoint.py"
] | [
"#!/usr/bin/env python3\n\nimport numpy as np\nimport xarray as xr\nfrom pathlib import Path\nfrom datetime import datetime\nfrom shutil import copy2\n\n\ndef crop_snap(in_file, out_file, tau_slice: slice, reset_tau=False):\n\n new_dataset = xr.open_dataset(in_file)\n new_dataset.encoding = {\"unlimited_dims\... | [
[
"numpy.max"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jshirius/kaggle_cassava | [
"1660b26df18e6347b2d914c6717186659908c0ed"
] | [
"src/learning.py"
] | [
"# 訓練と評価\n\nimport time\nfrom tqdm import tqdm\nimport torch\nfrom torch import nn\nfrom torch.cuda.amp import autocast, GradScaler\nimport numpy as np\nimport pandas as pd\nfrom src.data_set import TestDataset, LABEL_NUM\nfrom src.model.train_model import CassvaImgClassifier, LabelSmoothingLoss, TaylorCrossEntropy... | [
[
"numpy.sqrt",
"torch.zeros",
"torch.randperm",
"torch.load",
"torch.utils.data.DataLoader",
"pandas.DataFrame",
"torch.cuda.amp.autocast",
"numpy.concatenate",
"numpy.int",
"numpy.mean",
"torch.no_grad",
"torch.device",
"numpy.random.randint",
"torch.nn.Cros... | [
{
"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": []
}
] |
ITBA-Consulting/soc-app-served | [
"310643deca5ad2fff5e8bf617ee952cb991a37b2"
] | [
"app/data_cleaning.py"
] | [
"import pandas as pd\nfrom scipy import stats\nimport numpy as np\nfrom scipy import stats\n\n\ndef od(df_soc, numerical_col, type):\n #print(\"type\" , type, type == 'naive')\n if type == 'naive':\n for e in numerical_col:\n print(\"enter od\")\n removed_outliers = df_soc[e].betw... | [
[
"scipy.stats.zscore"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
haribharadwaj/PLOSBiol_ASD_ObjectFormation | [
"5ea164876b00b3d11965e7f4a443abbfcfa7b252"
] | [
"generate_Fig3A_3B.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Dec 21 15:50:07 2018\r\n\r\n@author: hari\r\n\"\"\"\r\n\r\nfrom scipy import io\r\nimport pylab as pl\r\nimport numpy as np\r\n\r\nfname = 'ERPsummary_zscore.mat'\r\ndat = io.loadmat(fname)\r\nt = dat['t'].flatten()\r\nc6 = dat['c6']\r\nc12 = dat['c12']\r\nc18 = ... | [
[
"scipy.io.loadmat",
"numpy.mean"
]
] | [
{
"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"... |
talesa/syne-tune | [
"282156294a64a0cd260ccd908f3cf6b3e8c71003"
] | [
"syne_tune/optimizer/schedulers/searchers/kde_searcher.py"
] | [
"# Copyright 2021 Amazon.com, Inc. or its affiliates. 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# A copy of the License is located at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# or ... | [
[
"numpy.isfinite",
"numpy.clip",
"numpy.ceil",
"numpy.argsort",
"numpy.array",
"scipy.stats.truncnorm.rvs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gkanapathy/pebble-tutorial | [
"2bac6148f2fbb39ed8c2bc1187528e41bf0a3292"
] | [
"02_summarystats.py"
] | [
"import pandas as pd\n\n#load and process data into a global structure\ntitanic = pd.read_csv(\"https://raw.githubusercontent.com/gkanapathy/pebble-tutorial/main/data/titanic.csv\")\n\ndef mean(field=\"Age\"):\n return titanic[field].mean()\n\ndef median(field=\"Age\"):\n return titanic[field].median()\n\ndef... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
osushiski/gradio | [
"700b534d00e1ced1a1eb1228790f8cda1191a739"
] | [
"demo/webcam/run.py"
] | [
"import numpy as np\n\nimport gradio as gr\n\n\ndef snap(image):\n return np.flipud(image)\n\n\niface = gr.Interface(snap, gr.inputs.Image(source=\"webcam\", tool=None), \"image\")\nif __name__ == \"__main__\":\n iface.launch()\n"
] | [
[
"numpy.flipud"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vincentbonnetcg/Numerical-Bric-a-Brac | [
"e71f2305d7452de985e5e9fa8935da611b6d9992"
] | [
"implicit_solver/lib/objects/jit/algorithms/area_lib.py"
] | [
"\"\"\"\n@author: Vincent Bonnet\n@description : Area constraint helper functions\n\"\"\"\n\nimport math\nimport numpy as np\nimport numba\n\nfrom lib.objects.jit.data import Area\nimport core.code_gen as generate\nimport lib.objects.jit.algorithms.data_accessor as db\nimport core.jit.math_2d as math2D\nfrom lib.ob... | [
[
"numpy.sign",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Abdus-Samee/CSE-218 | [
"154b0d8abe56bf4e11f2d86c5043f7c022baf7f2"
] | [
"Non Linear Eqn/graph.py"
] | [
"import matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nx = np.linspace(0, 1)\r\ny = x**3 - 2400*(x**2) - 3*x + 2\r\n\r\nplt.figure(figsize = (10, 10))\r\nplt.plot(x, y, 'r')\r\nplt.title('Molar dissociation of H20')\r\nplt.ylabel('Y Axis')\r\nplt.ylim(-1, 2)\r\nplt.grid()\r\nplt.show()"
] | [
[
"matplotlib.pyplot.title",
"numpy.linspace",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gpucce/respol_patents_code | [
"cfc5f4e53e67f431127fc21cd51777cf8225e53a"
] | [
"backward_cosine/step_04_cosine_similarity_five_years.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 3 14:15:00 2020\n\n@authors: Juan Carlos Gomez\n Sam Arts\n Jianan Hou\n\n@emails: jc.gomez@ugto.mx\n sam.arts@kuleuven.be\n jianan.hou@kuleuven.be\n\n@description: Computes the averegar cosine similarity of a focus patent\nregar... | [
[
"numpy.asarray",
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.preprocessing.normalize"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ssim/artis-tools | [
"5b81c660bb0d4563d10fa896954010ff3c0c3a15"
] | [
"artistools/estimators/estimators.py"
] | [
"#!/usr/bin/env python3\n\"\"\"Functions for reading and processing estimator files.\n\nExamples are temperatures, populations, and heating/cooling rates.\n\"\"\"\n# import math\nimport math\nimport multiprocessing\nimport sys\nfrom collections import namedtuple\nfrom functools import lru_cache, partial, reduce\n# ... | [
[
"pandas.DataFrame.from_records"
]
] | [
{
"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": []
}
] |
cphysics/gauge2d | [
"dce8d430b5f35af6750094ec68179490ae82d187"
] | [
"data/WNR.py"
] | [
"import numpy as np\n\n\nclass wnr(object):\n \n def __init__ (self,filename,ver):\n self.filename = filename\n self.ver = ver\n \n\n def writer(self):\n DataOut = np.column_stack(self.ver) \n np.savetxt(self.filename,DataOut)\n ... | [
[
"numpy.savetxt",
"numpy.loadtxt",
"numpy.column_stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ekmixon/Playground-1 | [
"08266d131735a7bcd72a975c6e402832f379d1cb"
] | [
"TensorFlow/mnist_softmax.py"
] | [
"##############################################################\n# A simple tf script to train onf the mnist data\n#\n# Author: Carl Cortright \n# Date: 9/29/2016\n# Updated 10/17/2016\n#\n# Tutorial: https://www.tensorflow.org/versions/r0.11/tutorials/mnist/beginners/index.html#about-this-tutorial\n###############... | [
[
"tensorflow.matmul",
"tensorflow.zeros",
"tensorflow.cast",
"tensorflow.placeholder",
"tensorflow.initialize_all_variables",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.log",
"tensorflow.Session",
"tensorflow.argmax",
"tensorflow.examples.tutorials.mnist.input_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
sally20921/Meta_Pseudo_Labels | [
"308b44b84cafb8aafc3264b336a2a795b839d287"
] | [
"code/data.py"
] | [
"import logging\nimport math\n\nimport numpy as np\nfrom PIL import Image\nfrom torchvision import datasets\nfrom torchvision import transforms\n\nfrom augmentation import RandAugment\n\nlogger = logging.getLogger(__name__)\n\ncifar10_mean = (0.4914, 0.4822, 0.4465)\ncifar10_std = (0.2471, 0.2435, 0.2616)\ncifar100... | [
[
"numpy.array",
"numpy.where",
"numpy.random.shuffle",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
paysonwallach/matrix-network | [
"599da49d1317c06f98aba7b4a0b7d1aae86ad37c"
] | [
"activations.py"
] | [
"# Module of activation functions for use in matrix-based neural networks\n\nimport numpy as np\n\n\ndef sigmoid(s, deriv=False):\n if not deriv:\n return 1.0 / (1.0 + np.exp(-s))\n else:\n return sigmoid(s) * (1.0 - sigmoid(s))\n\n\ndef softmax(s):\n z = np.sum(np.exp(s), axis=1)\n z = z.... | [
[
"numpy.exp",
"numpy.ma.size"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rahuldshetty/numpy_nn | [
"0d61b7fc47cecfd9701972de322c08ed799271b4",
"0d61b7fc47cecfd9701972de322c08ed799271b4"
] | [
"numpy_nn/activations/ELU.py",
"numpy_nn/optimizers/RMSProp.py"
] | [
"import numpy as np\nfrom ..layers import Layer\n\nclass ELU(Layer):\n '''\n '''\n def forward(self, x, alpha=0.01):\n self.alpha = alpha\n self.last_x = x\n self.result = np.where( x > 0, x, alpha*(np.exp(x) - 1) )\n return self.result\n\n def __call__(self, x, alpha=0.01):... | [
[
"numpy.exp"
],
[
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bryant1410/b4msa | [
"8e2a737058140298bfa57b2f21c78fca4fb2fad6"
] | [
"b4msa/tests/test_command_line.py"
] | [
"# Copyright 2016 Mario Graff (https://github.com/mgraffg) and Ranyart R. Suarez (https://github.com/RanyartRodrigo)\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://w... | [
[
"numpy.random.seed.assert_called_once_with"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dciborow/HyperdriveDeepLearning | [
"79994f0b019a4a283e24421beba0f3c976341827"
] | [
"scripts/engine.py"
] | [
"# Original source: https://github.com/pytorch/vision/blob/master/references/detection/engine.py\n\nimport math\nimport sys\nimport time\nimport torch\n\nimport torchvision.models.detection.mask_rcnn\n\nfrom coco_utils import get_coco_api_from_dataset\nfrom coco_eval import CocoEvaluator\nimport utils\n\n\ndef trai... | [
[
"torch.cuda.synchronize",
"torch.no_grad",
"torch.set_num_threads",
"torch.device",
"torch.get_num_threads"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
prabhat-xceedance/numpy | [
"4b3ddd74610d1975e0d0eb9c32df2a2d54c8dafd"
] | [
"setup.py"
] | [
"#!/usr/bin/env python3\n\"\"\" NumPy is the fundamental package for array computing with Python.\n\nIt provides:\n\n- a powerful N-dimensional array object\n- sophisticated (broadcasting) functions\n- tools for integrating C/C++ and Fortran code\n- useful linear algebra, Fourier transform, and random number capabi... | [
[
"numpy.distutils.command.build_clib.build_clib.build_a_library",
"numpy.distutils.misc_util.Configuration",
"numpy.distutils.command.build_ext.build_ext.build_extension"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.11",
"1.19",
"1.24",
"1.16",
"1.23",
"1.20",
"1.7",
"1.12",
"1.21",
"1.22",
"1.14",
"1.6",
"1.13",
"1.9",
"1.17",
"1.10",
"1.18",
"1.15",
"1.8"
],
"pand... |
mcoughlin/sncosmo | [
"f2933b269bd6d6e767ab93a23f3ab14ee90398f0"
] | [
"sncosmo/specmodel.py"
] | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\nimport astropy.units as u\nimport numpy as np\nfrom scipy.interpolate import splev, splrep\n\nfrom .bandpasses import get_bandpass\nfrom .constants import HC_ERG_AA, SPECTRUM_BANDFLUX_SPACING, FLAMBDA_UNIT\nfrom .utils import integration_grid\n\n__... | [
[
"numpy.asarray",
"scipy.interpolate.splrep",
"numpy.sum",
"scipy.interpolate.splev"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
iffsid/pyro | [
"0b6c423328dfaf7716ad81a965be3352e91a0bca"
] | [
"tests/infer/test_autoguide.py"
] | [
"# Copyright (c) 2017-2019 Uber Technologies, Inc.\n# SPDX-License-Identifier: Apache-2.0\n\nimport functools\nimport io\nimport warnings\nfrom operator import attrgetter\n\nimport numpy as np\nimport pytest\nimport torch\nfrom torch import nn\nfrom torch.distributions import constraints\n\nimport pyro\nimport pyro... | [
[
"torch.jit.save",
"torch.jit.load",
"torch.ones",
"numpy.isfinite",
"torch.zeros",
"torch.randn",
"torch.jit.trace_module",
"torch.eye",
"torch.tensor",
"torch.no_grad",
"torch.arange",
"torch.stack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
prabhasyadav/wells-app | [
"15354672069ad1d533835bd4328a85cac366fc88"
] | [
"wells.py"
] | [
"import streamlit as st\nimport matplotlib.pyplot as plt \nimport numpy as np \nimport pandas as pd\nimport scipy.special as ssp\npd.options.display.float_format = '{:.5f}'.format\n\nst.beta_set_page_config(page_icon=\"potable_water\")\nst.set_option('deprecation.showfileUploaderEncoding', False)\n\n\n\"## **The Pr... | [
[
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"scipy.special.expi",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel"
]
] | [
{
"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": []
}
] |
JFurness1/pyscf | [
"aff8a94003cc47ff5e741ce648d877b008a0c59e"
] | [
"pyscf/lib/numpy_helper.py"
] | [
"#!/usr/bin/env python\n# Copyright 2014-2020 The PySCF Developers. 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/LIC... | [
[
"numpy.dot",
"numpy.sqrt",
"numpy.take",
"numpy.asarray",
"numpy.ndarray",
"numpy.dtype",
"numpy.zeros_like",
"numpy.linalg.svd",
"numpy.tril_indices",
"numpy.arange",
"numpy.eye",
"numpy.linalg.cond",
"numpy.ndarray.__reduce__",
"numpy.zeros",
"numpy.as... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
prideout/snowy | [
"995c373bd751daf35d8b9a851de7a744329552d7"
] | [
"tests/test_dist.py"
] | [
"#!/usr/bin/env python3 -m pytest -s\n\n# The shebang runs the test with stdout enabled and must be invoked from\n# the repo root.\n\nimport snowy\nimport numpy as np\nimport pytest\n\nw, h = 1920 / 4, 1080 / 4\n\ndef smoothstep(edge0, edge1, x):\n t = np.clip((x - edge0) / (edge1 - edge0), 0.0, 1.0)\n return... | [
[
"numpy.hstack",
"numpy.sqrt",
"numpy.linspace",
"numpy.clip",
"numpy.logical_and",
"numpy.dstack",
"numpy.full",
"numpy.sign",
"numpy.meshgrid",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rogersamso/pysd_dev | [
"85606265aa92878c35a41dd81ce9588d23350e19"
] | [
"tests/unit_test_functions.py"
] | [
"import unittest\nimport warnings\n\nimport numpy as np\nimport xarray as xr\n\n\nclass TestInputFunctions(unittest.TestCase):\n def test_ramp(self):\n \"\"\"Test functions.ramp\"\"\"\n from pysd import functions\n\n self.assertEqual(functions.ramp(lambda: 14, .5, 10, 18), 2)\n\n self... | [
[
"numpy.arange",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KonstantinKorotaev/lightning-flash | [
"5b5be241345c7c9922787b044c33f0c51fbf2532"
] | [
"flash/text/classification/data.py"
] | [
"# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ssabit/simulation-modeling | [
"c084d9ba904b509b4cd4f95aec05a51d0b36b4e5"
] | [
"17-Inventory_system.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"sm-1-assignment-8-Inventory.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/drive/1uIiv_4XxSLfVAtvsJUQMOp3Jy5IGFHTI\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nm = int(input(\"Enter Maximum ... | [
[
"matplotlib.pyplot.title",
"numpy.random.choice",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rcoelho6/image-treatement | [
"8008486975583868f0738ba0410d1a1957e3e0f6"
] | [
"code/opening.py"
] | [
"import cv2\nimport numpy as np\n\nimage = cv2.imread('/home/rafael/workspace/deep-image-treatement/img/darkmenosmenos.jpeg', 0)\n\nkernel = np.ones((1, 1), np.uint8)\nopening = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)\n\ncv2.imwrite('/home/rafael/workspace/deep-image-treatement/img/darkmenosmenos.opening.2.... | [
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jianlong-yuan/SimpleBaseline | [
"e2ee7b82071299c06b572ec87ee71f050ad54550"
] | [
"segmentron/models/model_zoo.py"
] | [
"import logging\nimport torch\nfrom segmentron.modules.batch_norm import DSBN\nfrom collections import OrderedDict\nfrom segmentron.utils.registry import Registry\nfrom ..config import cfg\nimport os\n\nMODEL_REGISTRY = Registry(\"MODEL\")\nMODEL_REGISTRY.__doc__ = \"\"\"\nRegistry for segment model, i.e. the whole... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ayyappan-02/MalURL | [
"efe6b5d0b356d7bdf2a313e71b083fc7b035c9b0"
] | [
"models/model.py"
] | [
"import itertools\nimport pickle\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sn\nimport xgboost as xgb\nfrom mlxtend.feature_selection import SequentialFeatureSelector as sfs\nfrom sklearn import metrics\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.m... | [
[
"pandas.read_csv",
"sklearn.model_selection.cross_val_score",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.metrics.precision_score",
"sklearn.model_selection.train_test_split",
"sklearn.tree.DecisionTreeClassifier",
"sklearn.metrics.recall_score",
"sklearn.metrics.accuracy_s... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
MLinesCode/The-Complete-FAANG-Preparation | [
"2d0c7e8940eb2a58caaf4e978e548c08dd1f9a52"
] | [
"5]. Projects/Machine Learning & Data Science (ML-DS)/Python/Deep Learning Projects/Computer Vision/009]. Color Identification in Image/color_detection.py"
] | [
"import cv2\nimport pandas as pd\n\n#Saving the image and csv file path into variables\n\nimg_path = 'pic2.jpg'\ncsv_path = 'colors.csv'\n\n#Reading the .csv file(Containing all the colors,hex,RGB components) and naming the columns\n\nindex = ['color','color_name','hex','R','G','B']\ndf = pd.read_csv(csv_path, name... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
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