repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
young-oct/complex_sporco | [
"88488e69d77805ccf25030388dc9e03bcc6a6df1"
] | [
"tests/dictlrn/test_cbpdndl.py"
] | [
"from __future__ import division\nfrom builtins import object\n\nimport numpy as np\n\nfrom sporco.dictlrn import cbpdndl\n\n\n\nclass TestSet01(object):\n\n def setup_method(self, method):\n N = 16\n Nd = 5\n M = 4\n K = 3\n np.random.seed(12345)\n self.D0 = np.random.r... | [
[
"numpy.random.randn",
"numpy.random.seed"
]
] |
krantikiran68/EzPC | [
"cacf10f31cddf55e4a06908fcfc64f8d7d0f85bd"
] | [
"Athos/tests/tf/unittests/test_convolution.py"
] | [
"\"\"\"\n\nAuthors: Pratik Bhatu.\n\nCopyright:\nCopyright (c) 2021 Microsoft Research\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the... | [
[
"tensorflow.Graph",
"tensorflow.constant",
"tensorflow.as_dtype",
"tensorflow.compat.v1.Session",
"numpy.random.randn"
]
] |
ekunnii/APPIAN | [
"1460ef4e1b5c98a558b7f89753f1a1a5541374cf",
"1460ef4e1b5c98a558b7f89753f1a1a5541374cf"
] | [
"Test/validation_qc.py",
"Initialization/initialization.py"
] | [
"import matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom pyminc.volumes.factory import *\nimport sys\nimport os\nimport numpy as np\nfrom scipy.ndimage.measurements import center_of_mass\nif __name__ == \"__main__\":\n if os.path.exists(sys.argv[1]) :\n vol=volumeFromFile(sys.argv[... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.axis",
"numpy.array",
"numpy.sum"
],
[
"numpy.amax",
"numpy.split",
"numpy.isnan",
"numpy.mean",
"numpy.a... |
AndreaCoop/Video_creator | [
"2518a3527bff013466e887f6d1bc06fe2a8e4912"
] | [
"video_generator_script.py"
] | [
"# script to create an animated plot from a simulation\n\n# import data\n\n# import packages\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import animation\n\n# +\n# input \n\n# data from a .txt file\nfile_data = \"sin_expdecay.txt\"\n\n#set title and legth of the movie\ntitle_movie = 'examp... | [
[
"numpy.amax",
"numpy.amin",
"matplotlib.pyplot.axes",
"matplotlib.animation.FuncAnimation",
"matplotlib.pyplot.show",
"numpy.loadtxt",
"matplotlib.pyplot.figure"
]
] |
aturbofly/crab | [
"ca3d6a8f50573a09d154274dd14331ebc91abe17"
] | [
"scikits/crab/recommenders/knn/classes.py"
] | [
"\"\"\"\nGeneralized Recommender models.\n\nThis module contains basic memory recommender interfaces used throughout\nthe whole scikit-crab package.\n\nThe interfaces are realized as abstract base classes (ie., some optional\nfunctionality is provided in the interface itself, so that the interfaces\ncan be subclass... | [
[
"numpy.isnan",
"numpy.lexsort",
"numpy.setdiff1d",
"numpy.vectorize",
"numpy.array",
"numpy.sum"
]
] |
tristan-paul/TweetOff | [
"efaa020af8df43190f37a371272eee3a7731dd7e"
] | [
"tweetoff/predict.py"
] | [
"import numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import cross_val_score\nfrom .models import User\nfrom .twitter import BASILICA\n\ndef predict_user(user1_name, user2_name, tweet_text):\n user1 = User.query.filter(User.name == user1_name).one()\n user2 = U... | [
[
"numpy.concatenate",
"numpy.array",
"sklearn.linear_model.LogisticRegression",
"numpy.vstack"
]
] |
theimgclist/tensorflow | [
"fdfb6f190577416b402b0b113568222ff4e0d672"
] | [
"models/research/deep_speech/deep_speech_model.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.keras.layers.Lambda",
"tensorflow.shape",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.backend.ctc_batch_cost",
"tensorflow.keras.layers.Bidirectional",
"tensorflow.keras.layers.BatchNormalization",
"tensorflow.to_float",
"tensorflow.keras.layers.Flatten",
"te... |
EPFL-LCN/pub-illing2021-neurips | [
"f5c9d7380f123a155a7c2b913df6f1fddb787be1"
] | [
"vision/CLAPPVision/vision/models/Supervised_Loss.py"
] | [
"import torch.nn as nn\nimport torch\n\nfrom CLAPPVision.utils import utils\n\nclass Supervised_Loss(nn.Module):\n def __init__(self, opt, hidden_dim, calc_accuracy):\n super(Supervised_Loss, self).__init__()\n\n self.opt = opt\n\n self.pool = None\n self.hidden_dim = hidden_dim\n ... | [
[
"torch.nn.Linear",
"torch.nn.CrossEntropyLoss",
"torch.nn.functional.adaptive_avg_pool2d",
"torch.zeros"
]
] |
kristianeschenburg/ddCRP | [
"3c503418050b45e2156964c1e0165e92eb980fab"
] | [
"ddCRP/Priors.py"
] | [
"import numpy as np\nfrom numpy.linalg import det\nfrom scipy.special import gammaln, multigammaln\nfrom ddCRP.PriorBase import Prior\n\n\nclass NIW(Prior):\n\n \"\"\"\n Normal-Inverse-Chi-Squared prior model for connectivity features.\n\n Parameters:\n - - - - - -\n mu0, kappa0: float\n prior... | [
[
"numpy.linalg.det",
"numpy.log",
"scipy.special.multigammaln",
"scipy.special.gammaln"
]
] |
csala/RDT | [
"ca639dc1eeae5f1bb9f78e8b163659680ce627e3"
] | [
"tests/integration/test_hyper_transformer.py"
] | [
"import numpy as np\nimport pandas as pd\n\nfrom rdt import HyperTransformer\nfrom rdt.transformers import OneHotEncodingTransformer\n\n\ndef get_input_data_with_nan():\n data = pd.DataFrame({\n 'integer': [1, 2, 1, 3, 1],\n 'float': [0.1, 0.2, 0.1, np.nan, 0.1],\n 'categorical': ['a', 'a', ... | [
[
"pandas.to_datetime",
"pandas.testing.assert_frame_equal",
"pandas.DataFrame"
]
] |
ishansharma/open_cv_feature_detection | [
"34f09d6e144d8220cca9295f0a59dba7f9488516"
] | [
"image_operations/transformations.py"
] | [
"import cv2\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\n\nimage = mpimg.imread('../../dataset/Hands/Hand_0000083.jpg')\n\n\ndef resize():\n scaleup = cv2.resize(image, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)\n scaledown = cv2.resize(image, None, fx=0.5, fy=0.5, interpolation=c... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.image.imread",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] |
sylvainlapeyrade/LSTM_KDD99_Keras | [
"e07c05803dfd4ad454cf5043531bb3e205ec022b"
] | [
"src/unsw_processing.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom keras.utils import to_categorical\nfrom sklearn.preprocessing import (StandardScaler, OrdinalEncoder,\n LabelEncoder, MinMaxScaler)\npd.options.mode.chained_assignment = None # default='warn' | Disable warnings\n\n# ***** UNSW STRING ... | [
[
"pandas.read_csv",
"sklearn.preprocessing.OrdinalEncoder",
"sklearn.preprocessing.StandardScaler",
"sklearn.preprocessing.LabelEncoder",
"sklearn.preprocessing.MinMaxScaler"
]
] |
mkhorton/mp-dash-components | [
"b9af1b59f0120a90897631d9a7f8d9f0ae561de9"
] | [
"crystal_toolkit/core/legend.py"
] | [
"from pymatgen.core.periodic_table import Specie, Element\nfrom pymatgen.core.structure import Molecule\nfrom pymatgen.core.structure import SiteCollection, Site\nfrom pymatgen.analysis.molecule_structure_comparator import CovalentRadius\nfrom pymatgen.util.string import unicodeify_species\n\nfrom monty.json import... | [
[
"sklearn.preprocessing.LabelEncoder",
"numpy.array",
"matplotlib.cm.get_cmap"
]
] |
Russell-Ryan/pyLINEAR | [
"d68e44bc64d302b816db69d2becc4de3b15059f9"
] | [
"pylinear/modules/extract/groupcollection.py"
] | [
"import numpy as np\nfrom shapely import geometry\nimport h5py\n\n\nfrom ... import h5table\nfrom ...utilities import pool\nfrom ...constants import COMPARGS,SEGTYPE\n\n\n\nclass GroupCollection(list):\n def __init__(self,minarea=0.1,ncpu=0,path='tables'):\n \n self.minarea=minarea\n self.path=path... | [
[
"numpy.array",
"numpy.loadtxt",
"numpy.unique"
]
] |
benjonesbenjones/silver | [
"4c037577851682aed07e3bd1daf05403cc00c5f0"
] | [
"app.py"
] | [
"# by ben with <3\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output, State\nimport dash_colorscales\nimport pandas as pd\nimport cufflinks as cf\nimport numpy as np\n\napp = dash.Dash(__name__)\nserver = app.server\n\ndf_lat_lon = pd.r... | [
[
"pandas.read_csv",
"numpy.linspace"
]
] |
sroet/regreg | [
"299ff18b8680872d4d85447953793bf438f78bba"
] | [
"regreg/problems/dual_problem.py"
] | [
"import numpy as np\nfrom scipy import sparse\nfrom warnings import warn\n\nfrom ..algorithms import FISTA\nfrom ..problems.composite import (composite, nonsmooth as nonsmooth_composite,\n smooth as smooth_composite)\nfrom ..affine import (vstack as afvstack, identity as afidentity, power_L,\... | [
[
"numpy.zeros"
]
] |
hongfz16/Garment4D | [
"9317dc262f3d35eb9e6cd6a7bfbb29f04560ca35"
] | [
"modules/mesh_encoder.py"
] | [
"import numpy as np\nimport torch\nfrom torch import nn\nimport pickle\nimport torch.nn.functional as F\nfrom .pointnet2encoder import Pointnet2MSGSEG\nimport sys\nsys.path.append('../')\nfrom utils import mesh_utils\nfrom smplx import batch_rodrigues\nfrom .pygcn import layers\nfrom .pygcn import utils as gcn_util... | [
[
"numpy.sqrt",
"torch.cat",
"torch.zeros",
"torch.no_grad",
"scipy.sparse.coo_matrix",
"torch.softmax",
"torch.ones",
"torch.mm",
"scipy.sparse.diags",
"torch.from_numpy",
"torch.nn.functional.relu",
"numpy.zeros",
"torch.isinf",
"numpy.power",
"torch.uns... |
rayyang29/pygaggle | [
"6a0a1261293428e8df4817ef835c558ba5fd7b01"
] | [
"pygaggle/run/evaluate_passage_ranker.py"
] | [
"from typing import Optional, List\nfrom pathlib import Path\nimport logging\n\nfrom pydantic import BaseModel, validator\nfrom transformers import (AutoModel,\n AutoTokenizer,\n AutoModelForSequenceClassification,\n BertForSequenceClassific... | [
[
"torch.device",
"torch.zeros"
]
] |
wenxichen/tensorflow_yolo2 | [
"f040d9932816d8b2f8d7a67231060f0beea821d4"
] | [
"yolo1-resnet-adv.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom tensorflow.python.ops import control_flow_ops\n# from datasets import dataset_factory\nfrom deployment import model_deploy\nfrom nets import nets_factory\n# from preproc... | [
[
"tensorflow.app.flags.DEFINE_string",
"tensorflow.train.AdamOptimizer",
"tensorflow.train.batch",
"tensorflow.gfile.IsDirectory",
"tensorflow.group",
"tensorflow.summary.scalar",
"tensorflow.Graph",
"tensorflow.python.ops.control_flow_ops.with_dependencies",
"tensorflow.get_col... |
rickatx/message_response_pipeline | [
"7433c83e3b1cfd201da448c7545117fd79a13cea"
] | [
"udacity_root/data/process_data.py"
] | [
"# AUTOGENERATED! DO NOT EDIT! File to edit: ETL_Pipeline_Preparation.ipynb (unless otherwise specified).\n\n__all__ = ['load_data', 'clean_data', 'get_engine', 'save_data']\n\n# Cell\n\nimport numpy as np\nimport pandas as pd\nfrom sqlalchemy import create_engine\nimport sys\n\n# Cell\n\ndef load_data(messages_fil... | [
[
"pandas.read_csv"
]
] |
hanaseleb/pycaret | [
"1fe6e1a6bee642351c4b6064d769f97294713f48"
] | [
"pycaret/tests/test_preprocess.py"
] | [
"import os\nimport sys\nimport pandas as pd\nimport numpy as np\nimport pytest\nimport pycaret.datasets\nimport pycaret.internal.preprocess\nimport pycaret.classification\n\nsys.path.insert(0, os.path.abspath(\"..\"))\n\n\ndef test_auto_infer_label():\n # loading dataset\n data = pycaret.datasets.get_data(\"j... | [
[
"numpy.random.randint"
]
] |
NetVoobrazhenia/pandas_task | [
"0289391e47bb136dacce1cec700ef0f52a028801"
] | [
"lec/5.py"
] | [
"import pandas as pd\n\nworks = pd.read_csv(\"./works.csv\")\n\nnNa = works['skills'].notna()\ndf = works['skills'].dropna().str.lower().str.contains('питон|python')\n\nprint(f\"зарплата тех, у кого в скиллах есть python (питон):\\n\",\n ', '.join(f\"{i}\" for i in works[nNa][df]['salary'].values))\n"
] | [
[
"pandas.read_csv"
]
] |
guialfredo/mmtracking | [
"05926b4d824a9f7b05e78a6375e6e63530a55df9"
] | [
"mmtrack/models/sot/base.py"
] | [
"from abc import ABCMeta, abstractmethod\nfrom collections import OrderedDict\n\nimport mmcv\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nfrom mmcv.runner import auto_fp16, load_checkpoint\nfrom mmcv.utils import print_log\n\nfrom mmtrack.utils import get_root_logger\n... | [
[
"torch.distributed.get_world_size",
"torch.distributed.is_available",
"torch.distributed.is_initialized"
]
] |
jamesmtuck/DNA_stability | [
"52b8561807969c09db8ba81e1198f6cbcee28857"
] | [
"stability.py"
] | [
"# Contributed by James Tuck (jtuck@ncsu.edu) \n\nimport sys\nimport csv\nimport math\nfrom math import factorial\n\nimport matplotlib.pyplot as plt\nimport gmpy2 as g\nfrom gmpy2 import mpfr\n\n# Tune precision of gmpy2 module\ng.get_context().precision = 1000\n\ndef dumpXY(name, XY, labels):\n \"\"\" Write a c... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.suptitle",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] |
dsj96/TITS | [
"ea9c4dc812ff871c7ccb2e3748e35d3b634920d0"
] | [
"FNN.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nfrom torch.nn import Module\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\nfrom torch.autograd import Variable\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nimport math\r\nimport numpy as np\r\nimport argparse\r\n\r\nfrom metrics impor... | [
[
"torch.nn.functional.dropout",
"sklearn.model_selection.train_test_split",
"torch.nn.Linear",
"torch.nn.functional.sigmoid",
"numpy.mean"
]
] |
prakhargurawa/Machine-Learning-A-Z | [
"f1b2dfbdfe67525c2d5061e66c3d31d612d35309",
"f1b2dfbdfe67525c2d5061e66c3d31d612d35309"
] | [
"3_Classification/Model_Selection_Classification/random_forest_classification.py",
"2_Regression/Model_Selection_Regression/polynomial_regression.py"
] | [
"# Random Forest Classification\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('ENTER_THE_NAME_OF_YOUR_DATASET_HERE.csv')\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, -1].values\n\n# Splitting the data... | [
[
"pandas.read_csv",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.metrics.confusion_matrix",
"sklearn.model_selection.train_test_split",
"sklearn.preprocessing.StandardScaler",
"sklearn.metrics.accuracy_score"
],
[
"pandas.read_csv",
"sklearn.metrics.r2_score",
"numpy.... |
baptistesoulard/Production-plan-optimization | [
"38cd0501315c11fd0635d09e2869c54e2336f0bf"
] | [
"temp/Model5.py"
] | [
"# Import required packages\nimport pandas as pd\nimport gurobipy\nfrom matplotlib import pyplot as plt\nimport datetime\nfrom typing import List, Dict\n\n\ndef optimize_planning(\n timeline: List[str],\n workcenters: List[str],\n needs: Dict[str, int],\n wc_cost_reg: Dict[str, int],\n wc_cost_ot: Di... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"pandas.DataFrame",
"pandas.DataFrame.from_dict",
"matplotlib.pyplot.show"
]
] |
bensondaled/puffsopto | [
"fdcf2fabb50c2bd2521a3d2702ad2e41217c0032"
] | [
"figs/figure_s7.py"
] | [
"\"\"\"\nFigure S7: logistic regression model, by subj\n\"\"\"\nimport matplotlib.pyplot as pl\nfrom figure_panels import *\n\n## Setup figure ##\nfig_id = 'sfig7'\nfigw,figh = 7.2,5.\nfig = pl.figure(fig_id, figsize=(figw,figh))\n\nrow_bottoms = [.85,.7,.55,.4,.25,.1]\nletter_ys = [.8, .6, 0, 0, 0, 0, 0, 0]\nlette... | [
[
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure"
]
] |
Velocities/stocker | [
"a163971c60dd70a2c20a6d23add33c25036efe46"
] | [
"stocker/get_data.py"
] | [
"import pandas as pd\r\nimport yfinance as yf\r\nimport requests\r\nimport datetime as dt\r\nfrom pytrends.request import TrendReq\r\n\r\n\r\ndef main(stock, years=1): # function to get data from Yahoo Finance\r\n end = dt.datetime.today().strftime('%Y-%m-%d') # today as the end date\r\n start = (dt.datetim... | [
[
"pandas.to_datetime",
"pandas.DataFrame"
]
] |
aryaman4/ludwig | [
"76ddea4634e4dcc1c0f956f2e61d80b0c3621a81"
] | [
"ludwig/utils/math_utils.py"
] | [
"#! /usr/bin/env python\n# coding=utf-8\n# Copyright (c) 2019 Uber Technologies, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENS... | [
[
"numpy.max",
"numpy.iinfo"
]
] |
denizetkar/lstms.pth | [
"c1d6af1e106e17c51604ae8acdb5114828adff19"
] | [
"test/test_correctness.py"
] | [
"#!/usr/bin/env python\n\nimport torch as th\nimport torch.nn as nn\nfrom torch.autograd import Variable as V\n\nfrom lstms import SlowLSTM, LSTM, GalLSTM, MoonLSTM, SemeniutaLSTM\n\n\nif __name__ == '__main__':\n lstms = [\n (SlowLSTM, 'SlowLSTM'),\n (LSTM, 'LSTM'),\n ]\n for lstm, name in l... | [
[
"torch.zeros",
"torch.nn.LSTM",
"torch.manual_seed",
"torch.sum",
"torch.equal",
"torch.rand"
]
] |
lisiyuan656/datasets | [
"b097e0985eaaadc6b0c1f4dfa3b3cf88d116c607"
] | [
"tensorflow_datasets/core/deprecated/text/subword_text_encoder_test.py"
] | [
"# coding=utf-8\n# Copyright 2020 The TensorFlow Datasets Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless ... | [
[
"tensorflow.compat.v2.io.gfile.GFile",
"tensorflow.compat.v2.compat.as_text"
]
] |
ConnorJL/ProgGAN-PyTorch | [
"a64aec9640a094f5dc09184677c13236574f69a2"
] | [
"ProgGAN.py"
] | [
"import math\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom scipy.misc import imsave\nfrom torch.autograd import Variable\nfrom torch.nn.init import kaiming_normal, calculate_gain\n\n\nclass ProgGAN(object):\n def __init__(self, nz=512, lr=0.0010):\n self... | [
[
"torch.nn.functional.upsample",
"torch.mean",
"torch.transpose",
"torch.cat",
"torch.sum",
"torch.FloatTensor",
"torch.autograd.Variable",
"torch.nn.init.calculate_gain",
"torch.sqrt",
"torch.std",
"torch.rand",
"numpy.zeros",
"torch.squeeze",
"torch.nn.init... |
An-Dang/clustinator | [
"d1737628cd65745eb7034eb4e0d59996f8976f8b",
"d1737628cd65745eb7034eb4e0d59996f8976f8b"
] | [
"clustinator/clustering.py",
"poc/src/clustering.py"
] | [
"from sklearn.cluster import DBSCAN\nimport numpy as np\n\n\nstates = [\"INITIAL\",\"login\",\"View_Items\",\"home\",\"logout\",\"View_Items_quantity\",\"Add_to_Cart\",\"shoppingcart\",\n \"remove\",\"deferorder\",\"purchasecart\",\"inventory\",\"sellinventory\",\"clearcart\",\"cancelorder\",\"$\"]\n\n# Da... | [
[
"numpy.mean",
"sklearn.cluster.DBSCAN",
"numpy.unique"
],
[
"numpy.mean",
"sklearn.cluster.DBSCAN",
"numpy.unique"
]
] |
KelvinKramp/Autotune123 | [
"f6948e6da650bed0c05577e9565449e7e488ea2a"
] | [
"get_filtered_data.py"
] | [
"import numpy as np\nfrom scipy.signal import savgol_filter\n\n\ndef get_filtered_data(df, filter=\"No filter\"):\n # clean lists by removing sensitivity, removing IC ratio, removing empty values and converting strings\n # with ratios to floats.\n\n # x\n l = df[\"Parameter\"].to_list()\n l_time = []... | [
[
"numpy.asarray",
"numpy.isnan",
"scipy.signal.savgol_filter"
]
] |
fgkcv25/LMC | [
"1c13ac671ef325448fb3f8264398648cbb3d56b7"
] | [
"Leitura de Arquivo/Leitura de arquivo.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue May 21 07:51:07 2019\r\n\r\n@author: 05873472955\r\n\"\"\"\r\n\r\n\r\nimport numpy as np\r\n\r\nwith open('C:\\\\Users\\\\05873472955\\\\Desktop\\\\LEitura de Arquivo\\\\A.txt') as arquivo:\r\n dados = arquivo.readlines()\r\n \r\ndados.remove(dados[1001])\r... | [
[
"numpy.asarray"
]
] |
njaupan/SASAR | [
"b32e7eef9c85663656fdf19a58f7f39a768a8116"
] | [
"SASAR.py"
] | [
"#!/usr/bin/env python3\n\"\"\"\n# June 2021\n# If using this pipeline please cite : XXXXXXXXXX\n#--------------------------------------------------------------------------+ \n# \n#\tSASAR is a meta-assembly tool \n# to reconcile different long read assembl... | [
[
"pandas.merge",
"pandas.read_csv"
]
] |
dingmyu/psa | [
"e27539bbd569cd1100a339336b9e3a2b0dad67fc"
] | [
"fail_wok/voc_deeplab/resnet.py"
] | [
"import torch.nn as nn\nimport torch\nimport math\nimport torch.utils.model_zoo as model_zoo\nfrom torchE.nn import SyncBatchNorm2d\n\nBatchNorm = nn.BatchNorm2d\n\n__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',\n 'resnet152']\n\n\nmodel_urls = {\n 'resnet18': 'https://download.p... | [
[
"torch.nn.Sequential",
"torch.load",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.AvgPool2d",
"torch.nn.ReLU",
"torch.utils.model_zoo.load_url"
]
] |
BrunoKrinski/segtool | [
"cb604b5f38104c43a76450136e37c3d1c4b6d275"
] | [
"final_plot.py"
] | [
"import os\nimport cv2\nimport glob\nimport json\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.cbook as cbook\n\ndef getminmax(x1, x2):\n aux1 = sorted(x1)\n aux2 = sorted(x2)\n\n #print(aux1[0], aux2[0])\n if aux1[0] < aux2[0]:\n p = aux1[0]\n else:\n ... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xticks",
"numpy.array",
"matp... |
telnoratti/burningwheel-tools | [
"78277752ddcd6f5b257603920385b61b113effac"
] | [
"gen_function.py"
] | [
"import sympy\nfrom sympy import rsolve, Function, expand, Rational, Sum, Poly\nfrom sympy.abc import *\n\nfrom sympy import O\nimport numpy as np\nfrom numpy.polynomial import polynomial as np_poly\n\n### Recurrence relations precomputed, needed for exploding cases\n# These are kept as recurrence relations because... | [
[
"numpy.zeros",
"numpy.polynomial.polynomial.polymul"
]
] |
wdecay/ShapeClassification | [
"0592a837f272c709322a1d7e74948268e8c82cce"
] | [
"layers/Output.py"
] | [
"import tensorflow as tf\n\nclass Output(tf.keras.layers.Layer):\n def __init__(self, num_classes, **kwargs):\n self.num_classes = num_classes\n super(Output, self).__init__(**kwargs)\n\n def build(self, input_shape):\n tfn_output_shape = input_shape[0][0].as_list()\n\n self.fully_... | [
[
"tensorflow.map_fn",
"tensorflow.squeeze",
"tensorflow.einsum"
]
] |
sylvainletourneau/env_canada | [
"ec146f98b6b556a483358789f2a963b9fc421478"
] | [
"env_canada/ec_radar.py"
] | [
"from concurrent.futures import as_completed\nimport datetime\nfrom io import BytesIO\nimport json\nimport os\nfrom PIL import Image\nimport xml.etree.ElementTree as et\n\nimport cv2\nimport dateutil.parser\nimport imageio\nimport numpy as np\nimport requests\nfrom requests_futures.sessions import FuturesSession\n\... | [
[
"numpy.radians",
"numpy.degrees",
"numpy.cos",
"numpy.sin",
"numpy.array"
]
] |
PaipaPsyche/SuperClusterCharacterization | [
"8d86ad31aa34dfa6a44592b94c42cba38487216d"
] | [
"Densidades (test)/plot_divergence.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 6 13:06:41 2018\n\n@author: David\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom astropy.convolution import Gaussian2DKernel,convolve\n\n\n\n\n#Data Loading========\ndata1 = np.loadtxt(\"Halo.txt\")\ndata1=np.transpose(data1)\n\n\nprints=0#... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.axes",
"numpy.transpose",
"numpy.zeros",
"numpy.sum",
"numpy.loadtxt",
"matplotlib.pyplot.figure"
]
] |
Saiprasad16/agents | [
"9e0972fc0878b29925ae496e883d80e7da3928aa"
] | [
"tf_agents/utils/nest_utils.py"
] | [
"# coding=utf-8\n# Copyright 2020 The TF-Agents Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required ... | [
[
"tensorflow.convert_to_tensor",
"numpy.expand_dims",
"tensorflow.concat",
"tensorflow.stack",
"numpy.squeeze",
"tensorflow.rank",
"tensorflow.nest.flatten",
"tensorflow.nest.pack_sequence_as",
"numpy.stack",
"tensorflow.compat.v2.where",
"tensorflow.TensorShape",
"t... |
Feverdreams/BMI | [
"53d59f996f21ad29bf2e8961eb9b45bfe1776252",
"53d59f996f21ad29bf2e8961eb9b45bfe1776252"
] | [
"biosppy/plotting.py",
"biosppy/signals/eeg.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nbiosppy.plotting\n----------------\n\nThis module provides utilities to plot data.\n\n:copyright: (c) 2015-2018 by Instituto de Telecomunicacoes\n:license: BSD 3-clause, see LICENSE for more details.\n\"\"\"\n\n# Imports\n# compat\nfrom __future__ import absolute_import, division, ... | [
[
"numpy.abs",
"numpy.nonzero",
"numpy.min",
"numpy.linspace",
"numpy.sqrt",
"matplotlib.pyplot.get_cmap",
"numpy.ones",
"numpy.max",
"numpy.mean",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.close",
"numpy.angle",
"numpy.array",
"matplotlib.pyplot.show... |
shania3322/joeynmt | [
"5afe9d00930f19949b2078141771bf4621f6e9ae"
] | [
"github/joeynmt/transformer_layers.py"
] | [
"# -*- coding: utf-8 -*-\r\n\r\nimport math\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torch import Tensor\r\n\r\n\r\n# pylint: disable=arguments-differ\r\nclass MultiHeadedAttention(nn.Module):\r\n \"\"\"\r\n Multi-Head Attention module from \"Attention is All You Need\"\r\n\r\n Implementation modif... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.zeros",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.matmul",
"torch.arange",
"torch.nn.ReLU"
]
] |
c-lai/3D-ResNets-PyTorch | [
"488d0d7c4760e60ead4db80fe6f017a8778318ff"
] | [
"model.py"
] | [
"import torch\nfrom torch import nn\n\nfrom models import (resnet, resnet2p1d, pre_act_resnet,\n wide_resnet, resnext, densenet, googlenet)\n\n\ndef get_module_name(name):\n name = name.split('.')\n if name[0] == 'module':\n i = 1\n else:\n i = 0\n if name[i] == 'feature... | [
[
"torch.cuda.set_device",
"torch.load",
"torch.nn.Linear",
"torch.nn.DataParallel",
"torch.nn.parallel.DistributedDataParallel"
]
] |
adit98/google-research | [
"0714e9a5a3934d922c0b9dd017943a8e511eb5bc"
] | [
"jaxnerf/nerf/datasets.py"
] | [
"# coding=utf-8\n# Copyright 2020 The Google Research Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"numpy.split",
"numpy.sqrt",
"numpy.linspace",
"numpy.concatenate",
"numpy.mean",
"numpy.cross",
"numpy.moveaxis",
"numpy.random.randint",
"numpy.square",
"numpy.ones_like",
"numpy.reshape",
"numpy.arange",
"numpy.eye",
"numpy.stack",
"numpy.sin",
"n... |
stacksmashing/TensorKartRealHW | [
"bb5e1eb164db0a78738ac6c0c7f07909c4d89dff"
] | [
"train3.py"
] | [
"#!/usr/bin/env python\n#model like in https://www.youtube.com/watch?v=tcpmucSLKo8\n\nimport numpy as np\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Flatten\nfrom tensorflow.keras.layers import Conv2D\nfrom tensorflow.keras import optimizers\nfrom tensorflow.... | [
[
"tensorflow.keras.layers.Dense",
"sklearn.utils.shuffle",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.backend.square",
"tensorflow.keras.optimizers.Adam",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.models.Sequential",
"tensorflow.keras.layers.Flatten"
]
] |
epahfk84/shape_as_points | [
"bcda2fdaab22c26e33b074d3c993fbb55fd567a1"
] | [
"generate.py"
] | [
"import torch\nfrom torch.utils.data import Dataset, DataLoader\nimport numpy as np; np.set_printoptions(precision=4)\nimport shutil, argparse, time, os\nimport pandas as pd\nfrom collections import defaultdict\nfrom src import config\nfrom src.utils import mc_from_psr, export_mesh, export_pointcloud\nfrom src.dpsr... | [
[
"torch.__version__.split",
"numpy.set_printoptions",
"torch.utils.data.DataLoader",
"pandas.DataFrame",
"torch.cuda.is_available",
"torch.device"
]
] |
kevin-michael-cs230/bigbird | [
"38ef761f56d89be11f3a64e4aabac554c8454050"
] | [
"bigbird/core/modeling.py"
] | [
"# Copyright 2020 The BigBird Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law o... | [
[
"tensorflow.compat.v2.zeros_like",
"tensorflow.compat.v2.ones_like",
"tensorflow.compat.v2.math.count_nonzero",
"tensorflow.compat.v2.slice",
"tensorflow.compat.v2.cast",
"tensorflow.compat.v2.compat.v1.variable_scope",
"tensorflow.compat.v2.ones",
"tensorflow.compat.v2.identity",
... |
ruslanmv/Machine-Learning-Codes | [
"dfc0ce1321c9953800d4238b3f4ab8f164bf26fc"
] | [
"rl2/cartpole/dqn_theano.py"
] | [
"# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python\n# https://www.udemy.com/deep-reinforcement-learning-in-python\nfrom __future__ import print_function, division\nfrom builtins import range\n# Note: you may need to update your version of future\n# sudo pip install -U future\n\nimport gym\ni... | [
[
"numpy.random.random",
"numpy.sqrt",
"matplotlib.pyplot.title",
"numpy.random.choice",
"matplotlib.pyplot.plot",
"numpy.atleast_2d",
"numpy.random.randn",
"matplotlib.pyplot.show",
"numpy.zeros",
"numpy.empty"
]
] |
JAAlvarado-Montes/huntsman-pocs | [
"eb5dfdc07e3084cb86b8f02373e83b7b27ecfe5a"
] | [
"src/huntsman/pocs/utils/dither.py"
] | [
"import numpy as np\nimport astropy.units as u\nfrom astropy.coordinates import SkyCoord, SkyOffsetFrame, ICRS\nfrom astropy.wcs import WCS\n\nimport matplotlib.pyplot as plt\n\n# Pattern for dice 9 3x3 grid (sequence of (RA offset, dec offset) pairs)\ndice9 = ((0, 0),\n (0, 1),\n (1, 1),\n ... | [
[
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.subplot",
"numpy.random.uniform",
"numpy.zeros"
]
] |
Shirling-VT/Tdiff_Validation | [
"a19c5c8b62b09d0cd60749154c4d744f1f56dfeb"
] | [
"py/get_fit_data.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"get_fit_data.py: utility module to fetch fitacf<v> level data.\"\"\"\n\n__author__ = \"Chakraborty, S.\"\n__copyright__ = \"Copyright 2020, SuperDARN@VT\"\n__credits__ = []\n__license__ = \"MIT\"\n__version__ = \"1.0.\"\n__maintainer__ = \"Chakraborty, S.\"\n__email__ = \"shibaji7@vt... | [
[
"numpy.abs",
"numpy.unique",
"numpy.mean",
"pandas.DataFrame.from_records",
"numpy.array"
]
] |
abhinavarora/text | [
"69f67f3a775f3d3c6f85cfaa4ac3819500b90696",
"69f67f3a775f3d3c6f85cfaa4ac3819500b90696"
] | [
"torchtext/transforms.py",
"test/data/test_modules.py"
] | [
"import json\nfrom copy import deepcopy\nfrom functools import lru_cache\nfrom typing import Any, List, Optional, Union\n\nimport torch\nimport torchtext # noqa: F401\nfrom torch import Tensor\nfrom torch.nn import Module\nfrom torchtext._torchtext import CLIPEncoder as CLIPEncoderPyBind, GPT2BPEEncoder as GPT2BPE... | [
[
"torch.classes.torchtext.Vocab",
"torch.classes.torchtext.CLIPEncoder",
"torch.classes.torchtext.GPT2BPEEncoder",
"torch.jit.isinstance"
],
[
"torch.randint",
"torch.cat",
"torch.zeros",
"torch.nn.functional.multi_head_attention_forward",
"torch.nn.Linear",
"torch.rand"... |
grlee77/chainer | [
"c064bb33701bc35fee9500a334a8fc76e4179bfc"
] | [
"tests/onnx_chainer_tests/functions_tests/test_arrays.py"
] | [
"import chainer\nimport chainer.functions as F\nfrom chainer import testing\nimport numpy as np\nimport onnx\nimport pytest\n\nfrom onnx_chainer import export\nfrom onnx_chainer.testing import input_generator\nfrom onnx_chainer_tests.helper import ONNXModelChecker\nfrom onnx_chainer_tests.helper import ONNXModelTes... | [
[
"numpy.arange",
"numpy.array",
"numpy.zeros"
]
] |
etrulls/d2-net | [
"95e5557d7f64641ba7991d7370b845c5a036f183"
] | [
"lib/pyramid.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom lib.exceptions import EmptyTensorError\nfrom lib.utils import interpolate_dense_features, upscale_positions\n\n\ndef process_multiscale(image, model, scales=[.5, 1, 2]):\n b, _, h_init, w_init = image.size()\n device = image.device\... | [
[
"torch.nn.functional.normalize",
"torch.abs",
"torch.max",
"torch.cat",
"torch.zeros",
"torch.min",
"torch.nn.functional.interpolate",
"torch.nonzero",
"torch.stack"
]
] |
tho15/tfplusplus | [
"e151986f7d449ee5ccb440fbb947fbc64fd62f49"
] | [
"experimental/model.py"
] | [
"import numpy as np\nimport tensorflow as tf\n#import cv2\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport csv\nimport math\nimport os\nfrom keras.layers import Dense, Flatten, Lambda, Activation, MaxPooling2D, ELU, Dropout\nfrom keras.layers.convolutional import Conv2D\nfrom keras.models import Sequ... | [
[
"sklearn.utils.shuffle",
"matplotlib.pyplot.imread",
"tensorflow.global_variables",
"sklearn.model_selection.train_test_split",
"tensorflow.python.framework.graph_util.convert_variables_to_constants",
"numpy.array"
]
] |
graehl/pytorch-transformers | [
"59292fe230ee2c2c681b7966bf2bc1f374ce67d4"
] | [
"run_sent.py"
] | [
" #!/usr/bin/python3\n\n# pip3 install --user torch==1.4.0+cpu torchvision==0.5.0+cpu -f https://download.pytorch.org/whl/torch_stable.html\n# pip3 install --user transformers\n\n\n\nimport torch\nimport argparse\nimport sys\n\n\nmodelname=\"finmodel_distilbert\" #\"bert-base-cased-finetuned-mrpc\"\n\n\nparser = a... | [
[
"torch.softmax"
]
] |
harshkasyap/PyAriesFL | [
"dd78dcebc771971abfee301b80cdd5d246c14840"
] | [
"data/generate_model.py"
] | [
"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport torch\nimport sys\nimport traceback\n\n\n# prep\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import preprocessing\nfrom ... | [
[
"torch.nn.Linear",
"torch.nn.Sigmoid",
"torch.save"
]
] |
unsw-cse-soc/REST2Bot | [
"b4dc549ee61611afb8cfbee612f7b7c7ce9ee8a5"
] | [
"swagger/swagger_utils.py"
] | [
"import os\nimport re\n\nimport wordninja\nfrom nltk.stem import WordNetLemmatizer\nfrom pandas import read_csv\nfrom tabulate import tabulate\n\nfrom swagger.entities import Param\nfrom utils.preprocess import remove_stopword\nfrom utils.language_tool import LanguageChecker\nfrom utils.text import is_singular\n\nl... | [
[
"pandas.read_csv"
]
] |
DeltaMarine101/neural_net | [
"fd0b9793a9b3dffd1ee2330ff8e3e9bda98cec33"
] | [
"nn.py"
] | [
"import math\nimport random as r\nimport numpy as np\nimport pickle\nimport time\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as cplt\n\n# data = [(np.array(list(map(float, i[38:].split()))) / 256., np.array(list(map(float, i[8:28].split())))) for i in open('data/mnist.txt').read().strip().split('\\n'... | [
[
"numpy.dot",
"numpy.reshape",
"matplotlib.pyplot.subplots",
"numpy.random.rand",
"matplotlib.pyplot.subplots_adjust",
"numpy.exp",
"matplotlib.colors.LinearSegmentedColormap.from_list",
"matplotlib.pyplot.show",
"numpy.zeros",
"matplotlib.pyplot.style.use"
]
] |
calmarazlopez/Deep-Learning-Udacity-Nanodegree | [
"19114fe796043c550a6ba3b3ae67c1e239002c11"
] | [
"Predicting Bike-Sharing Patterns/my_answers.py"
] | [
"import numpy as np\n\n\nclass NeuralNetwork(object):\n def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n # Set number of nodes in input, hidden and output layers.\n self.input_nodes = input_nodes\n self.hidden_nodes = hidden_nodes\n self.output_nodes = out... | [
[
"numpy.dot",
"numpy.random.normal",
"numpy.exp",
"numpy.zeros"
]
] |
dhowardCS/git_vscode_demo | [
"edbe4397c3b27cd31cfd3036ebf465c40c79432b"
] | [
"SKlearn Course/knn_classifier1/main.py"
] | [
"import numpy as np \nimport pandas as pd\nfrom sklearn import neighbors, metrics\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\n\ndata = pd.read_csv('car.data')\nprint(data.head())"
] | [
[
"pandas.read_csv"
]
] |
dotrungkien3210/paper_faceNet | [
"2a371e00e0d5faa717c66e289f102993ec712311"
] | [
"plot.py"
] | [
"import numpy as np\nimport os.path\nfrom model import create_model\nimport cv2\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport warnings\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import LinearSVC\nfrom sklearn.metrics import f... | [
[
"numpy.square",
"pandas.read_csv",
"numpy.expand_dims",
"matplotlib.pyplot.title",
"sklearn.metrics.confusion_matrix",
"pandas.DataFrame",
"matplotlib.pyplot.xlabel",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] |
Jannkar/doom_actionspace | [
"37663341f60a05943202b77394a4203d070fad95"
] | [
"agent_stable_baselines/stable_baselines/deepq/experiments/enjoy_mountaincar.py"
] | [
"import argparse\r\n\r\nimport gym\r\nimport numpy as np\r\n\r\nfrom stable_baselines.deepq import DQN\r\n\r\n\r\ndef main(args):\r\n \"\"\"\r\n Run a trained model for the mountain car problem\r\n\r\n :param args: (ArgumentParser) the input arguments\r\n \"\"\"\r\n env = gym.make(\"MountainCar-v0\")... | [
[
"numpy.random.random"
]
] |
fmfn/lifelines | [
"fec81897674ebeb3223efba48b99e7b1302cdf9e"
] | [
"lifelines/fitters/SBGSurvival.py"
] | [
"from __future__ import print_function\nfrom lifelines.utils.DataHandler import DataHandler\nfrom lifelines.utils.ShiftedBetaGeometric import ShiftedBetaGeometric\nimport numpy as np\nimport pandas as pd\n\n\nclass SBGSurvival(object):\n \"\"\"\n This class implements an extended version of the Shifted-Beta-G... | [
[
"numpy.vstack",
"pandas.DataFrame"
]
] |
Space0726/FontTools | [
"a322a9bc403e93b0b32856a461fa6bf384d921e9"
] | [
"tools/derivativetools.py"
] | [
"\"\"\" Font tools for calculating derivative and using it.\n\nLast modified date: 2019/08/17\n\nCreated by Seongju Woo.\n\"\"\"\nimport math\nimport numpy as np\nimport bezier\nfrom fwig.tools import appendtools\n\ndef _calculate_distance(point_1, point_2):\n return math.sqrt(pow(point_1[0]-point_2[0], 2)\n ... | [
[
"numpy.asfortranarray"
]
] |
francescobarbara/idad | [
"7931daeec5ae7db0c212d0b13f3c13d4784ecfdb"
] | [
"neural/critics.py"
] | [
"from collections import OrderedDict\n\nimport torch\nfrom torch import nn\n\n\n## MI critics\nclass CriticDotProd(nn.Module):\n \"\"\"\n Separable critic\n\n returns:\n scores_joint: tensor of shape [batch_size, batch_size] where only non-zero terms are on the diagonal\n scores_prod: tensor of shape... | [
[
"torch.nn.Softplus",
"torch.cat",
"torch.eye",
"torch.nn.Linear",
"torch.matmul",
"torch.distributions.Normal",
"torch.nn.ReLU"
]
] |
Juanjoglvz/MachineLearning | [
"2d979978448bf14c628dad0d8b87062e5687a101"
] | [
"src/visualization/Interpretation.py"
] | [
"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import preprocessing \nfrom sklearn.cluster import KMeans\nfrom sklearn import metrics\nfrom sklearn.decomposition import PCA\n\n# Read the data and load it into memory\ndf_T = pd.read_csv(\"../../data/processed/T2_Accelerometer... | [
[
"pandas.read_csv",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.show",
"sklearn.decomposition.PCA",
"sklearn.preprocessing.MinMaxScaler"
]
] |
zhivko/tensortrade | [
"af7a4a323415457d8ddb3befa3dabeac1844fdd0"
] | [
"examples/myexample/main.py"
] | [
"from tensortrade.oms.instruments import Instrument, BTC, USD\nfrom tensortrade.env.default.actions import BSH\n\nfrom tensortrade.env.generic import Renderer\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport ray\nimport numpy as np\nimport pandas as pd\n\nimport tensortrade.env.default as default\n\... | [
[
"pandas.Series",
"torch.zeros",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"torch.cuda.is_available",
"pandas.DataFrame.from_dict",
"matplotlib.pyplot.show"
]
] |
Originofamonia/DANN | [
"97541e913e050855818f562574b28b5f2b550a1f"
] | [
"train/main.py"
] | [
"import random\nimport os\nimport sys\nimport torch.backends.cudnn as cudnn\nimport torch.optim as optim\nimport torch.utils.data\nfrom torchvision import datasets\nfrom torchvision import transforms\nimport numpy as np\n\n\ndef add_path(path):\n if path not in sys.path:\n print('Adding {}'.format(path))\... | [
[
"numpy.exp"
]
] |
94mia/DeepLEGO | [
"9458b13da6117f6054ce406bdb3942358e2cd764"
] | [
"heads/pspnet.py"
] | [
"'''\nRe-implementation of head module of PSPNet introduced in paper [1]\nThe structure of this module refers to the Caffe implementation from [2]\n\nReference:\n[1] Pyramid Scene Parsing Network\n https://arxiv.org/abs/1612.01105\n[2] hszhao/PSPNet/evaluation/prototxt/pspnet101_cityscapes_713.prototxt\n http... | [
[
"torch.nn.functional.upsample",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] |
iisys-hof/HUI-Audio-Corpus-German | [
"4d2de2ed538a6b943166e1e35c10ee8b0b266be6"
] | [
"huiAudioCorpus/converter/ListToHistogramConverter.py"
] | [
"from huiAudioCorpus.model.Histogram import Histogram\nfrom typing import List, TypeVar\n\nimport numpy as np\nnumber = TypeVar('number', int, float)\n\nclass ListToHistogramConverter:\n def __init__(self, stepSize: int):\n self.stepSize =stepSize\n\n def convert(self, list: List[number]):\n bin... | [
[
"numpy.histogram"
]
] |
nnn112358/python-control_test | [
"58e1b5e6feec0477fd4bad3683fb8af470faed4f"
] | [
"pvtol-nested.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n#このファイルは、Python制御パッケージの基本的な機能を実証することを目的としています。\n#AstromとMrurayの平面垂直離着陸（PVTOL）機に対応する、かなり複雑な制御設計と解析を動作します。\n#\n\n\n# pvtol-nested.py - aircraftのスラスタベクトルの内外ループ制御設計\n\n\nfrom __future__ import print_function\nfrom matplotlib.pyplot import * # MATLAB プロット関数\nfrom contr... | [
[
"numpy.squeeze"
]
] |
Gorilla-Lab-SCUT/SRDC-CVPR2020 | [
"9cd07156e5c520e955a7df33c42819777d012ecb"
] | [
"data/prepare_data.py"
] | [
"import os\nimport shutil\nimport torch\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\nimport torch.nn.functional as F\nfrom utils.folder import ImageFolder\nimport numpy as np\nimport cv2\n\ndef generate_dataloader(args):\n # Data loading code\n traindir = os.path.join... | [
[
"torch.utils.data.DataLoader",
"torch.from_numpy",
"torch.cuda.FloatTensor",
"numpy.random.normal",
"numpy.transpose"
]
] |
aky15/espnet | [
"1dc734839d34e2f2dd13cfa375713aecf232ae25"
] | [
"espnet/nets/pytorch_backend/transformer/decoder.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Copyright 2019 Shigeki Karita\n# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)\n\n\"\"\"Decoder definition.\"\"\"\n\nimport logging\n\nfrom typing import Any\nfrom typing import List\nfrom typing import Tuple\n\nimport torch\n\nfrom espnet.nets.pytor... | [
[
"torch.nn.Dropout",
"torch.nn.Module.__init__",
"torch.nn.Embedding",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.ReLU"
]
] |
gugarosa/dropout_rbm | [
"1b16e71315e1577b468ea2cd0d3c7fcf48bc6851"
] | [
"optimization.py"
] | [
"import argparse\n\nimport numpy as np\nimport torch\n\nimport utils.loader as l\nimport utils.objects as m\nimport utils.opt as o\nimport utils.target as t\n\n\ndef get_arguments():\n \"\"\"Gets arguments from the command line.\n\n Returns:\n A parser with the input arguments.\n\n \"\"\"\n\n # C... | [
[
"torch.manual_seed",
"numpy.random.seed"
]
] |
srijannnd/Data-Set-Visualization-App | [
"cbac1a5c076c056cfb5d26795c0505b60e2850b4"
] | [
"uploads/core/views.py"
] | [
"from django.shortcuts import render, redirect, get_object_or_404\nfrom uploads.core.models import Document\nfrom uploads.core.forms import DocumentForm\nimport pandas as pd\nimport seaborn as sns\n\n\ndef home(request):\n documents = Document.objects.all()\n if request.method == 'POST':\n form = Docum... | [
[
"pandas.read_csv"
]
] |
VERITAS-Observatory/V2DL3 | [
"3b4691cbb3a06805b722e494d4ae84ce7a866dd4"
] | [
"pyV2DL3/vegas/fillRESPONSE_not_safe.py"
] | [
"import logging\n\nimport numpy as np\n\nfrom pyV2DL3.vegas.irfloader import IRFLoader\nfrom pyV2DL3.vegas.util import getThetaSquareCut\n\nlogger = logging.getLogger(__name__)\n\n\ndef __fillRESPONSE_not_safe__(\n effectiveAreaIO, azimuth, zenith, noise, irf_to_store=None\n):\n\n if irf_to_store is None:\n ... | [
[
"numpy.sqrt"
]
] |
SuperToxicCat/Drowsiness-Detection | [
"fac50f762719e76cb41a8a7d0206f63ae35cab89"
] | [
"face_landmarks.py"
] | [
"\n\nimport cv2\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\n\n\ndef get_landmark_model(saved_model='models/pose_model'):\n \"\"\"\n \n Parameters\n ----------\n saved_model : string, optional\n Path to facial landmarks model. The default is 'models/pose_model'.\n\n... | [
[
"numpy.reshape",
"numpy.array",
"tensorflow.constant",
"tensorflow.saved_model.load"
]
] |
AnonSubm2021/TransStyleGAN | [
"0194cd6f0e96c801d55c0cb9683e1f552bcf6d48"
] | [
"metrics/prdc.py"
] | [
"\"\"\"\nprdc\nCopyright (c) 2020-present NAVER Corp.\nMIT license\n\"\"\"\nimport numpy as np\nimport sklearn.metrics\n\n__all__ = ['compute_prdc']\n\n\ndef compute_pairwise_distance(data_x, data_y=None):\n \"\"\"\n Args:\n data_x: numpy.ndarray([N, feature_dim], dtype=np.float32)\n data_y: num... | [
[
"numpy.take_along_axis",
"numpy.expand_dims",
"numpy.argpartition"
]
] |
rigdenlab/SWAMP | [
"3e93ab27f4acf0124f7cb2d78a151cc3352b9c6e"
] | [
"swamp/clustering/spectral.py"
] | [
"from swamp.clustering.clustering import Clustering\nfrom sklearn.cluster import SpectralClustering\nfrom scipy.stats import randint, expon\n\n\nclass Spectral(Clustering):\n \"\"\"This class implements methods and datastructures to work with :py:obj:`sklearn.cluster.Spectral`\n\n :example:\n\n >>> from sw... | [
[
"scipy.stats.randint",
"scipy.stats.expon",
"sklearn.cluster.SpectralClustering"
]
] |
BrunoCoimbra/decisionengine_modules | [
"bfd14644eb2e16b72b75fdcc3ebe8ad1323b904f",
"bfd14644eb2e16b72b75fdcc3ebe8ad1323b904f"
] | [
"src/decisionengine_modules/glideinwms/resource_dist_plugins.py",
"src/decisionengine_modules/glideinwms/sources/factory_entries.py"
] | [
"# SPDX-FileCopyrightText: 2017 Fermi Research Alliance, LLC\n# SPDX-License-Identifier: Apache-2.0\n\nimport pandas as pd\n\n_RESOURCE_FROM_COLUMN_MAP = {\n \"Grid_Figure_Of_Merit\": \"Grid_Figure_Of_Merit\",\n \"GCE_Figure_Of_Merit\": \"FigureOfMerit\",\n \"AWS_Figure_Of_Merit\": \"AWS_Figure_Of_Merit\",... | [
[
"pandas.DataFrame"
],
[
"pandas.concat",
"pandas.DataFrame"
]
] |
moshes7/Competitions | [
"7434ed9ef51c6f8e61f87d180c025f4f0a7c32b9"
] | [
"models/pretrained_xception.py"
] | [
"from __future__ import print_function, division, absolute_import\n\n# -*- coding: utf-8 -*-\n\"\"\"pretrained-Xception.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/drive/1nrkV8ju4mc1uw5CqV7h8u7QInWkO0asL\n\"\"\"\n\nimport math\nimport torch... | [
[
"torch.nn.Sequential",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.functional.adaptive_avg_pool2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.init.xavier_uniform_",
"torch.no_grad",
"torch.rand",
"torch.cuda.is_available",
"torch.nn.BatchNorm2d",
... |
hannesfelipe/oemof-solph | [
"a29802c73b9f3a1240a9ea6cec28f9d52bf1001c"
] | [
"tests/test_scripts/test_solph/test_simple_model/test_simple_invest.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\" This example shows how to create an energysystem with oemof objects and\nsolve it with the solph module.\n\nData: example_data.csv\n\nThis file is part of project oemof (github.com/oemof/oemof). It's copyrighted\nby the contributors recorded in the version control history of the f... | [
[
"pandas.read_csv",
"pandas.date_range"
]
] |
BCV-Uniandes/SIMBA | [
"1f25cdd5005ad71918938ea6bef4544a4c24281b"
] | [
"simba/train.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nBone Age Assessment SIMBA train routine.\n\"\"\"\n\n# Standard lib imports\nimport os\nimport csv\nimport glob\nimport time\nimport argparse\nimport warnings\nimport pandas as pd\nimport os.path as osp\n\n# PyTorch imports\nimport torch\nimport torch.nn as nn\nimport torch.optim ... | [
[
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.utils.data.distributed.DistributedSampler",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.is_available",
"torch.nn.L1Loss"
]
] |
chbehrens/brian2 | [
"46b5264caa5375ae13084508b5c1049e0c9e019e"
] | [
"brian2/core/variables.py"
] | [
"from __future__ import absolute_import\n'''\nClasses used to specify the type of a function, variable or common\nsub-expression.\n'''\nimport collections\nimport functools\nimport numbers\n\nimport numpy as np\nimport sympy\nfrom past.builtins import basestring\n\nfrom brian2.units.fundamentalunits import (Quantit... | [
[
"numpy.can_cast",
"numpy.issubdtype",
"numpy.asanyarray",
"numpy.prod",
"numpy.array"
]
] |
xdralex/pytorch-wheel5 | [
"336529e354a45908cf3f8f12cd401a95fb2a5351"
] | [
"wheel5/dataset/functional.py"
] | [
"from typing import Optional, Tuple, List\n\nimport numpy as np\nimport torch\nfrom numpy.random.mtrand import RandomState\n\nfrom wheel5.tricks.heatmap import heatmap_to_selection_mask\n\n\ndef mixup(img_src: torch.Tensor, lb_src: torch.Tensor,\n img_dst: torch.Tensor, lb_dst: torch.Tensor,\n alp... | [
[
"torch.lerp",
"numpy.sqrt",
"numpy.clip",
"numpy.round",
"numpy.random.mtrand.RandomState"
]
] |
1036225283/Reinforcement-learning-with-tensorflow | [
"0422669b36faccfb1b96581a6345b8f39805ce7e"
] | [
"contents/Double_DQN/run_Pendulum.py"
] | [
"\"\"\"\nDouble DQN & Natural DQN comparison,\nThe Pendulum example.\n\nView more on my tutorial page: https://morvanzhou.github.io/tutorials/\n\nUsing:\nTensorflow: 1.0\ngym: 0.8.0\n\"\"\"\n\n\nimport gym\nfrom contents.Double_DQN.RL_brain import DoubleDQN\nimport numpy as np\nimport matplotlib.pyplot as plt\nimpo... | [
[
"matplotlib.pyplot.legend",
"tensorflow.global_variables_initializer",
"matplotlib.pyplot.grid",
"tensorflow.Session",
"tensorflow.variable_scope",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] |
TeaKatz/AI_Training | [
"881a7176965a267a5d966f1f50edd29b39906c26"
] | [
"utilities/SignalGenerator.py"
] | [
"import numpy as np\n\n\nclass SignalGenerator:\n\tdef __init__(self, period, signal_type=\"sine\", amplitude=1, vertical_shift=0, phase_shift=0):\n\t\tassert signal_type.lower() in [\"sine\", \"cos\", \"half-sine\", \"half-cos\", \"sawtooth\"], \"get unknown signal_type: '{}'\".format(signal_type)\n\t\t\n\t\tself.... | [
[
"numpy.mod",
"numpy.arange",
"numpy.cos",
"numpy.sin"
]
] |
staoxiao/LibVQ | [
"f844c60055ace872279daa272b0bad1005c02e2b"
] | [
"examples/NQ/learnable_index/train_index.py"
] | [
"import sys\nsys.path.append('./')\nimport os\nimport pickle\nimport gc\n\nimport faiss\nimport numpy as np\nfrom transformers import HfArgumentParser\nfrom torch.optim import AdamW\n\nfrom LibVQ.base_index import FaissIndex\nfrom LibVQ.dataset.dataset import load_rel, write_rel\nfrom LibVQ.learnable_index import L... | [
[
"numpy.memmap"
]
] |
AI-Assistant/FEMAG-Python | [
"ff86e8f41485ae9df6034e6b8e810b59f8094c70"
] | [
".venv/Lib/site-packages/ipopt-0.1.9/setup.py"
] | [
"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\ncyipopt: Python wrapper for the Ipopt optimization package, written in Cython.\n\nCopyright (C) 2012-2015 Amit Aides\nCopyright (C) 2015-2018 Matthias Kümmerer\n\nAuthor: Matthias Kümmerer <matthias.kuemmerer@bethgelab.org>\n(original Author: Amit Aides <ami... | [
[
"numpy.get_include"
]
] |
yuriharrison/ml-algorithms | [
"b69c7e666006d43b10ef8f0d95fe745a430f04f1"
] | [
"mlalgorithms/kMeans.py"
] | [
"\"\"\"K-Means clustering Algorithm\"\"\"\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\n\nclass KMeans:\n \"\"\"K-Means clustering\n\n -- Arguments\n k: int, optional, default 2\n The number of clusters to form as well as the number of centroids to generate.\n ... | [
[
"matplotlib.pyplot.scatter",
"numpy.linalg.norm",
"numpy.average",
"numpy.array",
"numpy.sum",
"matplotlib.pyplot.show"
]
] |
felixhao28/oneflow | [
"e558af6ef6c4ed90e4abc7bc1ba895f55795626d"
] | [
"python/oneflow/test/modules/test_deconv2d.py"
] | [
"\"\"\"\nCopyright 2020 The OneFlow Authors. All rights reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by ap... | [
[
"numpy.array"
]
] |
Kingsford-Group/polarset | [
"26da7debd5a4c4a456fcf7ac3749f06527db0226"
] | [
"anchor_sets_bit.py"
] | [
"# these are re-implementations of CoverageChecker and KMerChain that\n# uses bitvectors (array in Python) to improve performance.\n\nfrom collections import defaultdict\nfrom anchor_sets import CoverageChecker, KMerChain\nfrom array import array\nimport numpy as np\nimport pickle\nimport logging\nfrom tqdm import ... | [
[
"numpy.argsort"
]
] |
kioma/densenet | [
"e03a590aa38159c5099f641b630cb4016e9ab6cf"
] | [
"test_inference.py"
] | [
"\"\"\"Test ImageNet pretrained DenseNet\"\"\"\nimport cv2\nimport numpy as np\nfrom keras.optimizers import SGD\nimport keras.backend as K\n\n# We only test DenseNet-161 in this script for demo purpose\nfrom models.densenet161 import DenseNet\n\nim = cv2.resize(cv2.imread('resources/cat.jpg'), (224, 224)).astype(... | [
[
"numpy.expand_dims",
"numpy.argmax"
]
] |
debodeepkar/adni_research | [
"d8061d0a68e1aca6517b8f15089a3331dd6819e6"
] | [
"entropy.py"
] | [
"import nibabel as nb\nimport os\nfrom skimage import data\nfrom skimage.measure.entropy import shannon_entropy\nfrom skimage.color import rgb2gray\nimport numpy as np\n\npath = \"/Users/debodeepkar/Documents/ADNI/NORMALIZED/ANTS/CN/\"\nos.chdir(path)\nfile = os.listdir(path) #returns a list with path\n\nslice=[]\n... | [
[
"numpy.append",
"numpy.argmax"
]
] |
rl-navigation/deployable | [
"c06f0913297069ac2a064124ea591a17552a3d75"
] | [
"src/renderer.py"
] | [
"from __future__ import print_function, division\nimport numpy as np, cv2, networkx as nx\n\n#==============================================================================\n# ENVIRONMENT RENDERER\n#==============================================================================\n\n_render_cache = {}\n_optimal_paths... | [
[
"numpy.hstack",
"numpy.pad",
"numpy.abs",
"numpy.clip",
"numpy.cos",
"numpy.full",
"numpy.sin",
"numpy.ceil",
"numpy.mean",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] |
G-arj/qdk-python | [
"4bb4fa371347dc76b0448dfc79c557d468002f74"
] | [
"azure-quantum/tests/unit/test_target.py"
] | [
"import unittest\nimport warnings\nimport pytest\n\nimport numpy as np\n\nfrom azure.core.exceptions import HttpResponseError\nfrom azure.quantum.job.job import Job\nfrom azure.quantum._client.models import CostEstimate, UsageEvent\nfrom azure.quantum.target import IonQ, Honeywell, Quantinuum\n\nfrom common import ... | [
[
"numpy.round"
]
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.