repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
jorag/pgnlm | [
"798a1c08505713ad6176be3568df2fe05308843f"
] | [
"pgnlm/datasets.py"
] | [
"#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author: jorag\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport os \r\nfrom osgeo import gdal\r\nfrom helperfuncs import iq2complex, norm01\r\n\r\n\r\ndef _test_dataset(load_options):\r\n \"\"\" Load and crop the simulated test dataset.\r\n\r\n ... | [
[
"numpy.max",
"numpy.transpose",
"numpy.min"
]
] |
harrys17451/CryptocurrencyPrediction | [
"7ec542bcd6bf960b115638484737f097120badcd"
] | [
"DataProcessor.py"
] | [
"\n# coding: utf-8\n\n# In[2]:\n\n\nimport pandas as pd\nimport numpy as np\nimport h5py\n\n\n# In[24]:\n\n\ninput_step_size = 50\noutput_size = 30\nsliding_window = False\nfile_name= 'bitcoin2012_2017_50_30_prediction.h5' \n\n\n# In[19]:\n\n\ndf = pd.read_csv('data/bitstampUSD_1-min_data_2012-01-01_to_2017-05-31.c... | [
[
"pandas.to_datetime",
"numpy.array",
"pandas.read_csv"
]
] |
DCGM/pero_ocr_web | [
"e901027712827278f9ace914f6ccba16d3ac280f"
] | [
"app/ocr/general.py"
] | [
"import os\nimport numpy as np\nimport shutil\n\nfrom app.db.model import RequestState, RequestType, Request, DocumentState, TextLine, Annotation, TextRegion, Document\nfrom app.db.user import User\nfrom app.db.general import get_text_region_by_id, get_text_line_by_id\nfrom app import db_session\nfrom flask import ... | [
[
"numpy.asarray"
]
] |
lammySup/ncscli | [
"1758b7c894f2b890c7462d63a9c46ce47bf1262b"
] | [
"examples/batchMode/plotGatlingOutput.py"
] | [
"#!/usr/bin/env python3\n\"\"\"\nplots loadtest results produced by runBatchJMeter\n\"\"\"\n# standard library modules\nimport argparse\nimport csv\nimport glob\nimport json\nimport logging\nimport math\nimport os\nimport re\nimport sys\nimport warnings\n# third-party modules\nimport matplotlib as mpl\nimport matpl... | [
[
"numpy.sin",
"matplotlib.pyplot.xlim",
"matplotlib.ticker.MultipleLocator",
"numpy.zeros",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.title",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.Polygon",
"matplotlib.py... |
pagiux/passflow | [
"602d86896c0ed69fa828b51cafb1584286e2782c"
] | [
"models/real_nvp/coupling_layer.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom enum import IntEnum\nimport numpy as np\n\nfrom models.nn import MLP, ResNet\n\n\nclass MaskType(IntEnum):\n CHECKERBOARD = 0\n HORIZONTAL = 1\n CHAR_RUN = 2\n\n\nclass AffineTransform(nn.Module):\n def __init__(self, dim, device, mask_type, mask_pattern, net... | [
[
"torch.zeros",
"numpy.zeros",
"numpy.ones",
"numpy.tile",
"torch.tanh",
"torch.exp"
]
] |
KedoKudo/tomoproc | [
"b20270e87af4ce7459004a6ed928037ae8573b1e"
] | [
"tomoproc/prep/correction.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\n\"\"\"\nContain functions operating on tomography sinograsm\n\nNOTE:\n Different experiment requires different type of correction, the choice of\n which should be established via trial and error.\n\"\"\"\nimport tomopy\nimport numpy as ... | [
[
"numpy.array",
"numpy.ceil",
"scipy.ndimage.shift",
"numpy.ones",
"scipy.signal.medfilt2d",
"numpy.where",
"numpy.sqrt",
"numpy.absolute",
"numpy.average",
"numpy.random.random",
"numpy.linspace",
"numpy.linalg.svd"
]
] |
AJTYNAN/rlcard | [
"7370c4c81bd5fc3e087df29d1d73cfc4514c081a"
] | [
"rlcard/games/nolimitholdem/game.py"
] | [
"from enum import Enum\n\nimport numpy as np\nfrom copy import deepcopy\nfrom rlcard.games.limitholdem import Game\nfrom rlcard.games.limitholdem import PlayerStatus\n\nfrom rlcard.games.nolimitholdem import Dealer\nfrom rlcard.games.nolimitholdem import Player\nfrom rlcard.games.nolimitholdem import Judger\nfrom r... | [
[
"numpy.sum",
"numpy.random.RandomState"
]
] |
ricoai/ricar_ryc | [
"811aefaf9893f3fe9c61d2070d8dc3a949f36697"
] | [
"manage.py"
] | [
"#!/usr/bin/env python3\n\"\"\"\nScripts to drive a donkey 2 car and train a model for it. \n\nUsage:\n manage.py drive [--model=<model>] [--web=<True/False>] [--throttle=<Throttle 0.0-1.0>] [--js]\n manage.py train (--tub=<tub>) (--model=<model>)\n manage.py calibrate\n manage.py (calibrate)\n manag... | [
[
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.show"
]
] |
fy-meng/lunarlander-saliency | [
"294a2008ad010a42ebbde4fa039c711611e96044"
] | [
"lunarLander.py"
] | [
"\"\"\"\nFile name: lunarLander.py\n Agent for landing successfully the 'Lunar Lander' which is implemented in\n OpenAI gym (reference [1]).\n\nUsage: python lunarLander.py -h\n\nusage: lunarLander.py [-h] [-v {0,1,2}] -e {train,test} [-a A]\n\nLunar Lander with DQN\n\noptional arguments:\n -h, --help ... | [
[
"matplotlib.pyplot.switch_backend",
"numpy.max",
"numpy.array",
"numpy.random.rand",
"pandas.to_pickle",
"numpy.zeros",
"numpy.random.seed",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.title",
"matplotlib.pyplot... |
Gjain234/AdaptiveQLearning | [
"4bb9751a3cb76604bdc5ac2225da84e5daa32755"
] | [
"create_fig_multiple_ambulance.py"
] | [
"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport matplotlib.patches as patches\nimport pickle\nimport numpy as np\nfrom src import agent\nfrom adaptive_Agent import AdaptiveDiscretization\nfrom eNet_Agent import eNet\nimport pandas as pd\n\nepLen = 5\nnEps = 2000\n\nproblem_type = 'ambulance'\nproble... | [
[
"matplotlib.pyplot.rcParams.update",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.tight_layout",
"matplotlib.pypl... |
ishipachev/UdacitySDCND-CarND-Advanced-Lane-Lines-P4 | [
"a34d728f0062acc29aee312cb3f7b739edcc5c08"
] | [
"calibrate.py"
] | [
"import numpy as np\nimport cv2\nimport glob\nimport os\nimport pickle\nimport matplotlib.pyplot as plt\n\n# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)\nnx = 9\nny = 6\nobjp = np.zeros((ny*nx, 3), np.float32)\nobjp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)\n\n# Arrays to store object poi... | [
[
"numpy.zeros"
]
] |
jukiewiczm/guildai | [
"478cc29cb102a8bd0bed693ce9626fe4949257a2"
] | [
"guild/commands/tensorflow_impl.py"
] | [
"# Copyright 2017-2019 TensorHub, 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/LICENSE-2.0\n#\n# Unless required by applicable law or ag... | [
[
"tensorflow.python.tools.inspect_checkpoint.main",
"tensorflow.Graph",
"tensorflow.Session",
"tensorflow.GraphDef",
"tensorflow.import_graph_def",
"tensorflow.gfile.FastGFile"
]
] |
abgcsc/CDANs | [
"7113ed836df1369895054b11c121071faa8392af"
] | [
"KerasSupplementary.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 06 11:28:39 2016\n\n@author: Drew\n\"\"\"\n\nimport numpy as np\nimport os\nfrom keras.models import model_from_json, Model, Sequential\nfrom keras.layers import merge, Input\nfrom keras.layers.core import Lambda, Masking, Reshape, Dense, Flatten, Dropout\nfrom k... | [
[
"numpy.asarray",
"numpy.zeros",
"numpy.ones",
"numpy.prod",
"numpy.random.randint",
"numpy.argmax",
"numpy.power",
"numpy.unique"
]
] |
haquebd/robotic-warehouse | [
"55036332f82e79cf3f60c377c73d2d39733ec9e0"
] | [
"rware/warehouse.py"
] | [
"import logging\n\nfrom collections import defaultdict, OrderedDict\nimport gym\nfrom gym import spaces\n\nfrom rware.utils import MultiAgentActionSpace, MultiAgentObservationSpace\n\nfrom enum import Enum\nimport numpy as np\n\nfrom typing import List, Tuple, Optional, Dict\n\nimport networkx as nx\nimport astar\n... | [
[
"numpy.rot90",
"numpy.pad",
"numpy.array",
"numpy.random.choice",
"numpy.zeros",
"numpy.ones",
"numpy.unravel_index",
"numpy.stack",
"numpy.arange",
"numpy.random.randint",
"numpy.indices"
]
] |
GChen-ai/simple-deeplearning-framework | [
"0ad48f107457f576e4952e75637d68fa3da4f5a7"
] | [
"test_pool.py"
] | [
"from src.layers import*\nimport numpy as np\npool=Avgpool2D(2)\nimg=np.array([[[[3,0,4,2],[6,5,4,1],[3,0,2,2],[1,1,1,1]]\n ],\n [[[3,0,4,2],[6,5,4,1],[3,0,2,2],[1,1,1,1]]\n ]])\nprint(img.shape)\nout=pool.forward(img)\nprint(out)\nback=pool.backward(out)"
] | [
[
"numpy.array"
]
] |
pelperscience/arctic-connectivity | [
"946e90c5310682d7e2499e2ca9e2fd5ace71535c"
] | [
"community_detection/cooc.py"
] | [
"\"\"\"Create a matrix that shows how often bin pairs fall in the same degenerate solution.\"\"\"\n\nimport numpy as np\nfrom scipy import sparse\nimport xarray as xr ######################### xr\n\nfrom itertools import combinations\n\nimport sys\nimport pickle\nfrom glob import glob\nfrom importlib import reloa... | [
[
"numpy.matmul",
"numpy.zeros",
"numpy.sum",
"numpy.degrees",
"numpy.unique"
]
] |
rongou/cudf | [
"23cafcf0ae1a2fe5e6b7138f4c92c2dbfa2ec93b"
] | [
"python/cudf/cudf/core/series.py"
] | [
"# Copyright (c) 2018-2021, NVIDIA CORPORATION.\n\nfrom __future__ import annotations\n\nimport functools\nimport inspect\nimport pickle\nimport warnings\nfrom collections import abc as abc\nfrom hashlib import sha256\nfrom numbers import Number\nfrom shutil import get_terminal_size\nfrom typing import Any, Mutable... | [
[
"numpy.result_type",
"numpy.array",
"numpy.asarray",
"pandas._config.get_option",
"numpy.sort",
"pandas.Series",
"numpy.issubdtype",
"numpy.iinfo"
]
] |
anhaidgroup/py_entitymatching | [
"6724081d7d95c547e5a51625b4a8207c6c1737f8"
] | [
"py_entitymatching/dask/daskmlmatcher.py"
] | [
"\"\"\"\nThis module contains functions related to ML-matcher, that is common across\nall the ML-matchers.\n\"\"\"\nimport logging\n\nimport pandas as pd\nimport numpy as np\n\n# import dask\nimport dask\nfrom dask import delayed\nfrom dask.diagnostics import ProgressBar\n\nimport py_entitymatching.catalog.catalog_... | [
[
"pandas.concat",
"numpy.delete",
"numpy.array_split"
]
] |
pyc-ycy/qtpandas | [
"5adf5f0cee8d3409e25342ecb390cce19beb6ff1"
] | [
"tests/test_CustomDelegates.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\nfrom builtins import super\nfrom builtins import str\nfrom future import standard_library\nstandard_library.install_aliases()\nfrom qtp... | [
[
"numpy.int8",
"numpy.uint8",
"numpy.uint32",
"numpy.float16",
"numpy.uint16",
"pandas.DataFrame",
"numpy.int64",
"numpy.float64",
"numpy.finfo",
"numpy.float32",
"numpy.uint64",
"numpy.int32",
"numpy.iinfo",
"numpy.dtype",
"numpy.int16"
]
] |
JiehangXie/PaddleSpeech | [
"60090b49ec27437127ab62358026dd5bb95fccc7"
] | [
"paddlespeech/text/exps/ernie_linear/avg_model.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LIC... | [
[
"numpy.array",
"numpy.argsort"
]
] |
jchkoch/pycalculix | [
"c943c0408297873de104bd60c404c42eea66b895"
] | [
"pycalculix/results_file.py"
] | [
"\"\"\"This module stores the Results_File class.\"\"\"\n\nimport collections\nimport math # used for metric number conversion\nimport os #need to check if results file exists\nimport re # used to get info from frd file\nimport subprocess # used to check ccx version\n\nimport matplotlib.pyplot as plt\nimport matplo... | [
[
"matplotlib.cm.ScalarMappable",
"matplotlib.pyplot.tricontourf",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.tripcolor",
"numpy.arange",
"numpy.polyfit",
"numpy.core.function_base.linspace",
"numpy.poly1d",
"matplotlib.pyplot.gca",
"nu... |
JerryX1110/VFS | [
"22b915318935f459c9ee2d854d741b3f01a2ce9a"
] | [
"mmaction/models/backbones/resnet3d_slowfast.py"
] | [
"import torch\nimport torch.nn as nn\nfrom mmcv.cnn import ConvModule, kaiming_init\nfrom mmcv.runner import _load_checkpoint, load_checkpoint\nfrom mmcv.utils import print_log\n\nfrom ...utils import get_root_logger\nfrom ..registry import BACKBONES\nfrom .resnet3d import ResNet3d\n\n\nclass ResNet3dPathway(ResNet... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.zeros"
]
] |
burgalon/spinningup | [
"6ae9e69d795919f8775ded5d2dd6d6b60ae8ffea"
] | [
"spinup/utils/plot.py"
] | [
"import seaborn as sns\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport json\nimport os\nimport os.path as osp\nimport numpy as np\n\nDIV_LINE_WIDTH = 50\n\n# Global vars for tracking and labeling data at load time.\nexp_idx = 0\nunits = dict()\n\ndef plot_data(data, xaxis='Epoch', value=\"AverageEpRet... | [
[
"numpy.asarray",
"numpy.ones",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.tight_layout",
"pandas.concat",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ticklabel_format",
"numpy.convolve"
]
] |
hukkelas/full_body_anonymization | [
"c61745b137c84ffb742ef6ab2f4721db4acf22b7"
] | [
"fba/data/build.py"
] | [
"from .transforms import build_transforms\nfrom .utils import DataPrefetcher, InfiniteSampler\nfrom .datasets import build_dataset\nimport torch\nfrom fba import utils\nfrom torch.utils.data._utils.collate import default_collate\n\n\ndef get_dataloader(cfg, is_train: bool):\n imsize = cfg.imsize\n if is_train... | [
[
"torch.utils.data.DistributedSampler",
"torch.utils.data._utils.collate.default_collate",
"torch.utils.data.DataLoader"
]
] |
Knoxantropicen/rlkit | [
"c60fb3794bdd8a6fc4480e668dc3832c5f5f3ab5"
] | [
"rlkit/pytorch/sac/policies.py"
] | [
"import numpy as np\nimport torch\nfrom torch import nn as nn\n\nfrom rlkit.policies.base import ExplorationPolicy, Policy\nfrom rlkit.pytorch.distributions import TanhNormal\nfrom rlkit.pytorch.networks import Mlp\n\n\nLOG_SIG_MAX = 2\nLOG_SIG_MIN = -20\n\n\nclass TanhGaussianPolicy(Mlp, ExplorationPolicy):\n \... | [
[
"torch.nn.Linear",
"numpy.log",
"torch.clamp",
"torch.tanh",
"torch.exp"
]
] |
DrLachie/pyclesperanto_prototype | [
"56843fac2543265c40f108fd40eac3ecf85c8458"
] | [
"tests/test_voronoi_otsu_labeling.py"
] | [
"import pyclesperanto_prototype as cle\nimport numpy as np\n\ndef test_voronoi_otsu_labeling():\n \n gpu_input = cle.push(np.asarray([\n\n [0, 0, 1, 1, 0, 0],\n [0, 1, 8, 9, 1, 0],\n [0, 1, 7, 6, 1, 0],\n [0, 0, 1, 1, 1, 0],\n [0, 0, 1, 1, 1, 0],\n ... | [
[
"numpy.array_equal",
"numpy.asarray"
]
] |
herman-nside/spektral | [
"58bb524ec783f187145c3afe53db491dbc1f0ba0"
] | [
"examples/graph_prediction/tud_mincut.py"
] | [
"\"\"\"\nThis example shows how to perform molecule regression with the\n[Open Graph Benchmark](https://ogb.stanford.edu) `mol-esol` dataset, using a\nsimple GCN with MinCutPool in batch mode.\nExpect unstable training due to the small-ish size of the dataset.\n\"\"\"\n\nimport numpy as np\nfrom tensorflow.keras.ca... | [
[
"tensorflow.keras.layers.Input",
"numpy.split",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.models.Model",
"tensorflow.keras.optimizers.Adam",
"tensorflow.keras.callbacks.EarlyStopping"
]
] |
hankyul2/Show_Attend_Tell | [
"1fc76af8f62e5ba84307f91622ba243fff49b943"
] | [
"main.py"
] | [
"import json\nimport os\n\nimport math\nimport warnings\n\nimport torch\nfrom torch.nn.utils.rnn import pack_padded_sequence\n\nwarnings.filterwarnings('ignore')\nos.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n\nfrom torch.optim import SGD, Adam, AdamW\nfrom torch.optim.lr_scheduler import OneCycleLR\nfrom pytorch... | [
[
"torch.max",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.figure",
"torch.tensor",
"torch.nn.utils.rnn.pack_padded_sequence",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.imshow"
]
] |
XuehaiPan/Soft-Actor-Critic | [
"9018199f28351f4106dab73a9dc3631c52b72260"
] | [
"common/network.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\n\n\n__all__ = [\n 'build_encoder',\n 'Container', 'NetworkBase',\n 'VanillaNeuralNetwork', 'VanillaNN',\n 'MultilayerPerceptron', 'MLP',\n 'GRUHidden', 'cat_hidden',\n 'RecurrentNeuralNetwork', 'RNN',\n 'ConvolutionalNeuralNetwork', 'CNN... | [
[
"torch.nn.Linear",
"torch.cat",
"torch.nn.ParameterList",
"torch.nn.GRU",
"torch.nn.ModuleList",
"torch.nn.BatchNorm2d",
"torch.load",
"torch.FloatTensor",
"numpy.sqrt",
"torch.Tensor",
"torch.zeros",
"torch.device",
"torch.nn.Identity",
"torch.save",
"t... |
CK-er/mmdet | [
"9bea4068efbcf7bf739dbe41917a68d525c29868"
] | [
"mmdet/core/bbox/transforms.py"
] | [
"import numpy as np\nimport torch\n\ndef bbox2delta(proposals, gt, means=[0, 0, 0, 0], stds=[1, 1, 1, 1]):\n assert proposals.size() == gt.size()\n\n proposals = proposals.float()\n gt = gt.float()\n px = (proposals[..., 0] + proposals[..., 2]) * 0.5\n py = (proposals[..., 1] + proposals[..., 3]) * 0... | [
[
"torch.cat",
"torch.stack",
"numpy.log",
"numpy.zeros",
"torch.log",
"torch.addcmul"
]
] |
kaist-dmlab/Ada-Boundary | [
"9514a2a005aaf79db7eac84c55cbcefbb72a4011"
] | [
"src/reader/batch_patcher.py"
] | [
"import numpy as np\nimport time, os, math, operator, statistics, sys\nimport tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\nfrom random import Random\n\nclass Sample(object):\n def __init__(self, id, image, label):\n self.id = id\n self.image = image\n self.label = label\n\nclass Min... | [
[
"numpy.random.choice",
"numpy.zeros",
"tensorflow.compat.v1.disable_v2_behavior",
"numpy.sum",
"numpy.ones"
]
] |
ThisIsIsaac/high-res-stereo | [
"55341fd4b3205162a01ad6aebff0086e49e1a909"
] | [
"unlabeled_util/argoverse_make_pseudo_gt.py"
] | [
"import argparse\nimport cv2\nfrom models import hsm\nimport numpy as np\nimport os\nimport pdb\nimport skimage.io\nimport torch\nimport torch.nn as nn\nimport torch.backends.cudnn as cudnn\nfrom torch.autograd import Variable\nimport time\nfrom models.submodule import *\nfrom utils.eval import mkdir_p, save_pfm\nf... | [
[
"numpy.logical_or",
"numpy.reshape",
"numpy.zeros",
"torch.cuda.synchronize",
"numpy.lib.pad",
"torch.no_grad",
"torch.FloatTensor",
"torch.cuda.empty_cache",
"torch.squeeze",
"torch.load",
"torch.nn.DataParallel"
]
] |
LitianD/ObjDetect | [
"849f63467ce9e25c8ba0c24ca7bfdea9d836b0dd"
] | [
"gif.py"
] | [
"from PIL import Image\nimport image2gif\nimport numpy as np\nimport os\n\noutfilename = \"D:\\PyCharmProject\\objDetect\\keras-yolo3\\gif\\\\1\\\\1.gif\" # 转化的GIF图片名称\nl = os.listdir(\"D:\\PyCharmProject\\objDetect\\keras-yolo3\\gif\\\\1\")\nframes = []\nfor image_name in l: # 索引各自目录\n im = Image... | [
[
"numpy.array"
]
] |
Saravji/pmdarima | [
"7f42e36beb888d9e1e7e41b0d9c9f7419c730a3a"
] | [
"examples/arima/example_persisting_a_model.py"
] | [
"\"\"\"\n=========================\nPersisting an ARIMA model\n=========================\n\n\nThis example demonstrates how we can persist an ARIMA model to disk after\nfitting it. It can then be loaded back up and used to generate forecasts.\n\n.. raw:: html\n\n <br/>\n\"\"\"\nprint(__doc__)\n\n# Author: Taylor ... | [
[
"sklearn.externals.joblib.load",
"sklearn.externals.joblib.dump"
]
] |
facebookresearch/uimnet | [
"d7544cf5fb4c65cb262dca203afb0db4ba6c569d"
] | [
"uimnet/algorithms/mixup.py"
] | [
"#!/usr/bin/env python3\n#\n# # Copyright (c) 2021 Facebook, inc. and its affiliates. All Rights Reserved\n#\n#\nimport torch\nfrom uimnet import utils\nfrom uimnet.algorithms.erm import ERM\nimport numpy as np\n\n\nclass Mixup(ERM):\n HPARAMS = dict(ERM.HPARAMS)\n HPARAMS.update({\n \"alpha\": (0.3, l... | [
[
"torch.zeros",
"torch.nn.functional.one_hot",
"numpy.random.choice",
"torch.arange",
"torch.distributions.Beta"
]
] |
lmsac/GproDIA | [
"3fc1cdee535c9743806b7be423aba29daca24406"
] | [
"src/filter_assays.py"
] | [
"import argparse\n\nparser = argparse.ArgumentParser(\n description='Filter assays.'\n)\nparser.add_argument(\n '--in', nargs='+',\n help='input assay files'\n)\nparser.add_argument(\n '--out',\n help='output assay file'\n)\n\nparser.add_argument(\n '--swath_windows',\n help='SWATH isolation wi... | [
[
"pandas.read_csv"
]
] |
idchlife/tf2-mobile-pose-estimation | [
"a1f1f52eecbb841fa878bff4d3c311b79864835d"
] | [
"models/simplepose_coco.py"
] | [
"\"\"\"\n SimplePose for COCO Keypoint, implemented in TensorFlow.\n Original paper: 'Simple Baselines for Human Pose Estimation and Tracking,' https://arxiv.org/abs/1804.06208.\n\"\"\"\n\n__all__ = ['SimplePose', 'simplepose_resnet18_coco', 'simplepose_resnet50b_coco', 'simplepose_resnet101b_coco',\n ... | [
[
"tensorflow.keras.Sequential",
"tensorflow.executing_eagerly",
"tensorflow.keras.layers.Conv2DTranspose",
"tensorflow.keras.backend.get_value",
"tensorflow.keras.layers.Cropping2D"
]
] |
samuelyu2002/PACS | [
"5010b2f0d20933b0647e3d6230d673e1830249ec"
] | [
"experiments/CLIP/predict.py"
] | [
"import torch\nimport numpy as np\nimport torch.nn as nn\nfrom utils import load_model\nfrom torchvision import transforms\nfrom PIL import Image\nimport json\nimport clip\nfrom collections import defaultdict\nimport argparse\nimport os\n\nparser = argparse.ArgumentParser(description='Extracting frames and audio')\... | [
[
"torch.cat",
"numpy.sum",
"torch.no_grad",
"torch.cuda.is_available",
"torch.load",
"torch.nn.CosineSimilarity"
]
] |
guanyuliu0818/ITU-Rpy | [
"524f30f73a6fed9be4416637d8d83ad7717f0a00"
] | [
"itur/models/itu676.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport warnings\n\nimport numpy as np\nfrom astropy import units as u\n\nfrom itur import utils\nfrom itur.models.itu453 import radio_refractive_index\nfrom itur.mod... | [
[
"numpy.arccos",
"numpy.minimum",
"numpy.exp",
"numpy.where",
"numpy.cos",
"numpy.cumsum",
"numpy.deg2rad",
"numpy.zeros_like",
"numpy.sin",
"numpy.vectorize",
"numpy.log",
"numpy.logical_and",
"numpy.arange",
"numpy.sqrt",
"numpy.mod",
"numpy.array",... |
TCherici/StRADRL | [
"528bf4fbd61f91a79dfc24fc6d6c9caa66a4a5a5"
] | [
"settings/options3.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\n\ndef get_options(option_type):\n \"\"\"\n option_type: string\n 'training' or 'diplay' or 'visualize'\n \"\"\" \n # name\n tf.app.flags.D... | [
[
"tensorflow.app.flags.DEFINE_float",
"tensorflow.app.flags.DEFINE_integer",
"tensorflow.app.flags.DEFINE_boolean",
"tensorflow.app.flags.DEFINE_string"
]
] |
wjm41/soapgp | [
"ef57cebb7413abb96b54983141e188dff5166d03"
] | [
"data/Malaria/generate_smiles.py"
] | [
"import pandas as pd\nimport sys\n\ncsv_name = sys.argv[1]+'.csv'\nsmiles_name = sys.argv[1]+'.can'\n\nSMILES_df = pd.read_csv(csv_name,header=0, index_col=False)\nfile=open(smiles_name,'w')\nfor i,row in SMILES_df.iterrows():\n\tfile.write(row['SMILES']+'\\t'+str(row['Percentage_inhibition_3D7'])+'\\t'+str(row['Pe... | [
[
"pandas.read_csv"
]
] |
leying95/stereopy | [
"1580a88a091a2ebc0f177ea73409e2c4b4dd4c7e"
] | [
"stereo/tools/cell_type_anno.py"
] | [
"#!/usr/bin/env python3\n# coding: utf-8\n\"\"\"\n@author: Ping Qiu qiuping1@genomics.cn\n@last modified by: Ping Qiu\n@file:cell_type_anno.py\n@time:2021/03/09\n\"\"\"\nimport pandas as pd\nimport numpy as np\nimport os\nfrom multiprocessing import Pool\nimport traceback\nfrom ..log_manager import logger\nfrom ..... | [
[
"scipy.sparse.issparse",
"numpy.random.choice",
"pandas.DataFrame",
"numpy.log1p",
"pandas.Series",
"pandas.read_csv",
"numpy.unique"
]
] |
TylerYep/ml-toolkit | [
"095bdce961133acc720f90b6d1bbb0a7becbfc9f"
] | [
"ai_toolkit/datasets/dataset.py"
] | [
"from __future__ import annotations\n\nimport sys\nfrom pathlib import Path\nfrom typing import Any, Callable, Tuple\n\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset, random_split\nfrom torch.utils.data.dataloader import default_collate\n\nfrom ai_toolkit.args import Arguments\n\nTensorDataLo... | [
[
"torch.cuda.is_available",
"torch.utils.data.random_split",
"torch.Generator",
"torch.utils.data.dataloader.default_collate"
]
] |
bearpelican/PSSR | [
"a90b7d208d4369946500a70a6f31c44e3367e4c7"
] | [
"utils/resnet.py"
] | [
"import torch.nn as nn\nimport torch,math,sys\nimport torch.utils.model_zoo as model_zoo\nfrom functools import partial\nimport fastai.vision.learner as fvl\n\n__all__ = ['WNResNet', 'wnresnet18', 'wnresnet34', 'wnresnet50', 'wnresnet101', 'wnresnet152']\n\n# or: ELU+init (a=0.54; gain=1.55)\nact_fn = nn.ReLU(inpla... | [
[
"torch.nn.Linear",
"torch.nn.init.constant_",
"torch.nn.Sequential",
"torch.nn.AvgPool2d",
"torch.nn.MaxPool2d",
"torch.nn.init.kaiming_normal_",
"torch.utils.model_zoo.load_url",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.AdaptiveAvgPool2d"
]
] |
thejus-vm/dopamine | [
"d2f4128f056f781e70ea926ab071a621f955a23c"
] | [
"dopamine/jax/agents/quantile/quantile_agent.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Dopamine 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 b... | [
[
"tensorflow.compat.v1.Summary.Value"
]
] |
janvi16/-HACKTOBERFEST2K20 | [
"aa9c8b6f7feb245793c0a003ba6fbea3fca9ca22"
] | [
"Python/Read Excel File/excel_file.py"
] | [
"import pandas\n\nexcel_data_df = pandas.read_excel('records.xlsx', sheet_name='Employees')\n\n# print whole sheet data\nprint(excel_data_df)"
] | [
[
"pandas.read_excel"
]
] |
hyabe/onnx-chainer | [
"339ff390957d9dd29843add015533290fdd051c0"
] | [
"onnx_chainer/functions/normalization.py"
] | [
"import sys\n\nimport chainer\nimport numpy as np\n\nfrom onnx_chainer.functions.opset_version import support\nfrom onnx_chainer import onnx_helper\n\n\n@support((1, 6, 7))\ndef convert_BatchNormalization(func, opset_version, input_names,\n num_outputs, context, parameters):\n if le... | [
[
"numpy.zeros_like",
"numpy.ones_like"
]
] |
lucasalexsorensen/mlops | [
"2d8157eb493061775bdab9a8e176d2bdcc2c166e"
] | [
"src/models/train_model.py"
] | [
"from ..data import MaskDataset\nfrom torch.utils.data import DataLoader\nimport kornia as K\nimport torch.nn as nn\nimport torchvision\nimport torch\n\nimport wandb\nimport numpy as np\nimport pandas as pd\nfrom torchvision.transforms import ToTensor\nfrom .model import Net\nimport argparse\nimport torchmetrics\n\... | [
[
"numpy.array",
"torch.no_grad",
"numpy.diff",
"torch.cuda.is_available",
"torch.utils.data.DataLoader",
"numpy.append",
"torch.nn.CrossEntropyLoss"
]
] |
hg-zhang/deepst | [
"fc35cfd40785a3a0cc56a83c151c629e53eaf6bd"
] | [
"deepst/datasets/TaxiBJ.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n load BJ Data from multiple sources as follows:\n meteorologic data\n\"\"\"\nfrom __future__ import print_function\n\nimport os\n#import cPickle as pickle\nimport pickle\nfrom copy import copy\nimport numpy as np\nimport h5py\nfrom . import load_stdata, stat\nfrom ..prepr... | [
[
"numpy.hstack",
"numpy.asarray",
"numpy.vstack"
]
] |
marcorainone/TropPOLoRaTools | [
"d89e24f29325c50be050b79c2cdce75225fdfaa6"
] | [
"src/graph-rsigra-interval.py"
] | [
"#!/usr/bin/env python3\n# ===================================================================================\n# Project: TropPo \n# v. 1.0 2020-03-01, ICTP Wireless Lab\n# Programmer: Marco Rainone - ICTP Wireless Lab\n# Specifications, revisions and verifications: \n# Marco Zennaro, ... | [
[
"pandas.DataFrame.from_records",
"pandas.read_csv",
"numpy.isfinite"
]
] |
hui2000ji/masif | [
"70a76c5f4639f70c546d5603612c7cc9f47a35b8"
] | [
"source/data_preparation/01-pdb_extract_and_triangulate.py"
] | [
"#!/usr/bin/python\nimport numpy as np\nimport os\nimport Bio\nimport shutil\nfrom Bio.PDB import * \nimport sys\nimport importlib\nfrom IPython.core.debugger import set_trace\n\n# Local includes\nfrom default_config.masif_opts import masif_opts\nfrom triangulation.computeMSMS import computeMSMS\nfrom triangulation... | [
[
"sklearn.neighbors.KDTree",
"numpy.where",
"numpy.square"
]
] |
UpSea/ZipLineMid | [
"1e0cdcfa7974f412dbee32809cffdaf2de6b4971"
] | [
"xpower/Strategies/testTemp/testParams01.py"
] | [
"import numpy as np\n\nt = np.arange(0.0, 10.0, 0.01)\ns = np.sin(2*np.pi*t)\nparams={}\nparams['s']=s\nparams['t']=t"
] | [
[
"numpy.sin",
"numpy.arange"
]
] |
IEM-Computer-Vision/pytorch3d | [
"e3819a49dfa855de1a7c99c0583fb69f9bdad75b"
] | [
"pytorch3d/structures/meshes.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n\nfrom typing import List\n\nimport torch\n\nfrom . import utils as struct_utils\nfrom .textures import Textures\n\n\nclass Meshes(object):\n \"\"\"\n This class provides functions for working with batches of triangulated\n meshes wi... | [
[
"torch.zeros",
"torch.device",
"torch.cat",
"torch.nn.functional.normalize",
"torch.stack",
"torch.unique",
"torch.arange",
"torch.is_tensor",
"torch.sparse.FloatTensor",
"torch.ones",
"torch.full",
"torch.empty",
"torch.cross",
"torch.zeros_like",
"torc... |
nderako/NeuroFeedback-api | [
"07be188a751c8e68a97f788d74c666dc158cd0f4"
] | [
"pythonModules/parser.py"
] | [
"import requests, json, sys, getopt, time, random\nfrom scipy.io import loadmat\nimport mne\nimport numpy as np\n\ndef main(argv):\n inputfile = ''\n try:\n opts, args = getopt.getopt(argv,\"hi:\",[\"ifile=\"])\n except getopt.GetoptError:\n print('transmissorClient.py -i <inputfile>')\n ... | [
[
"numpy.concatenate",
"scipy.io.loadmat"
]
] |
xieliaing/scikit-learn | [
"9b210ae8ffdc40e210f30f24656779ac690b899a",
"9b210ae8ffdc40e210f30f24656779ac690b899a"
] | [
"examples/compose/plot_compare_reduction.py",
"examples/compose/plot_transformed_target.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n=================================================================\nSelecting dimensionality reduction with Pipeline and GridSearchCV\n=================================================================\n\nThis example constructs a pipeline that does dimensionality\nreduction followed... | [
[
"numpy.array",
"sklearn.datasets.load_digits",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.title",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.figure",
"sklearn.decomposition.NMF",
"matplotlib.pyplot.ylabel",
"sklearn.feature_selection.Select... |
GitHubChuanYu/T3Project4_SystemIntegration | [
"1cd2224c5b94292927441e46df137749a0520f09"
] | [
"ros/src/tl_detector/tl_detector.py"
] | [
"#!/usr/bin/env python\nimport rospy\nfrom std_msgs.msg import Int32\nfrom geometry_msgs.msg import PoseStamped, Pose\nfrom styx_msgs.msg import TrafficLightArray, TrafficLight\nfrom styx_msgs.msg import Lane, Waypoint\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge\nfrom light_classification.tl_... | [
[
"scipy.spatial.KDTree",
"numpy.array",
"numpy.dot"
]
] |
SenseTime-Knowledge-Mining/BridgeDPI | [
"0dbbecb73d7ffe982ff4fbbf05a58b65591343ba"
] | [
"nnLayer.py"
] | [
"from torch import nn as nn\nfrom torch.nn import functional as F\nimport torch,time,os,random\nimport numpy as np\nfrom collections import OrderedDict\n\nclass TextSPP(nn.Module):\n def __init__(self, size=128, name='textSpp'):\n super(TextSPP, self).__init__()\n self.name = name\n self.spp... | [
[
"torch.nn.Linear",
"torch.cat",
"torch.nn.LSTM",
"torch.nn.GRU",
"torch.nn.ModuleList",
"torch.nn.functional.elu",
"torch.nn.AdaptiveAvgPool1d",
"torch.nn.utils.rnn.pack_padded_sequence",
"numpy.cos",
"torch.exp",
"torch.nn.CrossEntropyLoss",
"torch.sum",
"torch... |
DrewAlexander98/Couch-Mk2 | [
"d9dc7768d0e6714c2eb58dcd901988617303ce95"
] | [
"FormulaTest.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\n\ndef main():\n angleList = np.arange(1, 90, 10)\n numVals = angleList.size\n xValues = np.array(numVals)\n yValues = np.array(numVals)\n turnWheelVal1 = np.array(numVals)\n turnWheelVal2 = np.array(numVals)\n lvr = np.array(numVals)\n xv... | [
[
"numpy.array",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"numpy.radians",
"numpy.arange",
"numpy.power",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.show"
]
] |
cbim-medical-group/pytorch-template | [
"7f76ee095420e23efe22df560d8e0a314fbc16dd",
"7f76ee095420e23efe22df560d8e0a314fbc16dd"
] | [
"data_loader/my_transforms/to_tensor.py",
"loss/bce_loss.py"
] | [
"import numpy as np\nimport torch\nfrom skimage.transform import resize\n\n\nclass ToTensor:\n def __init__(self, training=True):\n \"\"\"\n Convert numpy array to Torch.Tensor\n \"\"\"\n self.training = training\n\n def __call__(self, sample):\n image, mask = sample['image'... | [
[
"torch.tensor"
],
[
"torch.unique",
"torch.Tensor",
"torch.nn.functional.binary_cross_entropy"
]
] |
chaitanya2334/stain-normalization-tool | [
"8f7aab84466bf96c344f93f46f2103a8e906fe51"
] | [
"mat_estimation/scd.py"
] | [
"import cv2\nimport numpy as np\n\nfrom training.scd_trainer import SCDTrainer\n\n\ndef est_using_scd(img, trainer):\n assert isinstance(trainer, SCDTrainer)\n prob_maps, _ = trainer.classify_stain_regions(img)\n\n double_img = cv2.normalize(img.astype('float'), None, 0.0, 1.0, cv2.NORM_MINMAX)\n\n col_... | [
[
"numpy.zeros",
"numpy.sum",
"numpy.ones",
"numpy.mean",
"numpy.cross"
]
] |
astepanian1/gpt-2-simple | [
"6fd93398dc4f2df2910cebc7cf09d22c8704d5c3"
] | [
"gpt_2_simple/gpt_2.py"
] | [
"import tarfile\nimport os\nimport json\nimport requests\nimport sys\nimport shutil\nimport re\nfrom tqdm import tqdm, trange\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.core.protobuf import rewriter_config_pb2\nfrom tensorflow.python.client import device_lib\nimport time\nfrom datetime import dat... | [
[
"tensorflow.compat.v1.placeholder",
"tensorflow.compat.v1.summary.FileWriter",
"tensorflow.compat.v1.global_variables_initializer",
"tensorflow.train.latest_checkpoint",
"tensorflow.compat.v1.train.AdamOptimizer",
"numpy.random.seed",
"tensorflow.compat.v1.train.Saver",
"tensorflow... |
madhukar-m-rao/deephyper | [
"d280701d9e4cae3e639be054bf1c5ef918d9a1a7"
] | [
"deephyper/search/hps/automl/classifier/autosklearn1/run.py"
] | [
"import inspect\nfrom inspect import signature\nfrom pprint import pprint\n\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\n\nfrom deephyper.search.hps.automl.classifier.mapping import CLASSIFIERS\nfrom deephyper.search.nas.model.preprocessing import minmaxstdscale... | [
[
"sklearn.model_selection.train_test_split",
"sklearn.metrics.accuracy_score"
]
] |
MiVaVo/Antispoof-3d | [
"03ee614f4daf85069ce22c80cb6ed4642bdf762e"
] | [
"src/icp/utils.py"
] | [
"import copy\nimport functools\n\nimport numpy as np\nimport open3d as o3d\n\n\ndef points_to_pointcloud(func):\n @functools.wraps(func)\n def ensure_is_pointcloud(*args, **kwargs):\n if 'left_points_array' in kwargs.keys() and 'right_points_array' in kwargs.keys():\n if not isinstance(kwarg... | [
[
"numpy.any",
"numpy.asarray",
"numpy.asanyarray"
]
] |
upscale-project/hslink_phy | [
"741f78da673d2e633da05d292aa6645125ebae32"
] | [
"DaVE/mLingua/examples_ncsim/serdes/ch_response/plot.py"
] | [
"#! /usr/bin/env python \n\n\"\"\"\n Generate eyediagram and save it to eye.png\n\"\"\"\n\nimport numpy as np\nfrom scipy.interpolate import interp1d\nimport matplotlib.pylab as plt\nimport matplotlib\nfrom scipy.signal import lsim, zpk2tf\n\nfont = { 'size' : 19}\nmatplotlib.rc('font', **font)\n\ndef main():\n ... | [
[
"matplotlib.pylab.savefig",
"matplotlib.pylab.ylabel",
"matplotlib.pylab.legend",
"matplotlib.pylab.close",
"matplotlib.rc",
"numpy.loadtxt",
"matplotlib.pylab.subplot",
"matplotlib.pylab.tight_layout",
"matplotlib.pylab.title",
"matplotlib.pylab.plot"
]
] |
songquanpeng/BigGAN-PyTorch | [
"6988f1f3ccfa4f6794ce269f056422da4ce9baf6"
] | [
"BigGAN.py"
] | [
"import numpy as np\nimport math\nimport functools\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn import init\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.nn import Parameter as P\n\nimport layers\nfrom sync_batchnorm import SynchronizedBatchNorm2d as SyncBatchNorm2d\n\n\n# Archi... | [
[
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.AvgPool2d",
"torch.split",
"torch.nn.init.xavier_uniform_",
"torch.nn.ReLU",
"torch.nn.init.normal_",
"torch.nn.init.orthogonal_",
"torch.set_grad_enabled"
]
] |
Koriban/Hummingbot-Demex | [
"0ed19e41285b41999eb4ea7c69b5c4a16722e00b"
] | [
"test/test_pmm_take_if_cross.py"
] | [
"#!/usr/bin/env python\n\nfrom os.path import join, realpath\nimport sys; sys.path.insert(0, realpath(join(__file__, \"../../\")))\n\nfrom typing import List\nfrom decimal import Decimal\nimport logging; logging.basicConfig(level=logging.ERROR)\nimport pandas as pd\nimport unittest\n\nfrom hummingbot.strategy.marke... | [
[
"pandas.Timestamp"
]
] |
changyu98/GoogLeNet-PyTorch | [
"a2fae2b8b14e830a3f64c81bc4e62dadb6cfe5b7"
] | [
"examples/simple/test.py"
] | [
"# Copyright 2020 Lorna Authors. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required b... | [
[
"torch.no_grad",
"torch.softmax",
"torch.topk"
]
] |
polltooh/FineGrainedAction | [
"4582b4179e643119448c7c20ab06044fb211163e"
] | [
"nn/test_nn_fc7.py"
] | [
"import tensorflow as tf\nfrom bvlc_alexnet_fc7 import AlexNet\nimport nt\nimport numpy as np\nimport utility_function as uf\nimport os\nimport time\nimport cv2\nimport image_io\nimport sys\nimport math\n# the dimension of the final layer = feature dim\nNN_DIM = 100\n\n# TEST_TXT = 'file_list_test_nba_dunk_fc7.txt'... | [
[
"tensorflow.train.start_queue_runners",
"tensorflow.app.flags.DEFINE_integer",
"tensorflow.concat",
"tensorflow.train.batch",
"tensorflow.app.flags.DEFINE_string",
"tensorflow.TextLineReader",
"tensorflow.initialize_all_variables",
"tensorflow.train.Coordinator",
"tensorflow.Se... |
soham97/Predictive-Threat-Intelligence | [
"fa0bfbea905c9179aa67791d26f1f219e59a1b32"
] | [
"codes/kafka/flask-server/deploy_functions.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nfrom tqdm import tqdm_notebook as tqdm\n\nenc = LabelEncoder()\nenc.fit(['cowrie.session.connect', 'cowrie.client.version',\n 'cowrie.login.success', 'cowrie.session.closed',\n 'cowrie.login.failed', 'cowrie.log.cl... | [
[
"pandas.DataFrame",
"sklearn.preprocessing.LabelEncoder",
"numpy.argmax"
]
] |
Hunter8moon/h8m2 | [
"9cb2ced9d701650258f5e4d14e6036a3b56b0b96"
] | [
"source/util/image_util.py"
] | [
"import os\nfrom random import randint, random\n\nimport numpy as np\nfrom PIL import Image\n\n\nclass ImageUtil:\n @staticmethod\n def file_to_array(file_path, width, height, augment=True):\n \"\"\"\n Loads a image from disk and returns an np.array pf that image.\n\n :param file_path: Pa... | [
[
"numpy.asarray"
]
] |
jawad26/numpy | [
"07447fd215ebffbce2f4e516ef02629e91fca6b0"
] | [
"numpy/ma/core.py"
] | [
"\"\"\"\nnumpy.ma : a package to handle missing or invalid values.\n\nThis package was initially written for numarray by Paul F. Dubois\nat Lawrence Livermore National Laboratory.\nIn 2006, the package was completely rewritten by Pierre Gerard-Marchant\n(University of Georgia) to make the MaskedArray class a subcla... | [
[
"numpy.core.umath.equal",
"numpy.ndarray.sort",
"numpy.isclose",
"numpy.dot",
"numpy.void",
"numpy.core.umath.less",
"numpy.core.umath.greater",
"numpy.where",
"numpy.resize",
"numpy.iscomplexobj",
"numpy.finfo",
"numpy.unique",
"numpy.outer",
"numpy.issubdt... |
giuliovv/cryptotrading | [
"9627f68ef318e17f14784e3275a849885fb3710a"
] | [
"utils/technical.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom tqdm.auto import tqdm\n\nfrom utils import ultimate_cycle\n\n# OSCILLATORS\n\ndef macd(prices: pd.Series, long: int, short: int, strategy=False, getgains=False, winning=False, commissions=0.005) -> pd.Series:\n '''\n Return the MACD\n\n :param pd.Series prices... | [
[
"numpy.zeros",
"pandas.Series"
]
] |
avant1/server | [
"effbc03644de60ed97242811e0933e3611f14cd8"
] | [
"qa/L0_infer/infer_test.py"
] | [
"# Copyright (c) 2018-2021, NVIDIA CORPORATION. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n# * Redistributions of source code must retain the above copyright\n# notice, this list... | [
[
"numpy.dtype"
]
] |
R1704/SpeechRecognitionSNN | [
"4b788d1bd20d8ce201da6da8b200b3ca722c7efa"
] | [
"SpykeTorch/utils.py"
] | [
"import torch\nimport torch.nn.functional as fn\nimport numpy as np\nimport math\nfrom torchvision import transforms\nfrom torchvision import datasets\nimport os\n\ndef to_pair(data):\n\tr\"\"\"Converts a single or a tuple of data into a pair. If the data is a tuple with more than two elements, it selects\n\tthe fi... | [
[
"numpy.max",
"torch.nonzero",
"numpy.sin",
"torch.stack",
"torch.split",
"torch.save",
"numpy.exp",
"numpy.mean",
"torch.from_numpy",
"torch.tensor",
"numpy.cos",
"torch.load",
"torch.zeros_like",
"torch.nn.functional.pad",
"torch.abs_",
"torch.nn.fu... |
tusharsarkar3/TLA | [
"a898617765e2af8ce4f416d8430a8ee9c92aba94"
] | [
"build/lib/TLA/Lang_Classify/predict.py"
] | [
"from TLA.Lang_Classify.model import get_model, BERT_Arch\nimport argparse\nimport torch\nimport pandas as pd\nimport numpy as np\nfrom transformers import AutoModel, BertTokenizerFast\nimport pickle\nfrom distutils.sysconfig import get_python_lib\n\ndef predict(val_text,model):\n try:\n if isinstance(pd.... | [
[
"numpy.array",
"torch.no_grad",
"torch.cuda.is_available",
"torch.tensor",
"numpy.argmax",
"pandas.read_csv"
]
] |
hhelm10/graspy | [
"bbf93b069af426885261d64a6225228ff5aa049b"
] | [
"graspy/utils/utils.py"
] | [
"#!/usr/bin/env python\n\n# utils.py\n# Created by Eric Bridgeford on 2018-09-07.\n# Email: ebridge2@jhu.edu\n# Copyright (c) 2018. All rights reserved.\n\nimport warnings\nfrom collections import Iterable\nfrom functools import reduce\nfrom pathlib import Path\n\nimport networkx as nx\nimport numpy as np\nfrom skl... | [
[
"numpy.max",
"numpy.isinf",
"numpy.array",
"numpy.count_nonzero",
"numpy.array_equal",
"numpy.errstate",
"numpy.sum",
"numpy.triu",
"numpy.mean",
"numpy.allclose",
"numpy.stack",
"numpy.sqrt",
"sklearn.utils.check_array",
"numpy.diag",
"numpy.tril"
]
] |
zongdaoming/TinyTransformer | [
"8e64f8816117048c388b4b20e3a56760ce149fe3"
] | [
"unn/models/heads/pair_head_clean/pair.py"
] | [
"#coding: utf-8\nfrom ..utils import bbox_helper\nfrom ..utils.pair_helper import pair_nms\nfrom ..utils.pair_helper import pair_box_transform\nfrom ..utils.pair_helper import pair_pos_score\nimport pdb\nimport ctypes\n\nimport math\nimport numpy as np\nimport torch\nfrom torch.autograd import Variable\nimport logg... | [
[
"numpy.array",
"numpy.zeros",
"torch.is_tensor",
"numpy.random.shuffle",
"torch.from_numpy",
"numpy.where",
"numpy.unique",
"numpy.hstack",
"numpy.vstack"
]
] |
rystylee/pytorch-3DGAN | [
"768f53182183c123a7cbac16581fb777fcf8f726"
] | [
"trainer.py"
] | [
"import os\n\nfrom tqdm import tqdm\n\nimport torch\nimport torch.optim as optim\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torchsummary import summary\n\nfrom model import Discriminator\nfrom model import Generator\nfrom losses import GANLoss\nfrom data_loader import load_dataloader\nfrom utils impor... | [
[
"torch.no_grad",
"torch.cat",
"torch.utils.tensorboard.SummaryWriter",
"torch.load"
]
] |
sms1097/imodels | [
"b2b062c8ff1b12c02271f041674a11af85fcfea6"
] | [
"experiments/combine.py"
] | [
"import argparse\nimport glob\nimport os\nimport pickle as pkl\nimport warnings\n\nimport numpy as np\nimport pandas as pd\n\nfrom experiments.compare_models import compute_meta_auc, MODEL_COMPARISON_PATH\n\n\ndef combine_comparisons(path, model, test):\n all_files = glob.glob(path + '*')\n model_files = list... | [
[
"numpy.unique",
"pandas.concat"
]
] |
amandanic11/shopping-cart | [
"a7a4a11ba452582a83e5a01bc69ec8834edbad5b"
] | [
"shopping-cart-pandas.py"
] | [
"# shopping_cart_pandas.py\nfrom __future__ import print_function\nimport datetime\nimport os\nimport pandas as pd \nimport csv\nfrom dotenv import load_dotenv\nfrom sendgrid import SendGridAPIClient\nfrom sendgrid.helpers.mail import Mail\nimport functools\n\n\nnow = datetime.datetime.now()\npd.options.display.flo... | [
[
"pandas.read_csv"
]
] |
chrisdxie/rice | [
"c3e42822226af9ac28d95d434cd582386122b679"
] | [
"src/data_augmentation.py"
] | [
"import torch\nimport random\nimport numpy as np\nimport cv2\n\nfrom .util import utilities as util_\n\n\n##### Useful Utilities #####\n\ndef array_to_tensor(array):\n \"\"\"Convert a numpy.ndarray to torch.FloatTensor.\n\n numpy.ndarray [N, H, W, C] -> torch.FloatTensor [N, C, H, W]\n OR\n numpy.nd... | [
[
"numpy.random.normal",
"numpy.array",
"numpy.zeros_like",
"numpy.random.rand",
"numpy.random.choice",
"numpy.count_nonzero",
"numpy.logical_or",
"numpy.random.gamma",
"numpy.mean",
"torch.from_numpy",
"numpy.where",
"numpy.random.uniform",
"numpy.random.randint"... |
Mushroomcat9998/U-2-Net | [
"290d82b087b5eb6e7b781cacea18f270badc51e3"
] | [
"u2net_test.py"
] | [
"import os\nimport glob\nimport torch\nimport argparse\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom skimage import io\nfrom torchvision import transforms\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\n\nfrom model import U2NET\nfrom model import U2NETP\n\nfrom data_loader impo... | [
[
"torch.min",
"torch.max",
"torch.autograd.Variable",
"torch.cuda.is_available",
"torch.utils.data.DataLoader",
"torch.load"
]
] |
msAlcantara/tsgen | [
"b8b5e1d5bf99997135b696284261b9fe41a2a614"
] | [
"tests/gen_test.py"
] | [
"import pandas as pd\nimport numpy as np\nimport os.path\nfrom unittest import TestCase\nfrom tsgen.gen import TimeSerieGenerator\n\n\nclass TimeSerieGeneratorTestCase(TestCase):\n def test_generate_df_freq(self):\n ts_gen = TimeSerieGenerator(\n date_start=\"1990-01-01\",\n date_end... | [
[
"pandas.datetime.now"
]
] |
jamesthesnake/bonvoyage | [
"60c4d442138a65262496fd7dea0c8c8837b6c5a7"
] | [
"bonvoyage/tests/test_visualize.py"
] | [
"import matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport pytest\n\n\n@pytest.fixture(params=['hexbin', 'scatter'])\ndef kind(request):\n return request.param\n\n\ndef test_waypointplot(waypoints, kind):\n from bonvoyage import waypointplot\n\n fig, ax = plt.subplots()\n waypointplot(waypoints,... | [
[
"matplotlib.pyplot.subplots"
]
] |
RK900/learna | [
"c61f88fff5275fb627ab16d539ccc9c81e5a4b46"
] | [
"src/analyse/analyse_experiment_group.py"
] | [
"from pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport scikits.bootstrap as sci\n\nfrom .read_data import read_data_from_method_path, read_sequence_lengths\nfrom .process_data import (\n solved_across_time_per_run,\n solved_across_time_min,\n runs_solve_instance,\n solved_per_time_li... | [
[
"pandas.DataFrame",
"pandas.to_csv"
]
] |
Munyola/dnc | [
"d3d94b3b1f1efc282481910054f82047caf37f65"
] | [
"train.py"
] | [
"# Copyright 2017 Google 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/LICENSE-2.0\n#\n# Unless required by applicable law or agreed... | [
[
"tensorflow.train.SingularMonitoredSession",
"tensorflow.trainable_variables",
"tensorflow.logging.set_verbosity",
"tensorflow.zeros_initializer",
"tensorflow.flags.DEFINE_string",
"tensorflow.train.CheckpointSaverHook",
"tensorflow.expand_dims",
"tensorflow.sigmoid",
"tensorfl... |
feifzhou/deepmind-research | [
"769bfdbeafbcb472cb8e2c6cfa746b53ac82efc2"
] | [
"hierarchical_transformer_memory/pycolab_ballet/ballet_environment.py"
] | [
"# Copyright 2021 DeepMind Technologies Limited. 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# Unle... | [
[
"numpy.array",
"numpy.zeros",
"numpy.ones",
"numpy.random.default_rng",
"numpy.tensordot"
]
] |
yzh119/tvm | [
"19400c9967020ca822399f57de0253c3dc98845b"
] | [
"tests/python/unittest/test_tir_intrin.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.nextafter",
"numpy.arccos",
"numpy.arcsin",
"numpy.log1p",
"numpy.arange",
"numpy.random.randint",
"numpy.sinh",
"numpy.hypot",
"numpy.log10",
"numpy.arctanh",
"numpy.zeros",
"numpy.arccosh",
"numpy.arctan",
"numpy.copysign",
"numpy.power",
"n... |
NunoEdgarGFlowHub/gandissect | [
"1a162a6bd3d4842139feb9f191aa1fad565dee4e"
] | [
"netdissect/upsegmodel/prroi_pool/functional.py"
] | [
"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n# File : functional.py\n# Author : Jiayuan Mao, Tete Xiao\n# Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com\n# Date : 07/13/2018\n# \n# This file is part of PreciseRoIPooling.\n# Distributed under terms of the MIT license.\n# Copyright (c) 2017 Megvii Te... | [
[
"torch.zeros",
"torch.zeros_like"
]
] |
dongzhi0312/can | [
"067d19844f1bf0e058acd03c23f47449686570ac"
] | [
"solver/can_solver.py"
] | [
"import torch\nimport torch.nn as nn\nimport os\nfrom . import utils as solver_utils\nfrom utils.utils import to_cuda, to_onehot\nfrom torch import optim\nfrom . import clustering\nfrom discrepancy.cdd import CDD\nfrom math import ceil as ceil\nfrom .base_solver import BaseSolver\nfrom copy import deepcopy\n\n\ncla... | [
[
"torch.no_grad"
]
] |
robert-mijakovic/hoomd-blue | [
"f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a"
] | [
"hoomd/md/pytest/test_dihedral.py"
] | [
"# Copyright (c) 2009-2022 The Regents of the University of Michigan.\n# Part of HOOMD-blue, released under the BSD 3-Clause License.\n\nimport hoomd\nimport pytest\nimport numpy\n\n# Test parameters include the class, class keyword arguments, bond params,\n# force, and energy.\ndihedral_test_parameters = [\n (\... | [
[
"numpy.testing.assert_allclose",
"numpy.sin",
"numpy.cos"
]
] |
petakajaib/dia-kata | [
"5e3f90498352eb4fbf8d7b95e807d4d31df139a2"
] | [
"ml_pipeline/vectorization/relative_entity_position_vector.py"
] | [
"import numpy as np\nfrom .entity_position_vector import get_entity_position_vector\n\ndef get_relative_entity_position_vector(entry, enriched_collection):\n entity_position = get_entity_position_vector(entry, enriched_collection)\n\n sorted_map = {}\n for idx, elem in enumerate(sorted(set(entity_position)... | [
[
"numpy.array"
]
] |
strates-git/mathematics | [
"1271033400cece3e987cd6e0d7a8db82abeea769"
] | [
"CDCmodels.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Sep 30 18:17:48 2020\n\n@author: Shane Strate\n\"\"\"\nimport pandas as pd\nimport glob, os\nfrom sklearn import preprocessing\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, confusion_matrix\nimport numpy as np\nimport seaborn as sns\nimport ma... | [
[
"pandas.to_datetime",
"numpy.array",
"sklearn.metrics.mean_squared_error",
"pandas.merge",
"pandas.DataFrame",
"sklearn.metrics.mean_absolute_error",
"sklearn.preprocessing.MinMaxScaler",
"numpy.sqrt",
"pandas.unique",
"pandas.read_csv"
]
] |
amazon-research/cmaxent | [
"a18998b5b02a7d1ef96fceadbea6a9c1aae8cae1"
] | [
"experiments/ace_bounds_experiment.py"
] | [
"# ./experiments/ace_bounds_experiment.py\n\"\"\" Experiments testing our derived ACE bounds.\n\nCopyright Amazon.com, Inc. or its affiliates. All Rights Reserved. \nSPDX-License-Identifier: Apache-2.0\n\"\"\"\nimport numpy as np\nfrom tqdm import trange\n\nfrom experiments.synthetic_data_generation import *\n\nfro... | [
[
"numpy.array",
"numpy.clip"
]
] |
15thai/Gibb_ringing | [
"0e019d0da60d6da7c933a85d2407b655ada206bc"
] | [
"unring_parallel.py"
] | [
"import time\nimport numpy as np\nimport multiprocessing\nimport ctypes\nfrom contextlib import closing\nfrom unring import unring_2d\n\ndef unring_wrapper(vol):\n inp = np.frombuffer(shared_input)\n sh_input= inp.reshape(arr_shape)\n\n out = np.frombuffer(shared_output)\n sh_out= out.reshape(arr_shape)\n\n... | [
[
"numpy.frombuffer"
]
] |
mjziebarth/gmt-python | [
"0005152780528c7248369fb1446a9670383f2b19"
] | [
"gmt/helpers/tempfile.py"
] | [
"\"\"\"\nUtilities for dealing with temporary file management.\n\"\"\"\nimport os\nfrom tempfile import NamedTemporaryFile\n\nimport numpy as np\n\n\ndef unique_name():\n \"\"\"\n Generate a unique name with the prefix 'gmt-python-'.\n\n Useful for generating unique names for figures (otherwise GMT will pl... | [
[
"numpy.loadtxt"
]
] |
Peterror/CPS-2018 | [
"e63c17032e9af0a0cd2e0c30a9b31a8bc3018888"
] | [
"Transformata/FFT.py"
] | [
"import numpy as np\n\n\nclass FFT(object):\n def __init__(self, sampling_frequency, samples_power_of_two):\n \"\"\"\n samples - 2^samples_power_of_two\n sampling_frequency must be equal to the number of samples\n \"\"\"\n try:\n self._number_of_samples = 1 <... | [
[
"numpy.complex",
"numpy.sin",
"numpy.cos"
]
] |
guy-amir/swt | [
"9f7f1de45318d2cfb1903f777a7fbb965cd845ac"
] | [
"src/utils.py"
] | [
"\"\"\"\nsome utiliy functions for data processing and visualization.\n\"\"\"\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import ConnectionPatch\nimport numpy as np\nimport torch\n\n# class name for CIFAR-10 dataset\ncifar10_class_name = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', \n ... | [
[
"matplotlib.pyplot.rcParams.update",
"torch.cat",
"matplotlib.pyplot.figure",
"torch.abs",
"matplotlib.patches.ConnectionPatch",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.show",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.axis",
... |
vinsis/attention-seeking-in-pytorch | [
"fe9cab2cd9def3efb1837d70cd0179b0fb04b2c0"
] | [
"code/content_based_concat_attention.py"
] | [
"import torch\nimport torch.nn as nn\nfrom torch.optim import Adam\nimport torch.nn.functional as F\n\nfrom loader import loader, sequence_length\n\n# sequence_length is equal to 10\nencoder_input_size = 32\nencoder_hidden_size = 32\n\ndecoder_input_size = 32\ndecoder_output_size = 32*2\n\ndevice = 'cuda' if torch.... | [
[
"torch.nn.Linear",
"torch.zeros",
"torch.cat",
"torch.nn.LSTM",
"torch.nn.CrossEntropyLoss",
"torch.optim.Adam",
"torch.no_grad",
"torch.bmm",
"torch.sort",
"torch.cuda.is_available",
"torch.nn.functional.softmax",
"torch.Tensor",
"torch.nn.Embedding"
]
] |
tillbiskup/cwepr | [
"fb5df019238b63f36c7cfdf2d88264e5d18078be"
] | [
"cwepr/plotting.py"
] | [
"\"\"\"\nPlotting: Graphical representations of data extracted from datasets.\n\nGraphical representations of cw-EPR data are an indispensable aspect of data\nanalysis. To facilitate this, a series of different plotters are available.\n\nPlotting relies on `matplotlib <https://matplotlib.org/>`_, and mainly its\nob... | [
[
"numpy.power",
"numpy.sqrt"
]
] |
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