repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
qmlcode/qml-interface | [
"328d3bc522291c896fc6fdfd4f64f005b99bed3f",
"328d3bc522291c896fc6fdfd4f64f005b99bed3f"
] | [
"test/test_representations.py",
"qml/ml/kernels/kernels.py"
] | [
"# MIT License\n#\n# Copyright (c) 2017 Anders Steen Christensen\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to... | [
[
"numpy.asarray",
"numpy.concatenate",
"numpy.allclose",
"numpy.loadtxt"
],
[
"numpy.asarray",
"numpy.sqrt",
"numpy.sum",
"numpy.empty"
]
] |
dmft-wien2k/dmft-wien2k-v2 | [
"83481be27e8a9ff14b9635d6cc1cd9d96f053487"
] | [
"src/putils/MMoment/transformations.py"
] | [
"# -*- coding: utf-8 -*-\n# transformations.py\n\n# Copyright (c) 2006-2012, Christoph Gohlke\n# Copyright (c) 2006-2012, The Regents of the University of California\n# Produced at the Laboratory for Fluorescence Dynamics\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or witho... | [
[
"numpy.diag",
"numpy.dot",
"numpy.radians",
"numpy.expand_dims",
"numpy.sqrt",
"numpy.vstack",
"numpy.fabs",
"numpy.concatenate",
"numpy.mean",
"numpy.cross",
"numpy.negative",
"numpy.trace",
"numpy.roll",
"numpy.linalg.svd",
"numpy.allclose",
"numpy... |
gamedaygeorge/datacube-applications-library | [
"1b6314ee3465f9f17930391a4c241e981a9e200e"
] | [
"DCAL_utils_special/dc_mosaic.py"
] | [
"# Copyright 2016 United States Government as represented by the Administrator\n# of the National Aeronautics and Space Administration. All Rights Reserved.\n#\n# Portion of this code is Copyright Geoscience Australia, Licensed under the\n# Apache License, Version 2.0 (the \"License\"); you may not use this file\n#... | [
[
"numpy.invert",
"numpy.issubdtype",
"numpy.stack",
"numpy.full",
"numpy.zeros",
"numpy.isin"
]
] |
quantshah/qgrad | [
"db13e8463de49a8df1bf59524bc1b0a875405ee9"
] | [
"qgrad/qgrad_qutip.py"
] | [
"\"\"\"\nImplementation of some common quantum mechanics functions that work with JAX\n\"\"\"\nfrom jax.ops import index, index_update\nimport jax.numpy as jnp\nfrom jax.random import PRNGKey, uniform\nimport numpy as np\nfrom scipy.linalg import expm, sqrtm\nfrom numpy.linalg import matrix_power\n\n\ndef fidelity(... | [
[
"numpy.random.randint",
"numpy.zeros",
"numpy.fill_diagonal",
"scipy.linalg.expm"
]
] |
lorinczb/pytorch-dc-tts | [
"9dae50678113e2f60ad0752b99b959bb0b11dfc9"
] | [
"pretrained_voxceleb_model/DatasetLoader.py"
] | [
"#! /usr/bin/python\n# -*- encoding: utf-8 -*-\n\nimport torch\nimport torchaudio\nimport numpy\nimport random\nimport pdb\nimport os\nimport threading\nimport time\nimport math\nfrom scipy.io import wavfile\nfrom queue import Queue\n\n# torchfb = torchaudio.transforms.MelSpectrogram(sample_rate=16000, n_fft=2048, ... | [
[
"numpy.dot",
"numpy.maximum",
"numpy.abs",
"torch.Tensor",
"numpy.clip",
"numpy.asarray",
"torch.cat",
"numpy.append"
]
] |
fengxiaoshuai/CNN_model_optimizer | [
"4c48420989ffe31a4075d36a5133fee0d999466a"
] | [
"distillation/build_student.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\n\nwith tf.variable_scope(\"student\"):\n input_label = tf.placeholder(dtype=tf.float32, shape=[10, 10], name=\"label\")\n input_image = tf.placeholder(dtype=tf.float32, shape=[10, 224, 224, 3], name=\"input\")\n conv1 = tf.layers.conv2d(inputs=input_image, fi... | [
[
"tensorflow.layers.conv2d",
"tensorflow.nn.softmax",
"tensorflow.reshape",
"tensorflow.placeholder",
"tensorflow.layers.dense",
"numpy.ones",
"tensorflow.losses.softmax_cross_entropy",
"tensorflow.global_variables_initializer",
"tensorflow.Session",
"tensorflow.variable_sco... |
cccaaannn/background_subtractor | [
"2832faa1a049e99a1125ee3d6802f13d8c8a387c"
] | [
"main.py"
] | [
"import os\nimport cv2\nimport numpy as np\nimport random as rnd\n\n\n\ndef path_creator(path, img_count,staring_string,extension):\n img_count_str = str(img_count)\n\n while True:\n if(len(img_count_str)<6):\n img_count_str = \"0\" + img_count_str\n else:\n break\n img_... | [
[
"numpy.median",
"numpy.zeros"
]
] |
yoyoFC/Energy_DB_Project | [
"fd6f2bc82e5a8ac031458f9e2e57578e62fafca7"
] | [
".ipynb_checkpoints/ES_Customer_Class-checkpoint.py"
] | [
"import streamlit as st\nimport pandas as pd\nimport numpy as np\nimport psycopg2 as db_connect\nimport altair as alt\n\n\nhost_name = \"dataviz.cgq2ewzuuqs1.us-east-2.rds.amazonaws.com\"\ndb_user = \"postgres\"\ndb_password = \"ElPeruano_2021\"\ndb_name = \"postgres\"\ndb_port = 5432\nconnection = db_connect.conne... | [
[
"pandas.DataFrame"
]
] |
thorben-frank/netket | [
"33e7a2c2ae5cf7b2a3d9b34b34ecbfb31b5865af"
] | [
"netket/operator/_abstract_operator.py"
] | [
"# Copyright 2021 The NetKet 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 required... | [
[
"scipy.sparse.csr_matrix",
"numpy.empty",
"numpy.ones"
]
] |
ninikolov/low_resource_summarization | [
"3655e1d0538f082942649cdbaa9fee3efc9c4c0c"
] | [
"summarization_systems/oracle.py"
] | [
"\"\"\"Extractive oracle\"\"\"\n\n\n\nimport argparse\nimport itertools\nimport logging\nimport nltk\nimport numpy as np\nimport sys\nfrom multiprocessing import Process, Manager, cpu_count\nfrom tqdm import *\nfrom nltk.tokenize import ToktokTokenizer\ntoktok = ToktokTokenizer().tokenize\n\n\ndef set_overlap(sourc... | [
[
"numpy.argsort",
"numpy.mean"
]
] |
LSheneman/texas_rangers_modeler | [
"ebaa63e639beb715d108068cbf4e22f24848b4f7"
] | [
"src/features/tweetCleaner.py"
] | [
"import re \nimport sys\nimport tweepy \nfrom tweepy import OAuthHandler \nfrom textblob import TextBlob \nimport pickle\nimport string\nimport config\nimport pandas as pd\n\nclass TwitterCleaner(object): \n ''' \n Generic Twitter Class for sentiment analysis. \n '''\n def __init__(self): \n ''' ... | [
[
"pandas.DataFrame"
]
] |
kenichinakanishi/gen-efficientnet-pytorch | [
"76f18aaf5a42e4b521a2cc482241575702075a43"
] | [
"geffnet/gen_efficientnet.py"
] | [
"\"\"\" Generic Efficient Networks\n\nA generic MobileNet class with building blocks to support a variety of models:\n\n* EfficientNet (B0-B8, L2 + Tensorflow pretrained AutoAug/RandAug/AdvProp/NoisyStudent ports)\n - EfficientNet: Rethinking Model Scaling for CNNs - https://arxiv.org/abs/1905.11946\n - CondConv:... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.nn.functional.dropout",
"torch.nn.Flatten",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d"
]
] |
diagccmc/pyblock | [
"9195e4231ce5fdbae1046bec456867099a40adfa"
] | [
"pyblock/pd_utils.py"
] | [
"'''Pandas-based wrapper around :mod:`pyblock.blocking`.'''\n\n# copyright: (c) 2014 James Spencer\n# license: modified BSD license; see LICENSE for further details.\n\nimport numpy\nimport pandas as pd\nimport pyblock.blocking\n\ndef reblock(data, axis=0, weights=None):\n '''Blocking analysis of correlated data... | [
[
"pandas.concat",
"pandas.Series",
"numpy.min",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"numpy.array"
]
] |
rudibakaal/Predicting-Credit-Card-Defaults-with-Tensorflow | [
"c5dde5d06adecce0e5e85564af922f11e79f89ec"
] | [
"cc_default.py"
] | [
"import tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import LabelEncoder\nfrom keras.utils.vis_utils import plot_model\nimport matplotlib.style as style\n\nds = p... | [
[
"pandas.read_csv",
"tensorflow.keras.layers.LeakyReLU",
"matplotlib.pyplot.title",
"matplotlib.style.use",
"tensorflow.keras.layers.Dense",
"pandas.DataFrame",
"numpy.random.permutation",
"numpy.mean",
"matplotlib.pyplot.xlabel",
"sklearn.preprocessing.StandardScaler",
... |
CityU-AIM-Group/D2Net | [
"c42f45addf9ca6c734a1335fd466abd38aa2968c"
] | [
"utils/lovasz_loss.py"
] | [
"\"\"\"\nLovasz-Softmax and Jaccard hinge loss in PyTorch\nMaxim Berman 2018 ESAT-PSI KU Leuven (MIT License)\n\"\"\"\n\nimport torch\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport numpy as np\nfrom itertools import filterfalse as ifilterfalse\n\n\ndef lovasz_grad(gt_sorted):\n \"... | [
[
"torch.nn.functional.softmax",
"torch.sort",
"torch.nn.functional.elu",
"numpy.array",
"torch.autograd.Variable"
]
] |
liepeiming/captcha_trainer | [
"51459fc0d18324145a0dbdeb0ef6cc2ce47c71a5"
] | [
"utils/data.py"
] | [
"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Author: kerlomz <kerlomz@gmail.com>\nimport os\nimport hashlib\nimport utils\nimport random\nimport utils.sparse\nimport tensorflow as tf\nimport numpy as np\nfrom constants import RunMode, ModelField, DatasetType, LossFunction\nfrom config import ModelConfig, EXCE... | [
[
"tensorflow.compat.v1.data.make_one_shot_iterator",
"tensorflow.data.TFRecordDataset",
"tensorflow.cast",
"numpy.concatenate",
"tensorflow.io.FixedLenFeature",
"tensorflow.compat.v1.python_io.tf_record_iterator",
"tensorflow.keras.preprocessing.sequence.pad_sequences"
]
] |
yongzhuo/Macadam | [
"794a29c760ce25264388c3a85a6b118733afb023"
] | [
"macadam/tc/t00_predict.py"
] | [
"# !/usr/bin/python\n# -*- coding: utf-8 -*-\n# @time : 2020/5/8 21:38\n# @author : Mo\n# @function: class of model predict\n\n\n# 适配linux\nimport sys\nimport os\npath_root = os.path.abspath(os.path.join(os.path.dirname(__file__), \"../..\"))\nsys.path.append(path_root)\n## cpu-gpu与tf.keras\n# os.environ[\"CUDA... | [
[
"numpy.array"
]
] |
G-Wang/Text2Speech-Pytorch | [
"7bacdd0880825c3eeb08b6899b5c499416e53d0c"
] | [
"tts/preprocess/audio.py"
] | [
"import librosa\nimport librosa.filters\nimport math\nimport numpy as np\nfrom scipy import signal\nfrom hparams import hparams\nfrom scipy.io import wavfile\n\nimport lws\n\n\ndef load_wav(path):\n return librosa.core.load(path, sr=hparams.sample_rate)[0]\n\n\ndef save_wav(wav, path):\n wav = wav * 32767 / m... | [
[
"numpy.dot",
"numpy.log",
"numpy.maximum",
"numpy.abs",
"numpy.clip",
"numpy.power"
]
] |
taruma/hidrokit | [
"c8b949aa6a81981684a24e5dd1e498ec82cbe0ca"
] | [
"hidrokit/contrib/taruma/hk99.py"
] | [
"\"\"\"manual:\nhttps://gist.github.com/taruma/8dd920bee9fa95cf6eba39cc9d694953\"\"\"\n\nimport numpy as np\nimport pandas as pd\n\n\ndef thiessen_weight(area):\n area_val = list(area.values())\n area_percent = area_val / np.sum(area_val)\n key = list(area.keys())\n return dict(zip(key, area_percent))\n... | [
[
"numpy.sum",
"numpy.stack"
]
] |
NetEase-FuXi/EET | [
"f827cef4bfcf8b18e2d4169469052440fe2b216f"
] | [
"example/python/gpt2_transformers_example.py"
] | [
"import torch\r\nimport numpy as np\r\nfrom eet.transformers.modeling_gpt2 import EETGPT2Model\r\nusing_half = False\r\nseq_len = 128\r\nbatch = 5\r\n\r\ndef main():\r\n input = np.random.randint(1000,9000,seq_len * batch,dtype=\"int64\")\r\n inputs = np.random.randint(1000,9000,1 * batch,dtype=\"int64\")\r\n... | [
[
"torch.from_numpy",
"numpy.random.randint"
]
] |
kevinkovalchik/my_prosit | [
"befc201df38bbba78e467649d5c27a1d8577bfad"
] | [
"prysit/converters/generic.py"
] | [
"import pandas as pd\nimport numpy as np\nimport multiprocessing as mp\nimport pyteomics.mass\n\nfrom ..constants import MAX_ION, ION_TYPES, MAX_FRAG_CHARGE\nfrom .. import utils\n\n\naa_comp = dict(pyteomics.mass.std_aa_comp)\naa_comp[\"o\"] = pyteomics.mass.Composition({\"O\": 1})\ntranslate2spectronaut = {\"C\":... | [
[
"pandas.concat",
"numpy.zeros",
"pandas.DataFrame"
]
] |
cirno1w/transport | [
"0eb972c78f9154c0a3f780f197ef9af406b2bb71"
] | [
"src/transbigdata/tests/test_bikedata.py"
] | [
"import transbigdata as tbd\nimport pandas as pd\n\n\nclass TestBikedata:\n def setup_method(self):\n self.data =pd.DataFrame([['713ED7A4B5EA3233E0533C0BA8C09291', '2018-08-27 8:41:46', 0,\n 121.432966, 31.130154],\n ['713ED7A4B5EA3233E0533C0BA8C09291', '2018-08-27 8:58:46', 1,\n 121.4... | [
[
"pandas.DataFrame"
]
] |
mjdroz/StatisticsCalculator | [
"6be77b650b16e1c3e8ed6160905d99e58449e9b4"
] | [
"RandomGenerator/randomListSelectionSeed.py"
] | [
"from numpy import random\nfrom RandomGenerator.randomListSelection import randomListSelection\n\ndef randomListSelectionSeed(list, seed):\n state = random.get_state()\n random.seed(seed)\n try:\n seeded_selection = randomListSelection(list)\n return seeded_selection\n finally:\n ra... | [
[
"numpy.random.get_state",
"numpy.random.set_state",
"numpy.random.seed"
]
] |
carbonplan/cmip6-downscaling | [
"41401d99d3beef7e80485cc54161cbc8653f583e"
] | [
"cmip6_downscaling/methods/regions.py"
] | [
"from typing import Any, Dict, Tuple, Union\n\nimport numpy as np\nimport regionmask\nimport xarray as xr\n\n\ndef generate_subdomains(\n ex_output_grid: Union[xr.Dataset, xr.DataArray],\n buffer_size: Union[float, int],\n region_def: str = 'ar6',\n) -> Tuple[Dict[Union[int, float], Any], xr.DataArray]:\n ... | [
[
"numpy.isfinite",
"numpy.unique"
]
] |
ajits-github/yolov4 | [
"31d833983286d2da942226b589140fc69bc79ba2"
] | [
"tool/tv_reference/coco_utils.py"
] | [
"import copy\nimport os\nfrom PIL import Image\n\nimport torch\nimport torch.utils.data\nimport torchvision\n\nfrom pycocotools import mask as coco_mask\nfrom pycocotools.coco import COCO\n\nfrom . import transforms as T\n\n\nclass FilterAndRemapCocoCategories(object):\n def __init__(self, categories, remap=True... | [
[
"torch.zeros",
"torch.tensor",
"torch.as_tensor",
"torch.utils.data.Subset",
"torch.stack"
]
] |
eisenhauer/ADIOS2-Examples | [
"15505deab8f61f395d530ae9b66e24d65f6d97ca"
] | [
"source/cpp/gray-scott/plot/gsplot.py"
] | [
"#!/usr/bin/env python3\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nimport adios2\nimport argparse\nfrom mpi4py import MPI\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport decomp\nimport time\nimport os\n\n\ndef SetupArgs()... | [
[
"numpy.squeeze",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.clf",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.ion",
"matplotlib.pyplot.tick_params",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] |
whutddk/AlphaZero_Quoridor | [
"52744b64f643ed6c01adad86adea29d3d2b14177"
] | [
"quoridor.py"
] | [
"import numpy as np\nfrom queue import Queue\nimport time\n\nclass Quoridor(object):\n\tHORIZONTAL = 1\n\tVERTICAL = -1\n\n\tdef __init__(self, safe=False):\n\t\tself.safe = safe\n\n\t\t#self.action_space = 140 # 140 64+64 + 4 + 8possible actions in total \n\t\t\n\t\tself.action_space = 84 # 84 36+36 + 4 + 8 pos... | [
[
"numpy.int8",
"numpy.stack",
"numpy.ones",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
]
] |
cjdans5545/khaiii | [
"328d5a8af456a5941130383354c07d1cd0e47cf5"
] | [
"src/main/python/khaiii/train/trainer.py"
] | [
"# -*- coding: utf-8 -*-\n\n\n\"\"\"\ntraining related library\n__author__ = 'Jamie (jamie.lim@kakaocorp.com)'\n__copyright__ = 'Copyright (C) 2019-, Kakao Corp. All rights reserved.'\n\"\"\"\n\n\n###########\n# imports #\n###########\nfrom argparse import Namespace\nimport copy\nfrom datetime import datetime, time... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.functional.softmax",
"torch.cuda.is_available",
"torch.load"
]
] |
princeton-vl/oasis | [
"5835d24c331d78e91becba29f7e4a53ccd3e376e"
] | [
"experiments/depth/train2.py"
] | [
"import argparse\nimport os\n\nimport cv2\nimport math\nimport torch\nimport torch.nn.parallel\nimport numpy as np\n\nimport valid2\n\nimport config\nimport TBLogger\n\nfrom utils import makedir_if_not_exist, StoreDictKeyPair, save_obj\nfrom torch import optim\nfrom torch.utils import data\nfrom torch.autograd impo... | [
[
"torch.nn.parallel.DataParallel",
"numpy.min",
"numpy.unique",
"torch.load",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"numpy.max",
"torch.cuda.is_available",
"numpy.zeros"
]
] |
ondrejdyck/pyTEMlib | [
"b8ed2000f1bb44c7add966cef444a02e456258cb"
] | [
"pyTEMlib/interactive_eels.py"
] | [
"\"\"\" Interactive routines for EELS analysis\n\nthis file provides additional dialogs for EELS quantification\n\nAuthor: Gerd Duscher\n\"\"\"\n\nimport numpy as np\n\nfrom PyQt5 import QtWidgets, QtCore, QtGui\nimport sidpy\nimport matplotlib.patches as patches\nfrom matplotlib.widgets import RectangleSelector, S... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.patches.Rectangle",
"matplotlib.patches.Circle",
"matplotlib.pyplot.subplot",
"numpy.searchsorted",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.figure"
]
] |
bkmi/schnetpack | [
"3c58fd1a0b9fa2b046a88e89eb0d0c9051973046"
] | [
"src/schnetpack/datasets/qm9.py"
] | [
"import logging\nimport os\nimport re\nimport shutil\nimport tarfile\nimport tempfile\nfrom urllib import request as request\n\nimport numpy as np\nfrom ase.io.extxyz import read_xyz\nfrom ase.units import Debye, Bohr, Hartree, eV\n\nimport schnetpack as spk\nfrom schnetpack.datasets import DownloadableAtomsData\n\... | [
[
"numpy.array",
"numpy.zeros",
"numpy.setdiff1d"
]
] |
shaoeric/BNN_NoBN | [
"e1d007c7ec15c4793f15375508752eee3ad7e4e0"
] | [
"models/Qa_reactnet_18_none.py"
] | [
"'''\nReact-birealnet-18(modified from resnet)\n\nBN setting: remove all BatchNorm layers\nConv setting: original Conv2d\nBinary setting: only activation are binarized\n\n'''\n\n\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.model_zoo as model_zoo\nimport torch.nn.functional as F\n\nfrom layers import ... | [
[
"torch.nn.Sequential",
"torch.nn.PReLU",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.Identity",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.AvgPool2d"
]
] |
mevtorres/pvextractor | [
"0048a43ae06f39313645205e087c4fbec1168a52"
] | [
"pvextractor/gui.py"
] | [
"from __future__ import print_function\n\nimport os\nimport math\nimport warnings\n\nimport numpy as np\n\nfrom matplotlib.collections import LineCollection\nfrom matplotlib.transforms import Bbox\nfrom matplotlib.patches import Polygon\n\nfrom .geometry.path import Path, get_endpoints\nfrom . import extract_pv_sli... | [
[
"numpy.hstack",
"numpy.isfinite",
"matplotlib.widgets.Button",
"matplotlib.use",
"matplotlib.widgets.Slider",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] |
cseveriano/evolving_clustering | [
"50dd2b4e38ee11aba9382f1a8e04f530b7c9c949"
] | [
"src/models/evolving/util/util.py"
] | [
"import numpy as np\nimport math\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\n\ndef adjust_labels(y_pred, y):\n new_y_pred = np.array(y_pred.copy())\n\n pred_labels = np.unique(y_pred)\n\n for l in pred_labels:\n labels = y[y_pred == l]\n\n uniqueValues, occurCount = np.unique... | [
[
"matplotlib.pyplot.gca",
"numpy.unique",
"matplotlib.pyplot.draw",
"matplotlib.pyplot.Circle",
"numpy.max",
"numpy.argmax"
]
] |
hsiang271828/vnpy | [
"54e1658b253b4e32c36dcb27b51134980cae6411"
] | [
"examples/vn_trader/save_tdx_data.py"
] | [
"'''\n使用说明:\n\nfuture_download状态，下载期货数据时设置为True，下载股票数据时设置为False\nfile_path:通达信数据保存路径大家自行替换\n通达信期货数据对齐datetime到文华财经后数据与文化财经完全一致，包括指数合约\n单个文件较大时多进程只有两个进程在运行，原因不明\n通达信股票只能下载最近100天数据，期货数据下载没有时间限制\n期货数据存储路径:D:\\tdx\\vipdoc\\ds,上交所股票数据路径:D:\\tdx\\vipdoc\\sh,深交所股票数据路径:D:\\tdx\\vipdoc\\sz\n建议下载通达信期货通可以同时下载股票和期货数据，enjoy it！... | [
[
"numpy.fromfile",
"pandas.to_datetime",
"numpy.dtype",
"pandas.DataFrame",
"numpy.timedelta64",
"numpy.datetime64"
]
] |
SSS135/ppo-pytorch | [
"04cd026116bfbd7353274f8dbb4951cddfc66e6b",
"04cd026116bfbd7353274f8dbb4951cddfc66e6b"
] | [
"ppo_pytorch/common/repeat_env.py",
"ppo_pytorch/common/rl_base.py"
] | [
"import random\n\nimport gym\nimport numpy as np\nfrom gym import spaces\nfrom gym.envs.registration import register\n\nregister(\n id='Repeat-v0',\n entry_point='ppo_pytorch.common.repeat_env:RepeatEnv',\n)\n\nregister(\n id='RepeatNondeterministic-v0',\n entry_point='ppo_pytorch.common.repeat_env:Repe... | [
[
"numpy.array"
],
[
"numpy.asarray",
"numpy.reshape"
]
] |
CloudChaoszero/Theano-PyMC | [
"c32c1d34f9ea7e11e877bd454cb9b08305812720"
] | [
"tests/tensor/random/test_op.py"
] | [
"import numpy as np\nfrom pytest import fixture, raises\n\nimport aesara.tensor as aet\nfrom aesara import config\nfrom aesara.assert_op import Assert\nfrom aesara.gradient import NullTypeGradError, grad\nfrom aesara.tensor.math import eq\nfrom aesara.tensor.random.op import RandomVariable, default_shape_from_param... | [
[
"numpy.eye",
"numpy.array",
"numpy.zeros",
"numpy.ones"
]
] |
braidedlogix/pymodelfit | [
"de8a02a27d13646b1f4b011d056edbed76540473"
] | [
"pymodelfit/utils.py"
] | [
"#Copyright 2008 Erik Tollerud\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... | [
[
"numpy.radians",
"numpy.arctan2",
"numpy.max",
"numpy.zeros_like",
"numpy.histogram",
"matplotlib.pyplot.isinteractive",
"matplotlib.pyplot.gcf",
"numpy.sin",
"numpy.log",
"numpy.min",
"matplotlib.pyplot.savefig",
"numpy.array",
"matplotlib.pyplot.show",
"ma... |
maxkferg/mink-reality | [
"6eed945af797f164dddd0d69a7f47183b621db22"
] | [
"src/simulator/sim/simulation/environment/simulation_env.py"
] | [
"import os\nimport gym\nimport math\nfrom gym import spaces\nfrom gym.utils import seeding\nimport numpy as np\nimport time\nimport pybullet\nimport transforms3d\nfrom . import SimRobot\nfrom . import bullet_client\nfrom .robot_models import Husky\nfrom .robot_models import Turtlebot\nfrom .config import URDF_ROOT\... | [
[
"numpy.dot",
"numpy.reshape",
"numpy.eye",
"numpy.ones",
"numpy.random.normal",
"numpy.array",
"numpy.zeros"
]
] |
ChamiLamelas/Math36B_FinalProject | [
"0bdb5d17769553a4edb163534c21cc641860a07a"
] | [
"code/statistical_tests.py"
] | [
"import scipy.stats\nimport numpy as np\n\n\ndef f_test(sample_x, sample_y, larger_varx_alt):\n \"\"\"\n Computes the F-value and corresponding p-value for a pair of samples and alternative hypothesis.\n\n Parameters\n ----------\n sample_x : list\n A random sample x1,...,xnx. Let its (underly... | [
[
"numpy.median",
"numpy.max",
"numpy.mean",
"numpy.var",
"numpy.array",
"numpy.sum"
]
] |
dorogam/autarquicas | [
"9295ef112f247cb5d3d33bce89ec6c993f51b0b9"
] | [
"app.py"
] | [
"# -*- coding: utf-8 -*-\n\n# import libraries \n\nimport pandas as pd \nimport json \n\n# Open json files \n\n\t# 2009 \n\nwith open('json_files/autarquicas_2009.json') as f2009:\n data_2009 = json.load(f2009)\n\n\t# 2013\n\nwith open('json_files/autarquicas_2013.json') as f2013:\n data_2013 = json.load(f201... | [
[
"pandas.json_normalize"
]
] |
OldMetalmind/daily_weather_report | [
"fe652ba5bf836509e04f1c53396739d70ec3f998"
] | [
"app.py"
] | [
"# -*- coding: utf-8 -*-\n# Original Code: Jorge Gomes \n# Optimization: João Pina \n\n# ------------------------------\n# DESCRIPTION\n# ------------------------------\n\n# This app scrapes information from IPMA and generates an image\n# The image is to be shared automatically on social media \n\n# ---------... | [
[
"pandas.json_normalize",
"pandas.Series",
"pandas.DataFrame"
]
] |
Z-yq/audioSamples.github.io | [
"084519b5a0464f465e1d72c24cba07c1ec55cd26"
] | [
"asr/models/layers/transpose_time_major.py"
] | [
"# Copyright 2020 Huy Le Nguyen (@usimarit)\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 ... | [
[
"tensorflow.transpose"
]
] |
fianfian237/uro_pred_backend | [
"5986d9c38189362014cbfe0318a18b72c8386a7b"
] | [
"app.py"
] | [
"from flask import Flask, Response, jsonify\nfrom model import Model\nfrom flask import request\nimport pandas as pd\n\napp = Flask(__name__)\nmodel_grade = Model('Modeles/model_grade.joblib')\nmodel_stade = Model('Modeles/model_stade.joblib')\n\n\n@app.route('/predict_grade_n_stade', methods=[\"GET\"])\ndef predic... | [
[
"pandas.DataFrame"
]
] |
djl11/ivy | [
"209f74b5a1a82ca69ad712788ae0469c3f8614d9"
] | [
"ivy/backends/numpy/core/general.py"
] | [
"\"\"\"\nCollection of Numpy general functions, wrapped to fit Ivy syntax and signature.\n\"\"\"\n\n# global\nimport logging\nimport numpy as _np\nimport math as _math\nfrom operator import mul as _mul\nfrom functools import reduce as _reduce\nimport multiprocessing as _multiprocessing\n\n# local\nfrom ivy.core.dev... | [
[
"numpy.take_along_axis",
"numpy.split",
"numpy.expand_dims",
"numpy.take",
"numpy.linspace",
"numpy.asarray",
"numpy.squeeze",
"numpy.cumsum",
"numpy.dtype",
"numpy.concatenate",
"numpy.round",
"numpy.maximum.at",
"numpy.argmin",
"numpy.zeros_like",
"num... |
wh629/unqover | [
"6c499c6b965bd12433ebdffedde2a1d1639fe0ee"
] | [
"utils/convert_hdf5_to_hf.py"
] | [
"import sys\nimport argparse\nimport h5py\nimport numpy as np\nimport torch\nfrom utils.holder import *\nfrom utils.util import *\nimport qa.pipeline\nfrom transformers import *\nimport logging\n\nlogging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n\nparser = argparse.ArgumentParser(description=__doc__, fo... | [
[
"torch.device",
"torch.save"
]
] |
tilschaef/genomepy | [
"4c10e69b6886cf52381caf6498395391834a675b"
] | [
"genomepy/providers/base.py"
] | [
"\"\"\"BaseProvider class, the parent of the provider classes\"\"\"\nimport gzip\nimport os\nimport shutil\nimport subprocess as sp\nimport time\nfrom tempfile import TemporaryDirectory, mkdtemp\nfrom typing import Iterator, List, Union\nfrom urllib.request import urlopen\n\nimport pandas as pd\nfrom loguru import ... | [
[
"pandas.read_csv"
]
] |
0oshowero0/gnn_profile | [
"fafe1ae8da167c1ece51a73921c2f11d54021620"
] | [
"hanzhenyu/ogb/ogbn-arxiv/gat.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport argparse\nimport math\nimport os\nimport random\nimport time\n\nimport dgl\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom matplotlib import pyplot as plt\nfrom matplotlib.ticker import AutoMinorLocator, ... | [
[
"torch.mean",
"matplotlib.pyplot.legend",
"torch.nn.functional.softmax",
"matplotlib.ticker.MultipleLocator",
"numpy.linspace",
"torch.zeros",
"torch.cat",
"matplotlib.ticker.AutoMinorLocator",
"torch.no_grad",
"numpy.mean",
"torch.cuda.manual_seed_all",
"torch.devi... |
infinitemugen/genrl | [
"602587417ce167380c90a726764a3efa4643dc38"
] | [
"genrl/deep/common/noise.py"
] | [
"from abc import ABC, abstractmethod\n\nimport numpy as np\n\n\nclass ActionNoise(ABC):\n \"\"\"\n Base class for Action Noise\n\n :param mean: Mean of noise distribution\n :param std: Standard deviation of noise distribution\n :type mean: float\n :type std: float\n \"\"\"\n\n def __init__(s... | [
[
"numpy.random.normal",
"numpy.zeros_like",
"numpy.sqrt"
]
] |
Yihao-Sun/pytorch-mopo | [
"33a81aae8b221de01007e7b27a8a02ee19c27eb3"
] | [
"models/tf_dynamics_models/constructor.py"
] | [
"import numpy as np\nimport tensorflow.compat.v1 as tf\ntf.disable_eager_execution()\nimport pdb\n\nfrom models.tf_dynamics_models.fc import FC\nfrom models.tf_dynamics_models.bnn import BNN\n\ndef construct_model(obs_dim=11, act_dim=3, rew_dim=1, hidden_dim=200, num_networks=7,\n\t\t\t\t\tnum_elites=5, session=Non... | [
[
"numpy.concatenate",
"tensorflow.compat.v1.get_collection",
"tensorflow.compat.v1.initialize_vars",
"tensorflow.compat.v1.disable_eager_execution"
]
] |
lnicoletti/EPA1361-G21 | [
"e8dce7169101fd620a3e36f7c38df7f4db598a69"
] | [
"epa1361_open/final assignment/funs_generate_network.py"
] | [
"from __future__ import division, unicode_literals, print_function\n\nimport numpy as np\nimport networkx as nx\nimport pandas as pd\nfrom funs_dikes import Lookuplin # @UnresolvedImport\n\n\ndef to_dict_dropna(data):\n return dict((str(k), v.dropna().to_dict())\n for k, v in pd.compat.iteritems(... | [
[
"pandas.read_excel",
"pandas.compat.iteritems",
"numpy.loadtxt",
"numpy.column_stack"
]
] |
syedraza2/Variational_Quantum_Embedding | [
"780d6a976c4a3d8912df93d20335a6d4a15481ca"
] | [
"generate_data.py"
] | [
"import numpy as np\n\ndef generate_data(finename_X, finename_Y, number_of_data):\n\tX = []\n\tY = []\n\tfor i in range(40):\n\t x =[]\n\t y = 0\n\t r1 = np.random.uniform(0,1)\n\t if r1 < 0.5:\n\t x.append(np.random.uniform(-1.0,1.0))\n\t x.append(np.random.uniform(-1.0,1.0))\n\t y... | [
[
"numpy.random.uniform"
]
] |
CodingOfZero/Detection | [
"4196d364b7ee8de928c6e6fc92473bce0f5e8628"
] | [
"demo.py"
] | [
"#!/usr/bin/env python\n\n# --------------------------------------------------------\n# Tensorflow Faster R-CNN\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Xinlei Chen, based on code from Ross Girshick\n# --------------------------------------------------------\n\n\"\"\"\nDemo script s... | [
[
"numpy.hstack",
"tensorflow.ConfigProto",
"tensorflow.Session",
"tensorflow.train.Saver",
"numpy.where"
]
] |
FENGShuanglang/CPFNet_Project | [
"57f7455f008841555eaffef61945ba606445fc0f"
] | [
"OCT/CPFNet/dataset/PiFu.py"
] | [
"import torch\nimport glob\nimport os\nfrom torchvision import transforms\nfrom torchvision.transforms import functional as F\n#import cv2\nfrom PIL import Image\n# import pandas as pd\nimport numpy as np\nfrom imgaug import augmenters as iaa\nimport imgaug as ia\n#from utils import get_label_info, one_hot_it\nimpo... | [
[
"numpy.reshape",
"numpy.array"
]
] |
jherzberg/article-tagging | [
"769b06061502af1517af359ea8adee51aede4fa5"
] | [
"lib/tagnews/crimetype/benchmark.py"
] | [
"from __future__ import division, print_function\n\nimport numpy as np\nimport pandas as pd\n\n\ndef get_kfold_split(N, k=4):\n \"\"\"\n Create groups used for k-fold cross validation.\n\n Parameters\n ----------\n N : number of samples to split\n k : number of groups used for cross validation\n\n... | [
[
"numpy.logical_not",
"numpy.random.seed",
"numpy.arange",
"pandas.DataFrame",
"numpy.ones",
"numpy.random.permutation",
"pandas.set_option",
"numpy.array_split",
"numpy.zeros",
"numpy.sum"
]
] |
bdh-team-12/sleep-predictions-through-deep-learning | [
"7664cdffc0a0b0e732bffc95fd01e3ea27687025"
] | [
"CNN_CNN_Model/Train_Model.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Apr 24 14:11:51 2019\r\n\r\n@author: CRNZ\r\n\"\"\"\r\nimport numpy as np\r\nimport pandas as pd\r\nimport glob\r\nimport os\r\nfrom glob import glob\r\nfrom Models import get_base_model,get_model_cnn\r\nfrom keras import optimizers, losses, activations, models\r... | [
[
"numpy.load",
"numpy.expand_dims",
"sklearn.model_selection.train_test_split"
]
] |
WoojunePark/BasicSR | [
"7a6f13d3933db6c5be3319e37815c57ff72ab374"
] | [
"test_scripts/test_esrgan.py"
] | [
"import argparse\nimport cv2\nimport glob\nimport numpy as np\nimport os\nimport torch\n\nfrom basicsr.models.archs.rrdbnet_arch import RRDBNet\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--model_path',\n type=str,\n default= # noqa: E251\n 'exp... | [
[
"torch.device",
"torch.no_grad",
"torch.load",
"numpy.transpose"
]
] |
asitnayak/MLFlow-FashionMNIST-PyTorch | [
"bada0eeee45f91599fd135ffdb5e720770f3761c"
] | [
"src/stage_03_training_model.py"
] | [
"import argparse\nfrom calendar import EPOCH\nimport os\nimport shutil\nfrom tqdm import tqdm\nimport logging\nimport torch\nimport torch.nn as nn\nfrom src.utils.common import read_yaml, create_directories\nfrom src.stage_01_get_data import main as loader_main\nfrom src.stage_02_base_model_creation import CNN\nimp... | [
[
"torch.nn.CrossEntropyLoss",
"torch.save",
"torch.cuda.is_available",
"torch.load"
]
] |
dmeybohm/intellij-community | [
"7fcc441fd5902ec3d237c34ee93f5ed1faf23629"
] | [
"python/helpers/pycharm_matplotlib_backend/backend_interagg.py"
] | [
"import matplotlib\nimport os\nimport socket\nimport struct\nfrom matplotlib._pylab_helpers import Gcf\nfrom matplotlib.backend_bases import FigureManagerBase, ShowBase\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg\nfrom matplotlib.figure import Figure\n\nHOST = 'localhost'\nPORT = os.getenv(\"PYCHAR... | [
[
"matplotlib.backend_bases.FigureManagerBase.__init__",
"matplotlib.is_interactive",
"matplotlib._pylab_helpers.Gcf.get_all_fig_managers",
"matplotlib.backends.backend_agg.FigureCanvasAgg.draw",
"matplotlib.backends.backend_agg.FigureCanvasAgg.__init__",
"matplotlib._pylab_helpers.Gcf.get_a... |
wjyamada/BaleIdentification | [
"42280bc70af985691ad3c1d6519b96ad6d89f464"
] | [
"yolov3/train.py"
] | [
"import argparse\n\nimport torch.distributed as dist\nimport torch.optim as optim\nimport torch.optim.lr_scheduler as lr_scheduler\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport test # import test.py to get mAP after each epoch\nfrom models import *\nfrom utils.datasets import *\nfrom utils.utils imp... | [
[
"torch.optim.Adam",
"torch.optim.lr_scheduler.LambdaLR",
"torch.distributed.init_process_group",
"torch.utils.tensorboard.SummaryWriter",
"torch.distributed.destroy_process_group",
"torch.optim.SGD"
]
] |
yadala1998/Triangulation-of-convex-layers | [
"5da10fac0478e088c2725f1abe0b20c87380009b"
] | [
"input_file_generator.py"
] | [
"#!/usr/bin/env python3\n\"\"\"\nUnivertity of Wisconsin-Madison\nYaqi Zhang\n\"\"\"\nimport sys\nimport numpy as np\n\ndef main():\n if len(sys.argv) != 7:\n print(\"Usage: >> python {} <filename> <npoints> <xmin> <xmax> <ymin> <ymax>\".format(sys.argv[0]))\n sys.exit(1)\n filename = sys.argv[1... | [
[
"numpy.random.uniform"
]
] |
hhelm10/primitives-interfaces | [
"15766d77dae016fa699a46bade0fe66711b23459"
] | [
"jhu_primitives/sgm/ben_sgm/backends/fused.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\n backends/fused.py\n\"\"\"\n\nfrom time import time\nfrom ..common import _BaseSGM, _JVMixin\nfrom .. import lap_solvers\n\nimport numpy as np\nfrom scipy import sparse\n\n# --\n# SGM loop\n\nclass BaseSGMFused(_BaseSGM):\n def run(self, num_iters, tolerance, verbose=True):\n... | [
[
"scipy.sparse.random",
"numpy.arange",
"numpy.ones"
]
] |
ekrim/latent-tracking | [
"2688cd9adad18b301552b033b20acecbcce17868"
] | [
"geometry.py"
] | [
"import os\nimport sys\nimport PIL\nimport colorsys\nfrom collections import namedtuple\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport torch\n\nimport data\n\n\nJOINTS = 'wrist index_mcp index_pip index_dip index_tip middle_mcp middle_pip m... | [
[
"numpy.sqrt",
"numpy.linspace",
"torch.load",
"numpy.arctan2",
"numpy.max",
"numpy.concatenate",
"numpy.mean",
"numpy.any",
"numpy.arange",
"numpy.sin",
"numpy.float32",
"numpy.zeros",
"matplotlib.pyplot.figure",
"numpy.min",
"numpy.random.rand",
"nu... |
dat-boris/pandas | [
"cc429cf41502ffca677bab2af2386f3387710ed4"
] | [
"pandas/plotting/_core.py"
] | [
"# being a bit too dynamic\n# pylint: disable=E1101\nfrom __future__ import division\n\nimport warnings\nimport re\nfrom collections import namedtuple\nfrom distutils.version import LooseVersion\n\nimport numpy as np\n\nfrom pandas.util._decorators import cache_readonly, Appender\nfrom pandas.compat import range, l... | [
[
"numpy.nanmax",
"pandas.core.common.try_sort",
"pandas.plotting._tools._flatten",
"numpy.nanmin",
"pandas.plotting._timeseries._decorate_axes",
"pandas.core.dtypes.missing.notna",
"pandas.compat.map",
"matplotlib.artist.setp",
"pandas.plotting._compat._mpl_ge_3_0_0",
"scipy... |
hrosc/TrainYourOwnYOLO | [
"bcffa3f6b1acd37bd003635b471239fbb804a19f"
] | [
"Utils/Convert_Format.py"
] | [
"from os import path, makedirs\nimport pandas as pd\nimport numpy as np\nimport re\nimport os\nfrom PIL import Image\nfrom Get_File_Paths import GetFileList, ChangeToOtherMachine\n\n\ndef convert_vott_csv_to_yolo(\n vott_df,\n labeldict=dict(\n zip(\n [\"Cat_Face\"],\n [\n ... | [
[
"pandas.read_csv",
"pandas.Series",
"pandas.DataFrame"
]
] |
yu9824/yikit | [
"d2a0732f543e70b8be985b22847504a06c9837fc"
] | [
"yikit/models/models.py"
] | [
"\n'''\nCopyright (c) 2021 yu9824\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in wri... | [
[
"sklearn.ensemble.RandomForestRegressor",
"sklearn.utils.validation.check_is_fitted",
"sklearn.model_selection._validation._score",
"sklearn.metrics._scorer._check_multimetric_scoring",
"sklearn.metrics.mean_squared_error",
"sklearn.base.clone",
"numpy.mean",
"sklearn.utils.Bunch",... |
Eveneko/SUSTech-Courses | [
"0420873110e91e8d13e6e85a974f1856e01d28d6"
] | [
"CS303_Artifical-Intelligence/Gomoku/Chessboard/graph.py"
] | [
"import numpy as np\nfrom graphics import *\n\nGRID_WIDTH = 20\nCOLUMN = 15\nROW = 15\n\nlist1 = [] # black\nlist2 = [] # white\nlist3 = [] # all\nchessboard = np.zeros((15, 15))\n\nlist_all = [] # 整个棋盘的\n\n\ndef game_win(list):\n for m in range(COLUMN):\n for n in range(ROW):\n\n if n < RO... | [
[
"numpy.zeros"
]
] |
thomasg3/energy-price-aware-scheduling | [
"fdde23dff891a382f2f3d8f2b852675832f83e8d"
] | [
"learners/nearest_neighbors.py"
] | [
"#!/usr/bin/env python\n\nimport core\nfrom sklearn import neighbors\n\n\nbasic_features = ['HolidayFlag', 'DayOfWeek', 'PeriodOfDay', 'ForecastWindProduction', 'SystemLoadEA', 'SMPEA']\nall_features = ['HolidayFlag', 'DayOfWeek', 'WeekOfYear', 'Day', 'Month', 'Year', 'PeriodOfDay',\n 'ForecastWindPr... | [
[
"sklearn.neighbors.KNeighborsRegressor"
]
] |
mina-payout/mina | [
"4fab6c9366292b9d0c964e498fea743eb47623f7"
] | [
"automation/services/mina-bp-stats/payout-process/main_app/payouts_validate.py"
] | [
"from numpy.core.numeric import NaN\nimport pandas as pd\nimport psycopg2\nfrom google.cloud import storage\nimport os\nimport json\nfrom payouts_config import BaseConfig\nfrom datetime import datetime, timezone, timedelta\nimport math\nimport sys\nfrom validate_email import second_mail\nfrom logger_util import log... | [
[
"pandas.json_normalize",
"pandas.DataFrame"
]
] |
joe-siyuan-qiao/mmdetection | [
"2fcac9660cd40c374bf713dcf333d4b7a51bea06"
] | [
"mmdet/models/roi_heads/roi_extractors/groie.py"
] | [
"\"\"\"Generic RoI Extractor.\n\nA novel Region of Interest Extraction Layer for Instance Segmentation.\n\"\"\"\n\nfrom torch import nn\n\nfrom mmdet.core import force_fp32\nfrom mmdet.models.builder import ROI_EXTRACTORS\nfrom mmdet.ops.plugin import build_plugin_layer\nfrom .single_level import SingleRoIExtractor... | [
[
"torch.nn.ReLU"
]
] |
bhneo/SparsePooling | [
"6575774ad95cd782bbd228fb08c588b475035fc6"
] | [
"models/res/ex1.py"
] | [
"import os\nimport sys\n\nsys.path.append(os.getcwd())\n\nimport tensorflow as tf\ntf.get_logger().setLevel('ERROR')\nfrom common.inputs import data_input\nfrom common import layers, utils, train, res_blocks, attacks\n\nimport config\n\n\nWEIGHT_DECAY = 1e-4\nBATCH_NORM_EPSILON = 1e-3\nBATCH_NORM_DECAY = 0.99\n\nke... | [
[
"tensorflow.cast",
"tensorflow.keras.callbacks.LearningRateScheduler",
"tensorflow.image.random_crop",
"tensorflow.keras.initializers.he_normal",
"tensorflow.keras.optimizers.SGD",
"tensorflow.keras.Input",
"tensorflow.image.random_flip_left_right",
"tensorflow.keras.regularizers.l... |
min942773/parlai_wandb | [
"1d9ba1a0df2199d0247cee8c4929a2598ac7e41a"
] | [
"parlai/core/torch_generator_agent.py"
] | [
"#!/usr/bin/env python3\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\n\n\"\"\"\nGeneric PyTorch-based Generator agent.\n\nImplements quite a bit of boilerplate, including forced-d... | [
[
"torch.LongTensor",
"torch.softmax",
"torch.nn.CrossEntropyLoss",
"torch.nn.functional.log_softmax",
"torch.cat",
"torch.Tensor",
"torch.zeros",
"torch.multinomial",
"torch.arange",
"torch.topk",
"torch.index_select",
"torch.nn.parallel.DistributedDataParallel"
]
... |
lawrendran/mesh-transformer-jax | [
"49bdec254f626dddbd9d16ea6d6edb6b49e459ad"
] | [
"train.py"
] | [
"import argparse\nimport json\nimport time\n\nimport numpy as np\nimport wandb\nfrom tqdm import tqdm\n\nfrom mesh_transformer.build_model import build_model\nfrom lm_eval import evaluator, tasks\nfrom tasks.eval_harness import EvalHarnessAdaptor\nfrom tfrecord_loader import TFRecordNewInputs\nimport multiprocessin... | [
[
"numpy.array"
]
] |
rstodden/TS-scale-interpretations | [
"4acc197c5ef6f950509227f47e6e69342be3829f"
] | [
"src/rebuild_hsplit.py"
] | [
"#!/usr/bin/env python\n# Copyright (c) Regina Stodden.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n#\n\nimport pandas as pd\nimport os\n\n\nwith open(\"data/test.8turkers.tok.norm\") as f:\n original_conten... | [
[
"pandas.isna",
"pandas.read_excel",
"pandas.DataFrame"
]
] |
subhacom/mbnet | [
"b0ab55079ed31614f923ee15ed65defae156332b"
] | [
"analysis/pn_kc_ggn_plot.py"
] | [
"# pn_kc_ggn_plot.py --- \n# \n# Filename: pn_kc_ggn_plot.py\n# Description: \n# Author: Subhasis Ray\n# Maintainer: \n# Created: Fri Feb 16 13:08:41 2018 (-0500)\n# Last-Updated: Fri Feb 16 16:11:54 2018 (-0500)\n# By: Subhasis Ray\n# Update #: 263\n# \n# Code:\n\nimport sys\nimport os\nfrom timeit i... | [
[
"numpy.concatenate",
"numpy.arange",
"numpy.zeros",
"numpy.random.choice"
]
] |
utsavnandi/Kaggle-SIIM-ISIC-Melanoma-Classification | [
"5790c50b9cc266f82326a84093fa067880447397"
] | [
"plots.py"
] | [
"import time\nimport datetime\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import roc_curve\n\n\ndef plot_roc(y_true, y_pred, show=False):\n testy, lr_probs = y_true, y_pred\n ns_probs = [0 for _ in range(len(testy))]\n # calculate roc curves\n ns_fpr, ns_tpr, _ = roc_curve... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"sklearn.metrics.roc_curve",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"numpy.where",
"matplotli... |
mchant/pandas-ta | [
"360a26d71c83fe87e4042e4f86298b1dc3023704"
] | [
"pandas_ta/overlap/kama.py"
] | [
"# -*- coding: utf-8 -*-\nfrom numpy import NaN as npNaN\nfrom pandas import Series\nfrom pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series\n\n\ndef kama(close, length=None, fast=None, slow=None, drift=None, offset=None, **kwargs):\n \"\"\"Indicator: Kaufman's Adaptive Moving Average (K... | [
[
"pandas.Series"
]
] |
avito-tech/abito | [
"9071eecd9526ee5c268cfacd7ac9a49b6ee185e5"
] | [
"abito/lib/stats/plain.py"
] | [
"import numpy as np\nfrom typing import Union\n\n\n__all__ = ['sum', 'mean', 'var', 'std', 'mean_std', 'quantile', 'median', 'ratio']\n\n\ndef sum(obs: np.ndarray) -> np.float:\n return obs.sum(axis=0)\n\n\ndef mean(obs: np.ndarray) -> np.float:\n return np.divide(obs.sum(axis=0), obs.shape[0])\n\n\ndef demea... | [
[
"numpy.quantile",
"numpy.sqrt"
]
] |
ludc/rlstructures | [
"99fa91bb4e955d31348bed007f25b41641c9fa73"
] | [
"rlalgos/ppo/run_cartpole_pomdp.py"
] | [
"#\n# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n#\n\n\nfrom rlstructures import logging\nfrom rlstructures.env_wrappers import GymEnv, GymEnvInf\nfrom rlstructures.tools import we... | [
[
"torch.multiprocessing.set_start_method"
]
] |
ketank1000/pancake_prediction_bot | [
"146b4b9f1c924abaf8f81e864473dbf085955c49"
] | [
"backtest/backtest.py"
] | [
"\"\"\"\nCurrent results: (15000 epochs)\n Win/lose: 38/23 (62.295081967213115%)\n Profit: 3.599999999999998 $\n\nCurrent results: (15000 epochs)\n Win/lose: 186/153 (54.86725663716814%)\n Profit: -22.80000000000001 $\n\"\"\"\n\nimport json\nfrom os import EX_SOFTWARE\nfrom typing_extensions import Requ... | [
[
"pandas.read_json",
"pandas.DataFrame"
]
] |
tburnett/pointlike | [
"a556f07650c2f17d437c86fdafe9f9a33f59758e"
] | [
"python/uw/darkmatter/spectral.py"
] | [
"\"\"\" Dark Matter spectral models\n\n $Header: /nfs/slac/g/glast/ground/cvs/pointlike/python/uw/darkmatter/spectral.py,v 1.16 2014/08/06 00:52:49 echarles Exp $\n\n author: Alex Drlica-Wagner, Joshua Lande\n\"\"\"\nimport operator\nimport copy\nimport collections\nfrom collections import OrderedDict\nimport... | [
[
"numpy.append"
]
] |
johne13/dataset2binary | [
"de2bd5deb4f1f3935a6697093fc19c3c7a8b2d88"
] | [
"dataset2binary.py"
] | [
"'''\nfile: dataset2binary.py \n\ndescription: converts a dataset (aka dataframe) to a binary data file, and\n alse creates c and fortran code for reading the binary data\n\ninput: data in sas, stata, or csv format. may include a mix of character,\n integer, and float data, in multiple sizes.\n\n... | [
[
"pandas.read_csv",
"numpy.issubdtype",
"numpy.rec.fromarrays",
"pandas.read_sas",
"pandas.read_stata",
"pandas.to_numeric"
]
] |
covid-models/ventilator-supply-demand | [
"2fe52854833af0d074d942f6352eaea0b9612ce0"
] | [
"model.py"
] | [
"import math\r\nimport pandas as pd\r\nimport numpy as np\r\nimport scipy.integrate\r\nfrom datetime import timedelta\r\nimport shared\r\n# import world_data\r\n# import population\r\n\r\ndef model(Y, x, N, beta0, days0, beta1, gamma, sigma):\r\n # :param array x: Time step (days)\r\n # :param int N: Populati... | [
[
"numpy.arange",
"pandas.DataFrame"
]
] |
Marticles/ml-in-action | [
"7b8a13fdd73a210ee4338dce400bd764eb9abf75"
] | [
"LogisticRegression/lr.py"
] | [
"# -*- coding: utf-8 -*-\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# 《机器学习实战》 - 第5章 - Logistic回归\n\n# 示例1:采用梯度上升法找到Logistic回归分类器的最佳回归系数\n\ndef loadDataSet():\n \"\"\"\n 读取数据集\n \"\"\"\n dataMat = []\n labelMat = []\n fr = open('TestSet.txt')\n for line in fr.readlines():\n ... | [
[
"numpy.arange",
"numpy.ones",
"matplotlib.pyplot.ylabel",
"numpy.shape",
"numpy.exp",
"matplotlib.pyplot.xlabel",
"numpy.array",
"numpy.mat",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] |
JiaqiLiZju/NvTK | [
"6b887670a03d63c1747d9854ecbbac13cc06461c"
] | [
"NvTK/Explainer/MotifVisualize.py"
] | [
"'''Motif Visualization in NvTK.\n\nCurrently, this module only support DNA MOTIF Visualization.\n\nProtein Sequence Motif Visualization was under development.\n'''\n\n# Modified motif visualization functions from DeepOmic\n# Jiaqili@zju.edu.cn\n\n__all__ = [\"filter_heatmap\", \"plot_filter_heatmap\", \"plot_filte... | [
[
"matplotlib.pyplot.imshow",
"numpy.minimum",
"numpy.sqrt",
"numpy.max",
"numpy.mean",
"numpy.where",
"matplotlib.pyplot.gca",
"numpy.arange",
"numpy.ceil",
"matplotlib.pyplot.subplot",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.close",
"matplotlib.pyplot... |
dojinkimm/Object_Detection_Video_AllInOne | [
"ef2e3ca1ad5e731db43c7786a12f9f5ab42d52e3"
] | [
"p_utils/utils.py"
] | [
"from __future__ import division\nimport tqdm\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport cv2\n\n\ndef to_cpu(tensor):\n return tensor.detach().cpu()\n\n\ndef prep_image(img, inp_dim):\n \"\"\"\n Prepare image for inputting to the neural network.\n\n Returns a Variable\n \"\"\"\n... | [
[
"numpy.maximum",
"torch.max",
"numpy.unique",
"torch.nn.init.constant_",
"torch.min",
"torch.from_numpy",
"numpy.full",
"numpy.concatenate",
"torch.log",
"torch.nn.init.normal_",
"torch.stack",
"numpy.argsort",
"torch.clamp",
"numpy.array",
"numpy.where"... |
seasker/current | [
"f040cec106e9758d4c3a04a1e9b0a4e384b3c7b4"
] | [
"src/train_yanan.py"
] | [
"import cv2\r\nimport sys\r\nimport time\r\nimport imageio\r\n\r\nimport tensorflow as tf\r\nimport scipy.misc as sm\r\nimport numpy as np\r\nimport scipy.io as sio\r\nimport math\r\n\r\nfrom mcnet import MCNET\r\nfrom utils import *\r\nfrom os import listdir, makedirs, system\r\nfrom os.path import exists\r\nfrom ... | [
[
"tensorflow.device",
"tensorflow.summary.FileWriter",
"numpy.squeeze",
"tensorflow.ConfigProto",
"tensorflow.global_variables_initializer",
"tensorflow.train.AdamOptimizer",
"numpy.mod",
"numpy.array",
"numpy.zeros",
"tensorflow.summary.merge"
]
] |
XingyuGuUCSD/deep-speaker | [
"4f16612af09f414eaabb39eef352af9b14f8a1c8"
] | [
"models_train.py"
] | [
"import logging\nfrom time import time\n\nimport numpy as np\nimport sys\n\nimport constants as c\nfrom librispeech_wav_reader import read_librispeech_structure\nfrom models import convolutional_model\nfrom next_batch import stochastic_mini_batch\nfrom triplet_loss import deep_speaker_loss\nfrom utils import get_la... | [
[
"numpy.reshape",
"numpy.random.uniform"
]
] |
mraabo/Dissertation--Bayesian-Neural-Networks | [
"629b1c5f4bbdb80ef1d1037b4a0a1b7f95ac710b"
] | [
"Python_code/Boston_BNN_1hidden_hiera.py"
] | [
"# # ----------------------------- INFO ---------------------------\n# In this python script we implement and run a BNN for predicting house prices\n# in Boston. The sampler is based on the NUTS sampler\n\n# # ----------------------------- IMPORTS ---------------------------\nimport warnings\nimport tensorflow as t... | [
[
"sklearn.metrics.mean_squared_error",
"numpy.ones",
"numpy.random.randn",
"numpy.insert",
"tensorflow.random.set_seed"
]
] |
robosyn/TensorFlow2.0-Examples | [
"6b71ba04eae5e12cc0390ec48c95baf6a17d5765"
] | [
"4-Object_Detection/YOLOV3/test_model.py"
] | [
"import numpy as np\nimport tensorflow as tf\nimport cv2\nimport os\nimport time\nimport shutil\nimport numpy as np\nimport tensorflow as tf\nimport core.utils as utils\nfrom tqdm import tqdm\nfrom core.dataset import Dataset\nfrom core.yolov3 import YOLOv3, decode, compute_loss\nfrom core.config import cfg\n\n#pri... | [
[
"tensorflow.concat",
"tensorflow.shape",
"numpy.reshape",
"tensorflow.keras.Model",
"tensorflow.keras.layers.Input"
]
] |
mathisme/scikit-learn | [
"8b23a61d87b97ec9445d8b151ce5b2ebc92ce555"
] | [
"sklearn/linear_model/_stochastic_gradient.py"
] | [
"# Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)\n# Mathieu Blondel (partial_fit support)\n#\n# License: BSD 3 clause\n\"\"\"Classification, regression and One-Class SVM using Stochastic Gradient\nDescent (SGD).\n\"\"\"\n\nimport numpy as np\nimport warnings\n\nfrom abc import AB... | [
[
"numpy.unique",
"numpy.asarray",
"numpy.clip",
"numpy.ones",
"numpy.atleast_1d",
"numpy.iinfo",
"numpy.any",
"numpy.array",
"numpy.zeros"
]
] |
alexmalins/actions-ci-test | [
"a83bfc42aecce58a3cef54db2aad46c3f7532b19"
] | [
"tests/test_somecode.py"
] | [
"\"\"\"Unit tests for functions exported from actionscicd package\"\"\"\r\n\r\nimport unittest\r\nimport numpy as np\r\nfrom actionscicd import add_arrays, load_datafile\r\n\r\n\r\nclass TestSomeCode(unittest.TestCase):\r\n \"\"\"Unit tests for functions in somecode.py\"\"\"\r\n\r\n def test_add_arrays(self) ... | [
[
"numpy.array"
]
] |
arthpatel573/google-research | [
"eee881ac0ca58299cf6540618a34fc6f6924d268"
] | [
"tft/libs/tft_model.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... | [
[
"pandas.Series",
"tensorflow.keras.layers.InputLayer",
"numpy.concatenate",
"numpy.ones_like",
"tensorflow.keras.layers.Embedding",
"tensorflow.keras.layers.TimeDistributed",
"tensorflow.keras.layers.Multiply",
"tensorflow.keras.callbacks.EarlyStopping",
"numpy.zeros",
"ten... |
mzgubic/fair-hmumu | [
"dd5caa3bc0bcc459d8500c837cd633333ac741d2"
] | [
"fair_hmumu/configuration.py"
] | [
"import os\nimport ast\nimport itertools\nimport configparser\nimport pandas as pd\nfrom fair_hmumu import utils\n\n\nclass Configuration:\n\n def __init__(self, path):\n\n # path\n self.path = os.path.abspath(path)\n self.name = os.path.basename(self.path)\n self.loc = os.path.dirnam... | [
[
"pandas.DataFrame"
]
] |
lukoucky/swimming_pool_attendance_prediction | [
"016fd81df6fb56019c9a48ec6d904da119e5d3d4",
"016fd81df6fb56019c9a48ec6d904da119e5d3d4"
] | [
"models/tree_models.py",
"models/neural_network_base.py"
] | [
"from sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.ensemble import ExtraTreesRegressor\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error, make_scorer\nfrom data_helper import DataHelper\nimport numpy... | [
[
"sklearn.ensemble.RandomForestRegressor",
"sklearn.ensemble.ExtraTreesClassifier",
"sklearn.ensemble.RandomForestClassifier",
"sklearn.ensemble.ExtraTreesRegressor"
],
[
"tensorflow.device",
"tensorflow.random.set_seed"
]
] |
prkhrv/High-Performance-Python-with-CUDA | [
"5ac07e0ace3cd07f3583af8a3954bb52ac74b6e8"
] | [
"vector_add.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\nimport numpy as np\nimport time\n\nfrom numba import vectorize, cuda\n\n@vectorize(['float32(float32, float32)'], target='cuda')\ndef VectorAdd(a, b):\n return a + b\n\ndef main():\n N = 32000000\n\n A = np.ones(... | [
[
"numpy.ones"
]
] |
mycal-tucker/IC3Net | [
"bd71cc92d3ec5a5bfc12860babdb5a570421021d"
] | [
"nns/probe.py"
] | [
"import torch.nn as nn\n\n\nclass Probe(nn.Module):\n def __init__(self, input_dim, output_dim, hidden_size=64, num_layers=1, dropout_rate=0.8):\n super(Probe, self).__init__()\n self.layers = nn.ModuleList()\n self.out_dim = output_dim\n prev_size = input_dim\n self.dropout1 =... | [
[
"torch.nn.Dropout",
"torch.nn.Linear",
"torch.nn.ModuleList"
]
] |
kumar10725/windpowerlib | [
"865a6b697edbccf815d1e98cad994239b5ccd395"
] | [
"windpowerlib/wind_turbine.py"
] | [
"\"\"\"\nThe ``wind_turbine`` module contains the class WindTurbine that implements\na wind turbine in the windpowerlib and functions needed for the modelling of a\nwind turbine.\n\nSPDX-FileCopyrightText: 2019 oemof developer group <contact@oemof.org>\nSPDX-License-Identifier: MIT\n\"\"\"\nimport pandas as pd\nimp... | [
[
"pandas.reset_option",
"pandas.merge",
"pandas.read_csv",
"pandas.DataFrame"
]
] |
jakevdp/Mmani | [
"681b6cdbd358b207e8b6c4a482262c84bea15bd7"
] | [
"megaman/geometry/tests/test_adjacency.py"
] | [
"# LICENSE: Simplified BSD https://github.com/mmp2/megaman/blob/master/LICENSE\n\nfrom nose import SkipTest\n\nimport numpy as np\nfrom numpy.testing import assert_allclose, assert_raises, assert_equal\nfrom scipy.sparse import isspmatrix\nfrom scipy.spatial.distance import cdist, pdist, squareform\n\nfrom megaman.... | [
[
"scipy.sparse.isspmatrix",
"numpy.arange",
"scipy.spatial.distance.cdist",
"scipy.spatial.distance.pdist",
"numpy.testing.assert_raises",
"numpy.random.rand",
"numpy.random.RandomState"
]
] |
aahmadian-liu/ood-likefree-invertible | [
"977e70eccaa7f2eb09724b5bf6f28156f4940461"
] | [
"Codes/Resflow_Procs/preprocessing/convert_to_pth.py"
] | [
"import sys\nimport re\nimport numpy as np\nimport torch\n\ninfile='celeba_full_64x64_5bit.npy'\nimg = torch.tensor(np.load(infile))\nimg = img.permute(0, 3, 1, 2)\ntorch.save(img, re.sub('.npy$', '.pth', infile))\n"
] | [
[
"numpy.load"
]
] |
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