repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
benmaier/GillEpi | [
"60f76fb25c6548e6886f68b38e0ed7873e4804ff"
] | [
"GillEpi/SIR.py"
] | [
"from __future__ import print_function\nimport random\nimport sys\n\nimport numpy as np\nimport networkx as nx\n\nfrom GillEpi import SIR_node\n\nclass SIR():\n\n def __init__(self,\n G, # (dynamic) network. beware! this is going to change during simulation\n infectio... | [
[
"numpy.concatenate",
"numpy.random.exponential",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JonasStankevicius/ConvPoint_Keras | [
"3850e09ad5c39c892bc031c050b30ea48866a4d8"
] | [
"data.py"
] | [
"from tensorflow.keras.utils import Sequence\nimport tensorflow as tf\nfrom imports import np\nfrom PIL import Image, ImageEnhance, ImageOps\nfrom dataTool import *\nfrom configs import Config\nfrom configs.SDE import SDE\nfrom configs.NMG import NMG\n\nclass TrainSequence(Sequence):\n def __init__(self, iterati... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
adamklaff/pyEX | [
"74a4cfa5978ccff95261aeb54f526dedc579aa6b"
] | [
"pyEX/stocks/keyStats.py"
] | [
"# *****************************************************************************\n#\n# Copyright (c) 2020, the pyEX authors.\n#\n# This file is part of the pyEX library, distributed under the terms of\n# the Apache License 2.0. The full license can be found in the LICENSE file.\n#\nfrom functools import wraps\n\ni... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
creotiv/hdrnet | [
"e5c00f11b8ee9afe8444014ce682e6c997df7003"
] | [
"hdrnet/hdrnet_ops.py"
] | [
"# Copyright 2016 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.python.framework.ops.RegisterShape",
"tensorflow.load_op_library",
"tensorflow.python.framework.ops.RegisterGradient"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
u201815044/AutoX | [
"febbd718ead54dfef0cea15bba9f6562b4af62f3"
] | [
"autox/feature_engineer/fe_diff.py"
] | [
"import pandas as pd\nfrom ..CONST import FEATURE_TYPE\nfrom autox.process_data import Feature_type_recognition\nfrom tqdm import tqdm\n\nclass FeatureDiff:\n def __init__(self):\n self.target = None\n self.df_feature_type = None\n self.silence_group_cols = []\n self.silence_agg_cols ... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jlimmm/distiller | [
"e82d938077f9c5abc81b79cfadf245f44fabe0ba"
] | [
"examples/object_detection_compression/compress_detector.py"
] | [
"# This code is originally from:\n# https://github.com/pytorch/vision/tree/v0.4.2/references/detection/train.py\n# It contains code to support compression (distiller)\nr\"\"\"PyTorch Detection Training.\n\nTo run in a multi-gpu environment, use the distributed launcher::\n\n python -m torch.distributed.launch ... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.utils.data.distributed.DistributedSampler",
"torch.load",
"torch.utils.data.SequentialSampler",
"torch.utils.data.DataLoader",
"torch.utils.data.RandomSampler",
"torch.distributed.barrier",
"torch.nn.parallel.DistributedDataParallel",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YashNita/Bird_Sound_Classification_Thesis- | [
"96993d0813f8279a78fa82f2861cd6ea94b603aa"
] | [
"scripts/split_data.py"
] | [
"import glob\nimport os\nimport tqdm\nimport numpy as np\nimport shutil\nfrom bird import loader as l\n\nsource_dir = \"/disk/martinsson-spring17/birdClef2016Subset\"\nclasses = os.listdir(os.path.join(source_dir, \"train\"))\n\npercentage_validation_sampels = 0.10\n\nprogress = tqdm.tqdm(range(len(classes)))\nclas... | [
[
"numpy.ceil"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Shivam9091/GamestonkTerminal | [
"0368a3b25ab574d3e19ddbddaab0128716dbe61b"
] | [
"gamestonk_terminal/common/prediction_techniques/neural_networks_view.py"
] | [
"\"\"\" Neural Networks View\"\"\"\n__docformat__ = \"numpy\"\n\nfrom typing import List, Any\nimport traceback\nimport numpy as np\nimport pandas as pd\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import (\n LSTM,\n Simple... | [
[
"tensorflow.keras.layers.MaxPool1D",
"tensorflow.keras.layers.SimpleRNN",
"tensorflow.keras.layers.Dense",
"numpy.median",
"tensorflow.keras.layers.Conv1D",
"pandas.DataFrame",
"tensorflow.keras.layers.LSTM",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras.layers.AvgPool1D... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
... |
droftware/homework | [
"03c2f4f9b61ad04fc343f8561d7253733c22f541"
] | [
"hw2/train_pg_f18.py"
] | [
"\"\"\"\nOriginal code from John Schulman for CS294 Deep Reinforcement Learning Spring 2017\nAdapted for CS294-112 Fall 2017 by Abhishek Gupta and Joshua Achiam\nAdapted for CS294-112 Fall 2018 by Michael Chang and Soroush Nasiriany\n\"\"\"\nimport numpy as np\nimport tensorflow as tf\nimport gym\nimport logz\nimpo... | [
[
"tensorflow.get_variable",
"tensorflow.nn.log_softmax",
"tensorflow.reduce_sum",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"tensorflow.train.AdamOptimizer",
"tensorflow.layers.dense",
"tensorflow.ConfigProto",
"numpy.std",
"tensorflow.Session",
"numpy.min",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
MosyMosy/StylishTENT | [
"aa59f13e0cbd8ee7021093cac63a91207678ec6a"
] | [
"tent.py"
] | [
"from copy import deepcopy\n\nimport torch\nimport torch.nn as nn\nimport torch.jit\n\n\nclass Tent(nn.Module):\n \"\"\"Tent adapts a model by entropy minimization during testing.\n\n Once tented, a model adapts itself by updating on every forward.\n \"\"\"\n def __init__(self, model, optimizer, steps=1... | [
[
"torch.enable_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kazuhito00/landmarks-classifier-asia-onnx-sample | [
"f11c1868563cd552a22ee085b7622bf0f2591ebc"
] | [
"sample_onnx.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport copy\nimport time\nimport argparse\n\nimport cv2 as cv\nimport numpy as np\nimport pandas as pd\nimport onnxruntime\n\n\ndef run_inference(onnx_session, input_size, image):\n # Pre process:Resize, expand dimensions, float32 cast\n input_image = cv.resize... | [
[
"numpy.argsort",
"numpy.expand_dims",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
almath123/pystruct | [
"f08743d85b86447a55eb361b35726a3f3266ee4e"
] | [
"setup.py"
] | [
"from setuptools import setup\nfrom setuptools.extension import Extension\nimport numpy as np\n\nimport os\n\nif os.path.exists('MANIFEST'):\n os.remove('MANIFEST')\n\nsetup(name=\"pystruct\",\n version=\"0.2.5\",\n install_requires=[\"ad3\"],\n packages=['pystruct', 'pystruct.learners', 'pystruct... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jamesvuc/jax-bayes | [
"b91432cbf39dc0faebd1879a021fb2939d6072da"
] | [
"examples/deep/nn_regression/mlp_regression_mcmc.py"
] | [
"\n\nimport numpy as np\nnp.random.seed(0)\n\nimport haiku as hk\n\nimport jax.numpy as jnp\nfrom jax.experimental import optimizers\nimport jax\n\nfrom tqdm import tqdm, trange\nfrom matplotlib import pyplot as plt\n\nfrom jax_bayes.utils import confidence_bands\nfrom jax_bayes.mcmc import (\n\t# langevin_fns,\n\t... | [
[
"numpy.log",
"numpy.random.seed",
"numpy.linspace",
"matplotlib.pyplot.subplots",
"numpy.sin",
"numpy.mean",
"numpy.random.randn",
"numpy.random.rand",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dreaquil/anomalib | [
"0199f05e09a67967c8512a923059ae0105f849a2",
"0199f05e09a67967c8512a923059ae0105f849a2"
] | [
"tests/pre_merge/utils/callbacks/visualizer_callback/dummy_lightning_model.py",
"anomalib/data/btech.py"
] | [
"from pathlib import Path\nfrom typing import Union\n\nimport pytorch_lightning as pl\nimport torch\nfrom omegaconf.dictconfig import DictConfig\nfrom omegaconf.listconfig import ListConfig\nfrom torch import nn\nfrom torch.utils.data import DataLoader, Dataset\n\nfrom anomalib.models.components import AnomalyModul... | [
[
"torch.ones",
"torch.rand",
"torch.Tensor",
"torch.zeros"
],
[
"torch.utils.data.DataLoader",
"numpy.zeros",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"... |
dnandha/iGibson | [
"bbd8c294aad1ddffce868244a474dd40c2976590"
] | [
"gibson2/utils/motion_planning_wrapper.py"
] | [
"import gibson2\nfrom gibson2.envs.igibson_env import iGibsonEnv\nfrom time import time, sleep\nimport os\nfrom gibson2.utils.assets_utils import download_assets, download_demo_data\nimport numpy as np\nfrom gibson2.external.pybullet_tools.utils import control_joints\nfrom gibson2.external.pybullet_tools.utils impo... | [
[
"numpy.min",
"numpy.asarray",
"numpy.arange",
"numpy.arctan2",
"numpy.max",
"numpy.append",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kuttim/followers-growth | [
"65f08b55b5ea31a893b4a3d0c7edd35790fefacc"
] | [
"scripts/clean_csv.py"
] | [
"import pandas as pd\n\ndata = pd.read_csv(\"data/2016.csv\")\n\ndf = pd.DataFrame(data)\n\ndf = df.replace('[Mæ(=)]', '', regex=True)\nprint(df)\n\ndf.to_csv(\"data/2022.csv\", index=False)\n"
] | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
jina-ai/bert-as-service | [
"4b88e99263a29903312f52bae01465b44b7a0cce"
] | [
"scripts/benchmark.py"
] | [
"import random\nimport time\nfrom typing import Optional\nimport threading\nimport click\nimport numpy as np\nfrom docarray import Document, DocumentArray\n\n\ndef warn(*args, **kwargs):\n pass\n\n\nimport warnings\n\nwarnings.warn = warn\n\n\nnp.random.seed(123)\n\n\nclass BenchmarkClient(threading.Thread):\n ... | [
[
"numpy.mean",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
henghuiding/BFP | [
"98f54bafb4387bd0ca989ca0fb1be6455009aec6"
] | [
"utils/datasets/camvid.py"
] | [
"###########################################################################\r\n# Created by: NTU EEE\r\n# Email: ding0093@e.ntu.edu.sg\r\n# Copyright (c) 2019\r\n###########################################################################\r\n\r\nimport os\r\nimport sys\r\nimport numpy as np\r\nimport random\r\nimpo... | [
[
"numpy.array",
"torch.from_numpy",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cakkaya/pytext | [
"5869b839411556dbbed85aef0b5b098336e56657"
] | [
"pytext/exporters/exporter.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nfrom typing import Callable, Dict, List, Tuple, Union\n\nimport torch\nfrom caffe2.python import core\nfrom caffe2.python.onnx.backend_rep import Caffe2Rep\nfrom pytext.config import ConfigBase\nfrom pytext.config.com... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
theferrit32/dataset-partitioning | [
"5c29437f33da79aaf54ee1644e3a57f49a83f002"
] | [
"dataset_partitioning/merge_parquet.py"
] | [
"import os, sys, gc\nimport pandas as pd\nimport logging\nimport binascii\n\nfrom multiprocessing import Pool\n\n# logging.basicConfig(level=logging.DEBUG)\nfrom dataset_partitioning import logging_util\nlogger = logging_util.get_logger(__name__)\n\ndef rand_string(length):\n return binascii.hexlify(os.urandom(i... | [
[
"pandas.read_parquet"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"0.24",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
TarentoTechnologies/FaceRecognitionBasedAttendance | [
"2d33f9a466b3a0cef83d1c63dc2a0087a905814a"
] | [
"scripts/dataset/rotate.py"
] | [
"from scipy.ndimage import rotate\nfrom scipy.misc import imread, imshow\n\nimg = imread('John Doe.jpg') # example\n\nrotate_img = rotate(img, 90)\n\n"
] | [
[
"scipy.misc.imread",
"scipy.ndimage.rotate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.10",
"0.16",
"0.19",
"0.18",
"0.12",
"1.0",
"0.17",
"1.2"
],
"tensorflow": []
}
] |
Tiamat-Tech/just-ask | [
"80725161e12ad0682b4c2091f61a5889a335ba21"
] | [
"preproc/preproc_activitynetqa.py"
] | [
"import json\r\nimport os\r\nimport collections\r\nimport pandas as pd\r\n\r\nfrom global_parameters import ACT_PATH\r\n\r\nos.chdir(ACT_PATH)\r\n\r\ntrain_q = json.load(open(\"train_q.json\", \"r\"))\r\nval_q = json.load(open(\"val_q.json\", \"r\"))\r\ntest_q = json.load(open(\"test_q.json\", \"r\"))\r\n\r\ntrain_... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jpolchlo/raster-vision | [
"8aa9601d168de2c6bbc00d7f5ba2b70f1d5f7f13"
] | [
"rastervision2/pytorch_learner/regression_learner.py"
] | [
"import warnings\nwarnings.filterwarnings('ignore') # noqa\nfrom os.path import join, isdir\nimport csv\n\nimport matplotlib\nmatplotlib.use('Agg') # noqa\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\n\nimport torch\nfrom torchvision import models\nimport torch.nn as nn\nimport torch.n... | [
[
"torch.abs",
"torch.nn.functional.l1_loss",
"torch.cat",
"matplotlib.use",
"matplotlib.pyplot.savefig",
"torch.tensor",
"torch.nn.Linear",
"torch.utils.data.ConcatDataset",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DSouzaM/mesh-testsuite | [
"8fb4a0a97a81183f0920e55042ea96c4a994ed27"
] | [
"original_analysis/3-ruby.py"
] | [
"#!/usr/bin/env python3\n\nimport argparse\nimport sys\nimport os\nimport csv\nimport numpy\n\nfrom os import path\nfrom collections import defaultdict\n\n\nBASE_DIR = 'results/3-ruby'\nMEMORY_DIR = path.join(BASE_DIR, 'memory')\nSPEED_DIR = path.join(BASE_DIR, 'speed')\n\nTEST_END = 7.5 # seconds\n\n\ndef slurp(fi... | [
[
"numpy.std",
"numpy.median",
"numpy.array",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mcsosa121/KSRFILS | [
"75995933771d8338de33cc9bbb5e9416e4242c6b"
] | [
"experiment4_plot.py"
] | [
"import os\r\nimport numpy\r\nimport matplotlib.pyplot as plt\r\nimport pickle\r\nfrom scipy import linalg,io,sparse\r\nfrom scipy.interpolate import UnivariateSpline\r\n\r\ndef plot_with_trend(x,y,marker,label,color,deg):\r\n #trendline is not helpful, removed.\r\n plt.plot(x,y,marker=marker,label=label,colo... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.title",
"matplotlib.pyplot.yscale",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.xscale",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Koukyosyumei/secure_ml | [
"1242148e0686d0374795f99143fcb0a8f34f71f2"
] | [
"secure_ml/defense/purifier.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Purifier_Cifar10(nn.Module):\n \"\"\" autoencoder for purification on Cifar10\n reference https://arxiv.org/abs/2005.03915\n \"\"\"\n\n def __init__(self):\n super(Purifier_Cifar10, self).__init__()\n self... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.CrossEntropyLoss",
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.nn.MSELoss",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mwmarkland/Eleven_Line_Neural_Network | [
"53bf8239c465013f0fc9d8bdd8d72127a208d7a7"
] | [
"original.py"
] | [
"# This is the original eleven line neural network code\n# from the website/blog article.\n\n# Looking at wikipedia, there is a lot of details hiding here that can be filled in. This is an example of a simple \"backpropagation\" network \n\n# Not exactly a copy/paste as I'm going to annotate the\n# code with commen... | [
[
"numpy.dot",
"numpy.random.random",
"numpy.random.seed",
"numpy.array",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CharvyJain/Rotten-Scripts | [
"c44e69754bbecb8a547fe2cc3a29be5acf97c46a",
"c44e69754bbecb8a547fe2cc3a29be5acf97c46a"
] | [
"Python/Linkedin_post_emails_scrapper/app.py",
"Python/Generate_Fake_Data/generate_fake_data.py"
] | [
"from selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nimport selenium\nimport time \nimport re\nimport pandas as pd\nimport os\nimport sys\n\n\ndef scroll_down(driver):\... | [
[
"pandas.DataFrame"
],
[
"numpy.savez",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
CancerHenry/TA_Winrate | [
"bc9ee0d6ef9ad3559ab2ac7360f400d08a375f30"
] | [
"test.py"
] | [
"import pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv('MA_result.csv')\nslow = df['best_revenue_slow']\nfast = df['best_revenue_fast']\n\nplt.plot(slow, fast, 'o')\n\nplt.show()\n"
] | [
[
"matplotlib.pyplot.plot",
"pandas.read_csv",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dave-lab41/pelops | [
"292af80dba190f9506519c8e13432fef648a2291",
"292af80dba190f9506519c8e13432fef648a2291",
"292af80dba190f9506519c8e13432fef648a2291"
] | [
"etl/makeFeaturesResNet50.py",
"pelops/analysis/comparecameras.py",
"pelops/datasets/featuredataset.py"
] | [
"# coding: utf-8\nimport json\nimport os\nimport sys\nimport time\n\nimport numpy as np\nimport scipy.spatial.distance\nfrom keras.applications.resnet50 import ResNet50, preprocess_input\nfrom keras.models import Model\nfrom keras.preprocessing import image\n\n\ndef load_image(img_path):\n data = image.load_img(... | [
[
"numpy.expand_dims",
"numpy.zeros"
],
[
"numpy.array"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Babars7/SDPS-Net | [
"ea96c6933485c5a50e4151179b6d2fea898b1898"
] | [
"models/solver_utils.py"
] | [
"import torch\nimport os\nfrom utils import eval_utils\nfrom utils.losses import LabelSmoothingCrossEntropy\n\nclass Stage1ClsCrit(object): # First Stage, Light classification criterion\n def __init__(self, args):\n print('==> Using Stage1ClsCrit for lighting classification')\n self.s1_est_d = args... | [
[
"torch.optim.lr_scheduler.MultiStepLR",
"torch.optim.Adam",
"torch.load",
"torch.split",
"torch.nn.CosineEmbeddingLoss",
"torch.optim.SGD",
"torch.nn.MSELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
trungthanhnguyen0502/Text-to-speech-VLSP | [
"0fae144bd731995ddb2f70ebf7c0b97bfb5c5b37"
] | [
"tacotron2/waveglow/denoiser.py"
] | [
"import sys\nsys.path.append('tacotron2')\nimport torch\nfrom layers import STFT\n\n\nclass Denoiser(torch.nn.Module):\n \"\"\" Removes model bias from audio produced with waveglow \"\"\"\n\n def __init__(self, waveglow, is_cuda=True, filter_length=1024, n_overlap=4,\n win_length=1024, mode='z... | [
[
"torch.randn",
"torch.clamp",
"torch.no_grad",
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
computergeek125/ffxiv-fc-stats | [
"e4da027c5ffb5dee086a3313d8498d1b49b18330"
] | [
"ffxiv-fc-stats.py"
] | [
"#!/usr/bin/env python3\r\n\r\n# Written by computergeek125\r\n# Originally created 02 AUG 2019\r\n# See attached LICENSE file\r\n\r\nimport aiohttp\r\nimport argparse\r\nimport asyncio\r\nimport json\r\nimport logging\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\nfrom pprint import pprint\r\nimport ra... | [
[
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.show",
"matplotlib.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cmmclaug/Workflows_showcase | [
"27ba8f3c1c6ce101edc2fcaa313211a43ddf1fb3"
] | [
"src/generate_eda.py"
] | [
"\"\"\"\nAuthor: Zeliha Ural Merpez\n\nDate: Nov 25, 2020\n\nThis script reads combined data and generates images and tables to be used in further analysis.\n\nUsage: generate_eda.py -i=<input> -o=<output> [-v]\n\nOptions:\n-i <input>, --input <input> Local raw data csv filename and path\n-o <output>, --output ... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
JOCh1958/openvino | [
"070201feeec5550b7cf8ec5a0ffd72dc879750be",
"070201feeec5550b7cf8ec5a0ffd72dc879750be",
"070201feeec5550b7cf8ec5a0ffd72dc879750be"
] | [
"model-optimizer/extensions/middle/ONNXResize11ToInterpolate.py",
"inference-engine/tools/cross_check_tool/utils.py",
"model-optimizer/extensions/front/sub_test.py"
] | [
"# Copyright (C) 2018-2021 Intel Corporation\n# SPDX-License-Identifier: Apache-2.0\n\nimport logging as log\n\nimport numpy as np\n\nfrom extensions.ops.Cast import Cast\nfrom extensions.ops.activation_ops import Floor\nfrom extensions.ops.elementwise import Add, Div, Mul\nfrom extensions.ops.interpolate import In... | [
[
"numpy.arange"
],
[
"numpy.absolute",
"numpy.abs",
"numpy.min",
"numpy.reshape",
"numpy.isnan",
"numpy.max",
"numpy.savez_compressed",
"numpy.random.normal",
"numpy.count_nonzero",
"numpy.load",
"numpy.isinf"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sameerkhurana10/DSOL_rv0.2 | [
"9cc5da72f1cf3f46350470a06a4d04abbf171f32"
] | [
"gpu_test.py"
] | [
"from theano import function, config, shared, tensor\nimport numpy\nimport time\n\nvlen = 10 * 30 * 768 # 10 x #cores x # threads per core\niters = 1000\n\nrng = numpy.random.RandomState(22)\nx = shared(numpy.asarray(rng.rand(vlen), config.floatX))\nf = function([], tensor.exp(x))\nprint(f.maker.fgraph.toposort())... | [
[
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
reinforcement-learning-kr/minecraft | [
"f48c3cefe17cbea555994d55140982e1ffa0b4dc"
] | [
"mdqn/minecraft_env/env.py"
] | [
"import logging\nimport time\nimport numpy as np\nimport json\nimport xml.etree.ElementTree as ET\nimport gym\nfrom gym import spaces, error\nfrom minecraft_env.eating_env import *\n\nreshape = False\n\ntry:\n import malmo.MalmoPython as MalmoPython\nexcept ImportError as e:\n err = e\n try:\n impor... | [
[
"numpy.frombuffer",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ESMValGroup/ESMValTool | [
"0d983b1e694157474801e71d33e5e9c1c33426bf"
] | [
"esmvaltool/diag_scripts/ocean/diagnostic_model_vs_obs.py"
] | [
"\"\"\"\nModel vs Observations maps Diagnostic.\n======================================\n\nDiagnostic to produce comparison of model and data.\nThe first kind of image shows four maps and the other shows a scatter plot.\n\nThe four pane image is a latitude vs longitude figures showing:\n\n* Top left: model\n* Top r... | [
[
"numpy.linspace",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.ma.array",
"numpy.ma.masked_where",
"matplotlib.pyplot.gca",
"numpy.arange",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
"matplotlib.p... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
Matrixeigs/EnergyManagementSourceCodes | [
"1ea824941fe87528622ec7aa8148024752a3947c",
"1ea824941fe87528622ec7aa8148024752a3947c"
] | [
"distribution_system_optimization/optimal_power_flow/optimal_power_flow_ADMM_centralized.py",
"unit_commitment/test_cases/profiles.py"
] | [
"\"\"\"\nADMM based distributed optimal power flow\nThe power flow modelling is based on the branch power flow\nThe purpose is to introduce slack variables to formulate a decentralized optimization problem.\nThe centralized model (24a)\nReferences:\n [1]Peng, Qiuyu, and Steven H. Low. \"Distributed optimal power... | [
[
"numpy.shape",
"numpy.where",
"numpy.ones"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
msHujindou/Tetris-DQN | [
"8d8f83b151262dd2c392f8d247b1cee76c0bf0fd",
"8d8f83b151262dd2c392f8d247b1cee76c0bf0fd"
] | [
"src/train/train_dqn9_img.py",
"src/train/train_dqn9_fc.py"
] | [
"\"\"\"\r\n基于train_dqn9，添加如下更改：\r\n1. 训练由state转换成灰色图片\r\n2. 局面由 20x10 变为 21x10\r\n3. 因为内存有限，batch设置成2048就能导致16G机器崩溃\r\n4. 因为内存有限，每个进程的局数也没法设置高\r\n\r\n将episode设置成200000，训练了160+小时，仍然没有出结果，改成20000\r\n将MAX_Batch_Size设置成128，会出现内存不够错误\r\n\r\nRun 102 test_1626833346_3eac8b9c 结果如下：\r\nepisode设置成10000需要的训练时间为75小时，训练出来的模型对识别... | [
[
"torch.nn.SmoothL1Loss",
"torch.tensor",
"torch.from_numpy",
"torch.cat"
],
[
"torch.nn.SmoothL1Loss",
"torch.tensor",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
crest-cassia/caravan | [
"0a8e606e31d2d36a9379bdc00fafe55cf9144da6"
] | [
"samples/sensitivity_analysis/analyze.py"
] | [
"import sys\nfrom caravan import Tables,Task\nfrom SALib.analyze import sobol\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\n\nif len(sys.argv) != 2:\n print(\"Usage: python analyze.py tasks.pickle\")\n raise Exception(\"invalid usage\")\n\nproblem = {\n '... | [
[
"matplotlib.use",
"numpy.arange",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.bar",
"matplotlib.pyplot.xticks",
"numpy.array",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SankaW/teamfx | [
"88d4a6295b4c5e050fbb96c23d632097956e5cf8",
"88d4a6295b4c5e050fbb96c23d632097956e5cf8"
] | [
"anomalies/local_outlier_factor_reducer.py",
"backtesting/backtester/Strategies/MACD/MACD.py"
] | [
"from flask import Flask,redirect, url_for, request\nimport pandas as pd\nimport os\nimport gc\nimport anomalies.config as config\n\ndef detect_lof_reducer():\n\n if os.path.exists(\"static/anomalies/local_outlier_factors/\"+request.form[\"currency\"] + '_' + request.form[\"year\"]+\"_merged_local_outlier_factor... | [
[
"pandas.read_csv"
],
[
"pandas.ewma",
"numpy.where",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.21",
"0.20",
"0.19"
],
"scipy": [],
"ten... |
lanpartis/tianshou | [
"7be1b183c4c1df26ad0e63218836aac201db48ea"
] | [
"examples/box2d/bipedal_hardcore_sac.py"
] | [
"import os\nimport gym\nimport torch\nimport pprint\nimport argparse\nimport numpy as np\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom tianshou.env import SubprocVectorEnv\nfrom tianshou.trainer import offpolicy_trainer\nfrom tianshou.data import Collector, ReplayBuffer\nfrom tianshou.policy import SAC... | [
[
"torch.optim.Adam",
"numpy.random.random",
"numpy.random.seed",
"torch.zeros",
"torch.load",
"torch.manual_seed",
"torch.utils.tensorboard.SummaryWriter",
"numpy.prod",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
G-Thomson/DAJIN | [
"e702f465c015da33fabcfc43213f346acd7e0415"
] | [
"src/ml_simulated.py"
] | [
"import os\n\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\nos.environ[\"TF_FORCE_GPU_ALLOW_GROWTH\"] = \"true\"\n\nimport sys\nimport random as rn\nimport numpy as np\nimport pandas as pd\n\nimport pickle\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder, Label... | [
[
"pandas.read_csv",
"numpy.random.seed",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv1D",
"pandas.factorize",
"tensorflow.keras.layers.MaxPooling1D",
"numpy.save",
"sklearn.neighbors.LocalOutlierFactor",
"sklearn.preprocessi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
Atlas200dk/sample_bodypose | [
"148000d84a2c51fea9954174cb9a10b1e740ae73"
] | [
"atlas_utils/acl_image.py"
] | [
"import numpy as np\nfrom PIL import Image\nimport copy\n\nimport acl\n#from utils import *\nfrom atlas_utils.constants import *\n\n\nclass AclImage():\n def __init__(self, image, width=0, height=0,\n size=0, memory_type=MEMORY_NORMAL): \n self._data = None\n self._np_array = Non... | [
[
"numpy.fromfile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
humanattn/humanattn2022 | [
"1ccf8aa03ad42f692bf840925f6e0e20268a4a1c"
] | [
"models/attendgru_bio_base.py"
] | [
"from tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Input, Dense, Embedding, Reshape, GRU, LSTM, Dropout, BatchNormalization, Activation, concatenate, multiply, MaxPooling1D, Conv1D, Flatten, Bidirectional, RepeatVector, Permute, TimeDistributed, dot, Lambda\nfrom tensorflow.keras.optimi... | [
[
"tensorflow.keras.layers.Activation",
"tensorflow.keras.models.Model",
"tensorflow.keras.layers.Embedding",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.concatenate",
"tensorflow.keras.layers.GRU",
"tensorflow.keras.layers.dot",
"tensorflow.keras.layers.Flatten",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
lzhnb/gorillatorch | [
"53a97c6fff3007c74f275204b5cae5d526300220",
"53a97c6fff3007c74f275204b5cae5d526300220"
] | [
"gorilla/ops/roi_align/roi_align.py",
"gorilla/ops/dcn/deform_conv.py"
] | [
"from torch import nn\nfrom torch.autograd import Function\nfrom torch.autograd.function import once_differentiable\nfrom torch.nn.modules.utils import _pair\n\n# TODO fix import error\n# from . import roi_align_ext\n\n\nclass RoIAlignFunction(Function):\n\n @staticmethod\n def forward(ctx,\n f... | [
[
"torch.nn.modules.utils._pair"
],
[
"torch.sigmoid",
"torch.nn.modules.utils._single",
"torch.Tensor",
"torch.cat",
"torch.zeros_like",
"torch.nn.modules.utils._pair",
"torch.chunk",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CaptainEven/MOTEvaluate | [
"4d9ea6b230cc7898cf177e256508b4d030acfe94"
] | [
"utils/measurements.py"
] | [
"\"\"\"\n2D MOT2016 Evaluation Toolkit\nAn python reimplementation of toolkit in\n2DMOT16(https://motchallenge.net/data/MOT16/)\n\nThis file lists the matching algorithms.\n1. clear_mot_hungarian: Compute CLEAR_MOT metrics\n\n- Bernardin, Keni, and Rainer Stiefelhagen. \"Evaluating multiple object\ntracking perform... | [
[
"numpy.unique",
"numpy.ones",
"scipy.optimize.linear_sum_assignment",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.4",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
"1.8"
],
"tensorflow": []
}
] |
AllanYangZhou/mj_envs | [
"0539d5c92a900d1cc732c0825aef1d2f2caa5aa2"
] | [
"mj_envs/hand_manipulation_suite/relocate_v0.py"
] | [
"import numpy as np\nfrom gym import utils\nfrom mjrl.envs import mujoco_env\nfrom mujoco_py import MjViewer\nimport os\n\nADD_BONUS_REWARDS = True\n\nclass RelocateEnvV0(mujoco_env.MujocoEnv, utils.EzPickle):\n def __init__(self):\n self.target_obj_sid = 0\n self.S_grasp_sid = 0\n self.obj_... | [
[
"numpy.ones_like",
"numpy.clip",
"numpy.linalg.norm",
"numpy.concatenate",
"numpy.mean",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cliffrwong/quality_estimation | [
"e2c65afa194f8d07fd3c9e2268de09a0e7016a5e"
] | [
"paradet/paradet.py"
] | [
"import math\nimport os\nimport random\nimport sys\nimport time\nimport logging\n\nimport numpy as np\nimport scipy.spatial.distance\nimport tensorflow as tf\nimport pickle\n\nfrom tensorflow.python import debug as tf_debug\nimport data_utils\nimport paradet_model\nfrom tensorflow.python.training import saver as sa... | [
[
"tensorflow.train.get_checkpoint_state",
"tensorflow.summary.FileWriter",
"tensorflow.gfile.GFile",
"numpy.set_printoptions",
"tensorflow.app.flags.DEFINE_integer",
"numpy.random.random_sample",
"tensorflow.global_variables_initializer",
"tensorflow.Summary.Value",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
XieResearchGroup/PLANS | [
"479e97f5944dcc036d5f4204890a371ebafb394a"
] | [
"src/models/hmlc.py"
] | [
"import tensorflow as tf\nfrom tensorflow import math as tfm\nfrom tensorflow.keras import Model\nfrom tensorflow.keras.layers import Dense, Add, Dropout\n\n\nclass HMLC(Model):\n def __init__(\n self, fp_len=2048, l1_len=1, l2_len=3, l3_len=5, beta=0.5, drop_rate=0.3\n ):\n super(HMLC, self).__... | [
[
"tensorflow.clip_by_value",
"tensorflow.multiply",
"tensorflow.concat",
"tensorflow.reduce_mean",
"tensorflow.greater",
"tensorflow.keras.layers.Dense",
"tensorflow.less",
"tensorflow.ones_like",
"tensorflow.expand_dims",
"tensorflow.keras.layers.Add",
"tensorflow.math.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
aria-systems-group/CROWN-Robustness-Certification | [
"f8686bcbad98865ff09c9321093cd3c984091323"
] | [
"general_fit.py"
] | [
"##\n## Copyright (C) IBM Corp, 2018\n## Copyright (C) Huan Zhang <huan@huan-zhang.com>, 2018\n## Copyright (C) Tsui-Wei Weng <twweng@mit.edu>, 2018\n## \n## This program is licenced under the Apache-2.0 licence,\n## contained in the LICENCE file in this directory.\n##\n\nimport numpy as np\nfrom numba import jit\... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.tight_layout",
"numpy.ones_like",
"numpy.cosh",
"numpy.arctan",
"numpy.linspace",
"matplotlib.pyplot.locator_params",
"numpy.empty_like",
"matplotlib.pyplot.ylim",
"numpy.abs",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
souravrs999/Speech-emotion-recognition | [
"5784926411f19fb2cff6b06a920210ddbb2bdcb2",
"5784926411f19fb2cff6b06a920210ddbb2bdcb2"
] | [
"Raw_files/random_forest.py",
"Raw_files/data_preprocessing.py"
] | [
"# Random Forest Classifier\n\n\nimport os\nimport joblib\n\n# Loading saved models\n\nX = joblib.load('C:/Users/SOURAV R S/Desktop/Emotion-Classification-Ravdess/features/Xsmall.joblib')\ny = joblib.load('C:/Users/SOURAV R S/Desktop/Emotion-Classification-Ravdess/features/ysmall.joblib')\n\n\nprint('-----Random Fo... | [
[
"sklearn.metrics.classification_report",
"sklearn.model_selection.train_test_split",
"sklearn.ensemble.RandomForestClassifier"
],
[
"numpy.asarray",
"matplotlib.pyplot.show",
"numpy.concatenate",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andrea-ortalda/CarND-Traffic-Sign-Classifier-Project | [
"860000fb9d16a11745f5fe789b16df3e8b068e89"
] | [
"utils/multilayer_perceptron.py"
] | [
"import numpy as np\n\ndef sigmoid(x):\n \"\"\"\n Calculate sigmoid\n \"\"\"\n return 1/(1+np.exp(-x))\n\n# Network size\nN_input = 4\nN_hidden = 3\nN_output = 2\n\nnp.random.seed(42)\n# Make some fake data\nX = np.random.randn(4)\n\nweights_input_to_hidden = np.random.normal(0, scale=0.1, size=(N_input... | [
[
"numpy.dot",
"numpy.random.seed",
"numpy.random.normal",
"numpy.random.randn",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZhanqiZhang66/dcgan_code | [
"46d2a850c86ec6cba53247950c69b9e0aefc6b09"
] | [
"scratch_1.py"
] | [
"import sys\nsys.path.append('..')\nimport tensorflow as tf\nimport numpy as np\nimport theano\nimport theano.tensor as T\n#from theano.sandbox.cuda.dnn import dnn_conv\n\n# from lib import costs\n# from lib import inits\n# from lib import updates\n# from lib import activations\n# from lib.vis import color_grid_vis... | [
[
"torch.nn.ConvTranspose2d",
"torch.from_numpy",
"torch.nn.Tanh",
"torch.nn.Linear",
"torch.FloatTensor",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"sklearn.externals.joblib.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wengrx/GRET | [
"416d688b89ded263bdc63c93b67f05677981455d"
] | [
"src/cn.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\nx= np.random.randn(4,5,20)\ninput = tf.constant(x)\nx = tf.cast(input,'float32')\ncon = tf.get_variable(\"weight\",[20, 10])\n\n\nz=tf.dot(x,con)\n# z=tf.nn.conv2d(tf.cast(input,'float32'),con,strides=[1,1,1,1],padding=\"VALID\")\n\nsess=tf.Session()\nsess.run(tf.glob... | [
[
"tensorflow.get_variable",
"tensorflow.constant",
"tensorflow.cast",
"tensorflow.global_variables_initializer",
"numpy.random.randn",
"tensorflow.dot",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
proteus1991/RawVSR | [
"56686859498a07c83fde191fa1fc109d7aafb3da"
] | [
"models/color_correction.py"
] | [
"\"\"\"\npaper: Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference\nfile: color_correction.py\nauthor: Xiaohong Liu\ndate: 17/09/19\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom blocks import ChannelAttention\n\n\nclass RawVSR(nn.Module):\n ... | [
[
"torch.nn.Conv2d",
"torch.cat",
"torch.nn.ConvTranspose2d",
"torch.nn.functional.interpolate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hyanwong/pyslim | [
"7203b743e30e330729a73fa9b23f971565095202"
] | [
"tests/test_tree_sequence.py"
] | [
"\"\"\"\nTest cases for tree sequences.\n\"\"\"\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport tests\nimport unittest\nimport random\nimport os\nimport numpy as np\nimport pytest\nimport tskit\nimport msprime\nimport pyslim\n\n\nclass TestSlimTreeSequence(tests.PyslimTestCase):\n\... | [
[
"numpy.array_equal",
"numpy.isnan",
"numpy.arange",
"numpy.repeat",
"numpy.array",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
frc1678/server-2018 | [
"a400b9b3f4c0932f993a08716b98904f317b1fe8"
] | [
"SPR.py"
] | [
"#Last Updated: 2/9/18\nimport utils\nimport Math\nimport random\nimport numpy as np\nimport scipy.stats as stats\nimport CSVExporter\nimport pprint\nimport firebaseCommunicator\nimport json\n\npbc = firebaseCommunicator.PyrebaseCommunicator()\n\n#Scout Performance Analysis\nclass ScoutPrecision(object):\n\t'''Scor... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
daytonb/pandas | [
"a0d92b65614629086ae15ed9af703a8b2494bece"
] | [
"pandas/plotting/_matplotlib/hist.py"
] | [
"from typing import TYPE_CHECKING\n\nimport numpy as np\n\nfrom pandas.core.dtypes.common import is_integer, is_list_like\nfrom pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass\nfrom pandas.core.dtypes.missing import isna, remove_na_arraylike\n\nfrom pandas.io.formats.printing import pprint_thing\nfrom... | [
[
"numpy.nanmax",
"numpy.nanmin",
"scipy.stats.gaussian_kde",
"pandas.core.dtypes.missing.remove_na_arraylike",
"matplotlib.pyplot.gcf",
"numpy.ravel",
"matplotlib.pyplot.figure",
"pandas.core.dtypes.common.is_list_like",
"pandas.plotting._matplotlib.tools._flatten",
"pandas.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5... |
HackelLab-UMN/DevRep | [
"e05023a8abe7be6c2e22f42d523b20bd76cd8da5"
] | [
"main_paper_one/figure1b/figure1B2.py"
] | [
"import numpy as np\nimport matplotlib as mpl\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns \n\ndef load_datasets():\n library=pd.read_pickle('../make_datasets/seq_to_assay_training_data.pkl')\n # test=pd.read_pickle('./seq_to_dot_test_data.pkl')\n # all10=pd.... | [
[
"pandas.concat",
"matplotlib.use",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.close",
"pandas.read_pickle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
chyld/demoX | [
"27f26a553aeb6682173f6b1b8dc8969101993324",
"27f26a553aeb6682173f6b1b8dc8969101993324"
] | [
"gcontent-app/model.py",
"stats-essentials/scripts/pdf-cdf-plot.py"
] | [
"#!/usr/bin/env python\n\nimport re\nimport pandas as pd\n\ndef get_user_table():\n \"\"\"\n return user table\n \"\"\"\n\n df = pd.read_csv(\"./data/lessons.csv\")\n return([[i+1, df[\"lesson\"][i], \", \".join(re.split(\";\",df[\"topics\"][i])), df[\"repo\"][i], df[\"checkpoint\"][i]] for i in df.i... | [
[
"pandas.read_csv"
],
[
"numpy.arange",
"numpy.cumsum",
"numpy.exp",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anonymousgithubid/code | [
"7d34dc6ca175f09cc9a34843b9a6108ea2cc9744"
] | [
"network/gra_transf_inpt5_new_dropout_2layerMLP_3_adj_mtx.py"
] | [
"import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom .graph_transformer_layers_new_dropout_3_adj_mtx import *\n\nimport ipdb\n\n\nclass GraphTransformerEncoder(nn.Module):\n \n def __init__(self, coord_input_dim, feat_input_dim, feat_dict_size, n_layers=6, n_heads=8, \n ... | [
[
"torch.nn.ReLU",
"torch.nn.Linear",
"torch.nn.Dropout",
"torch.nn.Embedding"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
phd-jaybie/3d-spatial-privacy-1 | [
"1df4905ad963b31063d827f7a1d404c51cf7f297"
] | [
"info3d.py"
] | [
"import numpy as np\n#import quaternion\nimport sys\nimport matplotlib.pyplot as plt\nimport math\nimport pickle\nimport pandas as pd\nimport scipy.io\n#import cv2\nimport time\n\nfrom numpy import linalg as LA\nfrom scipy.spatial import Delaunay\n\n#from pyntcloud import PyntCloud\nfrom sklearn.neighbors import Ne... | [
[
"numpy.dot",
"numpy.amax",
"numpy.asarray",
"numpy.around",
"numpy.concatenate",
"numpy.round",
"numpy.max",
"numpy.all",
"numpy.mean",
"numpy.nanmean",
"numpy.argmin",
"numpy.cross",
"numpy.nanstd",
"numpy.where",
"numpy.unique",
"numpy.reshape",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lewlin/models | [
"fbdbe12b9f53403b5195ee60648a316fb83b11fb"
] | [
"official/recommendation/ncf_keras_benchmark.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.compat.v1.logging.set_verbosity"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Rockett8855/gym-minigrid | [
"e8f50fae6f0eb64b2c5a91db2164cd8114a7bf4a"
] | [
"gym_minigrid/agent.py"
] | [
"import numpy as np\n\nfrom enum import IntEnum\n\nfrom .minigrid import COLORS, DIR_TO_VEC\n\n\nclass PusherActions(IntEnum):\n left = 0\n right = 1\n forward = 2\n backward = 3\n\n none = 4\n\n\nclass PusherAgent(object):\n def __init__(self, position, direction):\n self.position = positi... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
calmisential/EfficientDet_TensorFlow2 | [
"af126ebc9d20cdbc84974e265b9a211ec2249ba5"
] | [
"core/efficientdet.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\nfrom configuration import Config\nfrom core.anchor import Anchors\nfrom core.efficientnet import get_efficient_net\nfrom core.bifpn import BiFPN\nfrom core.loss import FocalLoss\nfrom core.prediction_net import BoxClassPredict\nfrom utils.nms import NMS\n\n\nclass Eff... | [
[
"tensorflow.math.argmax",
"tensorflow.reduce_mean",
"numpy.clip",
"tensorflow.math.reduce_max",
"numpy.squeeze",
"tensorflow.math.reduce_mean",
"numpy.stack",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rsheftel/pandas_exchange_calendars | [
"447ff90e022801fa343c6192fd7aaee33a6c6d8d"
] | [
"pandas_market_calendars/holidays_cme_globex.py"
] | [
"from dateutil.relativedelta import (MO, TU, WE, TH, FR, SA, SU)\nfrom pandas import (DateOffset, Timestamp, date_range)\nfrom datetime import timedelta\nfrom pandas.tseries.holiday import (Holiday, nearest_workday, sunday_to_monday, Easter)\nfrom pandas.tseries.offsets import Day, CustomBusinessDay\n\nfrom panda... | [
[
"pandas.tseries.holiday.Easter",
"pandas.tseries.offsets.Day",
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
khushmeeet/char-conv-classification | [
"92c9d10c0e3e0011138fbbada8125dc9f2be7f34"
] | [
"main.py"
] | [
"import tensorflow as tf\nimport helper\nfrom sklearn.utils import shuffle\nimport time\n\n# ----- Params -----\nsent_limit = 200\nn_classes = 4\nnum_feature_map = 256\nnodes = 1024\nepochs = 10\npath = 'ag_news_csv/'\nembedding_size = 300\nbatch_size = 128\n\n# ----- Getting the data -----\ntrain_X, train_Y = help... | [
[
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.nn.max_pool",
"tensorflow.cast",
"tensorflow.train.AdamOptimizer",
"tensorflow.summary.scalar",
"tensorflow.nn.conv2d",
"tensorflow.name_scope",
"tensorflow.Session",
"tensorflow.argmax",
"tensorflow.nn.dropout"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
MachengShen/torchbeast | [
"3853fdda44db4d91d773ff2a3db3658a02fa1a15"
] | [
"multiagent-competition/gym-compete/gym_compete/new_envs/agents/humanoid_kicker.py"
] | [
"from .humanoid import Humanoid\nfrom gym.spaces import Box\nimport numpy as np\nfrom .agent import Agent\nimport six\n\n\ndef mass_center(mass, xpos):\n return (np.sum(mass * xpos, 0) / np.sum(mass))\n\n\nclass HumanoidKicker(Humanoid):\n\n def __init__(self, agent_id, xml_path=None):\n if xml_path is... | [
[
"numpy.asscalar",
"numpy.isfinite",
"numpy.clip",
"numpy.asarray",
"numpy.concatenate",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nour-7/detectron2 | [
"0528575139c14bdc9c33840f446c8f3aec6f4f36"
] | [
"detectron2/data/detection_utils.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\n\"\"\"\nCommon data processing utilities that are used in a\ntypical object detection data pipeline.\n\"\"\"\nimport logging\nimport numpy as np\nimport pycocotools.mask as mask_util\nimport torch\nfrom fvcore.common... | [
[
"numpy.expand_dims",
"numpy.asarray",
"numpy.ascontiguousarray",
"torch.tensor",
"numpy.ceil",
"numpy.floor",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ajmal017/pandas-ta | [
"98099f71de7c4a8b293b8de4dd62fa2399e5a12a"
] | [
"pandas_ta/core.py"
] | [
"# -*- coding: utf-8 -*-\nfrom functools import wraps\nfrom multiprocessing import cpu_count, Pool\nfrom random import random\nfrom time import perf_counter\n\nimport pandas as pd\nfrom pandas.core.base import PandasObject\n\nfrom pandas_ta.candles import *\nfrom pandas_ta.momentum import *\nfrom pandas_ta.overlap... | [
[
"pandas.api.extensions.register_dataframe_accessor",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Cai631/MBMFN | [
"9a48744d7de87a6a7ec08ad87b2d0bd727e1d23c"
] | [
"FLOPs/count_hooks.py"
] | [
"import argparse\n\nimport torch\nimport torch.nn as nn\n\nmultiply_adds = 1\n\n\ndef count_convNd(m, x, y):\n x = x[0]\n cin = m.in_channels\n batch_size = x.size(0)\n\n kernel_ops = m.weight.size()[2:].numel()\n bias_ops = 1 if m.bias is not None else 0\n ops_per_element = kernel_ops + bias_ops\... | [
[
"torch.prod",
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ShihaoYing/douban250 | [
"abbe18b9c2cd4b0b4b5b128d79ac9ca949feaebc",
"abbe18b9c2cd4b0b4b5b128d79ac9ca949feaebc"
] | [
"douban_movie/douban_movie/spiders/movie_comment_spider225.py",
"douban_movie/douban_movie/spiders/movie_people_spider25000.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport scrapy\nimport numpy as np\nfrom faker import Factory\nfrom douban_movie.dns_cache import _setDNSCache\nfrom douban_movie.items import DoubanMovieItem, DoubanMovieCommentItem, DoubanMovieUser\n# import urlparse # python2.x\nfrom urllib import parse as urlparse # python3.x\nf = F... | [
[
"numpy.loadtxt"
],
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
goriunov/yahooquery | [
"c013e372b7db3c15d1b068090980e1a210b75284"
] | [
"yahooquery/ticker.py"
] | [
"from datetime import datetime, timedelta\nimport re\n\nimport pandas as pd\n\nfrom yahooquery.base import _YahooFinance\nfrom yahooquery.utils import _convert_to_timestamp, _flatten_list, _history_dataframe\n\n\nclass Ticker(_YahooFinance):\n \"\"\"\n Base class for interacting with Yahoo Finance API\n\n ... | [
[
"pandas.concat",
"pandas.to_datetime",
"pandas.Series",
"pandas.DataFrame",
"pandas.DataFrame.from_records"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
SoulTop/Automatic_Face_Mosaic | [
"63571bb459f19550e7a98c426f83d76cf8edb56b"
] | [
"main.py"
] | [
"from facedetect.facedetect import *\nfrom mosaic import *\nimport os\nimport argparse\nimport time\nimport cv2\nfrom cv2 import dnn\nimport numpy as np\n\n\n\n\ndef mosaicFaces():\n net = dnn.readNetFromONNX(args.onnx_path) # onnx version\n input_size = [int(v.strip()) for v in args.input_size.split(\",\")]... | [
[
"numpy.reshape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jguhlin/funannotate | [
"a0f38b208e71834f8963b73534fcf269c21bf695"
] | [
"funannotate/utilities/contrast.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import division\nimport sys\nimport os\nimport argparse\nimport itertools\nfrom Bio import SeqIO\nfrom interlap import InterLap\nfrom collections import defaultdict\nfrom collections import OrderedDict\nfrom natsort import natsorted\nimport numpy as... | [
[
"numpy.subtract",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jaswanthbjk/3D-Object-Detection | [
"dec5ec6f4c799018c858240202ae4381c4a7023e"
] | [
"dataset/pkl_to_tfrec.py"
] | [
"import pickle\nimport tensorflow as tf\nimport numpy as np\n\nNUM_HEADING_BIN = 12\nNUM_OBJECT_POINT = 512\nNUM_SIZE_CLUSTER = 8\n\ng_type2class = {'car': 0, 'Van': 1, 'Truck': 2, 'pedestrian': 3,\n 'Person_sitting': 4, 'bicycle': 5, 'Tram': 6, 'Misc': 7}\ng_class2type = {g_type2class[t]: t for t in... | [
[
"tensorflow.io.TFRecordWriter",
"numpy.expand_dims",
"tensorflow.constant",
"numpy.random.random",
"numpy.random.choice",
"numpy.cos",
"numpy.sin",
"numpy.copy",
"numpy.random.randn",
"tensorflow.train.BytesList",
"numpy.transpose",
"tensorflow.train.FloatList",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danijar/ffnn | [
"c1c09d95f90057a91ae24c80b74f415680b97338"
] | [
"setup.py"
] | [
"import os\nimport sys\nimport subprocess\nimport setuptools\nfrom setuptools.command.build_ext import build_ext\nfrom setuptools.command.test import test\n\n\nclass TestCommand(test):\n\n description = 'run tests, linters and create a coverage report'\n user_options = []\n\n def __init__(self, *args, **kw... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DebankurS/handtracking | [
"a3baf4bdc68cad4846749d24274fb1a8684bb4fc"
] | [
"generate_tfrecord.py"
] | [
"\"\"\"\nUsage:\n # From tensorflow/models/\n # Create train data:\n python generate_tfrecord.py --csv_input=images/train/train_labels.csv --output_path=train.record --image_dir=images/train\n\n # Create test data:\n python generate_tfrecord.py --csv_input=images/test/test_labels.csv --output_path=test.recor... | [
[
"tensorflow.app.run",
"pandas.read_csv",
"tensorflow.python_io.TFRecordWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
dmklee/nuro-arm | [
"78a21e17e0140ed73c022bd5e5caef8a71470f21"
] | [
"nuro_arm/cube.py"
] | [
"import pybullet as pb\nimport os\nfrom PIL import Image\nimport cv2\nimport numpy as np\n\nfrom nuro_arm.constants import URDF_DIR\n\nclass Cube:\n def __init__(self,\n pos,\n rot=[0,0,0,1],\n size=0.025,\n pb_client=0,\n tag_id=Non... | [
[
"numpy.zeros",
"numpy.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LSaffin/scripts | [
"100fc442229ea11f8766a6d78b4db8790c607326"
] | [
"myscripts/models/speedy/deterministic_errors.py"
] | [
"\"\"\"\nCompare a set of runs with reduced precision to a reference double precision\nsimulation. The forecasts use the same initial conditions and if SPPT is on each\nforecast uses the same random-number seed in the SPPT forcing so all errors are\n'deterministic'.\n\"\"\"\n\nimport os\nimport matplotlib.pyplot as... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.axhline",
"matplotlib.pyplot.title",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gurpreet-Singh121/transformers | [
"669e3c50c98ad5b506555a551d2ecbf72ceb3c99"
] | [
"src/transformers/modeling_tf_utils.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. 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... | [
[
"tensorflow.convert_to_tensor",
"numpy.asarray",
"tensorflow.python.keras.backend.int_shape",
"tensorflow.keras.utils.register_keras_serializable",
"tensorflow.python.keras.saving.hdf5_format.load_attributes_from_hdf5_group",
"tensorflow.rank",
"tensorflow.python.keras.engine.data_adap... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
SanderBorgmans/pyiron | [
"81121b767b1d6371eb7c07be8e9301eba48aa557"
] | [
"pyiron/atomistics/master/convergence_volume.py"
] | [
"# coding: utf-8\n# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department\n# Distributed under the terms of \"New BSD License\", see the LICENSE file.\n\nfrom __future__ import print_function\nfrom pyiron.atomistics.master.serial import SerialMaster\n\n__author__... | [
[
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Eurus-Holmes/VAG-NMT | [
"b982038ea1295cc038b8dcbca11aa81d318f7a49",
"b982038ea1295cc038b8dcbca11aa81d318f7a49"
] | [
"machine_translation_vision/layers/NMT_Decoder_V2.py",
"machine_translation_vision/layers/NMT_Decoder_V3.py"
] | [
"#This NMT Decoder will return two things: (1) The second decoder hidden state (2) The first decoder hidden state\n\nimport torch\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nimport random\nimport numpy as np\n\nclass BahdanauAttn(nn.Module):\n def __... | [
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.cat",
"torch.nn.GRU",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.bmm",
"torch.rand",
"torch.nn.init.constant"
],
[
"torch.nn.Softmax",
"torch.nn.Dropout",
"torch.nn.GRU",
"torch.nn.Embedding",
"torch.nn... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
28Smiles/SAS-AIED2020 | [
"13eb600fedb6130f2a353d1a66e7eace774345e3"
] | [
"bert-large-cased-whole-word-masking.py"
] | [
"import os\n\n# -- GPU TO USE --\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n# -- PARAMETERS --\nMODEL_NAME = 'bert-large-cased-whole-word-masking'\nMODEL_PREFIX = 'Bert'\nDATASET = 'union'\nLANGS = ['en']\nTRAIN_BATCH_SIZE = 4\nACCUMULATION_STEPS = 4\nLEARN_RA... | [
[
"torch.utils.data.DataLoader",
"torch.utils.data.RandomSampler"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Trybnetic/paat | [
"b3ff42f51423ae323da1bc776497d6432168d1b6",
"b3ff42f51423ae323da1bc776497d6432168d1b6"
] | [
"tests/test_features.py",
"paat/wear_time.py"
] | [
"import os\nimport tempfile\n\nimport numpy as np\nimport numpy.testing as npt\nimport pandas as pd\nimport h5py\nimport pytest\n\nfrom paat import io, features\n\n\ndef test_calculate_actigraph_counts(load_gt3x_file, test_root_path):\n data, sample_freq = load_gt3x_file\n\n # Test 1sec count processing\n ... | [
[
"numpy.testing.assert_almost_equal"
],
[
"tensorflow.keras.models.load_model",
"pandas.Series",
"numpy.min",
"numpy.ptp",
"pandas.DataFrame",
"numpy.all",
"numpy.max",
"numpy.std",
"numpy.diff",
"pandas.DataFrame.from_dict",
"numpy.zeros",
"numpy.where"
]
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"... |
Luoxd1996/Rank2nuclearSegmentation | [
"bd85ac13eec7ce18c286efd521a27486483da904"
] | [
"dcan/loss.py"
] | [
"import torch\nimport torch.nn as nn\nfrom torch.nn.functional import binary_cross_entropy\n\n\nclass BinaryCrossEntropyLoss2d(nn.Module):\n def __init__(self, weight=None, size_average=True):\n super().__init__()\n self.bce_loss = nn.BCELoss(weight, size_average)\n\n def forward(self, inputs, t... | [
[
"torch.exp",
"torch.nn.functional.binary_cross_entropy",
"torch.nn.BCELoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sukanyasaha007/virtual-stylist | [
"7b8fb1efb2fbef78e6a95a046635c4560c41ecc4"
] | [
"code/image_generation/TryOnGAN/projector.py"
] | [
"# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all intellectual property\n# and proprietary rights in and to this software, related documentation\n# and any modifications thereto. Any use, reproduction, disclosure or\n# distribution of this softwa... | [
[
"torch.randn_like",
"torch.jit.load",
"torch.zeros",
"numpy.concatenate",
"numpy.mean",
"torch.no_grad",
"torch.nn.functional.interpolate",
"torch.device",
"pandas.read_csv",
"torch.from_numpy",
"torch.tensor",
"torch.roll",
"torch.nn.functional.avg_pool2d",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
jb753/turbigen | [
"bdad31d8185a44fceccee13b8aa3ec49998643b6"
] | [
"run_resolution.py"
] | [
"\"\"\"Run a resolution study on datum design.\"\"\"\n\nfrom turbigen import hmesh, geometry\n# from turbigen import submit, turbostream, hmesh, geometry\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ny = geometry.cluster_wall_solve_npts(1.1, 0.001)\n\ny = geometry.cluster_wall_solve_ER(65, 0.001)\n\n# rst... | [
[
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xkcd1838/bench-DGNN | [
"299fee7dda057c6e9479bcb713afbc1a7c31b16b"
] | [
"experiment/analysis/log_analyzer.py"
] | [
"import glob\nfrom shutil import copy2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport sys\nimport pprint\nimport pandas as pd\nimport plotly.express as px\nfrom plotly.subplots import make_subplots\n\nprompt = lambda q : input(\"{} (y/n): \".format(q)).lower().strip()[:1] == \"y\"\n\ndef pa... | [
[
"pandas.melt",
"pandas.DataFrame.from_dict"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
hito0512/Vitis-AI | [
"996459fb96cb077ed2f7e789d515893b1cccbc95",
"996459fb96cb077ed2f7e789d515893b1cccbc95",
"996459fb96cb077ed2f7e789d515893b1cccbc95",
"996459fb96cb077ed2f7e789d515893b1cccbc95"
] | [
"tools/Vitis-AI-Quantizer/vai_q_pytorch/example/resnet18_quant_custom_op.py",
"tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/decent_q/python/graphsurgeon/node_manipulation.py",
"tools/RNN/rnn_quantizer/tensorflow/tf_nndct/quantization/api.py",
"tools/Vitis-AI-Quantizer/vai_q_pytorch/pytorch_... | [
"import os\nimport re\nimport sys\nimport argparse\nimport time\nimport pdb\nimport random\nfrom pytorch_nndct.apis import torch_quantizer\nfrom pytorch_nndct.utils import register_custom_op\nimport torch\nimport torch.nn as nn\nimport torchvision\nimport torchvision.transforms as transforms\nfrom torchvision.model... | [
[
"torch.nn.CrossEntropyLoss",
"torch.utils.data.distributed.DistributedSampler",
"torch.load",
"torch.randn",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.cuda.is_available"
],
[
"numpy.frombuffer",
"tensorflow.core.framework.node_def_pb2.NodeDef",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"1.10",
"1.12",
"2.7",
"2.6",
"1.4",
"1.13",
"2.3",
"2.... |
MrH101/stock_prediction | [
"32d23d3eb550871a0994ea2b4c1077805c2e22c8"
] | [
"app.py"
] | [
"import streamlit as st\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tempfile\nimport matplotlib.dates as mdates\nimport tensorflow\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense,Activation,Dropout\nfrom tensorflow.keras.layers import ... | [
[
"matplotlib.pyplot.legend",
"pandas.to_datetime",
"numpy.sqrt",
"matplotlib.pyplot.plot",
"sklearn.preprocessing.MinMaxScaler",
"pandas.read_csv",
"numpy.reshape",
"tensorflow.keras.models.Sequential",
"matplotlib.pyplot.figure",
"matplotlib.dates.DateFormatter",
"matpl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
kth-tcs/chaos-engineering-research | [
"0d05cee16614154da3cfef79f4f3cbef5296b434"
] | [
"pobs/experiments/hawkbit/overhead_evaluation.py"
] | [
"#!/usr/bin/python\n# -*- coding:utf-8 -*-\n\nimport os, time, re, datetime, tempfile, subprocess\nimport logging\nimport numpy\n\ndef workload_generator(duration):\n t_end = time.time() + 60 * duration\n # if the response contains this point information, it is considered as a successful one\n cmd_workload... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
beckernick/cuml | [
"36b4c7fe2bb98d12bec12a22781c5ad0a7f0d964"
] | [
"python/cuml/test/test_fil.py"
] | [
"# Copyright (c) 2019, NVIDIA CORPORATION.\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 l... | [
[
"numpy.allclose",
"numpy.asarray",
"numpy.around",
"sklearn.model_selection.train_test_split",
"numpy.stack",
"sklearn.metrics.mean_squared_error",
"numpy.round",
"numpy.shape",
"numpy.random.RandomState",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aliciapj/intro_ml_python | [
"703e1050c2a8bce706e1d34076aaade032710ae3"
] | [
"utils/plot_utils.py"
] | [
"from matplotlib.colors import ListedColormap\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import svm, datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.utils.multiclass import unique_labels\n\n\ndef plot_decision_regio... | [
[
"matplotlib.pyplot.contourf",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"numpy.unique",
"numpy.arange",
"matplotlib.pyplot.subplots",
"sklearn.metrics.confusion_matrix",
"sklearn.utils.multiclass.unique_labels",
"matplotlib.pyplot.subplot",
"matplotlib.pyplot.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
roatienza/straug | [
"d3bd2b4354e840d1ba322d70f59dba8a4b5fe5b3"
] | [
"straug/process.py"
] | [
"\"\"\"\nAll other image transformations used in object recognition data \naugmentation literature that may be applicable in STR can be\nfound here:\n1) Posterize\n2) Solarize,\n3) Invert, \n4) Equalize, \n5) AutoContrast, \n6) Sharpness and \n7) Color.\n\nBased on AutoAugment and FastAugment:\n https://github.c... | [
[
"numpy.random.default_rng"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
IntenF/QFTSampler | [
"5324e1a11ed77bfc67aaef0902da4b32543e96cc"
] | [
"QFTSampler/ExpTargetDists/BaseTarget.py"
] | [
"import numpy as np\n\nclass BaseTarget():\n def __init__(self, N, M, dim, sample_num=None):\n self.N = N\n self.M = M\n self.dim = dim\n self.Z = 1\n if sample_num is None:\n sample_num = min(1000000, (2**N)**dim)\n if sample_num!=0:\n self.check(s... | [
[
"numpy.sum",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
boru-roylu/Paddle | [
"b15c675530db541440ddb5b7e774d522ecaf1533"
] | [
"python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py"
] | [
"# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.\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 r... | [
[
"numpy.concatenate",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kaniblu/pytorch-nlu | [
"947e147c837e3e0fac42072ede87ccadcf93b04d"
] | [
"embedding/fasttext.py"
] | [
"import subprocess\n\nimport numpy as np\n\nfrom .common import Embeddings\n\n\nclass FastText(object):\n\n def __init__(self, fasttext_path, model_path, dtype=np.float32):\n self.fasttext_path = fasttext_path\n self.model_path = model_path\n self.args = [fasttext_path, \"print-word-vectors\... | [
[
"numpy.fromstring"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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