repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
THUMNLab/AutoGL | [
"7b551961e90f5042d9b91d92c083f3f09dd9dbdd",
"7b551961e90f5042d9b91d92c083f3f09dd9dbdd"
] | [
"autogl/module/nas/estimator/one_shot.py",
"autogl/solver/classifier/graph_classifier.py"
] | [
"import torch.nn as nn\nimport torch.nn.functional as F\n\nfrom . import register_nas_estimator\nfrom ..space import BaseSpace\nfrom .base import BaseEstimator\n\n\n@register_nas_estimator(\"oneshot\")\nclass OneShotEstimator(BaseEstimator):\n \"\"\"\n One shot estimator.\n\n Use model directly to get esti... | [
[
"torch.nn.functional.softmax"
],
[
"torch.device",
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kumagai-group/vise | [
"8adfe61ad8f31767ec562f02f271e2495f357cd4",
"8adfe61ad8f31767ec562f02f271e2495f357cd4",
"8adfe61ad8f31767ec562f02f271e2495f357cd4"
] | [
"vise/analyzer/dielectric_function.py",
"vise/util/plotly_util.py",
"vise/analyzer/vasp/make_diele_func.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright (c) 2020. Distributed under the terms of the MIT License.\nfrom dataclasses import dataclass\nfrom math import sqrt, pi\nfrom typing import List\n\nimport numpy as np\nfrom monty.json import MSONable\nfrom tqdm import tqdm\nfrom vise.util.mix_in import ToJsonFileMixIn\nfrom sc... | [
[
"numpy.array",
"numpy.allclose",
"numpy.count_nonzero",
"numpy.argwhere"
],
[
"numpy.dot",
"numpy.linalg.norm",
"numpy.concatenate",
"numpy.linalg.det",
"numpy.cross",
"numpy.average"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RobinCondat/pytorch-retinanet | [
"14a2085cd3785a667454898dc65f5324b1b9c6b8"
] | [
"retinanet/losses_vehicle.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nfrom retinanet.config_experiment_2 import INDEXES_MIX, VEHICLE_INDEXES\n\ndef calc_iou(a, b):\n area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1])\n\n iw = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, ... | [
[
"torch.abs",
"torch.ge",
"torch.ones",
"torch.max",
"torch.Tensor",
"torch.zeros",
"torch.eq",
"torch.lt",
"torch.unsqueeze",
"torch.tensor",
"torch.le",
"torch.log",
"torch.cuda.is_available",
"torch.stack",
"torch.clamp",
"torch.pow",
"torch.ne... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PeterouZh/PyTorch-StudioGAN | [
"faef6048d25dadee4fa31b2955f16f7d1ca8e1e2"
] | [
"src/main.py"
] | [
"# PyTorch StudioGAN: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN\n# The MIT License (MIT)\n# See license file or visit https://github.com/POSTECH-CVLab/PyTorch-StudioGAN for details\n\n# src/main.py\n\n\nimport json\nimport os\nimport sys\nimport random\nimport warnings\nfrom argparse import ArgumentParser\... | [
[
"torch.cuda.device_count",
"torch.multiprocessing.spawn",
"torch.autograd.set_detect_anomaly",
"torch.cuda.current_device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anigasan/tensorflow | [
"5b780b4983007661ca479bf4d7ed9a260d8ce43f"
] | [
"tensorflow/lite/python/convert.py"
] | [
"# Lint as: python2, python3\n# 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/... | [
[
"tensorflow.python.platform.resource_loader.get_path_to_datafile",
"tensorflow.lite.python.util.convert_dtype_to_tflite_type",
"tensorflow.lite.python.wrap_toco.wrapped_toco_convert",
"tensorflow.lite.python.util.get_tensor_name",
"tensorflow.python.util.tf_export.tf_export",
"tensorflow.l... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
cverluise/parseEPO | [
"be1171a0f8e6fcafa711fa291aebb1fc2260d5e6"
] | [
"parseepo/serialize.py"
] | [
"import html2text\nimport pandas as pd\nfrom wasabi import Printer\n\nfrom parseepo import validate\nfrom parseepo.exception import SingleAttrException\nfrom parseepo.utils import prepare_name\n\nh = html2text.HTML2Text()\nmsg = Printer()\nNAMES = [\"EP\", \"Num\", \"Ext\", \"publication_date\", \"language\", \"att... | [
[
"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": []
}
] |
luigiluz/pyampd | [
"cd247030f5a4ccd971da837b9b873cacbd7adfb3"
] | [
"pyampd/ampd.py"
] | [
"import numpy as np\nfrom scipy.ndimage import uniform_filter1d\nfrom scipy.signal import detrend\n\n\ndef find_peaks_original(x, scale=None, debug=False):\n \"\"\"Find peaks in quasi-periodic noisy signals using AMPD algorithm.\n\n Automatic Multi-Scale Peak Detection originally proposed in\n \"An Efficie... | [
[
"numpy.min",
"numpy.arange",
"numpy.indices",
"numpy.flatnonzero",
"numpy.ones",
"numpy.argmax",
"scipy.ndimage.uniform_filter1d",
"numpy.zeros",
"scipy.signal.detrend"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
bionlplab/heart_failure_mortality | [
"f3bbfe65fe6f2c2a076acb38697133b472bf2231"
] | [
"extract_features.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom utils import *\nfrom sklearn.preprocessing import StandardScaler\nfrom collections import defaultdict\nimport re\n\ndef format_labels(file_path, timelines, mapping):\n\tmost_recent = mapping.sort_values([\"subject_id\", \"ordering_date\"], ascending=False).drop_duplica... | [
[
"pandas.merge",
"pandas.read_csv",
"numpy.load",
"sklearn.preprocessing.StandardScaler",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
derekmpham/mindmeld | [
"18189f956e4e3eb92df61fde95ec82f73b9efa91"
] | [
"mindmeld/converter/dialogflow.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2015 Cisco Systems, Inc. and others. All rights reserved.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/lic... | [
[
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
offy284/Keras-GAN | [
"6652c626ba584ffd1c25ca4e925e6f131077395c"
] | [
"music_preprocessor/music_preprocessor.py"
] | [
"import itertools\nimport shutil\nimport os\nfrom os import listdir\nfrom os.path import isfile, join\nfrom tqdm import tqdm\nimport numpy as np\nimport scipy\nfrom scipy.io.wavfile import write, read\nfrom scipy.fftpack import fft\nfrom scipy import signal\nfrom scipy.fft import fftshift\nimport matplotlib.pyplot ... | [
[
"scipy.signal.spectrogram",
"numpy.save",
"numpy.concatenate",
"scipy.io.wavfile.read",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"0.16",
"1.8"
],
"tensorflow": []
}
] |
Didou09/tofu | [
"4a4e1f058bab8e7556ed9d518f90807cec605476"
] | [
"tofu/geom/_core_optics.py"
] | [
"\n\"\"\"\nThis module is the geometrical part of the ToFu general package\nIt includes all functions and object classes necessary for tomography on Tokamaks\n\"\"\"\n\n# Built-in\nimport sys\nimport os\nimport warnings\nimport copy\n\n\n# Common\nimport numpy as np\nimport scipy.interpolate as scpinterp\nimport sc... | [
[
"numpy.nanmax",
"numpy.sqrt",
"numpy.linspace",
"matplotlib.colors.to_rgba",
"numpy.nanmin",
"numpy.arctan2",
"numpy.concatenate",
"numpy.round",
"numpy.nanargmin",
"numpy.max",
"numpy.any",
"numpy.nanmean",
"numpy.cross",
"matplotlib.pyplot.plot",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
srihari-nagaraj/anuvaad | [
"b09b01a033a033e97db6e404c088e0e6332053e4",
"b09b01a033a033e97db6e404c088e0e6332053e4",
"b09b01a033a033e97db6e404c088e0e6332053e4"
] | [
"anuvaad-etl/anuvaad-extractor/document-processor/evaluator/evaluator_string/src/notebooks/tesseract_ocr_evaluation_local.py",
"anuvaad-etl/anuvaad-extractor/document-processor/block-segmenter/src/utilities/yolov5/datasets.py",
"anuvaad-etl/anuvaad-extractor/document-processor/layout-detector/prima/src/utilitie... | [
"import glob\nimport uuid\nimport json\nimport requests\nimport copy,time\nimport os\nimport cv2\nimport numpy as np\nfrom time import sleep\nimport pandas as pd\nimport logging\nfrom collections import Counter\nimport pytesseract\nfrom pytesseract import Output\n#from pytesseract import pytesseract\nfrom difflib i... | [
[
"pandas.read_csv",
"pandas.DataFrame",
"numpy.frombuffer",
"numpy.float32",
"numpy.array"
],
[
"numpy.minimum",
"torch.zeros",
"torch.cat",
"torch.load",
"numpy.flipud",
"numpy.concatenate",
"torch.save",
"numpy.random.beta",
"torch.utils.data.distribute... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
... |
phanvanthinh98/keras_LSTM | [
"b22cff1e9fd762226ec3dc9d3af3e300484dd833",
"2d551d31915906120f3f6a3ed3c4de94ba4bb288",
"2d551d31915906120f3f6a3ed3c4de94ba4bb288",
"b22cff1e9fd762226ec3dc9d3af3e300484dd833"
] | [
"keras/wrappers/scikit_learn.py",
"keras/distribute/custom_training_loop_optimizer_test.py",
"keras/mixed_precision/loss_scale_optimizer.py",
"keras/legacy_tf_layers/core.py"
] | [
"# Copyright 2015 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... | [
[
"numpy.hstack",
"numpy.unique",
"numpy.arange",
"tensorflow.python.util.tf_export.keras_export",
"numpy.searchsorted",
"numpy.array"
],
[
"tensorflow.compat.v2.Variable",
"tensorflow.compat.v2.test.main",
"tensorflow.compat.v2.__internal__.test.combinations.combine",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
daniil-lyakhov/deep-object-reid | [
"b0f7d6a2d4cff8c417a66d82c09d16788d81ec67",
"b0f7d6a2d4cff8c417a66d82c09d16788d81ec67"
] | [
"torchreid/models/mobilenetv3.py",
"torchreid/engine/image/multilabel.py"
] | [
"import math\n\nimport torch\nimport torch.nn as nn\nfrom torch.cuda.amp import autocast\n\nfrom torchreid.losses import AngleSimpleLinear\nfrom torchreid.ops import Dropout, EvalModeSetter, rsc\nfrom .common import HSigmoid, HSwish, ModelInterface, make_divisible\nimport timm\n\nfrom torchreid.integration.nncf.com... | [
[
"torch.nn.Sequential",
"torch.nn.BatchNorm1d",
"torch.nn.PReLU",
"torch.nn.Conv2d",
"torch.cuda.amp.autocast",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.InstanceNorm2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
],
[
"torch.nn... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Harshs27/lingvo | [
"bd396e651488b2e2c4a7416be077b4a0226c87c8",
"bd396e651488b2e2c4a7416be077b4a0226c87c8",
"bd396e651488b2e2c4a7416be077b4a0226c87c8",
"bd396e651488b2e2c4a7416be077b4a0226c87c8",
"bd396e651488b2e2c4a7416be077b4a0226c87c8"
] | [
"lingvo/core/conv_layers_builder_test.py",
"lingvo/tools/audio_lib.py",
"lingvo/core/cluster.py",
"lingvo/core/steps/attention_steps_test.py",
"lingvo/core/test_utils.py"
] | [
"# Lint as: python3\n# Copyright 2020 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... | [
[
"numpy.random.normal",
"numpy.array",
"numpy.full"
],
[
"tensorflow.python.ops.gen_audio_ops.mfcc",
"tensorflow.python.ops.gen_audio_ops.audio_spectrogram"
],
[
"numpy.empty"
],
[
"numpy.array",
"numpy.random.rand",
"numpy.random.seed"
],
[
"numpy.zeros"
]... | [
{
"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.... |
541867329/pydata-notebook | [
"867f204d7abac96dbae80e6cdd2e3661e554d1dd"
] | [
"mydemo/matplotlibDemo/clickEvent.py"
] | [
"from matplotlib.pyplot import figure, show\nimport numpy as npy\nfrom numpy.random import rand\n\nif 1: # picking on a scatter plot (matplotlib.collections.RegularPolyCollection)\n\n x, y, c, s = rand(4, 100)\n\n\n def onpick3(event):\n ind = event.ind\n print('onpick3 scatter:', ind, npy.take... | [
[
"numpy.take",
"matplotlib.pyplot.show",
"numpy.random.rand",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
suresh-guttikonda/iGibson | [
"a69e623058180146466cd52d4bb3c00d1facdacf",
"a69e623058180146466cd52d4bb3c00d1facdacf",
"a69e623058180146466cd52d4bb3c00d1facdacf",
"a69e623058180146466cd52d4bb3c00d1facdacf",
"a69e623058180146466cd52d4bb3c00d1facdacf"
] | [
"igibson/robots/jr2_robot.py",
"igibson/utils/data_utils/ext_object/scripts_wip/get_obj_stable_rotations.py",
"igibson/test/test_render_tensor.py",
"igibson/robots/fetch_robot.py",
"igibson/metrics/agent.py"
] | [
"import gym\nimport numpy as np\n\nfrom igibson.robots.robot_locomotor import LocomotorRobot\n\n\nclass JR2(LocomotorRobot):\n \"\"\"\n JR2 robot (no arm)\n Reference: https://cvgl.stanford.edu/projects/jackrabbot/\n Uses joint velocity control\n \"\"\"\n\n def __init__(self, config):\n sel... | [
[
"numpy.ones"
],
[
"numpy.eye",
"numpy.matmul"
],
[
"numpy.array"
],
[
"numpy.array"
],
[
"numpy.abs",
"numpy.linalg.norm",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
teja-ambati1202/Insurance-Fraud-Detection | [
"a9bbdd5a2af68e0e90f8e16ba43129bab709614b"
] | [
"Training_Raw_data_validation/rawValidation.py"
] | [
"import sqlite3\r\nfrom datetime import datetime\r\nfrom os import listdir\r\nimport os\r\nimport re\r\nimport json\r\nimport shutil\r\nimport pandas as pd\r\nfrom application_logging.logger import App_Logger\r\n\r\n\r\n\r\n\r\n\r\nclass Raw_Data_validation:\r\n\r\n \"\"\"\r\n This class shall be use... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
inqlee0704/pyqct | [
"304612ed558e7c46fe987ecfea8145cbc5721700"
] | [
"QCT/get_S_norm.py"
] | [
"# ##############################################################################\n# Usage: python get_S_norm.py Subj I1 I2\n# Time: ~ 20s\n# Ref: \n# ##############################################################################\n# 20220118, In Kyu Lee\n# No version suffix\n# ######################################... | [
[
"numpy.float16",
"numpy.std",
"pandas.read_csv",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
a-maumau/pixel_objectness.pytorch | [
"f5acb972be694662d839b99eb33e66a807d6031e"
] | [
"trainer.py"
] | [
"import os\nimport math\nimport argparse\nfrom datetime import datetime\n\nimport torch \nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport numpy as np\nfrom tqdm import tqdm\nfrom PIL import Image\n\nimport data_loader\nfrom mau_ml_util.train_logger import TrainLogger\n#from mau_ml_util.metric impor... | [
[
"matplotlib.pylab.show",
"numpy.uint8",
"numpy.concatenate",
"torch.no_grad",
"numpy.mean",
"torch.cuda.is_available",
"torch.nn.functional.interpolate",
"torch.device",
"matplotlib.pylab.imshow",
"numpy.array",
"numpy.zeros",
"numpy.where",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
solad5/acgan-gpt2 | [
"52901a996fd235355f8c3f6b83037c85b1fdb415",
"52901a996fd235355f8c3f6b83037c85b1fdb415"
] | [
"gpt2_model.py",
"embedders.py"
] | [
"'''\n code by TaeHwan Jung(@graykode)\n Original Paper and repository here : https://github.com/openai/gpt-2\n GPT2 Pytorch Model : https://github.com/huggingface/pytorch-pretrained-BERT\n'''\n\nimport copy\nimport torch\nimport math\nimport torch.nn as nn\nfrom torch.nn.parameter import Parameter\n\ndef ... | [
[
"torch.nn.Softmax",
"torch.ones",
"torch.empty",
"torch.zeros",
"torch.sqrt",
"torch.cat",
"torch.nn.Embedding",
"torch.matmul",
"torch.nn.Linear",
"torch.nn.init.normal_",
"torch.nn.parameter.Parameter",
"torch.pow"
],
[
"torch.mean",
"torch.nn.utils.rn... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ManuLado/Enviar-comandos-a-marlin | [
"5ba596c9b0db47125e2e29ed8084e61d326e8777",
"5ba596c9b0db47125e2e29ed8084e61d326e8777"
] | [
"take_images.py",
"pano_libs/P0.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Graba video leido desde la arducam\n# Se le debe indicar el archivo de video a grabar y\n# la duración de la captura en segundos.\n\n# SINTAXIS: python capturar_video.py VIDEO TIEMPO\n# 1- Ruta del video\n# 2- Tiempo de grabacion en segundos\n\nfrom ctypes impo... | [
[
"numpy.reshape",
"numpy.frombuffer",
"numpy.zeros"
],
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.title",
"numpy.asarray",
"numpy.concatenate",
"numpy.float32",
"numpy.array",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AndrewFalkowski/SODIS_SIM | [
"4d5da3e0872ee747d399d66fdee1633e7d2b8ab1"
] | [
"BoxThermal.py"
] | [
"import numpy as np\nfrom math import sqrt\nimport matplotlib.pyplot as plt\nimport numba\nimport time\nfrom scipy.integrate import odeint\n\n\n\n# a sample differential equation dy/dx = (x-y)/2\n\n# def dydx(x,y):\n# return ((x-y)/2)\n\n# # find the value of y for a given x using step size h\n# # and an initia... | [
[
"numpy.sqrt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rivei/pm4py_with_dash | [
"05ed524c11b44932783864a4465d400ea1300910"
] | [
"python/pm4pyPlus.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 1 22:17:20 2019\n\n@author: Wei\n\"\"\"\n\n#from dash_app import default_log as log\nimport pandas as pd\nimport numpy as np\n#import pytz\nfrom datetime import datetime, tzinfo,timedelta\n\nfrom pm4py.statistics.traces.log import case_statistics\nfrom pm4py.alg... | [
[
"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": []
}
] |
guptarohit994/ECE143_group25_project | [
"e31d0425b2a6114eed6c55bdb0491c2c996b94be"
] | [
"statistical_analysis/gpa_scatter.py"
] | [
"\nimport helper\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd \n\ndef plot_gpa_scatter():\n \"\"\"Plotting scatterplot of grades expected and grade received, using the general department list\n \"\"\"\n # obtaining data\n department_df = helper.generate_depts_df(helper.gener... | [
[
"numpy.linalg.lstsq",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AK391/stylegan_xl | [
"9854d3d0e96eccaad10cab22379c018e1e031cf0"
] | [
"viz/renderer.py"
] | [
"# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. 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 o... | [
[
"numpy.linspace",
"torch.sin",
"numpy.asarray",
"torch.kaiser_window",
"torch.device",
"torch.nn.functional.affine_grid",
"numpy.eye",
"torch.from_numpy",
"torch.arange",
"torch.ones_like",
"torch.nn.functional.pad",
"torch.empty",
"torch.cuda.current_stream",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jggatter/cumulus | [
"1dfd9dfce5a44ff867859db6f24a356f72c6ccdd"
] | [
"docker/demultiplexing/demuxlet/generate_zarr.py"
] | [
"import argparse\n\nimport pegasusio as pio\nimport pandas as pd\n\nparser = argparse.ArgumentParser(description='Merge demuxlet result with gene-count matrix.')\nparser.add_argument('demux_res', metavar = 'demux_result.best', help = 'Demuxlet demultiplexing results.')\nparser.add_argument('raw_mat', metavar = 'raw... | [
[
"pandas.read_csv",
"pandas.Index"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
HuangHaoyu1997/gym-miniworld | [
"77dc24bf1b1ca8c2cfefadfe3e35a0deb2d08a1a",
"77dc24bf1b1ca8c2cfefadfe3e35a0deb2d08a1a"
] | [
"gym_miniworld/miniworld.py",
"gym_miniworld/entity.py"
] | [
"import math\nfrom enum import IntEnum\nimport numpy as np\nimport gym\nfrom gym import spaces\nfrom .random import *\nfrom .opengl import *\nfrom .objmesh import *\nfrom .entity import *\nfrom .math import *\nfrom .params import *\n\n# Default wall height for room\nDEFAULT_WALL_HEIGHT=2.74\n\n# Texture size/densit... | [
[
"numpy.dot",
"numpy.expand_dims",
"numpy.greater",
"numpy.linalg.norm",
"numpy.stack",
"numpy.concatenate",
"numpy.insert",
"numpy.cross",
"numpy.array",
"numpy.flip",
"numpy.sum"
],
[
"numpy.dot",
"numpy.array",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Nitin-Mane/dense-ulearn-vos | [
"9e39d359a53a2343522ce5820fdf27223a4ffcb4"
] | [
"datasets/dataloader_infer.py"
] | [
"\"\"\"\nCopyright (c) 2021 TU Darmstadt\nAuthor: Nikita Araslanov <nikita.araslanov@tu-darmstadt.de>\nLicense: Apache License 2.0\n\"\"\"\n\nimport os\nimport torch\n\nfrom PIL import Image\n\nimport numpy as np\nimport torchvision.transforms as tf\n\nfrom .dataloader_base import DLBase\n\n\nclass DataSeg(DLBase):... | [
[
"torch.LongTensor",
"torch.ones",
"numpy.unique",
"torch.zeros",
"torch.stack",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Carlosbogo/etna | [
"b6210f0e79ee92aa9ae8ff4fcfb267be9fb7cc94",
"b6210f0e79ee92aa9ae8ff4fcfb267be9fb7cc94",
"b6210f0e79ee92aa9ae8ff4fcfb267be9fb7cc94",
"b6210f0e79ee92aa9ae8ff4fcfb267be9fb7cc94"
] | [
"etna/analysis/eda_utils.py",
"tests/test_transforms/test_feature_importance_transform.py",
"tests/test_pipeline/test_autoregressive_pipeline.py",
"tests/test_transforms/test_gale_shapley.py"
] | [
"import math\nimport warnings\nfrom itertools import combinations\nfrom typing import TYPE_CHECKING\nfrom typing import Optional\nfrom typing import Sequence\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nimport statsmodels.api as sm\nfrom matplotlib.ticker import MaxNLocator\nfrom s... | [
[
"matplotlib.ticker.MaxNLocator",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"numpy.random.choice"
],
[
"sklearn.ensemble.RandomForestRegressor",
"sklearn.tree.ExtraTreeRegressor",
"pandas.concat",
"sklearn.tree.DecisionTreeRegressor",
"sklearn.metrics.r2_score"... | [
{
"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",
"... |
TimoleonLatinopoulos/MortalKombatOpenAI | [
"59dc89d1f50dd74690859e5e1fa18701a5246382"
] | [
"DDQN.py"
] | [
"import tensorflow as tf\nfrom keras.activations import relu\nfrom keras.initializers import VarianceScaling\nfrom keras.layers import Dense, Conv2D, Flatten\nfrom keras.losses import logcosh\n\n\nclass DDQN:\n \"\"\" Implements a Dueling Dual Deep Q-Network based on the frames of the Retro Environment \"\"\"\n\... | [
[
"tensorflow.multiply",
"tensorflow.reduce_mean",
"tensorflow.argmax",
"tensorflow.placeholder",
"tensorflow.one_hot",
"tensorflow.train.AdamOptimizer",
"tensorflow.split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
meokz/d3rlpy | [
"40504e2d8b424547558ab82786c523e8f4626a82",
"40504e2d8b424547558ab82786c523e8f4626a82"
] | [
"d3rlpy/models/torch/encoders.py",
"d3rlpy/metrics/comparer.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef _create_activation(activation_type):\n if activation_type == 'relu':\n return torch.relu\n elif activation_type == 'swish':\n return lambda x: x * torch.sigmoid(x)\n raise ValueError('invalid activation_type.')\n\n... | [
[
"torch.nn.BatchNorm1d",
"torch.sigmoid",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.no_grad",
"torch.rand",
"torch.nn.BatchNorm2d"
],
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NLP-Discourse-SoochowU/TDDiscourseParser | [
"2f9c7cef85c564c47b368ee4935caf1fad7c598d",
"2f9c7cef85c564c47b368ee4935caf1fad7c598d"
] | [
"treebuilder/partptr/train.py",
"segmenter/rnn/train.py"
] | [
"# coding: UTF-8\r\nimport argparse\r\nimport logging\r\nimport random\r\nimport torch\r\nimport copy\r\nimport numpy as np\r\nfrom dataset import CDTB\r\nfrom collections import Counter\r\nfrom itertools import chain\r\nfrom structure.vocab import Vocab, Label\r\nfrom structure.nodes import node_type_filter, EDU, ... | [
[
"numpy.random.seed",
"torch.manual_seed",
"torch.from_numpy",
"numpy.random.permutation",
"numpy.zeros",
"torch.save"
],
[
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"numpy.random.seed",
"torch.load",
"torch.random.manual_seed",
"torch.from_numpy",
"numpy.ran... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LaudateCorpus1/coremltools | [
"777a4460d6823e5e91dea4fa3eacb0b11c7d5dfc",
"777a4460d6823e5e91dea4fa3eacb0b11c7d5dfc",
"777a4460d6823e5e91dea4fa3eacb0b11c7d5dfc",
"5ece9069a1487d5083f00f56afe07832d88e3dfa",
"777a4460d6823e5e91dea4fa3eacb0b11c7d5dfc",
"777a4460d6823e5e91dea4fa3eacb0b11c7d5dfc"
] | [
"coremltools/converters/mil/mil/passes/conv_scale_fusion.py",
"coremltools/test/sklearn_tests/test_SVR.py",
"coremltools/converters/mil/experimental/passes/generic_linear_bias_fusion.py",
"coremltools/test/sklearn_tests/test_imputer.py",
"coremltools/converters/mil/backend/mil/load.py",
"coremltools/test/... | [
"# Copyright (c) 2021, Apple Inc. All rights reserved.\n#\n# Use of this source code is governed by a BSD-3-clause license that can be\n# found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause\n\nimport numpy as np\n\nfrom coremltools.converters.mil.mil.passes.pass_registry import regis... | [
[
"numpy.product",
"numpy.reshape",
"numpy.transpose",
"numpy.array",
"numpy.zeros"
],
[
"sklearn.svm.SVR",
"sklearn.preprocessing.OneHotEncoder",
"pandas.DataFrame",
"sklearn.datasets.load_boston"
],
[
"numpy.reshape",
"numpy.prod",
"numpy.random.rand",
"... | [
{
"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",
"... |
zeou1/maggot_models | [
"4e1b518c2981ab1ca9607099c3813e8429d94ca4",
"4e1b518c2981ab1ca9607099c3813e8429d94ca4",
"4e1b518c2981ab1ca9607099c3813e8429d94ca4",
"4e1b518c2981ab1ca9607099c3813e8429d94ca4",
"4e1b518c2981ab1ca9607099c3813e8429d94ca4",
"4e1b518c2981ab1ca9607099c3813e8429d94ca4",
"4e1b518c2981ab1ca9607099c3813e8429d94ca... | [
"notebooks/39.1-BDP-unbiased-clustering.py",
"notebooks/103.0-BDP-cascade-invert.py",
"notebooks/71.0-BDP-pdiff.py",
"notebooks/120.4-BDP-silly-models.py",
"notebooks/64.2-BDP-threshold-investigations.py",
"notebooks/127.0-BDP-more-silly-model.py",
"data/process_scripts/process_maggot_brain_connectome_2... | [
"# %% [markdown]\n# # Imports\nimport json\nimport os\nimport warnings\nfrom operator import itemgetter\nfrom pathlib import Path\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom joblib import Parallel, delayed\nfrom joblib.parallel import Parallel, delayed\nf... | [
[
"numpy.diag",
"numpy.sqrt",
"pandas.DataFrame",
"numpy.concatenate",
"numpy.mean",
"sklearn.metrics.adjusted_rand_score",
"numpy.where",
"numpy.ix_",
"matplotlib.pyplot.tight_layout",
"numpy.unique",
"matplotlib.cm.ScalarMappable",
"numpy.count_nonzero",
"numpy.... | [
{
"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... |
theoptips/PySyft | [
"4b68c3c6fbe0c18cdf87dfe6ddc3c2071a71f1cc",
"4b68c3c6fbe0c18cdf87dfe6ddc3c2071a71f1cc"
] | [
"examples/tutorials/advanced/websockets-example-MNIST-parallel/run_websocket_client.py",
"syft/federated/federated_client.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision import transforms, datasets\n\nimport logging\nimport argparse\nimport sys\nimport asyncio\nimport numpy as np\n\nimport syft as sy\nfrom syft import workers\nfrom syft.frameworks.torch.federated import utils\n\nlogger = logging... | [
[
"torch.jit.trace",
"numpy.histogram",
"torch.nn.functional.nll_loss",
"torch.nn.functional.log_softmax",
"torch.manual_seed",
"numpy.set_printoptions",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.no_grad",
"torch.cuda.is_available",
"torch.device",
"torch.nn.functi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ali-ry/azureml-examples | [
"817ae89d2766dcafd70937a22cb3a80f100a2906"
] | [
"python-sdk/tutorials/automl-with-azureml/forecasting-recipes-univariate/forecasting_script.py"
] | [
"\"\"\"\r\nThis is the script that is executed on the compute instance. It relies\r\non the model.pkl file which is uploaded along with this script to the\r\ncompute instance.\r\n\"\"\"\r\n\r\nimport argparse\r\nfrom azureml.core import Dataset, Run\r\nfrom azureml.automl.core.shared.constants import TimeSeriesInte... | [
[
"sklearn.externals.joblib.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nielsbril/best | [
"8a902293605f1bee1abf3ca66ae3708706658772"
] | [
"matching/matching.py"
] | [
"import pandas as pd\nimport argparse\nimport logging\nimport sys\nimport json\n\n\ndef get_best_logger(log_file, verbose):\n # Setup logger - (Python logger breaks PEP8 by default)\n logger = logging.getLogger(__name__)\n if verbose:\n logger.setLevel('DEBUG')\n # file_handler logs to file, stre... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
spyke/spyke | [
"20934521de9c557924911cf6190690ac1c6f8e80",
"20934521de9c557924911cf6190690ac1c6f8e80",
"20934521de9c557924911cf6190690ac1c6f8e80",
"20934521de9c557924911cf6190690ac1c6f8e80"
] | [
"spyke/sort.py",
"spyke/core.py",
"spyke/nsx.py",
"demo/threadpooltest2.py"
] | [
"\"\"\"Spike sorting classes and window\"\"\"\n\nfrom __future__ import division\nfrom __future__ import print_function\n\n__authors__ = ['Martin Spacek', 'Reza Lotun']\n\nimport os\nimport sys\nimport time\nimport datetime\nfrom copy import copy\nimport operator\nimport random\nimport shutil\nimport hashlib\nimpor... | [
[
"numpy.dot",
"numpy.split",
"scipy.signal.find_peaks",
"numpy.sqrt",
"numpy.asarray",
"sklearn.decomposition.FastICA",
"numpy.all",
"numpy.concatenate",
"sklearn.manifold.TSNE",
"numpy.any",
"numpy.histogram",
"numpy.where",
"numpy.hstack",
"numpy.unique",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"1.5",
"1.2",
"1.7",
"1.3",
"1.8"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflo... |
emmettmeinzer/hmwgen | [
"cd47733b5a34a6a3a9b56026eb5e73069e398033",
"cd47733b5a34a6a3a9b56026eb5e73069e398033"
] | [
"archive/reuUpdated.py",
"archive/attention.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Nov 11 13:41:14 2019\r\n\r\n@author: Emmett & Binyang\r\n\"\"\"\r\n\r\nfrom pprint import pprint\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom nltk.tokenize.punkt import PunktSentenceTokenizer, Pu... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.title",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"sklearn.manifold.TSNE",
"numpy.argmax",
"matplotlib.pyplot.axis",
"matplotlib.colors.TABLEAU_COLORS.items",
... | [
{
"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... |
LatencyTDH/DeepSpeed | [
"eecef309cb12528cfa78d932a6f073afb43847e5"
] | [
"deepspeed/runtime/engine.py"
] | [
"'''\nCopyright 2019 The Microsoft DeepSpeed Team\n'''\n\nimport os\nimport stat\nimport torch\nimport warnings\nimport hashlib\nimport torch.distributed as dist\nfrom collections import OrderedDict\nfrom shutil import copyfile\n\nfrom torch.nn.modules import Module\nfrom torch.distributed.distributed_c10d import _... | [
[
"torch.optim.Adam",
"torch.distributed.broadcast",
"torch.cuda.set_device",
"torch.cat",
"torch.load",
"torch.utils.data.SequentialSampler",
"torch.distributed.all_gather",
"torch.distributed.is_initialized",
"torch.is_tensor",
"torch.distributed.barrier",
"torch.optim.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
notmatthancock/notmatthancock.github.io | [
"abcd91cc7c2653c5243fe96ba2fd681ec03930bb"
] | [
"code/py/test_statsrecorder.py"
] | [
"import numpy as np\nimport statsrecorder as sr\n\nrs = np.random.RandomState(323)\n\nmystats = sr.StatsRecorder()\n\n# Hold all observations in \"data\" to check for correctness.\nndims = 42\ndata = np.empty((0, ndims))\n\nfor i in range(1000):\n nobserv = rs.randint(10,101)\n newdata = rs.randn(nobserv, ndi... | [
[
"numpy.vstack",
"numpy.random.RandomState",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
samtygier-stfc/SScanSS-2 | [
"0df2160c32fdc533f7d391735bd55d524e253f4d"
] | [
"sscanss/ui/dialogs/insert.py"
] | [
"import numpy as np\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom sscanss.config import path_for, settings\nfrom sscanss.core.math import Plane, Matrix33, Vector3, clamp, map_range, trunc, VECTOR_EPS\nfrom sscanss.core.geometry import mesh_plane_intersection\nfrom sscanss.core.util import Primitives, DockFlag, ... | [
[
"numpy.abs",
"numpy.array",
"numpy.expand_dims",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
cnheider/onnx | [
"f5bb59aa0f8b18b602763abe47d1d24d0d54b197",
"781545783a4e2bbbda48fc64318fb2c6d8bbb3cc",
"f5bb59aa0f8b18b602763abe47d1d24d0d54b197",
"8e9c7d57f7c5aa6f6eb7ee7abb0ba2a243781933",
"8e9c7d57f7c5aa6f6eb7ee7abb0ba2a243781933"
] | [
"onnx/backend/test/case/node/batchnorm.py",
"onnx/backend/test/case/base.py",
"onnx/backend/test/case/node/isnan.py",
"onnx/backend/test/case/node/max.py",
"onnx/backend/test/case/node/unsqueeze.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport numpy as np # type: ignore\n\nimport onnx\nfrom ..base import Base\nfrom . import expect\n\n\nclass BatchNormalization(Base):\n\n @staticmethod\n ... | [
[
"numpy.sqrt",
"numpy.array",
"numpy.random.randn",
"numpy.random.rand"
],
[
"numpy.random.seed"
],
[
"numpy.isnan",
"numpy.array"
],
[
"numpy.array",
"numpy.maximum",
"numpy.dtype"
],
[
"numpy.expand_dims",
"numpy.random.randn"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
nghitrampham/air_pollution_death_rate_related | [
"3fd72b9684e8362de5706ba37c1d90b844d4afe0"
] | [
"air_pollution_death_rate_related/scripts/air_pollution/predict_aqi.py"
] | [
"\"\"\"\nThis module is used to predict the Air Quality Index model for 2019 for all counties.\n\"\"\"\nimport pickle\nimport warnings\n\nimport pandas as pd\nimport numpy as np\nfrom keras.models import load_model\n\nimport helpers\n\nwarnings.filterwarnings(\"ignore\")\n\ndef main():\n\n data2019_raw = pd.read... | [
[
"pandas.merge",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
positivevaib/semi-supervised-imagenet-classification | [
"4fb6427f5a72951c1b866a1ddbc2599811bb5770",
"4fb6427f5a72951c1b866a1ddbc2599811bb5770"
] | [
"deep-clustering-conv-autoencoder/main.py",
"rotation-net/train.py"
] | [
"# import\nimport numpy as np\nimport sklearn as skl\nimport sklearn.cluster as cluster\nimport sklearn.metrics as metrics\nimport torch\nimport torch.distributions.kl as kl\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torch.utils.data as data\nimport torchvision\nimp... | [
[
"torch.transpose",
"numpy.sum",
"torch.empty",
"torch.nn.ConvTranspose2d",
"torch.cat",
"sklearn.cluster.kMeans",
"torch.nn.Conv2d",
"torch.utils.data.DataLoader",
"torch.distributions.kl.kl_divergence",
"torch.sum",
"torch.nn.Linear",
"torch.utils.data.ConcatDatase... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yhl111/PCNN | [
"2e0967aec962d55df1eb7d149a44b91c6c751a1a"
] | [
"model/config.py"
] | [
"import os\nimport numpy as np\n\nfrom .general_utils import get_logger\nfrom .data_utils import load_vocab, get_processing_word\n\nclass Config():\n def __init__(self, load=True):\n \"\"\"Initialize hyperparameters and load vocabs\n\n Args:\n load_embeddings: (bool) if True, load embedd... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thompson318/scikit-surgerycore | [
"22867073a5a3e87def68b4a76e70fe54d085be32"
] | [
"tests/algorithms/test_tracking_smoothing.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Tests for BARD pointer module\"\"\"\nimport math\nimport numpy as np\nimport pytest\nimport sksurgerycore.algorithms.tracking_smoothing as reg\n\n\ndef test_rvec_to_quaterion():\n \"\"\"\n Does it convert correctly\n \"\"\"\n\n #a 90 degree rotation about the x axis\n ... | [
[
"numpy.eye",
"numpy.array",
"numpy.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bourov/probability | [
"1e4053a0938b4773c3425bcbb07b3f1e5d50c7e2",
"1e4053a0938b4773c3425bcbb07b3f1e5d50c7e2",
"1e4053a0938b4773c3425bcbb07b3f1e5d50c7e2",
"1e4053a0938b4773c3425bcbb07b3f1e5d50c7e2",
"1e4053a0938b4773c3425bcbb07b3f1e5d50c7e2",
"1e4053a0938b4773c3425bcbb07b3f1e5d50c7e2",
"1e4053a0938b4773c3425bcbb07b3f1e5d50c7e... | [
"tensorflow_probability/python/distributions/deterministic.py",
"tensorflow_probability/python/internal/backend/numpy/debugging.py",
"tensorflow_probability/python/internal/backend/numpy/gen/linear_operator_diag.py",
"tensorflow_probability/examples/grammar_vae.py",
"tensorflow_probability/python/distributi... | [
"# Copyright 2018 The TensorFlow Probability Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by a... | [
[
"tensorflow.compat.v2.zeros_like",
"tensorflow.compat.v2.abs",
"tensorflow.compat.v2.rank",
"tensorflow.compat.v2.name_scope",
"tensorflow.compat.v2.shape",
"tensorflow.compat.v2.convert_to_tensor",
"tensorflow.compat.v2.broadcast_static_shape",
"tensorflow.compat.v2.identity",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
danielgrassinger/yt_new_frontend | [
"5f91d2fb8721c4c5da0af543a6256ed979cd9fc9",
"5f91d2fb8721c4c5da0af543a6256ed979cd9fc9",
"5f91d2fb8721c4c5da0af543a6256ed979cd9fc9",
"5f91d2fb8721c4c5da0af543a6256ed979cd9fc9",
"5f91d2fb8721c4c5da0af543a6256ed979cd9fc9",
"5f91d2fb8721c4c5da0af543a6256ed979cd9fc9"
] | [
"yt/frontends/athena/io.py",
"yt/frontends/tipsy/setup.py",
"yt/units/tests/test_ytarray.py",
"yt/frontends/enzo/data_structures.py",
"yt/utilities/tests/test_particle_generator.py",
"yt/fields/particle_fields.py"
] | [
"\"\"\"\nThe data-file handling functions\n\n\n\n\"\"\"\n\n#-----------------------------------------------------------------------------\n# Copyright (c) 2013, yt Development Team.\n#\n# Distributed under the terms of the Modified BSD License.\n#\n# The full license is in the file COPYING.txt, distributed with thi... | [
[
"numpy.prod",
"numpy.empty",
"numpy.fromfile",
"numpy.dtype"
],
[
"numpy.distutils.misc_util.Configuration"
],
[
"numpy.random.random",
"numpy.sqrt",
"numpy.ones_like",
"numpy.power",
"numpy.arange",
"numpy.union1d",
"numpy.testing.assert_array_equal",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [
"1.11",
"1.19",
"1.24",
"1.16",
"1.23",
"1.20",
"1.7",
"1.12",
"1.21",
"1.22",
"1.14",
"1.6",
"... |
nbingo/sMOOth | [
"aacdc5d24b931e534e984681923ec74f1103ca2f"
] | [
"src/configs/adult/adult_mlp_weighted.py"
] | [
"\"\"\"\nAn example config file to train a ImageNet classifier with detectron2.\nModel and dataloader both come from torchvision.\nThis shows how to use detectron2 as a general engine for any new models and tasks.\n\nTo run, use the following command:\n\npython tools/lazyconfig_train_net.py --config-file configs/Mi... | [
[
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FujitsuResearch/automatic_pruning | [
"b3bb525b736ca3e465cb6fb87f134748424a0fe5"
] | [
"examples/resnet34_imagenet/resnet34.py"
] | [
"# resnet34.py COPYRIGHT Fujitsu Limited 2022\n\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ndef zero_padding(x1, x2):\n num_ch1 = x1.size()[1]\n num_ch2 = x2.size()[1]\n ch_diff = num_ch1 - num_ch2\n # path1 < path2 : zero padding to path1 tensor\n if num_ch1 < num_ch2:\n ch_dif... | [
[
"torch.nn.Sequential",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.ReLU",
"torch.nn.functional.pad",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hobinkwak/Stock-Movements-Classification | [
"dac2e90d9ef2294f5c4dc8f6605b9051c71b3f45"
] | [
"utils/dataload.py"
] | [
"from itertools import combinations\nimport pandas as pd\n\nfrom utils.utils import *\n\n\ndef load_etf():\n etf_data = pd.read_csv(\n \"data/etf_data.csv\", encoding=\"euc_kr\", parse_dates=[\"tdate\"]\n )\n etf_ohlcv = etf_data.set_index([\"tdate\", \"etf_code\", \"data_name\"])[\n \"value\... | [
[
"pandas.read_csv",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
haideraltahan/datasets | [
"aad5c7ea705949d20817fcc49a892bb2a21532f0"
] | [
"tensorflow_datasets/testing/starcraft.py"
] | [
"# coding=utf-8\n# Copyright 2019 The TensorFlow Datasets Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless ... | [
[
"tensorflow.io.TFRecordWriter",
"tensorflow.train.FeatureLists",
"tensorflow.app.UsageError",
"tensorflow.train.FeatureList",
"numpy.random.randint",
"tensorflow.train.FloatList",
"tensorflow.train.Features",
"tensorflow.train.Int64List"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhao-david/ACORE-LFI | [
"91de88b77f0be110e42ed91bbb7a50b7ca83319a",
"91de88b77f0be110e42ed91bbb7a50b7ca83319a",
"91de88b77f0be110e42ed91bbb7a50b7ca83319a",
"91de88b77f0be110e42ed91bbb7a50b7ca83319a"
] | [
"acore/classifier_cov_pow_toy_pvalue.py",
"acore/classifier_power_multid_truth.py",
"acore/tests/test_sampling_mechanisms.py",
"acore/utils/pytorch_functions.py"
] | [
"from warnings import simplefilter\nsimplefilter(action='ignore', category=FutureWarning)\n\nimport numpy as np\nimport argparse\nimport pandas as pd\nfrom tqdm.auto import tqdm\nfrom datetime import datetime\nfrom sklearn.metrics import log_loss\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nfrom utils... | [
[
"matplotlib.pyplot.legend",
"numpy.log",
"matplotlib.pyplot.tight_layout",
"numpy.greater",
"numpy.random.seed",
"numpy.isfinite",
"numpy.isnan",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.savefig",
"sklearn.metrics.log_loss",
"numpy.std",
"numpy.mean",
"mat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
onlyrico/AliceMind | [
"a6a070b1610e4c4bfe84ee6c4195b2bc4f725ded",
"a6a070b1610e4c4bfe84ee6c4195b2bc4f725ded",
"a6a070b1610e4c4bfe84ee6c4195b2bc4f725ded"
] | [
"StructVBERT/tasks/vqa.py",
"StructVBERT/tasks/nlvr2.py",
"LatticeBERT/tokenization.py"
] | [
"# coding=utf-8\n# Copyleft 2019 project LXRT.\n\nimport os\nimport collections\n\nimport torch\nimport torch.nn as nn\nimport logging\nfrom torch.utils.data.dataloader import DataLoader\nfrom tqdm import tqdm\n\nfrom param import args\nfrom lxrt.qa_answer_table import load_lxmert_qa\nfrom tasks.vqa_model import VQ... | [
[
"torch.nn.Softmax",
"torch.load",
"torch.nn.BCEWithLogitsLoss",
"torch.no_grad",
"torch.utils.data.dataloader.DataLoader"
],
[
"torch.nn.CrossEntropyLoss",
"torch.utils.data.dataloader.DataLoader",
"torch.no_grad",
"torch.load"
],
[
"tensorflow.gfile.GFile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"... |
ziniuwan/maed | [
"9e1f1c37eba81da86c8d9c62dc9be41a01abff5b"
] | [
"lib/models/spin.py"
] | [
"\"\"\"\nThis script is brought from https://github.com/nkolot/SPIN\nAdhere to their licence to use this script\n\"\"\"\n\nimport math\nimport torch\nimport numpy as np\nimport os.path as osp\nimport torch.nn as nn\n\nfrom lib.core.config import DATA_DIR\nfrom lib.utils.geometry import rotation_matrix_to_angle_axis... | [
[
"torch.nn.Dropout",
"torch.zeros",
"torch.cat",
"torch.einsum",
"torch.eye",
"torch.from_numpy",
"torch.nn.Linear",
"torch.matmul",
"torch.nn.init.xavier_uniform_",
"torch.stack",
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
soma2000-lang/colour | [
"bb7ee23ac65e09613af78bd18dd98dffb1a2904a",
"bb7ee23ac65e09613af78bd18dd98dffb1a2904a",
"bb7ee23ac65e09613af78bd18dd98dffb1a2904a",
"bb7ee23ac65e09613af78bd18dd98dffb1a2904a",
"bb7ee23ac65e09613af78bd18dd98dffb1a2904a"
] | [
"colour/models/rgb/transfer_functions/canon_log.py",
"colour/models/rgb/datasets/p3_d65.py",
"colour/examples/appearance/examples_llab.py",
"colour/quality/tests/test_tm3018.py",
"colour/models/rgb/datasets/sharp.py"
] | [
"\"\"\"\nCanon Log Encodings\n===================\n\nDefines the *Canon Log* encodings:\n\n- :func:`colour.models.log_encoding_CanonLog`\n- :func:`colour.models.log_decoding_CanonLog`\n- :func:`colour.models.log_encoding_CanonLog2`\n- :func:`colour.models.log_decoding_CanonLog2`\n- :func:`colour.models.lo... | [
[
"numpy.log10",
"numpy.where",
"numpy.select"
],
[
"numpy.linalg.inv",
"numpy.array"
],
[
"numpy.array"
],
[
"numpy.testing.assert_almost_equal",
"numpy.array"
],
[
"numpy.linalg.inv",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
chaitanyamalaviya/NeuralFactorGraph | [
"6cd664b7edc43d56c6f1165baa7e7625eb0f7cd8"
] | [
"utils.py"
] | [
"from __future__ import division, print_function\nfrom conllu.parser import parse, parse_tree\nfrom tags import Tags, Tag, Label\n\nimport os\nimport re\nimport math\nimport numpy as np\nimport itertools\nimport pdb\nimport pickle\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nimpor... | [
[
"numpy.amax",
"torch.max",
"numpy.exp",
"torch.autograd.Variable",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure",
"torch.LongTensor",
"numpy.log",
"matplotlib.pyplot.savefig",
"torch.exp",
"torch.log",
"numpy.logaddexp.reduce",
"matplotlib.rc",
"matp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bwingert/ProDy | [
"7377a20b4a4841ec59dccaa93fa58e2ee0fe89bc"
] | [
"prody/utilities/catchall.py"
] | [
"\"\"\"This module defines miscellaneous utility functions that is public to users.\"\"\"\n\nimport numpy as np\nfrom numpy import unique, linalg, diag, sqrt, dot\n\nfrom Bio.Phylo.BaseTree import Tree, Clade\n\nfrom prody import PY3K\nfrom .misctools import addEnds, interpY, index, isListLike\nfrom .checkers impor... | [
[
"sklearn.manifold.SpectralEmbedding",
"matplotlib.pyplot.connect",
"sklearn.metrics.silhouette_score",
"numpy.asarray",
"matplotlib.ticker.AutoMinorLocator",
"matplotlib.ticker.AutoLocator",
"numpy.concatenate",
"sklearn.manifold.TSNE",
"matplotlib.colors.TwoSlopeNorm",
"sc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
ZHANG-CAIQI/COMP1001 | [
"abfad8101b4b58697dfbc8599eebf466beebb9ec"
] | [
"Assessments 1-8/Ass8/Q2_b_1.py"
] | [
"import matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\ndef stockUp(priceFile):\r\n\r\n # read the file\r\n infile = open(priceFile, \"r\")\r\n date = []\r\n stock = []\r\n\r\n # store only the dates and closing price\r\n day = 1\r\n firstLine = True\r\n for line in infile:\r\n ... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
steffi7574/lbann | [
"665797a112dc96d15bd1d958de61f48bf5d3d21f"
] | [
"bamboo/unit_tests/test_unit_layer_gather.py"
] | [
"import functools\nimport operator\nimport os\nimport os.path\nimport sys\nimport numpy as np\n\n# Bamboo utilities\ncurrent_file = os.path.realpath(__file__)\ncurrent_dir = os.path.dirname(current_file)\nsys.path.insert(0, os.path.join(os.path.dirname(current_dir), 'common_python'))\nimport tools\n\n# ============... | [
[
"numpy.random.seed",
"numpy.finfo",
"numpy.random.normal",
"numpy.mean",
"numpy.random.uniform",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bigvideoresearch/SCC | [
"f26cdb6aaf248b5112812dbdac1f1b5086aebccc",
"f26cdb6aaf248b5112812dbdac1f1b5086aebccc",
"f26cdb6aaf248b5112812dbdac1f1b5086aebccc"
] | [
"datasets/imagename_dataset.py",
"runner_master/runner/transforms/image/autoaugment_operators.py",
"runner_master/runner/setup.py"
] | [
"from runner_master import runner\nimport os\nimport io\nimport torch\nimport logging\nfrom PIL import Image, ImageFile\nfrom runner_master.runner.data import datasets\n# to fix \"OSError: image file is truncated\"\nImageFile.LOAD_TRUNCATED_IMAGES = True\n\nclass ImagenameDataset(datasets.ImglistDatasetV2):\n\n ... | [
[
"torch.rand"
],
[
"numpy.array",
"numpy.where",
"numpy.clip",
"numpy.random.randint"
],
[
"torch.manual_seed",
"torch.cuda.manual_seed",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hlzhang109/OpenPrompt | [
"8a1ec1ceac3805a11b09dda9b96ad7406d222f26",
"8a1ec1ceac3805a11b09dda9b96ad7406d222f26"
] | [
"openprompt/prompts/one2one_verbalizer.py",
"openprompt/prompt_base.py"
] | [
"import json\nfrom transformers.tokenization_utils import PreTrainedTokenizer\nfrom yacs.config import CfgNode\nfrom openprompt.data_utils.data_utils import InputFeatures\nimport re\nfrom openprompt import Verbalizer\nfrom typing import *\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom o... | [
[
"torch.nn.Parameter",
"torch.log",
"torch.tensor"
],
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ascend/modelzoo | [
"f018cfed33dbb1cc2110b9ea2e233333f71cc509",
"f018cfed33dbb1cc2110b9ea2e233333f71cc509",
"f018cfed33dbb1cc2110b9ea2e233333f71cc509",
"f018cfed33dbb1cc2110b9ea2e233333f71cc509",
"f018cfed33dbb1cc2110b9ea2e233333f71cc509",
"f018cfed33dbb1cc2110b9ea2e233333f71cc509",
"f018cfed33dbb1cc2110b9ea2e233333f71cc50... | [
"built-in/PyTorch/Official/cv/image_classification/Gluon_ResNet50_v1d_for_PyTorch/timm/optim/radam.py",
"built-in/ACL_TensorFlow/Official/recommendation/DCN_for_ACL/scripts/eval.py",
"built-in/ACL_TensorFlow/Official/recommendation/KGAT_for_ACL/Model/offline_inference/xacl_inference.py",
"built-in/PyTorch/Off... | [
"# Copyright [yyyy] [name of copyright owner]\n# Copyright 2021 Huawei Technologies Co., Ltd\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:/... | [
[
"torch.zeros_like"
],
[
"sklearn.metrics.roc_auc_score",
"sklearn.metrics.average_precision_score"
],
[
"numpy.fromfile"
],
[
"torch.save",
"torch.load"
],
[
"torch.is_grad_enabled",
"torch.nn.parallel.distributed._find_tensors"
],
[
"torch.npu.set_device",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
anglixjtu/MeshCNN_ | [
"83826e66d8989ed4967047c2ed6d099568c5781c",
"83826e66d8989ed4967047c2ed6d099568c5781c"
] | [
"src/util/losses.py",
"src/util/visualization.py"
] | [
"import torch\nimport torch.nn as nn\n\n\nclass ChamferLoss(nn.Module):\n\n def __init__(self):\n super(ChamferLoss, self).__init__()\n self.use_cuda = torch.cuda.is_available()\n\n def forward(self, preds, gts, reverse=True, bidirectional=True):\n def compute_loss(preds, gts):\n ... | [
[
"torch.min",
"torch.sum",
"torch.cuda.is_available",
"torch.arange"
],
[
"torch.utils.tensorboard.SummaryWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
morkovka1337/openvino_training_extensions | [
"846db45c264d6b061505213f51763520b9432ba9",
"846db45c264d6b061505213f51763520b9432ba9",
"846db45c264d6b061505213f51763520b9432ba9",
"846db45c264d6b061505213f51763520b9432ba9",
"846db45c264d6b061505213f51763520b9432ba9",
"846db45c264d6b061505213f51763520b9432ba9",
"846db45c264d6b061505213f51763520b9432ba... | [
"pytorch_toolkit/nncf/examples/object_detection/layers/modules/multibox_loss.py",
"tensorflow_toolkit/text_recognition/text_recognition/model.py",
"pytorch_toolkit/nncf/tests/modules/test_rnn.py",
"tensorflow_toolkit/ssd_detector/vlp/config.py",
"pytorch_toolkit/nncf/nncf/nncf_network.py",
"tensorflow_too... | [
"\"\"\"\n Copyright (c) 2019 Intel Corporation\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 http://www.apache.org/licenses/LICENSE-2.0\n Unless required by applicable law or agr... | [
[
"torch.LongTensor",
"torch.Tensor",
"torch.nn.functional.cross_entropy",
"torch.nn.functional.smooth_l1_loss"
],
[
"tensorflow.contrib.rnn.stack_bidirectional_dynamic_rnn",
"tensorflow.transpose",
"tensorflow.contrib.slim.dropout",
"tensorflow.contrib.slim.arg_scope",
"tens... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflo... |
CORAL-CMU/kalibr | [
"ebd759286944f156c3ae6202c27fe47667929744"
] | [
"aslam_offline_calibration/kalibr/python/kalibr_camera_calibration/CameraIntializers.py"
] | [
"import sm\nimport aslam_backend as aopt\nimport aslam_cv as cv\nimport numpy as np\n\ndef addPoseDesignVariable(problem, T0=sm.Transformation()):\n q_Dv = aopt.RotationQuaternionDv( T0.q() )\n q_Dv.setActive( True )\n problem.addDesignVariable(q_Dv)\n t_Dv = aopt.EuclideanPointDv( T0.t() )\n t_Dv.se... | [
[
"numpy.linalg.inv",
"numpy.eye",
"numpy.median",
"numpy.asmatrix",
"numpy.std",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ymaxgit/mxnet | [
"01ae629c6593e0352fd30979bccd0196854ef882"
] | [
"tests/python/unittest/test_gluon_rnn.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lamsoa729/FoXlink | [
"3c061b02968cdab1def752d5c145a6df4615504b"
] | [
"foxlink/me_zrl_bound_evolvers.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"@package docstring\nFile: me_zrl_bound_evolvers.py\nAuthor: Adam Lamson\nEmail: adam.lamson@colorado.edu\nDescription:\n\"\"\"\n\nimport numpy as np\n# from scipy.integrate import dblquad\nfrom .me_helpers import dr_dt, convert_sol_to_geom\nfrom .me_zrl_odes import (rod_geom_derivs_z... | [
[
"numpy.concatenate"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shuishoudage/music_generator | [
"7c17ef5bb3a5d872bff5ac8e1664f57f5b4ea08f"
] | [
"data_clean/preprocessing.py"
] | [
"from typing import List, Tuple, Dict, Any\nfrom collections import Counter\nimport pretty_midi\nimport matplotlib.pyplot as plt\nimport librosa.display\nimport os\nfrom os import listdir, walk\nfrom os.path import isfile, isdir, join\nfrom sys import argv\nimport traceback\nimport logging\nimport numpy as np\nfrom... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kushalj001/transformers | [
"0538820737bd8fb9ba1eb3a772412c6bbe2433ab"
] | [
"src/transformers/modeling_t5.py"
] | [
"# coding=utf-8\n# Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/lice... | [
[
"torch.all",
"torch.abs",
"torch.nn.functional.dropout",
"torch.cat",
"torch.nn.Embedding",
"torch.where",
"torch.full_like",
"torch.nn.Dropout",
"torch.nn.CrossEntropyLoss",
"torch.ones",
"torch.sqrt",
"torch.tensor",
"torch.nn.functional.relu",
"torch.aran... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
sepam/machine-learning-engineering-for-production-public | [
"cd6053459eee9b7f30bf86da63104b3f1381383a"
] | [
"course4/week3-ungraded-labs/C4_W3_Lab_4_Github_Actions/app/main.py"
] | [
"import pickle\nimport numpy as np\nfrom typing import List\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel, conlist\n\n\n\napp = FastAPI(title=\"Predicting Wine Class with batching\")\n\n# Open classifier in global scope\nwith open(\"models/wine-95-fixed.pkl\", \"rb\") as file:\n clf = pickle.load(... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GLaDO8/pytorch_playground | [
"3623de18881a37ce413c92d8a63ea9ba1cc401a5"
] | [
"nnwordembed.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\ntorch.manual_seed(1)\n\nword_to_ix = {\"hello\": 0, \"world\": 1}\n#first argument is the size of the embedded matrix. The second argument is the dimension of each word embedding. \nembeds = nn.Embedding(2, 5) # 2... | [
[
"torch.manual_seed",
"torch.nn.Embedding",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zactodd/mmdetection | [
"68532eb6f4643ddf0179a4384c8c9e004a2c1d07",
"84fbb2c6ee7346ea722cea3a4fa16d73e11fcafd",
"f8d5f6cafeafeac8beb22d855798327682f65f0a"
] | [
"mmdet/models/dense_heads/pisa_retinanet_head.py",
"mmdet/models/dense_heads/ssd_head.py",
"mmdet/models/roi_heads/mask_heads/maskiou_head.py"
] | [
"import torch\n\nfrom mmdet.core import force_fp32, images_to_levels\nfrom ..builder import HEADS\nfrom ..losses import carl_loss, isr_p\nfrom .retina_head import RetinaHead\n\n\n@HEADS.register_module()\nclass PISARetinaHead(RetinaHead):\n \"\"\"PISA Retinanet Head.\n\n The head owns the same structure with ... | [
[
"torch.no_grad",
"torch.cat"
],
[
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.functional.cross_entropy",
"torch.nn.Conv2d",
"torch.isfinite"
],
[
"torch.cat",
"torch.nn.ModuleList",
"numpy.stack",
"torch.nn.modules.utils._pair",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
microsoft/iclr2019-learning-to-represent-edits | [
"e5777d6aa6cdeda500cf076646177c48d1cb4622",
"e5777d6aa6cdeda500cf076646177c48d1cb4622"
] | [
"diff_representation/model/edit_encoder/bag_of_edits_change_encoder.py",
"diff_representation/model/encdec/sequential_decoder.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\nfrom itertools import chain\n\nimport numpy as np\nimport torch\nfrom torch import nn as nn\nfrom torch.autograd import Variable\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\nfrom tqdm import tqdm\nimport sys\n\... | [
[
"numpy.concatenate",
"torch.cat",
"torch.tensor"
],
[
"torch.nn.Dropout",
"torch.zeros",
"torch.cat",
"torch.sum",
"torch.tensor",
"torch.nn.LSTMCell",
"torch.nn.Linear",
"torch.stack",
"torch.nn.functional.tanh"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
junarwohn/tvm | [
"96c2e06cd063a695b3b485f2bdf8875df55fff1a",
"96c2e06cd063a695b3b485f2bdf8875df55fff1a",
"96c2e06cd063a695b3b485f2bdf8875df55fff1a"
] | [
"tvm_test/run_simple_mod_op2_pth.py",
"tvm_test/run_op2_pth.py",
"tvm_test/run_simple_mod_op3_pth.py"
] | [
"import tvm\nfrom tvm import relay\nfrom tvm import relay\nfrom tvm.runtime.vm import VirtualMachine\nfrom tvm.contrib.download import download_testdata\nfrom SimpleModel import Net\nimport numpy as np\nimport cv2\n\n# PyTorch imports\nimport torch\nimport torchvision\n\n# Time library for speed check\nimport time\... | [
[
"numpy.expand_dims",
"torch.jit.trace",
"torch.load",
"torch.no_grad",
"numpy.transpose",
"numpy.random.uniform"
],
[
"numpy.expand_dims",
"torch.jit.trace",
"torch.no_grad",
"numpy.transpose",
"numpy.random.uniform"
],
[
"numpy.expand_dims",
"torch.jit.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rogerfitz/tutorials | [
"dae6470bad63b71e755caaff0b69893f5c9a1d63",
"dae6470bad63b71e755caaff0b69893f5c9a1d63"
] | [
"travel_time_visualization/server.py",
"draft_kings_contests_scrape/main.py"
] | [
"from flask import Flask, jsonify,render_template,request\nfrom config import API_KEY\nimport datetime\nfrom collections import defaultdict\nimport requests\nimport pandas as pd\nimport sys\nimport logging\nfrom itertools import repeat\n\napp = Flask(__name__)\ngunicorn_error_logger = logging.getLogger('gunicorn.er... | [
[
"pandas.DataFrame.from_dict"
],
[
"pandas.concat",
"pandas.read_csv",
"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": []
},
{
"matplotlib": [],
"nump... |
mommy79/AuDi-GIT-turtlebot3_autorace | [
"fd1382246f1ee74ee70857006563184d672a6666"
] | [
"src/mission_node/src/intersection_detector.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport numpy as np\nimport cv2\nimport math\n\n\nclass IntersectionDetector:\n def __init__(self):\n self.lower_blue = np.array([85, 90, 120], np.uint8)\n self.upper_blue = np.array([115, 255, 255], np.uint8)\n\n def fn_find_intersection_line(self... | [
[
"numpy.array",
"numpy.zeros",
"numpy.vstack",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahoho/numpyro | [
"64e94e346c51a6c0c1ba51aa7b608e73513f158f",
"64e94e346c51a6c0c1ba51aa7b608e73513f158f"
] | [
"numpyro/distributions/transforms.py",
"numpyro/distributions/util.py"
] | [
"# Copyright Contributors to the Pyro project.\n# SPDX-License-Identifier: Apache-2.0\n\nimport math\nimport warnings\nimport weakref\n\nimport numpy as np\n\nfrom jax import lax, ops, tree_flatten, tree_map, vmap\nfrom jax.flatten_util import ravel_pytree\nfrom jax.nn import softplus\nimport jax.numpy as jnp\nfrom... | [
[
"numpy.less"
],
[
"numpy.reshape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tvieijra/netket | [
"ef3ff32b242f25b6a6ae0f08db1aada85775a2ea"
] | [
"Test/Machine/rbm.py"
] | [
"# Copyright 2019 The Simons Foundation, Inc. - 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... | [
[
"numpy.dot",
"numpy.cosh",
"numpy.concatenate",
"numpy.outer",
"numpy.tanh",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jameszhou-gl/CBRE | [
"53c952e0afc74518fc4223f0f20881336df20f95",
"53c952e0afc74518fc4223f0f20881336df20f95"
] | [
"cbre/cbre_net.py",
"cbre/loader.py"
] | [
"import tensorflow as tf\nimport numpy as np\nfrom cbre.util import *\n\n\nclass CBRENet(object):\n \"\"\"\n cbre_net implements the cycly-balanced representation learning for counterfactual inference\n\n The network is implemented as a tensorflow graph. The class constructor\n creates an object contain... | [
[
"tensorflow.concat",
"numpy.sqrt",
"tensorflow.zeros",
"tensorflow.nn.l2_loss",
"tensorflow.where",
"tensorflow.random_shuffle",
"tensorflow.Variable",
"tensorflow.nn.moments",
"tensorflow.gradients",
"tensorflow.gather",
"tensorflow.dynamic_stitch",
"tensorflow.squ... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caltech-netlab/gym-acnportal | [
"cacd2e4aa9159a3bf7f0b8e3db2dbb0832d76e46",
"cacd2e4aa9159a3bf7f0b8e3db2dbb0832d76e46"
] | [
"gym_acnportal/gym_acnsim/envs/tests/test_action_spaces.py",
"tutorials/lessonx_training_running_rl_agent.py"
] | [
"# coding=utf-8\n\"\"\" Tests for SimAction and action space functions. \"\"\"\nimport unittest\nfrom typing import Callable, Dict, List, Any\nfrom unittest.mock import create_autospec\n\nimport numpy as np\nfrom gym import Space\n\nfrom ..action_spaces import (\n SimAction,\n single_charging_schedule,\n z... | [
[
"numpy.testing.assert_equal",
"numpy.array"
],
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ckxy/part-of-hitogata | [
"76402d48a336fcd964d0e64bb01d959e8f07f296",
"76402d48a336fcd964d0e64bb01d959e8f07f296",
"76402d48a336fcd964d0e64bb01d959e8f07f296",
"76402d48a336fcd964d0e64bb01d959e8f07f296"
] | [
"datasets/readers/ccpd.py",
"utils/mask_tools.py",
"datasetsnx/readers/wflw.py",
"datasets/readers/lvis.py"
] | [
"import os\nimport numpy as np\nfrom addict import Dict\nfrom PIL import Image\nfrom .reader import Reader\nfrom .builder import READER\n\n\n__all__ = ['CCPD2019FolderReader']\n\n\n@READER.register_module()\nclass CCPD2019FolderReader(Reader):\n def __init__(self, root, **kwargs):\n super(CCPD2019FolderRe... | [
[
"numpy.array"
],
[
"numpy.unique",
"torch.sum",
"numpy.array",
"numpy.histogram",
"numpy.sum"
],
[
"numpy.asarray",
"numpy.array"
],
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
YeeCY/PASF | [
"95e548d365ea5da482c56408539d9a1514ef246b"
] | [
"rlkit/samplers/data_collector/path_collector.py"
] | [
"from collections import deque, OrderedDict\nfrom functools import partial\n\nimport numpy as np\n\nfrom rlkit.core.eval_util import create_stats_ordered_dict\nfrom rlkit.samplers.data_collector.base import PathCollector\nfrom rlkit.samplers.rollout_functions import rollout\n\n\nclass ActionAgent():\n def __init... | [
[
"numpy.hstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
charliec443/plaid-rl | [
"2e8fbf389af9efecd41361df80e40e0bf932056d",
"2e8fbf389af9efecd41361df80e40e0bf932056d",
"2e8fbf389af9efecd41361df80e40e0bf932056d",
"2e8fbf389af9efecd41361df80e40e0bf932056d"
] | [
"plaidrl/exploration_strategies/ou_strategy.py",
"plaidrl/torch/data_management/normalizer.py",
"plaidrl/torch/vae/vae_trainer.py",
"plaidrl/torch/data.py"
] | [
"import numpy as np\nimport numpy.random as nr\n\nfrom plaidrl.exploration_strategies.base import RawExplorationStrategy\n\n\nclass OUStrategy(RawExplorationStrategy):\n \"\"\"\n This strategy implements the Ornstein-Uhlenbeck process, which adds\n time-correlated noise to the actions taken by the determin... | [
[
"numpy.ones",
"numpy.prod",
"numpy.clip"
],
[
"torch.clamp"
],
[
"torch.optim.Adam",
"numpy.arange",
"numpy.uint8",
"torch.nn.functional.mse_loss",
"numpy.std",
"numpy.mean",
"numpy.any",
"torch.log",
"torch.distributions.Normal",
"torch.stack",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
sdwivedi/LightGBM | [
"f5ec54fbaca8bd5f72cdecbf755216c6278aafe3"
] | [
"examples/python-guide/advanced_example.py"
] | [
"# coding: utf-8\nimport json\nimport lightgbm as lgb\nimport pandas as pd\nimport numpy as np\nfrom sklearn.metrics import mean_squared_error\n\ntry:\n import cPickle as pickle\nexcept BaseException:\n import pickle\n\nprint('Loading data...')\n# load or create your dataset\ndf_train = pd.read_csv('../binary... | [
[
"numpy.exp",
"pandas.read_csv",
"numpy.mean",
"sklearn.metrics.mean_squared_error"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
afonchikk/Audio-Classification | [
"6acc7015ec847a64338f6300dca608a0752ba554"
] | [
"predict.py"
] | [
"from tensorflow.keras.models import load_model\nfrom clean import downsample_mono, envelope\nfrom kapre.time_frequency import STFT, Magnitude, ApplyFilterbank, MagnitudeToDecibel\nfrom sklearn.preprocessing import LabelEncoder\nimport numpy as np\nfrom glob import glob\nimport argparse\nimport os\nimport pandas as... | [
[
"tensorflow.keras.models.load_model",
"sklearn.preprocessing.LabelEncoder",
"numpy.argmax",
"numpy.mean",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
electr0de/APControllerProjectGit | [
"141ac08e716d6ac8cebe7b144b744744024d8939",
"141ac08e716d6ac8cebe7b144b744744024d8939"
] | [
"simglucose/controller/PaperController.py",
"simglucose/simulation/theta_init.py"
] | [
"from functools import partial\nfrom pprint import pprint\nimport matplotlib.pyplot as plt\n\n# import test2\nfrom simglucose.controller.base import Controller\n#from datetime import datetime, timedelta, time\nimport numpy as np\nimport math\n\npercent_value = 0.05\n\nsign = lambda x: math.copysign(1, x)\n\nnormali... | [
[
"numpy.random.normal",
"numpy.random.rand",
"numpy.random.seed"
],
[
"numpy.array",
"numpy.diff"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
0Miquel/LIIF-temporal | [
"b992cb87cb9bdeba6d4c9bc3960b36ba52a1ba75"
] | [
"models/rdn.py"
] | [
"# Residual Dense Network for Image Super-Resolution\r\n# https://arxiv.org/abs/1802.08797\r\n# modified from: https://github.com/thstkdgus35/EDSR-PyTorch\r\n\r\nfrom argparse import Namespace\r\n\r\nimport torch\r\nimport torch.nn as nn\r\n\r\nfrom models import register\r\n\r\n\r\nclass RDB_Conv(nn.Module):\r\n ... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.PixelShuffle",
"torch.nn.Conv3d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danagi/tianshou | [
"c97aa4065ee8464bd5897bb86f1f81abd8e2cff9",
"c97aa4065ee8464bd5897bb86f1f81abd8e2cff9"
] | [
"tianshou/policy/modelfree/discrete_sac.py",
"examples/mujoco/point_maze_td3.py"
] | [
"import torch\nimport numpy as np\nfrom torch.distributions import Categorical\nfrom typing import Any, Dict, Tuple, Union, Optional\n\nfrom tianshou.policy import SACPolicy\nfrom tianshou.data import Batch, ReplayBuffer, to_torch\n\n\nclass DiscreteSACPolicy(SACPolicy):\n \"\"\"Implementation of SAC for Discret... | [
[
"torch.min",
"torch.distributions.Categorical",
"torch.no_grad"
],
[
"torch.manual_seed",
"torch.cuda.is_available",
"torch.utils.tensorboard.SummaryWriter",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mayanks888/second.pytorch | [
"02d37885a543ee46516648dcab7db8f5d677a179",
"02d37885a543ee46516648dcab7db8f5d677a179",
"02d37885a543ee46516648dcab7db8f5d677a179"
] | [
"second/mayank_scripts/infer_ros_melodic_pretained_same_frame.py",
"second/mayank_scripts/infer_ros2.py",
"second/pytorch/models/voxelnet_mayank.py"
] | [
"#!/usr/bin/env python\n# ROS node libs\n\nimport time\n\nimport numpy as np\nimport rospy\nimport torch\n# from geometry_msgs.msg import Quaternion, Pose, Point, Vector3\nfrom pyquaternion import Quaternion\nfrom google.protobuf import text_format\nfrom sensor_msgs.msg import PointCloud2\nfrom std_msgs.msg import ... | [
[
"numpy.pad",
"torch.load",
"numpy.squeeze",
"torch.tensor",
"numpy.concatenate",
"numpy.ones",
"numpy.fromstring",
"torch.cuda.is_available",
"numpy.array",
"numpy.where"
],
[
"numpy.pad",
"torch.load",
"numpy.squeeze",
"torch.tensor",
"numpy.concate... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MaxSchambach/colour | [
"3f3685d616fda4be58cec20bc1e16194805d7e2d",
"3f3685d616fda4be58cec20bc1e16194805d7e2d",
"3f3685d616fda4be58cec20bc1e16194805d7e2d",
"3f3685d616fda4be58cec20bc1e16194805d7e2d",
"3f3685d616fda4be58cec20bc1e16194805d7e2d",
"3f3685d616fda4be58cec20bc1e16194805d7e2d",
"3f3685d616fda4be58cec20bc1e16194805d7e2... | [
"colour/corresponding/datasets/breneman1987.py",
"colour/models/rgb/transfer_functions/itur_bt_1886.py",
"colour/volume/tests/test_mesh.py",
"colour/utilities/common.py",
"colour/appearance/llab.py",
"colour/plotting/quality.py",
"colour/models/rgb/transfer_functions/tests/test_viper_log.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nBreneman Corresponding Chromaticities Dataset\n=============================================\n\nDefines *Breneman (1987)* results for corresponding chromaticities experiments.\n\nSee Also\n--------\n`Corresponding Chromaticities Prediction Jupyter Notebook\n<http://nbviewer.jupyter... | [
[
"numpy.array"
],
[
"numpy.maximum"
],
[
"numpy.reshape",
"numpy.array",
"numpy.tile"
],
[
"numpy.errstate",
"numpy.asarray"
],
[
"numpy.log",
"numpy.radians",
"numpy.linalg.inv",
"numpy.arctan2",
"numpy.log10",
"numpy.array"
],
[
"numpy.a... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
NestLakerJasonLIN/pipedream | [
"cad624f79a71f44ba79099f0c38321347b13e5c2",
"cad624f79a71f44ba79099f0c38321347b13e5c2",
"cad624f79a71f44ba79099f0c38321347b13e5c2",
"cad624f79a71f44ba79099f0c38321347b13e5c2",
"cad624f79a71f44ba79099f0c38321347b13e5c2",
"cad624f79a71f44ba79099f0c38321347b13e5c2",
"cad624f79a71f44ba79099f0c38321347b13e5c... | [
"profiler/torchmodules/torchlogger/activation_gradient_logger.py",
"runtime/image_classification/models/vgg16/gpus=16_straight/stage5.py",
"profiler/translation/seq2seq/train/smoothing.py",
"runtime/translation/main_with_runtime.py",
"runtime/translation/seq2seq/utils.py",
"runtime/image_classification/mo... | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport os\nimport pickle\nimport torch\n\n\nclass ActivationAndGradientLogger:\n def __init__(self, directory):\n self.directory = directory\n try:\n os.mkdir(self.directory)\n except:\n pass\... | [
[
"torch.save"
],
[
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.init.normal_",
"torch.nn.init.kaiming_normal_"
],
[
"torch.nn.functional.log_softmax"
],
[
"torch.cuda.set_device",
"torch.load",
"torch.Tensor",
"torch.cuda.memory_cached",
"torch.no_... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
PriyamvadaKumar/AWS_BioActive_Classification | [
"b6a4413618586712ca4dc196f2dfaa3ceca804fb"
] | [
"bioactive_lab.py"
] | [
"import os, sys\ndirpath = os.getcwd()\nsys.path.insert(0, dirpath + '/goal_tether_functions')\nsys.path.insert(0, dirpath + '/predictive_modelers')\nsys.path.insert(0, dirpath + '/predictive_modelers/assessment_resources')\nsys.path.insert(0, dirpath + '/active_learners')\nsys.path.insert(0, dirpath + '/data_acqui... | [
[
"pandas.read_csv",
"sklearn.cluster.KMeans",
"matplotlib.pyplot.subplots",
"numpy.savetxt",
"sklearn.preprocessing.LabelEncoder",
"numpy.zeros",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
polewczakp/pyAudioAnalysis | [
"7dc2d8e18da1ca2f2485a402bb7399b43bbb2b24"
] | [
"pyAudioAnalysis/audioSegmentation.py"
] | [
"from __future__ import print_function\nimport os\nimport csv\nimport glob\nimport scipy\nimport sklearn\nimport numpy as np\nimport hmmlearn.hmm\nimport sklearn.cluster\nimport pickle as cpickle\nimport matplotlib.pyplot as plt\nfrom scipy.spatial import distance\nimport sklearn.discriminant_analysis\nfrom pyAudio... | [
[
"sklearn.cluster.KMeans",
"matplotlib.pyplot.rc",
"matplotlib.pyplot.plot",
"numpy.max",
"numpy.concatenate",
"numpy.mean",
"sklearn.discriminant_analysis.LinearDiscriminantAnalysis",
"numpy.where",
"numpy.unique",
"scipy.signal.medfilt",
"numpy.arange",
"numpy.eye"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
BwCai/DCAA-UDA | [
"359c2122060aebfbe4384c918768c261fe2dc9c7"
] | [
"models/adaptation_model_stage1.py"
] | [
"from models.base_model import BaseModel\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport os, sys\nimport torch\nimport numpy as np\nimport itertools\n\nfrom torch.autograd import Variable\nfrom optimizers import get_optimizer\nfrom schedulers import get_scheduler\nfrom models.sync_batchnorm import S... | [
[
"torch.mean",
"torch.nn.functional.softmax",
"torch.cat",
"torch.load",
"torch.zeros",
"torch.sum",
"torch.nn.BCEWithLogitsLoss",
"numpy.mean",
"torch.no_grad",
"torch.nn.functional.interpolate",
"torch.cuda.is_available",
"torch.nn.L1Loss",
"torch.masked_select... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OSUrobotics/KinovaGrasping | [
"f22af60d3683fdc4ffecf49ccff179fbc6750748"
] | [
"gym-kinova-gripper/plotting_code/other_plots.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\n\n## Extra plotting functions that can be called for quick analysis\n\ndef plot_timestep_distribution(success_timesteps=None, fail_timesteps=None, all_timesteps=None, expert_saving_dir=None):\n \"\"\" Plot the distribution of time steps over successful and fa... | [
[
"matplotlib.pyplot.title",
"numpy.load",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wenyuC94/LogConcComp | [
"b17d6ba6a102ba83a8415774b0e6da27a362bd5d"
] | [
"src/utils.py"
] | [
"import os\nimport numpy as np\nimport numba as nb\n\ndef create_folder(storage_path):\n if not os.path.isdir(storage_path):\n os.makedirs(storage_path,exist_ok=True)\n lsdir = os.listdir(storage_path)\n for item in [\"info\",\"hist\",\"soln\",\"figs\"]:\n if item not in lsdir:\n ... | [
[
"numpy.random.RandomState",
"numpy.sqrt",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
1suancaiyu/STEP | [
"54195112990feaee137f5137775c736d07c2d26f",
"54195112990feaee137f5137775c736d07c2d26f"
] | [
"classifier_stgcn_real_only/utils/temp.py",
"classifier_hybrid/main.py"
] | [
"import h5py\nimport os\nimport numpy as np\n\nbase_path = os.path.dirname(os.path.realpath(__file__))\nfeature_file = '/media/uttaran/FCE1-7BF3/Gamma/Gait/classifier_stgcn/model_classifier_stgcn/featuresCombineddeep_features.txt'\nf = np.loadtxt(feature_file)\nfCombined = h5py.File('/media/uttaran/FCE1-7BF3/Gamma/... | [
[
"numpy.loadtxt"
],
[
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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