repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
Heegreis/pytorch-YOLOv4 | [
"99045748bd6fdbb55c7dac48ef82941c641e65c6"
] | [
"tool/utils.py"
] | [
"import sys\nimport os\nimport time\nimport math\nimport numpy as np\n\nimport itertools\nimport struct # get_image_size\nimport imghdr # get_image_size\n\n\ndef sigmoid(x):\n return 1.0 / (np.exp(-x) + 1.)\n\n\ndef softmax(x):\n x = np.exp(x - np.expand_dims(np.max(x, axis=1), axis=1))\n x = x / np.expa... | [
[
"numpy.max",
"numpy.array",
"numpy.minimum",
"numpy.copy",
"numpy.exp",
"numpy.where",
"numpy.loadtxt",
"numpy.argmax",
"numpy.maximum"
]
] |
waiting-gy/Caltech_Pedestrian2 | [
"41d7fb3e65866f7c50da556a0e4845149fdb0839"
] | [
"data/coco.py"
] | [
"from .config import HOME\nimport os\nimport os.path as osp\nimport sys\nimport torch\nimport torch.utils.data as data\nimport torchvision.transforms as transforms\nimport cv2\nimport numpy as np\n\n#COCO_ROOT = osp.join(HOME, 'data/coco/')\n#COCO_ROOT = \"/mnt/Younggao/coco/\"\nCOCO_ROOT = \"/kaggle/input/CaltechP... | [
[
"numpy.array",
"numpy.expand_dims",
"torch.from_numpy"
]
] |
icemtel/vedo | [
"b405064a10009b904e226b3f578e4c19aa50320a"
] | [
"examples/advanced/interpolateScalar2.py"
] | [
"\"\"\"Use scipy to interpolate the value of a scalar known on a set\nof points on a new set of points where the scalar is not defined.\n\nTwo interpolation methods are possible:\nRadial Basis Function (used here), and Nearest Point.\n\"\"\"\nimport numpy as np\nfrom vedo import *\nfrom scipy.interpolate import Rbf... | [
[
"scipy.interpolate.Rbf",
"numpy.split"
]
] |
ClementBM/coronavirus | [
"0cddd4042e163f1ac4460b7a97efcb9d419e99e2"
] | [
"coronavirus/tswindow.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\n\n\nclass WindowGenerator:\n def __init__(\n self,\n input_width,\n label_width,\n shift,\n train_df,\n validation_df,\n test_df,\n stride=10,\n label_columns=None,\n )... | [
[
"numpy.array",
"matplotlib.pyplot.xlabel",
"tensorflow.keras.preprocessing.timeseries_dataset_from_array",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.figure",
"numpy.arange",
"tensorflow.stack",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.sc... |
nkibrislioglu/NLP_Disaster_Response_Pipelines | [
"d1c8db73d0927b7600991fe5bc11b02967683984"
] | [
"app/run.py"
] | [
"import json\nimport plotly\nimport pandas as pd\n\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.tokenize import word_tokenize\n\nfrom flask import Flask\nfrom flask import render_template, request, jsonify\nfrom plotly.graph_objs import Bar\nfrom sklearn.externals import joblib\nfrom sqlalchemy import create... | [
[
"sklearn.externals.joblib.load",
"pandas.read_sql_table"
]
] |
microsoft/GLIP | [
"fd52c6361f013e70ae7682d90b3ab3ca2bd5e6bc"
] | [
"maskrcnn_benchmark/modeling/rpn/modeling_bert.py"
] | [
"# coding=utf-8\r\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\r\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\... | [
[
"torch.nn.Linear",
"torch.nn.Dropout",
"torch.nn.LayerNorm",
"torch.cat",
"torch.nn.Softmax",
"torch.einsum",
"torch.arange",
"torch.clamp",
"torch.matmul",
"torch.nn.Embedding"
]
] |
anferben/telemarketing_campaign_success | [
"68a60a63044f65f03afa66ef337e004380116133"
] | [
"scripts/train.py"
] | [
"import pickle\n\nimport pandas as pd\nimport numpy as np\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import KFold\n\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import roc_auc_score\n\n\n#... | [
[
"sklearn.ensemble.RandomForestClassifier",
"numpy.mean",
"sklearn.model_selection.KFold",
"numpy.std",
"sklearn.model_selection.train_test_split",
"pandas.read_csv",
"sklearn.feature_extraction.DictVectorizer",
"sklearn.metrics.roc_auc_score"
]
] |
saArbabi/rl-agents | [
"18ae779f015748eefb346e34b8406a3e4ff16208"
] | [
"rl_agents/agents/tree_search/abstract.py"
] | [
"import logging\nfrom collections import defaultdict\n\nimport gym\nimport numpy as np\nfrom gym.utils import seeding\n\nfrom rl_agents.agents.common.abstract import AbstractAgent\nfrom rl_agents.agents.common.factory import preprocess_env, safe_deepcopy_env\nfrom rl_agents.configuration import Configurable\nfrom r... | [
[
"numpy.amax",
"numpy.nonzero"
]
] |
denyslazarenko/neural_prophet | [
"868e3f23c2a565ea14d54fdea2ff6d199f30a5c8"
] | [
"neuralprophet/df_utils.py"
] | [
"from collections import OrderedDict\nfrom dataclasses import dataclass\nimport pandas as pd\nimport numpy as np\nimport logging\nimport math\n\n\nlog = logging.getLogger(\"NP.df_utils\")\n\n\n@dataclass\nclass ShiftScale:\n shift: float = 0.0\n scale: float = 1.0\n\n\ndef prep_copy_df_dict(df):\n \"\"\"Cr... | [
[
"numpy.quantile",
"numpy.min",
"numpy.mean",
"pandas.concat",
"numpy.issubdtype",
"numpy.max",
"pandas.DataFrame",
"numpy.argmax",
"pandas.to_datetime",
"numpy.round",
"numpy.std",
"numpy.argsort",
"pandas.to_numeric",
"pandas.infer_freq",
"numpy.isinf",... |
bfilippi/pandas-highcharts | [
"bf449b7db8b6966bcf95a0280bf2e4518f3e2419"
] | [
"pandas_highcharts/core.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport pandas\nimport copy\n\n\n_pd2hc_kind = {\n \"bar\": \"column\",\n \"barh\": \"bar\",\n \"area\": \"area\",\n \"line\": \"line\",\n \"pie\": \"pie\"\n}\n\n\ndef pd2hc_kind(kind):\n if kind not in _pd2hc_kind:\n raise ValueError(\"%(kind)s plots are not yet ... | [
[
"pandas.DataFrame",
"pandas.io.json.dumps"
]
] |
JKingKong/mmdetection | [
"cfa22397194c592c25bd19e2f9f2f60f1ea699d3",
"cfa22397194c592c25bd19e2f9f2f60f1ea699d3"
] | [
"violin_picture.py",
"mmdet/models/detectors/two_stage.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\n\nfig, axes = plt.subplots(figsize=(10, 10))\n\n\nall_data = [np.random.normal(0, std, 10) for std in range(9, 10)]\nall_data = [np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])]\naxes.violinplot(all_data,\n showmeans=False,\n showmed... | [
[
"matplotlib.pyplot.show",
"numpy.array",
"numpy.random.normal",
"matplotlib.pyplot.subplots"
],
[
"torch.zeros",
"torch.cat",
"torch.randn",
"torch.ones"
]
] |
condmat/simple-dmrg | [
"f80db9cb75b99d5627440174a888cd5868a07f28"
] | [
"simple_dmrg_02_finite_system.py"
] | [
"#!/usr/bin/env python\n#\n# Simple DMRG tutorial. This code integrates the following concepts:\n# - Infinite system algorithm\n# - Finite system algorithm\n#\n# Copyright 2013 James R. Garrison and Ryan V. Mishmash.\n# Open source under the MIT license. Source code at\n# <https://github.com/simple-dmrg/simple-... | [
[
"numpy.array",
"numpy.zeros",
"numpy.set_printoptions",
"numpy.linalg.eigh",
"scipy.sparse.identity",
"scipy.sparse.linalg.eigsh",
"scipy.sparse.kron"
]
] |
Henistein/tinygrad | [
"e3be28c825634fa92225a99ea712c2ea58d4dc6a"
] | [
"test/test_train.py"
] | [
"import os\nimport unittest\nimport time\nimport tinygrad.optim as optim\nimport numpy as np\nfrom tinygrad.tensor import Tensor\nfrom extra.training import train\nfrom extra.utils import get_parameters\nfrom models.efficientnet import EfficientNet\nfrom models.transformer import Transformer\nfrom models.resnet imp... | [
[
"numpy.prod",
"numpy.zeros"
]
] |
Fredpwol/pygnet | [
"02315c95ef6ee884bd55220a215c4bdd4f035317"
] | [
"graphnet/_vis/decorators.py"
] | [
"import numpy as np \n\n\n\ndef preproccess_plot(func):\n \"\"\"\n creates coordinate in the plot for nodes. \n \"\"\"\n def wrapper(graph, ax, n, weighted, shrinkA, shrinkB, layout, polygon_radius, attr, *args, **kwargs):\n space = np.linspace(0,1,n+1)\n wrapper.scale = 100 // (n+1)\n ... | [
[
"numpy.sin",
"numpy.random.shuffle",
"numpy.cos",
"numpy.random.random",
"numpy.linspace"
]
] |
Lucciola111/stream_autoencoder_windowing | [
"5456b07bd20220c987598db2cdb832d8195e1575",
"5456b07bd20220c987598db2cdb832d8195e1575"
] | [
"TrainingFunctions/DetectDriftADWINOnline.py",
"Evaluation/Generate_withIterations_TimeComplexityAnalysis.py"
] | [
"import tensorflow as tf\nimport numpy as np\nfrom skmultiflow.drift_detection.adwin import ADWIN\nfrom Utility_Functions.ComputeErrorPerDim import compute_error_per_dim\nfrom TrainingFunctions.BuildAutoencoder import build_autoencoder\n\n\ndef detect_drift_adwin_online(\n test_X, idx, dist, adwin, autoencod... | [
[
"numpy.linalg.norm",
"tensorflow.keras.callbacks.EarlyStopping"
],
[
"pandas.DataFrame",
"numpy.mean"
]
] |
hekun520/MEC_offloading | [
"42b17c4172f10ae15d13cc1c30f1389904be647f"
] | [
"simulation.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n MEC_offloading.simulation\n ~~~~~~~~~~~~~~~~~~~~~~~~~\n\n Simulation for the MEC_offloading\n\n :copyright: (c) 2018 by Giorgos Mitsis.\n :license: MIT License, see LICENSE for more details.\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom para... | [
[
"numpy.bincount",
"numpy.array",
"numpy.empty",
"numpy.random.seed",
"numpy.ones",
"numpy.append"
]
] |
TalhaUsuf/RetinaNet_W9_form | [
"2a66bba1de96bebd679775b841d95ac7dcfcbbbe",
"2a66bba1de96bebd679775b841d95ac7dcfcbbbe",
"2a66bba1de96bebd679775b841d95ac7dcfcbbbe"
] | [
"detectron_/engine/train_loop.py",
"tests/data/test_transforms.py",
"detectron_/modeling/roi_heads/rotated_fast_rcnn.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\n\nimport logging\nimport numpy as np\nimport time\nimport weakref\nfrom typing import List, Mapping, Optional\nimport torch\nfrom torch.nn.parallel import DataParallel, DistributedDataParallel\n\nimport detectron.utils.comm as comm\nfrom ... | [
[
"torch.cuda.amp.autocast",
"numpy.mean",
"torch.cuda.is_available",
"numpy.isfinite",
"torch.cuda.amp.GradScaler"
],
[
"numpy.array",
"numpy.random.rand",
"numpy.random.seed",
"numpy.allclose",
"numpy.random.randint",
"numpy.random.random"
],
[
"torch.isfini... |
boffomarco/hrp | [
"7017d358b8b53c289d0859f7dc61ca0d134843ed"
] | [
"am_driver_safe/src/Full_batch_EKF.py"
] | [
"#!/usr/bin/env python3\n\n# From https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter.py\n\nimport math\nfrom math import sin, cos, pi\n\nimport rospy\nimport tf\nfrom std_msgs.msg import Header\nfrom geometry_msgs.msg import Point, Pose, Quaternion,... | [
[
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.plot",
"numpy.linalg.pinv",
"matplotlib.pyplot.cla",
"numpy.eye",
"scipy.spatial.transform.Rotation.from_euler",
"numpy.arange",
"numpy.linalg.eig",
"matplotlib.pyplot.pause",
"matplotlib.pyp... |
AustinJAdams/statsmodels | [
"9271ced806b807a4dd325238df38b60f1aa363e2",
"e6632b6466dc7eb7062df0f26a6888da0e67e347"
] | [
"examples/python/glm.py",
"statsmodels/tsa/statespace/mlemodel.py"
] | [
"# coding: utf-8\n\n# DO NOT EDIT\n# Autogenerated from the notebook glm.ipynb.\n# Edit the notebook and then sync the output with this file.\n#\n# flake8: noqa\n# DO NOT EDIT\n\n# # Generalized Linear Models\n\nimport numpy as np\nimport statsmodels.api as sm\nfrom scipy import stats\nfrom matplotlib import pyplot... | [
[
"scipy.stats.zscore",
"scipy.stats.scoreatpercentile",
"numpy.random.rand",
"numpy.random.seed",
"numpy.exp",
"matplotlib.pyplot.subplots",
"numpy.arange",
"numpy.column_stack"
],
[
"scipy.stats.norm.pdf",
"numpy.ones_like",
"numpy.dot",
"numpy.outer",
"nump... |
2021rahul/Weakly-supervised-regression-for-ORDinal-labels | [
"1654b097c2f8fe3177fd929a8c58bbea677015f0"
] | [
"SOURCE/BALANCED/WORD/test_model.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 13 12:32:57 2019\n\n@author: ghosh128\n\"\"\"\n\nimport sys\nsys.path.append(\"../\")\nimport os\nimport numpy as np\nimport config\nimport tensorflow as tf\nfrom sklearn.metrics import mean_squared_error\nfrom math import sqrt\n\ntf.set_r... | [
[
"tensorflow.set_random_seed",
"tensorflow.zeros_initializer",
"sklearn.metrics.mean_squared_error",
"numpy.reshape",
"tensorflow.sigmoid",
"tensorflow.contrib.layers.xavier_initializer",
"tensorflow.reset_default_graph",
"tensorflow.matmul",
"tensorflow.train.Saver",
"tenso... |
drunckoder/yandex_school | [
"d080fee90b74977e0a671309662893b0f95fad60"
] | [
"yandex_school/resources.py"
] | [
"from typing import List, Dict, Tuple\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\nfrom flask import request\nfrom flask_restful import Resource\nfrom marshmallow import ValidationError\nimport numpy\n\nfrom yandex_school import db\nfrom yandex_school.models import Import, Cit... | [
[
"numpy.percentile"
]
] |
superporchetta/tide_prediction | [
"9946f0c97b7e1bbe0a791380d6a35e277f5d3a1c"
] | [
"LSTM.py"
] | [
"from sklearn.preprocessing import StandardScaler, MinMaxScaler\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\n# https://cnvrg.io/pytorch-lstm/\n\n\nclass LSTM1(nn.Module):\n def __init__(self, num_classes, input_size, hidden_size, num_layers, seq_length):\n super(LSTM1, self... | [
[
"torch.nn.Linear",
"torch.nn.LSTM",
"torch.nn.ReLU"
]
] |
Yuchong-Geng/lanefinder | [
"6ff65d36b681f1594cd2c2788a2dfa7632cac6d0"
] | [
"image/processing.py"
] | [
"import cv2\nimport numpy as np\n\n\ndef preprocessing(frame, mean, std):\n # normalize and quantize input\n # with paramaeters obtained during\n # model calibration\n frame *= (1 / 255)\n expd = np.expand_dims(frame, axis=0)\n quantized = (expd / std + mean)\n\n return quantized.astype(np.uint... | [
[
"numpy.expand_dims"
]
] |
wizardhead/pytti-core | [
"6030f6154ad7d17b93cf76e2d42905d4231a0abd"
] | [
"src/pytti/Image/ema_image.py"
] | [
"import torch\nfrom torch import nn\nfrom pytti.Image.differentiable_image import DifferentiableImage\n\n\nclass EMAImage(DifferentiableImage):\n \"\"\"\n Base class for differentiable images with Exponential Moving Average filtering\n Based on code by Katherine Crowson\n \"\"\"\n\n def __init__(self... | [
[
"torch.no_grad",
"torch.nn.Parameter",
"torch.tensor",
"torch.ones_like",
"torch.zeros_like"
]
] |
allenai/ViRB | [
"fbe1c42571ce0994b1e41bc4bdf88cf9658ae48b"
] | [
"models/DeepLabClassificationHead.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom models.ResNet50Encoder import Bottleneck\n\n\nclass DeepLabClassificationHead(nn.Module):\n\n def __init__(self, num_classes):\n super().__init__()\n self.aspp = ASPP(2048, 256)\n self.low_level_feature_reducer = n... | [
[
"torch.nn.Linear",
"torch.cat",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.Upsample",
"torch.nn.Conv2d",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.Flatten"
]
] |
DEVESHTARASIA/unidata-python-workshop | [
"6ce194a0515effbd0cddb50c2302d5160494747e"
] | [
"notebooks/Command_Line_Tools/skewt.py"
] | [
"# skewt.py - A simple Skew-T plotting tool\n\nimport argparse\nfrom datetime import datetime\n\nimport matplotlib.pyplot as plt\nimport metpy.calc as mpcalc\nfrom metpy.io.upperair import get_upper_air_data\nfrom metpy.plots import Hodograph, SkewT\nfrom metpy.units import units\nfrom mpl_toolkits.axes_grid1.inset... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] |
microsoft-fevieira/maro | [
"f9b4fcd06222109238be491ef64ce8affb2938d8"
] | [
"maro/simulator/scenarios/cim/business_engine.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\n\nimport os\nfrom math import ceil, floor\n\nimport numpy as np\nfrom yaml import safe_load\n\nfrom maro.backends.frame import FrameBase, SnapshotList\nfrom maro.data_lib.cim import CimDataContainerWrapper, Order, Stop\nfrom maro.event_bu... | [
[
"numpy.where",
"numpy.array"
]
] |
ReneRa/scikit-learn-extensions | [
"499c763b22e980d5add16b085d11915b3cd1c6d2",
"499c763b22e980d5add16b085d11915b3cd1c6d2"
] | [
"sklearnext/preprocessing/over_sampling/kmeans_smote.py",
"sklearnext/model_selection/search.py"
] | [
"\"\"\"\nThe :mod:`sklearnext.preprocessing.oversampling.kmeans_smote`\ncontains the implementation of the K-Means SMOTE oversampler.\n\"\"\"\n\n# Authors: Felix Last\n# Georgios Douzas <gdouzas@icloud.com>\n# License: BSD 3 clause\n\nimport warnings\nimport copy\nimport numpy as np\nfrom sklearn.metrics.p... | [
[
"numpy.concatenate",
"numpy.count_nonzero",
"numpy.delete",
"numpy.asarray",
"numpy.zeros",
"sklearn.cluster.KMeans",
"numpy.mean",
"numpy.eye",
"sklearn.metrics.pairwise.euclidean_distances",
"numpy.unique"
],
[
"sklearn.utils.validation.check_is_fitted"
]
] |
pfistfl/HPOBench | [
"a7ad8807bd2e058ff99f703ad057b64ecadd4b66"
] | [
"hpobench/benchmarks/nas/nasbench_101.py"
] | [
"\"\"\"\nInterface to NasBench101 for Hyperparameter Optimization and Neural Architecture Search\n\nhttps://github.com/automl/nas_benchmarks\n\nHow to use this benchmark:\n--------------------------\n\nWe recommend using the containerized version of this benchmark.\nIf you want to use this benchmark locally (withou... | [
[
"numpy.triu_indices",
"numpy.zeros",
"numpy.sum",
"numpy.mean",
"numpy.random.randint",
"numpy.argsort"
]
] |
Kaufi-Jonas/VaRA-Tool-Suite | [
"31563896ad7dd1c1a147202b0c5c9fffe772b803"
] | [
"varats/varats/plots/blame_interaction_graph_plots.py"
] | [
"\"\"\"Module for BlameInteractionGraph plots.\"\"\"\n\nimport typing as tp\nfrom datetime import datetime\nfrom pathlib import Path\n\nimport click\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport pandas as pd\nimport plotly.offline as offply\nfrom matplotlib import style\n\nfrom varats.data.reports... | [
[
"pandas.DataFrame",
"matplotlib.pyplot.subplots"
]
] |
tao-harald/geoopt | [
"d6fea4d44f146877c5a430e9fd6ba0fb7e821b92"
] | [
"tests/test_rlinesearch.py"
] | [
"import geoopt\nimport torch\nimport pytest\n\n\n@pytest.mark.parametrize(\n \"line_search_params\",\n [dict(), dict(c1=1e-3, c2=0.99), dict(amax=1, amin=1e-12), dict(stabilize=10)],\n)\n@pytest.mark.parametrize(\"batch_size\", [None, 1, 16])\n@pytest.mark.parametrize(\"line_search_method\", [\"armijo\", \"wo... | [
[
"torch.manual_seed",
"torch.no_grad",
"torch.randn"
]
] |
hircumg/Performance-RNN-PyTorch | [
"83ca93a2186ab5655fb2ca6e4ea9ce177e9d6111"
] | [
"preprocess.py"
] | [
"import os\nimport re\nimport sys\nimport torch\nimport hashlib\nfrom progress.bar import Bar\n\nfrom sequence import NoteSeq, EventSeq, ControlSeq\nimport utils\nimport config\n\ndef preprocess_midi(path):\n note_seq = NoteSeq.from_midi_file(path)\n note_seq.adjust_time(-note_seq.notes[0].start)\n event_s... | [
[
"torch.save"
]
] |
connorjward/PyOP2 | [
"35076d0cce1c64d322708a6f15fed85c0a2e30de"
] | [
"test/unit/test_linalg_complex.py"
] | [
"# This file is part of PyOP2\n#\n# PyOP2 is Copyright (c) 2012, Imperial College London and\n# others. Please see the AUTHORS file in the main source directory for\n# a full list of copyright holders. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, ar... | [
[
"numpy.allclose",
"numpy.arange",
"numpy.zeros"
]
] |
e-esteva/cipher | [
"44e240901f7fb5d28531012e83b63427a4726221"
] | [
"cipher/preprocess/encode_utils.py"
] | [
"import os\nimport numpy as np\nimport pandas as pd\nimport subprocess \nfrom .wrangle import filter_encode_metatable, extract_metatable_information\n\ndef _download_url(url, outpath=None):\n \"\"\"\n Download a file from a given url and save it with a specified output file \n if necessary. \n\n Paramet... | [
[
"pandas.DataFrame.from_dict",
"pandas.read_csv"
]
] |
Nicolinho/RLBench | [
"3014e872f518d5439e73e057e2251dee1f9df481"
] | [
"rlbench/demo.py"
] | [
"import numpy as np\n\n\nclass Demo(object):\n\n def __init__(self, observations, random_seed=None):\n self._observations = observations\n self.random_seed = random_seed\n\n def __len__(self):\n return len(self._observations)\n\n def __getitem__(self, i):\n return self._observat... | [
[
"numpy.random.set_state"
]
] |
aapeliv/tensorflow | [
"cd2a135c126f209ccc943555b85ca436ea27ffe3"
] | [
"tensorflow/python/estimator/estimator.py"
] | [
"# Copyright 2016 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.python.util.tf_export.estimator_export",
"tensorflow.python.eager.context.graph_mode",
"tensorflow.python.training.training.get_global_step",
"tensorflow.python.training.training_util._get_or_create_global_step_read",
"tensorflow.python.training.evaluation._evaluate_once",
"ten... |
ScarWar/DeepSTORM3D | [
"8ba5bc61120abedba9c1b24a994e616e280bdda2"
] | [
"DeepSTORM3D/postprocess_utils.py"
] | [
"# Import modules and libraries\nimport numpy as np\nimport torch\nfrom torch.nn import Module, MaxPool3d, ConstantPad3d\nfrom torch.nn.functional import conv3d\n\n\n# convert gpu tensors to numpy\ndef tensor_to_np(x):\n return np.squeeze(x.cpu().numpy())\n\n\n# post-processing on GPU: thresholding and local max... | [
[
"torch.zeros",
"torch.nonzero",
"torch.cat",
"numpy.not_equal",
"torch.nn.MaxPool3d",
"torch.FloatTensor",
"torch.squeeze",
"numpy.arange",
"torch.ones_like",
"torch.nn.ConstantPad3d",
"torch.nn.functional.conv3d",
"numpy.column_stack",
"numpy.meshgrid",
"to... |
dmadea/Spectra-Manipulator | [
"ddc1b27cb4f4691096dfa7b2975df350d2eaf40e"
] | [
"spectramanipulator/user_namespace.py"
] | [
"import numpy as np\nfrom typing import Iterable\nimport os\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt # we plot graphs with this library\nfrom matplotlib import cm\nfrom matplotlib.ticker import *\nimport matplotlib.gridspec as gridspec\n\nfrom matplotlib import colors as c\n\n# from copy import... | [
[
"scipy.stats.linregress",
"numpy.exp",
"numpy.gradient",
"matplotlib.gridspec.GridSpecFromSubplotSpec",
"numpy.nan_to_num",
"numpy.log",
"matplotlib.pyplot.savefig",
"numpy.interp",
"matplotlib.pyplot.subplots",
"scipy.linalg.lstsq",
"numpy.arange",
"matplotlib.pypl... |
ALWYNWU/stoic2021-baseline | [
"6372b7185a335a7ca78d8fac5dec775ce3b5c121"
] | [
"algorithm/i3d/i3dpt.py"
] | [
"# Altered from https://github.com/hassony2/kinetics_i3d_pytorch\n\nimport math\nimport os\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\n\ndef get_padding_shape(filter_shape, stride):\n def _pad_top_bottom(filter_dim, stride_val):\n pad_along = max(filter_dim - stride_val, 0)\n ... | [
[
"torch.nn.Linear",
"torch.nn.ConstantPad3d",
"torch.cat",
"torch.nn.Dropout",
"torch.flatten",
"torch.nn.Sequential",
"torch.nn.MaxPool3d",
"torch.from_numpy",
"torch.ones",
"numpy.transpose",
"torch.nn.Conv3d",
"torch.load",
"torch.nn.AvgPool3d",
"torch.nn.... |
liubaishuo-github/VTX550_post_processor | [
"d7f530efd89887b86510391c3a89ec2283094dd4"
] | [
"test1.py"
] | [
"\r\n\r\n\r\nimport re\r\n\r\nfrom numpy import mat, cross\r\n\r\n\r\naa = [1,2,3]\r\nbb = [3,4,8]\r\n\r\n\r\n\r\na = mat(aa).T.T\r\nb = mat(bb).T.T\r\n\r\n\r\nprint(a)\r\nprint(b)\r\n"
] | [
[
"numpy.mat"
]
] |
tekdogan/gcn | [
"068c79905f31c052270a70febe4e928067940562"
] | [
"py/pubmed/sag_inference/dgl_.py"
] | [
"#!/usr/bin/env python3\nimport dgl\nimport dgl.function as fn\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom dgl import DGLGraph\nimport sys\nimport torch.cuda.profiler as profiler\nimport pyprof\nimport dgl.data as da\nfrom dgl.nn import SAGEConv\n\npyprof.init()\n\nwith torch.autogra... | [
[
"torch.nn.Linear",
"torch.device",
"torch.autograd.profiler.emit_nvtx",
"torch.cuda.profiler.stop",
"torch.FloatTensor",
"torch.cuda.profiler.start",
"torch.LongTensor",
"torch.BoolTensor"
]
] |
dibschat/activity-recognition-KTH | [
"a01e9c4d0d70a68bffd6c4f446c22b4b0fd720bb"
] | [
"src/contextual_3x3.py"
] | [
"import numpy as np\nimport cv2\nfrom utils import window, mag_check, dir_check, create_hist_context\n\n# create Haar-cascade object\nbody_cascade = cv2.CascadeClassifier('cascadG.xml')\n\n# create background-subtraction object\nfgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = False)\nkernel = np.ones((5,5... | [
[
"numpy.zeros_like",
"numpy.array",
"numpy.ones",
"numpy.mean",
"numpy.std",
"numpy.hstack"
]
] |
sschrod/BITES | [
"64c76feebd8b0869e74938f79d93b1946dcf88b5"
] | [
"bites/utils/Simple_Network.py"
] | [
"\"\"\"Copyright (c) 2018, Haavard Kvamme\n 2021, Schrod Stefan\"\"\"\n\nimport numpy as np\nfrom torch import nn\n\n\nclass DenseVanillaBlock(nn.Module):\n def __init__(self, in_features, out_features, bias=True, batch_norm=True, dropout=0., activation=nn.ReLU,\n w_init_=lambda w... | [
[
"torch.nn.Linear",
"torch.nn.Dropout",
"torch.nn.Sequential",
"torch.nn.init.kaiming_normal_",
"torch.nn.BatchNorm1d",
"numpy.append"
]
] |
hanzz2007/mxnet | [
"cc0b2d67c40170aced702c9f80b4b7acbb1f2b79"
] | [
"tests/python/unittest/test_optimizer.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.random.seed",
"numpy.zeros_like"
]
] |
tabdelaal/SpaGE | [
"7533cbf2275c3049561e8a17b9f7866e0e324743"
] | [
"benchmark/osmFISH_AllenSSp/SpaGE/Precise_output.py"
] | [
"import os\r\nos.chdir('osmFISH_AllenSSp/')\r\n\r\nimport pickle\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib\r\nmatplotlib.rcParams['pdf.fonttype'] = 42\r\nmatplotlib.rcParams['ps.fonttype'] = 42\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nimport sys\r\nsys.path.insert(1,'Sp... | [
[
"matplotlib.pyplot.text",
"matplotlib.pyplot.xlabel",
"numpy.sum",
"matplotlib.pyplot.figure",
"numpy.arange",
"matplotlib.pyplot.ylabel",
"numpy.intersect1d",
"matplotlib.pyplot.show",
"matplotlib.pyplot.gca",
"numpy.diag"
]
] |
menrmenr/Psychotoolbox-3 | [
"b294355ea46bcd3bcd6876196d0c0f321b3a6ddc"
] | [
"setup.py"
] | [
"# setup.py -- Build-Script for building Psychtoolbox-3 \"mex\" files as Python extensions.\n#\n# (c) 2018 Mario Kleiner - Licensed under MIT license.\n#\n\n# from distutils.core import setup, Extension # Build system.\nfrom setuptools import setup, Extension, find_packages\nimport os, fnmatch, shutil ... | [
[
"numpy.get_include"
]
] |
astutespruce/sarp | [
"7ce503380440c47b762ed1a8efd1d3e3aab6605e",
"7ce503380440c47b762ed1a8efd1d3e3aab6605e"
] | [
"analysis/prep/barriers/lib/points.py",
"analysis/rank/rank_small_barriers.py"
] | [
"import pygeos as pg\nimport pandas as pd\nimport numpy as np\n\n\ndef connect_points(start, end):\n \"\"\"Convert a series or array of points to an array or series of lines.\n\n Parameters\n ----------\n start : Series or ndarray\n end : Series or ndarray\n\n Returns\n -------\n Series or n... | [
[
"numpy.array",
"pandas.Series"
],
[
"pandas.read_feather"
]
] |
caisarl76/TADE-AgnosticLT | [
"8a23f6609622dd30feb22101067e644666810400"
] | [
"dataset/flowers.py"
] | [
"import os\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torchvision\nimport tqdm\nfrom PIL import Image\nfrom matplotlib import pyplot as plt\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\n\n\nclass Flowers(Dataset):\n def __init__(self, root, train=True, download=... | [
[
"numpy.array",
"numpy.zeros",
"numpy.random.seed",
"numpy.random.shuffle",
"numpy.where",
"numpy.vstack",
"torch.utils.data.DataLoader",
"torch.tensor",
"pandas.read_csv",
"numpy.unique"
]
] |
rajasoun/app-analytics | [
"101ebb9795b3c80efee072dc79c37633cf0b241b"
] | [
"notebook/demand_forecast_by_ga.py"
] | [
"# flake8: noqa\n# coding: utf-8\n# # Setup\n# ## Jupyter Shell\n# In[1]:\n\n\nshell = 'ZMQInteractiveShell'\nIN_JUPYTER = 'get_ipython' in globals() and get_ipython().__class__.__name__ == shell\n\n# Allow modules and files to be loaded with relative paths\nfrom pkg_resources import resource_filename as fpath\nimp... | [
[
"numpy.isnan",
"numpy.log",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.title",
"numpy.exp",
"matplotlib.pyplot.ylabel",
"pandas.concat",
"matplotlib.pyplot.show",
"pandas.read_csv"
]
] |
peaceofmind123/esrgan_modified | [
"33a0f2478185eff90a7233b968b7901f7cf3a04a"
] | [
"train.py"
] | [
"# Copyright 2021 Dakewe Biotech Corporation. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unle... | [
[
"torch.utils.data.DataLoader"
]
] |
katiekeegan/StereoMatching_SVD_ANCC | [
"c0223d820ebfc21fbf8f39eae2812a0fac140d78"
] | [
"geometry.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 10 17:49:52 2022\n\n@author: katie\n\"\"\"\n\nimport cv2\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n\n\ndef grayImage(img):\n maxVal = np.max(img)\n minVal = np.min(img)\n alpha = 255. / (maxVal - minVal)\n beta = ... | [
[
"numpy.max",
"numpy.concatenate",
"numpy.array",
"numpy.dot",
"numpy.zeros_like",
"numpy.sin",
"numpy.zeros",
"numpy.min",
"numpy.mean",
"numpy.arctan",
"numpy.linalg.svd",
"numpy.cos",
"numpy.linalg.inv"
]
] |
kartik144/Language_Modelling-NTU | [
"19b388f9584ac9bea0685c9214f3548417a95a37"
] | [
"main.py"
] | [
"import argparse\nimport torch\nimport pickle\nfrom utils import data_test\nfrom utils import process\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nparser = argparse.ArgumentParser(description='PyTorch Sentence Completion Model')\n\n# Model parameters.\nparser.add_argument('--data', type=str, default='./d... | [
[
"torch.device",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots",
"torch.manual_seed",
"torch.cuda.is_available",
"torch.LongTensor",
"torch.load",
"matplotlib.pyplot.show",
"matplotlib.pyplot.xticks"
]
] |
wukailu/EDSR-PyTorch | [
"5625cf83ce88050b68e649beb4155b32c38018fa",
"5625cf83ce88050b68e649beb4155b32c38018fa"
] | [
"code/model/super_resolution_model/ddbpn_model.py",
"code/model/repdistiller_models/vgg.py"
] | [
"# Deep Back-Projection Networks For Super-Resolution\n# https://arxiv.org/abs/1803.02735\n\nfrom model.super_resolution_model import common\nfrom .utils import register_model, unpack_feature, pack_feature\n\nimport torch\nimport torch.nn as nn\n\n@register_model\ndef DDBPN(**hparams):\n return DDBPN_Model(**hpa... | [
[
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Sequential",
"torch.nn.Conv2d",
"torch.nn.PReLU"
],
[
"torch.nn.Linear",
"torch.nn.ModuleList",
"torch.nn.MaxPool2d",
"torch.nn.Sequential",
"torch.nn.BatchNorm2d",
"torch.nn.functional.relu",
"torch.nn.ReLU",
"t... |
sridhar551/ONNX | [
"69894f207dfcd72d1e70497d387201cec327efbc",
"69894f207dfcd72d1e70497d387201cec327efbc"
] | [
"onnx/backend/test/case/node/sub.py",
"onnx/backend/test/case/node/clip.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 Sub(Base):\n\n @staticmethod\n def export():\n... | [
[
"numpy.array",
"numpy.random.randn"
],
[
"numpy.array",
"numpy.random.randn",
"numpy.clip"
]
] |
mjawadak/COVID360 | [
"34587f0f0eb78886eb4590e4728eed0b869090c5"
] | [
"RecoveryModel.py"
] | [
"import pandas as pd\n\n##### death prediction model\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn import preprocessing\n\nclass RecoveryModel():\n\n\tdef __init__(self,MAX_DAYS_OF_INFECTION,NUMBER_ITERATIONS_PER_DAY):\n\n\t\t# Loads the recovery model data and builds a decision tree model with fix... | [
[
"sklearn.preprocessing.LabelEncoder",
"pandas.read_csv",
"sklearn.tree.DecisionTreeClassifier"
]
] |
SultanOrazbayev/dask | [
"fc1cea9cdb2ea31348204aa51e4f6f7327a2af33",
"fc1cea9cdb2ea31348204aa51e4f6f7327a2af33",
"fc1cea9cdb2ea31348204aa51e4f6f7327a2af33"
] | [
"dask/dataframe/io/parquet/utils.py",
"dask/dataframe/reshape.py",
"dask/dataframe/io/csv.py"
] | [
"import re\n\nimport pandas as pd\n\nfrom dask import config\nfrom dask.dataframe.io.utils import _is_local_fs\nfrom dask.utils import natural_sort_key\n\n\nclass Engine:\n \"\"\"The API necessary to provide a new Parquet reader/writer\"\"\"\n\n @classmethod\n def read_metadata(\n cls,\n fs,\... | [
[
"pandas.DataFrame"
],
[
"pandas.Index",
"pandas.api.types.is_list_like",
"pandas.api.types.is_scalar",
"pandas.CategoricalIndex",
"pandas.get_dummies"
],
[
"pandas.api.types.is_datetime64_any_dtype",
"pandas.api.types.is_object_dtype",
"pandas.api.types.is_float_dtype",... |
DaleBlomgren/DaleSC2 | [
"f6c82f68ed4359fb91e5c37eba3d93968359069b"
] | [
"attack_agent.py"
] | [
"import math\nimport random \n\nimport numpy as np \nimport pandas as pd \n\nfrom pysc2.agents import base_agent\nfrom pysc2.lib import actions\nfrom pysc2.lib import features\n\n_NO_OP = actions.FUNCTIONS.no_op.id\n_SELECT_POINT = actions.FUNCTIONS.select_point.id\n_BUILD_SUPPLY_DEPOT = actions.FUNCTIONS.Build_Sup... | [
[
"numpy.random.choice",
"numpy.zeros",
"pandas.DataFrame",
"numpy.random.permutation",
"numpy.random.uniform"
]
] |
HongyeGuo/DIRL-bidding_preference | [
"1451863af0c8195daa73051f82f33cbefa0024e2"
] | [
"deep_maxent.py"
] | [
"\"\"\"\nImplements deep maximum entropy inverse reinforcement learning based on\nZiebart et al., 2008 and Wulfmeier et al., 2015, using symbolic methods with\nTheano.\n\nMatthew Alger, 2015\nmatthew.alger@anu.edu.au\n\"\"\"\n\nfrom itertools import product\n\nimport numpy as np\nimport numpy.random as rn\nimport t... | [
[
"numpy.random.normal",
"numpy.random.uniform",
"numpy.savetxt",
"numpy.zeros"
]
] |
geronimocharlie/cognitive_modeling_playground | [
"8b69764d6167efafa89f99594117ec27a3825448"
] | [
"context_aware_image_captioning/train_justify.py"
] | [
"import torch\nfrom datasets import CubDataset, get_coco_loader\nimport torchvision.transforms as transforms\nimport torch.optim as optim\nfrom models import *\nfrom torch.nn.utils.rnn import pack_padded_sequence\nfrom tqdm import tqdm\nfrom hyperparameters import *\nimport numpy as np\n\nif data_mode == 'cub':\n ... | [
[
"torch.nn.utils.rnn.pack_padded_sequence",
"torch.load"
]
] |
jfzhuang/ST_Memory | [
"f253c05b7ecb37a1cbe9f312a628ba30b4555230"
] | [
"mmsegmentation/mmseg/datasets/custom.py"
] | [
"import os\nimport os.path as osp\nfrom collections import OrderedDict\nfrom functools import reduce\n\nimport mmcv\nimport numpy as np\nfrom mmcv.utils import print_log\nfrom prettytable import PrettyTable\nfrom torch.utils.data import Dataset\n\nfrom mmseg.core import eval_metrics\nfrom mmseg.utils import get_roo... | [
[
"numpy.round",
"numpy.nanmean",
"numpy.unique"
]
] |
alexfikl/loopy | [
"814612b63fe14dd94af12d4238c387a9929b97b9"
] | [
"test/test_apps.py"
] | [
"from __future__ import division, absolute_import, print_function\n\n__copyright__ = \"Copyright (C) 2012 Andreas Kloeckner\"\n\n__license__ = \"\"\"\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the... | [
[
"numpy.array",
"numpy.cumsum"
]
] |
seungbin79/pystock | [
"1a5120ac6cbd3947515434bbdb7a2223d2d2b105"
] | [
"ch17/05.py"
] | [
"import sys\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QAxContainer import *\nfrom PyQt5.QtCore import *\nimport time\nimport pandas as pd\nimport sqlite3\n\nTR_REQ_TIME_INTERVAL = 0.2\n\n\nclass Kiwoom(QAxWidget):\n def __init__(self):\n super().__init__()\n self._create_kiwoom_instance()\n ... | [
[
"pandas.DataFrame"
]
] |
eug/bayesnet | [
"7376e67047afed298714b327c4897bef13f20385"
] | [
"main.py"
] | [
"import sys\nimport pandas as pd\nfrom sklearn.metrics import accuracy_score\nfrom bayesnet import BayesNetwork\nimport getopt\n\n\nclass Config:\n train_file = 'input/cmc_train.csv'\n test_file = 'input/cmc_test.csv'\n mode_predict = False\n samples = 0\n prob_event = None\n cond_events = None\n ... | [
[
"sklearn.metrics.confusion_matrix",
"sklearn.metrics.accuracy_score",
"pandas.concat",
"matplotlib.pyplot.show",
"pandas.read_csv"
]
] |
eric-wieser/4m20-coursework2 | [
"3b894563eb35336faa7e5f04dccb1a3fd9bfbc65"
] | [
"python/logger.py"
] | [
"import time\nimport pickle\n\nimport numpy as np\n\nimport config\n\nbasic_fields = [\n ('t', np.float64),\n ('target', np.float64, 3),\n ('actual', np.float64, 3),\n ('servo', np.float64, 3)\n]\nextra_fields = [\n ('error', np.float64, 3),\n ('in_bounds', np.bool, 3),\n ('actual_oob', np.floa... | [
[
"numpy.asarray",
"numpy.maximum.accumulate",
"numpy.recarray",
"numpy.where",
"numpy.arange",
"numpy.core.records.fromrecords",
"numpy.dtype"
]
] |
cdeepali/pytorch | [
"a8e45b596910f90013637ccc735d6ca21a93d852"
] | [
"test/test_overrides.py"
] | [
"# Owner(s): [\"module: __torch_function__\"]\n\nimport torch\nimport numpy as np\nimport inspect\nimport functools\nimport pprint\nimport pickle\nimport collections\nimport unittest\n\nfrom torch.testing._internal.common_utils import TestCase, run_tests\nfrom torch.overrides import (\n handle_torch_function,\n ... | [
[
"torch.overrides.wrap_torch_function",
"torch.einsum",
"torch.ones",
"torch.overrides.has_torch_function",
"torch.eye",
"torch.nn.RNN",
"torch.ger",
"torch.mul",
"torch.nn.functional.bilinear",
"numpy.eye",
"torch.abs",
"torch.testing._internal.common_utils.gradchec... |
maliagehan/apps-phenotyping | [
"698942d476692236f25b1e57784b8e18cf710887"
] | [
"appendix.3.camerastand/example.image.data/PlantCV_seeds.py"
] | [
"#!/usr/bin/env python\n\nimport os\nimport posixpath\nimport re\nimport string\nimport sys\nimport traceback\nsys.path.append('/Users/mgehan/Documents/github/plantcv/')\nimport argparse\nimport cv2\nimport numpy as np\nimport plantcv as pcv\n\n\ndef options():\n parser = argparse.ArgumentParser(description=\"Im... | [
[
"numpy.array",
"numpy.copy"
]
] |
enjoyneer87/SciDataTool | [
"37ddc4071f1edb1270ee03e43595c3f943fb9bd8",
"37ddc4071f1edb1270ee03e43595c3f943fb9bd8",
"37ddc4071f1edb1270ee03e43595c3f943fb9bd8"
] | [
"build/lib/SciDataTool/Methods/DataND/plot_3D_Data.py",
"SciDataTool/Functions/change_referential.py",
"SciDataTool/Methods/DataND/plot_2D_Data_Animated.py"
] | [
"from SciDataTool.Functions.Plot.plot_4D import plot_4D\nfrom SciDataTool.Functions.Plot.plot_3D import plot_3D\nfrom SciDataTool.Functions.Plot import unit_dict, norm_dict, axes_dict\nfrom SciDataTool.Functions.Load.import_class import import_class\nfrom SciDataTool.Classes.Norm_indices import Norm_indices\nfrom n... | [
[
"numpy.nanmin",
"numpy.log10",
"numpy.nanmax",
"numpy.meshgrid",
"numpy.array2string"
],
[
"numpy.concatenate",
"numpy.bincount",
"numpy.array",
"numpy.zeros",
"numpy.arange",
"numpy.abs",
"numpy.append",
"scipy.interpolate.RegularGridInterpolator",
"num... |
wangbingbing2022/milvus | [
"0b99da93010f29ac7408a0bb4767a8e95de033d3"
] | [
"tests/python_client/testcases/test_insert_20.py"
] | [
"import threading\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom pymilvus import Index\n\nfrom base.client_base import TestcaseBase\nfrom utils.util_log import test_log as log\nfrom common import common_func as cf\nfrom common import common_type as ct\nfrom common.common_type import CaseLabel, Chec... | [
[
"pandas.DataFrame",
"numpy.float32"
]
] |
eyalbetzalel/pytorch-generative-1 | [
"d491fa0a8ab37ad3b8aa1092b24ff7d863c9fbd8"
] | [
"pytorch_generative/datasets.py"
] | [
"\"\"\"Extra generative modeling benchmark datasets not provided by PyTorch.\"\"\"\n\nimport os\nimport urllib\n\nimport PIL\nimport numpy as np\nimport torch\nfrom torch.utils import data\nfrom torchvision.datasets import utils\nfrom torchvision.datasets import vision\n\n\ndef _read_image_file(path, shape):\n wit... | [
[
"torch.save",
"numpy.loadtxt",
"torch.from_numpy"
]
] |
akeemlh/gala | [
"0fdaf9159bccc59af2a3525f2926e04501754f48"
] | [
"gala/potential/potential/core.py"
] | [
"# Standard library\nimport abc\nfrom collections import OrderedDict\nimport copy as pycopy\nimport warnings\nimport uuid\n\n# Third-party\nimport numpy as np\nfrom astropy.constants import G\nimport astropy.units as u\nfrom astropy.utils import isiterable\ntry:\n from scipy.spatial.transform import Rotation\nex... | [
[
"numpy.array",
"numpy.zeros",
"numpy.sum",
"matplotlib.pyplot.subplots",
"numpy.diag",
"numpy.abs",
"numpy.meshgrid"
]
] |
jfacoustic/MyTwitterBot | [
"15a9509c41ba8c7049675048b4d05ab457270a7d"
] | [
"src/tftools/basic_functions.py"
] | [
"import tensorflow as tf\n\n\ndef init_wb(shape, name):\n \"\"\"\n Function initialize one matrix of weights and one bias vector.\n\n :type shape: tuple\n :type name: str\n :rtype: dictionary\n \"\"\"\n Winit = tf.truncated_normal(shape, mean=0, stddev=0.1)\n binit = tf.zeros(shape[-1])\n ... | [
[
"tensorflow.truncated_normal",
"tensorflow.zeros",
"tensorflow.matmul",
"tensorflow.get_variable"
]
] |
CarlosW1998/DeepLearningClass | [
"d19cd4961d223cce3b474c4e1732011ba0295ada"
] | [
"NeuralNetwork/NN.py"
] | [
"import numpy as np\n\nclass NeuralNetwork:\n def __init__(self, input_shape, neurons, learning_rate):\n self.wights = []\n self.wights.append(np.random.rand(input_shape, neurons))\n self.wights.append(np.random.rand(neurons, 1))\n self.baias = np.zeros(neurons)\n self.learning... | [
[
"numpy.random.rand",
"numpy.exp",
"numpy.zeros"
]
] |
Guigxs/AI-Quixo | [
"5ca625886ba28f41528b1eff291f1f1834947f05"
] | [
"ai/Quixo/ai.py"
] | [
"import cherrypy\nimport sys\nimport random\nimport numpy as np\n\nclass Server:\n @cherrypy.expose\n @cherrypy.tools.json_in()\n @cherrypy.tools.json_out()\n def move(self):\n # Deal with CORS\n cherrypy.response.headers['Access-Control-Allow-Origin'] = '*'\n cherrypy.response.head... | [
[
"numpy.resize"
]
] |
yxuansu/HCL | [
"b7a2871e8006d148958bb8dc65670d0491f6a446"
] | [
"SMN_MSN/main_utlis.py"
] | [
"# -*- coding:utf-8 -*-\nimport torch\nimport pickle\nimport collections\nimport time\nimport numpy as np\nimport os\n#from modules.msn import MSN\n# ------------------------------------------------------------------------------------- #\n# measurement functions\ndef compute_Rn_k(scores,labels, n=2, k=1):\n tota... | [
[
"numpy.asarray",
"numpy.intersect1d"
]
] |
genvex/fishswarm | [
"50b07da5264e08a1e312457dd4c7088d5a5557e6"
] | [
"couzinswarm/simulation.py"
] | [
"\"\"\"\nSimulation module\n=================\n\nContains the `Swarm` class, which is used for simulation.\n\"\"\"\nimport numpy as np\n\nfrom couzinswarm.objects import Fish\n\nfrom progressbar import ProgressBar as PB\n\nclass Swarm:\n \"\"\"A class for a swarm simulation.\n \n Attributes\n ----------... | [
[
"numpy.array",
"numpy.linalg.norm",
"numpy.empty",
"numpy.dot",
"numpy.arange",
"numpy.random.random"
]
] |
vndee/sentivi | [
"3df2c604d4f9934be9019ac3d6fdef48c6cc7c33"
] | [
"sentivi/classifier/lstm.py"
] | [
"import torch\nimport torch.nn as nn\n\nfrom typing import Optional\nfrom sentivi.classifier.nn_clf import NeuralNetworkClassifier\n\n\nclass LSTM(nn.Module):\n def __init__(self,\n num_labels: int,\n embedding_size: int,\n hidden_size: int,\n bidir... | [
[
"torch.nn.Linear",
"torch.zeros",
"torch.nn.LSTM",
"torch.bmm",
"torch.nn.functional.softmax"
]
] |
pandegroup/keras | [
"804e5b3d36896da23f168415e5ca2864868c0ede"
] | [
"keras/preprocessing/image.py"
] | [
"from __future__ import absolute_import\n\nimport numpy as np\nimport re\nfrom scipy import ndimage\nfrom scipy import linalg\n\nfrom os import listdir\nfrom os.path import isfile, join\nimport random\nimport math\nfrom six.moves import range\n\n'''\n Fairly basic set of tools for realtime data augmentation on i... | [
[
"numpy.max",
"scipy.ndimage.interpolation.shift",
"numpy.dot",
"numpy.asarray",
"numpy.reshape",
"numpy.random.seed",
"numpy.copy",
"scipy.linalg.svd",
"numpy.min",
"numpy.random.shuffle",
"numpy.mean",
"numpy.flipud",
"numpy.std",
"scipy.ndimage.interpolati... |
giovannic/fastms | [
"b54be22227062c3e53b44e898a587b00cd45d3ef"
] | [
"src/fastms/preprocessing.py"
] | [
"import numpy as np\nfrom sklearn.base import TransformerMixin\n\ndef format_runs(runs):\n # Time invariant parameters\n X = np.stack([entry['parameters'] for entry in runs])\n\n # Outputs\n y = np.stack([entry['outputs'] for entry in runs])\n\n # Time varying parameters\n period = y.shape[1]\n ... | [
[
"numpy.concatenate",
"numpy.mean",
"numpy.std",
"numpy.stack",
"numpy.repeat"
]
] |
ashikari/tutorial | [
"cc696ee25d94476872244222062f2ff9366d12ae"
] | [
"plotting/matplotlib/plotter_tut.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef main():\n\t\n\tx = np.linspace(0, 2, 100)\n\n\tplt.close('all')\n\tfig, ax = plt.subplots(1,1)\n\n\tax.plot(x, x, label = \"linear\")\n\tax.plot(x, x**2, label = \"Quadratic\")\n\tax.plot(x, x**3, label = \"Cubic\")\n\n\tplt.title(\"Simple Plot\")\n\tplt... | [
[
"matplotlib.pyplot.title",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show",
"numpy.linspace"
]
] |
cerules/sense2vec | [
"34a3af375e2df8c553d358093bda01296ba5b87d"
] | [
"scripts/05_export.py"
] | [
"#!/usr/bin/env python\nfrom sense2vec import Sense2Vec\nfrom sense2vec.util import split_key\nfrom pathlib import Path\nimport plac\nfrom wasabi import msg\nimport numpy\n\n\ndef _get_shape(file_):\n \"\"\"Return a tuple with (number of entries, vector dimensions). Handle\n both word2vec/FastText format, whi... | [
[
"numpy.asarray"
]
] |
qua-platform/qua-libs | [
"805a3b1a69980b939b370b3ba09434bc26dc45ec",
"805a3b1a69980b939b370b3ba09434bc26dc45ec"
] | [
"examples/advanced-topics/IIR-FIR/blackbox-filter-optimization.py",
"examples/multi-qubit/multiplexed-readout/configuration.py"
] | [
"from typing import List\n\nimport matplotlib.pyplot as plt\nfrom qm.qua import *\nfrom qm.QuantumMachinesManager import (\n SimulationConfig,\n QuantumMachinesManager,\n LoopbackInterface,\n)\nimport numpy as np\nimport scipy.signal as signal\nimport cma\n\nqmm = QuantumMachinesManager()\n\nwith program()... | [
[
"numpy.correlate",
"numpy.max",
"numpy.array",
"numpy.random.rand",
"numpy.sum",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"numpy.argmax",
"matplotlib.pyplot.show"
],
[
"numpy.array",
"numpy.sin",
"numpy.cos"
]
] |
wxwoods/mctorch | [
"7cd6eb51fdd01fa75ed9245039a4f145ba342de2"
] | [
"test/common_nn.py"
] | [
"import sys\nimport tempfile\nimport unittest\nfrom copy import deepcopy\nfrom itertools import product\nfrom functools import reduce\nfrom operator import mul\n\n\nimport torch\nimport torch.cuda\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn.functional import _Reduction\nfrom common_utils ... | [
[
"torch.autograd.gradcheck.iter_tensors",
"torch.cat",
"torch.nn.EmbeddingBag",
"torch.nn.Unfold",
"torch.randperm",
"torch.ones",
"torch.autograd.gradcheck.get_numerical_jacobian",
"torch.load",
"torch.nn.FractionalMaxPool3d",
"torch.exp",
"torch.where",
"torch.nn.C... |
dodohow1011/waveglow | [
"53d9883a73f7f0569b25eb665788ca59368f9413"
] | [
"train.py"
] | [
"# *****************************************************************************\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n# * Redis... | [
[
"torch.cuda.manual_seed",
"torch.arange",
"torch.max",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.utils.data.distributed.DistributedSampler",
"torch.load"
]
] |
cctrunz/landlab | [
"4e4ef12f4bae82bc5194f1dcc9af8ff1a7c20939",
"4e4ef12f4bae82bc5194f1dcc9af8ff1a7c20939",
"4e4ef12f4bae82bc5194f1dcc9af8ff1a7c20939"
] | [
"landlab/utils/fault_facet_finder.py",
"examples/flow_routing/simple_sp_driver_wlakes.py",
"landlab/grid/tests/test_raster_grid/test_BC_updates.py"
] | [
"#! /usr/bin/env python\n\n\"\"\"\nThis class is designed to provide functions to allow the automated\nidentification of planar facet surfaces above fault traces.\nModule is SLOW (e.g., minutes+ per full analysis of a \"large\" data set). It is\nonly intended for model post-analysis or DEM analysis. Do not loop thi... | [
[
"numpy.isclose",
"numpy.tan",
"numpy.mean",
"numpy.where",
"numpy.less",
"numpy.empty",
"numpy.logical_and",
"numpy.fabs",
"numpy.arange",
"numpy.polyfit",
"numpy.sqrt",
"numpy.poly1d",
"numpy.greater",
"numpy.argmax",
"numpy.empty_like",
"numpy.logi... |
DSCI-310/Group-10-Project | [
"cfc50ebcbbf160e0a72a1144e6f7ae8c345db4aa"
] | [
"tests/test_alpha_tuning.py"
] | [
"from pandas import DataFrame\nfrom sklearn.model_selection import train_test_split\nimport pytest\nfrom src.analysis.alpha_tuning import ridge_alpha_tuning\nfrom sklearn.pipeline import Pipeline, make_pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nfrom sklearn.linear_model import RidgeCV\n\n@pytest.... | [
[
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"sklearn.linear_model.RidgeCV",
"sklearn.preprocessing.StandardScaler"
]
] |
amerkel2/kartothek | [
"a555a7706a065c8f2ff1760c108c3be2e5be8b3a"
] | [
"kartothek/serialization/_generic.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nThis module contains functionality for persisting/serialising DataFrames.\n\"\"\"\n\nfrom typing import Dict\n\nimport numpy as np\nimport pandas as pd\nfrom pandas.api.types import is_list_like\n\nfrom kartothek.serialization._util import _check_contains_nul... | [
[
"numpy.isnan",
"pandas.api.types.is_list_like",
"numpy.asarray",
"numpy.errstate",
"numpy.logical_and",
"pandas.Timestamp",
"pandas.Series",
"pandas.api.types.is_categorical",
"numpy.isin"
]
] |
alexgallego1997/GamestonkTerminal | [
"1c6ce5c99111aa7195c51f6930fcdbb9dadd2f00",
"eb2b0d766bf1b6bb8656d6733083962efb152fe2"
] | [
"gamestonk_terminal/fundamental_analysis/yahoo_finance_api.py",
"gamestonk_terminal/technical_analysis/volatility.py"
] | [
"import argparse\nfrom datetime import datetime\nimport yfinance as yf\nimport pandas as pd\nfrom gamestonk_terminal.dataframe_helpers import clean_df_index\nfrom gamestonk_terminal.helper_funcs import (\n long_number_format,\n parse_known_args_and_warn,\n)\n\n\ndef info(l_args, s_ticker):\n parser = argpa... | [
[
"pandas.set_option"
],
[
"matplotlib.pyplot.ion",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel",
"... |
kamalabdel97/hvplot | [
"0301d73f36d49c9c5fc98604cf91ca4638b0b44d"
] | [
"hvplot/tests/testgeo.py"
] | [
"from unittest import TestCase, SkipTest, expectedFailure\n\nimport numpy as np\nimport pandas as pd\nimport holoviews as hv\n\n\nclass TestGeo(TestCase):\n\n def setUp(self):\n try:\n import xarray as xr\n import rasterio # noqa\n import geoviews # noqa\n imp... | [
[
"numpy.random.rand"
]
] |
YonatanSimson/OpenSfM | [
"358843738359f4b5d767b22df2f3960ded31c981",
"358843738359f4b5d767b22df2f3960ded31c981"
] | [
"opensfm/actions/create_submodels.py",
"opensfm/actions/export_openmvs.py"
] | [
"import logging\nfrom collections import defaultdict\n\nimport numpy as np\nfrom opensfm.large import metadataset\nfrom opensfm.large import tools\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef run_dataset(data):\n \"\"\" Split the dataset into smaller submodels. \"\"\"\n\n meta_data = metadataset.MetaDat... | [
[
"numpy.array",
"numpy.ceil",
"numpy.zeros"
],
[
"numpy.array"
]
] |
Rye-Catcher-ZCH/HITSZ-2020-Bioinformatics-Course-Project-Two-Team-zchnb | [
"80fb3abf62c95d5a09c54dc15d2ec48c3e2547e9"
] | [
"DeepConv-DTI-master/evaluate_performance.py"
] | [
"import pandas as pd\nimport argparse\nimport os\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import precision_recall_curve, auc, roc_curve\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"predictions\", help=\"prediction result to evaluate\")\nparser.add_argument(\"--test-name\",... | [
[
"pandas.read_table",
"sklearn.metrics.precision_recall_curve",
"pandas.DataFrame",
"pandas.Series",
"sklearn.metrics.auc",
"pandas.read_csv",
"sklearn.metrics.roc_curve"
]
] |
4QuantOSS/OpenDIGITS | [
"f7969aef0f91d074d09290261ca36e2e93128276"
] | [
"digits/utils/image.py"
] | [
"# Copyright (c) 2014-2017, NVIDIA CORPORATION. All rights reserved.\nfrom __future__ import absolute_import\nfrom __future__ import print_function\n\nimport math\nimport os.path\nimport requests\n\n# Find the best implementation available\ntry:\n from cStringIO import StringIO\nexcept ImportError:\n from io... | [
[
"numpy.concatenate",
"numpy.ndindex",
"numpy.pad",
"numpy.array",
"numpy.dot",
"numpy.zeros",
"numpy.minimum",
"numpy.ndarray",
"numpy.random.randint",
"numpy.sqrt",
"numpy.repeat",
"numpy.maximum"
]
] |
dingdanhao110/HINGCN | [
"281b73c03bd3b00e35bce4c5e1c27076233555e4"
] | [
"runEmbedGCN.py"
] | [
"from __future__ import division\nfrom __future__ import print_function\n\nimport time\nimport argparse\nimport numpy as np\nimport sys\n\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom utilities import *\nfrom hinmode... | [
[
"numpy.concatenate",
"torch.zeros",
"numpy.pad",
"torch.cuda.manual_seed",
"torch.cat",
"numpy.asarray",
"numpy.zeros",
"numpy.random.seed",
"torch.FloatTensor",
"numpy.random.permutation",
"numpy.ones",
"torch.manual_seed",
"torch.cuda.is_available",
"torch... |
aasavari-kakne/simclr | [
"2a6a1805416736d77bf015b02159b84721ce0831"
] | [
"fashion_labels.py"
] | [
"import csv\nimport os\nimport sys\nimport time\n\nimport numpy as np\nimport pandas as pd\nimport streamlit as st\nimport tensorflow as tf\n\nst.title(\"Fashion Labels\")\n\n\n@st.cache\ndef get_labels(label_name_file, add_value=False):\n \"\"\"\n get list of labels and position_by_attriubtes dict\n\n ... | [
[
"numpy.array",
"pandas.DataFrame",
"numpy.savez_compressed",
"tensorflow.gfile.FastGFile",
"numpy.unique"
]
] |
loaiabdalslam/Brainless | [
"d363e0d713fc9b024a4fac990b9c39cd59769454"
] | [
"tests/advanced_tests/advanced_install_tests.py"
] | [
"import datetime\nimport os\nimport random\nimport sys\nimport warnings\n\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\n\nimport tests.utils_testing as utils\nfrom brainless import Predictor\nfrom brainless.utils_models import load_ml_model\n\nsys.path = [os.path.abspath(os.path.dirname... | [
[
"numpy.random.seed",
"sklearn.model_selection.train_test_split"
]
] |
junghun73/Learning | [
"8b5a295c42f142a3b2f5fa13fc75434a2ea9235a",
"cc87204fcc4bd2f4702f7c29c83cb8ed5c94b7d6"
] | [
"advanced/mathematical_optimization/examples/plot_exercise_ill_conditioned.py",
"packages/scikit-learn/show_ica.py"
] | [
"\"\"\"\nAlternating optimization\n=========================\n\nThe challenge here is that Hessian of the problem is a very\nill-conditioned matrix. This can easily be seen, as the Hessian of the\nfirst term in simply 2*np.dot(K.T, K). Thus the conditioning of the\nproblem can be judged from looking at the conditio... | [
[
"scipy.optimize.fmin_ncg",
"numpy.random.normal",
"numpy.dot",
"numpy.reshape",
"scipy.optimize.fmin_l_bfgs_b",
"numpy.random.seed",
"scipy.optimize.fmin_powell",
"numpy.sum",
"scipy.optimize.fmin_bfgs",
"numpy.eye",
"numpy.apply_along_axis"
],
[
"numpy.random.n... |
gasongjian/easyml | [
"cd6bddef1f154c5a79e4e0e72ee858436d962caf"
] | [
"check.py"
] | [
"import statsmodels.api as sm\nfrom statsmodels.sandbox.nonparametric import kernels\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set()\n\n\n'''特征空间解析\n\n我们将特征类型分为如下四种\n- numeric：连续的特征，表现为可以定义序关系且唯一值可以有无数个\n- category：类别型特征\n- Multi-category：多类别\n- object：无结构... | [
[
"numpy.concatenate",
"numpy.zeros_like",
"numpy.ceil",
"numpy.random.choice",
"numpy.count_nonzero",
"numpy.round",
"numpy.sum",
"pandas.crosstab",
"pandas.DataFrame",
"matplotlib.pyplot.subplots",
"numpy.mean",
"numpy.random.randint",
"pandas.Series",
"nump... |
LBNL-UCB-STI/synthpop | [
"cd437c0855a30e51bf8a4c7454900b2f9db9f1ab"
] | [
"synthpop/recipes/starter2.py"
] | [
"import numpy as np\nimport pandas as pd\n\nfrom .. import categorizer as cat\nfrom ..census_helpers import Census\n\n\n# TODO DOCSTRINGS!!\nclass Starter:\n \"\"\"\n This is a recipe for getting the marginals and joint distributions to use\n to pass to the synthesizer using simple categories - population,... | [
[
"numpy.isnan",
"pandas.Series"
]
] |
gaetanmargueritte/ccg2esn | [
"3745ef23eb409837d20261045fad808af5ab82a8"
] | [
"RSSviz/RSSviz.py"
] | [
"\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib\r\nimport seaborn as sns\r\nimport umap\r\n\r\n\r\nclass RecurentStateSpaceVisualisation():\r\n def __init__(self, n_compo=2):\r\n self.reducer = umap.UMAP(n_components=n_compo, random_state = 42)\r\n\r\n def define_space(s... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.figure"
]
] |
gjkennedy/pyoptsparse | [
"fde73c62dc45c96be983f73cb62f60cf14a082a2"
] | [
"pyoptsparse/pySNOPT/setup.py"
] | [
"#!/usr/bin/env python\n\nimport os, sys\n\n\ndef configuration(parent_package=\"\", top_path=None):\n\n from numpy.distutils.misc_util import Configuration\n\n config = Configuration(\"pySNOPT\", parent_package, top_path)\n config.add_data_files(\"LICENSE\", \"README\")\n\n # Since snopt has a bunch of... | [
[
"numpy.distutils.misc_util.Configuration"
]
] |
ak-gupta/nbaspa | [
"db961717bb23854e0373b7732638021a18d909f5",
"db961717bb23854e0373b7732638021a18d909f5"
] | [
"tests/test_model/test_tasks/test_io.py",
"nbaspa/model/tasks/metrics.py"
] | [
"\"\"\"Test loading data.\"\"\"\n\nfrom pathlib import Path\n\nimport cloudpickle\nfrom lifelines import CoxTimeVaryingFitter\nfrom sklearn.isotonic import IsotonicRegression\n\nfrom nbaspa.model.tasks import LoadData, LoadModel\n\ndef test_load_model_data(data, gamelocation):\n \"\"\"Test loading fake model dat... | [
[
"sklearn.isotonic.IsotonicRegression"
],
[
"numpy.average",
"numpy.array",
"sklearn.metrics.roc_auc_score",
"numpy.round"
]
] |
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