repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
huangwenwenlili/vedadet | [
"1d66e07538792799c2f12c6b1d381e2c1374d623"
] | [
"vedadet/datasets/pipelines/transforms.py"
] | [
"# adapted from https://github.com/open-mmlab/mmcv or\n# https://github.com/open-mmlab/mmdetection\nimport inspect\nimport numpy as np\nfrom numpy import random\n\nimport vedacore.image as image\nfrom vedacore.misc import is_list_of, is_str, registry\nfrom vedadet.misc.bbox import bbox_overlaps\n\ntry:\n from im... | [
[
"numpy.clip",
"numpy.random.choice",
"numpy.random.random_sample",
"numpy.tile",
"numpy.concatenate",
"numpy.ceil",
"numpy.random.permutation",
"numpy.random.rand",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yyyjoe/deep-person-reid | [
"82380612ca87928c772ea8059da5cb8c6bb06e0c",
"82380612ca87928c772ea8059da5cb8c6bb06e0c"
] | [
"torchreid/datasets/mars.py",
"torchreid/models/densenet.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport glob\nimport re\nimport sys\nimport urllib\nimport tarfile\nimport zipfile\nimport os.path as osp\nfrom scipy.io import loadmat\nimport numpy as np\nimport h5py\nfrom scipy.misc impor... | [
[
"scipy.io.loadmat"
],
[
"torch.nn.Sequential",
"torch.nn.BatchNorm1d",
"torch.nn.Dropout",
"torch.cat",
"torch.nn.functional.dropout",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.AvgPool2d",
"torch.nn.functional.relu",
"torch.nn.Ad... | [
{
"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"... |
LukichevaPolina/dpnp | [
"5f5a679905d237ac7be1cc9ad1075877a9f77e39",
"5f5a679905d237ac7be1cc9ad1075877a9f77e39"
] | [
"tests/test_statistics.py",
"tests/test_strides.py"
] | [
"import pytest\n\nimport dpnp\n\nimport numpy\n\n\n@pytest.mark.parametrize(\"type\",\n [numpy.float64, numpy.float32, numpy.int64, numpy.int32],\n ids=['float64', 'float32', 'int64', 'int32'])\n@pytest.mark.parametrize(\"size\",\n [2, 4, 8, 16... | [
[
"numpy.arange",
"numpy.median",
"numpy.nanvar",
"numpy.testing.assert_array_equal",
"numpy.max",
"numpy.bincount",
"numpy.testing.assert_allclose",
"numpy.array"
],
[
"numpy.fmod",
"numpy.empty_like",
"numpy.arange",
"numpy.tan",
"numpy.copysign",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mapehe/pandas | [
"832d81fa55c0413fac441a05cef25a508ff0c308",
"8ddc0fd801d794fcd7735816790dff66d1c678e2"
] | [
"pandas/tests/test_panel.py",
"pandas/io/common.py"
] | [
"# -*- coding: utf-8 -*-\n# pylint: disable=W0612,E1101\n\nfrom warnings import catch_warnings\nfrom datetime import datetime\nimport operator\nimport pytest\n\nimport numpy as np\n\nfrom pandas.core.dtypes.common import is_float_dtype\nfrom pandas import (Series, DataFrame, Index, date_range, isna, notna,\n ... | [
[
"pandas.core.panel.Panel",
"pandas.util.testing.ensure_clean",
"pandas.Series",
"numpy.linspace",
"pandas.DataFrame.add",
"numpy.sqrt",
"numpy.asarray",
"numpy.around",
"pandas.util.testing.assert_produces_warning",
"pandas.io.excel.ExcelFile",
"pandas.MultiIndex.from_t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
cyber-meow/Noisy_active_query | [
"309c41f45c53311c32e2eeec9b179807237abce1"
] | [
"mnist/mnist_relabel_active.py"
] | [
"import torch\nimport torch.utils.data as data\nimport torchvision\nimport torchvision.transforms as transforms\n\nimport argparse\n# import matplotlib.pyplot as plt\nimport numpy as np\nfrom copy import deepcopy\nfrom collections import OrderedDict\n\nimport dataset\nimport settings\nfrom active_query import Rando... | [
[
"numpy.logical_or",
"torch.from_numpy",
"torch.cuda.is_available",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NCRAR/psiaudio | [
"fdb7629238ef7de62a511b5ab17458d46767db69"
] | [
"tests/test_pipeline.py"
] | [
"import pytest\n\nfrom collections import deque\nfrom functools import partial\n\nimport numpy as np\nfrom scipy import signal\n\nfrom psiaudio import pipeline\nfrom psiaudio.pipeline import normalize_index\nfrom psiaudio import util\n\n\n@pytest.fixture\ndef data1d(fs):\n md = {'foo': 'bar'}\n data = np.rand... | [
[
"scipy.signal.lfilter_zi",
"numpy.concatenate",
"numpy.testing.assert_array_equal",
"scipy.signal.lfilter",
"numpy.random.randint",
"numpy.mean",
"numpy.random.uniform",
"scipy.signal.detrend",
"scipy.signal.iirfilter",
"numpy.testing.assert_array_almost_equal"
]
] | [
{
"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"
... |
seyuboglu/terra | [
"7d5f8d8cdfbf819b52fb997b5b9746746d86b295"
] | [
"terra/pytorch.py"
] | [
"import torch\n\nfrom terra.io import reader, writer\n\n\n@writer(torch.Tensor)\ndef write_tensor(out, path):\n torch.save(out, path)\n return path\n\n\n@reader(torch.Tensor)\ndef read_tensor(path):\n return torch.load(path)\n\n\nclass TerraModule:\n def __terra_write__(self, path):\n torch.save(... | [
[
"torch.load",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Globe-Eater/Cali_Housing | [
"c4340ce86f13c8c4f50423c574d5ab857869400d"
] | [
"PCA.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 16 13:32:05 2020\n\n@author: kellenbullock\n\"\"\"\nimport pandas as pd\nfrom statsmodels.multivariate.pca import PCA\n\ndf = pd.read_excel('Final.xlsx')\ndf = df.drop(columns=['Unnamed: 0'])\n\nc = PCA(df, standardize=False)"
] | [
[
"pandas.read_excel"
]
] | [
{
"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": []
}
] |
Pseudomanifold/POT | [
"5861209f27fe8e022eca2ed2c8d0bb1da4a1146b"
] | [
"benchmarks/benchmark.py"
] | [
"# /usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom ot.backend import get_backend_list, jax, tf\nimport gc\n\n\ndef setup_backends():\n if jax:\n from jax.config import config\n config.update(\"jax_enable_x64\", True)\n\n if tf:\n from tensorflow.python.ops.numpy_ops import np_config... | [
[
"tensorflow.python.ops.numpy_ops.np_config.enable_numpy_behavior"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.8",
"2.7",
"2.9",
"2.5",
"2.6",
"2.10"
]
}
] |
warlock8hz/pointnet2 | [
"7e456909379c6d19946bf49b3f54346ec96e0fe5"
] | [
"part_seg/evaluate.py"
] | [
"import argparse\nimport math\nfrom datetime import datetime\nimport h5py\nimport numpy as np\n#import tensorflow as tf\nimport tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\nimport socket\nimport importlib\nimport os\nimport sys\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nROOT_DIR = os.path.dirn... | [
[
"tensorflow.compat.v1.ConfigProto",
"tensorflow.compat.v1.disable_v2_behavior",
"tensorflow.compat.v1.Session",
"tensorflow.compat.v1.placeholder",
"tensorflow.compat.v1.Graph",
"numpy.mean",
"numpy.argmax",
"numpy.array",
"tensorflow.compat.v1.train.Saver",
"numpy.zeros",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tstaffas/Lidar | [
"cd381826b6b45dd2a36aecb35617196b78ab32e8"
] | [
"Lidar code/intensity_lidar_0.2.py"
] | [
"#------IMPORTS-----\r\n#Packages for ETA backend\r\nimport json\r\nimport etabackend.eta #Available at: https://github.com/timetag/ETA, https://eta.readthedocs.io/en/latest/\r\nimport etabackend.tk as etatk\r\n\r\n#Packages used for analysis\r\nimport numpy as np\r\nfrom pathlib import Path\r\nimport os\r\nimport ... | [
[
"numpy.arange",
"numpy.amax",
"numpy.abs"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LinqCod/model_search | [
"d90bc39994bc2a5f5028035ac954f796eda03310"
] | [
"model_search/data/csv_data_for_binary.py"
] | [
"# Copyright 2021 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"pandas.read_csv",
"tensorflow.compat.v2.estimator.export.build_raw_serving_input_receiver_fn",
"tensorflow.compat.v2.compat.v1.placeholder",
"tensorflow.compat.v2.feature_column.numeric_column",
"tensorflow.compat.v2.compat.v1.disable_eager_execution",
"tensorflow.compat.v2.data.experimen... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
penghouwen/CDARTS | [
"7dddc8d5db4ed343979ed3687c6adfc39dfce284",
"7dddc8d5db4ed343979ed3687c6adfc39dfce284",
"3f2c153df1551e775c3d7a93c7f4a7b9931bd44c",
"3f2c153df1551e775c3d7a93c7f4a7b9931bd44c",
"7dddc8d5db4ed343979ed3687c6adfc39dfce284",
"7dddc8d5db4ed343979ed3687c6adfc39dfce284"
] | [
"CDARTS_segmentation/tools/utils/darts_utils.py",
"CDARTS_segmentation/train/seg_metrics.py",
"CDARTS_segmentation/train/cal_model.py",
"CDARTS_detection/mmdet/models/backbones/efficientnet_builder.py",
"CDARTS_detection/mmdet/models/utils/conv_module.py",
"lib/core/augment_function.py"
] | [
"import os\nimport math\nimport numpy as np\nimport torch\nimport shutil\nfrom torch.autograd import Variable\nimport time\nfrom tqdm import tqdm\nfrom genotypes import PRIMITIVES\nimport matplotlib\n# Force matplotlib to not use any Xwindows backend.\nmatplotlib.use('Agg')\nfrom matplotlib import pyplot as plt\nfr... | [
[
"matplotlib.pyplot.legend",
"torch.load",
"torch.no_grad",
"numpy.random.randn",
"numpy.copyto",
"torch.save",
"numpy.random.randint",
"torch.onnx.export",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.table",
"torch.cuda.synchronize",
"numpy.clip",
"torc... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
WAPAY/EPM | [
"4c4426f3b6a86d74f2d4c2c8d0c82f9475103eb7"
] | [
"model.py"
] | [
"# -*- coding: utf-8 -*-\nimport os\nos.environ['TF_KERAS'] = \"1\"\nimport tensorflow as tf\nimport config\nfrom bert4keras.models import build_transformer_model\nfrom module import label_smoothing, noam_scheme\nimport logging\nimport constant\nimport numpy as np\nimport law_accu_term_constraint\nlogging.basicConf... | [
[
"tensorflow.concat",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.train.AdamOptimizer",
"tensorflow.contrib.crf.crf_decode",
"tensorflow.contrib.crf.crf_log_likelihood",
"tensorflow.summary.scalar",
"tensorflow.layers.dense",
"tensorflow.train.get_or_create_global_st... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mlbench/mlbench-core | [
"4fd3c7e6f1a5be69e52383ab2eb64cad257218c2",
"4fd3c7e6f1a5be69e52383ab2eb64cad257218c2"
] | [
"mlbench_core/controlflow/pytorch/checkpoints_evaluation.py",
"mlbench_core/utils/pytorch/utils.py"
] | [
"\"\"\"Evaluate training/validation set using models in checkpoints\"\"\"\nimport logging\n\nimport torch\n\nfrom mlbench_core.aggregation.pytorch.centralized import AllReduceAggregation\nfrom mlbench_core.controlflow.pytorch.helpers import iterate_dataloader\nfrom mlbench_core.utils.pytorch.distributed import glob... | [
[
"torch.no_grad"
],
[
"torch.sum",
"torch.FloatTensor",
"torch.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PariksheetPinjari909/tvmdbg | [
"4ddd24485c554a1422289bffd6da8587f2a806bc"
] | [
"python/tvm/tools/debug/cli/tensor_format.py"
] | [
"# coding: utf-8\n# pylint: disable=fixme, too-many-arguments, too-many-locals, too-many-statements, too-many-branches, no-member,consider-using-enumerate\n# 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 fi... | [
[
"numpy.isinf",
"numpy.min",
"numpy.isnan",
"numpy.set_printoptions",
"numpy.issubdtype",
"numpy.isposinf",
"numpy.isneginf",
"numpy.max",
"numpy.size",
"numpy.std",
"numpy.shape",
"numpy.mean",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jdmcbr/geopandas | [
"1cabaaa860c6d407cae710b878e98f6ea83d1ae0"
] | [
"geopandas/tools/overlay.py"
] | [
"import warnings\nfrom functools import reduce\n\nimport numpy as np\nimport pandas as pd\n\nfrom geopandas import GeoDataFrame, GeoSeries\nfrom geopandas.array import _check_crs, _crs_mismatch_warn\n\n\ndef _ensure_geometry_column(df):\n \"\"\"\n Helper function to ensure the geometry column is called 'geome... | [
[
"numpy.split",
"pandas.concat",
"pandas.DataFrame",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
jpauwels/scikit-learn | [
"46969c2d1e8abf5cc5308b9a20fc89c747bce6b2"
] | [
"sklearn/model_selection/_validation.py"
] | [
"\"\"\"\nThe :mod:`sklearn.model_selection._validation` module includes classes and\nfunctions to validate the model.\n\"\"\"\n\n# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# Gael Varoquaux <gael.varoquaux@normalesup.org>\n# Olivier Grisel <olivier.grisel@ensta.org>\n# Raghav... | [
[
"numpy.split",
"numpy.linspace",
"numpy.asarray",
"numpy.issubdtype",
"numpy.concatenate",
"numpy.max",
"numpy.all",
"numpy.mean",
"numpy.zeros_like",
"scipy.sparse.vstack",
"scipy.sparse.issparse",
"numpy.unique",
"numpy.clip",
"numpy.stack",
"numpy.fin... | [
{
"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"... |
AljazBozic/TransformerFusion | [
"fe64be2e57a064403d6e0bf170ce42afcab1ab9f"
] | [
"src/evaluation/eval.py"
] | [
"import sys, os\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\n\nimport time\nimport argparse\nimport torch\nimport numpy as np\nfrom tqdm import tqdm\nimport open3d as o3d\n\nfrom nnutils.chamfer_distance import ChamferDistance \n\n\ndef visualize_occlusion_mask(occlusion_mask... | [
[
"torch.mean",
"numpy.asarray",
"torch.sqrt",
"torch.from_numpy",
"torch.inverse",
"torch.isfinite",
"torch.arange",
"numpy.load",
"torch.meshgrid",
"torch.ones_like",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
genomicsITER/NanoRTax | [
"c466dbc1371f597a976d004bc0fb8d4251fe4b8f"
] | [
"templates/kraken_push.py"
] | [
"#!/usr/bin/env python3\n\nimport datetime\nimport re\nimport pandas as pd\nimport skbio\n\n\ndf = pd.read_csv(\"$report\", delimiter=\"\\t\", names=['seq_id', 'tax_id', 'kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species'])\ntax_table = df['class'].value_counts()\ntax_table_class = pd.DataFrame(list... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
MugiPham/MEDIUM_NoteBook | [
"799b146469c99d8a94ab8684beb78271eec73cfb"
] | [
"Graph_TimeSeries_Forecasting/spektral_gcn.py"
] | [
"from tensorflow.keras import activations, initializers, regularizers, constraints\nfrom tensorflow.keras import backend as K\nfrom tensorflow.keras.layers import Layer\n\nfrom spektral_utilities import filter_dot, dot, localpooling_filter\n\n\nclass GraphConv(Layer):\n r\"\"\"\n A graph convolutional layer (... | [
[
"tensorflow.keras.backend.bias_add",
"tensorflow.keras.constraints.get",
"tensorflow.keras.activations.serialize",
"tensorflow.keras.constraints.serialize",
"tensorflow.keras.regularizers.get",
"tensorflow.keras.initializers.serialize",
"tensorflow.keras.regularizers.serialize",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
nmonath/awd-lstm-lm | [
"060650f5f8f7f9cff61faa8713ed5aca9679870b"
] | [
"generate.py"
] | [
"###############################################################################\n# Language Modeling on Penn Tree Bank\n#\n# This file generates new sentences sampled from the language model\n#\n###############################################################################\n\nimport argparse\n\nimport torch\nfrom... | [
[
"torch.cuda.manual_seed",
"torch.load",
"torch.manual_seed",
"torch.multinomial",
"torch.rand",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leotmc/simcse | [
"e0b62fcef5dadeeef55aef3b640ab219860c201b"
] | [
"SentEval/senteval/sick.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n#\n\n'''\nSICK Relatedness and Entailment\n'''\nfrom __future__ import absolute_import, division, unicode_literals\n\nim... | [
[
"numpy.abs",
"scipy.stats.pearsonr",
"sklearn.metrics.mean_squared_error",
"numpy.floor",
"scipy.stats.spearmanr",
"numpy.array",
"numpy.vstack"
]
] | [
{
"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"
... |
gmum/toolk | [
"936684590b06dedbc561cdbfe8927de9d19c37d6"
] | [
"pytorch_lightning_project_template/src/utils.py"
] | [
"\"\"\"\nMinor utilities\n\"\"\"\n\nimport sys\nfrom functools import reduce\n\nimport traceback\nimport logging\nimport argparse\nimport optparse\nimport datetime\nimport sys\nimport pprint\nimport types\nimport time\nimport copy\nimport subprocess\nimport glob\nfrom collections import OrderedDict\nimport os\nimpo... | [
[
"torch.nn.modules.module._addindent",
"pandas.concat",
"torch.save"
]
] | [
{
"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": []
}
] |
wangye707/continuous_integration | [
"6e38924a67e17a57f3e64d9e846f0ede6e47c93f"
] | [
"inference/inference_benchmark/cc/Paddle/bin/py_parse_log.py"
] | [
"import os\nimport re\nimport argparse\n\nimport pandas as pd\n\ndef parse_args():\n \"\"\"\n parse input args\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--log_path\", type=str, default=\"./log\",\n help=\"benchmark log path\")\n parser.add_argumen... | [
[
"pandas.isnull",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
vathes/DJ-NWB-Yu-Gutnisky-2016 | [
"85414f6b35da48fbb78a9a37108fb59a609079c6"
] | [
"scripts/ingest_wholecell.py"
] | [
"import os\nimport re\nimport pathlib\nimport h5py as h5\nimport numpy as np\nfrom decimal import Decimal\nfrom tqdm import tqdm\nimport glob\n\nimport datajoint as dj\nfrom pipeline import (reference, subject, acquisition, intracellular, behavior, stimulation, virus, utilities)\n\npath = pathlib.Path(dj.config['cu... | [
[
"numpy.array",
"numpy.diff",
"numpy.where",
"numpy.logical_and"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pvkraju80/leo | [
"f6ed6f4ee6eb34c47581d40d4d3fea69f42140f3"
] | [
"code/scripts/variance_swaps.py"
] | [
"#\n# Module with functions for\n# Variance Swaps Examples\n#\n# (c) Dr. Yves J. Hilpisch\n# Listed Volatility and Variance Derivatives\n#\nimport math\nimport numpy as np\nimport pandas as pd\n\ndef generate_path(S0, r, sigma, T, M, seed=100000):\n ''' Function to simulate a geometric Brownian motion.\n\n Pa... | [
[
"numpy.random.standard_normal",
"numpy.random.seed",
"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": []
}
] |
rcr-usfs/lamda | [
"dac61cca68d70f049d43a8162b7f2d80f3487d9c"
] | [
"Production/raster_processing_lib.py"
] | [
"\"\"\"\n\t Copyright 2021 Ian Housman, RedCastle Resources Inc.\n\n\t Licensed under the Apache License, Version 2.0 (the \"License\");\n\t you may not use this file except in compliance with the License.\n\t You may obtain a copy of the License at\n\n\t\t\t http://www.apache.org/licenses/LICENSE-2.0\n\n\t Unless ... | [
[
"numpy.max",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RiyaGupta99/preprocessy | [
"1cccf56e96f95394e939ea9aa2751857c071af75"
] | [
"preprocessy/feature_selection/_selectKBest.py"
] | [
"import numpy as np\nimport pandas as pd\nfrom sklearn.feature_selection import f_classif\nfrom sklearn.feature_selection import f_regression\n\n\nclass SelectKBest:\n \"\"\"Class for finding K highest scoring features among the set of all features. Takes a feature and finds its correlation with the\n target ... | [
[
"numpy.asarray",
"numpy.argsort",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hammadzz/great_expectations | [
"020c605000d9472e88a9da7b6baa2fae28fd02c7"
] | [
"great_expectations/dataset/sparkdf_dataset.py"
] | [
"import copy\nimport inspect\nimport json\nimport logging\nfrom collections import OrderedDict\nfrom datetime import datetime\nfrom functools import reduce, wraps\nfrom typing import List\n\nimport jsonschema\nimport numpy as np\nimport pandas as pd\nfrom dateutil.parser import parse\n\nfrom great_expectations.data... | [
[
"numpy.mean",
"pandas.Index"
]
] | [
{
"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": []
}
] |
okehkim/End-to-End-Speech-Recognition-Models | [
"7b4695bbc778e4d2c92470b56e2479c8d81d0079",
"7b4695bbc778e4d2c92470b56e2479c8d81d0079"
] | [
"deepspeech2/model.py",
"attention.py"
] | [
"# -*- coding: utf-8 -*-\n# Soohwan Kim @ https://github.com/sooftware/\n# This source code is licensed under the Apache 2.0 License license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import... | [
[
"torch.stack",
"torch.nn.ReLU",
"torch.no_grad",
"torch.nn.functional.log_softmax"
],
[
"torch.sigmoid",
"torch.nn.functional.softmax",
"numpy.sqrt",
"torch.bmm",
"torch.rand",
"torch.nn.Conv1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
boourns/eurorack | [
"1914e3c4c0a1b43b79fa18abdf28f16f0158d5c5",
"1914e3c4c0a1b43b79fa18abdf28f16f0158d5c5"
] | [
"warps/tools/generate_src_filters.py",
"clouds/resources/src_filters.py"
] | [
"#!/usr/bin/python2.5\n#\n# Copyright 2014 Olivier Gillet.\n#\n# Author: Olivier Gillet (pichenettes@mutable-instruments.net)\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software witho... | [
[
"numpy.arange",
"numpy.fft.rfft"
],
[
"numpy.arange",
"numpy.fft.rfft"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.21",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{... |
OMARI1988/bug_tracking | [
"4501805557359e476f54624956371e42e85f6a39"
] | [
"transform.py"
] | [
"# -*- coding: utf-8 -*-\n# transformations.py\n\n# Copyright (c) 2006-2015, Christoph Gohlke\n# Copyright (c) 2006-2015, The Regents of the University of California\n# Produced at the Laboratory for Fluorescence Dynamics\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or witho... | [
[
"numpy.diag",
"numpy.dot",
"numpy.radians",
"numpy.expand_dims",
"numpy.sqrt",
"numpy.vstack",
"numpy.fabs",
"numpy.concatenate",
"numpy.mean",
"numpy.cross",
"numpy.negative",
"numpy.trace",
"numpy.roll",
"numpy.linalg.svd",
"numpy.allclose",
"numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DSCI-310/DSCI-310-Group-7 | [
"f244e9005be3a4b73e77fed82c97d22ae2b0a971",
"f244e9005be3a4b73e77fed82c97d22ae2b0a971"
] | [
"src/knn_script.py",
"src/svm_lr_script.py"
] | [
"\"\"\"\nTakes the clean data location get the data from their do the model evaluation and train\nit using the best k for KNN and export the reports and results of KNN model to the export\nlocation\n\nUsage: src/knn_script.py data_loc export_loc\n\nOptions:\ndata_loc The location of the cleaned data that the mo... | [
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"matplotlib.pyplot.savefig",
"pandas.DataFrame",
"numpy.zeros"
],
[
"pandas.read_csv",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1... |
niltonlk/nrn | [
"464541abbf72fe58de77b16bf0e1df425a280b89",
"464541abbf72fe58de77b16bf0e1df425a280b89",
"464541abbf72fe58de77b16bf0e1df425a280b89"
] | [
"share/lib/python/neuron/rxdtests/run_all.py",
"share/lib/python/neuron/rxd/geometry3d/surface.py",
"share/lib/python/neuron/rxdtests/tests/cabuf.py"
] | [
"def test(files, correct_data):\n import os\n import sys\n import filecmp\n import subprocess\n import re, array, numpy\n\n tol = 1e-10\n dt_eps = 1e-20\n for dr in [\"wave1d\", \"ecs\", \"3d\", \"hybrid\"]:\n try:\n os.makedirs(os.path.join(\"test_data\", dr))\n exc... | [
[
"numpy.fromfile",
"numpy.interp"
],
[
"numpy.arange"
],
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marthaboyer/beast | [
"1ca71fb64ab60827e4e4e1937b64f319a98166c3",
"1ca71fb64ab60827e4e4e1937b64f319a98166c3",
"1ca71fb64ab60827e4e4e1937b64f319a98166c3"
] | [
"beast/tools/setup_batch_beast_trim.py",
"beast/physicsmodel/helpers/hdfstore.py",
"beast/observationmodel/noisemodel/toothpick.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nCode to setup the batch files for BEAST trim grid runs\n\"\"\"\n\nfrom __future__ import print_function\nimport os\nimport glob\n\nimport argparse\n\nimport numpy as np\n\n\ndef setup_batch_beast_trim(project,\n datafile,\n astfil... | [
[
"numpy.unique"
],
[
"numpy.asarray",
"numpy.arange",
"numpy.random.normal"
],
[
"numpy.amax",
"numpy.amin",
"numpy.arange",
"numpy.percentile",
"numpy.std",
"numpy.mean",
"numpy.interp",
"numpy.argsort",
"numpy.zeros",
"numpy.where",
"numpy.empty... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brandontrabucco/rlkit | [
"ac0bb660d6baa9c36fceb688cb147066f5ddc722",
"ac0bb660d6baa9c36fceb688cb147066f5ddc722"
] | [
"rlkit/torch/conv_networks.py",
"rlkit/core/rl_algorithm.py"
] | [
"import torch\nfrom torch import nn as nn\n\nfrom rlkit.pythonplusplus import identity\nfrom rlkit.torch.core import PyTorchModule\n\nimport numpy as np\n\n\nclass CNN(PyTorchModule):\n def __init__(\n self,\n input_width,\n input_height,\n input_channels,\n ... | [
[
"torch.nn.BatchNorm1d",
"torch.nn.ConvTranspose2d",
"torch.zeros",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.Linear",
"numpy.prod",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Mrqianduoduo/RSDet-8P-4R | [
"56e1c1ccb619975b515ecf7d1537f15282edcdd8"
] | [
"libs/configs/DOTA1.0/cfgs_res50_dota_v8.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import division, print_function, absolute_import\nimport os\nimport tensorflow as tf\nimport math\n\n\"\"\"\nv3 + 2x multi-gpu + REG_WEIGHT=1.0\nThis is your evaluation result for task 1:\n\n mAP: 0.627914092842082\n ap of each class:\n plane:0.8858093578644306,\n ... | [
[
"tensorflow.constant_initializer",
"tensorflow.random_normal_initializer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
ForrestHeiYing/Detectron | [
"787f5a9bf80b4149adb4913e86b2edd8a441bf09"
] | [
"detectron/utils/train.py"
] | [
"# Copyright (c) 2017-present, Facebook, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicabl... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
icaros-usc/pyribs | [
"ef289a930e7a8a51286cf657f7e4b29551277350",
"ef289a930e7a8a51286cf657f7e4b29551277350"
] | [
"tests/archives/grid_archive_test.py",
"tests/archives/archive_base_test.py"
] | [
"\"\"\"Tests for the GridArchive.\"\"\"\nimport numpy as np\nimport pytest\n\nfrom ribs.archives import AddStatus, GridArchive\n\nfrom .conftest import get_archive_data\n\n# pylint: disable = redefined-outer-name\n\n\n@pytest.fixture\ndef data():\n \"\"\"Data for grid archive tests.\"\"\"\n return get_archive... | [
[
"numpy.all",
"numpy.array",
"numpy.linspace",
"numpy.isclose"
],
[
"numpy.all",
"numpy.array",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Digitaltransform/tensorboard | [
"931fdb84ed125efe5ef6cb0bad869c52da73e18f"
] | [
"tensorboard/plugins/audio/audio_plugin_test.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.compat.v1.summary.merge_all",
"numpy.random.seed",
"tensorflow.test.main",
"tensorflow.compat.v1.summary.audio",
"tensorflow.compat.v1.Session",
"tensorflow.compat.as_bytes",
"tensorflow.compat.v1.placeholder",
"tensorflow.compat.v1.Graph",
"numpy.random.rand",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SalishSeaCast/tools | [
"4be9170bbe7ab700ab1b3afc3138e669053d43f8"
] | [
"SalishSeaTools/salishsea_tools/bio_tools.py"
] | [
"# Copyright 2013-2021 The Salish Sea NEMO Project and\n# The University of British Columbia\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/LI... | [
[
"numpy.minimum",
"numpy.power",
"numpy.ones",
"numpy.any",
"numpy.exp",
"numpy.logical_and",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
youansheng/PyTorchCV | [
"c7e717fe301ba338b3e9dbea70b51f3e2cd5dabe",
"3a51b2f209639e58620676bf19b3564ef8c92a75",
"c7e717fe301ba338b3e9dbea70b51f3e2cd5dabe",
"c7e717fe301ba338b3e9dbea70b51f3e2cd5dabe"
] | [
"models/seg/nets/pspnet.py",
"models/det/nets/faster_rcnn.py",
"demo/pseg/model/dmnetU.py",
"methods/seg/fcn_segmentor.py"
] | [
"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author: Donny You(youansheng@gmail.com)\n# Pytorch implementation of PSP net Synchronized Batch Normalization\n# this is pytorch implementation of PSP resnet101 (syn-bn) version\n\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom models... | [
[
"torch.nn.Dropout2d",
"torch.Tensor",
"torch.cat",
"torch.nn.ModuleList",
"torch.nn.Conv2d",
"torch.nn.AdaptiveAvgPool2d"
],
[
"torch.nn.Sequential",
"torch.nn.Conv2d"
],
[
"torch.cat",
"torch.load",
"torch.nn.PReLU",
"torch.nn.Conv2d",
"torch.nn.BatchNo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
heyudao/ISS | [
"d076fca9b8783825ffe688fbbddb7ef45eae7cd1"
] | [
"Day 2/workshop/selected-downloads (26)/ML_Classifiers.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Aug 23 17:03:52 2019\r\n\r\n@author: isswan\r\n\"\"\"\r\n\r\n\r\nimport sklearn\r\nimport nltk\r\nimport re \r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nfrom sklearn.model_selection import train_t... | [
[
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.metrics.confusion_matrix",
"sklearn.neighbors.KNeighborsClassifier",
"sklearn.feature_selection.SelectKBest",
"numpy.mean",
"sklearn.svm.LinearSVC",
"sklearn.metrics.clas... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
thisisi3/OpenMMLab-IoUNet | [
"23a9931627aa2bc15298d0379a81c2efde5e0297"
] | [
"mmdet/iounet/roi_generator.py"
] | [
"from mmdet.core import bbox_overlaps, bbox_xyxy_to_cxcywh, bbox_cxcywh_to_xyxy\nfrom .utils import clip_bboxes_to_image\nimport math, torch\n \nclass RoIGenerator(torch.nn.Module):\n \"\"\"It generates IoU balanced rois from a given set of GT bboxes.\n We use delta since it is scale-invariant. A large... | [
[
"torch.linspace",
"torch.cat",
"torch.randperm",
"torch.tensor",
"torch.stack",
"torch.meshgrid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jacobic/redmapper | [
"bda5bd6f486fd5f18d35aa9ae4b875628e905604",
"bda5bd6f486fd5f18d35aa9ae4b875628e905604"
] | [
"redmapper/calibration/selectspecseeds.py",
"tests/test_fitters.py"
] | [
"\"\"\"Select seed galaxies\n\"\"\"\nimport os\nimport numpy as np\nimport fitsio\nimport esutil\n\nfrom ..configuration import Configuration\nfrom ..galaxy import GalaxyCatalog\nfrom ..catalog import Catalog\n\nclass SelectSpecSeeds(object):\n \"\"\"\n Class to match a galaxy catalog to a spectroscopic catal... | [
[
"numpy.where",
"numpy.zeros"
],
[
"numpy.abs",
"numpy.random.seed",
"numpy.median",
"numpy.ones",
"numpy.testing.assert_almost_equal",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tanmay7270/Playing-with-PyTorch | [
"9ace9c93823010a91cdf9206c2d57d3dc10d821f"
] | [
"fashion_MNIST.py"
] | [
"import torch\nfrom torchvision import datasets, transforms\nfrom torch import nn, optim\nimport torch.nn.functional as F\n\n# Define a transform to normalize the data\ntransform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n#... | [
[
"torch.nn.NLLLoss",
"torch.utils.data.DataLoader",
"torch.exp",
"torch.nn.Linear",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KTH-dESA/UNECE-Capacity-building | [
"5163cf6c7c86aa2774c7f6072ada0e4b90cc8e9a"
] | [
"drina/packages.py"
] | [
"# This file imports all the packages\nimport pandas as pd\npd.set_option('mode.chained_assignment', None)\nimport numpy as np\nfrom IPython.display import HTML\nimport IPython.core.display as di\nimport plotly as py\nimport cufflinks\nimport plotly.offline as pyo\nfrom plotly.offline import plot, iplot, init_noteb... | [
[
"pandas.set_option"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Anpopaicoconat/Poly-Encoder | [
"779a6ec19bd6477947fcf44199fa06fc6353e18a",
"779a6ec19bd6477947fcf44199fa06fc6353e18a"
] | [
"encoder.py",
"telegram_bot.py"
] | [
"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom transformers import BertPreTrainedModel, BertModel\n\n\nclass BiEncoder(BertPreTrainedModel):\n def __init__(self, config, *inputs, **kwargs):\n super().__init__(config, *inputs, **kwargs)\n self.bert = ... | [
[
"torch.abs",
"torch.nn.functional.softmax",
"torch.nn.functional.log_softmax",
"torch.eye",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.matmul",
"torch.cdist",
"torch.nn.init.normal_",
"torch.arange"
],
[
"torch.load",
"torch.utils.data.DataLoader",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cburns/pynifti | [
"12c0314994a1061b573990dbd46ea8e35cec8e60",
"12c0314994a1061b573990dbd46ea8e35cec8e60",
"12c0314994a1061b573990dbd46ea8e35cec8e60"
] | [
"nifti/tests/test_analyze.py",
"nifti/spm2analyze.py",
"nifti/tests/transformations.py"
] | [
"''' Test analyze headers\n\nSee test_binary.py for general binary header tests\n\nThis - basic - analyze header cannot encode full affines (only\ndiagonal affines), and cannot do integer scaling.\n\nThe inability to do affines raises the problem of whether the image is\nneurological (left is left), or radiological... | [
[
"numpy.diag",
"numpy.allclose",
"numpy.ones",
"numpy.all",
"numpy.array"
],
[
"numpy.isfinite",
"numpy.dtype"
],
[
"numpy.dot",
"numpy.allclose",
"numpy.vstack",
"numpy.cumsum",
"numpy.finfo",
"numpy.linalg.det",
"numpy.identity",
"numpy.mean",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dmelis/POT | [
"7471b7a16577d66e907e10e08bf3bbcded19551c"
] | [
"ot/gromov.py"
] | [
"\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nGromov-Wasserstein transport method\r\n\r\n\r\n\"\"\"\r\n\r\n# Author: Erwan Vautier <erwan.vautier@gmail.com>\r\n# Nicolas Courty <ncourty@irisa.fr>\r\n# Rémi Flamary <remi.flamary@unice.fr>\r\n#\r\n# License: MIT License\r\n\r\nimport numpy as np\r\n\r\nfr... | [
[
"numpy.dot",
"numpy.log",
"numpy.asarray",
"numpy.linalg.norm",
"numpy.random.randn",
"numpy.outer",
"numpy.sum",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ji-Ping-Dai/CosmoCl | [
"5c6ca73c1a37d3b0de8751c772ad38eb60f89840"
] | [
"python/getdist/densities.py"
] | [
"import numpy as np\nfrom scipy.interpolate import splrep, splev, RectBivariateSpline, LinearNDInterpolator\n\n\nclass DensitiesError(Exception):\n pass\n\n\ndefaultContours = [0.68, 0.95]\n\n\ndef getContourLevels(inbins, contours=defaultContours, missing_norm=0, half_edge=True):\n \"\"\"\n Get contour l... | [
[
"scipy.interpolate.splrep",
"numpy.asarray",
"numpy.arange",
"numpy.cumsum",
"numpy.sort",
"scipy.interpolate.splev",
"numpy.atleast_1d",
"scipy.interpolate.RectBivariateSpline.__init__",
"numpy.max",
"numpy.argmax",
"numpy.searchsorted",
"scipy.interpolate.LinearND... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OpenHEC/SNN-simulator-on-PYNQcluster | [
"14f86a76edf4e8763b58f84960876e95d4efc43a",
"14f86a76edf4e8763b58f84960876e95d4efc43a",
"14f86a76edf4e8763b58f84960876e95d4efc43a",
"14f86a76edf4e8763b58f84960876e95d4efc43a"
] | [
"NEST-14.0-FPGA/pynest/nest/tests/test_stdp_multiplicity.py",
"NEST-14.0-FPGA/pynest/nest/tests/test_connect_fixed_outdegree.py",
"NEST-14.0-FPGA/topology/pynest/tests/test_plotting.py",
"NEST-14.0-FPGA/pynest/nest/tests/test_facetshw_stdp.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# test_stdp_multiplicity.py\n#\n# This file is part of NEST.\n#\n# Copyright (C) 2004 The NEST Initiative\n#\n# NEST is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either ve... | [
[
"numpy.round",
"numpy.array"
],
[
"numpy.diag",
"numpy.zeros",
"numpy.sum",
"numpy.ones"
],
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"numpy.ones_like",
"numpy.allclose",
"numpy.arange",
"numpy.atleast_1d",
"numpy.exp",
"numpy.a... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
KailinLi/nflows | [
"7c07a1d5e510beb681d1b11d6ffda95a086a8153"
] | [
"nflows/transforms/UMNN/MonotonicNormalizer.py"
] | [
"import torch\n\n# from UMNN import NeuralIntegral, ParallelNeuralIntegral\nimport torch.nn as nn\n\n\ndef _flatten(sequence):\n flat = [p.contiguous().view(-1) for p in sequence]\n return torch.cat(flat) if len(flat) > 0 else torch.tensor([])\n\n\nclass ELUPlus(nn.Module):\n def __init__(self):\n s... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.zeros",
"torch.nn.ELU",
"torch.tensor",
"torch.nn.Linear",
"torch.nn.ReLU",
"torch.ones_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mkoeppe/scipy | [
"70a76cbcea11d959bcfdf7ee9df983bfac30e170",
"70a76cbcea11d959bcfdf7ee9df983bfac30e170",
"70a76cbcea11d959bcfdf7ee9df983bfac30e170"
] | [
"scipy/optimize/__init__.py",
"scipy/stats/_mstats_extras.py",
"scipy/spatial/tests/test_kdtree.py"
] | [
"\"\"\"\n=====================================================\nOptimization and root finding (:mod:`scipy.optimize`)\n=====================================================\n\n.. currentmodule:: scipy.optimize\n\nSciPy ``optimize`` provides functions for minimizing (or maximizing)\nobjective functions, possibly sub... | [
[
"scipy._lib._testutils.PytestTester"
],
[
"numpy.dot",
"numpy.ma.fix_invalid",
"scipy.stats.distributions.norm.ppf",
"numpy.abs",
"numpy.sqrt",
"scipy.stats.distributions.norm.cdf",
"numpy.arange",
"scipy.stats.distributions.t.ppf",
"numpy.ma.median",
"numpy.ma.sort... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
beckdaniel/GPy | [
"37e835aa36b18c32bc500684cc10dd2b74240e1f",
"37e835aa36b18c32bc500684cc10dd2b74240e1f",
"37e835aa36b18c32bc500684cc10dd2b74240e1f",
"37e835aa36b18c32bc500684cc10dd2b74240e1f",
"37e835aa36b18c32bc500684cc10dd2b74240e1f"
] | [
"GPy/likelihoods/student_t.py",
"GPy/models/bayesian_gplvm_minibatch.py",
"GPy/kern/_src/kern.py",
"GPy/examples/regression.py",
"GPy/kern/_src/coregionalize.py"
] | [
"# Copyright (c) 2012-2014 Ricardo Andrade, Alan Saul\n# Licensed under the BSD 3-clause license (see LICENSE.txt)\n\nimport numpy as np\nfrom scipy import stats, special\nimport scipy as sp\nfrom . import link_functions\nfrom scipy import stats, integrate\nfrom scipy.special import gammaln, gamma\nfrom .likelihood... | [
[
"numpy.square",
"numpy.log",
"numpy.ones_like",
"numpy.sqrt",
"scipy.special.psi",
"numpy.atleast_1d",
"numpy.prod",
"scipy.special.gammaln",
"numpy.float",
"numpy.log1p",
"numpy.array",
"numpy.empty"
],
[
"numpy.square",
"numpy.dot",
"numpy.hstack",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.18",
"0.19"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
... |
w3ichen/models | [
"b378433a1bb2dc55ae9266796f63b787fe3206fd"
] | [
"official/modeling/performance.py"
] | [
"# 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-2.0\n#\n# Unless requ... | [
[
"tensorflow.keras.mixed_precision.experimental.LossScaleOptimizer",
"tensorflow.keras.mixed_precision.LossScaleOptimizer",
"tensorflow.keras.mixed_precision.set_global_policy",
"tensorflow.keras.mixed_precision.experimental.set_policy",
"tensorflow.keras.mixed_precision.experimental.Policy",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kazemakase/mne-python | [
"9dbb10156a8faf6610f3a8d979ffe9b853dd20dd"
] | [
"mne/preprocessing/ica.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Authors: Denis A. Engemann <denis.engemann@gmail.com>\n# Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# Juergen Dammers <j.dammers@fz-juelich.de>\n#\n# License: BSD (3-clause)\n\nfrom inspect import isfunction\nfrom collections import namedtuple\nfrom copy impor... | [
[
"numpy.dot",
"scipy.linalg.pinv",
"numpy.sqrt",
"numpy.asarray",
"sklearn.decomposition.FastICA",
"numpy.concatenate",
"numpy.round",
"numpy.exp",
"numpy.where",
"numpy.hstack",
"numpy.unique",
"numpy.arange",
"numpy.eye",
"numpy.std",
"numpy.argmax",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
welpaolo/ML2SQL | [
"a716c466dedfe0400d701de40beacd1fee16a778"
] | [
"python2sql/ml/ml_pipeline.py"
] | [
"import os\nimport pickle\nimport time\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sqlalchemy import create_engine\n\n\nclass MLPipeline(object):\n \"\"\"\n TODO\n \"\"\"\n\n def __init__(self, output_dir):\n self.output_dir = output_dir\... | [
[
"pandas.concat",
"pandas.read_csv",
"numpy.unique",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
codeboy5/cvpr20-scatter-text-recognizer | [
"4bd6cfbd4d7f64ce11864514f6b6b0646267c285"
] | [
"modules/prediction.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nfrom torch.nn.init import xavier_uniform_\nfrom torch.nn.init import constant_\nfrom torch.nn.init import xavier_normal_\nimport copy\nimport numpy as np\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n\ncl... | [
[
"torch.nn.functional.softmax",
"torch.cat",
"torch.zeros",
"torch.sin",
"torch.tanh",
"torch.FloatTensor",
"torch.cuda.is_available",
"torch.nn.Dropout",
"torch.ones",
"torch.nn.LSTMCell",
"torch.arange",
"torch.cos",
"torch.LongTensor",
"torch.empty",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
callysto/curriculum-notebooks | [
"871241c8f318cff74f46f42705a03020cd48cd17"
] | [
"Mathematics/GraphingTwoVariables/helper.py"
] | [
"import matplotlib.pyplot as plt\nimport plotly as py\nimport plotly.graph_objs as go\nimport numpy as np\nimport math\nimport ipywidgets as widgets\nfrom astropy.table import Table, Column\nfrom ipywidgets import interact, interactive, Layout\nfrom IPython.display import display, Math, Latex, HTML\n\npy.offline.in... | [
[
"matplotlib.pyplot.rc"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zlgenuine/pybamm | [
"5c43d17225710c5bea8e61b3863688eb7080e678",
"5c43d17225710c5bea8e61b3863688eb7080e678",
"5c43d17225710c5bea8e61b3863688eb7080e678"
] | [
"examples/scripts/thermal_lithium_ion.py",
"tests/unit/test_solvers/test_scipy_solver.py",
"tests/unit/test_expression_tree/test_symbolic_diff.py"
] | [
"#\n# Compares the full and lumped thermal models for a single layer Li-ion cell\n#\n\nimport pybamm\nimport numpy as np\n\n# load model\npybamm.set_logging_level(\"INFO\")\n\noptions = {\"thermal\": \"x-full\"}\nfull_thermal_model = pybamm.lithium_ion.SPMe(options)\n\noptions = {\"thermal\": \"x-lumped\"}\nlumped_... | [
[
"numpy.linspace"
],
[
"numpy.log",
"numpy.ones_like",
"numpy.sqrt",
"numpy.linspace",
"numpy.eye",
"numpy.ones",
"numpy.testing.assert_array_equal",
"numpy.testing.assert_array_less",
"numpy.block",
"numpy.max",
"numpy.testing.assert_allclose",
"numpy.exp",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fitancinpet/WMemPy | [
"ed9b3bece2aca465635e09526060165b2704cfcc"
] | [
"WMemPy/wmem_scannable.py"
] | [
"# pylint: disable=c-extension-no-member\n\"\"\"Scannable classes are used to represent live memory of a process that can be scanned\"\"\"\nimport os\nimport ctypes\nimport win32process\nimport numpy as np\nfrom wmempy.wmem_structs import MODULEINFO\n\nclass ProcScannable:\n \"\"\"\n Scannable interface has t... | [
[
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jlevy44/airlab | [
"7d8439339bea11e680716f1c70bf8a21559df0c6"
] | [
"airlab/regulariser/displacement.py"
] | [
"# Copyright 2018 University of Basel, Center for medical Image Analysis and Navigation\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/LICE... | [
[
"torch.sqrt",
"torch.abs",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mathco-wf/river | [
"2e0b25a2ef2d2ba9ec080cf86a491f7465433b18",
"2e0b25a2ef2d2ba9ec080cf86a491f7465433b18",
"c6ff38fa4ce4843ede1cba77248e0370a67a36f6"
] | [
"river/compat/test_sklearn.py",
"river/reco/funk_mf.py",
"river/naive_bayes/multinomial.py"
] | [
"import pytest\nfrom sklearn import linear_model as sk_linear_model\nfrom sklearn.utils import estimator_checks\n\nfrom river import base, cluster, compat, linear_model, preprocessing\n\n\n@pytest.mark.parametrize(\n \"estimator\",\n [\n pytest.param(estimator, id=str(estimator))\n for estimator... | [
[
"sklearn.linear_model.SGDRegressor",
"sklearn.utils.estimator_checks.check_estimator"
],
[
"numpy.dot"
],
[
"numpy.log",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4... |
nschloe/pyamg | [
"005a71754015f050ac3d39241365cbfa4998c374",
"005a71754015f050ac3d39241365cbfa4998c374",
"005a71754015f050ac3d39241365cbfa4998c374",
"005a71754015f050ac3d39241365cbfa4998c374",
"005a71754015f050ac3d39241365cbfa4998c374"
] | [
"pyamg/krylov/tests/test_simple_iterations.py",
"pyamg/classical/tests/test_classical.py",
"pyamg/tests/test_multilevel.py",
"pyamg/relaxation/tests/test_relaxation.py",
"pyamg/gallery/elasticity.py"
] | [
"from pyamg.krylov import minimal_residual, steepest_descent\nimport numpy as np\nfrom pyamg.util.linalg import norm\nimport pyamg\n\nfrom numpy.testing import TestCase\n\n\nclass TestSimpleIterations(TestCase):\n def setUp(self):\n self.definite_cases = []\n self.spd_cases = []\n\n # 1x1\n ... | [
[
"numpy.random.seed",
"numpy.ones",
"numpy.zeros_like",
"numpy.random.rand",
"numpy.ravel",
"numpy.array",
"numpy.zeros"
],
[
"scipy.sparse.coo_matrix",
"numpy.random.seed",
"numpy.cumsum",
"scipy.sparse.csr_matrix",
"numpy.zeros_like",
"numpy.random.rand",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
... |
phiandre/ToricCodeRL | [
"c62a33124e427307cc462caed02382cea0f74c47"
] | [
"QNet.py"
] | [
"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\nNeural network for approximation of Q-function.\n\nWill take in concatenated state- and action data\nand ouput the associated approximation Q(s,a).\n\n\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"... | [
[
"numpy.expand_dims"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LetteraUnica/neural_cellular_automata | [
"360aed252d31529b02460cdf50d752acbd6edaf8"
] | [
"pytorch_ca/src/utils/utils.py"
] | [
"import torch\n\nfrom random import randint\n\n\ndef make_seed(n_images: int,\n n_channels: int,\n image_size: int,\n n_CAs: int = 1,\n alpha_channel: int = -1,\n device: torch.device = \"cpu\") -> torch.Tensor:\n \"\"\"Makes n_images seeds to star... | [
[
"torch.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wingarlo/Aerolyzer | [
"16c91740ba561b988e67fdcd6ef802ed8a826da2"
] | [
"aerolyzer/image_restriction_functions.py"
] | [
"'''\nImage Restriction Function File\nDescription: This file contains all functions for the verifying image restrictions.\n'''\nimport os\nimport re\nimport cv2\nimport yaml\nfrom datetime import datetime\nimport exifread\nimport numpy as np\n\nclass imgRestFuncs(object):\n 'Class containing all image restricti... | [
[
"numpy.argmin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Photon26/wrs-main-210414 | [
"71efab6f8680af48ba269244136acf69c36cb730",
"71efab6f8680af48ba269244136acf69c36cb730",
"71efab6f8680af48ba269244136acf69c36cb730"
] | [
"modeling/_gimpact_cdhelper.py",
"0000_huri/tro/tro_locator.py",
"robotsim/_kinematics/jlchainik.py"
] | [
"import numpy as np\nimport gimpact as gi\n\n# util functions\ndef gen_cdmesh_vvnf(vertices, vertex_normals, faces):\n \"\"\"\n generate cdmesh given vertices, _, and faces\n :return: gimpact.TriMesh (require gimpact to be installed)\n author: weiwei\n date: 20210118\n \"\"\"\n return gi.TriMes... | [
[
"numpy.array"
],
[
"numpy.nonzero",
"numpy.vstack"
],
[
"numpy.diag",
"numpy.dot",
"numpy.sqrt",
"numpy.power",
"numpy.linalg.inv",
"numpy.linalg.eig",
"numpy.eye",
"numpy.linalg.norm",
"numpy.ones",
"numpy.zeros_like",
"numpy.identity",
"numpy.c... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chrism0dwk/probability | [
"ab260f15cae94c6802c2f2769fb448ad213b79cd"
] | [
"tensorflow_probability/python/distributions/probit_bernoulli.py"
] | [
"# 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.exp",
"tensorflow.compat.v2.less",
"tensorflow.compat.v2.math.ndtri",
"tensorflow.compat.v2.name_scope",
"tensorflow.compat.v2.cast",
"tensorflow.compat.v2.math.log1p",
"tensorflow.compat.v2.convert_to_tensor",
"tensorflow.compat.v2.identity",
"tensorflow.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anidhi/ParlAI | [
"aa684894300eececd6d000900e1821e9a7393da4"
] | [
"parlai_hred/seq2seq.py"
] | [
"#!/usr/bin/env python3\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom parlai_internal.agents.seq2seq.torch_generator_agent2 import TorchGeneratorAgent2\nfrom parlai.utils.mis... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.NLLLoss",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
npezolano/zipline | [
"e7a5e097c419bed7816d3cd6c370b5171db37b33",
"71effa5e98bd0425ac1863e1861c9b51fbc77242"
] | [
"zipline/utils/data.py",
"zipline/algorithm.py"
] | [
"#\n# Copyright 2013 Quantopian, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or... | [
[
"pandas.isnull",
"numpy.arange",
"pandas.Panel",
"numpy.all",
"numpy.roll",
"numpy.empty"
],
[
"numpy.allclose",
"pandas.DataFrame",
"numpy.datetime64"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
... |
ocefpaf/xroms | [
"763d6e678e28fe074e0aaab26fecd2b74e51a8b0"
] | [
"xroms/interp.py"
] | [
"from scipy.spatial import Delaunay\nimport matplotlib.tri as mtri\nimport numpy as np\nimport xroms\nimport xarray as xr\n\n\ndef setup(ds, whichgrids=None):\n '''Set up for using ll2xe().\n \n Set up Delaunay triangulation by calculating triangulation and functions for \n calculating grid coords from ... | [
[
"matplotlib.tri.TriAnalyzer",
"numpy.asarray",
"numpy.arange",
"scipy.spatial.Delaunay",
"numpy.column_stack",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
esheldon/galsim_extra | [
"1e93a35db16943f6ce0251cddcd253defe0d5c61"
] | [
"tests/test_focalplane.py"
] | [
"from __future__ import print_function\nimport galsim\nimport logging\nimport numpy as np\nimport os, sys\n\ndef test_truth():\n \"\"\"This test addressed Issue 10, where Niall found that the truth catalog wasn't being\n built correctly with the FocalPlane builder.\n \"\"\"\n config = galsim.config.Read... | [
[
"numpy.all",
"numpy.testing.assert_equal",
"numpy.not_equal",
"numpy.any"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
frankxu2004/knnlm | [
"7a668a916b08a0e82072c8f49eef4a10ad4a8505",
"7a668a916b08a0e82072c8f49eef4a10ad4a8505",
"7a668a916b08a0e82072c8f49eef4a10ad4a8505"
] | [
"fairseq/tasks/language_modeling.py",
"find_examples_wiki.py",
"analysis_histogram.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\nimport os\n\nimport numpy as np\nimport torch\n\nfrom fairseq import utils\nfrom fairseq.data import (\n data_uti... | [
[
"numpy.array",
"torch.no_grad"
],
[
"numpy.log",
"torch.load",
"numpy.memmap",
"torch.from_numpy",
"numpy.concatenate",
"torch.logsumexp",
"torch.stack",
"numpy.load",
"numpy.zeros",
"torch.ones_like"
],
[
"numpy.load",
"matplotlib.pyplot.subplots",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Chahalprincy/deepchem | [
"9d1a6a879cc74b065694b3ddb763d52151d57b7a",
"9d1a6a879cc74b065694b3ddb763d52151d57b7a"
] | [
"deepchem/utils/fake_data_generator.py",
"deepchem/models/tests/test_megnet.py"
] | [
"\"\"\"\nA fake data generator\n\"\"\"\nimport random\nimport numpy as np\nfrom deepchem.data import NumpyDataset\nfrom deepchem.feat import GraphData\n\n\nclass FakeGraphGenerator:\n \"\"\"Generates a random graphs which can be used for testing or other purposes.\n\n The generated graph supports both node-level ... | [
[
"numpy.array",
"numpy.random.rand",
"numpy.random.randint"
],
[
"numpy.all"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kkinist/karlib | [
"16c2daa11623b41297ddb9c6c2b1e663a8638cef"
] | [
"chem_subs.py"
] | [
"# Routines for general quantum chemistry (no particular software package)\n# Python3 and pandas\n# Karl Irikura \n#\nimport re, sys\n#import string, copy\nimport copy\nimport numpy as np\nimport pandas as pd\nimport quaternion\nfrom scipy.spatial.distance import cdist\nfrom scipy import interpolate\nfrom scipy imp... | [
[
"numpy.diag",
"numpy.dot",
"numpy.linalg.eigvals",
"numpy.sqrt",
"numpy.linspace",
"numpy.rad2deg",
"numpy.concatenate",
"numpy.max",
"numpy.round",
"numpy.zeros_like",
"numpy.any",
"numpy.argmin",
"numpy.cross",
"numpy.fill_diagonal",
"numpy.exp",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"1.3",
... |
JoJoJun/NLPCC2018_Multi_Turn_Response_Selection | [
"ba13e629e5094dca5d87e8aeea3d3371df6a9679"
] | [
"LCMN_and_SMN.py"
] | [
"import tensorflow as tf\r\nimport pickle\r\nimport utils\r\nfrom tensorflow.contrib.layers import xavier_initializer\r\nfrom tensorflow.contrib import rnn\r\nfrom tensorflow.python.keras.preprocessing.sequence import pad_sequences\r\nimport numpy as np\r\nimport Evaluate\r\nimport time\r\nfrom datetime import time... | [
[
"tensorflow.get_variable",
"tensorflow.nn.dynamic_rnn",
"tensorflow.contrib.rnn.GRUCell",
"tensorflow.contrib.keras.initializers.he_normal",
"tensorflow.stack",
"numpy.concatenate",
"tensorflow.orthogonal_initializer",
"tensorflow.contrib.layers.flatten",
"tensorflow.train.Adam... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
AutuanLiu/Machine-Learning-on-docker | [
"00eb7211a3a40a9da02114923647dfd6ac24f138",
"00eb7211a3a40a9da02114923647dfd6ac24f138",
"00eb7211a3a40a9da02114923647dfd6ac24f138",
"00eb7211a3a40a9da02114923647dfd6ac24f138"
] | [
"boost/XGBoost/xgboostree.py",
"TensorFlow/LinearRegression/LogisticRegression.py",
"boost/LightGBM/regression.py",
"ScikitLearn/featureSelection.py"
] | [
"\"\"\"\nCreated on 1 Apr 2015\n@author: Jamie Hall\n\"\"\"\n# https://github.com/dmlc/xgboost/blob/master/demo/guide-python/sklearn_examples.py\n\nimport pickle\n\nimport numpy as np\nimport xgboost as xgb\nfrom sklearn.datasets import load_iris, load_digits, load_boston\nfrom sklearn.metrics import confusion_matr... | [
[
"sklearn.model_selection.GridSearchCV",
"sklearn.datasets.load_iris",
"sklearn.model_selection.train_test_split",
"sklearn.model_selection.KFold",
"sklearn.metrics.confusion_matrix",
"sklearn.metrics.mean_squared_error",
"sklearn.datasets.load_digits",
"sklearn.datasets.load_boston... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflo... |
leoninekev/DeepCTR | [
"9bc93ae729f1d4d03fdf5fe8b9c111fbc59a2870"
] | [
"deepctr/layers/sequence.py"
] | [
"# -*- coding:utf-8 -*-\n\"\"\"\n\nAuthor:\n Weichen Shen,wcshen1994@163.com\n\n\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.python.keras import backend as K\nfrom tensorflow.python.keras.initializers import TruncatedNormal\nfrom tensorflow.python.keras.layers import LSTM, Lambda, Layer... | [
[
"tensorflow.zeros",
"tensorflow.cast",
"tensorflow.equal",
"tensorflow.python.keras.backend.variable",
"tensorflow.where",
"tensorflow.python.keras.backend.reverse",
"tensorflow.python.keras.layers.LSTM",
"tensorflow.squeeze",
"numpy.sin",
"tensorflow.nn.top_k",
"tensor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Micheal-Nguyen/reddit-stock-scraper | [
"779e2e463fab38567c3bb3c29b21665f7b0a0edd"
] | [
"reddit-stock-scraper.py"
] | [
"#! python3\nimport praw\nimport pandas as pd\nimport configparser\n\ndef scrape(name):\n config = configparser.ConfigParser()\n config.read(\"config.ini\")\n client_id = config.get(\"reddit\", \"client_id\")\n client_secret = config.get(\"reddit\", \"client_secret\")\n user_agent = config.get(\"redd... | [
[
"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": []
}
] |
llucid-97/FastDeepQLearning | [
"023d617ad86477763e424fd09fb496571083c50c",
"023d617ad86477763e424fd09fb496571083c50c"
] | [
"franQ/Env/classic_control_goal/classic_goal.py",
"franQ/Env/wrappers/common_image.py"
] | [
"from collections import OrderedDict\nimport typing as T\n\nimport numpy as np\nimport gym\n\nspaces = gym.spaces\nfrom franQ.Env import wrappers\nfrom franQ.Env.conf import EnvConf\n\n\nclass ClassicGoalEnv(wrappers.Wrapper):\n def __init__(self, conf: EnvConf):\n tasks = {\n \"CartPole-v1\": ... | [
[
"numpy.allclose",
"numpy.clip",
"numpy.asarray",
"numpy.cos",
"numpy.all",
"numpy.zeros_like",
"numpy.array"
],
[
"numpy.moveaxis"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
YorkUCVIL/Wavelet-Flow | [
"8d6d63fa116ec44299c32f37e66817594510f644",
"8d6d63fa116ec44299c32f37e66817594510f644"
] | [
"src/models/lsun_bedroom_64_haar/Validation_data.py",
"src/models/lsun_bedroom_64_haar/Training_data.py"
] | [
"import tensorflow as tf\nimport os\nfrom util import *\n\nclass Validation_data:\n\tdef __init__(self,batch_override=None,shuffle_repeat=True,partial_level=0):\n\t\twith tf.variable_scope(None,default_name='validation_data'):\n\t\t\tself.crop_factor = config.validation.partial_training_crops[partial_level]\n\t\t\t... | [
[
"tensorflow.read_file",
"tensorflow.shape",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.cast",
"tensorflow.floor",
"tensorflow.image.random_crop",
"tensorflow.variable_scope",
"tensorflow.random_uniform"
],
[
"tensorflow.image.random_flip_left_right",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensor... |
SebastianoF/pyro2 | [
"9d1787c2ee25d735a414db3da8c00287743a6fde",
"9d1787c2ee25d735a414db3da8c00287743a6fde",
"9d1787c2ee25d735a414db3da8c00287743a6fde"
] | [
"swe/tests/test_swe.py",
"compressible/problems/bubble.py",
"swe/interface.py"
] | [
"import numpy as np\nfrom numpy.testing import assert_array_equal\n\nfrom util import runparams\nimport swe.simulation as sn\n\n\nclass TestSimulation(object):\n @classmethod\n def setup_class(cls):\n \"\"\" this is run once for each class before any tests \"\"\"\n pass\n\n @classmethod\n ... | [
[
"numpy.testing.assert_array_equal",
"numpy.sqrt"
],
[
"numpy.exp",
"numpy.sqrt"
],
[
"numpy.dot",
"numpy.sqrt",
"numpy.zeros_like",
"numpy.copysign",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
youbaji/outlier | [
"a18daa6bd56015541ba77bb2650f8c4f135a041f"
] | [
"lof/lof.py"
] | [
"import numpy as np\nimport scipy.stats as stats\nfrom scipy.spatial.distance import pdist, squareform\nimport pdb\n\nclass Lof():\n def __init__(self, k):\n self.k = k\n\n def fit(self, data):\n #tree = sp.spatial.KDTree(data)\n distance = squareform(pdist(data))\n indices = stats... | [
[
"numpy.maximum",
"scipy.spatial.distance.pdist",
"numpy.random.random",
"scipy.stats.mstats.rankdata"
]
] | [
{
"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"
... |
WybeKoper/PASAF | [
"b7052eecb686f50a1988bdb7b1a88a26fc2240b5"
] | [
"data_processing/i_tables.py"
] | [
"import matplotlib.pyplot as plt\nfrom matplotlib.transforms import Bbox\nimport pandas as pd\nfrom adjustText import adjust_text\nimport os\nfrom mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\nfrom mpl_toolkits.axes_grid1.inset_locator import mark_inset\n\ndef number_of_rescales(taskmanager):\n ... | [
[
"pandas.set_option",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
GeekLogan/dcimg | [
"066f653a72a52db8cbca804832380d3d61c408f9"
] | [
"tests/test_dcimg.py"
] | [
"import unittest\nfrom ddt import ddt, data\n\nimport numpy as np\n\nfrom dcimg import DCIMGFile\n\n\ntest_vectors = [\n np.index_exp[...],\n np.index_exp[..., -1],\n np.index_exp[:, :, :],\n np.index_exp[..., ::-1],\n np.index_exp[..., ::-1, :],\n np.index_exp[::-1, ...],\n np.index_exp[::-1, ... | [
[
"numpy.array_equal",
"numpy.arange",
"numpy.copy",
"numpy.prod",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lsst/TS_wep | [
"c2e2b973d5da7e9e522d5490e9b6f94664ec8610",
"c2e2b973d5da7e9e522d5490e9b6f94664ec8610"
] | [
"ts_wep/CWFS/makeCornerMasklist.py",
"ts_wep/CWFS/interpMaskParam.py"
] | [
"import numpy as np\nfrom rotateMaskParam import rotateMaskParam\ndef makeCornerMasklist(obsR, ca, ra, cb, rb, fldx,fldy):\n\n cax, cay, cbx, cby = rotateMaskParam(ca,cb,fldx,fldy)\n masklist=np.array([[0, 0, 1, 1],[0, 0, obsR, 0],[cax, cay, ra, 1],[cbx, cby, rb, 0]])\n \n return masklist\n",
"import ... | [
[
"numpy.array"
],
[
"numpy.count_nonzero",
"numpy.dot",
"numpy.sqrt",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
robmarkcole/BERT_as_serverless_service | [
"fbc4004677ae3811b08f89d577b5a45ce0bfbbd0"
] | [
"config.py"
] | [
"import transformers\nimport torch\n\nMAX_LEN = 512\nTRAIN_BATCH_SIZE = 4\nVALID_BATCH_SIZE = 8\nNUM_CLASSES = 5\nDEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n\nEPOCHS = 2\nBERT_PATH = './input/prunebert-base-uncased-6-finepruned-w-distil-squad'\nMODEL_PATH = './model/pytorch... | [
[
"torch.device",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jheek/flax | [
"f4986e5154aa46b28fb75281ef67318f4f7cc569"
] | [
"examples/seq2seq/train.py"
] | [
"# Lint as: python3\n#\n# Copyright 2020 The Flax 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 require... | [
[
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
EmreOzkose/k2 | [
"818b138b33eabe440601df8910a2b97ac088594b",
"818b138b33eabe440601df8910a2b97ac088594b"
] | [
"k2/python/tests/ctc_loss_test.py",
"k2/python/k2/ops.py"
] | [
"#!/usr/bin/env python3\n#\n# Copyright 2020 Xiaomi Corporation (authors: Fangjun Kuang)\n#\n# See ../../../LICENSE for clarification regarding multiple 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 ma... | [
[
"torch.randint",
"torch.nn.functional.log_softmax",
"torch.cuda.set_device",
"torch.rand_like",
"torch.manual_seed",
"torch.nn.utils.rnn.pad_sequence",
"torch.tensor",
"torch.rand",
"torch.cuda.is_available",
"torch.arange",
"torch.device",
"torch.allclose",
"to... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chris48s/arcade | [
"930ecd8be2d47db1f65173c6f1afc00c0df545c8"
] | [
"arcade/sprite_list.py"
] | [
"\"\"\"\nThis module provides functionality to manage Sprites in a list.\n\n\"\"\"\n\nfrom typing import Iterable\nfrom typing import TypeVar\nfrom typing import Generic\nfrom typing import List\nfrom typing import Optional\n\nimport pyglet.gl as gl\n\nimport math\nimport numpy as np\n\nfrom PIL import Image\n\nfro... | [
[
"numpy.asarray",
"numpy.array",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
leichtrhino/nsf-torch | [
"1ad4495b476908a50c18ead391a8cd57bc2f4094",
"1ad4495b476908a50c18ead391a8cd57bc2f4094"
] | [
"test_generate_fake_data.py",
"test_wavenet_core.py"
] | [
"#!/usr/bin/env python\n\nimport os\nimport librosa\nimport random\nimport torch\nimport numpy as np\n\nfrom math import ceil\nfrom model import NSFModel\n\nsampling_rate = 16000\nframe_length = sampling_rate * 25 // 1000\nframe_shift = sampling_rate * 10 // 1000\n\nbatch_size = 2\nwaveform_length = 16000\ncontext_... | [
[
"numpy.expand_dims",
"torch.load",
"numpy.arange",
"numpy.random.shuffle",
"torch.tensor",
"numpy.array",
"numpy.zeros",
"numpy.vstack"
],
[
"torch.randn",
"torch.nn.MSELoss",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jpbottaro/anna | [
"e9a8be720cf0f95eb8b02fee469e9ed0c9c3ae6f"
] | [
"anna/model/metrics.py"
] | [
"\"\"\"Tensorflow implementation of metrics for Multi-label Classification.\n\nMetrics: Subset Accuracy, Hamming accuracy, example-based f1, and label-based\n micro/macro f1.\n\nDefinitions from: https://papers.nips.cc/paper/7125-maximizing-subset-accuracy-with-recurrent-neural-networks-in-multi-label-class... | [
[
"tensorflow.compat.v1.metrics.mean",
"tensorflow.python.ops.state_ops.assign_add",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.equal",
"tensorflow.python.ops.math_ops.greater",
"tensorflow.python.ops.array_ops.zeros",
"tensorflow.python.eager.context.executing_eagerly",... | [
{
"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... |
rainbowbismuth/bird-vision | [
"ea4a54a694be7c46906fcd703cb26d84048319a6"
] | [
"birdvision/quiet.py"
] | [
"\"\"\"\nUtility module to contain any code for any external libraries that get too noisy for my tastes.\n\"\"\"\n\n\ndef silence_tensorflow():\n \"\"\"Silence every warning of notice from tensorflow.\"\"\"\n import tensorflow as tf\n import logging\n import os\n logging.getLogger('tensorflow').setLe... | [
[
"tensorflow.autograph.set_verbosity",
"tensorflow.get_logger"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
dabrze/similarity_forest | [
"3d0f0f304b0106a6b2d86e6e84562ef2cb5ee81d"
] | [
"simforest/distance.py"
] | [
"import numpy as np\nfrom scipy.linalg.blas import sgemm\nimport numexpr as ne\nfrom numba import jit\n\n\ndef rbf(X, p, q, gamma=None):\n \"\"\"A function calculating rbf kernel based projection of data-points in matrix X\n X : array of shape=(n_examples, n_features),\n should be 2-dim... | [
[
"numpy.dot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ayan-b/xena-GDC-ETL | [
"107fabadd061ea1cbf2f3ec330c4277fed58284c"
] | [
"xena_gdc_etl/main.py"
] | [
"from __future__ import print_function\nimport argparse\nfrom datetime import date\nimport os\nimport pkg_resources\n\nimport pandas as pd\nfrom pandas.util.testing import assert_frame_equal\n\nfrom .utils import handle_merge_xena\nfrom .gdc import gdc_check_new, get_project_info\nfrom .constants import valid_dtype... | [
[
"pandas.util.testing.assert_frame_equal",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
gawelk/aqua | [
"98ce06289cc6e12d087982cba95a45353ce0cb3b",
"98ce06289cc6e12d087982cba95a45353ce0cb3b",
"98ce06289cc6e12d087982cba95a45353ce0cb3b"
] | [
"test/test_initial_state_zero.py",
"qiskit/aqua/components/optimizers/powell.py",
"test/test_graph_partition.py"
] | [
"# -*- coding: utf-8 -*-\n\n# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2018, 2019.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE... | [
[
"numpy.testing.assert_array_equal"
],
[
"scipy.optimize.minimize"
],
[
"numpy.random.seed",
"numpy.testing.assert_array_equal",
"numpy.binary_repr",
"numpy.count_nonzero",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
... |
EricCousineau-TRI/onnxruntime | [
"796948c6aee80df5562c3e1538bb675913946754"
] | [
"onnxruntime/test/python/onnxruntime_test_python_nuphar.py"
] | [
"# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\n# -*- coding: UTF-8 -*-\nimport numpy as np\nimport onnx\nfrom onnx import numpy_helper\nimport onnxruntime as onnxrt\nimport os\nfrom onnxruntime.nuphar.rnn_benchmark import perf_test, generate_model\nfrom pathlib im... | [
[
"numpy.concatenate",
"numpy.random.rand",
"numpy.allclose",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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