repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
RicardoHS/DeepCoinCounter | [
"b488cefeb327d7e795ec845e599ddf2e89ffdeee"
] | [
"lab/scripts/generate_data.py"
] | [
"import os\nfrom os import walk\nfrom typing import Dict\n\nfrom PIL import Image, ImageDraw, ImageFilter, ImageFont\nimport numpy as np\n\nimport config\n\n\ndef load_random_background(background_folder, base_ratio, mobile_camera_ratio=1.6624):\n # mobile_camera_ratio: 2048 x 1232\n # load background\n # ... | [
[
"numpy.random.choice",
"numpy.random.rand",
"numpy.random.uniform",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XiaohanZhangCMU/sderl | [
"9f8b0044b068d144400520c3b36b75403d394c7d",
"9f8b0044b068d144400520c3b36b75403d394c7d"
] | [
"sderl/exercises/tf1/problem_set_1_solutions/exercise1_2_soln.py",
"sderl/algos/pytorch/sac/sac.py"
] | [
"import tensorflow as tf\nimport numpy as np\n\n\nEPS = 1e-8\n\ndef mlp(x, hidden_sizes=(32,), activation=tf.tanh, output_activation=None):\n for h in hidden_sizes[:-1]:\n x = tf.layers.dense(x, units=h, activation=activation)\n return tf.layers.dense(x, units=hidden_sizes[-1], activation=output_activa... | [
[
"numpy.log",
"tensorflow.shape",
"tensorflow.reduce_sum",
"tensorflow.exp",
"tensorflow.layers.dense",
"numpy.ones"
],
[
"torch.optim.Adam",
"numpy.expand_dims",
"torch.min",
"numpy.squeeze",
"torch.sum",
"torch.as_tensor",
"torch.mul",
"torch.no_grad",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sonalake/blog-hypothesis-testing | [
"18f2bf759c970672b7f80d16c7bc845abe00b92b"
] | [
"examples/frequencies/independence.py"
] | [
"from scipy.stats import chi2_contingency\nimport numpy as np\n\n# can we assume anything from our sample\nsignificance = 0.05\n\npivot = np.array([\n # town votes\n [200, 150, 50],\n # country votes\n [250, 300, 50]\n])\n########################\n# Get the stat data\n(chi_stat, p_value, degrees_of_free... | [
[
"numpy.array",
"scipy.stats.chi2_contingency"
]
] | [
{
"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"... |
johnbywater/quantdsl | [
"81c1c69f27e094a6ed0542b28cf1ac8fcce5494a"
] | [
"quantdsl/tests/test_least_squares.py"
] | [
"import unittest\n\nimport scipy\n\nfrom quantdsl.semantics import LeastSquares\n\n\nclass TestLeastSquares(unittest.TestCase):\n\n DECIMALS = 12\n\n def assertFit(self, fixture_x, fixture_y, expected_values):\n assert expected_values is not None\n ls = LeastSquares(scipy.array(fixture_x), scipy... | [
[
"scipy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sanmit123/3D-ESPNet | [
"a4a2ea7531d3bdbaee2d1ed1f46f89bd03da82c0"
] | [
"utils.py"
] | [
"#============================================\n__author__ = \"Sachin Mehta\"\n__license__ = \"MIT\"\n__maintainer__ = \"Sachin Mehta\"\n#============================================\n\nimport numpy as np\n\ndef cropVolume(img, data=False):\n '''\n Helper function to remove the redundant black area from the 3... | [
[
"numpy.where",
"numpy.nonzero"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
myelintek/benchmarks | [
"16897fecaa24614796af94762c8b7f77cef20941"
] | [
"scripts/tf_cnn_benchmarks/test_util.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.device",
"tensorflow.Graph",
"tensorflow.constant",
"tensorflow.Variable",
"tensorflow.reduce_mean",
"tensorflow.reshape",
"tensorflow.placeholder",
"tensorflow.ConfigProto",
"tensorflow.global_variables_initializer",
"tensorflow.nn.l2_loss",
"tensorflow.con... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
rifatarefin/CodeSearchNet | [
"f1ec92321bc26331828a3edf05f44ce11d18fbba"
] | [
"src/predict.py"
] | [
"#!/usr/bin/env python\n\"\"\"\nRun predictions on a CodeSearchNet model.\n\nUsage:\n predict.py -m MODEL_FILE [-p PREDICTIONS_CSV]\n predict.py -r RUN_ID [-p PREDICTIONS_CSV]\n predict.py -h | --help\n\nOptions:\n -h --help Show this screen\n -m, --model_file FILENAME ... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
CoderZWei/mmdetection-1.2 | [
"c041492d830830d5a7ed279fe74c5076dd8fdfa8"
] | [
"mmdet/datasets/xml_style.py"
] | [
"import os.path as osp\nimport xml.etree.ElementTree as ET\n\nimport mmcv\nimport numpy as np\nfrom PIL import Image\n\nfrom .custom import CustomDataset\nfrom .registry import DATASETS\n\n\n@DATASETS.register_module\nclass XMLDataset(CustomDataset):\n\n def __init__(self, min_size=None, **kwargs):\n supe... | [
[
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sagarsetru/debiaser | [
"023f7dd888d5ceaaf5454a1350e12fc86fa19f1d"
] | [
"debiaser/debiaser_validation_function.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 7 07:17:48 2020\n\nNOTE: THIS FUNCTION WILL USE WHAT IS IN THE GOOGLE CLOUD FUNCTION\nAs such, there will be some redundancy between functions here,\nand functions defined in text_processing_function.\n\n@author: sagarsetru\n\"\"\"\n\nimp... | [
[
"pandas.read_csv",
"numpy.linalg.norm",
"numpy.std",
"numpy.append",
"numpy.mean",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
dieterch/dReliaCalc | [
"1e0a06e904f3a60527c3a6ae0f45c666a9b48128"
] | [
"Reliability_Tests/ex69.py"
] | [
"from reliability.Other_functions import convert_dataframe_to_grouped_lists\nimport pandas as pd\n# create some data in a dataframe\ndata = {'outcome': ['Failed', 'Censored', 'Failed', 'Failed', 'Censored'],\n 'cycles': [1253, 1500, 1342, 1489, 1500]}\ndf = pd.DataFrame(data, columns=['outcome', 'cycles'])\n... | [
[
"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": []
}
] |
Hammarn/Scripts | [
"eb9fb51b614d29aea425168aa16c58410d975f46"
] | [
"POPGEN/Mosaic_create_tped.py"
] | [
"#!/usr/bin/env python3\nimport pandas as pd\nimport argparse\nimport sys\nimport re\nimport sys\n\nimport pdb\npd.options.mode.chained_assignment = None # default='warn'\n\n\n__author__ = \"Rickard Hammarén @hammarn\"\n\n\ndef read_input(in_file):\n in_file = in_file\n T_DF = pd.read_csv(in_file, delimiter=... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
wpreimes/io_utils | [
"9ef4161a5bc65ab2fabee0e2c7cf873f19cf7a17"
] | [
"src/io_utils/read/geo_ts_readers/mixins.py"
] | [
"import warnings\nimport os\nimport numpy as np\nimport pandas as pd\nfrom datetime import datetime, timedelta\nimport netCDF4 as nc\nfrom pygeogrids.grids import CellGrid\nimport xarray as xr\n\nclass ContiguousRaggedTsCellReaderMixin:\n\n \"\"\"\n Adds functionality to read whole cells. Can be added to time... | [
[
"numpy.split",
"pandas.concat",
"numpy.unique",
"numpy.cumsum",
"pandas.DataFrame",
"pandas.MultiIndex.from_tuples",
"numpy.atleast_1d",
"numpy.append",
"numpy.vectorize",
"numpy.transpose",
"numpy.array",
"numpy.isin"
]
] | [
{
"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": []
}
] |
ciselab/OpenVocabCodeNLM | [
"8ab0ea9436349251a69f2628da8ad6a7c95ae0f3"
] | [
"code_nlm.py"
] | [
"# some structure based on https://github.com/wpm/tfrnnlm/blob/master/tfrnnlm/rnn.py\n# https://github.com/tensorflow/tensorflow/pull/2580/files#diff-083dd112b4600ecbaf63b2070951aad8\n\n\nfrom __future__ import print_function\n\nimport ast\n\nimport time\nfrom datetime import timedelta\n\nimport inspect\nimport ma... | [
[
"tensorflow.device",
"tensorflow.concat",
"numpy.asarray",
"tensorflow.reduce_sum",
"tensorflow.compat.v1.initialize_all_variables",
"tensorflow.compat.v1.app.run",
"tensorflow.compat.v1.summary.merge_all",
"tensorflow.Graph",
"tensorflow.Variable",
"tensorflow.compat.v1.gl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Jos3f/SOM-word-vectors | [
"ef3811249febf738adacab187a811f168bf6f30d"
] | [
"SOM.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport math\nfrom tqdm import tqdm\nfrom datetime import datetime\n\n\nclass SOM:\n \"\"\"SOM with 2d grid\"\"\"\n\n _data = None\n _nUnits = 0\n _map_width = 0\n _unit_weights = None\n\n def __init__(self, data, map_width):\n \"\"\"\n ... | [
[
"numpy.arange",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.annotate",
"numpy.linalg.norm",
"numpy.random.shuffle",
"numpy.random.normal",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.ylabel",
"numpy.floor",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"ma... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zooxiu/tensorflow | [
"c7126a56a0cb504d24c0f070965005963765a958"
] | [
"tensorflow/python/data/ops/dataset_ops.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.python.data.util.nest.map_structure_up_to",
"tensorflow.python.ops.gen_dataset_ops.window_dataset",
"tensorflow.python.ops.math_ops.log",
"tensorflow.python.data.util.structure.type_spec_from_value",
"tensorflow.python.ops.gen_dataset_ops.batch_dataset_v2",
"tensorflow.python.o... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.6",
"2.2",
"2.3",
"2.4",
"2.9",
"2.5",
"2.8",
"2.10"
]
}
] |
organic-chemistry/RepNano | [
"3f2e21f2006217f6438b16e6800b7f4f327c9210"
] | [
"misc/plot_content_alphabet.py"
] | [
"import tempfile\nimport ast\nfrom repnano.features.bam_tools import load_read_bam\nimport argparse\n\nimport numpy as np\nimport h5py\ndef get_h5_p(file_n, maxi=None,mods=[\"B\",\"I\"]):\n with h5py.File(file_n, \"r\") as f:\n\n print(\"After\",dict(f.attrs.items()))\n\n new_alphabet = f.attrs[\"a... | [
[
"matplotlib.use",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
devrimcavusoglu/COMET | [
"1fee80d74519febf54a93a91e43823cac76b7831"
] | [
"tests/integration/models/test_ranking_metric.py"
] | [
"# -*- coding: utf-8 -*-\nimport os\nimport shutil\nimport unittest\nimport warnings\n\nimport torch\nfrom comet.models import RankingMetric\nfrom pytorch_lightning import seed_everything\nfrom pytorch_lightning.trainer.trainer import Trainer\nfrom scipy.stats import pearsonr\nfrom tests.data import DATA_PATH\nfrom... | [
[
"scipy.stats.pearsonr"
]
] | [
{
"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"
... |
macroustc/nara_wpe | [
"25bf7b01f2f96d192a3cd996bae276a6194ad302"
] | [
"nara_wpe/wpe.py"
] | [
"import functools\nimport operator\n\nimport click\nimport numpy as np\n\n\ndef get_working_shape(shape):\n \"Flattens all but the last two dimension.\"\n product = functools.reduce(operator.mul, [1] + list(shape[:-2]))\n return [product] + list(shape[-2:])\n\n\ndef segment_axis(\n x,\n lengt... | [
[
"numpy.einsum",
"numpy.asarray",
"numpy.squeeze",
"numpy.lib.stride_tricks.as_strided",
"numpy.max",
"numpy.zeros_like",
"numpy.mean",
"numpy.iscomplexobj",
"numpy.moveaxis",
"numpy.roll",
"numpy.conjugate",
"numpy.pad",
"numpy.reshape",
"numpy.empty_like",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
germank/CommAI-env | [
"9dc8007dac3c0e075c2b06aa012ac8fb683be2e6"
] | [
"projects/continual-lm/learner/transformer_learner.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\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\nfrom .base_learner import BaseLearner\nimport model\nimport torch.nn as nn\nimport torch.optim as optim\n\nclass... | [
[
"torch.nn.CrossEntropyLoss"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Janie115/gridpath | [
"83b4bb497fc06ef20a67ab773a2c59e57cde895f"
] | [
"tests/project/operations/test_init.py"
] | [
"# Copyright 2016-2020 Blue Marble Analytics 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 appli... | [
[
"numpy.array",
"pandas.to_numeric",
"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": []
}
] |
gchrupala/platalea | [
"65833307bb6c5ad6cbdd6b17ad8ca59cf51fcd81"
] | [
"experiments/asr/run.py"
] | [
"import logging\nimport pickle\nimport torch\nimport torch.nn as nn\n\nimport platalea.asr as M\nimport platalea.dataset as D\n\ntorch.manual_seed(123)\n\n\nbatch_size = 8\nhidden_size = 1024\ndropout = 0.0\nfeature_fname = 'mfcc_delta_features.pt'\n\nlogging.basicConfig(level=logging.INFO)\n\nlogging.info('Loading... | [
[
"torch.manual_seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yangyongjx/lpcvc | [
"fb6de033c892a00103915c85c170c2d49846a8f6"
] | [
"image_classification/export_inference_graph.py"
] | [
"# Once-for-All: Train One Network and Specialize it for Efficient Deployment\n# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han\n# International Conference on Learning Representations (ICLR), 2020.\n\n# APQ: Joint Search for Network Architecture, Pruning and Quantization Policy\n# Tianzhe Wang, Kuan Wang... | [
[
"tensorflow.Graph",
"tensorflow.app.flags.DEFINE_bool",
"tensorflow.python.platform.gfile.GFile",
"tensorflow.app.flags.DEFINE_integer",
"tensorflow.placeholder",
"tensorflow.app.flags.DEFINE_string",
"tensorflow.logging.set_verbosity",
"tensorflow.app.flags.DEFINE_boolean",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mahfuz195/sdc_capstone | [
"9a8c09c65bd6c5abcb243d83d8ec51df17cb2385"
] | [
"ros/src/waypoint_updater/waypoint_updater.py"
] | [
"#!/usr/bin/env python\nimport numpy as np\nimport rospy\nfrom geometry_msgs.msg import PoseStamped\nfrom styx_msgs.msg import Lane, Waypoint\nfrom scipy.spatial import KDTree\nfrom std_msgs.msg import Int32\n\nimport math\n\n'''\nThis node will publish waypoints from the car's current position to some `x` distance... | [
[
"numpy.dot",
"numpy.array",
"scipy.spatial.KDTree"
]
] | [
{
"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"... |
Bobeye/hybrid-zero-RL-rabbit | [
"1b67d4082e279205e676972932345c3e83227538",
"1b67d4082e279205e676972932345c3e83227538"
] | [
"gym_customize/python2/gym/envs/mujoco/half_cheetah.py",
"gym_customize/gym/envs/mujoco/walker2d.py"
] | [
"import numpy as np\nfrom gym import utils\nfrom gym.envs.mujoco import mujoco_env\n\nclass HalfCheetahEnv(mujoco_env.MujocoEnv, utils.EzPickle):\n def __init__(self):\n mujoco_env.MujocoEnv.__init__(self, 'half_cheetah.xml', 5)\n utils.EzPickle.__init__(self)\n\n def _step(self, action):\n ... | [
[
"numpy.concatenate",
"numpy.square",
"numpy.clip"
],
[
"numpy.square",
"numpy.array",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
caoyi0905/auto-sklearn | [
"7d33420a644dd3b1e0db26664d01d856a7ca5fb2"
] | [
"test/test_pipeline/components/classification/test_multinomial_nb.py"
] | [
"import unittest\n\nfrom autosklearn.pipeline.components.classification.multinomial_nb import \\\n MultinomialNB\nfrom autosklearn.pipeline.util import _test_classifier, _test_classifier_iterative_fit, \\\n get_dataset, _test_classifier_predict_proba\n\nimport numpy as np\nimport sklearn.metrics\nimport sklea... | [
[
"numpy.random.random",
"numpy.nanmean",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gorold/neural-cellular-automata | [
"c5c4750ae80246bbc028467d880784182aaf7ded"
] | [
"train.py"
] | [
"import argparse\nimport os, time, json\n\nimport torch\nimport torch.optim as optim\n\nfrom NeuralCellularAutomata import *\nfrom utils import *\nfrom trainer import *\nfrom dataloaders import *\n\ndef get_options():\n parser = argparse.ArgumentParser()\n\n # General options\n parser.add_argument('--emoji... | [
[
"torch.device",
"torch.optim.lr_scheduler.ExponentialLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jaluebbe/ahrs | [
"4b4a33b1006e0d455a71ac8379a2697202361758"
] | [
"ahrs/utils/metrics.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nMetrics\n=======\n\nCommon metrics used in 3D Orientation representations.\n\nReferences\n----------\n.. [Huynh] Huynh, D.Q. Metrics for 3D Rotations: Comparison and Analysis. J\n Math Imaging Vis 35, 155-164 (2009).\n.. [Kuffner] Kuffner, J.J. Effective Sampling and Distance Me... | [
[
"numpy.allclose",
"numpy.eye",
"numpy.arccos",
"numpy.linalg.norm",
"numpy.copy",
"numpy.nansum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
muyang0320/tensorflow-deeplab-resnet-crf | [
"e3a9c293cb6017df1a16f36a60a6f3bba7d06686"
] | [
"inference.py"
] | [
"\"\"\"Run DeepLab-ResNet on a given image.\n\nThis script computes a segmentation mask for a given image.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport argparse\nfrom datetime import datetime\nimport os\nimport sys\nimport time\n\nfrom PIL import Image\n\nimport tensorflow as tf\nimport numpy as np\n\... | [
[
"tensorflow.nn.softmax",
"tensorflow.concat",
"tensorflow.read_file",
"tensorflow.shape",
"tensorflow.global_variables",
"tensorflow.argmax",
"tensorflow.expand_dims",
"tensorflow.ConfigProto",
"tensorflow.global_variables_initializer",
"tensorflow.Session",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
horribleheffalump/InteractingCMCResearch | [
"68cdd3163d32f83b7f3e80765ec3caf28e8937dd"
] | [
"ControlledDamsSimplified.py"
] | [
"from numba import jit\nimport numpy as np\nfrom scipy.optimize import minimize\n\nimport matplotlib.pyplot as plt\nfrom matplotlib import gridspec\n\nimport os\n\nfrom functools import partial\nfrom multiprocessing import Pool\nfrom time import time, strftime, localtime, gmtime\n\nfrom matplotlib import rc\n\nrc('... | [
[
"numpy.max",
"numpy.zeros_like",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.twinx",
"numpy.arange",
"numpy.eye",
"numpy.multiply.outer",
"matplotlib.pyplot.subplot",
"matplotlib.gridspec.GridSpec",
"matplotlib.pyplot.close",
"numpy.load",
"numpy.repeat",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cmheisel/better-project-forecast | [
"9bcba88d70c174ecedec09a020b02ac530bc4734"
] | [
"better/lib.py"
] | [
"import random\n\nfrom numpy import percentile, round, average\n\n\nclass Results(object):\n def __init__(self, results):\n self.results = results\n\n def percentile(self, pct):\n \"\"\"What's the Xth percentile of the results.\n Arguments:\n pct(float): The percentile you'd li... | [
[
"numpy.average",
"numpy.percentile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SysuCharon/transformers | [
"a69322c37ab355567043bb509322eed7aa58a485"
] | [
"src/transformers/trainer.py"
] | [
"# coding=utf-8\n# Copyright 2020-present the HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unles... | [
[
"torch.load",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.cuda.random.get_rng_state_all",
"torch.cuda.amp.autocast",
"numpy.concatenate",
"torch.no_grad",
"torch.cuda.is_available",
"torch.Generator",
"torch.utils.data.distributed.DistributedSampler",
"torch.dist... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ankitakashyap05/Merlion | [
"7dc95fbf64002e22bfce89625bdb76b7a3cbfbfc"
] | [
"examples/misc/generate_synthetic_tsad_dataset.py"
] | [
"#\n# Copyright (c) 2021 salesforce.com, inc.\n# All rights reserved.\n# SPDX-License-Identifier: BSD-3-Clause\n# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause\n#\nfrom os.path import abspath, dirname, join\nfrom collections import OrderedDict\nimport o... | [
[
"numpy.random.normal",
"numpy.random.seed",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mariocannistra/radio-astronomy-fftw | [
"9ee61e2ed855e53852b22c6c1bb60f08bfde3619"
] | [
"specview.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\ncode protions from hillshading routines by Joseph Barraud, Geophysics Labs.\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as mcolors\nimport matplotlib.cm as cm\nfrom skimage import exposure\n\nimport matplotlib.dates as mdates\nfrom matplot... | [
[
"numpy.nanmax",
"matplotlib.dates.DateFormatter",
"numpy.linspace",
"numpy.isnan",
"matplotlib.colors.LinearSegmentedColormap",
"numpy.arange",
"numpy.nanmin",
"numpy.interp",
"numpy.nanmean",
"matplotlib.ticker.FuncFormatter",
"matplotlib.pyplot.xlabel",
"numpy.nan... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tejeshreddy/car-price-forcasting | [
"b739085600e3b646d700e2bd1fe551ef3b9accfb"
] | [
"python_code.py"
] | [
"import pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(\"data/car data.csv\")\nfeatures = list(filter(lambda x: x!=\"Car_Name\" ,list(df.columns)))\n\nfinal_dataset = df[features]\nfinal_dataset['No_Year'] = 2020 - df['Year']\nfinal_dataset.drop(['Year']... | [
[
"sklearn.ensemble.RandomForestRegressor",
"pandas.read_csv",
"sklearn.model_selection.RandomizedSearchCV",
"numpy.linspace",
"sklearn.model_selection.train_test_split",
"sklearn.ensemble.ExtraTreesRegressor",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
speedcell4/houttuynia | [
"598ba06d70c1263a6d256991a52e424c03d73130"
] | [
"houttuynia/extensions/evaluation.py"
] | [
"from torch.utils.data import DataLoader\nimport torch\n\nfrom houttuynia.schedule import Extension, Schedule\nfrom houttuynia.context_managers import using_config\n\n__all__ = [\n 'Evaluation',\n]\n\n\nclass Evaluation(Extension):\n def __init__(self, data_loader: DataLoader, chapter: str) -> None:\n ... | [
[
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mortbopet/calyx | [
"cb9821a53c658cf9beb7a31bfbf4ff3022366770"
] | [
"fud/fud/stages/interpreter.py"
] | [
"from fud.stages import Stage, SourceType, Source\nfrom pathlib import Path\nimport simplejson as sjson\nimport numpy as np\nfrom fud.stages.verilator.numeric_types import FixedPoint, Bitnum\nfrom fud.errors import InvalidNumericType\nfrom fud.stages.verilator.json_to_dat import parse_fp_widths, float_to_fixed\nfro... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
netcadlabs/ndu-gate | [
"f479c293284fa6582d8682c98abf88e3da33b406"
] | [
"runners/point_occupied/point_occupied_runner.py"
] | [
"from threading import Thread\n\nimport cv2\nimport numpy as np\n\nfrom ndu_gate_camera.api.ndu_camera_runner import NDUCameraRunner\nfrom ndu_gate_camera.utility import constants, geometry_helper, image_helper\n\n\nclass PointOccupiedRunner(Thread, NDUCameraRunner):\n def __init__(self, config, connector_type):... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yaoshanliang/PaddleX | [
"fe40b6d10db0e4d46f3a73cc5e83c3236d6a5842"
] | [
"paddlex/cv/models/mask_rcnn.py"
] | [
"# copyright (c) 2020 PaddlePaddle 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 re... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nitarshan/robust-generalization-measures | [
"8e9012991ddef1603bab5b6ab31ace6fbfc67ac6"
] | [
"experiments/coupled_networks/supp_figure_monte_carlo_ablation.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport pickle\nimport seaborn as sns\n\nfrom sys import argv\n\nfrom common import pretty_measure\nfrom figure_cdf_all_measures import get_all_losses\n\n\nENVIRONMENT_CACHE_PATH = \"./environment_cache\"\n\n\ndef get_complexity_losses_per_hp(datasets, min_ess, f... | [
[
"numpy.hstack",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.axhline",
"numpy.abs",
"numpy.percentile",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chshih2/pytorch-a2c-ppo-acktr-gail | [
"9db0e442ce417e721179234d1c635e2269d8ab65"
] | [
"a2c_ppo_acktr/arguments.py"
] | [
"import argparse\n\nimport torch\n\n\ndef get_args():\n parser = argparse.ArgumentParser(description='RL')\n parser.add_argument(\n '--algo', default='a2c', help='algorithm to use: a2c | ppo | acktr')\n parser.add_argument(\n '--gail',\n action='store_true',\n default=False,\n ... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
isb-cgc/User-Data-Processor | [
"553c3d37a57904a5631b7e72f3a658f9cae85109"
] | [
"isb_cgc_user_data/user_gen/vcf_processing.py"
] | [
"#!/usr/bin/env python\n\n# Copyright 2015-2017, Institute for Systems Biology.\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# U... | [
[
"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": []
}
] |
hikaya-io/Superset | [
"5c531cede301d1b78c2f327f618ed590443ebab5"
] | [
"superset/db_engine_specs/base.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... | [
[
"pandas.concat",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
YuuuXie/flare | [
"5e63dbc876fdad32789b3d16e0d67972edd1b045"
] | [
"flare/rbcm.py"
] | [
"import json\nimport logging\nimport pickle\nimport time\n\nimport multiprocessing as mp\nimport numpy as np\n\nfrom collections import Counter\nfrom numpy.random import random\nfrom scipy.optimize import (\n minimize,\n basinhopping,\n differential_evolution,\n dual_annealing,\n)\nfrom scipy.cluster.hi... | [
[
"matplotlib.pyplot.imshow",
"numpy.sqrt",
"numpy.concatenate",
"numpy.max",
"numpy.zeros_like",
"numpy.where",
"scipy.optimize.basinhopping",
"numpy.hstack",
"matplotlib.pyplot.tight_layout",
"scipy.optimize.differential_evolution",
"numpy.matmul",
"matplotlib.pyplo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.6",
"1.10",
"1.4",
"1.3",
"1.9",
"1.5",
"1.7",
"1.2",
"1.8"
],
"tensorflow": []
}
] |
saikat-roy/Uni-Bonn-Pattern-Recognition | [
"43608b3ecec87f438bed8444b748aa7f823c10ad"
] | [
"Project1/task1_1/whExample.py"
] | [
"\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n\n\n\ndef plotData2D(X, filename=None):\n # create a figure and its axes\n fig = plt.figure()\n axs = fig.add_subplot(111)\n\n # see what happens, if you uncomment the next line\n # axs.set_aspect('equal')\n \n # plot t... | [
[
"numpy.vstack",
"matplotlib.pyplot.show",
"numpy.dtype",
"matplotlib.pyplot.savefig",
"numpy.all",
"numpy.copy",
"matplotlib.pyplot.close",
"numpy.array",
"numpy.loadtxt",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ikhwansong/ComputerSoftware_OCR | [
"e1664c7fd6081e00e39d9b5fbc48e902bbb5fdfc"
] | [
"find_character.py"
] | [
"import argparse\nimport tensorflow as tf\nimport os\nimport sys\nimport numpy as np\nimport yaml\nfrom tqdm import tqdm\n\nfrom anchor import generate_default_boxes\nfrom box_utils import decode, compute_nms\nfrom voc_data import create_batch_generator_with_customset\nfrom image_utils import ImageVisualizer\nfrom ... | [
[
"tensorflow.math.argmax",
"tensorflow.clip_by_value",
"tensorflow.concat",
"tensorflow.math.reduce_max",
"tensorflow.squeeze",
"tensorflow.gather",
"tensorflow.math.softmax",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
billbrod/synthesis-examples | [
"891c25c2396f556a93e2bd0dc12ca53731a434b9"
] | [
"synth/utils.py"
] | [
"#!/usr/bin/env python3\n\"\"\"various utils\n\"\"\"\nimport numpy as np\nimport warnings\nimport GPUtil\nimport os\nimport yaml\nimport os.path as op\nfrom contextlib import contextmanager\nfrom itertools import cycle\nimport pyrtools as pt\nimport imageio\n\n\ndef create_image(image_type, image_size, save_path=No... | [
[
"numpy.where",
"numpy.iinfo",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tiagovignatti/ezbench | [
"4042a2ee0bb1e470e7f55d59c491267db3dee455"
] | [
"stats/gen_report.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"\nCopyright (c) 2015, Intel Corporation\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n * Redistributions of source code must retain the above copyright notice,\n this list... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"scipy.stats.gaussian_kde",
"matplotlib.pyplot.subplot",
"matplotlib.pyplo... | [
{
"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"... |
rbdm/8755-project | [
"857a6bece0ad11c4ef367dd3afa9b6dc16931877"
] | [
"Global/detection.py"
] | [
"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT License.\n\nimport os\nimport numpy as np\nimport argparse\nimport time\n\nimport torch\nimport torchvision as tv\nimport torch.nn.functional as F\nfrom detection_util.util import *\nfrom detection_models import networks\nfrom PIL import Image, Image... | [
[
"numpy.array",
"torch.unsqueeze",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RonaldsonBellande/ML_robot_command_recognition | [
"345ab7b07a02f09e0f2be470db18a0ff888f939b"
] | [
"header_imports.py"
] | [
"# Copyright © 2021 Ronaldson Bellande\nfrom __future__ import print_function\nimport cv2, sys, math, random, warnings, os, os.path, json, pydicom, glob, shutil, datetime, zipfile, urllib.request, tensorflow as tf, time, trimesh, librosa, gym\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt... | [
[
"tensorflow.keras.mixed_precision",
"matplotlib.pyplot.style.use"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AzizRaies/testsafetystarteragent | [
"93411e927af22292f20a2e697c88bd165324b52a"
] | [
"safe_rl/pg/run_agent.py"
] | [
"import numpy as np\nimport tensorflow as tf\nimport gym\nfrom matplotlib import pyplot as plt\nimport time\nimport re\n\nimport os\nimport pickle\nimport safe_rl.pg.trust_region as tro\nfrom safe_rl.pg.agents import PPOAgent, TRPOAgent, CPOAgent\nfrom safe_rl.pg.buffer import CPOBuffer\nfrom safe_rl.pg.network imp... | [
[
"numpy.random.seed",
"tensorflow.reduce_mean",
"tensorflow.minimum",
"tensorflow.exp",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.variable_scope",
"tensorflow.Session",
"tensorflow.where",
"tensorflow.set_random_seed",
"numpy.exp",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
thaiminhpv/Doctor-Cyclop-Hackathon-2021 | [
"afb943f7d00ceccb408c895077517ddd06d87fd7"
] | [
"Server/server/model_inference/predictor.py"
] | [
"import numpy as np\nimport pandas as pd\n\nfrom server.model_inference.config import labels\nfrom server.model_inference.core_model import get_model_prediction\nfrom server.util.prediction_to_json import pandas_to_json\n\n\ndef get_predictions(images):\n ids = list(images.keys())\n out = np.hstack((np.asarra... | [
[
"numpy.asarray",
"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": []
}
] |
mark-velez/purplequery | [
"7c751da2419bb3a4e742c46392459c3f309ce87a"
] | [
"purplequery/join_test.py"
] | [
"# Copyright 2019 Verily Life Sciences LLC\n#\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport unittest\nfrom typing import List, Tuple, Type, Union # noqa: F401\n\nimport pandas as pd\nfrom ddt import data, ddt, unpack\n\nfrom purplequery.bq_abstrac... | [
[
"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": []
}
] |
georgetown-analytics/crash-severity | [
"990d736a43a3aba06ac41d92d7310307e9d9dc09"
] | [
"data output/data until 04-22-2017/occupancyConcatenation.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom datetime import datetime\n\n# auxiliar method to find the closest timestamp in a list to a given date\ndef nearest(tstamps, date):\n return min(tstamps, key=lambda x: abs(x - date))\n\nif __name__ == '__main__':\n occ = pd.read_csv('occupancy_data.csv')\n sen = p... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.to_datetime",
"pandas.isnull"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
dvsseed/pytorch_cnn | [
"2f4b9a2e8ed9b52de41bce8749798ed67d7504ab",
"2f4b9a2e8ed9b52de41bce8749798ed67d7504ab",
"2f4b9a2e8ed9b52de41bce8749798ed67d7504ab"
] | [
"vgg16.py",
"cnn_l3_battery_antialiased.py",
"squeezenet.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\ncfgs = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']\r\n\r\n\r\ndef make_layers():\r\n layers = []\r\n # in_channel = 3\r\n in_channel = 1\r\n for cfg in cfgs:\r\n if cfg ==... | [
[
"torch.nn.Sequential",
"torch.nn.Dropout",
"torch.nn.functional.log_softmax",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d",
"torch.flatten",
"torch.nn.ReLU"
],
[
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.max",
"torch.load",
"torch.cuda.e... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
StillerPatrick/NeuralSolvers | [
"5702b0507c3dc16d59cf3e77bc25f577f555fe4e"
] | [
"PINNFramework/PINN.py"
] | [
"import torch\nimport torch.nn as nn\nimport numpy as np\nfrom os.path import exists\nfrom itertools import chain\nfrom torch.utils.data import DataLoader\nfrom .InitalCondition import InitialCondition\nfrom .BoundaryCondition import BoundaryCondition, PeriodicBC, DirichletBC, NeumannBC, RobinBC, TimeDerivativeBC\n... | [
[
"torch.optim.Adam",
"numpy.random.get_state",
"numpy.random.seed",
"torch.zeros",
"torch.load",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.optim.LBFGS",
"torch.std",
"torch.device",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
smoorjani/rock-paper-scissor | [
"b12b3594e9681628f573e99fc260de03648e3286"
] | [
"recognition.py"
] | [
"'''\r\nPackHacks Rock Paper Scissors\r\n\r\nA computer-vision based version of rock-paper-scissors\r\n\r\n'''\r\n\r\n# Credit for gesture recognition tutorial: https://gogul09.github.io/software/hand-gesture-recognition-p1\r\n\r\nimport cv2\r\nimport numpy as np\r\nfrom keras.models import load_model\r\n\r\nbg = N... | [
[
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhangdddong/beautifulNLP | [
"295987cc03c9afb52008917d9d141fdb2eb66ba5",
"295987cc03c9afb52008917d9d141fdb2eb66ba5"
] | [
"文本生成/lstm/temp/model.py",
"命名实体识别/bert_bilstm_crf/bert_base/train/lstm_crf_layer.py"
] | [
"# coding: utf-8\nfrom __future__ import print_function\nimport tensorflow as tf\nimport numpy as np\nimport time\nimport os\n\ndef t_softmax(preds, t=1):\n preds = np.log(preds) / t\n exp_preds = np.exp(preds)\n prob = exp_preds / np.sum(exp_preds)\n return prob\n\ndef pick_top_n(preds, vocab_size, top... | [
[
"tensorflow.nn.dynamic_rnn",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.concat",
"tensorflow.device",
"tensorflow.get_variable",
"tensorflow.zeros",
"numpy.squeeze",
"tensorflow.train.AdamOptimizer",
"numpy.exp",
"tensorflow.gradients",
"tensorflow.r... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
achilleas-k/brian2 | [
"906563b6b1321585b082f79f74f1b4ab386347ec"
] | [
"brian2/tests/test_neurongroup.py"
] | [
"import uuid\n\nimport sympy\nimport numpy as np\nfrom numpy.testing.utils import assert_raises, assert_equal, assert_allclose\nfrom nose import SkipTest, with_setup\nfrom nose.plugins.attrib import attr\n\nfrom brian2.core.variables import linked_var\nfrom brian2.core.network import Network\nfrom brian2.core.prefe... | [
[
"numpy.testing.utils.assert_allclose",
"numpy.linspace",
"numpy.asarray",
"numpy.arange",
"numpy.nonzero",
"numpy.testing.utils.assert_equal",
"numpy.asanyarray",
"numpy.mean",
"numpy.testing.utils.assert_raises",
"numpy.var",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wagase/scraping | [
"81e886abe0f8a88830507c978f10f92faf4f19c6"
] | [
"sample/ch7/caltech101_keras.py"
] | [
"from keras.models import Sequential\nfrom keras.layers import Convolution2D, MaxPooling2D\nfrom keras.layers import Activation, Dropout, Flatten, Dense\nimport numpy as np\n\n# カテゴリの指定\ncategories = [\"chair\",\"camera\",\"butterfly\",\"elephant\",\"flamingo\"]\nnb_classes = len(categories)\n# 画像サイズを指定\nimage_w = ... | [
[
"numpy.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Paul1298/GPyOpt | [
"d26bd4fb57a0b83a5cdaa0e7e97aa3692eb31187"
] | [
"MyGPyOpt/testing/functional_tests/test_anchor_points_generator.py"
] | [
"import unittest\nimport numpy as np\n\nfrom MyGPyOpt.core.task.space import Design_space\nfrom MyGPyOpt.util.duplicate_manager import DuplicateManager\nfrom MyGPyOpt.optimization.anchor_points_generator import ObjectiveAnchorPointsGenerator, ThompsonSamplingAnchorPointsGenerator, RandomAnchorPointsGenerator\n\ntol... | [
[
"numpy.random.seed",
"numpy.arange",
"numpy.ones",
"numpy.all",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cyclone923/VAE_gumbel_softmax | [
"50e72e0cb89d2b56fb8aaa04a63343a1a96c792d"
] | [
"puzzle/get_view_data.py"
] | [
"from puzzle.sae import CubeSae\nfrom puzzle.dataset import get_train_and_test_dataset\nfrom puzzle.gumble import device\nfrom puzzle.train import MODEL_NAME\nfrom torch.utils.data import DataLoader\nimport torch\nfrom puzzle.train import load_data, TEMP_MIN_SAE, TEMP_BEGIN_AAE\nimport matplotlib.pyplot as plt\n\nN... | [
[
"matplotlib.pyplot.gca",
"matplotlib.pyplot.imshow",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.no_grad",
"matplotlib.pyplot.pause"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LMNS3d/sharpy | [
"af5964a2f8a7a1e133c0948cb2fdeb5cb5c46568",
"af5964a2f8a7a1e133c0948cb2fdeb5cb5c46568"
] | [
"tests/linear/assembly/test_assembly.py",
"tests/linear/rom/test_krylov.py"
] | [
"'''\nTest assembly\nS. Maraniello, 29 May 2018\n'''\n\nimport os\nimport copy\nimport warnings\nimport unittest\nimport itertools\nimport numpy as np\nimport scipy.linalg as scalg\n\nimport sharpy.utils.h5utils as h5utils\nimport sharpy.linear.src.assembly as assembly\nimport sharpy.linear.src.multisurfaces as mul... | [
[
"numpy.dot",
"numpy.unravel_index",
"numpy.absolute",
"numpy.abs",
"numpy.isfinite",
"scipy.linalg.block_diag",
"numpy.set_printoptions",
"numpy.max",
"numpy.block",
"numpy.argmax",
"numpy.random.rand",
"numpy.ravel_multi_index",
"numpy.array",
"numpy.zeros"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14... |
ATGSilva/ML-TTB-VBF-Detection | [
"2f21cc7a8549c2ed775b46d1ebc7954d1c99e49d"
] | [
"TFModelCreation/Tensorboard_example.py"
] | [
"'''Example for using TensorBoard to maximise model accuracy. \r\nThe code creates and trains new networks and evaluates their\r\naccuracy for combinations of given hyperparameters. Saves the accuracies \r\nand architectures in a folder which can then be viewed using TensorBoard.\r\nAlso includes functions for resh... | [
[
"numpy.amax",
"tensorflow.summary.scalar",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Dropout",
"numpy.array",
"tensorflow.keras.models.Sequential",
"tensorflow.keras.layers.Flatten",
"tensorflow.summary.create_file_writer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
trevor-crowley/jnb_trump_train | [
"93fa304f8c83858474e853af8a559110699108f2"
] | [
"develop/2017-01-15-tc-play.py"
] | [
"\n# coding: utf-8\n\n# In[2]:\n\nimport matplotlib as mpl\nfrom matplotlib import pyplot as plt\nfrom matplotlib.pyplot import GridSpec\nimport seaborn as sns\nimport mpld3\nimport numpy as np\nimport pandas as pd\nimport os, sys\nimport warnings\n\n\n# In[3]:\n\n# Scatter points\nfig, ax = plt.subplots()\nnp.rand... | [
[
"numpy.random.random",
"numpy.random.seed",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.NullFormatter",
"numpy.random.normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yongbowin/LARK_annotation | [
"713d63536fc3684bdf077c6fd99771e9e11ef6de"
] | [
"ERNIE/reader/task_reader.py"
] | [
"# Copyright (c) 2019 PaddlePaddle 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 ... | [
[
"numpy.random.shuffle",
"numpy.array",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nisheed75/RoboND-Rover-Project | [
"f28b0a5e087cdad1fe0f87eebac30250c99837ff"
] | [
"RoboND-Rover-Project/code/decision.py"
] | [
"import numpy as np\n\n\n# This is where you can build a decision tree for determining throttle, brake and steer \n# commands based on the output of the perception_step() function\ndef decision_step(Rover):\n\n # Implement conditionals to decide what to do given perception data\n # Here you're all set up with... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
srahul1222/progressive-learning | [
"1799923a084f36e8665638e5eda86de71efe4bf5"
] | [
"proglearn/voters.py"
] | [
"'''\nMain Author: Will LeVine \nCorresponding Email: levinewill@icloud.com\n'''\nimport numpy as np\n\nfrom sklearn.neighbors import KNeighborsClassifier\n\nfrom sklearn.utils.validation import (\n check_X_y,\n check_array,\n NotFittedError,\n)\n\nfrom sklearn.utils.multiclass import check_classification_... | [
[
"sklearn.utils.validation.check_array",
"numpy.unique",
"numpy.asarray",
"sklearn.utils.multiclass.check_classification_targets",
"sklearn.neighbors.KNeighborsClassifier",
"numpy.ones",
"sklearn.utils.validation.check_X_y",
"numpy.insert",
"numpy.array",
"numpy.where",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kamil-rafalko/Mask_RCNN | [
"dd505c8f8a94e3ccdaf64c8117f3cddc96552c86"
] | [
"mrcnn/model.py"
] | [
"\"\"\"\nMask R-CNN\nThe main Mask R-CNN model implementation.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\"\"\"\n\nimport os\nimport random\nimport datetime\nimport re\nimport math\nimport logging\nfrom collections import OrderedDict... | [
[
"numpy.amax",
"numpy.expand_dims",
"tensorflow.concat",
"tensorflow.control_dependencies",
"tensorflow.stack",
"tensorflow.reduce_sum",
"tensorflow.minimum",
"tensorflow.cast",
"tensorflow.image.non_max_suppression",
"tensorflow.equal",
"tensorflow.image.crop_and_resize... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
hubschne/ml-ops-bootcamp | [
"ff9b11d9c66a294cd292cc4c9ff62627b3ca6edc"
] | [
"bootcamp/lesson2/evaluate_model.py"
] | [
"# MIT License\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# This script is adapted from Francesca Lazzeri Energy Demand Forecast Workbench workshop.\n# Copyright (c) 2021 PyLadies Amsterdam, Alyona Galyeva\n\nimport os\nimport pickle\nimport sys\nimport matplotlib\nimport matplotlib.pyplot as plt... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bermeom/quadruped-robot | [
"5570c720a27b26f94236ebc2ff41f0a1549f10b8",
"5570c720a27b26f94236ebc2ff41f0a1549f10b8"
] | [
"ddpg/tf_/tensorflow_grad_inverter.py",
"ddpg/ou_noise.py"
] | [
"#Reference: https://github.com/MOCR/\n\nimport tensorflow as tf\n\nclass grad_inverter:\n def __init__(self, action_bounds):\n\n self.sess = tf.InteractiveSession() \n \n self.action_size = len(action_bounds[0])\n \n self.action_input = tf.placeholder(tf.float32, [None, ... | [
[
"tensorflow.multiply",
"tensorflow.constant",
"tensorflow.InteractiveSession",
"tensorflow.zeros",
"tensorflow.greater",
"tensorflow.placeholder",
"tensorflow.div"
],
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"1.0",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
anthonys01/snip2fumen | [
"43f5cda3cffd34108082b6653216df9a0480025a"
] | [
"snip2fumen/snipe2fumen.py"
] | [
"\"\"\"\nSnippet tool based on https://github.com/harupy/snipping-tool\n\nModified to work for multi-monitor\n\nDependencies :\npyqt5\nopencv-python\npyperclip\n\"\"\"\n\nimport sys\nfrom typing import Optional\n\nimport numpy as np\nfrom PyQt5 import QtWidgets, QtCore, QtGui\nfrom PyQt5.QtCore import QPoint\nfrom ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ianozsvald/dtype_pandas_numpy_speed_test | [
"5595e12f17a7e90397800de5fb6c9a4a0a9f894e"
] | [
"speed_test_combined.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport pickle\nimport time\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n#import ipython_memory_usage\n#get_ipython().run_line_magic('ipython_memory_usage_start', '')\n\n\nprint(f\"Pandas: {pd.__version__}\")\nprint(f\"NumPy: {np.... | [
[
"numpy.sqrt",
"matplotlib.pyplot.subplots",
"pandas.DataFrame",
"numpy.random.normal",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.xticks"
]
] | [
{
"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": []
}
] |
CavalloneChen/mpopt | [
"b418a3fa519146cc97fb8f29d454e27cca20ef4b"
] | [
"mpopt/algorithms/CMAES.py"
] | [
"import os\nimport time\nimport numpy as np\n\nfrom ..population.cmaes import CMAESPop\nfrom ..operator import operator as opt\nfrom ..tools.distribution import MultiVariateNormalDistribution as MVND\n\nEPS = 1e-8\n\nclass CMAES(object):\n\n def __init__(self):\n # params\n self.pop_size = None\n ... | [
[
"numpy.random.uniform",
"numpy.eye"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
congson1293/keras-contrib | [
"6614e21904fb63f836077ca21c3944b5a24a9014"
] | [
"tests/keras_contrib/layers/normalization/test_groupnormalization.py"
] | [
"import numpy as np\r\nimport pytest\r\nfrom keras import backend as K\r\nfrom keras import regularizers\r\nfrom keras.layers import Input\r\nfrom keras.models import Sequential, Model\r\nfrom numpy.testing import assert_allclose\r\n\r\nfrom keras_contrib.layers import GroupNormalization\r\nfrom keras_contrib.utils... | [
[
"numpy.random.random",
"numpy.arange",
"numpy.ones",
"numpy.random.normal",
"numpy.std",
"numpy.mean",
"numpy.testing.assert_allclose",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TTitscher/BitJug | [
"7f590a4cc5b0d17080ec9acb9896c3739791dad1"
] | [
"adaptation/topo_mma.py"
] | [
"import numpy as np\nimport math\nimport nlopt\nimport scipy\nfrom scipy.optimize import minimize\n\nfrom topopt.boundary_conditions import *\nfrom topopt.problems import *\nfrom topopt.guis import GUI\n\nnelx, nely = 30, 10 # Number of elements in the x and y\nnelx, nely = 200, 50 # Number of elements in the x a... | [
[
"scipy.sparse.coo_matrix",
"numpy.asarray",
"numpy.ones",
"numpy.ceil",
"scipy.optimize.minimize",
"numpy.zeros"
]
] | [
{
"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"
... |
jonrossclaytor/chicago-taxi | [
"8a617a3fc2dfa61f5b8987d944c51ed7a44d0a85"
] | [
"trainer/task.py"
] | [
"import argparse\nfrom google.cloud import storage\nimport glob\nimport joblib\nimport logging\nimport os\nfrom os import path\n\n#from keras.callbacks import Callback\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.callbacks import TensorBoard\nfrom keras.callbacks import EarlyStopping\n\nfrom tensorflow.... | [
[
"tensorflow.python.lib.io.file_io.FileIO"
]
] | [
{
"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... |
belosthomas/socr | [
"d78d9468061129922d5a592bb5687918e90efc5e",
"d78d9468061129922d5a592bb5687918e90efc5e"
] | [
"socr/text/dataset/iam_washington.py",
"socr/text/dataset/iam_oneline_handwriting_database.py"
] | [
"import os\n\nimport torch\nfrom PIL import Image\nfrom torch.utils.data.dataset import Dataset\n\nfrom socr.utils.image import image_pillow_to_numpy\n\n\nclass IAMWashington(Dataset):\n\n def __init__(self, helper, path, height=48, labels=\"\", transform=True, loss=None):\n self.height = height\n ... | [
[
"torch.from_numpy"
],
[
"torch.from_numpy"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
clbarnes/CATMAID-synapsesuggestor | [
"7b9ffd96c034ca47a6567ebbeb0eff4ece44cb33"
] | [
"synapsesuggestor/tests/apis/test_synapse_detection.py"
] | [
"# -*- coding: utf-8 -*-\nimport json\nfrom unittest import skip\n\nimport numpy as np\nfrom six import assertCountEqual\n\nfrom django.db import connection\n\nfrom synapsesuggestor.models import SynapseSliceSynapseObject, SynapseObject, SynapseSlice\nfrom synapsesuggestor.tests.common import SynapseSuggestorTestCa... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chinasonglei/data-etl-sloth | [
"e00ce6129b49b5f48fb0c657b244c6010b733bfc"
] | [
"source/etl_script/main.py"
] | [
"# coding:utf-8\n#!/usr/bin/python\n\nimport pymysql\nimport pandas as pd\nimport numpy as np\nfrom datetime import timedelta,datetime\ndt = datetime.now()\nimport xml.etree.ElementTree as ET\nfrom urllib import parse\nimport os,os.path\nimport zipfile\nfrom shutil import copy\nimport logging\nlogger = logging.getL... | [
[
"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": []
}
] |
dedkoster/tacotron2 | [
"6386f2a0d402ff677c150f5f8f04b0c258fa2316"
] | [
"data_utils.py"
] | [
"import random\nimport numpy as np\nimport torch\nimport torch.utils.data\n\nimport layers\nfrom utils import load_wav_to_torch, load_filepaths_and_text\nfrom text import text_to_sequence\n\n\nclass TextMelLoader(torch.utils.data.Dataset):\n \"\"\"\n 1) loads audio,text pairs\n 2) normalizes text a... | [
[
"numpy.load",
"torch.squeeze",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OLarionova-HORIS/lets-plot | [
"89e30a574fe2de3da17186acdbe1cf427d66d87f"
] | [
"python-package/lets_plot/geo_data/regions.py"
] | [
"import enum\nfrom abc import abstractmethod\nfrom typing import List, Dict, Optional, Union\n\nfrom pandas import DataFrame, Series\n\nfrom .gis.geocoding_service import GeocodingService\nfrom .gis.request import PayloadKind, RequestBuilder, RequestKind, MapRegion\nfrom .gis.response import GeocodedFeature, Namesa... | [
[
"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": []
}
] |
zipengxu-c/ADVSE-GuessWhat | [
"44ca8b1e2368b4afd90a74a8c22a6553e8fc351e"
] | [
"src/guesswhat/data_provider/guesser_qa_batchifier.py"
] | [
"import numpy as np\nimport collections\nimport copy\nfrom generic.data_provider.batchifier import AbstractBatchifier\n\nfrom generic.data_provider.image_preprocessors import get_spatial_feat\nfrom generic.data_provider.nlp_utils import padder, padder_3d\n\n\nclass GuesserBatchifier_RAH(AbstractBatchifier):\n\n ... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lkct/pytorch | [
"e5282c3cb8bf6ad8c5161f9d0cc271edb9abed25",
"ec62901a2c38b63d12843e0f079bdeb7644d8714"
] | [
"torch/ao/quantization/fx/prepare.py",
"torch/nn/parallel/distributed.py"
] | [
"import torch\nimport operator\nimport warnings\nfrom torch.fx import (\n GraphModule,\n)\nfrom torch.fx.graph import (\n Graph,\n Node,\n)\nfrom torch.fx.node import Argument\n\nfrom ..quantize import (\n propagate_qconfig_,\n)\nfrom ..observer import (\n ObserverBase,\n)\nfrom ..qconfig import QCon... | [
[
"torch.ao.quantization.quantize.convert",
"torch.tensor"
],
[
"torch.distributed._register_comm_hook",
"torch.autograd.profiler.record_function",
"torch.distributed.is_nccl_available",
"torch.zeros",
"torch.autograd.Variable._execution_engine.queue_callback",
"torch.distributed... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XanaduAI/pennylane-cirq | [
"cb6defaf448982769ae7e7bd5761d606d4d19fb6"
] | [
"tests/test_apply.py"
] | [
"# Copyright 2018 Xanadu Quantum Technologies 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 applica... | [
[
"numpy.diag",
"numpy.sqrt",
"scipy.linalg.block_diag",
"numpy.random.seed",
"numpy.allclose",
"numpy.abs",
"numpy.cos",
"numpy.sin",
"numpy.identity",
"numpy.ravel_multi_index",
"numpy.exp",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
ZTE/Adlik | [
"cac4b601ec8b06a3e382b19b3fed7f28ef0a80d9"
] | [
"model_compiler/tests/model_compiler/compilers/test_onnx_model_to_tensorrt_model.py"
] | [
"# Copyright 2019 ZTE corporation. All Rights Reserved.\n# SPDX-License-Identifier: Apache-2.0\n\nfrom unittest import TestCase\nfrom tempfile import TemporaryDirectory, NamedTemporaryFile\n\nimport os\nfrom torch.autograd import Variable\nimport torch.onnx\nimport torch\nimport torchvision\n\nimport pytest\nimport... | [
[
"torch.onnx.export",
"tensorflow.multiply",
"tensorflow.Graph",
"tensorflow.Variable",
"torch.randn",
"tensorflow.compat.v1.Session",
"tensorflow.compat.v1.placeholder",
"tensorflow.add",
"numpy.random.rand",
"torch.device"
]
] | [
{
"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... |
vatsalg29/btp | [
"2ad4c4cfe121e951db12efa3fe5b6f1cc0188da7"
] | [
"tests/modules/test_fusions.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\nimport unittest\n\nimport torch\nimport random\nimport operator\nimport functools\nimport numpy as np\nimport inspect\n\nimport pythia.modules.fusions as fusions\n\nclass TestModuleFusions(unittest.TestCase):\n def setUp(self):\n bsize = 2\n self... | [
[
"torch.randn",
"torch.Size",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chunyang-wen/elasticdl | [
"7b16b44f5314507494a552c11caaf3b2bce6d209"
] | [
"elasticdl/python/tests/servicer_test.py"
] | [
"import os\nimport random\nimport tempfile\nimport unittest\nfrom collections import defaultdict\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom elasticdl.proto import elasticdl_pb2\nfrom elasticdl.python.common.tensor import tensor_pb_to_ndarray\nfrom elasticdl.python.master.checkpoint_service import Checkp... | [
[
"tensorflow.keras.layers.Dense",
"numpy.array",
"tensorflow.optimizers.SGD"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
asafmaman101/transformer_exercise | [
"444c69daab33df706c5f2317a35056926e855dc0"
] | [
"submission_files/transformer.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 math\nfrom typing import Any, Dict, List, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nfrom fairseq import utils\n... | [
[
"torch.empty",
"torch.Tensor",
"torch.zeros",
"torch.nn.init.constant_",
"torch.nn.ModuleList",
"torch.nn.Embedding",
"torch.nn.Linear",
"torch.nn.init.normal_",
"torch.FloatTensor",
"torch.nn.init.xavier_uniform_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
venaissance/Reinforcement-learning-with-tensorflow | [
"78f5253d7e2bbd5aff5bacbbafd85ba9dbda686d"
] | [
"contents/6_OpenAI_gym/my_PG_CartPole.py"
] | [
"import gym\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\n\nif (tf.__version__ >= '2'):\n import tensorflow._api.v2.compat.v1 as tf\n\n tf.disable_v2_behavior()\n\n\nclass Memory(object):\n def __init__(self):\n self.ep_obs, self.ep_act, self.ep_rwd = [], [], []\n\n ... | [
[
"tensorflow._api.v2.compat.v1.Session",
"tensorflow._api.v2.compat.v1.nn.sparse_softmax_cross_entropy_with_logits",
"numpy.zeros_like",
"numpy.mean",
"tensorflow._api.v2.compat.v1.variable_scope",
"numpy.std",
"tensorflow._api.v2.compat.v1.train.AdamOptimizer",
"tensorflow._api.v2.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MoritzThomasHuebner/memestr | [
"acac0af27272304000007ead581333bf91ea4dbc"
] | [
"scripts/analyse_injection_study.py"
] | [
"import numpy as np\nimport json\n\nnetwork_snrs = []\nln_bfs = []\n\nfor i in range(2000):\n try:\n with open(f'injections/{i}_memory/IMR_mem_inj_non_mem_rec_result.json') as f:\n res = json.load(f)\n except Exception as e:\n print(e)\n with open(f'injections/{i}_memory/pp_result.... | [
[
"numpy.std",
"numpy.mean",
"numpy.array",
"numpy.where",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
robertofranceschi/Sentiment-Analysis-on-Tripadvisor-reviews | [
"5a3f2fc0b3536605e3ba83f71247e236fec883a6"
] | [
"code/main.py"
] | [
"# Project assignment - Winter Session, A.Y. 2019/2020\r\n# Data Science Lab: process and method - Politecnico di Torino\r\n\r\n# Student: Roberto Franceschi\r\n\r\n# BEST ACCURACY FOUND\r\n# Submission: 2020-01-22 23:32:48.334063\r\n# 0.9746452184675308 -> 0.975\r\n# SVC with param: {'C': 2, 'loss': 'hinge', 'pena... | [
[
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.feature_extraction.text.TfidfTransformer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nickyungyung/probability | [
"8edc2892658b5fac7f2e162e1abdc37d1f9858da",
"8edc2892658b5fac7f2e162e1abdc37d1f9858da"
] | [
"tensorflow_probability/python/sts/sum.py",
"tensorflow_probability/python/distributions/gumbel.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.convert_to_tensor",
"tensorflow.concat",
"tensorflow.control_dependencies",
"tensorflow.identity",
"tensorflow.assert_equal",
"tensorflow.name_scope",
"tensorflow.log",
"tensorflow.assert_same_float_dtype"
],
[
"tensorflow.convert_to_tensor",
"numpy.sqrt",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"1.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
... |
CheeseAnt/league-shard-counter | [
"bd6ce2a3d2b511c3de073f6cae86737db437a378"
] | [
"shard_counter.py"
] | [
"import numpy as np\nimport cv2\nimport pandas as pd\nimport os\n\nfrom time import time\nfrom pytesseract import image_to_string\nfrom re import search\nfrom glob import glob as listdir\nfrom collections import Counter\nfrom argparse import ArgumentParser\nfrom bs4 import BeautifulSoup as bs\nfrom requests import ... | [
[
"numpy.ones",
"numpy.array",
"pandas.DataFrame",
"numpy.int32"
]
] | [
{
"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": []
}
] |
databento/databento-python | [
"5e76841e10094713f29b3247cc0a66fc8fbb5869"
] | [
"tests/test_historical_bento.py"
] | [
"import os\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom databento import from_file\nfrom databento.common.enums import Compression, Encoding, Schema\nfrom databento.historical.bento import Bento, FileBento, MemoryBento\n\n\nTESTS_ROOT = os.path.dirname(os.path.abspath(__... | [
[
"pandas.Timestamp",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JoshuaQChurch/Parcel-Manifest | [
"3eddcc4a6898d1a9a84146327d931d479eb6fce0"
] | [
"macro_win_7.py"
] | [
"import tkinter as tk\nfrom tkinter import filedialog\nfrom tkinter import messagebox\nimport pandas as pd \nimport os \nimport pyautogui\nimport time \nfrom pathlib import Path \nimport copy\n\n\nclass MainApplication:\n \"\"\" \n This object creates a tkinter graphical\n user interface that allows the us... | [
[
"pandas.read_excel",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
doicbek/toast | [
"1d06a471c86a87845050ae2795fc053986d03159"
] | [
"src/toast/todmap/conviqt.py"
] | [
"# Copyright (c) 2015-2020 by the parties listed in the AUTHORS file.\n# All rights reserved. Use of this source code is governed by\n# a BSD-style license that can be found in the LICENSE file.\n\nimport warnings\n\nfrom ..mpi import use_mpi\n\nimport numpy as np\n\nfrom .. import qarray as qa\n\nfrom ..op import... | [
[
"numpy.hstack",
"numpy.radians",
"numpy.abs",
"numpy.cos",
"numpy.ones",
"numpy.sin",
"numpy.copy",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bovheat/bovheat | [
"05fd7d5bfff61ff2a78bd05ad8354198ac104996"
] | [
"tests/integration/test_validate_results.py"
] | [
"import io\nimport sys\nimport pandas as pd\n\nfrom bovheat_src import bovheat\n\n\ndef test_complete_runthrough(monkeypatch):\n # add custom argv\n additional_argv = \"example/data -s -5 30 -l eng -t 35 -o out_file -i 2\".split(\" \")\n monkeypatch.setattr(sys, \"argv\", [sys.argv[0]] + additional_argv)\n... | [
[
"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": []
}
] |
ouked/ParlPy | [
"c12402f70a30e9a3bda8e4b0bd58e0e5a48818b5"
] | [
"parlpy/mps/mp_fetcher.py"
] | [
"import datetime\nimport time\nimport pandas as pd\nimport requests\nimport json\n\n\ndef jprint(obj):\n # create a formatted string of the Python JSON object\n text = json.dumps(obj, sort_keys=True, indent=2)\n print(text)\n\n\n# def underscore_to_camelcase(name: str) -> str:\n# parts = [part[0].upper... | [
[
"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": []
}
] |
Chris-hughes10/YOLOX | [
"981df30285839469a23cb925ed0a0f3714e46514"
] | [
"yolox/data/dataloading.py"
] | [
"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport os\nimport random\nimport uuid\n\nimport numpy as np\n\nimport torch\n\n__all__ = [\n \"close_mosaic\",\n \"get_yolox_datadir\",\n \"worker_init_reset_seed\",\n]\n\n\ndef close_mosaic(dataloader):\n ... | [
[
"torch.manual_seed",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PingjunChen/LiverCancerSeg | [
"6d295b427c88d44560c351666f46f337facb539e"
] | [
"seg/dataload.py"
] | [
"# -*- coding: utf-8 -*-\n\nimport os, sys\nimport numpy as np\nfrom skimage import io\nfrom torchvision import transforms\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\n\n\nviable_rgb_mean = (0.790, 0.614, 0.739)\nviable_rgb_std = (0.093, 0.128, 0.102)\nwhole_rgb_mean = (0.765, 0.5... | [
[
"numpy.expand_dims",
"torch.utils.data.DataLoader"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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