repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
ghuls/weblogo | [
"7eab5d1b8a8ec38786fa426af84bd77950835524"
] | [
"tests/test_matrix.py"
] | [
"#!/usr/bin/env python\n\nimport unittest\nfrom io import StringIO\n\nimport numpy as np\n\nfrom weblogo import data\nfrom weblogo.matrix import AlphabeticArray, Motif, SubMatrix\nfrom weblogo.seq import Alphabet, protein_alphabet, unambiguous_protein_alphabet\n\nfrom . import data_stream\n\n\nclass test_Alphabetic... | [
[
"numpy.asarray",
"numpy.shape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
OfficialCodexplosive/RESKit | [
"e006e8c9923ddb044dab6951c95a15fa43489398"
] | [
"test/01_util/economic/test_lcoe.py"
] | [
"from reskit.util.economic.lcoe import levelized_cost_of_electricity, levelized_cost_of_electricity_simplified\nimport numpy as np\n\n\ndef test_levelized_cost_of_electricity():\n annual_expenditures = np.full(30, 100)\n annual_expenditures[0] = 500\n\n np.random.seed(0)\n annual_generation = np.random.... | [
[
"numpy.random.random",
"numpy.random.seed",
"numpy.full",
"numpy.array",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
meng630/GMD_E3SM_SCM | [
"990f84598b79f9b4763c3a825a7d25f4e0f5a565",
"990f84598b79f9b4763c3a825a7d25f4e0f5a565"
] | [
"components/mpas-source/testing_and_setup/compass/ocean/tendency_verification/all/Redi/plot.py",
"components/mpas-source/testing_and_setup/compass/ocean/Redi_verification/SouthernOceanSlice40/all/add_initial_state.py"
] | [
"#!/usr/bin/env python\n'''\nThis script plots results from MPAS-Ocean convergence test.\n'''\nimport numpy as np\nfrom netCDF4 import Dataset\nimport matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use('Agg')\n\n\n# mrp: read from file mesh after nx,ny attributes are added:\nnx = 10 # ncfileMesh.getncattr... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.title",
"numpy.reshape",
"matplotlib.use",
"matplotlib.pyplot.loglog",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.colorbar",
"numpy.max",
"matplotlib.pyplot.clf",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nevetscode23/docassemble | [
"e081281b3001baad12240a8bd9c19f24eefaef4d"
] | [
"docassemble_webapp/docassemble/webapp/machinelearning.py"
] | [
"import re\nimport random\nimport codecs\nimport pickle\nimport datetime\nimport os\nimport json\nimport threading\nfrom sqlalchemy import and_, select, delete\nfrom sklearn.ensemble import RandomForestClassifier\nimport pandas as pd\nfrom pandas.api.types import CategoricalDtype\nimport numpy as np\nimport yaml\nf... | [
[
"pandas.api.types.CategoricalDtype",
"pandas.Series",
"sklearn.ensemble.RandomForestClassifier",
"pandas.DataFrame",
"pandas.get_dummies"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
windows7lover/RegularizedNonlinearAcceleration | [
"5120b7cbbd159e2a1a5b0f6f99d14f5f0e4774f0"
] | [
"Python/rna_v2/online_rna.py"
] | [
"import torch as T\nimport numpy as np\nfrom numpy import linalg as LA\nfrom torch.optim import Optimizer, SGD\nimport copy\n\nclass online_rna(SGD):\n \n def __init__(self,params, lr,momentum=0,dampening=0,weight_decay=0,nesterov=False,K=10,reg_acc=1e-5,acceleration_type='online',do_average=False):\n ... | [
[
"numpy.linalg.solve",
"numpy.eye",
"numpy.linalg.norm",
"numpy.ones",
"numpy.concatenate",
"numpy.asmatrix",
"numpy.linalg.lstsq",
"numpy.shape",
"numpy.transpose",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhujiaxiaowang/Safety-helmet-detection-based-on-Yolov3 | [
"5f68bd391fc1f5ca796b955c28ea8f4074215b09"
] | [
"yolo.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nClass definition of YOLO_v3 style detection model on image and video\n\"\"\"\n\nimport colorsys\nimport os\nfrom timeit import default_timer as timer\n\nimport numpy as np\nfrom keras import backend as K\nfrom keras.models import load_model\nfrom keras.layers import Input\nfrom PIL... | [
[
"numpy.expand_dims",
"numpy.random.seed",
"numpy.asarray",
"numpy.random.shuffle",
"numpy.floor",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CompetitionDataResearch/recsys-spotify-challenge | [
"7468e82fcc69c45b46fd23ad924446d31f729a2e",
"7468e82fcc69c45b46fd23ad924446d31f729a2e"
] | [
"src/submit/submit.py",
"src/100songs_shuffle/auto_cf_test_submission_100_shuffle.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nThis script manage all slice files\nfor final submit\n\nCreated on Tue May 15 10:39:10 2018\n\n@author: bwhe\n\"\"\"\n\nimport os\nimport gc\nimport pickle\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nimport numpy as np\n\n\n\n\n__path__ = '.'\n\n\n# conver... | [
[
"numpy.concatenate",
"numpy.zeros",
"pandas.DataFrame"
],
[
"pandas.read_csv",
"sklearn.externals.joblib.load",
"scipy.sparse.csr_matrix",
"sklearn.svm.LinearSVC",
"sklearn.preprocessing.LabelEncoder",
"scipy.sparse.lil_matrix"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
Shawn-Guo-CN/spikesorters | [
"e67831289bfefb356c3fd8e5700934fdeaf2b894"
] | [
"spikesorters/utils/ssmdarecordingextractor.py"
] | [
"# Ultimately this file will be merged with mda extractors\n# of spikeextractors. But temporarily we have a local copy\n# here that works internally with ironclust\n\nimport json\nimport numpy as np\nimport os\n\nfrom spikeextractors import RecordingExtractor\nfrom spikeextractors import SortingExtractor\n\nfrom ..... | [
[
"numpy.unique",
"numpy.rint",
"numpy.genfromtxt",
"numpy.concatenate",
"numpy.ones",
"numpy.savetxt",
"numpy.argsort",
"numpy.array",
"numpy.zeros",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
syao13/pdb_eda | [
"3f622ac5345e8179b1e08813c6197d35339d3b00"
] | [
"pdb_eda/pdbParser.py"
] | [
"\"\"\"\nPDB Parser (pdb_eda.pdbParser)\n-------------------------------------------------------\n\nThis module provides methods to read and parse the PDB format files and returns PDB objects.\nFormat details of PDB can be found in ftp://ftp.wwpdb.org/pub/pdb/doc/format_descriptions/Format_v33_Letter.pdf.\n\"\"\"\n... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HibikiJie/MONet | [
"931400df28cb62aab90662abe00acd1d3688073d"
] | [
"models/monet_s_set.py"
] | [
"import numpy\n\n\nclass Set:\n\n def __init__(self):\n '''图片和标签的位置'''\n self.image_path = 'MONet/data/image'\n self.label_path = 'MONet/data'\n self.image_size = 640 # 图片尺寸\n size = 's'\n depth_multiple = None\n width_multiple = None\n '''设置锚定框的尺寸大小'''\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lcit/metrics_delin | [
"30f1ad9ccc901e63770f39a80b0e1ec6bbfb34d9"
] | [
"graph_based/holes_marbles_new_metric.py"
] | [
"import os\nimport sys\nimport numpy as np\nimport networkx as nx\nimport time\nimport copy\nimport random\nimport itertools\nimport scipy\nfrom scipy.spatial.distance import cdist\n\nfrom .. import utils\nfrom .common import extract_subgraph, compute_quantities, compute_scores\n \ndef node_matching_hung... | [
[
"numpy.random.seed",
"scipy.spatial.distance.cdist",
"numpy.ones",
"numpy.copy",
"numpy.argsort",
"numpy.array",
"numpy.unravel_index"
]
] | [
{
"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"
... |
muradkhan101/sound-emotion-nn | [
"3ab44541b29a847df2f5ab6a9393ef107ce0ec0d"
] | [
"classify.py"
] | [
"import matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nfrom keras.models import load_model\nfrom pyAudioAnalysis import audioFeatureExtraction, audioBasicIO\nimport pandas as pd\nimport numpy as np\nimport argparse\nimport imutils\nimport pickle\nimport cv2\nimport os\n\nap = argparse.Argum... | [
[
"numpy.expand_dims",
"matplotlib.use",
"numpy.concatenate",
"numpy.argmax",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ysulsky/metaworld | [
"33f3b90495be99f02a61da501d7d661e6bc675c5"
] | [
"metaworld/envs/mujoco/multitask_env.py"
] | [
"import gym\nfrom gym.spaces import Box\nimport numpy as np\n\nfrom metaworld.core.serializable import Serializable\n\n\nclass MultiTaskEnv(gym.Env, Serializable):\n def __init__(self,\n task_env_cls=None,\n task_args=None,\n task_kwargs=None,):\n Serializab... | [
[
"numpy.ones",
"numpy.concatenate",
"numpy.prod",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qstommyshu/deltahacks7 | [
"d4119d14fd5ba8bd5b45cd48cca8b0ba5f6290cd"
] | [
"assemble/Diabetes.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd\nfrom keras.models import Sequential\nfrom keras.layers import Dropout, Activation, Dense, Flatten\nfrom keras.callbacks import ModelCheckpoint\nimport numpy as... | [
[
"numpy.array",
"sklearn.model_selection.train_test_split",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
scottfabini/machine-learning-perceptron | [
"2bc4b7b415871bc73ac8c033983df73719df9422"
] | [
"6_q-learning/Board.py"
] | [
"#!/usr/bin/env python3\nimport pandas as pd\nimport numpy as np\nimport random\nfrom enum import Enum\nimport os\n\n# Enumeration of available actions\nclass Action(Enum):\n P = 0\n N = 1\n S = 2\n E = 3\n W = 4\n\n# Enumeration of available states\nclass State(Enum):\n Empty = 0\n Wall = 1\n ... | [
[
"numpy.ndenumerate",
"numpy.argmax",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
JRC1995/Continuous-RvNN | [
"b33bdbd2f80119dc0fa3ed6d44865a3d45bc1e81"
] | [
"inference/models/encoders/ordered_memory.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\n\n\nclass Distribution(nn.Module):\n def __init__(self, nslot, hidden_size, dropout):\n super(Distribution, self).__init__()\n\n self.query = nn.Sequential(\n nn.Dropout(dropout),\n nn.Linear(h... | [
[
"torch.nn.functional.normalize",
"torch.nn.Dropout",
"torch.cat",
"torch.zeros_like",
"torch.nn.LayerNorm",
"torch.exp",
"torch.nn.Linear",
"torch.nn.Sigmoid",
"torch.arange",
"torch.stack",
"torch.nn.ReLU",
"torch.cumsum",
"torch.ones_like",
"torch.nn.funct... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
SunAriesCN/tensorflow | [
"ac8f62a3eba894e35917d5ea974f9dc502148f13"
] | [
"tensorflow/python/compat/compat.py"
] | [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.python.platform.tf_logging.warning",
"tensorflow.python.util.tf_export.tf_export"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.5",
"1.7",
"1.4"
]
}
] |
juharris/PySyft | [
"dbb70f24cc55a7dca032fb06f1a8662cb15092a9"
] | [
"test/torch/tensors/test_additive_shared.py"
] | [
"import pytest\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport syft\nfrom syft.frameworks.torch.tensors.interpreters.additive_shared import AdditiveSharingTensor\n\n\ndef test_wrap(workers):\n \"\"\"\n Test the .on() wrap functionality for AdditiveSharingTensor\n \"\"\"\n\... | [
[
"torch.abs",
"torch.mean",
"torch.cat",
"torch.zeros",
"torch.sum",
"torch.mm",
"torch.ones",
"torch.tensor",
"torch.rand",
"torch.roll",
"torch.nn.functional.max_pool2d",
"torch.dot",
"torch.LongTensor",
"torch.nn.functional.avg_pool2d",
"torch.IntTenso... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hipros/TwitterBERT | [
"d6c2004796ece62d01950e891f68d8a5b6aae631"
] | [
"classifier.py"
] | [
"import torch\n\nfrom torch import nn\n\n\nclass BERTClassifier(nn.Module):\n def __init__(self, bert, hidden_size=768, num_classes=2, dr_rate=None, params=None):\n super(BERTClassifier, self).__init__()\n self.bert_model = bert\n self.dr_rate = dr_rate # dropout rate\n self.classifie... | [
[
"torch.nn.Linear",
"torch.nn.Dropout",
"torch.zeros_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cdluminate/robrank | [
"980141400525d295ac9642cd0f978d3e9e85ab90",
"980141400525d295ac9642cd0f978d3e9e85ab90"
] | [
"tools/grad.py",
"robrank/utils.py"
] | [
"'''\nCopyright (C) 2019-2021, Mo Zhou <cdluminate@gmail.com>\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable... | [
[
"torch.nn.functional.normalize",
"torch.nn.functional.pairwise_distance",
"torch.no_grad",
"torch.rand",
"torch.nn.functional.cosine_similarity",
"torch.dot"
],
[
"torch.nn.functional.normalize",
"sklearn.cluster.KMeans",
"torch.zeros",
"torch.sum",
"torch.distribut... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
translationalneurosurgery/tool-offspect | [
"011dafb697e8542fc7c3cf8af8523af3ff704a14"
] | [
"tests/mock/mock_smartmove.py"
] | [
"\"\"\"\nMock and test smartmove\n-----------------------\n\n:code-block: bash\n\n python mock_smartmove.py .\n \n offspect tms -t test.hdf5 -f documentation.txt VvNn_VvNn_2000-12-31_23-59-59.cnt \"VvNn 2000-12-31_23-59-59.cnt\" -r contralateral_mep -c Ch1 -pp 100 100\n\n\"\"\"\n\nimport libeep\nfrom pathl... | [
[
"numpy.arange"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
theGuyWithBlackTie/Deep-Extreme-Multi-Label-Learning | [
"e90e4fb5ebeba009358af529594db2201f8f434c"
] | [
"config.py"
] | [
"# Contains all static and live configurations about the project\r\nimport torch\r\n\r\n# paths\r\nDATASET = 'Bibtex' # Default dataset is set to Bibtex dataset\r\nDATAFOLDER = 'data/'\r\nMODEL_SAVED= 'outputs/{}/model/state_dict_model.pt'\r\nPREDICTIONS_PATH = 'outputs/{}/predictions/final_results_and_targets.... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dailab/elvis | [
"9b808d0777b014ee0c2d79e4b9a2390bea4841e6"
] | [
"elvis/result.py"
] | [
"from numpy import histogram\nfrom datetime import timedelta\nimport numpy as np\nfrom elvis.charging_point import ChargingPoint\nfrom elvis.config import ScenarioRealisation\nfrom elvis.utility.elvis_general import num_time_steps, create_time_steps\nfrom elvis.distribution import EquallySpacedInterpolatedDistribut... | [
[
"numpy.array",
"numpy.histogram",
"numpy.quantile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tobikasali/Disaster_response_pipleine | [
"aee12fc6ca27486490a5d90f7267740e4f7d6f1c"
] | [
"app/run.py"
] | [
"import json\nimport plotly\nimport pandas as pd\nimport numpy as np\n\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.tokenize import word_tokenize\n\nfrom flask import Flask\nfrom flask import render_template, request, jsonify\nfrom plotly.graph_objs import Bar\nfrom sklearn.externals import joblib\nfrom sqla... | [
[
"pandas.read_sql_table",
"sklearn.externals.joblib.load",
"numpy.sum"
]
] | [
{
"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": []
}
] |
LeosyHsu/tflite_on_microcontroller | [
"afb1a59fc1bc792ac72d1a3e22e2469020529788"
] | [
"slim/nets/nasnet/nasnet.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\... | [
[
"tensorflow.nn.softmax",
"tensorflow.transpose",
"tensorflow.contrib.layers.variance_scaling_initializer",
"tensorflow.identity",
"tensorflow.contrib.layers.flatten",
"tensorflow.logging.info",
"tensorflow.contrib.layers.l2_regularizer",
"tensorflow.variable_scope",
"tensorflow... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"1.2"
]
}
] |
mxl9999/LoanStats | [
"07d032a98f8dad5298b1edd1b2963d5fe26f1de4"
] | [
"LoanStats.py"
] | [
"\n# coding: utf-8\n\n# In[10]:\n\n\nimport numpy as np\nimport pandas as pd\nloandata = pd.DataFrame(pd.read_excel('D:\\data analysis\\Data capture\\loandata.xlsx'))\n\n\n# In[11]:\n\n\nloandata\n\n\n# In[31]:\n\n\nloandata.duplicated().value_counts()\n\n\n# In[13]:\n\n\nloandata.drop_duplicates()\n\n\n# In[14]:\n... | [
[
"matplotlib.pyplot.legend",
"pandas.to_datetime",
"sklearn.cross_validation.train_test_split",
"pandas.read_excel",
"matplotlib.pyplot.barh",
"matplotlib.pyplot.rc",
"sklearn.neighbors.KNeighborsClassifier",
"pandas.cut",
"matplotlib.pyplot.title",
"matplotlib.pyplot.pie",
... | [
{
"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": []
}
] |
giladcohen/darkon-examples | [
"29b0a880cc2ed7ac1b463e9e1e633ab521e04dc0"
] | [
"cifar10_resnet/hyper_parameters.py"
] | [
"# Coder: Wenxin Xu\n# Github: https://github.com/wenxinxu/resnet_in_tensorflow\n# ==============================================================================\nimport tensorflow as tf\n\nFLAGS = tf.app.flags.FLAGS\n\n## The following flags are related to save paths, tensorboard outputs and screen outputs\n\ntf.a... | [
[
"tensorflow.app.flags.DEFINE_boolean",
"tensorflow.app.flags.DEFINE_string",
"tensorflow.app.flags.DEFINE_integer",
"tensorflow.app.flags.DEFINE_float"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jinczing/AudioCLIP | [
"45327aa203839bfeb58681dd36c04fd493ee72f4"
] | [
"utils/transforms.py"
] | [
"import math\n\nimport numpy as np\n\nimport torch\nimport torchvision as tv\n\nimport ignite_trainer as it\n\n\ndef scale(old_value, old_min, old_max, new_min, new_max):\n old_range = (old_max - old_min)\n new_range = (new_max - new_min)\n new_value = (((old_value - old_min) * new_range) / old_range) + ne... | [
[
"torch.mean",
"torch.ones",
"torch.zeros",
"torch.rand",
"numpy.random.rand",
"torch.arange",
"torch.full_like",
"numpy.random.uniform",
"torch.log10",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zsyOAOA/VIRNet | [
"b66bea114d240f51a0c709fd132607c875c1ddc2"
] | [
"loss/loggamma_op.py"
] | [
"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Power by Zongsheng Yue 2020-03-30 14:59:29\n\nimport torch\nfrom scipy.special import gammaln\nfrom torch.autograd import Function as autoF\n\nclass LogGamma(autoF):\n '''\n Implement of the logarithm of gamma Function.\n '''\n @staticmethod\n def for... | [
[
"torch.digamma",
"torch.from_numpy",
"scipy.special.gammaln"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.18",
"0.19"
],
"tensorflow": []
}
] |
dhm-org/dhm_suite | [
"19f803fbe973bebf9bd63994e58ea94f475ba3f6"
] | [
"dhmsw/dhmsw/interface.py"
] | [
"\"\"\"\n###############################################################################\n# Copyright 2019, by the California Institute of Technology. ALL RIGHTS RESERVED.\n# United States Government Sponsorship acknowledged. Any commercial use must be\n# negotiated with the Office of Technology Transfer at the\... | [
[
"numpy.reshape",
"numpy.fromstring",
"numpy.dtype"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
idolumbantobing/vit-pytorch | [
"eb70d8dca041cc387b3e1f72d965d8814eeab29a"
] | [
"vit_pytorch/cct.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n# Pre-defined CCT Models\n__all__ = ['cct_2', 'cct_4', 'cct_6', 'cct_7', 'cct_8', 'cct_14', 'cct_16']\n\n\ndef cct_2(*args, **kwargs):\n return _cct(num_layers=2, num_heads=2, mlp_ratio=1, embedding_dim=128,\n *args, **kwargs... | [
[
"torch.nn.Dropout",
"torch.linspace",
"torch.sin",
"torch.cat",
"torch.nn.init.trunc_normal_",
"torch.zeros",
"torch.nn.init.constant_",
"torch.nn.Flatten",
"torch.nn.Conv2d",
"torch.nn.LayerNorm",
"torch.nn.Linear",
"torch.nn.Identity",
"torch.nn.MaxPool2d",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vikingMei/incubator-mxnet | [
"02f1e23a0cc36d3d6cf4eba4db7103b8967f9b8d"
] | [
"example/rnn/nce/src/loader/corpusiter.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n#\n# Usage: \n# Author: wxm71(weixing.mei@aispeech.com)\n\nimport pdb\nimport logging\nimport threading\nimport multiprocessing\n\nimport numpy as np\nimport mxnet as mx\n\nfrom .utils import batchify\n\n\nclass CorpusIter(mx.io.DataIter):\n def __init__(self, source, bat... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lechatquidanse/bicing-prediction-api | [
"cc71f9a57d1151fa768ad2b3769b9a4df77289f6"
] | [
"src/user_interface/rest/flask/response/FlaskPredictByStationIdResponse.py"
] | [
"\"\"\"\nIn charge of response handle for predictions by station\n\"\"\"\nfrom flask import jsonify\nfrom pandas import DataFrame, DatetimeIndex\n\nfrom application.use_case.query.StationAvailabilitiesPredictionByDateTimeInPeriodFilterView import \\\n StationAvailabilitiesPredictionByDateTimeInPeriodFilterView\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": []
}
] |
GidLev/cepy | [
"98fee44c9cb17e228c77b35f107702c0eaf0d8f0"
] | [
"cepy/embed_align.py"
] | [
"import numpy as np\nfrom collections.abc import Iterable\nimport copy\n\ndef align(base_ce, target_ce, base_index = 0, target_indices ='all'):\n '''\n Aligned connectome embeddings originated from independent fitting iteration\n\n Parameters\n ----------\n base_ce : CE\n Containes the latent ... | [
[
"numpy.linalg.pinv",
"numpy.dot"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zlinao/COMP5212-project1 | [
"fa6cb10d238de187fbb891499916c6b44a0cd7b7"
] | [
"neural_network.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 1 13:05:41 2018\n\n@author: lin\n\"\"\"\nimport numpy as np\nfrom sklearn.metrics import accuracy_score,confusion_matrix,classification_report,log_loss\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import GridSearchCV\nfr... | [
[
"sklearn.neural_network.MLPClassifier",
"sklearn.model_selection.GridSearchCV",
"sklearn.metrics.confusion_matrix",
"sklearn.metrics.log_loss",
"numpy.load",
"sklearn.metrics.classification_report",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
boronhub/tensorflow | [
"0c98d8b8e2971d312d46fdcb739e6ce333aafe5a"
] | [
"tensorflow/python/data/experimental/ops/readers.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.ops.dataset_ops.MapDataset",
"tensorflow.python.framework.dtypes.int64.as_numpy_dtype",
"tensorflow.python.ops.gen_experimental_dataset_ops.sql_dataset",
"tensorflow.python.data.ops.dataset_ops.get_legacy_output_types",
"tensorflow.python.framework.dtypes.int32.as_numpy... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.2",
"1.13",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
lantunes/deepdab | [
"0e30f102b9d7c37d3691540496b1649f2704d586"
] | [
"deepdab/util/plotter_multiple2.py"
] | [
"import matplotlib.pyplot as plt\nimport pylab\n\ndata = pylab.loadtxt('../out/plotdata.txt', delimiter=',', usecols=(0, 1, 2, 3, 4, 5, 6))\n\npylab.plot(data[:, 0], data[:, 1], linestyle='solid', linewidth=0.5, marker='.', markersize=2, label='L0', color='blue')\npylab.plot(data[:, 0], data[:, 2], linestyle='dashe... | [
[
"matplotlib.pyplot.legend",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
latasianguy/progressive-learning | [
"c15299cf3c87f431250722e7f682f4abb43b5c0c"
] | [
"proglearn/forest.py"
] | [
"\"\"\"\nMain Author: Will LeVine\nCorresponding Email: levinewill@icloud.com\n\"\"\"\nfrom .progressive_learner import ClassificationProgressiveLearner\nfrom .transformers import TreeClassificationTransformer\nfrom .voters import TreeClassificationVoter\nfrom .deciders import SimpleArgmaxAverage\nimport numpy as n... | [
[
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tensorflow-pool/insightface | [
"d27ad2d3e8b15a9abaddc86dc12c59437db6ee80"
] | [
"src/image_iter.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport logging\nimport numbers\nimport os\nimport random\nimport sys\nfrom io import BytesIO\n\nimport mxnet as mx\nimport numpy as np\nfrom PIL import Image\nfrom mxnet import io\nfrom mxnet import nd... | [
[
"numpy.asarray",
"numpy.fliplr",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
htung0101/pytorch-dense-correspondence | [
"5a6eb07e4fec63d0d24a25712c0094255fb310e3"
] | [
"dense_correspondence/correspondence_tools/correspondence_finder.py"
] | [
"# torch\nimport torch\n\n# system\nimport numpy as numpy\nimport numpy as np\nfrom numpy.linalg import inv\nimport random\nimport warnings\n\n# io\nfrom PIL import Image\n\n# torchvision\nimport sys\nsys.path.insert(0, '../pytorch-segmentation-detection/vision/') # from subrepo\nfrom torchvision import transforms\... | [
[
"numpy.dot",
"torch.abs",
"torch.cat",
"torch.ones",
"torch.from_numpy",
"numpy.copy",
"torch.rand",
"torch.nonzero",
"torch.index_select",
"numpy.zeros",
"torch.ones_like",
"torch.squeeze",
"torch.LongTensor",
"torch.floor",
"numpy.linalg.inv",
"tor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
UFSCar-CS/operating-systems-2 | [
"c966ce65cfcc24697dbf114b4af29609f46a4fdd"
] | [
"projects/quick-parallel/tests/quick_parallel_tests.py"
] | [
"from unittest import TestCase\n\nimport time\nimport numpy as np\nfrom numpy import testing\n\nfrom quick_parallel import Sort\n\n\nclass QuickParallelTest(TestCase):\n def test_is_quick_sorting_correctly(self):\n for i in range(1, 1001, 100):\n x = np.random.rand(i)\n sorted_x = So... | [
[
"numpy.random.rand",
"numpy.sort"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tanglef/keops | [
"58b2c5f7822a7468a6da2ce439939e7dad04d7f3"
] | [
"pykeops/torch/cluster/matrix.py"
] | [
"import torch\n\ndef from_matrix( ranges_i, ranges_j, keep ) :\n r\"\"\"Turns a boolean matrix into a KeOps-friendly **ranges** argument.\n\n This routine is a helper for the **block-sparse** reduction mode of KeOps,\n allowing you to turn clustering information (**ranges_i**,\n **ranges_j**) and a clus... | [
[
"torch.arange",
"torch.Tensor",
"torch.IntTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
milot-mirdita/alphafold | [
"8a24cc8d22feb2b7ae9bf78f910ce97e9d4403e8",
"8a24cc8d22feb2b7ae9bf78f910ce97e9d4403e8"
] | [
"alphafold/model/all_atom_test.py",
"alphafold/model/tf/input_pipeline.py"
] | [
"# Copyright 2021 DeepMind Technologies Limited\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 applic... | [
[
"numpy.expand_dims",
"numpy.ones_like",
"numpy.asarray",
"numpy.ones",
"numpy.deg2rad",
"numpy.zeros_like",
"numpy.array",
"numpy.zeros"
],
[
"tensorflow.compat.v1.constant",
"tensorflow.compat.v1.range"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
desam47/Machine_Learning_Project-2 | [
"f7ab069179879650859467f451b9997a495833a2"
] | [
"Adam.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# https://github.com/duaraanalytics/bankmarketing/blob/master/Analyzing%20Employee%20Churn%20with%20Keras.ipynb\n\n\"\"\"\nCreated on Wed Mar 6 08:51:19 2019\n\n@author: dipesh\n\"\"\"\n\n# Import necessary libraries\n\nimport numpy as np\nimport pandas as pd\nim... | [
[
"matplotlib.pyplot.legend",
"pandas.read_csv",
"sklearn.model_selection.cross_val_score",
"matplotlib.pyplot.title",
"pandas.get_dummies",
"numpy.set_printoptions",
"sklearn.metrics.accuracy_score",
"sklearn.model_selection.train_test_split",
"sklearn.metrics.confusion_matrix",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chnghia/pytorch-lightning-gan | [
"3e484ea8eca2a3365a6b209a979a95a5b2e4a6f2"
] | [
"models/pix2pixHD_model.py"
] | [
"import os\nfrom argparse import ArgumentParser, Namespace\nfrom collections import OrderedDict\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\nimport torchvision.transforms as transforms\nfrom torch.optim import lr_scheduler\nfrom torch.autograd impo... | [
[
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mys007/awesome-semantic-segmentation-pytorch | [
"fa83563d45ce85a9deb0284de85e75b5aa24883a"
] | [
"tests/test_model.py"
] | [
"\"\"\"Model overfitting test\"\"\"\nimport argparse\nimport time\nimport os\nimport sys\n\nimport torch\nimport torch.nn as nn\nimport torch.backends.cudnn as cudnn\nimport numpy as np\n\ncur_path = os.path.abspath(os.path.dirname(__file__))\nroot_path = os.path.split(cur_path)[0]\nsys.path.append(root_path)\n\nfr... | [
[
"numpy.array",
"torch.from_numpy",
"torch.cuda.is_available",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RobertArbon/NMpathAnalysis | [
"f428d6b11ebefcf080de5737448963ed251b18c2"
] | [
"nmpath/ensembles.py"
] | [
"#!/usr/bin/env python\n\nimport numpy as np\nfrom copy import deepcopy\nimport networkx as nx\nfrom math import log\n\nfrom interval import Interval\nfrom auxfunctions import get_shape, weighted_choice\nfrom auxfunctions import reverse_sort_lists\nfrom mfpt import directional_mfpt\nfrom mfpt import direct_mfpts\n\... | [
[
"numpy.random.uniform",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kuronekodaisuki/pytorch | [
"459270ac01f1bfcbeaffb20f1c94622561af94e0"
] | [
"test/test_quantization.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom torch.testing._internal.common_utils import run_tests\n\n# Quantization core tests. These include tests for\n# - quantized kernels\n# - quantized functional operators\n# - quantized workflow modules\n# - quantized workflow operators\n# - quantized tensor\n\n# 1. Quantized Kernels\n#... | [
[
"torch.testing._internal.common_utils.run_tests"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ForrestHurley/compPhys | [
"34c2d93b77858150a1c099deff812d961ab6378d"
] | [
"hmwk/0/4.py"
] | [
"import numpy as np\nfrom matplotlib import pyplot as plt\nfrom abc import ABC, abstractmethod\n\nclass integrator(ABC):\n def __init__(self,\n minimum = 0,\n maximum = 1,\n steps = 10):\n self.minimum = minimum\n self.maximum = maximum\n self.steps = steps\n self... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.cumsum",
"numpy.sin",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Zabamund/misc | [
"e1a2bec1b8e36b039807ec02c53ee3970bb4e255"
] | [
"scripts/demos/activation_funcs.py"
] | [
"#!/bin/python\n\nimport numpy as np\n\ndef binary_step(x):\n return np.where(x < 0, 0, 1)\n\ndef binary_step_(x):\n return np.where(x != 0, 0, np.inf)\n\ndef linear(x, a=1):\n return a * x\n\ndef linear_(x, a=1):\n return a\n\ndef sigmoid(x):\n return 1 / (1 + np.exp(-x))\n \ndef sigmoid_(x):\n ... | [
[
"numpy.exp",
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Pimax1/DrQA | [
"a4940b38c3491d5e9a06f48ef90ea3ab65019e98"
] | [
"scripts/pipeline/interactive.py"
] | [
"#!/usr/bin/env python3\n# Copyright 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\"\"\"Interactive interface to full DrQA pipeline.\"\"\"\n\nimport torch\nimport argparse\nimport co... | [
[
"torch.cuda.set_device",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
forkbabu/BS-Nets-Implementation-Pytorch | [
"5375456639f18ef25915df5a2e368b922fdf2aaf"
] | [
"global_module/train.py"
] | [
"import time\nimport torch\nimport numpy as np\nimport sys\nsys.path.append('../global_module/')\nimport d2lzh_pytorch as d2l\n\n\ndef evaluate_accuracy(data_iter, net, loss, device):\n acc_sum, n = 0.0, 0\n with torch.no_grad():\n for X, y in data_iter:\n test_l_sum, test_num = 0, 0\n ... | [
[
"torch.no_grad",
"torch.optim.lr_scheduler.CosineAnnealingLR",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
edeneault/pyfe_capstone_hikingtrails | [
"849328a47095933f2e8ce8a8a5a0d56d04dd3807"
] | [
"visualpass.py"
] | [
"################################################################################\r\n# #### visualpass.py ##### #\r\n# #### written by: Etienne Deneault ##### #\r\n###############################################################... | [
[
"pandas.read_sql_query",
"pandas.read_excel",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
degtiarev/hexapod | [
"71c451e13b534e798d8dc02b0478a9660e97d6b7",
"71c451e13b534e798d8dc02b0478a9660e97d6b7",
"71c451e13b534e798d8dc02b0478a9660e97d6b7"
] | [
"examples/pybullet/gym/pybullet_envs/bullet/minitaur_gym_env.py",
"examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py",
"examples/pybullet/gym/pybullet_envs/robot_pendula.py"
] | [
"\"\"\"This file implements the gym environment of minitaur.\n\n\"\"\"\n\nimport os, inspect\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparentdir = os.path.dirname(os.path.dirname(currentdir))\nos.sys.path.insert(0,parentdir)\n\n\nimport math\nimport time\nimport gym\nf... | [
[
"numpy.asarray",
"numpy.random.normal",
"numpy.array"
],
[
"numpy.reshape",
"numpy.array",
"numpy.finfo"
],
[
"numpy.clip",
"numpy.cos",
"numpy.isfinite",
"numpy.sin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CharlieKC/ACE | [
"ba8909e49dd5408881684f080f2de81b6ec224c2"
] | [
"flair/trainers/finetune_trainer.py"
] | [
"\"\"\"\r\nFine-tune trainer: a trainer for finetuning BERT and able to be parallelized based on flair\r\nAuthor: Xinyu Wang\r\nContact: wangxy1@shanghaitech.edu.cn\r\n\"\"\"\r\n\r\nfrom .distillation_trainer import *\r\nfrom transformers import (\r\n\tAdamW,\r\n\tget_linear_schedule_with_warmup,\r\n)\r\nfrom flair... | [
[
"torch.optim.lr_scheduler.LambdaLR",
"torch.utils.tensorboard.SummaryWriter",
"torch.optim.lr_scheduler.ExponentialLR"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Guangyi-Zhang/low-expected-cost-decision-trees | [
"745138d4c6637dc5fc5fe4fa13de7de0e09611e2"
] | [
"util.py"
] | [
"import numpy as np\nfrom itertools import groupby, accumulate\nfrom collections import Counter\n\n########## Incense ##########\nfrom incense import ExperimentLoader\n\n# Try to locate config file for Mongo DB\nimport importlib\nspec = importlib.util.find_spec('mongodburi')\nif spec is not None:\n from mongodbu... | [
[
"numpy.arange",
"numpy.sort",
"numpy.random.rand",
"numpy.bincount",
"numpy.argsort",
"numpy.array",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
db12138/Online_Courses_and_Materials | [
"6a113056f4fd2667556942b3bcc9608bdf9c2968"
] | [
"Stanford_CS224n NLP with Deep Learning/lec6/tensorflow_toturial_code/word2vec_starter.py"
] | [
"\"\"\" starter code for word2vec skip-gram model with NCE loss\nCS 20: \"TensorFlow for Deep Learning Research\"\ncs20.stanford.edu\nChip Huyen (chiphuyen@cs.stanford.edu)\nLecture 04\n\"\"\"\n\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL']='2'\n\nimport numpy as np\nfrom tensorflow.contrib.tensorboard.plugins imp... | [
[
"tensorflow.TensorShape",
"tensorflow.summary.FileWriter",
"tensorflow.random_uniform_initializer",
"tensorflow.zeros",
"tensorflow.truncated_normal_initializer",
"tensorflow.global_variables_initializer",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.Session",
"tens... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
chrmertz/synth_train_data | [
"181fca14ed7b92953ff977cc3a7924311d145a97",
"181fca14ed7b92953ff977cc3a7924311d145a97"
] | [
"syndata-generation/dataset_generator.py",
"traffic_sign/tools/im2mask.py"
] | [
"import argparse\nimport glob\nimport sys\nimport os\nfrom xml.etree.ElementTree import Element, SubElement, tostring\nimport xml.dom.minidom\nimport cv2\nimport numpy as np\nimport random\nfrom PIL import Image\nfrom PIL import ImageEnhance\nfrom PIL import ImageFilter\nimport PIL.ImageOps \nimport scipy\nfrom ... | [
[
"numpy.linspace",
"numpy.any",
"numpy.array",
"numpy.where",
"numpy.zeros"
],
[
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
amanjha8100/alibi-detect | [
"13b2664dca601aebc25c774fd6814bd5d380d6e7"
] | [
"alibi_detect/utils/fetching.py"
] | [
"import cloudpickle as cp\nimport logging\nimport os\nimport pickle\nimport tensorflow as tf\nfrom tensorflow.python.keras import backend\nfrom typing import Tuple, Union\nfrom urllib.request import urlopen\nfrom alibi_detect.base import BaseDetector\nfrom alibi_detect.ad import AdversarialAE, ModelDistillation\nfr... | [
[
"tensorflow.keras.models.load_model",
"tensorflow.keras.utils.get_file"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
navn-r/scikit-learn | [
"24babc68d510a827ced4159f313edf8488f41b15"
] | [
"sklearn/ensemble/_hist_gradient_boosting/tests/test_binning.py"
] | [
"import numpy as np\nimport scipy.sparse as sp\nfrom numpy.testing import assert_array_equal, assert_allclose\nimport pytest\n\nfrom sklearn.ensemble._hist_gradient_boosting.binning import (\n _BinMapper,\n _find_binning_thresholds,\n _map_to_bins,\n)\nfrom sklearn.ensemble._hist_gradient_boosting.common i... | [
[
"numpy.linspace",
"numpy.all",
"numpy.zeros_like",
"sklearn.ensemble._hist_gradient_boosting.binning._map_to_bins",
"sklearn.ensemble._hist_gradient_boosting.binning._BinMapper",
"numpy.allclose",
"numpy.unique",
"sklearn.utils._openmp_helpers._openmp_effective_n_threads",
"num... | [
{
"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"... |
Silicon-He/Speech-enhancement-and-separation | [
"7524941c19f3950b88abab6dc74edd26541d325b"
] | [
"speech_separation_RNN.py"
] | [
"# -*- coding = utf-8 -*-\r\n# @Author:何欣泽\r\n# @Time:2020/10/18 19:14\r\n# @File:speech_separation_IRM.py\r\n# @Software:PyCharm\r\n\r\n\r\nimport librosa\r\nimport librosa.display\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom tensorflow.keras.models import load_model\r\n\r\n\r\ndef get_audio_se... | [
[
"tensorflow.keras.models.load_model",
"numpy.abs",
"numpy.multiply",
"numpy.reshape",
"numpy.shape",
"numpy.angle",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
sumersumerdjl/kozistr-Awesome-GANs | [
"9add33fdfcb9fead75c37dd7afdbede625a303c9"
] | [
"AdaGAN/adagan_model.py"
] | [
"import tensorflow as tf\n\nimport sys\n\nsys.path.append('../')\nimport tfutil as t\n\n\ntf.set_random_seed(777) # reproducibility\n\n\nclass AdaGAN:\n\n def __init__(self, s, batch_size=64, height=28, width=28, channel=1, n_classes=10,\n sample_num=64, sample_size=8,\n n_input=... | [
[
"tensorflow.summary.FileWriter",
"tensorflow.nn.sigmoid",
"tensorflow.reshape",
"tensorflow.placeholder",
"tensorflow.trainable_variables",
"tensorflow.summary.merge_all",
"tensorflow.variable_scope",
"tensorflow.train.AdamOptimizer",
"tensorflow.set_random_seed",
"tensorfl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
XianyuanLiu/EPIC-KITCHENS-100_UDA_TA3N | [
"30e1c494eadb165e5c71c6f89406707d58153308"
] | [
"dataset.py"
] | [
"import torch.utils.data as data\n\nimport os\nimport os.path\nimport numpy as np\nfrom numpy.random import randint\nimport torch\nimport pickle\nimport pandas as pd\nfrom colorama import init\nfrom colorama import Fore, Back, Style\n\ninit(autoreset=True)\n\n\nclass VideoRecord(object):\n def __init__(self, i, ... | [
[
"numpy.expand_dims",
"numpy.ones",
"numpy.append",
"torch.stack",
"pandas.read_pickle",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"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": []
}
] |
cswarth/whylogs | [
"6805b252f1d07efde84836d3924949f7ec2d97b1",
"6805b252f1d07efde84836d3924949f7ec2d97b1"
] | [
"src/whylogs/core/datasetprofile.py",
"tests/unit/app/test_segments.py"
] | [
"\"\"\"\nDefines the primary interface class for tracking dataset statistics.\n\"\"\"\nimport datetime\nimport io\nimport logging\nfrom typing import Dict, List, Mapping, Optional, Union\nfrom uuid import uuid4\n\nimport numpy as np\nimport pandas as pd\nfrom google.protobuf.internal.decoder import _DecodeVarint32\... | [
[
"numpy.ndim",
"numpy.arange",
"numpy.asanyarray",
"pandas.DataFrame"
],
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
bluebibi/rl_book_codes | [
"ef7fc9993eb66618e4b4e80e59cc2879a8db3522"
] | [
"chapter_11/REINFORCE.py"
] | [
"import torch\nimport gym\nimport numpy as np\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nimport matplotlib.pyplot as plt\n\n# Constants\nGAMMA = 0.99\n\n\nclass PolicyNetwork(nn.Module):\n def __init__(self, num_inputs, num_actions, ... | [
[
"torch.from_numpy",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"torch.nn.Linear",
"matplotlib.pyplot.ylabel",
"torch.autograd.Variable",
"torch.FloatTensor",
"matplotlib.pyplot.grid",
"torch.stack",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.rcParams.upd... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
agorshk/daal4py | [
"58a9b2301c47cd2d5144a403a59c210e10b75f8f"
] | [
"examples/multivariate_outlier_batch.py"
] | [
"\n#*******************************************************************************\n# Copyright 2014-2020 Intel Corporation\n# All Rights Reserved.\n#\n# This software is licensed under the Apache License, Version 2.0 (the\n# \"License\"), the following terms apply:\n#\n# You may not use this file except in compli... | [
[
"pandas.read_csv",
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lurosenb/Swap_Auditor | [
"63f1ba22a1c834ad2cb961e5e25b9c3a36c12cec"
] | [
"fairness/data/objects/ProcessedData.py"
] | [
"import pandas as pd\nimport numpy\nimport numpy.random\n\nTAGS = [\"original\"]#, \"numerical\", \"numerical-binsensitive\", \"categorical-binsensitive\"]\nTRAINING_PERCENT = 2.0 / 3.0\n\nclass ProcessedData():\n def __init__(self, data_obj):\n self.data = data_obj\n self.dfs = dict((k, pd.read_cs... | [
[
"numpy.arange",
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nestauk/mapping_parenting_tech | [
"17ae403e2eb6be7c73092bc49bc62f9a24fdf0de",
"17ae403e2eb6be7c73092bc49bc62f9a24fdf0de"
] | [
"mapping_parenting_tech/analysis/prototyping/visualisation_app_landscape.py",
"mapping_parenting_tech/analysis/prototyping/archived/clustering_reviews.py"
] | [
"# ---\n# jupyter:\n# jupytext:\n# cell_metadata_filter: -all\n# comment_magics: true\n# formats: ipynb,py:percent\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.13.8\n# kernelspec:\n# display_name: Py... | [
[
"pandas.read_csv",
"numpy.random.random",
"numpy.sqrt",
"sklearn.cluster.KMeans",
"numpy.random.seed",
"scipy.spatial.distance.cdist",
"pandas.DataFrame",
"numpy.round",
"numpy.argmax",
"pandas.read_json"
],
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"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... |
italogsfernandes/emg-moviments-classifier | [
"7a58f162fa6c5bc1a2906c539dac3e0210115522"
] | [
"python-hand-movements-classifier/convert_database_to_new_format.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCoding at 6:48 and listening to:\n Rock is Dead - Marylin Manson\n Dance D'Amour - The 69 Eyes\n Wake Up - Rage Against the Machine\n Clubbed to Death - Robert D.\n@author: italo\n\"\"\"\n#%% Importing the libraries\nimport pandas as pd # reading... | [
[
"pandas.concat",
"numpy.arange",
"matplotlib.pyplot.ylim",
"pandas.DataFrame",
"numpy.ones",
"matplotlib.pyplot.plot",
"pandas.read_table",
"matplotlib.pyplot.subplot",
"numpy.diff",
"matplotlib.pyplot.grid",
"numpy.array",
"numpy.zeros",
"matplotlib.pyplot.show... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
dolcos/driverlessai-recipes | [
"e1eb2787daeaae387d80ce24d4d3398c4fddb585"
] | [
"models/mli/model_gam.py"
] | [
"\"\"\"Generalized Additive Model\"\"\"\n\nimport uuid\nimport os\nimport datatable as dt\nimport numpy as np\nfrom h2oaicore.models import CustomModel\nfrom sklearn.preprocessing import LabelEncoder\nfrom h2oaicore.systemutils import physical_cores_count\nfrom h2oaicore.systemutils import user_dir, remove, config,... | [
[
"numpy.isin",
"matplotlib.pyplot.title",
"numpy.random.choice",
"numpy.isnan",
"sklearn.preprocessing.OneHotEncoder",
"pandas.DataFrame",
"numpy.array",
"sklearn.preprocessing.LabelEncoder",
"numpy.isinf",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
ii-research-ranking/ptranking | [
"2794e6e086bcd87ce177f40194339e9b825e9f4c"
] | [
"ptranking/ltr_adhoc/listwise/st_listnet.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"Description\nauthor = {Bruch, Sebastian and Han, Shuguang and Bendersky, Michael and Najork, Marc},\ntitle = {A Stochastic Treatment of Learning to Rank Scoring Functions},\nyear = {2020},\nbooktitle = {Proceedings of the 13th International Conference on Web ... | [
[
"torch.nn.functional.softmax",
"torch.log",
"torch.nn.functional.log_softmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
williamthegrey/ml-agents | [
"c75eacbdc40f9afffaedfd79d861b83a88cf3b8d"
] | [
"ml-agents/mlagents/trainers/ppo/trainer.py"
] | [
"# # Unity ML-Agents Toolkit\n# ## ML-Agent Learning (PPO)\n# Contains an implementation of PPO as described in: https://arxiv.org/abs/1707.06347\n\nfrom collections import defaultdict\nfrom typing import cast\n\nimport numpy as np\n\nfrom mlagents_envs.logging_util import get_logger\nfrom mlagents_envs.base_env im... | [
[
"numpy.append",
"numpy.mean",
"numpy.zeros_like",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Azmarie/COVID-Doctor-Chatbot | [
"e516fc9b722713b450b73c6c1d004977b2e265b1"
] | [
"Transformer/transformer_perplexity.py"
] | [
"import torch\nimport torch.nn.functional as F\nfrom torch.utils.data import TensorDataset, DataLoader\nimport numpy as np\nfrom transformers import AdamW, get_linear_schedule_with_warmup\nfrom transformers_model import transformers_model\n\nimport fire\nimport time\nimport os\n\n# uses allennlp modules\nfrom allen... | [
[
"torch.load",
"torch.utils.data.TensorDataset",
"torch.utils.data.DataLoader",
"torch.no_grad",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
isLinXu/DatasetMarkerTool | [
"b324315ed5c679f9dc764a6b1d88be246ee7f2db"
] | [
"dataset/core/tools/crop/crop_bounds.py"
] | [
"import argparse\nimport cv2\nimport numpy as np\nimport os\nimport shutil\n\ndef crop_image_only_outside(img,tol=0):\n # img is 2D or 3D image data\n # tol is tolerance\n mask = img>tol\n if img.ndim==3:\n mask = mask.all(2)\n m,n = mask.shape\n mask0,mask1 = mask.any(0),mask.any(1)\n ... | [
[
"numpy.int0",
"numpy.median",
"numpy.ones",
"numpy.array",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
moldis-group/DesignBODIPY | [
"92d06bc7fdb070760588e96e6c8c1b5f6359636c",
"92d06bc7fdb070760588e96e6c8c1b5f6359636c"
] | [
"GenerateSLATM.py",
"DesignBodipy_Bayes.py"
] | [
"from qml.representations import get_slatm_mbtypes\nfrom qml import Compound\nimport numpy as np\n\n\nclass GenerateSLATM:\n \"\"\"\n Simple class wrapper to initialize qml SLATM routines\n call dunder overloaded for generating slatm descriptor\n \"\"\"\n\n def __init__(self):\n # Declare mbty... | [
[
"numpy.array"
],
[
"numpy.abs",
"sklearn.gaussian_process.kernels.RBF",
"numpy.random.choice",
"scipy.stats.norm.cdf",
"scipy.stats.norm.pdf",
"numpy.squeeze",
"numpy.max",
"sklearn.gaussian_process.GaussianProcessRegressor",
"scipy.optimize.minimize",
"numpy.random... | [
{
"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",
... |
aaysan/facenet | [
"ba0d690e137b89c5d76f22bcc7a12deab98325d9"
] | [
"src/facenet.py"
] | [
"\"\"\"Functions for building the face recognition network.\n\"\"\"\n# MIT License\n#\n# Copyright (c) 2016 David Sandberg\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 without ... | [
[
"numpy.sqrt",
"tensorflow.control_dependencies",
"tensorflow.cast",
"sklearn.model_selection.KFold",
"numpy.concatenate",
"numpy.max",
"tensorflow.train.ExponentialMovingAverage",
"numpy.mean",
"tensorflow.image.decode_image",
"tensorflow.train.AdamOptimizer",
"tensorfl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.19",
"0.18",
"1.2",
"0.12",
"1.0",
"0.17",
"0.16"
],
"tensorflow": []
}
] |
mcgill-cpslab/spiral | [
"22290a0684f8f1f276db2b05569452fbbe0e125d"
] | [
"nineturn/dtdg/models/decoder/torch/sequentialDecoder/rnnFamily.py"
] | [
"# Copyright 2022 The Nine Turn 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 requi... | [
[
"torch.randn",
"torch.nn.GRU",
"torch.nn.RNN",
"torch.nn.LSTM"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mreitm/neurokernel | [
"8195a500ba1127f719e963465af9f43d6019b884"
] | [
"neurokernel/pattern.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"\nRepresent connectivity pattern using pandas DataFrame.\n\"\"\"\n\nfrom collections import OrderedDict\nimport itertools\nimport re\n\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\n\nfrom plsel import Selector, SelectorMethods\nfrom pm import BasePortMapper\n\nclas... | [
[
"pandas.merge",
"pandas.read_csv",
"pandas.DataFrame.from_csv",
"pandas.isnull",
"pandas.MultiIndex",
"pandas.MultiIndex.from_tuples",
"pandas.DataFrame",
"numpy.isscalar",
"numpy.iterable"
]
] | [
{
"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": []
}
] |
xdr940/TSLa | [
"97f8626cb29e956f77fc34d84100771ab84bea52"
] | [
"components/measurement.py"
] | [
"\nimport pandas as pd\nimport numpy as np\nfrom collections import Counter\nimport matplotlib.pyplot as plt\nimport math\n\n\nclass Measurer:\n def __init__(self,procedures):\n self.procedures = procedures\n self.alg_cnt = Counter([item[4:] for item in list(self.procedures.keys())])\n\n\n def ... | [
[
"numpy.array",
"numpy.ones_like",
"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": []
}
] |
Lion-HuangGz/CS231nAssignment | [
"9adfdeda1c892c468c7bab9661fe8eb4d1063510"
] | [
"assignment1/cs231n/classifiers/linear_svm.py"
] | [
"import numpy as np\n\n\ndef svm_loss_naive(W, X, y, reg):\n \"\"\"\n Structured SVM loss function, naive implementation (with loops).\n\n Inputs have dimension D, there are C classes, and we operate on minibatches\n of N examples.\n\n Inputs:\n - W: A numpy array of shape (D, C) containing weight... | [
[
"numpy.arange",
"numpy.zeros",
"numpy.sum",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stlukyanenko/mne-python | [
"89647f3363fcb5de306cc18e55e7b9fa89fe0315",
"89647f3363fcb5de306cc18e55e7b9fa89fe0315",
"89647f3363fcb5de306cc18e55e7b9fa89fe0315",
"89647f3363fcb5de306cc18e55e7b9fa89fe0315"
] | [
"mne/io/proj.py",
"mne/forward/_field_interpolation.py",
"mne/time_frequency/tfr.py",
"mne/rank.py"
] | [
"# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# Matti Hämäläinen <msh@nmr.mgh.harvard.edu>\n# Denis Engemann <denis.engemann@gmail.com>\n# Teon Brooks <teon.brooks@gmail.com>\n#\n# License: BSD (3-clause)\n\nfrom copy import deepcopy\nfrom itertools import count\nfrom math... | [
[
"numpy.dot",
"scipy.linalg.svd",
"numpy.unique",
"numpy.eye",
"numpy.ones",
"numpy.all",
"numpy.array",
"numpy.zeros",
"numpy.sum",
"numpy.empty"
],
[
"numpy.logical_not",
"numpy.dot",
"scipy.linalg.svd",
"numpy.linspace",
"numpy.cumsum",
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0... |
amirsaleh-salehzadeh/BehaveNet | [
"1dcdfa3d9efd022f29f37495be15f9657c5e7374"
] | [
"DeepEEGSingleElectrode/models/HAR_CNN/HAR_CNN.py"
] | [
"import sys\nsys.path.insert(0, \"/home/cirl/Amir/Human-Activity-EEG-Accelerometer\")\nimport numpy as np\nimport os\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Activation, Dropout, Flatten\nimport time\n\nimport tensorflow as tf\nimport random as rn\nfrom keras import backend as K, o... | [
[
"tensorflow.ConfigProto",
"tensorflow.get_default_graph",
"numpy.random.seed",
"tensorflow.set_random_seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
KumarLabJax/deep-hrnet-mouse | [
"13014885861f87bf9fd5a99afdfe3153cca852a5",
"13014885861f87bf9fd5a99afdfe3153cca852a5"
] | [
"lib/core/evaluate.py",
"tools/rendervidoverlay.py"
] | [
"# ------------------------------------------------------------------------------\n# Copyright (c) Microsoft\n# Licensed under the MIT License.\n# Written by Bin Xiao (Bin.Xiao@microsoft.com)\n# ------------------------------------------------------------------------------\n\nfrom __future__ import absolute_import\... | [
[
"numpy.less",
"numpy.linalg.norm",
"numpy.ones",
"numpy.not_equal",
"numpy.array",
"numpy.zeros"
],
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sonsus/vtt_qa_pipeline | [
"23fc8e19b094c76113ac91d7b49212246984f00f"
] | [
"startup/model/temporal_graph.py"
] | [
"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nfrom .modules import Conv1dIn, MultiHeadAttention\nfrom .acc_model import Encoder, Decoder\n\n\nclass TemporalGraph(nn.Module):\n def __init__(self, vocab, n_dim, image_dim, layers, dropout):\n super().__init__()\n\n self.vocab... | [
[
"torch.nn.Linear",
"torch.nn.functional.relu",
"torch.nn.Embedding",
"torch.nn.LayerNorm"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AmitNikhade/Vision-Transformer | [
"f8d5b02670ca7d3f9ec365c6944a9411c70efec8"
] | [
"src/test.py"
] | [
"import torch\nimport sys\nimport matplotlib.pyplot as plt\nimport numpy as np\nsys.path.append(\"VIT/src/utils\")\nfrom utils.preprocess import test_loader\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel=torch.load('VIT/src/data/model_weight_MNIST.pt')\nmodel.eval()\n\n\n\npredi... | [
[
"torch.max",
"torch.load",
"torch.no_grad",
"torch.cuda.is_available",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ZHTushar23/breaching | [
"3e4d336ed3c631b8bf70974a9a5e1f923ddb039a",
"3e4d336ed3c631b8bf70974a9a5e1f923ddb039a"
] | [
"breaching/attacks/auxiliaries/conv2circulant.py",
"breaching/attacks/auxiliaries/make_functional.py"
] | [
"\"\"\"This is the file conv2circulant from https://github.com/JunyiZhu-AI/R-GAP/blob/main/conv2circulant.py\"\"\"\n\nimport numpy as np\n\n\ndef generate_coordinates(x_shape, kernel, stride, padding):\n assert len(x_shape) == 4\n assert len(kernel.shape) == 4\n assert x_shape[1] == kernel.shape[1]\n k_... | [
[
"numpy.array",
"numpy.zeros"
],
[
"torch.stack",
"torch.nn.Parameter",
"torch.empty_like"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alexisperakis/arviz | [
"edfbcf9c177f6be25139e20317400133ab1180ed"
] | [
"arviz/plots/backends/bokeh/distplot.py"
] | [
"\"\"\"Bokeh Distplot.\"\"\"\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom ....stats.density_utils import get_bins, histogram\nfrom ...kdeplot import plot_kde\nfrom ...plot_utils import _scale_fig_size, set_bokeh_circular_ticks_labels, vectorized_to_hex\nfrom .. import show_layout\nfrom . import back... | [
[
"numpy.asarray",
"numpy.deg2rad",
"numpy.diff",
"numpy.cumsum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
wqi18433/text-to-text-transfer-transformer | [
"56fbff6d26af1ded529f598dd4eaa4a506002e32"
] | [
"t5/data/preprocessors.py"
] | [
"# Copyright 2020 The T5 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 applicable law or agr... | [
[
"tensorflow.compat.v1.not_equal",
"tensorflow.compat.v1.logical_and",
"tensorflow.compat.v1.random.uniform",
"tensorflow.compat.v1.strings.regex_replace",
"tensorflow.compat.v1.equal",
"tensorflow.compat.v1.concat",
"tensorflow.compat.v1.logging.warn",
"tensorflow.compat.v1.strings... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
daniel-zeng/SegSort | [
"7a50e6253df23a7719f962b34acff2626c916354"
] | [
"pyscripts/inference/prototype_embedding_rgb.py"
] | [
"from __future__ import print_function\n\nimport argparse\nimport os\nimport time\nimport math\nfrom tqdm import tqdm\n\nimport tensorflow as tf\nimport numpy as np\nimport scipy.io\nimport scipy.misc\nimport network.vmf.common_utils as common_utils\nimport network.vmf.eval_utils as eval_utils\nfrom PIL import Imag... | [
[
"tensorflow.concat",
"numpy.linspace",
"tensorflow.global_variables",
"numpy.concatenate",
"tensorflow.ConfigProto",
"tensorflow.gather",
"tensorflow.name_scope",
"tensorflow.Session",
"tensorflow.train.Saver",
"numpy.zeros",
"tensorflow.image.resize_bilinear",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
low-sky/otfmap | [
"d9b6b776cff36931a527f5066bc4e036f7f62955"
] | [
"setup.py"
] | [
"from distutils.core import setup\nfrom Cython.Build import cythonize\nimport numpy as np\n\nsetup(\n name = \"On-the-Fly Gridder\",\n ext_modules = cythonize(\"src/*.pyx\", include_path = [np.get_include()]),\n include_dirs = [np.get_include()]\n)\n"
] | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Savioor/pepsiWorkshop2 | [
"b2cfcd80381d068d927dda3ef01d31592a63df72"
] | [
"question_4.py"
] | [
"from rocket_pather import *\nfrom constants import *\nimport math\nimport matplotlib.pyplot as plt\n\nif __name__ == '__main__':\n data = RocketData(0, 0, ROCKET_VEL, 0, 0)\n theta_50_rocket = RocketData(0, 0, ROCKET_VEL, math.radians(50), 0)\n theta_70_rocket = RocketData(0, 0, ROCKET_VEL, math.radians(7... | [
[
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
julimueller/dtld_parsin | [
"f9575de42a1951017db44fcf07584b4ef143b397"
] | [
"python/dtld_parsing/calibration.py"
] | [
"from __future__ import print_function\n\n__author__ = \"Andreas Fregin, Julian Mueller and Klaus Dietmayer\"\n__maintainer__ = \"Julian Mueller\"\n__email__ = \"julian.mu.mueller@daimler.com\"\n\nimport yaml\nimport numpy as np\n\n\nclass IntrinsicCalibration:\n \"\"\"\n Intrinsic calibration\n\n Attribut... | [
[
"numpy.reshape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NRathod-tech/ga-learner-dst-repo | [
"b3c6b545e08951388c12c3fb2a24ed029daee2c3"
] | [
"Superhero-Statistics-data/code.py"
] | [
"# --------------\n#Header files\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\n#Reading of the file\r\ndata=pd.read_csv(path)\r\ndata.head()\r\ndata.isnull().sum()\r\n# Code starts here\r\ndata['Gender'].value_counts()\r\ndata['Gender'] = data['Gender'].replace('-', 'Age... | [
[
"pandas.read_csv",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
JasonLiJT/SF2-image-processing-python | [
"1752382c43986ed49f2d835ce147b6aa3110b14f"
] | [
"SF2_Python/helpers_jpeg.py"
] | [
"# Authors:\n# Jason Li - jl944@cam.ac.uk,\n# Karthik Suresh - ks800@cam.ac.uk\n# 2019 SF2 Group 7\n\nimport numpy as np\nfrom helpers import *\nimport scipy.io as sio\n\n############# Initial provided methods #############\n\n\nclass HuffmanHelper:\n '''Base Helper class for Huffman coding'''\n\n def __init_... | [
[
"numpy.concatenate",
"numpy.max",
"numpy.round",
"numpy.all",
"numpy.argmin",
"numpy.any",
"numpy.where",
"numpy.square",
"numpy.ix_",
"numpy.reshape",
"numpy.arange",
"numpy.block",
"numpy.zeros",
"numpy.delete",
"numpy.array",
"numpy.sum",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
andreanelli/scikit-cosmo | [
"97adbd5459bc738f994ded2ac385a13d4ad20afb"
] | [
"tests/test_standard_flexible_scaler.py"
] | [
"import unittest\nfrom skcosmo.preprocessing.flexible_scaler import StandardFlexibleScaler\nfrom sklearn.preprocessing import StandardScaler\nimport sklearn\nimport numpy as np\n\n\nclass ScalerTests(unittest.TestCase):\n def test_fit_transform_pf(self):\n \"\"\"Checks that in the case of normalization by... | [
[
"numpy.sqrt",
"numpy.around",
"numpy.random.uniform",
"sklearn.preprocessing.StandardScaler",
"numpy.array",
"numpy.isclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ntunlp/ptrnet-depparser | [
"2b6ebdb63825eafd63d86700bbbc278cabfafeb2",
"2b6ebdb63825eafd63d86700bbbc278cabfafeb2"
] | [
"neuronlp2/io/conllx_data.py",
"neuronlp2/nn/modules/skipconnect_rnn.py"
] | [
"__author__ = 'max'\n\nimport os.path\nimport random\nimport numpy as np\nfrom .alphabet import Alphabet\nfrom .logger import get_logger\nfrom . import utils\nimport torch\n\n# Special vocabulary symbols - we always put them at the start.\nPAD = b\"_PAD\"\nPAD_POS = b\"_PAD_POS\"\nPAD_TYPE = b\"_<PAD>\"\nPAD_CHAR =... | [
[
"torch.randperm",
"numpy.arange",
"numpy.random.random_sample",
"numpy.random.shuffle",
"numpy.random.binomial",
"torch.from_numpy",
"torch.device",
"numpy.zeros",
"numpy.empty"
],
[
"torch.nn.init.constant_",
"torch.Tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TakahitoMotoki/qiskit-terra | [
"531e62f3a3c218fee6db116f54ed41ce4e88d9a9"
] | [
"qiskit/quantum_info/states/utils.py"
] | [
"# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2017, 2020.\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-2.0.\n#\n# Any modificatio... | [
[
"numpy.log",
"numpy.log2",
"scipy.linalg.svd",
"numpy.reshape",
"numpy.eye",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
pierrefdz/dino | [
"8adde6022bdfcb27f20f5938e25d541aa2d6e112"
] | [
"main_dino.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\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 ... | [
[
"torch.nn.functional.softmax",
"torch.max",
"numpy.linspace",
"torch.zeros",
"torch.utils.data.DataLoader",
"torch.sum",
"torch.nn.SyncBatchNorm.convert_sync_batchnorm",
"torch.cuda.amp.autocast",
"torch.no_grad",
"torch.nn.PairwiseDistance",
"torch.cuda.synchronize",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Frmissjing/berryconda | [
"0207e46ff539728d5077196470fa9ce85887834d"
] | [
"recipes/scipy/run_test.py"
] | [
"import sys\nimport os\n\n# Use OpenBLAS with 1 thread only as it seems to be using too many\n# on the CIs apparently.\n\nimport scipy\nimport scipy.cluster._hierarchy\nimport scipy.cluster._vq\nimport scipy.fftpack._fftpack\nimport scipy.fftpack.convolve\nimport scipy.integrate._dop\nimport scipy.integrate._odepac... | [
[
"scipy.test"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AidanKosik/HiveMind | [
"385006e1777e475d8ed8d71bb26e53b8a76acdd5"
] | [
"src/QLearningTable.py"
] | [
"import numpy as np\nimport pandas as pd\n\n\n# Stolen from https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow\nclass QLearningTable:\n def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9):\n self.actions = actions # a list\n self.lr = learning_rate\n ... | [
[
"numpy.random.uniform",
"numpy.random.permutation",
"pandas.DataFrame",
"numpy.random.choice"
]
] | [
{
"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": []
}
] |
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