repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
initzhang/Hetu | [
"447111a358e4dc6df5db9c216bdb3590fff05f84",
"447111a358e4dc6df5db9c216bdb3590fff05f84",
"447111a358e4dc6df5db9c216bdb3590fff05f84"
] | [
"python/hetu/gpu_ops/BatchNorm.py",
"python/hetu/gpu_ops/Split.py",
"examples/cnn/tf_models/tf_LeNet.py"
] | [
"from __future__ import absolute_import\nfrom .Node import Op\nimport numpy as np\nfrom .. import ndarray\nfrom .._base import DNNL_LIB\nfrom ..cpu_links import batch_norm as cpu_batch_norm\nfrom ..cpu_links import batch_norm_inference as cpu_batch_norm_inference\nfrom ..cpu_links import batch_norm_gradient as cpu_... | [
[
"numpy.ones",
"numpy.zeros"
],
[
"numpy.zeros"
],
[
"numpy.random.normal",
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.nn.relu",
"tensorflow.nn.conv2d",
"tensorflow.matmul",
"tensorflow.transpose",
"tensorflow.reshape",
"tensorflow.reduce_mean... |
ITMO-NSS-team/nas-fedot | [
"3b886f48f66f7ce77dca10b3cf0229e056b4948b"
] | [
"fedot/core/composer/optimisers/gp_optimiser.py"
] | [
"import math\r\nfrom dataclasses import dataclass\r\nfrom functools import partial\r\nfrom typing import (\r\n List,\r\n Callable,\r\n Any,\r\n Optional\r\n)\r\n\r\nimport numpy as np\r\nfrom core.composer.optimisers.crossover import CrossoverTypesEnum, crossover\r\nfrom core.composer.optimisers.heredit... | [
[
"numpy.argsort"
]
] |
ChuChuIgbokwe/DeepReinforcementLearningNanodegree | [
"d1e0ddde31b56f1f4f534fcd7e32d9512105874a"
] | [
"p1_navigation/dqn_agent.py"
] | [
"import numpy as np\nimport random\nfrom collections import namedtuple, deque\n\nfrom model import QNetwork\n\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nBUFFER_SIZE = int(1e5) # replay buffer size\nBATCH_SIZE = 64 # minibatch size\nGAMMA = 0.99 # discount fact... | [
[
"torch.no_grad",
"torch.from_numpy",
"torch.nn.functional.mse_loss",
"torch.cuda.is_available",
"numpy.arange",
"numpy.vstack"
]
] |
willsheffler/tcdock | [
"c7b8614221f4a94750054bfe5dfb12298e8d05b8"
] | [
"rpxdock/search/dockspec.py"
] | [
"from abc import abstractmethod\nimport numpy as np\nimport rpxdock.homog as hm\nfrom rpxdock.geom import sym\n\nallowed_twocomp_architectures = \"\"\"\nD22 D32 D42 D52 D62 D72 D82\nT32 T33 O32 O42 O43 I32 I52 I53\nT32D T23D T33D\nO32D O23D O42D O24D O43D O34D\nI32D I23D I52D I54D I53D I35D\nAXEL_1_2_3 AXEL_1_2_4 A... | [
[
"numpy.concatenate",
"numpy.array",
"numpy.linalg.norm",
"numpy.sin",
"numpy.tan",
"numpy.mean",
"numpy.eye",
"numpy.allclose",
"numpy.sign",
"numpy.argmax"
]
] |
mesudepolat/CART_DIABETES | [
"ebb94754a227178f5abfacce771a9808f609605e"
] | [
"CART_DIABETES.py"
] | [
"################################\r\n# DIABETES PREDICTION with CART\r\n################################\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.metrics import *\r\nfrom sklearn.model_selection import *\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nimport pydotplus\r\nfrom sklearn.tree ... | [
[
"pandas.DataFrame",
"pandas.read_csv",
"sklearn.tree.DecisionTreeClassifier",
"pandas.set_option"
]
] |
osisbo/on-safety-in-safe-bayesian-optimization | [
"fa0f09df065e41e05f6b0185d4817dccc60b164a"
] | [
"experiments_losbo.py"
] | [
"from __future__ import print_function, division, absolute_import\n\nimport GPy\nimport numpy as np\nimport losbo as safeopt\nimport scipy\nimport math\nimport time\nimport os\nimport datetime\nimport pickle\nimport multiprocessing as mp\nimport sys\nimport pathlib\n\nstore_path = str(pathlib.Path(__file__).parent.... | [
[
"numpy.logical_not",
"numpy.logical_or",
"numpy.linalg.norm",
"numpy.zeros",
"numpy.random.seed",
"numpy.sum",
"scipy.stats.ttest_ind",
"numpy.nonzero",
"numpy.where",
"numpy.argmax"
]
] |
urschrei/convertbng | [
"16557cea756a0ec6994b4148ae8caa67c065f686"
] | [
"setup.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nsetup.py\n\nCreated by Stephan Hügel on 2016-07-25\n\"\"\"\n\nimport sys\nfrom setuptools import setup, Extension\nfrom Cython.Build import cythonize\nimport numpy\n\n\n# # Set dynamic RPATH differently, depending on platform\nldirs = []\nddirs = []\nif \"lin... | [
[
"numpy.get_include"
]
] |
404-Brain-Not-Found/Bird-Watcher | [
"2a9e2033faa17c4a28ae1be5d1145d556f2bc7b8"
] | [
"yolo_utils.py"
] | [
"import numpy as np\nfrom tensorflow.keras import backend as k\nfrom image_utils import non_max_suppression\n\n\ndef xywh2minmax(xy, wh):\n xy_min = xy - wh / 2\n xy_max = xy + wh / 2\n\n return xy_min, xy_max\n\n\ndef iou(pred_mins, pred_maxes, true_mins, true_maxes):\n intersect_mins = k.maximum(pred_... | [
[
"tensorflow.keras.backend.expand_dims",
"tensorflow.keras.backend.dtype",
"tensorflow.keras.backend.sum",
"tensorflow.keras.backend.sqrt",
"tensorflow.keras.backend.minimum",
"tensorflow.keras.backend.transpose",
"tensorflow.keras.backend.stack",
"tensorflow.keras.backend.square",
... |
Fisher33318/yolov3-motebus | [
"96bf731cda088a18ebdc2e5a863adfef812e1095"
] | [
"utils/torch_utils.py"
] | [
"import math\nimport os\nimport time\nfrom copy import deepcopy\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef init_seeds(seed=0):\n torch.manual_seed(seed)\n\n # Remove randomness (may be slower on Tesla GPUs) # https://pytorch.org/docs/... | [
[
"torch.zeros",
"torch.device",
"torch.sqrt",
"torch.cuda.synchronize",
"torch.nn.functional.interpolate",
"torch.no_grad",
"torch.cuda.get_device_properties",
"torch.cuda.device_count",
"torch.manual_seed",
"torch.mm",
"torch.nn.Conv2d",
"torch.cuda.is_available",
... |
volodymyrss/Concordance | [
"2bdbf551f85be7cfa924a4c8fefc77e08f6d1067"
] | [
"CalConcordanceCode/generatehistogram.py"
] | [
"import numpy as np\nimport math as math\nimport matplotlib.pyplot as plt\n\n\ndef generatehistogramfunc(mcmcChainBG, mapResultBG, N, M, B, G, sigma,\n figsize1=20, figsize2=6, ncolfig=5, sigmatheory=float('nan'),\n savefigname='fig.pdf', plotwhich=[1, 0, 1],\n ... | [
[
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.axhline",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.title",
"matplotlib.pyplot.savefig",
"numpy.exp",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.hist",
"numpy.a... |
tahentx/ecological-inference | [
"720217eeaf5b56fbc7337056f3f663731f66970a"
] | [
"pyei/data.py"
] | [
"\"\"\"Helpers for managing data files.\"\"\"\nfrom dataclasses import dataclass\n\nimport pandas as pd\n\n__all__ = [\"Datasets\"]\n\n\n@dataclass\nclass _DataSet:\n \"\"\"Class to hold datasets and related information.\n\n TODO: Add description, provenance, and other metadata here.\n \"\"\"\n\n url: s... | [
[
"pandas.read_csv"
]
] |
mohitmunjal/pmdarima | [
"cd333d6d661bf9d9b4afa37fa8747b3dcccc7f3f"
] | [
"pmdarima/datasets/lynx.py"
] | [
"# -*- coding: utf-8 -*-\n#\n# Author: Taylor Smith <taylor.smith@alkaline-ml.com>\n#\n# This is the lynx dataset found in R.\n\nimport numpy as np\nimport pandas as pd\n\nfrom ..compat import DTYPE\n\n__all__ = [\n 'load_lynx'\n]\n\n\ndef load_lynx(as_series=False, dtype=DTYPE):\n \"\"\"Annual numbers of lyn... | [
[
"numpy.array"
]
] |
saramsv/CCT | [
"27b4fd838a174a3c0fca582aa163e5bd426b055a"
] | [
"pseudo_labels/make_cam.py"
] | [
"import torch\nfrom torch import multiprocessing, cuda\nfrom torch.utils.data import DataLoader\nimport torch.nn.functional as F\nfrom torch.backends import cudnn\n\nimport numpy as np\nimport importlib\nimport os\n\nimport voc12.dataloader\nfrom misc import torchutils, imutils\n\ncudnn.enabled = True\n\ndef _work(... | [
[
"torch.nonzero",
"torch.stack",
"torch.nn.functional.adaptive_max_pool2d",
"torch.no_grad",
"torch.multiprocessing.spawn",
"torch.cuda.device",
"torch.cuda.device_count",
"torch.unsqueeze",
"torch.cuda.empty_cache",
"torch.utils.data.DataLoader",
"torch.load"
]
] |
AmateurZhang/EnjoyWithDataOnPowerSystems | [
"64227d0505012d2b5650874c65268e85d9751a17"
] | [
"AnEnsembleForPointEstimate/Ensemble.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Oct 3 11:00:38 2017\n\n@author: thuzhang\n\"\"\"\n\n# Ensemble\n\n\n# packages\nfrom PredictWindSpd import PredictWindSpd\nfrom Reduced_Noise import NoiseReduce\nfrom Former_Data import FormerData\n#from ReadTrueValue import ReadTrueValue\nimport matplotlib.pyplot a... | [
[
"matplotlib.pyplot.show",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.figure"
]
] |
schmolly/timemachine | [
"7d13a0406dc2d09ac67892988641ba4965bfb206"
] | [
"examples/rhfe_dual.py"
] | [
"# relative hydration free energy\n\nimport numpy as np\n\nfrom rdkit import Chem\nfrom rdkit.Chem import AllChem\nfrom fe import pdb_writer\nfrom fe import topology\nfrom md import builders\nfrom md import minimizer\n\nfrom timemachine.lib import potentials, custom_ops\nfrom timemachine.lib import LangevinIntegrat... | [
[
"numpy.concatenate",
"numpy.zeros_like",
"numpy.eye",
"numpy.any",
"numpy.linspace"
]
] |
srakhe/olympics | [
"0b85e33f51333802ba93d46eaaf1e40889861a0f"
] | [
"web/utils/predict_host.py"
] | [
"import pandas as pd\nfrom datetime import datetime\nimport pickle\nimport numpy as np\nimport plotly.graph_objects as go\nfrom sklearn.preprocessing import StandardScaler\n\n\nclass PredictHost:\n\n def __init__(self, data_path, web_data_path):\n self.data_path = data_path\n self.web_data_path = w... | [
[
"numpy.array",
"sklearn.preprocessing.StandardScaler",
"pandas.DataFrame",
"numpy.cbrt",
"pandas.read_csv"
]
] |
hhieuu/garch-option-pricing | [
"bd5ccc2ce55d97768dddfcc13da95f2babff7c02"
] | [
"utils.py"
] | [
"import numpy as np\nfrom numba import jit\n\n\n@jit(nopython=True)\ndef normal_pdf(x, mean=0, sd=1):\n \"\"\"\n Calculate probability of x given X ~ N(mean, sd ** 2)\n \"\"\"\n return 1 / (sd * np.sqrt(2 * np.pi)) \\\n * np.exp(- 1 / 2 * ((x - mean) / sd) ** 2)"
] | [
[
"numpy.exp",
"numpy.sqrt"
]
] |
wakame1367/Titanic_submit_script | [
"d96fdf23e4f86caaa534dc1734d33c71d7371b01"
] | [
"titanic_sample/dataset.py"
] | [
"from pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\nfrom titanic_sample.utils import ON_KAGGLE\n\nDATA_ROOT = Path('../input/titanic' if ON_KAGGLE else './resources')\nCategorical_Features = ['Embarked', 'Pclass', 'Sex']\ntrain_path = DATA_RO... | [
[
"sklearn.model_selection.train_test_split",
"pandas.read_csv",
"numpy.mean",
"pandas.concat"
]
] |
ByzanTine/AutoHOOT | [
"007bb423bfc8eefa64e4d1b0f8dad80b440bcf7a"
] | [
"examples/tucker.py"
] | [
"# Copyright 2020 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ... | [
[
"numpy.core.einsumfunc._parse_einsum_input"
]
] |
TimothyDelille/tgn | [
"44ff0843467e152bb54099dc6a34f668cd9749f7"
] | [
"utils/data_processing.py"
] | [
"import numpy as np\nimport random\nimport pandas as pd\n\n\nclass Data:\n def __init__(self, sources, destinations, timestamps, edge_idxs, labels, game_ids, batch_idx):\n self.sources = sources\n self.destinations = destinations\n self.timestamps = timestamps\n self.edge_idxs = edge_idxs\n self.lab... | [
[
"numpy.quantile",
"numpy.random.rand",
"numpy.mean",
"numpy.logical_and",
"numpy.std"
]
] |
mldmort/adVNTR | [
"412398924bc7f2ed1fa38c0c5456998d9cdd5b5a"
] | [
"advntr/coverage_bias.py"
] | [
"import logging\nfrom math import sqrt\nimport sys\n\nimport pysam\nimport numpy\n\nfrom advntr.settings import *\nfrom advntr.utils import get_chromosome_reference_sequence, get_gc_content\n\n\nclass CoverageBiasDetector:\n \"\"\"Find the coverage distribution based on GC content.\"\"\"\n\n def __init__(self... | [
[
"numpy.array"
]
] |
uzair789/faster-rcnn.pytorch | [
"f9d984d27b48a067b29792932bcb5321a39c1f09"
] | [
"lib/model/rpn/bbox_transform.py"
] | [
"# --------------------------------------------------------\n# Fast R-CNN\n# Copyright (c) 2015 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ross Girshick\n# --------------------------------------------------------\n# --------------------------------------------------------\n#... | [
[
"torch.stack",
"torch.min",
"torch.max",
"torch.log",
"torch.exp"
]
] |
marco-zangari/math | [
"8eea68b0f7c0457593814250b488343b7504972b"
] | [
"graphs/newtons_law.py"
] | [
"\"\"\"Newton's law of universal gravitation.\"\"\"\n\nimport matplotlib.pyplot as plt\n\n\ndef draw_graph(x, y):\n \"\"\"Draw relation between gravitational force & distance between bodies.\"\"\"\n plt.plot(x, y, marker='o')\n plt.xlabel('Distance in Meters')\n plt.ylabel('Gravitational Force in Newton... | [
[
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.title",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.show"
]
] |
Dumpkin1996/clipper | [
"457088be2ebe68c68b94d90389d1308e35b4c844",
"457088be2ebe68c68b94d90389d1308e35b4c844"
] | [
"applications/fatigue_bigball/container/container4/app/predict.py",
"applications/prediction_clipper/container/c8_KNN/app/predict.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 22 15:03:22 2019\n\n@author: davidzhou\n\"\"\"\nimport time\nimport cv2\nimport numpy as np\nimport os\nimport json\n\ndef image_string(image):\n image_encode=cv2.imencode('.jpg',image)[1]\n imagelist=image_encode.tolist()\n image... | [
[
"numpy.uint8",
"numpy.array",
"numpy.copy"
],
[
"pandas.DataFrame",
"pandas.read_json",
"sklearn.preprocessing.MinMaxScaler",
"sklearn.model_selection.GridSearchCV",
"sklearn.neighbors.KNeighborsRegressor"
]
] |
harrisonzhu508/Bayesian-Inference-on-Football | [
"08bd8bfd3aa1800ca59c7572405be2405641d881"
] | [
"wcAnalysis.py"
] | [
"\"\"\"\nThe aim of this project is to predict the scores head-to-head matches of world cup 2018 in Russia.\n\nIn particular, we work under the Bayesian framework, using the Poisson regression likelihood with a normalised Gaussian prior. Our 3\napproximations will be the:\n\n- Laplace approximation, \n- Metropolis-... | [
[
"torch.zeros",
"numpy.random.binomial",
"numpy.trace",
"numpy.zeros",
"pandas.DataFrame",
"numpy.random.poisson",
"matplotlib.pyplot.plot",
"scipy.stats.poisson.pmf",
"numpy.mean",
"numpy.random.uniform",
"torch.eye",
"matplotlib.pyplot.show",
"pandas.read_csv",... |
fmelinscak/cognibench | [
"372513b8756a342c0df222dcea5ff6d1d69fbcec"
] | [
"cognibench/scores.py"
] | [
"import numpy as np\nfrom scipy.stats.mstats import pearsonr\nfrom sciunit import scores\nfrom sciunit import errors\nfrom cognibench.capabilities import PredictsLogpdf, ReturnsNumParams\nfrom overrides import overrides\nfrom cognibench.utils import negloglike\n\n\nclass BoundedScore(scores.FloatScore):\n @overr... | [
[
"numpy.dot",
"numpy.asarray",
"numpy.log",
"scipy.stats.mstats.pearsonr",
"numpy.sum",
"numpy.mean",
"numpy.sqrt",
"numpy.clip",
"numpy.abs",
"numpy.squeeze"
]
] |
mortenblaa/lut-generator | [
"ab00879a095582deb74eb760c7cbb603bb711785"
] | [
"lut-generator.py"
] | [
"import argparse\nimport math\nimport sys\n\nimport cv2\nimport numpy as np\n\n\ndef make_image_strip(samples=16, flipy=False):\n \"\"\"\n Creates a list containing the image data in a strip format.\n\n :param samples: The number of samples, should be 16, 32 or 64\n :type samples: int\n :param flipy:... | [
[
"numpy.array"
]
] |
TcheL/Road2Filter | [
"0f18053824de6d654ac95c63271cd93077359139"
] | [
"IIR/o4zpsbwlpf.py"
] | [
"#!/usr/bin/env python3\n\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import signal\n\nfs = 1000 # sampling frequency\nfc = 6 # cut-off frequency\nt = np.arange(1000)/fs\nsga = np.sin(2*np.pi*2*t) # signal with f = 2\nsgb = np.sin(2*np.pi*10*t) # signal with f = 10\nsgo = sga +... | [
[
"numpy.sin",
"numpy.savetxt",
"matplotlib.pyplot.plot",
"scipy.signal.butter",
"matplotlib.pyplot.legend",
"scipy.signal.filtfilt",
"numpy.loadtxt",
"scipy.signal.lfilter",
"numpy.arange",
"matplotlib.pyplot.show"
]
] |
JonasStankevicius/CenterPoint | [
"d3459c508385645b88301267aafdfcff999a01af"
] | [
"det3d/torchie/trainer/utils.py"
] | [
"\"\"\"\nThis file contains primitives for multi-gpu communication.\nThis is useful when doing distributed training.\n\"\"\"\n\nimport functools\nimport pickle\nimport sys\nimport time\nfrom getpass import getuser\nfrom socket import gethostname\n\nimport torch\nimport torch.distributed as dist\nfrom det3d import t... | [
[
"torch.distributed.get_world_size",
"torch.distributed.is_available",
"torch.cat",
"torch.stack",
"torch.IntTensor",
"torch.no_grad",
"torch.distributed.all_gather",
"torch.ByteTensor",
"torch.distributed.is_initialized",
"torch.ByteStorage.from_buffer",
"torch.distribu... |
amangoel185/awkward-1.0 | [
"892b5abca4a2e86842d160cede9836b1c4352e45",
"892b5abca4a2e86842d160cede9836b1c4352e45"
] | [
"tests/v2/test_0404-array-validity-check.py",
"tests/v2/test_0447-preserve-regularness-in-reduce.py"
] | [
"# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/master/LICENSE\n\nfrom __future__ import absolute_import\n\nimport pytest # noqa: F401\nimport numpy as np # noqa: F401\nimport awkward as ak # noqa: F401\n\nto_list = ak._v2.operations.convert.to_list\n\n\ndef test_BitMaskedArray():\n ... | [
[
"numpy.array",
"numpy.arange",
"numpy.unique"
],
[
"numpy.sum",
"numpy.array",
"numpy.arange"
]
] |
Indumathi31/rpp | [
"1380bcd27130a36e3ba26763209148c2275fae5b"
] | [
"utilities/rpp-performancetests/HIP_NEW/generatePerformanceLogs.py"
] | [
"import os\nimport subprocess\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--profiling', type=str, default='NO', help='Run with profiler? - (YES/NO)')\nparser.add_argument('--case_start', type=str, default='0', help='Testing range starting case # - (0-84)')\nparser.add_argument('--ca... | [
[
"pandas.read_csv"
]
] |
leezu/gluon-nlp | [
"19de74c2b03f22dde8311a0225b4571c2deef0e4"
] | [
"scripts/machine_translation/train_transformer.py"
] | [
"\"\"\"\nTransformer\n=================================\n\nThis example shows how to implement the Transformer model with GluonNLP Toolkit.\n\n@inproceedings{vaswani2017attention,\n title={Attention is all you need},\n author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones,\n ... | [
[
"numpy.concatenate",
"numpy.array",
"numpy.random.seed",
"numpy.load",
"numpy.exp",
"numpy.savez"
]
] |
facebookresearch/alma | [
"02a6d0e76fedc6af315dd906698153edabbaea5a"
] | [
"crlapi/sl/clmodels/agg_ensemble.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#\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom crlapi.core import CLModel\nfrom c... | [
[
"torch.stack",
"torch.nn.ModuleList",
"torch.no_grad",
"numpy.mean",
"torch.nn.functional.cross_entropy",
"numpy.prod"
]
] |
pondus314/MVPproject | [
"166b7b9ac982afb5ec005d543ef38580de2a61ef"
] | [
"zs3/modeling/aspp.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom zs3.modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d\n\n\nclass _ASPPModule(nn.Module):\n def __init__(self, inplanes, planes, kernel_size, padding, dilation, BatchNorm):\n super().__init__()\n self.atrous... | [
[
"torch.cat",
"torch.nn.Dropout",
"torch.nn.init.kaiming_normal_",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.AdaptiveAvgPool2d"
]
] |
jwkanggist/tpu | [
"1def89d0a750844bbff58d27ff1f1fcf6b304669"
] | [
"models/official/retinanet/anchors.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... | [
[
"numpy.minimum",
"numpy.exp",
"tensorflow.reshape",
"numpy.where",
"tensorflow.cast",
"numpy.concatenate",
"tensorflow.concat",
"numpy.unravel_index",
"numpy.swapaxes",
"numpy.arange",
"numpy.column_stack",
"numpy.vstack",
"numpy.expand_dims",
"numpy.array",... |
ummadiviany/image-segmenter | [
"906457a16765f7e1995a8dca7b222ba3abd13a3f"
] | [
"inference.py"
] | [
"import torch\nfrom torchvision.models.segmentation import fcn_resnet50\nfrom torchvision.utils import draw_segmentation_masks\nfrom PIL import Image\nimport io\nfrom torchvision.transforms import transforms\nfrom torchvision.utils import save_image\nimport torchvision.transforms.functional as F\n\ndef get_model():... | [
[
"torch.arange",
"torch.nn.functional.softmax"
]
] |
shenyann/NASLib | [
"6fad875f21e41bb9c91647bbd0620aa6e6dc8c7f"
] | [
"naslib/predictors/zerocost_estimators.py"
] | [
"# Author: Robin Ru @ University of Oxford\n# This is an implementation of zero-cost estimators based on:\n# https://github.com/BayesWatch/nas-without-training (Jacov)\n# and https://github.com/gahaalt/SNIP-pruning (SNIP)\n\nimport numpy as np\nimport torch\nimport logging\nimport gc\n\nfrom naslib.predictors.predi... | [
[
"numpy.array",
"torch.cat",
"numpy.log",
"torch.no_grad",
"torch.cuda.empty_cache",
"torch.cuda.is_available",
"numpy.linalg.eig",
"torch.ones_like",
"numpy.corrcoef",
"torch.nn.CrossEntropyLoss"
]
] |
Teradata/techbytes-using-python-with-vantage | [
"422cd3e4f79c56fda9fe51f9143c3e958418d5ae"
] | [
"Inputs/stoRFScoreSB.py"
] | [
"################################################################################\n# * The contents of this file are Teradata Public Content and have been released\n# * to the Public Domain.\n# * Please see license.txt file in the package for more information.\n# * Alexander Kolovos and Tim Miller - May 2021 - v.2.... | [
[
"pandas.DataFrame",
"pandas.to_numeric"
]
] |
soldierofhell/yolov3-tf2 | [
"36a40edece7b01051bb357f4153a13a40aaac4de"
] | [
"yolov3_tf2/dataset.py"
] | [
"import tensorflow as tf\n\n\n@tf.function\ndef transform_targets_for_output(y_true, grid_size, anchor_idxs, classes):\n # y_true: (N, boxes, (x1, y1, x2, y2, class, best_anchor))\n N = tf.shape(y_true)[0]\n\n # y_true_out: (N, grid, grid, anchors, [x, y, w, h, obj, class])\n y_true_out = tf.zeros(\n ... | [
[
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.image.decode_jpeg",
"tensorflow.cast",
"tensorflow.shape",
"tensorflow.concat",
"tensorflow.argmax",
"tensorflow.io.FixedLenFeature",
"tensorflow.constant",
"tensorflow.pad",
"tensorflow.TensorArray",
"tensorflow... |
matsuren/crownconv360depth | [
"8f8f30b6739409e5cd762af92206fe72d74b0d54"
] | [
"models/icosweepnet.py"
] | [
"import random\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .costvolume_regularization import CostRegularization\nfrom .feature_extraction import FeatureExtraction\nfrom .icospherical_sweeping import IcoSphericalSweeping\n\n\nclass IcoSweepNet(nn.Module):\n ... | [
[
"torch.zeros",
"numpy.finfo",
"torch.nn.functional.softmax",
"numpy.linspace",
"torch.sum"
]
] |
strawberrypie/simpletransformers | [
"20ad03322281e3c0f594b35fa885bff3dd526ca6"
] | [
"simpletransformers/t5/t5_utils.py"
] | [
"import logging\nimport os\nfrom os import truncate\nimport pickle\nfrom multiprocessing import Pool\nfrom typing import Tuple\n\nfrom tqdm.auto import tqdm\n\nimport pandas as pd\nimport torch\nfrom tokenizers.implementations import ByteLevelBPETokenizer\nfrom tokenizers.processors import BertProcessing\nfrom torc... | [
[
"torch.flatten"
]
] |
jeffwdoak/pycalphad | [
"c5c8df6a0630d9bed20a79eb8617849b260c5925"
] | [
"pycalphad/tests/test_calculate.py"
] | [
"\"\"\"\nThe calculate test module verifies that calculate() calculates\nModel quantities correctly.\n\"\"\"\n\nimport nose.tools\nfrom pycalphad import Database, calculate\nimport numpy as np\ntry:\n # Python 2\n from StringIO import StringIO\nexcept ImportError:\n # Python 3\n from io import StringIO\... | [
[
"numpy.testing.assert_array_equal"
]
] |
StefekVUT/Aberation_tool | [
"bb4dfa319a8d7a633321492a10a1cc47302e9b07"
] | [
"Aberrations_refactored.py"
] | [
"import numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nfrom math import pi\nfrom skimage.color import rgb2gray\n\n# functions\n\n\ndef load_image(filename):\n image = cv2.imread(filename, 0)\n print(image)\n return image\n\n\ndef pipe_convert_normalize(image):\n gray_image = rgb2gray(image)\n... | [
[
"numpy.sin",
"numpy.fft.ifft2",
"numpy.fft.fft2",
"numpy.log",
"numpy.multiply",
"numpy.arctan",
"numpy.sqrt",
"numpy.cos",
"numpy.fft.fftshift",
"numpy.size",
"numpy.abs"
]
] |
wykys/MIKS-FSK | [
"a28255a1a184fb0b9753fcb133ea12e1b75ae93d"
] | [
"src/bell202.py"
] | [
"# wykys 2019\n\nimport numpy as np\nfrom numpy import array, uint8\nfrom bin_print import bin_print\n\nFREQ_L = 2200\nFREQ_H = 1200\nSAMPLE_RATE = 9600\nDATA_RATE = 1200\nBIT_SIZE = SAMPLE_RATE/DATA_RATE\n\n\ndef find_data_bytes(s):\n l_cnt = 0\n h_cnt = 0\n\n flag_frame_start = False\n flag_change_fro... | [
[
"numpy.round",
"numpy.uint8"
]
] |
sapan-ostic/deep_prediction | [
"e4709e4a66477755e6afe39849597ae1e3e969b5"
] | [
"scripts/sgan/losses.py"
] | [
"import torch\nimport random\n\n\ndef bce_loss(input, target):\n \"\"\"\n Numerically stable version of the binary cross-entropy loss function.\n As per https://github.com/pytorch/pytorch/issues/751\n See the TensorFlow docs for a derivation of this formula:\n https://www.tensorflow.org/api_docs/pyth... | [
[
"torch.zeros_like",
"torch.numel",
"torch.ones_like",
"torch.sum"
]
] |
yazanobeidi/sentience | [
"f0556817f4673530719682253a9f140a00fb1d39"
] | [
"src/python/memory/dnc/access_test.py"
] | [
"# Copyright 2017 Google Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed... | [
[
"numpy.random.rand",
"numpy.sum",
"tensorflow.python.ops.rnn.dynamic_rnn",
"numpy.ones",
"numpy.random.randn",
"tensorflow.test.compute_gradient_error",
"tensorflow.constant",
"tensorflow.reduce_sum",
"tensorflow.test.main",
"tensorflow.global_variables_initializer",
"t... |
ToBraun/RecLac | [
"0b4edef3322cef6e8d449176f980da3a1a130a68"
] | [
"RECLAC/boxcount.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 18 11:22:17 2021\n\n@author: tobraun\n\"\"\"\n\nimport numpy as np\nimport scipy.stats as stats\nimport scipy.optimize as opt\n\nfrom .recurrence_plot import RP\n\n\nclass Boxcount(RP):\n \n def __init__(self, x, boxes, glide = False... | [
[
"numpy.rot90",
"numpy.random.normal",
"numpy.array",
"numpy.isnan",
"scipy.stats.norm.logpdf",
"numpy.zeros",
"numpy.random.seed",
"numpy.sum",
"numpy.copy",
"numpy.mean",
"numpy.where",
"numpy.arange",
"numpy.log10",
"scipy.optimize.minimize",
"numpy.va... |
rbSparky/umit-hack-backend | [
"a9402d35d07693b78498a2ba2d4ff08fcb6cab44"
] | [
"app.py"
] | [
"import pickle\nfrom flask import Flask, request, jsonify, session\nfrom flask_cors import CORS, cross_origin\nimport sklearn\nfrom sklearn.decomposition import TruncatedSVD\nimport pandas as pd\nimport numpy as np\n\n\nranks = []\napp = Flask(__name__)\ncors = CORS(app)\napp.config['CORS_HEADERS'] = 'Content-Type'... | [
[
"numpy.corrcoef",
"sklearn.decomposition.TruncatedSVD",
"pandas.DataFrame"
]
] |
shrutipulstya/vision | [
"85982ac695e78af80bf59cd9c855e1729b7376f5"
] | [
"torchvision/models/detection/keypoint_rcnn.py"
] | [
"import torch\nfrom torch import nn\n\nfrom torchvision.ops import MultiScaleRoIAlign\n\nfrom ._utils import overwrite_eps\nfrom ..._internally_replaced_utils import load_state_dict_from_url\n\nfrom .faster_rcnn import FasterRCNN\nfrom .backbone_utils import resnet_fpn_backbone, _validate_trainable_layers\n\n\n__al... | [
[
"torch.nn.init.constant_",
"torch.nn.init.kaiming_normal_",
"torch.nn.ConvTranspose2d",
"torch.nn.ReLU",
"torch.nn.Conv2d"
]
] |
xidonett/pyprojects | [
"004ee23f01681301eeabc1b06f833a7efd7b89b3"
] | [
"odessa_news_scrapper/odessa_news_scrapper.py"
] | [
"from bs4 import BeautifulSoup\r\nimport pandas as pd\r\nimport requests\r\n\r\nclass OdessaNewsScrapper():\r\n \r\n def __init__( self, first_page:int = 1, last_page:int = 2, link:str = \"https://on.od.ua/category/news/2-odessa/page/\", csv_file:str = \"odessa_news.csv\" ) -> None:\r\n \r\n tit... | [
[
"pandas.DataFrame"
]
] |
rudrapatel/numba | [
"5111ad5da17f914ca203e67f3026d2c0d890dfbb"
] | [
"numba/tests/test_record_dtype.py"
] | [
"from __future__ import print_function, division, absolute_import\n\nimport sys\n\nimport numpy as np\nimport ctypes\nfrom numba import jit, numpy_support, types\nfrom numba import unittest_support as unittest\nfrom numba.compiler import compile_isolated\nfrom numba.itanium_mangler import mangle_type\nfrom numba.ut... | [
[
"numpy.asarray",
"numpy.zeros",
"numpy.testing.assert_equal",
"numpy.recarray",
"numpy.arange",
"numpy.dtype"
]
] |
kaiogu/rasa | [
"9dadb8058f3a723096dcd96333801ecf11c12780"
] | [
"tests/nlu/featurizers/test_regex_featurizer.py"
] | [
"from typing import Text, List, Any, Tuple\n\nimport numpy as np\nimport pytest\n\nfrom rasa.shared.nlu.training_data.training_data import TrainingData\nfrom rasa.shared.nlu.training_data.message import Message\nfrom rasa.nlu.config import RasaNLUModelConfig\nfrom rasa.nlu.tokenizers.whitespace_tokenizer import Whi... | [
[
"numpy.allclose",
"numpy.array"
]
] |
lbny/albatros | [
"304889fa2ff96ac573f7e069c7ed2575803bdcf9"
] | [
"scripts/split_train_test.py"
] | [
"\"\"\"\nAuthor: Lucas Bony\n\nSplits train and valid csv\n\"\"\"\nimport os.path as osp\n\nimport argparse\n\nimport numpy as np\nimport pandas as pd\n\nparser: argparse.ArgumentParser = argparse.ArgumentParser()\nparser.add_argument('--train_ratio', type=float, default=0.8)\nparser.add_argument('--valid_ratio', t... | [
[
"pandas.read_csv",
"numpy.arange",
"numpy.intersect1d"
]
] |
aaroexxt/Tacotron | [
"86a0a25d2bc574e7663f7c24c71e3f7d16ce3342"
] | [
"synthesizer.py"
] | [
"import io\nimport numpy as np\nimport tensorflow as tf\nfrom hparams import hparams\nfrom librosa import effects\nfrom models import create_model\nfrom text import text_to_sequence\nfrom util import audio\n\n\nclass Synthesizer:\n def load(self, checkpoint_path, model_name='tacotron'):\n print('Constructing mo... | [
[
"numpy.asarray",
"tensorflow.Session",
"tensorflow.train.Saver",
"tensorflow.variable_scope",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer"
]
] |
bastianalt/correlation_priors_for_rl | [
"9a98f345ac10e9767d854cd7a9681057a50a9737"
] | [
"simulations/sim_imitation_learning.py"
] | [
"import sys\nsys.path.append('..')\n\nimport numpy as np\nimport os\nimport pickle\nimport matplotlib.pyplot as plt\nimport packages.subgoal.inference as sg\nfrom packages.mdp.gridworld import Gridworld\nfrom packages.mdp.mdp import MDP, policyDivergence, normalizeQ, det2stoch\nfrom packages.policy.exploration impo... | [
[
"numpy.rot90",
"numpy.zeros",
"numpy.random.seed",
"numpy.prod",
"numpy.random.random",
"numpy.logspace"
]
] |
wuyaoyao99/mssdk | [
"9373e1a6d2b6e347319de3890b6a77b48a7e6922"
] | [
"mssdk/index/index_sw.py"
] | [
"# -*- coding:utf-8 -*-\n# /usr/bin/env python\n\"\"\"\nAuthor: Albert King\ndate: 2019/12/6 14:34\ncontact: jindaxiang@163.com\ndesc: 获取申万指数-申万一级\nhttp://www.swsindex.com/IdxMain.aspx\n部分代码要感谢: PKUJson\n\"\"\"\nimport time\nimport json\nimport datetime\n\nimport pandas as pd\nimport requests\nfrom bs4 import Beaut... | [
[
"pandas.DataFrame"
]
] |
nikita-astronaut/poliastro | [
"7f675d76da413618f3bcc25317de750d74ea667e"
] | [
"src/poliastro/integrator_params.py"
] | [
"import numpy as np\n\nA = [np.array([]),\n np.array([5.26001519587677318785587544488e-2]),\n np.array([1.97250569845378994544595329183e-2, 5.91751709536136983633785987549e-2]),\n np.array([2.95875854768068491816892993775e-2, 0, 8.87627564304205475450678981324e-2]),\n np.array([2.4136513415926668550... | [
[
"numpy.array"
]
] |
mrudula14/incubator-superset | [
"fdc645c1319f4dd72859059bac37327498af731a"
] | [
"tests/core_tests.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.Timestamp"
]
] |
albanie/mcnPyTorch | [
"75d49c597e09f38fea7bea96123b40be9a9c43d5"
] | [
"python/import_pytorch.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# pytorch model importer\n\n# --------------------------------------------------------\n# mcnPyTorch\n# Licensed under The MIT License [see LICENSE.md for details]\n# Copyright (C) 2017 Samuel Albanie\n# --------------------------------------------------------\n\n... | [
[
"numpy.array",
"numpy.empty"
]
] |
junqi-jiang/mmxai | [
"08ae70a6d443fbdfa92dba6cd285429e85ac851f"
] | [
"web_app/interpretability4mmf/lime_mmf.py"
] | [
"from mmxai.interpretability.classification.lime.lime_multimodal import (\n LimeMultimodalExplainer,\n)\nfrom skimage.segmentation import mark_boundaries\nfrom skimage import img_as_ubyte\nimport numpy as np\nfrom PIL import Image\n\n\ndef lime_multimodal_explain(image_path, text, model, label_to_exp, num_sample... | [
[
"numpy.uint8",
"numpy.array"
]
] |
ruichen-v/normPredict | [
"9bc5cc03ba255652a79b7712d419c6b27e0a601c"
] | [
"testtf/digit/train_digit.py"
] | [
"\"\"\" Convolutional Neural Network.\nBuild and train a convolutional neural network with TensorFlow.\nThis example is using the MNIST database of handwritten digits\n(http://yann.lecun.com/exdb/mnist/)\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n\"\"\"\n\nfrom __future... | [
[
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.reshape",
"tensorflow.data.Iterator.from_structure",
"numpy.frombuffer",
"tensorflow.nn.softmax",
"tensorflow.contrib.layers.flatten",
"tensorflow.global_variables_initializ... |
wraith1995/Exasim | [
"ad475c7066c5bde1a7941e1703650e3a0db34fbb"
] | [
"tests/Euler/naca0012/pdeapp.py"
] | [
"# import external modules\nimport numpy, os\n\n# Add Exasim to Python search path\ncdir = os.getcwd(); ii = cdir.find(\"Exasim\");\nexec(open(cdir[0:(ii+6)] + \"/Installation/setpath.py\").read());\n\n# import internal modules\nimport Preprocessing, Postprocessing, Gencode, Mesh\n\n# Create pde object and mesh obj... | [
[
"numpy.array",
"numpy.sin",
"numpy.ones",
"numpy.arange",
"numpy.cos",
"numpy.sqrt",
"numpy.abs"
]
] |
PinkDiamond1/LandBOSSE | [
"1920f8e9be37c91b5d17c774845159a1fa24d5f9"
] | [
"landbosse/tests/model/test_CollectionCost.py"
] | [
"from unittest import TestCase\nimport os\n\nimport pandas as pd\n\nfrom landbosse.model import Cable, Array, ArraySystem\nfrom landbosse.tests.model.test_WeatherDelay import generate_a_year\nfrom landbosse.tests.model.test_filename_functions import landbosse_test_input_dir\n\nimport pytest\n\npd.set_option('displa... | [
[
"pandas.read_csv",
"pandas.set_option"
]
] |
u6579559/fairseq | [
"0c36db41012c1fbd1dc570ff3ba1f0ef9b830191"
] | [
"fairseq/data/data_utils.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\ntry:\n from collections.abc import Iterable\nexcept ImportError:\n from collections import Iterable\nimport contextlib\nimport... | [
[
"numpy.random.rand",
"numpy.random.choice",
"numpy.copy",
"numpy.min",
"numpy.full",
"numpy.random.normal",
"numpy.random.poisson",
"numpy.random.get_state",
"numpy.random.randint",
"numpy.array",
"torch.max",
"numpy.random.set_state",
"numpy.asarray",
"torc... |
sonatsen/raven | [
"30764491e7ecaa16de2a4e0ddab3bc9e169e5f95"
] | [
"framework/Steps.py"
] | [
"# Copyright 2017 Battelle Energy Alliance, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ... | [
[
"numpy.atleast_1d"
]
] |
Chonim/aqicn.org-Crawler | [
"3c47958acea7072b980a235b0e12847ba0349b97"
] | [
"aqlWorld.py"
] | [
"import csv\nimport time\nimport json\nimport requests\nimport numpy as np\nimport gevent.monkey\nimport urllib\nfrom datetime import datetime\nfrom urllib.request import urlopen\n\nstart_time = time.time()\nheader_row = False\ncount = 0\nfile_name = \"dust_result_\" + time.strftime(\"%Y%m%d-%H%M%S\") + \".csv\"\n\... | [
[
"numpy.arange"
]
] |
cocoxu/OpenNMT-py | [
"820ad912dda0b5cbe49c53762374deb6bedd1299"
] | [
"tools/extract_embeddings.py"
] | [
"import argparse\n\nimport torch\n\nimport onmt\nimport onmt.model_builder\nimport onmt.inputters as inputters\nimport onmt.opts\n\nfrom onmt.utils.misc import use_gpu\nfrom onmt.utils.logging import init_logger, logger\n\nparser = argparse.ArgumentParser(description='translate.py')\n\nparser.add_argument('-model',... | [
[
"torch.cuda.set_device",
"torch.load"
]
] |
KevinGetandGive/flurs | [
"d64912a9c90b441de517e8264a7698434384a12d"
] | [
"flurs/recommender/user_knn.py"
] | [
"from ..base import RecommenderMixin\nfrom ..model import UserKNN\n\nimport numpy as np\n\n\nclass UserKNNRecommender(UserKNN, RecommenderMixin):\n\n \"\"\"User k-Nearest-Neighbor (kNN; user-based collaborative filtering) recommender\n\n References\n ----------\n\n - M. Pepagelis et al.\n **Increme... | [
[
"numpy.concatenate",
"numpy.abs",
"numpy.argsort",
"numpy.zeros"
]
] |
peter850706/VIPCUP2018 | [
"76510531d57ba78e9c2bc364a10db05c4e9d0035"
] | [
"models/densehighres3dnet_multitasking/densehighres3dnet_multitasking.py"
] | [
"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, print_function\n\nfrom six.moves import range\n\nimport tensorflow as tf\nfrom niftynet.layer import layer_util\nfrom niftynet.layer.activation import ActiLayer\nfrom niftynet.layer.base_layer import TrainableLayer, LayerFromCallable\nfrom niftynet.l... | [
[
"tensorflow.concat",
"tensorflow.reshape",
"tensorflow.squeeze",
"tensorflow.slice",
"tensorflow.split"
]
] |
dmoebius-dm/prototorch_models | [
"71602bf38a09148eab13d98c9f89589b345ac570"
] | [
"examples/lvqmln_iris.py"
] | [
"\"\"\"LVQMLN example using all four dimensions of the Iris dataset.\"\"\"\n\nimport argparse\n\nimport prototorch as pt\nimport pytorch_lightning as pl\nimport torch\n\n\nclass Backbone(torch.nn.Module):\n def __init__(self, input_size=4, hidden_size=10, latent_size=2):\n super().__init__()\n self... | [
[
"torch.nn.Linear",
"torch.utils.data.DataLoader",
"torch.nn.Sigmoid"
]
] |
jsngalloway/night-trader | [
"520e94e58f99e8670e2dbddf66955a737be9e0d6"
] | [
"night_trader/predictors/lstm/lstm_creator.py"
] | [
"from typing import List, Tuple\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow.keras import layers, optimizers\nfrom sklearn.preprocessing import MinMaxScaler\n\n\ndef scaleData(\n paths_to_datasets: List[str], sub_sampling\n) -> Tuple[List[pd.DataFrame], MinMaxScaler]:\n s... | [
[
"numpy.array",
"pandas.DataFrame",
"tensorflow.keras.Sequential",
"tensorflow.keras.layers.Dense",
"sklearn.preprocessing.MinMaxScaler",
"tensorflow.keras.layers.LSTM",
"pandas.read_csv"
]
] |
selinozdas/Mona | [
"f1cd3e31f1a607c454566337423cd3e7031c4374"
] | [
"mona.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"selin(1).ipynb\r\n\r\nAutomatically generated by Colaboratory.\r\n\r\nOriginal file is located at\r\n https://colab.research.google.com/drive/17_eVt28SluHB_hQNwNlwvH2KqPftxkh3\r\n\"\"\"\r\n\r\nimport os\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotli... | [
[
"matplotlib.pyplot.subplot",
"numpy.array",
"sklearn.preprocessing.LabelEncoder",
"numpy.zeros",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.title",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.yticks",
"matplotlib.pyplot.ylabel",
"sklearn... |
murali513/pythonsample | [
"991b61ebdc7b1315a5618c0081ebdb45a217f223"
] | [
"application.py"
] | [
"from flask import request, Flask\nfrom flask_cors import CORS\n\napp = Flask(__name__)\napp.config['DEBUG'] = True\nCORS(app)\n\n# Setup configuration file\nimport configparser\n\nconfig = configparser.ConfigParser()\nconfig.read('api_config.ini')\n\n# Setup logs test\nimport logging\n\nlogging.basicConfig(filenam... | [
[
"tensorflow.expand_dims",
"tensorflow.get_default_graph",
"tensorflow.Graph",
"tensorflow.Session",
"tensorflow.import_graph_def",
"tensorflow.GraphDef",
"tensorflow.gfile.GFile",
"matplotlib.pyplot.figure",
"tensorflow.cast",
"tensorflow.squeeze",
"tensorflow.greater",... |
zehengl/ez-cuisine-classifier | [
"ba8f3f6a4bbeb738f75be400aec38ceda0c893ae"
] | [
"try_language_models.py"
] | [
"import json\nimport os\n\nimport pandas as pd\nfrom fastai.text import (\n AWD_LSTM,\n TextClasDataBunch,\n TextLMDataBunch,\n language_model_learner,\n text_classifier_learner,\n)\nfrom sklearn.model_selection import train_test_split\n\n\ndata = \"data\"\nmodel = \"model_fastai\"\n\nif __name__ == ... | [
[
"sklearn.model_selection.train_test_split",
"pandas.DataFrame"
]
] |
HephaestusProject/pytorch-ReCoSa | [
"eca171582a9021845009ade542cd99c2e5ddf701"
] | [
"train.py"
] | [
"\"\"\"\n This script was made by soeque1 at 24/07/20.\n To implement code for training your model.\n\"\"\"\n\nimport logging\nfrom argparse import ArgumentParser, Namespace\nfrom logging import getLogger\n\nimport pytorch_lightning as pl\nimport torch\nimport torch.nn.functional as F\nfrom pytorch_lightning ... | [
[
"torch.nn.functional.cross_entropy",
"torch.cuda.is_available",
"torch.exp",
"torch.argmax"
]
] |
kachiann/QMCSoftware | [
"0ed9da2f10b9ac0004c993c01392b4c86002954c",
"0ed9da2f10b9ac0004c993c01392b4c86002954c"
] | [
"qmcpy/integrand/ml_call_options.py",
"qmcpy/true_measure/lebesgue.py"
] | [
"from ._integrand import Integrand\nfrom ..discrete_distribution import Sobol\nfrom ..true_measure import Gaussian\nfrom ..util import ParameterError\nfrom numpy import *\nfrom scipy.stats import norm\n\n\nclass MLCallOptions(Integrand):\n \"\"\"\n Various call options from finance using Milstein discretizati... | [
[
"scipy.stats.norm.cdf"
],
[
"scipy.stats.norm.pdf",
"numpy.array",
"scipy.stats.norm.ppf",
"numpy.tile",
"numpy.isscalar",
"numpy.isfinite",
"scipy.stats.norm.cdf"
]
] |
Addalin/cameranetwork | [
"edb8bfc83ab3cd4be90b8c50d45190d98d91571a"
] | [
"scripts_sunphotometer/analyze_readings.py"
] | [
"##\n## Copyright (C) 2017, Amit Aides, all rights reserved.\n## \n## This file is part of Camera Network\n## (see https://bitbucket.org/amitibo/cameranetwork_git).\n## \n## Redistribution and use in source and binary forms, with or without modification,\n## are permitted provided that the following conditions are ... | [
[
"matplotlib.use",
"numpy.array",
"numpy.ascontiguousarray",
"matplotlib.pyplot.plot",
"numpy.mean",
"matplotlib.pyplot.figure",
"numpy.std",
"matplotlib.pyplot.show",
"matplotlib.pyplot.imshow"
]
] |
qwindelzorf/owon_hds200 | [
"1914ca18b30927dc786513cba7758a6ce56df904"
] | [
"hds_stream.py"
] | [
"from typing import Any, Dict, List\nfrom owonHDS import owonHDS\nimport sys\nimport time\nimport re\nimport numpy as np\nimport pyqtgraph as pg\n\nfrom pyqtgraph.Qt import QtCore\n\nCHANNEL_COLORS = {\"CH1\": \"y\", \"CH2\": \"b\"}\n\n\ndef float_from_str(val: str) -> float:\n _prefix = {\n \"p\": 1e-12,... | [
[
"numpy.arange"
]
] |
Mrmoore98/hedwig | [
"dc8c2f1f5e6886b9ce9999bbd071bce02cfbbaf1"
] | [
"common/trainers/bert_trainer.py"
] | [
"import datetime\nimport os\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader, RandomSampler, TensorDataset\nfrom tqdm import tqdm\nfrom tqdm import trange\n\nfrom common.evaluators.bert_evaluator import BertEvaluator\nfrom datasets.bert_processors.abstract_processor import c... | [
[
"torch.utils.data.RandomSampler",
"torch.argmax",
"torch.save",
"torch.tensor",
"torch.utils.data.DataLoader",
"torch.utils.data.TensorDataset"
]
] |
anyboby/mbpo | [
"98b75cb4cb13a2640fce1fbe1ddef466b864342e"
] | [
"mbpo/models/bnn.py"
] | [
"from __future__ import division\nfrom __future__ import print_function\nfrom __future__ import absolute_import\n\nimport os\nimport time\nimport pdb\nimport itertools\nfrom collections import OrderedDict\n\nimport tensorflow as tf\nimport numpy as np\nfrom tqdm import trange\nfrom scipy.io import savemat, loadmat\... | [
[
"tensorflow.exp",
"numpy.random.choice",
"numpy.set_printoptions",
"numpy.tile",
"numpy.sort",
"tensorflow.add_n",
"tensorflow.ConfigProto",
"tensorflow.variable_scope",
"numpy.random.randint",
"tensorflow.nn.softplus",
"numpy.arange",
"tensorflow.Session",
"ten... |
acmbo/MarketSimulation | [
"a6f5678f788118c27c8a17abfa494373e3cc2f48"
] | [
"tests/test.py"
] | [
"import unittest\nimport pandas as pd\n\nfrom context import market\nfrom market.Marketactor import marketactor, market_Buyer, market_Seller\n\nclass TestActors(unittest.TestCase):\n\n def test_is_actor(self):\n \n for obj in [marketactor, market_Buyer, market_Seller]:\n \n print('Tes... | [
[
"pandas.DataFrame"
]
] |
peteWT/cbm_fia | [
"62de14a9f6ec3bc1d2af4158d0fd747e6531597e"
] | [
"cbm_curves.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom scipy.optimize import curve_fit\n\ndf = pd.read_csv('cabioage.csv')\n\n\ndef beta(data, pred='totage', resp='drybio_bole'):\n pmin = min(data[pred])\n pmax = max(data[pred])\n dmin = min(data[data[pred] == pmin][resp])\n dmax = max(data[data[pred] == pmax][... | [
[
"pandas.read_csv",
"numpy.power",
"numpy.exp"
]
] |
adit-negi/cowin-alerts-app | [
"af34f717ba52677bd78852a0dd37b40ebe244760"
] | [
"alerts/tasks.py"
] | [
"import requests\nimport json\nimport smtplib, ssl\nimport time\nfrom datetime import datetime, timedelta\nfrom .models import *\nfrom django.conf import settings\nfrom django.template import Context\nfrom django.template.loader import render_to_string\nfrom django.core.mail import EmailMultiAlternatives, EmailMess... | [
[
"numpy.asarray",
"numpy.random.RandomState"
]
] |
hannahbrucemacdonald/molssi_project | [
"49fe030f7cc43638bab4dfd50faa39742aee6900"
] | [
"molssi_project/tests/test_math.py"
] | [
"import pytest\nimport molssi_project as mp\nimport numpy as np\n\n\n@pytest.mark.parametrize('n, answer', [(0, 0.), (1, 1.), (2, 2.), (3, 2.5), (4, 2.667)])\ndef test_euler(n, answer):\n assert (mp.math.euler(n) == pytest.approx(answer,\n abs=2)), 'Euler function for... | [
[
"numpy.random.seed"
]
] |
sdgds/dnnbrain | [
"a28ef0dcc20fb16d30a1158f15558a8844827173"
] | [
"dnnbrain/dnn/tests/test_core.py"
] | [
"import os\nimport copy\nimport h5py\nimport pytest\nimport numpy as np\n\nfrom os.path import join as pjoin\nfrom dnnbrain.io import fileio as fio\nfrom dnnbrain.dnn import core as dcore\nfrom dnnbrain.dnn.base import dnn_mask, array_statistic, dnn_fe\n\nDNNBRAIN_TEST = pjoin(os.environ['DNNBRAIN_DATA'], 'test')\n... | [
[
"numpy.concatenate",
"numpy.array",
"numpy.asarray",
"numpy.zeros",
"numpy.testing.assert_equal",
"numpy.testing.assert_almost_equal",
"numpy.ones",
"numpy.random.randn",
"numpy.random.randint",
"numpy.arange",
"numpy.all"
]
] |
yaojin17/adversarial-project | [
"76af16f126ae701fb3a0a83152b37cbec5e7b28f",
"76af16f126ae701fb3a0a83152b37cbec5e7b28f"
] | [
"ChangedResnet18.py",
"trades_awp_utils.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom torch.nn import Parameter\r\n\r\n\r\nclass CosineLinear(nn.Module):\r\n def __init__(self, in_features, out_features):\r\n super(CosineLinear, self).__init__()\r\n\r\n self.in_features = in_features\r\n self.ou... | [
[
"torch.nn.functional.normalize",
"torch.nn.functional.avg_pool2d",
"torch.nn.Sequential",
"torch.nn.BatchNorm2d",
"torch.nn.init.xavier_uniform_",
"torch.nn.Conv2d",
"torch.nn.functional.relu",
"torch.Tensor"
],
[
"torch.max",
"torch.autograd.Variable",
"torch.no_gr... |
electricbrainio/hyperopt | [
"68839dc3a82f146aad2307dd2eab33bf6ed5f3cb"
] | [
"hyperopt/mongoexp.py"
] | [
"\"\"\"\nMongodb-based Trials Object\n===========================\n\nComponents involved:\n\n- mongo\n e.g. mongod ...\n\n- driver\n e.g. hyperopt-mongo-search mongo://address bandit_json bandit_algo_json\n\n- worker\n e.g. hyperopt-mongo-worker --loop mongo://address\n\n\nMongo\n=====\n\nMongo (daemon pro... | [
[
"numpy.invert",
"numpy.array",
"numpy.random.randint",
"numpy.random.rand"
]
] |
estanislaoledesma/genper | [
"5996b8bc199d8cecc74b7f6d03b67a4c356b4beb"
] | [
"tests/utils/test_file_manager.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport unittest\n\nimport numpy as np\n\nfrom dataloader.image.image import Image\nfrom utils.file_manager import FileManager\n\nROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))\n\n\nclass TestFileManager(unittest.TestCase):\n\... | [
[
"numpy.random.rand"
]
] |
notreal1995/once-for-all | [
"be6b47173e8d365e0712bade60a7cc6495e65d8e"
] | [
"ofa/model_zoo.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\nimport json\nimport torch\nimport torch.nn as nn\n# from layers import *\nfrom ofa.layers import MyModul... | [
[
"torch.squeeze",
"torch.nn.ModuleList"
]
] |
Build-Week-Airbnb-Optimal-Price-4/Deployment-Test | [
"60a7bd3bbfb890fd771ae1052ecf14ef586d384c"
] | [
"airbnb_api/application.py"
] | [
"\"\"\"Flask App for Predicting Optimal AirBnB prices in Berlin\"\"\"\r\nfrom flask import Flask, jsonify, request\r\nimport numpy as np\r\nimport pandas as pd\r\nimport pickle\r\nfrom joblib import load\r\n# import xgboost as xgb\r\nimport os\r\n\r\n# local import:\r\nfrom .api_function import get_lemmas\r\nfrom d... | [
[
"pandas.DataFrame"
]
] |
rainarit/segmentation-benchmark | [
"bbdadf56ed2ff1049e7dd5925f61f524d0440401"
] | [
"semseg/train.py"
] | [
"import datetime\nimport os\nimport time\nimport torch\nimport torch.utils.data\nimport transforms as T\nfrom torch import nn\nimport torchvision\nimport numpy as np\nimport scipy.io\nimport random\nfrom PIL import Image\nimport matplotlib.image as mpimg\nfrom tqdm import tqdm\nfrom coco_utils import get_coco\nimpo... | [
[
"torch.cuda.manual_seed",
"torch.utils.data.RandomSampler",
"torch.cuda.amp.autocast",
"torch.Generator",
"numpy.min",
"torch.nn.functional.cross_entropy",
"torch.load",
"torch.nn.SyncBatchNorm.convert_sync_batchnorm",
"torch.initial_seed",
"torch.manual_seed",
"torch.t... |
linesd/SSGPR | [
"3728bf5aa25334c40955a211aa391a8721839901"
] | [
"test/test_predict.py"
] | [
"import sys\nsys.path.append(\"..\")\nimport numpy as np\nfrom model.ssgpr import SSGPR\nfrom math import floor\n\ndef test_predict():\n np.random.seed(1) # set seed\n precision = 5\n\n # load the data\n data = np.load(\"../data/test_data/test_predict_data.npy\")\n solution = np.load(\"../data/test_... | [
[
"numpy.random.seed",
"numpy.load",
"numpy.round"
]
] |
iitm-sysdl/FuSeConv | [
"04cdf54abfdbf359235d1b4c0848f188b1abbf2d"
] | [
"yet_another_mobilenet_series/common.py"
] | [
"import copy\nimport importlib\nimport logging\nimport math\nimport os\n\nimport torch\nimport torch.distributed as dist\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom utils import distributed as udist\nfrom utils.model_profiling import model_profiling\nfrom utils.config import FLAGS\nfrom utils.meters ... | [
[
"torch.cat",
"torch.distributed.all_gather",
"torch.cuda.device_count",
"torch.tensor",
"torch.ones_like",
"torch.mean",
"torch.nn.DataParallel"
]
] |
HARDIntegral/ADHD_Classifier | [
"f86c8eafa78ca241919d12134afe796c665f8021"
] | [
"Data/norm_plot.py"
] | [
"#!/usr/bin/env python3\n\nfrom numpy import linspace\nimport matplotlib.pyplot as plt\nimport scipy.stats as ss\n\nplt.style.use('seaborn') # pretty matplotlib plots\nplt.rcParams['figure.figsize'] = (8,5)\n\ndef plot_normal(data_points,x_range,mu,sigma,color,label):\n \n x = x_range\n y = ss.norm.pdf(x,m... | [
[
"scipy.stats.norm.pdf",
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.hist",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.show",
"... |
spradius/TensorRT | [
"eb5de99b523c76c2f3ae997855ad86d3a1e86a31"
] | [
"tools/Polygraphy/tests/util/test_util.py"
] | [
"#\n# Copyright (c) 2021, NVIDIA CORPORATION. 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.array_equal"
]
] |
khangthk/ITK | [
"f3c12edaf9cef07dbc34107e1a8aec9859204116"
] | [
"Modules/Bridge/NumPy/wrapping/test/itkImageMetaDataSetGetItem.py"
] | [
"# ==========================================================================\n#\n# Copyright NumFOCUS\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# https:/... | [
[
"numpy.allclose",
"numpy.array"
]
] |
prasastoadi/tensor2tensor | [
"2e65bf2a4581761396ca0e737bcb2b79e700fee6"
] | [
"tensor2tensor/rl/model_rl_experiment.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Tensor2Tensor 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 requir... | [
[
"tensorflow.Summary",
"tensorflow.logging.set_verbosity",
"tensorflow.expand_dims",
"tensorflow.gfile.Copy",
"tensorflow.Graph",
"tensorflow.gfile.Exists",
"tensorflow.Session",
"tensorflow.logging.info",
"tensorflow.reshape",
"tensorflow.map_fn",
"tensorflow.app.run",
... |
danielschulz/SDV | [
"0a346f09a751490a0b0165ef0c11267604c99646"
] | [
"sdv/constraints/tabular.py"
] | [
"\"\"\"Table constraints.\n\nThis module contains constraints that are evaluated within a single table,\nand which can affect one or more columns at a time, as well as one or more\nrows.\n\nCurrently implemented constraints are:\n\n * CustomConstraint: Simple constraint to be set up by passing the python\n ... | [
[
"numpy.exp"
]
] |
xuyannus/Machine-Learning-Collection | [
"425d196e9477dbdbbd7cc0d19d29297571746ab5"
] | [
"ML/Projects/text_generation_babynames/generating_names.py"
] | [
"\"\"\"\nText generation using a character LSTM, specifically we want to\ngenerate new names as inspiration for those having a baby :) \n\nAlthough this is for name generation, the code is general in the\nway that you can just send in any large text file (shakespear text, etc)\nand it will generate it.\n\nProgramme... | [
[
"torch.nn.Linear",
"torch.zeros",
"torch.nn.LSTM",
"torch.nn.CrossEntropyLoss",
"torch.multinomial",
"torch.cuda.is_available",
"torch.nn.Embedding"
]
] |
soufiomario/VideoSuperResolution | [
"82c3347554561ff9dfb5e86d9cf0a55239ca662e"
] | [
"VSR/Backend/TF/Framework/Motion.py"
] | [
"\"\"\"\nCopyright: Wenyi Tang 2017-2018\nAuthor: Wenyi Tang\nEmail: wenyi.tang@intel.com\nCreated Date: Aug 21st 2018\n\nUtility for motion compensation\n\"\"\"\nimport numpy as np\nimport tensorflow as tf\n\n\ndef _grid_norm(width, height, bounds=(-1.0, 1.0)):\n \"\"\"generate a normalized mesh grid\n\n Args:... | [
[
"tensorflow.scatter_nd",
"tensorflow.sqrt",
"tensorflow.clip_by_value",
"tensorflow.stack",
"tensorflow.tile",
"tensorflow.to_float",
"tensorflow.cast",
"tensorflow.shape",
"tensorflow.transpose",
"tensorflow.add_n",
"tensorflow.squared_difference",
"tensorflow.floo... |
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