repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
civisanalytics/muffnn | [
"0ec92ddb5a0158a3e46e68974ff958101e3b874d"
] | [
"muffnn/mlp/mlp_regressor.py"
] | [
"\"\"\"\nA Deep Neural Network (multilayer Perceptron) sklearn-style regressor.\n\nSimilar to sklearn.neural_network.MLPRegressor, but using TensorFlow.\n\"\"\"\n\nimport logging\nfrom warnings import warn\n\nimport numpy as np\nfrom sklearn.base import RegressorMixin\n\nimport tensorflow as tf\nfrom tensorflow.pyt... | [
[
"tensorflow.multiply",
"tensorflow.contrib.nn.alpha_dropout",
"numpy.mean",
"tensorflow.reshape",
"numpy.std",
"tensorflow.placeholder",
"tensorflow.reduce_sum",
"tensorflow.nn.dropout"
]
] |
gangserver/py_test | [
"869bdfa5c94c3b6a15b87e0c3de6b2cdaca821f4"
] | [
"python/hongong/ch07/07_3.py"
] | [
"from tensorflow import keras\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n(train_input, train_target), (test_input, test_target) =\\\n keras.datasets.fashion_mnist.load_data()\nprint(train_input.shape, train_target.shape)\nprint(test_input.shape, ... | [
[
"tensorflow.keras.layers.Flatten",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"tensorflow.keras.datasets.fashion_mnist.load_data",
"tensorflow.keras.callbacks.ModelCheckpoint",
"tensorflow.keras.Sequential",
"tensorflow.keras.models.load_model... |
donchesworth/pytorch-quik | [
"e59ea3393bf017a17ab92991f14fe3bd6c5b2d0c"
] | [
"pytorch_quik/tensor.py"
] | [
"import torch\nimport torch.utils.data as td\nfrom typing import Optional, Dict, Union\nfrom transformers import BatchEncoding\nfrom argparse import Namespace\nimport numpy as np\nimport pandas as pd\nfrom pytorch_quik import io\n\nTensor_Target = Union[str, np.ndarray]\nTensor_Data = Union[pd.DataFrame, torch.Tens... | [
[
"torch.LongTensor",
"torch.save",
"torch.tensor",
"torch.utils.data.TensorDataset"
]
] |
cclauss/statarb | [
"a59366f70122c355fc93a2391362a3e8818a290e",
"a59366f70122c355fc93a2391362a3e8818a290e"
] | [
"load_data_live.py",
"salamander/get_borrow.py"
] | [
"#!/usr/bin/env python\n\nimport sys\nimport os\nimport glob\nimport re\nimport math\n\nfrom dateutil import parser as dateparser\nimport time\nfrom datetime import datetime\nfrom datetime import timedelta\n\nimport numpy as np\nimport pandas as pd\nimport pandas.io.sql as psql\nimport sqlite3 as lite\n\nfrom util ... | [
[
"pandas.read_csv"
],
[
"pandas.read_csv",
"pandas.concat"
]
] |
ademidun/mordred | [
"8337a088b96e35fda86235ebf37f9c72d35cf72f"
] | [
"mordred/_graph_matrix.py"
] | [
"import numpy as np\nfrom rdkit import Chem\n\nfrom ._base import Descriptor\n\n\nclass DistanceMatrix(Descriptor):\n __slots__ = (\"explicit_hydrogens\", \"useBO\", \"useAtomWts\")\n\n hermitian = True\n\n def parameters(self):\n return self.explicit_hydrogens, self.useBO, self.useAtomWts\n\n de... | [
[
"numpy.sum"
]
] |
YiY-Xu/11775-hws | [
"fdbdefb8c90d600f9336e3a7491c1f68798cccd4"
] | [
"hw2_code/select_frames.py"
] | [
"#!/bin/python\n# Randomly select \nimport yaml\nimport numpy as np\nimport os\nimport sys\n\nif __name__ == '__main__':\n if len(sys.argv) != 4:\n print (\"Usage: {0} file_list select_ratio output_file\".format(sys.argv[0]))\n print (\"file_list -- the list of video names\")\n print(\"confi... | [
[
"numpy.random.seed",
"numpy.float32",
"numpy.genfromtxt",
"numpy.random.shuffle"
]
] |
hjw-1014/Multi-Objective-Reactive-Motion-Planning-in-Mobile-Manipulators | [
"9a8801e9c663174b753c4852b2313c5a3f302434"
] | [
"scripts/darias_energy_control/_test_simple_tiago_lefthand_base.py"
] | [
"import numpy as np\nimport pybullet as p\nimport time\nimport matplotlib.pyplot as plt\n\nfrom cep.envs import Tiago_LeftParallelHand_Base\nfrom cep.cep_models import cep_simple_model_tiago_lefthand_base\nimport torch\n\njoint_limit_buffers = 0.02\njoint_limits = np.array([2.5, 2.5, 3.1416, 2.75, 1.57, 3.53, 2.35,... | [
[
"torch.device",
"numpy.array",
"matplotlib.pyplot.subplots",
"numpy.arange",
"numpy.clip",
"matplotlib.pyplot.show"
]
] |
giuliasantarsieri/open_ML | [
"db8cfab3e67b246553db27868183f71cfe3ef4f8"
] | [
"src/get_dataset.py"
] | [
"from pathlib import Path\n\nimport requests\nimport pandas as pd\nimport os\n\n\ndef latest_catalog():\n \"\"\"This function returns the pandas dataframe of the latest version of dgf resource catalog\n (https://www.data.gouv.fr/en/datasets/catalogue-des-donnees-de-data-gouv-fr/#_)\"\"\"\n # dgf_catalog = ... | [
[
"pandas.read_csv",
"pandas.read_table",
"pandas.set_option",
"pandas.read_excel"
]
] |
jedibobo/S2ANet-custom-dataset | [
"869b196d4c33713a5c61bd80064d10a453fb76ef"
] | [
"DOTA_devkit/ImgSplit_multi_process.py"
] | [
"\"\"\"\n-------------\nThis is the multi-process version\n\"\"\"\nimport codecs\nimport copy\nimport math\nimport os\nfrom functools import partial\nfrom multiprocessing import Pool\n\nimport cv2\nimport dota_utils as util\nimport numpy as np\nimport shapely.geometry as shgeo\nfrom dota_utils import GetFileFromThi... | [
[
"numpy.sum",
"numpy.array",
"numpy.zeros",
"numpy.shape"
]
] |
Wrosinski/dl_frameworks_benchmark | [
"62d7e408a825c8c287d6fa6afdd53f6cc543f95c",
"62d7e408a825c8c287d6fa6afdd53f6cc543f95c"
] | [
"pytorch/models_pretrained/densenet169.py",
"pytorch/models_pretrained/polynet.py"
] | [
"import numpy as np\nimport torch\nimport torchvision\nfrom torch import nn\nfrom torch.nn import functional as F\n\nmean = [0.485, 0.456, 0.406]\nstd = [0.229, 0.224, 0.225]\n\nmean_ = np.mean(mean)\nstd_ = np.mean(std)\n\n\nclass DenseNet169(nn.Module):\n\n def __init__(self, parameters):\n\n super().__... | [
[
"torch.nn.Linear",
"torch.nn.AdaptiveMaxPool2d",
"numpy.mean",
"torch.nn.functional.relu"
],
[
"torch.nn.Linear",
"torch.nn.AdaptiveMaxPool2d"
]
] |
wenke727/RoadNetworkCreator | [
"a359d2e5c5f0921b1af514c3a88b5e3a25707407"
] | [
"src/db/db_process.py"
] | [
"#%%\nimport os, sys\nimport geopandas as gpd\nimport pandas as pd\nfrom sqlalchemy import create_engine\nfrom shapely.geometry import Point, box\n\nsys.path.append(\"..\") \nfrom utils.utils import load_config\n\n\nconfig = load_config()\npano_dir = config['data']['pano_dir']\nENGINE = create_engine(config['da... | [
[
"scipy.stats.mode",
"pandas.DataFrame",
"pandas.read_csv",
"pandas.read_hdf"
]
] |
samkberry/prysm | [
"3c17cb7b6049a36a1f8b6a0035c216ca1078aee1"
] | [
"prysm/mtf_utils.py"
] | [
"\"\"\"Utilities for working with MTF data.\"\"\"\nimport operator\n\nfrom scipy.interpolate import griddata, RegularGridInterpolator as RGI\n\nfrom .util import share_fig_ax\nfrom .io import read_trioptics_mtf_vs_field, read_trioptics_mtfvfvf\n\nfrom prysm import mathops as m\n\n\nclass MTFvFvF(object):\n \"\"\... | [
[
"pandas.DataFrame",
"scipy.interpolate.RegularGridInterpolator"
]
] |
pasinducw/scs-4224-fyp | [
"9ff208e31ddd17146dac2226ac5474bd7fe98ab2"
] | [
"comparing-rnn-params/model/trainer.py"
] | [
"import copy\nimport math\nimport os\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport librosa\nimport librosa.display\n\nfrom model import Model\nfrom dataset import Covers80DatasetPerformanceChunks\n\nimport argparse\n\nparser = argparse.Argu... | [
[
"torch.device",
"matplotlib.pyplot.xlabel",
"torch.no_grad",
"matplotlib.pyplot.legend",
"numpy.mean",
"numpy.argmax",
"matplotlib.pyplot.clf",
"torch.nn.CrossEntropyLoss"
]
] |
artemmavrin/SLTools | [
"04525b5d6777be3ccdc6ad44e4cbfe24a8875933"
] | [
"stattools/mixture/gaussian.py"
] | [
"\"\"\"Estimation and simulation for Gaussian mixture models.\"\"\"\n\nimport itertools\nimport numbers\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.stats as st\n\nfrom ..cluster import KMeansCluster\nfrom ..generic import Classifier\nfrom ..utils.validation import validate_bool\nfrom ..util... | [
[
"numpy.meshgrid",
"numpy.mean",
"scipy.stats.multivariate_normal.pdf",
"numpy.empty",
"numpy.eye",
"numpy.arange",
"numpy.ndim",
"matplotlib.pyplot.gca",
"numpy.zeros",
"numpy.shape",
"numpy.cov",
"numpy.asarray",
"numpy.random.RandomState",
"numpy.sum",
... |
jiabijue/md_mrle | [
"21830842ca4e663153b9abb94ca2db604059a91f"
] | [
"models/hedln.py"
] | [
"import torch\r\nimport torch.nn as nn\r\n\r\n\r\nclass HEDLN(nn.Module):\r\n def __init__(self, input_size, hidden_size, num_layers,\r\n dropout, bidirectional, num_classes1, num_classes2):\r\n super(HEDLN, self).__init__()\r\n self.num_directions = 2 if bidirectional else 1\r\n ... | [
[
"torch.nn.Linear",
"torch.cat",
"torch.nn.LSTM",
"torch.nn.Sigmoid"
]
] |
louni-g/MedCAT | [
"5ffa96b9cef2520ab3a4b406aa2ab880b38f1e13"
] | [
"medcat/deprecated/cat_ann.py"
] | [
"\"\"\" I would just ignore this whole class, it's just a lot of rules that work nicely for UMLS with garbage\nonce the software is trained the main thing are the context vectors.\n\"\"\"\n\nimport numpy as np\nimport operator\n\nclass CatAnn(object):\n def __init__(self, cdb, spacy_cat):\n self.cdb = cdb... | [
[
"numpy.max"
]
] |
harry-uglow/Curriculum-Reinforcement-Learning | [
"cb050556e1fdc7b7de8d63ad932fc712a35ac144"
] | [
"graphing/training_time_vs_tsr.py"
] | [
"import numpy as np\nimport torch\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# two_stage_baseline_data = [torch.load(f\"sparse_dr_{i}M_eval.pt\") for i in range(1, 5)]\n# curl_data = torch.load(f\"curl_eval.pt\")\n# dense_dr_data = torch.load(f\"dense_dr_eval.pt\")\nclrs = [\n '#1f77b4', # muted... | [
[
"numpy.array",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.subplots",
"numpy.mean",
"numpy.std",
"matplotlib.pyplot.draw",
"matplotlib.pyplot.ylabel",
"torch.load",
"matplotlib.pyplot.show",
"matplotlib.pyplot.xticks"
]
] |
thiago9864/calculo_numerico | [
"e4b6f059bdb31460130093bd446fb3ea906dc542"
] | [
"Lista6/Erros.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\n@author: Renan, Thiago\n\"\"\"\n\n#Calculo do Erro\n\nimport numpy as np\nfrom MetodosIntegracao import NewtonCotes\nfrom MinimosQuadrados import MinimosQuadrados\n\nclass Erro():\n \n \n def erroDaNorma(self,K,a,b,f,coeficientes, n, particoes): #1\n #K = Numero de Pa... | [
[
"numpy.abs",
"numpy.sqrt"
]
] |
vishalbelsare/avalanche | [
"128491b5712ffac826fe30eb798cd22ae147396b"
] | [
"tests/training/test_plugins.py"
] | [
"import itertools\nimport sys\n\nimport torch\nfrom torch import nn\nimport unittest\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\n\nfrom torch.nn import CrossEntropyLoss\nfrom torch.optim import SGD\nfrom torch.optim.lr_scheduler import MultiStepLR, Reduce... | [
[
"torch.nn.Linear",
"torch.utils.data.dataloader.DataLoader",
"torch.nn.Sequential",
"torch.nn.CrossEntropyLoss",
"torch.no_grad",
"torch.optim.lr_scheduler.MultiStepLR",
"torch.random.manual_seed",
"torch.from_numpy",
"torch.nn.ReLU",
"torch.optim.lr_scheduler.ReduceLROnPla... |
LorePep/deepyeast | [
"4f35276b8d3a9ae03a14105c84b0746431fbf409"
] | [
"examples/learning_rate_finder.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nimport keras\nfrom keras import backend as K\nfrom keras.preprocessing.image import ImageDataGenerator\n\nfrom deepyeast.dataset import load_data\nfrom deepyeast.utils import preprocess_input\nfrom deepyeast.models import DeepYeast\n\nclass LearningRateFinder:\n... | [
[
"numpy.isinf",
"numpy.isnan",
"matplotlib.pyplot.xscale",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"numpy.min",
"matplotlib.pyplot.ylabel"
]
] |
begab/mamus | [
"94005a809fd8b547c2054d51adf117b8646035ed"
] | [
"IO_helper.py"
] | [
"import re\nimport gzip\nimport numpy as np\n\nfrom zipfile import ZipFile\n\ndef load_corpus(corpus_file, load_tags=False):\n if corpus_file.endswith('.gz'):\n corpus = []\n with gzip.open(corpus_file, 'r') as f:\n for line in f:\n corpus.append(line.decode(\"utf-8\").spl... | [
[
"numpy.array"
]
] |
indervirbanipal/building-deep-learning-models-with-tensorflow | [
"6317f5d96bfe4e88f3c357c43a20d86a87a4a35e"
] | [
"restricted_boltzmann_machines.py"
] | [
"import urllib.request\nwith urllib.request.urlopen(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DL0120EN-SkillsNetwork/labs/Week4/data/utils.py\") as url:\n response = url.read()\ntarget = open('utils.py', 'w')\ntarget.write(response.decode('utf-8'))\ntarget.clo... | [
[
"numpy.random.normal",
"tensorflow.zeros",
"tensorflow.shape",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.keras.layers.Flatten",
"matplotlib.pyplot.xlabel",
"tensorflow.round",
"tensorflow.matmul",
"matplotlib.pyplot.plot",
"tensorflow.transpose",
"tensor... |
abhithosar/chartocr_cv | [
"388b95710a02ded0532b021f64c58d8d3e1cc639"
] | [
"test_pipeline.py"
] | [
"#!/usr/bin/env python\nimport os\nimport json\nimport torch\nimport pprint\nimport argparse\n\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport cv2\nfrom tqdm import tqdm\nfrom config import system_configs\nfrom nnet.py_factory import NetworkFactory\nfrom db.datasets import datasets\nimport importlib\nfrom RuleG... | [
[
"matplotlib.use",
"torch.no_grad",
"torch.cuda.is_available"
]
] |
opentensor/BitTensor | [
"59de2d0fe48f3bd02ba5bff6159e6625bd6cb945",
"59de2d0fe48f3bd02ba5bff6159e6625bd6cb945"
] | [
"bittensor/_neuron/text/advanced_server/run.py",
"bittensor/utils/__init__.py"
] | [
"#!/bin/python3\n# The MIT License (MIT)\n# Copyright © 2021 Yuma Rao\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the “Software”), to deal in the Software without restriction, including without limitation\n# the rights to ... | [
[
"torch.zeros",
"torch.autograd.set_detect_anomaly",
"torch.arange"
],
[
"pandas.DataFrame",
"torch.randperm",
"torch.topk"
]
] |
maotong/deepnet | [
"a896006391f85695b2005cb775acf6b86f9dbb7b"
] | [
"cudamat/test_cudamat.py"
] | [
"import pdb\nimport numpy as np\nimport nose\nimport cudamat as cm\nimport cudamat_conv as cm_cv\n\ndef setup():\n cm.cublas_init()\n\ndef teardown():\n cm.cublas_shutdown()\n\ndef test_reshape():\n m = 256\n n = 1\n cm1 = np.array(np.random.rand(n, m)*10, dtype=np.float32, order='F')\n cm2 = np.a... | [
[
"numpy.array",
"numpy.dot",
"numpy.empty",
"numpy.random.rand",
"numpy.log",
"numpy.sum",
"numpy.random.randn",
"numpy.exp",
"numpy.mean",
"numpy.sign",
"numpy.sqrt",
"numpy.abs",
"numpy.vdot"
]
] |
JoeriHermans/constraining-dark-matter-with-stellar-streams-and-ml | [
"9dca4a508a19c514422a9b177d4fcf1dad6ea693"
] | [
"experiments/experiment-simulations/makeGD1stream.py"
] | [
"import os, os.path\nimport pickle\nimport numpy\nfrom optparse import OptionParser\nimport gd1_util\nfrom galpy.util import save_pickles\nfrom galpy.util import bovy_conversion\nfrom galpy.potential import MWPotential2014\nimport astropy.units as u\n\n\n#code to generate streams\ndef get_options():\n usage = \"... | [
[
"numpy.round",
"numpy.arange"
]
] |
vinhill/keras | [
"71d6d6eb0412f4c66ff40cf2393bb157cec281e0"
] | [
"keras/engine/base_preprocessing_layer.py"
] | [
"# Copyright 2019 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.eager.context.async_wait",
"tensorflow.compat.v2.sparse.to_dense",
"tensorflow.python.util.tf_export.keras_export",
"tensorflow.compat.v2.range",
"tensorflow.compat.v2.function",
"tensorflow.compat.v2.Variable",
"tensorflow.compat.v2.inside_function",
"tensorflow... |
jimkon/rl | [
"f917624607748c186b794fe783a462064be5dcfa"
] | [
"examples/mountain_car_stds.py"
] | [
"import numpy as np\nimport time\nimport gym\nimport pandas as pd\n\nfrom rl_lib.utils.utils import running_average\nfrom rl_lib.agents.q_learning import QLearningAgent\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import LogNorm\nplt.style.use(\"bmh\")\n\n\n################### VARIABLES ##############... | [
[
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.colorbar",
"numpy.linalg.norm",
"pandas.DataFrame",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.subplot",
"numpy.array",
"matplotlib.pyplot.title",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.hist",
"matplotlib.py... |
wangcongcong123/covidsearch | [
"f9a4a7ce0a127015e1b8cb5075238b3aaefe894e"
] | [
"cord/sentence_search.py"
] | [
"__author__=\"congcong wang\"\n\nimport pickle\nimport shutil\nimport time\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sentence_transformers import SentenceTransformer\nfrom tqdm import tqdm\nimport nltk\nimport os\nimport logging\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(messag... | [
[
"sklearn.metrics.pairwise.cosine_similarity"
]
] |
kkirtac/3D-ResNets-PyTorch | [
"263969d1b4f9d93260f0bd06c4e9cc8fd214632e"
] | [
"predict.py"
] | [
"import torch\nfrom torch import nn\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport time\nimport os\nimport csv\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport itertools\nfrom sklearn.metrics import confusion_matrix\nimport subprocess\nfrom models impo... | [
[
"sklearn.metrics.confusion_matrix",
"torch.stack",
"numpy.set_printoptions",
"torch.load",
"pandas.read_csv",
"torch.nn.DataParallel",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.colorbar",
"torch.autograd.Variable",
"matplotlib.pyplot.tight_layout",
"torch.utils.dat... |
tttslab/armos | [
"6003a9e90ea5ed586f8e45d7d8b872fdad58e26a"
] | [
"policy_gradient/TractSeqToWave_Func.py"
] | [
"import ctypes\t\t\t# for accessing C libraries \r\nimport os\t\t\t# for retrieving path names\r\nimport numpy # for array operations\r\nimport scipy.io.wavfile # to write wav file\r\nimport configparser\r\n\r\nconf = configparser.SafeConfigParser()\r\nconf.read(\"config.ini\")\r\nframeRate_Hz = int(conf.get('main... | [
[
"numpy.abs"
]
] |
phlpphns/ASCIItoXRDML | [
"974da632691f07a9743a8a73c3cee03593976333"
] | [
"scripts/ASCSynchGeneral_toXRDML.py"
] | [
"#!/usr/bin/python3\n\n#\n# ASCSynchMythen_toXRDML.py\n#\n# takes Mhythen input that has gaps. Does interpolation and saves to the PANalytical xrdml format \n# some sections are commented\n#\n# serious improvement appreciated\n# give wavelength as first argument in command line \n#\n\nimport numpy\nimport... | [
[
"scipy.interpolate.interp1d",
"numpy.genfromtxt",
"numpy.arange"
]
] |
tuoyl/timing_lib | [
"72d4467d932dbbd8d7ceb79bc5dfaa292a55679f"
] | [
"tatpulsar/pulsar_timing/ToA.py"
] | [
"#!/usr/bin/env python\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import curve_fit\nfrom tatpulsar.pulsar_timing.utils import *\nfrom tatpulsar.pulsar_timing.utils import njit, HAS_NUMBA\nfrom tatpulsar.pulsar_timing.Profile import resampling_profile, norm_profile, Profile\n\nimport s... | [
[
"numpy.max",
"numpy.array",
"matplotlib.pyplot.errorbar",
"numpy.sum",
"matplotlib.pyplot.plot",
"numpy.roll",
"numpy.min",
"numpy.mean",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.legend",
"numpy.argmax",
"numpy.sqrt",
"numpy.append",
"matplotlib.pyplot... |
meelgroup/mgt | [
"ccd51c9094e63eee8234ed7cde128746a481f897"
] | [
"general_lp_interface.py"
] | [
"import cplex\nimport numpy as np\nimport time as t\n\nalpha = 1\n\n\n# solve an instance of a Linear Programming relaxed instance for Group Testing\n# as designed in the malyuotov paper\n# y is the vector of result for each group tested\n# A is the recovery Matrix\n# nvar is the number of variables\n# noiseless is... | [
[
"numpy.multiply",
"numpy.diag"
]
] |
lsDrizzle/FreeFormDeformation-SketchDetection | [
"8b9da9b8e40ba9c98f012e03eb0388f4a7d5c613"
] | [
"generate_video.py"
] | [
"import manimlib.config\nimport manimlib.constants\nimport manimlib.extract_scene\nimport numpy as np\n\n\ndef generate_video(original_mesh, current_mesh, mesh_spacing, file=\"ffd_visualization.py\", low_quality=True, resolution=\"700,700\",\n scene_names=None, preview=False):\n \"\"\"\n gen... | [
[
"numpy.size",
"numpy.ones",
"numpy.flip"
]
] |
pocokhc/r2d3 | [
"b438e58c3c7a0296d613cbffe26bb15e87a3b4ac"
] | [
"src/memory.py"
] | [
"import numpy as np\n\nimport random\nimport bisect\nimport math\n\nclass Memory():\n \"\"\" Abstract base class for all implemented Memory. \"\"\"\n def add(self, exp, priority=0):\n raise NotImplementedError()\n\n def update(self, idx, exp, priority):\n raise NotImplementedError()\n\n de... | [
[
"numpy.empty"
]
] |
NateThom/contrastive_learning | [
"047f2628d7ff7bb8842f35311c4477df9b7e6566"
] | [
"old/random_blur.py"
] | [
"import torch\nimport torchvision.transforms.functional as TF\n\nclass MyRandomBlur(object):\n \"\"\"Crop randomly the image and masks in a sample\n output_size (tuple or int): Desired output size. If int, square crop\n is made.\n \"\"\"\n\n def __init__(self, kernel_size):\n ... | [
[
"torch.rand"
]
] |
CLC-HCMUS/models | [
"0102dfb62644602c319164aea6530510656046e4"
] | [
"syntaxnet/syntaxnet/beam_reader_ops_test.py"
] | [
"# Copyright 2016 Google Inc. 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 required by appl... | [
[
"tensorflow.assign",
"tensorflow.python.platform.googletest.main",
"tensorflow.Graph",
"tensorflow.python.platform.logging.info",
"tensorflow.test.get_temp_dir"
]
] |
Nishant173/pyutils | [
"f9a369b227b3f87d44cafa2a00696462cdc0cc37"
] | [
"pyutils/data_wrangler/explore.py"
] | [
"from typing import Dict, List, Optional, Union\n\nimport pandas as pd\n\nfrom pyutils.core.exceptions import InvalidDataFrameError\nfrom pyutils.core.type_annotations import NumberOrString\n\n\ndef is_dataframe_full(data: pd.DataFrame) -> bool:\n \"\"\"Returns True if there are no missing values in a non-empty ... | [
[
"pandas.DataFrame",
"pandas.Series"
]
] |
amimem/gym-multigrid | [
"7fff0316bf1c1d481a93bc131fe8da5f776de979"
] | [
"gym_multigrid/multigrid.py"
] | [
"import math\nimport gym\nfrom enum import IntEnum\nimport numpy as np\nfrom gym import error, spaces, utils\nfrom gym.utils import seeding\nfrom .rendering import *\nfrom .window import Window\nimport numpy as np\n\n# Size in pixels of a tile in the full-scale human view\nTILE_PIXELS = 32\n\n# Map of color names t... | [
[
"numpy.array",
"numpy.array_equal",
"numpy.zeros",
"numpy.ones",
"numpy.prod"
]
] |
snudatalab/Negotiation_Learning | [
"7c0f13781aec2d7efe0870c2bcd60eaef5d342de",
"e85c8088cc75337226688e3c153c9fc44c04924a",
"7c0f13781aec2d7efe0870c2bcd60eaef5d342de",
"7c0f13781aec2d7efe0870c2bcd60eaef5d342de"
] | [
"src/NL_Transformer_Enc_Dec/fairseq/criterions/sentence_prediction.py",
"src/NL_BERT/BERT/pytorch_pretrained_bert/convert_transfo_xl_checkpoint_to_pytorch.py",
"src/NL_BERT/finetune_tinybert.py",
"src/NL_Transformer_Enc_Dec/fairseq/models/speech_to_text/modules/emformer.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\r\n#\r\n# This source code is licensed under the MIT license found in the\r\n# LICENSE file in the root directory of this source tree.\r\n\r\nimport math\r\n\r\nimport torch\r\nimport torch.nn.functional as F\r\nfrom fairseq import metrics, utils\r\nfrom fairseq.c... | [
[
"torch.nn.functional.mse_loss",
"torch.nn.functional.nll_loss",
"torch.nn.functional.log_softmax"
],
[
"torch.save"
],
[
"numpy.array",
"torch.cuda.manual_seed_all",
"torch.stack",
"numpy.random.seed",
"torch.manual_seed",
"torch.tensor"
],
[
"torch.nn.Linea... |
piyush2896/Autoencoder-Implementations | [
"baf38cff97a4baad4c04ec0b0a98a1b114c32568"
] | [
"Simple-Autoecnoders/model.py"
] | [
"import tensorflow as tf\n\ndef dense(in_, units, name=None):\n return tf.layers.dense(inputs=in_,\n units=units,\n activation=tf.nn.relu,\n kernel_regularizer=tf.contrib.layers.xavier_initializer,\n name=name... | [
[
"tensorflow.estimator.EstimatorSpec",
"tensorflow.train.AdamOptimizer",
"tensorflow.losses.mean_squared_error",
"tensorflow.layers.dense",
"tensorflow.train.get_global_step"
]
] |
yogeshmj/AD-DL | [
"76b9b564061581effe8f3698992bfea3ffb055fa"
] | [
"clinicadl/clinicadl/tools/tsv/kfold_split.py"
] | [
"# coding: utf8\n\nfrom .tsv_utils import baseline_df\nimport shutil\nfrom sklearn.model_selection import StratifiedKFold\nfrom os import path\nimport os\nimport pandas as pd\nimport numpy as np\n\nsex_dict = {'M': 0, 'F': 1}\n\n\ndef split_diagnoses(formatted_data_path,\n n_splits=5, subset_name... | [
[
"pandas.DataFrame",
"sklearn.model_selection.StratifiedKFold",
"pandas.concat"
]
] |
wholmgren/pandas | [
"608882de01bbc6d08f12c514bb61642c52480774"
] | [
"pandas/tests/test_frame.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\n# pylint: disable-msg=W0612,E1101\nfrom copy import deepcopy\nfrom datetime import datetime, timedelta, time\nimport sys\nimport operator\nimport re\nimport csv\nimport nose\nimport functools\nimport itertools\nfrom itertools import product, permuta... | [
[
"numpy.ma.masked_all",
"numpy.random.rand",
"numpy.array_equal",
"numpy.random.choice",
"numpy.median",
"pandas.core.nanops.nansem",
"pandas.compat.OrderedDict",
"numpy.tile",
"scipy.stats.skew",
"pandas.core.common.pprint_thing",
"pandas.util.testing.getSeriesData",
... |
sirimullalab/kinasepkipred | [
"fbb8833fc511fc1fd9ed8a02c2a3110e90b740a5"
] | [
"scripts/evaluation/ConInterCal2/metk.py"
] | [
"#!/usr/bin/env python\n\n\"\"\"Usage: metk.py --in INFILE_NAME --prefix OUTFILE_PREFIX [--units UNIT_NAME] [--example]\n\n--in INFILE_NAME input file name\n--prefix OUTFILE_PREFIX prefix for output file names\n--units UNIT_NAME units to display (uM (default) or nM)\n--example show ex... | [
[
"pandas.read_csv"
]
] |
mehrdad-shokri/cryptotrader | [
"65a4b4b502182a3df5ab581fc882edc61185279f"
] | [
"cryptotrader/utils.py"
] | [
"import logging\nfrom datetime import datetime, timedelta, timezone\nfrom decimal import Decimal, InvalidOperation, DivisionByZero, getcontext, Context\nfrom functools import partialmethod\nimport zmq\nimport msgpack\nfrom socket import gaierror\nfrom time import time, sleep\nimport smtplib\n\nimport numpy as np\n#... | [
[
"numpy.vectorize",
"numpy.log",
"numpy.frombuffer",
"numpy.exp",
"numpy.float64",
"numpy.finfo",
"numpy.alltrue",
"numpy.sort",
"numpy.arange",
"numpy.cumsum",
"numpy.maximum"
]
] |
billy000400/Mu2e_MLTracking | [
"675e62d844ff8a5ccba9019e316c374c40658101"
] | [
"python/frcnn_train/statistics_anchors.py"
] | [
"import sys\nfrom pathlib import Path\nimport pickle\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nutil_dir = Path.cwd().parent.joinpath('Utility')\nsys.path.insert(1, str(util_dir))\nfrom Abstract import *\nfrom Information import *\nfrom Configuration import frcnn_config\n\ndef an... | [
[
"numpy.array",
"numpy.count_nonzero",
"numpy.isnan",
"numpy.load",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.show",
"pandas.read_csv"
]
] |
sejavichan/rva_basic_tools | [
"138d5df854e5869718cd9577cbc9d47f832bfdd2"
] | [
"scripts/coll_avoidance_pot.py"
] | [
"#!/usr/bin/env python\nimport math\nimport rospy\nimport tf\nimport numpy as np\nfrom sensor_msgs.msg import LaserScan\nfrom geometry_msgs.msg import Point\nfrom visualization_msgs.msg import Marker\nfrom geometry_msgs.msg import Twist\n\nclass Coll_Avoidance_Pot:\n def __init__(self):\n rospy.Subscriber... | [
[
"numpy.array"
]
] |
IIT-PAVIS/acoustic-images-self-supervision | [
"5d4e0e88aecbe49bf14b852a01dc687ce4de1f85"
] | [
"trainer/trainer_audio.py"
] | [
"from datetime import datetime\nimport tensorflow as tf\nimport numpy as np\nimport tensorflow.contrib.slim as slim\nfrom tensorflow.python.ops import nn_ops\n\nflags = tf.app.flags\nFLAGS = flags.FLAGS\n_FRAMES_PER_SECOND = 12\n\n\nclass Trainer(object):\n \n def __init__(self, model_1, model_2, model_transf... | [
[
"tensorflow.reduce_min",
"tensorflow.logical_not",
"tensorflow.reshape",
"tensorflow.sqrt",
"tensorflow.to_float",
"tensorflow.tile",
"tensorflow.control_dependencies",
"tensorflow.greater",
"tensorflow.global_variables_initializer",
"tensorflow.GPUOptions",
"tensorflow... |
PeterouZh/BigGAN-PyTorch-1 | [
"722fe2e3b721f350c8e9b991d2839b0291f5cea7"
] | [
"inception_utils.py"
] | [
"''' Inception utilities\n This file contains methods for calculating IS and FID, using either\n the original numpy code or an accelerated fully-pytorch version that \n uses a fast newton-schulz approximation for the matrix sqrt. There are also\n methods for acquiring a desired number of samples from th... | [
[
"torch.cat",
"numpy.load",
"numpy.exp",
"numpy.mean",
"numpy.iscomplexobj",
"torch.eye",
"torch.nn.DataParallel",
"torch.sqrt",
"torch.trace",
"numpy.log",
"numpy.eye",
"torch.tensor",
"numpy.isfinite",
"numpy.atleast_2d",
"torch.nn.functional.dropout",
... |
LimeThaw/ABM | [
"73b2e9193f72fb3d0c65be6d35e74eb10f7c5a83"
] | [
"plot.py"
] | [
"import matplotlib.pyplot as plt\nimport re\nimport json\n\ninfile = open(\"out.txt\", \"r\")\nvalues = infile.read()\ninfile.close()\n\npopCount = []\nhappiness = []\ncrimes = []\ngunCrimes = []\navgConnectedness = []\ngunPossession = []\nvalues = map(lambda x: x.split(\", \"), values[2:-2].split(\"), (\"))\nfor v... | [
[
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.subplot"
]
] |
DaniMarts/bayesrace | [
"3d0d2b26dac2e33ad7e38513304cfb259abe351c"
] | [
"bayes_race/gp/validate_model_f110.py"
] | [
"\"\"\"\tValidate a trained GP model for error discrepancy between kinematic and dynamic models.\n\"\"\"\n\n__author__ = 'Achin Jain'\n__email__ = 'achinj@seas.upenn.edu'\n\n\nimport time\nimport numpy as np\nimport _pickle as pickle\nimport matplotlib.pyplot as plt\n\nfrom sklearn.gaussian_process import GaussianP... | [
[
"sklearn.metrics.mean_squared_error",
"sklearn.metrics.explained_variance_score",
"numpy.sqrt",
"sklearn.metrics.r2_score",
"matplotlib.pyplot.show"
]
] |
aleksandra-kim/consumption_model_ch | [
"01d923751aec2880dafab1e4373e694e145d8210"
] | [
"dev/potentially_useful.py"
] | [
"import bw2data as bd\nimport bw2calc as bc\nimport bw2io as bi\nfrom pypardiso import spsolve\nimport numpy as np\nfrom pathlib import Path\nimport json\n\nfrom consumption_model_ch.strategies.allocation import modify_exchanges\n\ndirpath = Path(__file__).parent.resolve() / \"data\"\n\n\nclass exiobaseLCA:\n\n ... | [
[
"numpy.where",
"numpy.log"
]
] |
jason-weirather/pythologist-reader | [
"b1e4fd5fd51b1a2412c81f3d053c4b68be0b991f"
] | [
"pythologist_reader/formats/inform/frame.py"
] | [
"import os, re, json, sys\nfrom collections import OrderedDict\nimport pandas as pd\nimport numpy as np\nfrom pythologist_reader import CellFrameGeneric\nfrom uuid import uuid4\nfrom pythologist_image_utilities import read_tiff_stack, map_image_ids, \\\n image_edges, watershed... | [
[
"numpy.array",
"numpy.zeros",
"pandas.DataFrame",
"numpy.ones",
"pandas.concat",
"pandas.read_csv"
]
] |
FinMind/Class | [
"74610a45ec169bff2bc841565973545adf57dee4"
] | [
"courses/01_Building_a_distributed_system_and_monitoring_system_with_Python/chapter03/3.4/practice/financialdata/crawler/taiwan_twse_stock_price.py"
] | [
"import datetime\nimport json\nimport typing\n\nimport pandas as pd\nimport requests\n\nURL = \"https://www.twse.com.tw/exchangeReport/MI_INDEX?response=json&date={}&type=ALLBUT0999&_={}\"\n# 網頁瀏覽時, 所帶的 request header 參數, 模仿瀏覽器發送 request\nHEADER = {\n \"Accept\": \"application/json, text/javascript, */*; q=0.01\... | [
[
"pandas.DataFrame"
]
] |
AshHarvey/ray | [
"f35339b5ff3d5e85c20720637e28bd5a380a545e"
] | [
"python/ray/tests/test_actor_failures.py"
] | [
"import collections\nimport numpy as np\nimport os\nimport pytest\nimport signal\nimport sys\nimport time\n\nimport ray\nimport ray.test_utils\nimport ray.cluster_utils\nfrom ray.test_utils import (\n wait_for_condition,\n wait_for_pid_to_exit,\n generate_system_config_map,\n get_other_nodes,\n Signa... | [
[
"numpy.random.rand"
]
] |
franzhaas/pgcolorbar | [
"0c1ed11bbac50c5a24ecd295d3542d8e0d48f336"
] | [
"pgcolorbar/colorlegend.py"
] | [
"\"\"\" Color legend.\n\n Consists of a vertical color bar with a draggable axis that displays the values of the colors.\n Optionally a histogram can be displayed.\n\"\"\"\nfrom __future__ import print_function, division\n\nimport cProfile\nimport logging\nimport os\nimport pstats\n\nimport numpy as np\nimpor... | [
[
"numpy.ceil",
"numpy.isnan",
"numpy.array_equal",
"numpy.percentile",
"numpy.copy",
"numpy.nanmin",
"numpy.isscalar",
"numpy.arange",
"numpy.append",
"numpy.linspace",
"numpy.nanmax"
]
] |
petehunt/dagster | [
"1df8496851a7a50a19053759fdac32753cc087a1"
] | [
"examples/hacker_news_assets/hacker_news_assets/assets/activity_stats.py"
] | [
"import json\nimport os\n\nimport pandas as pd\nfrom dagster_dbt import dbt_cli_resource\nfrom dagster_dbt.asset_defs import load_assets_from_dbt_manifest\nfrom hacker_news_assets.resources.snowflake_io_manager import (\n SHARED_SNOWFLAKE_CONF,\n connect_snowflake,\n)\n\nfrom dagster import MetadataValue\nfro... | [
[
"pandas.read_sql"
]
] |
fanqi203/pycrtm | [
"6fbdc8e45aa25c8030605b7489bd006d6757b63a"
] | [
"testCases/plot_images.py"
] | [
"# run ipdb debug:\n\n#import ipdb\n#ipdb.set_trace()\n\nimport xarray as xr\nimport matplotlib\n#matplotlib.use('AGG')# or PDF, SVG or PS\n \nimport matplotlib.pyplot as plt\n\nimport numpy as np\n\nimport os \nfrom pathlib import Path\n\ndef preprocess(ds):\n import os\n ds=ds.expand_dims(\"cases\")\n fi... | [
[
"numpy.round",
"matplotlib.pyplot.subplots",
"numpy.where",
"numpy.arange",
"matplotlib.pyplot.show"
]
] |
somnathrakshit/mri-sim-py | [
"2034357d1c5a89ee48f5dc38484a7ea33cc04db7"
] | [
"epg/epgcpmg_torch_image.py"
] | [
"#!/usr/bin/python\n\n# EPG CPMG simulation code, based off of Matlab scripts from Brian Hargreaves <bah@stanford.edu> \n# 2015 Jonathan Tamir <jtamir@eecs.berkeley.edu>\n# 2019 Ke Wang <kewang@eecs.berkeley.edu> rewrite it in Pytorch\n\nfrom __future__ import division\nimport torch\nimport torch.backends.cudnn as ... | [
[
"torch.zeros",
"torch.cos",
"torch.cat",
"numpy.array",
"torch.stack",
"torch.sin",
"numpy.zeros",
"torch.mm",
"torch.abs",
"torch.tensor",
"torch.Tensor",
"torch.exp"
]
] |
Roy-Tuhin/maskrcnn_sophisticate- | [
"a5a2300abbe2633d66847cdbfa7ed2bc2f901ec3",
"a5a2300abbe2633d66847cdbfa7ed2bc2f901ec3"
] | [
"apps/falcon/utils/visualize.py",
"apps/falcon/utils/CocoDataset.py"
] | [
"\"\"\"\nMask R-CNN\nDisplay and Visualization Functions.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\"\"\"\n\nimport os\nimport sys\nimport random\nimport itertools\nimport colorsys\n\nimport numpy as np\nfrom skimage.measure import ... | [
[
"matplotlib.lines.Line2D",
"numpy.zeros",
"matplotlib.pyplot.title",
"matplotlib.patches.Polygon",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.figure",
"numpy.where",
"numpy.any",
"numpy.fliplr",
"numpy.arange",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show... |
DrTodd13/numba | [
"de35af55d295f1677cca76646691d8c51c79d3cf"
] | [
"numba/dppl/examples/pa_examples/test1-2d.py"
] | [
"from numba import njit, gdb\nimport numpy as np\n\n@njit(parallel={'offload':True})\ndef f1(a, b):\n c = a + b\n return c\n\nN = 1000\nprint(\"N\", N)\n\na = np.ones((N,N), dtype=np.float32)\nb = np.ones((N,N), dtype=np.float32)\n\nprint(\"a:\", a, hex(a.ctypes.data))\nprint(\"b:\", b, hex(b.ctypes.data))\nc... | [
[
"numpy.ones"
]
] |
justin13601/AICancer | [
"b84f76ecfea90b352b40285778e136d7fc16b04a"
] | [
"models/CNN4_LungMalignant.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchsummary import summary\nimport torch.optim as optim\n\ntorch.manual_seed(1)\n\n\nclass CNN4_LungMalignant(nn.Module):\n def __init__(self):\n super(CNN4_LungMalignant, self).__init__()\n self.name = 'CNN4_LungMalignant... | [
[
"torch.manual_seed",
"torch.nn.Conv2d",
"torch.nn.Linear",
"torch.nn.MaxPool2d"
]
] |
Ethan-ye/Efficient-Segmentation-Networks | [
"27272e43126a507a6d93b21cd2372f5432f61237",
"27272e43126a507a6d93b21cd2372f5432f61237",
"27272e43126a507a6d93b21cd2372f5432f61237",
"27272e43126a507a6d93b21cd2372f5432f61237"
] | [
"model/FastSCNNX5.py",
"model/ContextNetX10.py",
"model/ESPNet_v2/SegmentationModelX4.py",
"model/BiSeNetV2X28.py"
] | [
"##################################################################################\r\n# Fast-SCNN: Fast Semantic Segmentation Network\r\n# Paper-Link: https://arxiv.org/pdf/1902.04502.pdf\r\n##################################################################################\r\n\r\n\r\nimport torch\r\nimport torch.n... | [
[
"torch.cat",
"torch.nn.Dropout",
"torch.nn.Sequential",
"torch.nn.functional.interpolate",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.cuda.is_available",
"torch.nn.AdaptiveAvgPool2d",
"torch.randn"
],
[
"torch.nn.Dropout",
"torch.nn.Seque... |
yasufumy/torchdata | [
"ed837afa366638fb19656bcc234903d266ac2910"
] | [
"pytorch_pipeline/core.py"
] | [
"from typing import Any, Iterator, Iterable, Tuple, List, Callable\nimport warnings\nimport _collections_abc\nimport random\nimport itertools\n\nimport lineflow as lf\nfrom torch.utils.data import IterableDataset\nfrom torch.utils.data import get_worker_info\n\n\nclass Dataset(IterableDataset):\n def __init__(se... | [
[
"torch.utils.data.get_worker_info"
]
] |
mahnen/gamma-limits-sensitivity | [
"8047914428bb9e4f0f6fc366111d7a9d42701502"
] | [
"gamma_limits_sensitivity/__main__.py"
] | [
"'''\nThis is the main of the ul method paper demonstration\n\nUsage:\n gamma_limits_sensitivity ul --l_lim=<arg> --t_obs=<arg> --A_eff=<file> [--E_0=<arg>] [--out=<path>]\n gamma_limits_sensitivity sens --s_bg=<arg> --alpha=<arg> --t_obs=<arg> --A_eff=<file> [--E_0=<arg>] [--out=<path>]\n gamma_limits_sensitivi... | [
[
"matplotlib.pyplot.show"
]
] |
ayushisaxena12/dscc202-402-spring2022 | [
"9a12f5d981befe64a076fc186aae5b61009a30b8"
] | [
"project3-mlops/05-Model-Management.py"
] | [
"# Databricks notebook source\n# MAGIC %md\n# MAGIC # Model Management\n# MAGIC \n# MAGIC An MLflow model is a standard format for packaging models that can be used on a variety of downstream tools. This lesson provides a generalizable way of handling machine learning models created in and deployed to a variety of... | [
[
"tensorflow.random.set_seed",
"pandas.read_csv",
"sklearn.ensemble.RandomForestRegressor",
"tensorflow.keras.layers.Dense"
]
] |
RealBrandonChen/AirSim | [
"c43b1e821454668e5c9f7181acd8f9334b83f7c0"
] | [
"venv/Lib/site-packages/gym/envs/toy_text/guessing_game.py"
] | [
"import numpy as np\n\nimport gym\nfrom gym import spaces\nfrom gym.utils import seeding\n\n\nclass GuessingGame(gym.Env):\n \"\"\"Number guessing game\n\n The object of the game is to guess within 1% of the randomly chosen number\n within 200 time steps\n\n After each step the agent is provided with on... | [
[
"numpy.array"
]
] |
fabiorodp/IN5550_Neural_Methods_in_Natural_Language_Processing | [
"4d3b2ed56b56e016413ae1544e19ad2a2c0ef047"
] | [
"Oblig1/evaluation.py"
] | [
"# Author: Fabio Rodrigues Pereira\n# E-mail: fabior@uio.no\n\n# Author: Per Morten Halvorsen\n# E-mail: pmhalvor@uio.no\n\n# Author: Eivind Grønlie Guren\n# E-mail: eivindgg@ifi.uio.no\n\nfrom packages.preprocessing import Signal20Dataset\nfrom packages.ann_models import MLPModel_wl\nfrom packages.ann_models impor... | [
[
"torch.device"
]
] |
zhl2007/pytorch-image-quality-param-ctrl | [
"78fa5a00fda9b54a1c558ee54b9b6d0a4f30f915"
] | [
"ppo/main.py"
] | [
"import copy\nimport glob\n\nimport torch.optim as optim\nfrom torch.autograd import Variable\nfrom torch.utils.data.sampler import BatchSampler, SubsetRandomSampler\n\nfrom arguments import get_args\nfrom model import CNNPolicy, MLPPolicy\nfrom storage import RolloutStorage\nfrom usb_cam_env import *\n\nENV_IMG_W ... | [
[
"torch.autograd.Variable"
]
] |
SpencerEricksen/PCBA_downloads_oxphos | [
"62f5b7a6faf2743afa31368d3e8d529e248c815d"
] | [
"analysis/cluster_HAC_get_medoids.py"
] | [
"#!/home/ssericksen/anaconda2/bin/python\n\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.neighbors import DistanceMetric\nfrom itertools import combinations\n\n\ndef clst_medoids( num_clusters, fps_file ):\n '''given num_clusters and a CSV of chemical fingerprints (binary strings), return\n ... | [
[
"sklearn.cluster.AgglomerativeClustering",
"sklearn.neighbors.DistanceMetric.get_metric"
]
] |
pulkitkatdare/baby-ai-game | [
"e00e35d40a0d873f63098527751c3db87ccfae41"
] | [
"pytorch_rl/main.py"
] | [
"import copy\nimport glob\nimport os\nimport time\nimport operator\nfrom functools import reduce\n\nimport gym\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.autograd import Variable\n\nfrom common.arguments import get_args\nfrom ag... | [
[
"torch.manual_seed",
"torch.cuda.manual_seed"
]
] |
axru5812/pysynphot | [
"527cb4ce55355c78ace5130a2fd8ca81a31ac1e6"
] | [
"setup.py"
] | [
"#!/usr/bin/env python\nimport os\nimport pkgutil\nimport sys\nfrom glob import glob\nfrom numpy import get_include as np_include\nfrom setuptools import setup, Extension\nfrom subprocess import check_call, CalledProcessError\n\n\nif not pkgutil.find_loader('relic'):\n relic_local = os.path.exists('relic')\n ... | [
[
"numpy.get_include"
]
] |
velexi-corporation/rsi-2020-bemish | [
"3454ecdff66802494d5c3c1678767fddc42d72b6"
] | [
"lib/nodes/util.py"
] | [
"from math import exp\nfrom numba import jit\nimport numpy as np\n\nimport scipy.integrate\n\nimport time\n\n@jit(nopython=True, parallel=True)\ndef foldiak_func(l, inside):\n return 1.0/(1.0+np.exp(-1 * l * inside))\n\n\ndef weightsum(xarr, qarr):\n return np.matmul(xarr,qarr)\n \n\n\nclass FoldiakShapedD... | [
[
"numpy.where",
"numpy.full",
"numpy.matmul",
"numpy.exp"
]
] |
yaramohajerani/read-GRACE-harmonics | [
"aa48506a64860809249164a9bcaebf679d41f6ff",
"aa48506a64860809249164a9bcaebf679d41f6ff"
] | [
"gravity_toolkit/plm_holmes.py",
"scripts/grace_mean_harmonics.py"
] | [
"#!/usr/bin/env python\nu\"\"\"\nplm_holmes.py\nWritten by Tyler Sutterley (08/2020)\n\nComputes fully-normalized associated Legendre Polynomials\n for a vector of x values (can also be singular)\n\nUses Holmes and Featherstone (2002) recursion relation\n\nThis recursion relation is better conditioned for high\n... | [
[
"numpy.int",
"numpy.zeros",
"numpy.finfo",
"numpy.atleast_1d",
"numpy.float128",
"numpy.sqrt"
],
[
"numpy.int",
"numpy.copy",
"numpy.mean"
]
] |
cristianmtr/magenta | [
"8f930263b7cfd67f27eb12cd871b4e5fa87d382e",
"8f930263b7cfd67f27eb12cd871b4e5fa87d382e",
"ac2d8ae455fdd07f4b46dec82aedab22fcb6bbbd"
] | [
"magenta/pipelines/pipeline.py",
"magenta/models/gansynth/lib/network_functions.py",
"magenta/models/score2perf/vocab_decoding_test.py"
] | [
"# Copyright 2018 The Magenta 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 o... | [
[
"tensorflow.gfile.IsDirectory",
"tensorflow.python_io.tf_record_iterator",
"tensorflow.gfile.Exists",
"tensorflow.logging.info",
"tensorflow.python_io.TFRecordWriter",
"tensorflow.gfile.MakeDirs",
"tensorflow.gfile.ListDirectory"
],
[
"tensorflow.variable_scope",
"tensorflo... |
miker83z/cloud-chain | [
"0f5c43159544da547173ee0425e78bede261513b"
] | [
"statistics-web3py/main.py"
] | [
"import argparse\n\nimport numpy as np\nimport pandas as pd\n\nfrom settings import experiments, lambdas, functions, TRANSIENT_VALUE, RESULT_DIR\nfrom statistics import response_time_blockchain, number_users_system, calculate_transient, mean_error, \\\n bar_plot_metrics, bar_plot_one_metric, plot_transient, new_... | [
[
"numpy.float64",
"pandas.DataFrame",
"pandas.concat"
]
] |
jstarck/cosmostat | [
"f686efe4c00073272487417da15e207a529f07e7"
] | [
"pycs/tools/cosmostat_init.py"
] | [
"# @cosmmostat_init.py\n#\n# Some convenient ROUTINES for interactive use\n#\n# Functions which makes the life easier for iSAP users.\n#\n# @author Jean-Luc Starck\n# @version 1.0\n# @date 2020\n#\n\nimport numpy as np\nfrom os import remove\nfrom subprocess import check_call\nfrom datetime import datetime\ni... | [
[
"numpy.rot90",
"numpy.fft.fft2",
"numpy.copy",
"scipy.ndimage.filters.gaussian_filter",
"numpy.min",
"numpy.mean",
"numpy.sign",
"numpy.where",
"matplotlib.pyplot.cm.get_cmap",
"numpy.max",
"numpy.iscomplex",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.save... |
Bluedragon137/tfmodisco | [
"d7c56b21e1bb58b07695ef3035f173b7d1a039e6"
] | [
"modisco/visualization/viz_sequence.py"
] | [
"import matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\nimport numpy as np\nfrom .. import util\n\nfrom PIL import Image\nimport base64\nimport io\n\n\ndef ic_scale(pwm,background):\n per_position_ic = util.compute_per_position_ic(\n ppm=pwm, backgroun... | [
[
"numpy.max",
"numpy.array",
"matplotlib.patches.Rectangle",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.title",
"numpy.min",
"matplotlib.pyplot.figure",
"matplotlib.patches.Ellipse",
"numpy.arange",
"matplotlib.pyplot.gcf",
"matplotlib.pyplot.show",
"matplotlib.pyp... |
wardVD/serve | [
"a9f7c3f0ff0e86c6dadcf36765a63d01758fed37"
] | [
"ts/torch_handler/base_handler.py"
] | [
"\"\"\"\nBase default handler to load torchscript or eager mode [state_dict] models\nAlso, provides handle method per torch serve custom model specification\n\"\"\"\nimport abc\nimport logging\nimport os\nimport importlib.util\nimport time\nimport torch\n\nfrom ..utils.util import list_classes_from_module, load_lab... | [
[
"torch.no_grad",
"torch.jit.load",
"torch.cuda.is_available",
"torch.load",
"torch.as_tensor"
]
] |
Unathi-Skosana/ptycho | [
"95e49a092d1212e2d02349ad4c50ea7db93e2687"
] | [
"tests/test_patterns.py"
] | [
"import cv2\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import ImageGrid\n\nfrom numpy import pi\nfrom utils import stripes, get_gradation_2d,\\\n get_gradation_3d, checkerboard, radial_gradient\nfrom utils.filters import gau_kern, circ_mask\nfrom ski... | [
[
"matplotlib.pyplot.imsave",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] |
MatthewMasters/grover | [
"737a340754bc4c63134ef84019a0a84023fd69a3"
] | [
"grover/data/molfeaturegenerator.py"
] | [
"\"\"\"\nThe registered feature generator for molecules.\nThis implementation is adapted from\nhttps://github.com/chemprop/chemprop/blob/master/chemprop/features/features_generators.py\n\"\"\"\n\nfrom typing import Callable, List, Union\n\nimport numpy as np\nfrom rdkit import Chem, DataStructs\nfrom rdkit.Chem imp... | [
[
"numpy.zeros"
]
] |
Saqibm128/examples | [
"56f2b059ca959a97354decfd006d4a1381d79f79"
] | [
"time_sequence_prediction/train.py"
] | [
"from __future__ import print_function\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nimport numpy as np\r\nimport matplotlib\r\nmatplotlib.use('Agg')\r\nimport matplotlib.pyplot as plt\r\n\r\nclass Sequence(nn.Module):\r\n def __init__(self):\r\n super(Sequence, self).__init__... | [
[
"matplotlib.use",
"torch.nn.Linear",
"torch.nn.LSTMCell",
"torch.nn.MSELoss",
"torch.stack",
"numpy.random.seed",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.title",
"torch.no_grad",
"matplotlib.pyplot.close",
"matplotlib.pyplot.ytick... |
gaurvigoyal/lifting_events_to_3d_hpe | [
"66d27eb7534f81a95d9f68e17cc534ef2a2c9b1c"
] | [
"experimenting/dataset/core/dhp19core.py"
] | [
"\"\"\"\nCore dataset implementation. BaseCore may be inherhit to create a new\nDatasetCore\n\"\"\"\n\nimport os\nfrom abc import ABC, abstractmethod\n\nimport cv2\nimport numpy as np\nimport torch\nfrom scipy import io\n\nfrom experimenting.utils import Skeleton\n\nfrom ..utils import get_file_paths\nfrom .base im... | [
[
"numpy.max",
"numpy.zeros",
"scipy.io.loadmat",
"numpy.load",
"torch.tensor",
"numpy.expand_dims"
]
] |
vincealdrin/Tutu | [
"05cf7e9cca8bb95c3bce782d6d899a3b0525bd10"
] | [
"detector/detector_api.py"
] | [
"from functools import reduce\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\nfrom newspaper import Article\nimport os\nfrom fake_useragent import UserAgent\nimport pandas as pd\nfrom requests import get\nfrom lxml import etree\nfrom scipy.sparse import hstack\nfrom flask import Flask, request, jsonif... | [
[
"pandas.DataFrame",
"scipy.sparse.hstack"
]
] |
maxfrei750/FibeR-CNN | [
"14c3a050b594817ed3016cc3419c82d35154c1c6"
] | [
"fibercnn/modeling/error_correction_and_detection.py"
] | [
"import numpy as np\nfrom skimage.transform import downscale_local_mean, rescale\n\nfrom fibercnn.modeling.spline import calculate_length, interpolation, to_mask\n\n\ndef _calculate_point_distances(As, Bs):\n return np.sqrt(np.sum((As - Bs) ** 2, axis=1))\n\n\ndef _calculate_segment_lengths(keypoints):\n leng... | [
[
"numpy.logical_or",
"numpy.array",
"numpy.delete",
"numpy.sum",
"numpy.logical_and",
"numpy.any",
"numpy.where",
"numpy.argmax",
"numpy.argsort"
]
] |
GreatWei/pythonStates | [
"543037fa5768be773a3ba31fba06e16a9edea46a"
] | [
"examples/python/generic_mle.py"
] | [
"# coding: utf-8\n\n# DO NOT EDIT\n# Autogenerated from the notebook generic_mle.ipynb.\n# Edit the notebook and then sync the output with this file.\n#\n# flake8: noqa\n# DO NOT EDIT\n\n# # 极大似然估计(通用模型)\n\n# 本教程说明了如何在 statsmodels 中快速实现新的极大似然模型。 我们举两个例子：\n#\n# 1. 两分类因变量的 Probit 模型\n# 2. 计数数据的负二项式模型\n#\n# GenericLik... | [
[
"scipy.stats.nbinom.logpmf",
"numpy.dot",
"numpy.zeros"
]
] |
fossabot/pycvcqv | [
"2de1504c056316c9557eb82f5ee3594ece8aace8"
] | [
"pycvcqv/is_numeric.py"
] | [
"\"\"\"The is_numeric trait.\"\"\"\n# --------------------------- Import libraries and functions --------------------------\nimport functools\n\nimport pandas as pd\nfrom pandas.api.types import is_numeric_dtype\n\n# -------------------------------- function definition --------------------------------\n\n\ndef is_n... | [
[
"pandas.Series"
]
] |
RoryBarnes/approxposterior | [
"f80b068919732573471fbb488fcd72d35926c112"
] | [
"approxposterior/tests/test_2DBayesOpt.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n\nTest Bayesian optimization of 2D Rosenbrock function\n\n@author: David P. Fleming [University of Washington, Seattle], 2019\n@email: dflemin3 (at) uw (dot) edu\n\n\"\"\"\n\nfrom approxposterior import approx, likelihood as lh, gpUtils\nimport numpy as np\nf... | [
[
"numpy.random.seed",
"numpy.allclose"
]
] |
karttur/geoimagine02-grass | [
"09c207707ddd0dae04a871e006e184409aa87d99"
] | [
"imaging/images2swf.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright (C) 2012, Almar Klein\n#\n# This code is subject to the (new) BSD license:\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n# * Redistributions of source code mus... | [
[
"numpy.zeros_like",
"numpy.asarray",
"numpy.zeros",
"numpy.ones",
"numpy.flipud",
"numpy.frombuffer"
]
] |
yubozhao/BentoML | [
"0d56b35e7a6969947c77a8cea685190f2196440f"
] | [
"tests/integration/test_tensorflow_v2_2_savedmodel_artifact.py"
] | [
"# pylint: disable=redefined-outer-name\nimport json\n\nimport numpy as np\nimport pytest\nimport tensorflow as tf\n\nimport bentoml\nfrom tests.bento_service_examples.tensorflow_classifier import Tensorflow2Classifier\nfrom tests.integration.api_server.conftest import (\n build_api_server_docker_image,\n exp... | [
[
"numpy.asfarray",
"tensorflow.TensorSpec",
"tensorflow.matmul",
"tensorflow.keras.initializers.Ones"
]
] |
raimis/torchani | [
"f2bf5fb16a65c2226f75858bc6603a9f7b029fe4"
] | [
"tests/test_forces.py"
] | [
"import torch\nimport torchani\nimport unittest\nimport os\nimport pickle\n\npath = os.path.dirname(os.path.realpath(__file__))\nN = 97\n\n\nclass TestForce(unittest.TestCase):\n\n def setUp(self):\n self.tolerance = 1e-5\n model = torchani.models.ANI1x(model_index=0)\n self.aev_computer = m... | [
[
"torch.autograd.grad",
"torch.jit.script",
"torch.from_numpy"
]
] |
coinse/arachne | [
"e19c86c07d675568f30f12b2130398afe61a5aa4"
] | [
"arachne/utils/data_loader.py"
] | [
"#!/usr/bin/python\n\n'''Loads data as numpy data form'''\n\nimport torch\nimport torchvision.datasets as dss\nfrom torchvision import transforms\nfrom torch.utils import data\nimport os\nimport numpy as np\nfrom PIL import Image\nimport pandas\n\nclass CelebAFolder(data.Dataset):\n \"\"\"CelebA folder\"\"\"\n ... | [
[
"pandas.read_csv",
"torch.utils.data.DataLoader"
]
] |
JamesMcGuigan/udacity-deep-reinforcement-learning | [
"e093db535fb12dbfb8bc2b5764133e1f52bbbccd"
] | [
"p1_navigation/src/dqn_agent.py"
] | [
"import os\nimport random\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom src.ReplayBuffer import ReplayBuffer\nfrom src.SumTree import SumTreeReplayBuffer\nfrom src.model import QNetwork\n\ndevice = torch.device(\"cuda:0\" if torch... | [
[
"torch.no_grad",
"torch.from_numpy",
"torch.nn.functional.mse_loss",
"torch.abs",
"torch.cuda.is_available",
"numpy.arange",
"torch.load"
]
] |
tum-db/mlinspect4sql | [
"863f1a98baff92341722b4fb180008cf9b518b80"
] | [
"example_to_sql/data_generation/compas_data_generation.py"
] | [
"import pandas\nfrom example_to_sql._benchmark_utility import ROOT_DIR\n\n\ndef generate_compas_dataset(sizes):\n \"\"\"\n As the compas pipeline does not use any joins, the data will just be augmented, by replicating the existing one.\n \"\"\"\n train_src = pandas.read_csv(ROOT_DIR / r\"data_generation... | [
[
"pandas.read_csv"
]
] |
dycloud-chan/pandas | [
"39ccb352b53e9b5b694c4f7f044774f9c3677e98"
] | [
"pandas/io/html.py"
] | [
"\"\"\"\n:mod:`pandas.io.html` is a module containing functionality for dealing with\nHTML IO.\n\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom collections import abc\nimport numbers\nimport re\nfrom typing import (\n Pattern,\n Sequence,\n)\n\nfrom pandas._typing import (\n FilePath,\n ReadBuffe... | [
[
"pandas.errors.AbstractMethodError",
"pandas.io.common.get_handle",
"pandas.core.construction.create_series_with_explicit_dtype",
"pandas.io.parsers.TextParser",
"pandas.compat._optional.import_optional_dependency",
"pandas.io.common.urlopen",
"pandas.io.common.validate_header_arg",
... |
endrikacupaj/LASAGNE | [
"6321ab5161999905b357bd9b67906dcac04b8644"
] | [
"constants.py"
] | [
"import os\nimport torch\nfrom pathlib import Path\nfrom args import get_parser\n\n# set root path\nROOT_PATH = Path(os.path.dirname(__file__))\n\n# read parser\nparser = get_parser()\nargs = parser.parse_args()\n\n# model name\nMODEL_NAME = 'LASAGNE'\n\n# define device\nCUDA = 'cuda'\nCPU = 'cpu'\nDEVICE = torch.d... | [
[
"torch.cuda.is_available"
]
] |
nasim-aust/Pattern-Lab-Work | [
"5300f837c5e66b260f070d772d56c9646366adb6"
] | [
"lab 3/Pattern lab 3/Pattern/untitled2.py"
] | [
"import numpy as np\nfrom matplotlib import pyplot as plt\nx=np.linspace(0,20,200)\ny1=np.exp(-0.1*x)*np.sin(x)\ny2=np.exp(-0.3*x)*np.sin(x)\nplt.plot(x,y1)\nplt.plot(x,y2)\nplt.title('Just Enough!')\nplt.show()"
] | [
[
"numpy.sin",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.title",
"numpy.exp",
"matplotlib.pyplot.show",
"numpy.linspace"
]
] |
lucasw/chrono | [
"e79d8c761c718ecb4c796725cff37026f357da8c"
] | [
"src/demos/python/irrlicht/demo_IRR_crank_plot.py"
] | [
"# =============================================================================\n# PROJECT CHRONO - http://projectchrono.org\n#\n# Copyright (c) 2019 projectchrono.org\n# All rights reserved.\n#\n# Use of this source code is governed by a BSD-style license that can be found\n# in the LICENSE file at the top level ... | [
[
"numpy.linspace",
"matplotlib.pyplot.subplots"
]
] |
LiquidFun/stegowav | [
"89ef0b40c52c834febffeeefba30eccbb0862e29"
] | [
"error_correction/hamming_error_correction.py"
] | [
"import numpy as np\n\nfrom error_correction.error_correction_type import ErrorCorrectionType\nfrom error_correction.generic_error_correction import GenericErrorCorrection\n\n\nclass HammingErrorCorrection(GenericErrorCorrection):\n\n def __init__(self):\n super().__init__(ErrorCorrectionType.HAMMING)\n\n... | [
[
"numpy.binary_repr"
]
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.