repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
FlorianWilhelm/mlstm4reco | [
"023ff281c7cdb5aa8a9ae3ef08e0629276865424"
] | [
"experiments/run.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport sys\nimport os\nimport shutil\nimport pickle\nimport time\nimport argparse\n\nimport torch\nimport numpy as np\nfrom IPython.core import ultratb\nfrom spotlight.sequence.implicit import ImplicitSequenceModel\nfrom spotlight.cross_validation import user_based_t... | [
[
"numpy.isnan",
"numpy.random.RandomState",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lukaswenzl/High-Redshift-Quasars-with-Random-Forests | [
"edf67a0129277cfa17392a0808498404d8a26c6a"
] | [
"result_analysis/kstar_sample.py"
] | [
"import numpy as np\nimport pandas as pd\n\ndata = pd.read_csv(\"../data/results/full14.csv\")\nkstars = data.query(\"pred_class == 'K'\")\n\nkstars = kstars.sample(n=1000000)\nkstars.to_csv(\"../data/analysis/kstars_1mio.csv\")\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ShGanesh/Simple_Python_Calculator_with_GUI | [
"f81c0a96c96c51e6ac9bda38f7d9d9b3879481e1"
] | [
"graphing_calci.py"
] | [
"import matplotlib.pyplot as plt\nimport numpy as np\nimport tkinter as tk\nfrom tkinter import *\nimport re\n\nreplacements = {\n 'sin' : 'np.sin',\n 'cos' : 'np.cos',\n 'exp': 'np.exp',\n 'sqrt': 'np.sqrt',\n '^': '**',\n}\n\nallowed_words = [\n 'x',\n 'sin',\n 'cos',\n 'sqrt',\n 'ex... | [
[
"matplotlib.pyplot.xlim",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.show",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
charles-cowart/microsetta-private-api | [
"96d3c09c8e30e00c87ce82c5e7d16ba4eb93ce74"
] | [
"microsetta_private_api/repo/metadata_repo/_transforms.py"
] | [
"from ._constants import MISSING_VALUE, UNSPECIFIED\nfrom functools import reduce\nfrom operator import or_\nimport pandas as pd\nimport numpy as np\n\n\nWEIGHT_KG = 'weight_kg'\nWEIGHT_UNITS = 'weight_units'\nHEIGHT_CM = 'height_cm'\nHEIGHT_UNITS = 'height_units'\nBIRTH_YEAR = 'birth_year'\nBIRTH_MONTH = 'birth_mo... | [
[
"numpy.timedelta64",
"pandas.to_datetime",
"pandas.to_numeric",
"pandas.isnull"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
FablabHome/PCMS_home_robotics | [
"21202fb73811edfcbdfde204ba33fb8bd4360d4b",
"21202fb73811edfcbdfde204ba33fb8bd4360d4b"
] | [
"RCJ_pcms_base/scripts/tests/PersonFollower/DrawWaypoint.py",
"RCJ_pcms_base/scripts/GenderDetectionNode.py"
] | [
"import cv2 as cv\nimport numpy as np\nimport rospy\nfrom home_robot_msgs.msg import PFWaypoints\n\n\ndef callback(wpts: PFWaypoints):\n global waypoints\n waypoints = wpts.waypoints\n\n\nrospy.init_node('draw_waypoint')\nrate = rospy.Rate(30)\n\nwaypoints = []\ncm2pixel_ratio = 1 / 10\n\nrospy.Subscriber(\n ... | [
[
"numpy.zeros"
],
[
"numpy.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vladvelici/dbxlogger | [
"75fc34d5fe7479727c37eb22e80cdd4e5917bb6b"
] | [
"examples/torchbearer/cifar10.py"
] | [
"import argparse\n\nimport torch\nimport torch.nn as nn\nimport torchvision\nimport torch.optim as optim\nfrom torchvision import transforms\n\nimport torchbearer\nfrom torchbearer import Trial\nfrom torchbearer.cv_utils import DatasetValidationSplitter\n\nimport dbxlogger as dbx\nfrom dbxlogger.integrations.torchb... | [
[
"torch.nn.CrossEntropyLoss",
"torch.nn.Conv2d",
"torch.utils.data.DataLoader",
"torch.nn.Linear",
"torch.cuda.is_available",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Daffan/APPLR | [
"ceb394cd337b4b4ccb3915d05d1f42fd317c5c8e"
] | [
"continuous/net.py"
] | [
"import torch\nfrom torch import nn\nfrom tianshou.data import to_torch\nimport numpy as np\n\nclass Net(nn.Module):\n def __init__(self, num_layers=2, state_shape=728, action_shape=0, concat=False, hidden_layer_size=512, device=\"cpu\"):\n super().__init__()\n self.device = device\n state_s... | [
[
"torch.nn.Sequential",
"torch.nn.BatchNorm1d",
"torch.cat",
"torch.nn.MaxPool1d",
"torch.nn.Linear",
"numpy.prod",
"torch.nn.Conv1d",
"torch.nn.ReLU",
"torch.nn.AvgPool1d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
18150167970/knowledge-distillation | [
"90989b6b4a696645841b892d9a0e0019d186d642"
] | [
"model/faster_rcnn.py"
] | [
"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nimport torch as t\nimport numpy as np\nimport cupy as cp\nfrom utils import array_tool as at\nfrom model.utils.bbox_tools import loc2bbox\nfrom model.utils.nms import non_maximum_suppression\n\nf... | [
[
"torch.optim.Adam",
"torch.Tensor",
"numpy.concatenate",
"torch.no_grad",
"torch.optim.SGD"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gustavo1046/WebCrawler---Python | [
"9180cc111629e9bda3ddcb309b2ff94a77822425"
] | [
"mining.py"
] | [
"\r\nimport requests\r\nfrom bs4 import BeautifulSoup as bs\r\nimport pandas as pd\r\nfrom time import sleep\r\nfrom selenium import webdriver\r\n\r\nurl = 'https://www.usquidditch.org/teams'\r\n\r\n\r\n#open selenium - use your own info for PATH\r\nPATH = (r\"C:\\\\Users\\\\Gustavo\\\\Desktop\\\\scoutfy\\\\Team US... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Wangbaiyue007/nmt-multi30k-pytorch | [
"3b1724ef3a049a6c55cfb21217508195b8b653e6"
] | [
"utils.py"
] | [
"import copy\nimport torch\nfrom collections import Counter\nfrom torchtext.vocab import Vocab\nfrom torch.nn.utils import clip_grad_norm_\nfrom torch.nn.utils.rnn import pad_sequence\n\ndef train(model, train_iter, optimizer, loss_fn, device):\n # global steps\n model.train()\n losses = 0\n for (src, t... | [
[
"torch.LongTensor",
"torch.ones",
"torch.max",
"torch.zeros",
"torch.nn.utils.rnn.pad_sequence",
"torch.tensor",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
razofz/scarf-1 | [
"2c97ed6d9433ff95154ce8b26f382139a708207d"
] | [
"scarf/knn_utils.py"
] | [
"\"\"\"\nUtility functions for running the KNN algorithm.\n\"\"\"\nimport numpy as np\nfrom .writers import create_zarr_dataset\nfrom .ann import AnnStream\nfrom .utils import tqdmbar\nimport pandas as pd\nfrom scipy.sparse import csr_matrix, coo_matrix\nfrom typing import List\nfrom numba import jit\n\n\n__all__ =... | [
[
"numpy.hstack",
"scipy.sparse.coo_matrix",
"numpy.unique",
"numpy.arange",
"pandas.DataFrame",
"numpy.argsort",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.1... |
cbrnr/gsoc2021 | [
"b0663b74445681ad4e7d1bb0937488be1aa37b4b"
] | [
"prototypes/originals/plot_pyqtgraph.py"
] | [
"\"\"\"\nThis is a prototype of a Raw-Plot based on pyqtgraph.\nIt was originally created by Clemens Brunner (https://github.com/cbrnr).\n\"\"\"\nimport sys\nimport numpy as np\nfrom PyQt5.QtWidgets import QApplication\nimport pyqtgraph as pg\nfrom pyqtgraph import PlotCurveItem\nimport mne\n\n\nclass RawCurveItem(... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jbussemaker/pyCycle | [
"656a14fc89cd397fd00913c0ac7465a5f7e5cb95"
] | [
"pycycle/elements/heat_exchanger.py"
] | [
"import numpy as np\nimport openmdao.api as om\n\nfrom pycycle.thermo.thermo import Thermo\n\nfrom pycycle.thermo.cea.species_data import janaf\nfrom pycycle.constants import AIR_ELEMENTS\nfrom pycycle.flow_in import FlowIn\nfrom pycycle.passthrough import PassThrough\n\n\nclass Areas(om.ExplicitComponent):\n \"... | [
[
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lauriraudla/pOliver | [
"ef2a6cc7c04a2d6946854c15b084e75e64038625"
] | [
"LUT.py"
] | [
"import numpy as np\n\n# distances_LUT = np.array(\n# [0.5581873069080149, 0.673282329238654, 0.775489401649724, 0.880352084715722, 1.0049236875481926,\n# 1.1211567901331803, 1.2531766413769805, 1.3515307222188782, 1.437071400857621, 1.5519779219846181,\n# 1.728146900469168, 1.8134751192793028, 1.9... | [
[
"numpy.array",
"numpy.absolute"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lukedottec/ai-gym | [
"1a94777c7d6d4c53871855758ea0c0b8d325f297"
] | [
"unity/p3_collab-compet/agent.py"
] | [
"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom collections import namedtuple, deque\nimport copy\nimport random\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom model import Actor, Critic\n\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available()... | [
[
"numpy.clip",
"numpy.random.standard_normal",
"torch.from_numpy",
"numpy.ones",
"torch.nn.functional.mse_loss",
"torch.no_grad",
"torch.cuda.is_available",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ereide/pyga-camcal | [
"fd25748ddb11c5b05ef24a2deca2689e0d899875"
] | [
"tests/testbenchmark.py"
] | [
"import numpy as np\n\nfrom pygacal.common.cgatools import *\n\nfrom pygacal.rotation import minimizeError\nfrom pygacal.rotation.benchmark import *\nfrom pygacal.rotation.costfunction import *\nfrom pygacal.rotation.mapping import ( RotorLineMapping, BivectorLineMapping, LinePropertyBivectorMapping, \n ... | [
[
"numpy.random.seed",
"numpy.linspace",
"numpy.array",
"numpy.meshgrid",
"numpy.zeros",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
b06611016/tade_final | [
"0ba5d63a478580497a168d5814dc4c2b0b06f53b"
] | [
"model/metric.py"
] | [
"import torch\r\n\r\ndef accuracy(output, target, return_length=False):\r\n with torch.no_grad():\r\n pred = torch.argmax(output, dim=1)\r\n assert pred.shape[0] == len(target)\r\n correct = 0\r\n correct += torch.sum(pred == target).item()\r\n if return_length:\r\n return c... | [
[
"torch.topk",
"torch.sum",
"torch.no_grad",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Kiri23/facial_landmark-detection | [
"6377b0f99b5537abaf5a77252ebe2b18dbaff875"
] | [
"facial_landmark_detection.py"
] | [
"# pyimage search tutorial #http://www.pyimagesearch.com/2017/04/03/facial-landmarks-dlib-opencv-python/\n# import the necessary packages\nfrom imutils import face_utils\nimport numpy as np\nimport argparse\nimport imutils\nimport dlib\nimport cv2\n\ndef rect_to_bb(rect):\n\t# take a bounding predicted by dlib and ... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
TradeRES/toolbox-amiris-emlab | [
"11e6e7101bfbc0d71753e3892d4463c4955d2c34"
] | [
"scripts/combine_AMIRIS_results_ingrid.py"
] | [
"import sys\nimport os\nfrom pathlib import Path\nfrom glob import glob\n# from datetime import timedelta\nimport datetime as dt\nimport pandas as pd\nfrom fameio.scripts.convert_results import run as convert_results\nfrom fameio.source.cli import Config\nfrom fameio.source.time import FameTime, Constants\nimport m... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
janbolle/neptune-client | [
"33b1876b361d9a7184f557d7bd6e016cb08bd59f"
] | [
"neptune/new/internal/utils/images.py"
] | [
"#\n# Copyright (c) 2020, Neptune Labs Sp. z o.o.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by appli... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Supeking/darts_sk | [
"d87b5624d2f6795e842af74664aa42d85a41fa0b"
] | [
"cnn/architect.py"
] | [
"import torch\nimport numpy as np\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\n\ndef _concat(xs):\n return torch.cat([x.view(-1) for x in xs])\n\n\nclass Architect(object):\n\n def __init__(self, model, args):\n self.network_momentum = args.momentum\n self.network_weight_decay ... | [
[
"torch.zeros_like",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NSaal/seiresnet | [
"2633f780c2e7bd66b722a1727227a6e4e8f6529d"
] | [
"models/resstage.py"
] | [
"import torch\nimport torch.nn as nn\nimport os\nfrom div.download_from_url import download_from_url\n\ntry:\n from torch.hub import _get_torch_home\n torch_cache_home = _get_torch_home()\nexcept ImportError:\n torch_cache_home = os.path.expanduser(\n os.getenv('TORCH_HOME', os.path.join(\n ... | [
[
"torch.nn.Sequential",
"torch.nn.init.constant_",
"torch.hub._get_torch_home",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Linear",
"torch.nn.AdaptiveAvgPool2d",
"torch.nn.ReLU",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
HeyLucasLeao/HeyLucasLeao.github.io | [
"5c5da105763bbaeb3391ca716a09e87490b6eb1f"
] | [
"raspagem_dos_boletins_diarios/updating.py"
] | [
"from shutil import Error\nimport pandas as pd\nimport urllib.request\nfrom os import listdir\nfrom datetime import datetime\nimport csv\nimport numpy as np\nfrom tabula import read_pdf\nimport requests\nfrom datetime import timedelta\nfrom time import sleep\n\nPATH_PDF = r'../raspagem_dos_boletins_diarios/relatori... | [
[
"pandas.to_datetime",
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
Shenzhen-Cloudatawalk-Technology-Co-Ltd/EdgeML | [
"ef9f8a77f096acbdeb941014791f8eda1c1bc35b"
] | [
"tools/SeeDot/seedot/compiler/converter/test.py"
] | [
"# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT license.\n\nimport math\nimport numpy as np\n\n'''\nTest codes for the converter.\n'''\n\ndef getScale(maxabs: float, bits):\n return int(np.ceil(np.log2(maxabs) - np.log2((1 << (bits - 2)) - 1)))\n\ndef printUspsRNN():\n i... | [
[
"numpy.ceil",
"numpy.log2",
"numpy.ldexp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ChaseKnowlden/airflow | [
"6b71eac1997a7c0db3b8e3aed6b4e65d01871440"
] | [
"airflow/providers/presto/hooks/presto.py"
] | [
"#\n# 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\")... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
MikyasDesta/NeMo | [
"4995477e6ce49de55b123723e42021c9eff8e2c0"
] | [
"nemo/collections/tts/modules/radtts.py"
] | [
"# Copyright (c) 2022, 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 re... | [
[
"torch.cat",
"torch.nn.Embedding",
"torch.tanh",
"torch.detach",
"torch.nn.utils.rnn.pad_packed_sequence",
"torch.no_grad",
"torch.nn.utils.remove_weight_norm",
"torch.sigmoid",
"torch.LongTensor",
"torch.nn.ReplicationPad1d",
"torch.nn.ModuleList",
"torch.zeros_lik... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gergely-flamich/relative-entropy-coding | [
"c99d90cabec4395de2d01d889bd2b7ed7b7453d7",
"c99d90cabec4395de2d01d889bd2b7ed7b7453d7"
] | [
"rec/models/resnet_vae.py",
"rec/io/tests/coding_test.py"
] | [
"from typing import Tuple\n\nimport numpy as np\n\nimport tensorflow as tf\nimport tensorflow_probability as tfp\n\nfrom rec.models.custom_modules import ReparameterizedConv2D, ReparameterizedConv2DTranspose, AutoRegressiveMultiConv2D, \\\n SignalConv2D\nfrom rec.coding import GaussianCoder, BeamSearchCoder\nfro... | [
[
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.Variable",
"tensorflow.tile",
"tensorflow.nn.elu",
"tensorflow.nn.sigmoid",
"tensorflow.exp",
"tensorflow.math.exp",
"tensorflow.math.square",
"tensorflow.math.floor",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
... |
vpaliy/CIFAR-10-Image-Classification | [
"f05d31b8fcbd20c1ec7baf2a1b4f35b51b41a376"
] | [
"main.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import accuracy_score\n\nimport pickle as pickle\nimport numpy as np\nimport os\n\nfrom softmax import SGDSoftmaxClassifier, SoftmaxClassifier\n\n\ndef load_data(train_paths, test_path):\n X_train, y_train = get_batch(train_paths[0])\n ... | [
[
"matplotlib.pyplot.title",
"numpy.min",
"sklearn.metrics.accuracy_score",
"matplotlib.pyplot.show",
"numpy.concatenate",
"numpy.max",
"matplotlib.pyplot.subplot",
"numpy.mean",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.xlabel",
"numpy.array",
"numpy.divide",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LzVv123456/Deep-Reinforced-Tree-Traversal | [
"8e117590c8cd51c9fc9c033232658876160fa638",
"8e117590c8cd51c9fc9c033232658876160fa638"
] | [
"tracer/validation.py",
"tracer/model.py"
] | [
"import torch\nimport copy\nimport time\nimport collections\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom skimage.feature import peak_local_max\nfrom utilities import *\nfrom agent_env import *\n\n\nclass Evaluate_Agent():\n\n def __init... | [
[
"torch.tensor",
"matplotlib.animation.FuncAnimation",
"matplotlib.pyplot.grid",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.show",
"matplotlib.pyplot.pause",
"matplotlib.pyplot.figure"
],
[
"torch.max",
"torch.zeros",
"torch.cat",
"torch.tensor",
"torch.nn.Conv... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aliciasantos/molpy | [
"b3c5c12926882c33927c17e94696fde04b44b1d1"
] | [
"molpy/util.py"
] | [
"import numpy as np\n\n\ndef distance(point1, point2):\n \"\"\"\n Calculate distance between two points.\n\n Parameters\n ----------\n point1 : array_like\n The first point.\n point2 : array_like\n The second point.\n\n Returns\n -------\n float\n The distance between... | [
[
"numpy.asarray",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
andrespp/uetl | [
"a7d59998fc98584c214158d2481b6f320a43aadf"
] | [
"src/uetl.py"
] | [
"\"\"\"utel.py\n\"\"\"\nimport pandas as pd\nimport pandas.io.sql as sqlio\nfrom sqlalchemy import create_engine\nimport psycopg2\nfrom psycopg2.extensions import ISOLATION_LEVEL_AUTOCOMMIT\n\nclass DataWarehouse():\n \"\"\"DataWarehouse class\n \"\"\"\n\n def __init__(self, name, dbms, host, port, base, u... | [
[
"pandas.DataFrame",
"pandas.io.sql.read_sql_query"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
HDRUK/MedCAT | [
"69c36d1da484ad32520a9b3333adf8f6ebfcbde7"
] | [
"medcat/cat.py"
] | [
"import os\nimport json\nimport pandas\nimport spacy\nfrom time import sleep\nfrom functools import partial\nfrom multiprocessing import Process, Manager, Queue, Pool, Array\nfrom medcat.cdb import CDB\nfrom medcat.spacy_cat import SpacyCat\nfrom medcat.preprocessing.tokenizers import spacy_split_all\nfrom medcat.u... | [
[
"torch.set_num_threads"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nikste/ClassMix | [
"fd0ec39c440cf8b10e40ce5ea0b69c5e4a60fa55"
] | [
"models/resnet.py"
] | [
"import torch\nimport torch.nn as nn\n\n__all__ = [\n \"ResNet\",\n \"resnet18\",\n \"resnet34\",\n \"resnet50\",\n \"resnet101\",\n \"resnet152\",\n \"resnext50_32x4d\",\n \"resnext101_32x8d\",\n \"wide_resnet50_2\",\n \"wide_resnet101_2\",\n \"resnext101_32x16d_wsl\",\n]\n\n\ndef ... | [
[
"torch.nn.Sequential",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.ReLU",
"torch.nn.init.kaiming_normal_"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Okery/PyTorch-Simple-MaskRCNN | [
"5e57a353f211c7130bfcf1d55cacd80057d81423"
] | [
"pytorch_mask_rcnn/utils.py"
] | [
"import os\nimport re\nimport random\nimport torch\n\n\n__all__ = [\"save_ckpt\", \"Meter\"]\n\ndef save_ckpt(model, optimizer, epochs, ckpt_path, **kwargs):\n checkpoint = {}\n checkpoint[\"model\"] = model.state_dict()\n checkpoint[\"optimizer\"] = optimizer.state_dict()\n checkpoint[\"epochs\"] = ep... | [
[
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zhaotao1987/jcvi | [
"748fcdbbd1db5eb8a4ccfe19eec6072006ffd501"
] | [
"graphics/synteny.py"
] | [
"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\n\"\"\"\n%prog mcscan.txt all.bed layout.csv\n\nIllustrate MCscan multiple collinearity alignments. Use layout.csv to indicate\nthe positions of tracks. For example:\n\n#x, y, rotation, ha, va, color, ratio\n0.5, 0.6, 0, left, center, g\n0.25, 0.7, 45, top, center, ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aashnamsft/elastic | [
"5372d6acaf07d130ab0f0ccaf52958a7fde88902"
] | [
"test/timer/local_timer_example.py"
] | [
"#!/usr/bin/env python3\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree.\nimport logging\nimport multiprocessing as mp\nimport signal\nimport time\nimpo... | [
[
"torch.multiprocessing.start_process"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ariercole/Cambridge_COVID-19_ICU | [
"31de37868af26ee0dd16e95c56d37ba6b9966511"
] | [
"graphs.py"
] | [
"\"\"\"Graphs file.\"\"\"\n\nimport dash_core_components as dcc\nimport datetime\nimport numpy as np\nimport os\nimport pandas as pd\nimport pickle\nimport plotly.express as px\nimport plotly.graph_objs as go\nimport numpy as np\nimport os\nimport shutil\nimport statsmodels.api as sm\n\nfrom dateutil import parser\... | [
[
"numpy.random.lognormal",
"numpy.log",
"numpy.maximum",
"numpy.minimum",
"numpy.arange",
"numpy.stack",
"numpy.random.normal",
"numpy.random.gamma",
"numpy.exp",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AlexanderDavid/AutomaticExtractiveArticleSummarization | [
"8791d44dcacd9f0ac663f5788ac8ebe885409543"
] | [
"TextRank.py"
] | [
"import networkx as nx # Graphs\nfrom gensim.models.doc2vec import Doc2Vec, TaggedDocument # Sent2Vec functionality\nfrom nltk.tokenize import word_tokenize, sent_tokenize # Sentence and word tokenization\nfrom numpy import dot # Fast dot pr... | [
[
"numpy.dot",
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
manojgupta3051994/ga-learner-dsmp-repo | [
"5b6231d3d07f55428bc462c4a8302c7fb3e0718d"
] | [
"Stats_GA/code.py"
] | [
"# --------------\n#Header files\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\n#path of the data file- path\r\n\r\n#Code starts here \r\ndata = pd.read_csv(path)\r\ndata['Gender'].replace('-','Agender',inplace=True)\r\ngender_count = data['Gender'].value_counts()\r\ngend... | [
[
"pandas.read_csv",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.title"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
david-yd-hao/Vision-RB-Robotics | [
"c9fc812d2b08291f47f046ed1b4c97967bf9f098"
] | [
"Vision/mask.py"
] | [
"import cv2\nimport numpy as np\n\ndef red_mask(img, lower=[(0,80,100), (5,255,255)], higher=[(165,80,100), (180,255,255)]):\n img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n mask1 = cv2.inRange(img_hsv, lower[0], lower[1])\n mask2 = cv2.inRange(img_hsv, higher[0], higher[1])\n mask = cv2.bitwise_or(ma... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
bahalbach/nbaproject | [
"b2336452ebefa1251dee9ff19a6f189756c5a3dd",
"b2336452ebefa1251dee9ff19a6f189756c5a3dd"
] | [
"nbaproject/load_roster_br.py",
"nbaproject/build_simple_rebound_model.py"
] | [
"import requests\nfrom bs4 import BeautifulSoup, Comment\nimport pandas as pd\nimport datetime\nimport unidecode\nimport time\n\nfrom nba_utils import get_team_abr, map_id\n\n\ndef load_roster_br(team_id, season_name):\n team_abr = get_team_abr(team_id, season_name)\n season_year = season_name[:2]+season_name... | [
[
"pandas.read_html",
"pandas.DataFrame"
],
[
"tensorflow.keras.models.load_model",
"tensorflow.keras.Input",
"tensorflow.keras.losses.SparseCategoricalCrossentropy",
"tensorflow.keras.layers.Dense",
"sklearn.preprocessing.OneHotEncoder",
"sklearn.model_selection.train_test_split... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
chaitjo/lstm-context-embeddings | [
"ab5894eb727ede8daa394ebe6e87735a6207f292"
] | [
"data_helpers.py"
] | [
"import numpy as np\nimport re\nimport itertools\nfrom collections import Counter\n\n\ndef clean_str(string):\n \"\"\"\n Tokenization/string cleaning\n \"\"\"\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\n string = re.sub(r\"\\'s\", \" \\'s\", string)\n string = re.sub(r\"\\'ve\"... | [
[
"numpy.concatenate",
"numpy.arange",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
brettkoonce/jraph | [
"fbb3cf2a04015d4d5a878f4f0ab9fad961392618"
] | [
"jraph/examples/sat.py"
] | [
"# Copyright 2020 DeepMind Technologies Limited.\n\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://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by applicable... | [
[
"numpy.asarray",
"numpy.arange",
"numpy.eye"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pixelart7/hadoop | [
"82919a1e7af2ec22993f273e9ee64512a688c99b"
] | [
"hadoop-yarn-project/hadoop-yarn/hadoop-yarn-applications/hadoop-yarn-submarine/src/main/docker/with-cifar10-models/ubuntu-16.04/cifar10_estimator_tf_1.8.0/cifar10_main.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.device",
"tensorflow.train.LoggingTensorHook",
"tensorflow.concat",
"tensorflow.metrics.accuracy",
"tensorflow.contrib.training.GreedyLoadBalancingStrategy",
"tensorflow.contrib.learn.Experiment",
"tensorflow.nn.l2_loss",
"tensorflow.GPUOptions",
"tensorflow.group",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
newlawrence/Poliastro | [
"9cc569ed4421f1e05d69de5df260ea919fa83a30"
] | [
"src/poliastro/tests/test_coordinates.py"
] | [
"import astropy.units as u\nfrom astropy import time\nfrom astropy.tests.helper import assert_quantity_allclose\nfrom numpy.linalg import norm\n\nfrom poliastro import coordinates, bodies\nfrom poliastro.examples import molniya\nfrom poliastro.twobody.orbit import Orbit\n\n\n# Note that function are tested using as... | [
[
"numpy.linalg.norm"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.24",
"1.13",
"1.16",
"1.9",
"1.18",
"1.23",
"1.21",
"1.22",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
... |
guy1992l/functions | [
"ecc7bc18501d377d10d2b40edccf5c4b256a79d3"
] | [
"sql_to_file/sql_to_file.py"
] | [
"# Generated by nuclio.export.NuclioExporter\n\nimport pandas as pd\nimport pyhive\nfrom sqlalchemy.engine import create_engine\nfrom mlrun.execution import MLClientCtx\n\n\ndef sql_to_file(\n context: MLClientCtx,\n sql_query: str,\n database_url: str,\n file_ext: str = \"parquet\",\n) -> None:\n \"... | [
[
"pandas.read_sql"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Othinus099/interior | [
"5c3eaa59722d71374422d34f42342c659d7ce8b0"
] | [
"projects/UniDet/unidet/modeling/roi_heads/split_roi_heads.py"
] | [
"import json\nimport torch\nfrom torch import nn\nfrom torch.autograd.function import Function\nimport torch.nn.functional as F\nimport numpy as np\n\nfrom detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference\nfrom detectron2.modeling.roi_heads.roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads\nfr... | [
[
"torch.device",
"torch.tensor",
"torch.zeros",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rogeroyer/df_singularity_plan_zhaoshang | [
"dab27cfaa039b1fda380409e56d11b453194230f"
] | [
"train_gbdt_model.py"
] | [
"# -*- coding:utf-8 -*-\r\n\r\nimport random\r\nfrom divide_data import *\r\nfrom sklearn.ensemble import GradientBoostingClassifier\r\nfrom sklearn.metrics import roc_auc_score\r\nfrom extract_features import extract_feature\r\nfrom extract_features import extract_one_hot_feature\r\nfrom extract_features import ex... | [
[
"sklearn.metrics.roc_auc_score",
"sklearn.ensemble.GradientBoostingClassifier"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
MertEfeSevim/legendaryPokemonClassification | [
"17452d8d5192b5e527ae0e5e1df139e5f00cde4a"
] | [
"classifier.py"
] | [
"import pandas as pd\nimport numpy as np\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import linear_model\n\npokemon_csv = pd.read_csv('Pokemon.csv')\n\ndf = pd.DataFrame(pokemon_csv, columns = ['Name', 'T... | [
[
"pandas.read_csv",
"sklearn.naive_bayes.GaussianNB",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"numpy.array",
"sklearn.linear_model.SGDClassifier",
"sklearn.metrics.accuracy_score"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
yimuw/yimu-blog | [
"280ab2eca1fa48602d1695d69366842ea40debda"
] | [
"random/my_git/my_git.py"
] | [
"import os\nimport hashlib\nimport pickle\nimport argparse\nimport sys\nimport shutil\nimport copy\n\njoin = os.path.join\n\n\nclass TreeNode:\n def __init__(self, id, blobs=[], parents=[]):\n self.id = id\n self.blobs = blobs\n self.parents = parents\n self.children = []\n\n\nclass B... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
resgen/skills-ml | [
"5fa1fc975892230931b002fe5cac2579b242cd03"
] | [
"tests/test_storage.py"
] | [
"from skills_ml.storage import open_sesame, ModelStorage, S3Store, FSStore, PersistedJSONDict, ProxyObjectWithStorage, SerializedByStorage\nfrom skills_ml.algorithms.preprocessing import IterablePipeline\nfrom skills_ml.algorithms import nlp\n\nfrom skills_utils.s3 import upload, list_files\n\nimport joblib\n\nfrom... | [
[
"numpy.random.rand",
"sklearn.ensemble.RandomForestClassifier",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vineetjnair9/GridCal | [
"5b63cbae45cbe176b015e5e99164a593f450fe71"
] | [
"src/GridCal/Engine/Core/snapshot_opf_data.py"
] | [
"# This file is part of GridCal.\n#\n# GridCal is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# GridCal is distributed in t... | [
[
"scipy.sparse.csc_matrix",
"numpy.ix_",
"numpy.conj",
"numpy.abs",
"numpy.power",
"numpy.sqrt",
"numpy.arange",
"scipy.sparse.diags",
"numpy.ones",
"numpy.exp",
"numpy.zeros",
"numpy.empty",
"scipy.sparse.lil_matrix"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
lucaspbastos/soundata | [
"8b60852debea7cdc49e2c6853b033a29e503b67c"
] | [
"soundata/datasets/fsdnoisy18k.py"
] | [
"\"\"\"FSDnoisy18K Dataset Loader\n\n.. admonition:: Dataset Info\n :class: dropdown\n\n *Created By:*\n Eduardo Fonseca, Mercedes Collado, Manoj Plakal, Daniel P. W. Ellis, Frederic Font, Xavier Favory, Xavier Serra.\n Music Technology Group, Universitat Pompeu Fabra (Barcelona). Version 1.0\n\... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Alejandro-Valdes/fetal-brain-segmentation | [
"09f7ecea1b0935f96a2bc7b014092439285fea69"
] | [
"models/experimental_models/unet_resnet_upconv_se.py"
] | [
"from losses import *\nfrom keras.models import Model\nfrom keras.optimizers import RMSprop, Adam, SGD\nfrom keras.losses import binary_crossentropy\nfrom keras import backend as K\nfrom keras import layers\nimport numpy as np\n\nimport tensorflow as tf\n\n\ndef down_conv(init, nb_filter, se_version):\n x = laye... | [
[
"tensorflow.reset_default_graph",
"tensorflow.Session"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
sanixa/gan-leaks-custom | [
"f2efd8c8f4d267dd728bf00c8936d6f04a63736e"
] | [
"attack_models/tools/eval_roc.py"
] | [
"import numpy as np\nimport os\nimport argparse\nimport matplotlib.pyplot as plt\nfrom sklearn import metrics\n\n\n############################################################################\n# visualization functions\n############################################################################\ndef plot_roc(pos_r... | [
[
"sklearn.metrics.roc_auc_score",
"matplotlib.pyplot.legend",
"sklearn.metrics.confusion_matrix",
"numpy.concatenate",
"matplotlib.pyplot.plot",
"numpy.zeros_like",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.close",
"numpy.load",
"matplotlib.pyplot.figure",
"ma... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
WNoxchi/fastai_docs | [
"7a53964531a67c193c19235ed135c4793e65256c"
] | [
"dev_course/dl2/exp/nb_01.py"
] | [
"\n#################################################\n### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###\n#################################################\n# file to edit: dev_nb/01_matmul_workbook.ipynb\n\nfrom exp.nb_00 import *\nimport operator\n\ndef test(a,b,cmp,cname=None):\n if cname is None: cname=cmp.__... | [
[
"torch.allclose"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lensonp/hwaves | [
"0f566d8380515a5e64a77d72466900d1676a93ee"
] | [
"tests/test_hwf_density.py"
] | [
"import os\n\nimport numpy as np\n\nfrom hwaves.hwf_density import cartesian_density, pack_cartesian_data, write_cartesian\nfrom hwaves.hwf import radial_wf_integral\n\ntests_path = os.path.join(os.getcwd(),'tests')\n\nnx = 30\nny = 30\nnz = 30\ndx = 0.1\ndy = 0.1\ndz = 0.1\n\ndef test_cartesian_density():\n\n x... | [
[
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
stuartcrobinson/ImageAI | [
"654f91b55fad98752f650a4fd7b26d3edfd64175"
] | [
"examples/object_detectionSTUART_croppingVideoFrames.py"
] | [
"import json\nimport os\nimport statistics as s\nimport time\nfrom os import listdir\n\nimport cv2\nfrom scipy.spatial import distance\n\nfrom imageai.Detection import ObjectDetection\n\nexecution_path = os.getcwd()\n\ndetectorY = ObjectDetection()\ndetectorY.setModelTypeAsYOLOv3()\ndetectorY.setModelPath(\"/Users/... | [
[
"scipy.spatial.distance.euclidean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
AITuringSAS/images_for_codecommit | [
"e2d383fbcaead9024f9d26751ee0aa3d39688258"
] | [
"efficientdet_aituring/automl/efficientdet/utils.py"
] | [
"# Copyright 2020 Google Research. 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... | [
[
"tensorflow.compat.v1.io.gfile.exists",
"tensorflow.compat.v1.io.gfile.rename",
"tensorflow.compat.v1.io.gfile.makedirs",
"tensorflow.compat.v1.random.uniform",
"tensorflow.compat.v1.GradientTape",
"tensorflow.compat.v1.profiler.ProfileOptionBuilder.float_operation",
"tensorflow.compat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kangyongxin/deepmind-research | [
"5b22f165327a263a31b2a467b9446fd1bf74d7db"
] | [
"curl/training.py"
] | [
"################################################################################\n# Copyright 2019 DeepMind Technologies Limited\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the Li... | [
[
"tensorflow.scatter_update",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.equal",
"numpy.concatenate",
"numpy.max",
"tensorflow.train.AdamOptimizer",
"tensorflow.get_default_graph",
"tensorflow.scatter_nd_update",
"tensorflow.get_collection",
"numpy.arange",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
noamsgl/RL201_HW4 | [
"76bd3b173eaf07d3d4c47a3448ec6bdebbe7a7aa"
] | [
"main.py"
] | [
"\"\"\"\nNoam Siegel and Dolev Orgad,\nReinforcement Learning,\nBen Gurion University of the Negev, 2020\n\"\"\"\n\nfrom datetime import datetime\n\nimport gym\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom numpy.linalg import inv\nfrom tqdm import tqdm\n\n\"\"\"\nAssumes installation of tqdm:\npip inst... | [
[
"numpy.diag",
"numpy.random.random",
"numpy.linspace",
"matplotlib.pyplot.title",
"numpy.asarray",
"numpy.linalg.inv",
"numpy.ones",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.ylabel",
"numpy.mean",
"numpy.random.randint",
"numpy.savetxt",
"matplotlib.pyplot.x... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jjhenkel/nteract | [
"088222484b59af14b1da22de4d0990d8925adf95"
] | [
"applications/jupyter-extension/nteract_on_jupyter/notebooks/utils/cb/python/lowlevel.py"
] | [
"import json\nimport regex\nimport pickle\nimport os.path\nimport xxhash\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.dataset as ds \nimport pyarrow.parquet as pq \nimport pyarrow.gandiva as gd\n\nTYPE_TO_BYTES = [\n 'aliased_import',\n 'argument_list',\n 'assert_statement',\n 'assignment'... | [
[
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
flying-sheep/pyRserve | [
"7bc53fe3ebda026df3e5ed27484fb9fbeabddcff"
] | [
"pyRserve/taggedContainers.py"
] | [
"\"\"\"\nSome specialized list and array classes to store results obtained from R. These\nclasses provide means not to only access object items by index but also - sort\nof like a dictionary - by key. However keys must not be unique or can even be\nNone. In those cases only the first item with that key is found.\n\... | [
[
"numpy.array",
"numpy.ndarray.__getitem__"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
satyenataagyaa/drl-portfolio-management | [
"7fccc39bd3c19b6ea5d6511444e7c4bb60a49a8b"
] | [
"src/model/ddpg/critic.py"
] | [
"\"\"\"\nCritic Network definition, the input is (o, a_{t-1}, a_t) since (o, a_{t-1}) is the state.\nBasically, it evaluates the value of (current action, previous action and observation) pair\n\"\"\"\n\nimport tensorflow as tf\nimport tflearn\n\n\nclass CriticNetwork(object):\n \"\"\"\n Input to the network ... | [
[
"tensorflow.multiply",
"tensorflow.gradients",
"tensorflow.placeholder",
"tensorflow.train.AdamOptimizer",
"tensorflow.trainable_variables"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Yash-567/ClassyVision | [
"356ddca8c12c871cc950f085ab514e95aa590e11"
] | [
"classy_vision/tasks/classification_task.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport copy\nimport enum\nimport json\nimport logging\nimport math\nimport multiprocessing as mp\nimport tim... | [
[
"torch.distributed.broadcast",
"torch.enable_grad",
"torch.nn.SyncBatchNorm.convert_sync_batchnorm",
"torch.tensor",
"torch.no_grad",
"torch.cuda.is_available",
"torch.cuda.device_count"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hencockable/deep-head-pose | [
"f6aa02edb7f7b243aa2df4f9cc233310ed73d7c0"
] | [
"code/my_scripts/make_train_val_test_sets.py"
] | [
"import pandas as pd\nimport os\n\n# all vps\n# 'VP3', 'VP8', 'VP7', 'VP14', 'VP2', 'VP13', 'VP16', 'VP12', 'VP10', 'VP19', 'VP18', 'VP5', 'VP11'\n\ndata_path = \"../../source/train_val_test_vps/data/\"\nl4_path = \"../../source/train_val_test_vps/l4/\"\nl4_no_pca_path = \"../../source/train_val_test_vps/l4_no_pca/... | [
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
MZSHAN/SparseReconstruction | [
"c6f553acbd6cf5ad72038fae5d5454e42e11cdcb"
] | [
"EpipolarHelpers.py"
] | [
"import math\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport utils\n\n\n# Function has been tested\ndef triangulate(projection_matrix1, pts1, projection_matrix2, pts2):\n \"\"\"\n Function takes a the projection matrices of two cameras and the corresponding points in the pixel space of the\n 2... | [
[
"numpy.diag",
"numpy.dot",
"matplotlib.pyplot.imshow",
"numpy.expand_dims",
"numpy.squeeze",
"numpy.concatenate",
"numpy.round",
"numpy.exp",
"numpy.random.randint",
"numpy.linalg.svd",
"numpy.reshape",
"numpy.arange",
"numpy.eye",
"numpy.roots",
"numpy.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ayushdg/cudf | [
"2a82eca39149d474dc9ffb1cb95dcea4a58a78cf"
] | [
"python/cudf/cudf/tests/test_indexing.py"
] | [
"# Copyright (c) 2021, NVIDIA CORPORATION.\n\nfrom itertools import combinations\n\nimport cupy\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nimport cudf\nfrom cudf.core._compat import PANDAS_GE_110, PANDAS_GE_120\nfrom cudf.tests import utils\nfrom cudf.tests.utils import INTEGER_TYPES, assert_eq, ass... | [
[
"pandas.to_datetime",
"pandas.Series",
"pandas.DataFrame",
"numpy.dtype",
"numpy.random.sample",
"numpy.random.randint",
"numpy.arange",
"numpy.int8",
"numpy.float32",
"pandas.MultiIndex",
"pandas.Categorical",
"pandas.Timedelta",
"numpy.random.rand",
"panda... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
STAR-Center/IncrementalTopo | [
"befbcd55f8a438ff3dd1a2d1d5d4fbb467dc149f"
] | [
"gp_topo/RMsaddle.py"
] | [
"#!/usr/bin/env python\n'''\nThis script will remove some saddle point\nHowever there're some degree one point that is not saddle point and some low density saddle that may not be a passage.\n'''\n'''\nHere we just let the non-saddle point removed that's ok\n'''\nimport numpy as np\nimport pdb\ndef removeSaddle(gau... | [
[
"numpy.where",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
weigq/classification | [
"16f587af0c01a66f7c8a50c80372daf288ca4fd6"
] | [
"utils/misc.py"
] | [
"'''Some helper functions for PyTorch, including:\n - get_mean_and_std: calculate the mean and std value of dataset.\n - msr_init: net parameter initialization.\n - progress_bar: progress bar mimic xlua.progress.\n'''\nimport errno\nimport os\nimport sys\nimport time\nimport math\n\nimport torch.nn as nn\n... | [
[
"torch.nn.init.kaiming_normal",
"torch.nn.init.constant",
"torch.nn.init.normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AJJLagerweij/topopt | [
"b0f8f0f22363c62678d96d659d4015bec4fc59e5"
] | [
"src_Actuator/constraints.py"
] | [
"\"\"\"\nConstraints class used to specify the density constraints of the topology\noptimisation problem. It contains functions for minimum and maximum element\ndensity in the upcomming iteration and the magnitude of the volume constraint\nfunction itself of the current design. This version of the code is used for ... | [
[
"numpy.sum",
"numpy.maximum",
"numpy.minimum",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rwl/pylon | [
"916514255db1ae1661406f0283df756baf960d14"
] | [
"pyreto/roth_erev.py"
] | [
"#------------------------------------------------------------------------------\n# Copyright (C) 2006 Charles Gieseler\n# Copyright (C) 2010 Richard Lincoln\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free... | [
[
"scipy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dilawar/pypet | [
"2769c74eff55c165c9002cc67611b96b1d2377ea"
] | [
"pypet/tests/integration/environment_test.py"
] | [
"__author__ = 'Robert Meyer'\n\nimport os\nimport platform\nimport logging\nimport time\nimport numpy as np\n\nfrom pypet.trajectory import Trajectory, load_trajectory\nfrom pypet.utils.explore import cartesian_product\nfrom pypet.environment import Environment\nfrom pypet.storageservice import HDF5StorageService\n... | [
[
"numpy.array",
"numpy.random.rand",
"scipy.sparse.lil_matrix"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
jitdee-ai/darts-models | [
"d200cdf5591959d212fa4535804a5a502ad2a19d"
] | [
"darmo/layers/tresnetv1/general_layers.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .avg_pool import FastAvgPool2d\n\nclass Flatten(nn.Module):\n def forward(self, x):\n return x.view(x.size(0), -1)\n\nclass DepthToSpace(nn.Module):\n\n def __init__(self, block_size):\n super().__init__()\n sel... | [
[
"torch.nn.functional.relu6",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.Sigmoid"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
manipopopo/TC-ResNet | [
"7dff6f4f865f1e63ff705d8e0267cf3b9a0d70a3"
] | [
"audio_nets/res.py"
] | [
"import tensorflow as tf\n\nslim = tf.contrib.slim\n\n\ndef conv_relu_bn(inputs, num_outputs, kernel_size, stride, idx, use_dilation, bn=False):\n scope = f\"conv{idx}\"\n with tf.variable_scope(scope, values=[inputs]):\n if use_dilation:\n assert stride == 1\n rate = int(2**(idx ... | [
[
"tensorflow.variable_scope",
"tensorflow.reshape"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
coursekevin/AerospikeDesign | [
"2385e53fa6af51fb09b8f1280cbb052e7a5c7aea"
] | [
"angelinoNozzle_py/angelino_nozzle_design.py"
] | [
"import numpy as np \nimport gasdynamics as gd \nimport matplotlib.pyplot as plt\nfrom scipy import optimize\nfrom scipy import interpolate\nfrom matplotlib import cm \nimport os\nfrom MOC import chr_mesh\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\n\nclass plug_nozzle:\n def __init__(self,design_al... | [
[
"numpy.sqrt",
"numpy.linspace",
"numpy.arctan",
"numpy.asarray",
"scipy.interpolate.griddata",
"numpy.sin",
"matplotlib.pyplot.close",
"matplotlib.pyplot.axis",
"numpy.zeros",
"matplotlib.pyplot.title",
"numpy.arccos",
"matplotlib.pyplot.savefig",
"scipy.interpo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"0.16",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"1.3",
"1.8"
... |
yux121/CS282a | [
"03781ad645a68f3dc23fcee7b6103fe878abe7f9"
] | [
"utils.py"
] | [
"import numpy as np\nimport time\nimport math\nimport torch\nfrom torch.nn import functional as F\n\nPAD_ID, SOS_ID, EOS_ID, UNK_ID, DEL_ID = [0, 1, 2, 3, 10000]\n\ndef cos_approx(data1,data2):\n \"\"\"numpy implementation of cosine similarity for matrix\"\"\"\n #print(\"warning: the second matrix will be tra... | [
[
"numpy.dot",
"numpy.multiply",
"numpy.vstack",
"numpy.linalg.norm",
"numpy.transpose",
"numpy.exp",
"numpy.zeros",
"numpy.divide"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ap3xx/great_expectations | [
"67251ff3fcb60b1a52a6ece1bec98fb8e96f6a96"
] | [
"great_expectations/util.py"
] | [
"import importlib\nimport json\nimport logging\nimport os\nimport time\nfrom functools import wraps\nfrom inspect import getcallargs\nfrom pathlib import Path\nfrom types import ModuleType\nfrom typing import Callable, Union\n\nimport black\nimport importlib_metadata\nfrom pkg_resources import Distribution\n\nfrom ... | [
[
"pandas.read_excel",
"pandas.read_csv",
"pandas.read_table",
"pandas.read_parquet",
"pandas.read_feather",
"pandas.read_json",
"pandas.read_pickle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
mercuries-ai/ml-agents | [
"80c5dbf08a8834a5802e218abc408ecb74da1694"
] | [
"ml-agents/mlagents/trainers/policy/torch_policy.py"
] | [
"from typing import Any, Dict, List, Tuple, Optional\nimport numpy as np\nfrom mlagents.torch_utils import torch, default_device\nimport copy\n\nfrom mlagents.trainers.action_info import ActionInfo\nfrom mlagents.trainers.behavior_id_utils import get_global_agent_id\nfrom mlagents.trainers.policy import Policy\nfro... | [
[
"numpy.concatenate",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
quochungto/stanford-cs231n-assignment-solution | [
"22f6a97f29c4188d57a581822ce1dbd37a36b882"
] | [
"assignment1/cs231n/classifiers/neural_net.py"
] | [
"from __future__ import print_function\n\nfrom builtins import range\nfrom builtins import object\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom past.builtins import xrange\n\nclass TwoLayerNet(object):\n \"\"\"\n A two-layer fully-connected neural network. The net has an input dimension of\n N... | [
[
"numpy.maximum",
"numpy.random.choice",
"numpy.arange",
"numpy.random.randn",
"numpy.exp",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xwshi/Semantic-Segmentation | [
"4cc89a22ffc9018d2b44e69e85672c7bdd1ab706"
] | [
"deeplab_Xception/nets/deeplab.py"
] | [
"import tensorflow as tf\nfrom keras import backend as K\nfrom keras import layers\nfrom keras.activations import relu\nfrom keras.applications.imagenet_utils import preprocess_input\nfrom keras.layers import (Activation, Add, BatchNormalization, Concatenate,\n Conv2D, DepthwiseConv2D, Drop... | [
[
"tensorflow.keras.backend.int_shape",
"tensorflow.image.resize_images"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
sstolle/scikit-lego | [
"a8543b7246db5cca8e89173c66b96bc63bc2a42a"
] | [
"tests/test_api.py"
] | [
"from collections import defaultdict\n\nimport pytest\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.utils import estimator_checks\n\nfrom sklego.dummy import RandomRegressor\nfrom sklego.transformers import EstimatorTransformer, RandomAdder\nfrom tests.conftest import id_func\n\n\n@pytest.mark.pa... | [
[
"sklearn.utils.estimator_checks.check_estimator",
"sklearn.linear_model.LinearRegression"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
meliani09/smt | [
"af729143be09b012257bf81dcd3e2c8c40f65c96",
"ab3bda1e38ca3a785dfc0ed692f1437bf7e78bd3"
] | [
"smt/surrogate_models/surrogate_model.py",
"smt/surrogate_models/kpls.py"
] | [
"'''\nAuthor: Dr. Mohamed A. Bouhlel <mbouhlel@umich.edu>\n Dr. John T. Hwang <hwangjt@umich.edu>\n\nThis package is distributed under New BSD license.\n'''\n#TODO: Extend to multifidelity problems by adding training_points = {'approx': {}}\n#TODO: Complete the mixture of expert model: verify from if self.op... | [
[
"numpy.array"
],
[
"sklearn.cross_decomposition.pls_.PLSRegression"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
fjaragones/pyacq | [
"be4d7a485d3c851a6e8f28a1197ba6118ad082ea"
] | [
"pyacq/core/tests/test_tools.py"
] | [
"# -*- coding: utf-8 -*-\n# Copyright (c) 2016, French National Center for Scientific Research (CNRS)\n# Distributed under the (new) BSD License. See LICENSE for more info.\n\nfrom pyacq.core import OutputStream, InputStream\nfrom pyacq.core.tools import ThreadPollInput, StreamConverter, ChannelSplitter\nfrom pyqtg... | [
[
"numpy.random.rand"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
marcintustin/xnmt | [
"f315fc5e493d25746bbde46d2c89cea3410d43df"
] | [
"test/test_training.py"
] | [
"import unittest\n\nimport dynet as dy\nimport numpy as np\n\nfrom xnmt.translator import DefaultTranslator\nfrom xnmt.embedder import SimpleWordEmbedder\nfrom xnmt.lstm import LSTMSeqTransducer\nfrom xnmt.pyramidal import PyramidalLSTMSeqTransducer\nfrom xnmt.attender import MlpAttender, DotAttender\nfrom xnmt.dec... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nnayar7/FEELVOS | [
"fc6387101127d1b94625b5ddd2be0d95b706d038"
] | [
"feelvos/models/Backbone.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass UNet(nn.Module):\n def __init__(self, n_ch, n_classes, bilinear=True):\n super(UNet, self).__init__()\n self.n_ch = n_ch\n self.n_classes = n_classes\n self.bilinear = bilinear\n\n self.inc = Doubl... | [
[
"torch.nn.ConvTranspose2d",
"torch.cat",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.Upsample",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qgking/CKDNet | [
"bc7273e0f3001e7f07241c842d7c49fff077fe5d"
] | [
"module/Critierion.py"
] | [
"import torch.nn as nn\nimport torch.nn.functional as F\nimport torch\nimport numpy as np\n\n\ndef make_one_hot(labels, classes):\n one_hot = torch.cuda.FloatTensor(labels.size()[0], classes, labels.size()[2], labels.size()[3]).zero_()\n target = one_hot.scatter_(1, labels.data, 1)\n return target\n\n\ncla... | [
[
"torch.nn.CrossEntropyLoss",
"torch.ones",
"numpy.asarray",
"numpy.reshape",
"torch.sub",
"torch.exp",
"torch.log",
"torch.clamp",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Justin-Tan/invariant_reps | [
"a6a647b03193e02af9f6a289a9443266564de15d"
] | [
"network.py"
] | [
"\"\"\" Network wiring \"\"\"\n\nimport tensorflow as tf\nimport numpy as np\nimport glob, time, os\nimport functools\n\nfrom utils import Utils\n\nclass Network(object):\n\n @staticmethod\n def _spectral_norm(w):\n w_shape = w.shape.as_list()\n w = tf.reshape(w, [-1, w_shape[-1]])\n\n wi... | [
[
"tensorflow.concat",
"tensorflow.reduce_sum",
"tensorflow.cast",
"tensorflow.layers.batch_normalization",
"tensorflow.nn.moments",
"tensorflow.layers.dense",
"tensorflow.squeeze",
"tensorflow.divide",
"tensorflow.stop_gradient",
"tensorflow.truncated_normal_initializer",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
rick1270/DS-Unit-3-Sprint-1-Software-Engineering | [
"3e0b0b09ce365452a50980d18d5479deeb19e6bc"
] | [
"acme.py"
] | [
"\n# coding: utf-8\n\n# In[1]:\n\n\nfrom numpy import random\n\nclass Product(object):\n def __init__(self, name, price=10, weight=20, flammability=0.5, identifier=random.randint(1000000,999999999)):\n self.name = name\n self.price = price\n self.weight = weight\n self.flammability = ... | [
[
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
danielmlow/vfp | [
"f43d170f25fa413c60b4d1930d658cb38cdd3265"
] | [
"analyze_results.py"
] | [
"\n\nimport os\nimport pickle\nimport json\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n\ndef performance_table(results, permute_order, output_dir, score_i = 0,score_name='roc_auc_score', round = 2):\n\t# score = {N}, choose which metric to compute, if run has ... | [
[
"pandas.concat",
"pandas.read_csv",
"matplotlib.pyplot.figure",
"numpy.median",
"pandas.DataFrame",
"numpy.percentile",
"numpy.round",
"matplotlib.pyplot.clf",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
photoszzt/onnxruntime | [
"523db6ef443b0a41de1401004ae4b394dad3314c"
] | [
"orttraining/orttraining/python/training/ortmodule/_io.py"
] | [
"# -------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n# --------------------------------------------------------------------------\n\nfrom collections import abc\nimport copy\nimport inspect\nimpo... | [
[
"torch.nn.Identity",
"torch.no_grad",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PhiladelphiaController/covid19-forecaster | [
"e2b4718b51c4b250db74269a73451f616db43bb5"
] | [
"covid19_forecaster/forecasters.py"
] | [
"import pandas as pd\n\n\ndef check_date_bounds(date, start_date, stop_date):\n \"\"\"Check the bounds of a specific date.\"\"\"\n\n # Check min date\n if date < pd.to_datetime(start_date):\n date_str = date.strftime(\"%Y-%m-%d\")\n raise ValueError(\n f\"Date {date_str} before min... | [
[
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Sillte/fairyimage | [
"1b84136984d392b187712333064da707e42bbb6e"
] | [
"src/fairyimage/captioner.py"
] | [
"from typing import Dict, List\nimport numpy as np\nfrom PIL import Image \n\nimport fairyimage as fi\nfrom fairyimage import AlignMode\n\nclass Captioner:\n \"\"\"Present images with words.\"\"\"\n\n def __init__(\n self,\n fontsize=None,\n backcolor=None,\n frame_width=None,\n ... | [
[
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tsbiosky/Imbalanced-Dataset-Project | [
"26037dc1edc51228c22372638a1187f5f0ae15e4"
] | [
"predictor.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nimport cv2\nimport torch\nfrom torchvision import transforms as T\nfrom torchvision.transforms import functional as F\nfrom maskrcnn_benchmark.modeling.detector import build_detection_model\nfrom maskrcnn_benchmark.utils.checkpoint import Det... | [
[
"torch.zeros",
"torch.load",
"torch.cat",
"matplotlib.pyplot.get_cmap",
"torch.tensor",
"numpy.copy",
"torch.no_grad",
"torch.nonzero",
"torch.device"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Tarekbouamer/Azure_Kinect_ROS_Driver | [
"17442bbc0a1ed18e60dfad2a1f6040900aa30d70"
] | [
"scripts/readDepthPFM.py"
] | [
"import glob, os\nimport re\nimport numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\nimport sys\n\nimport cv2\n\nimport argparse\n\n\nparser = argparse.ArgumentParser(description=\"Data Annotation \")\nparser.add_argument(\"data\", metavar=\"DIR\", type=str, help=\"Path to dataset\")\n\ndef re... | [
[
"numpy.reshape",
"numpy.fromfile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jingnanshi/torque_limited_simple_pendulum | [
"82a9379dffe063f863aeeae4fd2e2ef112b1c1fb",
"82a9379dffe063f863aeeae4fd2e2ef112b1c1fb"
] | [
"software/python/simple_pendulum/controllers/ddpg/ddpg_controller.py",
"software/python/simple_pendulum/reinforcement_learning/ddpg/replay_buffer.py"
] | [
"import numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.models import load_model\n\nfrom simple_pendulum.controllers.abstract_controller import AbstractController\n\ntf.compat.v1.disable_eager_execution()\n\n\nclass ddpg_controller(AbstractController):\n def __init__(self, model_path, torque_limit, s... | [
[
"tensorflow.keras.models.load_model",
"numpy.clip",
"numpy.squeeze",
"numpy.cos",
"numpy.sin",
"numpy.atleast_2d",
"numpy.copy",
"tensorflow.compat.v1.disable_eager_execution",
"numpy.array"
],
[
"tensorflow.convert_to_tensor",
"tensorflow.cast",
"numpy.zeros",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1... |
ZhengPeng7/OCR_TIANCHI_ICPR | [
"8d3865dd83628995f3c81fa4e7adb219fb875fad"
] | [
"single_ctpn/checkpoints_convert.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless ... | [
[
"tensorflow.python.platform.app.run",
"tensorflow.python.framework.ops.Graph",
"tensorflow.python.platform.tf_logging.info",
"tensorflow.python.pywrap_tensorflow.NewCheckpointReader",
"tensorflow.python.ops.variables.Variable",
"tensorflow.python.client.session.Session",
"tensorflow.py... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"1.2"
]
}
] |
dassh-dev/dassh | [
"5cd53fc7883897e59ec1126ee955e6a664995e25"
] | [
"dassh/__init__.py"
] | [
"########################################################################\n# Copyright 2021, UChicago Argonne, LLC\n#\n# Licensed under the BSD-3 License (the \"License\"); you may not use\n# this file except in compliance with the License. You may obtain a\n# copy of the License at\n#\n# https://opensource.org... | [
[
"numpy.set_printoptions"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thccheung/cellpose | [
"873641ed5b78c3fd8eff7eeb08bf30c0d751e272"
] | [
"cellpose/transforms.py"
] | [
"import numpy as np\nimport warnings\nimport cv2\n\nimport logging\ntransforms_logger = logging.getLogger(__name__)\n\nfrom . import dynamics, utils\n\ndef _taper_mask(ly=224, lx=224, sig=7.5):\n bsize = max(224, max(ly, lx))\n xm = np.arange(bsize)\n xm = np.abs(xm - xm.mean())\n mask = 1/(1 + np.exp((... | [
[
"numpy.pad",
"numpy.linspace",
"numpy.arange",
"numpy.int32",
"numpy.ptp",
"numpy.percentile",
"numpy.ones",
"numpy.transpose",
"numpy.ceil",
"numpy.sin",
"numpy.cos",
"numpy.zeros_like",
"numpy.random.rand",
"numpy.moveaxis",
"numpy.exp",
"numpy.arr... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ntropy-network/ML-tools | [
"279cc6c8918ab4f1407a891f9f80480b9d8cc178"
] | [
"SNGP/test_gp.py"
] | [
"import torch\nimport numpy as np\nimport itertools as it\nimport gaussian_process\nfrom tqdm import tqdm\nimport pytest\nfrom gaussian_process import RandomFeatureGaussianProcess, LaplaceRandomFeatureCovariance, mean_field_logits\nfrom random_fourier_features import RandomFourierFeatures\n\n\nclass Dataset(torch.u... | [
[
"numpy.linalg.svd",
"numpy.sqrt",
"torch.load",
"torch.randn",
"numpy.eye",
"torch.eye",
"torch.tensor",
"torch.exp",
"numpy.random.randn",
"numpy.testing.assert_allclose",
"torch.diag",
"numpy.random.RandomState"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.