repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
macdaliot/exist | [
"65244f79c602c5a00c3ea6a7eef512ce9c21e60a"
] | [
"scripts/insert2db/reputation/plugins/dshield_medium.py"
] | [
"import sys\nimport os\nimport configparser\nimport requests\nimport pandas as pd\nimport hashlib\nfrom io import StringIO\nfrom datetime import datetime, timezone\n\n## Django Setup\nimport django\nimport pymysql\npymysql.install_as_MySQLdb()\nconffile = os.path.join(os.path.dirname(__file__), \"../../conf/insert2... | [
[
"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": []
}
] |
derekdylu/mgt2001 | [
"b228d5e75e75a2f3f170e35db1bea999b765bec8"
] | [
"mgt2001/hyp/non.py"
] | [
"from matplotlib import pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport math\nimport scipy.stats as stats\n\n\ndef inter_p_value(p_value):\n # interpretation\n if p_value >= 0 and p_value < 0.01:\n inter_p = 'Overwhelming Evidence'\n elif p_value >= 0.01 and p_value < 0.05:\n i... | [
[
"scipy.stats.chi2.ppf",
"scipy.stats.norm.ppf",
"pandas.merge",
"scipy.stats.norm.cdf",
"numpy.abs",
"numpy.isnan",
"scipy.stats.chi2.cdf",
"pandas.DataFrame",
"scipy.stats.t.ppf",
"numpy.cov",
"scipy.stats.binom.cdf",
"scipy.stats.norm.sf",
"numpy.corrcoef",
... | [
{
"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": [
"0.13",
"1.6",
"0.14",
"1.10",
"0... |
cosmoscope/qt-client | [
"c6cb59267c8be9149a95fb853a4f181d9092c86b"
] | [
"qt_client/components/plot_data_model.py"
] | [
"import numpy as np\nfrom PyQt5.QtCore import (QAbstractTableModel, QModelIndex, QObject, Qt,\n QVariant, pyqtProperty, pyqtSignal, pyqtSlot)\n\nfrom ..hub import Hub, Message\n\n\nclass PlotDataModel(QAbstractTableModel):\n # DataRole = Qt.UserRole + 1\n\n def __init__(self, *args, *... | [
[
"numpy.arange",
"numpy.random.sample"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xXEminenTXx/ImageClassifier | [
"e0e63e12108b523270ea7d615afcbfc696b07996"
] | [
"predict_functions.py"
] | [
"# python imports\nimport numpy as np\nfrom PIL import Image\nimport torch\nfrom torch import nn, optim\nimport torch.nn.functional as F\nfrom torchvision import datasets, transforms, models\nfrom collections import OrderedDict\nfrom sys import exit\n\n# File containing all of the functions used in the predict prog... | [
[
"torch.exp",
"numpy.array",
"torch.from_numpy",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Gordonbuck/ml-oov-we | [
"ce28cd8b556a16125ba36cd41781a3e60bb26422"
] | [
"src/train.py"
] | [
"import higher\nfrom leap import Leap\nimport numpy as np\nimport os\nimport torch\nimport torch.nn as nn\nimport gc\n\n\ndef train(model, source_corpus, char2idx, args, device):\n model = model.to(device)\n optimizer = torch.optim.Adam(model.parameters(), lr=args.lr_init)\n lr_scheduler = torch.optim.lr_s... | [
[
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"numpy.arange",
"torch.nn.functional.cross_entropy",
"torch.no_grad",
"torch.nn.functional.cosine_similarity",
"torch.backends.cudnn.flags",
"numpy.average"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
krisbuote/Reinforcement-Learning-Trader | [
"ae8c3af0856a480c88546c2a7e478a735585e0af",
"ae8c3af0856a480c88546c2a7e478a735585e0af"
] | [
"Reinforcement-Learning-Trader/Agent.py",
"vanilla-LSTM/run.py"
] | [
"import keras\nfrom keras.models import Sequential\nfrom keras.models import load_model\nfrom keras.layers import Dense, LSTM, Dropout\nfrom keras.optimizers import Adam\n\nimport numpy as np\nimport random\nfrom collections import deque\n\nclass Agent:\n def __init__(self, state_size, is_eval=False, model_name=... | [
[
"numpy.argmax"
],
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show",
"sklearn.metrics.mean_squared_error"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mussard/share_data_benchmark | [
"c02bfa4017b9008800cabe47d7c7959f82c26060"
] | [
"MRPT/vdz/atoms/V_0/mrpt.py"
] | [
"import json\nfrom pyscf import gto,scf,mcscf, fci, lo, ci, cc\nfrom pyscf.scf import ROHF, UHF,ROKS\nimport numpy as np\nimport pandas as pd\n\n# THIS IS WERE IT STARTS ====================================\n\ndf=json.load(open(\"../../../trail.json\"))\n\nspins={'Sc':1, 'Ti':2, 'V':3, 'Cr':6, 'Mn':5, 'Fe':4, 'Cu':... | [
[
"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": []
}
] |
PariseC/osm2rail | [
"dfc373aedba4a82fd144192cb6a855e8a11b0601"
] | [
"osm2rail/plotter.py"
] | [
"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection,PolyCollection\n\ndef showNetwork(network,savefig=None):\n node_x_coords=[]\n node_y_coords=[]\n link_coords=[]\n poi_coords=[]\n\n for _,node in network.node_dict.items():\n node_x_coords.a... | [
[
"matplotlib.pyplot.tight_layout",
"matplotlib.collections.LineCollection",
"matplotlib.pyplot.subplots",
"matplotlib.collections.PolyCollection",
"matplotlib.pyplot.xlabel",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mikee385/fbsrankings | [
"2b50e26a302b53c21cd8f5c965943d6fbf0680a1"
] | [
"src/fbsrankings/domain/service/srs_ranking_service.py"
] | [
"from typing import Dict\nfrom typing import List\n\nimport numpy\n\nfrom fbsrankings.domain.model.affiliation import Subdivision\nfrom fbsrankings.domain.model.game import Game\nfrom fbsrankings.domain.model.game import GameStatus\nfrom fbsrankings.domain.model.ranking import Ranking\nfrom fbsrankings.domain.model... | [
[
"numpy.linalg.lstsq",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jamaps/tracc | [
"0f71b07b6560ed2f5a9a9f6f94a07e487af254c5"
] | [
"build/lib/tracc/tracc.py"
] | [
"import tracc\nimport pandas as pd\nimport numpy as np\n\n\nclass costs:\n\n def __init__(self,\n travelcosts_df,\n columns = None\n ):\n\n \"\"\"\n Inputs data and prunes columns if desired\n \"\"\"\n\n if columns is not None:\n self.data = travelcosts... | [
[
"pandas.merge",
"numpy.where",
"pandas.concat"
]
] | [
{
"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": []
}
] |
ShashankBice/pygeotools | [
"5bc74f96cf79f3089572cab7e4f3632ca36b22bc"
] | [
"pygeotools/lib/iolib.py"
] | [
"#! /usr/bin/env python\n\"\"\"\nFunctions for IO, mostly wrapped around GDAL\n\nNote: This was all written before RasterIO existed, which might be a better choice. \n\"\"\"\n\nimport os\nimport subprocess\n\nimport numpy as np\nfrom osgeo import gdal, gdal_array, osr\n\n#Define drivers\nmem_drv = gdal.GetDriverByN... | [
[
"numpy.isnan",
"numpy.loadtxt",
"numpy.ma.masked_values"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.10",
"1.12",
"1.11",
"1.19",
"1.13",
"1.16",
"1.9",
"1.18",
"1.20",
"1.7",
"1.15",
"1.14",
"1.17",
"1.8"
],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
chenxiaoyu523/RPNet-Pytorch | [
"7beceb9f39e66eba5283536b478f86523fcc96c7"
] | [
"data/utils.py"
] | [
"import os\nfrom PIL import Image\nimport numpy as np\n\n\ndef get_files(folder, name_filter=None, extension_filter=None):\n \"\"\"Helper function that returns the list of files in a specified folder\n with a specified extension.\n\n Keyword arguments:\n - folder (``string``): The path to a folder.\n ... | [
[
"numpy.log",
"numpy.median",
"numpy.zeros_like",
"numpy.bincount",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rpuntaie/tensorflow_examples | [
"1958f7f0de9d96859dc3961a1695e1543fec9fd3",
"1958f7f0de9d96859dc3961a1695e1543fec9fd3",
"1958f7f0de9d96859dc3961a1695e1543fec9fd3"
] | [
"mask.py",
"course_v2/_09nlp.py",
"course_v1/sixi.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"\nChain models.\n\nMasking.\n\nShow output of layer.\n\"\"\"\n\nimport numpy as np\nfrom tensorflow.keras import Input\nfrom tensorflow.keras.layers import Masking, Dense\nfrom tensorflow.keras.regularizers import l2\nfrom tensorflow.keras.models import Sequential, Model\n\nX_train ... | [
[
"tensorflow.keras.layers.Masking",
"tensorflow.keras.Input",
"tensorflow.keras.regularizers.l2",
"numpy.random.rand",
"tensorflow.keras.models.Sequential"
],
[
"matplotlib.pyplot.legend",
"tensorflow.keras.layers.GlobalAveragePooling1D",
"matplotlib.pyplot.plot",
"pandas.re... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.2"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": [
"1.10",
... |
pyomeca/BiorbdOptim | [
"f07094668788d3e1b5e8cd1c65fbf0c7dc7cc978"
] | [
"bioptim/limits/penalty_option.py"
] | [
"from typing import Any, Union, Callable\n\nimport biorbd_casadi as biorbd\nfrom casadi import horzcat, vertcat, Function, MX, SX\nimport numpy as np\n\nfrom .penalty_node import PenaltyNodeList\nfrom ..misc.enums import Node, PlotType, ControlType, ConstraintType, IntegralApproximation\nfrom ..misc.mapping import ... | [
[
"numpy.issubdtype",
"numpy.ndarray",
"numpy.repeat",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nerdslab/SwapVAE | [
"f43e59c93d0b9f7f1de51a63e25b17b7be1da2d9"
] | [
"vae_kits/classification.py"
] | [
"import torch\r\nfrom torch.utils.data import DataLoader, Dataset\r\nfrom tqdm import tqdm\r\n\r\nclass Simple_Trans(Dataset):\r\n def __init__(self, data, transform=None):\r\n # [reps, labels]\r\n self.reps = data[0]\r\n self.labels = data[1]\r\n # print(self.reps.shape, self.labels.... | [
[
"torch.nn.CrossEntropyLoss",
"torch.max",
"torch.cat",
"torch.utils.data.DataLoader",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
opennlp/Large-Scale-Text-Classification | [
"a803c8d89357e5ec897031a41dda807d91f00431"
] | [
"interpretation/instance_explanation.py"
] | [
"from factory import vectorizer_factory\nfrom sklearn.base import TransformerMixin\nfrom sklearn.pipeline import make_pipeline\nfrom lime.lime_text import LimeTextExplainer\n\n\nclass VectorTransformer(TransformerMixin):\n def __init__(self, vectorizer_name):\n self.vectorizer_name = vectorizer_name\n\n ... | [
[
"sklearn.pipeline.make_pipeline"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lefevre-fraser/openmeta-mms | [
"08f3115e76498df1f8d70641d71f5c52cab4ce5f",
"08f3115e76498df1f8d70641d71f5c52cab4ce5f",
"08f3115e76498df1f8d70641d71f5c52cab4ce5f",
"08f3115e76498df1f8d70641d71f5c52cab4ce5f",
"08f3115e76498df1f8d70641d71f5c52cab4ce5f",
"08f3115e76498df1f8d70641d71f5c52cab4ce5f",
"08f3115e76498df1f8d70641d71f5c52cab4ce5... | [
"bin/Python27/Lib/site-packages/numpy/core/tests/test_regression.py",
"bin/Python27/Lib/site-packages/scipy/linalg/tests/test_decomp.py",
"bin/Python27/Lib/site-packages/scipy/sparse/base.py",
"bin/Python27/Lib/site-packages/scipy/stats/_multivariate.py",
"bin/Python27/Lib/site-packages/scipy/sparse/linalg/... | [
"from __future__ import division, absolute_import, print_function\r\n\r\nimport copy\r\nimport pickle\r\nimport sys\r\nimport platform\r\nimport gc\r\nimport warnings\r\nimport tempfile\r\nfrom os import path\r\nfrom io import BytesIO\r\nfrom itertools import chain\r\n\r\nimport numpy as np\r\nfrom numpy.testing im... | [
[
"numpy.setbufsize",
"numpy.all",
"numpy.binary_repr",
"numpy.where",
"numpy.unique",
"numpy.zeros",
"numpy.testing.assert_raises",
"numpy.chararray",
"numpy.compat.sixu",
"numpy.find_common_type",
"numpy.array",
"numpy.fromfile",
"numpy.indices",
"numpy.rand... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
NunoEdgarGFlowHub/torchbearer | [
"940e75ec88acd59d5a97aa8c721f7cfa30a5c4d0",
"940e75ec88acd59d5a97aa8c721f7cfa30a5c4d0",
"940e75ec88acd59d5a97aa8c721f7cfa30a5c4d0"
] | [
"torchbearer/callbacks/weight_decay.py",
"torchbearer/callbacks/between_class.py",
"tests/metrics/test_aggregators.py"
] | [
"import torchbearer\n\nfrom torchbearer.callbacks import Callback\n\nimport torch\n\n\nclass WeightDecay(Callback):\n \"\"\"Create a WeightDecay callback which uses the given norm on the given parameters and with the given decay rate.\n If params is None (default) then the parameters will be retrieved from th... | [
[
"torch.norm"
],
[
"torch.zeros_like",
"torch.nn.functional.log_softmax"
],
[
"torch.Size",
"torch.FloatTensor",
"torch.Tensor",
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
datalayer-externals/papermill-scrapbook | [
"911220a26c7f6606f6370a75a4cdac4284675bdc"
] | [
"scrapbook/models.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nmodels.py\n\nProvides the various model wrapper objects for scrapbook\n\"\"\"\nfrom __future__ import unicode_literals\nimport os\nimport copy\nimport nbformat\nimport collections\nimport pandas as pd\n\nfrom six import string_types\nfrom collections import OrderedDict\nfrom IPytho... | [
[
"pandas.concat",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
PacktPublishing/Computer-Vision-YOLO-Custom-Object-Detection-with-Colab-GPU | [
"f90db3c5f3326d89282f249ede92234812c824a5"
] | [
"pretrained_yolo_video_nms.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n\n@author: abhilash\n\"\"\"\n\nimport numpy as np\nimport cv2\n\n#get the webcam video stream\nfile_video_stream = cv2.VideoCapture('images/testing/video_sample2.mp4')\n\n#create a while loop \nwhile (file_video_stream.isOpened):\n #get the current frame from video stream\n ... | [
[
"numpy.array",
"numpy.argmax",
"numpy.tile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ishaiqbal/sqlalchemy-challenge- | [
"5b2b7bbb954e371bd1777b5cb04bfb22d7a5a25c"
] | [
"app.py"
] | [
"import numpy as np\nimport datetime as dt\nimport pandas as pd\n\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func, inspect\n\nfrom flask import Flask, jsonify\n\n\n#################################################\n#... | [
[
"numpy.ravel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ekatef/oemof-examples | [
"4805d5cef03141a917fd8a9e1141acfa8cc9d781",
"4805d5cef03141a917fd8a9e1141acfa8cc9d781",
"f16511d20008c30889a6e75a788a3a1a0bc632c2"
] | [
"oemof_examples/tespy/heat_pump/heat_pump_water.py",
"oemof_examples/oemof.solph/v0.4.x/activity_costs/activity_costs.py",
"oemof_examples/oemof.solph/v0.4.x/storage_investment/v2_invest_optimize_only_gas_and_storage.py"
] | [
"# -*- coding: utf-8 -*-\nfrom tespy.networks import Network\nfrom tespy.components import (\n Sink, Source, Splitter, Compressor, Condenser, Pump, HeatExchangerSimple,\n Valve, Drum, HeatExchanger, CycleCloser\n)\nfrom tespy.connections import Connection, Ref\nfrom tespy.tools.characteristics import CharLine... | [
[
"numpy.array",
"pandas.DataFrame"
],
[
"matplotlib.pyplot.show",
"numpy.full",
"pandas.date_range"
],
[
"pandas.read_csv",
"pandas.date_range"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
fitbenchmarking/fitbenchmarking | [
"ea398efa61f071dc64fe7c3b484d5bb4e1897856",
"ea398efa61f071dc64fe7c3b484d5bb4e1897856"
] | [
"examples/benchmark_problems/scripts/generate_simulated_mantid.py",
"fitbenchmarking/results_processing/base_table.py"
] | [
"\"\"\"\nThis script is used to generate simulated count data based on a Mantid\nscript.\n\"\"\"\n\nimport os\n\nimport numpy\n\n\ndef VariableStatsData(N, A0, omega, phi, sigma, bg):\n x = numpy.linspace(start=0.0, stop=32.0, num=2001)\n y = (1+A0*numpy.cos(omega*x+phi)*numpy.exp(-(sigma*x)**2)) * \\\n ... | [
[
"numpy.linspace",
"numpy.cos",
"numpy.random.poisson",
"numpy.exp",
"numpy.sum"
],
[
"numpy.linspace",
"numpy.isnan",
"matplotlib.pyplot.subplots",
"pandas.MultiIndex.from_tuples",
"matplotlib.pyplot.get_cmap",
"matplotlib.pyplot.savefig",
"numpy.log10",
"pa... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"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",
"... |
Zerwer/EEGMachineLearning | [
"d0dfcf617b22317a88018a86545c4f7e37a290b9"
] | [
"data/live_predict.py"
] | [
"# Unsure majority of time but more correct then wrong when thinking of\n# Requires more data for training\nfrom data import *\nfrom tkinter import *\nfrom keras.models import load_model\nimport numpy as np\nimport threading\nimport time\n\n# Time variables\nstart_wait = 10000\nwait = 2100\n\n# Set dimensions\nw = ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
st2yang/garage | [
"50186a9630df038aeba36d6b06b006ab32ed48f5",
"50186a9630df038aeba36d6b06b006ab32ed48f5",
"50186a9630df038aeba36d6b06b006ab32ed48f5"
] | [
"tests/garage/sampler/test_sampler.py",
"tests/garage/tf/baselines/test_baselines.py",
"tests/garage/torch/policies/test_deterministic_mlp_policy.py"
] | [
"from dowel import logger\nimport numpy as np\n\nfrom garage.sampler.utils import truncate_paths\n\nfrom tests.fixtures.logger import NullOutput\n\n\nclass TestSampler:\n\n def setup_method(self):\n logger.add_output(NullOutput())\n\n def teardown_method(self):\n logger.remove_all()\n\n def t... | [
[
"numpy.zeros"
],
[
"tensorflow.compat.v1.global_variables_initializer"
],
[
"numpy.prod",
"torch.ones",
"numpy.array_equal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lorentzenchr/scipy | [
"393a05ee927883ad6316b7092c851afea8f16816",
"393a05ee927883ad6316b7092c851afea8f16816",
"393a05ee927883ad6316b7092c851afea8f16816",
"393a05ee927883ad6316b7092c851afea8f16816"
] | [
"scipy/signal/tests/test_savitzky_golay.py",
"scipy/sparse/tests/test_array_api.py",
"scipy/sparse/linalg/setup.py",
"scipy/optimize/_tstutils.py"
] | [
"import numpy as np\nfrom numpy.testing import (assert_allclose, assert_equal,\n assert_almost_equal, assert_array_equal,\n assert_array_almost_equal)\n\nfrom scipy.ndimage import convolve1d\n\nfrom scipy.signal import savgol_coeffs, savgol_filter\nfrom scipy.sign... | [
[
"numpy.testing.assert_equal",
"numpy.polyfit",
"numpy.ones_like",
"numpy.testing.assert_array_almost_equal",
"numpy.linspace",
"numpy.arange",
"scipy.signal.savgol_coeffs",
"numpy.testing.assert_array_equal",
"numpy.full_like",
"numpy.testing.assert_almost_equal",
"nump... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.14",
"1.6",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"1.0",
"0.17",
"0.16",
"1.8"
],
"tensorflow": []
... |
DelphianCalamity/PrivateKube | [
"14f575e77021ab7baca30f4061140ec83bdc96a7",
"14f575e77021ab7baca30f4061140ec83bdc96a7"
] | [
"evaluation/macrobenchmark/workload/models/classification.py",
"examples/pipeline/dp_train/src/main.py"
] | [
"import sys, os, shutil\nimport h5py\nimport time\nimport io\nimport random\nimport tempfile\nfrom tqdm import tqdm\nfrom absl import app, flags, logging\nfrom ray.util.multiprocessing import Pool\nimport gcsfs\nimport numpy as np\nfrom pathlib import Path\n\nfrom sklearn.linear_model import LogisticRegression\nfro... | [
[
"torch.nn.CrossEntropyLoss",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"numpy.sqrt",
"numpy.random.seed",
"torch.zeros",
"numpy.random.choice",
"torch.utils.data.DataLoader",
"torch.zeros_like",
"torch.nn.BCEWithLogitsLoss",
"torch.no_grad",
"torch.ones_like"
],
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
LEGOS-CTOH/xarray | [
"d543d09aaa7fdfc4f5f92edcd4e3c0af1207c95b"
] | [
"xarray/core/nanops.py"
] | [
"import numpy as np\n\nfrom . import dtypes, nputils, utils\nfrom .duck_array_ops import _dask_or_eager_func, count, fillna, isnull, where_method\nfrom .pycompat import dask_array_type\n\ntry:\n import dask.array as dask_array\nexcept ImportError:\n dask_array = None\n\n\ndef _replace_nan(a, val):\n \"\"\"... | [
[
"numpy.nanmean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
thilinicooray/mac-network-pytorch | [
"0e4bf3f7f301570b652490f697758361c866f3c1",
"0e4bf3f7f301570b652490f697758361c866f3c1",
"0e4bf3f7f301570b652490f697758361c866f3c1"
] | [
"main_verbq_working.py",
"model_roles_working.py",
"main_agentplace365_verbq_withnone_diffeval.py"
] | [
"import torch\n#from imsitu_encoder_verbq import imsitu_encoder\nfrom imsitu_encoder_roleqverbq_embdhz import imsitu_encoder\nfrom imsitu_loader import imsitu_loader_roleq_updated\nfrom imsitu_scorer_log import imsitu_scorer\nimport json\nimport model_verbq_working\nimport os\nimport utils\nimport time\nimport rand... | [
[
"torch.cuda.manual_seed",
"torch.manual_seed",
"torch.utils.data.DataLoader",
"torch.optim.lr_scheduler.ExponentialLR",
"torch.autograd.Variable",
"torch.no_grad",
"torch.nn.DataParallel",
"torch.optim.Adamax"
],
[
"torch.nn.Dropout",
"torch.zeros",
"torch.cat",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
GabrielWen/spartan | [
"ce3bf7f2bb551d7f996a1884acef819b620cc854"
] | [
"spartan/examples/ssvd/qr.py"
] | [
"import spartan\nfrom spartan import expr, core\nimport numpy as np\nfrom sys import stderr\n\ndef qr(Y):\n ''' Compute the thin qr factorization of a matrix.\n Factor the matrix Y as QR, where Q is orthonormal and R is\n upper-triangular.\n\n Parameters\n ----------\n Y: Spartan array of shape (M, K).\n\n N... | [
[
"numpy.linalg.inv",
"numpy.linalg.cholesky"
]
] | [
{
"matplotlib": [],
"numpy": [
"1.11",
"1.10",
"1.12",
"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": [],
... |
GrantRoss-Tenki/Malawi-CQC-CSC-OSU-Work | [
"a720e0451579945ba10eafdafe2e0d59a86d5cfb",
"a720e0451579945ba10eafdafe2e0d59a86d5cfb"
] | [
"Graphing_Summaries.py",
"Sensitivity_Fuel_Threshold.py"
] | [
"import matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\n#from pylab import plot, show, xlim,figure,hold, ylim,legend, boxplot, setup, axes\r\n\r\nimport seaborn as sns\r\n\r\n# Is this a personal or work computer\r\n# Are you graphing for hood or no hood\r\n\r\nComputer = 'personal' #or 'pe... | [
[
"matplotlib.pyplot.text",
"matplotlib.pyplot.boxplot",
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.title",
"numpy.arange",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.subplots",
"numpy.percentile",
"pandas.DataFrame",
"matplotlib.pyplot.xlim",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
icml2020submission6857/metarl | [
"9b66cefa2b6bcb6a38096d629ce8853b47c7171d",
"9b66cefa2b6bcb6a38096d629ce8853b47c7171d",
"9b66cefa2b6bcb6a38096d629ce8853b47c7171d",
"9b66cefa2b6bcb6a38096d629ce8853b47c7171d",
"9b66cefa2b6bcb6a38096d629ce8853b47c7171d",
"ae4825d21478fa1fd0aa6b116941ea40caa152a5",
"9b66cefa2b6bcb6a38096d629ce8853b47c7171... | [
"tests/metarl/torch/algos/test_torch_algo_utils.py",
"tests/metarl/tf/baselines/test_gaussian_cnn_baseline.py",
"src/metarl/tf/policies/categorical_mlp_policy.py",
"src/metarl/tf/q_functions/discrete_cnn_q_function.py",
"src/metarl/envs/dm_control/dm_control_env.py",
"tests/metarl/envs/wrappers/test_noop.... | [
"\"\"\"Test torch algo utility functions.\"\"\"\nimport numpy as np\nimport pytest\nimport tensorflow as tf\nimport torch\nimport torch.nn.functional as F\n\nimport metarl.tf.misc.tensor_utils as tf_utils\nimport metarl.torch.algos._utils as torch_algo_utils\nfrom tests.fixtures import TfGraphTestCase\n\n\ndef stac... | [
[
"numpy.expand_dims",
"torch.Tensor",
"numpy.arange",
"numpy.ones",
"torch.nn.Linear",
"tensorflow.compat.v1.placeholder",
"numpy.zeros"
],
[
"tensorflow.Graph",
"numpy.array_equal",
"tensorflow.compat.v1.get_variable",
"tensorflow.compat.v1.trainable_variables",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
jhbrito/HelloWorlds | [
"7e2247ca7f312a516ce6a5054913d59e2f1de0f9"
] | [
"HelloWorldOpenCV.py"
] | [
"# Demo with a few examples of using OpenCV functions and UI\n# packages: opencv-python\n# uses lena: https://upload.wikimedia.org/wikipedia/en/7/7d/Lenna_%28test_image%29.png\n\nimport numpy as np\nimport cv2\n\nprint(\"Hello World OpenCV\")\nprint(\"OpenCV Version:\", cv2.__version__)\n\nimage = np.ones((256, 256... | [
[
"numpy.uint8",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rish-16/gym-navmaze | [
"cc21d730ec6ab1e96a4a1a8f602a5bbb951d2929"
] | [
"src/cartpole.py"
] | [
"import numpy as np\nfrom collections import deque\nimport pickle\nimport torch\nfrom utils import collect_trajectories, random_sample\nfrom PPO import PPO\nimport matplotlib.pyplot as plt\nfrom parallelEnv import *\nimport gym\n\nenv = gym.make(\"CartPole-v0\")\nenv.reset()\nenv.seed(2)\n\nobs_dim = env.observatio... | [
[
"matplotlib.pyplot.title",
"torch.from_numpy",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.subplot",
"numpy.zeros_like",
"numpy.mean",
"torch.cuda.is_available",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"numpy.sum",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
williamberrios/lofo-importance | [
"34967cf47dc1c2797d3a77f8926918ae91e4197a"
] | [
"lofo/infer_defaults.py"
] | [
"import numpy as np\nfrom sklearn.preprocessing import LabelEncoder\nfrom lightgbm import LGBMClassifier, LGBMRegressor\n\n\ndef infer_model(df, features, y, n_jobs):\n model_class = LGBMRegressor\n if len(np.unique(y)) == 2:\n y = LabelEncoder().fit_transform(y)\n model_class = LGBMClassifier\n... | [
[
"sklearn.preprocessing.LabelEncoder",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kolibril13/napari | [
"b39647d94e587f0255b0d4cc3087855e160a8929",
"b39647d94e587f0255b0d4cc3087855e160a8929"
] | [
"napari/_vispy/overlays/axes.py",
"napari/components/viewer_model.py"
] | [
"import numpy as np\nfrom vispy.scene.visuals import Compound, Line, Mesh, Text\nfrom vispy.visuals.transforms import STTransform\n\nfrom ...layers.shapes._shapes_utils import triangulate_ellipse\nfrom ...utils.colormaps.standardize_color import transform_color\nfrom ...utils.theme import get_theme\nfrom ...utils.t... | [
[
"numpy.linspace",
"numpy.subtract",
"numpy.concatenate",
"numpy.log10",
"numpy.array",
"numpy.zeros",
"numpy.empty"
],
[
"numpy.multiply",
"numpy.min",
"numpy.round",
"numpy.max",
"numpy.repeat",
"numpy.array",
"numpy.zeros",
"numpy.divide",
"num... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PaperCodeReview/MoCo-TF | [
"1ea01b2d005de3e030229f79a37135468fa1631e"
] | [
"dataloader.py"
] | [
"import os\r\nimport random\r\nimport numpy as np\r\nimport pandas as pd\r\nimport tensorflow as tf\r\n\r\nfrom augment import Augment\r\n\r\n\r\nAUTO = tf.data.experimental.AUTOTUNE\r\n\r\n\r\ndef set_dataset(task, data_path):\r\n trainset = pd.read_csv(\r\n os.path.join(\r\n data_path, 'image... | [
[
"tensorflow.data.Dataset.from_tensors",
"tensorflow.concat",
"tensorflow.zeros",
"tensorflow.io.decode_jpeg",
"numpy.random.choice",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.identity",
"tensorflow.expand_dims",
"tensorflow.image.extract_jpeg_shape",
"tensor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
leeamen/k_means | [
"dfa9cad22033c108e3988a99f4d58c685eb06921"
] | [
"user_modeling.py"
] | [
"#!/usr/bin/python\n#coding:utf-8\nimport numpy as np\nimport logging\nimport mylog\nimport mykmeans as ml\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.ERROR)\n\ndef str2num(s):\n a = ['very_low', 'Low', 'Middle', 'High']\n for i in range(0, len(a)):\n if a[i] == s:\n return float(i)\nif... | [
[
"numpy.loadtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
esnet/hps-rl | [
"8426652e622394a955a44c42201e2204f6bfa0f2"
] | [
"searchmethods/modularGA.py"
] | [
"\nimport numpy, random\n\nclass Individual:\n def __init__(self,genome, llimits =[], ulimits=[], type=[], LEN = 1,fitness_func = None):\n if genome is None:\n self.genome = numpy.zeros(LEN,dtype=float)\n for gene in range(LEN):\n if type[gene] == \"integer\":\n ... | [
[
"numpy.random.random",
"numpy.random.seed",
"numpy.mean",
"numpy.random.uniform",
"numpy.zeros",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Bruce-zxy/deep-study-lenet5 | [
"bba6531c9234c077107f79ff852f141cfed58229"
] | [
"data_creation.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport os\nimport sys\nimport random\nimport numpy as np\nimport pandas as pd\nimport h5py\nimport matplotlib.pyplot as plt\nfrom math import cos, sin, atan2, sqrt, pi, radians, degrees, ceil, isnan\nfrom skimage import io, transform\n\nBASE_DIR = os.path.dirname(os... | [
[
"numpy.genfromtxt",
"numpy.append",
"numpy.delete",
"numpy.insert",
"numpy.array",
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
skinnider/low-data-generative-models | [
"6e743b6d1ba3265f58fcbd33f2c60e633cf25999",
"6e743b6d1ba3265f58fcbd33f2c60e633cf25999"
] | [
"python/train_model.py",
"python/clean-SMILES.py"
] | [
"\"\"\"\nTrain a language model to generate SMILES.\n\"\"\"\n\nimport argparse\nimport os\nimport numpy as np\nimport pandas as pd\nimport random\nimport sys\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\n# suppress Chem.MolFromSm... | [
[
"numpy.random.seed",
"torch.load",
"torch.manual_seed",
"pandas.DataFrame",
"torch.cuda.is_available",
"torch.cuda.manual_seed_all"
],
[
"pandas.read_csv",
"numpy.unique"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
rlaehgns5399/GoogLeNet-Inception-tf | [
"eb9597634eec9a7b511e967ad8c7b2552563755f"
] | [
"src/helper/trainer.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# File: trainer.py\n# Author: Qian Ge <geqian1001@gmail.com>\n\nimport os\nimport numpy as np\nimport tensorflow as tf\n\n\ndef display(global_step,\n step,\n scaler_sum_list,\n name_list,\n collection,\n summary... | [
[
"tensorflow.Summary"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CalebEverett/fastai-dl2 | [
"64d23592eddca6ca1f3647e73c319e97c8eb392b"
] | [
"fastai/torch_imports.py"
] | [
"import os\nimport torch, torchvision, torchtext\nfrom torch import nn, cuda, backends, FloatTensor, LongTensor, optim\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom torch.utils.data import Dataset, TensorDataset\nfrom torch.nn.init import kaiming_uniform, kaiming_normal\nfrom torchvisi... | [
[
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
qq456cvb/CPPF | [
"79366978854ae18b14c69ac850ea64b9dc286081"
] | [
"models/model.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom .sprin import GlobalInfoProp, SparseSO3Conv\nimport numpy as np\n\n\nclass ResLayer(torch.nn.Module):\n def __init__(self, dim_in, dim_out, bn=False) -> None:\n super().__init__()\n assert(bn is False)\n self.fc1 = t... | [
[
"torch.nn.BatchNorm1d",
"torch.norm",
"torch.cat",
"torch.nn.ModuleList",
"torch.sum",
"torch.nn.Linear",
"torch.arange",
"torch.topk"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Ram81/habitat-imitation-baselines | [
"c6e11c8ebadbf1260e1bed58a5b8dfb7faf6a505"
] | [
"habitat/tasks/nav/object_nav_task.py"
] | [
"# Copyright (c) Facebook, Inc. and its affiliates.\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 os\nfrom typing import Any, List, Optional\n\nimport attr\nfrom cv2 import log\nimport numpy as np\nfrom gym import spaces\n\nfr... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ehwa009/Eye_Motion_Dataset | [
"42a1c897dc4209c6bb2de94c915ab36995855202"
] | [
"run_preprocessing.py"
] | [
"import pickle\nimport argparse\nimport pandas as pd\nimport numpy as np\nimport math\n\nfrom tqdm import tqdm\nfrom sklearn import decomposition\n\nCENTER_X = int(960 / 3 / 2)\nCENTER_Y = int(540 / 3 / 2)\n\n# CENTER_X = 0\n# CENTER_Y = 0\n\n\ndef load_data(path, data_size=None):\n with open(path, 'rb') as f:\n... | [
[
"numpy.sqrt",
"numpy.isnan",
"numpy.array",
"sklearn.decomposition.PCA",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
FischbachLab/hCom_variable_regions | [
"6f1108c461a7e31964d1d81a83c03b9f4dad4c76"
] | [
"summarize_clstr_table.py"
] | [
"#!/usr/bin/env python3\n## How many clusters have more than one organisms as it's members\nimport sys\nimport pandas as pd\nimport logging\n\n\ndef main():\n clstr_table = sys.argv[1]\n output = sys.argv[2]\n\n clstr_df = pd.read_table(clstr_table, header=0)\n clstr_df[\"organism\"] = clstr_df[\"id\"].... | [
[
"pandas.read_table"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ZZIQIN/FATE | [
"cc6783927564cbb15c067d5010f1cdf82a5de20a",
"cc6783927564cbb15c067d5010f1cdf82a5de20a"
] | [
"federatedml/ftl/hetero_ftl/hetero_ftl_host.py",
"federatedml/ftl/encrypted_ftl.py"
] | [
"#\n# Copyright 2019 The FATE 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 r... | [
[
"numpy.squeeze",
"numpy.array"
],
[
"numpy.log",
"numpy.expand_dims",
"numpy.squeeze",
"numpy.tile",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
warlock8hz/h5pyViewer | [
"4955aa6fdd66255738bd86d7b8947282133c5b82",
"4955aa6fdd66255738bd86d7b8947282133c5b82"
] | [
"h5pyViewer/FrmPyFAI.py",
"h5pyViewer/hdfImage.py"
] | [
"#!/usr/bin/env python\n#*-----------------------------------------------------------------------*\n#| |\n#| Copyright (c) 2013 by Paul Scherrer Institute (http://www.psi.ch) |\n#| ... | [
[
"matplotlib.figure.Figure",
"matplotlib.use",
"numpy.arange",
"scipy.ndimage.median_filter",
"numpy.average",
"matplotlib.backends.backend_wxagg.FigureCanvasWxAgg.__init__"
],
[
"numpy.sqrt",
"numpy.arctan2",
"numpy.max",
"numpy.ma.power",
"numpy.ma.max",
"matpl... | [
{
"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"... |
abbasegbeyemi/pyqtgraph | [
"6aeafce477d1d7eebb9d2fe824d4c5573ef9ceed",
"6aeafce477d1d7eebb9d2fe824d4c5573ef9ceed",
"6aeafce477d1d7eebb9d2fe824d4c5573ef9ceed"
] | [
"examples/optics/pyoptic.py",
"examples/SimplePlot.py",
"examples/verlet_chain/chain.py"
] | [
"# -*- coding: utf-8 -*-\nimport pyqtgraph as pg\nfrom pyqtgraph.Qt import QtGui, QtCore\nimport numpy as np\nimport csv, gzip, os\nfrom pyqtgraph import Point\n\nclass GlassDB:\n \"\"\"\n Database of dispersion coefficients for Schott glasses\n + Corning 7980\n \"\"\"\n def __init__(self, fileName=... | [
[
"numpy.sqrt",
"numpy.clip",
"numpy.arcsin",
"numpy.cos",
"numpy.sin",
"numpy.arctan2"
],
[
"numpy.random.normal"
],
[
"numpy.array",
"numpy.zeros",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XinyueZ/some-python-codes | [
"2d7296a4deebb0cd086be34ad7d66f5042cdf6e6",
"2d7296a4deebb0cd086be34ad7d66f5042cdf6e6"
] | [
"machine_learning/tf_notMNIST_Training_Gradient_Descent.py",
"machine_learning/tf_notMNIST_Training_Convolutional_Layer.py"
] | [
"#\n# Run NN, multinomial logistic regression using simple gradient descent.\n#\nimport config\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import (Variable, constant, global_variables_initializer,\n truncated_normal, zeros)\n\nfrom tf_training_helper import TrainingHelper\n\... | [
[
"tensorflow.nn.softmax",
"tensorflow.truncated_normal",
"tensorflow.constant",
"tensorflow.zeros",
"tensorflow.global_variables_initializer",
"tensorflow.Session"
],
[
"tensorflow.nn.softmax",
"tensorflow.constant",
"tensorflow.truncated_normal",
"tensorflow.zeros",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
mone27/fastai | [
"af8dfc07ca3f333f8c1bdbea1803af669a53738f"
] | [
"fastai/callback/tensorboard.py"
] | [
"# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/71_callback.tensorboard.ipynb (unless otherwise specified).\n\n__all__ = ['TensorBoardCallback']\n\n# Cell\nfrom ..basics import *\n\n# Cell\nimport tensorboard\nfrom torch.utils.tensorboard import SummaryWriter\nfrom .fp16 import ModelToHalf\n\n# Cell\nclass TensorB... | [
[
"torch.utils.tensorboard.SummaryWriter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
alarca94/senti-transfer | [
"da83a072c8d471bc74aa25b237b5e301502db869"
] | [
"utils/inout.py"
] | [
"import os\nimport yaml\n\nimport pandas as pd\nimport xml.etree.ElementTree as ET\n\nfrom types import SimpleNamespace\nfrom sklearn.model_selection import train_test_split\n\nfrom utils.experiment_utils import create_linspace\nfrom utils.preprocess import *\n\n\nSOURCE_PATH = './source_data'\nDATA_PATH = './data'... | [
[
"sklearn.model_selection.train_test_split",
"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": []
}
] |
cafltar/CAF_EC_Column_Rename | [
"7375678081d8931f34e7ab8b4a6e02eca112e721"
] | [
"LTAR_Flux_QC.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 21 15:53:46 2018\n\n@author: Eric S. Russell\nLaboratory for Atmospheric Research\nDept. of Civil and Environmental Engineering\nWashington State University\neric.s.russell@wsu.edu\n\nNot all of these functions are used in the column rename script; these are pote... | [
[
"numpy.hstack",
"pandas.concat",
"pandas.to_datetime",
"pandas.read_csv",
"pandas.DataFrame",
"numpy.nanmean",
"numpy.nanstd",
"numpy.exp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
n1ckfg/RoutedFusion | [
"1733911c7fe025b461b75e48461658709996e39c"
] | [
"voxelgrid/tsdf/run_tsdf_fusion.py"
] | [
"#!/scratch_net/nudel/esandstroem/venvs/tsdf_fusion_env/bin/python\nimport os\napp_path = '/scratch_net/nudel/esandstroem/venvs/tsdf_fusion_env/bin'\nos.environ[\"PATH\"] = app_path + os.pathsep + os.environ[\"PATH\"]\n\nfrom TSDFHandle import *\nimport numpy as np\nimport cv2\nfrom utils import extract_mesh_marchi... | [
[
"numpy.array",
"numpy.matmul",
"numpy.loadtxt",
"numpy.ones"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
xiangze/edward | [
"6419751d1d849c84c502e5ff3f7249b9bbc7b3aa",
"6419751d1d849c84c502e5ff3f7249b9bbc7b3aa",
"6419751d1d849c84c502e5ff3f7249b9bbc7b3aa"
] | [
"tests/util/test_get_descendants.py",
"tests/models/test_param_mixture_stats.py",
"docs/parser/public_api.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom edward.models import Bernoulli, Normal\nfrom edward.util import get_descendants\n\n\nclass test_get_descendants_class(tf.test.TestCase):\n\n def test_v_structure(self):... | [
[
"tensorflow.scan",
"tensorflow.constant",
"tensorflow.cast",
"tensorflow.test.main",
"tensorflow.ones"
],
[
"tensorflow.Graph",
"tensorflow.test.main",
"numpy.exp",
"numpy.zeros_like",
"tensorflow.set_random_seed",
"numpy.array",
"numpy.histogram",
"numpy.ze... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"1.12",
"2.6",
"2.2",
"1.13",
"2.3",
"2.4",
"1.4",
"2.9",
"1.5",
"1.7",
"2.5",
"0.12",
"1.0",
"2.8",
"1... |
dongfangyixi/ParlAI | [
"424a2b3c7086593f699c76612dffd1d925986177"
] | [
"parlai/agents/transformer/mixer.py"
] | [
"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates.\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\"\"\"\nTransformer Agents.\n\"\"\"\nfrom typing import Optional\nfrom parlai.core.params import ParlaiParser\nfr... | [
[
"torch.LongTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ReyesDeJong/Deep-SVDD-PyTorch | [
"1fc7eae1474556f869d5c5422da74fd4fe2f1aed"
] | [
"src/datasets/hits_dataset.py"
] | [
"import os\nimport sys\n\nimport numpy as np\nimport pandas as pd\nfrom torch.utils.data import Subset\nfrom torch.utils.data.dataset import Dataset # For custom datasets\nfrom torchvision import transforms\n\nPROJECT_PATH = os.path.abspath(\n os.path.join(os.path.dirname(__file__), '..', '..'))\nsys.path.appen... | [
[
"numpy.nanmax",
"pandas.read_pickle",
"numpy.nanmin",
"torch.utils.data.Subset",
"numpy.array"
]
] | [
{
"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": []
}
] |
Amitdedhia6/DrugDiscovery | [
"c70dec96cee4d0d643a8b9de30530b6871fdf05e"
] | [
"generate_embeddings.py"
] | [
"import torch\nimport torch.nn as nn\nimport os\nfrom common import base_data_path\nfrom typing import List\nimport pandas as pd\n\n\nCONTEXT_SIZE = 1 # 1 words to the left, 1 to the right\nEMDEDDING_DIM = 3\nword_to_ix = {}\nix_to_word = {}\n\n\ndef make_context_vector(context, word_to_ix):\n idxs = [word_to_i... | [
[
"torch.nn.NLLLoss",
"pandas.read_csv",
"torch.nn.LogSoftmax",
"torch.LongTensor",
"torch.nn.Embedding",
"torch.tensor",
"torch.nn.Linear",
"torch.nn.ReLU"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
ehsanul/brick | [
"291c0783f3b062cf73887cb3581dd92342891165"
] | [
"heuristic/train/nn/train-nn.py"
] | [
"from __future__ import absolute_import, division, print_function\n\nimport sys\nimport pathlib\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\nEPOCHS = 1000\n\n# The patience parameter is... | [
[
"tensorflow.contrib.saved_model.save_keras_model",
"matplotlib.pyplot.legend",
"pandas.read_csv",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.ylim",
"tensorflow.keras.layers.Dense",
"pandas.DataFrame",
"matplotlib.pyplot.plot",
"matplotlib.py... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
juanpablordz/moog.github.io | [
"d7995d3563492378d0877ce8d16f5ca9a8031794",
"d7995d3563492378d0877ce8d16f5ca9a8031794"
] | [
"moog/action_spaces/joystick.py",
"moog/state_initialization/sprite_generators.py"
] | [
"\"\"\"Joystick action space for controlling agent avatars.\"\"\"\n\nfrom . import abstract_action_space\nfrom dm_env import specs\nimport numpy as np\n\n\nclass Joystick(abstract_action_space.AbstractActionSpace):\n \"\"\"Joystick action space.\"\"\"\n\n def __init__(self, scaling_factor=1., action_layers='a... | [
[
"numpy.random.uniform",
"numpy.zeros",
"numpy.clip"
],
[
"numpy.random.shuffle",
"numpy.random.choice"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yyliu01/TraCoCo | [
"eecbc92c961d393deaa31726739a94b7f495d893"
] | [
"Code/VnetLA/validate.py"
] | [
"import os\nimport math\nimport torch\nimport argparse\nimport numpy as np\nfrom tqdm import tqdm\nfrom medpy import metric\nimport torch.nn.functional as F\nfrom Configs.config import config\nfrom Model.Vnet import VNet as Vnet\nfrom cc3d import connected_components\nfrom Dataloader.dataset import LAHeartDataset\n... | [
[
"torch.nn.functional.softmax",
"numpy.expand_dims",
"numpy.pad",
"torch.load",
"numpy.asarray",
"torch.from_numpy",
"numpy.argmax",
"numpy.array",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dcstrandberg/aspect-sentiment | [
"0177888d4fe96d49b78e44f5bd24be619c93bf00",
"0177888d4fe96d49b78e44f5bd24be619c93bf00"
] | [
"aspect_sentiment.py",
"tweet_handlers.py"
] | [
"import spacy\nfrom textblob import TextBlob\nimport pandas as pd\n\n# Import functions from other files\nfrom tweet_handlers import pullTweetsFromCSV, tweetPulls\n\n### Declare functions to standardize, identify, and analyze input text\n# Will ultimately take in a list of tweets and return:\n# - Word counts\n# - S... | [
[
"pandas.read_csv",
"pandas.DataFrame"
],
[
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1... |
williamhowardsnyder/OnClass | [
"07b2917dbdf01a1de54771de3383bbaa4bb2f283",
"07b2917dbdf01a1de54771de3383bbaa4bb2f283"
] | [
"utils.py",
"script/run_marker_gene_based_prediction.py"
] | [
"from anndata import read_h5ad\nimport sys\nfrom time import time\nfrom scipy import stats, sparse\nimport numpy as np\nimport collections\nimport pickle\nfrom sklearn.preprocessing import normalize\nimport os\nfrom collections import Counter\nimport pandas as pd\nfrom sklearn.model_selection import train_test_spli... | [
[
"numpy.diag",
"numpy.dot",
"sklearn.metrics.roc_auc_score",
"numpy.sqrt",
"numpy.nan_to_num",
"numpy.concatenate",
"numpy.max",
"numpy.mean",
"numpy.nanmean",
"scipy.sparse.vstack",
"sklearn.metrics.f1_score",
"numpy.exp",
"numpy.where",
"numpy.unique",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"1.6",
"0.14",
"1.10",
"0.15",
"1.4",
"1.3",
"1.9",
"0.19",
"1.5",
"0.18",
"1.2",
"1.7",
"0.12",
"1.0",
"0.17",
"0.16",
"1.8"
... |
HongLabTHU/dual-mVEPs | [
"f387584865a45a7257d8203fcb9522820e1311de"
] | [
"Offline/dataset.py"
] | [
"import glob\nimport os\nimport warnings\nfrom datetime import datetime\nfrom copy import deepcopy\n\nimport numpy as np\nimport pyedflib\nimport scipy.io as sio\n\nfrom config import cfg\nfrom thirdparty.cerebus import NsxFile, NevFile\nfrom thirdparty.nex import Reader as NexReader\nfrom .utils import find_neares... | [
[
"numpy.logical_and",
"numpy.unique",
"scipy.io.loadmat",
"numpy.round",
"numpy.diff",
"numpy.array"
]
] | [
{
"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"... |
pabloserna/SentimentAnalysisinAWS | [
"d94572665442ef6f49deb07ed78f8104654fefc3"
] | [
"train/model_stack.py"
] | [
"import torch.nn as nn\n\nclass LSTMClassifier(nn.Module):\n \"\"\"\n This is the simple RNN model we will be using to perform Sentiment Analysis.\n \"\"\"\n\n def __init__(self, embedding_dim, hidden_dim, vocab_size):\n \"\"\"\n Initialize the model by settingg up the various layers.\n ... | [
[
"torch.nn.Linear",
"torch.nn.Sigmoid",
"torch.nn.Embedding",
"torch.nn.LSTM"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
KlausBSautter/Kratos | [
"1ceb900dbacfab344e27e32285250eafc52093ec"
] | [
"applications/RomApplication/python_scripts/structural_mechanics_analysis_rom.py"
] | [
"import KratosMultiphysics\nimport KratosMultiphysics.RomApplication as romapp\nimport KratosMultiphysics.StructuralMechanicsApplication\nfrom KratosMultiphysics.RomApplication.empirical_cubature_method import EmpiricalCubatureMethod\nfrom KratosMultiphysics.RomApplication import python_solvers_wrapper_rom as solve... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sentinel-hub/multi-temporal-super-resolution | [
"5ef642304a980db87bdb935a7a7450bd649f8912"
] | [
"sr/data_loader.py"
] | [
"import os\nfrom collections import OrderedDict\nfrom typing import Tuple, List, Callable\n\nfrom fs_s3fs import S3FS\n\nimport numpy as np\nimport pandas as pd\n\nimport torch\nfrom torch.utils.data import Dataset\nfrom skimage.exposure import match_histograms\nfrom datetime import datetime\nfrom eolearn.core impo... | [
[
"numpy.rot90",
"numpy.swapaxes",
"numpy.random.random",
"numpy.random.choice",
"numpy.arange",
"torch.from_numpy",
"numpy.ones",
"numpy.concatenate",
"numpy.random.permutation",
"numpy.mean",
"numpy.moveaxis",
"numpy.load",
"numpy.array",
"numpy.flip",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aaparikh/Intermediate-Python-Practice | [
"6f49bea8f677e7ed500cd1ec91df4c8531832abb"
] | [
"ranndom_m.py"
] | [
"#there are many ways we can do random numbers\n\n#1. import random\n#used to produce pseudo-random numbers. \n# They are called pseudo-random because they are not truly random and can be reproduced.\nimport random\n\na = random.random() #random float between 0 and 1\nb = random.uniform(1,10) #random float between ... | [
[
"numpy.random.random",
"numpy.random.seed",
"numpy.random.choice",
"numpy.random.randrange",
"numpy.random.randn",
"numpy.random.uniform",
"numpy.random.randint"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Subarna578/pythainlp | [
"9650a40396719284add17bb09f50e948dea41053"
] | [
"pythainlp/transliterate/thai2rom.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nRomanization of Thai words based on machine-learnt engine (\"thai2rom\")\n\"\"\"\n\nimport random\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom pythainlp.corpus import download, get_corpus_path\n\ndevice = torch.device(\"cuda:0\" ... | [
[
"torch.nn.Dropout",
"torch.nn.functional.softmax",
"torch.nn.LSTM",
"torch.load",
"torch.cat",
"torch.zeros",
"torch.sum",
"torch.from_numpy",
"torch.nn.Embedding",
"torch.tensor",
"numpy.sort",
"torch.nn.Linear",
"torch.nn.utils.rnn.pad_packed_sequence",
"t... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
RaphaelOlivier/speechbrain | [
"142dc6caa4b46ca4c9341b0cd39627f489808749"
] | [
"speechbrain/lobes/models/huggingface_wav2vec.py"
] | [
"\"\"\"This lobe enables the integration of huggingface pretrained wav2vec2/hubert/wavlm models.\n\nReference: https://arxiv.org/abs/2006.11477\nReference: https://arxiv.org/abs/1904.05862\nReference: https://arxiv.org/abs/2110.13900\nTransformer from HuggingFace needs to be installed:\nhttps://huggingface.co/trans... | [
[
"torch.nn.functional.layer_norm",
"torch.load",
"torch.tensor",
"numpy.ones",
"torch.no_grad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sbrml/pilco | [
"77b6d8b9033ffdb23cae4936b028f42144f37846"
] | [
"pilco/environments/custom/continuous_mountaincar.py"
] | [
"\"\"\"\nOur modification of the OpenAI Gym Continuous Mountain Car by Olivier Sigaud:\nhttps://github.com/openai/gym/blob/master/gym/envs/classic_control/continuous_mountain_car.py\n\nwhich was (ultimately) based on Sutton's implementation:\nhttp://incompleteideas.net/sutton/MountainCar/MountainCar1.cp\n\"\"\"\n\n... | [
[
"numpy.linspace",
"numpy.clip",
"numpy.cos",
"numpy.sin",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
eltociear/NiaPy | [
"7884aefec8f013d9f8db5c1af7080a61dd19a31d"
] | [
"examples/custom_problem.py"
] | [
"# encoding=utf8\n# This is temporary fix to import module from parent folder\n# It will be removed when package is published on PyPI\nimport sys\n\nsys.path.append('../')\n\nimport numpy as np\nfrom niapy.task import StoppingTask\nfrom niapy.problems import Problem\nfrom niapy.algorithms.basic import ParticleSwarm... | [
[
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mrzhuzhe/Kaggle_Lux_AI_2021 | [
"08b795e71e78c768d28c648290a15d58ca718776"
] | [
"lux_ai/lux_gym/multi_subtask.py"
] | [
"from abc import ABC, abstractmethod\nimport numpy as np\nimport random\nfrom typing import Callable, Dict, Optional, Tuple, Sequence\n\nfrom .reward_spaces import Subtask\nfrom ..lux.game import Game\n\n\nclass SubtaskSampler(ABC):\n def __init__(self, subtask_constructors: Sequence[Callable[..., Subtask]]):\n ... | [
[
"numpy.maximum",
"numpy.array",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Acetonen/Interkamen_career | [
"75cc0a5832b7c1e303967cc337bb001e3383eb9e"
] | [
"interkamen_career/modules/mechanics_economic.py"
] | [
"#!/usr/bin/env python3\n\n\"\"\"Visualise statistic by machine economic.\"\"\"\n\n\nfrom __future__ import annotations\n\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom typing import Dict\nfrom .mechanic_report import MechReports\nfrom .administration.logger_cfg import Logs\nfrom .support_modules.... | [
[
"matplotlib.pyplot.show",
"pandas.DataFrame",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
robbycostales/multiagent-particle-envs | [
"22a00b18e13b629a206a8ffc8d8319d06dd5d7b0"
] | [
"multiagent/scenarios/simple_speaker_listener.py"
] | [
"import numpy as np\nfrom multiagent.core import World, Agent, Landmark\nfrom multiagent.scenario import BaseScenario\n\nclass Scenario(BaseScenario):\n def make_world(self, dim_c=3):\n world = World()\n # set any world properties first\n world.dim_c = dim_c\n num_landmarks = 3\n ... | [
[
"numpy.square",
"numpy.random.choice",
"numpy.concatenate",
"numpy.random.uniform",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
t27/carla-scenic-data-collector | [
"3f38fa0e23a9f0ed85726292c5703c8505330870"
] | [
"carla_python_api_recorder.py"
] | [
"# Recorder that records agent states as dataframes and also stores a carla recording, in synchronous mode\n\n\n#!/usr/bin/env python\n\n# Copyright (c) 2019 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy,... | [
[
"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": []
}
] |
dcambie/spectrochempy | [
"e376082d66be7a4c528b7d83be076d77534e39bd",
"4707c51dba0032c160afc40682fa16d4b9855ded",
"4707c51dba0032c160afc40682fa16d4b9855ded",
"4707c51dba0032c160afc40682fa16d4b9855ded",
"4707c51dba0032c160afc40682fa16d4b9855ded",
"4707c51dba0032c160afc40682fa16d4b9855ded",
"4707c51dba0032c160afc40682fa16d4b9855de... | [
"spectrochempy/core/dataset/nddataset.py",
"spectrochempy/core/readers/readcsv.py",
"spectrochempy/api.py",
"spectrochempy/core/dataset/meta.py",
"spectrochempy/core/readers/readjdx.py",
"tests/test_analysis/test_svd.py",
"tests/test_processors/test_autosub.py"
] | [
"# -*- coding: utf-8 -*-\n\n#\n# ======================================================================================================================\n# Copyright (©) 2015-2019 LCS\n# Laboratoire Catalyse et Spectrochimie, Caen, France.\n# CeCILL-B FREE SOFTWARE LICENSE AGREEMENT\n# See full LICENSE agreement in ... | [
[
"numpy.ma.array",
"numpy.array"
],
[
"numpy.array",
"numpy.loadtxt"
],
[
"matplotlib.get_backend",
"matplotlib.use"
],
[
"numpy.all"
],
[
"numpy.linspace",
"numpy.around",
"numpy.concatenate",
"numpy.array",
"numpy.zeros"
],
[
"numpy.testing.... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
bicepjai/Deep-Survey-on-Text-Classification | [
"d935f0d4fc09213644d0291a0d64873912b2e331"
] | [
"lib/global_utils.py"
] | [
"import sys\nimport os\n\nimport re\nimport collections\nimport itertools\nimport bcolz\nimport pickle\n\nimport numpy as np\nimport pandas as pd\nimport gc\nimport random\nimport smart_open\nimport h5py\nimport csv\nimport tensorflow as tf\nimport gensim\n\nimport datetime as dt\nfrom tqdm import tqdm_notebook as ... | [
[
"numpy.array",
"numpy.float",
"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": []
}
] |
Sensors-in-Paradise/OpportunityML | [
"a123b4842de45f735d517be6bcd96ca35171db91",
"a123b4842de45f735d517be6bcd96ca35171db91"
] | [
"src/loader/load_opportunity_dataset.py",
"archive/model_archive/ConvModel.py"
] | [
"import itertools\nimport os\n\nimport numpy as np\nimport pandas as pd\n\nfrom utils.Recording import Recording\nimport utils.settings as settings\n\n\ndef load_opportunity_dataset(opportunity_dataset_path: str) -> \"list[Recording]\":\n \"\"\"\n Returns a list of Recordings from the opportunity dataset\n ... | [
[
"pandas.read_csv"
],
[
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv1D",
"tensorflow.keras.layers.MaxPooling1D",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.models.Sequential",
"tensorflow.keras.layers.Flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"2.7",
"2.2",
"2... |
evertdeman/HD-BET | [
"817a50d2fe9b8663646cc74652cb50e26f343a3b",
"41ebe0d3360b86ae6949412c9ea2a5f653612540"
] | [
"HD_BET/utils.py",
"HD_BET/network_architecture.py"
] | [
"from urllib.request import urlopen\nimport torch\nfrom torch import nn\nimport numpy as np\nfrom skimage.morphology import label\nimport os\nfrom HD_BET.paths import folder_with_parameter_files\n\n\ndef get_params_fname(fold):\n return os.path.join(folder_with_parameter_files, \"%d.model\" % fold)\n\n\ndef mayb... | [
[
"torch.nn.init.kaiming_normal",
"numpy.unique",
"torch.exp",
"numpy.argmax",
"torch.nn.init.constant",
"numpy.sum"
],
[
"torch.nn.Module.__init__",
"torch.cat",
"torch.nn.Dropout3d",
"torch.nn.Conv3d",
"torch.nn.functional.interpolate",
"torch.nn.InstanceNorm3d"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mdhasan8/Machine-Learning-in-Python | [
"d66607d3003e8279e35cf176851f506aa833a9fe"
] | [
"Neural_Network_Tensorflow.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Dec 16 22:30:11 2020\r\n\r\n@author: Easin\r\n\"\"\"\r\nfrom __future__ import absolute_import, division, print_function\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow.keras import Model, layers\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n#... | [
[
"tensorflow.nn.softmax",
"tensorflow.reduce_mean",
"numpy.reshape",
"tensorflow.keras.layers.Dense",
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.cast",
"tensorflow.keras.datasets.mnist.load_data",
"tensorflow.nn.sparse_softmax_cross_entropy_with_logits",
"tensorfl... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.13",
"1.10",
"1.12"
]
}
] |
983632847/covid19_pocus_ultrasound | [
"3625e95bbf189926dbd12966ef59ee71ed10e453"
] | [
"pocovidnet/scripts/eval_vid_classifier.py"
] | [
"import argparse\nimport json\nimport os\nimport pickle\nimport numpy as np\nfrom pocovidnet.evaluate_genesis import GenesisEvaluator\nfrom pocovidnet.evaluate_video import VideoEvaluator\nfrom tensorflow.keras import backend as K\nfrom pocovidnet.videoto3d import Videoto3D\n\n\ndef main():\n parser = argparse.A... | [
[
"numpy.mean",
"numpy.unique",
"tensorflow.keras.backend.set_image_data_format"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.2",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
pworinger/kornia | [
"a8bddbc5412694d778b1a7338e0d001910bb8024",
"a8bddbc5412694d778b1a7338e0d001910bb8024",
"a8bddbc5412694d778b1a7338e0d001910bb8024",
"a8bddbc5412694d778b1a7338e0d001910bb8024"
] | [
"test/geometry/transform/crop/test_crop2d.py",
"test/geometry/test_linalg.py",
"test/geometry/transform/test_flip.py",
"test/morphology/test_gradient.py"
] | [
"from typing import Tuple\n\nimport pytest\n\nimport kornia as kornia\nimport kornia.testing as utils # test utils\n\nimport torch\nfrom torch.testing import assert_allclose\nfrom torch.autograd import gradcheck\n\n\nclass TestBoundingBoxInferring:\n def test_bounding_boxes_dim_inferring(self, device, dtype):\n... | [
[
"torch.jit.script",
"torch.ones",
"torch.testing.assert_allclose",
"torch.randn",
"torch.tensor",
"torch.rand",
"torch.autograd.gradcheck"
],
[
"torch.jit.script",
"torch.ones",
"torch.testing.assert_allclose",
"torch.sin",
"torch.zeros_like",
"torch.eye",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
GonzalezDiazJ/pyviz_geoviews_clone | [
"cac9afd1bc0d25313c84ea617300bbe40207d044"
] | [
"geoviews/operation/projection.py"
] | [
"import param\nimport numpy as np\n\nfrom cartopy import crs as ccrs\nfrom cartopy.img_transform import warp_array, _determine_bounds\nfrom holoviews.core.util import cartesian_product, get_param_values\nfrom holoviews.operation import Operation\nfrom shapely.geometry import Polygon, LineString, MultiPolygon, Multi... | [
[
"numpy.hstack",
"numpy.isfinite",
"numpy.linspace",
"numpy.isnan",
"numpy.asarray",
"numpy.flipud",
"numpy.dstack",
"numpy.concatenate",
"numpy.diff",
"numpy.any",
"numpy.column_stack",
"numpy.errstate",
"numpy.array",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
necromuralist/resampling | [
"0b48a51cb5f8e21a3f52508ecc74f12fa03d9b25"
] | [
"resampling/foundations_for_inference/gender_discrimination.py"
] | [
"\n# pandas standard library\nimport sys\n\n# third-party\nimport pandas\nimport matplotlib\nimport matplotlib.pyplot as plot\n\nmatplotlib.style.use('ggplot')\n\nGENDER_COUNT = 24\nMALES_PROMOTED = 21\nFEMALES_PROMOTED = 14\nGENDER_DIFFERENCE = MALES_PROMOTED - FEMALES_PROMOTED\nFEMALES_NOT_PROMOTED = GENDER_COUNT... | [
[
"pandas.concat",
"matplotlib.style.use",
"pandas.Index",
"pandas.DataFrame",
"matplotlib.pyplot.figure"
]
] | [
{
"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": []
}
] |
hugeinteger/InterFaceGAN | [
"59e75c0b4dcdbcea693b31ff11cf239c39e14ed1"
] | [
"utils/manipulator.py"
] | [
"# python3.7\n\"\"\"Utility functions for latent codes manipulation.\"\"\"\n\nimport numpy as np\nfrom sklearn import svm\n\nfrom .logger import setup_logger\n\n__all__ = ['train_boundary', 'project_boundary', 'linear_interpolate']\n\n\ndef train_boundary(latent_codes,\n scores,\n ... | [
[
"numpy.linspace",
"numpy.arange",
"numpy.linalg.norm",
"numpy.random.shuffle",
"numpy.ones",
"numpy.concatenate",
"sklearn.svm.SVC",
"numpy.argsort",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
NREL/reVX | [
"4d62eb2c003c3b53b959f7a58bdc342d18098884",
"4d62eb2c003c3b53b959f7a58bdc342d18098884"
] | [
"reVX/least_cost_xmission/least_cost_xmission.py",
"reVX/offshore/dist_to_ports_converter.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nModule to compute least cost xmission paths, distances, and costs one or\nmore SC points\n\"\"\"\nfrom concurrent.futures import as_completed\nimport geopandas as gpd\nimport json\nimport logging\nimport numpy as np\nimport os\nimport pandas as pd\nfrom pyproj.crs import CRS\nimpor... | [
[
"pandas.concat",
"pandas.Series",
"numpy.unique",
"numpy.min",
"numpy.round",
"numpy.concatenate",
"numpy.max",
"numpy.ceil",
"numpy.array",
"numpy.where",
"scipy.spatial.cKDTree"
],
[
"numpy.minimum",
"numpy.any"
]
] | [
{
"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... |
Nikoula86/organoidSegment | [
"b5d00256c15302ccd76b8b7a412852750476504b"
] | [
"morgana/GUIs/fluo.py"
] | [
"from PyQt5.QtWidgets import (QApplication, QComboBox, QGridLayout, QGroupBox, QLabel, QPushButton,\n QFileDialog, QMessageBox, QWidget, QSizePolicy, QCheckBox)\nfrom matplotlib.backends.backend_qt5agg import FigureCanvas \nfrom matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationTool... | [
[
"numpy.pad",
"numpy.linspace",
"matplotlib.figure.Figure",
"numpy.min",
"numpy.clip",
"numpy.isnan",
"matplotlib.backends.backend_qt5agg.FigureCanvas",
"numpy.nan_to_num",
"numpy.max",
"matplotlib.backends.backend_qt5agg.NavigationToolbar2QT",
"scipy.interpolate.interp1... | [
{
"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"
... |
libinruan/hierarchical_bayesian_target_encoder | [
"7510028a8ad1dea308802c4ca3d3a05533a9c89b"
] | [
"BayCatEncoder/code.py"
] | [
"#%%\nimport numpy as np\nimport pandas as pd\nimport time\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom collections import defaultdict\nfrom sklearn.model_selection import KFold, StratifiedKFold \n\nclass Timer:\n def __enter__(self):\n self.start=time.time()\n return self\n... | [
[
"pandas.merge",
"numpy.random.seed",
"numpy.random.choice",
"sklearn.model_selection.StratifiedKFold",
"pandas.DataFrame",
"numpy.random.randint"
]
] | [
{
"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": []
}
] |
xinwang1/Quantum | [
"0f56e36e9e6111547547ae1b6cd5df307b41c1ac"
] | [
"paddle_quantum/QAOA/example/main.py"
] | [
"# Copyright (c) 2020 Institute for Quantum Computing, Baidu 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/LICEN... | [
[
"numpy.diag",
"matplotlib.pyplot.legend",
"numpy.min",
"matplotlib.pyplot.plot",
"numpy.max",
"matplotlib.pyplot.xlabel",
"numpy.load",
"matplotlib.pyplot.show",
"matplotlib.pyplot.ylabel"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Rishank2610/gammapy | [
"3cd64fdb2c53c8e5c697a9b85ef8d0486bff0b76",
"3cd64fdb2c53c8e5c697a9b85ef8d0486bff0b76",
"3cd64fdb2c53c8e5c697a9b85ef8d0486bff0b76",
"3cd64fdb2c53c8e5c697a9b85ef8d0486bff0b76",
"3cd64fdb2c53c8e5c697a9b85ef8d0486bff0b76",
"3cd64fdb2c53c8e5c697a9b85ef8d0486bff0b76",
"3cd64fdb2c53c8e5c697a9b85ef8d0486bff0b7... | [
"gammapy/utils/testing.py",
"gammapy/datasets/tests/test_datasets.py",
"gammapy/maps/wcs/core.py",
"gammapy/irf/psf/tests/test_map.py",
"gammapy/estimators/flux.py",
"gammapy/modeling/models/tests/test_spectral.py",
"gammapy/estimators/tests/test_parameter_estimator.py",
"docs/astro/source/plot_pwn_ev... | [
"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\"\"\"Utilities for testing\"\"\"\nimport os\nimport sys\nfrom numpy.testing import assert_allclose\nimport astropy.units as u\nfrom astropy.coordinates import SkyCoord\nfrom astropy.time import Time\n\n__all__ = [\n \"requires_dependency\",\n ... | [
[
"matplotlib.pyplot.figure",
"matplotlib.pyplot.close",
"numpy.testing.assert_allclose"
],
[
"numpy.testing.assert_allclose"
],
[
"numpy.ndenumerate",
"numpy.ones",
"numpy.concatenate",
"numpy.ravel_multi_index",
"numpy.ravel",
"numpy.where"
],
[
"numpy.meshg... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
... |
Zihang97/PAGAN | [
"9233fc54ecf49d6a82bb0794333d61f707439a68",
"9233fc54ecf49d6a82bb0794333d61f707439a68"
] | [
"src/snlayers/snconv1d.py",
"torch_stft/util.py"
] | [
"# coding=utf-8\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn.modules import conv\nfrom torch.nn.modules.utils import _single\nfrom ..functions.max_sv import max_singular_value\n\nclass SNConv1d(conv._ConvNd):\n\n def __init__(self, in_channels, out_channels, kernel_size, s... | [
[
"torch.nn.functional.conv1d",
"torch.nn.modules.utils._single",
"torch.Tensor"
],
[
"numpy.zeros",
"scipy.signal.get_window"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
... |
samirsahoo007/Naive-Bayes-and-Decision-Tree-Classifiers | [
"619c5c0b17438d1014f7ca7e4ce13cc44c45de3c"
] | [
"src/classifiers.py"
] | [
"# -*- coding: utf-8 -*- #\n\"\"\"*********************************************************************************************\"\"\"\n# FileName [ classifiers.py ]\n# Synopsis [ 'Naive Bayes' and 'Decision Tree' training, testing, and tunning functions ]\n# Author [ Ting-Wei Liu (Andi611) ]\n# ... | [
[
"sklearn.tree.export_graphviz",
"sklearn.naive_bayes.ComplementNB",
"sklearn.model_selection.cross_val_score",
"sklearn.naive_bayes.GaussianNB",
"numpy.arange",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.tree.DecisionTreeClassifier",
"sklearn.naive_bayes.BernoulliNB",
"sklea... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jphacks/C_2008 | [
"65d7a1d3a90045b149397cdd1e038ab648bb842e"
] | [
"sound_factory/sound_factory.py"
] | [
"import os\nimport re\nimport argparse\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.preprocessing.image import load_img, img_to_array\n\n\nIMAGE_SHAPE = [(224, 224), (240, 240), (260, 260), (300, 300), (380, 380), (456, 456), (528, 528), (600, 600)]\n\ndef main(p... | [
[
"tensorflow.keras.preprocessing.image.load_img"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.3",
"2.4",
"2.5",
"2.6"
]
}
] |
bhomaidan1990/reinforcement-learning-an-introduction | [
"fbf020d9da2daec3194a17f968ef29d12ebde6f6"
] | [
"chapter05/blackjack.py"
] | [
"#######################################################################\n# Copyright (C) #\n# 2016-2018 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #\n# 2016 Kenta Shimada(hyperkentakun@gmail.com) #\n# 2017 Nicky van Forees... | [
[
"matplotlib.pyplot.legend",
"numpy.asarray",
"numpy.flipud",
"numpy.max",
"numpy.where",
"numpy.random.randint",
"numpy.arange",
"numpy.argmax",
"matplotlib.pyplot.close",
"matplotlib.pyplot.subplots_adjust",
"numpy.zeros",
"numpy.random.choice",
"numpy.power",
... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hxwork/OMNet | [
"be88a734e7327def365e1875bbc7cd2fea1539b0"
] | [
"common/manager.py"
] | [
"import os\r\nfrom collections import defaultdict\r\n\r\nimport numpy as np\r\nimport torch\r\nfrom termcolor import colored\r\nfrom torch.utils.tensorboard import SummaryWriter\r\n\r\nfrom common import utils\r\n\r\n\r\nclass Manager():\r\n def __init__(self, model, optimizer, scheduler, params, dataloaders, lo... | [
[
"numpy.sqrt",
"torch.save",
"torch.utils.tensorboard.SummaryWriter",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
shinhaha/tensorflow | [
"4647017a727985d64c5b0addee92f0ec516952c1"
] | [
"Inflearn_SungKim/1.LinearRegression/LinearRegression(placeholders).py"
] | [
"import tensorflow as tf\n\n#placeholder variable(scalar)\nX=tf.placeholder(tf.float32,shape=[None])\nY=tf.placeholder(tf.float32,shape=[None])\n\nW=tf.Variable(tf.random_normal([1]),name='weight')\nb=tf.Variable(tf.random_normal([1]),name='bias')\n\nhypothesis=X*W+b\n#average\ncost=tf.reduce_mean(tf.square(hypothe... | [
[
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.square",
"tensorflow.Session",
"tensorflow.random_normal"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
tizian/layer-laboratory | [
"008cc94b76127e9eb74227fcd3d0145da8ddec30",
"6beefe4b656504673b8d6c3f0e8eb198265c84de"
] | [
"src/films/tests/test_hdrfilm.py",
"src/python/python/autodiff.py"
] | [
"import mitsuba\nimport pytest\nimport os\nimport enoki as ek\n\n\ndef test01_construct(variant_scalar_rgb):\n from mitsuba.core.xml import load_string\n\n # With default reconstruction filter\n film = load_string(\"\"\"<film version=\"2.0.0\" type=\"hdrfilm\"></film>\"\"\")\n assert film is not None\n ... | [
[
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.colorbar",
"matplotlib.pyplot.subplot",
"numpy.array",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
],
[
"torch.cuda.empty_cache"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kvzhao/pytorch-metric-learning | [
"9eb792bcfc1616b599e6ee457514e3cb3a7235dd"
] | [
"src/pytorch_metric_learning/utils/logging_presets.py"
] | [
"import logging\r\nfrom . import common_functions as c_f\r\nimport os\r\nimport torch\r\nfrom collections import defaultdict\r\nimport sqlite3\r\n\r\n# You can write your own hooks for logging.\r\n# But if you'd like something that just works, then use this HookContainer.\r\n# You'll need to install record-keeper a... | [
[
"torch.utils.tensorboard.SummaryWriter",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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