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# Author: Ritchie Lee, ritchie.lee@sv.cmu.edu
# Date: 11/21/2014
#ACASX implementation: based on interfacing to the the Julia ADD
module ACASX_ADD_Impl
export
addObserver,
ACASX_ADD,
ACASXInput,
ACASXOutput,
initialize,
update
import Compat.ASCIIString
using AbstractCollisionAvoidanceSyste... | {"hexsha": "5075a7779de7483fd58a083b9ed8e1919be8279b", "size": 1781, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/CollisionAvoidanceSystem/ACASX_ADD_Impl/ACASX_ADD_Impl.jl", "max_stars_repo_name": "weirdindiankid/cs542", "max_stars_repo_head_hexsha": "67e5e71fdd368c9e212dc847185dcec9733a78ae", "max_stars_r... |
"""
readSRF(fname::AbstractString)
Import surface data in the BESA loc format
fname = location and name of file to import
verbose = should the function output user feedback
surface = readSRF(verbose=false)
"""
function readSRF(fname::AbstractString = Pkg.dir("Private", "test", "data", "Default50Brain.srf"); ve... | {"hexsha": "00968e852793bd0c4570f17985107ec987676c83", "size": 2196, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/read_write/srf.jl", "max_stars_repo_name": "HanneDeprez1/codeRob", "max_stars_repo_head_hexsha": "120b8497bca885f15992b4a85bd1b37fa3273629", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#include <iostream>
#include <bustache/model.hpp>
#include <boost/iostreams/device/mapped_file.hpp>
int main()
{
using bustache::object;
using bustache::array;
using namespace bustache::literals;
boost::unordered_map<std::string, bustache::format> context
{
{"href", "href=\"{{url}}\""_fmt}... | {"hexsha": "4a7aea662675c097bb7cd63d39a836ccbe7b0b72", "size": 2062, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "thirdparty/bustache/example/main.cpp", "max_stars_repo_name": "travelping/nifi-minifi-cpp", "max_stars_repo_head_hexsha": "5ad6154607ce6a6cda2d97e402d1e767fc0c6971", "max_stars_repo_licenses": ["Apa... |
import numpy as np
from lib.LinUCB import *
import math
from scipy.sparse.csgraph import connected_components
from scipy.sparse import csr_matrix
from lib.BaseAlg import BaseAlg
class CLUBUserStruct(LinUCBUserStruct):
def __init__(self, featureDimension, lambda_, userID):
LinUCBUserStruct.__init__(
... | {"hexsha": "6ac531096481ffc08eb9ab017349276799a618db", "size": 5161, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/CLUB.py", "max_stars_repo_name": "yilingjia/BanditLib", "max_stars_repo_head_hexsha": "aab74f65d576f964e233a685e98bc6c1fd940686", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
"""Read c3d files.
"""
__author__ = "Marcos Duarte, https://github.com/demotu/"
__version__ = "0.0.1"
__license__ = "MIT"
import os
import copy
import pprint
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
import ezc3d
xr.set_options(keep_attrs=True)
def read_c3d(fname,... | {"hexsha": "9cd78c75cdfcea5cb08d6c096be0b8c411a7af7e", "size": 22024, "ext": "py", "lang": "Python", "max_stars_repo_path": "functions/read_c3d_xr.py", "max_stars_repo_name": "lucassantanasilva/bmc", "max_stars_repo_head_hexsha": "8e828ea4110e35689657529db89ef6cd81313412", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import pathlib
import numpy as np
import pandas as pd
import xarray as xr
import pybedtools
import dask
import subprocess
from collections import defaultdict
from ALLCools.mcds import RegionDS
import pyBigWig
import warnings
from concurrent.futures import ProcessPoolExecutor, as_completed
from sklearn.model_selection i... | {"hexsha": "c8677977acf6d471fefd3736f84b76225f78389f", "size": 28601, "ext": "py", "lang": "Python", "max_stars_repo_path": "ALLCools/reptile/reptile.py", "max_stars_repo_name": "jksr/ALLCools", "max_stars_repo_head_hexsha": "0788ce4405c25361b45701ffe5b03a962c154d25", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import argparse
import pickle
import numpy as np
import warnings
import os
import sys
warnings.simplefilter(action='ignore', category=FutureWarning)
def get_arguments():
parser = argparse.ArgumentParser(description='scenario')
parser.add_argument('--cwd', type=str, default='./',
help='... | {"hexsha": "8c8c578100b20c435106fb975e9ff56633e28571", "size": 3875, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmark_launch_scripts/paper/Experiment_6-2-1.py", "max_stars_repo_name": "jboilard1994/disentanglement_lib", "max_stars_repo_head_hexsha": "9cb6bdbafeb0247864f94d5c5c0853a310c86b9e", "max_stars... |
#Copyright (c) 2015 Rex Computing and Isaac Yonemoto
#see LICENSE.txt
#this work was supported in part by DARPA Contract D15PC00135
#unum-bitwalk.jl
#implements a "bitwalking" functional. Said functional takes a ulp that isn't
#at maximal fraction length and then breaks it into two ulps that has one extra
#bit of len... | {"hexsha": "aa58527c569729887e994c24938914943107070d", "size": 1964, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/methods/unum_bitwalk.jl", "max_stars_repo_name": "REX-Computing/unumjl", "max_stars_repo_head_hexsha": "5441eecd983943dcc5f2ad737b3f2144b3ed12e6", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
Copyright (c) 2021 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writin... | {"hexsha": "4434268d72f7cb33dcf33e7a7cc1660f853de308", "size": 4692, "ext": "py", "lang": "Python", "max_stars_repo_path": "nncf/common/batchnorm_adaptation.py", "max_stars_repo_name": "xiao1228/nncf", "max_stars_repo_head_hexsha": "307262119ee3f50eec2fa4022b2ef96693fd8448", "max_stars_repo_licenses": ["Apache-2.0"], "... |
#include <array>
#include <algorithm>
//#include <iterator>
#include <iostream>
#include <thread>
#include <mutex>
#include <vector>
#include <deque>
#include <boost/asio.hpp>
#include <boost/function.hpp>
#include <boost/bind.hpp>
//#include <boost/thread.hpp>
#include <boost/program_options.hpp>
#include <stdint.h>
... | {"hexsha": "865aaa6495b23a2e2b740aafb74b821368022965", "size": 23513, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "main.cpp", "max_stars_repo_name": "jeanleflambeur/wifibidicast", "max_stars_repo_head_hexsha": "2a30f300f08a5cf5ff2f51e4b7d4f2cfe9be5d3f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import os
import sys
from collections import OrderedDict
from matplotlib import pyplot as plt
from PIL import Image
import numpy as np
class History():
def __init__(self):
self.epoch_log = []
self.batch_log = {}
def update_epoch_log(self, log):
if type(log) not in [OrderedDict]:
... | {"hexsha": "f0936ef9d85a161efdee9f0a37562832c584ff84", "size": 3233, "ext": "py", "lang": "Python", "max_stars_repo_path": "torchfit/utils.py", "max_stars_repo_name": "amaiya/torchfit", "max_stars_repo_head_hexsha": "29819e27ba9b8703dc792a56b5bb49f8e9a4dc9c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "m... |
C @(#)loaddc.f 20.3 2/13/96
subroutine loaddc (iver,histin,rcflag,jtape,ldflow,stab)
integer iver, histin, jtape
logical stab, rcflag, ldflow
* this is a dummy routine called from RDDTAI.FOR for EPRI dc
logical done
done = .true.
if (done) stop 'LOADDC FOR EPRI'
... | {"hexsha": "4b3684690fbe1abf400f9cdff98317289150c865", "size": 339, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ipf/loaddc.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "m... |
"""
mbs_experiment.py
---
Source code for figure: bar_mbs_avg20runs_eta15.png
---
This program runs an experiment using the neural network.
Several values of mini batch size are tested, for each value the number of
epochs needed to correctly classify 80% of the validation data is found.
As well as the percentage of cor... | {"hexsha": "1139fc8ebb82df60f3fc1e78f1f1b5b3e32dd8f1", "size": 4090, "ext": "py", "lang": "Python", "max_stars_repo_path": "mbs_experiment.py", "max_stars_repo_name": "brullenbakken/Neural-Network-PJ-2", "max_stars_repo_head_hexsha": "eef5707ceb215aa490fda9fffade64407b9f5a2c", "max_stars_repo_licenses": ["MIT"], "max_s... |
# Armijo线搜索准则
def armijo(funcs, args, x_0, d, gamma=0.5, c=0.1):
'''
Parameters
----------
funcs : sympy.matrices.dense.MutableDenseMatrix
当前目标方程
args : sympy.matrices.dense.MutableDenseMatrix
参数列表
x_0 : list
初始迭代点列表
d : numpy.array
... | {"hexsha": "4f04ecdba2f18d22c8cad49c8bd381f6ba1fd27c", "size": 6697, "ext": "py", "lang": "Python", "max_stars_repo_path": "optimtool/functions/linear_search.py", "max_stars_repo_name": "linjing-lab/optimtool", "max_stars_repo_head_hexsha": "9ca298b91ba755b4dab4028879af2c5a06c2e6d6", "max_stars_repo_licenses": ["MIT"],... |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 02 21:17:50 2014
@author: Andreas
"""
import numpy as np
import gdal
#from osgeo import gdal_array
class NDEM():
def __init__(self, model_dem, model_dom,outFile, noData):
self.model_dem = str(model_dem)
self.model_dom = str(model_dom)
... | {"hexsha": "7d9eef4189ae94123bdfa3ec4338d692e395f836", "size": 2882, "ext": "py", "lang": "Python", "max_stars_repo_path": "modul/module_ndem.py", "max_stars_repo_name": "AndyLoewe/CityEX", "max_stars_repo_head_hexsha": "9df0fe9df721bab4c5341b411fa69a180993ce41", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# This import below is needed to run TensorFlow on top of the he-transformer.
import subprocess
import time
import ngraph_bridge
import numpy as np
import tensorflow as tf
from consts import out_server_name, out_final_name, argmax_times_name, \
inference_no_network_times_name
# Add parent directory to path
from m... | {"hexsha": "9b3646d9071fc7e2884bb438b7c3b4719bae707f", "size": 7131, "ext": "py", "lang": "Python", "max_stars_repo_path": "server_client.py", "max_stars_repo_name": "cleverhans-lab/capc-iclr", "max_stars_repo_head_hexsha": "bc4c47fcd178a5da4950c84222bc232d64ea4f83", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
[STATEMENT]
lemma is_contraction_\<L>: "is_contraction \<L>\<^sub>b"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. is_contraction \<L>\<^sub>b
[PROOF STEP]
using contraction_\<L> zero_le_disc disc_lt_one
[PROOF STATE]
proof (prove)
using this:
dist (\<L>\<^sub>b ?v) (\<L>\<^sub>b ?u) \<le> l * dist ?v ?u
0 \<le> l
... | {"llama_tokens": 315, "file": "MDP-Rewards_MDP_reward", "length": 3} |
import gzip
import os
import subprocess
import xml.etree.ElementTree as ET
import graphviz
import networkx as nx
import tex2pix
from particle import latex_to_html_name
from particle.converters.bimap import DirectionalMaps
from pylhe._version import version as __version__
from pylhe.awkward import register_awkward, to... | {"hexsha": "a3ab03a446707a58f3da54d1f0836c59561fae25", "size": 11906, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pylhe/__init__.py", "max_stars_repo_name": "lukasheinrich/pylhe", "max_stars_repo_head_hexsha": "8beb29cca38762b1b1a50af0221c3ea7a23f187d", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
# Not working with Aiida 1.0
from aiida.common.exceptions import InputValidationError
from aiida.orm import ArrayData, Dict
from aiida_phonopy.common.raw_parsers import (
get_force_constants,
get_FORCE_SETS_txt,
get_poscar_txt,
)
import numpy as np
from aiida_lammps.calculations.lammps import BaseLammpsCa... | {"hexsha": "089cd22ae40081ef5e985211f7c000ef9b6a6f75", "size": 7263, "ext": "py", "lang": "Python", "max_stars_repo_path": "aiida_lammps/calculations/lammps/combinate.py", "max_stars_repo_name": "JPchico/aiida-lammps", "max_stars_repo_head_hexsha": "8f618541784bbd6360efc653350570cf76398e83", "max_stars_repo_licenses": ... |
import os
import numpy as np
import torch
import pandas as pd
from experiment_interface.logger import get_train_logger, get_test_logger
class Evaluator():
def __init__(self,
net,
test_dataset,
batch_size,
predict_module,
metric,
num_workers,
result_dir=None,... | {"hexsha": "6d1b7e95e565743850f68d46625c51f764d8dded", "size": 4973, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiment_interface/evaluator/core.py", "max_stars_repo_name": "dnnspark/trainer", "max_stars_repo_head_hexsha": "cdf28eaf22e4b97e11b08d4d04274e2e178f20e3", "max_stars_repo_licenses": ["MIT"], "m... |
[STATEMENT]
lemma connected_Int_frontier:
"\<lbrakk>connected s; s \<inter> t \<noteq> {}; s - t \<noteq> {}\<rbrakk> \<Longrightarrow> (s \<inter> frontier t \<noteq> {})"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>connected s; s \<inter> t \<noteq> {}; s - t \<noteq> {}\<rbrakk> \<Longrightarrow>... | {"llama_tokens": 1007, "file": null, "length": 5} |
import cdat_info
import cdms2
import unittest
import numpy
import os
class TestCDMSAutobounds(unittest.TestCase):
def createFile(self, minLon, maxLon, offset):
# Create a test netCDF file and load it with one grid of data.
#
testFile = cdms2.open('testFile.nc', 'w')
latitudes = nu... | {"hexsha": "8039eaf99c817bdbd6e8db80e97c05757542bd8f", "size": 4044, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_CDMSBounds.py", "max_stars_repo_name": "jasonb5/cdms", "max_stars_repo_head_hexsha": "dd41a8dd3b5bac10a4bfdf6e56f6465e11efc51d", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
from genericpath import exists
from logging import raiseExceptions
from numpy import cumsum
from .sphere import sphere_2D,sphere_3D
import random
import math
from matplotlib import pyplot as plt
import numpy as np
class shot_stream:
"""A class that describes the shot stream
Attributes:
number_of_sphere... | {"hexsha": "ee5695424b86fb041426fc1bda64890994060c66", "size": 10855, "ext": "py", "lang": "Python", "max_stars_repo_path": "sphere_generator/shot_stream_generator.py", "max_stars_repo_name": "aalamprou/sFEre", "max_stars_repo_head_hexsha": "3001d88961e434e01800a995e1742b04d5e36110", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma Prop3: "Op A \<longleftrightarrow> \<bullet>\<^bold>\<midarrow>A \<^bold>\<approx> \<^bold>\<bottom>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. [\<^bold>\<turnstile> \<lambda>w. \<I> A w = A w] = [\<^bold>\<turnstile> \<lambda>w. op_det\<^sup>c (\<^bold>\<midarrow>A) w = \<^bold>\<bottom> w]
[... | {"llama_tokens": 149, "file": "Topological_Semantics_ex_LFUs", "length": 1} |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | {"hexsha": "3a77c55384f1417a75ed62b736411813ae6680f8", "size": 13197, "ext": "py", "lang": "Python", "max_stars_repo_path": "OpenRoboRL/envs/quadruped_robot/quadruped_gym_env.py", "max_stars_repo_name": "Derek-TH-Wang/OpenRoboRL", "max_stars_repo_head_hexsha": "b81333f034acff7252322322b8d499cd2c3c49e9", "max_stars_repo... |
import os
import numpy as np
import json
import random
from PIL import Image
from PIL import ImageDraw
import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
class DatasetBase(Dataset):
"""Base dataset for VITON-GAN.
"""
def __init__(self, opt, mode, data_... | {"hexsha": "151cc6b641c8c997c552df4739b9b9a462d319cf", "size": 8083, "ext": "py", "lang": "Python", "max_stars_repo_path": "viton_gan/dataset.py", "max_stars_repo_name": "gkeng/viton-gan", "max_stars_repo_head_hexsha": "ea16b39c84e7b8a8a8ccd928165a144a3f13be57", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 97... |
# from keras.models import Sequential
# from keras.layers import Dense
import keras
import numpy as np
from classifier.prepareData import Classifier_dataset
class MusicClassifier:
"""
This class is used for applying the exist model to the project
It has to be loaded from a existing model (file or keras mod... | {"hexsha": "342cd399dbfe4f20d7eb782308a26a3d7722bafe", "size": 2811, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "Anne-Fern/shazam-air", "max_stars_repo_head_hexsha": "e51f9a11b896410599e9574417509646b962f86e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19, "max_... |
# -*- coding: utf-8 -*-
##
# @file func_plot_app.py
# @brief Contain a GUI for plotting functions
# @author Gabriel H Riqueti
# @email gabrielhriqueti@gmail.com
# @date 28/04/2021
#
from PySide2 import QtWidgets
from PySide2.QtWidgets import QApplication
import sys
import numpy as np
from matplotlib.backe... | {"hexsha": "a3569ca4ec41b064cd0f1eed572d65acb59d34e5", "size": 5580, "ext": "py", "lang": "Python", "max_stars_repo_path": "biomedical_signal_processing/tools/func_plot_app.py", "max_stars_repo_name": "gabrielriqu3ti/biomedical_signal_processing", "max_stars_repo_head_hexsha": "84cdc5e5b65facba70c6945d62b18bcf0e5dc3e2"... |
// Copyright 2010 Dean Michael Berris.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#define BOOST_TEST_MODULE HTTP 1.0 Get Test
#include <boost/network/include/http/client.hpp>
#include <boost/test/unit_test... | {"hexsha": "1a2b558b163a8600cdefce3b4bda24b95916e345", "size": 1612, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/network/test/http/client_get_test.cpp", "max_stars_repo_name": "antoinelefloch/cpp-netlib", "max_stars_repo_head_hexsha": "5eb9b5550a10d06f064ee9883c7d942d3426f31b", "max_stars_repo_licenses": ... |
\documentclass[twoside]{article}
\setlength{\oddsidemargin}{0.25 in}
\setlength{\evensidemargin}{-0.25 in}
\setlength{\topmargin}{-0.6 in}
\setlength{\textwidth}{6.5 in}
\setlength{\textheight}{8.5 in}
\setlength{\headsep}{0.75 in}
\setlength{\parindent}{0 in}
\setlength{\parskip}{0.1 in}
%
% ADD PACKAGES here:
%
\us... | {"hexsha": "7e4370078b18dddb574d7a67fd9e5a5a49a6771f", "size": 170744, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "MATH2923/exam-notes.tex", "max_stars_repo_name": "chrishyland/uni-notes", "max_stars_repo_head_hexsha": "c19b260657f4c3e4a0c2a3a1248cc0baf23d3e55", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma mono_Par_ref: "\<lbrakk>P \<sqsubseteq> P'; Q \<sqsubseteq> Q'\<rbrakk> \<Longrightarrow> (P || Q) \<sqsubseteq> (P' || Q')"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>P \<sqsubseteq> P'; Q \<sqsubseteq> Q'\<rbrakk> \<Longrightarrow> (P||Q) \<sqsubseteq> (P'||Q')
[PROOF STEP]
by (rule ... | {"llama_tokens": 141, "file": "HOL-CSP_CSP", "length": 1} |
#!/usr/bin/env python3
# Imports
# Standard lib
import unittest
import pathlib
# 3rd party
import numpy as np
from PIL import Image
# Our own imports
from deep_hipsc_tracking.model import preproc
from deep_hipsc_tracking.model._preproc import composite_mask
from .. import helpers
# Helper Classes
class FakeDet... | {"hexsha": "10645e09f7ed08fc24a4719d2ab24dbcb4d5b6f4", "size": 53613, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/model/test_preproc.py", "max_stars_repo_name": "JackToppen/deep-hipsc-tracking", "max_stars_repo_head_hexsha": "9d7e07814f26e3f76603bba1a945ae05e88733db", "max_stars_repo_licenses": ["BSD-3... |
#encoding=utf-8
import numpy as np
import cv2 as cv
import os
import tensorflow as tf
def getTrianList():
root_dir = "/Users/zhuxiaoxiansheng/Desktop/doc/SICA_data/YaleB"
with open('/Users/zhuxiaoxiansheng/Desktop'+"/Yaledata.txt","w") as f:
for file in os.listdir(root_dir):
if len(file) =... | {"hexsha": "fb6df07e69b634022822f8e30d102e1aed0f9e2d", "size": 2363, "ext": "py", "lang": "Python", "max_stars_repo_path": "Batch Geneator.py", "max_stars_repo_name": "LiangjunFeng/Create-dataset-for-Tensorflow", "max_stars_repo_head_hexsha": "1533dea183e77a1bdaa0327926b0ee9c02590252", "max_stars_repo_licenses": ["MIT"... |
import numpy as np
import os
import pandas as pd
from xml.etree.cElementTree import iterparse
import logging
logger = logging.getLogger('parse_database')
def mf_from_inchi(inchi):
return inchi.split('/')[1]
class MolecularDatabase():
def __init__(self, filename):
self.database = {}
self._requ... | {"hexsha": "66232a92df9c0bc5b198ca7d4924e80b6e164f6b", "size": 6010, "ext": "py", "lang": "Python", "max_stars_repo_path": "parsedatabases/io.py", "max_stars_repo_name": "METASPACE2020/parsedatabases", "max_stars_repo_head_hexsha": "2beb82d672b814c3c0393ce004edc342d3133d10", "max_stars_repo_licenses": ["Apache-2.0"], "... |
from numpy import clip, inf
class NaivePredictor(object):
def __init__(self, data_in, num_prediction_periods):
self.__history = data_in
self.__num_prediction_periods = num_prediction_periods
@property
def configuration(self):
return ""
def predict_counts(self):
y_lis... | {"hexsha": "20d30e90acaa6df6727b219ad68520267561d763", "size": 412, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/algorithms/naive_predictor.py", "max_stars_repo_name": "ExesiosPB/libm", "max_stars_repo_head_hexsha": "09c2638d895a4ba69e0d7f4f0e353f27d4b7911f", "max_stars_repo_licenses": ["MIT"], "max_s... |
# Copyright 2019 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... | {"hexsha": "3837ba400c03f433c492e83801a84ef2d589b3fd", "size": 10991, "ext": "py", "lang": "Python", "max_stars_repo_path": "assistant/functions/intent_slot/preprocessor/preprocess.py", "max_stars_repo_name": "SunLemuria/JointBERT-Tensorflow1", "max_stars_repo_head_hexsha": "92a30f9274f8c75035e7c39e0a7b77c3a1343a87", "... |
import sys
sys.path.append('/media/nox/OS/Linux/Documents/Masterarbeit/shared/dlabb/')
sys.path.append('/home/dladmin/Documents/arthurma/shared/dlabb')
import csv
import datetime as dt
import math
import os
import random
import time
import pandas as pd
import numpy as np
import tensorflow as tf
slim = tf.contrib.sl... | {"hexsha": "9ad224ca3e6be7572ae02719defa91fbbde16d99", "size": 39450, "ext": "py", "lang": "Python", "max_stars_repo_path": "abb_supervised_networks/regression/nn_regression_prediction_low_memory.py", "max_stars_repo_name": "habichta/ETHZDeepReinforcementLearning", "max_stars_repo_head_hexsha": "e1ae22159753724290f2006... |
import numpy as np
import tensorflow as tf
from tf_model_base import TfModelBase
import warnings
__author__ = 'Chris Potts'
# Ignore the TensorFlow warning
# Converting sparse IndexedSlices to a dense Tensor of unknown shape.
# This may consume a large amount of memory.
warnings.filterwarnings("ignore", category=... | {"hexsha": "9f862f55af9be4e3c8b71b78eee8b46f889a54b4", "size": 9827, "ext": "py", "lang": "Python", "max_stars_repo_path": "tf_rnn_classifier.py", "max_stars_repo_name": "andrewquirk/cs191w", "max_stars_repo_head_hexsha": "0fc0c8996e7e8eb570a7e347ff7ead6f8b5bd4ce", "max_stars_repo_licenses": ["Apache-2.0", "MIT"], "max... |
import queue
from threading import Thread
import numpy as np
import cv2 as cv2
import torch
class InferenceEngine(Thread):
def __init__(self, net, use_gpu=False):
Thread.__init__(self)
self.net = net
self.use_gpu = use_gpu
if use_gpu:
self.net.cuda()
self._que... | {"hexsha": "62a7de0d1e5febc7e48bcb39eb526c20d7645b1b", "size": 4886, "ext": "py", "lang": "Python", "max_stars_repo_path": "realtimenet/engine.py", "max_stars_repo_name": "doguaraci/20bn-realtimenet", "max_stars_repo_head_hexsha": "007e342e81c605feaa35abdd34960eb3011d6661", "max_stars_repo_licenses": ["MIT"], "max_star... |
testOnAllBackends("Vector Algos") do af
println("\twhere")
afArr = array(af, [0.0, 0.1, 0.0, 0.3, 0.0, 0.5, 0.0, 0.7, 0.8, 0.0])
result = where(afArr)
@test host(result) == [0x00000001,0x00000003,0x00000005,0x00000007,0x00000008]
end
| {"hexsha": "73251d9161b5916faac924869c9ad109ba8a1fa1", "size": 238, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "ArrayFire/test/vectorAlgos.jl", "max_stars_repo_name": "unbornchikken/julia-ml-proto", "max_stars_repo_head_hexsha": "ade4dbe78890be9f81ddc9e03f4f6fc2cfe31825", "max_stars_repo_licenses": ["Apache-2... |
theory MinimalHEAPLemmas
imports MinimalHEAP1
begin
text {* First we start with just one function symbol (foreground; even if with more in background).
Next, we add some side conditiosn that are "obvious" (inferrable/knownable from context).
Finally, we have some lemmas that don't have trivial conditio... | {"author": "leouk", "repo": "VDM_Toolkit", "sha": "791013909961d45949fcd96d937ae18f0174c7ec", "save_path": "github-repos/isabelle/leouk-VDM_Toolkit", "path": "github-repos/isabelle/leouk-VDM_Toolkit/VDM_Toolkit-791013909961d45949fcd96d937ae18f0174c7ec/experiments/vdm/Heap/isa/MinimalHEAPLemmas.thy"} |
#Exercícios Numpy-07
#*******************
import numpy as np
arr=np.arange(10,50)
print('arr=',arr)
| {"hexsha": "867c0851d118454920efbb1cc937589466372de7", "size": 103, "ext": "py", "lang": "Python", "max_stars_repo_path": "Ex.07-Numpy.py", "max_stars_repo_name": "aguinaldolorandi/100-exercicios-Numpy", "max_stars_repo_head_hexsha": "276c721bd8b161153223b353168a1c15936edbd1", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python3
import os
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
import sys
from adaptiveumbrella.wham2d import WHAM2DRunner
sys.path.append('..')
class MyUmbrellaRunner(WHAM2DRunner):
def __init__(self):
WHAM2DRunner.__init__(self)
cum_frames = [0... | {"hexsha": "c96fbe43d78f2df5f4c1ab70a660319232707db4", "size": 2719, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/example.py", "max_stars_repo_name": "danijoo/adaptiveumbrella", "max_stars_repo_head_hexsha": "9bae718c02fddfe43f9f65ae43028de6bc803132", "max_stars_repo_licenses": ["CC0-1.0"], "max_star... |
import json
from os.path import dirname, join
import numpy as np
import pandas as pd
from sklearn.preprocessing import KBinsDiscretizer
def _discretize(vector, **kwargs):
"""Discretizes vector with sklearn.preprocessing.KBinsDiscretizer.
Parameters
----------
vector : np.array
kwargs
Arg... | {"hexsha": "cbd2b90768dd2029f101128def224673fb162645", "size": 4938, "ext": "py", "lang": "Python", "max_stars_repo_path": "bcselector/datasets.py", "max_stars_repo_name": "Kaketo/bc-selector", "max_stars_repo_head_hexsha": "c7acd1033bee741530735fb601f9e464c3ccc26f", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
Contains code for defining the dropout layer (currently unused).
"""
import theano.tensor as tensor
def dropout_layer(state_before, use_noise, trng):
proj = tensor.switch(use_noise,
(state_before *
trng.binomial(state_before.shape,
... | {"hexsha": "b085c69a28f7cca695a31aa16a6a22cdcd334530", "size": 480, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn_dropout.py", "max_stars_repo_name": "Sentimentron/Dracula", "max_stars_repo_head_hexsha": "878f81c1c56a8ac12cf02d8f15bd93c544e29611", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
# Copyright 2021 Tensorforce Team. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable la... | {"hexsha": "64144f7d65e37fc9a098cc70f14568ccd7b90b4d", "size": 6331, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorforce/core/layers/attention.py", "max_stars_repo_name": "DLPerf/tensorforce", "max_stars_repo_head_hexsha": "33a2d84fa850e8842dfe2cef3901de32cf7cd221", "max_stars_repo_licenses": ["Apache-2.... |
### FieldTuple types
# FieldTuple is a thin wrapper around a Tuple or NamedTuple holding some Fields
# and behaving like a Field itself
struct FieldTuple{B<:Basis,FS<:Union{Tuple,NamedTuple},T} <: Field{B,Spin,Pix,T}
fs::FS
# the constructor for FieldTuples is a bit complex because there's alot of
# diff... | {"hexsha": "1f643bd3b01425325f3d6cb8389ebe06402285e3", "size": 8586, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/field_tuples.jl", "max_stars_repo_name": "JuliaTagBot/CMBLensing.jl", "max_stars_repo_head_hexsha": "59913ff7a889587e706f3cf634c1e6b379bc1574", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import matplotlib.pyplot as plt
import numpy as np
import dynpy
bn = dynpy.bn.BooleanNetwork(rules=dynpy.sample_nets.budding_yeast_bn)
initState = np.zeros(bn.num_vars, 'uint8')
initState[ [1,3,6] ] = 1
plt.spy(bn.get_trajectory(start_state=initState, max_time=15))
plt.xlabel('Node')
plt.ylabel('Time')
| {"hexsha": "bfd3d2f1a294eea42d2a23c7c6f18a267eb372fb", "size": 306, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/test_pyplots/bn_trajectory.py", "max_stars_repo_name": "artemyk/dynpy", "max_stars_repo_head_hexsha": "c2914ac315083ad76707a7fcb2c8800a2ec52944", "max_stars_repo_licenses": ["BSD-2-Clause"], "... |
from __future__ import print_function, division
import numpy as np
from scipy.linalg import inv
import matplotlib.pyplot as plt
n = 20
s = 2.0
m = 2 * (n + 1)
M = np.empty((m, n))
for i in range(m):
for j in range(n):
M[i, j] = np.exp(-2*(i / s - j)**2)
M = M.dot(inv((M.T).dot(M))).dot(M.T)
xM = np.arang... | {"hexsha": "9828aca61deb18c4bcd1a36e5942a46a221b8c81", "size": 625, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/transform_methods/basex-vert.py", "max_stars_repo_name": "beverlyru/PyAbel", "max_stars_repo_head_hexsha": "8f8d7e864475ae823837b0925a88eaac7b3ffdc7", "max_stars_repo_licenses": ["MIT"], "max_s... |
import re, sys
import os, json
CURRENT_FOLDER = os.path.dirname(os.path.abspath(__file__))
import numpy as np
import random
UNKNOWN_TOKEN = '<unnown>'
PADDING_TOKEN = '<paddingword>'
def extract_text_from_line_numb(line):
match = re.search('\d+ your persona:', line)
if match is None:
match = re.search(... | {"hexsha": "4037a226ebca751e656705720ef8d6ec67784a68", "size": 10198, "ext": "py", "lang": "Python", "max_stars_repo_path": "parlai/agents/mai_model/data_reader.py", "max_stars_repo_name": "khaimaitien/Mai_convai2", "max_stars_repo_head_hexsha": "83bf99ba09d678455aad49239f3daefc5a7b8fc9", "max_stars_repo_licenses": ["B... |
import numpy as np
import yaml
import pickle
from os import listdir
from os.path import join
import faiss
import main.utils as utils
# load global config yaml
yaml_path = './config.yaml'
cont = None
with open(yaml_path, 'r', encoding='utf-8') as f:
cont = f.read()
arg = yaml.load(cont)
arg_dataset = arg['dataset'... | {"hexsha": "67edc72c9911cb3aab039cce58682345acd26f1e", "size": 1569, "ext": "py", "lang": "Python", "max_stars_repo_path": "main/eval_desc.py", "max_stars_repo_name": "Rick0514/VPR_SMCN", "max_stars_repo_head_hexsha": "7a00dc8e4de0c21438474c05a4a7be18d05367fa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import math
import argparse
import pickle
import numpy as np
from numpy.core.umath_tests import inner1d
import scipy
import scipy.sparse as sp
import MDAnalysis.analysis.distances
import MDAnalysis as md
from tqdm import tqdm
import boo
def pbc(ref_pos_mat, pos_mat, box):
box = box[:3]
pbc_pos_mat = np.cop... | {"hexsha": "2041993d2cde2859311745d91d3245788e9e2fa0", "size": 5555, "ext": "py", "lang": "Python", "max_stars_repo_path": "prepare/prepare.py", "max_stars_repo_name": "QHwan/GCIceNet", "max_stars_repo_head_hexsha": "5792f5fa7bd2989b54eddeae5c9f8fca3f004bb5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
"""
Copyright (C) <2010> Autin L. TSRI
This file git_upy/autodesk3dsmax/v2015/maxHelper.py is part of upy.
upy is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the Li... | {"hexsha": "ea9bf40e3163c4b54f8ff19bec5d534e8e6c8be7", "size": 13745, "ext": "py", "lang": "Python", "max_stars_repo_path": "cellpack/mgl_tools/upy/autodesk3dsmax/v2016/maxHelper.py", "max_stars_repo_name": "mesoscope/cellpack", "max_stars_repo_head_hexsha": "ec6b736fc706c1fae16392befa814b5337a3a692", "max_stars_repo_l... |
[STATEMENT]
lemma rel_interior_injective_linear_image:
fixes f :: "'m::euclidean_space \<Rightarrow> 'n::euclidean_space"
assumes "bounded_linear f"
and "inj f"
shows "rel_interior (f ` S) = f ` (rel_interior S)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. rel_interior (f ` S) = f ` rel_interior S
[PROO... | {"llama_tokens": 316, "file": null, "length": 2} |
# -*- coding: utf-8 -*-
""" BrainProp implementation.
Usage:
python main.py <dataset> <architecture> <algorithm>
Use the optional argument -s to save training outputs or -l to load weights (specify then the file name)
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import sys, o... | {"hexsha": "5828d20c594495a3e1f736db7acce8cb5ffdbf66", "size": 9531, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "isapome/BrainProp", "max_stars_repo_head_hexsha": "ae4d60186c9cd2cba06008d0dbe97538f81149fe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17, "max_star... |
#find row based on the code, to improve this function
# and the find_column could do a hi_lo funtion
# and then call that for both find_row and find_column
function find_row(code)
row_code = code[1:7]
lo_n = 0
hi_n = 127
count = 1
for i in row_code
#find the bounds of the range depending if we can ... | {"hexsha": "4f58e45f9bb204aad559040f51de06ce761a65a5", "size": 1906, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Lupe/day5_part2.jl", "max_stars_repo_name": "efacks68/codingjuntos_advent2020", "max_stars_repo_head_hexsha": "1d7ef6278616716ad62a5f3af3fa0419173b6dc1", "max_stars_repo_licenses": ["MIT"], "max_st... |
[STATEMENT]
lemma wp3charn[rule_format]:
assumes domAllow: "dom (C (AllowPortFromTo a b po)) \<noteq> {}"
and wp3: "wellformed_policy3 (xs @ [DenyAllFromTo a b])"
shows "AllowPortFromTo a b po \<notin> set xs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. AllowPortFromTo a b po \<noti... | {"llama_tokens": 1697, "file": "UPF_Firewall_FWNormalisation_NormalisationIntegerPortProof", "length": 11} |
# Copyright (c) 2020, Fabio Muratore, Honda Research Institute Europe GmbH, and
# Technical University of Darmstadt.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source ... | {"hexsha": "cb71b0cf28136f54d56447175749e6ba5aaac14e", "size": 7555, "ext": "py", "lang": "Python", "max_stars_repo_path": "Pyrado/pyrado/environments/quanser/quanser_qube.py", "max_stars_repo_name": "theogruner/SimuRLacra", "max_stars_repo_head_hexsha": "4893514ccdeb10a736c55de9aa7753fd51c5afec", "max_stars_repo_licen... |
[STATEMENT]
lemma mset_lt_single_right_iff[simp]: "M < {#y#} \<longleftrightarrow> (\<forall>x \<in># M. x < y)" for y :: "'a::linorder"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (M < {#y#}) = (\<forall>x\<in>#M. x < y)
[PROOF STEP]
proof (rule iffI)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. M < {#y#} ... | {"llama_tokens": 1898, "file": null, "length": 24} |
import sys
sys.path.append('../HubCCD')
import ham
import numpy as np
import CCD
import CCSD
import UCCSD
from ast import literal_eval
import cGCCSD
from scf import twoe_MO_tran
fle = '16x1_complex_ghf'
#This function reads complex_GHF MOs and data from Kitou's Fortran output files
def read_K(fle,slow="False"):
#Get... | {"hexsha": "7f9b455deb1eb25506190b5756a5ff525e244c4d", "size": 2121, "ext": "py", "lang": "Python", "max_stars_repo_path": "read_K.py", "max_stars_repo_name": "jag20/HubCCD", "max_stars_repo_head_hexsha": "674ecbc3689f57672eb1b760cfccf444e838ac35", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars... |
[STATEMENT]
lemma abc_list_crsp_simp3[simp]:
"\<lbrakk>abc_list_crsp lma lmb; \<not> m < length lma; m < length lmb\<rbrakk> \<Longrightarrow>
abc_list_crsp (lma @ 0 \<up> (m - length lma) @ [n]) (lmb[m := n])"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>abc_list_crsp lma lmb; \<not> m < length lma;... | {"llama_tokens": 944, "file": "Universal_Turing_Machine_Recursive", "length": 6} |
SUBROUTINE CEDREAD(id,nid,iun,panel)
C
C THIS SUBROUTINE READS IN A CEDRIC FORMAT DISK VOLUME AND
C RETURNS ID HEADER INFORMATION ABOUT THE VOLUME AS WELL AS
C THE DATA FOR THE VARIOUS FIELDS AND LEVELS IN THE VOLUME.
C
C ID - 510 WORD ID HEADER FOR VOLUME
C ITEM - INTEGER SCRATCH ARRA... | {"hexsha": "3d5e30c46bc7036b2bcfdf5bd0b45a7d87e10d23", "size": 4184, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "zipfile/CEDREAD.f", "max_stars_repo_name": "NCAR/lrose-grid2ps", "max_stars_repo_head_hexsha": "fb7d368a7f96adcbe4f78b488f323cff35799cc9", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
import sys
import numpy as np
with np.load('uhat_dataset.npz') as data:
train_data = data['x_chars_train']
test_data = data['x_chars_test']
train_labels = data['y_chars_train']
test_labels = data['y_chars_test']
# reshape to flatten and normalize image data
train_data = train_data.reshape(len(train_da... | {"hexsha": "edb62318fa3a27061399e938336fe06d1d4a8ee0", "size": 3046, "ext": "py", "lang": "Python", "max_stars_repo_path": "uhat_model.py", "max_stars_repo_name": "curt-mitch/uhat_deep_nn", "max_stars_repo_head_hexsha": "4152dbd46687b9778272aa0e800a3e888e9fe15b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import typing
from IPython.core.display import JSON
import ibis
from numpy.lib.function_base import disp
import opentracing
import IPython.display
__all__ = [
"_expr_map",
"DATA_NAME_PREFIX",
"get_fallback",
"set_fallback",
"get_active_span",
"set_active_span",
"enable_debug",
"disable_... | {"hexsha": "2425256c3831f369ae5d8668874a498732ecfd8c", "size": 1439, "ext": "py", "lang": "Python", "max_stars_repo_path": "ibis_vega_transform/globals.py", "max_stars_repo_name": "xmnlab/ibis-vega-transform", "max_stars_repo_head_hexsha": "75b4c5e4a8452dbeb5a81e19eae817b4d9c64999", "max_stars_repo_licenses": ["Apache-... |
from __future__ import print_function
import numpy as np
import tensorflow as tf
from image_reader import ImageReader
from tools import decode_labels, prepare_label
from mobilenet import MobileNet
import time
import os
slim = tf.contrib.slim
#Directory Paths
tf.app.flags.DEFINE_string(
'data_dir', '/home/n1703... | {"hexsha": "8b0ac11403bce03f766adb71d9a29bd9cd4e6012", "size": 9835, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "ayshrv/mobilenet_psp", "max_stars_repo_head_hexsha": "08d376e322f0985a36f29a23fa6cd9cf180bcdd4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_s... |
% !TEX encoding = UTF-8
%Koma article
\documentclass[fontsize=12pt,paper=letter,twoside]{scrartcl}
\usepackage{float}
\usepackage{listings}
%Standard Pre-amble
\input{sty/defns.tex}
%Useful definitions
%\newcommand{\mv}[1]{\textit{m\_#1}}
%\newcommand{\cv}[1]{\textit{c\_#1}}
%\newcommand{\degree}[1]{^{\circ}\mathrm{#1... | {"hexsha": "44c6cbe81593fc1b4014d937a40a4282a4c94bf4", "size": 27005, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/users-manual/admin/Latex/admin-manual.tex", "max_stars_repo_name": "ssh24/eecs-gradapps", "max_stars_repo_head_hexsha": "7642f021ca963049abccc7b828b40a6513e17d1f", "max_stars_repo_licenses": ["... |
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
import numpy as np
import nltk
import string
import random
# getting my hands dirty :p
file = open('./chatbot.txt', 'r', errors='ign... | {"hexsha": "670613e9660243075ce66be59078da02d841c6ba", "size": 2701, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "RohanPunjani/DEVER-chatbot", "max_stars_repo_head_hexsha": "9a972fcdb9b3112a8231553c7af2fc820ee72085", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "... |
import os
import sys
import pandas as pd
import numpy as np
import h5py
import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
import alt_splice_heatmap_hl as hmhelp
import alt_splice_embeddings as ebhelp
BASEDIR = os.path.dirname(os.path.dirname(__file__))
sys.path.append(BAS... | {"hexsha": "3abe2ad35862d33ff1b5c036fcb6946bccb24957", "size": 13903, "ext": "py", "lang": "Python", "max_stars_repo_path": "sqtl/sqtl.py", "max_stars_repo_name": "yjzhang2013/pancanatlas_code_public", "max_stars_repo_head_hexsha": "cd095a7ac1f9e8124a6849be5aa9a4ccf932c31b", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import warnings
import logging
from openff.evaluator.utils import setup_timestamp_logging
from openff.evaluator.datasets import PhysicalPropertyDataSet
from openff.evaluator.forcefield import SmirnoffForceFieldSource
from openff.evaluator.properties import Density, EnthalpyOfMixing
from openff.evaluator.client import R... | {"hexsha": "fa7d99fa5b9b8826db555404d15923380c5f7f48", "size": 6734, "ext": "py", "lang": "Python", "max_stars_repo_path": "LJ_surrogates/run_simulations.py", "max_stars_repo_name": "ocmadin/LJ_surrogates", "max_stars_repo_head_hexsha": "5e3873c521d90dc93d90e41c434a89b74d609d3d", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import sys
from scipy.interpolate import interp1d
startIndex=14
endIndex=64
computerStreamNums=[8,4,4]
streamNum=np.sum(computerStreamNums)
scriptFiles=[]
for i in range(len(computerStreamNums)):
runnerFile=open('./scripts2/XXL_runner_'+str(i)+'.sh', 'w')
for j in range(computerStreamNu... | {"hexsha": "ee15b126fe2bb0163af4d8a454cd3e5ab2c17b3f", "size": 2313, "ext": "py", "lang": "Python", "max_stars_repo_path": "runner_LC_MXXL_lin.py", "max_stars_repo_name": "beckrob/AvERA_ISW", "max_stars_repo_head_hexsha": "2eb3fe6d0346513e1b50bbfdbfa1714c6353a35d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""
Functions in this files will ONLY use NumPy, and are therefore candidates for speed up with Numba.
"""
import numpy as np
from numba import jit
@jit(nopython=True)
def distance_matrix(test: np.ndarray, ref: np.ndarray, weight_matrix: np.ndarray):
# TODO: allow user to specify `band`. The code below assumes t... | {"hexsha": "b6675a04e9a5125899f556cbf5345b12178f4fc5", "size": 2632, "ext": "py", "lang": "Python", "max_stars_repo_path": "process_improve/batch/alignment_helpers.py", "max_stars_repo_name": "kgdunn/process-improve", "max_stars_repo_head_hexsha": "ceb9657344fda085450dcc6818fcd438c893dd0c", "max_stars_repo_licenses": [... |
"""
Deep Learning and Neural Networks
Advanced Research Seminar I/III
Graduate School of Information Science
Nara Institute of Science and Technology
January 2014
Instructor:
Kevin Duh, IS Building Room A-705
Office hours: after class, or appointment by email (x@is.naist.jp where x=kevinduh)
http://cl.naist.jp/~kevin... | {"hexsha": "66d0ebc19d3a773cb83573957fbe27e8ee370849", "size": 1205, "ext": "py", "lang": "Python", "max_stars_repo_path": "lecture1_code00.py", "max_stars_repo_name": "autodrive/NAIST_DeepLearning", "max_stars_repo_head_hexsha": "ac2c0512c43f71ea7df68567c5e24e689ac18aea", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import torch
import math
import matplotlib.pyplot as plt
import numpy as np
import torch
import math
import matplotlib.pyplot as plt
import numpy as np
class Torch_SOM(torch.nn.Module):
'''
A Torch implementation of a Self Oranizing Map (SOM).
Method: forward(input): Forward pass ... | {"hexsha": "dbbea3803b730b6d41f4af4ec96e01ad149e35c9", "size": 5532, "ext": "py", "lang": "Python", "max_stars_repo_path": "kohenen/models/kohenen_torch.py", "max_stars_repo_name": "BDJohnston/py_som", "max_stars_repo_head_hexsha": "450e4d3a158fbbb4f4a5ce241e2a63a05dd741fb", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#ifndef PCL_SIMULATION_IO_
#define PCL_SIMULATION_IO_
#include <boost/shared_ptr.hpp>
#include <GL/glew.h>
#include <pcl/pcl_config.h>
#ifdef OPENGL_IS_A_FRAMEWORK
# include <OpenGL/gl.h>
# include <OpenGL/glu.h>
#else
# include <GL/gl.h>
# include <GL/glu.h>
#endif
#ifdef GLUT_IS_A_FRAMEWORK
# include <GLUT/glut.h>... | {"hexsha": "9607d14b83398ea271e81cb4501e2c0632d119f9", "size": 2080, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "kinect_sim/include/kinect_sim/simulation_io.hpp", "max_stars_repo_name": "Tacha-S/perception", "max_stars_repo_head_hexsha": "aefbb5612c84b46a745c7db4fe860a2456d6e7ef", "max_stars_repo_licenses": ["... |
module InfiniteOpt
# Import and export JuMP
import Reexport
Reexport.@reexport using JuMP
# Import the necessary packages.
import MathOptInterface
import Distributions
import DataStructures
import FastGaussQuadrature
# Make useful aliases (note we get MOI and MOIU from JuMP)
const JuMPC = JuMP.Containers
const MOI... | {"hexsha": "3386aa59bbea85c14dd3472e83c8c7289e568fb4", "size": 6097, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/InfiniteOpt.jl", "max_stars_repo_name": "azev77/InfiniteOpt.jl", "max_stars_repo_head_hexsha": "db734856e6d89fd105f7bdb4fb5b8e16a72bd7fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
module partition
using Random
using Plots
using Statistics
const MAX_GEN = 500
const NBANDITS = 10
const POP_SIZE = 500
const NELITES = 50
const GENS_PER_FRAME = 50
#mutation probabilities
const SWAP_PROB = 0.5
const BALANCE_PROB = 0.5
const CROSS_PROB = 0.1
const NEXPERIMENTS = 1;
const EXPERIMENT_NAME = "eas... | {"hexsha": "7f40989396c73dcc4571f0a1b6f5b8aa1b856714", "size": 7342, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/partition.jl", "max_stars_repo_name": "OndrejKincl/evaTeaching-python", "max_stars_repo_head_hexsha": "76e33e7928bfd3c1e336ea3d3f3a9f6487c7bdfd", "max_stars_repo_licenses": ["MIT"], "max_star... |
spd = matrixdepot("symmetric", "pos-def")
data = matrixdepot("data")
| {"hexsha": "84786a23296ff6eb3ac531a968b053f7a495930c", "size": 71, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/property.jl", "max_stars_repo_name": "JuliaPackageMirrors/MatrixDepot.jl", "max_stars_repo_head_hexsha": "86b9c9ce3ad7bf0ea8f282624696c9174c157bcc", "max_stars_repo_licenses": ["MIT"], "max_star... |
\documentclass[../main.tex]{subfiles}
\begin{document}
\chapter{Iteration and Recursion}
\label{chapter_iteration_recursion}
\begin{chapquote}
{Niklaus Wirth, \textit{Algorithms + Data Structures = Programs, 1976}}
``The power of recursion evidently lies in the possibility of defining an infinite set of objects by a fi... | {"hexsha": "790fd8c946f4d7f136d27e9687d8c4a6090f95f6", "size": 19989, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Easy-Book/chapters/chapter_3_iteration_recursion.tex", "max_stars_repo_name": "stungkit/Algorithms-and-Coding-Interviews", "max_stars_repo_head_hexsha": "131199fea0b082d92c0f272a495c7a56a3242b71", ... |
"""
Plots metrics that assess quality of single units. Some functions here generate plots for the
output of functions in the brainbox `single_units.py` module.
Run the following to set-up the workspace to run the docstring examples:
>>> from brainbox import processing
>>> import alf.io as aio
>>> import numpy as np
>>... | {"hexsha": "e6a18fd7dff4e745fe6cc254eabfaf1fb9de9929", "size": 33854, "ext": "py", "lang": "Python", "max_stars_repo_path": "brainbox/plot.py", "max_stars_repo_name": "nbonacchi/ibllib", "max_stars_repo_head_hexsha": "9066c00a8e9a65a1d209144a2ac54d0b87bec0b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import os
import argparse
import numpy as np
from six.moves import cPickle
import hickle
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = [8.0, 6.0]
mpl.rcParams['font.size'] = 20
mpl.rcParams['xtick.labelsize'] = 16
mp... | {"hexsha": "2d1ac2207e913f114a7d34ed5d559b94994c8ad9", "size": 4217, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot_prediction.py", "max_stars_repo_name": "ukky17/prednet", "max_stars_repo_head_hexsha": "962f278b14c3e66f8583e33e6ad116ebbcd8bb40", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: Isa Restrepo
# @Date: 2014-05-01 16:00:07
# @Last Modified by: Isa Restrepo
# @Last Modified time: 2015-03-14 22:40:14
import numpy as np
from scipy.signal import butter, lfilter
from scipy import interpolate, fftpack
# from sklearn import preprocessing
... | {"hexsha": "55391b2aa7c61f8f3dde949e99216a3c181068db", "size": 13266, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/signal_process_util.py", "max_stars_repo_name": "brown-ccv/pulsedetector", "max_stars_repo_head_hexsha": "37a23ea932e0846886e433e11a79a01836b7a7cc", "max_stars_repo_licenses": ["Apache-2.0"],... |
[STATEMENT]
lemma dg_1_is_arrD:
assumes "f : a \<mapsto>\<^bsub>dg_1 \<aa> \<ff>\<^esub> b"
shows "a = \<aa>" and "b = \<aa>" and "f = \<ff>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. a = \<aa> &&& b = \<aa> &&& f = \<ff>
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
f : a \<mapsto>\<^bsu... | {"llama_tokens": 227, "file": "CZH_Foundations_czh_digraphs_CZH_DG_Simple", "length": 2} |
/////////1/////////2/////////3/////////4/////////5/////////6/////////7/////////8
// Name :
// Author : Avi
// Revision : $Revision: #29 $
//
// Copyright 2009- ECMWF.
// This software is licensed under the terms of the Apache Licence version 2.0
// which can be obtained at http://www.apache.org/license... | {"hexsha": "6af5623524702eda24097ee2657b559e7513b128", "size": 12357, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Base/src/cts/GroupCTSCmd.cpp", "max_stars_repo_name": "mpartio/ecflow", "max_stars_repo_head_hexsha": "ea4b89399d1e7b897ff48c59b1e885e6d53cc8d6", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
from keras.datasets import mnist
import os
os.chdir("d:/data")
from keras import models # model unit
from keras import layers # layer
from keras.utils import to_categorical # one-hot encoder
import numpy as np
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
network = models.Sequential() #... | {"hexsha": "a2e89f3008bbe255f6b67d49196b82f1db549311", "size": 1167, "ext": "py", "lang": "Python", "max_stars_repo_path": "lec2-1.py", "max_stars_repo_name": "cutz-j/keras", "max_stars_repo_head_hexsha": "a2a567861fd52c4c21522151d45af2bb2ee21893", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars... |
import numpy as np
from trainLinearReg import trainLinearReg
from linearRegCost import linearRegCostFunction
# TODO
# Input: training set (X, y) & Validation set (Xval, yval)
# Output: Lambda set corresponding with train and validation error
def validation_curve(X, y, Xvali, yvali):
# Selected set of lam... | {"hexsha": "f569b77116bf019a116657d13656a68051820171", "size": 975, "ext": "py", "lang": "Python", "max_stars_repo_path": "(Ex5)_Upgrade_Neural_network_model/validationCurve.py", "max_stars_repo_name": "HarryPham0123/Coursera_Machine_learning_AndrewNg", "max_stars_repo_head_hexsha": "ae1fa34969fa0dafd44aa6606f6749c09b4... |
using QuantumWalk
using LightGraphs
importall QuantumWalk
##
abstract type AbstractStochastic <: QWModelDiscr end
struct UniformStochastic{G<:SimpleGraph} <: AbstractStochastic
graph::G
end
UniformScaling(digraph::G) where G= UniformStochastic{G}(digraph)
function check_qwdynamics(::Type{QWSearch},
... | {"hexsha": "74b86fba3e169e0bc35b3a207c6cb902d5f6a2a9", "size": 2247, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/example_code/qwsearch_example.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/QuantumWalk.jl-715ab00d-497d-5f5e-92b0-16e551a8d81b", "max_stars_repo_head_hexsha": "710e18f04bf55fe10... |
import sys, os
import subprocess
repo_dir = subprocess.Popen(['git', 'rev-parse', '--show-toplevel'], stdout=subprocess.PIPE).communicate()[0].rstrip()
#base = os.path.join(repo_dir, "comp_astro_evan", "refactored")
#libs = os.path.join(repo_dir, base, "libs")
base = os.path.join(repo_dir)
libs = os.path.join(repo_di... | {"hexsha": "515141db592719060418db79fb90b86262355627", "size": 2603, "ext": "py", "lang": "Python", "max_stars_repo_path": "input_data.py", "max_stars_repo_name": "FSUcilab/Compartmental_model_astrocytes", "max_stars_repo_head_hexsha": "b97139eea2570594269b08cf6033f135c42fdc94", "max_stars_repo_licenses": ["MIT"], "max... |
from vector_calculus.containers import Tensor
from sympy import symbols, S
from numpy import eye, array
import unittest
class TestTensor(unittest.TestCase):
'''UnitTest of Tensor class.'''
def test_len(self):
for i in range(2, 4):
self.assertEqual(len(Tensor(eye(i))), i)
def test_add... | {"hexsha": "f80c8e46e5fcf274324b34645364270ed78cb423", "size": 1425, "ext": "py", "lang": "Python", "max_stars_repo_path": "vector_calculus/tests/test_tensor.py", "max_stars_repo_name": "MiroK/vector_calculus", "max_stars_repo_head_hexsha": "29351b22da84e629fd03aa7bec4e74c726bf963a", "max_stars_repo_licenses": ["MIT"],... |
# Turbojet_Super.py
#
# Created: May 2015, Tim MacDonald
# Modified:
# ----------------------------------------------------------------------
# Imports
# ----------------------------------------------------------------------
# suave imports
import SUAVE
# package imports
import numpy as np
import scipy as sp
im... | {"hexsha": "6a2023517e634a46a40a92fa1915242e0d51a91a", "size": 14259, "ext": "py", "lang": "Python", "max_stars_repo_path": "References/Geovana Neves/TCC_Geovana_Neves_GitHub/SUAVE_modifications/SUAVE-feature-constant_throttle_EAS/trunk/SUAVE/Components/Energy/Networks/Turbojet_Super.py", "max_stars_repo_name": "Vinici... |
import numpy as np
import functools as ft
from collections import Counter
with open("input6.txt") as f:
lines = f.read().replace("\n","")
lines=np.fromstring(lines,dtype="int",sep=",")
lines=dict(Counter(lines))
def add_zero(dic):
dic2={}
for i in range(0,9):
if i not in dic:
dic2[i]=0
else:
di... | {"hexsha": "107ea6b9bb9a4cbbed19bd635bb9fe8948f97472", "size": 533, "ext": "py", "lang": "Python", "max_stars_repo_path": "d6.py", "max_stars_repo_name": "Prithiviksit/adcentofcode2021", "max_stars_repo_head_hexsha": "40f17dade63196f80407703131a8d1f0b924ad3a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import matplotlib.pyplot as plt
import sympy
#
def plot_one_digit_freqs(f1):
"""
Plot one digit frequency counts using matplotlib.
"""
ax = plt.plot(f1,'bo-')
plt.title('Single digit counts in pi')
plt.xlabel('Digit')
plt.ylabel('Count')
return ax
#
def one_digit_freqs(digits, normalize=... | {"hexsha": "04399a8d878244bf4417412adc99abf0a8f3187f", "size": 749, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter12/c12_12_pi_01.py", "max_stars_repo_name": "andrewjcoxon/Hands-On-Data-Science-with-Anaconda", "max_stars_repo_head_hexsha": "82504a059ecd284b3599fa9af2b3eb6bbd6e28f3", "max_stars_repo_lice... |
import pandas as pd
import numpy as np
from trading_gym.envs.portfolio_gym.portfolio_gym import PortfolioTradingGym
np.random.seed(64)
def create_mock_data(order_book_ids, start_date="2019-01-01", end_date="2022-01-02", number_feature=3):
trading_dates = pd.date_range(start=start_date, end=end_date, freq="D")
... | {"hexsha": "9d75d7cbf830fdc41f985cb0e6f327e5e0b3e4b2", "size": 2357, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_envs/test_mul_realnumber.py", "max_stars_repo_name": "zhaoshiying97/trading_gym", "max_stars_repo_head_hexsha": "d4af8d724efa17420e6ebb430f6f9d4f08c6f83a", "max_stars_repo_licenses": ["A... |
'''
Created on Dec 3, 2017
@author: halil
'''
'''
see: http://www.astroml.org/book_figures/chapter3/fig_bivariate_gaussian.html
'''
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.patches import Ellipse
from astroML.stats.random import bivariate_normal
from astroML.plotting import setup_tex... | {"hexsha": "b96fc34eacc99e639f3d2eba81f2f006ca42c50d", "size": 2366, "ext": "py", "lang": "Python", "max_stars_repo_path": "second-round-intreview/parcoord-brushing/backend/src/paper2declutter/MultivariateNormalPlot.py", "max_stars_repo_name": "halilagin/parcoord-brushing", "max_stars_repo_head_hexsha": "71dde2d9b24038... |
""" Astropy coordinate class for the Palomar 5 stream coordinate system """
# Third-party
import numpy as np
import astropy.units as u
import astropy.coordinates as coord
from astropy.coordinates import frame_transform_graph
from astropy.coordinates.matrix_utilities import matrix_transpose
__all__ = ["Pal5PriceWhela... | {"hexsha": "c131faad5f0ff8f6c3523b9db1991ed411dedd53", "size": 4737, "ext": "py", "lang": "Python", "max_stars_repo_path": "gala/coordinates/pal5.py", "max_stars_repo_name": "zilishen/gala", "max_stars_repo_head_hexsha": "f7184e6b09fbc42a349f6b5a2bca6242f1e9936e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import sys
from argparse import ArgumentParser
from sympy import diff, dsolve, integrate, solve, var
# TODO: fix it
sys.path.append("./")
from calculus_of_variations.abstract_problem import AbstractSolver
from calculus_of_variations.utils import ( # noqa: F401
sympy_eval,
t,
x,
x_diff,
x_diff_2,
... | {"hexsha": "219508b58daf585444e8cec9be3844e72af9313e", "size": 6009, "ext": "py", "lang": "Python", "max_stars_repo_path": "calculus_of_variations/higher_derivatives_problem.py", "max_stars_repo_name": "dayyass/calculus_of_variations", "max_stars_repo_head_hexsha": "d016c4824bfb5595569b156fd38f2a841c92d3ec", "max_stars... |
#!usr/bin/env python
import matplotlib.animation
import matplotlib.pyplot as plt
import numpy as np
import sys
if len(sys.argv) < 2 or sys.argv[1] not in ('python', 'matlab'):
print("Usage: python %s ( python | matlab ) [savename.gif] " % sys.argv[0])
sys.exit(1)
order_type = sys.argv[1]
fig, axes = plt.subp... | {"hexsha": "afbc2b7ced03559bd14a975bf999f1a4101246a2", "size": 1608, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/index_order.py", "max_stars_repo_name": "scottstanie/scottstanie.github.io", "max_stars_repo_head_hexsha": "69863e119e996cf939b420a973d5153e0baf3b7a", "max_stars_repo_licenses": ["MIT"], "... |
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 10 18:05:33 2019
@author: RGB
"""
import numpy as np
import keras
from keras.layers import Dense, Dropout, Flatten
from keras.applications import VGG16
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model, load_model
from k... | {"hexsha": "dfe6767cdd0050de64e29c29c6498a9a4cd8fbd3", "size": 5353, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot.py", "max_stars_repo_name": "dhruvin88/Masters_Project", "max_stars_repo_head_hexsha": "802a66cd4bb9f5a62e96f3698987fe9af88798fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "m... |
# Copyright (c) 2017-2020 Sony Corporation. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | {"hexsha": "044cf68de32fc09cfb79ddfd4755bd118ace19cf", "size": 5140, "ext": "py", "lang": "Python", "max_stars_repo_path": "shape-reconstruction/implicit-geometric-regularization/visualize.py", "max_stars_repo_name": "shikisawamura/nnabla-examples", "max_stars_repo_head_hexsha": "baf4e4cc620dedbf4368683325c0fb868676850... |
#include <stdio.h>
#include <bitset>
#include <stdlib.h>
#include <unistd.h>
#include <string.h>
#include <iostream>
#include <net/if.h>
#include <sys/types.h>
#include <sys/socket.h>
#include <sys/ioctl.h>
#include <linux/can.h>
#include <linux/can/raw.h>
#include <boost/asio.hpp>
#include <boost/bind.hpp>
#includ... | {"hexsha": "a0fd61cccc4ee8e1f78fceb5aa1a9257da005bae", "size": 3851, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/test_moteus_query.cpp", "max_stars_repo_name": "slovak194/pibot", "max_stars_repo_head_hexsha": "a58ce60f0bfcfbb0fdd66c95ed8ffdaa5549318a", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import threading
import random
import numpy as np
import time
import argparse
from skimage.transform import resize
from skimage.color import rgb2gray
from collections import deque
import matplotlib.pyplot as ... | {"hexsha": "234ad07602d4f7df7af3cb5b0ffa8cc4eb086919", "size": 12504, "ext": "py", "lang": "Python", "max_stars_repo_path": "drqn/eval.py", "max_stars_repo_name": "neale/A4C", "max_stars_repo_head_hexsha": "acbbb3cf14e31a19c12f27306971b4db4feafe09", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_r... |
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