text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy as np
from collections import deque
import layers
class Point(object):
__array_priority__ = 1000
def __init__(self, *coordinates):
if len(coordinates) == 1:
if type(coordinates) == np.array:
self.coordinates = coordinates[0]
return
self... | {"hexsha": "585f3fa30641e982282350f9fad3830b46badab8", "size": 16288, "ext": "py", "lang": "Python", "max_stars_repo_path": "geometry.py", "max_stars_repo_name": "SymJAX/DeeSect", "max_stars_repo_head_hexsha": "93636ec495e391b06dc304704d5671da488bf75c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null... |
%
% perf.tex - the final performance report which includes the rest
% of the data.
%
% Copyright (c) 1998 Phil Maker <pjm@gnu.org>
% 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. Redistr... | {"hexsha": "0b876803dbc0b41cb89ff2281eca474918e511ca", "size": 5458, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "perf/perf.tex", "max_stars_repo_name": "pjmaker/nana", "max_stars_repo_head_hexsha": "6d70617db8b9972e6c1008265fc228aba91c2042", "max_stars_repo_licenses": ["BSD-2-Clause", "BSD-3-Clause"], "max_sta... |
module DATools
mutable struct FixedSizeBinaryMaxHeap
heap::Array{UInt16}
ind::UInt16
FixedSizeBinaryMaxHeap(m_max::Int) = new(Array{UInt16}(m_max), 0)
end
function Base.length(bmh::FixedSizeBinaryMaxHeap)
return bmh.ind
end
function Base.push!{T <: Integer}(bmh::Fi... | {"hexsha": "f124f07ba5392d2ba93e4bba2a6a45a44d07c5d8", "size": 1909, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/tools.jl", "max_stars_repo_name": "nswa17/DA_alg.jl", "max_stars_repo_head_hexsha": "0e5ff1765b1a175cb529d786ba17029282e586ee", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": n... |
import cupy
# Collection of activation functions
# Reference: https://en.wikipedia.org/wiki/Activation_function
class Sigmoid():
def __call__(self, x):
return 1 / (1 + cupy.exp(-x))
def gradient(self, x):
return self.__call__(x) * (1 - self.__call__(x))
class Softmax():
def __call__(sel... | {"hexsha": "fc9bccff41713083f11b5e069019cf2e5b1c82b2", "size": 2025, "ext": "py", "lang": "Python", "max_stars_repo_path": "MLCtr/graduateutil/graduateutil/activation_functions.py", "max_stars_repo_name": "devillove084/CollageDesign", "max_stars_repo_head_hexsha": "e2a85a8d15f82d1f72b754de04af78126eae9a1c", "max_stars_... |
/**
* Copyright (c) 2013, Akamai Technologies
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* Redistributions of source code must retain the above copyright
* notice, this lis... | {"hexsha": "ec39de0c0f973e841df7ccf8b5f65428e7683a29", "size": 18834, "ext": "cc", "lang": "C++", "max_stars_repo_path": "ads-df/test/http-operator-test.cc", "max_stars_repo_name": "lairofthegoldinblair/trecul", "max_stars_repo_head_hexsha": "41953c22f18f76e5add7a35a13775f70459fcd96", "max_stars_repo_licenses": ["BSD-3... |
# coding=utf-8
# Author: Kay Hartmann <kg.hartma@gmail.com>
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.autograd import Variable
from eeggan.pytorch.modules.module import Module
def exponential_sum(x, n):
nom = 1 - np.exp(1j * n * x)
denom = 1 - np.exp(1... | {"hexsha": "3fb112c6e6d23241853d74c460aa9e18aef3f8f4", "size": 3852, "ext": "py", "lang": "Python", "max_stars_repo_path": "eeggan/pytorch/modules/scaling/filtering.py", "max_stars_repo_name": "kahartma/eeggan", "max_stars_repo_head_hexsha": "1fd5b45938ea6f1033f301430a5c7fb3b9bf4fb4", "max_stars_repo_licenses": ["BSD-3... |
MoSS <- function(n, alpha, m){
p.score <- runif(n*alpha)**m
n.score <- 1 - runif( round(n*(1-alpha), digits=0) )**m
scores <- cbind(c(p.score, n.score),c(rep(1,length(p.score)), rep(2,length(n.score))))
scores <- cbind(scores[,1], scores[,1], scores[,2])
return(scores)
} | {"hexsha": "9ccae422d9df4072ae91e89ef7d25e5b1cd9be8a", "size": 289, "ext": "r", "lang": "R", "max_stars_repo_path": "proposals/MoSS.r", "max_stars_repo_name": "andregustavom/icdm21_paper", "max_stars_repo_head_hexsha": "ee4f5247ae6574ab69f5a29134846d50d9e305b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
// ====================================================================
// This file is part of FlexibleSUSY.
//
// FlexibleSUSY 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 Licens... | {"hexsha": "51f4610f99ae21e6a0cde0504102d46b2f42c3dc", "size": 2863, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "contrib/MassSpectra/flexiblesusy/src/gsl_utils.hpp", "max_stars_repo_name": "sebhoof/gambit_1.5", "max_stars_repo_head_hexsha": "f9a3f788e3331067c555ae1a030420e903c6fdcd", "max_stars_repo_licenses":... |
module sindy
using LinearAlgebra
# one needs to create a library matrix
# solve the least squares problem
# resolve the least squares problem for the relevant contributing indices/variables
struct LinsolverOptions
singular_value_tol::Float64
end
function LinsolverOptions()
return Linsolve... | {"hexsha": "3fc89c630a20a8bf9b9599bc0339fb3e37251c47", "size": 10614, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "sindy/src/sindy_module.jl", "max_stars_repo_name": "HomoModelicus/julia", "max_stars_repo_head_hexsha": "26be81348032ccd2728046193ce627c823a3804b", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from flask import Flask, jsonify, request
from datetime import datetime
from pymodm import connect, MongoModel, fields
from LogIn import LogIn
from UserData import UserData
from UserMetrics import UserMetrics
import base64
import json
from skimage import util, exposure, io, color
from bson.binary import Binary
import p... | {"hexsha": "9fafae5c66fc60cc55abee491d45fa834fb54197", "size": 28906, "ext": "py", "lang": "Python", "max_stars_repo_path": "server.py", "max_stars_repo_name": "cdong223/bme547-final-project", "max_stars_repo_head_hexsha": "67ab2d9dd704c82c56b19866e50e2178ac036e04", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
import matplotlib.pyplot as plt
import threading
import time
import pickle
from .scale import Scale
from .audiogenerator import Audiogenerator
from .midiprocessing import Midiprocessing
from .audioanalyzer import Audioanalyzer
from .dissonancereduction import Dissonancereduction
def plot_session_lo... | {"hexsha": "c0238e874d03713a766c770405a92f8b0ea33f81", "size": 23474, "ext": "py", "lang": "Python", "max_stars_repo_path": "adaptivetuning/tuner.py", "max_stars_repo_name": "ArneKramerSunderbrink/adaptivetuning", "max_stars_repo_head_hexsha": "80dce0c8d031918a9d45dc84fdd6cd64f6df7a8a", "max_stars_repo_licenses": ["MIT... |
from scipy.stats.mstats import winsorize
import numpy as np
def winsorise(x: np.ndarray,
lower_limit: float = 0.1,
upper_limit: float = 0.1):
""" Winsorisation
:param x: a numeric sequence
:param lower_limit: float
:param upper_limit: float
:return: np.ndarray
"""
... | {"hexsha": "95b16b5fe316a5809c053c485e9b1005f298b7fe", "size": 452, "ext": "py", "lang": "Python", "max_stars_repo_path": "vest/transformations/winsorisation.py", "max_stars_repo_name": "vcerqueira/vest-python", "max_stars_repo_head_hexsha": "146e1ee50463637c89e32112283cf857e2eb190a", "max_stars_repo_licenses": ["MIT"]... |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 27 09:08:06 2020
@author: ZeeshanNisar
"""
from keras.preprocessing.image import load_img, img_to_array
from tqdm import tqdm as tqdm
import os
import numpy as np
img_rows = 256
img_cols = 256
channels = 1
os.chdir('/content/drive/My Drive/GitHub Repositories')
baseDir... | {"hexsha": "0620c79c0851b8693e65e02269f116bc63e80da5", "size": 1286, "ext": "py", "lang": "Python", "max_stars_repo_path": "Cascaded Model/BRATS Data/implementation/Save BRATS data to Numpy Files.py", "max_stars_repo_name": "zeeshannisar/Reseacrh-Paper-Contribution", "max_stars_repo_head_hexsha": "1e01bfd3c20111257b8ee... |
#include <boost/spirit/home/classic/utility/chset_operators.hpp>
| {"hexsha": "790c948bb07a43e4de09699420eddc9d1cea8d9a", "size": 65, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_spirit_home_classic_utility_chset_operators.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_rep... |
#include "TcpAcceptor.h"
#include <boost/bind.hpp>
TcpAcceptor::TcpAcceptor(boost::asio::io_service &io_service,
TcpAcceptorCallback &callback) :
NetworkAcceptor(io_service),
m_callback(callback)
{
}
void TcpAcceptor::start_accept()
{
tcp::socket *socket = new tcp::socket(m_io_ser... | {"hexsha": "439a9f8f18a25910114c7227b36f3d818a25e54e", "size": 1043, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/net/TcpAcceptor.cpp", "max_stars_repo_name": "Hawkheart/Astron", "max_stars_repo_head_hexsha": "3a15606ab15b63b666fdff1e0145417470232dbc", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
//==============================================================================
// Copyright 2003 - 2011 LASMEA UMR 6602 CNRS/Univ. Clermont II
// Copyright 2009 - 2011 LRI UMR 8623 CNRS/Univ Paris Sud XI
//
// Distributed under the Boost Software License, Version 1.0.
// Se... | {"hexsha": "90109f3ca7e126d3dc7bdb752979a23ceb454bd0", "size": 988, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "modules/boost/simd/sdk/include/boost/simd/sdk/simd/pack/meta/cardinal_of.hpp", "max_stars_repo_name": "pbrunet/nt2", "max_stars_repo_head_hexsha": "2aeca0f6a315725b335efd5d9dc95d72e10a7fb7", "max_sta... |
\documentstyle[11pt]{article}
\newcommand{\Cpp}{C\protect\raisebox{.18ex}{++}}
\title{
Interactively Testing Remote Servers Using the Python Programming Language
}
\author{
Guido van Rossum \\
Dept. AA, CWI, P.O. Box 94079 \\
1090 GB Amsterdam, The Netherlands \\
E-mail: {\tt guido@cwi.nl}
\and
Jelke de Boer \\
... | {"hexsha": "88f5778fa5a12ccdeeb1a993987f91c63901f428", "size": 59914, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Doc/qua.tex", "max_stars_repo_name": "AtjonTV/Python-1.4", "max_stars_repo_head_hexsha": "2a80562c5a163490f444181cb75ca1b3089759ec", "max_stars_repo_licenses": ["Unlicense", "TCL", "DOC", "AAL", "X... |
export DSOSPoly, SDSOSPoly, SOSPoly
function PolyJuMP.bridges(::Type{<:PositiveSemidefinite2x2ConeTriangle})
return [Bridges.Variable.PositiveSemidefinite2x2Bridge]
end
function PolyJuMP.bridges(::Type{<:ScaledDiagonallyDominantConeTriangle})
return [Bridges.Variable.ScaledDiagonallyDominantBridge]
end
functio... | {"hexsha": "de402c17e7936be79c2482fe0657052cbfb8321f", "size": 3320, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/variable.jl", "max_stars_repo_name": "ericphanson/SumOfSquares.jl", "max_stars_repo_head_hexsha": "3f524f2f8dcf22a9e1c4e0ea549e7166736faeac", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
function [ X Y ] = selPts
%[ X Y ] = selPts
% manually select several points in current figure
% selection is stopped when ESC is pressed
X = []; Y = [];
ctrlLen = 0;
while 1
[X1 Y1] = ginput(1);
if isempty(X1), break; end
X = [X;X1];
Y = [Y;Y1];
plot(X1,Y1,'bo');
ctrlLen = ctrlLen+1;
% text(X1+3,Y1,num2str(c... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/30822-lucas-kanade-tracker-with-pyramid-and-... |
import numpy as np
from scipy.integrate import trapz
from scipy.interpolate import InterpolatedUnivariateSpline, interp1d
"""
Physics constants and utility functions.
"""
# Masses (MeV)
higgs_mass = 125.1e3
electron_mass = 0.510998928 # electron
muon_mass = 105.6583715 # muon
neutral_pion_mass = 134.9766 # neutral... | {"hexsha": "b6af0cc43893931ec09e66205e40aa84f8f5a0df", "size": 6730, "ext": "py", "lang": "Python", "max_stars_repo_path": "hazma/parameters.py", "max_stars_repo_name": "LoganAMorrison/Hazma", "max_stars_repo_head_hexsha": "e9612729767ff48d5ce50633393f81ee021242d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#pragma once
#include <boost/functional/hash.hpp>
#include <functional>
#include <tuple>
namespace std {
template <typename... TTypes>
class hash<std::tuple<TTypes...>>
{
private:
typedef std::tuple<TTypes...> Tuple;
template <int N>
size_t operator()(const Tuple& value __attribute__((unused))) const
{
... | {"hexsha": "95d74dd269eebc716f58f6a31173fd2b4da24f93", "size": 783, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/aux/hash_specializations.hpp", "max_stars_repo_name": "simonpintarelli/2dBoltzmann", "max_stars_repo_head_hexsha": "bc6b7bbeffa242ce80937947444383b416ba3fc9", "max_stars_repo_licenses": ["BSD-3-C... |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | {"hexsha": "be606f836c5851ca4e1ed7e2aa6f2ab5129d9b66", "size": 48470, "ext": "py", "lang": "Python", "max_stars_repo_path": "ocw/plotter.py", "max_stars_repo_name": "Peter-Gibson/climate", "max_stars_repo_head_hexsha": "513dcc438d20bc987f6291497bac89727d01c184", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
from __future__ import print_function
import tensorflow as tf
import numpy as np
import TensorflowUtils as utils
#########################Load Weigths function############################################################
def loadWeights(i,weights,LayerName):
kernels, bias = weights[i][0][0][0][0]
# matconvnet:... | {"hexsha": "27727f69b4597052d8cf175ab5823b321c25fad5", "size": 22925, "ext": "py", "lang": "Python", "max_stars_repo_path": "Build_Net.py", "max_stars_repo_name": "sagieppel/Reconstruct-image-from-sparsely-sampled-pixels-using-fully-convolutional-neural-network-FCN-with-v", "max_stars_repo_head_hexsha": "679b6ab16eb8ba... |
#!/usr/bin/env python
import os
import sys
import gzip
from scipy.spatial import *
import bisect
import math
import numpy as np
try:
from scripts import plot_read_depth
except ImportError:
import plot_read_depth
try:
from scripts import my_utils
except ImportError:
import my_utils
try:
from scrip... | {"hexsha": "073b30519848c78b7db47580cb6bd616539922c2", "size": 19347, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/filter_calls.py", "max_stars_repo_name": "kskuchin/LinkedSV_debug", "max_stars_repo_head_hexsha": "c912d193ca4490581735c005a7fea1b7ec62c612", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env julia
fb = open("/dev/fb0", "w")
width = 1280
height = 1024
nframes = 0
nsec = 10
frame = zeros(UInt32, width, height)
t0 = time()
while time() - t0 < nsec
frame[:] = 0x00000000 + nframes
seekstart(fb)
write(fb, frame)
#sleep(0.1)
nframes += 1
end
fps = nframes / nsec
@show fps
| {"hexsha": "b243eb4b12ad37663f1de9c7de30005917da8ab9", "size": 316, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "pixel.jl", "max_stars_repo_name": "vtjnash/orange-winner", "max_stars_repo_head_hexsha": "ec4a83de0c38ca8f56cf9230d46fc68ecd877515", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_st... |
export build_tarballs, autobuild, print_buildjl, product_hashes_from_github_release, build
import GitHub: gh_get_json, DEFAULT_API
import SHA: sha256
"""
build_tarballs(ARGS, src_name, src_version, sources, script, platforms,
products, dependencies; kwargs...)
This should be the top-level funct... | {"hexsha": "9c9ce2ad1b48fcb905773337bf311ad9e212956a", "size": 29060, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/AutoBuild.jl", "max_stars_repo_name": "iblis17/BinaryBuilder.jl", "max_stars_repo_head_hexsha": "8164b23768ef2a82a0a957c28a97e65674e8a04f", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
Big Bang problem in cpmpy.
This a port of a OR-tools CP-Solver model ported from
a MiniZinc model based on a Comet model by Thore Graepel
(which was based on a Comet model of mine):
'''
Nontransitive dice a la The Big Bang Theory in Comet
Thore Graepel (thoregraepel@googlemail.com)
The idea is to create a set of... | {"hexsha": "5a76cac1ada8343bacea354a9f8622677ec0a8b5", "size": 4036, "ext": "py", "lang": "Python", "max_stars_repo_path": "cpmpy/big_bang2.py", "max_stars_repo_name": "tias/hakank", "max_stars_repo_head_hexsha": "87b7f180c9393afce440864eb9e5fb119bdec1a4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
import itertools
import logging
import numpy
from implicit.als import AlternatingLeastSquares
from implicit.approximate_als import augment_inner_product_matrix
log = logging.getLogger("implicit")
class AnnoyALSWrapper:
"""A wrapper of the :class:`~implicit.als.AlternatingLeastSquares` that uses an
`Annoy <h... | {"hexsha": "fb678e2b45b585e151b70bd1aec13a863e114e8d", "size": 5297, "ext": "py", "lang": "Python", "max_stars_repo_path": "implicit/annoy_als.py", "max_stars_repo_name": "redbubble/implicit", "max_stars_repo_head_hexsha": "fe85f79f8b547a75e42186bf5357ad2f395366a4", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# The MIT License (MIT)
#
# Copyright (c) 2014 Johannes Schlatow
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy,... | {"hexsha": "f2807c73f4d8bd0e75c83bea79bfe11aa05cd04f", "size": 11042, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyplot_helper/barchart.py", "max_stars_repo_name": "ValiValpas/pyplot_helper", "max_stars_repo_head_hexsha": "87001140ec46eca741e72701e72d40a6c4223714", "max_stars_repo_licenses": ["MIT"], "max_s... |
'''Example model specification for SIR over a scale-free network.
@author: Joe Schaul <joe.schaul@gmail.com>
'''
import networkx as nx
from ComplexNetworkSim import NetworkSimulation, AnimationCreator, PlotCreator
from agent_SIR import INFECTED, RECOVERED, SUSCEPTIBLE
from agent_SIR import SIRSimple as agent... | {"hexsha": "88712d1a8f23611105e86482e0bf0fbd70067329", "size": 2038, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/SIR_model/model_scale_free.py", "max_stars_repo_name": "Juliet-Chunli/cnss", "max_stars_repo_head_hexsha": "534c7e0b0338e831ec20b5002d1fdf1cc0879a2c", "max_stars_repo_licenses": ["BSD-2-C... |
"""
test the polynomial expansion
"""
import pytest
import numpy as np
from gsMk import PCE
from gsMk.GSA.train_construct import build_xy
def test_poly_expand1():
pce = PCE(nvar=2, nord=2)
orders = pce.order_list
x = [[0, 0],
[1, 2]]
x = np.array(x)
xexpand = [[1, 0, 0, 0, 0, 0],
... | {"hexsha": "db296f7657d4d7f37f70acfccfc4b15d351594f8", "size": 1615, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_expansion.py", "max_stars_repo_name": "thj2009/gsMk", "max_stars_repo_head_hexsha": "bae3da556afd976c12bbf7cfbbbc8f913e4fbb6d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
\section{Research Interests}
\begin{itemize}[noitemsep,nolistsep]
\item Scalable compiler directed workload analysis
\item Hardware software co-design for specialized architectures
\item Core micro-architecture with a focus on the cache memory hierarchy
\end{itemize}
| {"hexsha": "82541ab622e56b915950ca93ab90bfa7982d35c8", "size": 271, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/common/01.tex", "max_stars_repo_name": "snehasish/cv", "max_stars_repo_head_hexsha": "2537e87b8186846b59e2066422de9baa6c369de0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_sta... |
module TMGlib
# wrap some routines from the LAPACK test matrix generator library
export latme!, latmr!
using LinearAlgebra
import LinearAlgebra.BlasInt
import LinearAlgebra.BLAS.@blasfunc
# CHECKME: routines linked here are included in many BLAS/LAPACK distributions
# (OpenBLAS, MKL), but do we need to allow for othe... | {"hexsha": "5bde86087d35240965a8729678fc7859cb4e9344", "size": 14415, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/TMGlib.jl", "max_stars_repo_name": "alfredjmduncan/GenericSchur.jl", "max_stars_repo_head_hexsha": "1f897e5f9796ca7c9d5a384fdae4953daf3d4462", "max_stars_repo_licenses": ["BSD-3-Clause-Open-M... |
[STATEMENT]
lemma ucast_ucast_len:
"\<lbrakk> x < 2 ^ LENGTH('b) \<rbrakk> \<Longrightarrow> ucast (ucast x::'b::len word) = (x::'a::len word)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x < 2 ^ LENGTH('b) \<Longrightarrow> ucast (ucast x) = x
[PROOF STEP]
apply (subst ucast_ucast_mask)
[PROOF STATE]
proof (pr... | {"llama_tokens": 234, "file": "Word_Lib_More_Word", "length": 3} |
# Copyright 2021 The TensorFlow Authors. 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 applica... | {"hexsha": "f01667ae84e31e98431148da0f8b59054275deb8", "size": 1836, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/compiler/mlir/tfrt/python_tests/tf_broadcast_to_test.py", "max_stars_repo_name": "wainshine/tensorflow", "max_stars_repo_head_hexsha": "dc7a8dc8546c679b9c7b3df7494ce4506bfc1a6d", "max_s... |
// Stéphane Adam Garnier - 2012
// include Cinder lib(s)
#include "cinder/app/AppCocoaTouch.h"
#include "cinder/app/Renderer.h"
#include "cinder/Surface.h"
#include "cinder/gl/Texture.h"
#include "cinder/Camera.h"
#include "cinder/thread.h"
// include standard lib(s)
#include <string>
#include <cstring>
#include <io... | {"hexsha": "db9e7234e001787e069fe8358d25b0d779648d8b", "size": 2706, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "MixedCinderTouchProject/src/MixedCinderTouchProjectApp.cpp", "max_stars_repo_name": "stephaneAG/Cinder_test", "max_stars_repo_head_hexsha": "7453be4dcb40bfc772be63d30ec43d8485584290", "max_stars_rep... |
import numpy as np
from sklearn.utils.validation import (check_array, check_consistent_length,
check_is_fitted, column_or_1d)
from confounds.base import BaseDeconfound
class ComBat(BaseDeconfound):
"""ComBat method to remove batch effects."""
def __init__(self,
... | {"hexsha": "a91ec85ba6c83439c858c5ec70764bb6d41f1d28", "size": 13296, "ext": "py", "lang": "Python", "max_stars_repo_path": "confounds/combat.py", "max_stars_repo_name": "vishalbelsare/confounds", "max_stars_repo_head_hexsha": "c4d0f70dd4b66279086fd6cb685b6f112981730c", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
from zappy.LF_elements.bus import ACbus as Bus
from zappy.LF_elements.line import ACline as Line
from zappy.LF_elements.generator import ACgenerator as Generator
from zappy.LF_elements.load import ACload as Load
from openmdao.api import Group, IndepVarComp
from openmdao.api import DirectSolver, BoundsEnforceLS, Newton... | {"hexsha": "2ffde164a1e01b036400064f72c326c19617cfc1", "size": 9472, "ext": "py", "lang": "Python", "max_stars_repo_path": "zappy/LF_examples/load_flow_example1.py", "max_stars_repo_name": "OpenMDAO/zappy", "max_stars_repo_head_hexsha": "2c72048b4c4e0ce0ae83221e4ee5788978254340", "max_stars_repo_licenses": ["Apache-2.0... |
from __future__ import division, absolute_import
from __future__ import print_function, unicode_literals
import nose.tools as nt
import numpy as np
import theano
import theano.tensor as T
import treeano
import treeano.nodes as tn
from treeano.sandbox.nodes import input_scaling
fX = theano.config.floatX
def test_... | {"hexsha": "95d668b86ceff8501a8332751fb4aa1bb94536c3", "size": 1659, "ext": "py", "lang": "Python", "max_stars_repo_path": "u24_lymphocyte/third_party/treeano/sandbox/nodes/tests/input_scaling_test.py", "max_stars_repo_name": "ALSM-PhD/quip_classification", "max_stars_repo_head_hexsha": "7347bfaa5cf11ae2d7a528fbcc43322... |
from pretrained_networks import *
import time
import argparse
import os
import numpy as np
from PIL import Image
from pathlib import Path
def generate_from_vector(network_pkl: str, vector_fpath: str, output_fpath: str):
time_0= time.time()
# Load network pkl
_, _, Gs = load_networks(network_pkl)
t... | {"hexsha": "12012f7cb9a0d95c64dfd58608b5540fe7306f16", "size": 2620, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate_from_vector.py", "max_stars_repo_name": "PolaeCo/stylegan2-ada", "max_stars_repo_head_hexsha": "26aa96a7a2c217779d7663dc450866011b059460", "max_stars_repo_licenses": ["BSD-Source-Code"], ... |
import math
import heapq
import numpy as np
import scipy.sparse as sp
from opendr.topology import get_vert_connectivity, get_vertices_per_edge
from menpo.shape import PointCloud, TriMesh
from menpo3d.vtkutils import trimesh_from_vtk, trimesh_to_vtk, VTKClosestPointLocator
from vtk.util.numpy_support import vtk_to_nu... | {"hexsha": "3fefee7cb2a3ae9271c0ce6635b0759324baf751", "size": 9385, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/mesh_sampling.py", "max_stars_repo_name": "eosulliv/coma", "max_stars_repo_head_hexsha": "c2f68b460aa3e062577cf3eca30f17711f27d1e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
"""
Code verification using the benchmark of a rod under uniformly distributed load.
"""
import numpy as np
from fenics import Mesh
from dynamic import initialise_results, run_dynamic
from axes_world import one_by_two, fontsize
# =============================================================================
# Veri... | {"hexsha": "94e5bb631a3e87650836d14ccb0ed6f4ae50ec5f", "size": 2938, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/verification_dynamic.py", "max_stars_repo_name": "mou3adb/RodiCS", "max_stars_repo_head_hexsha": "caafe8f6427943cb6d82cf3245a3d774ba7664f1", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
using ..Tagging, ..Traits, ..Var, ..Space, ..Basis
# @inline hasintervene(ω) = hastag(ω, Val{:intervene})
@inline tagintervene(::trait(Intervene), ω, intervention) =
let i = mergeinterventions(intervention, ω.tags.intervene.intervention)
mergetag(ω, (intervene = (intervention = i, intctx = ω.tags.intervene),))
... | {"hexsha": "443d2eae791e192b7e54fbf4b9909fb2cb9d62b7", "size": 4380, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "OmegaCore/src/interventions/intervenepass.jl", "max_stars_repo_name": "zenna/expect", "max_stars_repo_head_hexsha": "48bd661df410777eeb8940876a5cc8817eed2ac5", "max_stars_repo_licenses": ["MIT"], "... |
import numpy as np
class FunctionObjective:
def __init__(self, map_points, prizes):
self.map_points = map_points
self.prizes = prizes
self.distance_matrix_calculate()
def distance_matrix_calculate(self):
qtd = self.map_points.shape[0]
distancias = np.zeros([qtd, qtd])... | {"hexsha": "37d96159d386d6be956466b4fd5d95e608a4d0a9", "size": 1852, "ext": "py", "lang": "Python", "max_stars_repo_path": "solution/FunctionObjective.py", "max_stars_repo_name": "killdary/genetic_algorithm_route_calculation", "max_stars_repo_head_hexsha": "f7d9c114d8780bad6124ee61214b7dce0557d312", "max_stars_repo_lic... |
@testset "LeafNode" begin
node = @inferred LeafNode{Float64, 2, 2}()
@test TreeArrays.childtype(node) === nothing
@test TreeArrays.leaftype(node) == LeafNode{Float64, 2, 2}
@test TreeArrays.leafeltype(node) == Float64
@test size(node) === (4, 4)
# getindex/setindex!
node[1] = 2
node[3] ... | {"hexsha": "37cc9c30005d0eab458c4374ef2d487044cac763", "size": 577, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/LeafNode.jl", "max_stars_repo_name": "KeitaNakamura/TreeArrays.jl", "max_stars_repo_head_hexsha": "9ee871a206123d1525e49c516f2e222cf55ace32", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
From Test Require Import tactic.
Section FOFProblem.
Variable Universe : Set.
Variable UniverseElement : Universe.
Variable wd_ : Universe -> Universe -> Prop.
Variable col_ : Universe -> Universe -> Universe -> Prop.
Variable col_swap1_1 : (forall A B C : Universe, (col_ A B C -> col_ B A C)).
Variable col_swap2_... | {"author": "janicicpredrag", "repo": "Larus", "sha": "a095ca588fbb0e4a64a26d92946485bbf85e1e08", "save_path": "github-repos/coq/janicicpredrag-Larus", "path": "github-repos/coq/janicicpredrag-Larus/Larus-a095ca588fbb0e4a64a26d92946485bbf85e1e08/benchmarks/coq-problems/col-trans/col_trans_1142.v"} |
import pandas as pd
import numpy as np
from model.common.topic import beauty_columns, fashion_columns, mobile_columns
concat_ratio_dict = {'Beauty_Colour_group': {'fastai': 0.6, 'lgb': 0.4},
'Beauty_Brand': {'fastai': 0.5, 'lgb': 0.5},
'Beauty_Benefits': {'fastai': 0.4, 'lgb': 0.6},
'Beauty_Product_texture': {'fast... | {"hexsha": "cc3e02906ec85016ab06a2a530112ea01258e479", "size": 7937, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/text/common/prediction.py", "max_stars_repo_name": "AdityaSidharta/shopee_data_science", "max_stars_repo_head_hexsha": "6f32d52964067937e2538240446e26b5dd746652", "max_stars_repo_licenses": ... |
from manim import *
from manim.utils import tex
import numpy as np
import math
import textwrap
import solarized
import tree_data
from util import *
class TheBook(Scene):
def construct(self):
text_color = solarized.BASE00
erdos_img = ImageMobject("img/erdos.jpg") # wiki
erdos_img.height =... | {"hexsha": "f9958cbdffc55c891cc5ce0af205d204079e482a", "size": 24371, "ext": "py", "lang": "Python", "max_stars_repo_path": "part1_trees.py", "max_stars_repo_name": "polylog-cs/longest-path-video", "max_stars_repo_head_hexsha": "ac77f71371ace11bb66fc8deb3e7b01c9a370a6f", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
\begin{center}
\vspace*{25pt}
\includegraphics{Images/COBOL-Programming-Course.png}
\hypertarget{cobol-programming-course-2}{%
\section*{
\\[35pt]
\Huge COBOL Programming Course 2 \\[10pt]
\Huge Advanced Topics \\[15pt]
\Large Version 2.3.0}\label{cobol-programming-course-2}}
\end{center}
\pagebreak
\hyperta... | {"hexsha": "ad91bbe5fbeb25b6a3a7ae9d71b18e7f09bef99d", "size": 726, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "COBOL Programming Course #2 - Advanced Topics/Front_Matter.tex", "max_stars_repo_name": "paulofa001/cobol-programming-course", "max_stars_repo_head_hexsha": "cb95c5236abbdc48290cf42189384911e878c92c"... |
using Mocking
using RecipesBase
using Test
using TimeZones
using TimeZones: PKG_DIR
using TimeZones.TZData: ARCHIVE_DIR, TZSource, compile, build
using Unicode
Mocking.activate()
const TZDATA_VERSION = "2016j"
const TZ_SOURCE_DIR = get(ENV, "TZ_SOURCE_DIR", joinpath(PKG_DIR, "test", "tzsource"))
const TZFILE_DIR = ... | {"hexsha": "b631e70f7cf1f163210700b5268763b7cf4ebdf9", "size": 2645, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "NHDaly/TimeZones.jl", "max_stars_repo_head_hexsha": "71178fefd23a8ad00f43aacfcde74720f1abfd07", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
[STATEMENT]
lemma pp_of_term_sminus [term_simps]: "pp_of_term (v \<ominus> t) = pp_of_term v - t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. pp_of_term (v \<ominus> t) = pp_of_term v - t
[PROOF STEP]
by (simp add: sminus_def term_simps) | {"llama_tokens": 113, "file": "Polynomials_MPoly_Type_Class", "length": 1} |
-- exercises in "Type-Driven Development with Idris"
-- chapter 4
-- check that all functions are total
%default total
--
-- Expressions
--
||| An integer arithmetic expression
data Expr = ||| A single integer
Value Int
| ||| Addition of an expression to an expression
Addition Expr ... | {"hexsha": "0fb3b8bc7fd8183eea2215ff5814d1fc7bda7faa", "size": 760, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "chapter4/Expr.idr", "max_stars_repo_name": "pascalpoizat/idris-book", "max_stars_repo_head_hexsha": "f1ef0ed0a8b8c1690d7ce65258f04322b37ff956", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
# Copyright 2020 Keren Ye, University of Pittsburgh
#
# 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... | {"hexsha": "13a01c9eb83f27752d2af6d16005760df9d6e628", "size": 8406, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset-tools/create_coco_cap_graph_tf_record.py", "max_stars_repo_name": "yekeren/WSSGG", "max_stars_repo_head_hexsha": "4d20dadffe7584ac2c7f26419960512380b8d06e", "max_stars_repo_licenses": ["Ap... |
##################################################
# Padilha et al., "Temporally sorting images from real-world events",
# Pattern Recognition Letters, 2021
#
# Code for testing the Hierarchical Pipeline
#
# usage:
# python testing_allClassifiers_Hierarchical.py <setAorB>
# params:
# setAorB - either 'setA' or 's... | {"hexsha": "8844262f9b1c7688c6353c74c85baf2c3152bc28", "size": 4614, "ext": "py", "lang": "Python", "max_stars_repo_path": "hierarchical/testing_allClassifiers_Hierarchical.py", "max_stars_repo_name": "rafaspadilha/temporal-sorting-event", "max_stars_repo_head_hexsha": "7acd2ec59c0c796405d985e92e2f13035d878887", "max_s... |
"""Helpers classes and functions."""
import time
import torch as th
import numpy as np
import logging
try:
import coloredlogs
coloredlogs.install()
HAS_COLORED_LOGS = True
except:
HAS_COLORED_LOGS = False
__all__ = ["ExponentialMovingAverage", "Averager", "Timer", "tensor2image", "get_logger", "set_... | {"hexsha": "a50901c448acd045128fd0b902586933c77f05fa", "size": 4043, "ext": "py", "lang": "Python", "max_stars_repo_path": "ttools/utils.py", "max_stars_repo_name": "sutkarsh/ttools", "max_stars_repo_head_hexsha": "a2e5fbf308566c0c54ab9d6ad1d9f8bc63f8fe99", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
from typing import Tuple, Union
import numpy as np
from common.exceptionmanager import catch_error_exception
from imageoperators.boundingboxes import BoundingBoxes, BoundBox3DType, BoundBox2DType
from imageoperators.imageoperator import CropImage
from preprocessing.imagegenerator import ImageGenerator
class RandomW... | {"hexsha": "8a47ae03539d98b59be3d05b8c5df916ec2a0aa5", "size": 4986, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/preprocessing/randomwindowimages.py", "max_stars_repo_name": "AntonioGUJ/AirwaySegmentation_Keras", "max_stars_repo_head_hexsha": "7da4c88dfde6f0dd2f8f181b2d3fd07dc2d28638", "max_stars_repo_li... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import GridSpec
import os
import cv2
from tqdm import tqdm
# the three first letters on the code on the plot signifies the ordering, eg.
# adrradaad means autoencoder, deep ranking, then random
np.random.seed(42069)
for d in tqdm(os.listdir("... | {"hexsha": "c0d39101a458303bff5f27b5bbceea814d8e51dc", "size": 2536, "ext": "py", "lang": "Python", "max_stars_repo_path": "survey/survey_images_plot.py", "max_stars_repo_name": "natashanorsker/fagprojekt", "max_stars_repo_head_hexsha": "ef9a8cc2128c43d891c8a7a47e14916af2b9c602", "max_stars_repo_licenses": ["MIT"], "ma... |
import logging
from numpy import array, inf, where
from NiaPy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')
__all__ = ['AntColonyOptimization']
class AntColonyOptimization(Algorithm):
r"""Implementation of Ant Colony Opt... | {"hexsha": "091707a290fa04636429b20968625fb65855b545", "size": 8357, "ext": "py", "lang": "Python", "max_stars_repo_path": "NiaPy/algorithms/basic/aco.py", "max_stars_repo_name": "kozulic/NiaPy", "max_stars_repo_head_hexsha": "08ea02f9928a052d32cc9b6282b84316740b24a2", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import unittest
import neuralnetsim
import networkx as nx
class TestNetworkAnalysis(unittest.TestCase):
def test_calc_mu(self):
graph = nx.DiGraph()
graph.add_node(1, com=1)
graph.add_node(2, com=1)
graph.add_node(4, com=2)
graph.add_node(5, com=3)
graph.add_edge(1,... | {"hexsha": "25bfe65f1e2d389f31d2b3ee9584188c1224ff50", "size": 2749, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_network_analysis.py", "max_stars_repo_name": "Nathaniel-Rodriguez/neuralnetsim", "max_stars_repo_head_hexsha": "c353af92fb3f44539370220963b07bdfd9822149", "max_stars_repo_licenses": ["M... |
import torch.utils.data as data
import torchvision.transforms as transforms
import os
from PIL import Image
import random
import numpy as np
def vkt_loader(filepath):
all_limg = []
all_rimg = []
all_disp = []
img_path = os.path.join(filepath, 'vkitti_2.0.3_rgb')
depth_path = os.path.join(filepath... | {"hexsha": "89f359a62da1081ab3ece3d790b7bdba87d28d9a", "size": 3671, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataloader/vKITTI_loader.py", "max_stars_repo_name": "SpadeLiu/Graft-PSMNet", "max_stars_repo_head_hexsha": "1f2950d5afd85237f8d3604caab20dd47a8c9889", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma agree_func:"Vagree \<nu> \<nu>' (FVDiff ($f var args)) \<Longrightarrow> (\<And>i. Vagree \<nu> \<nu>' (FVDiff (args i)))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Vagree \<nu> \<nu>' (FVDiff ($f var args)) \<Longrightarrow> (\<And>i. Vagree \<nu> \<nu>' (FVDiff (args i)))
[PROOF STEP]
proof ... | {"llama_tokens": 1760, "file": "Differential_Dynamic_Logic_Static_Semantics", "length": 16} |
import boto3
import re
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import clear_output
import time
import tabulate
import sagemaker
def get_training_metrics(training_job_name, train_metric_name, val_metric_name):
"""
This function uses Amazon CloudWatch to ext... | {"hexsha": "c423b1faf7b08087f73c8e885e3684753b2fd738", "size": 5858, "ext": "py", "lang": "Python", "max_stars_repo_path": "nasa-turbofan-rul-xgboost/notebooks/utils.py", "max_stars_repo_name": "michaelhoarau/sagemaker-predictive-maintenance", "max_stars_repo_head_hexsha": "5f35d75d12d1e398a9d77508d11a2ffbe0c413ae", "m... |
import pickle
import os
import numpy as np
import logging
import torch
from torch.utils.data import TensorDataset, Dataset
from itertools import chain
import sys
import string
logger = logging.getLogger(__name__)
class BatchedDataset(Dataset):
def __init__(self, tensors):
self.tensor0 = tensors[0]
... | {"hexsha": "50dab56b6860361a12107ca06abb86abe9216c66", "size": 28244, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "kathrynchapman/LA_MC2C", "max_stars_repo_head_hexsha": "62c2477a77dc1e8c8ba435e8dd37c4e4a33bbc78", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": nu... |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 L.A. Barba, C.D. Cooper, G.F. Forsyth.
# Spreading out
Welcome to the fifth, and last, notebook of Module 4 "_Spreading out: diffusion problems,"_ of our fabulous course **"Practical Numerical Methods with Python."**
... | {"hexsha": "febc52da87d68a1a12316212b21f40632e71aad0", "size": 135415, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "lessons/04_spreadout/04_05_Crank-Nicolson.ipynb", "max_stars_repo_name": "rolando-contribute/numerical-mooc", "max_stars_repo_head_hexsha": "5f2115666006bf6e6367320fff46ddc1e0e32044... |
/*
* Copyright (c) 2020-2021 Adrian Georg Herrmann
*
* These are unit tests for the Data Storage Module.
*/
#include <boost/archive/text_oarchive.hpp>
#include <boost/filesystem.hpp>
#include <iostream>
#include <string>
#include <vector>
#include "../test.hpp"
#include "../extras/dummy_logger.hpp"
#include "..... | {"hexsha": "74158fadaea5a82a97379dfec3a72a217812ff74", "size": 28155, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/storage/test_handler_settings.cpp", "max_stars_repo_name": "adrianghc/HEMS", "max_stars_repo_head_hexsha": "94ffd85a050211efc6ef785b873ee39e906a8b78", "max_stars_repo_licenses": ["MIT"], "max... |
# coding=utf-8
# 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 writing, software
# distr... | {"hexsha": "2ddcf8d29696c3ae4efeb5010a89b3819a7138e0", "size": 24939, "ext": "py", "lang": "Python", "max_stars_repo_path": "tk/data_generators/allen_brain.py", "max_stars_repo_name": "cwbeitel/tk", "max_stars_repo_head_hexsha": "ed6096b696e30255121f3cad10fa72c337883a6f", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
[STATEMENT]
lemma scalarE[elim]:
assumes "scalar A f"
obtains "nop A 0 f"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (nop A []\<^sub>\<circ> f \<Longrightarrow> thesis) \<Longrightarrow> thesis
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
scalar A f
goal (1 subgoal):
1. (nop A []\<^sub>... | {"llama_tokens": 158, "file": "CZH_Foundations_czh_sets_CZH_Sets_NOP", "length": 2} |
# Copyright (c) 2021-2022, NVIDIA 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 agre... | {"hexsha": "34375c2ecf40afb35c92294148742e058f4097d6", "size": 9847, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/cuml/test/test_compose.py", "max_stars_repo_name": "nickpowersys/cuml", "max_stars_repo_head_hexsha": "b0cccb5b37f2c8b159a9c10cd8b54192b7f33b9c", "max_stars_repo_licenses": ["Apache-2.0"], ... |
import gzip
import os
import json
import urllib.request
import numpy as np
import pickle
import torch
from torch.utils.data import TensorDataset, DataLoader
device = "cuda:0" if torch.cuda.is_available() else "cpu"
def get_raw_data_go():
''' Returns the set of samples from the local file or download it if it do... | {"hexsha": "6afed9e42dda6140381d2a5463c02289ca606d13", "size": 3252, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn/src/dataset.py", "max_stars_repo_name": "Theomat/go-enseirb-2020", "max_stars_repo_head_hexsha": "ae842888dfd61a23d3556c5f63c4474bdbb1685f", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
// Copyright (c) 2015-2016
// Author: Chrono Law
#ifndef _NGX_TIMER_HPP
#define _NGX_TIMER_HPP
#include <deque>
#include <boost/function.hpp>
#include <boost/functional/factory.hpp>
#include "NgxValue.hpp"
template<typename T>
class NgxTimerEv final
{
public:
typedef NgxTimerEv this_type;
typedef T ... | {"hexsha": "6277a2374d4cc5528feb93d02f737ee43d4a1f35", "size": 3002, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ngxpp/NgxTimer.hpp", "max_stars_repo_name": "Swanzzy/ngx_cpp_dev", "max_stars_repo_head_hexsha": "bd479d627294eaaa2a51b47a3d50928a2a85d018", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
#include "Core.h"
#include <iostream>
#include <boost/log/trivial.hpp>
#include <boost/log/utility/setup.hpp>
Core::Core() {}
Core::~Core() {}
void Core::Run() {
// Configure logging
// Output message to console
boost::log::add_console_log(
std::cout,
boost::log::keywords::format = "[... | {"hexsha": "1600ee9c58dcd09bce0284d829af93de7889e8bc", "size": 534, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Source/Core/Core.cpp", "max_stars_repo_name": "CoreTrackProject/Prototype", "max_stars_repo_head_hexsha": "f952ba3c82cee77bf3a8c6001fc8ccb4c7433042", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
# retrieves map images from arcgis server given a zipped shape file
# import packages
import json
import logging
import os
import re
import subprocess
from zipfile import ZipFile
import matplotlib.pyplot as plt
import numpy as np
import pycrs
import rasterio
import requests
import shapefile as shp
from pascal_voc_wr... | {"hexsha": "9f8b548059ed2ce0498b115572ad57c3c8246429", "size": 14555, "ext": "py", "lang": "Python", "max_stars_repo_path": "MapRetrieve.py", "max_stars_repo_name": "constant5/ShadeMyRun", "max_stars_repo_head_hexsha": "eb4367f9f11235139b669f4c9f0985cd1b45e524", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
[STATEMENT]
lemma len_downshift_helper:
"x |\<in>| P \<Longrightarrow> Suc (fMax ((\<lambda>x. x - Suc 0) |`| (P |-| {|0|}))) \<noteq> fMax P \<Longrightarrow> xa |\<in>| P \<Longrightarrow> xa = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>x |\<in>| P; Suc (fMax ((\<lambda>x. x - Suc 0) |`| (P |-| {... | {"llama_tokens": 2293, "file": "Formula_Derivatives_WS1S_Prelim", "length": 19} |
/**
* Copyright Soramitsu Co., Ltd. All Rights Reserved.
* SPDX-License-Identifier: Apache-2.0
*/
#ifndef IROHA_PG_CONNECTION_INIT_HPP
#define IROHA_PG_CONNECTION_INIT_HPP
#include <soci/soci.h>
#include <soci/callbacks.h>
#include <soci/postgresql/soci-postgresql.h>
#include <boost/algorithm/string.hpp>
#include... | {"hexsha": "4b34da7ca21ee8b281b4c7a16ec64f8591e246d2", "size": 4380, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "iroha-master/irohad/main/impl/pg_connection_init.hpp", "max_stars_repo_name": "rogelcorral/My-Iroha-Playground", "max_stars_repo_head_hexsha": "c370a114e6039f8667c2b68b7dcb10e4c387c67d", "max_stars_... |
import argparse
import datetime
import os
import tensorflow as tf
import numpy as np
import socket
import tensorflow.keras.backend as K
from tensorflow.keras.models import model_from_json
import TimeSeriesSR_Final.data_loader_helpers as dataloaders
from tensorflow.python.ops import math_ops
import stippy
import matplot... | {"hexsha": "2a4df86b67a67dafaab480e3c0c40fdf2ac3b3f2", "size": 17519, "ext": "py", "lang": "Python", "max_stars_repo_path": "TimeSeriesSR/PredictionForModel.py", "max_stars_repo_name": "paahunik/satnet-fineet-satellite_image-imputations", "max_stars_repo_head_hexsha": "08907e885feaf2a37653ed40d9902db886de1a32", "max_st... |
import os
import glob
import pickle
import numpy as np
import torch
import math
from easydict import EasyDict as edict
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.gaussian_process.kernels import Matern
from model import *
from utils.gp_helper import cal_kern_spec_mix_nomu_sep, cal_marg_likelihood, stan... | {"hexsha": "bbb672fd1b86497c7d5f820307416ef79c75ff25", "size": 5283, "ext": "py", "lang": "Python", "max_stars_repo_path": "get_data_gp.py", "max_stars_repo_name": "ikanher/AHGP", "max_stars_repo_head_hexsha": "8750823790ec6abece78e31cc0ec7a6162656a75", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19, "max_st... |
import numpy as np
def gen_samples(gen_dic_list,min_max_dic,X_train,number,target_class=None):
'''
Inputs:
condition and thresholds dictionary - generated by TreePathDictionary function,
min_max value of each feature in X_train as dictionary -generated by TreePathDictionary function... | {"hexsha": "58198f5755bd6bd35e20ad335d32c6462cae37fc", "size": 1775, "ext": "py", "lang": "Python", "max_stars_repo_path": "new_master/SampleGeneration.py", "max_stars_repo_name": "Arnab9Codes/Thesis_pure_v-1", "max_stars_repo_head_hexsha": "ca2220fbe5579aa25c11a9ae608c6a933b6e1c96", "max_stars_repo_licenses": ["BSD-3-... |
function solve(tree::NDTree, x::Vector{Float64})
y = x[tree.p]
maxLevel = length(tree.t)
# Forward
for l = 1:maxLevel
for s = 1:2^(maxLevel-l)
Lss = tree.t[l][s].APiv[1,1]
p = get_dofs(tree.t[l][s])
# Pivot
BLAS.trsv!('L', 'N', 'U', Lss, view(y, ... | {"hexsha": "c04f1e15f7306bedb14c75ddf1535021cffc5a4e", "size": 1108, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/solve.jl", "max_stars_repo_name": "leopoldcambier/LU_MND", "max_stars_repo_head_hexsha": "0fd0f61e9043305b672dfa1041c09e707f2328f1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Este NÃO é um programa ROS
from __future__ import print_function, division
import cv2
import os,sys, os.path
import numpy as np
print("Rodando Python versão ", sys.version)
print("OpenCV versão: ", cv2.__version__)
print("Diretório de trabalho: ", os.getcwd())
def cros... | {"hexsha": "71c59d435d8c8a627a8c5a9a6ad95919cfb322d6", "size": 1721, "ext": "py", "lang": "Python", "max_stars_repo_path": "studies/1 - OpenCV/ex3.py", "max_stars_repo_name": "FelixLuciano/Elements-of-Computer-Vision", "max_stars_repo_head_hexsha": "22dc19d1f4b8f346d79ca2e55986216fc1b58e9b", "max_stars_repo_licenses": ... |
/*
Copyright (c) 2005-2016, University of Oxford.
All rights reserved.
University of Oxford means the Chancellor, Masters and Scholars of the
University of Oxford, having an administrative office at Wellington
Square, Oxford OX1 2JD, UK.
This file is part of Chaste.
Redistribution and use in source and binary forms... | {"hexsha": "7312d22653f4df07c21941a2366fcc53fad5eb1e", "size": 13052, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "cell_based/src/population/pdes/CellBasedPdeHandler.hpp", "max_stars_repo_name": "uofs-simlab/ChasteOS", "max_stars_repo_head_hexsha": "04d98998e2ebad3f29086b8eaa1d89c08c6fccf6", "max_stars_repo_lic... |
## Data Preparation for ROC Analysis
struct ROCData{T <: Real}
thresholds::Vector{T}
P::Int
N::Int
TP::Vector{Int}
TN::Vector{Int}
FP::Vector{Int}
FN::Vector{Int}
FPR::Vector{Float64}
TPR::Vector{Float64}
end
function _thresholds(used_scores, distances::Bool)
unique_scores = unique(used_scores)
if distance... | {"hexsha": "9a935e9366187203ef695e3be797d2a80ac7ab24", "size": 3422, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/rocdata.jl", "max_stars_repo_name": "JuliaTagBot/ROC.jl", "max_stars_repo_head_hexsha": "08f927e8af3247be15e48437714dd09dac508550", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "m... |
using Pkg
Pkg.activate(dirname(@__FILE__))
Pkg.instantiate()
using Kuber
ctx = Kuber.KuberContext()
kubqueens_pod = kuber_obj(ctx, """{
"kind": "Pod",
"metadata":{
"name": "kubqueens-pod",
"namespace": "default",
"l... | {"hexsha": "99f3e508a7abaf2470e760a59f2b80c984d2c828", "size": 1840, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter 14/kubernetes/kubserver.jl", "max_stars_repo_name": "bpbpublications/Hands-on-Julia-Programming", "max_stars_repo_head_hexsha": "e1c0618d89b8c2d8bb46422c3f1546c169a8259f", "max_stars_repo_l... |
import numpy as np
import os
import numbers
try:
from PyQt4.QtGui import QFileDialog
from PyQt4 import QtCore, QtGui
from PyQt4.QtGui import QMainWindow
except ImportError:
from PyQt5.QtWidgets import QFileDialog
from PyQt5 import QtCore, QtGui
from PyQt5.QtWidgets import QApplication, QMainWin... | {"hexsha": "bf928902a980e66b3b7182ede4b0eaa227782d55", "size": 10093, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/__code/display_imaging_resonance_sample_definition.py", "max_stars_repo_name": "mabrahamdevops/python_notebooks", "max_stars_repo_head_hexsha": "6d5e7383b60cc7fd476f6e85ab93e239c9c32330... |
from datetime import datetime
from math import sqrt
import numpy as np
import random
import re
from typing import List
from azure.quantum import Workspace
from azure.quantum.optimization import Problem , ProblemType , Term , ParallelTempering , SimulatedAnnealing , Tabu
from util.tFunctions import *
from util.benchma... | {"hexsha": "219abd1545ea63a0f29d74b2e8a9a6e5d73417f4", "size": 11375, "ext": "py", "lang": "Python", "max_stars_repo_path": "cvrp.py", "max_stars_repo_name": "delbert/qio", "max_stars_repo_head_hexsha": "724cc0c214e36157b8e2839b92186fccbdc66e16", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_r... |
[STATEMENT]
lemma lens_indep_vwb_iff:
assumes "vwb_lens x" "vwb_lens y"
shows "x \<bowtie> y \<longleftrightarrow> (\<forall> u v \<sigma>. put\<^bsub>x\<^esub> (put\<^bsub>y\<^esub> \<sigma> v) u = put\<^bsub>y\<^esub> (put\<^bsub>x\<^esub> \<sigma> u) v)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (x \<bow... | {"llama_tokens": 2849, "file": "Optics_Lens_Laws", "length": 18} |
from pathlib import Path
import numpy as np
import cv2
class Frame:
def __init__(self, idx, frames_dir, extract_features=False):
"""Initializes instance of class Frame.
Args:
idx (int): Index of the frame.
frames_dir (str): Directory containing all frames.
ext... | {"hexsha": "1692c815b69a834a651b598586f7dfd2c1a0b783", "size": 2585, "ext": "py", "lang": "Python", "max_stars_repo_path": "videokf/keyframe_manager/frame_manager.py", "max_stars_repo_name": "averdones/video-kf", "max_stars_repo_head_hexsha": "65a81a0b5352e3ad4d7f394f32772e911de4abbc", "max_stars_repo_licenses": ["MIT"... |
import torch.utils.data as data
import torch
import h5py
import os
import math
import numpy as np
from PIL import Image
class prepareDataset(data.Dataset):
def __init__(self, path):
super(prepareDataset, self).__init__()
file = h5py.File(path, 'r')
self.data = file.get('data')
self.... | {"hexsha": "0a9bc70c27d94ba8c7e294126b827464dc6b7ad5", "size": 1847, "ext": "py", "lang": "Python", "max_stars_repo_path": "super_resolution/VDSR_PyTorch/util.py", "max_stars_repo_name": "kumayu0108/model-zoo", "max_stars_repo_head_hexsha": "4285779f6ff51fa1efb0625d67b428e90c343c0c", "max_stars_repo_licenses": ["MIT"],... |
/* Copyright 2016-2017 Joaquin M Lopez Munoz.
* 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)
*
* See http://www.boost.org/libs/poly_collection for library home page.
*/
#ifndef BOOST_POLY_COLLECTION_DETAIL_F... | {"hexsha": "590664b319bfb539b43c42f72df3cbcd732beeae", "size": 3773, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/external/boost/boost_1_68_0/boost/poly_collection/detail/function_model.hpp", "max_stars_repo_name": "Bpowers4/turicreate", "max_stars_repo_head_hexsha": "73dad213cc1c4f74337b905baea2b3a1e5a0266... |
[STATEMENT]
lemma add_update:
"i < length ns \<Longrightarrow> foldl (+) m (ns[i := Suc (ns ! i)]) = Suc (foldl (+) m ns)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. i < length ns \<Longrightarrow> foldl (+) m (ns[i := Suc (ns ! i)]) = Suc (foldl (+) m ns)
[PROOF STEP]
by (induction ns arbitrary: i m, simp_all ... | {"llama_tokens": 142, "file": "Generalized_Counting_Sort_Algorithm", "length": 1} |
"""
AgedJacobianFactors
Type containing the Jacobian Factors and the age of the Jacobian.
This allows for the Shamanskii method to not update the Jacobian at each iterate.
"""
mutable struct AgedJacobianFactors
fac # Jacobian factors — can be Real, Complex, Dual, or HyperDual
age::Int # age of the ... | {"hexsha": "5f7676e7731c8c9ec8a4510e239037d20f64426b", "size": 566, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/newTypes.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/AIBECS.jl-ace601d6-714c-11e9-04e5-89b7fad23838", "max_stars_repo_head_hexsha": "75f81609ca4ff67d81bda9f7031ad38e52b61556", "m... |
import numpy as np
from eqsig.single import Signal, AccSignal
def load_values_and_dt(ffp):
"""
Loads values and time step that were saved in eqsig input format.
Parameters
----------
ffp: str
Full file path to output file
Returns
-------
values: array_like
An array of... | {"hexsha": "5cf26de352c147200516f5fcea8a2bbea681d2d4", "size": 4277, "ext": "py", "lang": "Python", "max_stars_repo_path": "eqsig/loader.py", "max_stars_repo_name": "geosharma/eqsig", "max_stars_repo_head_hexsha": "3083022ab9e48ee422eff261560ee60846e766e2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
[STATEMENT]
lemma bind_spmf_assoc [simp]:
fixes x :: "'a spmf" and f :: "'a \<Rightarrow> 'b spmf" and g :: "'b \<Rightarrow> 'c spmf"
shows "(x \<bind> f) \<bind> g = x \<bind> (\<lambda>y. f y \<bind> g)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<bind> f \<bind> g = x \<bind> (\<lambda>y. f y \<bind> ... | {"llama_tokens": 195, "file": null, "length": 1} |
import numpy as np
import networkx as nx
import neighborhoods.permutationtest as perm_test
import neighborhoods.neighborhoods as nbr
from scipy import sparse
from datetime import datetime
#import yappi
if __name__ == '__main__':
# Data Loading
A = np.load('../results/sample_adjmat_20200601.npy')
B = np... | {"hexsha": "0ec0ef687cb4b911521482bfbaecde053e1ff9ed", "size": 2089, "ext": "py", "lang": "Python", "max_stars_repo_path": "spatialpower/main.py", "max_stars_repo_name": "klarman-cell-observatory/PowerAnalysisForSpatialOmics", "max_stars_repo_head_hexsha": "257e5663bb5476c7d9a22230741b5507fd621352", "max_stars_repo_lic... |
export get_projector
function get_projector(constraint,comp_grid,special_operator_list::Array{String,1},A,TD_n::Tuple,TF::DataType)
if constraint.set_type == "bounds"
if constraint.app_mode[1] in ["matrix","tensor"]
if constraint.TD_OP in special_operator_list
P = x -> copyto!(x,A'*project_bounds!... | {"hexsha": "0bade1803c60f77f94e03ddd2a5976b7102b3a26", "size": 3630, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/get_projector.jl", "max_stars_repo_name": "slimgroup/SetIntersectionProjection", "max_stars_repo_head_hexsha": "d7dd8cf018bb960fa626e761e62b865e2725b466", "max_stars_repo_licenses": ["MIT"], "m... |
from numpy import mean
import logging
logger = logging.getLogger(__name__)
logger.setLevel(level=logging.INFO)
def distances(x):
avg_dis = mean([x[n]-x[n-1] for n in range(1,len(x))])
return(avg_dis)
def counts(data_read, def_info, indef_info, null_info, null_token):
"""
produce a count of each a... | {"hexsha": "ddd7ecf502b803b42d1f2b75d79887cf22417fe7", "size": 4682, "ext": "py", "lang": "Python", "max_stars_repo_path": "ndl_tense/file_anlysis/article_counts.py", "max_stars_repo_name": "ooominds/ndltenses", "max_stars_repo_head_hexsha": "19d38510158e9a8c9e5b2f262587da0875d11c43", "max_stars_repo_licenses": ["MIT"]... |
import argparse, time, logging, os, math, random
os.environ["MXNET_USE_OPERATOR_TUNING"] = "0"
import numpy as np
from scipy import stats
import mxnet as mx
from mxnet import gluon, nd
from mxnet import autograd as ag
from mxnet.gluon import nn
from mxnet.gluon.data.vision import transforms
from gluoncv.model_zoo im... | {"hexsha": "6566d348180ea70902d3a1dcae3fe0c853c1960f", "size": 9690, "ext": "py", "lang": "Python", "max_stars_repo_path": "sgd.py", "max_stars_repo_name": "xcgoner/robust_fedavg", "max_stars_repo_head_hexsha": "11eda45a6b37f09c18a5741e14e84bb911bca5ba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
import random
import numpy as np
import pnoise
from .geometry import interpolate
from .model import LineCollection
__all__ = ["squiggles"]
def squiggles(
lines: LineCollection, ampl: float, period: float, quantization: float
) -> LineCollection:
"""Apply a squiggle filter to a :class:`LineCollection`.
... | {"hexsha": "43c17ce10651c9872ed55e4515551780b5330176", "size": 1532, "ext": "py", "lang": "Python", "max_stars_repo_path": "vpype/filters.py", "max_stars_repo_name": "tatarize/vpype", "max_stars_repo_head_hexsha": "ee1c20d9f920b74206034624571f854fa470cf38", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 453, "m... |
Require Import ssreflect ssrbool ssrfun eqtype ssrnat seq choice fintype.
Require Import div finfun bigop prime binomial ssralg finset fingroup finalg.
Require Import mxalgebra perm zmodp matrix ssrint refinements funperm.
Require Import seq seqpoly pol square_free casteljau desc rat.
Require Import ssrnum ssrint real... | {"author": "math-comp", "repo": "trajectories", "sha": "cc6e1298208a93592230f5b4ee3228a024aa03e7", "save_path": "github-repos/coq/math-comp-trajectories", "path": "github-repos/coq/math-comp-trajectories/trajectories-cc6e1298208a93592230f5b4ee3228a024aa03e7/attic/CAD_COQ/ssr_descartes/isolate.v"} |
% --------------------------------
% studies, performance
% ==============
%
% --------------
\section{Parallel Strong Scaling and Comparison with OpenCMISS Iron}\label{sec:parallel_strong_scaling_opencmiss}
After the performance of different optimization types has been evaluated for a scenario with a single number... | {"hexsha": "5253ca5ac8ab0d8a8d197e6e1ffe0ba6338e7492", "size": 36460, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "document/08_results_2b.tex", "max_stars_repo_name": "maierbn/phd_thesis_source", "max_stars_repo_head_hexsha": "babee64f01f15d93cb75140eb8c8424883b33c6c", "max_stars_repo_licenses": ["CC-BY-4.0"], ... |
from typing import Any, Dict
import numpy as np
import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
from module.detector.Detector import Detector
class ArimaDetector(Detector):
def __init__(self, dataset: pd.DataFrame, ground_truth_outliers: np.ndarray,
configuration_name: str, )... | {"hexsha": "3230fa543268a365e0c5baa16309bc1fb64b3268", "size": 800, "ext": "py", "lang": "Python", "max_stars_repo_path": "Task3/module/detector/ArimaDetector.py", "max_stars_repo_name": "KKowalewski24/ADZ", "max_stars_repo_head_hexsha": "8a04570a1f6f08506572386b2312a259a8308f56", "max_stars_repo_licenses": ["MIT"], "m... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.