testParaMonte_testing_mpi.m

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%%%%    ParaMonte: Parallel Monte Carlo and Machine Learning Library.                                                           %%%%
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%%%%    Copyright (C) 2012-present, The Computational Data Science Lab                                                          %%%%
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%%%%    This file is part of the ParaMonte library.                                                                             %%%%
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%%%%    LICENSE                                                                                                                 %%%%
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%%%%       https://github.com/cdslaborg/paramonte/blob/main/LICENSE.md                                                          %%%%
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% clear MATLAB space
 
clc;
clear all;
close all;
clear classes;
format compact; format long;
 
% set path to the ParaMonte MATLAB library
 
pmlibRootDir = './'; % set this path to the paramonte library root dir
addpath(genpath(pmlibRootDir));
 
% change MATLAB's working directory to the folder containing this script
 
filePath = mfilename('fullpath');
[currentDir,fileName,fileExt] = fileparts(filePath); cd(currentDir); % Change working directory to source code directory.
cd(fileparts(mfilename('fullpath'))); % Change working directory to source code directory.
 
% define the objective function
 
NDIM = 4; % number of dimensions of the domain of the distribution
 
% create a ParaMonte object
 
pm = paramonte(); % use paramonte("matlab") to use the MATLAB kernel instead of interface
 
% create a ParaDRAM simulation object
 
pmpd = pm.ParaDRAM();
 
% specifity path to the input specification file. 
% This is optional: all simulation specifications can be 
% also set as attributes of the pmpd.spec component of the object.
% KEEP IN MIND: if you set pmpd.inputFile to any non-empty value, then
% the inputFile will override any values specified via pmpd.spec properties.
% pmpd.inputFile = fullfile( string(currentDir) , "paramonte.in" );
 
pmpd.bldtype = "testing";
pmpd.mpiEnabled = true;
pmpd.spec.chainSize = 10000;
pmpd.spec.randomSeed = 31731;
 
pmpd.runSampler ( NDIM          ... number of dimensions of the objective function
                , @getLogFunc   ... the objective function: multivariate normal distribution
                );
 
function logFunc = getLogFunc(point)
    mean    =   [ 0.0;0.0;0.0;0.0 ];      % mean of the Multivariate Normal distribution
    covmat  =   [ 1.0,0.5,0.5,0.5       ... covariance matrix of the Multivariate Normal distribution
                ; 0.5,1.0,0.5,0.5       ...
                ; 0.5,0.5,1.0,0.5       ...
                ; 0.5,0.5,0.5,1.0 ];
    logFunc = log(mvnpdf(point,mean,covmat));
end