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002e7d7b06
This is faster than the library functionality that it replaces, as it allows the compiler to do inlining. It also does not utilise any static state so generators do not interfere with each other. It is also faster than the the array lookup in cachedRandom. The cachedRandom class therefore offers no advantage over Random and has been removed.
293 lines
8.0 KiB
C
293 lines
8.0 KiB
C
/*---------------------------------------------------------------------------*\
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========= |
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\\ / F ield | OpenFOAM: The Open Source CFD Toolbox
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\\ / O peration |
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\\ / A nd | Copyright (C) 2011-2018 OpenFOAM Foundation
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\\/ M anipulation |
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-------------------------------------------------------------------------------
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License
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This file is part of OpenFOAM.
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OpenFOAM is free software: you can redistribute it and/or modify it
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under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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OpenFOAM is distributed in the hope that it will be useful, but WITHOUT
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ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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for more details.
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You should have received a copy of the GNU General Public License
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along with OpenFOAM. If not, see <http://www.gnu.org/licenses/>.
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Application
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Test-Distribution
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Description
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Test the Distribution class
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Plot normal distribution test in gnuplot using:
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\verbatim
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normalDistribution(mean, sigma, x) = \
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sqrt(1.0/(2.0*pi*sigma**2))*exp(-(x - mean)**2.0/(2.0*sigma**2))
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plot normalDistribution(8.5, 2.5, x), "Distribution_scalar_test_x" w p
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\endverbatim
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\*---------------------------------------------------------------------------*/
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#include "vector.H"
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#include "labelVector.H"
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#include "tensor.H"
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#include "Distribution.H"
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#include "Random.H"
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#include "dimensionedTypes.H"
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#include "argList.H"
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#include "PstreamReduceOps.H"
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// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //
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using namespace Foam;
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int main(int argc, char *argv[])
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{
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#include "setRootCase.H"
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Random R(918273);
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{
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// scalar
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label randomDistributionTestSize = 50000000;
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Distribution<scalar> dS(scalar(5e-2));
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Info<< nl << "Distribution<scalar>" << nl
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<< "Sampling "
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<< randomDistributionTestSize
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<< " times from a standard normal distribution."
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<< endl;
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for (label i = 0; i < randomDistributionTestSize; i++)
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{
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dS.add(2.5*R.scalarNormal() + 8.5);
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}
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Info<< "Mean " << dS.mean() << nl
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<< "Median " << dS.median()
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<< endl;
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dS.write("Distribution_scalar_test_1");
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Distribution<scalar> dS2(scalar(1e-2));
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Info<< nl << "Distribution<scalar>" << nl
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<< "Sampling "
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<< randomDistributionTestSize
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<< " times from a standard normal distribution."
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<< endl;
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for (label i = 0; i < randomDistributionTestSize; i++)
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{
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dS2.add(1.5*R.scalarNormal() -6.0);
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}
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Info<< "Mean " << dS2.mean() << nl
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<< "Median " << dS2.median()
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<< endl;
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dS2.write("Distribution_scalar_test_2");
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Info<< nl << "Adding previous two Distribution<scalar>" << endl;
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dS = dS + dS2;
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dS.write("Distribution_scalar_test_1+2");
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}
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if (Pstream::parRun())
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{
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// scalar in parallel
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label randomDistributionTestSize = 100000000;
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Distribution<scalar> dS(scalar(1e-1));
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Pout<< "Distribution<scalar>" << nl
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<< "Sampling "
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<< randomDistributionTestSize
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<< " times from uniform distribution."
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<< endl;
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for (label i = 0; i < randomDistributionTestSize; i++)
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{
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dS.add(R.scalar01() + 10*Pstream::myProcNo());
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}
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Pout<< "Mean " << dS.mean() << nl
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<< "Median " << dS.median()
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<< endl;
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reduce(dS, sumOp<Distribution<scalar>>());
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if (Pstream::master())
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{
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Info<< "Reducing parallel Distribution<scalar>" << nl
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<< "Mean " << dS.mean() << nl
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<< "Median " << dS.median()
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<< endl;
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dS.write("Distribution_scalar_test_parallel_reduced");
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}
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}
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{
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// vector
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Distribution<vector> dV(vector(0.1, 0.05, 0.15));
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label randomDistributionTestSize = 1000000;
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Info<< nl << "Distribution<vector>" << nl
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<< "Sampling "
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<< randomDistributionTestSize
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<< " times from uniform and a standard normal distribution."
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<< endl;
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for (label i = 0; i < randomDistributionTestSize; i++)
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{
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dV.add(R.sample01<vector>());
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// Adding separate standard normal components with component
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// weights
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dV.add
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(
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vector
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(
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R.scalarNormal()*3.0 + 1.5,
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R.scalarNormal()*0.25 + 4.0,
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R.scalarNormal()*3.0 - 1.5
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),
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vector(1.0, 2.0, 5.0)
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);
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}
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Info<< "Mean " << dV.mean() << nl
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<< "Median " << dV.median()
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<< endl;
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dV.write("Distribution_vector_test");
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}
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// {
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// // labelVector
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// Distribution<labelVector> dLV(labelVector::one*10);
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// label randomDistributionTestSize = 2000000;
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// Info<< nl << "Distribution<labelVector>" << nl
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// << "Sampling "
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// << randomDistributionTestSize
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// << " times from uniform distribution."
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// << endl;
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// for (label i = 0; i < randomDistributionTestSize; i++)
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// {
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// dLV.add
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// (
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// labelVector
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// (
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// R.sampleAB<label>(-1000, 1001),
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// R.sampleAB<label>(-5000, 5001),
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// R.sampleAB<label>(-2000, 7001)
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// )
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// );
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// }
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// Info<< "Mean " << dLV.mean() << nl
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// << "Median " << dLV.median()
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// << endl;
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// dLV.write("Distribution_labelVector_test");
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// }
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{
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// tensor
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Distribution<tensor> dT(tensor::one*1e-2);
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label randomDistributionTestSize = 2000000;
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Info<< nl << "Distribution<tensor>" << nl
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<< "Sampling "
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<< randomDistributionTestSize
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<< " times from uniform distribution."
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<< endl;
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for (label i = 0; i < randomDistributionTestSize; i++)
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{
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dT.add(R.sample01<tensor>());
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}
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Info<< "Mean " << dT.mean() << nl
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<< "Median " << dT.median()
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<< endl;
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dT.write("Distribution_tensor_test");
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}
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{
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// symmTensor
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Distribution<symmTensor> dSyT(symmTensor::one*1e-2);
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label randomDistributionTestSize = 2000000;
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Info<< nl << "Distribution<symmTensor>" << nl
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<< "Sampling "
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<< randomDistributionTestSize
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<< " times from uniform distribution."
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<< endl;
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for (label i = 0; i < randomDistributionTestSize; i++)
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{
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dSyT.add(R.sample01<symmTensor>());
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}
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Info<< "Mean " << dSyT.mean() << nl
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<< "Median " << dSyT.median()
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<< endl;
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dSyT.write("Distribution_symmTensor_test");
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}
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{
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// sphericalTensor
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Distribution<sphericalTensor> dSpT(sphericalTensor::one*1e-2);
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label randomDistributionTestSize = 50000000;
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Info<< nl << "Distribution<sphericalTensor>" << nl
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<< "Sampling "
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<< randomDistributionTestSize
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<< " times from uniform distribution."
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<< endl;
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for (label i = 0; i < randomDistributionTestSize; i++)
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{
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dSpT.add(R.sample01<sphericalTensor>());
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}
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Info<< "Mean " << dSpT.mean() << nl
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<< "Median " << dSpT.median()
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<< endl;
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dSpT.write("Distribution_sphericalTensor_test");
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}
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Info<< nl << "End" << nl << endl;
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return 0;
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}
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// ************************************************************************* //
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