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/*
* Copyright (c) 2017, Miroslav Stoyanov
*
* This file is part of
* Toolkit for Adaptive Stochastic Modeling And Non-Intrusive ApproximatioN: TASMANIAN
*
* 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 code must retain the above copyright notice, this list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions
* and the following disclaimer in the documentation and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse
* or promote products derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
* INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
* OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
* OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* UT-BATTELLE, LLC AND THE UNITED STATES GOVERNMENT MAKE NO REPRESENTATIONS AND DISCLAIM ALL WARRANTIES, BOTH EXPRESSED AND IMPLIED.
* THERE ARE NO EXPRESS OR IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, OR THAT THE USE OF THE SOFTWARE WILL NOT INFRINGE ANY PATENT,
* COPYRIGHT, TRADEMARK, OR OTHER PROPRIETARY RIGHTS, OR THAT THE SOFTWARE WILL ACCOMPLISH THE INTENDED RESULTS OR THAT THE SOFTWARE OR ITS USE WILL NOT RESULT IN INJURY OR DAMAGE.
* THE USER ASSUMES RESPONSIBILITY FOR ALL LIABILITIES, PENALTIES, FINES, CLAIMS, CAUSES OF ACTION, AND COSTS AND EXPENSES, CAUSED BY, RESULTING FROM OR ARISING OUT OF,
* IN WHOLE OR IN PART THE USE, STORAGE OR DISPOSAL OF THE SOFTWARE.
*/
#include "TasmanianAddons.hpp"
#include "gridtestCLICommon.hpp"
inline bool testLikelySendRecv(){
int me = TasGrid::getMPIRank(MPI_COMM_WORLD);
TasDREAM::LikelihoodGaussIsotropic ref_isolike(10.0, {1.0, 2.0, 3.0});
if (me == 0){
if (TasDREAM::MPILikelihoodSend(ref_isolike, 1, 11, MPI_COMM_WORLD) != MPI_SUCCESS) return false;
}else if (me == 1){
TasDREAM::LikelihoodGaussIsotropic isolike;
if (TasDREAM::MPILikelihoodRecv(isolike, 0, 11, MPI_COMM_WORLD) != MPI_SUCCESS) return false;
std::vector<double> model = {1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0}; // full rank matrix to cover all entries
std::vector<double> result(3), true_result(3);
isolike.getLikelihood(TasDREAM::logform, model, result);
ref_isolike.getLikelihood(TasDREAM::logform, model, true_result);
for(size_t i=0; i<3; i++) if (std::abs(result[i] - true_result[i]) > TasGrid::Maths::num_tol) return false;
}
TasDREAM::LikelihoodGaussAnisotropic ref_alike({4.0, 5.0, 6.0}, {1.0, 2.0, 3.0});
if (me == 1){
if (TasDREAM::MPILikelihoodSend(ref_alike, 2, 12, MPI_COMM_WORLD) != MPI_SUCCESS) return false;
}else if (me == 2){
TasDREAM::LikelihoodGaussAnisotropic alike;
if (TasDREAM::MPILikelihoodRecv(alike, 1, 12, MPI_COMM_WORLD) != MPI_SUCCESS) return false;
std::vector<double> model = {1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0}; // full rank matrix to cover all entries
std::vector<double> result(3), true_result(3);
alike.getLikelihood(TasDREAM::logform, model, result);
ref_alike.getLikelihood(TasDREAM::logform, model, true_result);
for(size_t i=0; i<3; i++) if (std::abs(result[i] - true_result[i]) > TasGrid::Maths::num_tol) return false;
}
return true;
}
inline bool testLikelyScatter(){
int me = TasGrid::getMPIRank(MPI_COMM_WORLD);
int tag = 11;
TasDREAM::LikelihoodGaussIsotropic source(10.0, {1.0, 2.0, 3.0});
TasDREAM::LikelihoodGaussIsotropic reference;
TasDREAM::LikelihoodGaussIsotropic destination;
if (me == 0){
reference.setData(10.0, {1.0});
MPILikelihoodScatter(source, destination, 0, tag, MPI_COMM_WORLD);
}else if (me == 1){
reference.setData(10.0, {2.0});
MPILikelihoodScatter(TasDREAM::LikelihoodGaussIsotropic(), destination, 0, tag, MPI_COMM_WORLD);
}else{
reference.setData(10.0, {3.0});
MPILikelihoodScatter(TasDREAM::LikelihoodGaussIsotropic(), destination, 0, tag, MPI_COMM_WORLD);
}
std::vector<double> model = {1.0}; // full rank matrix to cover all entries
std::vector<double> result(1), true_result(1);
destination.getLikelihood(TasDREAM::logform, model, result);
reference.getLikelihood(TasDREAM::logform, model, true_result);
if (std::abs(result[0] - true_result[0]) > TasGrid::Maths::num_tol) return false;
TasDREAM::LikelihoodGaussAnisotropic asource({14.0, 15.0}, {1.0, 2.0});
TasDREAM::LikelihoodGaussAnisotropic areference;
TasDREAM::LikelihoodGaussAnisotropic adestination;
if (me == 0){
areference.setData({14.0}, {1.0});
MPILikelihoodScatter(asource, adestination, 0, tag, MPI_COMM_WORLD);
}else if (me == 1){
areference.setData({15.0}, {2.0});
MPILikelihoodScatter(TasDREAM::LikelihoodGaussAnisotropic(), adestination, 0, tag, MPI_COMM_WORLD);
}else{
MPILikelihoodScatter(TasDREAM::LikelihoodGaussAnisotropic(), adestination, 0, tag, MPI_COMM_WORLD);
}
if (me != 2){
result = {0.0};
true_result = {11.0};
adestination.getLikelihood(TasDREAM::logform, model, result);
areference.getLikelihood(TasDREAM::logform, model, true_result);
if (std::abs(result[0] - true_result[0]) > TasGrid::Maths::num_tol) return false;
}else{
if (adestination.getNumOutputs() != 0){
std::cout << "last rank did not receive empty likelihood." << std::endl;
return false;
}
}
return true;
}
void testMPIDream(){
int num_chains = 10;
int me = TasGrid::getMPIRank(MPI_COMM_WORLD);
auto full_grid = TasGrid::makeSequenceGrid(2, 7, 2, TasGrid::type_level, TasGrid::rule_rleja);
TasGrid::loadNeededPoints<mode_sequential>([&](std::vector<double> const &x, std::vector<double> &y, size_t)->void{
y = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0};
for(size_t i=0; i<y.size(); i++)
y[i] += 0.1 * x[i%2]; // add perturbation to y
}, full_grid, 0);
auto grid = TasGrid::makeEmpty();
TasDREAM::LikelihoodGaussAnisotropic full_likelihood({0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0});
TasDREAM::LikelihoodGaussAnisotropic likely;
TasDREAM::TasmanianDREAM full_state(num_chains, 2);
TasDREAM::TasmanianDREAM state = (me == 0) ? full_state : TasDREAM::TasmanianDREAM();
TasGrid::MPIGridScatterOutputs(full_grid, grid, 0, 11, MPI_COMM_WORLD);
TasDREAM::MPILikelihoodScatter(full_likelihood, likely, 0, 13, MPI_COMM_WORLD);
std::minstd_rand park_miller_init(42), park_miller1(77), park_miller2(77);
std::uniform_real_distribution<double> unif(0.0, 1.0);
std::vector<double> initial_state;
TasDREAM::genGaussianSamples({0.0, 0.0}, {0.2, 0.2}, num_chains, initial_state, [&]()->double{ return unif(park_miller_init); });
if (me == 0) state.setState(initial_state);
full_state.setState(initial_state);
TasDREAM::SampleDREAM(10, 10,
TasDREAM::DistributedPosterior<TasDREAM::regform>(grid, likely, TasDREAM::uniform_prior, 2, num_chains, 0, MPI_COMM_WORLD),
grid.getDomainInside(),
state,
TasDREAM::dist_uniform, 0.05,
TasDREAM::const_percent<50>,
[&]()->double{ return unif(park_miller1); }
);
TasDREAM::SampleDREAM(10, 10,
TasDREAM::posterior<TasDREAM::regform>(full_grid, full_likelihood, TasDREAM::uniform_prior),
grid.getDomainInside(),
full_state,
TasDREAM::dist_uniform, 0.05,
TasDREAM::const_percent<50>,
[&]()->double{ return unif(park_miller2); }
);
if (me == 0){
std::vector<double> mean, variance;
state.getHistoryMeanVariance(mean, variance);
std::vector<double> ref_mean, ref_variance;
full_state.getHistoryMeanVariance(ref_mean, ref_variance);
if (((std::abs(mean[0] - ref_mean[0]) + std::abs(mean[1] - ref_mean[1])) > 1.E-9) ||
((std::abs(variance[0] - ref_variance[0]) + std::abs(variance[1] - ref_variance[1])) > 1.E-9))
throw std::runtime_error("ERROR: mismatch in sampling between reference and computed DREAM.");
}
}
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