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|
/* +------------------------------------------------------------------------+
| Mobile Robot Programming Toolkit (MRPT) |
| https://www.mrpt.org/ |
| |
| Copyright (c) 2005-2023, Individual contributors, see AUTHORS file |
| See: https://www.mrpt.org/Authors - All rights reserved. |
| Released under BSD License. See: https://www.mrpt.org/License |
+------------------------------------------------------------------------+ */
#include <mrpt/bayes/CParticleFilterCapable.h>
#include <mrpt/io/vector_loadsave.h>
#include <mrpt/math/data_utils.h>
#include <mrpt/math/ops_vectors.h>
#include <mrpt/math/utils.h>
#include <mrpt/random.h>
#include <mrpt/system/filesystem.h>
#include <iostream>
#include <map>
using namespace mrpt::bayes;
using namespace mrpt::math;
using namespace mrpt::random;
using namespace mrpt::system;
using namespace mrpt::io;
using namespace std;
double MIN_LOG_WEIG = -1.0;
unsigned int N_TESTS = 500;
int N_PARTICLES = 100;
// For batch experiment:
CVectorDouble min_log_ws;
map<string, CVectorDouble> results;
// vectorToTextFile( out_indxs, #ALGOR, true, true); /* By rows, append */
#define TEST_RESAMPLING(ALGOR) \
mrpt::system::deleteFile(#ALGOR); \
/*printf(#ALGOR);*/ \
/*printf("\n");*/ \
ERR_MEANs.clear(); \
ERR_STDs.clear(); \
for (size_t i = 0; i < N_TESTS; i++) \
{ \
mrpt::random::getRandomGenerator().drawUniformVector( \
log_ws, MIN_LOG_WEIG, 0.0); \
CParticleFilterCapable::log2linearWeights(log_ws, lin_ws); \
CParticleFilterCapable::computeResampling( \
CParticleFilter::ALGOR, log_ws, out_indxs); \
hist_parts = mrpt::math::histogram(out_indxs, 0, M - 1, M, true); \
vector<double> errs_hist = lin_ws - hist_parts; \
ERR_MEANs.push_back(mrpt::math::mean(errs_hist)); \
ERR_STDs.push_back(mrpt::math::stddev(errs_hist)); \
} \
printf("%s: ERR_MEAN %e\n", #ALGOR, mrpt::math::mean(ERR_MEANs)); \
printf("%s: ERR_STD %f\n", #ALGOR, mrpt::math::mean(ERR_STDs)); \
results[#ALGOR].push_back(mrpt::math::mean(ERR_STDs));
// ------------------------------------------------------
// TestResampling
// ------------------------------------------------------
void TestResampling()
{
vector<double> log_ws;
std::vector<size_t> out_indxs;
const size_t M = N_PARTICLES;
log_ws.resize(M);
// vectorToTextFile( log_ws, "log_ws.txt");
// Compute normalized linear weights:
vector<double> lin_ws;
vector<double> hist_parts;
vector<double> ERR_MEANs;
vector<double> ERR_STDs;
// prMultinomial
TEST_RESAMPLING(prMultinomial)
// prResidual
TEST_RESAMPLING(prResidual)
// prStratified
TEST_RESAMPLING(prStratified)
// prSystematic
TEST_RESAMPLING(prSystematic)
}
void TestBatch()
{
for (double LL = -2; LL <= 2.01; LL += 0.08)
{
double L = pow(10.0, LL);
min_log_ws.push_back(L);
printf("MIN_LOG_W=%f\n", L);
MIN_LOG_WEIG = L;
TestResampling();
}
// Save results to files:
CVectorDouble R;
vectorToTextFile(min_log_ws, "min_log_ws.txt");
R = results["prMultinomial"];
vectorToTextFile(R, "prMultinomial.txt");
R = results["prResidual"];
vectorToTextFile(R, "prResidual.txt");
R = results["prStratified"];
vectorToTextFile(R, "prStratified.txt");
R = results["prSystematic"];
vectorToTextFile(R, "prSystematic.txt");
}
// ------------------------------------------------------
// MAIN
// ------------------------------------------------------
int main(int argc, char** argv)
{
try
{
getRandomGenerator().randomize();
if (argc > 1) N_PARTICLES = atoi(argv[1]);
// TestResampling();
TestBatch();
return 0;
}
catch (exception& e)
{
std::cerr << "MRPT error: " << mrpt::exception_to_str(e) << std::endl;
return -1;
}
catch (...)
{
cerr << "Untyped excepcion!!";
return -1;
}
}
|
