131 lines
3.5 KiB
C++
131 lines
3.5 KiB
C++
/**
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* @file testChordalBayesNet.cpp
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* @brief Unit tests for ChordalBayesNet
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* @author Frank Dellaert
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*/
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// STL/C++
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#include <iostream>
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#include <sstream>
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#include <CppUnitLite/TestHarness.h>
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#include <boost/tuple/tuple.hpp>
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#include <boost/foreach.hpp>
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#ifdef HAVE_BOOST_SERIALIZATION
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#include <boost/archive/text_oarchive.hpp>
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#include <boost/archive/text_iarchive.hpp>
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#endif //HAVE_BOOST_SERIALIZATION
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#include "ChordalBayesNet.h"
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#include "smallExample.h"
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using namespace gtsam;
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/* ************************************************************************* */
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TEST( ChordalBayesNet, constructor )
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{
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// small Bayes Net x <- y
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// x y d
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// 1 1 9
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// 1 5
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Matrix R11 = Matrix_(1,1,1.0), S12 = Matrix_(1,1,1.0);
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Matrix R22 = Matrix_(1,1,1.0);
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Vector d1(1), d2(1);
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d1(0) = 9; d2(0) = 5;
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// define nodes and specify in reverse topological sort (i.e. parents last)
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ConditionalGaussian x(d1,R11,"y",S12), y(d2,R22);
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// check small example which uses constructor
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ChordalBayesNet cbn = createSmallChordalBayesNet();
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CHECK( x.equals(*cbn["x"]) );
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CHECK( y.equals(*cbn["y"]) );
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}
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/* ************************************************************************* */
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TEST( ChordalBayesNet, matrix )
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{
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// Create a test graph
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ChordalBayesNet cbn = createSmallChordalBayesNet();
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Matrix R; Vector d;
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boost::tie(R,d) = cbn.matrix(); // find matrix and RHS
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Matrix R1 = Matrix_(2,2,
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1.0, 1.0,
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0.0, 1.0
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);
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Vector d1 = Vector_(2, 9.0, 5.0);
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EQUALITY(R,R1);
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CHECK(d==d1);
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}
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/* ************************************************************************* */
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TEST( ChordalBayesNet, optimize )
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{
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// optimize small Bayes Net
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ChordalBayesNet cbn = createSmallChordalBayesNet();
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boost::shared_ptr<FGConfig> actual = cbn.optimize();
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FGConfig expected;
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Vector x(1), y(1);
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x(0) = 4; y(0) = 5;
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expected.insert("x",x);
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expected.insert("y",y);
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CHECK(actual->equals(expected));
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}
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/* ************************************************************************* */
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#ifdef HAVE_BOOST_SERIALIZATION
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TEST( ChordalBayesNet, serialize )
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{
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// //create a starting CBN
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// ChordalBayesNet cbn = createSmallChordalBayesNet();
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//
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// //serialize the CBN
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// std::ostringstream in_archive_stream;
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// boost::archive::text_oarchive in_archive(in_archive_stream);
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// in_archive << cbn;
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// std::string serialized = in_archive_stream.str();
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//
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// //DEBUG
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// std::cout << "CBN Raw string: [" << serialized << "]" << std::endl;
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//
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// //remove newlines/carriage returns
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// std::string clean;
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// BOOST_FOREACH(char s, serialized)
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// {
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// if (s != '\n')
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// {
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// //copy in character
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// clean.append(std::string(1,s));
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// }
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// else
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// {
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// std::cout << " Newline character found!" << std::endl;
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// //replace with an identifiable string
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// clean.append(std::string(1,' '));
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// }
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// }
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//
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//
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// std::cout << "Cleaned CBN String: [" << clean << "]" << std::endl;
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//
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// //deserialize the CBN
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// std::istringstream out_archive_stream(clean);
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// boost::archive::text_iarchive out_archive(out_archive_stream);
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// ChordalBayesNet output;
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// out_archive >> output;
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// CHECK(cbn.equals(output));
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}
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#endif //HAVE_BOOST_SERIALIZATION
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
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/* ************************************************************************* */
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