675 lines
19 KiB
C++
675 lines
19 KiB
C++
/**
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* @file testLinearFactorGraph.cpp
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* @brief Unit tests for Linear Factor Graph
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* @author Christian Potthast
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**/
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#include <string.h>
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#include <iostream>
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using namespace std;
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#include <boost/foreach.hpp>
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#include <boost/tuple/tuple.hpp>
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#include <boost/assign/std/list.hpp> // for operator +=
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using namespace boost::assign;
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#include <CppUnitLite/TestHarness.h>
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#include "Matrix.h"
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#include "Ordering.h"
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#include "smallExample.h"
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#include "GaussianBayesNet.h"
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#include <FactorGraph-inl.h> // needed for FactorGraph::eliminate
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using namespace gtsam;
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double tol=1e-4;
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/* ************************************************************************* */
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/* unit test for equals (LinearFactorGraph1 == LinearFactorGraph2) */
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/* ************************************************************************* */
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TEST( LinearFactorGraph, equals ){
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LinearFactorGraph fg = createLinearFactorGraph();
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LinearFactorGraph fg2 = createLinearFactorGraph();
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CHECK(fg.equals(fg2));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, error )
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{
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LinearFactorGraph fg = createLinearFactorGraph();
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VectorConfig cfg = createZeroDelta();
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// note the error is the same as in testNonlinearFactorGraph as a
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// zero delta config in the linear graph is equivalent to noisy in
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// non-linear, which is really linear under the hood
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double actual = fg.error(cfg);
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DOUBLES_EQUAL( 5.625, actual, 1e-9 );
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}
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/* ************************************************************************* */
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/* unit test for find seperator */
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/* ************************************************************************* */
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TEST( LinearFactorGraph, find_separator )
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{
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LinearFactorGraph fg = createLinearFactorGraph();
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set<string> separator = fg.find_separator("x2");
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set<string> expected;
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expected.insert("x1");
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expected.insert("l1");
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CHECK(separator.size()==expected.size());
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set<string>::iterator it1 = separator.begin(), it2 = expected.begin();
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for(; it1!=separator.end(); it1++, it2++)
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CHECK(*it1 == *it2);
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, combine_factors_x1 )
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{
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// create a small example for a linear factor graph
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LinearFactorGraph fg = createLinearFactorGraph();
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// create sigmas
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double sigma1 = 0.1;
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double sigma2 = 0.1;
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double sigma3 = 0.2;
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Vector sigmas = Vector_(6, sigma1, sigma1, sigma2, sigma2, sigma3, sigma3);
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// combine all factors
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LinearFactor::shared_ptr actual = removeAndCombineFactors(fg,"x1");
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// the expected linear factor
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Matrix Al1 = Matrix_(6,2,
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0., 0.,
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0., 0.,
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0., 0.,
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0., 0.,
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1., 0.,
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0., 1.
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);
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Matrix Ax1 = Matrix_(6,2,
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1., 0.,
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0.00, 1.,
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-1., 0.,
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0.00,-1.,
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-1., 0.,
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00., -1.
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);
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Matrix Ax2 = Matrix_(6,2,
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0., 0.,
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0., 0.,
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1., 0.,
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+0.,1.,
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0., 0.,
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0., 0.
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);
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// the expected RHS vector
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Vector b(6);
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b(0) = -1*sigma1;
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b(1) = -1*sigma1;
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b(2) = 2*sigma2;
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b(3) = -1*sigma2;
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b(4) = 0*sigma3;
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b(5) = 1*sigma3;
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vector<pair<string, Matrix> > meas;
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meas.push_back(make_pair("l1", Al1));
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meas.push_back(make_pair("x1", Ax1));
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meas.push_back(make_pair("x2", Ax2));
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LinearFactor expected(meas, b, sigmas);
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//LinearFactor expected("l1", Al1, "x1", Ax1, "x2", Ax2, b);
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// check if the two factors are the same
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CHECK(assert_equal(expected,*actual));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, combine_factors_x2 )
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{
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// create a small example for a linear factor graph
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LinearFactorGraph fg = createLinearFactorGraph();
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// determine sigmas
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double sigma1 = 0.1;
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double sigma2 = 0.2;
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Vector sigmas = Vector_(4, sigma1, sigma1, sigma2, sigma2);
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// combine all factors
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LinearFactor::shared_ptr actual = removeAndCombineFactors(fg,"x2");
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// the expected linear factor
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Matrix Al1 = Matrix_(4,2,
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// l1
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0., 0.,
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0., 0.,
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1., 0.,
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0., 1.
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);
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Matrix Ax1 = Matrix_(4,2,
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// x1
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-1., 0., // f2
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0.00,-1., // f2
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0.00, 0., // f4
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0.00, 0. // f4
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);
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Matrix Ax2 = Matrix_(4,2,
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// x2
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1., 0.,
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+0.,1.,
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-1., 0.,
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+0.,-1.
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);
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// the expected RHS vector
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Vector b(4);
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b(0) = 2*sigma1;
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b(1) = -1*sigma1;
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b(2) = -1*sigma2;
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b(3) = 1.5*sigma2;
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vector<pair<string, Matrix> > meas;
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meas.push_back(make_pair("l1", Al1));
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meas.push_back(make_pair("x1", Ax1));
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meas.push_back(make_pair("x2", Ax2));
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LinearFactor expected(meas, b, sigmas);
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// check if the two factors are the same
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CHECK(assert_equal(expected,*actual));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, eliminateOne_x1 )
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{
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LinearFactorGraph fg = createLinearFactorGraph();
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ConditionalGaussian::shared_ptr actual = fg.eliminateOne("x1");
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// create expected Conditional Gaussian
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Matrix R11 = Matrix_(2,2,
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1.0, 0.0,
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0.0, 1.0
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);
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Matrix S12 = Matrix_(2,2,
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-0.111111, 0.00,
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+0.00,-0.111111
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);
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Matrix S13 = Matrix_(2,2,
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-0.444444, 0.00,
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+0.00,-0.444444
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);
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Vector d(2); d(0) = -0.133333; d(1) = -0.0222222;
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Vector sigma(2); sigma(0) = 1./15; sigma(1) = 1./15;
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ConditionalGaussian expected("x1",d,R11,"l1",S12,"x2",S13,sigma);
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CHECK(assert_equal(expected,*actual,tol));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, eliminateOne_x2 )
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{
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LinearFactorGraph fg = createLinearFactorGraph();
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ConditionalGaussian::shared_ptr actual = fg.eliminateOne("x2");
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// create expected Conditional Gaussian
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Matrix R11 = Matrix_(2,2,
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1.0, 0.0,
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0.0, 1.0
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);
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Matrix S12 = Matrix_(2,2,
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-0.2, 0.0,
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+0.0,-0.2
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);
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Matrix S13 = Matrix_(2,2,
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-0.8, 0.0,
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+0.0,-0.8
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);
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Vector d(2); d(0) = 0.2; d(1) = -0.14;
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Vector sigma(2); sigma(0) = 0.0894427; sigma(1) = 0.0894427;
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ConditionalGaussian expected("x2",d,R11,"l1",S12,"x1",S13,sigma);
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CHECK(assert_equal(expected,*actual,tol));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, eliminateOne_l1 )
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{
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LinearFactorGraph fg = createLinearFactorGraph();
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ConditionalGaussian::shared_ptr actual = fg.eliminateOne("l1");
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// create expected Conditional Gaussian
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Matrix R11 = Matrix_(2,2,
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1.0, 0.0,
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0.0, 1.0
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);
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Matrix S12 = Matrix_(2,2,
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-0.5, 0.0,
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+0.0,-0.5
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);
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Matrix S13 = Matrix_(2,2,
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-0.5, 0.0,
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+0.0,-0.5
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);
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Vector d(2); d(0) = -0.1; d(1) = 0.25;
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Vector sigma(2); sigma(0) = 0.141421; sigma(1) = 0.141421;
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ConditionalGaussian expected("l1",d,R11,"x1",S12,"x2",S13,sigma);
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CHECK(assert_equal(expected,*actual,tol));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, eliminateAll )
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{
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// create expected Chordal bayes Net
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double data1[] = { 1.0, 0.0,
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0.0, 1.0};
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Matrix R1 = Matrix_(2,2, data1);
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Vector d1(2); d1(0) = -0.1; d1(1) = -0.1;
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Vector sigma1(2); sigma1(0) = 0.1; sigma1(1) = 0.1;
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ConditionalGaussian::shared_ptr cg1(new ConditionalGaussian("x1",d1, R1, sigma1));
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double data21[] = { 1.0, 0.0,
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0.0, 1.0};
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Matrix R2 = Matrix_(2,2, data21);
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double data22[] = { -1.0, 0.0,
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0.0, -1.0};
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Matrix A1 = Matrix_(2,2, data22);
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Vector d2(2); d2(0) = 0.0; d2(1) = 0.2;
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Vector sigma2(2); sigma2(0) = 0.149071; sigma2(1) = 0.149071;
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ConditionalGaussian::shared_ptr cg2(new ConditionalGaussian("l1",d2, R2,"x1", A1,sigma2));
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double data31[] = { 1.0, 0.0,
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0.0, 1.0};
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Matrix R3 = Matrix_(2,2, data31);
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double data32[] = { -0.2, 0.0,
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0.0, -0.2};
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Matrix A21 = Matrix_(2,2, data32);
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double data33[] = { -0.8, 0.0,
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0.0, -0.8};
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Matrix A22 = Matrix_(2,2, data33);
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Vector d3(2); d3(0) = 0.2; d3(1) = -0.14;
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Vector sigma3(2); sigma3(0) = 0.0894427; sigma3(1) = 0.0894427;
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ConditionalGaussian::shared_ptr cg3(new ConditionalGaussian("x2",d3, R3,"l1", A21, "x1", A22, sigma3));
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GaussianBayesNet expected;
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expected.push_back(cg3);
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expected.push_back(cg2);
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expected.push_back(cg1);
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// Check one ordering
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LinearFactorGraph fg1 = createLinearFactorGraph();
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Ordering ord1;
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ord1 += "x2","l1","x1";
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GaussianBayesNet actual = fg1.eliminate(ord1);
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CHECK(assert_equal(expected,actual,tol));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, add_priors )
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{
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LinearFactorGraph fg = createLinearFactorGraph();
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LinearFactorGraph actual = fg.add_priors(3);
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LinearFactorGraph expected = createLinearFactorGraph();
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Matrix A = eye(2);
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Vector b = zero(2);
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double sigma = 3.0;
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expected.push_back(LinearFactor::shared_ptr(new LinearFactor("l1",A,b,sigma)));
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expected.push_back(LinearFactor::shared_ptr(new LinearFactor("x1",A,b,sigma)));
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expected.push_back(LinearFactor::shared_ptr(new LinearFactor("x2",A,b,sigma)));
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CHECK(assert_equal(expected,actual)); // Fails
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, copying )
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{
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// Create a graph
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LinearFactorGraph actual = createLinearFactorGraph();
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// Copy the graph !
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LinearFactorGraph copy = actual;
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// now eliminate the copy
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Ordering ord1;
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ord1 += "x2","l1","x1";
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GaussianBayesNet actual1 = copy.eliminate(ord1);
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// Create the same graph, but not by copying
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LinearFactorGraph expected = createLinearFactorGraph();
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// and check that original is still the same graph
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CHECK(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, matrix )
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{
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// Create a graph
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LinearFactorGraph fg = createLinearFactorGraph();
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// render with a given ordering
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Ordering ord;
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ord += "x2","l1","x1";
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Matrix A; Vector b;
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boost::tie(A,b) = fg.matrix(ord);
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Matrix A1 = Matrix_(2*4,3*2,
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+0., 0., 0., 0., 10., 0., // unary factor on x1 (prior)
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+0., 0., 0., 0., 0., 10.,
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10., 0., 0., 0.,-10., 0., // binary factor on x2,x1 (odometry)
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+0., 10., 0., 0., 0.,-10.,
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+0., 0., 5., 0., -5., 0., // binary factor on l1,x1 (z1)
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+0., 0., 0., 5., 0., -5.,
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-5., 0., 5., 0., 0., 0., // binary factor on x2,l1 (z2)
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+0., -5., 0., 5., 0., 0.
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);
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Vector b1 = Vector_(8,-1., -1., 2., -1., 0., 1., -1., 1.5);
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EQUALITY(A,A1);
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CHECK(b==b1);
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, sparse )
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{
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// create a small linear factor graph
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LinearFactorGraph fg = createLinearFactorGraph();
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// render with a given ordering
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Ordering ord;
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ord += "x2","l1","x1";
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Matrix ijs = fg.sparse(ord);
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EQUALITY(ijs, Matrix_(3, 14,
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// f(x1) f(x2,x1) f(l1,x1) f(x2,l1)
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+1., 2., 3., 4., 3., 4., 5.,6., 5., 6., 7., 8.,7.,8.,
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+5., 6., 1., 2., 5., 6., 3.,4., 5., 6., 1., 2.,3.,4.,
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10.,10., 10.,10.,-10.,-10., 5.,5.,-5.,-5., -5.,-5.,5.,5.));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, CONSTRUCTOR_GaussianBayesNet )
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{
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LinearFactorGraph fg = createLinearFactorGraph();
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// render with a given ordering
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Ordering ord;
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ord += "x2","l1","x1";
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GaussianBayesNet CBN = fg.eliminate(ord);
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// True LinearFactorGraph
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LinearFactorGraph fg2(CBN);
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GaussianBayesNet CBN2 = fg2.eliminate(ord);
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CHECK(assert_equal(CBN,CBN2));
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// Base FactorGraph only
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FactorGraph<LinearFactor> fg3(CBN);
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GaussianBayesNet CBN3 = _eliminate<LinearFactor,ConditionalGaussian>(fg3,ord);
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CHECK(assert_equal(CBN,CBN3));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, GET_ORDERING)
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{
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Ordering expected;
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expected += "l1","x1","x2";
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LinearFactorGraph fg = createLinearFactorGraph();
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Ordering actual = fg.getOrdering();
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CHECK(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, OPTIMIZE )
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{
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// create a graph
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LinearFactorGraph fg = createLinearFactorGraph();
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// create an ordering
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Ordering ord = fg.getOrdering();
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// optimize the graph
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VectorConfig actual = fg.optimize(ord);
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// verify
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VectorConfig expected = createCorrectDelta();
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CHECK(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, COMBINE_GRAPHS_INPLACE)
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{
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// create a test graph
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LinearFactorGraph fg1 = createLinearFactorGraph();
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// create another factor graph
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LinearFactorGraph fg2 = createLinearFactorGraph();
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// get sizes
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int size1 = fg1.size();
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int size2 = fg2.size();
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// combine them
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fg1.combine(fg2);
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CHECK(size1+size2 == fg1.size());
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, COMBINE_GRAPHS)
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{
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// create a test graph
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LinearFactorGraph fg1 = createLinearFactorGraph();
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// create another factor graph
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LinearFactorGraph fg2 = createLinearFactorGraph();
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// get sizes
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int size1 = fg1.size();
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int size2 = fg2.size();
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// combine them
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LinearFactorGraph fg3 = LinearFactorGraph::combine2(fg1, fg2);
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CHECK(size1+size2 == fg3.size());
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}
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/* ************************************************************************* */
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// print a vector of ints if needed for debugging
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void print(vector<int> v) {
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for (int k = 0; k < v.size(); k++)
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cout << v[k] << " ";
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cout << endl;
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, factor_lookup)
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{
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// create a test graph
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LinearFactorGraph fg = createLinearFactorGraph();
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// ask for all factor indices connected to x1
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list<int> x1_factors = fg.factors("x1");
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int x1_indices[] = { 0, 1, 2 };
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list<int> x1_expected(x1_indices, x1_indices + 3);
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CHECK(x1_factors==x1_expected);
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// ask for all factor indices connected to x2
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list<int> x2_factors = fg.factors("x2");
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int x2_indices[] = { 1, 3 };
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list<int> x2_expected(x2_indices, x2_indices + 2);
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CHECK(x2_factors==x2_expected);
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}
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/* ************************************************************************* */
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TEST( LinearFactorGraph, findAndRemoveFactors )
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{
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// create the graph
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LinearFactorGraph fg = createLinearFactorGraph();
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|
// We expect to remove these three factors: 0, 1, 2
|
|
LinearFactor::shared_ptr f0 = fg[0];
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|
LinearFactor::shared_ptr f1 = fg[1];
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|
LinearFactor::shared_ptr f2 = fg[2];
|
|
|
|
// call the function
|
|
vector<LinearFactor::shared_ptr> factors = fg.findAndRemoveFactors("x1");
|
|
|
|
// Check the factors
|
|
CHECK(f0==factors[0]);
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|
CHECK(f1==factors[1]);
|
|
CHECK(f2==factors[2]);
|
|
|
|
// CHECK if the factors are deleted from the factor graph
|
|
LONGS_EQUAL(1,fg.nrFactors());
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( LinearFactorGraph, findAndRemoveFactors_twice )
|
|
{
|
|
// create the graph
|
|
LinearFactorGraph fg = createLinearFactorGraph();
|
|
|
|
// We expect to remove these three factors: 0, 1, 2
|
|
LinearFactor::shared_ptr f0 = fg[0];
|
|
LinearFactor::shared_ptr f1 = fg[1];
|
|
LinearFactor::shared_ptr f2 = fg[2];
|
|
|
|
// call the function
|
|
vector<LinearFactor::shared_ptr> factors = fg.findAndRemoveFactors("x1");
|
|
|
|
// Check the factors
|
|
CHECK(f0==factors[0]);
|
|
CHECK(f1==factors[1]);
|
|
CHECK(f2==factors[2]);
|
|
|
|
factors = fg.findAndRemoveFactors("x1");
|
|
CHECK(factors.size() == 0);
|
|
|
|
// CHECK if the factors are deleted from the factor graph
|
|
LONGS_EQUAL(1,fg.nrFactors());
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(LinearFactorGraph, createSmoother)
|
|
{
|
|
LinearFactorGraph fg1 = createSmoother(2);
|
|
LONGS_EQUAL(3,fg1.size());
|
|
LinearFactorGraph fg2 = createSmoother(3);
|
|
LONGS_EQUAL(5,fg2.size());
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( LinearFactorGraph, variables )
|
|
{
|
|
LinearFactorGraph fg = createLinearFactorGraph();
|
|
Dimensions expected;
|
|
insert(expected)("l1", 2)("x1", 2)("x2", 2);
|
|
Dimensions actual = fg.dimensions();
|
|
CHECK(expected==actual);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
// Tests ported from ConstrainedLinearFactorGraph
|
|
/* ************************************************************************* */
|
|
|
|
/* ************************************************************************* */
|
|
TEST( LinearFactorGraph, constrained_simple )
|
|
{
|
|
// get a graph with a constraint in it
|
|
LinearFactorGraph fg = createSimpleConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
Ordering ord;
|
|
ord += "x", "y";
|
|
VectorConfig actual = fg.optimize(ord);
|
|
|
|
// verify
|
|
VectorConfig expected = createSimpleConstraintConfig();
|
|
CHECK(assert_equal(actual, expected));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( LinearFactorGraph, constrained_single )
|
|
{
|
|
// get a graph with a constraint in it
|
|
LinearFactorGraph fg = createSingleConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
Ordering ord;
|
|
ord += "x", "y";
|
|
VectorConfig actual = fg.optimize(ord);
|
|
|
|
// verify
|
|
VectorConfig expected = createSingleConstraintConfig();
|
|
CHECK(assert_equal(actual, expected));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( LinearFactorGraph, constrained_single2 )
|
|
{
|
|
// get a graph with a constraint in it
|
|
LinearFactorGraph fg = createSingleConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
Ordering ord;
|
|
ord += "y", "x";
|
|
VectorConfig actual = fg.optimize(ord);
|
|
|
|
// verify
|
|
VectorConfig expected = createSingleConstraintConfig();
|
|
CHECK(assert_equal(actual, expected));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( LinearFactorGraph, constrained_multi1 )
|
|
{
|
|
// get a graph with a constraint in it
|
|
LinearFactorGraph fg = createMultiConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
Ordering ord;
|
|
ord += "x", "y", "z";
|
|
VectorConfig actual = fg.optimize(ord);
|
|
|
|
// verify
|
|
VectorConfig expected = createMultiConstraintConfig();
|
|
CHECK(assert_equal(actual, expected));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( LinearFactorGraph, constrained_multi2 )
|
|
{
|
|
// get a graph with a constraint in it
|
|
LinearFactorGraph fg = createMultiConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
Ordering ord;
|
|
ord += "z", "x", "y";
|
|
VectorConfig actual = fg.optimize(ord);
|
|
|
|
// verify
|
|
VectorConfig expected = createMultiConstraintConfig();
|
|
CHECK(assert_equal(actual, expected));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
|
/* ************************************************************************* */
|