848 lines
25 KiB
C++
848 lines
25 KiB
C++
/**
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* @file testGaussianFactorGraph.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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#include <boost/assign/std/vector.hpp> // for operator +=
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using namespace boost::assign;
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#include <CppUnitLite/TestHarness.h>
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#define GTSAM_MAGIC_KEY
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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 "numericalDerivative.h"
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#include "inference-inl.h" // needed for eliminate and marginals
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using namespace gtsam;
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using namespace example;
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double tol=1e-5;
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/* ************************************************************************* */
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/* unit test for equals (GaussianFactorGraph1 == GaussianFactorGraph2) */
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, equals ){
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GaussianFactorGraph fg = createGaussianFactorGraph();
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GaussianFactorGraph fg2 = createGaussianFactorGraph();
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CHECK(fg.equals(fg2));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, error )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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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( GaussianFactorGraph, find_separator )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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set<Symbol> separator = fg.find_separator("x2");
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set<Symbol> 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<Symbol>::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( GaussianFactorGraph, combine_factors_x1 )
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{
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// create a small example for a linear factor graph
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GaussianFactorGraph fg = createGaussianFactorGraph();
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// combine all factors
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GaussianFactor::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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5., 0.,
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0., 5.
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);
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Matrix Ax1 = Matrix_(6,2,
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10., 0.,
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0., 10.,
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-10., 0.,
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0.,-10.,
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-5., 0.,
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0.,-5.
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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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10., 0.,
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0., 10.,
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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;
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b(1) = -1;
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b(2) = 2;
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b(3) = -1;
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b(4) = 0;
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b(5) = 1;
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vector<pair<Symbol, 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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GaussianFactor expected(meas, b, ones(6));
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//GaussianFactor 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( GaussianFactorGraph, combine_factors_x2 )
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{
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// create a small example for a linear factor graph
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GaussianFactorGraph fg = createGaussianFactorGraph();
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// combine all factors
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GaussianFactor::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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5., 0.,
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0., 5.
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);
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Matrix Ax1 = Matrix_(4,2,
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// x1
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-10., 0., // f2
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0.,-10., // f2
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0., 0., // f4
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0., 0. // f4
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);
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Matrix Ax2 = Matrix_(4,2,
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// x2
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10., 0.,
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0., 10.,
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-5., 0.,
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0.,-5.
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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;
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b(1) = -1;
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b(2) = -1;
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b(3) = 1.5;
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vector<pair<Symbol, 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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GaussianFactor expected(meas, b, ones(4));
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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( GaussianFactorGraph, eliminateOne_x1 )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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GaussianConditional::shared_ptr actual = fg.eliminateOne("x1");
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// create expected Conditional Gaussian
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Matrix I = 15*eye(2), R11 = I, S12 = -0.111111*I, S13 = -0.444444*I;
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Vector d = Vector_(2, -0.133333, -0.0222222), sigma = ones(2);
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GaussianConditional expected("x1",15*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( GaussianFactorGraph, eliminateOne_x2 )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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GaussianConditional::shared_ptr actual = fg.eliminateOne("x2");
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// create expected Conditional Gaussian
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double sig = 0.0894427;
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Matrix I = eye(2)/sig, R11 = I, S12 = -0.2*I, S13 = -0.8*I;
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Vector d = Vector_(2, 0.2, -0.14)/sig, sigma = ones(2);
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GaussianConditional 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( GaussianFactorGraph, eliminateOne_l1 )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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GaussianConditional::shared_ptr actual = fg.eliminateOne("l1");
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// create expected Conditional Gaussian
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double sig = sqrt(2)/10.;
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Matrix I = eye(2)/sig, R11 = I, S12 = -0.5*I, S13 = -0.5*I;
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Vector d = Vector_(2, -0.1, 0.25)/sig, sigma = ones(2);
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GaussianConditional 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( GaussianFactorGraph, eliminateOne_x1_fast )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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GaussianConditional::shared_ptr actual = fg.eliminateOne("x1", false);
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// create expected Conditional Gaussian
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Matrix I = 15*eye(2), R11 = I, S12 = -0.111111*I, S13 = -0.444444*I;
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Vector d = Vector_(2, -0.133333, -0.0222222), sigma = ones(2);
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GaussianConditional expected("x1",15*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( GaussianFactorGraph, eliminateOne_x2_fast )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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GaussianConditional::shared_ptr actual = fg.eliminateOne("x2", false);
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// create expected Conditional Gaussian
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double sig = 0.0894427;
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Matrix I = eye(2)/sig, R11 = I, S12 = -0.2*I, S13 = -0.8*I;
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Vector d = Vector_(2, 0.2, -0.14)/sig, sigma = ones(2);
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GaussianConditional 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( GaussianFactorGraph, eliminateOne_l1_fast )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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GaussianConditional::shared_ptr actual = fg.eliminateOne("l1", false);
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// create expected Conditional Gaussian
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double sig = sqrt(2)/10.;
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Matrix I = eye(2)/sig, R11 = I, S12 = -0.5*I, S13 = -0.5*I;
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Vector d = Vector_(2, -0.1, 0.25)/sig, sigma = ones(2);
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GaussianConditional 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( GaussianFactorGraph, eliminateAll )
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{
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// create expected Chordal bayes Net
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Matrix I = eye(2);
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Vector d1 = Vector_(2, -0.1,-0.1);
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GaussianBayesNet expected = simpleGaussian("x1",d1,0.1);
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double sig1 = 0.149071;
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Vector d2 = Vector_(2, 0.0, 0.2)/sig1, sigma2 = ones(2);
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push_front(expected,"l1",d2, I/sig1,"x1", (-1)*I/sig1,sigma2);
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double sig2 = 0.0894427;
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Vector d3 = Vector_(2, 0.2, -0.14)/sig2, sigma3 = ones(2);
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push_front(expected,"x2",d3, I/sig2,"l1", (-0.2)*I/sig2, "x1", (-0.8)*I/sig2, sigma3);
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// Check one ordering
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GaussianFactorGraph fg1 = createGaussianFactorGraph();
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Ordering ordering;
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ordering += "x2","l1","x1";
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GaussianBayesNet actual = fg1.eliminate(ordering);
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CHECK(assert_equal(expected,actual,tol));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, eliminateAll_fast )
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{
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// create expected Chordal bayes Net
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Matrix I = eye(2);
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Vector d1 = Vector_(2, -0.1,-0.1);
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GaussianBayesNet expected = simpleGaussian("x1",d1,0.1);
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double sig1 = 0.149071;
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Vector d2 = Vector_(2, 0.0, 0.2)/sig1, sigma2 = ones(2);
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push_front(expected,"l1",d2, I/sig1,"x1", (-1)*I/sig1,sigma2);
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double sig2 = 0.0894427;
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Vector d3 = Vector_(2, 0.2, -0.14)/sig2, sigma3 = ones(2);
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push_front(expected,"x2",d3, I/sig2,"l1", (-0.2)*I/sig2, "x1", (-0.8)*I/sig2, sigma3);
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// Check one ordering
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GaussianFactorGraph fg1 = createGaussianFactorGraph();
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Ordering ordering;
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ordering += "x2","l1","x1";
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GaussianBayesNet actual = fg1.eliminate(ordering, false);
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CHECK(assert_equal(expected,actual,tol));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, add_priors )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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GaussianFactorGraph actual = fg.add_priors(3);
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GaussianFactorGraph expected = createGaussianFactorGraph();
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Matrix A = eye(2);
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Vector b = zero(2);
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SharedDiagonal sigma = sharedSigma(2,3.0);
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expected.push_back(GaussianFactor::shared_ptr(new GaussianFactor("l1",A,b,sigma)));
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expected.push_back(GaussianFactor::shared_ptr(new GaussianFactor("x1",A,b,sigma)));
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expected.push_back(GaussianFactor::shared_ptr(new GaussianFactor("x2",A,b,sigma)));
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CHECK(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, copying )
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{
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// Create a graph
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GaussianFactorGraph actual = createGaussianFactorGraph();
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// Copy the graph !
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GaussianFactorGraph 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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GaussianFactorGraph expected = createGaussianFactorGraph();
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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( GaussianFactorGraph, matrix )
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{
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// Create a graph
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GaussianFactorGraph fg = createGaussianFactorGraph();
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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( GaussianFactorGraph, sparse )
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{
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// create a small linear factor graph
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GaussianFactorGraph fg = createGaussianFactorGraph();
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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(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., 5., 6., 1., 2., 3.,4., 5., 6., 3., 4., 1., 2.,
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10.,10., -10.,-10., 10., 10., 5.,5.,-5.,-5., 5., 5.,-5.,-5.), ijs);
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, CONSTRUCTOR_GaussianBayesNet )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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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 GaussianFactorGraph
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GaussianFactorGraph 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<GaussianFactor> fg3(CBN);
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GaussianBayesNet CBN3 = gtsam::eliminate<GaussianFactor,GaussianConditional>(fg3,ord);
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CHECK(assert_equal(CBN,CBN3));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, getOrdering)
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{
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Ordering expected;
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expected += "l1","x1","x2";
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GaussianFactorGraph fg = createGaussianFactorGraph();
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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( GaussianFactorGraph, optimize )
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{
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// create a graph
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GaussianFactorGraph fg = createGaussianFactorGraph();
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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( GaussianFactorGraph, combine)
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{
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// create a test graph
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GaussianFactorGraph fg1 = createGaussianFactorGraph();
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// create another factor graph
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GaussianFactorGraph fg2 = createGaussianFactorGraph();
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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( GaussianFactorGraph, combine2)
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{
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// create a test graph
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GaussianFactorGraph fg1 = createGaussianFactorGraph();
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// create another factor graph
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GaussianFactorGraph fg2 = createGaussianFactorGraph();
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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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GaussianFactorGraph fg3 = GaussianFactorGraph::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( GaussianFactorGraph, factor_lookup)
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{
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// create a test graph
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GaussianFactorGraph fg = createGaussianFactorGraph();
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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);
|
|
CHECK(x2_factors==x2_expected);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, findAndRemoveFactors )
|
|
{
|
|
// create the graph
|
|
GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
|
|
// We expect to remove these three factors: 0, 1, 2
|
|
GaussianFactor::shared_ptr f0 = fg[0];
|
|
GaussianFactor::shared_ptr f1 = fg[1];
|
|
GaussianFactor::shared_ptr f2 = fg[2];
|
|
|
|
// call the function
|
|
vector<GaussianFactor::shared_ptr> factors = fg.findAndRemoveFactors("x1");
|
|
|
|
// Check the factors
|
|
CHECK(f0==factors[0]);
|
|
CHECK(f1==factors[1]);
|
|
CHECK(f2==factors[2]);
|
|
|
|
// CHECK if the factors are deleted from the factor graph
|
|
LONGS_EQUAL(1,fg.nrFactors());
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, findAndRemoveFactors_twice )
|
|
{
|
|
// create the graph
|
|
GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
|
|
// We expect to remove these three factors: 0, 1, 2
|
|
GaussianFactor::shared_ptr f0 = fg[0];
|
|
GaussianFactor::shared_ptr f1 = fg[1];
|
|
GaussianFactor::shared_ptr f2 = fg[2];
|
|
|
|
// call the function
|
|
vector<GaussianFactor::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(GaussianFactorGraph, createSmoother)
|
|
{
|
|
GaussianFactorGraph fg1 = createSmoother(2);
|
|
LONGS_EQUAL(3,fg1.size());
|
|
GaussianFactorGraph fg2 = createSmoother(3);
|
|
LONGS_EQUAL(5,fg2.size());
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, variables )
|
|
{
|
|
GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
Dimensions expected;
|
|
insert(expected)("l1", 2)("x1", 2)("x2", 2);
|
|
Dimensions actual = fg.dimensions();
|
|
CHECK(expected==actual);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, keys )
|
|
{
|
|
GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
Ordering expected;
|
|
expected += "l1","x1","x2";
|
|
CHECK(assert_equal(expected,fg.keys()));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, involves )
|
|
{
|
|
GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
CHECK(fg.involves("l1"));
|
|
CHECK(fg.involves("x1"));
|
|
CHECK(fg.involves("x2"));
|
|
CHECK(!fg.involves("x3"));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
double error(const VectorConfig& x) {
|
|
GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
return fg.error(x);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, gradient )
|
|
{
|
|
GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
|
|
// Construct expected gradient
|
|
VectorConfig expected;
|
|
|
|
// 2*f(x) = 100*(x1+c["x1"])^2 + 100*(x2-x1-[0.2;-0.1])^2 + 25*(l1-x1-[0.0;0.2])^2 + 25*(l1-x2-[-0.2;0.3])^2
|
|
// worked out: df/dx1 = 100*[0.1;0.1] + 100*[0.2;-0.1]) + 25*[0.0;0.2] = [10+20;10-10+5] = [30;5]
|
|
expected.insert("l1",Vector_(2, 5.0,-12.5));
|
|
expected.insert("x1",Vector_(2, 30.0, 5.0));
|
|
expected.insert("x2",Vector_(2,-25.0, 17.5));
|
|
|
|
// Check the gradient at delta=0
|
|
VectorConfig zero = createZeroDelta();
|
|
VectorConfig actual = fg.gradient(zero);
|
|
CHECK(assert_equal(expected,actual));
|
|
|
|
// Check it numerically for good measure
|
|
Vector numerical_g = numericalGradient<VectorConfig>(error,zero,0.001);
|
|
CHECK(assert_equal(Vector_(6,5.0,-12.5,30.0,5.0,-25.0,17.5),numerical_g));
|
|
|
|
// Check the gradient at the solution (should be zero)
|
|
Ordering ord;
|
|
ord += "x2","l1","x1";
|
|
GaussianFactorGraph fg2 = createGaussianFactorGraph();
|
|
VectorConfig solution = fg2.optimize(ord); // destructive
|
|
VectorConfig actual2 = fg.gradient(solution);
|
|
CHECK(assert_equal(zero,actual2));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, multiplication )
|
|
{
|
|
GaussianFactorGraph A = createGaussianFactorGraph();
|
|
VectorConfig x = createCorrectDelta();
|
|
Errors actual = A * x;
|
|
Errors expected;
|
|
expected += Vector_(2,-1.0,-1.0);
|
|
expected += Vector_(2, 2.0,-1.0);
|
|
expected += Vector_(2, 0.0, 1.0);
|
|
expected += Vector_(2,-1.0, 1.5);
|
|
CHECK(assert_equal(expected,actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, transposeMultiplication )
|
|
{
|
|
GaussianFactorGraph A = createGaussianFactorGraph();
|
|
Errors e;
|
|
e += Vector_(2, 0.0, 0.0);
|
|
e += Vector_(2,15.0, 0.0);
|
|
e += Vector_(2, 0.0,-5.0);
|
|
e += Vector_(2,-7.5,-5.0);
|
|
|
|
VectorConfig expected, actual = A ^ e;
|
|
expected.insert("l1",Vector_(2, -37.5,-50.0));
|
|
expected.insert("x1",Vector_(2,-150.0, 25.0));
|
|
expected.insert("x2",Vector_(2, 187.5, 25.0));
|
|
CHECK(assert_equal(expected,actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, rhs )
|
|
{
|
|
GaussianFactorGraph Ab = createGaussianFactorGraph();
|
|
Errors expected, actual = Ab.rhs();
|
|
expected.push_back(Vector_(2,-1.0,-1.0));
|
|
expected.push_back(Vector_(2, 2.0,-1.0));
|
|
expected.push_back(Vector_(2, 0.0, 1.0));
|
|
expected.push_back(Vector_(2,-1.0, 1.5));
|
|
CHECK(assert_equal(expected,actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
// Extra test on elimination prompted by Michael's email to Frank 1/4/2010
|
|
TEST( GaussianFactorGraph, elimination )
|
|
{
|
|
// Create Gaussian Factor Graph
|
|
GaussianFactorGraph fg;
|
|
Matrix Ap = eye(1), An = eye(1) * -1;
|
|
Vector b = Vector_(1, 0.0);
|
|
SharedDiagonal sigma = sharedSigma(2,2.0);
|
|
fg.add("x1", An, "x2", Ap, b, sigma);
|
|
fg.add("x1", Ap, b, sigma);
|
|
fg.add("x2", Ap, b, sigma);
|
|
|
|
// Eliminate
|
|
Ordering ord;
|
|
ord += "x1", "x2";
|
|
GaussianBayesNet bayesNet = fg.eliminate(ord);
|
|
|
|
// Check sigma
|
|
DOUBLES_EQUAL(1.0,bayesNet["x2"]->get_sigmas()(0),1e-5);
|
|
|
|
// Check matrix
|
|
Matrix R;Vector d;
|
|
boost::tie(R,d) = matrix(bayesNet);
|
|
Matrix expected = Matrix_(2,2,
|
|
0.707107, -0.353553,
|
|
0.0, 0.612372);
|
|
CHECK(assert_equal(expected,R,1e-6));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
// Tests ported from ConstrainedGaussianFactorGraph
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, constrained_simple )
|
|
{
|
|
// get a graph with a constraint in it
|
|
GaussianFactorGraph fg = createSimpleConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
Ordering ord;
|
|
ord += "x", "y";
|
|
VectorConfig actual = fg.optimize(ord);
|
|
|
|
// verify
|
|
VectorConfig expected = createSimpleConstraintConfig();
|
|
CHECK(assert_equal(expected, actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, constrained_single )
|
|
{
|
|
// get a graph with a constraint in it
|
|
GaussianFactorGraph fg = createSingleConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
Ordering ord;
|
|
ord += "x", "y";
|
|
VectorConfig actual = fg.optimize(ord);
|
|
|
|
// verify
|
|
VectorConfig expected = createSingleConstraintConfig();
|
|
CHECK(assert_equal(expected, actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, constrained_single2 )
|
|
{
|
|
// get a graph with a constraint in it
|
|
GaussianFactorGraph fg = createSingleConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
Ordering ord;
|
|
ord += "y", "x";
|
|
VectorConfig actual = fg.optimize(ord);
|
|
|
|
// verify
|
|
VectorConfig expected = createSingleConstraintConfig();
|
|
CHECK(assert_equal(expected, actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, constrained_multi1 )
|
|
{
|
|
// get a graph with a constraint in it
|
|
GaussianFactorGraph fg = createMultiConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
Ordering ord;
|
|
ord += "x", "y", "z";
|
|
VectorConfig actual = fg.optimize(ord);
|
|
|
|
// verify
|
|
VectorConfig expected = createMultiConstraintConfig();
|
|
CHECK(assert_equal(expected, actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, constrained_multi2 )
|
|
{
|
|
// get a graph with a constraint in it
|
|
GaussianFactorGraph fg = createMultiConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
Ordering ord;
|
|
ord += "z", "x", "y";
|
|
VectorConfig actual = fg.optimize(ord);
|
|
|
|
// verify
|
|
VectorConfig expected = createMultiConstraintConfig();
|
|
CHECK(assert_equal(expected, actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
|
|
SharedDiagonal model = sharedSigma(2,1);
|
|
|
|
TEST( GaussianFactorGraph, findMinimumSpanningTree )
|
|
{
|
|
GaussianFactorGraph g;
|
|
Matrix I = eye(2);
|
|
Vector b = Vector_(0, 0, 0);
|
|
g.add("x1", I, "x2", I, b, model);
|
|
g.add("x1", I, "x3", I, b, model);
|
|
g.add("x1", I, "x4", I, b, model);
|
|
g.add("x2", I, "x3", I, b, model);
|
|
g.add("x2", I, "x4", I, b, model);
|
|
g.add("x3", I, "x4", I, b, model);
|
|
|
|
map<string, string> tree = g.findMinimumSpanningTree<string, GaussianFactor>();
|
|
CHECK(tree["x1"].compare("x1")==0);
|
|
CHECK(tree["x2"].compare("x1")==0);
|
|
CHECK(tree["x3"].compare("x1")==0);
|
|
CHECK(tree["x4"].compare("x1")==0);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, split )
|
|
{
|
|
GaussianFactorGraph g;
|
|
Matrix I = eye(2);
|
|
Vector b = Vector_(0, 0, 0);
|
|
g.add("x1", I, "x2", I, b, model);
|
|
g.add("x1", I, "x3", I, b, model);
|
|
g.add("x1", I, "x4", I, b, model);
|
|
g.add("x2", I, "x3", I, b, model);
|
|
g.add("x2", I, "x4", I, b, model);
|
|
|
|
PredecessorMap<string> tree;
|
|
tree["x1"] = "x1";
|
|
tree["x2"] = "x1";
|
|
tree["x3"] = "x1";
|
|
tree["x4"] = "x1";
|
|
|
|
GaussianFactorGraph Ab1, Ab2;
|
|
g.split<string, GaussianFactor>(tree, Ab1, Ab2);
|
|
LONGS_EQUAL(3, Ab1.size());
|
|
LONGS_EQUAL(2, Ab2.size());
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
|
/* ************************************************************************* */
|