1001 lines
33 KiB
C++
1001 lines
33 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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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/set.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 <gtsam/base/Matrix.h>
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#include <gtsam/slam/smallExample.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <gtsam/inference/SymbolicFactorGraph.h>
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//#include <gtsam/inference/BayesTree-inl.h>
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#include <gtsam/linear/GaussianSequentialSolver.h>
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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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Ordering ordering; ordering += "x1","x2","l1";
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianFactorGraph fg2 = createGaussianFactorGraph(ordering);
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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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Ordering ordering; ordering += "x1","x2","l1";
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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VectorValues cfg = createZeroDelta(ordering);
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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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// SL-NEEDED? TEST( GaussianFactorGraph, find_separator )
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//{
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// GaussianFactorGraph fg = createGaussianFactorGraph();
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//
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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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//
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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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// SL-FIX 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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//
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// // combine all factors
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// GaussianFactor::shared_ptr actual = removeAndCombineFactors(fg,"x1");
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//
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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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//
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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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//
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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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//
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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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//
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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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//
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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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///* ************************************************************************* */
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// SL-FIX 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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//
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// // combine all factors
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// GaussianFactor::shared_ptr actual = removeAndCombineFactors(fg,"x2");
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//
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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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//
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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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//
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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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//
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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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//
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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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//
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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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// Ordering ordering; ordering += "x1","l1","x2";
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// GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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// GaussianConditional::shared_ptr actual = GaussianSequentialSolver::EliminateUntil(fg, 1);
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//
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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(ordering["x1"],15*d,R11,ordering["l1"],S12,ordering["x2"],S13,sigma);
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//
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// CHECK(assert_equal(expected,*actual,tol));
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//}
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//
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///* ************************************************************************* */
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//TEST( GaussianFactorGraph, eliminateOne_x2 )
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//{
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// Ordering ordering; ordering += "x2","l1","x1";
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// GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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// GaussianConditional::shared_ptr actual = GaussianSequentialSolver::EliminateUntil(fg, 1);
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//
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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(ordering["x2"],d,R11,ordering["l1"],S12,ordering["x1"],S13,sigma);
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//
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// CHECK(assert_equal(expected,*actual,tol));
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//}
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//
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///* ************************************************************************* */
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//TEST( GaussianFactorGraph, eliminateOne_l1 )
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//{
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// Ordering ordering; ordering += "l1","x1","x2";
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// GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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// GaussianConditional::shared_ptr actual = GaussianSequentialSolver::EliminateUntil(fg, 1);
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//
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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(ordering["l1"],d,R11,ordering["x1"],S12,ordering["x2"],S13,sigma);
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//
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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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//
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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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//
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// CHECK(assert_equal(expected,*actual,tol));
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//}
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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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//
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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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//
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// CHECK(assert_equal(expected,*actual,tol));
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//}
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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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//
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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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//
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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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Ordering ordering;
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ordering += "x2","l1","x1";
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Vector d1 = Vector_(2, -0.1,-0.1);
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GaussianBayesNet expected = simpleGaussian(ordering["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,ordering["l1"],d2, I/sig1,ordering["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,ordering["x2"],d3, I/sig2,ordering["l1"], (-0.2)*I/sig2, ordering["x1"], (-0.8)*I/sig2, sigma3);
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// Check one ordering
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GaussianFactorGraph fg1 = createGaussianFactorGraph(ordering);
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GaussianBayesNet actual = *GaussianSequentialSolver(fg1).eliminate();
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CHECK(assert_equal(expected,actual,tol));
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GaussianBayesNet actualET = *GaussianSequentialSolver(fg1).eliminate();
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CHECK(assert_equal(expected,actualET,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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//
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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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//
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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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//
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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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//
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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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Ordering ordering; ordering += "l1","x1","x2";
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianFactorGraph actual = fg.add_priors(3, vector<size_t>(3,2));
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GaussianFactorGraph expected = createGaussianFactorGraph(ordering);
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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(ordering["l1"],A,b,sigma)));
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expected.push_back(GaussianFactor::shared_ptr(new GaussianFactor(ordering["x1"],A,b,sigma)));
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expected.push_back(GaussianFactor::shared_ptr(new GaussianFactor(ordering["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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Ordering ordering; ordering += "x2","l1","x1";
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GaussianFactorGraph actual = createGaussianFactorGraph(ordering);
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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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GaussianBayesNet actual1 = *GaussianSequentialSolver(copy).eliminate();
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// Create the same graph, but not by copying
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GaussianFactorGraph expected = createGaussianFactorGraph(ordering);
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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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// SL-FIX TEST( GaussianFactorGraph, matrix )
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//{
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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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//
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// // Create a graph
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// GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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//
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// Matrix A; Vector b;
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// boost::tie(A,b) = fg.matrix();
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//
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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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//
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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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// SL-FIX TEST( GaussianFactorGraph, sizeOfA )
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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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//
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// pair<size_t, size_t> mn = fg.sizeOfA();
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// CHECK(8 == mn.first);
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// CHECK(6 == mn.second);
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//}
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///* ************************************************************************* */
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//SL-FIX 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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//
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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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//
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// Matrix ijs = fg.sparse(ord);
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//
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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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Ordering ord;
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ord += "x2","l1","x1";
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GaussianFactorGraph fg = createGaussianFactorGraph(ord);
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// render with a given ordering
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GaussianBayesNet CBN = *GaussianSequentialSolver(fg).eliminate();
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// True GaussianFactorGraph
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GaussianFactorGraph fg2(CBN);
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GaussianBayesNet CBN2 = *GaussianSequentialSolver(fg2).eliminate();
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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 original; original += "l1","x1","x2";
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FactorGraph<IndexFactor> symbolic(createGaussianFactorGraph(original));
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Permutation perm(*Inference::PermutationCOLAMD(VariableIndex(symbolic)));
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Ordering actual = original; actual.permuteWithInverse((*perm.inverse()));
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Ordering expected; expected += "l1","x2","x1";
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CHECK(assert_equal(expected,actual));
|
|
}
|
|
|
|
// SL-FIX TEST( GaussianFactorGraph, getOrdering2)
|
|
//{
|
|
// Ordering expected;
|
|
// expected += "l1","x1";
|
|
// GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
// set<Symbol> interested; interested += "l1","x1";
|
|
// Ordering actual = fg.getOrdering(interested);
|
|
// CHECK(assert_equal(expected,actual));
|
|
//}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, optimize )
|
|
{
|
|
// create an ordering
|
|
Ordering ord; ord += "x2","l1","x1";
|
|
|
|
// create a graph
|
|
GaussianFactorGraph fg = createGaussianFactorGraph(ord);
|
|
|
|
// optimize the graph
|
|
VectorValues actual = *GaussianSequentialSolver(fg).optimize();
|
|
|
|
// verify
|
|
VectorValues expected = createCorrectDelta(ord);
|
|
|
|
CHECK(assert_equal(expected,actual));
|
|
}
|
|
|
|
///* ************************************************************************* */
|
|
// SL-FIX TEST( GaussianFactorGraph, optimizeMultiFrontlas )
|
|
//{
|
|
// // create an ordering
|
|
// Ordering ord; ord += "x2","l1","x1";
|
|
//
|
|
// // create a graph
|
|
// GaussianFactorGraph fg = createGaussianFactorGraph(ord);
|
|
//
|
|
// // optimize the graph
|
|
// VectorValues actual = fg.optimizeMultiFrontals(ord);
|
|
//
|
|
// // verify
|
|
// VectorValues expected = createCorrectDelta();
|
|
//
|
|
// CHECK(assert_equal(expected,actual));
|
|
//}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, combine)
|
|
{
|
|
// create an ordering
|
|
Ordering ord; ord += "x2","l1","x1";
|
|
|
|
// create a test graph
|
|
GaussianFactorGraph fg1 = createGaussianFactorGraph(ord);
|
|
|
|
// create another factor graph
|
|
GaussianFactorGraph fg2 = createGaussianFactorGraph(ord);
|
|
|
|
// get sizes
|
|
size_t size1 = fg1.size();
|
|
size_t size2 = fg2.size();
|
|
|
|
// combine them
|
|
fg1.combine(fg2);
|
|
|
|
CHECK(size1+size2 == fg1.size());
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, combine2)
|
|
{
|
|
// create an ordering
|
|
Ordering ord; ord += "x2","l1","x1";
|
|
|
|
// create a test graph
|
|
GaussianFactorGraph fg1 = createGaussianFactorGraph(ord);
|
|
|
|
// create another factor graph
|
|
GaussianFactorGraph fg2 = createGaussianFactorGraph(ord);
|
|
|
|
// get sizes
|
|
size_t size1 = fg1.size();
|
|
size_t size2 = fg2.size();
|
|
|
|
// combine them
|
|
GaussianFactorGraph fg3 = GaussianFactorGraph::combine2(fg1, fg2);
|
|
|
|
CHECK(size1+size2 == fg3.size());
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
// print a vector of ints if needed for debugging
|
|
void print(vector<int> v) {
|
|
for (size_t k = 0; k < v.size(); k++)
|
|
cout << v[k] << " ";
|
|
cout << endl;
|
|
}
|
|
|
|
///* ************************************************************************* */
|
|
// SL-NEEDED? TEST( GaussianFactorGraph, factor_lookup)
|
|
//{
|
|
// // create a test graph
|
|
// GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
//
|
|
// // ask for all factor indices connected to x1
|
|
// list<size_t> x1_factors = fg.factors("x1");
|
|
// size_t x1_indices[] = { 0, 1, 2 };
|
|
// list<size_t> x1_expected(x1_indices, x1_indices + 3);
|
|
// CHECK(x1_factors==x1_expected);
|
|
//
|
|
// // ask for all factor indices connected to x2
|
|
// list<size_t> x2_factors = fg.factors("x2");
|
|
// size_t x2_indices[] = { 1, 3 };
|
|
// list<size_t> x2_expected(x2_indices, x2_indices + 2);
|
|
// CHECK(x2_factors==x2_expected);
|
|
//}
|
|
|
|
///* ************************************************************************* */
|
|
// SL-NEEDED? 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, createSmoother)
|
|
{
|
|
GaussianFactorGraph fg1 = createSmoother(2).first;
|
|
LONGS_EQUAL(3,fg1.size());
|
|
GaussianFactorGraph fg2 = createSmoother(3).first;
|
|
LONGS_EQUAL(5,fg2.size());
|
|
}
|
|
|
|
///* ************************************************************************* */
|
|
// SL-NEEDED? TEST( GaussianFactorGraph, variables )
|
|
//{
|
|
// GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
// Dimensions expected;
|
|
// insert(expected)("l1", 2)("x1", 2)("x2", 2);
|
|
// Dimensions actual = fg.dimensions();
|
|
// CHECK(expected==actual);
|
|
//}
|
|
|
|
///* ************************************************************************* */
|
|
// SL-NEEDED? TEST( GaussianFactorGraph, keys )
|
|
//{
|
|
// GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
// Ordering expected;
|
|
// expected += "l1","x1","x2";
|
|
// CHECK(assert_equal(expected,fg.keys()));
|
|
//}
|
|
|
|
///* ************************************************************************* */
|
|
// SL-NEEDED? 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 VectorValues& x) {
|
|
// create an ordering
|
|
Ordering ord; ord += "x2","l1","x1";
|
|
|
|
GaussianFactorGraph fg = createGaussianFactorGraph(ord);
|
|
return fg.error(x);
|
|
}
|
|
|
|
///* ************************************************************************* */
|
|
// SL-NEEDED? TEST( GaussianFactorGraph, gradient )
|
|
//{
|
|
// GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
//
|
|
// // Construct expected gradient
|
|
// VectorValues 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
|
|
// VectorValues zero = createZeroDelta();
|
|
// VectorValues actual = fg.gradient(zero);
|
|
// CHECK(assert_equal(expected,actual));
|
|
//
|
|
// // Check it numerically for good measure
|
|
// Vector numerical_g = numericalGradient<VectorValues>(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();
|
|
// VectorValues solution = fg2.optimize(ord); // destructive
|
|
// VectorValues actual2 = fg.gradient(solution);
|
|
// CHECK(assert_equal(zero,actual2));
|
|
//}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, multiplication )
|
|
{
|
|
// create an ordering
|
|
Ordering ord; ord += "x2","l1","x1";
|
|
|
|
GaussianFactorGraph A = createGaussianFactorGraph(ord);
|
|
VectorValues x = createCorrectDelta(ord);
|
|
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));
|
|
}
|
|
|
|
///* ************************************************************************* */
|
|
// SL-NEEDED? TEST( GaussianFactorGraph, transposeMultiplication )
|
|
//{
|
|
// // create an ordering
|
|
// Ordering ord; ord += "x2","l1","x1";
|
|
//
|
|
// GaussianFactorGraph A = createGaussianFactorGraph(ord);
|
|
// 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);
|
|
//
|
|
// VectorValues expected = createZeroDelta(ord), actual = A ^ e;
|
|
// expected[ord["l1"]] = Vector_(2, -37.5,-50.0);
|
|
// expected[ord["x1"]] = Vector_(2,-150.0, 25.0);
|
|
// expected[ord["x2"]] = Vector_(2, 187.5, 25.0);
|
|
// CHECK(assert_equal(expected,actual));
|
|
//}
|
|
|
|
///* ************************************************************************* */
|
|
// SL-NEEDED? TEST( GaussianFactorGraph, rhs )
|
|
//{
|
|
// // create an ordering
|
|
// Ordering ord; ord += "x2","l1","x1";
|
|
//
|
|
// GaussianFactorGraph Ab = createGaussianFactorGraph(ord);
|
|
// Errors expected = createZeroDelta(ord), 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 )
|
|
{
|
|
Ordering ord;
|
|
ord += "x1", "x2";
|
|
// Create Gaussian Factor Graph
|
|
GaussianFactorGraph fg;
|
|
Matrix Ap = eye(1), An = eye(1) * -1;
|
|
Vector b = Vector_(1, 0.0);
|
|
SharedDiagonal sigma = sharedSigma(1,2.0);
|
|
fg.add(ord["x1"], An, ord["x2"], Ap, b, sigma);
|
|
fg.add(ord["x1"], Ap, b, sigma);
|
|
fg.add(ord["x2"], Ap, b, sigma);
|
|
|
|
// Eliminate
|
|
GaussianBayesNet bayesNet = *GaussianSequentialSolver(fg).eliminate();
|
|
|
|
// Check sigma
|
|
DOUBLES_EQUAL(1.0,bayesNet[ord["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);
|
|
Matrix expected2 = Matrix_(2,2,
|
|
0.707107, -0.353553,
|
|
0.0, -0.612372);
|
|
CHECK(equal_with_abs_tol(expected, R, 1e-6) || equal_with_abs_tol(expected2, 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
|
|
VectorValues actual = *GaussianSequentialSolver(fg).optimize();
|
|
|
|
// verify
|
|
VectorValues expected = createSimpleConstraintValues();
|
|
CHECK(assert_equal(expected, actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, constrained_single )
|
|
{
|
|
// get a graph with a constraint in it
|
|
GaussianFactorGraph fg = createSingleConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
VectorValues actual = *GaussianSequentialSolver(fg).optimize();
|
|
|
|
// verify
|
|
VectorValues expected = createSingleConstraintValues();
|
|
CHECK(assert_equal(expected, actual));
|
|
}
|
|
|
|
///* ************************************************************************* */
|
|
//SL-FIX TEST( GaussianFactorGraph, constrained_single2 )
|
|
//{
|
|
// // get a graph with a constraint in it
|
|
// GaussianFactorGraph fg = createSingleConstraintGraph();
|
|
//
|
|
// // eliminate and solve
|
|
// Ordering ord;
|
|
// ord += "y", "x";
|
|
// VectorValues actual = fg.optimize(ord);
|
|
//
|
|
// // verify
|
|
// VectorValues expected = createSingleConstraintValues();
|
|
// CHECK(assert_equal(expected, actual));
|
|
//}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, constrained_multi1 )
|
|
{
|
|
// get a graph with a constraint in it
|
|
GaussianFactorGraph fg = createMultiConstraintGraph();
|
|
|
|
// eliminate and solve
|
|
VectorValues actual = *GaussianSequentialSolver(fg).optimize();
|
|
|
|
// verify
|
|
VectorValues expected = createMultiConstraintValues();
|
|
CHECK(assert_equal(expected, actual));
|
|
}
|
|
|
|
///* ************************************************************************* */
|
|
//SL-FIX TEST( GaussianFactorGraph, constrained_multi2 )
|
|
//{
|
|
// // get a graph with a constraint in it
|
|
// GaussianFactorGraph fg = createMultiConstraintGraph();
|
|
//
|
|
// // eliminate and solve
|
|
// Ordering ord;
|
|
// ord += "z", "x", "y";
|
|
// VectorValues actual = fg.optimize(ord);
|
|
//
|
|
// // verify
|
|
// VectorValues expected = createMultiConstraintValues();
|
|
// CHECK(assert_equal(expected, actual));
|
|
//}
|
|
|
|
/* ************************************************************************* */
|
|
|
|
SharedDiagonal model = sharedSigma(2,1);
|
|
|
|
// SL-FIX 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);
|
|
//}
|
|
|
|
///* ************************************************************************* */
|
|
// SL-FIX 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());
|
|
//}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(GaussianFactorGraph, replace)
|
|
{
|
|
Ordering ord; ord += "x1","x2","x3","x4","x5","x6";
|
|
SharedDiagonal noise(sharedSigma(3, 1.0));
|
|
|
|
GaussianFactorGraph::sharedFactor f1(new GaussianFactor(
|
|
ord["x1"], eye(3,3), ord["x2"], eye(3,3), zero(3), noise));
|
|
GaussianFactorGraph::sharedFactor f2(new GaussianFactor(
|
|
ord["x2"], eye(3,3), ord["x3"], eye(3,3), zero(3), noise));
|
|
GaussianFactorGraph::sharedFactor f3(new GaussianFactor(
|
|
ord["x3"], eye(3,3), ord["x4"], eye(3,3), zero(3), noise));
|
|
GaussianFactorGraph::sharedFactor f4(new GaussianFactor(
|
|
ord["x5"], eye(3,3), ord["x6"], eye(3,3), zero(3), noise));
|
|
|
|
GaussianFactorGraph actual;
|
|
actual.push_back(f1);
|
|
// actual.checkGraphConsistency();
|
|
actual.push_back(f2);
|
|
// actual.checkGraphConsistency();
|
|
actual.push_back(f3);
|
|
// actual.checkGraphConsistency();
|
|
actual.replace(0, f4);
|
|
// actual.checkGraphConsistency();
|
|
|
|
GaussianFactorGraph expected;
|
|
expected.push_back(f4);
|
|
// actual.checkGraphConsistency();
|
|
expected.push_back(f2);
|
|
// actual.checkGraphConsistency();
|
|
expected.push_back(f3);
|
|
// actual.checkGraphConsistency();
|
|
|
|
CHECK(assert_equal(expected, actual));
|
|
}
|
|
|
|
///* ************************************************************************* */
|
|
//TEST ( GaussianFactorGraph, combine_matrix ) {
|
|
// // create a small linear factor graph
|
|
// GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
// Dimensions dimensions = fg.dimensions();
|
|
//
|
|
// // get two factors from it and insert the factors into a vector
|
|
// vector<GaussianFactor::shared_ptr> lfg;
|
|
// lfg.push_back(fg[4 - 1]);
|
|
// lfg.push_back(fg[2 - 1]);
|
|
//
|
|
// // combine in a factor
|
|
// Matrix Ab; SharedDiagonal noise;
|
|
// Ordering order; order += "x2", "l1", "x1";
|
|
// boost::tie(Ab, noise) = combineFactorsAndCreateMatrix(lfg, order, dimensions);
|
|
//
|
|
// // the expected augmented matrix
|
|
// Matrix expAb = Matrix_(4, 7,
|
|
// -5., 0., 5., 0., 0., 0.,-1.0,
|
|
// +0., -5., 0., 5., 0., 0., 1.5,
|
|
// 10., 0., 0., 0.,-10., 0., 2.0,
|
|
// +0., 10., 0., 0., 0.,-10.,-1.0);
|
|
//
|
|
// // expected noise model
|
|
// SharedDiagonal expModel = noiseModel::Unit::Create(4);
|
|
//
|
|
// CHECK(assert_equal(expAb, Ab));
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// CHECK(assert_equal(*expModel, *noise));
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//}
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/* ************************************************************************* */
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/**
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* x2 x1 x3 b
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* 1 1 1 1 1 0 1
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* 1 1 1 -> 1 1 1
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* 1 1 1 1
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*/
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// SL-NEEDED? TEST ( GaussianFactorGraph, eliminateFrontals ) {
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// typedef GaussianFactorGraph::sharedFactor Factor;
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// SharedDiagonal model(Vector_(1, 0.5));
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// GaussianFactorGraph fg;
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// Factor factor1(new GaussianFactor("x1", Matrix_(1,1,1.), "x2", Matrix_(1,1,1.), Vector_(1,1.), model));
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// Factor factor2(new GaussianFactor("x2", Matrix_(1,1,1.), "x3", Matrix_(1,1,1.), Vector_(1,1.), model));
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// Factor factor3(new GaussianFactor("x3", Matrix_(1,1,1.), "x3", Matrix_(1,1,1.), Vector_(1,1.), model));
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// fg.push_back(factor1);
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// fg.push_back(factor2);
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// fg.push_back(factor3);
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//
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// Ordering frontals; frontals += "x2", "x1";
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// GaussianBayesNet bn = fg.eliminateFrontals(frontals);
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//
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// GaussianBayesNet bn_expected;
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// GaussianBayesNet::sharedConditional conditional1(new GaussianConditional("x2", Vector_(1, 2.), Matrix_(1, 1, 2.),
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// "x1", Matrix_(1, 1, 1.), "x3", Matrix_(1, 1, 1.), Vector_(1, 1.)));
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// GaussianBayesNet::sharedConditional conditional2(new GaussianConditional("x1", Vector_(1, 0.), Matrix_(1, 1, -1.),
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// "x3", Matrix_(1, 1, 1.), Vector_(1, 1.)));
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// bn_expected.push_back(conditional1);
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// bn_expected.push_back(conditional2);
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// CHECK(assert_equal(bn_expected, bn));
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//
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// GaussianFactorGraph::sharedFactor factor_expected(new GaussianFactor("x3", Matrix_(1, 1, 2.), Vector_(1, 2.), SharedDiagonal(Vector_(1, 1.))));
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// GaussianFactorGraph fg_expected;
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// fg_expected.push_back(factor_expected);
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// CHECK(assert_equal(fg_expected, fg));
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//}
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|
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
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/* ************************************************************************* */
|