406 lines
12 KiB
C++
406 lines
12 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 testGaussianFactor.cpp
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* @brief Unit tests for Linear Factor
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* @author Christian Potthast
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* @author Frank Dellaert
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**/
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#include <gtsam/slam/smallExample.h>
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#include <gtsam/nonlinear/Symbol.h>
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#include <gtsam/nonlinear/Ordering.h>
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#include <gtsam/linear/GaussianConditional.h>
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#include <gtsam/base/Matrix.h>
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#include <gtsam/base/Testable.h>
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#include <CppUnitLite/TestHarness.h>
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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>
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#include <boost/assign/std/map.hpp> // for insert
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using namespace boost::assign;
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#include <iostream>
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using namespace std;
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using namespace gtsam;
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// Convenience for named keys
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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static SharedDiagonal
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sigma0_1 = sharedSigma(2,0.1), sigma_02 = sharedSigma(2,0.2),
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constraintModel = noiseModel::Constrained::All(2);
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const Key kx1 = X(1), kx2 = X(2), kl1 = L(1);
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/* ************************************************************************* */
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TEST( GaussianFactor, linearFactor )
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{
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Ordering ordering; ordering += kx1,kx2,kl1;
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Matrix I = eye(2);
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Vector b = Vector_(2, 2.0, -1.0);
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JacobianFactor expected(ordering[kx1], -10*I,ordering[kx2], 10*I, b, noiseModel::Unit::Create(2));
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// create a small linear factor graph
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FactorGraph<JacobianFactor> fg = example::createGaussianFactorGraph(ordering);
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// get the factor kf2 from the factor graph
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JacobianFactor::shared_ptr lf = fg[1];
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// check if the two factors are the same
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EXPECT(assert_equal(expected,*lf));
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}
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///* ************************************************************************* */
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// SL-FIX TEST( GaussianFactor, keys )
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//{
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// // get the factor kf2 from the small linear factor graph
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// Ordering ordering; ordering += kx1,kx2,kl1;
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// GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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// GaussianFactor::shared_ptr lf = fg[1];
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// list<Symbol> expected;
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// expected.push_back(kx1);
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// expected.push_back(kx2);
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// EXPECT(lf->keys() == expected);
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//}
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///* ************************************************************************* */
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// SL-FIX TEST( GaussianFactor, dimensions )
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//{
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// // get the factor kf2 from the small linear factor graph
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// Ordering ordering; ordering += kx1,kx2,kl1;
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// GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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//
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// // Check a single factor
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// Dimensions expected;
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// insert(expected)(kx1, 2)(kx2, 2);
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// Dimensions actual = fg[1]->dimensions();
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// EXPECT(expected==actual);
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//}
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/* ************************************************************************* */
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TEST( GaussianFactor, getDim )
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{
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// get a factor
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Ordering ordering; ordering += kx1,kx2,kl1;
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GaussianFactorGraph fg = example::createGaussianFactorGraph(ordering);
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GaussianFactor::shared_ptr factor = fg[0];
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// get the size of a variable
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size_t actual = factor->getDim(factor->find(ordering[kx1]));
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// verify
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size_t expected = 2;
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EXPECT_LONGS_EQUAL(expected, actual);
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}
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///* ************************************************************************* */
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// SL-FIX TEST( GaussianFactor, combine )
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//{
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// // create a small linear factor graph
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// Ordering ordering; ordering += kx1,kx2,kl1;
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// GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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//
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// // get two factors from it and insert the factors into a vector
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// vector<GaussianFactor::shared_ptr> lfg;
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// lfg.push_back(fg[4 - 1]);
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// lfg.push_back(fg[2 - 1]);
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//
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// // combine in a factor
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// GaussianFactor combined(lfg);
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//
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// // sigmas
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// double sigma2 = 0.1;
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// double sigma4 = 0.2;
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// Vector sigmas = Vector_(4, sigma4, sigma4, sigma2, sigma2);
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//
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// // the expected combined linear factor
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// Matrix Ax2 = Matrix_(4, 2, // x2
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// -5., 0.,
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// +0., -5.,
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// 10., 0.,
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// +0., 10.);
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//
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// Matrix Al1 = Matrix_(4, 2, // l1
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// 5., 0.,
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// 0., 5.,
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// 0., 0.,
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// 0., 0.);
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//
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// Matrix Ax1 = Matrix_(4, 2, // x1
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// 0.00, 0., // f4
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// 0.00, 0., // f4
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// -10., 0., // f2
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// 0.00, -10. // f2
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// );
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//
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// // the RHS
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// Vector b2(4);
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// b2(0) = -1.0;
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// b2(1) = 1.5;
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// b2(2) = 2.0;
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// b2(3) = -1.0;
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//
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// // use general constructor for making arbitrary factors
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// vector<pair<Symbol, Matrix> > meas;
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// meas.push_back(make_pair(kx2, Ax2));
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// meas.push_back(make_pair(kl1, Al1));
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// meas.push_back(make_pair(kx1, Ax1));
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// GaussianFactor expected(meas, b2, noiseModel::Diagonal::Sigmas(ones(4)));
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// EXPECT(assert_equal(expected,combined));
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//}
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/* ************************************************************************* */
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TEST( GaussianFactor, error )
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{
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// create a small linear factor graph
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Ordering ordering; ordering += kx1,kx2,kl1;
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GaussianFactorGraph fg = example::createGaussianFactorGraph(ordering);
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// get the first factor from the factor graph
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GaussianFactor::shared_ptr lf = fg[0];
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// check the error of the first factor with noisy config
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VectorValues cfg = example::createZeroDelta(ordering);
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// calculate the error from the factor kf1
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// note the error is the same as in testNonlinearFactor
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double actual = lf->error(cfg);
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DOUBLES_EQUAL( 1.0, actual, 0.00000001 );
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}
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///* ************************************************************************* */
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// SL-FIX TEST( GaussianFactor, eliminate )
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//{
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// // create a small linear factor graph
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// Ordering ordering; ordering += kx1,kx2,kl1;
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// GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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//
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// // get two factors from it and insert the factors into a vector
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// vector<GaussianFactor::shared_ptr> lfg;
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// lfg.push_back(fg[4 - 1]);
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// lfg.push_back(fg[2 - 1]);
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//
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// // combine in a factor
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// GaussianFactor combined(lfg);
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//
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// // eliminate the combined factor
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// GaussianConditional::shared_ptr actualCG;
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// GaussianFactor::shared_ptr actualLF;
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// boost::tie(actualCG,actualLF) = combined.eliminate(kx2);
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//
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// // create expected Conditional Gaussian
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// Matrix I = eye(2)*sqrt(125.0);
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// Matrix R11 = I, S12 = -0.2*I, S13 = -0.8*I;
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// Vector d = I*Vector_(2,0.2,-0.14);
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//
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// // Check the conditional Gaussian
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// GaussianConditional
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// expectedCG(kx2, d, R11, kl1, S12, kx1, S13, repeat(2, 1.0));
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//
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// // the expected linear factor
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// I = eye(2)/0.2236;
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// Matrix Bl1 = I, Bx1 = -I;
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// Vector b1 = I*Vector_(2,0.0,0.2);
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//
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// GaussianFactor expectedLF(kl1, Bl1, kx1, Bx1, b1, repeat(2,1.0));
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//
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// // check if the result matches
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// EXPECT(assert_equal(expectedCG,*actualCG,1e-3));
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// EXPECT(assert_equal(expectedLF,*actualLF,1e-3));
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//}
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/* ************************************************************************* */
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TEST( GaussianFactor, matrix )
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{
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// create a small linear factor graph
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Ordering ordering; ordering += kx1,kx2,kl1;
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FactorGraph<JacobianFactor> fg = example::createGaussianFactorGraph(ordering);
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// get the factor kf2 from the factor graph
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//GaussianFactor::shared_ptr lf = fg[1]; // NOTE: using the older version
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Vector b2 = Vector_(2, 0.2, -0.1);
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Matrix I = eye(2);
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// render with a given ordering
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Ordering ord;
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ord += kx1,kx2;
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JacobianFactor::shared_ptr lf(new JacobianFactor(ord[kx1], -I, ord[kx2], I, b2, sigma0_1));
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// Test whitened version
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Matrix A_act1; Vector b_act1;
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boost::tie(A_act1,b_act1) = lf->matrix(true);
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Matrix A1 = Matrix_(2,4,
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-10.0, 0.0, 10.0, 0.0,
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000.0,-10.0, 0.0, 10.0 );
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Vector b1 = Vector_(2, 2.0, -1.0);
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EQUALITY(A_act1,A1);
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EQUALITY(b_act1,b1);
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// Test unwhitened version
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Matrix A_act2; Vector b_act2;
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boost::tie(A_act2,b_act2) = lf->matrix(false);
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Matrix A2 = Matrix_(2,4,
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-1.0, 0.0, 1.0, 0.0,
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000.0,-1.0, 0.0, 1.0 );
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//Vector b2 = Vector_(2, 2.0, -1.0);
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EQUALITY(A_act2,A2);
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EQUALITY(b_act2,b2);
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// Ensure that whitening is consistent
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boost::shared_ptr<noiseModel::Gaussian> model = lf->get_model();
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model->WhitenSystem(A_act2, b_act2);
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EQUALITY(A_act1, A_act2);
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EQUALITY(b_act1, b_act2);
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}
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/* ************************************************************************* */
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TEST( GaussianFactor, matrix_aug )
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{
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// create a small linear factor graph
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Ordering ordering; ordering += kx1,kx2,kl1;
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FactorGraph<JacobianFactor> fg = example::createGaussianFactorGraph(ordering);
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// get the factor kf2 from the factor graph
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//GaussianFactor::shared_ptr lf = fg[1];
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Vector b2 = Vector_(2, 0.2, -0.1);
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Matrix I = eye(2);
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// render with a given ordering
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Ordering ord;
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ord += kx1,kx2;
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JacobianFactor::shared_ptr lf(new JacobianFactor(ord[kx1], -I, ord[kx2], I, b2, sigma0_1));
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// Test unwhitened version
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Matrix Ab_act1;
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Ab_act1 = lf->matrix_augmented(false);
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Matrix Ab1 = Matrix_(2,5,
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-1.0, 0.0, 1.0, 0.0, 0.2,
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00.0,- 1.0, 0.0, 1.0, -0.1 );
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EQUALITY(Ab_act1,Ab1);
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// Test whitened version
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Matrix Ab_act2;
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Ab_act2 = lf->matrix_augmented(true);
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Matrix Ab2 = Matrix_(2,5,
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-10.0, 0.0, 10.0, 0.0, 2.0,
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00.0, -10.0, 0.0, 10.0, -1.0 );
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EQUALITY(Ab_act2,Ab2);
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// Ensure that whitening is consistent
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boost::shared_ptr<noiseModel::Gaussian> model = lf->get_model();
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model->WhitenInPlace(Ab_act1);
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EQUALITY(Ab_act1, Ab_act2);
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}
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/* ************************************************************************* */
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// small aux. function to print out lists of anything
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template<class T>
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void print(const list<T>& i) {
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copy(i.begin(), i.end(), ostream_iterator<T> (cout, ","));
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cout << endl;
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}
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///* ************************************************************************* */
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// SL-FIX TEST( GaussianFactor, sparse )
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//{
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// // create a small linear factor graph
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// Ordering ordering; ordering += kx1,kx2,kl1;
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// GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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//
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// // get the factor kf2 from the factor graph
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// GaussianFactor::shared_ptr lf = fg[1];
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//
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// // render with a given ordering
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// Ordering ord;
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// ord += kx1,kx2;
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//
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// list<int> i,j;
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// list<double> s;
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// boost::tie(i,j,s) = lf->sparse(fg.columnIndices(ord));
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//
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// list<int> i1,j1;
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// i1 += 1,2,1,2;
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// j1 += 1,2,3,4;
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//
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// list<double> s1;
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// s1 += -10,-10,10,10;
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//
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// EXPECT(i==i1);
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// EXPECT(j==j1);
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// EXPECT(s==s1);
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//}
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///* ************************************************************************* */
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// SL-FIX TEST( GaussianFactor, sparse2 )
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//{
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// // create a small linear factor graph
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// Ordering ordering; ordering += kx1,kx2,kl1;
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// GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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//
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// // get the factor kf2 from the factor graph
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// GaussianFactor::shared_ptr lf = fg[1];
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//
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// // render with a given ordering
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// Ordering ord;
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// ord += kx2,kl1,kx1;
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//
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// list<int> i,j;
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// list<double> s;
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// boost::tie(i,j,s) = lf->sparse(fg.columnIndices(ord));
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//
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// list<int> i1,j1;
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// i1 += 1,2,1,2;
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// j1 += 5,6,1,2;
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//
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// list<double> s1;
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// s1 += -10,-10,10,10;
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//
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// EXPECT(i==i1);
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// EXPECT(j==j1);
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// EXPECT(s==s1);
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//}
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/* ************************************************************************* */
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TEST( GaussianFactor, size )
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{
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// create a linear factor graph
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Ordering ordering; ordering += kx1,kx2,kl1;
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GaussianFactorGraph fg = example::createGaussianFactorGraph(ordering);
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// get some factors from the graph
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boost::shared_ptr<GaussianFactor> factor1 = fg[0];
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boost::shared_ptr<GaussianFactor> factor2 = fg[1];
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boost::shared_ptr<GaussianFactor> factor3 = fg[2];
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EXPECT_LONGS_EQUAL(1, factor1->size());
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EXPECT_LONGS_EQUAL(2, factor2->size());
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EXPECT_LONGS_EQUAL(2, factor3->size());
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}
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
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/* ************************************************************************* */
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