533 lines
18 KiB
C++
533 lines
18 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 testGaussianFactorGraphB.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 <tests/smallExample.h>
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#include <gtsam/nonlinear/Symbol.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/GaussianSequentialSolver.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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#include <gtsam/inference/SymbolicFactorGraph.h>
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#include <gtsam/base/numericalDerivative.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/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 <string.h>
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#include <iostream>
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using namespace std;
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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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using symbol_shorthand::X;
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using symbol_shorthand::L;
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, equals ) {
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Ordering ordering; ordering += X(1),X(2),L(1);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianFactorGraph fg2 = createGaussianFactorGraph(ordering);
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EXPECT(fg.equals(fg2));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, error ) {
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Ordering ordering; ordering += X(1),X(2),L(1);
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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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TEST( GaussianFactorGraph, eliminateOne_x1 )
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{
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Ordering ordering; ordering += X(1),L(1),X(2);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianConditional::shared_ptr conditional;
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GaussianFactorGraph remaining;
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boost::tie(conditional,remaining) = inference::eliminateOne(fg, 0, EliminateQR);
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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[X(1)],15*d,R11,ordering[L(1)],S12,ordering[X(2)],S13,sigma);
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EXPECT(assert_equal(expected,*conditional,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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Ordering ordering; ordering += X(2),L(1),X(1);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianConditional::shared_ptr actual = inference::eliminateOne(fg, 0, EliminateQR).first;
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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[X(2)],d,R11,ordering[L(1)],S12,ordering[X(1)],S13,sigma);
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EXPECT(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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Ordering ordering; ordering += L(1),X(1),X(2);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianConditional::shared_ptr actual = inference::eliminateOne(fg, 0, EliminateQR).first;
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// create expected Conditional Gaussian
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double sig = sqrt(2.0)/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[L(1)],d,R11,ordering[X(1)],S12,ordering[X(2)],S13,sigma);
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EXPECT(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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Ordering ordering; ordering += X(1),L(1),X(2);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianConditional::shared_ptr conditional;
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GaussianFactorGraph remaining;
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boost::tie(conditional,remaining) = inference::eliminateOne(fg, ordering[X(1)], EliminateQR);
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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[X(1)],15*d,R11,ordering[L(1)],S12,ordering[X(2)],S13,sigma);
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// Create expected remaining new factor
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JacobianFactor expectedFactor(1, Matrix_(4,2,
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4.714045207910318, 0.,
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0., 4.714045207910318,
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0., 0.,
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0., 0.),
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2, Matrix_(4,2,
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-2.357022603955159, 0.,
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0., -2.357022603955159,
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7.071067811865475, 0.,
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0., 7.071067811865475),
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Vector_(4, -0.707106781186547, 0.942809041582063, 0.707106781186547, -1.414213562373094), noiseModel::Unit::Create(4));
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EXPECT(assert_equal(expected,*conditional,tol));
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EXPECT(assert_equal((const GaussianFactor&)expectedFactor,*remaining.back(),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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Ordering ordering; ordering += X(1),L(1),X(2);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianConditional::shared_ptr actual = inference::eliminateOne(fg, ordering[X(2)], EliminateQR).first;
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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[X(2)],d,R11,ordering[X(1)],S13,ordering[L(1)],S12,sigma);
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EXPECT(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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Ordering ordering; ordering += X(1),L(1),X(2);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianConditional::shared_ptr actual = inference::eliminateOne(fg, ordering[L(1)], EliminateQR).first;
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// create expected Conditional Gaussian
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double sig = sqrt(2.0)/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[L(1)],d,R11,ordering[X(1)],S12,ordering[X(2)],S13,sigma);
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EXPECT(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 += X(2),L(1),X(1);
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Vector d1 = Vector_(2, -0.1,-0.1);
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GaussianBayesNet expected = simpleGaussian(ordering[X(1)],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[L(1)],d2, I/sig1,ordering[X(1)], (-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[X(2)],d3, I/sig2,ordering[L(1)], (-0.2)*I/sig2, ordering[X(1)], (-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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EXPECT(assert_equal(expected,actual,tol));
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GaussianBayesNet actualQR = *GaussianSequentialSolver(fg1, true).eliminate();
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EXPECT(assert_equal(expected,actualQR,tol));
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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 += X(2),L(1),X(1);
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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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EXPECT(assert_equal(expected,actual));
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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 += X(2),L(1),X(1);
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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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EXPECT(assert_equal(CBN,CBN2));
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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 += L(1),X(1),X(2);
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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 += L(1),X(2),X(1);
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EXPECT(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, optimize_Cholesky )
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{
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// create an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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// create a graph
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GaussianFactorGraph fg = createGaussianFactorGraph(ord);
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// optimize the graph
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VectorValues actual = *GaussianSequentialSolver(fg, false).optimize();
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// verify
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VectorValues expected = createCorrectDelta(ord);
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EXPECT(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, optimize_QR )
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{
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// create an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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// create a graph
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GaussianFactorGraph fg = createGaussianFactorGraph(ord);
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// optimize the graph
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VectorValues actual = *GaussianSequentialSolver(fg, true).optimize();
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// verify
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VectorValues expected = createCorrectDelta(ord);
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EXPECT(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 an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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// create a test graph
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GaussianFactorGraph fg1 = createGaussianFactorGraph(ord);
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// create another factor graph
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GaussianFactorGraph fg2 = createGaussianFactorGraph(ord);
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// get sizes
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size_t size1 = fg1.size();
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size_t size2 = fg2.size();
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// combine them
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fg1.combine(fg2);
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EXPECT(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 an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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// create a test graph
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GaussianFactorGraph fg1 = createGaussianFactorGraph(ord);
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// create another factor graph
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GaussianFactorGraph fg2 = createGaussianFactorGraph(ord);
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// get sizes
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size_t size1 = fg1.size();
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size_t size2 = fg2.size();
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// combine them
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GaussianFactorGraph fg3 = GaussianFactorGraph::combine2(fg1, fg2);
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EXPECT(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 (size_t 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, createSmoother)
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{
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GaussianFactorGraph fg1 = createSmoother(2).first;
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LONGS_EQUAL(3,fg1.size());
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GaussianFactorGraph fg2 = createSmoother(3).first;
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LONGS_EQUAL(5,fg2.size());
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}
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/* ************************************************************************* */
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double error(const VectorValues& x) {
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// create an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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GaussianFactorGraph fg = createGaussianFactorGraph(ord);
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return fg.error(x);
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, multiplication )
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{
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// create an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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GaussianFactorGraph A = createGaussianFactorGraph(ord);
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VectorValues x = createCorrectDelta(ord);
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Errors actual = A * x;
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Errors expected;
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expected += Vector_(2,-1.0,-1.0);
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expected += Vector_(2, 2.0,-1.0);
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expected += Vector_(2, 0.0, 1.0);
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expected += Vector_(2,-1.0, 1.5);
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EXPECT(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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// Extra test on elimination prompted by Michael's email to Frank 1/4/2010
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TEST( GaussianFactorGraph, elimination )
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{
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Ordering ord;
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ord += X(1), X(2);
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// Create Gaussian Factor Graph
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GaussianFactorGraph fg;
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Matrix Ap = eye(1), An = eye(1) * -1;
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Vector b = Vector_(1, 0.0);
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SharedDiagonal sigma = noiseModel::Isotropic::Sigma(1,2.0);
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fg.add(ord[X(1)], An, ord[X(2)], Ap, b, sigma);
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fg.add(ord[X(1)], Ap, b, sigma);
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fg.add(ord[X(2)], Ap, b, sigma);
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// Eliminate
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GaussianBayesNet bayesNet = *GaussianSequentialSolver(fg).eliminate();
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// Check sigma
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EXPECT_DOUBLES_EQUAL(1.0,bayesNet[ord[X(2)]]->get_sigmas()(0),1e-5);
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// Check matrix
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Matrix R;Vector d;
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boost::tie(R,d) = matrix(bayesNet);
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Matrix expected = Matrix_(2,2,
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0.707107, -0.353553,
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0.0, 0.612372);
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Matrix expected2 = Matrix_(2,2,
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0.707107, -0.353553,
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0.0, -0.612372);
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EXPECT(equal_with_abs_tol(expected, R, 1e-6) || equal_with_abs_tol(expected2, R, 1e-6));
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}
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/* ************************************************************************* */
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// Tests ported from ConstrainedGaussianFactorGraph
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, constrained_simple )
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{
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// get a graph with a constraint in it
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GaussianFactorGraph fg = createSimpleConstraintGraph();
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EXPECT(hasConstraints(fg));
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// eliminate and solve
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VectorValues actual = *GaussianSequentialSolver(fg).optimize();
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// verify
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VectorValues expected = createSimpleConstraintValues();
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, constrained_single )
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{
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// get a graph with a constraint in it
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GaussianFactorGraph fg = createSingleConstraintGraph();
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EXPECT(hasConstraints(fg));
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// eliminate and solve
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VectorValues actual = *GaussianSequentialSolver(fg).optimize();
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// verify
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VectorValues expected = createSingleConstraintValues();
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, constrained_multi1 )
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{
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// get a graph with a constraint in it
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GaussianFactorGraph fg = createMultiConstraintGraph();
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EXPECT(hasConstraints(fg));
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// eliminate and solve
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VectorValues actual = *GaussianSequentialSolver(fg).optimize();
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// verify
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VectorValues expected = createMultiConstraintValues();
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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static SharedDiagonal model = noiseModel::Isotropic::Sigma(2,1);
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/* ************************************************************************* */
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TEST(GaussianFactorGraph, replace)
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{
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Ordering ord; ord += X(1),X(2),X(3),X(4),X(5),X(6);
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SharedDiagonal noise(noiseModel::Isotropic::Sigma(3, 1.0));
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GaussianFactorGraph::sharedFactor f1(new JacobianFactor(
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ord[X(1)], eye(3,3), ord[X(2)], eye(3,3), zero(3), noise));
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GaussianFactorGraph::sharedFactor f2(new JacobianFactor(
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ord[X(2)], eye(3,3), ord[X(3)], eye(3,3), zero(3), noise));
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GaussianFactorGraph::sharedFactor f3(new JacobianFactor(
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ord[X(3)], eye(3,3), ord[X(4)], eye(3,3), zero(3), noise));
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GaussianFactorGraph::sharedFactor f4(new JacobianFactor(
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ord[X(5)], eye(3,3), ord[X(6)], eye(3,3), zero(3), noise));
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GaussianFactorGraph actual;
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actual.push_back(f1);
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actual.push_back(f2);
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actual.push_back(f3);
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actual.replace(0, f4);
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GaussianFactorGraph expected;
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expected.push_back(f4);
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expected.push_back(f2);
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expected.push_back(f3);
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|
|
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EXPECT(assert_equal(expected, actual));
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|
}
|
|
|
|
/* ************************************************************************* */
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|
TEST(GaussianFactorGraph, createSmoother2)
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|
{
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|
using namespace example;
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|
GaussianFactorGraph fg2;
|
|
Ordering ordering;
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boost::tie(fg2,ordering) = createSmoother(3);
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LONGS_EQUAL(5,fg2.size());
|
|
|
|
// eliminate
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|
vector<Index> x3var; x3var.push_back(ordering[X(3)]);
|
|
vector<Index> x1var; x1var.push_back(ordering[X(1)]);
|
|
GaussianBayesNet p_x3 = *GaussianSequentialSolver(
|
|
*GaussianSequentialSolver(fg2).jointFactorGraph(x3var)).eliminate();
|
|
GaussianBayesNet p_x1 = *GaussianSequentialSolver(
|
|
*GaussianSequentialSolver(fg2).jointFactorGraph(x1var)).eliminate();
|
|
CHECK(assert_equal(*p_x1.back(),*p_x3.front())); // should be the same because of symmetry
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(GaussianFactorGraph, hasConstraints)
|
|
{
|
|
FactorGraph<GaussianFactor> fgc1 = createMultiConstraintGraph();
|
|
EXPECT(hasConstraints(fgc1));
|
|
|
|
FactorGraph<GaussianFactor> fgc2 = createSimpleConstraintGraph() ;
|
|
EXPECT(hasConstraints(fgc2));
|
|
|
|
Ordering ordering; ordering += X(1), X(2), L(1);
|
|
GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
|
|
EXPECT(!hasConstraints(fg));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
|
/* ************************************************************************* */
|