66 lines
2.4 KiB
C++
66 lines
2.4 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 testInference.cpp
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* @brief Unit tests for functionality declared in inference.h
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* @author Frank Dellaert
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*/
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#include <gtsam/CppUnitLite/TestHarness.h>
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#define GTSAM_MAGIC_KEY
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#include <gtsam/slam/smallExample.h>
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#include <gtsam/inference/inference-inl.h>
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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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/* ************************************************************************* */
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// The tests below test the *generic* inference algorithms. Some of these have
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// specialized versions in the derived classes GaussianFactorGraph etc...
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/* ************************************************************************* */
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/* ************************************************************************* */
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TEST(GaussianFactorGraph, createSmoother)
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{
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GaussianFactorGraph fg2;
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Ordering ordering;
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boost::tie(fg2,ordering) = createSmoother(3);
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LONGS_EQUAL(5,fg2.size());
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// eliminate
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list<Index> x3var; x3var.push_back(ordering["x3"]);
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list<Index> x1var; x1var.push_back(ordering["x1"]);
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GaussianBayesNet p_x3 = *Inference::Eliminate(Inference::Marginal(fg2, x3var));
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GaussianBayesNet p_x1 = *Inference::Eliminate(Inference::Marginal(fg2, x1var));
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CHECK(assert_equal(*p_x1.back(),*p_x3.front())); // should be the same because of symmetry
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}
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/* ************************************************************************* */
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TEST( Inference, marginals )
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{
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// create and marginalize a small Bayes net on "x"
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GaussianBayesNet cbn = createSmallGaussianBayesNet();
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list<Index> xvar; xvar.push_back(0);
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GaussianBayesNet actual = *Inference::Eliminate(Inference::Marginal(GaussianFactorGraph(cbn), xvar));
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// expected is just scalar Gaussian on x
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GaussianBayesNet expected = scalarGaussian(0, 4, sqrt(2));
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CHECK(assert_equal(expected,actual));
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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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