171 lines
5.5 KiB
C++
171 lines
5.5 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 testNonlinearFactorGraph.cpp
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* @brief Unit tests for Non-Linear Factor NonlinearFactorGraph
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* @brief testNonlinearFactorGraph
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* @author Carlos Nieto
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* @author Christian Potthast
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*/
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/*STL/C++*/
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#include <iostream>
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using namespace std;
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#include <boost/assign/std/list.hpp>
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#include <boost/assign/std/set.hpp>
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using namespace boost::assign;
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#include <CppUnitLite/TestHarness.h>
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#include <gtsam/base/Testable.h>
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#include <gtsam/base/Matrix.h>
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#include <tests/smallExample.h>
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#include <gtsam/inference/FactorGraph.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/symbolic/SymbolicFactorGraph.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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using namespace gtsam;
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using namespace example;
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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( NonlinearFactorGraph, equals )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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NonlinearFactorGraph fg2 = createNonlinearFactorGraph();
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CHECK( fg.equals(fg2) );
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, error )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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Values c1 = createValues();
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double actual1 = fg.error(c1);
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DOUBLES_EQUAL( 0.0, actual1, 1e-9 );
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Values c2 = createNoisyValues();
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double actual2 = fg.error(c2);
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DOUBLES_EQUAL( 5.625, actual2, 1e-9 );
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, keys )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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FastSet<Key> actual = fg.keys();
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LONGS_EQUAL(3, (long)actual.size());
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FastSet<Key>::const_iterator it = actual.begin();
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LONGS_EQUAL((long)L(1), (long)*(it++));
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LONGS_EQUAL((long)X(1), (long)*(it++));
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LONGS_EQUAL((long)X(2), (long)*(it++));
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, GET_ORDERING)
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{
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Ordering expected; expected += L(1), X(2), X(1); // For starting with l1,x1,x2
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NonlinearFactorGraph nlfg = createNonlinearFactorGraph();
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Ordering actual = Ordering::colamd(nlfg);
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EXPECT(assert_equal(expected,actual));
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// Constrained ordering - put x2 at the end
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Ordering expectedConstrained; expectedConstrained += L(1), X(1), X(2);
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FastMap<Key, int> constraints;
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constraints[X(2)] = 1;
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Ordering actualConstrained = Ordering::colamdConstrained(nlfg, constraints);
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EXPECT(assert_equal(expectedConstrained, actualConstrained));
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, probPrime )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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Values cfg = createValues();
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// evaluate the probability of the factor graph
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double actual = fg.probPrime(cfg);
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double expected = 1.0;
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DOUBLES_EQUAL(expected,actual,0);
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, linearize )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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Values initial = createNoisyValues();
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GaussianFactorGraph linearized = *fg.linearize(initial);
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GaussianFactorGraph expected = createGaussianFactorGraph();
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CHECK(assert_equal(expected,linearized)); // Needs correct linearizations
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, clone )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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NonlinearFactorGraph actClone = fg.clone();
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EXPECT(assert_equal(fg, actClone));
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for (size_t i=0; i<fg.size(); ++i)
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EXPECT(fg[i] != actClone[i]);
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, rekey )
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{
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NonlinearFactorGraph init = createNonlinearFactorGraph();
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map<Key,Key> rekey_mapping;
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rekey_mapping.insert(make_pair(L(1), L(4)));
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NonlinearFactorGraph actRekey = init.rekey(rekey_mapping);
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// ensure deep clone
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LONGS_EQUAL((long)init.size(), (long)actRekey.size());
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for (size_t i=0; i<init.size(); ++i)
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EXPECT(init[i] != actRekey[i]);
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NonlinearFactorGraph expRekey;
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// original measurements
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expRekey.push_back(init[0]);
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expRekey.push_back(init[1]);
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// updated measurements
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Point2 z3(0, -1), z4(-1.5, -1.);
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SharedDiagonal sigma0_2 = noiseModel::Isotropic::Sigma(2,0.2);
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expRekey += simulated2D::Measurement(z3, sigma0_2, X(1), L(4));
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expRekey += simulated2D::Measurement(z4, sigma0_2, X(2), L(4));
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EXPECT(assert_equal(expRekey, actRekey));
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, symbolic )
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{
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NonlinearFactorGraph graph = createNonlinearFactorGraph();
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SymbolicFactorGraph expected;
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expected.push_factor(X(1));
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expected.push_factor(X(1), X(2));
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expected.push_factor(X(1), L(1));
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expected.push_factor(X(2), L(1));
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SymbolicFactorGraph actual = *graph.symbolic();
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EXPECT(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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