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								/* ----------------------------------------------------------------------------
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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 Graph
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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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											2010-01-18 03:34:57 +08:00
										 
									 
								 
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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/inference/FactorGraph-inl.h>
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								#include <gtsam/nonlinear/NonlinearFactorGraph-inl.h>
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								using namespace gtsam;
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								using namespace example;
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								/* ************************************************************************* */
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								TEST( Graph, equals )
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								{
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									Graph fg = createNonlinearFactorGraph();
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									Graph fg2 = createNonlinearFactorGraph();
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									CHECK( fg.equals(fg2) );
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								}
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								/* ************************************************************************* */
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								TEST( Graph, error )
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								{
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									Graph 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( Graph, keys )
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								{
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									Graph fg = createNonlinearFactorGraph();
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									set<Symbol> actual = fg.keys();
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									LONGS_EQUAL(3, actual.size());
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									set<Symbol>::const_iterator it = actual.begin();
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									CHECK(assert_equal(Symbol('l', 1), *(it++)));
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									CHECK(assert_equal(Symbol('x', 1), *(it++)));
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									CHECK(assert_equal(Symbol('x', 2), *(it++)));
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								}
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								/* ************************************************************************* */
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								TEST( Graph, GET_ORDERING)
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								{
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								  Ordering expected; expected += "x1","l1","x2";
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								  Graph nlfg = createNonlinearFactorGraph();
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								  Ordering actual = *nlfg.orderingCOLAMD(createNoisyValues());
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								  CHECK(assert_equal(expected,actual));
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								}
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								/* ************************************************************************* */
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								TEST( Graph, probPrime )
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								{
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									Graph 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( Graph, linearize )
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								{
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									Graph fg = createNonlinearFactorGraph();
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									Values initial = createNoisyValues();
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									boost::shared_ptr<FactorGraph<GaussianFactor> > linearized = fg.linearize(initial, *initial.orderingArbitrary());
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									FactorGraph<GaussianFactor> expected = createGaussianFactorGraph(*initial.orderingArbitrary());
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									CHECK(assert_equal(expected,*linearized)); // Needs correct linearizations
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								}
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								/* ************************************************************************* */
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								int main() {
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									TestResult tr;
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									return TestRegistry::runAllTests(tr);
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								}
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								/* ************************************************************************* */
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