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Reenabled dogleg unit tests and moved relevant parts to other test files

release/4.3a0
Richard Roberts 2013-08-11 18:17:32 +00:00
parent f983026225
commit 3c8d482271
3 changed files with 210 additions and 319 deletions
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65
gtsam/linear/tests/testGaussianBayesNetUnordered.cpp
View File

@ -26,6 +26,8 @@
using namespace boost::assign; using namespace boost::assign;
#include <gtsam/base/Testable.h> #include <gtsam/base/Testable.h>
#include <gtsam/base/LieVector.h>
#include <gtsam/base/numericalDerivative.h>
#include <gtsam/linear/GaussianBayesNet.h> #include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/JacobianFactor.h> #include <gtsam/linear/JacobianFactor.h>
#include <gtsam/linear/GaussianFactorGraph.h> #include <gtsam/linear/GaussianFactorGraph.h>
@ -127,6 +129,69 @@ TEST( GaussianBayesNet, DeterminantTest )
EXPECT_DOUBLES_EQUAL( expectedDeterminant, actualDeterminant, 1e-9); EXPECT_DOUBLES_EQUAL( expectedDeterminant, actualDeterminant, 1e-9);
} }
/* ************************************************************************* */
namespace {
double computeError(const GaussianBayesNet& gbn, const LieVector& values)
{
pair<Matrix,Vector> Rd = GaussianFactorGraph(gbn).jacobian();
return 0.5 * (Rd.first * values - Rd.second).squaredNorm();
}
}
/* ************************************************************************* */
TEST(GaussianBayesNet, ComputeSteepestDescentPoint) {
// Create an arbitrary Bayes Net
GaussianBayesNet gbn;
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
0, Vector_(2, 1.0,2.0), Matrix_(2,2, 3.0,4.0,0.0,6.0),
3, Matrix_(2,2, 7.0,8.0,9.0,10.0),
4, Matrix_(2,2, 11.0,12.0,13.0,14.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
1, Vector_(2, 15.0,16.0), Matrix_(2,2, 17.0,18.0,0.0,20.0),
2, Matrix_(2,2, 21.0,22.0,23.0,24.0),
4, Matrix_(2,2, 25.0,26.0,27.0,28.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
2, Vector_(2, 29.0,30.0), Matrix_(2,2, 31.0,32.0,0.0,34.0),
3, Matrix_(2,2, 35.0,36.0,37.0,38.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
3, Vector_(2, 39.0,40.0), Matrix_(2,2, 41.0,42.0,0.0,44.0),
4, Matrix_(2,2, 45.0,46.0,47.0,48.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
4, Vector_(2, 49.0,50.0), Matrix_(2,2, 51.0,52.0,0.0,54.0)));
// Compute the Hessian numerically
Matrix hessian = numericalHessian(
boost::function<double(const LieVector&)>(boost::bind(&computeError, gbn, _1)),
LieVector(Vector::Zero(GaussianFactorGraph(gbn).jacobian().first.cols())));
// Compute the gradient numerically
Vector gradient = numericalGradient(
boost::function<double(const LieVector&)>(boost::bind(&computeError, gbn, _1)),
LieVector(Vector::Zero(GaussianFactorGraph(gbn).jacobian().first.cols())));
// Compute the gradient using dense matrices
Matrix augmentedHessian = GaussianFactorGraph(gbn).augmentedHessian();
LONGS_EQUAL(11, augmentedHessian.cols());
Vector denseMatrixGradient = -augmentedHessian.col(10).segment(0,10);
EXPECT(assert_equal(gradient, denseMatrixGradient, 1e-5));
// Compute the steepest descent point
double step = -gradient.squaredNorm() / (gradient.transpose() * hessian * gradient)(0);
Vector expected = gradient * step;
// Compute the steepest descent point with the dogleg function
VectorValues actual = gbn.optimizeGradientSearch();
// Check that points agree
EXPECT(assert_equal(expected, actual.vector(), 1e-5));
// Check that point causes a decrease in error
double origError = GaussianFactorGraph(gbn).error(VectorValues::Zero(actual));
double newError = GaussianFactorGraph(gbn).error(actual);
EXPECT(newError < origError);
}
/* ************************************************************************* */ /* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);} int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */ /* ************************************************************************* */

111
gtsam/linear/tests/testGaussianBayesTreeUnordered.cpp
View File

@ -24,6 +24,8 @@
using namespace boost::assign; using namespace boost::assign;
#include <gtsam/base/debug.h> #include <gtsam/base/debug.h>
#include <gtsam/base/LieVector.h>
#include <gtsam/base/numericalDerivative.h>
#include <gtsam/geometry/Rot2.h> #include <gtsam/geometry/Rot2.h>
#include <gtsam/linear/GaussianJunctionTree.h> #include <gtsam/linear/GaussianJunctionTree.h>
#include <gtsam/linear/GaussianBayesTree.h> #include <gtsam/linear/GaussianBayesTree.h>
@ -173,6 +175,115 @@ TEST(GaussianBayesTree, complicatedMarginal) {
EXPECT(assert_equal(expectedCov, actualCov, 1e1)); EXPECT(assert_equal(expectedCov, actualCov, 1e1));
} }
namespace {
/* ************************************************************************* */
double computeError(const GaussianBayesTree& gbt, const LieVector& values)
{
pair<Matrix,Vector> Rd = GaussianFactorGraph(gbt).jacobian();
return 0.5 * (Rd.first * values - Rd.second).squaredNorm();
}
}
/* ************************************************************************* */
TEST(GaussianBayesTree, ComputeSteepestDescentPointBT) {
// Create an arbitrary Bayes Tree
GaussianBayesTree bt;
bt.insertRoot(MakeClique(GaussianConditional(
pair_list_of
(2, Matrix_(6,2,
31.0,32.0,
0.0,34.0,
0.0,0.0,
0.0,0.0,
0.0,0.0,
0.0,0.0))
(3, Matrix_(6,2,
35.0,36.0,
37.0,38.0,
41.0,42.0,
0.0,44.0,
0.0,0.0,
0.0,0.0))
(4, Matrix_(6,2,
0.0,0.0,
0.0,0.0,
45.0,46.0,
47.0,48.0,
51.0,52.0,
0.0,54.0)),
3, Vector_(6, 29.0,30.0,39.0,40.0,49.0,50.0)), list_of
(MakeClique(GaussianConditional(
pair_list_of
(0, Matrix_(4,2,
3.0,4.0,
0.0,6.0,
0.0,0.0,
0.0,0.0))
(1, Matrix_(4,2,
0.0,0.0,
0.0,0.0,
17.0,18.0,
0.0,20.0))
(2, Matrix_(4,2,
0.0,0.0,
0.0,0.0,
21.0,22.0,
23.0,24.0))
(3, Matrix_(4,2,
7.0,8.0,
9.0,10.0,
0.0,0.0,
0.0,0.0))
(4, Matrix_(4,2,
11.0,12.0,
13.0,14.0,
25.0,26.0,
27.0,28.0)),
2, Vector_(4, 1.0,2.0,15.0,16.0))))));
// Compute the Hessian numerically
Matrix hessian = numericalHessian(
boost::function<double(const LieVector&)>(boost::bind(&computeError, bt, _1)),
LieVector(Vector::Zero(GaussianFactorGraph(bt).jacobian().first.cols())));
// Compute the gradient numerically
Vector gradient = numericalGradient(
boost::function<double(const LieVector&)>(boost::bind(&computeError, bt, _1)),
LieVector(Vector::Zero(GaussianFactorGraph(bt).jacobian().first.cols())));
// Compute the gradient using dense matrices
Matrix augmentedHessian = GaussianFactorGraph(bt).augmentedHessian();
LONGS_EQUAL(11, augmentedHessian.cols());
Vector denseMatrixGradient = -augmentedHessian.col(10).segment(0,10);
EXPECT(assert_equal(gradient, denseMatrixGradient, 1e-5));
// Compute the steepest descent point
double step = -gradient.squaredNorm() / (gradient.transpose() * hessian * gradient)(0);
Vector expected = gradient * step;
// Known steepest descent point from Bayes' net version
VectorValues expectedFromBN = pair_list_of
(0, Vector_(2, 0.000129034, 0.000688183))
(1, Vector_(2, 0.0109679, 0.0253767))
(2, Vector_(2, 0.0680441, 0.114496))
(3, Vector_(2, 0.16125, 0.241294))
(4, Vector_(2, 0.300134, 0.423233));
// Compute the steepest descent point with the dogleg function
VectorValues actual = bt.optimizeGradientSearch();
// Check that points agree
EXPECT(assert_equal(expected, actual.vector(), 1e-5));
EXPECT(assert_equal(expectedFromBN, actual, 1e-5));
// Check that point causes a decrease in error
double origError = GaussianFactorGraph(bt).error(VectorValues::Zero(actual));
double newError = GaussianFactorGraph(bt).error(actual);
EXPECT(newError < origError);
}
/* ************************************************************************* */ /* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);} int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */ /* ************************************************************************* */

353
tests/testDoglegOptimizer.cpp
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@ -17,14 +17,11 @@
#include <CppUnitLite/TestHarness.h> #include <CppUnitLite/TestHarness.h>
#if 0
#include <tests/smallExample.h> #include <tests/smallExample.h>
#include <gtsam/nonlinear/DoglegOptimizerImpl.h> #include <gtsam/nonlinear/DoglegOptimizerImpl.h>
#include <gtsam/nonlinear/Symbol.h> #include <gtsam/nonlinear/Symbol.h>
#include <gtsam/linear/JacobianFactor.h> #include <gtsam/linear/JacobianFactor.h>
#include <gtsam/linear/GaussianBayesTree.h> #include <gtsam/linear/GaussianBayesTree.h>
#include <gtsam/inference/BayesTree.h>
#include <gtsam/base/numericalDerivative.h> #include <gtsam/base/numericalDerivative.h>
#ifdef __GNUC__ #ifdef __GNUC__
@ -46,282 +43,6 @@ using namespace gtsam;
using symbol_shorthand::X; using symbol_shorthand::X;
using symbol_shorthand::L; using symbol_shorthand::L;
/* ************************************************************************* */
double computeError(const GaussianBayesNet& gbn, const LieVector& values) {
// Convert Vector to VectorValues
VectorValues vv = *allocateVectorValues(gbn);
internal::writeVectorValuesSlices(values, vv,
boost::make_counting_iterator(size_t(0)), boost::make_counting_iterator(vv.size()));
// Convert to factor graph
GaussianFactorGraph gfg(gbn);
return gfg.error(vv);
}
/* ************************************************************************* */
double computeErrorBt(const BayesTree<GaussianConditional>& gbt, const LieVector& values) {
// Convert Vector to VectorValues
VectorValues vv = *allocateVectorValues(gbt);
internal::writeVectorValuesSlices(values, vv,
boost::make_counting_iterator(size_t(0)), boost::make_counting_iterator(vv.size()));
// Convert to factor graph
GaussianFactorGraph gfg(gbt);
return gfg.error(vv);
}
/* ************************************************************************* */
TEST(DoglegOptimizer, ComputeSteepestDescentPoint) {
// Create an arbitrary Bayes Net
GaussianBayesNet gbn;
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
0, Vector_(2, 1.0,2.0), Matrix_(2,2, 3.0,4.0,0.0,6.0),
3, Matrix_(2,2, 7.0,8.0,9.0,10.0),
4, Matrix_(2,2, 11.0,12.0,13.0,14.0), ones(2)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
1, Vector_(2, 15.0,16.0), Matrix_(2,2, 17.0,18.0,0.0,20.0),
2, Matrix_(2,2, 21.0,22.0,23.0,24.0),
4, Matrix_(2,2, 25.0,26.0,27.0,28.0), ones(2)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
2, Vector_(2, 29.0,30.0), Matrix_(2,2, 31.0,32.0,0.0,34.0),
3, Matrix_(2,2, 35.0,36.0,37.0,38.0), ones(2)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
3, Vector_(2, 39.0,40.0), Matrix_(2,2, 41.0,42.0,0.0,44.0),
4, Matrix_(2,2, 45.0,46.0,47.0,48.0), ones(2)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
4, Vector_(2, 49.0,50.0), Matrix_(2,2, 51.0,52.0,0.0,54.0), ones(2)));
// Compute the Hessian numerically
Matrix hessian = numericalHessian(
boost::function<double(const LieVector&)>(boost::bind(&computeError, gbn, _1)),
LieVector(VectorValues::Zero(*allocateVectorValues(gbn)).asVector()));
// Compute the gradient numerically
VectorValues gradientValues = *allocateVectorValues(gbn);
Vector gradient = numericalGradient(
boost::function<double(const LieVector&)>(boost::bind(&computeError, gbn, _1)),
LieVector(VectorValues::Zero(gradientValues).asVector()));
internal::writeVectorValuesSlices(gradient, gradientValues,
boost::make_counting_iterator(size_t(0)), boost::make_counting_iterator(gradientValues.size()));
// Compute the gradient using dense matrices
Matrix augmentedHessian = GaussianFactorGraph(gbn).augmentedHessian();
LONGS_EQUAL(11, augmentedHessian.cols());
VectorValues denseMatrixGradient = *allocateVectorValues(gbn);
internal::writeVectorValuesSlices(-augmentedHessian.col(10).segment(0,10), denseMatrixGradient,
boost::make_counting_iterator(size_t(0)), boost::make_counting_iterator(gradientValues.size()));
EXPECT(assert_equal(gradientValues, denseMatrixGradient, 1e-5));
// Compute the steepest descent point
double step = -gradient.squaredNorm() / (gradient.transpose() * hessian * gradient)(0);
VectorValues expected = gradientValues; scal(step, expected);
// Compute the steepest descent point with the dogleg function
VectorValues actual = optimizeGradientSearch(gbn);
// Check that points agree
EXPECT(assert_equal(expected, actual, 1e-5));
// Check that point causes a decrease in error
double origError = GaussianFactorGraph(gbn).error(VectorValues::Zero(*allocateVectorValues(gbn)));
double newError = GaussianFactorGraph(gbn).error(actual);
EXPECT(newError < origError);
}
/* ************************************************************************* */
TEST(DoglegOptimizer, BT_BN_equivalency) {
// Create an arbitrary Bayes Tree
BayesTree<GaussianConditional> bt;
bt.insert(BayesTree<GaussianConditional>::sharedClique(new BayesTree<GaussianConditional>::Clique(
GaussianConditional::shared_ptr(new GaussianConditional(
boost::assign::pair_list_of
(2, Matrix_(6,2,
31.0,32.0,
0.0,34.0,
0.0,0.0,
0.0,0.0,
0.0,0.0,
0.0,0.0))
(3, Matrix_(6,2,
35.0,36.0,
37.0,38.0,
41.0,42.0,
0.0,44.0,
0.0,0.0,
0.0,0.0))
(4, Matrix_(6,2,
0.0,0.0,
0.0,0.0,
45.0,46.0,
47.0,48.0,
51.0,52.0,
0.0,54.0)),
3, Vector_(6, 29.0,30.0,39.0,40.0,49.0,50.0), ones(6))))));
bt.insert(BayesTree<GaussianConditional>::sharedClique(new BayesTree<GaussianConditional>::Clique(
GaussianConditional::shared_ptr(new GaussianConditional(
boost::assign::pair_list_of
(0, Matrix_(4,2,
3.0,4.0,
0.0,6.0,
0.0,0.0,
0.0,0.0))
(1, Matrix_(4,2,
0.0,0.0,
0.0,0.0,
17.0,18.0,
0.0,20.0))
(2, Matrix_(4,2,
0.0,0.0,
0.0,0.0,
21.0,22.0,
23.0,24.0))
(3, Matrix_(4,2,
7.0,8.0,
9.0,10.0,
0.0,0.0,
0.0,0.0))
(4, Matrix_(4,2,
11.0,12.0,
13.0,14.0,
25.0,26.0,
27.0,28.0)),
2, Vector_(4, 1.0,2.0,15.0,16.0), ones(4))))));
// Create an arbitrary Bayes Net
GaussianBayesNet gbn;
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
0, Vector_(2, 1.0,2.0), Matrix_(2,2, 3.0,4.0,0.0,6.0),
3, Matrix_(2,2, 7.0,8.0,9.0,10.0),
4, Matrix_(2,2, 11.0,12.0,13.0,14.0), ones(2)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
1, Vector_(2, 15.0,16.0), Matrix_(2,2, 17.0,18.0,0.0,20.0),
2, Matrix_(2,2, 21.0,22.0,23.0,24.0),
4, Matrix_(2,2, 25.0,26.0,27.0,28.0), ones(2)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
2, Vector_(2, 29.0,30.0), Matrix_(2,2, 31.0,32.0,0.0,34.0),
3, Matrix_(2,2, 35.0,36.0,37.0,38.0), ones(2)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
3, Vector_(2, 39.0,40.0), Matrix_(2,2, 41.0,42.0,0.0,44.0),
4, Matrix_(2,2, 45.0,46.0,47.0,48.0), ones(2)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional(
4, Vector_(2, 49.0,50.0), Matrix_(2,2, 51.0,52.0,0.0,54.0), ones(2)));
GaussianFactorGraph expected(gbn);
GaussianFactorGraph actual(bt);
EXPECT(assert_equal(expected.augmentedHessian(), actual.augmentedHessian()));
}
/* ************************************************************************* */
TEST(DoglegOptimizer, ComputeSteepestDescentPointBT) {
// Create an arbitrary Bayes Tree
BayesTree<GaussianConditional> bt;
bt.insert(BayesTree<GaussianConditional>::sharedClique(new BayesTree<GaussianConditional>::Clique(
GaussianConditional::shared_ptr(new GaussianConditional(
boost::assign::pair_list_of
(2, Matrix_(6,2,
31.0,32.0,
0.0,34.0,
0.0,0.0,
0.0,0.0,
0.0,0.0,
0.0,0.0))
(3, Matrix_(6,2,
35.0,36.0,
37.0,38.0,
41.0,42.0,
0.0,44.0,
0.0,0.0,
0.0,0.0))
(4, Matrix_(6,2,
0.0,0.0,
0.0,0.0,
45.0,46.0,
47.0,48.0,
51.0,52.0,
0.0,54.0)),
3, Vector_(6, 29.0,30.0,39.0,40.0,49.0,50.0), ones(6))))));
bt.insert(BayesTree<GaussianConditional>::sharedClique(new BayesTree<GaussianConditional>::Clique(
GaussianConditional::shared_ptr(new GaussianConditional(
boost::assign::pair_list_of
(0, Matrix_(4,2,
3.0,4.0,
0.0,6.0,
0.0,0.0,
0.0,0.0))
(1, Matrix_(4,2,
0.0,0.0,
0.0,0.0,
17.0,18.0,
0.0,20.0))
(2, Matrix_(4,2,
0.0,0.0,
0.0,0.0,
21.0,22.0,
23.0,24.0))
(3, Matrix_(4,2,
7.0,8.0,
9.0,10.0,
0.0,0.0,
0.0,0.0))
(4, Matrix_(4,2,
11.0,12.0,
13.0,14.0,
25.0,26.0,
27.0,28.0)),
2, Vector_(4, 1.0,2.0,15.0,16.0), ones(4))))));
// Compute the Hessian numerically
Matrix hessian = numericalHessian(
boost::function<double(const LieVector&)>(boost::bind(&computeErrorBt, bt, _1)),
LieVector(VectorValues::Zero(*allocateVectorValues(bt)).asVector()));
// Compute the gradient numerically
VectorValues gradientValues = *allocateVectorValues(bt);
Vector gradient = numericalGradient(
boost::function<double(const LieVector&)>(boost::bind(&computeErrorBt, bt, _1)),
LieVector(VectorValues::Zero(gradientValues).asVector()));
internal::writeVectorValuesSlices(gradient, gradientValues,
boost::make_counting_iterator(size_t(0)), boost::make_counting_iterator(gradientValues.size()));
// Compute the gradient using dense matrices
Matrix augmentedHessian = GaussianFactorGraph(bt).augmentedHessian();
LONGS_EQUAL(11, augmentedHessian.cols());
VectorValues denseMatrixGradient = *allocateVectorValues(bt);
internal::writeVectorValuesSlices(-augmentedHessian.col(10).segment(0,10), denseMatrixGradient,
boost::make_counting_iterator(size_t(0)), boost::make_counting_iterator(gradientValues.size()));
EXPECT(assert_equal(gradientValues, denseMatrixGradient, 1e-5));
// Compute the steepest descent point
double step = -gradient.squaredNorm() / (gradient.transpose() * hessian * gradient)(0);
VectorValues expected = gradientValues; scal(step, expected);
// Known steepest descent point from Bayes' net version
VectorValues expectedFromBN(5,2);
expectedFromBN[0] = Vector_(2, 0.000129034, 0.000688183);
expectedFromBN[1] = Vector_(2, 0.0109679, 0.0253767);
expectedFromBN[2] = Vector_(2, 0.0680441, 0.114496);
expectedFromBN[3] = Vector_(2, 0.16125, 0.241294);
expectedFromBN[4] = Vector_(2, 0.300134, 0.423233);
// Compute the steepest descent point with the dogleg function
VectorValues actual = optimizeGradientSearch(bt);
// Check that points agree
EXPECT(assert_equal(expected, actual, 1e-5));
EXPECT(assert_equal(expectedFromBN, actual, 1e-5));
// Check that point causes a decrease in error
double origError = GaussianFactorGraph(bt).error(VectorValues::Zero(*allocateVectorValues(bt)));
double newError = GaussianFactorGraph(bt).error(actual);
EXPECT(newError < origError);
}
/* ************************************************************************* */ /* ************************************************************************* */
TEST(DoglegOptimizer, ComputeBlend) { TEST(DoglegOptimizer, ComputeBlend) {
// Create an arbitrary Bayes Net // Create an arbitrary Bayes Net
@ -329,33 +50,33 @@ TEST(DoglegOptimizer, ComputeBlend) {
gbn += GaussianConditional::shared_ptr(new GaussianConditional( gbn += GaussianConditional::shared_ptr(new GaussianConditional(
0, Vector_(2, 1.0,2.0), Matrix_(2,2, 3.0,4.0,0.0,6.0), 0, Vector_(2, 1.0,2.0), Matrix_(2,2, 3.0,4.0,0.0,6.0),
3, Matrix_(2,2, 7.0,8.0,9.0,10.0), 3, Matrix_(2,2, 7.0,8.0,9.0,10.0),
4, Matrix_(2,2, 11.0,12.0,13.0,14.0), ones(2))); 4, Matrix_(2,2, 11.0,12.0,13.0,14.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional( gbn += GaussianConditional::shared_ptr(new GaussianConditional(
1, Vector_(2, 15.0,16.0), Matrix_(2,2, 17.0,18.0,0.0,20.0), 1, Vector_(2, 15.0,16.0), Matrix_(2,2, 17.0,18.0,0.0,20.0),
2, Matrix_(2,2, 21.0,22.0,23.0,24.0), 2, Matrix_(2,2, 21.0,22.0,23.0,24.0),
4, Matrix_(2,2, 25.0,26.0,27.0,28.0), ones(2))); 4, Matrix_(2,2, 25.0,26.0,27.0,28.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional( gbn += GaussianConditional::shared_ptr(new GaussianConditional(
2, Vector_(2, 29.0,30.0), Matrix_(2,2, 31.0,32.0,0.0,34.0), 2, Vector_(2, 29.0,30.0), Matrix_(2,2, 31.0,32.0,0.0,34.0),
3, Matrix_(2,2, 35.0,36.0,37.0,38.0), ones(2))); 3, Matrix_(2,2, 35.0,36.0,37.0,38.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional( gbn += GaussianConditional::shared_ptr(new GaussianConditional(
3, Vector_(2, 39.0,40.0), Matrix_(2,2, 41.0,42.0,0.0,44.0), 3, Vector_(2, 39.0,40.0), Matrix_(2,2, 41.0,42.0,0.0,44.0),
4, Matrix_(2,2, 45.0,46.0,47.0,48.0), ones(2))); 4, Matrix_(2,2, 45.0,46.0,47.0,48.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional( gbn += GaussianConditional::shared_ptr(new GaussianConditional(
4, Vector_(2, 49.0,50.0), Matrix_(2,2, 51.0,52.0,0.0,54.0), ones(2))); 4, Vector_(2, 49.0,50.0), Matrix_(2,2, 51.0,52.0,0.0,54.0)));
// Compute steepest descent point // Compute steepest descent point
VectorValues xu = optimizeGradientSearch(gbn); VectorValues xu = gbn.optimizeGradientSearch();
// Compute Newton's method point // Compute Newton's method point
VectorValues xn = optimize(gbn); VectorValues xn = gbn.optimize();
// The Newton's method point should be more "adventurous", i.e. larger, than the steepest descent point // The Newton's method point should be more "adventurous", i.e. larger, than the steepest descent point
EXPECT(xu.asVector().norm() < xn.asVector().norm()); EXPECT(xu.vector().norm() < xn.vector().norm());
// Compute blend // Compute blend
double Delta = 1.5; double Delta = 1.5;
VectorValues xb = DoglegOptimizerImpl::ComputeBlend(Delta, xu, xn); VectorValues xb = DoglegOptimizerImpl::ComputeBlend(Delta, xu, xn);
DOUBLES_EQUAL(Delta, xb.asVector().norm(), 1e-10); DOUBLES_EQUAL(Delta, xb.vector().norm(), 1e-10);
} }
/* ************************************************************************* */ /* ************************************************************************* */
@ -365,73 +86,67 @@ TEST(DoglegOptimizer, ComputeDoglegPoint) {
gbn += GaussianConditional::shared_ptr(new GaussianConditional( gbn += GaussianConditional::shared_ptr(new GaussianConditional(
0, Vector_(2, 1.0,2.0), Matrix_(2,2, 3.0,4.0,0.0,6.0), 0, Vector_(2, 1.0,2.0), Matrix_(2,2, 3.0,4.0,0.0,6.0),
3, Matrix_(2,2, 7.0,8.0,9.0,10.0), 3, Matrix_(2,2, 7.0,8.0,9.0,10.0),
4, Matrix_(2,2, 11.0,12.0,13.0,14.0), ones(2))); 4, Matrix_(2,2, 11.0,12.0,13.0,14.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional( gbn += GaussianConditional::shared_ptr(new GaussianConditional(
1, Vector_(2, 15.0,16.0), Matrix_(2,2, 17.0,18.0,0.0,20.0), 1, Vector_(2, 15.0,16.0), Matrix_(2,2, 17.0,18.0,0.0,20.0),
2, Matrix_(2,2, 21.0,22.0,23.0,24.0), 2, Matrix_(2,2, 21.0,22.0,23.0,24.0),
4, Matrix_(2,2, 25.0,26.0,27.0,28.0), ones(2))); 4, Matrix_(2,2, 25.0,26.0,27.0,28.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional( gbn += GaussianConditional::shared_ptr(new GaussianConditional(
2, Vector_(2, 29.0,30.0), Matrix_(2,2, 31.0,32.0,0.0,34.0), 2, Vector_(2, 29.0,30.0), Matrix_(2,2, 31.0,32.0,0.0,34.0),
3, Matrix_(2,2, 35.0,36.0,37.0,38.0), ones(2))); 3, Matrix_(2,2, 35.0,36.0,37.0,38.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional( gbn += GaussianConditional::shared_ptr(new GaussianConditional(
3, Vector_(2, 39.0,40.0), Matrix_(2,2, 41.0,42.0,0.0,44.0), 3, Vector_(2, 39.0,40.0), Matrix_(2,2, 41.0,42.0,0.0,44.0),
4, Matrix_(2,2, 45.0,46.0,47.0,48.0), ones(2))); 4, Matrix_(2,2, 45.0,46.0,47.0,48.0)));
gbn += GaussianConditional::shared_ptr(new GaussianConditional( gbn += GaussianConditional::shared_ptr(new GaussianConditional(
4, Vector_(2, 49.0,50.0), Matrix_(2,2, 51.0,52.0,0.0,54.0), ones(2))); 4, Vector_(2, 49.0,50.0), Matrix_(2,2, 51.0,52.0,0.0,54.0)));
// Compute dogleg point for different deltas // Compute dogleg point for different deltas
double Delta1 = 0.5; // Less than steepest descent double Delta1 = 0.5; // Less than steepest descent
VectorValues actual1 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta1, optimizeGradientSearch(gbn), optimize(gbn)); VectorValues actual1 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta1, gbn.optimizeGradientSearch(), gbn.optimize());
DOUBLES_EQUAL(Delta1, actual1.asVector().norm(), 1e-5); DOUBLES_EQUAL(Delta1, actual1.vector().norm(), 1e-5);
double Delta2 = 1.5; // Between steepest descent and Newton's method double Delta2 = 1.5; // Between steepest descent and Newton's method
VectorValues expected2 = DoglegOptimizerImpl::ComputeBlend(Delta2, optimizeGradientSearch(gbn), optimize(gbn)); VectorValues expected2 = DoglegOptimizerImpl::ComputeBlend(Delta2, gbn.optimizeGradientSearch(), gbn.optimize());
VectorValues actual2 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta2, optimizeGradientSearch(gbn), optimize(gbn)); VectorValues actual2 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta2, gbn.optimizeGradientSearch(), gbn.optimize());
DOUBLES_EQUAL(Delta2, actual2.asVector().norm(), 1e-5); DOUBLES_EQUAL(Delta2, actual2.vector().norm(), 1e-5);
EXPECT(assert_equal(expected2, actual2)); EXPECT(assert_equal(expected2, actual2));
double Delta3 = 5.0; // Larger than Newton's method point double Delta3 = 5.0; // Larger than Newton's method point
VectorValues expected3 = optimize(gbn); VectorValues expected3 = gbn.optimize();
VectorValues actual3 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta3, optimizeGradientSearch(gbn), optimize(gbn)); VectorValues actual3 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta3, gbn.optimizeGradientSearch(), gbn.optimize());
EXPECT(assert_equal(expected3, actual3)); EXPECT(assert_equal(expected3, actual3));
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST(DoglegOptimizer, Iterate) { TEST(DoglegOptimizer, Iterate) {
// really non-linear factor graph // really non-linear factor graph
boost::shared_ptr<example::Graph> fg(new example::Graph( NonlinearFactorGraph fg = example::createReallyNonlinearFactorGraph();
example::createReallyNonlinearFactorGraph()));
// config far from minimum // config far from minimum
Point2 x0(3,0); Point2 x0(3,0);
boost::shared_ptr<Values> config(new Values); Values config;
config->insert(X(1), x0); config.insert(X(1), x0);
// ordering
boost::shared_ptr<Ordering> ord(new Ordering());
ord->push_back(X(1));
double Delta = 1.0; double Delta = 1.0;
for(size_t it=0; it<10; ++it) { for(size_t it=0; it<10; ++it) {
GaussianSequentialSolver solver(*fg->linearize(*config, *ord)); GaussianBayesNet gbn = *fg.linearize(config)->eliminateSequential();
GaussianBayesNet gbn = *solver.eliminate();
// Iterate assumes that linear error = nonlinear error at the linearization point, and this should be true // Iterate assumes that linear error = nonlinear error at the linearization point, and this should be true
double nonlinearError = fg->error(*config); double nonlinearError = fg.error(config);
double linearError = GaussianFactorGraph(gbn).error(VectorValues::Zero(*allocateVectorValues(gbn))); double linearError = GaussianFactorGraph(gbn).error(config.zeroVectors());
DOUBLES_EQUAL(nonlinearError, linearError, 1e-5); DOUBLES_EQUAL(nonlinearError, linearError, 1e-5);
// cout << "it " << it << ", Delta = " << Delta << ", error = " << fg->error(*config) << endl; // cout << "it " << it << ", Delta = " << Delta << ", error = " << fg->error(*config) << endl;
DoglegOptimizerImpl::IterationResult result = DoglegOptimizerImpl::Iterate(Delta, DoglegOptimizerImpl::SEARCH_EACH_ITERATION, gbn, *fg, *config, *ord, fg->error(*config)); VectorValues dx_u = gbn.optimizeGradientSearch();
VectorValues dx_n = gbn.optimize();
DoglegOptimizerImpl::IterationResult result = DoglegOptimizerImpl::Iterate(Delta, DoglegOptimizerImpl::SEARCH_EACH_ITERATION, dx_u, dx_n, gbn, fg, config, fg.error(config));
Delta = result.Delta; Delta = result.Delta;
EXPECT(result.f_error < fg->error(*config)); // Check that error decreases EXPECT(result.f_error < fg.error(config)); // Check that error decreases
Values newConfig(config->retract(result.dx_d, *ord)); Values newConfig(config.retract(result.dx_d));
(*config) = newConfig; config = newConfig;
DOUBLES_EQUAL(fg->error(*config), result.f_error, 1e-5); // Check that error is correctly filled in DOUBLES_EQUAL(fg.error(config), result.f_error, 1e-5); // Check that error is correctly filled in
} }
} }
#endif
/* ************************************************************************* */ /* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); } int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
/* ************************************************************************* */ /* ************************************************************************* */