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Enabled and made GaussianBayesTree unit tests pass

release/4.3a0
Richard Roberts 2013-07-29 23:54:38 +00:00
parent a7540a7305
commit 5746dedacb
7 changed files with 193 additions and 14 deletions
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2
gtsam/base/treeTraversal-inst.h
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@ -104,7 +104,7 @@ namespace gtsam {
}
// If we have child tasks, start subtasks and wait for them to complete
set_ref_count(1 + node->children.size());
set_ref_count(1 + (int)node->children.size());
spawn(childTasks);
wait_for_all();
}

4
gtsam/inference/JunctionTreeUnordered-inst.h
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@ -96,7 +96,7 @@ namespace gtsam {
size_t nrMergedChildren = 0;
assert(myData.myJTNode->children.size() == myData.childSymbolicConditionals.size());
// Loop over children
int combinedProblemSize = symbolicElimResult.first->size();
int combinedProblemSize = (int)symbolicElimResult.first->size();
for(size_t child = 0; child < myData.childSymbolicConditionals.size(); ++child) {
// Check if we should merge the child
if(myNrParents + 1 == myData.childSymbolicConditionals[child]->nrParents()) {
@ -105,7 +105,7 @@ namespace gtsam {
const typename JunctionTreeUnordered<BAYESTREE,GRAPH>::Node& childToMerge =
*myData.myJTNode->children[child - nrMergedChildren];
// Merge keys, factors, and children.
myData.myJTNode->keys.insert(myData.myJTNode->keys.end(), childToMerge.keys.begin(), childToMerge.keys.end());
myData.myJTNode->keys.insert(myData.myJTNode->keys.begin(), childToMerge.keys.begin(), childToMerge.keys.end());
myData.myJTNode->factors.insert(myData.myJTNode->factors.end(), childToMerge.factors.begin(), childToMerge.factors.end());
myData.myJTNode->children.insert(myData.myJTNode->children.end(), childToMerge.children.begin(), childToMerge.children.end());
// Remove child from list.

1
gtsam/inference/VariableSlots.h
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@ -20,6 +20,7 @@
#include <gtsam/global_includes.h>
#include <gtsam/base/FastMap.h>
#include <gtsam/base/timing.h>
#include <iostream>

19
gtsam/linear/JacobianFactorUnordered-inl.h
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@ -19,7 +19,6 @@
#pragma once
#include <gtsam/linear/linearExceptions.h>
#include <boost/range/adaptor/map.hpp>
#include <boost/range/adaptor/transformed.hpp>
#include <boost/range/join.hpp>
#include <boost/assign/list_of.hpp>
@ -66,8 +65,8 @@ namespace gtsam {
/* ************************************************************************* */
namespace {
const Matrix& _getPairSecond(const std::pair<Key,Matrix>& p) {
return p.second;
DenseIndex _getCols(const std::pair<Key,Matrix>& p) {
return p.second.cols();
}
}
@ -85,14 +84,12 @@ namespace gtsam {
// Gather dimensions - uses boost range adaptors to take terms, extract .second which are the
// matrices, then extract the number of columns e.g. dimensions in each matrix. Then joins with
// a single '1' to add a dimension for the b vector.
using boost::adaptors::map_values;
using boost::adaptors::transformed;
using boost::join;
Ab_ = VerticalBlockMatrix(join(
terms
| transformed(&_getPairSecond)
| transformed(boost::mem_fn(&Matrix::cols)),
boost::assign::cref_list_of<1,DenseIndex>(1)), b.size());
{
using boost::adaptors::transformed;
using boost::join;
using boost::assign::cref_list_of;
Ab_ = VerticalBlockMatrix(join(terms | transformed(_getCols), cref_list_of<1,DenseIndex>(1)), b.size());
}
// Check and add terms
typedef std::pair<Key, Matrix> Term;

1
gtsam/linear/JacobianFactorUnordered.cpp
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@ -46,6 +46,7 @@
#endif
#include <boost/range/algorithm/copy.hpp>
#include <boost/range/adaptor/indirected.hpp>
#include <boost/range/adaptor/map.hpp>
#include <cmath>
#include <sstream>

180
gtsam/linear/tests/testGaussianBayesTreeUnordered.cpp Normal file
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@ -0,0 +1,180 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file testGaussianBayesTree.cpp
* @date Jul 8, 2010
* @author Kai Ni
*/
#include <iostream>
#include <CppUnitLite/TestHarness.h>
#include <boost/assign/list_of.hpp>
#include <boost/assign/std/list.hpp> // for operator +=
#include <boost/assign/std/set.hpp> // for operator +=
using namespace boost::assign;
#include <gtsam/base/debug.h>
#include <gtsam/geometry/Rot2.h>
#include <gtsam/linear/GaussianJunctionTreeUnordered.h>
#include <gtsam/linear/GaussianBayesTreeUnordered.h>
#include <gtsam/linear/GaussianConditionalUnordered.h>
using namespace std;
using namespace gtsam;
#define TEST TEST_UNSAFE
namespace {
const Key x1=1, x2=2, x3=3, x4=4;
const SharedDiagonal chainNoise = noiseModel::Isotropic::Sigma(1, 0.5);
const GaussianFactorGraphUnordered chain = list_of
(JacobianFactorUnordered(x2, Matrix_(1,1,1.), x1, Matrix_(1,1,1.), Vector_(1,1.), chainNoise))
(JacobianFactorUnordered(x2, Matrix_(1,1,1.), x3, Matrix_(1,1,1.), Vector_(1,1.), chainNoise))
(JacobianFactorUnordered(x3, Matrix_(1,1,1.), x4, Matrix_(1,1,1.), Vector_(1,1.), chainNoise))
(JacobianFactorUnordered(x4, Matrix_(1,1,1.), Vector_(1,1.), chainNoise));
const OrderingUnordered chainOrdering = OrderingUnordered(list_of(x2)(x1)(x3)(x4));
/* ************************************************************************* */
// Helper functions for below
GaussianBayesTreeCliqueUnordered::shared_ptr MakeClique(const GaussianConditionalUnordered& conditional)
{
return boost::make_shared<GaussianBayesTreeCliqueUnordered>(
boost::make_shared<GaussianConditionalUnordered>(conditional));
}
template<typename CHILDREN>
GaussianBayesTreeCliqueUnordered::shared_ptr MakeClique(
const GaussianConditionalUnordered& conditional, const CHILDREN& children)
{
GaussianBayesTreeCliqueUnordered::shared_ptr clique =
boost::make_shared<GaussianBayesTreeCliqueUnordered>(
boost::make_shared<GaussianConditionalUnordered>(conditional));
clique->children = children;
BOOST_FOREACH(const GaussianBayesTreeCliqueUnordered::shared_ptr& child, children)
child->parent_ = clique;
return clique;
}
}
/* ************************************************************************* */
/**
* x1 - x2 - x3 - x4
* x3 x4
* x2 x1 : x3
*
* x2 x1 x3 x4 b
* 1 1 1
* 1 1 1
* 1 1 1
* 1 1
*
* 1 0 0 1
*/
TEST( GaussianBayesTree, eliminate )
{
GaussianBayesTreeUnordered bt = *chain.eliminateMultifrontal(chainOrdering);
Matrix two = Matrix_(1,1,2.);
Matrix one = Matrix_(1,1,1.);
GaussianBayesTreeUnordered bayesTree_expected;
bayesTree_expected.insertRoot(
MakeClique(GaussianConditionalUnordered(pair_list_of (x3, Matrix_(2,1, 2., 0.)) (x4, Matrix_(2,1, 2., 2.)), 2, Vector_(2, 2., 2.)), list_of
(MakeClique(GaussianConditionalUnordered(pair_list_of (x2, Matrix_(2,1, -2.*sqrt(2.), 0.)) (x1, Matrix_(2,1, -sqrt(2.), -sqrt(2.))) (x3, Matrix_(2,1, -sqrt(2.), sqrt(2.))), 2, Vector_(2, -2.*sqrt(2.), 0.))))));
EXPECT(assert_equal(bayesTree_expected, bt));
}
/* ************************************************************************* */
TEST( GaussianBayesTree, optimizeMultiFrontal )
{
VectorValuesUnordered expected = pair_list_of
(x1, Vector_(1, 0.))
(x2, Vector_(1, 1.))
(x3, Vector_(1, 0.))
(x4, Vector_(1, 1.));
VectorValuesUnordered actual = chain.eliminateMultifrontal(chainOrdering)->optimize();
EXPECT(assert_equal(expected,actual));
}
/* ************************************************************************* */
TEST(GaussianBayesTree, complicatedMarginal) {
// Create the conditionals to go in the BayesTree
GaussianBayesTreeUnordered bt;
bt.insertRoot(
MakeClique(GaussianConditionalUnordered(pair_list_of (11, (Matrix(3,1) << 0.0971, 0, 0).finished())
(12, (Matrix(3,2) << 0.3171, 0.4387, 0.9502, 0.3816, 0, 0.7655).finished()),
2, (Vector(3) << 0.2638, 0.1455, 0.1361).finished()), list_of
(MakeClique(GaussianConditionalUnordered(pair_list_of (9, (Matrix(3,1) << 0.7952, 0, 0).finished())
(10, (Matrix(3,2) << 0.4456, 0.7547, 0.6463, 0.2760, 0, 0.6797).finished())
(11, (Matrix(3,1) << 0.6551, 0.1626, 0.1190).finished())
(12, (Matrix(3,2) << 0.4984, 0.5853, 0.9597, 0.2238, 0.3404, 0.7513).finished()),
2, (Vector(3) << 0.4314, 0.9106, 0.1818).finished())))
(MakeClique(GaussianConditionalUnordered(pair_list_of (7, (Matrix(3,1) << 0.2551, 0, 0).finished())
(8, (Matrix(3,2) << 0.8909, 0.1386, 0.9593, 0.1493, 0, 0.2575).finished())
(11, (Matrix(3,1) << 0.8407, 0.2543, 0.8143).finished()),
2, (Vector(3) << 0.3998, 0.2599, 0.8001).finished()), list_of
(MakeClique(GaussianConditionalUnordered(pair_list_of (5, (Matrix(3,1) << 0.2435, 0, 0).finished())
(6, (Matrix(3,2) << 0.4733, 0.1966, 0.3517, 0.2511, 0.8308, 0.0).finished())
// NOTE the non-upper-triangular form
// here since this test was written when we had column permutations
// from LDL. The code still works currently (does not enfore
// upper-triangularity in this case) but this test will need to be
// redone if this stops working in the future
(7, (Matrix(3,1) << 0.5853, 0.5497, 0.9172).finished())
(8, (Matrix(3,2) << 0.2858, 0.3804, 0.7572, 0.5678, 0.7537, 0.0759).finished()),
2, (Vector(3) << 0.8173, 0.8687, 0.0844).finished()), list_of
(MakeClique(GaussianConditionalUnordered(pair_list_of (3, (Matrix(3,1) << 0.0540, 0, 0).finished())
(4, (Matrix(3,2) << 0.9340, 0.4694, 0.1299, 0.0119, 0, 0.3371).finished())
(6, (Matrix(3,2) << 0.1622, 0.5285, 0.7943, 0.1656, 0.3112, 0.6020).finished()),
2, (Vector(3) << 0.9619, 0.0046, 0.7749).finished())))
(MakeClique(GaussianConditionalUnordered(pair_list_of (1, (Matrix(3,1) << 0.2630, 0, 0).finished())
(2, (Matrix(3,2) << 0.7482, 0.2290, 0.4505, 0.9133, 0, 0.1524).finished())
(5, (Matrix(3,1) << 0.8258, 0.5383, 0.9961).finished()),
2, (Vector(3) << 0.0782, 0.4427, 0.1067).finished())))))))));
// Marginal on 5
Matrix expectedCov = (Matrix(1,1) << 236.5166).finished();
//GaussianConditionalUnordered actualJacobianChol = *bt.marginalFactor(5, EliminateCholesky);
GaussianConditionalUnordered actualJacobianQR = *bt.marginalFactor(5, EliminateQRUnordered);
//EXPECT(assert_equal(actualJacobianChol, actualJacobianQR)); // Check that Chol and QR obtained marginals are the same
LONGS_EQUAL(1, (long)actualJacobianQR.rows());
LONGS_EQUAL(1, (long)actualJacobianQR.size());
LONGS_EQUAL(5, (long)actualJacobianQR.keys()[0]);
Matrix actualA = actualJacobianQR.getA(actualJacobianQR.begin());
Matrix actualCov = inverse(actualA.transpose() * actualA);
EXPECT(assert_equal(expectedCov, actualCov, 1e-1));
// Marginal on 6
// expectedCov = (Matrix(2,2) <<
// 8471.2, 2886.2,
// 2886.2, 1015.8).finished();
expectedCov = (Matrix(2,2) <<
1015.8, 2886.2,
2886.2, 8471.2).finished();
//actualJacobianChol = bt.marginalFactor(6, EliminateCholesky);
actualJacobianQR = *bt.marginalFactor(6, EliminateQRUnordered);
//EXPECT(assert_equal(actualJacobianChol, actualJacobianQR)); // Check that Chol and QR obtained marginals are the same
LONGS_EQUAL(2, (long)actualJacobianQR.rows());
LONGS_EQUAL(1, (long)actualJacobianQR.size());
LONGS_EQUAL(6, (long)actualJacobianQR.keys()[0]);
actualA = actualJacobianQR.getA(actualJacobianQR.begin());
actualCov = inverse(actualA.transpose() * actualA);
EXPECT(assert_equal(expectedCov, actualCov, 1e1));
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */

0
gtsam/linear/tests/testGaussianJunctionTree.cpp → gtsam/linear/tests/testGaussianJunctionTreeObsolete.cpp
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