Renamed ConditionalGaussian -> GaussianConditional
Alex Cunningham
parent
40f8ba740d
commit
c7b86cec97
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@ -353,9 +353,9 @@
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<useDefaultCommand>true</useDefaultCommand>
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<runAllBuilders>true</runAllBuilders>
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</target>
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<target name="testConditionalGaussian.run" path="cpp" targetID="org.eclipse.cdt.build.MakeTargetBuilder">
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<target name="testGaussianConditional.run" path="cpp" targetID="org.eclipse.cdt.build.MakeTargetBuilder">
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<buildCommand>make</buildCommand>
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<buildTarget>testConditionalGaussian.run</buildTarget>
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<buildTarget>testGaussianConditional.run</buildTarget>
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<stopOnError>true</stopOnError>
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<useDefaultCommand>true</useDefaultCommand>
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<runAllBuilders>true</runAllBuilders>
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@ -23,7 +23,7 @@ namespace gtsam {
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* Bayes network
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* This is the base class for SymbolicBayesNet, DiscreteBayesNet, and GaussianBayesNet
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* corresponding to what is used for the "Conditional" template argument:
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* a SymbolicConditional, ConditionalProbabilityTable, or a ConditionalGaussian
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* a SymbolicConditional, ConditionalProbabilityTable, or a GaussianConditional
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*/
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template<class Conditional>
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class BayesNet: public Testable<BayesNet<Conditional> > {
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@ -23,7 +23,7 @@ namespace gtsam {
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/**
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* Bayes tree
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* Templated on the Conditional class, the type of node in the underlying Bayes chain.
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* This could be a ConditionalProbabilityTable, a ConditionalGaussian, or a SymbolicConditional
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* This could be a ConditionalProbabilityTable, a GaussianConditional, or a SymbolicConditional
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*/
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template<class Conditional>
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class BayesTree: public Testable<BayesTree<Conditional> > {
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@ -16,7 +16,7 @@ using namespace gtsam;
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// Explicitly instantiate so we don't have to include everywhere
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#include "BayesNet-inl.h"
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template class BayesNet<ConditionalGaussian>;
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template class BayesNet<GaussianConditional>;
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// trick from some reading group
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#define FOREACH_PAIR( KEY, VAL, COL) BOOST_FOREACH (boost::tie(KEY,VAL),COL)
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@ -27,8 +27,8 @@ namespace gtsam {
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/* ************************************************************************* */
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GaussianBayesNet scalarGaussian(const string& key, double mu, double sigma) {
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GaussianBayesNet bn;
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ConditionalGaussian::shared_ptr
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conditional(new ConditionalGaussian(key, Vector_(1,mu), eye(1), Vector_(1,sigma)));
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GaussianConditional::shared_ptr
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conditional(new GaussianConditional(key, Vector_(1,mu), eye(1), Vector_(1,sigma)));
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bn.push_back(conditional);
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return bn;
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}
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@ -37,8 +37,8 @@ GaussianBayesNet scalarGaussian(const string& key, double mu, double sigma) {
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GaussianBayesNet simpleGaussian(const string& key, const Vector& mu, double sigma) {
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GaussianBayesNet bn;
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size_t n = mu.size();
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ConditionalGaussian::shared_ptr
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conditional(new ConditionalGaussian(key, mu, eye(n), repeat(n,sigma)));
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GaussianConditional::shared_ptr
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conditional(new GaussianConditional(key, mu, eye(n), repeat(n,sigma)));
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bn.push_back(conditional);
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return bn;
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}
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@ -46,14 +46,14 @@ GaussianBayesNet simpleGaussian(const string& key, const Vector& mu, double sigm
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/* ************************************************************************* */
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void push_front(GaussianBayesNet& bn, const string& key, Vector d, Matrix R,
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const string& name1, Matrix S, Vector sigmas) {
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ConditionalGaussian::shared_ptr cg(new ConditionalGaussian(key, d, R, name1, S, sigmas));
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GaussianConditional::shared_ptr cg(new GaussianConditional(key, d, R, name1, S, sigmas));
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bn.push_front(cg);
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}
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/* ************************************************************************* */
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void push_front(GaussianBayesNet& bn, const string& key, Vector d, Matrix R,
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const string& name1, Matrix S, const string& name2, Matrix T, Vector sigmas) {
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ConditionalGaussian::shared_ptr cg(new ConditionalGaussian(key, d, R, name1, S, name2, T, sigmas));
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GaussianConditional::shared_ptr cg(new GaussianConditional(key, d, R, name1, S, name2, T, sigmas));
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bn.push_front(cg);
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}
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@ -63,7 +63,7 @@ VectorConfig optimize(const GaussianBayesNet& bn)
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VectorConfig result;
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/** solve each node in turn in topological sort order (parents first)*/
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BOOST_REVERSE_FOREACH(ConditionalGaussian::shared_ptr cg, bn) {
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BOOST_REVERSE_FOREACH(GaussianConditional::shared_ptr cg, bn) {
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Vector x = cg->solve(result); // Solve for that variable
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result.insert(cg->key(),x); // store result in partial solution
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}
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@ -76,7 +76,7 @@ pair<Matrix,Vector> matrix(const GaussianBayesNet& bn) {
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// add the dimensions of all variables to get matrix dimension
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// and at the same time create a mapping from keys to indices
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size_t N=0; map<string,size_t> mapping;
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BOOST_FOREACH(ConditionalGaussian::shared_ptr cg,bn) {
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BOOST_FOREACH(GaussianConditional::shared_ptr cg,bn) {
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mapping.insert(make_pair(cg->key(),N));
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N += cg->dim();
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}
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@ -87,7 +87,7 @@ pair<Matrix,Vector> matrix(const GaussianBayesNet& bn) {
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string key; size_t I;
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FOREACH_PAIR(key,I,mapping) {
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// find corresponding conditional
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ConditionalGaussian::shared_ptr cg = bn[key];
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GaussianConditional::shared_ptr cg = bn[key];
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// get RHS and copy to d
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const Vector& d_ = cg->get_d();
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@ -102,7 +102,7 @@ pair<Matrix,Vector> matrix(const GaussianBayesNet& bn) {
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R(I+i,I+j) = R_(i,j);
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// loop over S matrices and copy them into R
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ConditionalGaussian::const_iterator keyS = cg->parentsBegin();
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GaussianConditional::const_iterator keyS = cg->parentsBegin();
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for (; keyS!=cg->parentsEnd(); keyS++) {
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Matrix S = keyS->second; // get S matrix
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const size_t m = S.size1(), n = S.size2(); // find S size
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@ -11,13 +11,13 @@
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#include <list>
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#include "ConditionalGaussian.h"
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#include "GaussianConditional.h"
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#include "BayesNet.h"
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namespace gtsam {
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/** A Bayes net made from linear-Gaussian densities */
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typedef BayesNet<ConditionalGaussian> GaussianBayesNet;
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typedef BayesNet<GaussianConditional> GaussianBayesNet;
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/** Create a scalar Gaussian */
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GaussianBayesNet scalarGaussian(const std::string& key, double mu=0.0, double sigma=1.0);
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@ -1,5 +1,5 @@
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/**
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* @file ConditionalGaussian.cpp
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* @file GaussianConditional.cpp
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* @brief Conditional Gaussian Base class
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* @author Christian Potthast
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*/
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@ -7,26 +7,26 @@
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#include <string.h>
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#include <boost/numeric/ublas/vector.hpp>
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#include "Ordering.h"
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#include "ConditionalGaussian.h"
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#include "GaussianConditional.h"
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using namespace std;
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using namespace gtsam;
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/* ************************************************************************* */
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ConditionalGaussian::ConditionalGaussian(const string& key,Vector d, Matrix R, Vector sigmas) :
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GaussianConditional::GaussianConditional(const string& key,Vector d, Matrix R, Vector sigmas) :
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Conditional (key), R_(R),sigmas_(sigmas),d_(d)
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{
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}
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/* ************************************************************************* */
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ConditionalGaussian::ConditionalGaussian(const string& key, Vector d, Matrix R,
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GaussianConditional::GaussianConditional(const string& key, Vector d, Matrix R,
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const string& name1, Matrix S, Vector sigmas) :
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Conditional (key), R_(R), sigmas_(sigmas), d_(d) {
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parents_.insert(make_pair(name1, S));
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}
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/* ************************************************************************* */
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ConditionalGaussian::ConditionalGaussian(const string& key, Vector d, Matrix R,
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GaussianConditional::GaussianConditional(const string& key, Vector d, Matrix R,
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const string& name1, Matrix S, const string& name2, Matrix T, Vector sigmas) :
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Conditional (key), R_(R),sigmas_(sigmas), d_(d) {
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parents_.insert(make_pair(name1, S));
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@ -34,13 +34,13 @@ ConditionalGaussian::ConditionalGaussian(const string& key, Vector d, Matrix R,
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}
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/* ************************************************************************* */
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ConditionalGaussian::ConditionalGaussian(const string& key,
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GaussianConditional::GaussianConditional(const string& key,
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const Vector& d, const Matrix& R, const map<string, Matrix>& parents, Vector sigmas) :
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Conditional (key), R_(R),sigmas_(sigmas), d_(d), parents_(parents) {
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}
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/* ************************************************************************* */
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void ConditionalGaussian::print(const string &s) const
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void GaussianConditional::print(const string &s) const
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{
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cout << s << ": density on " << key_ << endl;
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gtsam::print(R_,"R");
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@ -54,9 +54,9 @@ void ConditionalGaussian::print(const string &s) const
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}
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/* ************************************************************************* */
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bool ConditionalGaussian::equals(const Conditional &c, double tol) const {
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bool GaussianConditional::equals(const Conditional &c, double tol) const {
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if (!Conditional::equals(c)) return false;
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const ConditionalGaussian* p = dynamic_cast<const ConditionalGaussian*> (&c);
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const GaussianConditional* p = dynamic_cast<const GaussianConditional*> (&c);
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if (p == NULL) return false;
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Parents::const_iterator it = parents_.begin();
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@ -85,7 +85,7 @@ bool ConditionalGaussian::equals(const Conditional &c, double tol) const {
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}
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/* ************************************************************************* */
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list<string> ConditionalGaussian::parents() const {
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list<string> GaussianConditional::parents() const {
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list<string> result;
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for (Parents::const_iterator it = parents_.begin(); it != parents_.end(); it++)
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result.push_back(it->first);
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@ -93,7 +93,7 @@ list<string> ConditionalGaussian::parents() const {
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}
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/* ************************************************************************* */
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Vector ConditionalGaussian::solve(const VectorConfig& x) const {
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Vector GaussianConditional::solve(const VectorConfig& x) const {
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Vector rhs = d_;
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for (Parents::const_iterator it = parents_.begin(); it
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!= parents_.end(); it++) {
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@ -1,5 +1,5 @@
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/**
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* @file ConditionalGaussian.h
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* @file GaussianConditional.h
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* @brief Conditional Gaussian Base class
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* @author Christian Potthast
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*/
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@ -28,12 +28,12 @@ class Ordering;
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* It has a set of parents y,z, etc. and implements a probability density on x.
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* The negative log-probability is given by || Rx - (d - Sy - Tz - ...)||^2
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*/
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class ConditionalGaussian : public Conditional {
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class GaussianConditional : public Conditional {
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public:
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typedef std::map<std::string, Matrix> Parents;
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typedef Parents::const_iterator const_iterator;
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typedef boost::shared_ptr<ConditionalGaussian> shared_ptr;
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typedef boost::shared_ptr<GaussianConditional> shared_ptr;
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protected:
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@ -52,41 +52,41 @@ protected:
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public:
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/** default constructor needed for serialization */
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ConditionalGaussian(){}
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GaussianConditional(){}
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/** constructor */
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ConditionalGaussian(const std::string& key) :
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GaussianConditional(const std::string& key) :
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Conditional (key) {}
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/** constructor with no parents
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* |Rx-d|
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*/
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ConditionalGaussian(const std::string& key, Vector d, Matrix R, Vector sigmas);
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GaussianConditional(const std::string& key, Vector d, Matrix R, Vector sigmas);
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/** constructor with only one parent
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* |Rx+Sy-d|
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*/
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ConditionalGaussian(const std::string& key, Vector d, Matrix R,
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GaussianConditional(const std::string& key, Vector d, Matrix R,
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const std::string& name1, Matrix S, Vector sigmas);
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/** constructor with two parents
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* |Rx+Sy+Tz-d|
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*/
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ConditionalGaussian(const std::string& key, Vector d, Matrix R,
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GaussianConditional(const std::string& key, Vector d, Matrix R,
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const std::string& name1, Matrix S, const std::string& name2, Matrix T, Vector sigmas);
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/**
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* constructor with number of arbitrary parents
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* |Rx+sum(Ai*xi)-d|
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*/
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ConditionalGaussian(const std::string& key, const Vector& d,
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GaussianConditional(const std::string& key, const Vector& d,
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const Matrix& R, const Parents& parents, Vector sigmas);
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/** deconstructor */
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virtual ~ConditionalGaussian() {}
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virtual ~GaussianConditional() {}
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/** print */
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void print(const std::string& = "ConditionalGaussian") const;
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void print(const std::string& = "GaussianConditional") const;
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/** equals function */
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bool equals(const Conditional &cg, double tol = 1e-9) const;
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/** return all parents */
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std::list<std::string> parents() const;
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/** return stuff contained in ConditionalGaussian */
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/** return stuff contained in GaussianConditional */
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const Vector& get_d() const {return d_;}
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const Matrix& get_R() const {return R_;}
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const Vector& get_sigmas() const {return sigmas_;}
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@ -11,7 +11,7 @@
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#include "Matrix.h"
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#include "Ordering.h"
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#include "ConditionalGaussian.h"
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#include "GaussianConditional.h"
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#include "GaussianFactor.h"
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using namespace std;
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@ -26,7 +26,7 @@ using namespace gtsam;
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typedef pair<const string, Matrix>& mypair;
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/* ************************************************************************* */
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GaussianFactor::GaussianFactor(const boost::shared_ptr<ConditionalGaussian>& cg) :
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GaussianFactor::GaussianFactor(const boost::shared_ptr<GaussianConditional>& cg) :
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b_(cg->get_d()) {
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As_.insert(make_pair(cg->key(), cg->get_R()));
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std::map<std::string, Matrix>::const_iterator it = cg->parentsBegin();
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@ -282,11 +282,11 @@ void GaussianFactor::append_factor(GaussianFactor::shared_ptr f, const size_t m,
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/* Note, in place !!!!
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* Do incomplete QR factorization for the first n columns
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* We will do QR on all matrices and on RHS
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* Then take first n rows and make a ConditionalGaussian,
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* Then take first n rows and make a GaussianConditional,
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* and last rows to make a new joint linear factor on separator
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*/
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/* ************************************************************************* */
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pair<ConditionalGaussian::shared_ptr, GaussianFactor::shared_ptr>
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pair<GaussianConditional::shared_ptr, GaussianFactor::shared_ptr>
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GaussianFactor::eliminate(const string& key) const
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{
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bool verbose = false;
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@ -297,7 +297,7 @@ GaussianFactor::eliminate(const string& key) const
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if (it==As_.end()) {
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// Conditional Gaussian is just a parent-less node with P(x)=1
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GaussianFactor::shared_ptr lf(new GaussianFactor);
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ConditionalGaussian::shared_ptr cg(new ConditionalGaussian(key));
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GaussianConditional::shared_ptr cg(new GaussianConditional(key));
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return make_pair(cg,lf);
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}
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@ -339,7 +339,7 @@ GaussianFactor::eliminate(const string& key) const
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}
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// create base conditional Gaussian
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ConditionalGaussian::shared_ptr cg(new ConditionalGaussian(key,
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GaussianConditional::shared_ptr cg(new GaussianConditional(key,
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sub(d, 0, n1), // form d vector
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sub(R, 0, n1, 0, n1), // form R matrix
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sub(newSigmas, 0, n1))); // get standard deviations
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@ -19,7 +19,7 @@
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namespace gtsam {
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class ConditionalGaussian;
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class GaussianConditional;
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class Ordering;
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/**
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}
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/** Construct from Conditional Gaussian */
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GaussianFactor(const boost::shared_ptr<ConditionalGaussian>& cg);
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GaussianFactor(const boost::shared_ptr<GaussianConditional>& cg);
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/**
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* Constructor that combines a set of factors
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* @param key the key of the node to be eliminated
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* @return a new factor and a conditional gaussian on the eliminated variable
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*/
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std::pair<boost::shared_ptr<ConditionalGaussian>, shared_ptr>
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std::pair<boost::shared_ptr<GaussianConditional>, shared_ptr>
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eliminate(const std::string& key) const;
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/**
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@ -42,9 +42,9 @@ set<string> GaussianFactorGraph::find_separator(const string& key) const
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}
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/* ************************************************************************* */
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ConditionalGaussian::shared_ptr
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GaussianConditional::shared_ptr
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GaussianFactorGraph::eliminateOne(const std::string& key) {
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return gtsam::eliminateOne<GaussianFactor,ConditionalGaussian>(*this, key);
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return gtsam::eliminateOne<GaussianFactor,GaussianConditional>(*this, key);
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}
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/* ************************************************************************* */
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@ -53,7 +53,7 @@ GaussianFactorGraph::eliminate(const Ordering& ordering)
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{
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GaussianBayesNet chordalBayesNet; // empty
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BOOST_FOREACH(string key, ordering) {
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ConditionalGaussian::shared_ptr cg = eliminateOne(key);
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GaussianConditional::shared_ptr cg = eliminateOne(key);
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chordalBayesNet.push_back(cg);
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}
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return chordalBayesNet;
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@ -75,7 +75,7 @@ GaussianFactorGraph::eliminate_(const Ordering& ordering)
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{
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boost::shared_ptr<GaussianBayesNet> chordalBayesNet(new GaussianBayesNet); // empty
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BOOST_FOREACH(string key, ordering) {
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ConditionalGaussian::shared_ptr cg = eliminateOne(key);
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GaussianConditional::shared_ptr cg = eliminateOne(key);
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chordalBayesNet->push_back(cg);
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}
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return chordalBayesNet;
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@ -66,7 +66,7 @@ namespace gtsam {
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* Eliminates the factors from the factor graph through findAndRemoveFactors
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* and adds a new factor on the separator to the factor graph
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*/
|
||||
ConditionalGaussian::shared_ptr eliminateOne(const std::string& key);
|
||||
GaussianConditional::shared_ptr eliminateOne(const std::string& key);
|
||||
|
||||
/**
|
||||
* eliminate factor graph in place(!) in the given order, yielding
|
||||
|
|
|
|||
|
|
@ -85,16 +85,16 @@ testSymbolicBayesNet_LDADD = libgtsam.la
|
|||
|
||||
# Gaussian inference
|
||||
headers += GaussianFactorSet.h
|
||||
sources += VectorConfig.cpp GaussianFactor.cpp GaussianFactorGraph.cpp ConditionalGaussian.cpp GaussianBayesNet.cpp
|
||||
check_PROGRAMS += testVectorConfig testGaussianFactor testGaussianFactorGraph testConditionalGaussian testGaussianBayesNet
|
||||
sources += VectorConfig.cpp GaussianFactor.cpp GaussianFactorGraph.cpp GaussianConditional.cpp GaussianBayesNet.cpp
|
||||
check_PROGRAMS += testVectorConfig testGaussianFactor testGaussianFactorGraph testGaussianConditional testGaussianBayesNet
|
||||
testVectorConfig_SOURCES = testVectorConfig.cpp
|
||||
testVectorConfig_LDADD = libgtsam.la
|
||||
testGaussianFactor_SOURCES = $(example) testGaussianFactor.cpp
|
||||
testGaussianFactor_LDADD = libgtsam.la
|
||||
testGaussianFactorGraph_SOURCES = $(example) testGaussianFactorGraph.cpp
|
||||
testGaussianFactorGraph_LDADD = libgtsam.la
|
||||
testConditionalGaussian_SOURCES = $(example) testConditionalGaussian.cpp
|
||||
testConditionalGaussian_LDADD = libgtsam.la
|
||||
testGaussianConditional_SOURCES = $(example) testGaussianConditional.cpp
|
||||
testGaussianConditional_LDADD = libgtsam.la
|
||||
testGaussianBayesNet_SOURCES = $(example) testGaussianBayesNet.cpp
|
||||
testGaussianBayesNet_LDADD = libgtsam.la
|
||||
|
||||
|
|
@ -112,13 +112,16 @@ timeGaussianFactorGraph: LDFLAGS += smallExample.o -L.libs -lgtsam -L../CppUnitL
|
|||
headers += NonlinearFactorGraph.h NonlinearFactorGraph-inl.h
|
||||
headers += NonlinearOptimizer.h NonlinearOptimizer-inl.h
|
||||
sources += NonlinearFactor.cpp
|
||||
check_PROGRAMS += testNonlinearFactor testNonlinearFactorGraph testNonlinearOptimizer
|
||||
sources += NonlinearEquality.cpp
|
||||
check_PROGRAMS += testNonlinearFactor testNonlinearFactorGraph testNonlinearOptimizer testNonlinearEquality
|
||||
testNonlinearFactor_SOURCES = $(example) testNonlinearFactor.cpp
|
||||
testNonlinearFactor_LDADD = libgtsam.la
|
||||
testNonlinearFactorGraph_SOURCES = $(example) testNonlinearFactorGraph.cpp
|
||||
testNonlinearFactorGraph_LDADD = libgtsam.la
|
||||
testNonlinearOptimizer_SOURCES = $(example) testNonlinearOptimizer.cpp
|
||||
testNonlinearOptimizer_LDADD = libgtsam.la
|
||||
testNonlinearEquality_SOURCES = testNonlinearEquality.cpp
|
||||
testNonlinearEquality_LDADD = libgtsam.la
|
||||
|
||||
# geometry
|
||||
sources += Point2.cpp Pose2.cpp Point3.cpp Rot3.cpp Pose3.cpp Cal3_S2.cpp
|
||||
|
|
|
|||
|
|
@ -0,0 +1,9 @@
|
|||
/*
|
||||
* @file NonlinearEquality.cpp
|
||||
* @brief
|
||||
* @author alexgc
|
||||
*/
|
||||
|
||||
#include "NonlinearEquality.h"
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,26 @@
|
|||
/*
|
||||
* @file NonlinearEquality.h
|
||||
* @brief Factor to handle enforced equality between factors
|
||||
* @author Alex Cunningham
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "NonlinearFactor.h"
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
/**
|
||||
* An equality factor that forces either one variable to a constant,
|
||||
* or a set of variables to be equal to each other.
|
||||
* Throws an error at linearization if the constraints are not met.
|
||||
*/
|
||||
template<class Config>
|
||||
class NonlinearEquality : public NonlinearFactor<Config>{
|
||||
public:
|
||||
NonlinearEquality();
|
||||
virtual ~NonlinearEquality();
|
||||
};
|
||||
|
||||
}
|
||||
|
||||
18
cpp/gtsam.h
18
cpp/gtsam.h
|
|
@ -52,19 +52,19 @@ class GaussianFactor {
|
|||
pair<Matrix,Vector> matrix(const Ordering& ordering) const;
|
||||
};
|
||||
|
||||
class ConditionalGaussian {
|
||||
ConditionalGaussian();
|
||||
ConditionalGaussian(string key,
|
||||
class GaussianConditional {
|
||||
GaussianConditional();
|
||||
GaussianConditional(string key,
|
||||
Vector d,
|
||||
Matrix R,
|
||||
Vector sigmas);
|
||||
ConditionalGaussian(string key,
|
||||
GaussianConditional(string key,
|
||||
Vector d,
|
||||
Matrix R,
|
||||
string name1,
|
||||
Matrix S,
|
||||
Vector sigmas);
|
||||
ConditionalGaussian(string key,
|
||||
GaussianConditional(string key,
|
||||
Vector d,
|
||||
Matrix R,
|
||||
string name1,
|
||||
|
|
@ -75,15 +75,15 @@ class ConditionalGaussian {
|
|||
void print(string s) const;
|
||||
Vector solve(const VectorConfig& x);
|
||||
void add(string key, Matrix S);
|
||||
bool equals(const ConditionalGaussian &cg, double tol) const;
|
||||
bool equals(const GaussianConditional &cg, double tol) const;
|
||||
};
|
||||
|
||||
class GaussianBayesNet {
|
||||
GaussianBayesNet();
|
||||
void print(string s) const;
|
||||
bool equals(const GaussianBayesNet& cbn, double tol) const;
|
||||
void push_back(ConditionalGaussian* conditional);
|
||||
void push_front(ConditionalGaussian* conditional);
|
||||
void push_back(GaussianConditional* conditional);
|
||||
void push_front(GaussianConditional* conditional);
|
||||
};
|
||||
|
||||
class GaussianFactorGraph {
|
||||
|
|
@ -96,7 +96,7 @@ class GaussianFactorGraph {
|
|||
void print(string s) const;
|
||||
bool equals(const GaussianFactorGraph& lfgraph, double tol) const;
|
||||
|
||||
ConditionalGaussian* eliminateOne(string key);
|
||||
GaussianConditional* eliminateOne(string key);
|
||||
GaussianBayesNet* eliminate_(const Ordering& ordering);
|
||||
VectorConfig* optimize_(const Ordering& ordering);
|
||||
pair<Matrix,Vector> matrix(const Ordering& ordering) const;
|
||||
|
|
|
|||
|
|
@ -204,9 +204,9 @@ GaussianBayesNet createSmallGaussianBayesNet()
|
|||
Vector tau(1); tau(0) = 1.0;
|
||||
|
||||
// define nodes and specify in reverse topological sort (i.e. parents last)
|
||||
ConditionalGaussian::shared_ptr
|
||||
Px_y(new ConditionalGaussian("x",d1,R11,"y",S12,tau)),
|
||||
Py(new ConditionalGaussian("y",d2,R22,tau));
|
||||
GaussianConditional::shared_ptr
|
||||
Px_y(new GaussianConditional("x",d1,R11,"y",S12,tau)),
|
||||
Py(new GaussianConditional("y",d2,R22,tau));
|
||||
GaussianBayesNet cbn;
|
||||
cbn.push_back(Px_y);
|
||||
cbn.push_back(Py);
|
||||
|
|
@ -502,11 +502,11 @@ VectorConfig createMultiConstraintConfig() {
|
|||
// Matrix R = eye(2);
|
||||
// Vector d = c["x1"];
|
||||
// double sigma = 0.1;
|
||||
// ConditionalGaussian::shared_ptr f1(new ConditionalGaussian(d/sigma, R/sigma));
|
||||
// GaussianConditional::shared_ptr f1(new GaussianConditional(d/sigma, R/sigma));
|
||||
// cbn.insert("x1", f1);
|
||||
//
|
||||
// // add a delta function to the cbn
|
||||
// ConstrainedConditionalGaussian::shared_ptr f2(new ConstrainedConditionalGaussian); //(c["x0"], "x0"));
|
||||
// ConstrainedGaussianConditional::shared_ptr f2(new ConstrainedGaussianConditional); //(c["x0"], "x0"));
|
||||
// cbn.insert_df("x0", f2);
|
||||
//
|
||||
// return cbn;
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ using namespace boost::assign;
|
|||
|
||||
using namespace gtsam;
|
||||
|
||||
typedef BayesTree<ConditionalGaussian> Gaussian;
|
||||
typedef BayesTree<GaussianConditional> Gaussian;
|
||||
|
||||
// Conditionals for ASIA example from the tutorial with A and D evidence
|
||||
SymbolicConditional::shared_ptr B(new SymbolicConditional("B")), L(
|
||||
|
|
@ -226,7 +226,7 @@ TEST( BayesTree, balanced_smoother_shortcuts )
|
|||
CHECK(assert_equal(empty,actual2,1e-4));
|
||||
|
||||
// Check the conditional P(C3|Root), which should be equal to P(x2|x4)
|
||||
ConditionalGaussian::shared_ptr p_x2_x4 = chordalBayesNet["x2"];
|
||||
GaussianConditional::shared_ptr p_x2_x4 = chordalBayesNet["x2"];
|
||||
GaussianBayesNet expected3; expected3.push_back(p_x2_x4);
|
||||
Gaussian::sharedClique C3 = bayesTree["x1"];
|
||||
GaussianBayesNet actual3 = C3->shortcut<GaussianFactor>(R);
|
||||
|
|
@ -252,7 +252,7 @@ TEST( BayesTree, balanced_smoother_clique_marginals )
|
|||
push_front(expected,"x1", zero(2), eye(2), "x2", A12, sigma);
|
||||
Gaussian::sharedClique R = bayesTree.root(), C3 = bayesTree["x1"];
|
||||
FactorGraph<GaussianFactor> marginal = C3->marginal<GaussianFactor>(R);
|
||||
GaussianBayesNet actual = eliminate<GaussianFactor,ConditionalGaussian>(marginal,C3->keys());
|
||||
GaussianBayesNet actual = eliminate<GaussianFactor,GaussianConditional>(marginal,C3->keys());
|
||||
CHECK(assert_equal(expected,actual,1e-4));
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -42,7 +42,7 @@ TEST( GaussianBayesNet, constructor )
|
|||
sigmas(0) = 1.;
|
||||
|
||||
// define nodes and specify in reverse topological sort (i.e. parents last)
|
||||
ConditionalGaussian x("x",d1,R11,"y",S12, sigmas), y("y",d2,R22, sigmas);
|
||||
GaussianConditional x("x",d1,R11,"y",S12, sigmas), y("y",d2,R22, sigmas);
|
||||
|
||||
// check small example which uses constructor
|
||||
GaussianBayesNet cbn = createSmallGaussianBayesNet();
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
/**
|
||||
* @file testConditionalGaussian.cpp
|
||||
* @file testGaussianConditional.cpp
|
||||
* @brief Unit tests for Conditional gaussian
|
||||
* @author Christian Potthast
|
||||
**/
|
||||
|
|
@ -15,14 +15,14 @@
|
|||
#endif //HAVE_BOOST_SERIALIZATION
|
||||
|
||||
#include "Matrix.h"
|
||||
#include "ConditionalGaussian.h"
|
||||
#include "GaussianConditional.h"
|
||||
|
||||
using namespace gtsam;
|
||||
|
||||
/* ************************************************************************* */
|
||||
/* unit test for equals */
|
||||
/* ************************************************************************* */
|
||||
TEST( ConditionalGaussian, equals )
|
||||
TEST( GaussianConditional, equals )
|
||||
{
|
||||
// create a conditional gaussian node
|
||||
Matrix A1(2,2);
|
||||
|
|
@ -44,7 +44,7 @@ TEST( ConditionalGaussian, equals )
|
|||
Vector d(2);
|
||||
d(0) = 0.2; d(1) = 0.5;
|
||||
|
||||
ConditionalGaussian
|
||||
GaussianConditional
|
||||
expected("x",d, R, "x1", A1, "l1", A2, tau),
|
||||
actual("x",d, R, "x1", A1, "l1", A2, tau);
|
||||
|
||||
|
|
@ -55,7 +55,7 @@ TEST( ConditionalGaussian, equals )
|
|||
/* ************************************************************************* */
|
||||
/* unit test for solve */
|
||||
/* ************************************************************************* */
|
||||
TEST( ConditionalGaussian, solve )
|
||||
TEST( GaussianConditional, solve )
|
||||
{
|
||||
//expected solution
|
||||
Vector expected(2);
|
||||
|
|
@ -76,7 +76,7 @@ TEST( ConditionalGaussian, solve )
|
|||
|
||||
Vector tau = ones(2);
|
||||
|
||||
ConditionalGaussian cg("x",d, R, "x1", A1, "l1", A2, tau);
|
||||
GaussianConditional cg("x",d, R, "x1", A1, "l1", A2, tau);
|
||||
|
||||
Vector sx1(2);
|
||||
sx1(0) = 1.0; sx1(1) = 1.0;
|
||||
|
|
@ -98,7 +98,7 @@ TEST( ConditionalGaussian, solve )
|
|||
/* unit test for serialization */
|
||||
/* ************************************************************************* */
|
||||
#ifdef HAVE_BOOST_SERIALIZATION
|
||||
TEST( ConditionalGaussian, serialize )
|
||||
TEST( GaussianConditional, serialize )
|
||||
{
|
||||
// create a conditional gaussion node
|
||||
Matrix A1(2,2);
|
||||
|
|
@ -116,7 +116,7 @@ TEST( ConditionalGaussian, serialize )
|
|||
Vector d(2);
|
||||
d(0) = 0.2; d(1) = 0.5;
|
||||
|
||||
ConditionalGaussian cg("x2", d, R, "x1", A1, "l1", A2);
|
||||
GaussianConditional cg("x2", d, R, "x1", A1, "l1", A2);
|
||||
|
||||
//serialize the CG
|
||||
std::ostringstream in_archive_stream;
|
||||
|
|
@ -127,7 +127,7 @@ TEST( ConditionalGaussian, serialize )
|
|||
//deserialize the CGg
|
||||
std::istringstream out_archive_stream(serialized);
|
||||
boost::archive::text_iarchive out_archive(out_archive_stream);
|
||||
ConditionalGaussian output;
|
||||
GaussianConditional output;
|
||||
out_archive >> output;
|
||||
|
||||
//check for equality
|
||||
|
|
@ -17,7 +17,7 @@ using namespace boost::assign;
|
|||
|
||||
#include "Matrix.h"
|
||||
#include "Ordering.h"
|
||||
#include "ConditionalGaussian.h"
|
||||
#include "GaussianConditional.h"
|
||||
#include "smallExample.h"
|
||||
|
||||
using namespace std;
|
||||
|
|
@ -314,7 +314,7 @@ TEST( GaussianFactor, eliminate )
|
|||
GaussianFactor combined(lfg);
|
||||
|
||||
// eliminate the combined factor
|
||||
ConditionalGaussian::shared_ptr actualCG;
|
||||
GaussianConditional::shared_ptr actualCG;
|
||||
GaussianFactor::shared_ptr actualLF;
|
||||
boost::tie(actualCG,actualLF) = combined.eliminate("x2");
|
||||
|
||||
|
|
@ -338,7 +338,7 @@ TEST( GaussianFactor, eliminate )
|
|||
sigmas(1) = 1/sqrt(125.0);
|
||||
|
||||
// Check the conditional Gaussian
|
||||
ConditionalGaussian expectedCG("x2", d,R11,"l1",S12,"x1",S13,sigmas);
|
||||
GaussianConditional expectedCG("x2", d,R11,"l1",S12,"x1",S13,sigmas);
|
||||
|
||||
// the expected linear factor
|
||||
double sigma = 0.2236;
|
||||
|
|
@ -403,7 +403,7 @@ TEST( GaussianFactor, eliminate2 )
|
|||
GaussianFactor combined(meas, b2, sigmas);
|
||||
|
||||
// eliminate the combined factor
|
||||
ConditionalGaussian::shared_ptr actualCG;
|
||||
GaussianConditional::shared_ptr actualCG;
|
||||
GaussianFactor::shared_ptr actualLF;
|
||||
boost::tie(actualCG,actualLF) = combined.eliminate("x2");
|
||||
|
||||
|
|
@ -422,7 +422,7 @@ TEST( GaussianFactor, eliminate2 )
|
|||
x2Sigmas(0) = 0.0894427;
|
||||
x2Sigmas(1) = 0.0894427;
|
||||
|
||||
ConditionalGaussian expectedCG("x2",d,R11,"l1x1",S12,x2Sigmas);
|
||||
GaussianConditional expectedCG("x2",d,R11,"l1x1",S12,x2Sigmas);
|
||||
|
||||
// the expected linear factor
|
||||
double sigma = 0.2236;
|
||||
|
|
@ -458,12 +458,12 @@ TEST( GaussianFactor, eliminate_empty )
|
|||
GaussianFactor f;
|
||||
|
||||
// eliminate the empty factor
|
||||
ConditionalGaussian::shared_ptr actualCG;
|
||||
GaussianConditional::shared_ptr actualCG;
|
||||
GaussianFactor::shared_ptr actualLF;
|
||||
boost::tie(actualCG,actualLF) = f.eliminate("x2");
|
||||
|
||||
// expected Conditional Gaussian is just a parent-less node with P(x)=1
|
||||
ConditionalGaussian expectedCG("x2");
|
||||
GaussianConditional expectedCG("x2");
|
||||
|
||||
// expected remaining factor is still empty :-)
|
||||
GaussianFactor expectedLF;
|
||||
|
|
@ -612,7 +612,7 @@ TEST( GaussianFactor, size )
|
|||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( GaussianFactor, CONSTRUCTOR_ConditionalGaussian )
|
||||
TEST( GaussianFactor, CONSTRUCTOR_GaussianConditional )
|
||||
{
|
||||
Matrix R11 = Matrix_(2,2,
|
||||
1.00, 0.00,
|
||||
|
|
@ -628,7 +628,7 @@ TEST( GaussianFactor, CONSTRUCTOR_ConditionalGaussian )
|
|||
sigmas(0) = 0.29907;
|
||||
sigmas(1) = 0.29907;
|
||||
|
||||
ConditionalGaussian::shared_ptr CG(new ConditionalGaussian("x2",d,R11,"l1x1",S12,sigmas));
|
||||
GaussianConditional::shared_ptr CG(new GaussianConditional("x2",d,R11,"l1x1",S12,sigmas));
|
||||
GaussianFactor actualLF(CG);
|
||||
// actualLF.print();
|
||||
GaussianFactor expectedLF("x2",R11,"l1x1",S12,d, sigmas(0));
|
||||
|
|
@ -645,7 +645,7 @@ TEST ( GaussianFactor, constraint_eliminate1 )
|
|||
GaussianFactor lc(key, eye(2), v, 0.0);
|
||||
|
||||
// eliminate it
|
||||
ConditionalGaussian::shared_ptr actualCG;
|
||||
GaussianConditional::shared_ptr actualCG;
|
||||
GaussianFactor::shared_ptr actualLF;
|
||||
boost::tie(actualCG,actualLF) = lc.eliminate("x0");
|
||||
|
||||
|
|
@ -654,7 +654,7 @@ TEST ( GaussianFactor, constraint_eliminate1 )
|
|||
|
||||
// verify conditional Gaussian
|
||||
Vector sigmas = Vector_(2, 0.0, 0.0);
|
||||
ConditionalGaussian expCG("x0", v, eye(2), sigmas);
|
||||
GaussianConditional expCG("x0", v, eye(2), sigmas);
|
||||
CHECK(assert_equal(expCG, *actualCG));
|
||||
}
|
||||
|
||||
|
|
@ -678,7 +678,7 @@ TEST ( GaussianFactor, constraint_eliminate2 )
|
|||
GaussianFactor lc("x", A1, "y", A2, b, 0.0);
|
||||
|
||||
// eliminate x and verify results
|
||||
ConditionalGaussian::shared_ptr actualCG;
|
||||
GaussianConditional::shared_ptr actualCG;
|
||||
GaussianFactor::shared_ptr actualLF;
|
||||
boost::tie(actualCG, actualLF) = lc.eliminate("x");
|
||||
|
||||
|
|
@ -694,7 +694,7 @@ TEST ( GaussianFactor, constraint_eliminate2 )
|
|||
1.0, 2.0,
|
||||
0.0, 0.0);
|
||||
Vector d = Vector_(2, 3.0, 0.6666);
|
||||
ConditionalGaussian expectedCG("x", d, R, "y", S, zero(2));
|
||||
GaussianConditional expectedCG("x", d, R, "y", S, zero(2));
|
||||
CHECK(assert_equal(expectedCG, *actualCG, 1e-4));
|
||||
}
|
||||
|
||||
|
|
@ -724,7 +724,7 @@ TEST ( GaussianFactor, constraint_eliminate3 )
|
|||
// eliminate y from original graph
|
||||
// NOTE: this will throw an exception, as
|
||||
// the leading matrix is rank deficient
|
||||
ConditionalGaussian::shared_ptr actualCG;
|
||||
GaussianConditional::shared_ptr actualCG;
|
||||
GaussianFactor::shared_ptr actualLF;
|
||||
try {
|
||||
boost::tie(actualCG, actualLF) = lc.eliminate("y");
|
||||
|
|
|
|||
|
|
@ -190,12 +190,12 @@ TEST( GaussianFactorGraph, combine_factors_x2 )
|
|||
TEST( GaussianFactorGraph, eliminateOne_x1 )
|
||||
{
|
||||
GaussianFactorGraph fg = createGaussianFactorGraph();
|
||||
ConditionalGaussian::shared_ptr actual = fg.eliminateOne("x1");
|
||||
GaussianConditional::shared_ptr actual = fg.eliminateOne("x1");
|
||||
|
||||
// create expected Conditional Gaussian
|
||||
Matrix I = eye(2), R11 = I, S12 = -0.111111*I, S13 = -0.444444*I;
|
||||
Vector d = Vector_(2, -0.133333, -0.0222222), sigma = repeat(2, 1./15);
|
||||
ConditionalGaussian expected("x1",d,R11,"l1",S12,"x2",S13,sigma);
|
||||
GaussianConditional expected("x1",d,R11,"l1",S12,"x2",S13,sigma);
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CHECK(assert_equal(expected,*actual,tol));
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}
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@ -205,12 +205,12 @@ TEST( GaussianFactorGraph, eliminateOne_x1 )
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TEST( GaussianFactorGraph, eliminateOne_x2 )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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ConditionalGaussian::shared_ptr actual = fg.eliminateOne("x2");
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GaussianConditional::shared_ptr actual = fg.eliminateOne("x2");
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// create expected Conditional Gaussian
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Matrix I = eye(2), R11 = I, S12 = -0.2*I, S13 = -0.8*I;
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Vector d = Vector_(2, 0.2, -0.14), sigma = repeat(2, 0.0894427);
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ConditionalGaussian expected("x2",d,R11,"l1",S12,"x1",S13,sigma);
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GaussianConditional expected("x2",d,R11,"l1",S12,"x1",S13,sigma);
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CHECK(assert_equal(expected,*actual,tol));
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||||
}
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|
|
@ -219,12 +219,12 @@ TEST( GaussianFactorGraph, eliminateOne_x2 )
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|||
TEST( GaussianFactorGraph, eliminateOne_l1 )
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{
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||||
GaussianFactorGraph fg = createGaussianFactorGraph();
|
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ConditionalGaussian::shared_ptr actual = fg.eliminateOne("l1");
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||||
GaussianConditional::shared_ptr actual = fg.eliminateOne("l1");
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||||
|
||||
// create expected Conditional Gaussian
|
||||
Matrix I = eye(2), R11 = I, S12 = -0.5*I, S13 = -0.5*I;
|
||||
Vector d = Vector_(2, -0.1, 0.25), sigma = repeat(2, 0.141421);
|
||||
ConditionalGaussian expected("l1",d,R11,"x1",S12,"x2",S13,sigma);
|
||||
GaussianConditional expected("l1",d,R11,"x1",S12,"x2",S13,sigma);
|
||||
|
||||
CHECK(assert_equal(expected,*actual,tol));
|
||||
}
|
||||
|
|
@ -353,7 +353,7 @@ TEST( GaussianFactorGraph, CONSTRUCTOR_GaussianBayesNet )
|
|||
|
||||
// Base FactorGraph only
|
||||
FactorGraph<GaussianFactor> fg3(CBN);
|
||||
GaussianBayesNet CBN3 = gtsam::eliminate<GaussianFactor,ConditionalGaussian>(fg3,ord);
|
||||
GaussianBayesNet CBN3 = gtsam::eliminate<GaussianFactor,GaussianConditional>(fg3,ord);
|
||||
CHECK(assert_equal(CBN,CBN3));
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -28,8 +28,8 @@ TEST(GaussianFactorGraph, createSmoother)
|
|||
Ordering ordering;
|
||||
GaussianBayesNet bayesNet = fg2.eliminate(ordering);
|
||||
bayesNet.print("bayesNet");
|
||||
FactorGraph<GaussianFactor> p_x3 = marginalize<GaussianFactor,ConditionalGaussian>(bayesNet, Ordering("x3"));
|
||||
FactorGraph<GaussianFactor> p_x1 = marginalize<GaussianFactor,ConditionalGaussian>(bayesNet, Ordering("x1"));
|
||||
FactorGraph<GaussianFactor> p_x3 = marginalize<GaussianFactor,GaussianConditional>(bayesNet, Ordering("x3"));
|
||||
FactorGraph<GaussianFactor> p_x1 = marginalize<GaussianFactor,GaussianConditional>(bayesNet, Ordering("x1"));
|
||||
CHECK(assert_equal(p_x1,p_x3)); // should be the same because of symmetry
|
||||
}
|
||||
|
||||
|
|
@ -39,10 +39,10 @@ TEST( Inference, marginals )
|
|||
// create and marginalize a small Bayes net on "x"
|
||||
GaussianBayesNet cbn = createSmallGaussianBayesNet();
|
||||
Ordering keys("x");
|
||||
FactorGraph<GaussianFactor> fg = marginalize<GaussianFactor, ConditionalGaussian>(cbn,keys);
|
||||
FactorGraph<GaussianFactor> fg = marginalize<GaussianFactor, GaussianConditional>(cbn,keys);
|
||||
|
||||
// turn into Bayes net to test easily
|
||||
BayesNet<ConditionalGaussian> actual = eliminate<GaussianFactor,ConditionalGaussian>(fg,keys);
|
||||
BayesNet<GaussianConditional> actual = eliminate<GaussianFactor,GaussianConditional>(fg,keys);
|
||||
|
||||
// expected is just scalar Gaussian on x
|
||||
GaussianBayesNet expected = scalarGaussian("x",4,sqrt(2));
|
||||
|
|
|
|||
|
|
@ -0,0 +1,16 @@
|
|||
/*
|
||||
* @file testNonlinearEquality.cpp
|
||||
* @author Alex Cunningham
|
||||
*/
|
||||
|
||||
|
||||
#include <CppUnitLite/TestHarness.h>
|
||||
#include "NonlinearEquality.h"
|
||||
|
||||
TEST ( NonlinearEquality, construction ) {
|
||||
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -69,7 +69,7 @@ int main()
|
|||
GaussianFactor combined("x2", Ax2, "l1", Al1, "x1", Ax1, b2);
|
||||
long timeLog = clock();
|
||||
int n = 1000000;
|
||||
ConditionalGaussian::shared_ptr conditional;
|
||||
GaussianConditional::shared_ptr conditional;
|
||||
GaussianFactor::shared_ptr factor;
|
||||
|
||||
for(int i = 0; i < n; i++)
|
||||
|
|
|
|||
Loading…
Reference in New Issue