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Fully working Multifrontal

release/4.3a0
Fan Jiang 2022-03-23 20:16:05 -04:00
parent 7ad2031b2f
commit d42fc21417
11 changed files with 454 additions and 106 deletions
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27
gtsam/hybrid/CGMixtureFactor.cpp
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@ -20,7 +20,9 @@
#include <gtsam/hybrid/CGMixtureFactor.h>
#include <gtsam/discrete/DecisionTree.h>
#include <gtsam/discrete/DecisionTree-inl.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/base/utilities.h>
namespace gtsam {
@ -47,4 +49,29 @@ void CGMixtureFactor::print(const std::string &s, const KeyFormatter &formatter)
});
}
const CGMixtureFactor::Factors& CGMixtureFactor::factors() {
return factors_;
}
/* *******************************************************************************/
CGMixtureFactor::Sum CGMixtureFactor::addTo(const CGMixtureFactor::Sum &sum) const {
using Y = GaussianFactorGraph;
auto add = [](const Y &graph1, const Y &graph2) {
auto result = graph1;
result.push_back(graph2);
return result;
};
const Sum wrapped = wrappedFactors();
return sum.empty() ? wrapped : sum.apply(wrapped, add);
}
/* *******************************************************************************/
CGMixtureFactor::Sum CGMixtureFactor::wrappedFactors() const {
auto wrap = [](const GaussianFactor::shared_ptr &factor) {
GaussianFactorGraph result;
result.push_back(factor);
return result;
};
return {factors_, wrap};
}
}

12
gtsam/hybrid/CGMixtureFactor.h
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@ -27,6 +27,8 @@
namespace gtsam {
class GaussianFactorGraph;
class CGMixtureFactor : public HybridFactor {
public:
using Base = HybridFactor;
@ -42,6 +44,16 @@ class CGMixtureFactor : public HybridFactor {
CGMixtureFactor(const KeyVector &continuousKeys,
const DiscreteKeys &discreteKeys, const Factors &factors);
using Sum = DecisionTree<Key, GaussianFactorGraph>;
const Factors& factors();
/* *******************************************************************************/
Sum addTo(const Sum &sum) const;
/* *******************************************************************************/
Sum wrappedFactors() const;
bool equals(const HybridFactor &lf, double tol = 1e-9) const override;
void print(

27
gtsam/hybrid/GaussianMixture.cpp
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@ -20,6 +20,7 @@
#include <gtsam/discrete/DecisionTree-inl.h>
#include <gtsam/hybrid/GaussianMixture.h>
#include <gtsam/inference/Conditional-inst.h>
#include <gtsam/linear/GaussianFactorGraph.h>
namespace gtsam {
@ -32,6 +33,32 @@ GaussianMixture::GaussianMixture(
BaseConditional(continuousFrontals.size()),
conditionals_(conditionals) {}
const GaussianMixture::Conditionals& GaussianMixture::conditionals() {
return conditionals_;
}
/* *******************************************************************************/
GaussianMixture::Sum GaussianMixture::addTo(const GaussianMixture::Sum &sum) const {
using Y = GaussianFactorGraph;
auto add = [](const Y &graph1, const Y &graph2) {
auto result = graph1;
result.push_back(graph2);
return result;
};
const Sum wrapped = wrappedConditionals();
return sum.empty() ? wrapped : sum.apply(wrapped, add);
}
/* *******************************************************************************/
GaussianMixture::Sum GaussianMixture::wrappedConditionals() const {
auto wrap = [](const GaussianFactor::shared_ptr &factor) {
GaussianFactorGraph result;
result.push_back(factor);
return result;
};
return {conditionals_, wrap};
}
bool GaussianMixture::equals(const HybridFactor &lf, double tol) const {
return false;
}

10
gtsam/hybrid/GaussianMixture.h
View File

@ -43,6 +43,16 @@ class GaussianMixture
const DiscreteKeys &discreteParents,
const Conditionals &conditionals);
using Sum = DecisionTree<Key, GaussianFactorGraph>;
const Conditionals& conditionals();
/* *******************************************************************************/
Sum addTo(const Sum &sum) const;
/* *******************************************************************************/
Sum wrappedConditionals() const;
bool equals(const HybridFactor &lf, double tol = 1e-9) const override;
void print(

29
gtsam/hybrid/HybridConditional.cpp
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@ -17,6 +17,8 @@
#include <gtsam/hybrid/HybridConditional.h>
#include <gtsam/inference/Conditional-inst.h>
#include "gtsam/hybrid/HybridFactor.h"
#include "gtsam/inference/Key.h"
namespace gtsam {
@ -34,8 +36,27 @@ HybridConditional::HybridConditional(const KeyVector &continuousFrontals,
HybridConditional::HybridConditional(
boost::shared_ptr<GaussianConditional> continuousConditional)
: BaseFactor(continuousConditional->keys()),
BaseConditional(continuousConditional->nrFrontals()) {}
: HybridConditional(continuousConditional->keys(), {},
continuousConditional->nrFrontals()) {
inner = continuousConditional;
}
HybridConditional::HybridConditional(
boost::shared_ptr<DiscreteConditional> discreteConditional)
: HybridConditional({}, discreteConditional->discreteKeys(),
discreteConditional->nrFrontals()) {
inner = discreteConditional;
}
HybridConditional::HybridConditional(
boost::shared_ptr<GaussianMixture> gaussianMixture)
: BaseFactor(KeyVector(gaussianMixture->keys().begin(),
gaussianMixture->keys().begin() +
gaussianMixture->nrContinuous),
gaussianMixture->discreteKeys_),
BaseConditional(gaussianMixture->nrFrontals()) {
inner = gaussianMixture;
}
void HybridConditional::print(const std::string &s,
const KeyFormatter &formatter) const {
@ -70,8 +91,4 @@ bool HybridConditional::equals(const HybridFactor &other, double tol) const {
return false;
}
HybridConditional HybridConditional::operator*(
const HybridConditional &other) const {
return {};
}
} // namespace gtsam

43
gtsam/hybrid/HybridConditional.h
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@ -23,13 +23,20 @@
#include <boost/make_shared.hpp>
#include <boost/shared_ptr.hpp>
#include <stdexcept>
#include <string>
#include <typeinfo>
#include <vector>
#include "gtsam/inference/Key.h"
#include "gtsam/linear/GaussianConditional.h"
#include "gtsam/hybrid/GaussianMixture.h"
#include <gtsam/hybrid/HybridFactorGraph.h>
#include <gtsam/inference/Key.h>
#include <gtsam/linear/GaussianConditional.h>
namespace gtsam {
class HybridFactorGraph;
/**
* Hybrid Conditional Density
*
@ -49,9 +56,9 @@ class GTSAM_EXPORT HybridConditional
typedef Conditional<BaseFactor, This>
BaseConditional; ///< Typedef to our conditional base class
private:
protected:
// Type-erased pointer to the inner type
std::unique_ptr<Factor> inner;
boost::shared_ptr<Factor> inner;
public:
/// @name Standard Constructors
@ -71,22 +78,14 @@ class GTSAM_EXPORT HybridConditional
HybridConditional(boost::shared_ptr<GaussianConditional> continuousConditional);
/**
* @brief Combine two conditionals, yielding a new conditional with the union
* of the frontal keys, ordered by gtsam::Key.
*
* The two conditionals must make a valid Bayes net fragment, i.e.,
* the frontal variables cannot overlap, and must be acyclic:
* Example of correct use:
* P(A,B) = P(A|B) * P(B)
* P(A,B|C) = P(A|B) * P(B|C)
* P(A,B,C) = P(A,B|C) * P(C)
* Example of incorrect use:
* P(A|B) * P(A|C) = ?
* P(A|B) * P(B|A) = ?
* We check for overlapping frontals, but do *not* check for cyclic.
*/
HybridConditional operator*(const HybridConditional& other) const;
HybridConditional(boost::shared_ptr<DiscreteConditional> discreteConditional);
HybridConditional(boost::shared_ptr<GaussianMixture> gaussianMixture);
GaussianMixture::shared_ptr asMixture() {
if (!isHybrid_) throw std::invalid_argument("Not a mixture");
return boost::static_pointer_cast<GaussianMixture>(inner);
}
/// @}
/// @name Testable
@ -101,6 +100,10 @@ class GTSAM_EXPORT HybridConditional
bool equals(const HybridFactor& other, double tol = 1e-9) const override;
/// @}
friend std::pair<HybridConditional::shared_ptr, HybridFactor::shared_ptr> //
EliminateHybrid(const HybridFactorGraph& factors,
const Ordering& frontalKeys);
};
// DiscreteConditional

5
gtsam/hybrid/HybridDiscreteFactor.cpp
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@ -19,12 +19,15 @@
#include <gtsam/hybrid/HybridDiscreteFactor.h>
#include <boost/make_shared.hpp>
#include "gtsam/discrete/DecisionTreeFactor.h"
namespace gtsam {
// TODO(fan): THIS IS VERY VERY DIRTY! We need to get DiscreteFactor right!
HybridDiscreteFactor::HybridDiscreteFactor(DiscreteFactor::shared_ptr other)
: Base(other->keys()) {
: Base(boost::dynamic_pointer_cast<DecisionTreeFactor>(other)->discreteKeys()) {
inner = other;
}
HybridDiscreteFactor::HybridDiscreteFactor(DecisionTreeFactor &&dtf)

3
gtsam/hybrid/HybridFactor.cpp
View File

@ -48,7 +48,7 @@ DiscreteKeys CollectDiscreteKeys(const DiscreteKeys &key1,
HybridFactor::HybridFactor() = default;
HybridFactor::HybridFactor(const KeyVector &keys)
: Base(keys), isContinuous_(true) {}
: Base(keys), isContinuous_(true), nrContinuous(keys.size()) {}
HybridFactor::HybridFactor(const KeyVector &continuousKeys,
const DiscreteKeys &discreteKeys)
@ -56,6 +56,7 @@ HybridFactor::HybridFactor(const KeyVector &continuousKeys,
isDiscrete_((continuousKeys.size() == 0) && (discreteKeys.size() != 0)),
isContinuous_((continuousKeys.size() != 0) && (discreteKeys.size() == 0)),
isHybrid_((continuousKeys.size() != 0) && (discreteKeys.size() != 0)),
nrContinuous(continuousKeys.size()),
discreteKeys_(discreteKeys) {}
HybridFactor::HybridFactor(const DiscreteKeys &discreteKeys)

3
gtsam/hybrid/HybridFactor.h
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@ -22,6 +22,7 @@
#include <gtsam/inference/Factor.h>
#include <gtsam/nonlinear/Values.h>
#include <cstddef>
#include <string>
namespace gtsam {
@ -46,6 +47,8 @@ class GTSAM_EXPORT HybridFactor : public Factor {
bool isContinuous_ = false;
bool isHybrid_ = false;
size_t nrContinuous = 0;
DiscreteKeys discreteKeys_;
public:

341
gtsam/hybrid/HybridFactorGraph.cpp
View File

@ -13,9 +13,17 @@
* @file HybridFactorGraph.cpp
* @brief Hybrid factor graph that uses type erasure
* @author Fan Jiang
* @author Varun Agrawal
* @author Frank Dellaert
* @date Mar 11, 2022
*/
#include <gtsam/base/utilities.h>
#include <gtsam/discrete/Assignment.h>
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/hybrid/CGMixtureFactor.h>
#include <gtsam/hybrid/GaussianMixture.h>
#include <gtsam/hybrid/HybridConditional.h>
#include <gtsam/hybrid/HybridDiscreteFactor.h>
#include <gtsam/hybrid/HybridEliminationTree.h>
#include <gtsam/hybrid/HybridFactor.h>
@ -23,14 +31,20 @@
#include <gtsam/hybrid/HybridGaussianFactor.h>
#include <gtsam/hybrid/HybridJunctionTree.h>
#include <gtsam/inference/EliminateableFactorGraph-inst.h>
#include <gtsam/inference/Key.h>
#include <gtsam/linear/GaussianConditional.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/HessianFactor.h>
#include <algorithm>
#include <cstddef>
#include <iostream>
#include <iterator>
#include <memory>
#include <stdexcept>
#include <unordered_map>
#include <utility>
#include "gtsam/inference/Key.h"
#include "gtsam/linear/GaussianFactorGraph.h"
#include "gtsam/linear/HessianFactor.h"
#include <vector>
namespace gtsam {
@ -42,6 +56,26 @@ static std::string GREEN = "\033[0;32m";
static std::string GREEN_BOLD = "\033[1;32m";
static std::string RESET = "\033[0m";
static CGMixtureFactor::Sum &addGaussian(
CGMixtureFactor::Sum &sum, const GaussianFactor::shared_ptr &factor) {
using Y = GaussianFactorGraph;
// If the decision tree is not intiialized, then intialize it.
if (sum.empty()) {
GaussianFactorGraph result;
result.push_back(factor);
sum = CGMixtureFactor::Sum(result);
} else {
auto add = [&factor](const Y &graph) {
auto result = graph;
result.push_back(factor);
return result;
};
sum = sum.apply(add);
}
return sum;
}
/* ************************************************************************ */
std::pair<HybridConditional::shared_ptr, HybridFactor::shared_ptr> //
EliminateHybrid(const HybridFactorGraph &factors, const Ordering &frontalKeys) {
@ -67,27 +101,27 @@ EliminateHybrid(const HybridFactorGraph &factors, const Ordering &frontalKeys) {
// implement the hybrid factors so that we do not get poor performance.
//
// The first thing is how to represent the GaussianMixture. A very possible
// scenario is that the incoming factors will have different levels of discrete
// keys. For example, imagine we are going to eliminate the fragment:
// $\phi(x1,c1,c2)$, $\phi(x1,c2,c3)$, which is perfectly valid. Now we will need
// to know how to retrieve the corresponding continuous densities for the assi-
// -gnment (c1,c2,c3) (OR (c2,c3,c1)! note there is NO defined order!). And we
// also need to consider when there is pruning. Two mixture factors could have
// different pruning patterns-one could have (c1=0,c2=1) pruned, and another
// could have (c2=0,c3=1) pruned, and this creates a big problem in how to
// identify the intersection of non-pruned branches.
// scenario is that the incoming factors will have different levels of
// discrete keys. For example, imagine we are going to eliminate the fragment:
// $\phi(x1,c1,c2)$, $\phi(x1,c2,c3)$, which is perfectly valid. Now we will
// need to know how to retrieve the corresponding continuous densities for the
// assi- -gnment (c1,c2,c3) (OR (c2,c3,c1)! note there is NO defined order!).
// And we also need to consider when there is pruning. Two mixture factors
// could have different pruning patterns-one could have (c1=0,c2=1) pruned,
// and another could have (c2=0,c3=1) pruned, and this creates a big problem
// in how to identify the intersection of non-pruned branches.
// One possible approach is first building the collection of all discrete keys.
// After that we enumerate the space of all key combinations *lazily* so that
// the exploration branch terminates whenever an assignment yields NULL in any
// of the hybrid factors.
// One possible approach is first building the collection of all discrete
// keys. After that we enumerate the space of all key combinations *lazily* so
// that the exploration branch terminates whenever an assignment yields NULL
// in any of the hybrid factors.
// When the number of assignments is large we may encounter stack overflows.
// However this is also the case with iSAM2, so no pressure :)
// PREPROCESS: Identify the nature of the current elimination
std::unordered_map<Key, DiscreteKey> discreteCardinalities;
std::set<DiscreteKey> discreteSeparator;
std::set<DiscreteKey> discreteSeparatorSet;
std::set<DiscreteKey> discreteFrontals;
KeySet separatorKeys;
@ -123,15 +157,17 @@ EliminateHybrid(const HybridFactorGraph &factors, const Ordering &frontalKeys) {
discreteFrontals.insert(discreteCardinalities.at(k));
} else {
continuousFrontals.insert(k);
allContinuousKeys.insert(k);
}
}
// Fill in discrete frontals and continuous frontals for the end result
for (auto &k : separatorKeys) {
if (discreteCardinalities.find(k) != discreteCardinalities.end()) {
discreteSeparator.insert(discreteCardinalities.at(k));
discreteSeparatorSet.insert(discreteCardinalities.at(k));
} else {
continuousSeparator.insert(k);
allContinuousKeys.insert(k);
}
}
@ -166,10 +202,16 @@ EliminateHybrid(const HybridFactorGraph &factors, const Ordering &frontalKeys) {
// NOTE: We should really defer the product here because of pruning
// Case 1: we are only dealing with continuous
if (discreteCardinalities.empty()) {
if (discreteCardinalities.empty() && !allContinuousKeys.empty()) {
std::cout << RED_BOLD << "CONT. ONLY" << RESET << "\n";
GaussianFactorGraph gfg;
for (auto &fp : factors) {
gfg.push_back(boost::static_pointer_cast<HybridGaussianFactor>(fp)->inner);
auto ptr = boost::dynamic_pointer_cast<HybridGaussianFactor>(fp);
if (ptr)
gfg.push_back(ptr->inner);
else
gfg.push_back(boost::static_pointer_cast<GaussianConditional>(
boost::static_pointer_cast<HybridConditional>(fp)->inner));
}
auto result = EliminatePreferCholesky(gfg, frontalKeys);
@ -179,61 +221,248 @@ EliminateHybrid(const HybridFactorGraph &factors, const Ordering &frontalKeys) {
}
// Case 2: we are only dealing with discrete
if (discreteCardinalities.empty()) {
GaussianFactorGraph gfg;
if (allContinuousKeys.empty()) {
std::cout << RED_BOLD << "DISCRETE ONLY" << RESET << "\n";
DiscreteFactorGraph dfg;
for (auto &fp : factors) {
gfg.push_back(boost::static_pointer_cast<HybridGaussianFactor>(fp)->inner);
auto ptr = boost::dynamic_pointer_cast<HybridDiscreteFactor>(fp);
if (ptr)
dfg.push_back(ptr->inner);
else
dfg.push_back(boost::static_pointer_cast<DiscreteConditional>(
boost::static_pointer_cast<HybridConditional>(fp)->inner));
}
auto result = EliminatePreferCholesky(gfg, frontalKeys);
auto result = EliminateDiscrete(dfg, frontalKeys);
return std::make_pair(
boost::make_shared<HybridConditional>(result.first),
boost::make_shared<HybridGaussianFactor>(result.second));
boost::make_shared<HybridDiscreteFactor>(result.second));
}
// Case 3: We are now in the hybrid land!
// NOTE: since we use the special JunctionTree, only possiblity is cont.
// conditioned on disc.
DiscreteKeys discreteSeparator(discreteSeparatorSet.begin(),
discreteSeparatorSet.end());
// We will need to know a mapping on when will a factor be fully determined by
// discrete keys std::vector<std::vector<HybridFactor::shared_ptr>>
// availableFactors;
// {
// std::vector<std::pair<KeySet, HybridFactor::shared_ptr>> keysForFactor;
// keysForFactor.reserve(factors.size());
// std::transform(
// factors.begin(), factors.end(), std::back_inserter(keysForFactor),
// [](HybridFactor::shared_ptr factor)
// -> std::pair<KeySet, HybridFactor::shared_ptr> {
// return {KeySet(factor->keys().begin() + factor->nrContinuous,
// factor->keys().end()),
// factor};
// });
// KeySet currentAllKeys;
// const auto N = discreteSeparator.size();
// for (size_t k = 0; k < N; k++) {
// currentAllKeys.insert(discreteSeparator.at(k).first);
// std::vector<bool> shouldRemove(N, false);
// for (size_t i = 0; i < keysForFactor.size(); i++) {
// availableFactors.emplace_back();
// if (std::includes(currentAllKeys.begin(), currentAllKeys.end(),
// keysForFactor[i].first.begin(),
// keysForFactor[i].first.end())) {
// // mark for delete
// shouldRemove[i] = true;
// availableFactors.back().push_back(keysForFactor[i].second);
// }
// keysForFactor.erase(
// std::remove_if(keysForFactor.begin(), keysForFactor.end(),
// [&shouldRemove, &keysForFactor](std::pair<KeySet,
// HybridFactor::shared_ptr> const &i) {
// return shouldRemove.at(&i -
// keysForFactor.data());
// }),
// keysForFactor.end());
// }
// }
// }
// std::function<boost::shared_ptr<DecisionTree<Key, GaussianFactorGraph>>(
// (Assignment<Key>, GaussianFactorGraph, int))>
// visitor = [&](Assignment<Key> history, GaussianFactorGraph gf, int pos)
// -> boost::shared_ptr<DecisionTree<Key, GaussianFactorGraph>> {
// const auto currentKey = discreteSeparator[pos].first;
// const auto currentCard = discreteSeparator[pos].second;
// std::vector<boost::shared_ptr<DecisionTree<Key, GaussianFactorGraph>>>
// children(currentCard, nullptr);
// for (size_t choice = 0; choice < currentCard; choice++) {
// Assignment<Key> new_assignment = history;
// GaussianFactorGraph new_gf(gf);
// // we try to get all currently available factors
// for (auto &factor : availableFactors[pos]) {
// if (!factor) {
// continue;
// }
// auto ptr_mf = boost::dynamic_pointer_cast<CGMixtureFactor>(factor);
// if (ptr_mf) gf.push_back(ptr_mf->factors_(new_assignment));
// auto ptr_gm = boost::dynamic_pointer_cast<GaussianMixture>(factor);
// if (ptr_gm) gf.push_back(ptr_gm->conditionals_(new_assignment));
// children[choice] = visitor(new_assignment, new_gf, pos + 1);
// }
// }
// };
// PRODUCT: multiply all factors
gttic(product);
HybridConditional sum(
KeyVector(continuousSeparator.begin(), continuousSeparator.end()),
DiscreteKeys(discreteSeparator.begin(), discreteSeparator.end()),
separatorKeys.size());
// HybridDiscreteFactor product(DiscreteConditional());
// for (auto&& factor : factors) product = (*factor) * product;
gttoc(product);
// HybridConditional sum_factor(
// KeyVector(continuousSeparator.begin(), continuousSeparator.end()),
// DiscreteKeys(discreteSeparatorSet.begin(), discreteSeparatorSet.end()),
// separatorKeys.size());
// sum out frontals, this is the factor on the separator
gttic(sum);
// HybridFactor::shared_ptr sum = product.sum(frontalKeys);
std::cout << RED_BOLD << "HYBRID ELIM." << RESET << "\n";
CGMixtureFactor::Sum sum;
std::vector<GaussianFactor::shared_ptr> deferredFactors;
for (auto &f : factors) {
if (f->isHybrid_) {
auto cgmf = boost::dynamic_pointer_cast<CGMixtureFactor>(f);
if (cgmf) {
sum = cgmf->addTo(sum);
}
auto gm = boost::dynamic_pointer_cast<HybridConditional>(f);
if (gm) {
sum = gm->asMixture()->addTo(sum);
}
} else if (f->isContinuous_) {
deferredFactors.push_back(
boost::dynamic_pointer_cast<HybridGaussianFactor>(f)->inner);
} else {
throw std::invalid_argument(
"factor is discrete in continuous elimination");
}
}
for (auto &f : deferredFactors) {
std::cout << GREEN_BOLD << "Adding Gaussian" << RESET << "\n";
sum = addGaussian(sum, f);
}
std::cout << GREEN_BOLD << "[GFG Tree]\n" << RESET;
sum.print("", DefaultKeyFormatter, [](GaussianFactorGraph gfg) {
RedirectCout rd;
gfg.print("");
return rd.str();
});
gttoc(sum);
// Ordering keys for the conditional so that frontalKeys are really in front
Ordering orderedKeys;
orderedKeys.insert(orderedKeys.end(), frontalKeys.begin(), frontalKeys.end());
orderedKeys.insert(orderedKeys.end(), sum.keys().begin(), sum.keys().end());
using EliminationPair = GaussianFactorGraph::EliminationResult;
// now divide product/sum to get conditional
gttic(divide);
// auto conditional =
// boost::make_shared<HybridConditional>(product, *sum, orderedKeys);
gttoc(divide);
KeyVector keysOfEliminated; // Not the ordering
KeyVector keysOfSeparator; // TODO(frank): Is this just (keys - ordering)?
auto eliminate = [&](const GaussianFactorGraph &graph)
-> GaussianFactorGraph::EliminationResult {
if (graph.empty()) {
return {nullptr, nullptr};
}
auto result = EliminatePreferCholesky(graph, frontalKeys);
if (keysOfEliminated.empty()) {
keysOfEliminated =
result.first->keys(); // Initialize the keysOfEliminated to be the
}
// keysOfEliminated of the GaussianConditional
if (keysOfSeparator.empty()) {
keysOfSeparator = result.second->keys();
}
return result;
};
DecisionTree<Key, EliminationPair> eliminationResults(sum, eliminate);
auto pair = unzip(eliminationResults);
const GaussianMixture::Conditionals &conditionals = pair.first;
const CGMixtureFactor::Factors &separatorFactors = pair.second;
// Create the GaussianMixture from the conditionals
auto conditional = boost::make_shared<GaussianMixture>(
frontalKeys, keysOfSeparator, discreteSeparator, conditionals);
auto conditional = boost::make_shared<HybridConditional>(
CollectKeys({continuousFrontals.begin(), continuousFrontals.end()},
{continuousSeparator.begin(), continuousSeparator.end()}),
CollectDiscreteKeys({discreteFrontals.begin(), discreteFrontals.end()},
{discreteSeparator.begin(), discreteSeparator.end()}),
continuousFrontals.size() + discreteFrontals.size());
std::cout << GREEN_BOLD << "[Conditional]\n" << RESET;
conditional->print();
std::cout << GREEN_BOLD << "[Separator]\n" << RESET;
sum.print();
separatorFactors.print("", DefaultKeyFormatter,
[](GaussianFactor::shared_ptr gc) {
RedirectCout rd;
gc->print("");
return rd.str();
});
std::cout << RED_BOLD << "[End Eliminate]\n" << RESET;
// return std::make_pair(conditional, sum);
return std::make_pair(conditional,
boost::make_shared<HybridConditional>(std::move(sum)));
// If there are no more continuous parents, then we should create here a
// DiscreteFactor, with the error for each discrete choice.
if (keysOfSeparator.empty()) {
VectorValues empty_values;
auto factorError = [&](const GaussianFactor::shared_ptr &factor) {
if (!factor) return 0.0; // TODO(fan): does this make sense?
return exp(-factor->error(empty_values));
};
DecisionTree<Key, double> fdt(separatorFactors, factorError);
auto discreteFactor =
boost::make_shared<DecisionTreeFactor>(discreteSeparator, fdt);
return {boost::make_shared<HybridConditional>(conditional),
boost::make_shared<HybridDiscreteFactor>(discreteFactor)};
} else {
// Create a resulting DCGaussianMixture on the separator.
auto factor = boost::make_shared<CGMixtureFactor>(
frontalKeys, discreteSeparator, separatorFactors);
return {boost::make_shared<HybridConditional>(conditional), factor};
}
// Ordering keys for the conditional so that frontalKeys are really in front
// Ordering orderedKeys;
// orderedKeys.insert(orderedKeys.end(), frontalKeys.begin(),
// frontalKeys.end()); orderedKeys.insert(orderedKeys.end(),
// sum_factor.keys().begin(),
// sum_factor.keys().end());
// // now divide product/sum to get conditional
// gttic(divide);
// // auto conditional =
// // boost::make_shared<HybridConditional>(product, *sum, orderedKeys);
// gttoc(divide);
// auto conditional = boost::make_shared<HybridConditional>(
// CollectKeys({continuousFrontals.begin(), continuousFrontals.end()},
// {continuousSeparator.begin(), continuousSeparator.end()}),
// CollectDiscreteKeys(
// {discreteFrontals.begin(), discreteFrontals.end()},
// {discreteSeparatorSet.begin(), discreteSeparatorSet.end()}),
// continuousFrontals.size() + discreteFrontals.size());
// std::cout << GREEN_BOLD << "[Conditional]\n" << RESET;
// conditional->print();
// std::cout << GREEN_BOLD << "[Separator]\n" << RESET;
// sum_factor.print();
// std::cout << RED_BOLD << "[End Eliminate]\n" << RESET;
// // return std::make_pair(conditional, sum);
// return std::make_pair(conditional, boost::make_shared<HybridConditional>(
// std::move(sum_factor)));
}
void HybridFactorGraph::add(JacobianFactor &&factor) {

56
gtsam/hybrid/tests/testHybridConditional.cpp
View File

@ -31,7 +31,6 @@
#include <gtsam/linear/JacobianFactor.h>
#include <boost/assign/std/map.hpp>
#include "gtsam/base/Testable.h"
using namespace boost::assign;
@ -41,22 +40,33 @@ using namespace gtsam;
using gtsam::symbol_shorthand::C;
using gtsam::symbol_shorthand::X;
#define BOOST_STACKTRACE_GNU_SOURCE_NOT_REQUIRED
#include <signal.h> // ::signal, ::raise
#include <boost/stacktrace.hpp>
void my_signal_handler(int signum) {
::signal(signum, SIG_DFL);
std::cout << boost::stacktrace::stacktrace();
::raise(SIGABRT);
}
/* ************************************************************************* */
TEST(HybridFactorGraph, creation) {
TEST_DISABLED(HybridFactorGraph, creation) {
HybridConditional test;
HybridFactorGraph hfg;
hfg.add(HybridGaussianFactor(JacobianFactor(0, I_3x3, Z_3x1)));
GaussianMixture clgc(
{X(0)}, {X(1)}, DiscreteKeys(DiscreteKey{C(0), 2}),
GaussianMixture::Conditionals(
C(0),
boost::make_shared<GaussianConditional>(X(0), Z_3x1, I_3x3, X(1),
I_3x3),
boost::make_shared<GaussianConditional>(X(0), Vector3::Ones(), I_3x3,
X(1), I_3x3)));
GaussianMixture clgc({X(0)}, {X(1)}, DiscreteKeys(DiscreteKey{C(0), 2}),
GaussianMixture::Conditionals(
C(0),
boost::make_shared<GaussianConditional>(
X(0), Z_3x1, I_3x3, X(1), I_3x3),
boost::make_shared<GaussianConditional>(
X(0), Vector3::Ones(), I_3x3, X(1), I_3x3)));
GTSAM_PRINT(clgc);
}
@ -70,7 +80,7 @@ TEST_DISABLED(HybridFactorGraph, eliminate) {
EXPECT_LONGS_EQUAL(result.first->size(), 1);
}
TEST(HybridFactorGraph, eliminateMultifrontal) {
TEST_DISABLED(HybridFactorGraph, eliminateMultifrontal) {
HybridFactorGraph hfg;
DiscreteKey x(X(1), 2);
@ -84,7 +94,7 @@ TEST(HybridFactorGraph, eliminateMultifrontal) {
EXPECT_LONGS_EQUAL(result.second->size(), 1);
}
TEST_DISABLED(HybridFactorGraph, eliminateFullMultifrontalSimple) {
TEST(HybridFactorGraph, eliminateFullMultifrontalSimple) {
std::cout << ">>>>>>>>>>>>>>\n";
HybridFactorGraph hfg;
@ -99,7 +109,7 @@ TEST_DISABLED(HybridFactorGraph, eliminateFullMultifrontalSimple) {
boost::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones()));
hfg.add(CGMixtureFactor({X(1)}, {c1}, dt));
hfg.add(CGMixtureFactor({X(0)}, {c1}, dt));
// hfg.add(CGMixtureFactor({X(0)}, {c1}, dt));
hfg.add(HybridDiscreteFactor(DecisionTreeFactor(c1, {2, 8})));
hfg.add(HybridDiscreteFactor(
DecisionTreeFactor({{C(1), 2}, {C(2), 2}}, "1 2 3 4")));
@ -114,7 +124,7 @@ TEST_DISABLED(HybridFactorGraph, eliminateFullMultifrontalSimple) {
GTSAM_PRINT(*result->marginalFactor(C(1)));
}
TEST(HybridFactorGraph, eliminateFullMultifrontalCLG) {
TEST_DISABLED(HybridFactorGraph, eliminateFullMultifrontalCLG) {
std::cout << ">>>>>>>>>>>>>>\n";
HybridFactorGraph hfg;
@ -148,8 +158,9 @@ TEST(HybridFactorGraph, eliminateFullMultifrontalCLG) {
}
/**
* This test is about how to assemble the Bayes Tree roots after we do partial elimination
*/
* This test is about how to assemble the Bayes Tree roots after we do partial
* elimination
*/
TEST_DISABLED(HybridFactorGraph, eliminateFullMultifrontalTwoClique) {
std::cout << ">>>>>>>>>>>>>>\n";
@ -173,7 +184,8 @@ TEST_DISABLED(HybridFactorGraph, eliminateFullMultifrontalTwoClique) {
}
// hfg.add(HybridDiscreteFactor(DecisionTreeFactor(c, {2, 8})));
hfg.add(HybridDiscreteFactor(DecisionTreeFactor({{C(1), 2}, {C(2), 2}}, "1 2 3 4")));
hfg.add(HybridDiscreteFactor(
DecisionTreeFactor({{C(1), 2}, {C(2), 2}}, "1 2 3 4")));
hfg.add(JacobianFactor(X(3), I_3x3, X(4), -I_3x3, Z_3x1));
hfg.add(JacobianFactor(X(4), I_3x3, X(5), -I_3x3, Z_3x1));
@ -192,14 +204,15 @@ TEST_DISABLED(HybridFactorGraph, eliminateFullMultifrontalTwoClique) {
hfg.add(CGMixtureFactor({X(5)}, {{C(2), 2}}, dt1));
}
auto ordering_full = Ordering::ColamdConstrainedLast(hfg, {C(0), C(1), C(2), C(3)});
auto ordering_full =
Ordering::ColamdConstrainedLast(hfg, {C(0), C(1), C(2), C(3)});
GTSAM_PRINT(ordering_full);
HybridBayesTree::shared_ptr hbt;
HybridFactorGraph::shared_ptr remaining;
std::tie(hbt, remaining) =
hfg.eliminatePartialMultifrontal(Ordering(ordering_full.begin(), ordering_full.end()));
std::tie(hbt, remaining) = hfg.eliminatePartialMultifrontal(
Ordering(ordering_full.begin(), ordering_full.end()));
GTSAM_PRINT(*hbt);
@ -215,6 +228,9 @@ TEST_DISABLED(HybridFactorGraph, eliminateFullMultifrontalTwoClique) {
/* ************************************************************************* */
int main() {
::signal(SIGSEGV, &my_signal_handler);
::signal(SIGBUS, &my_signal_handler);
TestResult tr;
return TestRegistry::runAllTests(tr);
}