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Resurrecting DiscreteBayesTree tests

release/4.3a0
Frank dellaert 2020-07-12 12:27:10 -04:00
parent f421a9316a
commit 58362579bb
4 changed files with 257 additions and 261 deletions
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3
gtsam/discrete/DiscreteBayesNet.h
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@ -20,13 +20,14 @@
#include <vector>
#include <map>
#include <boost/shared_ptr.hpp>
#include <gtsam/inference/BayesNet.h>
#include <gtsam/inference/FactorGraph.h>
#include <gtsam/discrete/DiscreteConditional.h>
namespace gtsam {
/** A Bayes net made from linear-Discrete densities */
class GTSAM_EXPORT DiscreteBayesNet: public FactorGraph<DiscreteConditional>
class GTSAM_EXPORT DiscreteBayesNet: public BayesNet<DiscreteConditional>
{
public:

13
gtsam/discrete/DiscreteBayesTree.cpp
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@ -29,10 +29,19 @@ namespace gtsam {
template class BayesTreeCliqueBase<DiscreteBayesTreeClique, DiscreteFactorGraph>;
template class BayesTree<DiscreteBayesTreeClique>;
/* ************************************************************************* */
double DiscreteBayesTreeClique::evaluate(
const DiscreteConditional::Values& values) const {
// evaluate all conditionals and multiply
double result = (*conditional_)(values);
for (const auto& child : children) {
result *= child->evaluate(values);
}
return result;
}
/* ************************************************************************* */
bool DiscreteBayesTree::equals(const This& other, double tol) const
{
bool DiscreteBayesTree::equals(const This& other, double tol) const {
return Base::equals(other, tol);
}

3
gtsam/discrete/DiscreteBayesTree.h
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@ -42,6 +42,9 @@ namespace gtsam {
typedef boost::weak_ptr<This> weak_ptr;
DiscreteBayesTreeClique() {}
DiscreteBayesTreeClique(const boost::shared_ptr<DiscreteConditional>& conditional) : Base(conditional) {}
//** evaluate conditional probability of subtree for given Values */
double evaluate(const DiscreteConditional::Values & values) const;
};
/* ************************************************************************* */

499
gtsam/discrete/tests/testDiscreteBayesTree.cpp
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@ -1,261 +1,245 @@
///* ----------------------------------------------------------------------------
//
// * 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 testDiscreteBayesTree.cpp
// * @date sept 15, 2012
// * @author Frank Dellaert
// */
//
//#include <gtsam/discrete/DiscreteBayesNet.h>
//#include <gtsam/discrete/DiscreteBayesTree.h>
//#include <gtsam/discrete/DiscreteFactorGraph.h>
//
//#include <boost/assign/std/vector.hpp>
//using namespace boost::assign;
//
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2020, 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 testDiscreteBayesTree.cpp
* @date sept 15, 2012
* @author Frank Dellaert
*/
#include <gtsam/base/Vector.h>
#include <gtsam/discrete/DiscreteBayesNet.h>
#include <gtsam/discrete/DiscreteBayesTree.h>
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/inference/BayesNet-inst.h>
#include <boost/assign/std/vector.hpp>
using namespace boost::assign;
#include <CppUnitLite/TestHarness.h>
//
//using namespace std;
//using namespace gtsam;
//
//static bool debug = false;
//
///**
// * Custom clique class to debug shortcuts
// */
////class Clique: public BayesTreeCliqueBaseOrdered<Clique, DiscreteConditional> {
////
////protected:
////
////public:
////
//// typedef BayesTreeCliqueBaseOrdered<Clique, DiscreteConditional> Base;
//// typedef boost::shared_ptr<Clique> shared_ptr;
////
//// // Constructors
//// Clique() {
//// }
//// Clique(const DiscreteConditional::shared_ptr& conditional) :
//// Base(conditional) {
//// }
//// Clique(
//// const std::pair<DiscreteConditional::shared_ptr,
//// DiscreteConditional::FactorType::shared_ptr>& result) :
//// Base(result) {
//// }
////
//// /// print index signature only
//// void printSignature(const std::string& s = "Clique: ",
//// const KeyFormatter& indexFormatter = DefaultKeyFormatter) const {
//// ((IndexConditionalOrdered::shared_ptr) conditional_)->print(s, indexFormatter);
//// }
////
//// /// evaluate value of sub-tree
//// double evaluate(const DiscreteConditional::Values & values) {
//// double result = (*(this->conditional_))(values);
//// // evaluate all children and multiply into result
//// for(boost::shared_ptr<Clique> c: children_)
//// result *= c->evaluate(values);
//// return result;
//// }
////
////};
//
////typedef BayesTreeOrdered<DiscreteConditional, Clique> DiscreteBayesTree;
////
/////* ************************************************************************* */
////double evaluate(const DiscreteBayesTree& tree,
//// const DiscreteConditional::Values & values) {
//// return tree.root()->evaluate(values);
////}
//
///* ************************************************************************* */
//
//TEST_UNSAFE( DiscreteBayesTree, thinTree ) {
//
// const int nrNodes = 15;
// const size_t nrStates = 2;
//
// // define variables
// vector<DiscreteKey> key;
// for (int i = 0; i < nrNodes; i++) {
// DiscreteKey key_i(i, nrStates);
// key.push_back(key_i);
// }
//
// // create a thin-tree Bayesnet, a la Jean-Guillaume
// DiscreteBayesNet bayesNet;
// bayesNet.add(key[14] % "1/3");
//
// bayesNet.add(key[13] | key[14] = "1/3 3/1");
// bayesNet.add(key[12] | key[14] = "3/1 3/1");
//
// bayesNet.add((key[11] | key[13], key[14]) = "1/4 2/3 3/2 4/1");
// bayesNet.add((key[10] | key[13], key[14]) = "1/4 3/2 2/3 4/1");
// bayesNet.add((key[9] | key[12], key[14]) = "4/1 2/3 F 1/4");
// bayesNet.add((key[8] | key[12], key[14]) = "T 1/4 3/2 4/1");
//
// bayesNet.add((key[7] | key[11], key[13]) = "1/4 2/3 3/2 4/1");
// bayesNet.add((key[6] | key[11], key[13]) = "1/4 3/2 2/3 4/1");
// bayesNet.add((key[5] | key[10], key[13]) = "4/1 2/3 3/2 1/4");
// bayesNet.add((key[4] | key[10], key[13]) = "2/3 1/4 3/2 4/1");
//
// bayesNet.add((key[3] | key[9], key[12]) = "1/4 2/3 3/2 4/1");
// bayesNet.add((key[2] | key[9], key[12]) = "1/4 8/2 2/3 4/1");
// bayesNet.add((key[1] | key[8], key[12]) = "4/1 2/3 3/2 1/4");
// bayesNet.add((key[0] | key[8], key[12]) = "2/3 1/4 3/2 4/1");
//
//// if (debug) {
//// GTSAM_PRINT(bayesNet);
//// bayesNet.saveGraph("/tmp/discreteBayesNet.dot");
//// }
//
// // create a BayesTree out of a Bayes net
// DiscreteBayesTree bayesTree(bayesNet);
// if (debug) {
// GTSAM_PRINT(bayesTree);
// bayesTree.saveGraph("/tmp/discreteBayesTree.dot");
// }
//
// // Check whether BN and BT give the same answer on all configurations
// // Also calculate all some marginals
// Vector marginals = zero(15);
// double joint_12_14 = 0, joint_9_12_14 = 0, joint_8_12_14 = 0, joint_8_12 = 0,
// joint82 = 0, joint12 = 0, joint24 = 0, joint45 = 0, joint46 = 0,
// joint_4_11 = 0;
// vector<DiscreteFactor::Values> allPosbValues = cartesianProduct(
// key[0] & key[1] & key[2] & key[3] & key[4] & key[5] & key[6] & key[7]
// & key[8] & key[9] & key[10] & key[11] & key[12] & key[13] & key[14]);
// for (size_t i = 0; i < allPosbValues.size(); ++i) {
// DiscreteFactor::Values x = allPosbValues[i];
// double expected = evaluate(bayesNet, x);
// double actual = evaluate(bayesTree, x);
// DOUBLES_EQUAL(expected, actual, 1e-9);
// // collect marginals
// for (size_t i = 0; i < 15; i++)
// if (x[i])
// marginals[i] += actual;
// // calculate shortcut 8 and 0
// if (x[12] && x[14])
// joint_12_14 += actual;
// if (x[9] && x[12] & x[14])
// joint_9_12_14 += actual;
// if (x[8] && x[12] & x[14])
// joint_8_12_14 += actual;
// if (x[8] && x[12])
// joint_8_12 += actual;
// if (x[8] && x[2])
// joint82 += actual;
// if (x[1] && x[2])
// joint12 += actual;
// if (x[2] && x[4])
// joint24 += actual;
// if (x[4] && x[5])
// joint45 += actual;
// if (x[4] && x[6])
// joint46 += actual;
// if (x[4] && x[11])
// joint_4_11 += actual;
// }
// DiscreteFactor::Values all1 = allPosbValues.back();
//
// Clique::shared_ptr R = bayesTree.root();
//
// // check separator marginal P(S0)
// Clique::shared_ptr c = bayesTree[0];
// DiscreteFactorGraph separatorMarginal0 = c->separatorMarginal(R,
// EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_8_12, separatorMarginal0(all1), 1e-9);
//
// // check separator marginal P(S9), should be P(14)
// c = bayesTree[9];
// DiscreteFactorGraph separatorMarginal9 = c->separatorMarginal(R,
// EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(marginals[14], separatorMarginal9(all1), 1e-9);
//
// // check separator marginal of root, should be empty
// c = bayesTree[11];
// DiscreteFactorGraph separatorMarginal11 = c->separatorMarginal(R,
// EliminateDiscrete);
// EXPECT_LONGS_EQUAL(0, separatorMarginal11.size());
//
// // check shortcut P(S9||R) to root
// c = bayesTree[9];
// DiscreteBayesNet shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_LONGS_EQUAL(0, shortcut.size());
//
// // check shortcut P(S8||R) to root
// c = bayesTree[8];
// shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_12_14/marginals[14], evaluate(shortcut,all1),
// 1e-9);
//
// // check shortcut P(S2||R) to root
// c = bayesTree[2];
// shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_9_12_14/marginals[14], evaluate(shortcut,all1),
// 1e-9);
//
// // check shortcut P(S0||R) to root
// c = bayesTree[0];
// shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_8_12_14/marginals[14], evaluate(shortcut,all1),
// 1e-9);
//
// // calculate all shortcuts to root
// DiscreteBayesTree::Nodes cliques = bayesTree.nodes();
// for(Clique::shared_ptr c: cliques) {
// DiscreteBayesNet shortcut = c->shortcut(R, EliminateDiscrete);
// if (debug) {
// c->printSignature();
// shortcut.print("shortcut:");
// }
// }
//
// // Check all marginals
// DiscreteFactor::shared_ptr marginalFactor;
// for (size_t i = 0; i < 15; i++) {
// marginalFactor = bayesTree.marginalFactor(i, EliminateDiscrete);
// double actual = (*marginalFactor)(all1);
// EXPECT_DOUBLES_EQUAL(marginals[i], actual, 1e-9);
// }
//
// DiscreteBayesNet::shared_ptr actualJoint;
//
// // Check joint P(8,2) TODO: not disjoint !
//// actualJoint = bayesTree.jointBayesNet(8, 2, EliminateDiscrete);
//// EXPECT_DOUBLES_EQUAL(joint82, evaluate(*actualJoint,all1), 1e-9);
//
// // Check joint P(1,2) TODO: not disjoint !
//// actualJoint = bayesTree.jointBayesNet(1, 2, EliminateDiscrete);
//// EXPECT_DOUBLES_EQUAL(joint12, evaluate(*actualJoint,all1), 1e-9);
//
// // Check joint P(2,4)
// actualJoint = bayesTree.jointBayesNet(2, 4, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint24, evaluate(*actualJoint,all1), 1e-9);
//
// // Check joint P(4,5) TODO: not disjoint !
//// actualJoint = bayesTree.jointBayesNet(4, 5, EliminateDiscrete);
//// EXPECT_DOUBLES_EQUAL(joint46, evaluate(*actualJoint,all1), 1e-9);
//
// // Check joint P(4,6) TODO: not disjoint !
//// actualJoint = bayesTree.jointBayesNet(4, 6, EliminateDiscrete);
//// EXPECT_DOUBLES_EQUAL(joint46, evaluate(*actualJoint,all1), 1e-9);
//
// // Check joint P(4,11)
// actualJoint = bayesTree.jointBayesNet(4, 11, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_4_11, evaluate(*actualJoint,all1), 1e-9);
//
//}
#include <vector>
using namespace std;
using namespace gtsam;
static bool debug = false;
// /**
// * Custom clique class to debug shortcuts
// */
// struct Clique : public BayesTreeCliqueBase<Clique, DiscreteConditional> {
// typedef BayesTreeCliqueBase<Clique, DiscreteConditional> Base;
// typedef boost::shared_ptr<Clique> shared_ptr;
// // Constructors
// Clique() {}
// explicit Clique(const DiscreteConditional::shared_ptr& conditional)
// : Base(conditional) {}
// Clique(const std::pair<DiscreteConditional::shared_ptr,
// DiscreteConditional::FactorType::shared_ptr>&
// result)
// : Base(result) {}
// /// print index signature only
// void printSignature(
// const std::string& s = "Clique: ",
// const KeyFormatter& indexFormatter = DefaultKeyFormatter) const {
// ((IndexConditionalOrdered::shared_ptr)conditional_)
// ->print(s, indexFormatter);
// }
// /// evaluate value of sub-tree
// double evaluate(const DiscreteConditional::Values& values) {
// double result = (*(this->conditional_))(values);
// // evaluate all children and multiply into result
// for (boost::shared_ptr<Clique> c : children_) result *=
// c->evaluate(values); return result;
// }
// };
// typedef BayesTreeOrdered<DiscreteConditional, Clique> DiscreteBayesTree;
/* ************************************************************************* */
TEST_UNSAFE(DiscreteBayesTree, thinTree) {
const int nrNodes = 15;
const size_t nrStates = 2;
// define variables
vector<DiscreteKey> key;
for (int i = 0; i < nrNodes; i++) {
DiscreteKey key_i(i, nrStates);
key.push_back(key_i);
}
// create a thin-tree Bayesnet, a la Jean-Guillaume
DiscreteBayesNet bayesNet;
bayesNet.add(key[14] % "1/3");
bayesNet.add(key[13] | key[14] = "1/3 3/1");
bayesNet.add(key[12] | key[14] = "3/1 3/1");
bayesNet.add((key[11] | key[13], key[14]) = "1/4 2/3 3/2 4/1");
bayesNet.add((key[10] | key[13], key[14]) = "1/4 3/2 2/3 4/1");
bayesNet.add((key[9] | key[12], key[14]) = "4/1 2/3 F 1/4");
bayesNet.add((key[8] | key[12], key[14]) = "T 1/4 3/2 4/1");
bayesNet.add((key[7] | key[11], key[13]) = "1/4 2/3 3/2 4/1");
bayesNet.add((key[6] | key[11], key[13]) = "1/4 3/2 2/3 4/1");
bayesNet.add((key[5] | key[10], key[13]) = "4/1 2/3 3/2 1/4");
bayesNet.add((key[4] | key[10], key[13]) = "2/3 1/4 3/2 4/1");
bayesNet.add((key[3] | key[9], key[12]) = "1/4 2/3 3/2 4/1");
bayesNet.add((key[2] | key[9], key[12]) = "1/4 8/2 2/3 4/1");
bayesNet.add((key[1] | key[8], key[12]) = "4/1 2/3 3/2 1/4");
bayesNet.add((key[0] | key[8], key[12]) = "2/3 1/4 3/2 4/1");
if (debug) {
GTSAM_PRINT(bayesNet);
bayesNet.saveGraph("/tmp/discreteBayesNet.dot");
}
// create a BayesTree out of a Bayes net
auto bayesTree = DiscreteFactorGraph(bayesNet).eliminateMultifrontal();
if (debug) {
GTSAM_PRINT(*bayesTree);
bayesTree->saveGraph("/tmp/discreteBayesTree.dot");
}
auto R = bayesTree->roots().front();
// Check whether BN and BT give the same answer on all configurations
vector<DiscreteFactor::Values> allPosbValues = cartesianProduct(
key[0] & key[1] & key[2] & key[3] & key[4] & key[5] & key[6] & key[7] &
key[8] & key[9] & key[10] & key[11] & key[12] & key[13] & key[14]);
for (size_t i = 0; i < allPosbValues.size(); ++i) {
DiscreteFactor::Values x = allPosbValues[i];
double expected = bayesNet.evaluate(x);
double actual = R->evaluate(x);
DOUBLES_EQUAL(expected, actual, 1e-9);
}
// Calculate all some marginals
Vector marginals = zero(15);
double joint_12_14 = 0, joint_9_12_14 = 0, joint_8_12_14 = 0, joint_8_12 = 0,
joint82 = 0, joint12 = 0, joint24 = 0, joint45 = 0, joint46 = 0,
joint_4_11 = 0;
for (size_t i = 0; i < allPosbValues.size(); ++i) {
DiscreteFactor::Values x = allPosbValues[i];
double px = R->evaluate(x);
for (size_t i = 0; i < 15; i++)
if (x[i]) marginals[i] += px;
// calculate shortcut 8 and 0
if (x[12] && x[14]) joint_12_14 += px;
if (x[9] && x[12] & x[14]) joint_9_12_14 += px;
if (x[8] && x[12] & x[14]) joint_8_12_14 += px;
if (x[8] && x[12]) joint_8_12 += px;
if (x[8] && x[2]) joint82 += px;
if (x[1] && x[2]) joint12 += px;
if (x[2] && x[4]) joint24 += px;
if (x[4] && x[5]) joint45 += px;
if (x[4] && x[6]) joint46 += px;
if (x[4] && x[11]) joint_4_11 += px;
}
DiscreteFactor::Values all1 = allPosbValues.back();
// check separator marginal P(S0)
auto c = (*bayesTree)[0];
DiscreteFactorGraph separatorMarginal0 =
c->separatorMarginal(EliminateDiscrete);
EXPECT_DOUBLES_EQUAL(joint_8_12, separatorMarginal0(all1), 1e-9);
// // check separator marginal P(S9), should be P(14)
// c = (*bayesTree)[9];
// DiscreteFactorGraph separatorMarginal9 =
// c->separatorMarginal(EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(marginals[14], separatorMarginal9(all1), 1e-9);
// // check separator marginal of root, should be empty
// c = (*bayesTree)[11];
// DiscreteFactorGraph separatorMarginal11 =
// c->separatorMarginal(EliminateDiscrete);
// EXPECT_LONGS_EQUAL(0, separatorMarginal11.size());
// // check shortcut P(S9||R) to root
// c = (*bayesTree)[9];
// DiscreteBayesNet shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_LONGS_EQUAL(0, shortcut.size());
// // check shortcut P(S8||R) to root
// c = (*bayesTree)[8];
// shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_12_14 / marginals[14], evaluate(shortcut, all1),
// 1e-9);
// // check shortcut P(S2||R) to root
// c = (*bayesTree)[2];
// shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_9_12_14 / marginals[14], evaluate(shortcut,
// all1),
// 1e-9);
// // check shortcut P(S0||R) to root
// c = (*bayesTree)[0];
// shortcut = c->shortcut(R, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_8_12_14 / marginals[14], evaluate(shortcut,
// all1),
// 1e-9);
// // calculate all shortcuts to root
// DiscreteBayesTree::Nodes cliques = bayesTree->nodes();
// for (auto c : cliques) {
// DiscreteBayesNet shortcut = c->shortcut(R, EliminateDiscrete);
// if (debug) {
// c->printSignature();
// shortcut.print("shortcut:");
// }
// }
// // Check all marginals
// DiscreteFactor::shared_ptr marginalFactor;
// for (size_t i = 0; i < 15; i++) {
// marginalFactor = bayesTree->marginalFactor(i, EliminateDiscrete);
// double actual = (*marginalFactor)(all1);
// EXPECT_DOUBLES_EQUAL(marginals[i], actual, 1e-9);
// }
// DiscreteBayesNet::shared_ptr actualJoint;
// Check joint P(8,2) TODO: not disjoint !
// actualJoint = bayesTree->jointBayesNet(8, 2, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint82, evaluate(*actualJoint,all1), 1e-9);
// Check joint P(1,2) TODO: not disjoint !
// actualJoint = bayesTree->jointBayesNet(1, 2, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint12, evaluate(*actualJoint,all1), 1e-9);
// Check joint P(2,4)
// actualJoint = bayesTree->jointBayesNet(2, 4, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint24, evaluate(*actualJoint, all1), 1e-9);
// Check joint P(4,5) TODO: not disjoint !
// actualJoint = bayesTree->jointBayesNet(4, 5, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint46, evaluate(*actualJoint,all1), 1e-9);
// Check joint P(4,6) TODO: not disjoint !
// actualJoint = bayesTree->jointBayesNet(4, 6, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint46, evaluate(*actualJoint,all1), 1e-9);
// Check joint P(4,11)
// actualJoint = bayesTree->jointBayesNet(4, 11, EliminateDiscrete);
// EXPECT_DOUBLES_EQUAL(joint_4_11, evaluate(*actualJoint, all1), 1e-9);
}
/* ************************************************************************* */
int main() {
@ -263,4 +247,3 @@ int main() {
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */