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/**
* @file testConstrainedLinearFactorGraph.cpp
* @author Alex Cunningham
*/
#include <iostream>
#include <CppUnitLite/TestHarness.h>
#include "ConstrainedLinearFactorGraph.h"
#include "LinearFactorGraph.h"
#include "Ordering.h"
#include "smallExample.h"
using namespace gtsam;
using namespace std;
/* ************************************************************************* */
TEST( ConstrainedLinearFactorGraph, elimination1 )
{
// get the graph
// *-X-x-Y
ConstrainedLinearFactorGraph fg = createSingleConstraintGraph();
// verify construction of the graph
CHECK(fg.size() == 2);
// eliminate x
Ordering ord;
ord.push_back("x");
GaussianBayesNet::shared_ptr cbn = fg.eliminate(ord);
// verify result of elimination
// CBN of size 1, as we only eliminated X now
CHECK(fg.size() == 1);
CHECK(cbn->size() == 1);
// We will have a "delta function" on X as a function of Y
// |1 2||x_1| = |1| - |10 0||y_1|
// |2 1||x_2| |2| |0 10||y_2|
Matrix Ax1(2, 2);
Ax1(0, 0) = 1.0; Ax1(0, 1) = 2.0;
Ax1(1, 0) = 2.0; Ax1(1, 1) = 1.0;
Matrix Ay1 = eye(2) * 10;
Vector b2 = Vector_(2, 1.0, 2.0);
ConstrainedConditionalGaussian expectedCCG1("x",b2, Ax1, "y", Ay1);
CHECK(expectedCCG1.equals(*((*cbn)["x"])));
// verify remaining factor on y
// Gaussian factor on X becomes different Gaussian factor on Y
Matrix Ap(2,2);
Ap(0, 0) = 1.0; Ap(0, 1) = -2.0;
Ap(1, 0) = -2.0; Ap(1, 1) = 1.0;
Ap = 33.3333 * Ap;
Vector bp = Vector_(2, 0.0, -10.0);
double sigma1 = 1;
LinearFactor expectedLF("y", Ap, bp,sigma1);
CHECK(expectedLF.equals(*(fg[0]), 1e-4));
// eliminate y
Ordering ord2;
ord2.push_back("y");
cbn = fg.eliminate(ord2);
// Check result
CHECK(fg.size() == 0);
Matrix R(2,2);
R(0, 0) = 74.5356; R(0, 1) = -59.6285;
R(1, 0) = 0.0; R(1, 1) = 44.7214;
Vector br = Vector_(2, 8.9443, 4.4721);
Vector tau(2);
tau(0) = R(0,0);
tau(1) = R(1,1);
// normalize the existing matrices
Matrix N = eye(2,2);
N(0,0) = 1/tau(0);
N(1,1) = 1/tau(1);
R = N*R;
ConditionalGaussian expected2("y",br, R, tau);
CHECK(expected2.equals(*((*cbn)["y"])));
}
/* ************************************************************************* */
TEST( ConstrainedLinearFactorGraph, optimize )
{
// create graph
ConstrainedLinearFactorGraph fg = createSingleConstraintGraph();
// perform optimization
Ordering ord;
ord.push_back("y");
ord.push_back("x");
VectorConfig actual = fg.optimize(ord);
VectorConfig expected;
expected.insert("x", Vector_(2, 1.0, -1.0));
expected.insert("y", Vector_(2, 0.2, 0.1));
CHECK(expected.size() == actual.size());
CHECK(assert_equal(expected["x"], actual["x"], 1e-4));
CHECK(assert_equal(expected["y"], actual["y"], 1e-4));
}
/* ************************************************************************* */
TEST( ConstrainedLinearFactorGraph, optimize2 )
{
// create graph
ConstrainedLinearFactorGraph fg = createSingleConstraintGraph();
// perform optimization
Ordering ord;
ord.push_back("x");
ord.push_back("y");
VectorConfig actual = fg.optimize(ord);
VectorConfig expected;
expected.insert("x", Vector_(2, 1.0, -1.0));
expected.insert("y", Vector_(2, 0.2, 0.1));
CHECK(expected.size() == actual.size());
CHECK(assert_equal(expected["x"], actual["x"], 1e-4)); // Fails here: gets x = (-3, 1)
CHECK(assert_equal(expected["y"], actual["y"], 1e-4));
}
/* ************************************************************************* */
TEST( ConstrainedLinearFactorGraph, is_constrained )
{
// very simple check
ConstrainedLinearFactorGraph fg;
CHECK(!fg.is_constrained("x"));
// create simple graph
Vector b = Vector_(2, 0.0, 0.0);
LinearFactor::shared_ptr f1(new LinearFactor("x", eye(2), "y", eye(2), b,1));
LinearFactor::shared_ptr f2(new LinearFactor("z", eye(2), "w", eye(2), b,1));
LinearConstraint::shared_ptr f3(new LinearConstraint("y", eye(2), "z", eye(2), b));
fg.push_back(f1);
fg.push_back(f2);
fg.push_back_constraint(f3);
CHECK(fg.is_constrained("y"));
CHECK(fg.is_constrained("z"));
CHECK(!fg.is_constrained("x"));
CHECK(!fg.is_constrained("w"));
}
/* ************************************************************************* */
TEST( ConstrainedLinearFactorGraph, get_constraint_separator )
{
ConstrainedLinearFactorGraph fg1 = createMultiConstraintGraph();
ConstrainedLinearFactorGraph fg2 = createMultiConstraintGraph();
LinearConstraint::shared_ptr lc1 = fg1.constraint_at(0);
LinearConstraint::shared_ptr lc2 = fg1.constraint_at(1);
vector<LinearConstraint::shared_ptr> actual1 = fg1.find_constraints_and_remove("y");
CHECK(fg1.size() == 2);
CHECK(actual1.size() == 1);
CHECK((*actual1.begin())->equals(*lc1));
vector<LinearConstraint::shared_ptr> actual2 = fg2.find_constraints_and_remove("x");
CHECK(fg2.size() == 1);
CHECK(actual2.size() == 2);
CHECK((*actual1.begin())->equals(*lc1));
LinearConstraint::shared_ptr act = *(++actual2.begin());
CHECK(act->equals(*lc2));
}
/* ************************************************************************* */
TEST( ConstrainedLinearFactorGraph, update_constraints )
{
// create a graph
ConstrainedLinearFactorGraph fg1 = createMultiConstraintGraph();
// process constraints - picking first constraint on x
vector<LinearConstraint::shared_ptr> constraints = fg1.find_constraints_and_remove("x");
CHECK(constraints.size() == 2);
CHECK(fg1.size() == 1); // both constraints removed
LinearConstraint::shared_ptr primary = constraints[0];
LinearConstraint::shared_ptr updatee = constraints[1];
fg1.update_constraints("x", constraints, primary);
CHECK(fg1.size() == 2); // induced constraint added back
// expected induced constraint
Matrix Ar(2,2);
Ar(0, 0) = -16.6666; Ar(0, 1) = -6.6666;
Ar(1, 0) = 10.0; Ar(1, 1) = 0.0;
Matrix A22(2,2);
A22(0,0) = 1.0 ; A22(0,1) = 1.0;
A22(1,0) = 1.0 ; A22(1,1) = 2.0;
Vector br = Vector_(2, 0.0, 5.0);
LinearConstraint::shared_ptr exp(new LinearConstraint("y", Ar, "z", A22, br));
// evaluate
CHECK(assert_equal(*(fg1.constraint_at(0)), *exp, 1e-4));
}
/* ************************************************************************* */
TEST( ConstrainedLinearFactorGraph, find_constraints_and_remove )
{
// constraint 1
Matrix A11(2,2);
A11(0,0) = 1.0 ; A11(0,1) = 2.0;
A11(1,0) = 2.0 ; A11(1,1) = 1.0;
Matrix A12(2,2);
A12(0,0) = 10.0 ; A12(0,1) = 0.0;
A12(1,0) = 0.0 ; A12(1,1) = 10.0;
Vector b1(2);
b1(0) = 1.0; b1(1) = 2.0;
LinearConstraint::shared_ptr lc1(new LinearConstraint("x", A11, "y", A12, b1));
// constraint 2
Matrix A21(2,2);
A21(0,0) = 3.0 ; A21(0,1) = 4.0;
A21(1,0) = -1.0 ; A21(1,1) = -2.0;
Matrix A22(2,2);
A22(0,0) = 1.0 ; A22(0,1) = 1.0;
A22(1,0) = 1.0 ; A22(1,1) = 2.0;
Vector b2(2);
b2(0) = 3.0; b2(1) = 4.0;
LinearConstraint::shared_ptr lc2(new LinearConstraint("x", A21, "z", A22, b2));
// construct the graph
ConstrainedLinearFactorGraph fg1;
fg1.push_back_constraint(lc1);
fg1.push_back_constraint(lc2);
// constraints on x
vector<LinearConstraint::shared_ptr> expected1, actual1;
expected1.push_back(lc1);
expected1.push_back(lc2);
actual1 = fg1.find_constraints_and_remove("x");
CHECK(fg1.size() == 0);
CHECK(expected1.size() == actual1.size());
vector<LinearConstraint::shared_ptr>::const_iterator exp1, act1;
for(exp1=expected1.begin(), act1=actual1.begin(); act1 != actual1.end(); ++act1, ++exp1) {
CHECK((*exp1)->equals(**act1));
}
}
/* ************************************************************************* */
TEST( ConstrainedLinearFactorGraph, eliminate_multi_constraint )
{
ConstrainedLinearFactorGraph fg = createMultiConstraintGraph();
// eliminate the constraint
ConstrainedConditionalGaussian::shared_ptr cg1 = fg.eliminate_constraint("x");
CHECK(cg1->nrParents() == 1);
CHECK(fg.nrFactors() == 1);
// eliminate the induced constraint
ConstrainedConditionalGaussian::shared_ptr cg2 = fg.eliminate_constraint("y");
CHECK(cg2->nrParents() == 1);
CHECK(fg.nrFactors() == 0);
// eliminate the linear factor
ConditionalGaussian::shared_ptr cg3 = fg.eliminateOne<ConditionalGaussian>("z");
CHECK(cg3->nrParents() == 0);
CHECK(fg.size() == 0);
// solve piecewise
VectorConfig actual;
Vector act_z = cg3->solve(actual);
actual.insert("z", act_z);
CHECK(assert_equal(act_z, Vector_(2, -4.0, 5.0), 1e-4));
Vector act_y = cg2->solve(actual);
actual.insert("y", act_y);
CHECK(assert_equal(act_y, Vector_(2, -0.1, 0.4), 1e-4));
Vector act_x = cg1->solve(actual);
CHECK(assert_equal(act_x, Vector_(2, -2.0, 2.0), 1e-4));
}
/* ************************************************************************* */
TEST( ConstrainedLinearFactorGraph, optimize_multi_constraint )
{
ConstrainedLinearFactorGraph fg = createMultiConstraintGraph();
// solve the graph
Ordering ord;
ord.push_back("x");
ord.push_back("y");
ord.push_back("z");
VectorConfig actual = fg.optimize(ord);
// verify
VectorConfig expected;
expected.insert("x", Vector_(2, -2.0, 2.0));
expected.insert("y", Vector_(2, -0.1, 0.4));
expected.insert("z", Vector_(2, -4.0, 5.0));
CHECK(expected.size() == actual.size());
CHECK(assert_equal(expected["x"], actual["x"], 1e-4));
CHECK(assert_equal(expected["y"], actual["y"], 1e-4));
CHECK(assert_equal(expected["z"], actual["z"], 1e-4));
}
/* ************************************************************************* */
// OLD TESTS - should be ported into the new structure when possible
/* ************************************************************************* */
/* ************************************************************************* */
//TEST( ConstrainedLinearFactorGraph, basic )
//{
// ConstrainedLinearFactorGraph fg = createConstrainedLinearFactorGraph();
//
// // expected equality factor
// Vector v1(2); v1(0)=1.;v1(1)=2.;
// LinearConstraint::shared_ptr f1(new LinearConstraint(v1, "x0"));
//
// // expected normal linear factor
// Matrix A21(2,2);
// A21(0,0) = -10 ; A21(0,1) = 0;
// A21(1,0) = 0 ; A21(1,1) = -10;
//
// Matrix A22(2,2);
// A22(0,0) = 10 ; A22(0,1) = 0;
// A22(1,0) = 0 ; A22(1,1) = 10;
//
// Vector b(2);
// b(0) = 20 ; b(1) = 30;
//
// LinearFactor::shared_ptr f2(new LinearFactor("x0", A21, "x1", A22, b));
//
// CHECK(f2->equals(*(fg[0])));
// CHECK(f1->equals(*(fg.eq_at(0))));
//}
//TEST ( ConstrainedLinearFactorGraph, copy )
//{
// LinearFactorGraph lfg = createLinearFactorGraph();
// LinearFactor::shared_ptr f1 = lfg[0];
// LinearFactor::shared_ptr f2 = lfg[1];
// LinearFactor::shared_ptr f3 = lfg[2];
// LinearFactor::shared_ptr f4 = lfg[3];
//
// ConstrainedLinearFactorGraph actual(lfg);
//
// ConstrainedLinearFactorGraph expected;
// expected.push_back(f1);
// expected.push_back(f2);
// expected.push_back(f3);
// expected.push_back(f4);
//
// CHECK(actual.equals(expected));
//}
//
//TEST( ConstrainedLinearFactorGraph, equals )
//{
// // basic equality test
// ConstrainedLinearFactorGraph fg = createConstrainedLinearFactorGraph();
// ConstrainedLinearFactorGraph fg2 = createConstrainedLinearFactorGraph();
// CHECK( fg.equals(fg2) );
//
// // ensuring that equality factors are compared
// LinearFactor::shared_ptr f2 = fg[0]; // get a linear factor from existing graph
// ConstrainedLinearFactorGraph fg3;
// fg3.push_back(f2);
// CHECK( !fg3.equals(fg) );
//}
//
//TEST( ConstrainedLinearFactorGraph, size )
//{
// LinearFactorGraph lfg = createLinearFactorGraph();
// ConstrainedLinearFactorGraph fg1(lfg);
//
// CHECK(fg1.size() == lfg.size());
//
// ConstrainedLinearFactorGraph fg2 = createConstrainedLinearFactorGraph();
//
// CHECK(fg2.size() == 2);
//}
//
//TEST( ConstrainedLinearFactorGraph, is_constrained )
//{
// ConstrainedLinearFactorGraph fg = createConstrainedLinearFactorGraph();
//
// CHECK(fg.is_constrained("x0"));
// CHECK(!fg.is_constrained("x1"));
//}
//
//TEST( ConstrainedLinearFactorGraph, optimize )
//{
// ConstrainedLinearFactorGraph fg1 = createConstrainedLinearFactorGraph();
// ConstrainedLinearFactorGraph fg2 = createConstrainedLinearFactorGraph();
//
// VectorConfig expected = createConstrainedConfig();
//
// Ordering ord1;
// ord1.push_back("x0");
// ord1.push_back("x1");
//
// Ordering ord2;
// ord2.push_back("x1");
// ord2.push_back("x0");
//
// VectorConfig actual1 = fg1.optimize(ord1);
// VectorConfig actual2 = fg2.optimize(ord2);
//
// CHECK(actual1.equals(expected));
// CHECK(actual1.equals(actual2));
//}
//
//TEST (ConstrainedLinearFactorGraph, eliminate )
//{
// ConstrainedLinearFactorGraph fg = createConstrainedLinearFactorGraph();
// VectorConfig c = createConstrainedConfig();
//
// Ordering ord1;
// ord1.push_back("x0");
// ord1.push_back("x1");
//
// ConstrainedGaussianBayesNet::shared_ptr actual = fg.eliminate(ord1);
//
// // create an expected bayes net
// ConstrainedGaussianBayesNet::shared_ptr expected(new ConstrainedGaussianBayesNet);
//
// ConstrainedConditionalGaussian::shared_ptr d(new ConstrainedConditionalGaussian);//(c["x0"], "x0"));
// expected->insert_df("x0", d);
//
// Matrix A = eye(2);
// double sigma = 0.1;
// Vector dv = c["x1"];
// ConditionalGaussian::shared_ptr cg(new ConditionalGaussian(dv/sigma, A/sigma));
// expected->insert("x1", cg);
//
// CHECK(actual->equals(*expected));
//}
//
//TEST (ConstrainedLinearFactorGraph, baseline_optimize)
//{
// // tests performance when there are no equality factors in the graph
// LinearFactorGraph lfg = createLinearFactorGraph();
// ConstrainedLinearFactorGraph clfg(lfg); // copy in the linear factor graph
//
// Ordering ord;
// ord.push_back("l1");
// ord.push_back("x1");
// ord.push_back("x2");
//
// VectorConfig actual = clfg.optimize(ord);
//
// VectorConfig expected = lfg.optimize(ord); // should be identical to regular lfg optimize
//
// CHECK(actual.equals(expected));
//}
//
//TEST (ConstrainedLinearFactorGraph, baseline_eliminate_one )
//{
// LinearFactorGraph fg = createLinearFactorGraph();
// ConstrainedLinearFactorGraph cfg(fg);
//
// ConditionalGaussian::shared_ptr actual = cfg.eliminate_one("x1");
//
// // create expected Conditional Gaussian
// Matrix R11 = Matrix_(2,2,
// 15.0, 00.0,
// 00.0, 15.0
// );
// Matrix S12 = Matrix_(2,2,
// -1.66667, 0.00,
// +0.00,-1.66667
// );
// Matrix S13 = Matrix_(2,2,
// -6.66667, 0.00,
// +0.00,-6.66667
// );
// Vector d(2); d(0) = -2; d(1) = -1.0/3.0;
// ConditionalGaussian expected(d,R11,"l1",S12,"x2",S13);
//
// CHECK( actual->equals(expected) );
//}
//
//TEST (ConstrainedLinearFactorGraph, eliminate_constraint)
//{
//// ConstrainedLinearFactorGraph fg = createConstrainedLinearFactorGraph();
//// ConstrainedConditionalGaussian::shared_ptr actual = fg.eliminate_constraint("x0");
////
//// VectorConfig c = createConstrainedConfig();
//// ConstrainedConditionalGaussian::shared_ptr expected(new ConstrainedConditionalGaussian);//(c["x0"], "x0"));
////
//// CHECK(assert_equal(*actual, *expected)); // check output for correct delta function
////
//// CHECK(fg.size() == 1); // check size
////
//// ConstrainedLinearFactorGraph::eq_const_iterator eit = fg.eq_begin();
//// CHECK(eit == fg.eq_end()); // ensure no remaining equality factors
////
//// // verify the remaining factor - should be a unary factor on x1
//// ConstrainedLinearFactorGraph::const_iterator it = fg.begin();
//// LinearFactor::shared_ptr factor_actual = *it;
////
//// CHECK(factor_actual->size() == 1);
//}
//
//TEST (ConstrainedLinearFactorGraph, constraintCombineAndEliminate )
//{
// // create a set of factors
// ConstrainedLinearFactorGraph fg = createConstrainedLinearFactorGraph();
// LinearConstraint::shared_ptr eq = fg.eq_at(0);
// LinearFactor::shared_ptr f1 = fg[0];
//
// // make a joint linear factor
// set<LinearFactor::shared_ptr> f1_set;
// f1_set.insert(f1);
// boost::shared_ptr<LinearFactor> joined(new LinearFactor(f1_set));
//
// // create a sample graph
// ConstrainedLinearFactorGraph graph;
//
// // combine linear factor and eliminate
// graph.constraintCombineAndEliminate(*eq, *joined);
//
// // verify structure
// CHECK(graph.size() == 1); // will have only one factor
// LinearFactor::shared_ptr actual = graph[0];
// CHECK(actual->size() == 1); // remaining factor will be unary
//
// // verify values
// VectorConfig c = createConstrainedConfig();
// Vector exp_v = c["x1"];
// Matrix A = actual->get_A("x1");
// Vector b = actual->get_b();
// Vector act_v = backsubstitution(A, b);
// CHECK(assert_equal(act_v, exp_v));
//}
//
//TEST (ConstrainedLinearFactorGraph, extract_eq)
//{
// ConstrainedLinearFactorGraph fg = createConstrainedLinearFactorGraph();
// LinearConstraint::shared_ptr actual = fg.extract_eq("x0");
//
// Vector v1(2); v1(0)=1.;v1(1)=2.;
// LinearConstraint::shared_ptr expected(new LinearConstraint(v1, "x0"));
//
// // verify output
// CHECK(assert_equal(*actual, *expected));
//
// // verify removal
// ConstrainedLinearFactorGraph::eq_const_iterator it = fg.eq_begin();
// CHECK(it == fg.eq_end());
//
// // verify full size
// CHECK(fg.size() == 1);
//}
//
//TEST( ConstrainedLinearFactorGraph, GET_ORDERING)
//{
// ConstrainedLinearFactorGraph fg = createConstrainedLinearFactorGraph();
// Ordering ord = fg.getOrdering();
// CHECK(ord[0] == string("x0"));
// CHECK(ord[1] == string("x1"));
//}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */
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