551 lines
17 KiB
C++
551 lines
17 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file testGaussianConditional.cpp
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* @brief Unit tests for Conditional gaussian
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* @author Christian Potthast
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**/
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#include <CppUnitLite/TestHarness.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <gtsam/base/Matrix.h>
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#include <gtsam/base/VerticalBlockMatrix.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/linear/JacobianFactor.h>
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#include <gtsam/linear/GaussianConditional.h>
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#include <gtsam/linear/GaussianDensity.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <boost/assign/std/list.hpp>
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#include <boost/assign/std/vector.hpp>
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#include <boost/assign/list_inserter.hpp>
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#include <boost/make_shared.hpp>
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#include <boost/assign/list_of.hpp>
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#include <iostream>
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#include <sstream>
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#include <vector>
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using namespace gtsam;
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using namespace std;
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using namespace boost::assign;
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using symbol_shorthand::X;
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using symbol_shorthand::Y;
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static const double tol = 1e-5;
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static Matrix R = (Matrix(2, 2) <<
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-12.1244, -5.1962,
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0., 4.6904).finished();
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/* ************************************************************************* */
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TEST(GaussianConditional, constructor)
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{
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Matrix S1 = (Matrix(2, 2) <<
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-5.2786, -8.6603,
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5.0254, 5.5432).finished();
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Matrix S2 = (Matrix(2, 2) <<
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-10.5573, -5.9385,
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5.5737, 3.0153).finished();
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Matrix S3 = (Matrix(2, 2) <<
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-11.3820, -7.2581,
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-3.0153, -3.5635).finished();
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Vector d = Vector2(1.0, 2.0);
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SharedDiagonal s = noiseModel::Diagonal::Sigmas(Vector2(3.0, 4.0));
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vector<pair<Key, Matrix> > terms = pair_list_of
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(1, R)
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(3, S1)
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(5, S2)
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(7, S3);
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GaussianConditional actual(terms, 1, d, s);
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GaussianConditional::const_iterator it = actual.beginFrontals();
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EXPECT(assert_equal(Key(1), *it));
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EXPECT(assert_equal(R, actual.R()));
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++ it;
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EXPECT(it == actual.endFrontals());
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it = actual.beginParents();
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EXPECT(assert_equal(Key(3), *it));
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EXPECT(assert_equal(S1, actual.S(it)));
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++ it;
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EXPECT(assert_equal(Key(5), *it));
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EXPECT(assert_equal(S2, actual.S(it)));
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++ it;
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EXPECT(assert_equal(Key(7), *it));
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EXPECT(assert_equal(S3, actual.S(it)));
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++it;
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EXPECT(it == actual.endParents());
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EXPECT(assert_equal(d, actual.d()));
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EXPECT(assert_equal(*s, *actual.get_model()));
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// test copy constructor
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GaussianConditional copied(actual);
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EXPECT(assert_equal(d, copied.d()));
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EXPECT(assert_equal(*s, *copied.get_model()));
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EXPECT(assert_equal(R, copied.R()));
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}
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/* ************************************************************************* */
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TEST( GaussianConditional, equals )
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{
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// create a conditional gaussian node
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Matrix A1(2,2);
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A1(0,0) = 1 ; A1(1,0) = 2;
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A1(0,1) = 3 ; A1(1,1) = 4;
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Matrix A2(2,2);
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A2(0,0) = 6 ; A2(1,0) = 0.2;
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A2(0,1) = 8 ; A2(1,1) = 0.4;
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Matrix R(2,2);
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R(0,0) = 0.1 ; R(1,0) = 0.3;
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R(0,1) = 0.0 ; R(1,1) = 0.34;
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SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector2(1.0, 0.34));
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Vector d = Vector2(0.2, 0.5);
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GaussianConditional
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expected(1, d, R, 2, A1, 10, A2, model),
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actual(1, d, R, 2, A1, 10, A2, model);
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EXPECT( expected.equals(actual) );
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}
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/* ************************************************************************* */
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namespace density {
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static const Key key = 77;
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static constexpr double sigma = 3.0;
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static const auto unitPrior =
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GaussianConditional(key, Vector1::Constant(5), I_1x1),
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widerPrior = GaussianConditional(
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key, Vector1::Constant(5), I_1x1,
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noiseModel::Isotropic::Sigma(1, sigma));
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} // namespace density
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/* ************************************************************************* */
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// Check that the evaluate function matches direct calculation with R.
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TEST(GaussianConditional, Evaluate1) {
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// Let's evaluate at the mean
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const VectorValues mean = density::unitPrior.solve(VectorValues());
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// We get the Hessian matrix, which has noise model applied!
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const Matrix invSigma = density::unitPrior.information();
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// A Gaussian density ~ exp (-0.5*(Rx-d)'*(Rx-d))
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// which at the mean is 1.0! So, the only thing we need to calculate is
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// the normalization constant 1.0/sqrt((2*pi*Sigma).det()).
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// The covariance matrix inv(Sigma) = R'*R, so the determinant is
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const double expected = sqrt((invSigma / (2 * M_PI)).determinant());
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const double actual = density::unitPrior.evaluate(mean);
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EXPECT_DOUBLES_EQUAL(expected, actual, 1e-9);
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using density::key;
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using density::sigma;
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// Let's numerically integrate and see that we integrate to 1.0.
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double integral = 0.0;
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// Loop from -5*sigma to 5*sigma in 0.1*sigma steps:
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for (double x = -5.0 * sigma; x <= 5.0 * sigma; x += 0.1 * sigma) {
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VectorValues xValues;
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xValues.insert(key, mean.at(key) + Vector1(x));
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const double density = density::unitPrior.evaluate(xValues);
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integral += 0.1 * sigma * density;
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}
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EXPECT_DOUBLES_EQUAL(1.0, integral, 1e-9);
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}
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/* ************************************************************************* */
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// Check the evaluate with non-unit noise.
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TEST(GaussianConditional, Evaluate2) {
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// See comments in test above.
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const VectorValues mean = density::widerPrior.solve(VectorValues());
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const Matrix R = density::widerPrior.R();
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const Matrix invSigma = density::widerPrior.information();
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const double expected = sqrt((invSigma / (2 * M_PI)).determinant());
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const double actual = density::widerPrior.evaluate(mean);
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EXPECT_DOUBLES_EQUAL(expected, actual, 1e-9);
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using density::key;
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using density::sigma;
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// Let's numerically integrate and see that we integrate to 1.0.
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double integral = 0.0;
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// Loop from -5*sigma to 5*sigma in 0.1*sigma steps:
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for (double x = -5.0 * sigma; x <= 5.0 * sigma; x += 0.1 * sigma) {
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VectorValues xValues;
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xValues.insert(key, mean.at(key) + Vector1(x));
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const double density = density::widerPrior.evaluate(xValues);
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integral += 0.1 * sigma * density;
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}
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EXPECT_DOUBLES_EQUAL(1.0, integral, 1e-5);
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}
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/* ************************************************************************* */
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TEST( GaussianConditional, solve )
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{
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//expected solution
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Vector expectedX(2);
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expectedX(0) = 20-3-11 ; expectedX(1) = 40-7-15;
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// create a conditional Gaussian node
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Matrix R = (Matrix(2, 2) << 1., 0.,
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0., 1.).finished();
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Matrix A1 = (Matrix(2, 2) << 1., 2.,
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3., 4.).finished();
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Matrix A2 = (Matrix(2, 2) << 5., 6.,
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7., 8.).finished();
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Vector d(2); d << 20.0, 40.0;
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GaussianConditional cg(1, d, R, 2, A1, 10, A2);
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Vector sx1(2); sx1 << 1.0, 1.0;
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Vector sl1(2); sl1 << 1.0, 1.0;
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VectorValues expected = map_list_of
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(1, expectedX)
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(2, sx1)
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(10, sl1);
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VectorValues solution = map_list_of
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(2, sx1) // parents
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(10, sl1);
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solution.insert(cg.solve(solution));
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EXPECT(assert_equal(expected, solution, tol));
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}
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/* ************************************************************************* */
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TEST( GaussianConditional, solve_simple )
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{
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// 2 variables, frontal has dim=4
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VerticalBlockMatrix blockMatrix(list_of(4)(2)(1), 4);
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blockMatrix.matrix() <<
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1.0, 0.0, 2.0, 0.0, 3.0, 0.0, 0.1,
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0.0, 1.0, 0.0, 2.0, 0.0, 3.0, 0.2,
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0.0, 0.0, 3.0, 0.0, 4.0, 0.0, 0.3,
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0.0, 0.0, 0.0, 3.0, 0.0, 4.0, 0.4;
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// solve system as a non-multifrontal version first
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GaussianConditional cg(list_of(1)(2), 1, blockMatrix);
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// partial solution
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Vector sx1 = Vector2(9.0, 10.0);
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// elimination order: 1, 2
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VectorValues actual = map_list_of
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(2, sx1); // parent
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VectorValues expected = map_list_of<Key, Vector>
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(2, sx1)
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(1, (Vector(4) << -3.1,-3.4,-11.9,-13.2).finished());
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// verify indices/size
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EXPECT_LONGS_EQUAL(2, (long)cg.size());
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EXPECT_LONGS_EQUAL(4, (long)cg.rows());
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// solve and verify
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actual.insert(cg.solve(actual));
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EXPECT(assert_equal(expected, actual, tol));
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}
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/* ************************************************************************* */
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TEST( GaussianConditional, solve_multifrontal )
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{
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// create full system, 3 variables, 2 frontals, all 2 dim
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VerticalBlockMatrix blockMatrix(list_of(2)(2)(2)(1), 4);
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blockMatrix.matrix() <<
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1.0, 0.0, 2.0, 0.0, 3.0, 0.0, 0.1,
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0.0, 1.0, 0.0, 2.0, 0.0, 3.0, 0.2,
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0.0, 0.0, 3.0, 0.0, 4.0, 0.0, 0.3,
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0.0, 0.0, 0.0, 3.0, 0.0, 4.0, 0.4;
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// 3 variables, all dim=2
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GaussianConditional cg(list_of(1)(2)(10), 2, blockMatrix);
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EXPECT(assert_equal(Vector(blockMatrix.full().rightCols(1)), cg.d()));
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// partial solution
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Vector sl1 = Vector2(9.0, 10.0);
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// elimination order; _x_, _x1_, _l1_
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VectorValues actual = map_list_of
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(10, sl1); // parent
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VectorValues expected = map_list_of<Key, Vector>
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(1, Vector2(-3.1,-3.4))
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(2, Vector2(-11.9,-13.2))
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(10, sl1);
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// verify indices/size
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EXPECT_LONGS_EQUAL(3, (long)cg.size());
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EXPECT_LONGS_EQUAL(4, (long)cg.rows());
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// solve and verify
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actual.insert(cg.solve(actual));
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EXPECT(assert_equal(expected, actual, tol));
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}
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/* ************************************************************************* */
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TEST( GaussianConditional, solveTranspose ) {
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/** create small Chordal Bayes Net x <- y
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* x y d
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* 1 1 9
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* 1 5
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*/
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Matrix R11 = (Matrix(1, 1) << 1.0).finished(), S12 = (Matrix(1, 1) << 1.0).finished();
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Matrix R22 = (Matrix(1, 1) << 1.0).finished();
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Vector d1(1), d2(1);
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d1(0) = 9;
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d2(0) = 5;
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// define nodes and specify in reverse topological sort (i.e. parents last)
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GaussianBayesNet cbn = list_of
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(GaussianConditional(1, d1, R11, 2, S12))
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(GaussianConditional(1, d2, R22));
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// x=R'*y, y=inv(R')*x
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// 2 = 1 2
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// 5 1 1 3
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VectorValues
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x = map_list_of<Key, Vector>
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(1, (Vector(1) << 2.).finished())
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(2, (Vector(1) << 5.).finished()),
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y = map_list_of<Key, Vector>
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(1, (Vector(1) << 2.).finished())
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(2, (Vector(1) << 3.).finished());
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// test functional version
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VectorValues actual = cbn.backSubstituteTranspose(x);
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CHECK(assert_equal(y, actual));
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}
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/* ************************************************************************* */
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TEST( GaussianConditional, information ) {
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// Create R matrix
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Matrix R(4,4); R <<
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1, 2, 3, 4,
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0, 5, 6, 7,
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0, 0, 8, 9,
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0, 0, 0, 10;
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// Create conditional
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GaussianConditional conditional(0, Vector::Zero(4), R);
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// Expected information matrix (using permuted R)
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Matrix IExpected = R.transpose() * R;
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// Actual information matrix (conditional should permute R)
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Matrix IActual = conditional.information();
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EXPECT(assert_equal(IExpected, IActual));
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}
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/* ************************************************************************* */
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TEST( GaussianConditional, isGaussianFactor ) {
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// Create R matrix
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Matrix R(4,4); R <<
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1, 2, 3, 4,
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0, 5, 6, 7,
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0, 0, 8, 9,
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0, 0, 0, 10;
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// Create a conditional
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GaussianConditional conditional(0, Vector::Zero(4), R);
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// Expected information matrix computed by conditional
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Matrix IExpected = conditional.information();
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// Expected information matrix computed by a factor
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JacobianFactor jf = conditional;
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Matrix IActual = jf.information();
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EXPECT(assert_equal(IExpected, IActual));
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}
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/* ************************************************************************* */
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// Test FromMeanAndStddev named constructors
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TEST(GaussianConditional, FromMeanAndStddev) {
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Matrix A1 = (Matrix(2, 2) << 1., 2., 3., 4.).finished();
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Matrix A2 = (Matrix(2, 2) << 5., 6., 7., 8.).finished();
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const Vector2 b(20, 40), x0(1, 2), x1(3, 4), x2(5, 6);
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const double sigma = 3;
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VectorValues values = map_list_of(X(0), x0)(X(1), x1)(X(2), x2);
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auto conditional1 =
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GaussianConditional::FromMeanAndStddev(X(0), A1, X(1), b, sigma);
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Vector2 e1 = (x0 - (A1 * x1 + b)) / sigma;
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double expected1 = 0.5 * e1.dot(e1);
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EXPECT_DOUBLES_EQUAL(expected1, conditional1.error(values), 1e-9);
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auto conditional2 = GaussianConditional::FromMeanAndStddev(X(0), A1, X(1), A2,
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X(2), b, sigma);
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Vector2 e2 = (x0 - (A1 * x1 + A2 * x2 + b)) / sigma;
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double expected2 = 0.5 * e2.dot(e2);
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EXPECT_DOUBLES_EQUAL(expected2, conditional2.error(values), 1e-9);
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}
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/* ************************************************************************* */
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// Test likelihood method (conversion to JacobianFactor)
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TEST(GaussianConditional, likelihood) {
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Matrix A1 = (Matrix(2, 2) << 1., 2., 3., 4.).finished();
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const Vector2 b(20, 40), x0(1, 2);
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const double sigma = 0.01;
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// |x0 - A1 x1 - b|^2
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auto conditional =
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GaussianConditional::FromMeanAndStddev(X(0), A1, X(1), b, sigma);
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VectorValues frontalValues;
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frontalValues.insert(X(0), x0);
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auto actual1 = conditional.likelihood(frontalValues);
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CHECK(actual1);
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// |(-A1) x1 - (b - x0)|^2
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JacobianFactor expected(X(1), -A1, b - x0,
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noiseModel::Isotropic::Sigma(2, sigma));
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EXPECT(assert_equal(expected, *actual1, tol));
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// Check single vector version
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auto actual2 = conditional.likelihood(x0);
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CHECK(actual2);
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EXPECT(assert_equal(expected, *actual2, tol));
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}
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/* ************************************************************************* */
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// Test sampling
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TEST(GaussianConditional, sample) {
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Matrix A1 = (Matrix(2, 2) << 1., 2., 3., 4.).finished();
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const Vector2 b(20, 40), x1(3, 4);
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const double sigma = 0.01;
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auto density = GaussianDensity::FromMeanAndStddev(X(0), b, sigma);
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auto actual1 = density.sample();
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EXPECT_LONGS_EQUAL(1, actual1.size());
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EXPECT(assert_equal(b, actual1[X(0)], 50 * sigma));
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VectorValues given;
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given.insert(X(1), x1);
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auto conditional =
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GaussianConditional::FromMeanAndStddev(X(0), A1, X(1), b, sigma);
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auto actual2 = conditional.sample(given);
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EXPECT_LONGS_EQUAL(1, actual2.size());
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EXPECT(assert_equal(A1 * x1 + b, actual2[X(0)], 50 * sigma));
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// Use a specific random generator
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std::mt19937_64 rng(4242);
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auto actual3 = conditional.sample(given, &rng);
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EXPECT_LONGS_EQUAL(1, actual2.size());
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// regression is not repeatable across platforms/versions :-(
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// EXPECT(assert_equal(Vector2(31.0111856, 64.9850775), actual2[X(0)], 1e-5));
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}
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/* ************************************************************************* */
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TEST(GaussianConditional, LogNormalizationConstant) {
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// Create univariate standard gaussian conditional
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auto std_gaussian =
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GaussianConditional::FromMeanAndStddev(X(0), Vector1::Zero(), 1.0);
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VectorValues values;
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values.insert(X(0), Vector1::Zero());
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double logDensity = std_gaussian.logDensity(values);
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// Regression.
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// These values were computed by hand for a univariate standard gaussian.
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EXPECT_DOUBLES_EQUAL(-0.9189385332046727, logDensity, 1e-9);
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EXPECT_DOUBLES_EQUAL(0.3989422804014327, exp(logDensity), 1e-9);
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|
|
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// Similar test for multivariate gaussian but with sigma 2.0
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double sigma = 2.0;
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auto conditional = GaussianConditional::FromMeanAndStddev(X(0), Vector3::Zero(), sigma);
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VectorValues x;
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x.insert(X(0), Vector3::Zero());
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Matrix3 Sigma = I_3x3 * sigma * sigma;
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double expectedLogNormalizingConstant = log(1 / sqrt((2 * M_PI * Sigma).determinant()));
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|
|
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EXPECT_DOUBLES_EQUAL(expectedLogNormalizingConstant, conditional.logNormalizationConstant(), 1e-9);
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}
|
|
|
|
/* ************************************************************************* */
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TEST(GaussianConditional, Print) {
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Matrix A1 = (Matrix(2, 2) << 1., 2., 3., 4.).finished();
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Matrix A2 = (Matrix(2, 2) << 5., 6., 7., 8.).finished();
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const Vector2 b(20, 40);
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const double sigma = 3;
|
|
|
|
GaussianConditional conditional(X(0), b, Matrix2::Identity(),
|
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noiseModel::Isotropic::Sigma(2, sigma));
|
|
|
|
// Test printing for no parents.
|
|
std::string expected =
|
|
"GaussianConditional p(x0)\n"
|
|
" R = [ 1 0 ]\n"
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" [ 0 1 ]\n"
|
|
" d = [ 20 40 ]\n"
|
|
" mean: 1 elements\n"
|
|
" x0: 20 40\n"
|
|
"isotropic dim=2 sigma=3\n";
|
|
EXPECT(assert_print_equal(expected, conditional, "GaussianConditional"));
|
|
|
|
auto conditional1 =
|
|
GaussianConditional::FromMeanAndStddev(X(0), A1, X(1), b, sigma);
|
|
|
|
// Test printing for single parent.
|
|
std::string expected1 =
|
|
"GaussianConditional p(x0 | x1)\n"
|
|
" R = [ 1 0 ]\n"
|
|
" [ 0 1 ]\n"
|
|
" S[x1] = [ -1 -2 ]\n"
|
|
" [ -3 -4 ]\n"
|
|
" d = [ 20 40 ]\n"
|
|
"isotropic dim=2 sigma=3\n";
|
|
EXPECT(assert_print_equal(expected1, conditional1, "GaussianConditional"));
|
|
|
|
// Test printing for multiple parents.
|
|
auto conditional2 = GaussianConditional::FromMeanAndStddev(X(0), A1, Y(0), A2,
|
|
Y(1), b, sigma);
|
|
std::string expected2 =
|
|
"GaussianConditional p(x0 | y0 y1)\n"
|
|
" R = [ 1 0 ]\n"
|
|
" [ 0 1 ]\n"
|
|
" S[y0] = [ -1 -2 ]\n"
|
|
" [ -3 -4 ]\n"
|
|
" S[y1] = [ -5 -6 ]\n"
|
|
" [ -7 -8 ]\n"
|
|
" d = [ 20 40 ]\n"
|
|
"isotropic dim=2 sigma=3\n";
|
|
EXPECT(assert_print_equal(expected2, conditional2, "GaussianConditional"));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() {
|
|
TestResult tr;
|
|
return TestRegistry::runAllTests(tr);
|
|
}
|
|
/* ************************************************************************* */
|