203 lines
6.1 KiB
C++
203 lines
6.1 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 testGaussianMixtureFactor.cpp
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* @brief Unit tests for GaussianMixtureFactor
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* @author Varun Agrawal
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* @author Fan Jiang
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* @author Frank Dellaert
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* @date December 2021
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*/
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#include <gtsam/base/TestableAssertions.h>
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#include <gtsam/discrete/DiscreteValues.h>
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#include <gtsam/hybrid/GaussianMixture.h>
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#include <gtsam/hybrid/GaussianMixtureFactor.h>
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#include <gtsam/hybrid/HybridValues.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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// Include for test suite
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#include <CppUnitLite/TestHarness.h>
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using namespace std;
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using namespace gtsam;
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using noiseModel::Isotropic;
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using symbol_shorthand::M;
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using symbol_shorthand::X;
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/* ************************************************************************* */
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// Check iterators of empty mixture.
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TEST(GaussianMixtureFactor, Constructor) {
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GaussianMixtureFactor factor;
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GaussianMixtureFactor::const_iterator const_it = factor.begin();
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CHECK(const_it == factor.end());
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GaussianMixtureFactor::iterator it = factor.begin();
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CHECK(it == factor.end());
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}
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/* ************************************************************************* */
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// "Add" two mixture factors together.
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TEST(GaussianMixtureFactor, Sum) {
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DiscreteKey m1(1, 2), m2(2, 3);
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auto A1 = Matrix::Zero(2, 1);
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auto A2 = Matrix::Zero(2, 2);
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auto A3 = Matrix::Zero(2, 3);
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auto b = Matrix::Zero(2, 1);
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Vector2 sigmas;
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sigmas << 1, 2;
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auto model = noiseModel::Diagonal::Sigmas(sigmas, true);
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auto f10 = std::make_shared<JacobianFactor>(X(1), A1, X(2), A2, b);
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auto f11 = std::make_shared<JacobianFactor>(X(1), A1, X(2), A2, b);
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auto f20 = std::make_shared<JacobianFactor>(X(1), A1, X(3), A3, b);
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auto f21 = std::make_shared<JacobianFactor>(X(1), A1, X(3), A3, b);
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auto f22 = std::make_shared<JacobianFactor>(X(1), A1, X(3), A3, b);
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std::vector<GaussianFactor::shared_ptr> factorsA{f10, f11};
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std::vector<GaussianFactor::shared_ptr> factorsB{f20, f21, f22};
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// TODO(Frank): why specify keys at all? And: keys in factor should be *all*
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// keys, deviating from Kevin's scheme. Should we index DT on DiscreteKey?
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// Design review!
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GaussianMixtureFactor mixtureFactorA({X(1), X(2)}, {m1}, factorsA);
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GaussianMixtureFactor mixtureFactorB({X(1), X(3)}, {m2}, factorsB);
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// Check that number of keys is 3
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EXPECT_LONGS_EQUAL(3, mixtureFactorA.keys().size());
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// Check that number of discrete keys is 1
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EXPECT_LONGS_EQUAL(1, mixtureFactorA.discreteKeys().size());
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// Create sum of two mixture factors: it will be a decision tree now on both
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// discrete variables m1 and m2:
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GaussianFactorGraphTree sum;
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sum += mixtureFactorA;
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sum += mixtureFactorB;
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// Let's check that this worked:
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Assignment<Key> mode;
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mode[m1.first] = 1;
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mode[m2.first] = 2;
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auto actual = sum(mode);
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EXPECT(actual.at(0) == f11);
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EXPECT(actual.at(1) == f22);
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}
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/* ************************************************************************* */
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TEST(GaussianMixtureFactor, Printing) {
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DiscreteKey m1(1, 2);
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auto A1 = Matrix::Zero(2, 1);
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auto A2 = Matrix::Zero(2, 2);
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auto b = Matrix::Zero(2, 1);
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auto f10 = std::make_shared<JacobianFactor>(X(1), A1, X(2), A2, b);
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auto f11 = std::make_shared<JacobianFactor>(X(1), A1, X(2), A2, b);
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std::vector<GaussianFactor::shared_ptr> factors{f10, f11};
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GaussianMixtureFactor mixtureFactor({X(1), X(2)}, {m1}, factors);
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std::string expected =
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R"(Hybrid [x1 x2; 1]{
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Choice(1)
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0 Leaf [1] :
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A[x1] = [
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0;
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0
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]
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A[x2] = [
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0, 0;
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0, 0
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]
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b = [ 0 0 ]
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No noise model
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1 Leaf [1] :
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A[x1] = [
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0;
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0
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]
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A[x2] = [
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0, 0;
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0, 0
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]
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b = [ 0 0 ]
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No noise model
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}
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)";
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EXPECT(assert_print_equal(expected, mixtureFactor));
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}
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/* ************************************************************************* */
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TEST(GaussianMixtureFactor, GaussianMixture) {
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KeyVector keys;
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keys.push_back(X(0));
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keys.push_back(X(1));
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DiscreteKeys dKeys;
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dKeys.emplace_back(M(0), 2);
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dKeys.emplace_back(M(1), 2);
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auto gaussians = std::make_shared<GaussianConditional>();
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GaussianMixture::Conditionals conditionals(gaussians);
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GaussianMixture gm({}, keys, dKeys, conditionals);
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EXPECT_LONGS_EQUAL(2, gm.discreteKeys().size());
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}
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/* ************************************************************************* */
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// Test the error of the GaussianMixtureFactor
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TEST(GaussianMixtureFactor, Error) {
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DiscreteKey m1(1, 2);
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auto A01 = Matrix2::Identity();
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auto A02 = Matrix2::Identity();
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auto A11 = Matrix2::Identity();
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auto A12 = Matrix2::Identity() * 2;
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auto b = Vector2::Zero();
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auto f0 = std::make_shared<JacobianFactor>(X(1), A01, X(2), A02, b);
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auto f1 = std::make_shared<JacobianFactor>(X(1), A11, X(2), A12, b);
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std::vector<GaussianFactor::shared_ptr> factors{f0, f1};
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GaussianMixtureFactor mixtureFactor({X(1), X(2)}, {m1}, factors);
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VectorValues continuousValues;
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continuousValues.insert(X(1), Vector2(0, 0));
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continuousValues.insert(X(2), Vector2(1, 1));
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// error should return a tree of errors, with nodes for each discrete value.
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AlgebraicDecisionTree<Key> error_tree = mixtureFactor.error(continuousValues);
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std::vector<DiscreteKey> discrete_keys = {m1};
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// Error values for regression test
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std::vector<double> errors = {1, 4};
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AlgebraicDecisionTree<Key> expected_error(discrete_keys, errors);
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EXPECT(assert_equal(expected_error, error_tree));
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// Test for single leaf given discrete assignment P(X|M,Z).
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DiscreteValues discreteValues;
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discreteValues[m1.first] = 1;
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EXPECT_DOUBLES_EQUAL(
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4.0, mixtureFactor.error({continuousValues, discreteValues}),
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1e-9);
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}
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/* ************************************************************************* */
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int main() {
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TestResult tr;
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return TestRegistry::runAllTests(tr);
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}
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/* ************************************************************************* */ |