remove LinearConstraint. It is replaced by LinearEquality
thduynguyen
parent
18481f21d0
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c4b574774a
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@ -1,107 +0,0 @@
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/* ----------------------------------------------------------------------------
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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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* LinearConstraint.h
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* @brief: LinearConstraint derived from Base with constrained noise model
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* @date: Nov 27, 2014
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* @author: thduynguyen
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*/
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#pragma once
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#include <gtsam/linear/JacobianFactor.h>
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namespace gtsam {
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/**
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* This class defines Linear constraints by inherit Base
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* with the special Constrained noise model
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*/
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class LinearConstraint: public JacobianFactor {
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public:
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typedef LinearConstraint This; ///< Typedef to this class
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typedef JacobianFactor Base; ///< Typedef to base class
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typedef boost::shared_ptr<This> shared_ptr; ///< shared_ptr to this class
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public:
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/** default constructor for I/O */
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LinearConstraint() : Base() {}
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/** Conversion from HessianFactor (does Cholesky to obtain Jacobian matrix) */
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explicit LinearConstraint(const HessianFactor& hf) {
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throw std::runtime_error("Cannot convert HessianFactor to LinearConstraint");
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}
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/** Construct unary factor */
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LinearConstraint(Key i1, const Matrix& A1, const Vector& b) :
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Base(i1, A1, b, noiseModel::Constrained::All(b.rows())) {
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}
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/** Construct binary factor */
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LinearConstraint(Key i1, const Matrix& A1, Key i2, const Matrix& A2,
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const Vector& b) :
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Base(i1, A1, i2, A2, b, noiseModel::Constrained::All(b.rows())) {
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}
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/** Construct ternary factor */
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LinearConstraint(Key i1, const Matrix& A1, Key i2, const Matrix& A2, Key i3,
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const Matrix& A3, const Vector& b) :
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Base(i1, A1, i2, A2, i3, A3, b,
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noiseModel::Constrained::All(b.rows())) {
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}
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/** Construct an n-ary factor
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* @tparam TERMS A container whose value type is std::pair<Key, Matrix>, specifying the
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* collection of keys and matrices making up the factor. */
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template<typename TERMS>
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LinearConstraint(const TERMS& terms, const Vector& b) :
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Base(terms, b, noiseModel::Constrained::All(b.rows())) {
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}
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/** Virtual destructor */
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virtual ~LinearConstraint() {
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}
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/** equals */
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virtual bool equals(const GaussianFactor& lf, double tol = 1e-9) const {
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return Base::equals(lf, tol);
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}
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/** print */
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virtual void print(const std::string& s = "", const KeyFormatter& formatter =
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DefaultKeyFormatter) const {
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Base::print(s, formatter);
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}
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/** Clone this LinearConstraint */
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virtual GaussianFactor::shared_ptr clone() const {
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return boost::static_pointer_cast<GaussianFactor>(
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boost::make_shared<LinearConstraint>(*this));
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}
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/** Special error_vector for constraints (A*x-b) */
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Vector error_vector(const VectorValues& c) const {
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return unweighted_error(c);
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}
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/** Special error for constraints.
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* I think it should be zero, as this function is meant for objective cost.
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* But the name "error" can be misleading.
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* TODO: confirm with Frank!! */
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virtual double error(const VectorValues& c) const {
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return 0.0;
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}
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}; // LinearConstraint
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} // gtsam
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@ -10,12 +10,12 @@
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* -------------------------------------------------------------------------- */
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/**
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* @file testLinearConstraint.cpp
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* @brief Unit tests for LinearConstraint
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* @file testLinearEquality.cpp
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* @brief Unit tests for LinearEquality
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* @author thduynguyen
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**/
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#include <gtsam_unstable/linear/LinearConstraint.h>
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#include <gtsam_unstable/linear/LinearEquality.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <gtsam/linear/HessianFactor.h>
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#include <gtsam/linear/VectorValues.h>
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@ -28,7 +28,7 @@ using namespace std;
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using namespace gtsam;
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using namespace boost::assign;
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GTSAM_CONCEPT_TESTABLE_INST(LinearConstraint)
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GTSAM_CONCEPT_TESTABLE_INST(LinearEquality)
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namespace {
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namespace simple {
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@ -45,16 +45,16 @@ namespace {
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}
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/* ************************************************************************* */
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TEST(LinearConstraint, constructors_and_accessors)
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TEST(LinearEquality, constructors_and_accessors)
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{
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using namespace simple;
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// Test for using different numbers of terms
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{
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// One term constructor
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LinearConstraint expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 1), b);
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LinearConstraint actual(terms[0].first, terms[0].second, b);
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LinearEquality expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 1), b, 0);
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LinearEquality actual(terms[0].first, terms[0].second, b, 0);
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EXPECT(assert_equal(expected, actual));
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LONGS_EQUAL((long)terms[0].first, (long)actual.keys().back());
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EXPECT(assert_equal(terms[0].second, actual.getA(actual.end() - 1)));
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@ -64,10 +64,10 @@ TEST(LinearConstraint, constructors_and_accessors)
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}
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{
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// Two term constructor
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LinearConstraint expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 2), b);
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LinearConstraint actual(terms[0].first, terms[0].second,
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terms[1].first, terms[1].second, b);
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LinearEquality expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 2), b, 0);
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LinearEquality actual(terms[0].first, terms[0].second,
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terms[1].first, terms[1].second, b, 0);
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EXPECT(assert_equal(expected, actual));
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LONGS_EQUAL((long)terms[1].first, (long)actual.keys().back());
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EXPECT(assert_equal(terms[1].second, actual.getA(actual.end() - 1)));
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@ -77,10 +77,10 @@ TEST(LinearConstraint, constructors_and_accessors)
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}
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{
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// Three term constructor
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LinearConstraint expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 3), b);
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LinearConstraint actual(terms[0].first, terms[0].second,
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terms[1].first, terms[1].second, terms[2].first, terms[2].second, b);
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LinearEquality expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 3), b, 0);
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LinearEquality actual(terms[0].first, terms[0].second,
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terms[1].first, terms[1].second, terms[2].first, terms[2].second, b, 0);
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EXPECT(assert_equal(expected, actual));
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LONGS_EQUAL((long)terms[2].first, (long)actual.keys().back());
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EXPECT(assert_equal(terms[2].second, actual.getA(actual.end() - 1)));
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@ -91,7 +91,7 @@ TEST(LinearConstraint, constructors_and_accessors)
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}
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/* ************************************************************************* */
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TEST(LinearConstraint, Hessian_conversion) {
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TEST(LinearEquality, Hessian_conversion) {
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HessianFactor hessian(0, (Matrix(4,4) <<
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1.57, 2.695, -1.1, -2.35,
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2.695, 11.3125, -0.65, -10.225,
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@ -101,7 +101,7 @@ TEST(LinearConstraint, Hessian_conversion) {
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73.1725);
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try {
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LinearConstraint actual(hessian);
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LinearEquality actual(hessian);
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EXPECT(false);
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}
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catch (const std::runtime_error& exception) {
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@ -110,9 +110,9 @@ TEST(LinearConstraint, Hessian_conversion) {
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}
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/* ************************************************************************* */
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TEST(LinearConstraint, error)
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TEST(LinearEquality, error)
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{
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LinearConstraint factor(simple::terms, simple::b);
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LinearEquality factor(simple::terms, simple::b, 0);
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VectorValues values;
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values.insert(5, Vector::Constant(3, 1.0));
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@ -134,10 +134,10 @@ TEST(LinearConstraint, error)
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}
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/* ************************************************************************* */
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TEST(LinearConstraint, matrices_NULL)
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TEST(LinearEquality, matrices_NULL)
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{
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// Make sure everything works with NULL noise model
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LinearConstraint factor(simple::terms, simple::b);
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LinearEquality factor(simple::terms, simple::b, 0);
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Matrix AExpected(3, 9);
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AExpected << simple::terms[0].second, simple::terms[1].second, simple::terms[2].second;
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@ -157,10 +157,10 @@ TEST(LinearConstraint, matrices_NULL)
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}
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/* ************************************************************************* */
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TEST(LinearConstraint, matrices)
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TEST(LinearEquality, matrices)
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{
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// And now witgh a non-unit noise model
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LinearConstraint factor(simple::terms, simple::b);
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LinearEquality factor(simple::terms, simple::b, 0);
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Matrix jacobianExpected(3, 9);
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jacobianExpected << simple::terms[0].second, simple::terms[1].second, simple::terms[2].second;
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@ -184,11 +184,11 @@ TEST(LinearConstraint, matrices)
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}
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/* ************************************************************************* */
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TEST(LinearConstraint, operators )
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TEST(LinearEquality, operators )
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{
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Matrix I = eye(2);
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Vector b = (Vector(2) << 0.2,-0.1).finished();
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LinearConstraint lf(1, -I, 2, I, b);
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LinearEquality lf(1, -I, 2, I, b, 0);
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VectorValues c;
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c.insert(1, (Vector(2) << 10.,20.).finished());
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@ -210,25 +210,25 @@ TEST(LinearConstraint, operators )
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// test gradient at zero
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Matrix A; Vector b2; boost::tie(A,b2) = lf.jacobian();
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VectorValues expectedG;
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expectedG.insert(1, (Vector(2) << -1,-1).finished());
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expectedG.insert(2, (Vector(2) << 1, 1).finished());
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expectedG.insert(1, (Vector(2) << 0.2, -0.1).finished());
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expectedG.insert(2, (Vector(2) << -0.2, 0.1).finished());
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VectorValues actualG = lf.gradientAtZero();
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EXPECT(assert_equal(expectedG, actualG));
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}
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/* ************************************************************************* */
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TEST(LinearConstraint, default_error )
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TEST(LinearEquality, default_error )
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{
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LinearConstraint f;
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LinearEquality f;
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double actual = f.error(VectorValues());
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DOUBLES_EQUAL(0.0, actual, 1e-15);
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}
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//* ************************************************************************* */
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TEST(LinearConstraint, empty )
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TEST(LinearEquality, empty )
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{
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// create an empty factor
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LinearConstraint f;
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LinearEquality f;
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EXPECT(f.empty());
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}
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