125 lines
4.6 KiB
C++
125 lines
4.6 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 expressionTesting.h
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* @date September 18, 2014
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* @author Frank Dellaert
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* @author Paul Furgale
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* @brief Test harness methods for expressions.
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*/
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#pragma once
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#include <gtsam_unstable/nonlinear/ExpressionFactor.h>
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#include <gtsam/nonlinear/Expression.h>
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#include <gtsam/base/Matrix.h>
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#include <gtsam/base/Testable.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <gtsam/linear/VectorValues.h>
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#include <CppUnitLite/TestResult.h>
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#include <CppUnitLite/Test.h>
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#include <CppUnitLite/Failure.h>
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namespace gtsam {
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/**
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* Linearize a nonlinear factor using numerical differentiation
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* The benefit of this method is that it does not need to know what types are
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* involved to evaluate the factor. If all the machinery of gtsam is working
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* correctly, we should get the correct numerical derivatives out the other side.
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*/
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JacobianFactor linearizeNumerically(const NoiseModelFactor& factor,
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const Values& values, double delta = 1e-5) {
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// We will fill a map of Jacobians
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std::map<Key, Matrix> jacobians;
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// Get size
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Eigen::VectorXd e = factor.whitenedError(values);
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const size_t rows = e.size();
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// Loop over all variables
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VectorValues dX = values.zeroVectors();
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BOOST_FOREACH(Key key, factor) {
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// Compute central differences using the values struct.
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const size_t cols = dX.dim(key);
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Matrix J = Matrix::Zero(rows, cols);
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for (size_t col = 0; col < cols; ++col) {
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Eigen::VectorXd dx = Eigen::VectorXd::Zero(cols);
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dx[col] = delta;
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dX[key] = dx;
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Values eval_values = values.retract(dX);
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Eigen::VectorXd left = factor.whitenedError(eval_values);
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dx[col] = -delta;
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dX[key] = dx;
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eval_values = values.retract(dX);
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Eigen::VectorXd right = factor.whitenedError(eval_values);
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J.col(col) = (left - right) * (1.0 / (2.0 * delta));
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}
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jacobians[key] = J;
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}
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// Next step...return JacobianFactor
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return JacobianFactor(jacobians, -e);
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}
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namespace internal {
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// CPPUnitLite-style test for linearization of a factor
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void testFactorJacobians(TestResult& result_, const std::string& name_,
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const NoiseModelFactor& factor, const gtsam::Values& values, double delta,
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double tolerance) {
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// Create expected value by numerical differentiation
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JacobianFactor expected = linearizeNumerically(factor, values, delta);
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// Create actual value by linearize
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boost::shared_ptr<JacobianFactor> actual = //
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boost::dynamic_pointer_cast<JacobianFactor>(factor.linearize(values));
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// Check cast result and then equality
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CHECK(actual);
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EXPECT( assert_equal(expected, *actual, tolerance));
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}
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}
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/// \brief Check the Jacobians produced by a factor against finite differences.
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/// \param factor The factor to test.
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/// \param values Values filled in for testing the Jacobians.
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/// \param numerical_derivative_step The step to use when computing the numerical derivative Jacobians
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/// \param tolerance The numerical tolerance to use when comparing Jacobians.
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#define EXPECT_CORRECT_FACTOR_JACOBIANS(factor, values, numerical_derivative_step, tolerance) \
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{ gtsam::internal::testFactorJacobians(result_, name_, factor, values, numerical_derivative_step, tolerance); }
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namespace internal {
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// CPPUnitLite-style test for linearization of an ExpressionFactor
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template<typename T>
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void testExpressionJacobians(TestResult& result_, const std::string& name_,
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const gtsam::Expression<T>& expression, const gtsam::Values& values,
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double nd_step, double tolerance) {
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// Create factor
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size_t size = traits<T>::dimension;
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ExpressionFactor<T> f(noiseModel::Unit::Create(size),
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expression.value(values), expression);
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testFactorJacobians(result_, name_, f, values, nd_step, tolerance);
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}
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}
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} // namespace gtsam
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/// \brief Check the Jacobians produced by an expression against finite differences.
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/// \param expression The expression to test.
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/// \param values Values filled in for testing the Jacobians.
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/// \param numerical_derivative_step The step to use when computing the finite difference Jacobians
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/// \param tolerance The numerical tolerance to use when comparing Jacobians.
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#define EXPECT_CORRECT_EXPRESSION_JACOBIANS(expression, values, numerical_derivative_step, tolerance) \
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{ gtsam::internal::testExpressionJacobians(result_, name_, expression, values, numerical_derivative_step, tolerance); }
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