Merged in feature/allowMap (pull request #18)
Replaced range adaptor pattern with more explicit loop to create dimensions, which also allows maps to be supplied as TERMS containers.release/4.3a0
commit
e408159545
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@ -13,6 +13,22 @@
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* @brief Incremental and batch solving, timing, and accuracy comparisons
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* @author Richard Roberts
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* @date August, 2013
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*
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* Here is an example. Below, to run in batch mode, we first generate an initialization in incremental mode.
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*
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* Solve in incremental and write to file w_inc:
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* ./SolverComparer --incremental -d w10000 -o w_inc
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*
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* You can then perturb that initialization to get batch something to optimize.
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* Read in w_inc, perturb it with noise of stddev 0.6, and write to w_pert:
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* ./SolverComparer --perturb 0.6 -i w_inc -o w_pert
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*
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* Then optimize with batch, read in w_pert, solve in batch, and write to w_batch:
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* ./SolverComparer --batch -d w10000 -i w_pert -o w_batch
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*
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* And finally compare solutions in w_inc and w_batch to check that batch converged to the global minimum
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* ./SolverComparer --compare w_inc w_batch
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*
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*/
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#include <gtsam/base/timing.h>
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@ -19,10 +19,6 @@
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#pragma once
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#include <gtsam/linear/linearExceptions.h>
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#include <boost/assign/list_of.hpp>
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#include <boost/range/adaptor/transformed.hpp>
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#include <boost/range/algorithm/for_each.hpp>
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#include <boost/foreach.hpp>
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namespace gtsam {
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@ -59,20 +55,10 @@ namespace gtsam {
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model_ = model;
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}
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/* ************************************************************************* */
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namespace internal {
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static inline DenseIndex getColsJF(const std::pair<Key,Matrix>& p) {
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return p.second.cols();
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}
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}
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/* ************************************************************************* */
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template<typename TERMS>
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void JacobianFactor::fillTerms(const TERMS& terms, const Vector& b, const SharedDiagonal& noiseModel)
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{
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using boost::adaptors::transformed;
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namespace br = boost::range;
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// Check noise model dimension
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if(noiseModel && (DenseIndex)noiseModel->dim() != b.size())
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throw InvalidNoiseModel(b.size(), noiseModel->dim());
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@ -80,25 +66,32 @@ namespace gtsam {
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// Resize base class key vector
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Base::keys_.resize(terms.size());
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// Construct block matrix - this uses the boost::range 'transformed' construct to apply
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// internal::getColsJF (defined just above here in this file) to each term. This
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// transforms the list of terms into a list of variable dimensions, by extracting the
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// number of columns in each matrix. This is done to avoid separately allocating an
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// array of dimensions before constructing the VerticalBlockMatrix.
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Ab_ = VerticalBlockMatrix(terms | transformed(&internal::getColsJF), b.size(), true);
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// Get dimensions of matrices
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std::vector<size_t> dimensions;
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dimensions.reserve(terms.size());
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for(typename TERMS::const_iterator it = terms.begin(); it != terms.end(); ++it) {
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const std::pair<Key, Matrix>& term = *it;
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const Matrix& Ai = term.second;
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dimensions.push_back(Ai.cols());
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}
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// Construct block matrix
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Ab_ = VerticalBlockMatrix(dimensions, b.size(), true);
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// Check and add terms
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DenseIndex i = 0; // For block index
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for(typename TERMS::const_iterator termIt = terms.begin(); termIt != terms.end(); ++termIt) {
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const std::pair<Key, Matrix>& term = *termIt;
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for(typename TERMS::const_iterator it = terms.begin(); it != terms.end(); ++it) {
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const std::pair<Key, Matrix>& term = *it;
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Key key = term.first;
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const Matrix& Ai = term.second;
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// Check block rows
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if(term.second.rows() != Ab_.rows())
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throw InvalidMatrixBlock(Ab_.rows(), term.second.rows());
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if(Ai.rows() != Ab_.rows())
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throw InvalidMatrixBlock(Ab_.rows(), Ai.rows());
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// Assign key and matrix
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Base::keys_[i] = term.first;
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Ab_(i) = term.second;
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Base::keys_[i] = key;
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Ab_(i) = Ai;
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// Increment block index
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++ i;
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@ -105,6 +105,24 @@ TEST(JacobianFactor, constructors_and_accessors)
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EXPECT(noise == expected.get_model());
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EXPECT(noise == actual.get_model());
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}
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{
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// Test three-term constructor with std::map
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JacobianFactor expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 3), b, noise);
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map<Key,Matrix> mapTerms;
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// note order of insertion plays no role: order will be determined by keys
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mapTerms.insert(terms[2]);
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mapTerms.insert(terms[1]);
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mapTerms.insert(terms[0]);
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JacobianFactor actual(mapTerms, b, noise);
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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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EXPECT(assert_equal(b, expected.getb()));
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EXPECT(assert_equal(b, actual.getb()));
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EXPECT(noise == expected.get_model());
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EXPECT(noise == actual.get_model());
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}
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{
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// VerticalBlockMatrix constructor
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JacobianFactor expected(
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