BORG Formatting
dellaert
parent
2c99f68ed7
commit
850501ed52
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@ -49,183 +49,185 @@
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using namespace std;
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using namespace boost::assign;
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namespace br { using namespace boost::range; using namespace boost::adaptors; }
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namespace br {
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using namespace boost::range;
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using namespace boost::adaptors;
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}
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namespace gtsam {
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/* ************************************************************************* */
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HessianFactor::HessianFactor() :
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info_(cref_list_of<1>(1))
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{
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info_(cref_list_of<1>(1)) {
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linearTerm().setZero();
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constantTerm() = 0.0;
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}
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/* ************************************************************************* */
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HessianFactor::HessianFactor(Key j, const Matrix& G, const Vector& g, double f) :
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GaussianFactor(cref_list_of<1>(j)), info_(cref_list_of<2>(G.cols())(1))
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{
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if(G.rows() != G.cols() || G.rows() != g.size()) throw invalid_argument(
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GaussianFactor(cref_list_of<1>(j)), info_(cref_list_of<2>(G.cols())(1)) {
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if (G.rows() != G.cols() || G.rows() != g.size())
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throw invalid_argument(
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"Attempting to construct HessianFactor with inconsistent matrix and/or vector dimensions");
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info_(0,0) = G;
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info_(0,1) = g;
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info_(1,1)(0,0) = f;
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info_(0, 0) = G;
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info_(0, 1) = g;
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info_(1, 1)(0, 0) = f;
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}
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/* ************************************************************************* */
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// error is 0.5*(x-mu)'*inv(Sigma)*(x-mu) = 0.5*(x'*G*x - 2*x'*G*mu + mu'*G*mu)
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// where G = inv(Sigma), g = G*mu, f = mu'*G*mu = mu'*g
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HessianFactor::HessianFactor(Key j, const Vector& mu, const Matrix& Sigma) :
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GaussianFactor(cref_list_of<1>(j)),
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info_(cref_list_of<2> (Sigma.cols()) (1) )
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{
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if (Sigma.rows() != Sigma.cols() || Sigma.rows() != mu.size()) throw invalid_argument(
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GaussianFactor(cref_list_of<1>(j)), info_(cref_list_of<2>(Sigma.cols())(1)) {
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if (Sigma.rows() != Sigma.cols() || Sigma.rows() != mu.size())
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throw invalid_argument(
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"Attempting to construct HessianFactor with inconsistent matrix and/or vector dimensions");
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info_(0,0) = Sigma.inverse(); // G
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info_(0,1) = info_(0,0).selfadjointView() * mu; // g
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info_(1,1)(0,0) = mu.dot(info_(0,1).knownOffDiagonal().col(0)); // f
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info_(0, 0) = Sigma.inverse(); // G
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info_(0, 1) = info_(0, 0).selfadjointView() * mu; // g
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info_(1, 1)(0, 0) = mu.dot(info_(0, 1).knownOffDiagonal().col(0)); // f
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}
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/* ************************************************************************* */
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HessianFactor::HessianFactor(Key j1, Key j2,
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const Matrix& G11, const Matrix& G12, const Vector& g1,
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const Matrix& G22, const Vector& g2, double f) :
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GaussianFactor(cref_list_of<2>(j1)(j2)),
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info_(cref_list_of<3> (G11.cols()) (G22.cols()) (1) )
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{
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info_(0,0) = G11;
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info_(0,1) = G12;
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info_(0,2) = g1;
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info_(1,1) = G22;
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info_(1,2) = g2;
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info_(2,2)(0,0) = f;
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HessianFactor::HessianFactor(Key j1, Key j2, const Matrix& G11,
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const Matrix& G12, const Vector& g1, const Matrix& G22, const Vector& g2,
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double f) :
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GaussianFactor(cref_list_of<2>(j1)(j2)), info_(
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cref_list_of<3>(G11.cols())(G22.cols())(1)) {
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info_(0, 0) = G11;
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info_(0, 1) = G12;
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info_(0, 2) = g1;
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info_(1, 1) = G22;
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info_(1, 2) = g2;
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info_(2, 2)(0, 0) = f;
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}
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/* ************************************************************************* */
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HessianFactor::HessianFactor(Key j1, Key j2, Key j3,
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const Matrix& G11, const Matrix& G12, const Matrix& G13, const Vector& g1,
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const Matrix& G22, const Matrix& G23, const Vector& g2,
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const Matrix& G33, const Vector& g3, double f) :
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GaussianFactor(cref_list_of<3>(j1)(j2)(j3)),
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info_(cref_list_of<4> (G11.cols()) (G22.cols()) (G33.cols()) (1) )
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{
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if(G11.rows() != G11.cols() || G11.rows() != G12.rows() || G11.rows() != G13.rows() || G11.rows() != g1.size() ||
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G22.cols() != G12.cols() || G33.cols() != G13.cols() || G22.cols() != g2.size() || G33.cols() != g3.size())
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throw invalid_argument("Inconsistent matrix and/or vector dimensions in HessianFactor constructor");
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info_(0,0) = G11;
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info_(0,1) = G12;
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info_(0,2) = G13;
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info_(0,3) = g1;
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info_(1,1) = G22;
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info_(1,2) = G23;
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info_(1,3) = g2;
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info_(2,2) = G33;
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info_(2,3) = g3;
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info_(3,3)(0,0) = f;
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HessianFactor::HessianFactor(Key j1, Key j2, Key j3, const Matrix& G11,
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const Matrix& G12, const Matrix& G13, const Vector& g1, const Matrix& G22,
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const Matrix& G23, const Vector& g2, const Matrix& G33, const Vector& g3,
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double f) :
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GaussianFactor(cref_list_of<3>(j1)(j2)(j3)), info_(
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cref_list_of<4>(G11.cols())(G22.cols())(G33.cols())(1)) {
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if (G11.rows() != G11.cols() || G11.rows() != G12.rows()
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|| G11.rows() != G13.rows() || G11.rows() != g1.size()
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|| G22.cols() != G12.cols() || G33.cols() != G13.cols()
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|| G22.cols() != g2.size() || G33.cols() != g3.size())
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throw invalid_argument(
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"Inconsistent matrix and/or vector dimensions in HessianFactor constructor");
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info_(0, 0) = G11;
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info_(0, 1) = G12;
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info_(0, 2) = G13;
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info_(0, 3) = g1;
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info_(1, 1) = G22;
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info_(1, 2) = G23;
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info_(1, 3) = g2;
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info_(2, 2) = G33;
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info_(2, 3) = g3;
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info_(3, 3)(0, 0) = f;
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}
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/* ************************************************************************* */
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namespace {
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DenseIndex _getSizeHF(const Vector& m) { return m.size(); }
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DenseIndex _getSizeHF(const Vector& m) {
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return m.size();
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}
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}
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/* ************************************************************************* */
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HessianFactor::HessianFactor(const std::vector<Key>& js, const std::vector<Matrix>& Gs,
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const std::vector<Vector>& gs, double f) :
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GaussianFactor(js), info_(gs | br::transformed(&_getSizeHF), true)
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{
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HessianFactor::HessianFactor(const std::vector<Key>& js,
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const std::vector<Matrix>& Gs, const std::vector<Vector>& gs, double f) :
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GaussianFactor(js), info_(gs | br::transformed(&_getSizeHF), true) {
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// Get the number of variables
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size_t variable_count = js.size();
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// Verify the provided number of entries in the vectors are consistent
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if(gs.size() != variable_count || Gs.size() != (variable_count*(variable_count+1))/2)
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throw invalid_argument("Inconsistent number of entries between js, Gs, and gs in HessianFactor constructor.\nThe number of keys provided \
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if (gs.size() != variable_count
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|| Gs.size() != (variable_count * (variable_count + 1)) / 2)
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throw invalid_argument(
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"Inconsistent number of entries between js, Gs, and gs in HessianFactor constructor.\nThe number of keys provided \
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in js must match the number of linear vector pieces in gs. The number of upper-diagonal blocks in Gs must be n*(n+1)/2");
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// Verify the dimensions of each provided matrix are consistent
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// Note: equations for calculating the indices derived from the "sum of an arithmetic sequence" formula
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for(size_t i = 0; i < variable_count; ++i){
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for (size_t i = 0; i < variable_count; ++i) {
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DenseIndex block_size = gs[i].size();
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// Check rows
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for(size_t j = 0; j < variable_count-i; ++j){
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size_t index = i*(2*variable_count - i + 1)/2 + j;
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if(Gs[index].rows() != block_size){
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throw invalid_argument("Inconsistent matrix and/or vector dimensions in HessianFactor constructor");
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for (size_t j = 0; j < variable_count - i; ++j) {
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size_t index = i * (2 * variable_count - i + 1) / 2 + j;
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if (Gs[index].rows() != block_size) {
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throw invalid_argument(
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"Inconsistent matrix and/or vector dimensions in HessianFactor constructor");
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}
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}
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// Check cols
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for(size_t j = 0; j <= i; ++j){
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size_t index = j*(2*variable_count - j + 1)/2 + (i-j);
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if(Gs[index].cols() != block_size){
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throw invalid_argument("Inconsistent matrix and/or vector dimensions in HessianFactor constructor");
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for (size_t j = 0; j <= i; ++j) {
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size_t index = j * (2 * variable_count - j + 1) / 2 + (i - j);
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if (Gs[index].cols() != block_size) {
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throw invalid_argument(
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"Inconsistent matrix and/or vector dimensions in HessianFactor constructor");
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}
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}
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}
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// Fill in the blocks
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size_t index = 0;
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for(size_t i = 0; i < variable_count; ++i){
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for(size_t j = i; j < variable_count; ++j){
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for (size_t i = 0; i < variable_count; ++i) {
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for (size_t j = i; j < variable_count; ++j) {
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info_(i, j) = Gs[index++];
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}
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info_(i, variable_count) = gs[i];
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}
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info_(variable_count, variable_count)(0,0) = f;
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info_(variable_count, variable_count)(0, 0) = f;
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}
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/* ************************************************************************* */
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namespace {
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void _FromJacobianHelper(const JacobianFactor& jf, SymmetricBlockMatrix& info)
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{
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void _FromJacobianHelper(const JacobianFactor& jf, SymmetricBlockMatrix& info) {
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gttic(HessianFactor_fromJacobian);
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const SharedDiagonal& jfModel = jf.get_model();
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if(jfModel)
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{
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if(jf.get_model()->isConstrained())
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throw invalid_argument("Cannot construct HessianFactor from JacobianFactor with constrained noise model");
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info.full().triangularView() = jf.matrixObject().full().transpose() *
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(jfModel->invsigmas().array() * jfModel->invsigmas().array()).matrix().asDiagonal() *
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jf.matrixObject().full();
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if (jfModel) {
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if (jf.get_model()->isConstrained())
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throw invalid_argument(
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"Cannot construct HessianFactor from JacobianFactor with constrained noise model");
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info.full().triangularView() =
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jf.matrixObject().full().transpose()
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* (jfModel->invsigmas().array() * jfModel->invsigmas().array()).matrix().asDiagonal()
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* jf.matrixObject().full();
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} else {
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info.full().triangularView() = jf.matrixObject().full().transpose() * jf.matrixObject().full();
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info.full().triangularView() = jf.matrixObject().full().transpose()
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* jf.matrixObject().full();
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}
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}
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}
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/* ************************************************************************* */
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HessianFactor::HessianFactor(const JacobianFactor& jf) :
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GaussianFactor(jf), info_(SymmetricBlockMatrix::LikeActiveViewOf(jf.matrixObject()))
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{
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GaussianFactor(jf), info_(
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SymmetricBlockMatrix::LikeActiveViewOf(jf.matrixObject())) {
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_FromJacobianHelper(jf, info_);
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}
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/* ************************************************************************* */
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HessianFactor::HessianFactor(const GaussianFactor& gf) :
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GaussianFactor(gf)
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{
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GaussianFactor(gf) {
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// Copy the matrix data depending on what type of factor we're copying from
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if(const JacobianFactor* jf = dynamic_cast<const JacobianFactor*>(&gf))
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{
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if (const JacobianFactor* jf = dynamic_cast<const JacobianFactor*>(&gf)) {
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info_ = SymmetricBlockMatrix::LikeActiveViewOf(jf->matrixObject());
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_FromJacobianHelper(*jf, info_);
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}
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else if(const HessianFactor* hf = dynamic_cast<const HessianFactor*>(&gf))
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{
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} else if (const HessianFactor* hf = dynamic_cast<const HessianFactor*>(&gf)) {
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info_ = hf->info_;
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}
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else
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{
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throw std::invalid_argument("In HessianFactor(const GaussianFactor& gf), gf is neither a JacobianFactor nor a HessianFactor");
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} else {
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throw std::invalid_argument(
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"In HessianFactor(const GaussianFactor& gf), gf is neither a JacobianFactor nor a HessianFactor");
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}
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}
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/* ************************************************************************* */
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HessianFactor::HessianFactor(const GaussianFactorGraph& factors,
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boost::optional<const Scatter&> scatter)
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{
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boost::optional<const Scatter&> scatter) {
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gttic(HessianFactor_MergeConstructor);
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boost::optional<Scatter> computedScatter;
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if(!scatter) {
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if (!scatter) {
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computedScatter = Scatter(factors);
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scatter = computedScatter;
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}
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@ -247,45 +249,46 @@ HessianFactor::HessianFactor(const GaussianFactorGraph& factors,
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// Form A' * A
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gttic(update);
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BOOST_FOREACH(const GaussianFactor::shared_ptr& factor, factors)
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if(factor)
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if (factor)
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factor->updateHessian(keys_, &info_);
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gttoc(update);
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}
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/* ************************************************************************* */
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void HessianFactor::print(const std::string& s, const KeyFormatter& formatter) const {
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void HessianFactor::print(const std::string& s,
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const KeyFormatter& formatter) const {
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cout << s << "\n";
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cout << " keys: ";
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for(const_iterator key=begin(); key!=end(); ++key)
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for (const_iterator key = begin(); key != end(); ++key)
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cout << formatter(*key) << "(" << getDim(key) << ") ";
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cout << "\n";
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gtsam::print(Matrix(info_.full().selfadjointView()), "Augmented information matrix: ");
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gtsam::print(Matrix(info_.full().selfadjointView()),
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"Augmented information matrix: ");
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}
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/* ************************************************************************* */
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bool HessianFactor::equals(const GaussianFactor& lf, double tol) const {
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if(!dynamic_cast<const HessianFactor*>(&lf))
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if (!dynamic_cast<const HessianFactor*>(&lf))
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return false;
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else {
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if(!Factor::equals(lf, tol))
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if (!Factor::equals(lf, tol))
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return false;
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Matrix thisMatrix = info_.full().selfadjointView();
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thisMatrix(thisMatrix.rows()-1, thisMatrix.cols()-1) = 0.0;
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Matrix rhsMatrix = static_cast<const HessianFactor&>(lf).info_.full().selfadjointView();
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rhsMatrix(rhsMatrix.rows()-1, rhsMatrix.cols()-1) = 0.0;
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thisMatrix(thisMatrix.rows() - 1, thisMatrix.cols() - 1) = 0.0;
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Matrix rhsMatrix =
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static_cast<const HessianFactor&>(lf).info_.full().selfadjointView();
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rhsMatrix(rhsMatrix.rows() - 1, rhsMatrix.cols() - 1) = 0.0;
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return equal_with_abs_tol(thisMatrix, rhsMatrix, tol);
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}
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}
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/* ************************************************************************* */
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Matrix HessianFactor::augmentedInformation() const
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{
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Matrix HessianFactor::augmentedInformation() const {
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return info_.full().selfadjointView();
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}
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/* ************************************************************************* */
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Matrix HessianFactor::information() const
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{
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Matrix HessianFactor::information() const {
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return info_.range(0, size(), 0, size()).selfadjointView();
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}
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@ -293,10 +296,10 @@ Matrix HessianFactor::information() const
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VectorValues HessianFactor::hessianDiagonal() const {
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VectorValues d;
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// Loop over all variables
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for (DenseIndex j = 0; j < (DenseIndex)size(); ++j) {
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for (DenseIndex j = 0; j < (DenseIndex) size(); ++j) {
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// Get the diagonal block, and insert its diagonal
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Matrix B = info_(j, j).selfadjointView();
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d.insert(keys_[j],B.diagonal());
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d.insert(keys_[j], B.diagonal());
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}
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return d;
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}
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@ -309,26 +312,24 @@ void HessianFactor::hessianDiagonal(double* d) const {
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}
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/* ************************************************************************* */
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map<Key,Matrix> HessianFactor::hessianBlockDiagonal() const {
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map<Key,Matrix> blocks;
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map<Key, Matrix> HessianFactor::hessianBlockDiagonal() const {
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map<Key, Matrix> blocks;
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// Loop over all variables
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for (DenseIndex j = 0; j < (DenseIndex)size(); ++j) {
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for (DenseIndex j = 0; j < (DenseIndex) size(); ++j) {
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// Get the diagonal block, and insert it
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Matrix B = info_(j, j).selfadjointView();
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blocks.insert(make_pair(keys_[j],B));
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blocks.insert(make_pair(keys_[j], B));
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}
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return blocks;
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}
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/* ************************************************************************* */
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Matrix HessianFactor::augmentedJacobian() const
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{
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Matrix HessianFactor::augmentedJacobian() const {
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return JacobianFactor(*this).augmentedJacobian();
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}
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/* ************************************************************************* */
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std::pair<Matrix, Vector> HessianFactor::jacobian() const
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{
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std::pair<Matrix, Vector> HessianFactor::jacobian() const {
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return JacobianFactor(*this).jacobian();
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}
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@ -363,10 +364,10 @@ void HessianFactor::updateHessian(const FastVector<Key>& infoKeys,
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}
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/* ************************************************************************* */
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GaussianFactor::shared_ptr HessianFactor::negate() const
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{
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GaussianFactor::shared_ptr HessianFactor::negate() const {
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shared_ptr result = boost::make_shared<This>(*this);
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result->info_.full().triangularView() = -result->info_.full().triangularView().nestedExpression(); // Negate the information matrix of the result
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result->info_.full().triangularView() =
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-result->info_.full().triangularView().nestedExpression(); // Negate the information matrix of the result
|
||||
return result;
|
||||
}
|
||||
|
||||
|
|
@ -383,7 +384,7 @@ void HessianFactor::multiplyHessianAdd(double alpha, const VectorValues& x,
|
|||
// Accessing the VectorValues one by one is expensive
|
||||
// So we will loop over columns to access x only once per column
|
||||
// And fill the above temporary y values, to be added into yvalues after
|
||||
for (DenseIndex j = 0; j < (DenseIndex)size(); ++j) {
|
||||
for (DenseIndex j = 0; j < (DenseIndex) size(); ++j) {
|
||||
// xj is the input vector
|
||||
Vector xj = x.at(keys_[j]);
|
||||
DenseIndex i = 0;
|
||||
|
|
@ -392,12 +393,12 @@ void HessianFactor::multiplyHessianAdd(double alpha, const VectorValues& x,
|
|||
// blocks on the diagonal are only half
|
||||
y[i] += info_(j, j).selfadjointView() * xj;
|
||||
// for below diagonal, we take transpose block from upper triangular part
|
||||
for (i = j + 1; i < (DenseIndex)size(); ++i)
|
||||
for (i = j + 1; i < (DenseIndex) size(); ++i)
|
||||
y[i] += info_(i, j).knownOffDiagonal() * xj;
|
||||
}
|
||||
|
||||
// copy to yvalues
|
||||
for(DenseIndex i = 0; i < (DenseIndex)size(); ++i) {
|
||||
for (DenseIndex i = 0; i < (DenseIndex) size(); ++i) {
|
||||
bool didNotExist;
|
||||
VectorValues::iterator it;
|
||||
boost::tie(it, didNotExist) = yvalues.tryInsert(keys_[i], Vector());
|
||||
|
|
@ -413,7 +414,7 @@ VectorValues HessianFactor::gradientAtZero() const {
|
|||
VectorValues g;
|
||||
size_t n = size();
|
||||
for (size_t j = 0; j < n; ++j)
|
||||
g.insert(keys_[j], -info_(j,n).knownOffDiagonal());
|
||||
g.insert(keys_[j], -info_(j, n).knownOffDiagonal());
|
||||
return g;
|
||||
}
|
||||
|
||||
|
|
@ -436,8 +437,7 @@ Vector HessianFactor::gradient(Key key, const VectorValues& x) const {
|
|||
if (i > j) {
|
||||
Matrix Gji = info(j, i);
|
||||
Gij = Gji.transpose();
|
||||
}
|
||||
else {
|
||||
} else {
|
||||
Gij = info(i, j);
|
||||
}
|
||||
// Accumulate Gij*xj to gradf
|
||||
|
|
@ -449,16 +449,17 @@ Vector HessianFactor::gradient(Key key, const VectorValues& x) const {
|
|||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
std::pair<boost::shared_ptr<GaussianConditional>, boost::shared_ptr<HessianFactor> >
|
||||
EliminateCholesky(const GaussianFactorGraph& factors, const Ordering& keys)
|
||||
{
|
||||
std::pair<boost::shared_ptr<GaussianConditional>,
|
||||
boost::shared_ptr<HessianFactor> > EliminateCholesky(
|
||||
const GaussianFactorGraph& factors, const Ordering& keys) {
|
||||
gttic(EliminateCholesky);
|
||||
|
||||
// Build joint factor
|
||||
HessianFactor::shared_ptr jointFactor;
|
||||
try {
|
||||
jointFactor = boost::make_shared<HessianFactor>(factors, Scatter(factors, keys));
|
||||
} catch(std::invalid_argument&) {
|
||||
jointFactor = boost::make_shared<HessianFactor>(factors,
|
||||
Scatter(factors, keys));
|
||||
} catch (std::invalid_argument&) {
|
||||
throw InvalidDenseElimination(
|
||||
"EliminateCholesky was called with a request to eliminate variables that are not\n"
|
||||
"involved in the provided factors.");
|
||||
|
|
@ -468,11 +469,14 @@ EliminateCholesky(const GaussianFactorGraph& factors, const Ordering& keys)
|
|||
GaussianConditional::shared_ptr conditional;
|
||||
try {
|
||||
size_t numberOfKeysToEliminate = keys.size();
|
||||
VerticalBlockMatrix Ab = jointFactor->info_.choleskyPartial(numberOfKeysToEliminate);
|
||||
conditional = boost::make_shared<GaussianConditional>(jointFactor->keys(), numberOfKeysToEliminate, Ab);
|
||||
VerticalBlockMatrix Ab = jointFactor->info_.choleskyPartial(
|
||||
numberOfKeysToEliminate);
|
||||
conditional = boost::make_shared<GaussianConditional>(jointFactor->keys(),
|
||||
numberOfKeysToEliminate, Ab);
|
||||
// Erase the eliminated keys in the remaining factor
|
||||
jointFactor->keys_.erase(jointFactor->begin(), jointFactor->begin() + numberOfKeysToEliminate);
|
||||
} catch(CholeskyFailed&) {
|
||||
jointFactor->keys_.erase(jointFactor->begin(),
|
||||
jointFactor->begin() + numberOfKeysToEliminate);
|
||||
} catch (CholeskyFailed&) {
|
||||
throw IndeterminantLinearSystemException(keys.front());
|
||||
}
|
||||
|
||||
|
|
@ -481,9 +485,9 @@ EliminateCholesky(const GaussianFactorGraph& factors, const Ordering& keys)
|
|||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
std::pair<boost::shared_ptr<GaussianConditional>, boost::shared_ptr<GaussianFactor> >
|
||||
EliminatePreferCholesky(const GaussianFactorGraph& factors, const Ordering& keys)
|
||||
{
|
||||
std::pair<boost::shared_ptr<GaussianConditional>,
|
||||
boost::shared_ptr<GaussianFactor> > EliminatePreferCholesky(
|
||||
const GaussianFactorGraph& factors, const Ordering& keys) {
|
||||
gttic(EliminatePreferCholesky);
|
||||
|
||||
// If any JacobianFactors have constrained noise models, we have to convert
|
||||
|
|
|
|||
Loading…
Reference in New Issue