Create JacobianFactor derived class for fixed size and add raw memory access

gtsam/slam/RegularJacobianFactor.h

@@ -1,4 +1,141 @@
-#ifndef REGULARJACOBIANFACTOR_H
+/* ----------------------------------------------------------------------------
-#define REGULARJACOBIANFACTOR_H
+
+ * GTSAM Copyright 2010, Georgia Tech Research Corporation,
+ * Atlanta, Georgia 30332-0415
+ * All Rights Reserved
+ * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
+
+ * See LICENSE for the license information
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ * @file    RegularJacobianFactor.h
+ * @brief   JacobianFactor class with fixed sized blcoks
+ * @author  Sungtae An
+ * @date    Nov 11, 2014
+ */
+
+#pragma once
+
+#include <gtsam/linear/JacobianFactor.h>
+#include <boost/foreach.hpp>
+#include <vector>
+
+namespace gtsam {
+
+template<size_t D>
+class RegularJacobianFactor: public JacobianFactor {
+
+private:
+
+  typedef Eigen::Matrix<double, D, D> MatrixDD; // camera hessian block
+  typedef Eigen::Matrix<double, D, 1> VectorD;
+  // Use eigen magic to access raw memory
+  typedef Eigen::Map<VectorD> DMap;
+  typedef Eigen::Map<const VectorD> ConstDMap;
+
+public:
+
+  /** Construct an n-ary factor
+   * @tparam TERMS A container whose value type is std::pair<Key, Matrix>, specifying the
+   *         collection of keys and matrices making up the factor. */
+  template<typename TERMS>
+  RegularJacobianFactor(const TERMS& terms, const Vector& b,
+      const SharedDiagonal& model = SharedDiagonal()) :
+      JacobianFactor(terms, b, model) {
+  }
+
+  /** Constructor with arbitrary number keys, and where the augmented matrix is given all together
+   *  instead of in block terms.  Note that only the active view of the provided augmented matrix
+   *  is used, and that the matrix data is copied into a newly-allocated matrix in the constructed
+   *  factor. */
+  template<typename KEYS>
+  RegularJacobianFactor(const KEYS& keys,
+      const VerticalBlockMatrix& augmentedMatrix,
+      const SharedDiagonal& sigmas = SharedDiagonal()) :
+      JacobianFactor(keys, augmentedMatrix, sigmas) {
+  }
+
+  /// Return the diagonal of the Hessian for this factor
+  VectorValues hessianDiagonal() const {
+    return JacobianFactor::hessianDiagonal();
+  }
+
+  /// Raw memory access version of hessianDiagonal
+  void hessianDiagonal(double* d) const {
+    // Loop over all variables in the factor
+    for (DenseIndex j = 0; j < (DenseIndex) size(); ++j) {
+      // Get the diagonal block, and insert its diagonal
+      DVector dj;
+      for (size_t k = 0; k < D; ++k)
+        dj(k) = Ab_(j).col(k).squaredNorm();
+
+      DMap(d + D * j) += dj;
+    }
+  }
+
+  /// y += alpha * A'*A*x
+  void multiplyHessianAdd(double alpha, const VectorValues& x,
+      VectorValues& y) const {
+    JacobianFactor::multiplyHessianAdd(alpha, x, y);
+  }
+
+  void multiplyHessianAdd(double alpha, const double* x, double* y,
+      std::vector<size_t> keys) const {
+    if (empty())
+      return;
+    Vector Ax = zero(Ab_.rows());
+
+    // Just iterate over all A matrices and multiply in correct config part
+    for (size_t pos = 0; pos < size(); ++pos)
+      Ax += Ab_(pos)
+          * ConstDMap(x + keys[keys_[pos]],
+              keys[keys_[pos] + 1] - keys[keys_[pos]]);
+
+    // Deal with noise properly, need to Double* whiten as we are dividing by variance
+    if (model_) {
+      model_->whitenInPlace(Ax);
+      model_->whitenInPlace(Ax);
+    }
+
+    // multiply with alpha
+    Ax *= alpha;
+
+    // Again iterate over all A matrices and insert Ai^e into y
+    for (size_t pos = 0; pos < size(); ++pos)
+      DMap(y + keys[keys_[pos]], keys[keys_[pos] + 1] - keys[keys_[pos]]) += Ab_(
+          pos).transpose() * Ax;
+  }
+
+  void multiplyHessianAdd(double alpha, const double* x, double* y) const {
+    if (empty()) return;
+    Vector Ax = zero(Ab_.rows());
+
+    // Just iterate over all A matrices and multiply in correct config part
+    for(size_t pos=0; pos<size(); ++pos)
+      Ax += Ab_(pos) * ConstDMap(x + D * keys_[pos]);
+
+    // Deal with noise properly, need to Double* whiten as we are dividing by variance
+    if  (model_) { model_->whitenInPlace(Ax); model_->whitenInPlace(Ax); }
+
+    // multiply with alpha
+    Ax *= alpha;
+
+    // Again iterate over all A matrices and insert Ai^e into y
+    for(size_t pos=0; pos<size(); ++pos)
+      DMap(y + D * keys_[pos]) += Ab_(pos).transpose() * Ax;
+  }
+
+  VectorValues gradientAtZero() const {
+    return JacobianFactor::gradientAtZero();
+  }
+
+  void gradientAtZero(double* d) const {
+    //throw std::runtime_error("gradientAtZero not implemented for Jacobian factor");
+  }
+
+};
+
+}
 
-#endif // REGULARJACOBIANFACTOR_H