Merge pull request #119 from cstew2/eigen-adaptor-rows-or-columns

Add optional argument for Eigen matrix layout

examples/matrix_example.cpp

@@ -96,6 +96,14 @@ void kdtree_demo(const size_t nSamples, const size_t dim) {
   //	typedef KDTreeEigenMatrixAdaptor<
   // Eigen::Matrix<num_t,Dynamic,Dynamic>,nanoflann::metric_L1>  my_kd_tree_t;
 
+  // Dimensionality set at compile-time: Explicit selection of the distance
+  // metric: L2
+  // Row Major matrix layout
+  // Eigen::Matrix<num_t, Dynamic, Dynamic> mat(dim, nSamples);
+  //	typedef KDTreeEigenMatrixAdaptor<
+  // Eigen::Matrix<num_t,Dynamic,Dynamic>,nanoflann::metric_L2, true>
+  // my_kd_tree_t;
+
   my_kd_tree_t mat_index(dim, std::cref(mat), 10 /* max leaf */);
   mat_index.index->buildIndex();
 

include/nanoflann.hpp

@@ -1933,8 +1933,8 @@ class KDTreeSingleIndexDynamicAdaptor {
 };
 
 /** An L2-metric KD-tree adaptor for working with data directly stored in an
- * Eigen Matrix, without duplicating the data storage. Each row in the matrix
- * represents a point in the state space.
+ * Eigen Matrix, without duplicating the data storage. You can select whether a 
+ * row or column in the matrix represents a point in the state space.
  *
  *  Example of usage:
  * \code
@@ -1948,11 +1948,14 @@ class KDTreeSingleIndexDynamicAdaptor {
  *  \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality
  * for the points in the data set, allowing more compiler optimizations. \tparam
  * Distance The distance metric to use: nanoflann::metric_L1,
- * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
+ * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam row_major 
+ * If set to true the rows of the matrix are used as the points, if set to false
+ * the columns of the matrix are used as the points.
  */
-template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2>
+template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2,
+	  bool row_major = true>
 struct KDTreeEigenMatrixAdaptor {
-  typedef KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance> self_t;
+  typedef KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance, row_major> self_t;
   typedef typename MatrixType::Scalar num_t;
   typedef typename MatrixType::Index IndexType;
   typedef
@@ -1969,7 +1972,7 @@ struct KDTreeEigenMatrixAdaptor {
                            const std::reference_wrapper<const MatrixType> &mat,
                            const int leaf_max_size = 10)
       : m_data_matrix(mat) {
-    const auto dims = mat.get().cols();
+    const auto dims = row_major ? mat.get().cols() : mat.get().rows();
     if (size_t(dims) != dimensionality)
       throw std::runtime_error(
           "Error: 'dimensionality' must match column count in data matrix");
@@ -2012,12 +2015,18 @@ struct KDTreeEigenMatrixAdaptor {
 
   // Must return the number of data points
   inline size_t kdtree_get_point_count() const {
-    return m_data_matrix.get().rows();
+    if(row_major)
+      return m_data_matrix.get().rows();
+    else
+      return m_data_matrix.get().cols();
   }
 
   // Returns the dim'th component of the idx'th point in the class:
   inline num_t kdtree_get_pt(const IndexType idx, size_t dim) const {
-    return m_data_matrix.get().coeff(idx, IndexType(dim));
+    if(row_major)
+      return m_data_matrix.get().coeff(idx, IndexType(dim));
+    else
+      return m_data_matrix.get().coeff(IndexType(dim), idx);
   }
 
   // Optional bounding-box computation: return false to default to a standard