Added the function for making kdtrees out of hyperrectangles.
This commit is contained in:
Dongryeol Lee
@@ -8,6 +8,8 @@ librule(
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"gen_range.h",
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"gen_kdtree.h",
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"gen_kdtree_impl.h",
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"gen_kdtree_hyper.h",
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"gen_kdtree_hyper_impl.h",
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"gen_metric_tree.h",
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"gen_metric_tree_impl.h",
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"general_spacetree.h"], # include files part of the 'lib'
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@@ -0,0 +1,93 @@
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// Copyright 2007 Georgia Institute of Technology. All rights reserved.
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// ABSOLUTELY NOT FOR DISTRIBUTION
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/**
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* @file tree/kdtree.h
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*
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* Tools for kd-trees.
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*
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* Eventually we hope to support KD trees with non-L2 (Euclidean)
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* metrics, like Manhattan distance.
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*
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* @experimental
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*/
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#ifndef TREE_GEN_KDTREE_HYPER_H
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#define TREE_GEN_KDTREE_HYPER_H
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#include "general_spacetree.h"
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#include "general_type_bounds.h"
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#include "fastlib/base/common.h"
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#include "fastlib/col/arraylist.h"
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#include "fastlib/fx/fx.h"
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#include "gen_kdtree_hyper_impl.h"
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/**
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* Regular pointer-style trees (as opposed to THOR trees).
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*/
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namespace proximity {
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public:
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/**
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* Creates a KD tree from hyperrectangles
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*
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* @experimental
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*
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* This requires you to pass in two unitialized ArrayLists which will contain
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* index mappings so you can account for the re-ordering of the matrix.
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* (By unitialized I mean don't call Init on it)
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*
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* @param matrix data where each column is a point, WHICH WILL BE RE-ORDERED
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* @param leaf_size the maximum points in a leaf
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* @param old_from_new pointer to an unitialized arraylist; it will map
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* new indices to original
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* @param new_from_old pointer to an unitialized arraylist; it will map
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* original indexes to new indices
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*/
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template<typename T, typename TKdTree, typename TKdTreeSplitter>
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TKdTree *MakeGenKdTree(GenMatrix<T>& lower_limit_matrix,
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GenMatrix<T>& upper_limit_matrix,
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index_t leaf_size,
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ArrayList<index_t> *old_from_new = NULL,
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ArrayList<index_t> *new_from_old = NULL) {
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TKdTree *node = new TKdTree();
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index_t *old_from_new_ptr;
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if (old_from_new) {
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old_from_new->Init(lower_limit_matrix.n_cols());
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for (index_t i = 0; i < lower_limit_matrix.n_cols(); i++) {
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(*old_from_new)[i] = i;
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}
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old_from_new_ptr = old_from_new->begin();
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}
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else {
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old_from_new_ptr = NULL;
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}
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node->Init(0, lower_limit_matrix.n_cols());
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node->bound().Init(lower_limit_matrix.n_rows());
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tree_gen_kdtree_private::FindBoundFromMatrix
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(lower_limit_matrix, upper_limit_matrix, 0, lower_limit_matrix.n_cols(),
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&node->bound());
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tree_gen_kdtree_private::SplitGenKdTree<T, TKdTree, TKdTreeSplitter>
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(lower_limit_matrix, upper_limit_matrix, node, leaf_size,
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old_from_new_ptr);
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if (new_from_old) {
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new_from_old->Init(lower_limit_matrix.n_cols());
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for (index_t i = 0; i < lower_limit_matrix.n_cols(); i++) {
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(*new_from_old)[(*old_from_new)[i]] = i;
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}
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}
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return node;
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}
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};
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#endif
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@@ -0,0 +1,177 @@
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/* Implementation for the regular pointer-style kd-tree builder. */
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#include "fastlib/fastlib_int.h"
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namespace tree_gen_kdtree_private {
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template<typename T, typename TBound>
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void FindBoundFromMatrix(const GenMatrix<T>& lower_limit_matrix,
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const GenMatrix<T>& upper_limit_matrix,
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index_t first, index_t count, TBound *bounds) {
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index_t end = first + count;
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for (index_t i = first; i < end; i++) {
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GenVector<T> col;
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lower_limit_matrix.MakeColumnVector(i, &col);
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*bounds |= col;
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col.Destruct();
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upper_limit_matrix.MakeColumnVector(i, &col);
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*bounds |= col;
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}
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}
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template<typename T, typename TBound>
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index_t MatrixPartition(GenMatrix<T>& lower_limit_matrix,
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GenMatrix<T>& upper_limit_matrix,
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index_t dim, double splitvalue,
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index_t first, index_t count, TBound* left_bound,
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TBound* right_bound, index_t *old_from_new) {
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index_t left = first;
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index_t right = first + count - 1;
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/* At any point:
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*
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* everything < left is correct
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* everything > right is correct
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*/
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for (;;) {
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// If the lower limit is at most the split value, then put it in
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// the left.
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while (lower_limit_matrix.get(dim, left) <= splitvalue &&
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likely(left <= right)) {
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GenVector<T> left_vector;
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lower_limit_matrix.MakeColumnVector(left, &left_vector);
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*left_bound |= left_vector;
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left_vector.Destruct();
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upper_limit_matrix.MakeColumnVector(left, &left_vector);
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*left_bound |= left_vector;
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left++;
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}
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// If the upper limit is at least the split value, then put it
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// in the right.
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while (upper_limit_matrix.get(dim, right) >= splitvalue &&
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likely(left <= right)) {
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GenVector<T> right_vector;
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lower_limit_matrix.MakeColumnVector(right, &right_vector);
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*right_bound |= right_vector;
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right_vector.Destruct();
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upper_limit_matrix.MakeColumnVector(right, &right_vector);
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*right_bound |= right_vector;
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right--;
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}
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if (unlikely(left > right)) {
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/* left == right + 1 */
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break;
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}
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GenVector<T> left_vector;
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GenVector<T> right_vector;
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// First swap the lower limits.
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lower_limit_matrix.MakeColumnVector(left, &left_vector);
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lower_limit_matrix.MakeColumnVector(right, &right_vector);
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left_vector.SwapValues(&right_vector);
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*left_bound |= left_vector;
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*right_bound |= right_vector;
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// Then swap the upper limits.
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left_vector.Destruct();
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right_vector.Destruct();
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upper_limit_matrix.MakeColumnVector(left, &left_vector);
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upper_limit_matrix.MakeColumnVector(right, &right_vector);
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left_vector.SwapValues(&right_vector);
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*left_bound |= left_vector;
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*right_bound |= right_vector;
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// Swap indices...
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if (old_from_new) {
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index_t t = old_from_new[left];
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old_from_new[left] = old_from_new[right];
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old_from_new[right] = t;
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}
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DEBUG_ASSERT(left <= right);
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right--;
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}
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DEBUG_ASSERT(left == right + 1);
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return left;
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}
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template<typename T, typename TKdTree, typename TKdTreeSplitter>
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void SplitGenKdTree(GenMatrix<T>& lower_limit_matrix,
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GenMatrix<T>& upper_limit_matrix, TKdTree *node,
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index_t leaf_size, index_t *old_from_new) {
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TKdTree *left = NULL;
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TKdTree *right = NULL;
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if (node->count() > leaf_size) {
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index_t split_dim = BIG_BAD_NUMBER;
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T max_width = -1;
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for (index_t d = 0; d < lower_limit_matrix.n_rows(); d++) {
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T w = node->bound().get(d).width();
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if (unlikely(w > max_width)) {
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max_width = w;
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split_dim = d;
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}
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}
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// choose the split value along the dimension to be splitted
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double split_val =
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TKdTreeSplitter::ChooseKdTreeSplitValue(lower_limit_matrix,
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upper_limit_matrix, node,
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split_dim);
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if (max_width < DBL_EPSILON) {
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// Okay, we can't do any splitting, because all these points are the
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// same. We have to give up.
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}
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else {
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left = new TKdTree();
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left->bound().Init(lower_limit_matrix.n_rows());
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right = new TKdTree();
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right->bound().Init(lower_limit_matrix.n_rows());
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index_t split_col =
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MatrixPartition(lower_limit_matrix, upper_limit_matrix,
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split_dim, split_val,
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node->begin(), node->count(),
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&left->bound(), &right->bound(),
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old_from_new);
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VERBOSE_MSG(3.0,"split (%d,[%d],%d) dim %d on %f (between %f, %f)",
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node->begin(), split_col,
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node->begin() + node->count(), split_dim, split_val,
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node->bound().get(split_dim).lo,
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node->bound().get(split_dim).hi);
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left->Init(node->begin(), split_col - node->begin());
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right->Init(split_col, node->begin() + node->count() - split_col);
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SplitGenKdTree<T, TKdTree, TKdTreeSplitter>
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(lower_limit_matrix, upper_limit_matrix, left, leaf_size,
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old_from_new);
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SplitGenKdTree<T, TKdTree, TKdTreeSplitter>
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(lower_limit_matrix, upper_limit_matrix, right, leaf_size,
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old_from_new);
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}
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}
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node->set_children(matrix, left, right);
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}
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};
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