bd_tree.cpp

c++实现的KNN库:建立高维度的K-d tree,实现K邻域搜索

//----------------------------------------------------------------------
// File:			bd_tree.cpp
// Programmer:		David Mount
// Description:		Basic methods for bd-trees.
// Last modified:	01/04/05 (Version 1.0)
//----------------------------------------------------------------------
// Copyright (c) 1997-2005 University of Maryland and Sunil Arya and
// David Mount.  All Rights Reserved.
// 
// This software and related documentation is part of the Approximate
// Nearest Neighbor Library (ANN).  This software is provided under
// the provisions of the Lesser GNU Public License (LGPL).  See the
// file ../ReadMe.txt for further information.
// 
// The University of Maryland (U.M.) and the authors make no
// representations about the suitability or fitness of this software for
// any purpose.  It is provided "as is" without express or implied
// warranty.
//----------------------------------------------------------------------
// History:
//	Revision 0.1  03/04/98
//		Initial release
//	Revision l.0  04/01/05
//		Fixed centroid shrink threshold condition to depend on the
//			dimension.
//		Moved dump routine to kd_dump.cpp.
//----------------------------------------------------------------------

#include "bd_tree.h"					// bd-tree declarations
#include "kd_util.h"					// kd-tree utilities
#include "kd_split.h"					// kd-tree splitting rules

#include <ANN/ANNperf.h>				// performance evaluation

//----------------------------------------------------------------------
//	Printing a bd-tree 
//		These routines print a bd-tree.   See the analogous procedure
//		in kd_tree.cpp for more information.
//----------------------------------------------------------------------

void ANNbd_shrink::print(				// print shrinking node
		int level,						// depth of node in tree
		ostream &out)					// output stream
{
	child[ANN_OUT]->print(level+1, out);		// print out-child

	out << "    ";
	for (int i = 0; i < level; i++)				// print indentation
		out << "..";
	out << "Shrink";
	for (int j = 0; j < n_bnds; j++) {			// print sides, 2 per line
		if (j % 2 == 0) {
			out << "\n";						// newline and indentation
			for (int i = 0; i < level+2; i++) out << "  ";
		}
		out << "  ([" << bnds[j].cd << "]"
			 << (bnds[j].sd > 0 ? ">=" : "< ")
			 << bnds[j].cv << ")";
	}
	out << "\n";

	child[ANN_IN]->print(level+1, out);			// print in-child
}

//----------------------------------------------------------------------
//	kd_tree statistics utility (for performance evaluation)
//		This routine computes various statistics information for
//		shrinking nodes.  See file kd_tree.cpp for more information.
//----------------------------------------------------------------------

void ANNbd_shrink::getStats(					// get subtree statistics
	int					dim,					// dimension of space
	ANNkdStats			&st,					// stats (modified)
	ANNorthRect			&bnd_box)				// bounding box
{
	ANNkdStats ch_stats;						// stats for children
	ANNorthRect inner_box(dim);					// inner box of shrink

	annBnds2Box(bnd_box,						// enclosing box
				dim,							// dimension
				n_bnds,							// number of bounds
				bnds,							// bounds array
				inner_box);						// inner box (modified)
												// get stats for inner child
	ch_stats.reset();							// reset
	child[ANN_IN]->getStats(dim, ch_stats, inner_box);
	st.merge(ch_stats);							// merge them
												// get stats for outer child
	ch_stats.reset();							// reset
	child[ANN_OUT]->getStats(dim, ch_stats, bnd_box);
	st.merge(ch_stats);							// merge them

	st.depth++;									// increment depth
	st.n_shr++;									// increment number of shrinks
}

//----------------------------------------------------------------------
// bd-tree constructor
//		This is the main constructor for bd-trees given a set of points.
//		It first builds a skeleton kd-tree as a basis, then computes the
//		bounding box of the data points, and then invokes rbd_tree() to
//		actually build the tree, passing it the appropriate splitting
//		and shrinking information.
//----------------------------------------------------------------------

ANNkd_ptr rbd_tree(						// recursive construction of bd-tree
	ANNpointArray		pa,				// point array
	ANNidxArray			pidx,			// point indices to store in subtree
	int					n,				// number of points
	int					dim,			// dimension of space
	int					bsp,			// bucket space
	ANNorthRect			&bnd_box,		// bounding box for current node
	ANNkd_splitter		splitter,		// splitting routine
	ANNshrinkRule		shrink);		// shrinking rule

ANNbd_tree::ANNbd_tree(					// construct from point array
	ANNpointArray		pa,				// point array (with at least n pts)
	int					n,				// number of points
	int					dd,				// dimension
	int					bs,				// bucket size
	ANNsplitRule		split,			// splitting rule
	ANNshrinkRule		shrink)			// shrinking rule
	: ANNkd_tree(n, dd, bs)				// build skeleton base tree
{
	pts = pa;							// where the points are
	if (n == 0) return;					// no points--no sweat

	ANNorthRect bnd_box(dd);			// bounding box for points
										// construct bounding rectangle
	annEnclRect(pa, pidx, n, dd, bnd_box);
										// copy to tree structure
	bnd_box_lo = annCopyPt(dd, bnd_box.lo);
	bnd_box_hi = annCopyPt(dd, bnd_box.hi);

	switch (split) {					// build by rule
	case ANN_KD_STD:					// standard kd-splitting rule
		root = rbd_tree(pa, pidx, n, dd, bs, bnd_box, kd_split, shrink);
		break;
	case ANN_KD_MIDPT:					// midpoint split
		root = rbd_tree(pa, pidx, n, dd, bs, bnd_box, midpt_split, shrink);
		break;
	case ANN_KD_SUGGEST:				// best (in our opinion)
	case ANN_KD_SL_MIDPT:				// sliding midpoint split
		root = rbd_tree(pa, pidx, n, dd, bs, bnd_box, sl_midpt_split, shrink);
		break;
	case ANN_KD_FAIR:					// fair split
		root = rbd_tree(pa, pidx, n, dd, bs, bnd_box, fair_split, shrink);
		break;
	case ANN_KD_SL_FAIR:				// sliding fair split
		root = rbd_tree(pa, pidx, n, dd, bs,
						bnd_box, sl_fair_split, shrink);
		break;
	default:
		annError("Illegal splitting method", ANNabort);
	}
}

//----------------------------------------------------------------------
//	Shrinking rules
//----------------------------------------------------------------------

enum ANNdecomp {SPLIT, SHRINK};			// decomposition methods

//----------------------------------------------------------------------
//	trySimpleShrink - Attempt a simple shrink
//
//		We compute the tight bounding box of the points, and compute
//		the 2*dim ``gaps'' between the sides of the tight box and the
//		bounding box.  If any of the gaps is large enough relative to
//		the longest side of the tight bounding box, then we shrink
//		all sides whose gaps are large enough.  (The reason for
//		comparing against the tight bounding box, is that after
//		shrinking the longest box size will decrease, and if we use
//		the standard bounding box, we may decide to shrink twice in
//		a row.  Since the tight box is fixed, we cannot shrink twice
//		consecutively.)
//----------------------------------------------------------------------
const float BD_GAP_THRESH = 0.5;		// gap threshold (must be < 1)
const int   BD_CT_THRESH  = 2;			// min number of shrink sides

ANNdecomp trySimpleShrink(				// try a simple shrink
	ANNpointArray		pa,				// point array
	ANNidxArray			pidx,			// point indices to store in subtree
	int					n,				// number of points
	int					dim,			// dimension of space
	const ANNorthRect	&bnd_box,		// current bounding box
	ANNorthRect			&inner_box)		// inner box if shrinking (returned)
{
	int i;
												// compute tight bounding box
	annEnclRect(pa, pidx, n, dim, inner_box);

	ANNcoord max_length = 0;					// find longest box side
	for (i = 0; i < dim; i++) {
		ANNcoord length = inner_box.hi[i] - inner_box.lo[i];
		if (length > max_length) {
			max_length = length;
		}
	}

	int shrink_ct = 0;							// number of sides we shrunk
	for (i = 0; i < dim; i++) {					// select which sides to shrink
												// gap between boxes
		ANNcoord gap_hi = bnd_box.hi[i] - inner_box.hi[i];
												// big enough gap to shrink?
		if (gap_hi < max_length*BD_GAP_THRESH)
			inner_box.hi[i] = bnd_box.hi[i];	// no - expand
		else shrink_ct++;						// yes - shrink this side