1172 lines
36 KiB
C++
1172 lines
36 KiB
C++
/***********************************************************************
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* Software License Agreement (BSD License)
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*
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* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
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* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
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*
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* THE BSD LICENSE
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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*
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* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
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* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
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* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
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* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
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* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*************************************************************************/
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#ifndef OPENCV_FLANN_KMEANS_INDEX_H_
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#define OPENCV_FLANN_KMEANS_INDEX_H_
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#include <algorithm>
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#include <map>
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#include <cassert>
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#include <limits>
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#include <cmath>
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#include "general.h"
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#include "nn_index.h"
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#include "dist.h"
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#include "matrix.h"
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#include "result_set.h"
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#include "heap.h"
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#include "allocator.h"
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#include "random.h"
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#include "saving.h"
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#include "logger.h"
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namespace cvflann
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{
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struct KMeansIndexParams : public IndexParams
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{
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KMeansIndexParams(int branching = 32, int iterations = 11,
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flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
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{
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(*this)["algorithm"] = FLANN_INDEX_KMEANS;
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// branching factor
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(*this)["branching"] = branching;
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// max iterations to perform in one kmeans clustering (kmeans tree)
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(*this)["iterations"] = iterations;
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// algorithm used for picking the initial cluster centers for kmeans tree
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(*this)["centers_init"] = centers_init;
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// cluster boundary index. Used when searching the kmeans tree
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(*this)["cb_index"] = cb_index;
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}
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};
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/**
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* Hierarchical kmeans index
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*
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* Contains a tree constructed through a hierarchical kmeans clustering
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* and other information for indexing a set of points for nearest-neighbour matching.
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*/
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template <typename Distance>
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class KMeansIndex : public NNIndex<Distance>
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{
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public:
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typedef typename Distance::ElementType ElementType;
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typedef typename Distance::ResultType DistanceType;
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typedef void (KMeansIndex::* centersAlgFunction)(int, int*, int, int*, int&);
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/**
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* The function used for choosing the cluster centers.
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*/
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centersAlgFunction chooseCenters;
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/**
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* Chooses the initial centers in the k-means clustering in a random manner.
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*
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* Params:
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* k = number of centers
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* vecs = the dataset of points
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* indices = indices in the dataset
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* indices_length = length of indices vector
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*
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*/
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void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length)
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{
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UniqueRandom r(indices_length);
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int index;
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for (index=0; index<k; ++index) {
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bool duplicate = true;
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int rnd;
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while (duplicate) {
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duplicate = false;
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rnd = r.next();
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if (rnd<0) {
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centers_length = index;
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return;
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}
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centers[index] = indices[rnd];
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for (int j=0; j<index; ++j) {
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DistanceType sq = distance_(dataset_[centers[index]], dataset_[centers[j]], dataset_.cols);
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if (sq<1e-16) {
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duplicate = true;
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}
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}
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}
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}
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centers_length = index;
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}
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/**
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* Chooses the initial centers in the k-means using Gonzales' algorithm
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* so that the centers are spaced apart from each other.
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*
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* Params:
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* k = number of centers
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* vecs = the dataset of points
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* indices = indices in the dataset
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* Returns:
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*/
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void chooseCentersGonzales(int k, int* indices, int indices_length, int* centers, int& centers_length)
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{
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int n = indices_length;
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int rnd = rand_int(n);
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assert(rnd >=0 && rnd < n);
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centers[0] = indices[rnd];
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int index;
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for (index=1; index<k; ++index) {
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int best_index = -1;
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DistanceType best_val = 0;
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for (int j=0; j<n; ++j) {
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DistanceType dist = distance_(dataset_[centers[0]],dataset_[indices[j]],dataset_.cols);
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for (int i=1; i<index; ++i) {
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DistanceType tmp_dist = distance_(dataset_[centers[i]],dataset_[indices[j]],dataset_.cols);
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if (tmp_dist<dist) {
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dist = tmp_dist;
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}
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}
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if (dist>best_val) {
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best_val = dist;
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best_index = j;
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}
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}
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if (best_index!=-1) {
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centers[index] = indices[best_index];
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}
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else {
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break;
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}
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}
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centers_length = index;
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}
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/**
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* Chooses the initial centers in the k-means using the algorithm
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* proposed in the KMeans++ paper:
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* Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding
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*
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* Implementation of this function was converted from the one provided in Arthur's code.
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*
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* Params:
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* k = number of centers
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* vecs = the dataset of points
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* indices = indices in the dataset
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* Returns:
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*/
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void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length)
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{
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int n = indices_length;
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double currentPot = 0;
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DistanceType* closestDistSq = new DistanceType[n];
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// Choose one random center and set the closestDistSq values
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int index = rand_int(n);
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assert(index >=0 && index < n);
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centers[0] = indices[index];
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for (int i = 0; i < n; i++) {
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closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
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closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
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currentPot += closestDistSq[i];
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}
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const int numLocalTries = 1;
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// Choose each center
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int centerCount;
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for (centerCount = 1; centerCount < k; centerCount++) {
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// Repeat several trials
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double bestNewPot = -1;
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int bestNewIndex = -1;
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for (int localTrial = 0; localTrial < numLocalTries; localTrial++) {
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// Choose our center - have to be slightly careful to return a valid answer even accounting
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// for possible rounding errors
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double randVal = rand_double(currentPot);
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for (index = 0; index < n-1; index++) {
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if (randVal <= closestDistSq[index]) break;
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else randVal -= closestDistSq[index];
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}
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// Compute the new potential
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double newPot = 0;
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for (int i = 0; i < n; i++) {
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DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
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newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
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}
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// Store the best result
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if ((bestNewPot < 0)||(newPot < bestNewPot)) {
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bestNewPot = newPot;
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bestNewIndex = index;
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}
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}
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// Add the appropriate center
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centers[centerCount] = indices[bestNewIndex];
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currentPot = bestNewPot;
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for (int i = 0; i < n; i++) {
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DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
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closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
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}
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}
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centers_length = centerCount;
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delete[] closestDistSq;
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}
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public:
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flann_algorithm_t getType() const
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{
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return FLANN_INDEX_KMEANS;
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}
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class KMeansDistanceComputer : public cv::ParallelLoopBody
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{
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public:
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KMeansDistanceComputer(Distance _distance, const Matrix<ElementType>& _dataset,
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const int _branching, const int* _indices, const Matrix<double>& _dcenters, const size_t _veclen,
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int* _count, int* _belongs_to, std::vector<DistanceType>& _radiuses, bool& _converged, cv::Mutex& _mtx)
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: distance(_distance)
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, dataset(_dataset)
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, branching(_branching)
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, indices(_indices)
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, dcenters(_dcenters)
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, veclen(_veclen)
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, count(_count)
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, belongs_to(_belongs_to)
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, radiuses(_radiuses)
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, converged(_converged)
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, mtx(_mtx)
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{
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}
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void operator()(const cv::Range& range) const
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{
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const int begin = range.start;
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const int end = range.end;
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for( int i = begin; i<end; ++i)
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{
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DistanceType sq_dist = distance(dataset[indices[i]], dcenters[0], veclen);
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int new_centroid = 0;
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for (int j=1; j<branching; ++j) {
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DistanceType new_sq_dist = distance(dataset[indices[i]], dcenters[j], veclen);
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if (sq_dist>new_sq_dist) {
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new_centroid = j;
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sq_dist = new_sq_dist;
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}
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}
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if (sq_dist > radiuses[new_centroid]) {
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radiuses[new_centroid] = sq_dist;
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}
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if (new_centroid != belongs_to[i]) {
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count[belongs_to[i]]--;
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count[new_centroid]++;
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belongs_to[i] = new_centroid;
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mtx.lock();
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converged = false;
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mtx.unlock();
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}
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}
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}
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private:
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Distance distance;
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const Matrix<ElementType>& dataset;
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const int branching;
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const int* indices;
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const Matrix<double>& dcenters;
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const size_t veclen;
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int* count;
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int* belongs_to;
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std::vector<DistanceType>& radiuses;
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bool& converged;
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cv::Mutex& mtx;
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KMeansDistanceComputer& operator=( const KMeansDistanceComputer & ) { return *this; }
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};
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/**
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* Index constructor
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*
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* Params:
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* inputData = dataset with the input features
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* params = parameters passed to the hierarchical k-means algorithm
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*/
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KMeansIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KMeansIndexParams(),
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Distance d = Distance())
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: dataset_(inputData), index_params_(params), root_(NULL), indices_(NULL), distance_(d)
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{
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memoryCounter_ = 0;
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size_ = dataset_.rows;
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veclen_ = dataset_.cols;
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branching_ = get_param(params,"branching",32);
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iterations_ = get_param(params,"iterations",11);
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if (iterations_<0) {
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iterations_ = (std::numeric_limits<int>::max)();
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}
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centers_init_ = get_param(params,"centers_init",FLANN_CENTERS_RANDOM);
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if (centers_init_==FLANN_CENTERS_RANDOM) {
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chooseCenters = &KMeansIndex::chooseCentersRandom;
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}
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else if (centers_init_==FLANN_CENTERS_GONZALES) {
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chooseCenters = &KMeansIndex::chooseCentersGonzales;
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}
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else if (centers_init_==FLANN_CENTERS_KMEANSPP) {
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chooseCenters = &KMeansIndex::chooseCentersKMeanspp;
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}
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else {
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throw FLANNException("Unknown algorithm for choosing initial centers.");
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}
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cb_index_ = 0.4f;
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}
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KMeansIndex(const KMeansIndex&);
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KMeansIndex& operator=(const KMeansIndex&);
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/**
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* Index destructor.
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*
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* Release the memory used by the index.
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*/
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virtual ~KMeansIndex()
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{
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if (root_ != NULL) {
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free_centers(root_);
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}
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if (indices_!=NULL) {
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delete[] indices_;
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}
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}
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/**
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* Returns size of index.
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*/
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size_t size() const
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{
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return size_;
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}
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/**
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* Returns the length of an index feature.
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*/
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size_t veclen() const
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{
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return veclen_;
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}
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void set_cb_index( float index)
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{
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cb_index_ = index;
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}
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/**
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* Computes the inde memory usage
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* Returns: memory used by the index
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*/
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int usedMemory() const
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{
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return pool_.usedMemory+pool_.wastedMemory+memoryCounter_;
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}
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/**
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* Builds the index
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*/
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void buildIndex()
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{
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if (branching_<2) {
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throw FLANNException("Branching factor must be at least 2");
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}
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indices_ = new int[size_];
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for (size_t i=0; i<size_; ++i) {
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indices_[i] = int(i);
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}
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root_ = pool_.allocate<KMeansNode>();
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std::memset(root_, 0, sizeof(KMeansNode));
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computeNodeStatistics(root_, indices_, (int)size_);
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computeClustering(root_, indices_, (int)size_, branching_,0);
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}
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void saveIndex(FILE* stream)
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{
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save_value(stream, branching_);
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save_value(stream, iterations_);
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save_value(stream, memoryCounter_);
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save_value(stream, cb_index_);
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save_value(stream, *indices_, (int)size_);
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save_tree(stream, root_);
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}
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void loadIndex(FILE* stream)
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{
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load_value(stream, branching_);
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load_value(stream, iterations_);
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load_value(stream, memoryCounter_);
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load_value(stream, cb_index_);
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if (indices_!=NULL) {
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delete[] indices_;
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}
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indices_ = new int[size_];
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load_value(stream, *indices_, size_);
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if (root_!=NULL) {
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free_centers(root_);
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}
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load_tree(stream, root_);
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index_params_["algorithm"] = getType();
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index_params_["branching"] = branching_;
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index_params_["iterations"] = iterations_;
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index_params_["centers_init"] = centers_init_;
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index_params_["cb_index"] = cb_index_;
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}
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/**
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* Find set of nearest neighbors to vec. Their indices are stored inside
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* the result object.
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*
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* Params:
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* result = the result object in which the indices of the nearest-neighbors are stored
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* vec = the vector for which to search the nearest neighbors
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* searchParams = parameters that influence the search algorithm (checks, cb_index)
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*/
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void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
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{
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int maxChecks = get_param(searchParams,"checks",32);
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if (maxChecks==FLANN_CHECKS_UNLIMITED) {
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findExactNN(root_, result, vec);
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}
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else {
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// Priority queue storing intermediate branches in the best-bin-first search
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Heap<BranchSt>* heap = new Heap<BranchSt>((int)size_);
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int checks = 0;
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findNN(root_, result, vec, checks, maxChecks, heap);
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BranchSt branch;
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while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
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KMeansNodePtr node = branch.node;
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findNN(node, result, vec, checks, maxChecks, heap);
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}
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assert(result.full());
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delete heap;
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}
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}
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/**
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* Clustering function that takes a cut in the hierarchical k-means
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* tree and return the clusters centers of that clustering.
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* Params:
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* numClusters = number of clusters to have in the clustering computed
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* Returns: number of cluster centers
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*/
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int getClusterCenters(Matrix<DistanceType>& centers)
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{
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int numClusters = centers.rows;
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if (numClusters<1) {
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throw FLANNException("Number of clusters must be at least 1");
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}
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DistanceType variance;
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KMeansNodePtr* clusters = new KMeansNodePtr[numClusters];
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int clusterCount = getMinVarianceClusters(root_, clusters, numClusters, variance);
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Logger::info("Clusters requested: %d, returning %d\n",numClusters, clusterCount);
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for (int i=0; i<clusterCount; ++i) {
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DistanceType* center = clusters[i]->pivot;
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for (size_t j=0; j<veclen_; ++j) {
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centers[i][j] = center[j];
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}
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}
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delete[] clusters;
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return clusterCount;
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}
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IndexParams getParameters() const
|
|
{
|
|
return index_params_;
|
|
}
|
|
|
|
|
|
private:
|
|
/**
|
|
* Struture representing a node in the hierarchical k-means tree.
|
|
*/
|
|
struct KMeansNode
|
|
{
|
|
/**
|
|
* The cluster center.
|
|
*/
|
|
DistanceType* pivot;
|
|
/**
|
|
* The cluster radius.
|
|
*/
|
|
DistanceType radius;
|
|
/**
|
|
* The cluster mean radius.
|
|
*/
|
|
DistanceType mean_radius;
|
|
/**
|
|
* The cluster variance.
|
|
*/
|
|
DistanceType variance;
|
|
/**
|
|
* The cluster size (number of points in the cluster)
|
|
*/
|
|
int size;
|
|
/**
|
|
* Child nodes (only for non-terminal nodes)
|
|
*/
|
|
KMeansNode** childs;
|
|
/**
|
|
* Node points (only for terminal nodes)
|
|
*/
|
|
int* indices;
|
|
/**
|
|
* Level
|
|
*/
|
|
int level;
|
|
};
|
|
typedef KMeansNode* KMeansNodePtr;
|
|
|
|
/**
|
|
* Alias definition for a nicer syntax.
|
|
*/
|
|
typedef BranchStruct<KMeansNodePtr, DistanceType> BranchSt;
|
|
|
|
|
|
|
|
|
|
void save_tree(FILE* stream, KMeansNodePtr node)
|
|
{
|
|
save_value(stream, *node);
|
|
save_value(stream, *(node->pivot), (int)veclen_);
|
|
if (node->childs==NULL) {
|
|
int indices_offset = (int)(node->indices - indices_);
|
|
save_value(stream, indices_offset);
|
|
}
|
|
else {
|
|
for(int i=0; i<branching_; ++i) {
|
|
save_tree(stream, node->childs[i]);
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
void load_tree(FILE* stream, KMeansNodePtr& node)
|
|
{
|
|
node = pool_.allocate<KMeansNode>();
|
|
load_value(stream, *node);
|
|
node->pivot = new DistanceType[veclen_];
|
|
load_value(stream, *(node->pivot), (int)veclen_);
|
|
if (node->childs==NULL) {
|
|
int indices_offset;
|
|
load_value(stream, indices_offset);
|
|
node->indices = indices_ + indices_offset;
|
|
}
|
|
else {
|
|
node->childs = pool_.allocate<KMeansNodePtr>(branching_);
|
|
for(int i=0; i<branching_; ++i) {
|
|
load_tree(stream, node->childs[i]);
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
/**
|
|
* Helper function
|
|
*/
|
|
void free_centers(KMeansNodePtr node)
|
|
{
|
|
delete[] node->pivot;
|
|
if (node->childs!=NULL) {
|
|
for (int k=0; k<branching_; ++k) {
|
|
free_centers(node->childs[k]);
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Computes the statistics of a node (mean, radius, variance).
|
|
*
|
|
* Params:
|
|
* node = the node to use
|
|
* indices = the indices of the points belonging to the node
|
|
*/
|
|
void computeNodeStatistics(KMeansNodePtr node, int* indices, int indices_length)
|
|
{
|
|
|
|
DistanceType radius = 0;
|
|
DistanceType variance = 0;
|
|
DistanceType* mean = new DistanceType[veclen_];
|
|
memoryCounter_ += int(veclen_*sizeof(DistanceType));
|
|
|
|
memset(mean,0,veclen_*sizeof(DistanceType));
|
|
|
|
for (size_t i=0; i<size_; ++i) {
|
|
ElementType* vec = dataset_[indices[i]];
|
|
for (size_t j=0; j<veclen_; ++j) {
|
|
mean[j] += vec[j];
|
|
}
|
|
variance += distance_(vec, ZeroIterator<ElementType>(), veclen_);
|
|
}
|
|
for (size_t j=0; j<veclen_; ++j) {
|
|
mean[j] /= size_;
|
|
}
|
|
variance /= size_;
|
|
variance -= distance_(mean, ZeroIterator<ElementType>(), veclen_);
|
|
|
|
DistanceType tmp = 0;
|
|
for (int i=0; i<indices_length; ++i) {
|
|
tmp = distance_(mean, dataset_[indices[i]], veclen_);
|
|
if (tmp>radius) {
|
|
radius = tmp;
|
|
}
|
|
}
|
|
|
|
node->variance = variance;
|
|
node->radius = radius;
|
|
node->pivot = mean;
|
|
}
|
|
|
|
|
|
/**
|
|
* The method responsible with actually doing the recursive hierarchical
|
|
* clustering
|
|
*
|
|
* Params:
|
|
* node = the node to cluster
|
|
* indices = indices of the points belonging to the current node
|
|
* branching = the branching factor to use in the clustering
|
|
*
|
|
* TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)
|
|
*/
|
|
void computeClustering(KMeansNodePtr node, int* indices, int indices_length, int branching, int level)
|
|
{
|
|
node->size = indices_length;
|
|
node->level = level;
|
|
|
|
if (indices_length < branching) {
|
|
node->indices = indices;
|
|
std::sort(node->indices,node->indices+indices_length);
|
|
node->childs = NULL;
|
|
return;
|
|
}
|
|
|
|
cv::AutoBuffer<int> centers_idx_buf(branching);
|
|
int* centers_idx = (int*)centers_idx_buf;
|
|
int centers_length;
|
|
(this->*chooseCenters)(branching, indices, indices_length, centers_idx, centers_length);
|
|
|
|
if (centers_length<branching) {
|
|
node->indices = indices;
|
|
std::sort(node->indices,node->indices+indices_length);
|
|
node->childs = NULL;
|
|
return;
|
|
}
|
|
|
|
|
|
cv::AutoBuffer<double> dcenters_buf(branching*veclen_);
|
|
Matrix<double> dcenters((double*)dcenters_buf,branching,veclen_);
|
|
for (int i=0; i<centers_length; ++i) {
|
|
ElementType* vec = dataset_[centers_idx[i]];
|
|
for (size_t k=0; k<veclen_; ++k) {
|
|
dcenters[i][k] = double(vec[k]);
|
|
}
|
|
}
|
|
|
|
std::vector<DistanceType> radiuses(branching);
|
|
cv::AutoBuffer<int> count_buf(branching);
|
|
int* count = (int*)count_buf;
|
|
for (int i=0; i<branching; ++i) {
|
|
radiuses[i] = 0;
|
|
count[i] = 0;
|
|
}
|
|
|
|
// assign points to clusters
|
|
cv::AutoBuffer<int> belongs_to_buf(indices_length);
|
|
int* belongs_to = (int*)belongs_to_buf;
|
|
for (int i=0; i<indices_length; ++i) {
|
|
|
|
DistanceType sq_dist = distance_(dataset_[indices[i]], dcenters[0], veclen_);
|
|
belongs_to[i] = 0;
|
|
for (int j=1; j<branching; ++j) {
|
|
DistanceType new_sq_dist = distance_(dataset_[indices[i]], dcenters[j], veclen_);
|
|
if (sq_dist>new_sq_dist) {
|
|
belongs_to[i] = j;
|
|
sq_dist = new_sq_dist;
|
|
}
|
|
}
|
|
if (sq_dist>radiuses[belongs_to[i]]) {
|
|
radiuses[belongs_to[i]] = sq_dist;
|
|
}
|
|
count[belongs_to[i]]++;
|
|
}
|
|
|
|
bool converged = false;
|
|
int iteration = 0;
|
|
while (!converged && iteration<iterations_) {
|
|
converged = true;
|
|
iteration++;
|
|
|
|
// compute the new cluster centers
|
|
for (int i=0; i<branching; ++i) {
|
|
memset(dcenters[i],0,sizeof(double)*veclen_);
|
|
radiuses[i] = 0;
|
|
}
|
|
for (int i=0; i<indices_length; ++i) {
|
|
ElementType* vec = dataset_[indices[i]];
|
|
double* center = dcenters[belongs_to[i]];
|
|
for (size_t k=0; k<veclen_; ++k) {
|
|
center[k] += vec[k];
|
|
}
|
|
}
|
|
for (int i=0; i<branching; ++i) {
|
|
int cnt = count[i];
|
|
for (size_t k=0; k<veclen_; ++k) {
|
|
dcenters[i][k] /= cnt;
|
|
}
|
|
}
|
|
|
|
// reassign points to clusters
|
|
cv::Mutex mtx;
|
|
KMeansDistanceComputer invoker(distance_, dataset_, branching, indices, dcenters, veclen_, count, belongs_to, radiuses, converged, mtx);
|
|
parallel_for_(cv::Range(0, (int)indices_length), invoker);
|
|
|
|
for (int i=0; i<branching; ++i) {
|
|
// if one cluster converges to an empty cluster,
|
|
// move an element into that cluster
|
|
if (count[i]==0) {
|
|
int j = (i+1)%branching;
|
|
while (count[j]<=1) {
|
|
j = (j+1)%branching;
|
|
}
|
|
|
|
for (int k=0; k<indices_length; ++k) {
|
|
if (belongs_to[k]==j) {
|
|
// for cluster j, we move the furthest element from the center to the empty cluster i
|
|
if ( distance_(dataset_[indices[k]], dcenters[j], veclen_) == radiuses[j] ) {
|
|
belongs_to[k] = i;
|
|
count[j]--;
|
|
count[i]++;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
converged = false;
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
DistanceType** centers = new DistanceType*[branching];
|
|
|
|
for (int i=0; i<branching; ++i) {
|
|
centers[i] = new DistanceType[veclen_];
|
|
memoryCounter_ += (int)(veclen_*sizeof(DistanceType));
|
|
for (size_t k=0; k<veclen_; ++k) {
|
|
centers[i][k] = (DistanceType)dcenters[i][k];
|
|
}
|
|
}
|
|
|
|
|
|
// compute kmeans clustering for each of the resulting clusters
|
|
node->childs = pool_.allocate<KMeansNodePtr>(branching);
|
|
int start = 0;
|
|
int end = start;
|
|
for (int c=0; c<branching; ++c) {
|
|
int s = count[c];
|
|
|
|
DistanceType variance = 0;
|
|
DistanceType mean_radius =0;
|
|
for (int i=0; i<indices_length; ++i) {
|
|
if (belongs_to[i]==c) {
|
|
DistanceType d = distance_(dataset_[indices[i]], ZeroIterator<ElementType>(), veclen_);
|
|
variance += d;
|
|
mean_radius += sqrt(d);
|
|
std::swap(indices[i],indices[end]);
|
|
std::swap(belongs_to[i],belongs_to[end]);
|
|
end++;
|
|
}
|
|
}
|
|
variance /= s;
|
|
mean_radius /= s;
|
|
variance -= distance_(centers[c], ZeroIterator<ElementType>(), veclen_);
|
|
|
|
node->childs[c] = pool_.allocate<KMeansNode>();
|
|
std::memset(node->childs[c], 0, sizeof(KMeansNode));
|
|
node->childs[c]->radius = radiuses[c];
|
|
node->childs[c]->pivot = centers[c];
|
|
node->childs[c]->variance = variance;
|
|
node->childs[c]->mean_radius = mean_radius;
|
|
computeClustering(node->childs[c],indices+start, end-start, branching, level+1);
|
|
start=end;
|
|
}
|
|
|
|
delete[] centers;
|
|
}
|
|
|
|
|
|
|
|
/**
|
|
* Performs one descent in the hierarchical k-means tree. The branches not
|
|
* visited are stored in a priority queue.
|
|
*
|
|
* Params:
|
|
* node = node to explore
|
|
* result = container for the k-nearest neighbors found
|
|
* vec = query points
|
|
* checks = how many points in the dataset have been checked so far
|
|
* maxChecks = maximum dataset points to checks
|
|
*/
|
|
|
|
|
|
void findNN(KMeansNodePtr node, ResultSet<DistanceType>& result, const ElementType* vec, int& checks, int maxChecks,
|
|
Heap<BranchSt>* heap)
|
|
{
|
|
// Ignore those clusters that are too far away
|
|
{
|
|
DistanceType bsq = distance_(vec, node->pivot, veclen_);
|
|
DistanceType rsq = node->radius;
|
|
DistanceType wsq = result.worstDist();
|
|
|
|
DistanceType val = bsq-rsq-wsq;
|
|
DistanceType val2 = val*val-4*rsq*wsq;
|
|
|
|
//if (val>0) {
|
|
if ((val>0)&&(val2>0)) {
|
|
return;
|
|
}
|
|
}
|
|
|
|
if (node->childs==NULL) {
|
|
if (checks>=maxChecks) {
|
|
if (result.full()) return;
|
|
}
|
|
checks += node->size;
|
|
for (int i=0; i<node->size; ++i) {
|
|
int index = node->indices[i];
|
|
DistanceType dist = distance_(dataset_[index], vec, veclen_);
|
|
result.addPoint(dist, index);
|
|
}
|
|
}
|
|
else {
|
|
DistanceType* domain_distances = new DistanceType[branching_];
|
|
int closest_center = exploreNodeBranches(node, vec, domain_distances, heap);
|
|
delete[] domain_distances;
|
|
findNN(node->childs[closest_center],result,vec, checks, maxChecks, heap);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Helper function that computes the nearest childs of a node to a given query point.
|
|
* Params:
|
|
* node = the node
|
|
* q = the query point
|
|
* distances = array with the distances to each child node.
|
|
* Returns:
|
|
*/
|
|
int exploreNodeBranches(KMeansNodePtr node, const ElementType* q, DistanceType* domain_distances, Heap<BranchSt>* heap)
|
|
{
|
|
|
|
int best_index = 0;
|
|
domain_distances[best_index] = distance_(q, node->childs[best_index]->pivot, veclen_);
|
|
for (int i=1; i<branching_; ++i) {
|
|
domain_distances[i] = distance_(q, node->childs[i]->pivot, veclen_);
|
|
if (domain_distances[i]<domain_distances[best_index]) {
|
|
best_index = i;
|
|
}
|
|
}
|
|
|
|
// float* best_center = node->childs[best_index]->pivot;
|
|
for (int i=0; i<branching_; ++i) {
|
|
if (i != best_index) {
|
|
domain_distances[i] -= cb_index_*node->childs[i]->variance;
|
|
|
|
// float dist_to_border = getDistanceToBorder(node.childs[i].pivot,best_center,q);
|
|
// if (domain_distances[i]<dist_to_border) {
|
|
// domain_distances[i] = dist_to_border;
|
|
// }
|
|
heap->insert(BranchSt(node->childs[i],domain_distances[i]));
|
|
}
|
|
}
|
|
|
|
return best_index;
|
|
}
|
|
|
|
|
|
/**
|
|
* Function the performs exact nearest neighbor search by traversing the entire tree.
|
|
*/
|
|
void findExactNN(KMeansNodePtr node, ResultSet<DistanceType>& result, const ElementType* vec)
|
|
{
|
|
// Ignore those clusters that are too far away
|
|
{
|
|
DistanceType bsq = distance_(vec, node->pivot, veclen_);
|
|
DistanceType rsq = node->radius;
|
|
DistanceType wsq = result.worstDist();
|
|
|
|
DistanceType val = bsq-rsq-wsq;
|
|
DistanceType val2 = val*val-4*rsq*wsq;
|
|
|
|
// if (val>0) {
|
|
if ((val>0)&&(val2>0)) {
|
|
return;
|
|
}
|
|
}
|
|
|
|
|
|
if (node->childs==NULL) {
|
|
for (int i=0; i<node->size; ++i) {
|
|
int index = node->indices[i];
|
|
DistanceType dist = distance_(dataset_[index], vec, veclen_);
|
|
result.addPoint(dist, index);
|
|
}
|
|
}
|
|
else {
|
|
int* sort_indices = new int[branching_];
|
|
|
|
getCenterOrdering(node, vec, sort_indices);
|
|
|
|
for (int i=0; i<branching_; ++i) {
|
|
findExactNN(node->childs[sort_indices[i]],result,vec);
|
|
}
|
|
|
|
delete[] sort_indices;
|
|
}
|
|
}
|
|
|
|
|
|
/**
|
|
* Helper function.
|
|
*
|
|
* I computes the order in which to traverse the child nodes of a particular node.
|
|
*/
|
|
void getCenterOrdering(KMeansNodePtr node, const ElementType* q, int* sort_indices)
|
|
{
|
|
DistanceType* domain_distances = new DistanceType[branching_];
|
|
for (int i=0; i<branching_; ++i) {
|
|
DistanceType dist = distance_(q, node->childs[i]->pivot, veclen_);
|
|
|
|
int j=0;
|
|
while (domain_distances[j]<dist && j<i) j++;
|
|
for (int k=i; k>j; --k) {
|
|
domain_distances[k] = domain_distances[k-1];
|
|
sort_indices[k] = sort_indices[k-1];
|
|
}
|
|
domain_distances[j] = dist;
|
|
sort_indices[j] = i;
|
|
}
|
|
delete[] domain_distances;
|
|
}
|
|
|
|
/**
|
|
* Method that computes the squared distance from the query point q
|
|
* from inside region with center c to the border between this
|
|
* region and the region with center p
|
|
*/
|
|
DistanceType getDistanceToBorder(DistanceType* p, DistanceType* c, DistanceType* q)
|
|
{
|
|
DistanceType sum = 0;
|
|
DistanceType sum2 = 0;
|
|
|
|
for (int i=0; i<veclen_; ++i) {
|
|
DistanceType t = c[i]-p[i];
|
|
sum += t*(q[i]-(c[i]+p[i])/2);
|
|
sum2 += t*t;
|
|
}
|
|
|
|
return sum*sum/sum2;
|
|
}
|
|
|
|
|
|
/**
|
|
* Helper function the descends in the hierarchical k-means tree by spliting those clusters that minimize
|
|
* the overall variance of the clustering.
|
|
* Params:
|
|
* root = root node
|
|
* clusters = array with clusters centers (return value)
|
|
* varianceValue = variance of the clustering (return value)
|
|
* Returns:
|
|
*/
|
|
int getMinVarianceClusters(KMeansNodePtr root, KMeansNodePtr* clusters, int clusters_length, DistanceType& varianceValue)
|
|
{
|
|
int clusterCount = 1;
|
|
clusters[0] = root;
|
|
|
|
DistanceType meanVariance = root->variance*root->size;
|
|
|
|
while (clusterCount<clusters_length) {
|
|
DistanceType minVariance = (std::numeric_limits<DistanceType>::max)();
|
|
int splitIndex = -1;
|
|
|
|
for (int i=0; i<clusterCount; ++i) {
|
|
if (clusters[i]->childs != NULL) {
|
|
|
|
DistanceType variance = meanVariance - clusters[i]->variance*clusters[i]->size;
|
|
|
|
for (int j=0; j<branching_; ++j) {
|
|
variance += clusters[i]->childs[j]->variance*clusters[i]->childs[j]->size;
|
|
}
|
|
if (variance<minVariance) {
|
|
minVariance = variance;
|
|
splitIndex = i;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (splitIndex==-1) break;
|
|
if ( (branching_+clusterCount-1) > clusters_length) break;
|
|
|
|
meanVariance = minVariance;
|
|
|
|
// split node
|
|
KMeansNodePtr toSplit = clusters[splitIndex];
|
|
clusters[splitIndex] = toSplit->childs[0];
|
|
for (int i=1; i<branching_; ++i) {
|
|
clusters[clusterCount++] = toSplit->childs[i];
|
|
}
|
|
}
|
|
|
|
varianceValue = meanVariance/root->size;
|
|
return clusterCount;
|
|
}
|
|
|
|
private:
|
|
/** The branching factor used in the hierarchical k-means clustering */
|
|
int branching_;
|
|
|
|
/** Maximum number of iterations to use when performing k-means clustering */
|
|
int iterations_;
|
|
|
|
/** Algorithm for choosing the cluster centers */
|
|
flann_centers_init_t centers_init_;
|
|
|
|
/**
|
|
* Cluster border index. This is used in the tree search phase when determining
|
|
* the closest cluster to explore next. A zero value takes into account only
|
|
* the cluster centres, a value greater then zero also take into account the size
|
|
* of the cluster.
|
|
*/
|
|
float cb_index_;
|
|
|
|
/**
|
|
* The dataset used by this index
|
|
*/
|
|
const Matrix<ElementType> dataset_;
|
|
|
|
/** Index parameters */
|
|
IndexParams index_params_;
|
|
|
|
/**
|
|
* Number of features in the dataset.
|
|
*/
|
|
size_t size_;
|
|
|
|
/**
|
|
* Length of each feature.
|
|
*/
|
|
size_t veclen_;
|
|
|
|
/**
|
|
* The root node in the tree.
|
|
*/
|
|
KMeansNodePtr root_;
|
|
|
|
/**
|
|
* Array of indices to vectors in the dataset.
|
|
*/
|
|
int* indices_;
|
|
|
|
/**
|
|
* The distance
|
|
*/
|
|
Distance distance_;
|
|
|
|
/**
|
|
* Pooled memory allocator.
|
|
*/
|
|
PooledAllocator pool_;
|
|
|
|
/**
|
|
* Memory occupied by the index.
|
|
*/
|
|
int memoryCounter_;
|
|
};
|
|
|
|
}
|
|
|
|
#endif //OPENCV_FLANN_KMEANS_INDEX_H_
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