/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* THE BSD LICENSE
*
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* modification, are permitted provided that the following conditions
* are met:
*
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*************************************************************************/
#ifndef OPENCV_FLANN_KMEANS_INDEX_H_
#define OPENCV_FLANN_KMEANS_INDEX_H_
//! @cond IGNORED
#include <algorithm>
#include <map>
#include <limits>
#include <cmath>
#include "general.h"
#include "nn_index.h"
#include "dist.h"
#include "matrix.h"
#include "result_set.h"
#include "heap.h"
#include "allocator.h"
#include "random.h"
#include "saving.h"
#include "logger.h"
#define BITS_PER_CHAR 8
#define BITS_PER_BASE 2 // for DNA/RNA sequences
#define BASE_PER_CHAR (BITS_PER_CHAR/BITS_PER_BASE)
#define HISTOS_PER_BASE (1<<BITS_PER_BASE)
namespace cvflann
{
struct KMeansIndexParams : public IndexParams
{
KMeansIndexParams(int branching = 32, int iterations = 11,
flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM,
float cb_index = 0.2, int trees = 1 )
{
(*this)["algorithm"] = FLANN_INDEX_KMEANS;
// branching factor
(*this)["branching"] = branching;
// max iterations to perform in one kmeans clustering (kmeans tree)
(*this)["iterations"] = iterations;
// algorithm used for picking the initial cluster centers for kmeans tree
(*this)["centers_init"] = centers_init;
// cluster boundary index. Used when searching the kmeans tree
(*this)["cb_index"] = cb_index;
// number of kmeans trees to search in
(*this)["trees"] = trees;
}
};
/**
* Hierarchical kmeans index
*
* Contains a tree constructed through a hierarchical kmeans clustering
* and other information for indexing a set of points for nearest-neighbour matching.
*/
template <typename Distance>
class KMeansIndex : public NNIndex<Distance>
{
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
typedef typename Distance::CentersType CentersType;
typedef typename Distance::is_kdtree_distance is_kdtree_distance;
typedef typename Distance::is_vector_space_distance is_vector_space_distance;
typedef void (KMeansIndex::* centersAlgFunction)(int, int*, int, int*, int&);
/**
* The function used for choosing the cluster centers.
*/
centersAlgFunction chooseCenters;
/**
* Chooses the initial centers in the k-means clustering in a random manner.
*
* Params:
* k = number of centers
* vecs = the dataset of points
* indices = indices in the dataset
* indices_length = length of indices vector
*
*/
void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length)
{
UniqueRandom r(indices_length);
int index;
for (index=0; index<k; ++index) {
bool duplicate = true;
int rnd;
while (duplicate) {
duplicate = false;
rnd = r.next();
if (rnd<0) {
centers_length = index;
return;
}
centers[index] = indices[rnd];
for (int j=0; j<index; ++j) {
DistanceType sq = distance_(dataset_[centers[index]], dataset_[centers[j]], dataset_.cols);
if (sq<1e-16) {
duplicate = true;
}
}
}
}
centers_length = index;
}
/**
* Chooses the initial centers in the k-means using Gonzales' algorithm
* so that the centers are spaced apart from each other.
*
* Params:
* k = number of centers
* vecs = the dataset of points
* indices = indices in the dataset
* Returns:
*/
void chooseCentersGonzales(int k, int* indices, int indices_length, int* centers, int& centers_length)
{
int n = indices_length;
int rnd = rand_int(n);
CV_DbgAssert(rnd >=0 && rnd < n);
centers[0] = indices[rnd];
int index;
for (index=1; index<k; ++index) {
int best_index = -1;
DistanceType best_val = 0;
for (int j=0; j<n; ++j) {
DistanceType dist = distance_(dataset_[centers[0]],dataset_[indices[j]],dataset_.cols);
for (int i=1; i<index; ++i) {
DistanceType tmp_dist = distance_(dataset_[centers[i]],dataset_[indices[j]],dataset_.cols);
if (tmp_dist<dist) {
dist = tmp_dist;
}
}
if (dist>best_val) {
best_val = dist;
best_index = j;
}
}
if (best_index!=-1) {
centers[index] = indices[best_index];
}
else {
break;
}
}
centers_length = index;
}
/**
* Chooses the initial centers in the k-means using the algorithm
* proposed in the KMeans++ paper:
* Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding
*
* Implementation of this function was converted from the one provided in Arthur's code.
*
* Params:
* k = number of centers
* vecs = the dataset of points
* indices = indices in the dataset
* Returns:
*/
void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length)
{
int n = indices_length;
double currentPot = 0;
DistanceType* closestDistSq = new DistanceType[n];
// Choose one random center and set the closestDistSq values
int index = rand_int(n);
CV_DbgAssert(index >=0 && index < n);
centers[0] = indices[index];
for (int i = 0; i < n; i++) {
closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
currentPot += closestDistSq[i];
}
const int numLocalTries = 1;
// Choose each center
int centerCount;
for (centerCount = 1; centerCount < k; centerCount++) {
// Repeat several trials
double bestNewPot = -1;
int bestNewIndex = -1;
for (int localTrial = 0; localTrial < numLocalTries; localTrial++) {
// Choose our center - have to be slightly careful to return a valid answer even accounting
// for possible rounding errors
double randVal = rand_double(currentPot);
for (index = 0; index < n-1; index++) {
if (randVal <= closestDistSq[index]) break;
else randVal -= closestDistSq[index];
}
// Compute the new potential
double newPot = 0;
for (int i = 0; i < n; i++) {
DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
// Store the best result
if ((bestNewPot < 0)||(newPot < bestNewPot)) {
bestNewPot = newPot;
bestNewIndex = index;
}
}
// Add the appropriate center
centers[centerCount] = indices[bestNewIndex];
currentPot = bestNewPot;
for (int i = 0; i < n; i++) {
DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
}
centers_length = centerCount;
delete[] closestDistSq;
}
public:
flann_algorithm_t getType() const CV_OVERRIDE
{
return FLANN_INDEX_KMEANS;
}
template<class CentersContainerType>
class KMeansDistanceComputer : public cv::ParallelLoopBody
{
public:
KMeansDistanceComputer(Distance _distance, const Matrix<ElementType>& _dataset,
const int _branching, const int* _indices, const CentersContainerType& _dcenters,
const size_t _veclen, std::vector<int> &_new_centroids,
std::vector<DistanceType> &_sq_dists)
: distance(_distance)
, dataset(_dataset)
, branching(_branching)
, indices(_indices)
, dcenters(_dcenters)
, veclen(_veclen)
, new_centroids(_new_centroids)
, sq_dists(_sq_dists)
{
}
void operator()(const cv::Range& range) const CV_OVERRIDE
{
const int begin = range.start;
const int end = range.end;
for( int i = begin; i<end; ++i)
{
DistanceType sq_dist(distance(dataset[indices[i]], dcenters[0], veclen));
int new_centroid(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) {
new_centroid = j;
sq_dist = new_sq_dist;
}
}
sq_dists[i] = sq_dist;
new_centroids[i] = new_centroid;
}
}
private:
Distance distance;
const Matrix<ElementType>& dataset;
const int branching;
const int* indices;
const CentersContainerType& dcenters;
const size_t veclen;
std::vector<int> &new_centroids;
std::vector<DistanceType> &sq_dists;
KMeansDistanceComputer& operator=( const KMeansDistanceComputer & ) { return *this; }
};
/**
* Index constructor
*
* Params:
* inputData = dataset with the input features
* params = parameters passed to the hierarchical k-means algorithm
*/
KMeansIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KMeansIndexParams(),
Distance d = Distance())
: dataset_(inputData), index_params_(params), root_(NULL), indices_(NULL), distance_(d)
{
memoryCounter_ = 0;
size_ = dataset_.rows;
veclen_ = dataset_.cols;
branching_ = get_param(params,"branching",32);
trees_ = get_param(params,"trees",1);
iterations_ = get_param(params,"iterations",11);
if (iterations_<0) {
iterations_ = (std::numeric_limits<int>::max)();
}
centers_init_ = get_param(params,"centers_init",FLANN_CENTERS_RANDOM);
if (centers_init_==FLANN_CENTERS_RANDOM) {
chooseCenters = &KMeansIndex::chooseCentersRandom;
}
else if (centers_init_==FLANN_CENTERS_GONZALES) {
chooseCenters = &KMeansIndex::chooseCentersGonzales;
}
else if (centers_init_==FLANN_CENTERS_KMEANSPP) {
chooseCenters = &KMeansIndex::chooseCentersKMeanspp;
}
else {
FLANN_THROW(cv::Error::StsBadArg, "Unknown algorithm for choosing initial centers.");
}
cb_index_ = 0.4f;
root_ = new KMeansNodePtr[trees_];
indices_ = new int*[trees_];
for (int i=0; i<trees_; ++i) {
root_[i] = NULL;
indices_[i] = NULL;
}
}
KMeansIndex(const KMeansIndex&);
KMeansIndex& operator=(const KMeansIndex&);
/**
* Index destructor.
*
* Release the memory used by the index.
*/
virtual ~KMeansIndex()
{
if (root_ != NULL) {
free_centers();
delete[] root_;
}
if (indices_!=NULL) {
free_indices();
delete[] indices_;
}
}
/**
* Returns size of index.
*/
size_t size() const CV_OVERRIDE
{
return size_;
}
/**
* Returns the length of an index feature.
*/
size_t veclen() const CV_OVERRIDE
{
return veclen_;
}
void set_cb_index( float index)
{
cb_index_ = index;
}
/**
* Computes the inde memory usage
* Returns: memory used by the index
*/
int usedMemory() const CV_OVERRIDE
{
return pool_.usedMemory+pool_.wastedMemory+memoryCounter_;
}
/**
* Builds the index
*/
void buildIndex() CV_OVERRIDE
{
if (branching_<2) {
FLANN_THROW(cv::Error::StsError, "Branching factor must be at least 2");
}
free_indices();
for (int i=0; i<trees_; ++i) {
indices_[i] = new int[size_];
for (size_t j=0; j<size_; ++j) {
indices_[i][j] = int(j);
}
root_[i] = pool_.allocate<KMeansNode>();
std::memset(root_[i], 0, sizeof(KMeansNode));
Distance* dummy = NULL;
computeNodeStatistics(root_[i], indices_[i], (unsigned int)size_, dummy);
computeClustering(root_[i], indices_[i], (int)size_, branching_,0);
}
}
void saveIndex(FILE* stream) CV_OVERRIDE
{
save_value(stream, branching_);
save_value(stream, iterations_);
save_value(stream, memoryCounter_);
save_value(stream, cb_index_);
save_value(stream, trees_);
for (int i=0; i<trees_; ++i) {
save_value(stream, *indices_[i], (int)size_);
save_tree(stream, root_[i], i);
}
}
void loadIndex(FILE* stream) CV_OVERRIDE
{
if (indices_!=NULL) {
free_indices();
delete[] indices_;
}
if (root_!=NULL) {
free_centers();
}
load_value(stream, branching_);
load_value(stream, iterations_);
load_value(stream, memoryCounter_);
load_value(stream, cb_index_);
load_value(stream, trees_);
indices_ = new int*[trees_];
for (int i=0; i<trees_; ++i) {
indices_[i] = new int[size_];
load_value(stream, *indices_[i], size_);
load_tree(stream, root_[i], i);
}
index_params_["algorithm"] = getType();
index_params_["branching"] = branching_;
index_params_["trees"] = trees_;
index_params_["iterations"] = iterations_;
index_params_["centers_init"] = centers_init_;
index_params_["cb_index"] = cb_index_;
}
/**
* Find set of nearest neighbors to vec. Their indices are stored inside
* the result object.
*
* Params:
* result = the result object in which the indices of the nearest-neighbors are stored
* vec = the vector for which to search the nearest neighbors
* searchParams = parameters that influence the search algorithm (checks, cb_index)
*/
void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
{
const int maxChecks = get_param(searchParams,"checks",32);
if (maxChecks==FLANN_CHECKS_UNLIMITED) {
findExactNN(root_[0], result, vec);
}
else {
// Priority queue storing intermediate branches in the best-bin-first search
const cv::Ptr<Heap<BranchSt>>& heap = Heap<BranchSt>::getPooledInstance(cv::utils::getThreadID(), (int)size_);
int checks = 0;
for (int i=0; i<trees_; ++i) {
findNN(root_[i], result, vec, checks, maxChecks, heap);
if ((checks >= maxChecks) && result.full())
break;
}
BranchSt branch;
while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
KMeansNodePtr node = branch.node;
findNN(node, result, vec, checks, maxChecks, heap);
}
CV_Assert(result.full());
}
}
/**
* Clustering function that takes a cut in the hierarchical k-means
* tree and return the clusters centers of that clustering.
* Params:
* numClusters = number of clusters to have in the clustering computed
* Returns: number of cluster centers
*/
int getClusterCenters(Matrix<CentersType>& centers)
{
int numClusters = centers.rows;
if (numClusters<1) {
FLANN_THROW(cv::Error::StsBadArg, "Number of clusters must be at least 1");
}
DistanceType variance;
KMeansNodePtr* clusters = new KMeansNodePtr[numClusters];
int clusterCount = getMinVarianceClusters(root_[0], clusters, numClusters, variance);
Logger::info("Clusters requested: %d, returning %d\n",numClusters, clusterCount);
for (int i=0; i<clusterCount; ++i) {
CentersType* center = clusters[i]->pivot;
for (size_t j=0; j<veclen_; ++j) {
centers[i][j] = center[j];
}
}
delete[] clusters;
return clusterCount;
}
IndexParams getParameters() const CV_OVERRIDE
{
return index_params_;
}
private:
/**
* Structure representing a node in the hierarchical k-means tree.
*/
struct KMeansNode
{
/**
* The cluster center.
*/
CentersType* 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, int num)
{
save_value(stream, *node);
save_value(stream, *(node->pivot), (int)veclen_);
if (node->childs==NULL) {
int indices_offset = (int)(node->indices - indices_[num]);
save_value(stream, indices_offset);
}
else {
for(int i=0; i<branching_; ++i) {
save_tree(stream, node->childs[i], num);
}
}
}
void load_tree(FILE* stream, KMeansNodePtr& node, int num)
{
node = pool_.allocate<KMeansNode>();
load_value(stream, *node);
node->pivot = new CentersType[veclen_];
load_value(stream, *(node->pivot), (int)veclen_);
if (node->childs==NULL) {
int indices_offset;
load_value(stream, indices_offset);
node->indices = indices_[num] + indices_offset;
}
else {
node->childs = pool_.allocate<KMeansNodePtr>(branching_);
for(int i=0; i<branching_; ++i) {
load_tree(stream, node->childs[i], num);
}
}
}
/**
* 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]);
}
}
}
void free_centers()
{
if (root_ != NULL) {
for(int i=0; i<trees_; ++i) {
if (root_[i] != NULL) {
free_centers(root_[i]);
}
}
}
}
/**
* Release the inner elements of indices[]
*/
void free_indices()
{
if (indices_!=NULL) {
for(int i=0; i<trees_; ++i) {
if (indices_[i]!=NULL) {
delete[] indices_[i];
indices_[i] = NULL;
}
}
}
}
/**
* Computes the statistics of a node (mean, radius, variance).
*
* Params:
* node = the node to use
* indices = array of indices of the points belonging to the node
* indices_length = number of indices in the array
*/
void computeNodeStatistics(KMeansNodePtr node, int* indices, unsigned int indices_length)
{
DistanceType variance = 0;
CentersType* mean = new CentersType[veclen_];
memoryCounter_ += int(veclen_*sizeof(CentersType));
memset(mean,0,veclen_*sizeof(CentersType));
for (unsigned int i=0; i<indices_length; ++i) {
ElementType* vec = dataset_[indices[i]];
for (size_t j=0; j<veclen_; ++j) {
mean[j] += vec[j];
}
variance += distance_(vec, ZeroIterator<ElementType>(), veclen_);
}
float length = static_cast<float>(indices_length);
for (size_t j=0; j<veclen_; ++j) {
mean[j] = cvflann::round<CentersType>( mean[j] / static_cast<double>(indices_length) );
}
variance /= static_cast<DistanceType>( length );
variance -= distance_(mean, ZeroIterator<ElementType>(), veclen_);
DistanceType radius = 0;
for (unsigned int i=0; i<indices_length; ++i) {
DistanceType tmp = distance_(mean, dataset_[indices[i]], veclen_);
if (tmp>radius) {
radius = tmp;
}
}
node->variance = variance;
node->radius = radius;
node->pivot = mean;
}
void computeBitfieldNodeStatistics(KMeansNodePtr node, int* indices,
unsigned int indices_length)
{
const unsigned int accumulator_veclen = static_cast<unsigned int>(
veclen_*sizeof(CentersType)*BITS_PER_CHAR);
unsigned long long variance = 0ull;
CentersType* mean = new CentersType[veclen_];
memoryCounter_ += int(veclen_*sizeof(CentersType));
unsigned int* mean_accumulator = new unsigned int[accumulator_veclen];
memset(mean_accumulator, 0, sizeof(unsigned int)*accumulator_veclen);
for (unsigned int i=0; i<indices_length; ++i) {
variance += static_cast<unsigned long long>( ensureSquareDistance<Distance>(
distance_(dataset_[indices[i]], ZeroIterator<ElementType>(), veclen_)));
unsigned char* vec = (unsigned char*)dataset_[indices[i]];
for (size_t k=0, l=0; k<accumulator_veclen; k+=BITS_PER_CHAR, ++l) {
mean_accumulator[k] += (vec[l]) & 0x01;
mean_accumulator[k+1] += (vec[l]>>1) & 0x01;
mean_accumulator[k+2] += (vec[l]>>2) & 0x01;
mean_accumulator[k+3] += (vec[l]>>3) & 0x01;
mean_accumulator[k+4] += (vec[l]>>4) & 0x01;
mean_accumulator[k+5] += (vec[l]>>5) & 0x01;
mean_accumulator[k+6] += (vec[l]>>6) & 0x01;
mean_accumulator[k+7] += (vec[l]>>7) & 0x01;
}
}
double cnt = static_cast<double>(indices_length);
unsigned char* char_mean = (unsigned char*)mean;
for (size_t k=0, l=0; k<accumulator_veclen; k+=BITS_PER_CHAR, ++l) {
char_mean[l] = static_cast<unsigned char>(
(((int)(0.5 + (double)(mean_accumulator[k]) / cnt)))
| (((int)(0.5 + (double)(mean_accumulator[k+1]) / cnt))<<1)
| (((int)(0.5 + (double)(mean_accumulator[k+2]) / cnt))<<2)
| (((int)(0.5 + (double)(mean_accumulator[k+3]) / cnt))<<3)
| (((int)(0.5 + (double)(mean_accumulator[k+4]) / cnt))<<4)
| (((int)(0.5 + (double)(mean_accumulator[k+5]) / cnt))<<5)
| (((int)(0.5 + (double)(mean_accumulator[k+6]) / cnt))<<6)
| (((int)(0.5 + (double)(mean_accumulator[k+7]) / cnt))<<7));
}
variance = static_cast<unsigned long long>(
0.5 + static_cast<double>(variance) / static_cast<double>(indices_length));
variance -= static_cast<unsigned long long>(
ensureSquareDistance<Distance>(
distance_(mean, ZeroIterator<ElementType>(), veclen_)));
DistanceType radius = 0;
for (unsigned int i=0; i<indices_length; ++i) {
DistanceType tmp = distance_(mean, dataset_[indices[i]], veclen_);
if (tmp>radius) {
radius = tmp;
}
}
node->variance = static_cast<DistanceType>(variance);
node->radius = radius;
node->pivot = mean;
delete[] mean_accumulator;
}
void computeDnaNodeStatistics(KMeansNodePtr node, int* indices,
unsigned int indices_length)
{
const unsigned int histos_veclen = static_cast<unsigned int>(
veclen_*sizeof(CentersType)*(HISTOS_PER_BASE*BASE_PER_CHAR));
unsigned long long variance = 0ull;
unsigned int* histograms = new unsigned int[histos_veclen];
memset(histograms, 0, sizeof(unsigned int)*histos_veclen);
for (unsigned int i=0; i<indices_length; ++i) {
variance += static_cast<unsigned long long>( ensureSquareDistance<Distance>(
distance_(dataset_[indices[i]], ZeroIterator<ElementType>(), veclen_)));
unsigned char* vec = (unsigned char*)dataset_[indices[i]];
for (size_t k=0, l=0; k<histos_veclen; k+=HISTOS_PER_BASE*BASE_PER_CHAR, ++l) {
histograms[k + ((vec[l]) & 0x03)]++;
histograms[k + 4 + ((vec[l]>>2) & 0x03)]++;
histograms[k + 8 + ((vec[l]>>4) & 0x03)]++;
histograms[k +12 + ((vec[l]>>6) & 0x03)]++;
}
}
CentersType* mean = new CentersType[veclen_];
memoryCounter_ += int(veclen_*sizeof(CentersType));
unsigned char* char_mean = (unsigned char*)mean;
unsigned int* h = histograms;
for (size_t k=0, l=0; k<histos_veclen; k+=HISTOS_PER_BASE*BASE_PER_CHAR, ++l) {
char_mean[l] = (h[k] > h[k+1] ? h[k+2] > h[k+3] ? h[k] > h[k+2] ? 0x00 : 0x10
: h[k] > h[k+3] ? 0x00 : 0x11
: h[k+2] > h[k+3] ? h[k+1] > h[k+2] ? 0x01 : 0x10
: h[k+1] > h[k+3] ? 0x01 : 0x11)
| (h[k+4]>h[k+5] ? h[k+6] > h[k+7] ? h[k+4] > h[k+6] ? 0x00 : 0x1000
: h[k+4] > h[k+7] ? 0x00 : 0x1100
: h[k+6] > h[k+7] ? h[k+5] > h[k+6] ? 0x0100 : 0x1000
: h[k+5] > h[k+7] ? 0x0100 : 0x1100)
| (h[k+8]>h[k+9] ? h[k+10]>h[k+11] ? h[k+8] >h[k+10] ? 0x00 : 0x100000
: h[k+8] >h[k+11] ? 0x00 : 0x110000
: h[k+10]>h[k+11] ? h[k+9] >h[k+10] ? 0x010000 : 0x100000
: h[k+9] >h[k+11] ? 0x010000 : 0x110000)
| (h[k+12]>h[k+13] ? h[k+14]>h[k+15] ? h[k+12] >h[k+14] ? 0x00 : 0x10000000
: h[k+12] >h[k+15] ? 0x00 : 0x11000000
: h[k+14]>h[k+15] ? h[k+13] >h[k+14] ? 0x01000000 : 0x10000000
: h[k+13] >h[k+15] ? 0x01000000 : 0x11000000);
}
variance = static_cast<unsigned long long>(
0.5 + static_cast<double>(variance) / static_cast<double>(indices_length));
variance -= static_cast<unsigned long long>(
ensureSquareDistance<Distance>(
distance_(mean, ZeroIterator<ElementType>(), veclen_)));
DistanceType radius = 0;
for (unsigned int i=0; i<indices_length; ++i) {
DistanceType tmp = distance_(mean, dataset_[indices[i]], veclen_);
if (tmp>radius) {
radius = tmp;
}
}
node->variance = static_cast<DistanceType>(variance);
node->radius = radius;
node->pivot = mean;
delete[] histograms;
}
template<typename DistType>
void computeNodeStatistics(KMeansNodePtr node, int* indices,
unsigned int indices_length,
const DistType* identifier)
{
(void)identifier;
computeNodeStatistics(node, indices, indices_length);
}
void computeNodeStatistics(KMeansNodePtr node, int* indices,
unsigned int indices_length,
const cvflann::HammingLUT* identifier)
{
(void)identifier;
computeBitfieldNodeStatistics(node, indices, indices_length);
}
void computeNodeStatistics(KMeansNodePtr node, int* indices,
unsigned int indices_length,
const cvflann::Hamming<unsigned char>* identifier)
{
(void)identifier;
computeBitfieldNodeStatistics(node, indices, indices_length);
}
void computeNodeStatistics(KMeansNodePtr node, int* indices,
unsigned int indices_length,
const cvflann::Hamming2<unsigned char>* identifier)
{
(void)identifier;
computeBitfieldNodeStatistics(node, indices, indices_length);
}
void computeNodeStatistics(KMeansNodePtr node, int* indices,
unsigned int indices_length,
const cvflann::DNAmmingLUT* identifier)
{
(void)identifier;
computeDnaNodeStatistics(node, indices, indices_length);
}
void computeNodeStatistics(KMeansNodePtr node, int* indices,
unsigned int indices_length,
const cvflann::DNAmming2<unsigned char>* identifier)
{
(void)identifier;
computeDnaNodeStatistics(node, indices, indices_length);
}
void refineClustering(int* indices, int indices_length, int branching, CentersType** centers,
std::vector<DistanceType>& radiuses, int* belongs_to, int* count)
{
cv::AutoBuffer<double> dcenters_buf(branching*veclen_);
Matrix<double> dcenters(dcenters_buf.data(), branching, veclen_);
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;
}
}
std::vector<int> new_centroids(indices_length);
std::vector<DistanceType> sq_dists(indices_length);
// reassign points to clusters
KMeansDistanceComputer<Matrix<double> > invoker(
distance_, dataset_, branching, indices, dcenters, veclen_, new_centroids, sq_dists);
parallel_for_(cv::Range(0, (int)indices_length), invoker);
for (int i=0; i < (int)indices_length; ++i) {
DistanceType sq_dist(sq_dists[i]);
int new_centroid(new_centroids[i]);
if (sq_dist > radiuses[new_centroid]) {
radiuses[new_centroid] = sq_dist;
}
if (new_centroid != belongs_to[i]) {
count[belongs_to[i]]--;
count[new_centroid]++;
belongs_to[i] = new_centroid;
converged = false;
}
}
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;
}
}
}
for (int i=0; i<branching; ++i) {
centers[i] = new CentersType[veclen_];
memoryCounter_ += (int)(veclen_*sizeof(CentersType));
for (size_t k=0; k<veclen_; ++k) {
centers[i][k] = (CentersType)dcenters[i][k];
}
}
}
void refineBitfieldClustering(int* indices, int indices_length, int branching, CentersType** centers,
std::vector<DistanceType>& radiuses, int* belongs_to, int* count)
{
for (int i=0; i<branching; ++i) {
centers[i] = new CentersType[veclen_];
memoryCounter_ += (int)(veclen_*sizeof(CentersType));
}
const unsigned int accumulator_veclen = static_cast<unsigned int>(
veclen_*sizeof(ElementType)*BITS_PER_CHAR);
cv::AutoBuffer<unsigned int> dcenters_buf(branching*accumulator_veclen);
Matrix<unsigned int> dcenters(dcenters_buf.data(), branching, accumulator_veclen);
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(unsigned int)*accumulator_veclen);
radiuses[i] = 0;
}
for (int i=0; i<indices_length; ++i) {
unsigned char* vec = (unsigned char*)dataset_[indices[i]];
unsigned int* dcenter = dcenters[belongs_to[i]];
for (size_t k=0, l=0; k<accumulator_veclen; k+=BITS_PER_CHAR, ++l) {
dcenter[k] += (vec[l]) & 0x01;
dcenter[k+1] += (vec[l]>>1) & 0x01;
dcenter[k+2] += (vec[l]>>2) & 0x01;
dcenter[k+3] += (vec[l]>>3) & 0x01;
dcenter[k+4] += (vec[l]>>4) & 0x01;
dcenter[k+5] += (vec[l]>>5) & 0x01;
dcenter[k+6] += (vec[l]>>6) & 0x01;
dcenter[k+7] += (vec[l]>>7) & 0x01;
}
}
for (int i=0; i<branching; ++i) {
double cnt = static_cast<double>(count[i]);
unsigned int* dcenter = dcenters[i];
unsigned char* charCenter = (unsigned char*)centers[i];
for (size_t k=0, l=0; k<accumulator_veclen; k+=BITS_PER_CHAR, ++l) {
charCenter[l] = static_cast<unsigned char>(
(((int)(0.5 + (double)(dcenter[k]) / cnt)))
| (((int)(0.5 + (double)(dcenter[k+1]) / cnt))<<1)
| (((int)(0.5 + (double)(dcenter[k+2]) / cnt))<<2)
| (((int)(0.5 + (double)(dcenter[k+3]) / cnt))<<3)
| (((int)(0.5 + (double)(dcenter[k+4]) / cnt))<<4)
| (((int)(0.5 + (double)(dcenter[k+5]) / cnt))<<5)
| (((int)(0.5 + (double)(dcenter[k+6]) / cnt))<<6)
| (((int)(0.5 + (double)(dcenter[k+7]) / cnt))<<7));
}
}
std::vector<int> new_centroids(indices_length);
std::vector<DistanceType> dists(indices_length);
// reassign points to clusters
KMeansDistanceComputer<ElementType**> invoker(
distance_, dataset_, branching, indices, centers, veclen_, new_centroids, dists);
parallel_for_(cv::Range(0, (int)indices_length), invoker);
for (int i=0; i < indices_length; ++i) {
DistanceType dist(dists[i]);
int new_centroid(new_centroids[i]);
if (dist > radiuses[new_centroid]) {
radiuses[new_centroid] = dist;
}
if (new_centroid != belongs_to[i]) {
count[belongs_to[i]]--;
count[new_centroid]++;
belongs_to[i] = new_centroid;
converged = false;
}
}
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]], centers[j], veclen_) == radiuses[j] ) {
belongs_to[k] = i;
count[j]--;
count[i]++;
break;
}
}
}
converged = false;
}
}
}
}
void refineDnaClustering(int* indices, int indices_length, int branching, CentersType** centers,
std::vector<DistanceType>& radiuses, int* belongs_to, int* count)
{
for (int i=0; i<branching; ++i) {
centers[i] = new CentersType[veclen_];
memoryCounter_ += (int)(veclen_*sizeof(CentersType));
}
const unsigned int histos_veclen = static_cast<unsigned int>(
veclen_*sizeof(CentersType)*(HISTOS_PER_BASE*BASE_PER_CHAR));
cv::AutoBuffer<unsigned int> histos_buf(branching*histos_veclen);
Matrix<unsigned int> histos(histos_buf.data(), branching, histos_veclen);
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(histos[i],0,sizeof(unsigned int)*histos_veclen);
radiuses[i] = 0;
}
for (int i=0; i<indices_length; ++i) {
unsigned char* vec = (unsigned char*)dataset_[indices[i]];
unsigned int* h = histos[belongs_to[i]];
for (size_t k=0, l=0; k<histos_veclen; k+=HISTOS_PER_BASE*BASE_PER_CHAR, ++l) {
h[k + ((vec[l]) & 0x03)]++;
h[k + 4 + ((vec[l]>>2) & 0x03)]++;
h[k + 8 + ((vec[l]>>4) & 0x03)]++;
h[k +12 + ((vec[l]>>6) & 0x03)]++;
}
}
for (int i=0; i<branching; ++i) {
unsigned int* h = histos[i];
unsigned char* charCenter = (unsigned char*)centers[i];
for (size_t k=0, l=0; k<histos_veclen; k+=HISTOS_PER_BASE*BASE_PER_CHAR, ++l) {
charCenter[l]= (h[k] > h[k+1] ? h[k+2] > h[k+3] ? h[k] > h[k+2] ? 0x00 : 0x10
: h[k] > h[k+3] ? 0x00 : 0x11
: h[k+2] > h[k+3] ? h[k+1] > h[k+2] ? 0x01 : 0x10
: h[k+1] > h[k+3] ? 0x01 : 0x11)
| (h[k+4]>h[k+5] ? h[k+6] > h[k+7] ? h[k+4] > h[k+6] ? 0x00 : 0x1000
: h[k+4] > h[k+7] ? 0x00 : 0x1100
: h[k+6] > h[k+7] ? h[k+5] > h[k+6] ? 0x0100 : 0x1000
: h[k+5] > h[k+7] ? 0x0100 : 0x1100)
| (h[k+8]>h[k+9] ? h[k+10]>h[k+11] ? h[k+8] >h[k+10] ? 0x00 : 0x100000
: h[k+8] >h[k+11] ? 0x00 : 0x110000
: h[k+10]>h[k+11] ? h[k+9] >h[k+10] ? 0x010000 : 0x100000
: h[k+9] >h[k+11] ? 0x010000 : 0x110000)
| (h[k+12]>h[k+13] ? h[k+14]>h[k+15] ? h[k+12] >h[k+14] ? 0x00 : 0x10000000
: h[k+12] >h[k+15] ? 0x00 : 0x11000000
: h[k+14]>h[k+15] ? h[k+13] >h[k+14] ? 0x01000000 : 0x10000000
: h[k+13] >h[k+15] ? 0x01000000 : 0x11000000);
}
}
std::vector<int> new_centroids(indices_length);
std::vector<DistanceType> dists(indices_length);
// reassign points to clusters
KMeansDistanceComputer<ElementType**> invoker(
distance_, dataset_, branching, indices, centers, veclen_, new_centroids, dists);
parallel_for_(cv::Range(0, (int)indices_length), invoker);
for (int i=0; i < indices_length; ++i) {
DistanceType dist(dists[i]);
int new_centroid(new_centroids[i]);
if (dist > radiuses[new_centroid]) {
radiuses[new_centroid] = dist;
}
if (new_centroid != belongs_to[i]) {
count[belongs_to[i]]--;
count[new_centroid]++;
belongs_to[i] = new_centroid;
converged = false;
}
}
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]], centers[j], veclen_) == radiuses[j] ) {
belongs_to[k] = i;
count[j]--;
count[i]++;
break;
}
}
}
converged = false;
}
}
}
}
void computeSubClustering(KMeansNodePtr node, int* indices, int indices_length,
int branching, int level, CentersType** centers,
std::vector<DistanceType>& radiuses, int* belongs_to, int* count)
{
// 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 += static_cast<DistanceType>( 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;
}
}
void computeAnyBitfieldSubClustering(KMeansNodePtr node, int* indices, int indices_length,
int branching, int level, CentersType** centers,
std::vector<DistanceType>& radiuses, int* belongs_to, int* count)
{
// 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];
unsigned long long variance = 0ull;
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 += static_cast<unsigned long long>( ensureSquareDistance<Distance>(d) );
mean_radius += ensureSimpleDistance<Distance>(d);
std::swap(indices[i],indices[end]);
std::swap(belongs_to[i],belongs_to[end]);
end++;
}
}
mean_radius = static_cast<DistanceType>(
0.5f + static_cast<float>(mean_radius) / static_cast<float>(s));
variance = static_cast<unsigned long long>(
0.5 + static_cast<double>(variance) / static_cast<double>(s));
variance -= static_cast<unsigned long long>(
ensureSquareDistance<Distance>(
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 = static_cast<DistanceType>(variance);
node->childs[c]->mean_radius = mean_radius;
computeClustering(node->childs[c],indices+start, end-start, branching, level+1);
start=end;
}
}
template<typename DistType>
void refineAndSplitClustering(
KMeansNodePtr node, int* indices, int indices_length, int branching,
int level, CentersType** centers, std::vector<DistanceType>& radiuses,
int* belongs_to, int* count, const DistType* identifier)
{
(void)identifier;
refineClustering(indices, indices_length, branching, centers, radiuses, belongs_to, count);
computeSubClustering(node, indices, indices_length, branching,
level, centers, radiuses, belongs_to, count);
}
/**
* The methods responsible with doing the recursive hierarchical clustering on
* binary vectors.
* As some might have heard that KMeans on binary data doesn't make sense,
* it's worth a little explanation why it actually fairly works. As
* with the Hierarchical Clustering algortihm, we seed several centers for the
* current node by picking some of its points. Then in a first pass each point
* of the node is then related to its closest center. Now let's have a look at
* the 5 central dimensions of the 9 following points:
*
* xxxxxx11100xxxxx (1)
* xxxxxx11010xxxxx (2)
* xxxxxx11001xxxxx (3)
* xxxxxx10110xxxxx (4)
* xxxxxx10101xxxxx (5)
* xxxxxx10011xxxxx (6)
* xxxxxx01110xxxxx (7)
* xxxxxx01101xxxxx (8)
* xxxxxx01011xxxxx (9)
* sum _____
* of 1: 66555
*
* Even if the barycenter notion doesn't apply, we can set a center
* xxxxxx11111xxxxx that will better fit the five dimensions we are focusing
* on for these points.
*
* Note that convergence isn't ensured anymore. In practice, using Gonzales
* as seeding algorithm should be fine for getting convergence ("iterations"
* value can be set to -1). But with KMeans++ seeding you should definitely
* set a maximum number of iterations (but make it higher than the "iterations"
* default value of 11).
*
* Params:
* node = the node to cluster
* indices = indices of the points belonging to the current node
* indices_length = number of points in the current node
* branching = the branching factor to use in the clustering
* level = 0 for the root node, it increases with the subdivision levels
* centers = clusters centers to compute
* radiuses = radiuses of clusters
* belongs_to = LookUp Table returning, for a given indice id, the center id it belongs to
* count = array storing the number of indices for a given center id
* identifier = dummy pointer on an instance of Distance (use to branch correctly among templates)
*/
void refineAndSplitClustering(
KMeansNodePtr node, int* indices, int indices_length, int branching,
int level, CentersType** centers, std::vector<DistanceType>& radiuses,
int* belongs_to, int* count, const cvflann::HammingLUT* identifier)
{
(void)identifier;
refineBitfieldClustering(
indices, indices_length, branching, centers, radiuses, belongs_to, count);
computeAnyBitfieldSubClustering(node, indices, indices_length, branching,
level, centers, radiuses, belongs_to, count);
}
void refineAndSplitClustering(
KMeansNodePtr node, int* indices, int indices_length, int branching,
int level, CentersType** centers, std::vector<DistanceType>& radiuses,
int* belongs_to, int* count, const cvflann::Hamming<unsigned char>* identifier)
{
(void)identifier;
refineBitfieldClustering(
indices, indices_length, branching, centers, radiuses, belongs_to, count);
computeAnyBitfieldSubClustering(node, indices, indices_length, branching,
level, centers, radiuses, belongs_to, count);
}
void refineAndSplitClustering(
KMeansNodePtr node, int* indices, int indices_length, int branching,
int level, CentersType** centers, std::vector<DistanceType>& radiuses,
int* belongs_to, int* count, const cvflann::Hamming2<unsigned char>* identifier)
{
(void)identifier;
refineBitfieldClustering(
indices, indices_length, branching, centers, radiuses, belongs_to, count);
computeAnyBitfieldSubClustering(node, indices, indices_length, branching,
level, centers, radiuses, belongs_to, count);
}
void refineAndSplitClustering(
KMeansNodePtr node, int* indices, int indices_length, int branching,
int level, CentersType** centers, std::vector<DistanceType>& radiuses,
int* belongs_to, int* count, const cvflann::DNAmmingLUT* identifier)
{
(void)identifier;
refineDnaClustering(
indices, indices_length, branching, centers, radiuses, belongs_to, count);
computeAnyBitfieldSubClustering(node, indices, indices_length, branching,
level, centers, radiuses, belongs_to, count);
}
void refineAndSplitClustering(
KMeansNodePtr node, int* indices, int indices_length, int branching,
int level, CentersType** centers, std::vector<DistanceType>& radiuses,
int* belongs_to, int* count, const cvflann::DNAmming2<unsigned char>* identifier)
{
(void)identifier;
refineDnaClustering(
indices, indices_length, branching, centers, radiuses, belongs_to, count);
computeAnyBitfieldSubClustering(node, indices, indices_length, branching,
level, centers, radiuses, belongs_to, count);
}
/**
* 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 = centers_idx_buf.data();
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;
}
std::vector<DistanceType> radiuses(branching);
cv::AutoBuffer<int> count_buf(branching);
int* count = count_buf.data();
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 = belongs_to_buf.data();
for (int i=0; i<indices_length; ++i) {
DistanceType sq_dist = distance_(dataset_[indices[i]], dataset_[centers_idx[0]], veclen_);
belongs_to[i] = 0;
for (int j=1; j<branching; ++j) {
DistanceType new_sq_dist = distance_(dataset_[indices[i]], dataset_[centers_idx[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]]++;
}
CentersType** centers = new CentersType*[branching];
Distance* dummy = NULL;
refineAndSplitClustering(node, indices, indices_length, branching, level,
centers, radiuses, belongs_to, count, dummy);
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,
const cv::Ptr<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();
if (isSquareDistance<Distance>())
{
DistanceType val = bsq-rsq-wsq;
if ((val>0) && (val*val > 4*rsq*wsq))
return;
}
else
{
if (bsq-rsq > wsq)
return;
}
}
if (node->childs==NULL) {
if ((checks>=maxChecks) && 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, const cv::Ptr<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] -= cvflann::round<DistanceType>(
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();
if (isSquareDistance<Distance>())
{
DistanceType val = bsq-rsq-wsq;
if ((val>0) && (val*val > 4*rsq*wsq))
return;
}
else
{
if (bsq-rsq > wsq)
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 splitting 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_;
/** Number of kmeans trees (default is one) */
int trees_;
/** 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_;
};
}
//! @endcond
#endif //OPENCV_FLANN_KMEANS_INDEX_H_
