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sustaining_gazes/lib/3rdParty/OpenCV3.4/include/opencv2/flann/kmeans_index.h

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Master commit of OpenFace.
2016-04-28 21:40:36 +02:00
/***********************************************************************
* 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
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef OPENCV_FLANN_KMEANS_INDEX_H_
#define OPENCV_FLANN_KMEANS_INDEX_H_
#include <algorithm>
#include <map>
#include <cassert>
#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"
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 )
{
(*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;
}
};
/**
* 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 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);
assert(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);
assert(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
{
return FLANN_INDEX_KMEANS;
}
class KMeansDistanceComputer : public cv::ParallelLoopBody
{
public:
KMeansDistanceComputer(Distance _distance, const Matrix<ElementType>& _dataset,
const int _branching, const int* _indices, const Matrix<double>& _dcenters, const size_t _veclen,
int* _count, int* _belongs_to, std::vector<DistanceType>& _radiuses, bool& _converged, cv::Mutex& _mtx)
: distance(_distance)
, dataset(_dataset)
, branching(_branching)
, indices(_indices)
, dcenters(_dcenters)
, veclen(_veclen)
, count(_count)
, belongs_to(_belongs_to)
, radiuses(_radiuses)
, converged(_converged)
, mtx(_mtx)
{
}
void operator()(const cv::Range& range) const
{
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;
}
}
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;
mtx.lock();
converged = false;
mtx.unlock();
}
}
}
private:
Distance distance;
const Matrix<ElementType>& dataset;
const int branching;
const int* indices;
const Matrix<double>& dcenters;
const size_t veclen;
int* count;
int* belongs_to;
std::vector<DistanceType>& radiuses;
bool& converged;
cv::Mutex& mtx;
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);
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 {
throw FLANNException("Unknown algorithm for choosing initial centers.");
}
cb_index_ = 0.4f;
}
KMeansIndex(const KMeansIndex&);
KMeansIndex& operator=(const KMeansIndex&);
/**
* Index destructor.
*
* Release the memory used by the index.
*/
virtual ~KMeansIndex()
{
if (root_ != NULL) {
free_centers(root_);
}
if (indices_!=NULL) {
delete[] indices_;
}
}
/**
* Returns size of index.
*/
size_t size() const
{
return size_;
}
/**
* Returns the length of an index feature.
*/
size_t veclen() const
{
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
{
return pool_.usedMemory+pool_.wastedMemory+memoryCounter_;
}
/**
* Builds the index
*/
void buildIndex()
{
if (branching_<2) {
throw FLANNException("Branching factor must be at least 2");
}
indices_ = new int[size_];
for (size_t i=0; i<size_; ++i) {
indices_[i] = int(i);
}
root_ = pool_.allocate<KMeansNode>();
std::memset(root_, 0, sizeof(KMeansNode));
computeNodeStatistics(root_, indices_, (int)size_);
computeClustering(root_, indices_, (int)size_, branching_,0);
}
void saveIndex(FILE* stream)
{
save_value(stream, branching_);
save_value(stream, iterations_);
save_value(stream, memoryCounter_);
save_value(stream, cb_index_);
save_value(stream, *indices_, (int)size_);
save_tree(stream, root_);
}
void loadIndex(FILE* stream)
{
load_value(stream, branching_);
load_value(stream, iterations_);
load_value(stream, memoryCounter_);
load_value(stream, cb_index_);
if (indices_!=NULL) {
delete[] indices_;
}
indices_ = new int[size_];
load_value(stream, *indices_, size_);
if (root_!=NULL) {
free_centers(root_);
}
load_tree(stream, root_);
index_params_["algorithm"] = getType();
index_params_["branching"] = branching_;
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)
{
int maxChecks = get_param(searchParams,"checks",32);
if (maxChecks==FLANN_CHECKS_UNLIMITED) {
findExactNN(root_, result, vec);
}
else {
// Priority queue storing intermediate branches in the best-bin-first search
Heap<BranchSt>* heap = new Heap<BranchSt>((int)size_);
int checks = 0;
findNN(root_, result, vec, checks, maxChecks, heap);
BranchSt branch;
while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
KMeansNodePtr node = branch.node;
findNN(node, result, vec, checks, maxChecks, heap);
}
assert(result.full());
delete heap;
}
}
/**
* 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<DistanceType>& centers)
{
int numClusters = centers.rows;
if (numClusters<1) {
throw FLANNException("Number of clusters must be at least 1");
}
DistanceType variance;
KMeansNodePtr* clusters = new KMeansNodePtr[numClusters];
int clusterCount = getMinVarianceClusters(root_, clusters, numClusters, variance);
Logger::info("Clusters requested: %d, returning %d\n",numClusters, clusterCount);
for (int i=0; i<clusterCount; ++i) {
DistanceType* center = clusters[i]->pivot;
for (size_t j=0; j<veclen_; ++j) {
centers[i][j] = center[j];
}
}
delete[] clusters;
return clusterCount;
}
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;
}
Starting the move to OpenCV 3.4
2018-02-01 21:10:10 +01:00
delete[] centers;
Master commit of OpenFace.
2016-04-28 21:40:36 +02:00
}
/**
* 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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