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opencv/3rdparty/flann/algorithms/kmeans_index.h

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/***********************************************************************
* 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 KMEANSTREE_H
#define KMEANSTREE_H
#include <algorithm>
#include <string>
#include <cstdlib>
#include <map>
#include <cassert>
#include <limits>
#include <cmath>
#include "constants.h"
#include "common.h"
#include "heap.h"
#include "allocator.h"
#include "matrix.h"
#include "result_set.h"
#include "random.h"
#include "nn_index.h"
using namespace std;
namespace cvflann
{
/**
* 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, const Matrix<float>& vecs, int* indices, int indices_length, float** 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] = vecs[indices[rnd]];
for (int j=0;j<index;++j) {
float sq = flann_dist(centers[index],centers[index]+vecs.cols,centers[j]);
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, const Matrix<float>& vecs, int* indices, int indices_length, float** centers, int& centers_length)
{
int n = indices_length;
int rnd = rand_int(n);
assert(rnd >=0 && rnd < n);
centers[0] = vecs[indices[rnd]];
int index;
for (index=1; index<k; ++index) {
int best_index = -1;
float best_val = 0;
for (int j=0;j<n;++j) {
float dist = flann_dist(centers[0],centers[0]+vecs.cols,vecs[indices[j]]);
for (int i=1;i<index;++i) {
float tmp_dist = flann_dist(centers[i],centers[i]+vecs.cols,vecs[indices[j]]);
if (tmp_dist<dist) {
dist = tmp_dist;
}
}
if (dist>best_val) {
best_val = dist;
best_index = j;
}
}
if (best_index!=-1) {
centers[index] = vecs[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, const Matrix<float>& vecs, int* indices, int indices_length, float** centers, int& centers_length)
{
int n = indices_length;
double currentPot = 0;
double* closestDistSq = new double[n];
// Choose one random center and set the closestDistSq values
int index = rand_int(n);
assert(index >=0 && index < n);
centers[0] = vecs[indices[index]];
for (int i = 0; i < n; i++) {
closestDistSq[i] = flann_dist(vecs[indices[i]], vecs[indices[i]] + vecs.cols, vecs[indices[index]]);
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 = 0;
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++)
newPot += min( (double)flann_dist(vecs[indices[i]], vecs[indices[i]] + vecs.cols, vecs[indices[index]]), closestDistSq[i] );
// Store the best result
if (bestNewPot < 0 || newPot < bestNewPot) {
bestNewPot = newPot;
bestNewIndex = index;
}
}
// Add the appropriate center
centers[centerCount] = vecs[indices[bestNewIndex]];
currentPot = bestNewPot;
for (int i = 0; i < n; i++)
closestDistSq[i] = min( (double)flann_dist(vecs[indices[i]], vecs[indices[i]]+vecs.cols, vecs[indices[bestNewIndex]]), closestDistSq[i] );
}
centers_length = centerCount;
delete[] closestDistSq;
}
namespace {
typedef void (*centersAlgFunction)(int, const Matrix<float>&, int*, int, float**, int&);
/**
* Associative array with functions to use for choosing the cluster centers.
*/
map<flann_centers_init_t,centersAlgFunction> centerAlgs;
/**
* Static initializer. Performs initialization befor the program starts.
*/
void centers_init()
{
centerAlgs[CENTERS_RANDOM] = &chooseCentersRandom;
centerAlgs[CENTERS_GONZALES] = &chooseCentersGonzales;
centerAlgs[CENTERS_KMEANSPP] = &chooseCentersKMeanspp;
}
struct Init {
Init() { centers_init(); }
};
Init __init;
}
/**
* Hierarchical kmeans index
*
* Contains a tree constructed through a hierarchical kmeans clustering
* and other information for indexing a set of points for nearest-neighbor matching.
*/
class KMeansIndex : public NNIndex
{
/**
* The branching factor used in the hierarchical k-means clustering
*/
int branching;
/**
* Maximum number of iterations to use when performing k-means
* clustering
*/
int max_iter;
/**
* 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 centers, 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<float> dataset;
/**
* Number of features in the dataset.
*/
int size_;
/**
* Length of each feature.
*/
int veclen_;
/**
* Struture representing a node in the hierarchical k-means tree.
*/
struct KMeansNodeSt {
/**
* The cluster center.
*/
float* pivot;
/**
* The cluster radius.
*/
float radius;
/**
* The cluster mean radius.
*/
float mean_radius;
/**
* The cluster variance.
*/
float variance;
/**
* The cluster size (number of points in the cluster)
*/
int size;
/**
* Child nodes (only for non-terminal nodes)
*/
KMeansNodeSt** childs;
/**
* Node points (only for terminal nodes)
*/
int* indices;
/**
* Level
*/
int level;
};
typedef KMeansNodeSt* KMeansNode;
/**
* Alias definition for a nicer syntax.
*/
typedef BranchStruct<KMeansNode> BranchSt;
/**
* Priority queue storing intermediate branches in the best-bin-first search
*/
Heap<BranchSt>* heap;
/**
* The root node in the tree.
*/
KMeansNode root;
/**
* Array of indices to vectors in the dataset.
*/
int* indices;
/**
* Pooled memory allocator.
*
* Using a pooled memory allocator is more efficient
* than allocating memory directly when there is a large
* number small of memory allocations.
*/
PooledAllocator pool;
/**
* Memory occupied by the index.
*/
int memoryCounter;
/**
* The function used for choosing the cluster centers.
*/
centersAlgFunction chooseCenters;
public:
flann_algorithm_t getType() const
{
return KMEANS;
}
/**
* Index constructor
*
* Params:
* inputData = dataset with the input features
* params = parameters passed to the hierarchical k-means algorithm
*/
KMeansIndex(const Matrix<float>& inputData, const KMeansIndexParams& params = KMeansIndexParams() )
: dataset(inputData), root(NULL), indices(NULL)
{
memoryCounter = 0;
size_ = dataset.rows;
veclen_ = dataset.cols;
branching = params.branching;
max_iter = params.iterations;
if (max_iter<0) {
max_iter = numeric_limits<int>::max();
}
flann_centers_init_t centersInit = params.centers_init;
if ( centerAlgs.find(centersInit) != centerAlgs.end() ) {
chooseCenters = centerAlgs[centersInit];
}
else {
throw FLANNException("Unknown algorithm for choosing initial centers.");
}
cb_index = 0.4f;
heap = new Heap<BranchSt>(size_);
}
/**
* Index destructor.
*
* Release the memory used by the index.
*/
virtual ~KMeansIndex()
{
if (root != NULL) {
free_centers(root);
}
delete heap;
if (indices!=NULL) {
delete[] indices;
}
}
/**
* Returns size of index.
*/
int size() const
{
return size_;
}
/**
* Returns the length of an index feature.
*/
int 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 (int i=0;i<size_;++i) {
indices[i] = i;
}
root = pool.allocate<KMeansNodeSt>();
computeNodeStatistics(root, indices, size_);
computeClustering(root, indices, size_, branching,0);
}
void saveIndex(FILE* stream)
{
save_header(stream, *this);
save_value(stream, branching);
save_value(stream, max_iter);
save_value(stream, memoryCounter);
save_value(stream, cb_index);
save_value(stream, *indices, size_);
save_tree(stream, root);
}
void loadIndex(FILE* stream)
{
IndexHeader header = load_header(stream);
if (header.rows!=size() || header.cols!=veclen()) {
throw FLANNException("The index saved belongs to a different dataset");
}
load_value(stream, branching);
load_value(stream, max_iter);
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);
}
/**
* 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& result, const float* vec, const SearchParams& searchParams)
{
int maxChecks = searchParams.checks;
if (maxChecks<0) {
findExactNN(root, result, vec);
}
else {
heap->clear();
int checks = 0;
findNN(root, result, vec, checks, maxChecks);
BranchSt branch;
while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
KMeansNode node = branch.node;
findNN(node, result, vec, checks, maxChecks);
}
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<float>& centers)
{
int numClusters = centers.rows;
if (numClusters<1) {
throw FLANNException("Number of clusters must be at least 1");
}
float variance;
KMeansNode* clusters = new KMeansNode[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) {
float* center = clusters[i]->pivot;
for (int j=0;j<veclen_;++j) {
centers[i][j] = center[j];
}
}
delete[] clusters;
return clusterCount;
}
// Params estimateSearchParams(float precision, Dataset<float>* testset = NULL)
// {
// Params params;
//
// return params;
// }
private:
void save_tree(FILE* stream, KMeansNode node)
{
save_value(stream, *node);
save_value(stream, *(node->pivot), veclen_);
if (node->childs==NULL) {
int indices_offset = 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, KMeansNode& node)
{
node = pool.allocate<KMeansNodeSt>();
load_value(stream, *node);
node->pivot = new float[veclen_];
load_value(stream, *(node->pivot), veclen_);
if (node->childs==NULL) {
int indices_offset;
load_value(stream, indices_offset);
node->indices = indices + indices_offset;
}
else {
node->childs = pool.allocate<KMeansNode>(branching);
for(int i=0; i<branching; ++i) {
load_tree(stream, node->childs[i]);
}
}
}
/**
* Helper function
*/
void free_centers(KMeansNode 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(KMeansNode node, int* indices, int indices_length) {
float radius = 0;
float variance = 0;
float* mean = new float[veclen_];
memoryCounter += veclen_*sizeof(float);
memset(mean,0,veclen_*sizeof(float));
for (int i=0;i<size_;++i) {
float* vec = dataset[indices[i]];
for (int j=0;j<veclen_;++j) {
mean[j] += vec[j];
}
variance += flann_dist(vec,vec+veclen_,zero);
}
for (int j=0;j<veclen_;++j) {
mean[j] /= size_;
}
variance /= size_;
variance -= flann_dist(mean,mean+veclen_,zero);
float tmp = 0;
for (int i=0;i<indices_length;++i) {
tmp = flann_dist(mean, mean + veclen_, dataset[indices[i]]);
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(KMeansNode node, int* indices, int indices_length, int branching, int level)
{
node->size = indices_length;
node->level = level;
if (indices_length < branching) {
node->indices = indices;
sort(node->indices,node->indices+indices_length);
node->childs = NULL;
return;
}
float** initial_centers = new float*[branching];
int centers_length;
chooseCenters(branching, dataset, indices, indices_length, initial_centers, centers_length);
if (centers_length<branching) {
node->indices = indices;
sort(node->indices,node->indices+indices_length);
node->childs = NULL;
return;
}
Matrix<double> dcenters(branching,veclen_);
for (int i=0; i<centers_length; ++i) {
for (int k=0; k<veclen_; ++k) {
dcenters[i][k] = double(initial_centers[i][k]);
}
}
delete[] initial_centers;
float* radiuses = new float[branching];
int* count = new int[branching];
for (int i=0;i<branching;++i) {
radiuses[i] = 0;
count[i] = 0;
}
// assign points to clusters
int* belongs_to = new int[indices_length];
for (int i=0;i<indices_length;++i) {
float sq_dist = flann_dist(dataset[indices[i]], dataset[indices[i]] + veclen_ ,dcenters[0]);
belongs_to[i] = 0;
for (int j=1;j<branching;++j) {
float new_sq_dist = flann_dist(dataset[indices[i]], dataset[indices[i]]+veclen_, dcenters[j]);
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<max_iter) {
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) {
float* vec = dataset[indices[i]];
double* center = dcenters[belongs_to[i]];
for (int k=0;k<veclen_;++k) {
center[k] += vec[k];
}
}
for (int i=0;i<branching;++i) {
int cnt = count[i];
for (int k=0;k<veclen_;++k) {
dcenters[i][k] /= cnt;
}
}
// reassign points to clusters
for (int i=0;i<indices_length;++i) {
float sq_dist = flann_dist(dataset[indices[i]], dataset[indices[i]]+veclen_ ,dcenters[0]);
int new_centroid = 0;
for (int j=1;j<branching;++j) {
float new_sq_dist = flann_dist(dataset[indices[i]], dataset[indices[i]]+veclen_,dcenters[j]);
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;
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) {
belongs_to[k] = i;
count[j]--;
count[i]++;
break;
}
}
converged = false;
}
}
}
float** centers = new float*[branching];
for (int i=0; i<branching; ++i) {
centers[i] = new float[veclen_];
memoryCounter += veclen_*sizeof(float);
for (int k=0; k<veclen_; ++k) {
centers[i][k] = (float)dcenters[i][k];
}
}
// compute kmeans clustering for each of the resulting clusters
node->childs = pool.allocate<KMeansNode>(branching);
int start = 0;
int end = start;
for (int c=0;c<branching;++c) {
int s = count[c];
float variance = 0;
float mean_radius =0;
for (int i=0;i<indices_length;++i) {
if (belongs_to[i]==c) {
float d = flann_dist(dataset[indices[i]],dataset[indices[i]]+veclen_,zero);
variance += d;
mean_radius += sqrt(d);
swap(indices[i],indices[end]);
swap(belongs_to[i],belongs_to[end]);
end++;
}
}
variance /= s;
mean_radius /= s;
variance -= flann_dist(centers[c],centers[c]+veclen_,zero);
node->childs[c] = pool.allocate<KMeansNodeSt>();
node->childs[c]->radius = radiuses[c];
node->childs[c]->pivot = centers[c];
node->childs[c]->variance = variance;
node->childs[c]->mean_radius = mean_radius;
node->childs[c]->indices = NULL;
computeClustering(node->childs[c],indices+start, end-start, branching, level+1);
start=end;
}
delete[] centers;
delete[] radiuses;
delete[] count;
delete[] belongs_to;
}
/**
* 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(KMeansNode node, ResultSet& result, const float* vec, int& checks, int maxChecks)
{
// Ignore those clusters that are too far away
{
float bsq = flann_dist(vec, vec+veclen_, node->pivot);
float rsq = node->radius;
float wsq = result.worstDist();
float val = bsq-rsq-wsq;
float 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) {
result.addPoint(dataset[node->indices[i]], node->indices[i]);
}
}
else {
float* domain_distances = new float[branching];
int closest_center = exploreNodeBranches(node, vec, domain_distances);
delete[] domain_distances;
findNN(node->childs[closest_center],result,vec, checks, maxChecks);
}
}
/**
* 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(KMeansNode node, const float* q, float* domain_distances)
{
int best_index = 0;
domain_distances[best_index] = flann_dist(q,q+veclen_,node->childs[best_index]->pivot);
for (int i=1;i<branching;++i) {
domain_distances[i] = flann_dist(q,q+veclen_,node->childs[i]->pivot);
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::make_branch(node->childs[i],domain_distances[i]));
}
}
return best_index;
}
/**
* Function the performs exact nearest neighbor search by traversing the entire tree.
*/
void findExactNN(KMeansNode node, ResultSet& result, const float* vec)
{
// Ignore those clusters that are too far away
{
float bsq = flann_dist(vec, vec+veclen_, node->pivot);
float rsq = node->radius;
float wsq = result.worstDist();
float val = bsq-rsq-wsq;
float 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) {
result.addPoint(dataset[node->indices[i]], node->indices[i]);
}
}
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(KMeansNode node, const float* q, int* sort_indices)
{
float* domain_distances = new float[branching];
for (int i=0;i<branching;++i) {
float dist = flann_dist(q, q+veclen_, node->childs[i]->pivot);
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
*/
float getDistanceToBorder(float* p, float* c, float* q)
{
float sum = 0;
float sum2 = 0;
for (int i=0;i<veclen_; ++i) {
float 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(KMeansNode root, KMeansNode* clusters, int clusters_length, float& varianceValue)
{
int clusterCount = 1;
clusters[0] = root;
float meanVariance = root->variance*root->size;
while (clusterCount<clusters_length) {
float minVariance = numeric_limits<float>::max();
int splitIndex = -1;
for (int i=0;i<clusterCount;++i) {
if (clusters[i]->childs != NULL) {
float 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
KMeansNode 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;
}
};
//register_index(KMEANS,KMeansTree)
}
#endif //KMEANSTREE_H
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