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Refactoring to prepare for other vector types while mutualizing some methods

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This commit is contained in:
Pierre-Emmanuel Viel 2020-06-26 23:08:04 +02:00
parent 7ec221e734
commit 98de57c6c4
1 changed files with 414 additions and 367 deletions
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781
modules/flann/include/opencv2/flann/kmeans_index.h
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@ -463,14 +463,10 @@ public:
root_[i] = pool_.allocate<KMeansNode>(); root_[i] = pool_.allocate<KMeansNode>();
std::memset(root_[i], 0, sizeof(KMeansNode)); std::memset(root_[i], 0, sizeof(KMeansNode));
if(is_kdtree_distance::val || is_vector_space_distance::val) { Distance* dummy = NULL;
computeNodeStatistics(root_[i], indices_[i], (unsigned int)size_); computeNodeStatistics(root_[i], indices_[i], (unsigned int)size_, dummy);
computeClustering(root_[i], indices_[i], (int)size_, branching_,0);
} computeClustering(root_[i], indices_[i], (int)size_, branching_,0);
else {
computeBitfieldNodeStatistics(root_[i], indices_[i], (unsigned int)size_);
computeBitfieldClustering(root_[i], indices_[i], (int)size_, branching_,0);
}
} }
} }
@ -829,6 +825,413 @@ private:
} }
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 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 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 heared 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);
}
/** /**
* The method responsible with actually doing the recursive hierarchical * The method responsible with actually doing the recursive hierarchical
@ -893,372 +1296,16 @@ private:
count[belongs_to[i]]++; count[belongs_to[i]]++;
} }
cv::AutoBuffer<double> dcenters_buf(branching*veclen_);
Matrix<double> dcenters(dcenters_buf.data(), 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]);
}
}
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;
}
}
}
CentersType** centers = new CentersType*[branching]; CentersType** centers = new CentersType*[branching];
for (int i=0; i<branching; ++i) { Distance* dummy = NULL;
centers[i] = new CentersType[veclen_]; refineAndSplitClustering(node, indices, indices_length, branching, level,
memoryCounter_ += (int)(veclen_*sizeof(CentersType)); centers, radiuses, belongs_to, count, dummy);
for (size_t k=0; k<veclen_; ++k) {
centers[i][k] = (CentersType)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 += 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;
}
delete[] centers; delete[] centers;
} }
/**
* The method responsible with doing the recursive hierarchical clustering on
* binary vectors.
* As some might have heared 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
*/
void computeBitfieldClustering(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;
}
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);
CentersType** centers = new CentersType*[branching];
for (int i=0; i<branching; ++i) {
centers[i] = new CentersType[veclen_];
memoryCounter_ += (int)(veclen_*sizeof(CentersType));
}
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 dist = distance_(dataset_[indices[i]], dataset_[centers_idx[0]], veclen_);
belongs_to[i] = 0;
for (int j=1; j<branching; ++j) {
DistanceType new_dist = distance_(dataset_[indices[i]], dataset_[centers_idx[j]], veclen_);
if (dist>new_dist) {
belongs_to[i] = j;
dist = new_dist;
}
}
if (dist>radiuses[belongs_to[i]]) {
radiuses[belongs_to[i]] = 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(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;
}
}
}
// 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;
computeBitfieldClustering(node->childs[c],indices+start, end-start, branching, level+1);
start=end;
}
delete[] centers;
}
/** /**
* Performs one descent in the hierarchical k-means tree. The branches not * Performs one descent in the hierarchical k-means tree. The branches not
* visited are stored in a priority queue. * visited are stored in a priority queue.