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// tree/cluster-utils.cc
// Copyright 2012 Arnab Ghoshal
// Copyright 2009-2011 Microsoft Corporation; Saarland University
// See ../../COPYING for clarification regarding multiple authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include <functional>
#include <queue>
#include <vector>
using std::vector;
#include "base/kaldi-math.h"
#include "util/stl-utils.h"
#include "tree/cluster-utils.h"
namespace kaldi {
typedef uint16 uint_smaller;
typedef int16 int_smaller;
// ============================================================================
// Some convenience functions used in the clustering routines
// ============================================================================
BaseFloat SumClusterableObjf(const std::vector<Clusterable*> &vec) {
BaseFloat ans = 0.0;
for (size_t i = 0; i < vec.size(); i++) {
if (vec[i] != NULL) {
BaseFloat objf = vec[i]->Objf();
if (KALDI_ISNAN(objf)) {
KALDI_WARN << "SumClusterableObjf, NaN objf";
} else {
ans += objf;
}
}
}
return ans;
}
BaseFloat SumClusterableNormalizer(const std::vector<Clusterable*> &vec) {
BaseFloat ans = 0.0;
for (size_t i = 0; i < vec.size(); i++) {
if (vec[i] != NULL) {
BaseFloat objf = vec[i]->Normalizer();
if (KALDI_ISNAN(objf)) {
KALDI_WARN << "SumClusterableObjf, NaN objf";
} else {
ans += objf;
}
}
}
return ans;
}
Clusterable* SumClusterable(const std::vector<Clusterable*> &vec) {
Clusterable *ans = NULL;
for (size_t i = 0; i < vec.size(); i++) {
if (vec[i] != NULL) {
if (ans == NULL)
ans = vec[i]->Copy();
else
ans->Add(*(vec[i]));
}
}
return ans;
}
void EnsureClusterableVectorNotNull(std::vector<Clusterable*> *stats) {
KALDI_ASSERT(stats != NULL);
std::vector<Clusterable*>::iterator itr = stats->begin(), end = stats->end();
if (itr == end) return; // Nothing to do.
Clusterable *nonNullExample = NULL;
for (; itr != end; ++itr) {
if (*itr != NULL) {
nonNullExample = *itr;
break;
}
}
if (nonNullExample == NULL) {
KALDI_ERR << "All stats are NULL."; // crash. logic error.
}
itr = stats->begin();
Clusterable *nonNullExampleCopy = nonNullExample->Copy();
// sets stats to zero. do this just once (on copy) for efficiency.
nonNullExampleCopy->SetZero();
for (; itr != end; ++itr) {
if (*itr == NULL) {
*itr = nonNullExampleCopy->Copy();
}
}
delete nonNullExampleCopy;
}
void AddToClusters(const std::vector<Clusterable*> &stats,
const std::vector<int32> &assignments,
std::vector<Clusterable*> *clusters) {
KALDI_ASSERT(assignments.size() == stats.size());
int32 size = stats.size();
if (size == 0) return; // Nothing to do.
KALDI_ASSERT(clusters != NULL);
int32 max_assignment = *std::max_element(assignments.begin(),
assignments.end());
if (static_cast<int32> (clusters->size()) <= max_assignment)
clusters->resize(max_assignment + 1, NULL); // extend with NULLs.
for (int32 i = 0; i < size; i++) {
if (stats[i] != NULL) {
if ((*clusters)[assignments[i]] == NULL)
(*clusters)[assignments[i]] = stats[i]->Copy();
else
(*clusters)[assignments[i]]->Add(*(stats[i]));
}
}
}
void AddToClustersOptimized(const std::vector<Clusterable*> &stats,
const std::vector<int32> &assignments,
const Clusterable &total,
std::vector<Clusterable*> *clusters) {
#ifdef KALDI_PARANOID
// Make sure "total" is actually the sum of stats in "stats".
{
BaseFloat stats_norm = SumClusterableNormalizer(stats),
tot_norm = total.Normalizer();
AssertEqual(stats_norm, tot_norm, 0.01);
}
#endif
KALDI_ASSERT(assignments.size() == stats.size());
int32 size = stats.size();
if (size == 0) return; // Nothing to do.
KALDI_ASSERT(clusters != NULL);
int32 num_clust = 1 + *std::max_element(assignments.begin(),
assignments.end());
if (static_cast<int32> (clusters->size()) < num_clust)
clusters->resize(num_clust, NULL); // extend with NULLs.
std::vector<int32> num_stats_for_cluster(num_clust, 0);
int32 num_total_stats = 0;
for (int32 i = 0; i < size; i++) {
if (stats[i] != NULL) {
num_total_stats++;
num_stats_for_cluster[assignments[i]]++;
}
}
if (num_total_stats == 0) return; // Nothing to do.
// it will only ever be efficient to subtract for at most one index.
int32 subtract_index = -1;
for (int32 c = 0; c < num_clust; c++) {
if (num_stats_for_cluster[c] > num_total_stats - num_stats_for_cluster[c]) {
subtract_index = c;
if ((*clusters)[c] == NULL)
(*clusters)[c] = total.Copy();
else
(*clusters)[c]->Add(total);
break;
}
}
for (int32 i = 0; i < size; i++) {
if (stats[i] != NULL) {
int32 assignment = assignments[i];
if (assignment != (int32) subtract_index) {
if ((*clusters)[assignment] == NULL)
(*clusters)[assignment] = stats[i]->Copy();
else
(*clusters)[assignment]->Add(*(stats[i]));
}
if (subtract_index != -1 && assignment != subtract_index)
(*clusters)[subtract_index]->Sub(*(stats[i]));
}
}
}
// ============================================================================
// Bottom-up clustering routines
// ============================================================================
class BottomUpClusterer {
public:
BottomUpClusterer(const std::vector<Clusterable*> &points,
BaseFloat max_merge_thresh,
int32 min_clust,
std::vector<Clusterable*> *clusters_out,
std::vector<int32> *assignments_out)
: ans_(0.0), points_(points), max_merge_thresh_(max_merge_thresh),
min_clust_(min_clust), clusters_(clusters_out != NULL? clusters_out
: &tmp_clusters_), assignments_(assignments_out != NULL ?
assignments_out : &tmp_assignments_) {
nclusters_ = npoints_ = points.size();
dist_vec_.resize((npoints_ * (npoints_ - 1)) / 2);
}
BaseFloat Cluster();
~BottomUpClusterer() { DeletePointers(&tmp_clusters_); }
private:
void Renumber();
void InitializeAssignments();
void SetInitialDistances(); ///< Sets up distances and queue.
/// CanMerge returns true if i and j are existing clusters, and the distance
/// (negated objf-change) "dist" is accurate (i.e. not outdated).
bool CanMerge(int32 i, int32 j, BaseFloat dist);
/// Merge j into i and delete j.
void MergeClusters(int32 i, int32 j);
/// Reconstructs the priority queue from the distances.
void ReconstructQueue();
void SetDistance(int32 i, int32 j);
BaseFloat& Distance(int32 i, int32 j) {
KALDI_ASSERT(i < npoints_ && j < i);
return dist_vec_[(i * (i - 1)) / 2 + j];
}
BaseFloat ans_;
const std::vector<Clusterable*> &points_;
BaseFloat max_merge_thresh_;
int32 min_clust_;
std::vector<Clusterable*> *clusters_;
std::vector<int32> *assignments_;
std::vector<Clusterable*> tmp_clusters_;
std::vector<int32> tmp_assignments_;
std::vector<BaseFloat> dist_vec_;
int32 nclusters_;
int32 npoints_;
typedef std::pair<BaseFloat, std::pair<uint_smaller, uint_smaller> > QueueElement;
// Priority queue using greater (lowest distances are highest priority).
typedef std::priority_queue<QueueElement, std::vector<QueueElement>,
std::greater<QueueElement> > QueueType;
QueueType queue_;
};
BaseFloat BottomUpClusterer::Cluster() {
KALDI_VLOG(2) << "Initializing cluster assignments.";
InitializeAssignments();
KALDI_VLOG(2) << "Setting initial distances.";
SetInitialDistances();
KALDI_VLOG(2) << "Clustering...";
while (nclusters_ > min_clust_ && !queue_.empty()) {
std::pair<BaseFloat, std::pair<uint_smaller, uint_smaller> > pr = queue_.top();
BaseFloat dist = pr.first;
int32 i = (int32) pr.second.first, j = (int32) pr.second.second;
queue_.pop();
if (CanMerge(i, j, dist)) MergeClusters(i, j);
}
KALDI_VLOG(2) << "Renumbering clusters to contiguous numbers.";
Renumber();
return ans_;
}
void BottomUpClusterer::Renumber() {
KALDI_VLOG(2) << "Freeing up distance vector.";
{
vector<BaseFloat> tmp;
tmp.swap(dist_vec_);
}
// Commented the following out since it was causing the process to take up too
// much memory with larger models. While the swap() method of STL types swaps
// the data pointers, std::swap() creates a temporary copy. -Arnab
// KALDI_VLOG(2) << "Freeing up the queue";
// // first free up memory by getting rid of queue. this is a special trick
// // to force STL to free memory.
// {
// QueueType tmp;
// std::swap(tmp, queue_);
// }
// called after clustering, renumbers to make clusters contiguously
// numbered. also processes assignments_ to remove chains of references.
KALDI_VLOG(2) << "Creating new copy of non-NULL clusters.";
std::vector<uint_smaller> mapping(npoints_, static_cast<uint_smaller> (-1)); // mapping from intermediate to final clusters.
std::vector<Clusterable*> new_clusters(nclusters_);
int32 clust = 0;
for (int32 i = 0; i < npoints_; i++) {
if ((*clusters_)[i] != NULL) {
KALDI_ASSERT(clust < nclusters_);
new_clusters[clust] = (*clusters_)[i];
mapping[i] = clust;
clust++;
}
}
KALDI_ASSERT(clust == nclusters_);
KALDI_VLOG(2) << "Creating new copy of assignments.";
std::vector<int32> new_assignments(npoints_);
for (int32 i = 0; i < npoints_; i++) { // now reprocess assignments_.
int32 ii = i;
while ((*assignments_)[ii] != ii)
ii = (*assignments_)[ii]; // follow the chain.
KALDI_ASSERT((*clusters_)[ii] != NULL); // cannot have assignment to nonexistent cluster.
KALDI_ASSERT(mapping[ii] != static_cast<uint_smaller>(-1));
new_assignments[i] = mapping[ii];
}
clusters_->swap(new_clusters);
assignments_->swap(new_assignments);
}
void BottomUpClusterer::InitializeAssignments() {
clusters_->resize(npoints_);
assignments_->resize(npoints_);
for (int32 i = 0; i < npoints_; i++) { // initialize as 1-1 mapping.
(*clusters_)[i] = points_[i]->Copy();
(*assignments_)[i] = i;
}
}
void BottomUpClusterer::SetInitialDistances() {
for (int32 i = 0; i < npoints_; i++) {
for (int32 j = 0; j < i; j++) {
BaseFloat dist = (*clusters_)[i]->Distance(*((*clusters_)[j]));
dist_vec_[(i * (i - 1)) / 2 + j] = dist;
if (dist <= max_merge_thresh_)
queue_.push(std::make_pair(dist, std::make_pair(static_cast<uint_smaller>(i),
static_cast<uint_smaller>(j))));
}
}
}
bool BottomUpClusterer::CanMerge(int32 i, int32 j, BaseFloat dist) {
KALDI_ASSERT(i != j && i < npoints_ && j < npoints_);
if ((*clusters_)[i] == NULL || (*clusters_)[j] == NULL)
return false;
BaseFloat cached_dist = dist_vec_[(i * (i - 1)) / 2 + j];
return (std::fabs(cached_dist - dist) <= 1.0e-05 * std::fabs(dist));
}
void BottomUpClusterer::MergeClusters(int32 i, int32 j) {
KALDI_ASSERT(i != j && i < npoints_ && j < npoints_);
(*clusters_)[i]->Add(*((*clusters_)[j]));
delete (*clusters_)[j];
(*clusters_)[j] = NULL;
// note that we may have to follow the chain within "assignment_" to get
// final assignments.
(*assignments_)[j] = i;
// subtract negated objective function change, i.e. add objective function
// change.
ans_ -= dist_vec_[(i * (i - 1)) / 2 + j];
nclusters_--;
// Now update "distances".
for (int32 k = 0; k < npoints_; k++) {
if (k != i && (*clusters_)[k] != NULL) {
if (k < i)
SetDistance(i, k); // SetDistance requires k < i.
else
SetDistance(k, i);
}
}
}
void BottomUpClusterer::ReconstructQueue() {
// empty queue [since there is no clear()]
{
QueueType tmp;
std::swap(tmp, queue_);
}
for (int32 i = 0; i < npoints_; i++) {
if ((*clusters_)[i] != NULL) {
for (int32 j = 0; j < i; j++) {
if ((*clusters_)[j] != NULL) {
BaseFloat dist = dist_vec_[(i * (i - 1)) / 2 + j];
if (dist <= max_merge_thresh_) {
queue_.push(std::make_pair(dist, std::make_pair(
static_cast<uint_smaller>(i), static_cast<uint_smaller>(j))));
}
}
}
}
}
}
void BottomUpClusterer::SetDistance(int32 i, int32 j) {
KALDI_ASSERT(i < npoints_ && j < i && (*clusters_)[i] != NULL
&& (*clusters_)[j] != NULL);
BaseFloat dist = (*clusters_)[i]->Distance(*((*clusters_)[j]));
dist_vec_[(i * (i - 1)) / 2 + j] = dist; // set the distance in the array.
if (dist < max_merge_thresh_) {
queue_.push(std::make_pair(dist, std::make_pair(static_cast<uint_smaller>(i),
static_cast<uint_smaller>(j))));
}
// every time it's at least twice the maximum possible size.
if (queue_.size() >= static_cast<size_t> (npoints_ * npoints_)) {
// Control memory use by getting rid of orphaned queue entries
ReconstructQueue();
}
}
BaseFloat ClusterBottomUp(const std::vector<Clusterable*> &points,
BaseFloat max_merge_thresh,
int32 min_clust,
std::vector<Clusterable*> *clusters_out,
std::vector<int32> *assignments_out) {
KALDI_ASSERT(max_merge_thresh >= 0.0 && min_clust >= 0);
KALDI_ASSERT(!ContainsNullPointers(points));
int32 npoints = points.size();
// make sure fits in uint_smaller and does not hit the -1 which is reserved.
KALDI_ASSERT(sizeof(uint_smaller)==sizeof(uint32) ||
npoints < static_cast<int32>(static_cast<uint_smaller>(-1)));
KALDI_VLOG(2) << "Initializing clustering object.";
BottomUpClusterer bc(points, max_merge_thresh, min_clust, clusters_out, assignments_out);
BaseFloat ans = bc.Cluster();
if (clusters_out) KALDI_ASSERT(!ContainsNullPointers(*clusters_out));
return ans;
}
// ============================================================================
// Compartmentalized bottom-up clustering routines
// ============================================================================
struct CompBotClustElem {
BaseFloat dist;
int32 compartment, point1, point2;
CompBotClustElem(BaseFloat d, int32 comp, int32 i, int32 j)
: dist(d), compartment(comp), point1(i), point2(j) {}
};
bool operator > (const CompBotClustElem &a, const CompBotClustElem &b) {
return a.dist > b.dist;
}
class CompartmentalizedBottomUpClusterer {
public:
CompartmentalizedBottomUpClusterer(
const vector< vector<Clusterable*> > &points, BaseFloat max_merge_thresh,
int32 min_clust)
: points_(points), max_merge_thresh_(max_merge_thresh),
min_clust_(min_clust) {
ncompartments_ = points.size();
nclusters_ = 0;
npoints_.resize(ncompartments_);
for (int32 comp = 0; comp < ncompartments_; comp++) {
npoints_[comp] = points[comp].size();
nclusters_ += npoints_[comp];
}
}
BaseFloat Cluster(vector< vector<Clusterable*> > *clusters_out,
vector< vector<int32> > *assignments_out);
~CompartmentalizedBottomUpClusterer() {
for (vector< vector<Clusterable*> >::iterator itr = clusters_.begin(),
end = clusters_.end(); itr != end; ++itr)
DeletePointers(&(*itr));
}
private:
// Renumbers to make clusters contiguously numbered. Called after clustering.
// Also processes assignments_ to remove chains of references.
void Renumber(int32 compartment);
void InitializeAssignments();
void SetInitialDistances(); ///< Sets up distances and queue.
/// CanMerge returns true if i and j are existing clusters, and the distance
/// (negated objf-change) "dist" is accurate (i.e. not outdated).
bool CanMerge(int32 compartment, int32 i, int32 j, BaseFloat dist);
/// Merge j into i and delete j. Returns obj function change.
BaseFloat MergeClusters(int32 compartment, int32 i, int32 j);
/// Reconstructs the priority queue from the distances.
void ReconstructQueue();
void SetDistance(int32 compartment, int32 i, int32 j);
const vector< vector<Clusterable*> > &points_;
BaseFloat max_merge_thresh_;
int32 min_clust_;
vector< vector<Clusterable*> > clusters_;
vector< vector<int32> > assignments_;
vector< vector<BaseFloat> > dist_vec_;
int32 ncompartments_, nclusters_;
vector<int32> npoints_;
// Priority queue using greater (lowest distances are highest priority).
typedef std::priority_queue< CompBotClustElem, std::vector<CompBotClustElem>,
std::greater<CompBotClustElem> > QueueType;
QueueType queue_;
};
BaseFloat CompartmentalizedBottomUpClusterer::Cluster(
vector< vector<Clusterable*> > *clusters_out,
vector< vector<int32> > *assignments_out) {
InitializeAssignments();
SetInitialDistances();
BaseFloat total_obj_change = 0;
while (nclusters_ > min_clust_ && !queue_.empty()) {
CompBotClustElem qelem = queue_.top();
queue_.pop();
if (CanMerge(qelem.compartment, qelem.point1, qelem.point2, qelem.dist))
total_obj_change += MergeClusters(qelem.compartment, qelem.point1,
qelem.point2);
}
for (int32 comp = 0; comp < ncompartments_; comp++)
Renumber(comp);
if (clusters_out != NULL) clusters_out->swap(clusters_);
if (assignments_out != NULL) assignments_out->swap(assignments_);
return total_obj_change;
}
void CompartmentalizedBottomUpClusterer::Renumber(int32 comp) {
// first free up memory by getting rid of queue. this is a special trick
// to force STL to free memory.
{
QueueType tmp;
std::swap(tmp, queue_);
}
// First find the number of surviving clusters in the compartment.
int32 clusts_in_compartment = 0;
for (int32 i = 0; i < npoints_[comp]; i++) {
if (clusters_[comp][i] != NULL)
clusts_in_compartment++;
}
KALDI_ASSERT(clusts_in_compartment <= nclusters_);
// mapping from intermediate to final clusters.
vector<uint_smaller> mapping(npoints_[comp], static_cast<uint_smaller> (-1));
vector<Clusterable*> new_clusters(clusts_in_compartment);
// Now copy the surviving clusters in a fresh array.
clusts_in_compartment = 0;
for (int32 i = 0; i < npoints_[comp]; i++) {
if (clusters_[comp][i] != NULL) {
new_clusters[clusts_in_compartment] = clusters_[comp][i];
mapping[i] = clusts_in_compartment;
clusts_in_compartment++;
}
}
// Next, process the assignments.
std::vector<int32> new_assignments(npoints_[comp]);
for (int32 i = 0; i < npoints_[comp]; i++) {
int32 ii = i;
while (assignments_[comp][ii] != ii)
ii = assignments_[comp][ii]; // follow the chain.
// cannot assign to nonexistent cluster.
KALDI_ASSERT(clusters_[comp][ii] != NULL);
KALDI_ASSERT(mapping[ii] != static_cast<uint_smaller>(-1));
new_assignments[i] = mapping[ii];
}
clusters_[comp].swap(new_clusters);
assignments_[comp].swap(new_assignments);
}
void CompartmentalizedBottomUpClusterer::InitializeAssignments() {
clusters_.resize(ncompartments_);
assignments_.resize(ncompartments_);
for (int32 comp = 0; comp < ncompartments_; comp++) {
clusters_[comp].resize(npoints_[comp]);
assignments_[comp].resize(npoints_[comp]);
for (int32 i = 0; i < npoints_[comp]; i++) { // initialize as 1-1 mapping.
clusters_[comp][i] = points_[comp][i]->Copy();
assignments_[comp][i] = i;
}
}
}
void CompartmentalizedBottomUpClusterer::SetInitialDistances() {
dist_vec_.resize(ncompartments_);
for (int32 comp = 0; comp < ncompartments_; comp++) {
dist_vec_[comp].resize((npoints_[comp] * (npoints_[comp] - 1)) / 2);
for (int32 i = 0; i < npoints_[comp]; i++)
for (int32 j = 0; j < i; j++)
SetDistance(comp, i, j);
}
}
bool CompartmentalizedBottomUpClusterer::CanMerge(int32 comp, int32 i, int32 j,
BaseFloat dist) {
KALDI_ASSERT(comp < ncompartments_ && i < npoints_[comp] && j < i);
if (clusters_[comp][i] == NULL || clusters_[comp][j] == NULL)
return false;
BaseFloat cached_dist = dist_vec_[comp][(i * (i - 1)) / 2 + j];
return (std::fabs(cached_dist - dist) <= 1.0e-05 * std::fabs(dist));
}
BaseFloat CompartmentalizedBottomUpClusterer::MergeClusters(int32 comp, int32 i,
int32 j) {
KALDI_ASSERT(comp < ncompartments_ && i < npoints_[comp] && j < i);
clusters_[comp][i]->Add(*(clusters_[comp][j]));
delete clusters_[comp][j];
clusters_[comp][j] = NULL;
// note that we may have to follow the chain within "assignment_" to get
// final assignments.
assignments_[comp][j] = i;
// objective function change.
BaseFloat ans = -dist_vec_[comp][(i * (i - 1)) / 2 + j];
nclusters_--;
// Now update "distances".
for (int32 k = 0; k < npoints_[comp]; k++) {
if (k != i && clusters_[comp][k] != NULL) {
if (k < i)
SetDistance(comp, i, k); // SetDistance requires k < i.
else
SetDistance(comp, k, i);
}
}
// Control memory use by getting rid of orphaned queue entries every time
// it's at least twice the maximum possible size.
if (queue_.size() >= static_cast<size_t> (nclusters_ * nclusters_)) {
ReconstructQueue();
}
return ans;
}
void CompartmentalizedBottomUpClusterer::ReconstructQueue() {
// empty queue [since there is no clear()]
{
QueueType tmp;
std::swap(tmp, queue_);
}
for (int32 comp = 0; comp < ncompartments_; comp++) {
for (int32 i = 0; i < npoints_[comp]; i++) {
if (clusters_[comp][i] == NULL) continue;
for (int32 j = 0; j < i; j++) {
if (clusters_[comp][j] == NULL) continue;
SetDistance(comp, i, j);
}
}
}
}
void CompartmentalizedBottomUpClusterer::SetDistance(int32 comp,
int32 i, int32 j) {
KALDI_ASSERT(comp < ncompartments_ && i < npoints_[comp] && j < i);
KALDI_ASSERT(clusters_[comp][i] != NULL && clusters_[comp][j] != NULL);
BaseFloat dist = clusters_[comp][i]->Distance(*(clusters_[comp][j]));
dist_vec_[comp][(i * (i - 1)) / 2 + j] = dist;
if (dist < max_merge_thresh_) {
queue_.push(CompBotClustElem(dist, comp, static_cast<uint_smaller>(i),
static_cast<uint_smaller>(j)));
}
}
BaseFloat ClusterBottomUpCompartmentalized(
const std::vector< std::vector<Clusterable*> > &points, BaseFloat thresh,
int32 min_clust, std::vector< std::vector<Clusterable*> > *clusters_out,
std::vector< std::vector<int32> > *assignments_out) {
KALDI_ASSERT(thresh >= 0.0 && min_clust >= 0);
int32 npoints = 0, num_non_empty_compartments = 0;
for (vector< vector<Clusterable*> >::const_iterator itr = points.begin(),
end = points.end(); itr != end; ++itr) {
KALDI_ASSERT(!ContainsNullPointers(*itr));
npoints += itr->size();
if (itr->size() > 0) num_non_empty_compartments++;
}
KALDI_ASSERT(min_clust >= num_non_empty_compartments); // Code does not merge compartments.
// make sure fits in uint_smaller and does not hit the -1 which is reserved.
KALDI_ASSERT(sizeof(uint_smaller)==sizeof(uint32) ||
npoints < static_cast<int32>(static_cast<uint_smaller>(-1)));
CompartmentalizedBottomUpClusterer bc(points, thresh, min_clust);
BaseFloat ans = bc.Cluster(clusters_out, assignments_out);
if (clusters_out) {
for (vector< vector<Clusterable*> >::iterator itr = clusters_out->begin(),
end = clusters_out->end(); itr != end; ++itr) {
KALDI_ASSERT(!ContainsNullPointers(*itr));
}
}
return ans;
}
// ============================================================================
// Clustering through refinement routines
// ============================================================================
class RefineClusterer {
public:
// size used in point_info structure (we store a lot of these so don't want
// to just make it int32). Also used as a time-id (cannot have more moves of
// points, than can fit in this time). Must be big enough to store num-clust.
typedef int32 LocalInt;
typedef uint_smaller ClustIndexInt;
RefineClusterer(const std::vector<Clusterable*> &points,
std::vector<Clusterable*> *clusters,
std::vector<int32> *assignments,
RefineClustersOptions cfg)
: points_(points), clusters_(clusters), assignments_(assignments),
cfg_(cfg) {
KALDI_ASSERT(cfg_.top_n >= 2);
num_points_ = points_.size();
num_clust_ = static_cast<int32> (clusters->size());
// so can fit clust-id in LocalInt
if (cfg_.top_n > (int32) num_clust_) cfg_.top_n
= static_cast<int32> (num_clust_);
KALDI_ASSERT(cfg_.top_n == static_cast<int32>(static_cast<ClustIndexInt>(cfg_.top_n)));
t_ = 0;
my_clust_index_.resize(num_points_);
// will set all PointInfo's to 0 too (they will be up-to-date).
clust_time_.resize(num_clust_, 0);
clust_objf_.resize(num_clust_);
for (int32 i = 0; i < num_clust_; i++)
clust_objf_[i] = (*clusters_)[i]->Objf();
info_.resize(num_points_ * cfg_.top_n);
ans_ = 0;
InitPoints();
}
BaseFloat Refine() {
if (cfg_.top_n <= 1) return 0.0; // nothing to do.
Iterate();
return ans_;
}
// at some point check cfg_.top_n > 1 after maxing to num_clust_.
private:
void InitPoint(int32 point) {
// Find closest clusters to this point.
// distances are really negated objf changes, ignoring terms that don't vary with the "other" cluster.
std::vector<std::pair<BaseFloat, LocalInt> > distances;
distances.reserve(num_clust_-1);
int32 my_clust = (*assignments_)[point];
Clusterable *point_cl = points_[point];
for (int32 clust = 0;clust < num_clust_;clust++) {
if (clust != my_clust) {
Clusterable *tmp = (*clusters_)[clust]->Copy();
tmp->Add(*point_cl);
BaseFloat other_clust_objf = clust_objf_[clust];
BaseFloat other_clust_plus_me_objf = (*clusters_)[clust]->ObjfPlus(* (points_[point]));
BaseFloat distance = other_clust_objf-other_clust_plus_me_objf; // negated delta-objf, with only "varying" terms.
distances.push_back(std::make_pair(distance, (LocalInt)clust));
delete tmp;
}
}
if ((cfg_.top_n-1-1) >= 0) {
std::nth_element(distances.begin(), distances.begin()+(cfg_.top_n-1-1), distances.end());
}
// top_n-1 is the # of elements we want to retain. -1 because we need the iterator
// that points to the end of that range (i.e. not potentially off the end of the array).
for (int32 index = 0;index < cfg_.top_n-1;index++) {
point_info &info = GetInfo(point, index);
int32 clust = distances[index].second;
info.clust = clust;
BaseFloat distance = distances[index].first;
BaseFloat other_clust_objf = clust_objf_[clust];
BaseFloat other_clust_plus_me_objf = -(distance - other_clust_objf);
info.objf = other_clust_plus_me_objf;
info.time = 0;
}
// now put the last element in, which is my current cluster.
point_info &info = GetInfo(point, cfg_.top_n-1);
info.clust = my_clust;
info.time = 0;
info.objf = (*clusters_)[my_clust]->ObjfMinus(*(points_[point]));
my_clust_index_[point] = cfg_.top_n-1;
}
void InitPoints() {
// finds, for each point, the closest cfg_.top_n clusters (including its own cluster).
// this may be the most time-consuming step of the algorithm.
for (int32 p = 0;p < num_points_;p++) InitPoint(p);
}
void Iterate() {
int32 iter, num_iters = cfg_.num_iters;
for (iter = 0;iter < num_iters;iter++) {
int32 cur_t = t_;
for (int32 point = 0;point < num_points_;point++) {
if (t_+1 == 0) {
KALDI_WARN << "Stopping iterating at int32 moves";
return; // once we use up all time points, must return-- this
// should rarely happen as int32 is large.
}
ProcessPoint(point);
}
if (t_ == cur_t) break; // nothing changed so we converged.
}
}
void MovePoint(int32 point, int32 new_index) {
// move point to a different cluster.
t_++;
int32 old_index = my_clust_index_[point]; // index into info
// array corresponding to current cluster.
KALDI_ASSERT(new_index < cfg_.top_n && new_index != old_index);
point_info &old_info = GetInfo(point, old_index),
&new_info = GetInfo(point, new_index);
my_clust_index_[point] = new_index; // update to new index.
int32 old_clust = old_info.clust, new_clust = new_info.clust;
KALDI_ASSERT( (*assignments_)[point] == old_clust);
(*assignments_)[point] = new_clust;
(*clusters_)[old_clust]->Sub( *(points_[point]) );
(*clusters_)[new_clust]->Add( *(points_[point]) );
UpdateClust(old_clust);
UpdateClust(new_clust);
}
void UpdateClust(int32 clust) {
KALDI_ASSERT(clust < num_clust_);
clust_objf_[clust] = (*clusters_)[clust]->Objf();
clust_time_[clust] = t_;
}
void ProcessPoint(int32 point) {
// note: calling code uses the fact
// that it only ever increases t_ by one.
KALDI_ASSERT(point < num_points_);
// (1) Make sure own-cluster like is updated.
int32 self_index = my_clust_index_[point]; // index <cfg_.top_n of own cluster.
point_info &self_info = GetInfo(point, self_index);
int32 self_clust = self_info.clust; // cluster index of own cluster.
KALDI_ASSERT(self_index < cfg_.top_n);
UpdateInfo(point, self_index);
float own_clust_objf = clust_objf_[self_clust];
float own_clust_minus_me_objf = self_info.objf; // objf of own cluster minus self.
// Now check the other "close" clusters and see if we want to move there.
for (int32 index = 0;index < cfg_.top_n;index++) {
if (index != self_index) {
UpdateInfo(point, index);
point_info &other_info = GetInfo(point, index);
BaseFloat other_clust_objf = clust_objf_[other_info.clust];
BaseFloat other_clust_plus_me_objf = other_info.objf;
BaseFloat impr = other_clust_plus_me_objf + own_clust_minus_me_objf
- other_clust_objf - own_clust_objf;
if (impr > 0) { // better to switch...
ans_ += impr;
MovePoint(point, index);
return; // the stuff we precomputed at the top is invalidated now, and it's
// easiest just to wait till next time we visit this point.
}
}
}
}
void UpdateInfo(int32 point, int32 idx) {
point_info &pinfo = GetInfo(point, idx);
if (pinfo.time < clust_time_[pinfo.clust]) { // it's not up-to-date...
Clusterable *tmp_cl = (*clusters_)[pinfo.clust]->Copy();
if (idx == my_clust_index_[point]) {
tmp_cl->Sub( *(points_[point]) );
} else{
tmp_cl->Add( *(points_[point]) );
}
pinfo.time = t_;
pinfo.objf = tmp_cl->Objf();
delete tmp_cl;
}
}
typedef struct {
LocalInt clust;
LocalInt time;
BaseFloat objf; // Objf of this cluster plus this point (or minus, if own cluster).
} point_info;
point_info &GetInfo(int32 point, int32 idx) {
KALDI_ASSERT(point < num_points_ && idx < cfg_.top_n);
int32 i = point*cfg_.top_n + idx;
KALDI_PARANOID_ASSERT(i < static_cast<int32>(info_.size()));
return info_[i];
}
const std::vector<Clusterable*> &points_;
std::vector<Clusterable*> *clusters_;
std::vector<int32> *assignments_;
std::vector<point_info> info_; // size is [num_points_ * cfg_.top_n].
std::vector<ClustIndexInt> my_clust_index_; // says for each point, which index 0...cfg_.top_n-1 currently
// corresponds to its own cluster.
std::vector<LocalInt> clust_time_; // Modification time of cluster.
std::vector<BaseFloat> clust_objf_; // [clust], objf for cluster.
BaseFloat ans_; // objf improvement.
int32 num_clust_;
int32 num_points_;
int32 t_;
RefineClustersOptions cfg_; // note, we change top_n in config; don't make this member a reference member.
};
BaseFloat RefineClusters(const std::vector<Clusterable*> &points,
std::vector<Clusterable*> *clusters,
std::vector<int32> *assignments,
RefineClustersOptions cfg) {
#ifndef KALDI_PARANOID // don't do this check in "paranoid" mode as we want to expose bugs.
if (cfg.num_iters <= 0) { return 0.0; } // nothing to do.
#endif
KALDI_ASSERT(clusters != NULL && assignments != NULL);
KALDI_ASSERT(!ContainsNullPointers(points) && !ContainsNullPointers(*clusters));
RefineClusterer rc(points, clusters, assignments, cfg);
BaseFloat ans = rc.Refine();
KALDI_ASSERT(!ContainsNullPointers(*clusters));
return ans;
}
// ============================================================================
// K-means like clustering routines
// ============================================================================
/// ClusterKMeansOnce is called internally by ClusterKMeans; it is equivalent
/// to calling ClusterKMeans with cfg.num_tries == 1. It returns the objective
/// function improvement versus everything being in one cluster.
BaseFloat ClusterKMeansOnce(const std::vector<Clusterable*> &points,
int32 num_clust,
std::vector<Clusterable*> *clusters_out,
std::vector<int32> *assignments_out,
ClusterKMeansOptions &cfg) {
std::vector<int32> my_assignments;
int32 num_points = points.size();
KALDI_ASSERT(clusters_out != NULL);
KALDI_ASSERT(num_points != 0);
KALDI_ASSERT(num_clust <= num_points);
KALDI_ASSERT(clusters_out->empty()); // or we wouldn't know what to do with pointers in there.
clusters_out->resize(num_clust, (Clusterable*)NULL);
assignments_out->resize(num_points);
{ // This block assigns points to clusters.
// This is done pseudo-randomly using Rand() so that
// if we call ClusterKMeans multiple times we get different answers (so we can choose
// the best if we want).
int32 skip; // randomly choose a "skip" that's coprime to num_points.
if (num_points == 1) {
skip = 1;
} else {
skip = 1 + (Rand() % (num_points-1)); // a number between 1 and num_points-1.
while (Gcd(skip, num_points) != 1) { // while skip is not coprime to num_points...
if (skip == num_points-1) skip = 0;
skip++; // skip is now still betweeen 1 and num_points-1. will cycle through
// all of 1...num_points-1.
}
}
int32 i, j, count = 0;
for (i = 0, j = 0; count != num_points;i = (i+skip)%num_points, j = (j+1)%num_clust, count++) {
// i cycles pseudo-randomly through all points; j skips ahead by 1 each time
// modulo num_points.
// assign point i to cluster j.
if ((*clusters_out)[j] == NULL) (*clusters_out)[j] = points[i]->Copy();
else (*clusters_out)[j]->Add(*(points[i]));
(*assignments_out)[i] = j;
}
}
BaseFloat normalizer = SumClusterableNormalizer(*clusters_out);
BaseFloat ans;
{ // work out initial value of "ans" (objective function improvement).
Clusterable *all_stats = SumClusterable(*clusters_out);
ans = SumClusterableObjf(*clusters_out) - all_stats->Objf(); // improvement just from the random
// initialization.
if (ans < -0.01 && ans < -0.01 * fabs(all_stats->Objf())) { // something bad happend.
KALDI_WARN << "ClusterKMeans: objective function after random assignment to clusters is worse than in single cluster: "<< (all_stats->Objf()) << " changed by " << ans << ". Perhaps your stats class has the wrong properties?";
}
delete all_stats;
}
for (int32 iter = 0;iter < cfg.num_iters;iter++) {
// Keep refining clusters by reassigning points.
BaseFloat objf_before;
if (cfg.verbose) objf_before =SumClusterableObjf(*clusters_out);
BaseFloat impr = RefineClusters(points, clusters_out, assignments_out, cfg.refine_cfg);
BaseFloat objf_after;
if (cfg.verbose) objf_after = SumClusterableObjf(*clusters_out);
ans += impr;
if (cfg.verbose)
KALDI_LOG << "ClusterKMeans: on iteration "<<(iter)<<", objf before = "<<(objf_before)<<", impr = "<<(impr)<<", objf after = "<<(objf_after)<<", normalized by "<<(normalizer)<<" = "<<(objf_after/normalizer);
if (impr == 0) break;
}
return ans;
}
BaseFloat ClusterKMeans(const std::vector<Clusterable*> &points,
int32 num_clust,
std::vector<Clusterable*> *clusters_out,
std::vector<int32> *assignments_out,
ClusterKMeansOptions cfg) {
if (points.size() == 0) {
if (clusters_out) KALDI_ASSERT(clusters_out->empty()); // or we wouldn't know whether to free the pointers.
if (assignments_out) assignments_out->clear();
return 0.0;
}
KALDI_ASSERT(cfg.num_tries>=1 && cfg.num_iters>=1);
if (clusters_out) KALDI_ASSERT(clusters_out->empty()); // or we wouldn't know whether to deallocate.
if (cfg.num_tries == 1) {
std::vector<int32> assignments;
return ClusterKMeansOnce(points, num_clust, clusters_out, (assignments_out != NULL?assignments_out:&assignments), cfg);
} else { // multiple tries.
if (clusters_out) {
KALDI_ASSERT(clusters_out->empty()); // we don't know the ownership of any pointers in there, otherwise.
}
BaseFloat best_ans = 0.0;
for (int32 i = 0;i < cfg.num_tries;i++) {
std::vector<Clusterable*> clusters_tmp;
std::vector<int32> assignments_tmp;
BaseFloat ans = ClusterKMeansOnce(points, num_clust, &clusters_tmp, &assignments_tmp, cfg);
KALDI_ASSERT(!ContainsNullPointers(clusters_tmp));
if (i == 0 || ans > best_ans) {
best_ans = ans;
if (clusters_out) {
if (clusters_out->size()) DeletePointers(clusters_out);
*clusters_out = clusters_tmp;
clusters_tmp.clear(); // suppress deletion of pointers.
}
if (assignments_out) *assignments_out = assignments_tmp;
}
// delete anything remaining in clusters_tmp (we cleared it if we used
// the pointers.
DeletePointers(&clusters_tmp);
}
return best_ans;
}
}
// ============================================================================
// Routines for clustering using a top-down tree
// ============================================================================
class TreeClusterer {
public:
TreeClusterer(const std::vector<Clusterable*> &points,
int32 max_clust,
TreeClusterOptions cfg):
points_(points), max_clust_(max_clust), ans_(0.0), cfg_(cfg)
{
KALDI_ASSERT(cfg_.branch_factor > 1);
Init();
}
BaseFloat Cluster(std::vector<Clusterable*> *clusters_out,
std::vector<int32> *assignments_out,
std::vector<int32> *clust_assignments_out,
int32 *num_leaves_out) {
while (static_cast<int32>(leaf_nodes_.size()) < max_clust_ && !queue_.empty()) {
std::pair<BaseFloat, Node*> pr = queue_.top();
queue_.pop();
ans_ += pr.first;
DoSplit(pr.second);
}
CreateOutput(clusters_out, assignments_out, clust_assignments_out,
num_leaves_out);
return ans_;
}
~TreeClusterer() {
for (int32 leaf = 0; leaf < static_cast<int32>(leaf_nodes_.size());leaf++) {
delete leaf_nodes_[leaf]->node_total;
DeletePointers(&(leaf_nodes_[leaf]->leaf.clusters));
delete leaf_nodes_[leaf];
}
for (int32 nonleaf = 0; nonleaf < static_cast<int32>(nonleaf_nodes_.size()); nonleaf++) {
delete nonleaf_nodes_[nonleaf]->node_total;
delete nonleaf_nodes_[nonleaf];
}
}
private:
struct Node {
bool is_leaf;
int32 index; // index into leaf_nodes or nonleaf_nodes as applicable.
Node *parent;
Clusterable *node_total; // sum of all data with this node.
struct {
std::vector<Clusterable*> points;
std::vector<int32> point_indices;
BaseFloat best_split;
std::vector<Clusterable*> clusters; // [branch_factor]... if we do split.
std::vector<int32> assignments; // assignments of points to clusters.
} leaf;
std::vector<Node*> children; // vector of size branch_factor. if non-leaf.
// pointers not owned here but in vectors leaf_nodes_, nonleaf_nodes_.
};
void CreateOutput(std::vector<Clusterable*> *clusters_out,
std::vector<int32> *assignments_out,
std::vector<int32> *clust_assignments_out,
int32 *num_leaves_out) {
if (num_leaves_out) *num_leaves_out = leaf_nodes_.size();
if (assignments_out)
CreateAssignmentsOutput(assignments_out);
if (clust_assignments_out)
CreateClustAssignmentsOutput(clust_assignments_out);
if (clusters_out)
CreateClustersOutput(clusters_out);
}
// This creates the output index corresponding to an index "index" into the array nonleaf_nodes_.
// reverse numbering so root node is last.
int32 NonleafOutputIndex(int32 index) {
return leaf_nodes_.size() + nonleaf_nodes_.size() - 1 - index;
}
void CreateAssignmentsOutput(std::vector<int32> *assignments_out) {
assignments_out->clear();
assignments_out->resize(points_.size(), (int32)(-1)); // fill with -1.
for (int32 leaf = 0; leaf < static_cast<int32>(leaf_nodes_.size()); leaf++) {
std::vector<int32> &indices = leaf_nodes_[leaf]->leaf.point_indices;
for (int32 i = 0; i < static_cast<int32>(indices.size()); i++) {
KALDI_ASSERT(static_cast<size_t>(indices[i]) < points_.size());
KALDI_ASSERT((*assignments_out)[indices[i]] == (int32)(-1)); // check we're not assigning twice.
(*assignments_out)[indices[i]] = leaf;
}
}
#ifdef KALDI_PARANOID
for (size_t i = 0;i<assignments_out->size();i++) KALDI_ASSERT((*assignments_out)[i] != (int32)(-1));
#endif
}
void CreateClustAssignmentsOutput(std::vector<int32> *clust_assignments_out) {
clust_assignments_out->resize(leaf_nodes_.size() + nonleaf_nodes_.size());
for (int32 leaf = 0; leaf < static_cast<int32>(leaf_nodes_.size()); leaf++) {
int32 parent_index;
if (leaf_nodes_[leaf]->parent == NULL) { // tree with only one node.
KALDI_ASSERT(leaf_nodes_.size() == 1&&nonleaf_nodes_.size() == 0 && leaf == 0);
parent_index = 0;
} else {
if (leaf_nodes_[leaf]->parent->is_leaf) parent_index = leaf_nodes_[leaf]->parent->index;
else parent_index = NonleafOutputIndex(leaf_nodes_[leaf]->parent->index);
}
(*clust_assignments_out)[leaf] = parent_index;
}
for (int32 nonleaf = 0; nonleaf < static_cast<int32>(nonleaf_nodes_.size()); nonleaf++) {
int32 index = NonleafOutputIndex(nonleaf);
int32 parent_index;
if (nonleaf_nodes_[nonleaf]->parent == NULL) parent_index = index; // top node. make it own parent.
else {
KALDI_ASSERT(! nonleaf_nodes_[nonleaf]->parent->is_leaf); // parent is nonleaf since child is nonleaf.
parent_index = NonleafOutputIndex(nonleaf_nodes_[nonleaf]->parent->index);
}
(*clust_assignments_out)[index] = parent_index;
}
}
void CreateClustersOutput(std::vector<Clusterable*> *clusters_out) {
clusters_out->resize(leaf_nodes_.size() + nonleaf_nodes_.size());
for (int32 leaf = 0; leaf < static_cast<int32>(leaf_nodes_.size()); leaf++) {
(*clusters_out)[leaf] = leaf_nodes_[leaf]->node_total;
leaf_nodes_[leaf]->node_total = NULL; // suppress delete.
}
for (int32 nonleaf = 0; nonleaf < static_cast<int32>(nonleaf_nodes_.size()); nonleaf++) {
int32 index = NonleafOutputIndex(nonleaf);
(*clusters_out)[index] = nonleaf_nodes_[nonleaf]->node_total;
nonleaf_nodes_[nonleaf]->node_total = NULL; // suppress delete.
}
}
void DoSplit(Node *node) {
KALDI_ASSERT(node->is_leaf && node->leaf.best_split > cfg_.thresh*0.999); // 0.999 is to avoid potential floating-point weirdness under compiler optimizations.
KALDI_ASSERT(node->children.size() == 0);
node->children.resize(cfg_.branch_factor);
for (int32 i = 0;i < cfg_.branch_factor;i++) {
Node *child = new Node;
node->children[i] = child;
child->is_leaf = true;
child->parent = node;
child->node_total = node->leaf.clusters[i];
if (i == 0) {
child->index = node->index; // assign node's own index in leaf_nodes_ to 1st child.
leaf_nodes_[child->index] = child;
} else {
child->index = leaf_nodes_.size(); // generate new indices for other children.
leaf_nodes_.push_back(child);
}
}
KALDI_ASSERT(node->leaf.assignments.size() == node->leaf.points.size());
KALDI_ASSERT(node->leaf.point_indices.size() == node->leaf.points.size());
for (int32 i = 0; i < static_cast<int32>(node->leaf.points.size()); i++) {
int32 child_index = node->leaf.assignments[i];
KALDI_ASSERT(child_index < static_cast<int32>(cfg_.branch_factor));
node->children[child_index]->leaf.points.push_back(node->leaf.points[i]);
node->children[child_index]->leaf.point_indices.push_back(node->leaf.point_indices[i]);
}
node->leaf.points.clear();
node->leaf.point_indices.clear();
node->leaf.clusters.clear(); // already assigned pointers to children.
node->leaf.assignments.clear();
node->is_leaf = false;
node->index = nonleaf_nodes_.size(); // new index at end of nonleaf_nodes_.
nonleaf_nodes_.push_back(node);
for (int32 i = 0;i < static_cast<int32>(cfg_.branch_factor);i++)
FindBestSplit(node->children[i]);
}
void FindBestSplit(Node *node) {
// takes a leaf node that has just been set up, and does ClusterKMeans with k = cfg_branch_factor.
KALDI_ASSERT(node->is_leaf);
if (node->leaf.points.size() == 0) {
KALDI_WARN << "Warning: tree clustering: leaf with no data";
node->leaf.best_split = 0; return;
}
if (node->leaf.points.size()<=1) { node->leaf.best_split = 0; return; }
else {
// use kmeans.
BaseFloat impr = ClusterKMeans(node->leaf.points,
cfg_.branch_factor,
&node->leaf.clusters,
&node->leaf.assignments,
cfg_.kmeans_cfg);
node->leaf.best_split = impr;
if (impr > cfg_.thresh)
queue_.push(std::make_pair(impr, node));
}
}
void Init() { // Initializes top node.
Node *top_node = new Node;
top_node->index = leaf_nodes_.size(); // == 0 currently.
top_node->parent = NULL; // no parent since root of tree.
top_node->is_leaf = true;
leaf_nodes_.push_back(top_node);
top_node->leaf.points = points_;
top_node->node_total = SumClusterable(points_);
top_node->leaf.point_indices.resize(points_.size());
for (size_t i = 0;i<points_.size();i++) top_node->leaf.point_indices[i] = i;
FindBestSplit(top_node); // this should always be called when new node is created.
}
std::vector<Node*> leaf_nodes_;
std::vector<Node*> nonleaf_nodes_;
const std::vector<Clusterable*> &points_;
int32 max_clust_;
BaseFloat ans_; // objf improvement.
std::priority_queue<std::pair<BaseFloat, Node*> > queue_; // contains leaves.
TreeClusterOptions cfg_;
};
BaseFloat TreeCluster(const std::vector<Clusterable*> &points,
int32 max_clust, // this is a max only.
std::vector<Clusterable*> *clusters_out,
std::vector<int32> *assignments_out,
std::vector<int32> *clust_assignments_out,
int32 *num_leaves_out,
TreeClusterOptions cfg) {
if (points.size() == 0) {
if (clusters_out) clusters_out->clear();
if (assignments_out) assignments_out->clear();
if (clust_assignments_out) clust_assignments_out->clear();
if (num_leaves_out) *num_leaves_out = 0;
return 0.0;
}
TreeClusterer tc(points, max_clust, cfg);
BaseFloat ans = tc.Cluster(clusters_out, assignments_out, clust_assignments_out, num_leaves_out);
if (clusters_out) KALDI_ASSERT(!ContainsNullPointers(*clusters_out));
return ans;
}
BaseFloat ClusterTopDown(const std::vector<Clusterable*> &points,
int32 max_clust, // max # of clusters.
std::vector<Clusterable*> *clusters_out,
std::vector<int32> *assignments_out,
TreeClusterOptions cfg) {
int32 num_leaves = 0;
BaseFloat ans = TreeCluster(points, max_clust, clusters_out, assignments_out, NULL, &num_leaves, cfg);
if (clusters_out != NULL) {
for (size_t j = num_leaves;j<clusters_out->size();j++) delete (*clusters_out)[j];
clusters_out->resize(num_leaves); // number of leaf-level clusters in tree.
}
return ans;
}
void RefineClustersOptions::Write(std::ostream &os, bool binary) const {
WriteToken(os, binary, "<RefineClustersOptions>");
WriteBasicType(os, binary, num_iters);
WriteBasicType(os, binary, top_n);
WriteToken(os, binary, "</RefineClustersOptions>");
}
void RefineClustersOptions::Read(std::istream &is, bool binary) {
ExpectToken(is, binary, "<RefineClustersOptions>");
ReadBasicType(is, binary, &num_iters);
ReadBasicType(is, binary, &top_n);
ExpectToken(is, binary, "</RefineClustersOptions>");
}
} // end namespace kaldi.
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