static std::vector<double> computeKMeans(
KMdata & datapoints,
uint64_t const k,
uint64_t const runs,
bool const debug
)
{
KMterm const term(runs, 0, 0, 0, 0.10, 0.10, 3, 0.50, 10, 0.95);
datapoints.buildKcTree();
KMfilterCenters ctrs(k, datapoints);
KMlocalHybrid kmAlg(ctrs, term);
ctrs = kmAlg.execute();
std::vector<double> centrevector;
for ( uint64_t i = 0; i < k; ++i )
{
centrevector.push_back(ctrs[i][0]);
if ( debug )
std::cerr << "centre[" << i << "]=" << ctrs[i][0] << std::endl;
}
std::sort(centrevector.begin(),centrevector.end());
return centrevector;
}
int
CVML_API
doKMeans(int dim, int k, int numofSamples, std::vector<std::vector<double>>& _samples,std::vector<std::vector<double>> & center)
{
typedef double T;
center.clear();
center.resize(k);
try{
// execution parameters (see KMterm.h and KMlocal.h)
KMterm term(100, 0, 0, 0, // run for 100 stages
0.10, 0.10, 3, // other typical parameter values
0.50, 10, 0.95);
//int dim = 1; // dimension
int nPts = numofSamples; // number of data points
KMdata dataPts(dim, nPts); // allocate data storage
//KMdata *dataPts = new KMdata(dim, nPts);
for ( unsigned int i = 0 ; i < numofSamples ; i++ ){
for(int j=0;i<dim;j++){
dataPts[i][j] = _samples[i][j];
}
}
dataPts.buildKcTree(); // build filtering structure
KMfilterCenters ctrs(k, dataPts); // allocate centers
// run the algorithm
//KMlocalLloyds kmAlg(ctrs, term); // repeated Lloyd's
// KMlocalSwap kmAlg(ctrs, term); // Swap heuristic
// KMlocalEZ_Hybrid kmAlg(ctrs, term); // EZ-Hybrid heuristic
KMlocalHybrid kmAlg(ctrs, term); // Hybrid heuristic
ctrs = kmAlg.execute(); // execute
for ( unsigned int i = 0 ; i < k ; i++ ){
for(int j=0;i<dim;j++){
T v = static_cast<T>(ctrs[i][j]);
center[i].push_back(v);
}
}
return 1;
}
catch(...)
{
//cvml::TRACK("doKMeans error \n");
return 0;
}
return 1;
}
vector<DataPoint> SpeechKMeans::WeightedKMeans(vector<DataPoint> &points,
vector<double> &weights, int k) {
KMterm term(100, 0, 0, 0, // run for 100 stages
0.10, 0.10, 3, // other typical parameter values
0.50, 10, 0.95);
int dim = problems_.num_features(); // dimension
int nPts = points.size(); // number of data points
KMdata dataPts(dim, nPts); // allocate data storage
for (int p = 0; p < nPts; ++p) {
dataPts[p] = new double[dim];
for (int i = 0; i < dim; ++i) {
dataPts[p][i] = points[p][i];
}
}
//kmUniformPts(dataPts.getPts(), nPts, dim); // generate random points
dataPts.buildKcTree(); // build filtering structure
KMfilterCenters ctrs(k, dataPts); // allocate centers
// run the algorithm
//KMlocalLloyds kmAlg(ctrs, term); // repeated Lloyd's
// KMlocalSwap kmAlg(ctrs, term); // Swap heuristic
// KMlocalEZ_Hybrid kmAlg(ctrs, term); // EZ-Hybrid heuristic
KMlocalHybrid kmAlg(ctrs, term); // Hybrid heuristic
ctrs = kmAlg.execute(); // execute
// print number of stages
cout << "Number of stages: " << kmAlg.getTotalStages() << "\n";
// print average distortion
cout << "Average distortion: " << ctrs.getDist(false)/nPts << "\n";
ctrs.print(); // print final centers
cerr << "copying points" << endl;
vector<DataPoint> results(k);
for (int j = 0; j < k; ++j) {
results[j].resize(dim);
for (int i = 0; i < dim; ++i) {
results[j][i] = ctrs.getCtrPts()[j][i];
}
}
cerr << "done copying points" << endl;
return results;
}
int
CVML_API
doKMeans(int dim, int k, int numofSamples, const double * _samples, double*& center)
{
typedef double T;
if(center)
{
delete [] center;
center=0;
}
center = new T[k*dim];
memset( center,0,sizeof(T)*k*dim );
try{
// execution parameters (see KMterm.h and KMlocal.h)
KMterm term(100, 0, 0, 0, // run for 100 stages
0.10, 0.10, 3, // other typical parameter values
0.50, 10, 0.95);
//int dim = 1; // dimension
int nPts = numofSamples; // number of data points
KMdata dataPts(dim, nPts); // allocate data storage
//KMdata *dataPts = new KMdata(dim, nPts);
for ( unsigned int i = 0 ; i < numofSamples ; i++ ){
for(int j=0;i<dim;j++){
int idx = i * dim + j;
dataPts[i][j] = _samples[idx];
}
}
dataPts.buildKcTree(); // build filtering structure
KMfilterCenters ctrs(k, dataPts); // allocate centers
// run the algorithm
//KMlocalLloyds kmAlg(ctrs, term); // repeated Lloyd's
// KMlocalSwap kmAlg(ctrs, term); // Swap heuristic
// KMlocalEZ_Hybrid kmAlg(ctrs, term); // EZ-Hybrid heuristic
KMlocalHybrid kmAlg(ctrs, term); // Hybrid heuristic
ctrs = kmAlg.execute(); // execute
for ( unsigned int i = 0 ; i < k ; i++ ){
for(int j=0;i<dim;j++){
int idx = i * dim + j;
center[idx] = static_cast<T>(ctrs[i][j]);
}
}
return 1;
}
catch(...)
{
//cvml::TRACK("doKMeans error \n");
return 0;
}
return 1;
}
