//----------------------------------------------------------------------
// Main program
//----------------------------------------------------------------------
int main(int argc, char **argv)
{
getArgs(argc, argv); // read command-line arguments
term.setAbsMaxTotStage(stages); // set number of stages
KMdata dataPts(dim, maxPts); // allocate data storage
int nPts = 0; // actual number of points
// generate points
if (dataIn != NULL) { // read points from file
while (nPts < maxPts && readPt(*dataIn, dataPts[nPts])) nPts++;
}
else { // generate points randomly
nPts = maxPts;
kmClusGaussPts(dataPts.getPts(), nPts, dim, k);
}
cout << "Data Points:\n"; // echo data points
for (int i = 0; i < nPts; i++)
printPt(cout, dataPts[i]);
dataPts.setNPts(nPts); // set actual number of pts
dataPts.buildKcTree(); // build filtering structure
KMfilterCenters ctrs(k, dataPts); // allocate centers
// run each of the algorithms
cout << "\nExecuting Clustering Algorithm: Lloyd's\n";
KMlocalLloyds kmLloyds(ctrs, term); // repeated Lloyd's
ctrs = kmLloyds.execute(); // execute
printSummary(kmLloyds, dataPts, ctrs); // print summary
cout << "\nExecuting Clustering Algorithm: Swap\n";
KMlocalSwap kmSwap(ctrs, term); // Swap heuristic
ctrs = kmSwap.execute();
printSummary(kmSwap, dataPts, ctrs);
cout << "\nExecuting Clustering Algorithm: EZ-Hybrid\n";
KMlocalEZ_Hybrid kmEZ_Hybrid(ctrs, term); // EZ-Hybrid heuristic
ctrs = kmEZ_Hybrid.execute();
printSummary(kmEZ_Hybrid, dataPts, ctrs);
cout << "\nExecuting Clustering Algorithm: Hybrid\n";
KMlocalHybrid kmHybrid(ctrs, term); // Hybrid heuristic
ctrs = kmHybrid.execute();
printSummary(kmHybrid, dataPts, ctrs);
kmExit(0);
}
//----------------------------------------------------------------------
// Main program
//----------------------------------------------------------------------
int main(int argc, char **argv)
{
getArgs(argc, argv); // read command-line arguments
term.setAbsMaxTotStage(stages); // set number of stages
KMdata dataPts(dim, maxPts); // allocate data storage
printf("Start Loading!\n");
int nPts = 0; // actual number of points
// generate points
if( !binary )
{
if (dataIn != NULL)
{ // read points from file
while (nPts < maxPts && readPt(*dataIn, dataPts[nPts]))
nPts++;
}
else
{ // generate points randomly
nPts = maxPts;
kmClusGaussPts(dataPts.getPts(), nPts, dim, k);
}
}
else
{
CFeaFileReader feaReader;
feaReader.openFile(infname.c_str() );
nPts = 0;
char* pBuf = feaReader.getNextSample4VarLen(true);
for(; ;)
{
if( nPts >= maxPts )
break;
if( !pBuf )
break;
KMpoint pPtFea = (dataPts[nPts]);
float *pF = (float *)pBuf;
for (int d = 0; d < dim; d++)
{
pPtFea[d] = pF[d];
}
nPts++;
pBuf = feaReader.getNextSample4VarLen();
}
}
printf("Load OK!\n");
dataPts.setNPts(nPts); // set actual number of pts
dataPts.buildKcTree(); // build filtering structure
KMfilterCenters ctrs(k, dataPts); // allocate centers
__int64 t1, t2;
t1 = cvGetTickCount();
cout << "\nExecuting Clustering Algorithm: Hybrid\n";
KMlocalHybrid kmHybrid(ctrs, term); // Hybrid heuristic
ctrs = kmHybrid.execute();
t2 = cvGetTickCount() - t1;
printSummary(kmHybrid, dataPts, ctrs);
printf( "Elapsed time = %10.2f (ms)\n", t2/(cvGetTickFrequency()*1000) );
kmExit(0);
}
//----------------------------------------------------------------------
// Main program
//----------------------------------------------------------------------
int nao_kmeans()
{
ifstream importMeans; // permettra d'utiliser les centres enregistrés dans l'étape de training
int nPts; // actual number of points
getArgs(); // read command-line arguments
term.setAbsMaxTotStage(stages); // set number of stages
// I- IMPORT STIPS
nPts = 0;
dim = STIPS_DIMENSION;
KMdata dataPts(dim, maxPts); // allocate data storage
if (dataIn != NULL){
laptevParser(*dataIn, dataPts, &nPts);
}
else{
perror("Pas de données à lire !!!");
return EXIT_FAILURE;
}
dataPts.setNPts(nPts); // set actual number of pts
dataPts.buildKcTree(); // build filtering structure
// II- IMPORT TRAINING CENTERS
KMfilterCenters ctrs(k, dataPts); // allocate centers
importMeans.open("/home/nao/data/activities_recognition/training.means", ios::in);
if(importMeans != NULL){
trainingMeansParser(importMeans,ctrs);
importMeans.close();
}
else{
cerr << "Error while importing Means" << endl;
return 2;
}
// III- GET ASSIGNMENTS
KMctrIdxArray closeCtr = new KMctrIdx[dataPts.getNPts()]; // dataPts = 1 label
double* sqDist = new double[dataPts.getNPts()];
ctrs.getAssignments(closeCtr, sqDist);
// IV- BAG OF WORDS STEP
// initialisation de l'histogramme
int* bowHistogram = NULL;
bowHistogram = new int[k];
for(int centre = 0; centre<k; centre++)
bowHistogram[centre]=0;
// remplissage de l'histogramme
for(int point = 0; point < nPts ; point++){
bowHistogram[closeCtr[point]]++;
}
delete closeCtr;
delete[] sqDist;
// exportation dans le fichier "testing.bow" sous un format pouvant être lu par libSVM
char* KMeansToBow = NULL;
KMeansToBow = new char[(256+1)];
sprintf(KMeansToBow,"%s","/home/nao/data/activities_recognition/stip"); //sprintf(KMeansToBow,"%s.bow",argv[1]);
ofstream testingBow(KMeansToBow, ios::out | ios::trunc); // ouverture en écriture avec effacement du fichier ouvert
if(testingBow){
testingBow << "0";
for(int centre = 0; centre<k ; centre++){
testingBow << " " << centre + 1 << ":" << bowHistogram[centre];
}
testingBow << endl;
testingBow.close();
}
else
cerr << "Impossible d'ouvrir le fichier erreur1" << endl;
// affichage
cout << "Bag Of Words histogram" << endl;
for(int centre = 0; centre<k; centre++){
cout << "Centre " << centre << ": ";
for (int i = 0; i<bowHistogram[centre]; i++)
cout << "*";
cout << endl;
}
delete[] bowHistogram;
//kmExit(0);
cout << "[kmeans.cpp]delete[]" << endl;
}
