コード例 #1
0
ファイル: KMLocalInterface.cpp プロジェクト: allenday/libmaus
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;
}
コード例 #2
0
ファイル: naomngt.cpp プロジェクト: no-ox/IMAR-C
void im_compute_bdd_bow(const IMbdd& bdd, 
			std::map <std::string, struct svm_problem>& peopleBOW){
  std::string path2bdd(bdd.getFolder());
  std::vector<std::string> activities = bdd.getActivities();
  std::vector<std::string> people = bdd.getPeople();

  int dim = bdd.getDim();
  int maxPts = bdd.getMaxPts();
  int k = bdd.getK();
  
  for(std::vector<std::string>::iterator person = people.begin();
      person != people.end();
      ++person){
    int currentActivity = 1;
    struct svm_problem svmPeopleBOW;
    svmPeopleBOW.l = 0;
    svmPeopleBOW.x = NULL;
    svmPeopleBOW.y = NULL;
    std::cout << "Computing the svmProblem of " << *person << std::endl;
    for(std::vector<std::string>::iterator activity = activities.begin();
	activity != activities.end();
	++activity){
      string rep(path2bdd + "/" + *person + "/" + *activity + "/fp");
      DIR * repertoire = opendir(rep.c_str());
      if (!repertoire){
	std::cerr << "Impossible to open the feature points directory!" << std::endl;
	exit(EXIT_FAILURE);
      }
      struct dirent * ent;
      while ( (ent = readdir(repertoire)) != NULL){
	std::string file = ent->d_name;
	if(file.compare(".") != 0 && file.compare("..") != 0){
	  std::string path2FPs(rep + "/" + file);
	  KMdata dataPts(dim,maxPts);
	  int nPts = importSTIPs(path2FPs, dim, maxPts, &dataPts);
	  if(nPts != 0){
	    dataPts.setNPts(nPts);
	    dataPts.buildKcTree();
	    
	    KMfilterCenters ctrs(k, dataPts);
	    importCenters(path2bdd + "/" + "training.means", dim, k, &ctrs);
	    
	    // Only one BOW
	    struct svm_problem svmBow = computeBOW(currentActivity,
						   dataPts,
						   ctrs);
	    addBOW(svmBow.x[0], svmBow.y[0], svmPeopleBOW);
	    destroy_svm_problem(svmBow);	  
	  }
	}
      }
      closedir(repertoire);
      currentActivity++;
    }
    peopleBOW.insert(std::make_pair<std::string, struct svm_problem>((*person), svmPeopleBOW));
  }
}
コード例 #3
0
ファイル: cvmlkmeans.cpp プロジェクト: handongfeng/CVMLLib
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;
}
Foam::pointField Foam::searchableBox::coordinates() const
{
    pointField ctrs(6);

    const pointField pts = treeBoundBox::points();
    const faceList& fcs = treeBoundBox::faces;

    forAll(fcs, i)
    {
        ctrs[i] = fcs[i].centre(pts);
    }
コード例 #5
0
Foam::pointField Foam::oldCyclicPolyPatch::calcFaceCentres
(
    const UList<face>& faces,
    const pointField& points
)
{
    pointField ctrs(faces.size());

    forAll(faces, facei)
    {
        ctrs[facei] = faces[facei].centre(points);
    }
コード例 #6
0
Foam::pointField Foam::perfectInterface::calcFaceCentres
(
    const primitivePatch& pp
)
{
    const pointField& points = pp.points();

    pointField ctrs(pp.size());

    forAll(ctrs, patchFaceI)
    {
        ctrs[patchFaceI] = pp[patchFaceI].centre(points);
    }
コード例 #7
0
ファイル: naokmeans.cpp プロジェクト: no-ox/IMAR-C
/**
 * \fn void createTrainingMeans(std::string stipFile, int dim, int maxPts, int k, std::string meansFile)
 * \brief Import HOG and HOF from a file and compute KMeans algorithm to create
 * the file training.means.
 * 
 * \param[in] stipFile The file containing the STIPs.
 * \param[in] dim Points and centers's dimension.
 * \param[in] maxPts The maximum number of data we can compute
 * \param[in] k The number of centers
 * \param[out] meansFile The file in wich we will save the KMeans centers.
 */
void createTrainingMeans(std::string stipFile,
			 int dim,
			 int maxPts,
			 int k,
                         std::string meansFile
                         ){

  KMdata dataPts(dim,maxPts);
  int nPts = importSTIPs(stipFile, dim, maxPts, &dataPts);
  dataPts.setNPts(nPts);
  dataPts.buildKcTree();
  
  KMfilterCenters ctrs(k, dataPts);    
  
  int ic = 3;
  kmIvanAlgorithm(ic, dim, dataPts, k, ctrs);  
  
  exportCenters(meansFile, dim, k, ctrs);
}
コード例 #8
0
ファイル: speech_kmeans.cpp プロジェクト: srush/clustering
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;
}
コード例 #9
0
ファイル: cvmlkmeans.cpp プロジェクト: handongfeng/CVMLLib
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;
}
コード例 #10
0
ファイル: naomngt.cpp プロジェクト: no-ox/IMAR-C
int im_create_specifics_training_means(IMbdd bdd,
				       const std::vector<std::string>& trainingPeople 
				       //std::vector <std::string> rejects
				       ){
  std::string path2bdd(bdd.getFolder());
  int dim = bdd.getDim();
  int maxPts = bdd.getMaxPts();
  
  std::vector <std::string> activities = bdd.getActivities();
  int nr_class = activities.size();
  
  int nr_people = trainingPeople.size();
  
  // The total number of centers
  int k = bdd.getK();
  int subK = k/nr_class;
  if(k%nr_class != 0){
    std::cerr << "K is no divisible by the number of activities !!" << std::endl;
    exit(EXIT_FAILURE);
  }
  double ***vDataPts = new double**[nr_class];
  int nrFP[nr_class]; // number of feature points for each class
  int currentActivity = 0;
  // For each activity
  for(std::vector<std::string>::iterator activity = activities.begin() ;
      activity != activities.end() ;
      ++activity){
    nrFP[currentActivity] = 0;
    
    // We concatenate all the training people
    int nrFPpP[nr_people]; // number of feature points per person
    double*** activityDataPts = new double**[nr_people];
    int currentPerson = 0;
    for(std::vector<std::string>::const_iterator person = trainingPeople.begin() ;
	person != trainingPeople.end() ;
	++person){
      nrFPpP[currentPerson] = 0;
      std::string rep(path2bdd + "/" + *person + "/" + *activity);
      DIR * repertoire = opendir(rep.c_str());
      if (!repertoire){
	std::cerr << "Impossible to open the feature points directory!" << std::endl;
	exit(EXIT_FAILURE);
      }
      
      // Checking that the file concatenate.<activity>.fp exists
      struct dirent * ent = readdir(repertoire);
      std::string file(ent->d_name);
      while (ent && (file.compare("concatenate." + *activity + ".fp")) != 0){
	ent = readdir(repertoire);
	file = ent->d_name;
      }
      if(!ent){
	std::cerr << "No file concatenate.<activity>.fp" << std::endl;
	exit(EXIT_FAILURE);
      }
      
      // Importing the feature points
      std::string path2FP(rep + "/" + file);
      KMdata kmData(dim,maxPts);
      nrFPpP[currentPerson] = importSTIPs(path2FP, dim, maxPts, &kmData);
      if(nrFPpP[currentPerson] != 0){
	activityDataPts[currentPerson] = new double*[nrFPpP[currentPerson]];
	for(int n = 0 ; n<nrFPpP[currentPerson] ; n++){
	  activityDataPts[currentPerson][n] = new double [dim];
	  for(int d = 0 ; d<dim ; d++)
	    activityDataPts[currentPerson][n][d] = kmData[n][d];
	}
      } // else the current person does not participate in this activity
      nrFP[currentActivity] += nrFPpP[currentPerson];
      currentPerson++;
    } // ++person
    
    // Saving people in vDataPts
    vDataPts[currentActivity] = new double*[nrFP[currentActivity]];
    int index=0;
    for(int p=0 ; p<nr_people ; p++){
      for(int fp=0 ; fp<nrFPpP[p] ; fp++){
	vDataPts[currentActivity][index] = new double[dim];
	for(int d=0 ; d<dim ; d++){
	  vDataPts[currentActivity][index][d] = activityDataPts[p][fp][d];
	}
	index++;
      }
    } // index must be equal to nrFP[currentActivity] - 1
    
    // Deleting activityDataPts
    for(int p=0 ; p<nr_people ; p++){
      for(int fp=0 ; fp < nrFPpP[p] ; fp++)
	delete[] activityDataPts[p][fp];
      delete[] activityDataPts[p];
    }
    delete[] activityDataPts;
    currentActivity++;
  } // ++activity
  
  // Total number of feature points
  int ttFP = 0;
  for(int a=0 ; a<nr_class ; a++){
    ttFP += nrFP[a];
  }
  
  // Memory allocation of the centers
  double** vCtrs = new double*[k];
  for(int i=0 ; i<k ; i++){
    vCtrs[i] = new double[dim];
  }
  
  // Doing the KMeans algorithm for each activities
  int ic = 3; // the iteration coefficient (Ivan's algorithm)
  int currCenter = 0;
  for(int i=0 ; i<nr_class ; i++){
    KMdata kmData(dim,nrFP[i]);
    for(int n=0 ; n<nrFP[i]; n++)
      for(int d=0 ; d<dim ; d++)
	kmData[n][d] = vDataPts[i][n][d];
    kmData.setNPts(nrFP[i]);
    kmData.buildKcTree();
    KMfilterCenters kmCtrs(subK,kmData);
    kmIvanAlgorithm(ic, dim, kmData, subK, kmCtrs);
    for(int n=0 ; n<subK ; n++){
      for(int d=0 ; d<dim ; d++){
	vCtrs[currCenter][d] = kmCtrs[n][d];
      }
      currCenter++;
    }
  }
  
  std::cout << "Concatenate KMdata" << std::endl;
  // Concatenate all the KMdata
  /* In reality it is not necessary
     but the objectif KMfilterCenters must be initialized with
     the KMdata */
  KMdata dataPts(dim,ttFP);
  int nPts = 0;
  for (int i=0 ; i<nr_class ; i++){
    for(int n=0 ; n<nrFP[i]; n++){
      for(int d=0 ; d<dim ; d++){
	dataPts[nPts][d] = vDataPts[i][n][d];
      }
      nPts++;
    }
  }
  // Releasing vDataPts
  for(int i=0 ; i<nr_class ; i++){
    for(int n=0 ; n<nrFP[i] ; n++)
      delete [] vDataPts[i][n];
    delete [] vDataPts[i];
  }
  delete[] vDataPts;
  
  dataPts.buildKcTree();
  // Returning the true centers
  KMfilterCenters ctrs(k,dataPts);
  for(int n=0 ; n<k ; n++){
    for(int d=0 ; d<dim ; d++){
      ctrs[n][d] = vCtrs[n][d];
    }
  }
  
  // Releasing vCtrs
  for(int i=0 ; i<k ; i++)
    delete [] vCtrs[i];
  delete[]vCtrs;
  
  exportCenters(bdd.getFolder() + "/" + bdd.getKMeansFile(),
		dim, k, ctrs);
  
  return k;
}
コード例 #11
0
ファイル: naomngt.cpp プロジェクト: no-ox/IMAR-C
/**
 * \fn void predictActivity(std::string, std::string bddName, int maxPts)
 * \brief Predict the activity done in a video with an existant trained BDD.
 *
 * \param[in] The path to the video to predict.
 * \param[in] bddName The name of the BDD containing videos.
 * \param[in] maxPts The maximum number of STIPs we can extract.
 * \return The name of the activity.
 */
void predictActivity(std::string videoPath,
		     std::string bddName
		     ){
  std::string path2bdd("bdd/" + bddName);   
  
  // Loading parameters
  IMbdd bdd(bddName,path2bdd);
  bdd.load_bdd_configuration(path2bdd.c_str(),"imconfig.xml");
  int scale_num = bdd.getScaleNum();
  std::string descriptor = bdd.getDescriptor();
  int dim = bdd.getDim();
  int k = bdd.getK();
  int maxPts = bdd.getMaxPts();
  
  //double p = getTrainProbability(path2bdd);
  
  // Computing feature points
  KMdata dataPts(dim,maxPts);
  int nPts = 0;
  nPts = extract_feature_points(videoPath,
				scale_num, descriptor, dim,
				maxPts, dataPts);		
  if(nPts == 0){
    std::cerr << "No activity detected !" << std::endl;
    exit(EXIT_FAILURE);
  }
  std::cout << nPts << " vectors extracted..." << std::endl;
  dataPts.setNPts(nPts);
  dataPts.buildKcTree();
  
  KMfilterCenters ctrs(k, dataPts);  
  importCenters(bdd.getFolder() + "/" + bdd.getKMeansFile(), dim, k, &ctrs);
  std::cout << "KMeans centers imported..." << std::endl;
  
  activitiesMap *am;
  int nbActivities = mapActivities(path2bdd,&am);
  
  
  struct svm_problem svmProblem = computeBOW(0,
					     dataPts,
					     ctrs);
  double means[k], stand_devia[k];
  load_gaussian_parameters(bdd, means, stand_devia);
  // simple, gaussian, both, nothing
  if(bdd.getNormalization().compare("") != 0)
    bow_normalization(bdd,svmProblem);
  std::cout << "Bag of words normalized..." << std::endl;
  
  struct svm_model** pSVMModels = new svm_model*[nbActivities];
  std::vector<std::string> modelFiles(bdd.getModelFiles());
  int i=0;
  for (std::vector<std::string>::iterator it = modelFiles.begin() ; it != modelFiles.end() ; ++it){
    pSVMModels[i]= svm_load_model((*it).c_str());
    i++;
  }
  std::cout << "SVM models imported..." << std::endl;
  double probs[nbActivities];
  double label = svm_predict_ovr_probs(pSVMModels,
				       svmProblem.x[0],
				       nbActivities,
				       probs,
				       2);
  std::cerr<<"Probs: ";
  for(int j=0 ; j<nbActivities ; j++){
    std::cout << setw(2) << setiosflags(ios::fixed) << probs[j]*100<<" "; 
  }
  
  destroy_svm_problem(svmProblem);
  int index = searchMapIndex(label, am, nbActivities);
  std::cout << "Activity predicted: ";
  std::cout << am[index].activity << "(" << am[index].label << ")";
  std::cout << std::endl;
  
  for(int m=0 ; m<nbActivities ; m++){
    svm_free_and_destroy_model(&pSVMModels[m]);
  }
}
コード例 #12
0
Foam::pointField Foam::searchableCylinder::coordinates() const
{
    pointField ctrs(1, 0.5*(point1_ + point2_));

    return ctrs;
}
コード例 #13
0
ファイル: KMLProxy.cpp プロジェクト: andreyto/imp-fork-proddl
void KMLProxy::run(Particles *initial_centers) {
  IMP_INTERNAL_CHECK(is_init_,"The proxy was not initialized");
  IMP_LOG(VERBOSE,"KMLProxy::run start \n");
  //use the initial centers if provided
  KMPointArray *kmc=nullptr;
  if (initial_centers != nullptr)  {
    IMP_INTERNAL_CHECK(kcenters_ == initial_centers->size(),
    "the number of initial points differs from the number of required"
    <<" centers\n");
    IMP_LOG(VERBOSE,"KMLProxy::run initial centers provided : \n");
    kmc = allocate_points(kcenters_,atts_.size());
    for (unsigned int i=0;i<kcenters_;i++){
      Particle *cen=(*initial_centers)[i];
      for(unsigned int j=0;j<atts_.size();j++) {
        (*(*kmc)[i])[j]=cen->get_value(atts_[j]);
       }
    }
  }
  IMP_LOG(VERBOSE,"KMLProxy::run load initial guess \n");
  //load the initail guess
  KMFilterCenters ctrs(kcenters_, data_, kmc,damp_factor_);

  //apply lloyd search
  IMP_LOG(VERBOSE,"KMLProxy::run load lloyd \n");
  lloyd_alg_ = new KMLocalSearchLloyd(&ctrs,&term_);
  log_header();
  IMP_CHECK_CODE(clock_t start = clock());
  IMP_LOG(VERBOSE,"KMLProxy::run excute lloyd \n");
  lloyd_alg_->execute();
  IMP_LOG(VERBOSE,"KMLProxy::run analyse \n");
  KMFilterCentersResults best_clusters = lloyd_alg_->get_best();
  IMP_CHECK_CODE(Float exec_time = elapsed_time(start));
  // print summary
  IMP_LOG_WRITE(TERSE,log_summary(&best_clusters,exec_time));
  IMP_LOG_WRITE(TERSE,best_clusters.show(IMP_STREAM));
  IMP_INTERNAL_CHECK(kcenters_
                     == (unsigned int) best_clusters.get_number_of_centers(),
             "The final number of centers does not match the requested one");
  IMP_INTERNAL_CHECK (dim_ == (unsigned int) best_clusters.get_dim(),
              "The dimension of the final clusters is wrong");
  //TODO clear the centroids list
  //set the centroids:
  Particle *p;
  IMP_LOG(VERBOSE,"KMLProxy::run load best results \n");
  for (unsigned int ctr_ind = 0; ctr_ind < kcenters_; ctr_ind++) {
    KMPoint *kmp = best_clusters[ctr_ind];
    //create a new particle
    p = new Particle(m_);
    centroids_.push_back(p);
    for (unsigned int att_ind = 0; att_ind < dim_; att_ind++) {
      p->add_attribute(atts_[att_ind],(*kmp)[att_ind],false);
    }
  }
  //set the assignment of particles to centers
  //array of number of all points
  //TODO - return this
  IMP_LOG(VERBOSE,"KMLProxy::run get assignments \n");
  const Ints *close_center = best_clusters.get_assignments();
  IMP_LOG(VERBOSE,"KMLProxy::run get assignments 2\n");
  for (int i=0;i<data_->get_number_of_points();i++) {
    //std::cout<<"ps number i: " << i << " close center : "
    //<< (*close_center)[i] << std::endl;
    assignment_[ps_[i]]=(*close_center)[i];
  }
}
コード例 #14
0
ファイル: kmeans.cpp プロジェクト: samy-/svmNAO
//----------------------------------------------------------------------
//  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;
}