ggmix.cc

/*  MELODIC - Multivariate exploratory linear optimized decomposition into 
              independent components
    
    ggmix.cc - Gaussian & Gaussian/Gamma Mixture Model

    Christian F. Beckmann, FMRIB Image Analysis Group
    
    Copyright (C) 1999-2008 University of Oxford */

/*  Part of FSL - FMRIB's Software Library
    http://www.fmrib.ox.ac.uk/fsl
    fsl@fmrib.ox.ac.uk
    
    Developed at FMRIB (Oxford Centre for Functional Magnetic Resonance
    Imaging of the Brain), Department of Clinical Neurology, Oxford
    University, Oxford, UK
    
    
    LICENCE
    
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    Oxford (the "Software")
    
    The Software remains the property of the University of Oxford ("the
    University").
    
    The Software is distributed "AS IS" under this Licence solely for
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    makes clear that no condition is made or to be implied, nor is any
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    You are not permitted under this Licence to use this Software
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#include "newimage/newimageall.h"
#include "ggmix.h"
#include "miscmaths/miscprob.h"
#include <time.h>
#include <strstream>


using namespace NEWIMAGE;

string float2str(float f,int width, int prec, bool scientif)
  {
    ostrstream os;
    int redw = int(std::abs(std::log10(std::abs(f))))+1;
    if(width>0)
      os.width(width);
    if(scientif)
      os.setf(ios::scientific);
    os.precision(redw+std::abs(prec));
    os.setf(ios::internal, ios::adjustfield);
    os << f << '\0';
    return os.str();
  } 
 
namespace GGMIX{
  
  void ggmix::setup(const RowVector& dat, 
		      const string dirname,
		      int cnum, volume<float> themask, 
		      volume<float> themean, 
		      int num_mix, float eps, bool fixit)
  {
    cnumber = cnum;
    Mask = themask;
    Mean = themean;
    prefix = string("IC_")+num2str(cnum);

    fitted = false;
    nummix = num_mix;
    numdata = dat.Ncols();
    
    //normalise data 
    datamean = mean(dat,2).AsScalar();
    datastdev= stdev(dat,2).AsScalar();
    data=(dat - datamean)/datastdev;

    props=zeros(1,nummix);
    vars=zeros(1,nummix);
    means=zeros(1,nummix);
    Params=zeros(1,nummix);
    logprobY = 1.0;

    props = std::pow(float(nummix),float(-1.0));

    Matrix tmp1;
    tmp1 = data * data.t() / numdata;
    vars = tmp1.AsScalar();

    float Dmin, Dmax, IntSize;
    Dmin =  min(data).AsScalar(); Dmax = max(data).AsScalar();
    IntSize = Dmax / nummix;

    means(1)=mean(data,2).AsScalar(); 
    for (int ctr=2; ctr <= means.Ncols(); ctr++){
      means(ctr) =  Dmax - (ctr - 1.5) * IntSize; 
    }
    means(2)=means(1)+2*sqrt(vars(1));
    //means(2)=means(1)+ 0.6*(Dmax-means(1));
    if(nummix>2)
      //means(3)=means(1)-0.6*(means(1)-Dmin);
            means(3)=means(1)-2*sqrt(vars(1));

    epsilon = eps;
    if(epsilon >=0 && epsilon < 0.0000001)
      {epsilon = std::log(float(dat.Ncols()))/1000 ;}
    fixdim = fixit;

  }


  Matrix ggmix::threshold(const RowVector& dat, string levels)
  { 
    Matrix Res;
    Res = 1.0;
    string tmpstr;
    tmpstr = levels;
    //cerr << " Levels : " << levels << endl << endl;
    Matrix levls(1,4);
    levls = 0;
    Matrix fpr;
    Matrix fdr;
    Matrix nht;

    char *p;
    char t[1024];
    const char *discard = ", [];{(})abcdeghijklmopqstuvwxyzABCEGHIJKLMNOQSTUVWXYZ~!@#$%^&*_-=+|\':></?";

    strcpy(t, tmpstr.c_str());
    p=strtok(t,discard);
    while(p){
      Matrix New(1,1);
      New(1,1) = atof(p);
      if(strchr(p,'d')){
	levls(1,3)++;
	if(fdr.Storage()>0){
	  fdr = fdr | New;}
	else{
	  fdr = New;
	}
      }else{
	if(strchr(p,'p')){
	  levls(1,2)++;
	  if(fpr.Storage()>0){
	    fpr = fpr | New;
	  }else{
	    fpr = New;
	  }
	}else{
	  if(strchr(p,'n')){
	    levls(1,4)++;
	    if(nht.Storage()>0){
	      nht = nht | New;
	    }else{
	      nht = New;
	    }
	  }else{
	    levls(1,1)++;
	    levls = levls | New;
	  }
	}
      }
      p=strtok(NULL,discard);
    }

    if(fpr.Storage()>0){levls = levls | fpr;}
    if(fdr.Storage()>0){levls = levls | fdr;}
    if(nht.Storage()>0){levls = levls | nht;}
    

    //cerr << " levles : " << levls << endl << endl;
    Res = threshold(data, levls);
    set_threshmaps(Res);

    return Res;
  }

  Matrix ggmix::threshold(const RowVector& dat, Matrix& levels)
  {  
    Matrix tests;
    tests=levels;
    Matrix Nprobs;

    //if only single Gaussian: resort to nht
    if(nummix==1||props(1)>0.999||probmap.Sum()<0.05){
      if(levels(1,4)==0){
	Matrix New(1,6);
	New(1,5)=0.05;
	New(1,6)=0.01;
	New(1,4)=2;New(1,1)=0;New(1,2)=0;New(1,3)=0;tests=New;
      }else{
	Matrix New;
	New = levels.Columns(int(1+levels(1,1)+levels(1,2)
				 +levels(1,3)),levels.Ncols());
	New(1,4) = levels(1,4);
	New(1,1)=0;New(1,1)=0;New(1,3)=0;
	tests=New;
      }
    }

    int numtests = int(tests(1,1)+tests(1,2)+tests(1,3)+tests(1,4));    

    Matrix Res(numtests,numdata);
    Res = 0.0;

    int next = 1;

    for(int ctr1=1;ctr1<=tests(1,1);ctr1++){
      if(4+next <= tests.Ncols()){
	//	message("   alternative hypothesis test at p > " << tests(1,4+next) << endl);
	add_infstr(" alternative hypothesis test at p > "+float2str(tests(1,4+next),0,2,false));
	Matrix tmpNull;
	tmpNull = dat;
	
	float cutoffpos, cutoffneg;
	//      cutoffpos = max(dat).AsScalar()+0.1;
	//      cutoffneg = min(dat).AsScalar()-0.1;
	cutoffpos = means(1)+6*std::sqrt(vars(1)+0.0000001);
	cutoffneg = means(1)-6*std::sqrt(vars(1)+0.0000001);
	
	for(int ctr=1; ctr<=tmpNull.Ncols(); ctr++)
	  if( probmap(ctr) > tests(1,4+next) ){
	    if( dat(ctr) > means(1) )
	      cutoffpos = std::min(cutoffpos, float(dat(ctr)));
	    else
	      cutoffneg = std::max(cutoffneg, float(dat(ctr)));
	  }
	
	//      cerr << " Cutoff " << cutoffneg << "  " << cutoffpos << endl;
	
	for(int ctr=1; ctr<=tmpNull.Ncols(); ctr++)
	  if( (dat(ctr) > cutoffneg) && (dat(ctr) < cutoffpos) )
	    tmpNull(1,ctr)=0.0;

	// for(int ctr=1; ctr<=tmpNull.Ncols(); ctr++)
	//   if( probmap(ctr) < tests(1,4+next))
	//          tmpNull(1,ctr)=0.0;
	Res.Row(next) << tmpNull;
      }
      next++;
    }
    
    for(int ctr1=1;ctr1<=tests(1,2);ctr1++){
      if(4+next <=tests.Ncols()){
	cerr << " false positives control " << tests(1,4+next)<<endl;
	Matrix tmp1;
	tmp1 = normcdf(dat,means(1),vars(1));
	Matrix tmpNull;
	tmpNull = dat; 
	for(int ctr=1; ctr<=tmp1.Ncols(); ctr++)
	  if(tmp1(1,ctr) < tests(1,4+next))
	    tmpNull(1,ctr)=0.0;
	Res.Row(next) << tmpNull;
      }
      next++;
    }

    for(int ctr1=1;ctr1<=tests(1,3);ctr1++){
      if(4+next <=tests.Ncols()){
	cerr << " false discovery rate " << tests(1,4+next)<< endl;
	Matrix newcdf(Nprobs);
	for(int ctr=1;ctr<=Nprobs.Nrows();ctr++){
	  newcdf.Row(ctr) << 1-props(ctr)*normcdf(dat,means(ctr),vars(ctr));
	}
	Matrix tmpNull;
	tmpNull = newcdf.Row(1);
	Matrix tmpAlt;
	tmpAlt = sum(newcdf.Rows(2,Nprobs.Nrows()),1);
	Matrix tmp;
	tmp = dat; 
	for(int ctr=1; ctr<=tmp.Ncols(); ctr++)
	  if(tmpNull(1,ctr) > (1-tests(1,4+next)) * tmpAlt(1,ctr))
	    tmp(1,ctr)=0.0;
	Res.Row(next) << tmp;
	next++;
      }
    }

    for(int ctr1=1;ctr1<=tests(1,4);ctr1++){
      if(4+next <=tests.Ncols()){
	//	message("   2-sided null hypothesis test at " << tests(1,4+next)<<endl);
	add_infstr(" 2-sided null hypothesis test at "+float2str(tests(1,4+next),0,2,false));
	double mu, sig;
	mu  = dat.Sum()/numdata;
	sig = var(dat,2).AsScalar();
	Matrix tmp1;
	tmp1 = normcdf(dat,mu,std::abs(sig));
	Matrix tmpNull;
	tmpNull = dat; 
	for(int ctr=1; ctr<=tmp1.Ncols(); ctr++)
	  if((tmp1(1,ctr) < 1-0.5*(tests(1,4+next))&&
	      (tmp1(1,ctr) > 0.5*(tests(1,4+next)))))
	    tmpNull(1,ctr)=0.0;
	Res.Row(next) << tmpNull;
      }
      next++;
    }
   
    return Res;
  }


  /* GMM fitting  */


  void ggmix::gmmupdate()
  {
    int it_ctr = 1;
    bool exitloop = false;
    float oldll;

    // cerr << " fit with : " << means.Ncols() << endl;

    Matrix tmp0;Matrix tmp1;Matrix prob_K__y_theta;
    Matrix kdata;
    RowVector prob_Y__theta;RowVector Nbar;
    RowVector mubar;RowVector sigmabar;RowVector pibar;
    
    do{
      oldll = logprobY;

      //make sure all variances are acceptable
      for(int ctr1=1; ctr1 <=vars.Ncols(); ctr1++)
      	if(vars(ctr1)<0.0001){
      	  vars(ctr1) = 0.0001;
      	}

      tmp0 = normpdf(data,means,vars);
      tmp1 = SP(props.t()*ones(1,numdata),tmp0);      
      prob_Y__theta << sum(tmp1,1);
      logprobY = log(prob_Y__theta).Sum();
      prob_K__y_theta = SP(tmp1,pow(ones(nummix,1)*prob_Y__theta,-1));
      Nbar << sum(prob_K__y_theta,2).t();
      pibar = Nbar / numdata;
      kdata = ones(nummix,1)*data;
      mubar <<SP(sum(SP(kdata,prob_K__y_theta),2).t(),pow(Nbar,-1));    
      kdata -= mubar.t()*ones(1,numdata);
      kdata = pow(kdata,2);
      sigmabar << SP(sum(SP(kdata,prob_K__y_theta),2).t(),pow(Nbar,-1));
      
      means = mubar;
      vars  = sigmabar;
      props = pibar;

   
      if(epsilon<0){exitloop = it_ctr >= -epsilon;}
      else{exitloop = (((logprobY-oldll < epsilon)&&(it_ctr>20))
		       ||(it_ctr>400));}      
      it_ctr++;
    }while(!exitloop);
  }

  
  void ggmix::gmmfit()
  {
    int i,j;

    //cerr << means << " " << vars << " " << props << endl;
    //cerr << fixdim << endl;
    if(fixdim){
      if(nummix>1){
	gmmupdate();
	add_params(means,vars,props,logprobY,MDL,Evi,true);
      }else{
	means.ReSize(1);
	means = data.Sum()/numdata;
	vars.ReSize(1);
	vars = var(data,2);
	props.ReSize(1);
	props = 1.0;
	gmmevidence();
      }
    }else{
      RowVector Score(Params.Ncols());
      do{
	//cerr << " fitting GMM with " << nummix << endl;
	//cerr << means << " " << vars << " " << props << endl;
    
	gmmupdate();

	//	cerr << means << " " << vars << " " << props << endl;

	Score(nummix) = gmmevidence();
       
	int idx1,idx2;
	RowVector pitmp = props;
     
	pitmp.MaximumAbsoluteValue1(idx1);
	pitmp(idx1)=0.0;
	pitmp.MaximumAbsoluteValue1(idx2);
	
	//status("   ");
	if(props.MaximumAbsoluteValue1(i)<0.9){
	  if((vars(idx2)>0.15)&&
	     (std::abs(means(idx2)-means(idx1))<0.5*vars(idx1))){
	    Score(nummix) = Score(nummix)/(2*(means(idx1)));
	  }	     
	}
	
	add_params(means,vars,props,logprobY,MDL,Evi,true);
	//cerr << " Evi : " << evidence() << "  ("<< nummix << ")" << endl;
       
	gmmreducemm();
	means = means.Columns(1,nummix);
	vars  = vars.Columns(1,nummix);
	props = props.Columns(1,nummix);

      }while(nummix>1);
      means.ReSize(1);
      means = data.Sum()/numdata;
      vars.ReSize(1);
      vars = var(data,2);
      props.ReSize(1);
      props = 1.0;
      Score(nummix) = gmmevidence();
      add_params(means,vars,props,logprobY,MDL,Evi,true);
      //identify best MM
      Score.MinimumAbsoluteValue2(i,j);
      means.ReSize(j);
      vars.ReSize(j);
      props.ReSize(j);
      nummix = j;
      int index; index = 3 + (j-1)*5; 
      means = Params.SubMatrix(index,index,1,j);
      vars  = Params.SubMatrix(index+1,index+1,1,j);
      props = Params.SubMatrix(index+2,index+2,1,j);
    }

    //make sure that maximum mode is first
    //     cerr <<" Max Abs : " << props.MaximumAbsoluteValue2(i,j) << " " << j << endl; 

    props.MaximumAbsoluteValue2(i,j);
    if(j>1){
      float tmp;
      tmp = means(1);means(1) = means(j);means(j)=tmp;
      tmp = vars(1);vars(1) = vars(j);vars(j)=tmp;
      tmp = props(1);props(1) = props(j);props(j)=tmp;
    }
    
    add_params(means,vars,props,logprobY,MDL,Evi,true);
    //write_ascii_matrix(mainhtml.appendDir(prefix+"-GMM_params.txt"),Params);

    if(nummix==1)
      probmap << normcdf(data,means(1),vars(1));
    else{
      Matrix Nprobs;
      Matrix tmp0;
      tmp0 = normpdf(data,means,vars);
      Nprobs = SP(props.t()*ones(1,numdata),tmp0);
      tmp0 = ones(Nprobs.Nrows(),1)*pow(sum(Nprobs,1),-1);
      Nprobs = SP(tmp0,Nprobs);
      probmap << SP(sum(Nprobs.Rows(2,Nprobs.Nrows()),1),
		    pow(sum(Nprobs,1),-1));
    } 
   }


  float ggmix::gmmevidence()
  {
    Matrix tmp0;
    if(means.Ncols()>1){
      tmp0 = normpdf(data,means,vars); 
    }else{
      tmp0 = normpdf(data,means.AsScalar(),vars.AsScalar());
    }
    Matrix tmp1;
    tmp1 = SP(props.t()*ones(1,numdata),tmp0);
    tmp0 = SP(tmp0,pow(ones(nummix,1)*sum(tmp1,1),-1));
    tmp0 = pow(tmp0-ones(nummix,1)*tmp0.Row(nummix),2);
    float logH = 0;
    if(means.Ncols()>1){
      logH = sum(log(sum(tmp0.Rows(1,nummix-1),2)),1).AsScalar();
    }
    logH = logH + 2*sum(log(std::sqrt(2.0)*numdata*props),2).AsScalar();
    logH = logH - 2*sum(props,2).AsScalar();
    
    RowVector prob_Y__theta;
    prob_Y__theta << sum(tmp1,1);
    
    logprobY = log(prob_Y__theta).Sum(); 
    
    MDL = -logprobY + (1.5*nummix-0.5)* std::log(float(numdata));
    
    Evi = -logprobY +nummix*std::log(2.0)-std::log(MISCMATHS::gamma(nummix))
      -3*nummix/2*std::log(M_PI)+0.5*logH;
  
    return Evi;
  }

  void ggmix::gmmreducemm()
  {
    Matrix dlm(nummix,nummix);
    Matrix mus(nummix,nummix);
    Matrix rs(nummix,nummix);

    for(int ctri=1;ctri<=nummix; ctri++){
      for(int ctrj=1;ctrj<=nummix; ctrj++){
	mus(ctri,ctrj) = (props(ctri)*means(ctri)+props(ctrj)*means(ctrj))
	                   /(props(ctri)+props(ctrj));
        rs(ctri,ctrj)  = (props(ctri)*(vars(ctri)+
			  std::pow(means(ctri)-mus(ctri,ctrj),2) ) 
                          + props(ctrj)*(vars(ctrj) 
                          + std::pow(means(ctrj)-mus(ctri,ctrj),2))) 
	                  / (props(ctri)+props(ctrj));
	dlm(ctri,ctrj) = 0.5*numdata*(
			 props(ctri)*std::log(
                           std::abs(rs(ctri,ctrj))/std::abs(vars(ctri))) 
			 + props(ctrj)*std::log(std::abs(rs(ctri,ctrj))
                         / std::abs(vars(ctrj))));
      }
    }

    dlm += IdentityMatrix(nummix)*dlm.Maximum();

    int i,j;
    float val;
    val=dlm.MinimumAbsoluteValue2(i,j);
    //cerr << "   " << val << " " << i << " " << j << endl;

    nummix--;
    //cerr << "NumMix" << nummix << endl;
    
    RowVector newmean(nummix);
    RowVector newvars(nummix);
    RowVector newprop(nummix);

    int ctr0 = 1;
    for(int ctr=1; ctr<=nummix+1; ctr++){
      if(ctr!=i&&ctr!=j){
	newmean(ctr0) = means(ctr);
	newvars(ctr0) = vars(ctr);
	newprop(ctr0) = props(ctr);
	ctr0++;
      }
    }
    //cerr << "ctr0   " << ctr0 << endl;
    if(ctr0<=nummix){
      newmean(ctr0) = mus(i,j);
      newvars(ctr0) = rs(i,j);
      newprop(ctr0) = props(i)+props(j);
      
      means = newmean;    
      vars=newvars;
      props=newprop;
    }
  }

  void ggmix::ggmfit()
  {// fit a mixture of a Gaussian and multiple Gamma functions to the histogram
  
    float log_p_y_theta = 1.0;
    float old_ll = 2.0;
    float g_eps = 0.000001;
    int it_ctr = 0;
    double Dmax, Dmin;
   
    Dmax = 2 * data.Maximum();
    Dmin = -2 * data.Minimum();

    //resize means, vars and props
    if(nummix > 3)
      nummix = 3;
    means = means.Columns(1,nummix);
    vars  = vars.Columns(1,nummix);
    props = props.Columns(1,nummix);

    means(1) = -2*mean(data,2).AsScalar();

    Matrix tmp1;Matrix prob_K__y_theta;
    Matrix kdata;
    RowVector prob_Y__theta;RowVector Nbar;
    RowVector mubar;RowVector sigmabar;RowVector pibar;
    Matrix p_ygx(nummix,numdata);
    offset = 0.0;
    float const2;
    Matrix negdata(data);
    negdata = -data;

    while((it_ctr<30) ||
	  ((std::abs(old_ll - log_p_y_theta)>g_eps) && (it_ctr<500)))
      { // fit GGM
	
 	it_ctr++;
	//offset = (std::min(0.2,1-props(1)))*std::sqrt(vars(1));

// 	//make sure all variances are acceptable
 	for(int ctr1=1; ctr1 <=nummix; ctr1++)
 	  if(vars(ctr1)<0.0001){
 	    vars(ctr1) = 0.0001;
 	  }

 	p_ygx = 0.0;
 	p_ygx.Row(1) << normpdf(data,means(1),vars(1));
       
 	const2 = (2.6-props(1))*sqrt(vars(1))+means(1); //min. nht level
 
	means(2) = (std::max(means(2), std::max(0.001,
	   0.5 * ( const2 + std::sqrt( const2*const2 + 4*vars(2))))));
	vars(2)  = std::max(std::min(vars(2), 0.5*std::pow(means(2),2)),0.0001);
	p_ygx.Row(2) << gammapdf(data,means(2),vars(2));
   
 	if(nummix>2){
	  const2 = (2.6-props(1))*sqrt(vars(1))-means(1);
	
	  means(3) = -(std::max(-means(3), std::max(0.001,
	      0.5 * ( const2 + std::sqrt( const2*const2 + 4*vars(3))))));
	  vars(3)  = std::max(std::min(vars(3), 0.5*std::pow(means(3),2)),0.0001);
 	  p_ygx.Row(3) << gammapdf(negdata,-means(3),vars(3));
	}

 	tmp1 = SP(props.t()*ones(1,numdata),p_ygx);
 	prob_Y__theta << sum(tmp1,1);
	
	//deal with non-modelled voxels
	for(int ctr=1; ctr<=tmp1.Ncols(); ctr++)
	  if(prob_Y__theta(ctr) < 0.0001)
	    prob_Y__theta(ctr) = 0.0001;

 	old_ll = log_p_y_theta;
 	log_p_y_theta = log(prob_Y__theta).Sum();
	// 	cerr << "calculated log_prob_Y__theta" <<endl;
	// 	cerr << old_ll << "   " << log_p_y_theta << "   " 
	//	cerr << float(std::abs(old_ll - log_p_y_theta)) << endl;
 	if((it_ctr<30) ||
	   ((std::abs(old_ll - log_p_y_theta)>g_eps) && (it_ctr<300))){//update
	  
 	  prob_K__y_theta = SP(tmp1,pow(ones(nummix,1)*prob_Y__theta,-1));
 	  Nbar << sum(prob_K__y_theta,2).t();
	  for(int ctr=1; ctr<=Nbar.Ncols(); ctr++)
	    if(Nbar(ctr) < 0.0001 * numdata)
	      Nbar = Nbar + 0.0001;
 	  pibar= Nbar / numdata;
	  // 	  cerr << "pibar :" << pibar << endl;
 	  kdata = ones(nummix,1)*data;
 	  mubar <<SP(sum(SP(kdata,prob_K__y_theta),2).t(),pow(Nbar,-1)); 
	  // 	  cerr << "mubar :" << mubar << endl;

 	  kdata -= mubar.t()*ones(1,numdata);
 	  kdata = pow(kdata,2);
 	  sigmabar << SP(sum(SP(kdata,prob_K__y_theta),2).t(),pow(Nbar,-1));
      
 	  means = mubar;
 	  vars  = sigmabar;
 	  props = pibar;
 	}//update
      } //while loop

    props = props / sum(props,2).AsScalar();
    add_params(means,vars,props,logprobY,MDL,Evi,true);
    
    probmap << SP(sum(tmp1.Rows(2,tmp1.Nrows()),1),
		    pow(sum(tmp1,1),-1));

    //if(probmap.Sum() < 0.01){
    //  message("    no Gamma survived " << endl);
    //  probmap << normcdf(data,means(1),vars(1));
    // }|| std::abs(mean(data,2).AsScalar()) > 1.0
      
    if(props(1)<0.4 ){
      //set up GMM
      //      message("    try Gaussian Mixture Model " << endl);
      props=zeros(1,nummix);
      vars=zeros(1,nummix);
      means=zeros(1,nummix);
      Params=zeros(1,nummix);
      logprobY = 1.0;
      
      props = std::pow(float(nummix),float(-1.0));

      tmp1 = data * data.t() / numdata;
      vars = tmp1.AsScalar();

      float Dmin, Dmax, IntSize;
      Dmin = min(data).AsScalar(); Dmax = max(data).AsScalar();
      IntSize = Dmax / nummix;

      means(1)=mean(data,2).AsScalar(); 
      for (int ctr=2; ctr <= means.Ncols(); ctr++){
	means(ctr) =  Dmax - (ctr - 1.5) * IntSize; 
      }
      means(2)=means(1)+sqrt(vars(1));
      if(nummix>2)
	means(3)=means(1)-sqrt(vars(1));
      
      fit(string("GMM"));
    }

    //cerr << prefix << "  " << it_ctr << endl;

  }

  /*  INPUT / OUTPUT  */

  void ggmix::add_params(Matrix& mu, Matrix& sig, Matrix& pi, 
		      float logLH, float MDL, float Evi, bool advance)
  { 
    int size = Params.Ncols();
    if(size<2){size=2;}
    Matrix New(5,size);
    New = 0;
    
    New.SubMatrix(3,3,1,mu.Ncols())=mu;
    New.SubMatrix(4,4,1,mu.Ncols())=sig;
    New.SubMatrix(5,5,1,mu.Ncols())=pi;
    New(1,1)=nummix;
  
    New(1,2)=-logLH;
    New(2,1)=Evi;
    New(2,2)=MDL;
    if(Params.Storage()>nummix){ 
      Params = New & Params;
    }else{ 
      Params =  New;
    }
  }

  void ggmix::get_params(int index, Matrix& mu, Matrix& sig, Matrix& pi, 
		      float logLH, float MDL, float Evi)
  { 
   
  }

  void ggmix::save()
  {
    
  }

  void ggmix::status(const string &txt)
  {
    cerr << txt << "epsilon " << epsilon << endl;
    cerr << txt << "nummix  " << nummix << endl;
    cerr << txt << "numdata " << numdata << endl;
    cerr << txt << "means   " << means << endl;
    cerr << txt << "vars    " << vars << endl;
    cerr << txt << "props   " << props << endl;
  }

}