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///////////////////////////////////////////////////////////////////////////////
// //
// This file is part of ModelBlocks. Copyright 2009, ModelBlocks developers. //
// //
// ModelBlocks is free software: you can redistribute it and/or modify //
// it under the terms of the GNU General Public License as published by //
// the Free Software Foundation, either version 3 of the License, or //
// (at your option) any later version. //
// //
// ModelBlocks is distributed in the hope that it will be useful, //
// but WITHOUT ANY WARRANTY; without even the implied warranty of //
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the //
// GNU General Public License for more details. //
// //
// You should have received a copy of the GNU General Public License //
// along with ModelBlocks. If not, see <http://www.gnu.org/licenses/>. //
// //
// ModelBlocks developers designate this particular file as subject to //
// the "Moses" exception as provided by ModelBlocks developers in //
// the LICENSE file that accompanies this code. //
// //
///////////////////////////////////////////////////////////////////////////////
#ifndef _NL_DTREE_CONTIN__
#define _NL_DTREE_CONTIN__
#include "nl-dtree.h"
typedef double Wt;
////////////////////////////////////////////////////////////////////////////////
//
// Cont DTree Node
//
////////////////////////////////////////////////////////////////////////////////
template<class Y, class P>
class ContDecisNode : public DecisNode<Y,P> {
private:
// Data members...
Wt wThreshold; // Threshold weight ("w_0")
map<A,Wt> awSeparator; // Hyperplane separator, weights on each attribute/dimension
Wt wSumSqr; // Sum of squares (parabolic) convolution coordinate weight
public:
// Constructor / destructor methods...
ContDecisNode ( ) : wThreshold(0.0), wSumSqr(0.0) { }
// Specification methods...
Wt& setWt ( ) { return wThreshold; }
Wt& setWt ( const A a ) { return (awSeparator.find(a)!=awSeparator.end()) ? awSeparator[a] : awSeparator[a]=0.0; }
Wt& setSsWt ( ) { return wSumSqr; }
// Extraction methods...
const Wt getWt ( ) const { return wThreshold; }
const Wt getWt ( const A a ) const { return ( (awSeparator.find(a)!=awSeparator.end()) ? awSeparator.find(a)->second : 0.0 ); }
const Wt getSsWt ( ) const { return wSumSqr; }
};
////////////////////////////////////////////////////////////////////////////////
//
// ContDTree Model
//
////////////////////////////////////////////////////////////////////////////////
template<class Y, class X, class P>
class ContDTree2DModel : public Generic2DModel<Y,X,P>, public Tree<ContDecisNode<Y,P> > {
public:
// Downcasts (safe b/c no new data)...
ContDTree2DModel<Y,X,P>& setLeft() { return static_cast<ContDTree2DModel<Y,X,P>&> ( Tree<ContDecisNode<Y,P> >::setLeft() ); }
ContDTree2DModel<Y,X,P>& setRight() { return static_cast<ContDTree2DModel<Y,X,P>&> ( Tree<ContDecisNode<Y,P> >::setRight() ); }
const ContDTree2DModel<Y,X,P>& getLeft() const { return static_cast<const ContDTree2DModel<Y,X,P>&> ( Tree<ContDecisNode<Y,P> >::getLeft() ); }
const ContDTree2DModel<Y,X,P>& getRight() const { return static_cast<const ContDTree2DModel<Y,X,P>&> ( Tree<ContDecisNode<Y,P> >::getRight() ); }
// Extraction methods...
const P getProb ( const Y y, const X& x ) const {
const Tree<ContDecisNode<Y,P> >* ptr = this;
while ( !ptr->isTerm() ) {
double sumsqr=0.0;
for(A a;a<X::getSize();a.setNext()) sumsqr += pow(x.get(a.toInt()),2.0) / X::getSize();
Wt wtdavg = -Tree<ContDecisNode<Y,P> >::getWt();
for(A a;a<X::getSize();a.setNext()) wtdavg += Tree<ContDecisNode<Y,P> >::getWt(a) * x.get(a.toInt());
wtdavg += Tree<ContDecisNode<Y,P> >::getSsWt() * sumsqr;
ptr = (wtdavg>0.0) ? &ptr->getRight() : &ptr->getLeft();
}
return ptr->getProb(y);
}
// Input / output methods...
bool readFields ( char*[], int ) ;
void writeFields ( FILE* pf, string sPref ) {
char psPath[1000] = "";
write ( pf, (sPref+"").c_str(), psPath, 0 );
}
void write ( FILE* pf, const char psPrefix[], char psPath[], int iEnd ) const {
if (Tree<ContDecisNode<Y,P> >::isTerm()) {
Y y;
psPath[iEnd]='\0';
for ( bool b=y.setFirst(); b; b=y.setNext() )
{ fprintf(pf, "%s [%s] : ", psPrefix, psPath); y.write(pf); fprintf(pf, " = %f\n", (double)Tree<ContDecisNode<Y,P> >::getProb(y)); }
////psPath[iEnd]='\0'; fprintf ( pf, "%s [%s] : 0 = %f\n", psPrefix, psPath, (double)Tree<ContDecisNode<Y,P> >::getProb("0") );
////psPath[iEnd]='\0'; fprintf ( pf, "%s [%s] : 1 = %f\n", psPrefix, psPath, (double)Tree<ContDecisNode<Y,P> >::getProb("1") );
} else {
psPath[iEnd]='\0'; fprintf ( pf, "%s [%s] =", psPrefix, psPath );
fprintf ( pf, " %f", Tree<ContDecisNode<Y,P> >::getWt() );
for(A a;a<X::getSize();a.setNext()) fprintf ( pf, "_%f", Tree<ContDecisNode<Y,P> >::getWt(a.toInt()) );
fprintf ( pf, "_%f", Tree<ContDecisNode<Y,P> >::getSsWt() );
fprintf ( pf, "\n" );
psPath[iEnd]='0'; psPath[iEnd+1]='\0'; getLeft().write ( pf, psPrefix, psPath, iEnd+1 );
psPath[iEnd]='1'; psPath[iEnd+1]='\0'; getRight().write ( pf, psPrefix, psPath, iEnd+1 );
}
}
};
////////////////////
template <class Y,class X, class P>
bool ContDTree2DModel<Y,X,P>::readFields ( char* aps[], int numFields ) {
if ( /*aps[0]==sId &&*/ (3==numFields || 4==numFields) ) {
//fprintf(stderr,"%s,%d\n",aps[3],numFields);
assert ( '['==aps[1][0] && ']'==aps[1][strlen(aps[1])-1] );
// Start at root...
Tree<ContDecisNode<Y,P> >* ptr = this;
assert(ptr);
// Find appropriate node, creating nodes as necessary...
for(int i=1; i<strlen(aps[1])-1; i++) {
assert ( '0'==aps[1][i] || '1'==aps[1][i] );
ptr = ( ('0'==aps[1][i]) ? &ptr->setLeft() : &ptr->setRight() ) ;
assert(ptr);
}
// Specify bit (at nonterminal) or distribution (at terminal)...
if ( 3==numFields) {
char* psT=NULL; Tree<ContDecisNode<Y,P> >::setWt() = atof(strtok_r(aps[2],"_",&psT)); ////atof(aps[2]);
for(A a;a<X::getSize();a.setNext()) Tree<ContDecisNode<Y,P> >::setWt(a) = atof(strtok_r(NULL,"_",&psT));
Tree<ContDecisNode<Y,P> >::setSsWt() = atof(strtok_r(NULL,"_",&psT)); }
// atof(aps[3+a.toInt()]); }
else if (4==numFields) ptr->setProb(aps[2]) = atof(aps[3]);
else assert(false);
} else return false;
return true;
}
////////////////////////////////////////////////////////////////////////////////
template<class Y, class X1,class X2, class P>
class ContDTree3DModel : public Generic3DModel<Y,X1,X2,P> {
private:
// Data members...
string sId;
SimpleHash<X1,ContDTree2DModel<Y,X2,P> > aqt;
public:
// Constructor / destructor methods...
ContDTree3DModel ( ) { }
ContDTree3DModel ( const string& s ) { sId = s; }
// Specification methods...
ContDTree2DModel<Y,X2,P>& setTree ( const X1& x1 ) { return aqt.set(x1); }
// Extraction methods...
bool setFirst ( Y& y ) const { return y.setFirst(); }
bool setNext ( Y& y ) const { return y.setNext(); }
P getProb ( const Y y, const X1& x1, const X2& x2 ) const { return aqt.get(x1).getProb(y,x2); }
// Input / output methods...
bool readFields ( char*[], int ) ;
void writeFields ( FILE* pf, string sPref ) {
char psPath[1000] = "";
X1 x1;
for ( bool b=x1.setFirst(); b; b=x1.setNext() )
aqt.get(x1).write ( pf, (sPref + " " + x1.getString()).c_str(), psPath, 0 );
}
};
////////////////////
template <class Y,class X1,class X2, class P>
bool ContDTree3DModel<Y,X1,X2,P>::readFields ( char* aps[], int numFields ) {
if ( /*aps[0]==sId &&*/ (4==numFields || 5==numFields) ) {
//fprintf(stderr,"%s,%d\n",aps[3],numFields);
assert ( '['==aps[2][0] && ']'==aps[2][strlen(aps[2])-1] );
// Start at root...
Tree<ContDecisNode<Y,P> >* ptr = &aqt.set(aps[1]);
assert(ptr);
// Find appropriate node, creating nodes as necessary...
for(int i=1; i<strlen(aps[2])-1; i++) {
assert ( '0'==aps[2][i] || '1'==aps[2][i] );
ptr = ( ('0'==aps[2][i]) ? &ptr->setLeft() : &ptr->setRight() ) ;
assert(ptr);
}
// Specify bit (at nonterminal) or distribution (at terminal)...
if ( 4==numFields) {
char* psT=NULL;
ptr->setWt() = atof(strtok_r(aps[3],"_",&psT)); ////atof(aps[3]);
for(A a;a<X2::getSize();a.setNext()) ptr->setWt(a) = atof(strtok_r(NULL,"_",&psT));
ptr->setSsWt() = atof(strtok_r(NULL,"_",&psT)); }
////for(A a;a<X2::getSize();a.setNext()) ptr->setWt(a) = atof(aps[4+a.toInt()]); }
else if (5==numFields) ptr->setProb(aps[3]) = atof(aps[4]);
//// else if (5==numFields && 0==strcmp(aps[3],"0")) ptr->setProb() = 1.0 - atof(aps[4]);
//// else if (5==numFields && 0==strcmp(aps[3],"1")) ptr->setProb() = atof(aps[4]);
else assert(false);
} else return false;
return true;
}
////////////////////////////////////////////////////////////////////////////////
//
// Trainable ContDTree Model
//
////////////////////////////////////////////////////////////////////////////////
template<class Y, class X, class P>
class TrainableContDTree2DModel : public ContDTree2DModel<Y,X,P> {
private:
List<Joint2DRV<X,Y> > lxy;
public:
// Downcasts (safe b/c no new data)...
TrainableContDTree2DModel<Y,X,P>& setLeft() { return static_cast<TrainableContDTree2DModel<Y,X,P>&>(ContDTree2DModel<Y,X,P>::setLeft()); }
TrainableContDTree2DModel<Y,X,P>& setRight() { return static_cast<TrainableContDTree2DModel<Y,X,P>&>(ContDTree2DModel<Y,X,P>::setRight()); }
const TrainableContDTree2DModel<Y,X,P>& getLeft() const { return static_cast<const TrainableContDTree2DModel<Y,X,P>&>(ContDTree2DModel<Y,X,P>::getLeft()); }
const TrainableContDTree2DModel<Y,X,P>& getRight() const { return static_cast<const TrainableContDTree2DModel<Y,X,P>&>(ContDTree2DModel<Y,X,P>::getRight()); }
// Specification methods...
void train ( List<Joint2DRV<X,Y> >&, const double ) ;
void train ( const double d ) { train(lxy,d); }
////// Input / output methods...
bool readData ( char* vs[], int numFields ) {
if ( 3==numFields ) lxy.add() = Joint2DRV<X,Y> ( X(vs[1]), Y(vs[2]) );
else return false;
return true;
}
};
////////////////////
template<class Y, class X, class P>
void TrainableContDTree2DModel<Y,X,P>::train ( List<Joint2DRV<X,Y> >& lxy, const double DTREE_CHISQR_LIMIT ) {
// Place to store counts...
//CPT3DModel<A,B,Y,double> aaaCounts; // hash was MUCH slower!!
SafeArray2D<B,Y,double> aaCounts ( 2, Y::getDomain().getSize(), 0.0 );
double dTot = lxy.getCard();
CPT1DModel<Y,double> modelY;
// if (11613==dTot) { //if (12940<=dTot && dTot<12950) { //if ( 20779==dTot ) { //// (bU)
// ListedObject<Joint2DRV<X,Y> >* pxy;
// for ( pxy = lxy.getFirst(); pxy; pxy = lxy.getNext(pxy) ) {
// fprintf(stdout,"Ohist "); pxy->getSub1().write(stdout); fprintf(stdout," "); pxy->getSub2().write(stdout); fprintf(stdout,"\n");
// }
// fprintf(stderr,"PRINTED\n");
// }
// For each datum in list...
ListedObject<Joint2DRV<X,Y> >* pxy;
for ( pxy = lxy.getFirst(); pxy; pxy = lxy.getNext(pxy) ) {
// Count Ys...
modelY.setProb(pxy->getSub2())++;
}
modelY.normalize();
double prRarest = (modelY.getProb("1")<modelY.getProb("0")) ? modelY.getProb("1") : modelY.getProb("0");
// // Set separator to pass through center of positives...
// for ( pxy = lxy.getFirst(); pxy; pxy = lxy.getNext(pxy) )
// for ( A a; a<X::getSize(); a.setNext() ) {
// if ( Y("1")==pxy->getSub2() ) {
// Tree<ContDecisNode<Y,P> >::setWt() -= (pxy->getSub1().get(a.toInt())+pow(pxy->getSub1().get(a.toInt()),2.0))/dTot; //// (dTot*prRarest);
// Tree<ContDecisNode<Y,P> >::setWt(a) += pxy->getSub1().get(a.toInt())/dTot; //// (dTot*prRarest);
// Tree<ContDecisNode<Y,P> >::setSsWt() += pow(pxy->getSub1().get(a.toInt()),2.0)/dTot; //// (dTot*prRarest);
// }
// }
// Set separator to pass through center of positives...
Tree<ContDecisNode<Y,P> >::setWt() = 1.0;
// For each gradient descent epoch...
for ( int epoch=1; epoch<=1000; epoch++ ) {
double dCtr=0.0;
double dPos = 0.0;
ListedObject<Joint2DRV<X,Y> >* pxy;
if(OUTPUT_NOISY) {
double lgprTot = 0.0;
// // For each datum in list...
// for ( pxy = lxy.getFirst(); pxy; pxy = lxy.getNext(pxy) ) {
// // Calc tot prob...
// double wtdavg = -Tree<ContDecisNode<Y,P> >::getWt();
// for(A a;a<X::getSize();a.setNext()) wtdavg += Tree<ContDecisNode<Y,P> >::getWt(a) * pxy->getSub1().get(a.toInt());
// // Calc est val of Y using sigmoid transfer fn...
// P prY = 1.0 / ( 1.0 + exp(-wtdavg) );
// if(epoch>1)fprintf(stderr," %f %f\n",(double)wtdavg,(double)prY);
// lgprTot += (pxy->getSub2()==1) ? log(prY) : log(1.0-prY) ;
// }
if (OUTPUT_NOISY && epoch%10==0) {
// Report...
fprintf(stderr," tot=%08d totlogprob=%g separator=%f",(int)dTot,lgprTot,Tree<ContDecisNode<Y,P> >::getWt());
for(A a;a<X::getSize();a.setNext()) fprintf(stderr,"_%f",Tree<ContDecisNode<Y,P> >::getWt(a));
fprintf(stderr,"_%f",Tree<ContDecisNode<Y,P> >::getSsWt());
fprintf(stderr,"\n");
}
}
fprintf(stderr," --- epoch %d ---\n",epoch);
// For each datum in list...
for ( pxy = lxy.getFirst(); pxy; pxy = lxy.getNext(pxy) ) {
// // Use random subset of more frequent Y val so total wts for 1 and 0 are equal (CODE REVIEW: should be subset nearest to centroid of fewer)...
// if ( double(rand())/double(RAND_MAX) < prRarest/modelY.getProb(pxy->getSub2()) ) {
dCtr++;
double gamma = dTot/(dTot+dCtr); // 1.0/(double(epoch)+dCtr/dTot); // 1.0/double(epoch); // 1.0/(double(epoch)+dCtr/(dTot*prRarest*2.0)); //
// Weight deltas for next epoch...
Wt wDelta = 0.0;
SafeArray1D<A,Wt> awDeltas (X::getSize(),0.0);
Wt wSsDelta = 0.0;
// Calc sum of squares for convolution coordinate...
double sumsqr=0.0;
for(A a;a<X::getSize();a.setNext()) sumsqr += pow(pxy->getSub1().get(a.toInt()),2.0) / X::getSize();
// Calc wtd avg of feats...
double wtdavg = -Tree<ContDecisNode<Y,P> >::getWt();
for(A a;a<X::getSize();a.setNext()) wtdavg += Tree<ContDecisNode<Y,P> >::getWt(a) * pxy->getSub1().get(a.toInt());
wtdavg += Tree<ContDecisNode<Y,P> >::getSsWt() * sumsqr;
//// Calc est val of Y using sigmoid transfer fn...
//P prY = ( ( ( (1.0/(1.0+exp(-wtdavg))) - .5 ) * exp(-wtdavg) ) + .5 ) ;
// Calc est val of Y using sigmoid transfer fn...
P prY = 1.0 / ( 1.0 + exp(-wtdavg) );
// Calc deltas for each feature/attribute/dimension...
double dEachWt = 1.0/dTot; // 1.0/dTot * modelY.getProb ( Y(1-pxy->getSub2().toInt()) ); // 1.0/(dTot*prRarest*2.0); //
wDelta += dEachWt * -1 * ( prY - P(double(pxy->getSub2().toInt())) );
for ( A a; a<X::getSize(); a.setNext() )
awDeltas.set(a) += dEachWt * pxy->getSub1().get(a.toInt()) * ( prY - P(double(pxy->getSub2().toInt())) );
wSsDelta += dEachWt * sumsqr * ( prY - P(double(pxy->getSub2().toInt())) );
// Update weights by deltas...
//Tree<ContDecisNode<Y,P> >::setWt() -= gamma * wDelta;
////double reldeduction = wDelta/Tree<ContDecisNode<Y,P> >::getWt();
for ( A a; a<X::getSize(); a.setNext() )
Tree<ContDecisNode<Y,P> >::setWt(a) -= gamma*awDeltas.get(a); //+ changeratio/Tree<ContDecisNode<Y,P> >::getWt(a);
Tree<ContDecisNode<Y,P> >::setSsWt() -= gamma*wSsDelta; //+ changeratio/Tree<ContDecisNode<Y,P> >::getSsWt();
dPos+=prY; // if (prY>0.5) dPos++;
// Report...
if(OUTPUT_VERYNOISY) {
fprintf(stderr," A tot=%08d vals = %f",(int)dTot,-1.00);
for(A a;a<X::getSize();a.setNext()) fprintf(stderr,"_%f",pxy->getSub1().get(a.toInt()));
fprintf(stderr,"_%f",sumsqr);
fprintf(stderr," --> %f %f (gold: %f)\n",wtdavg,(double)prY,double(pxy->getSub2().toInt()));
fprintf(stderr," D tot=%08d delt = %f",(int)dTot,wDelta);
for(A a;a<X::getSize();a.setNext()) fprintf(stderr,"_%f",awDeltas.get(a));
fprintf(stderr,"_%f",wSsDelta);
fprintf(stderr,"\n");
}
// Report...
if(OUTPUT_VERYNOISY) {
fprintf(stderr," _S tot=%08d sepr = %f",(int)dTot,Tree<ContDecisNode<Y,P> >::getWt());
for(A a;a<X::getSize();a.setNext()) fprintf(stderr,"_%f",Tree<ContDecisNode<Y,P> >::getWt(a));
fprintf(stderr,"_%f",Tree<ContDecisNode<Y,P> >::getSsWt());
fprintf(stderr,"\n");
}
// }
} // end loop pxy
/* // Report... */
/* if(OUTPUT_NOISY) { */
/* fprintf(stderr," tot:%08d +:%08d -:%08d\n",(int)dTot,(int)dPos,(int)(dTot-dPos)); */
/* fprintf(stderr," E tot=%08d separator=%f",(int)dTot,wDelta); */
/* for(A a;a<X::getSize();a.setNext()) fprintf(stderr,"_%f",awDeltas.get(a)); */
/* fprintf(stderr,"\n"); */
/* } */
} // end loop epoch
// Split list into each 0/1 child of this node...
List<Joint2DRV<X,Y> > alxy[2];
int actr[2] = {0,0};
// For each datum in list...
while ( !lxy.isEmpty() ) {
Joint2DRV<X,Y>* pxy = lxy.getFirst();
double sumsqr=0.0;
for(A a;a<X::getSize();a.setNext()) sumsqr += pow(pxy->getSub1().get(a.toInt()),2.0) / X::getSize();
Wt wtdavg=-Tree<ContDecisNode<Y,P> >::getWt();
for(A a;a<X::getSize();a.setNext()) wtdavg += Tree<ContDecisNode<Y,P> >::getWt(a) * pxy->getSub1().get(a.toInt());
wtdavg += Tree<ContDecisNode<Y,P> >::getSsWt() * sumsqr;
alxy[(wtdavg>0.0)?1:0].add() = *pxy;
aaCounts.set((wtdavg>0.0)?1:0,pxy->getSub2())++;
actr[(wtdavg>0.0)?1:0]++;
if(OUTPUT_VERYNOISY){fprintf(stderr,"classify "); pxy->write(stderr); fprintf(stderr," wtdavg=%f class=%d\n",wtdavg,(wtdavg>0.0)?1:0);}
lxy.pop();
}
// Calc chisqr...
double chisqr = 0.0;
fprintf(stderr," tot=%08d split=",(int)dTot);
for ( int b=0; b<2; b++ ) {
Y y;
for ( bool by=y.setFirst(); by; by=y.setNext() ) {
fprintf(stderr," (%s->%d:%f)",y.getString().c_str(),b,aaCounts.get(b,y));
if ( actr[b]>0.0 && modelY.getProb(y)>0.0 && dTot>0.0 ) {
double expect = actr[b] * modelY.getProb(y);
chisqr += pow ( aaCounts.get(b,y)-expect, 2.0 ) / expect;
}
}
}
fprintf(stderr,"\n");
// Report...
if(OUTPUT_NOISY) {
fprintf(stderr," tot=%08d separator=%f",(int)dTot,Tree<ContDecisNode<Y,P> >::getWt());
for(A a;a<X::getSize();a.setNext()) fprintf(stderr,"_%f",Tree<ContDecisNode<Y,P> >::getWt(a));
fprintf(stderr,"_%f",Tree<ContDecisNode<Y,P> >::getSsWt());
fprintf(stderr," chisqr=%g\n",chisqr);
}
// If separation is significant to chisqr limit...
if ( chisqr > DTREE_CHISQR_LIMIT ) {
// Recursively call train at each child...
setRight().train ( alxy[1], DTREE_CHISQR_LIMIT ); ////node*2LL+1LL);
setLeft().train ( alxy[0], DTREE_CHISQR_LIMIT ); ////node*2LL);
}
// If separation is not significant...
else {
// Add ratio as leaf...
Y y;
for ( bool by=y.setFirst(); by; by=y.setNext() )
ContDecisNode<Y,P>::setProb(y) = (dTot>0.0) ? modelY.getProb(y) : 1.0/Y::getDomain().getSize();
}
}
////////////////////////////////////////////////////////////////////////////////
template<class Y, class X1, class X2, class P>
class TrainableContDTree3DModel : public ContDTree3DModel<Y,X1,X2,P> {
private:
map<X1,List<Joint2DRV<X2,Y> > > mqlxy;
public:
////// Constructor...
TrainableContDTree3DModel() { }
TrainableContDTree3DModel(const char* ps) : ContDTree3DModel<Y,X1,X2,P>(ps) { }
////// setTree downcast...
TrainableContDTree2DModel<Y,X2,P>& setTree(const X1& x1) { return static_cast<TrainableContDTree2DModel<Y,X2,P>&>(ContDTree3DModel<Y,X1,X2,P>::setTree(x1)); }
////// Add training data to per-subphone lists...
bool readData ( char* vs[], int numFields ) {
if ( 4==numFields ) {
mqlxy[X1(vs[1])].add() = Joint2DRV<X2,Y> ( X2(vs[2]), Y(vs[3]) );
////mqlxy[X1(vs[1])].getLast()->write(stderr); fprintf(stderr,"\n");
}
else return false;
return true;
}
////// Train each subphone...
void train ( const double DTREE_CHISQR_LIMIT ) {
int ctr = 0;
// For each subphone...
X1 x1; for ( bool b=x1.setFirst(); b; b=x1.setNext() ) {
if(OUTPUT_NOISY)
fprintf(stderr,"***** x1:%s (number %d) *****\n",x1.getString().c_str(),ctr++);
setTree(x1).train ( mqlxy[x1], DTREE_CHISQR_LIMIT );
}
}
};
#endif // _NL_DTREE_CONTIN__
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