//the compute distance is undertaken an overhaull. I prefere to compute the probability instead of the
//euclidean distance.
void CRegionalMetaModel::ComputeDistance( vector< vector<REAL> >& mxInputs, vector<REAL>& vcPtCen, dist_pair_vector& vcDists )
{
int rows = mxInputs.size();
int cols = mxInputs.front().size();
/*compute the center of the hyperecllipsoid*/
vector<REAL> center(cols, 0.0);
for( vector< vector<REAL> >::iterator iter=mxInputs.begin(); iter!=mxInputs.end(); iter++ )
transform( center.begin(), center.end(), iter->begin(), center.begin(), plus<REAL>() );
transform( center.begin(), center.end(), center.begin(), bind2nd(divides<REAL>(), rows) );
/*compute the standard deviation of the columns*/
//compute sum( (x-u)*(x-u) ) for each column
vector<REAL> stds(cols, 0.0);
for( int i=0; i<rows; i++ )
for( int j=0; j<cols; j++ )stds[j] += sqr( center[j] - mxInputs[i][j] );
//using compose function operator to do "sqrt( sum/N-1 )"
transform( stds.begin(), stds.end(), stds.begin(),
compose_f_gx(
ptr_fun(sqrt), bind2nd(divides<double>(), rows-1)
)
);
/* compute the radius */
REAL alpha = 1.5;
vector<REAL> radius(cols);
transform( stds.begin(), stds.end(), radius.begin(), bind2nd(multiplies<REAL>(), alpha) );
for( i=0; i<cols; i++ )if( radius[i]<=0.0001 )radius[i]=0.0001;
/* compute the distance for each point */
vector<REAL> radius_sqr(cols);
transform( radius.begin(), radius.end(), radius.begin(), radius_sqr.begin(), multiplies<REAL>() );
for( i=0; i<rows; i++ ){
vector<REAL> temp(cols);
transform( mxInputs[i].begin(), mxInputs[i].end(), center.begin(), temp.begin(), diff_sqr<REAL>() );
REAL distance = inner_product( temp.begin(), temp.end(), radius_sqr.begin(), 0.0, plus<REAL>(), divides<REAL>() );
vcDists.push_back( dist_pair(i, distance) );
}
sort( vcDists.begin(), vcDists.end(), op_dist_less() );
}
std::ostream& operator<<(std::ostream&o,const String& s)
{
std::string stds(s.c_str());
o<<stds;
return o;
}
