WHcoord fileManager::meanCoordFromMask() const
{
if (m_maskMatrix.empty()) {
std::cerr<< "ERROR @ fileManager::storeFullTract(): Mask hast not been loaded, returning 0 coordinate"<<std::endl;
return WHcoord();
}
size_t sumX( 0 ), sumY( 0 ), sumZ( 0 ), sumElements( 0 );
for( int i=0 ; i<m_maskMatrix.size() ; ++i )
{
for( int j=0 ; j< m_maskMatrix[i].size() ; ++j )
{
for( int k=0 ; k<m_maskMatrix[i][j].size() ; ++k )
{
if (m_maskMatrix[i][j][k])
{
sumX += i;
sumY += j;
sumZ += k;
++sumElements;
}
}
}
}
size_t meanX( sumX/sumElements );
size_t meanY( sumY/sumElements );
size_t meanZ( sumZ/sumElements );
WHcoord meanCoord(meanX,meanY,meanZ);
return meanCoord;
} // end "meanCoordFromMask()" -----------------------------------------------------------------
treg_data_data Regression::mean_x(){
treg_data_data mean = 0.0;
mean = sumX() / num_data_points;
return( mean );
}
treg_data_data Regression::stand_dev_x(){ // Calculates the standard deviation of all x values
//http://onlinestatbook.com/2/summarizing_distributions/variability.html
treg_data_data stand_dev = 0.0;
treg_data_data variance = 0.0;
treg_data_data sumxsq = 0.0;
treg_data_data sumx_sqn = 0.0;
sumxsq = sumXX();
sumx_sqn = pow( sumX(), 2) / num_data_points;
variance = (sumxsq-sumx_sqn)/( num_data_points - 1 );
stand_dev = sqrt(variance);
return(stand_dev);
}
double LinearRegression::bottom()
{
double b = sumXX()-(sumX()*sumY()/(double)data.size());
return(b);
}
double LinearRegression::top()
{
double t = sumXY()-((sumX()*sumY())/ (double)data.size());
return(t);
}
double LinearRegression::offset()
{
double o = sumY() - gain()*sumX()/(double)data.size();
return(o);
}
