/// <summary>
/// Processes the spectra in matrix xMatrix for prediction.
/// </summary>
/// <param name="xMatrix">The matrix of spectra. Each spectrum is a row of the matrix.</param>
/// <param name="xMean">Not used.</param>
/// <param name="xScale">Not used.</param>
/// <param name="regionstart">Starting index of the region.</param>
/// <param name="regionend">End index of the region (one behind the last region element).</param>
public void Process(IMatrix xMatrix, IROVector xMean, IROVector xScale, int regionstart, int regionend)
{
int regionlength = regionend-regionstart;
int currentorder = Math.Min(_order,regionlength);
switch(currentorder)
{
case 0: // Detrending of order 0 - subtract mean
for(int n=0;n<xMatrix.Rows;n++)
{
// 1.) Get the mean response of a spectrum
double mean = 0;
for(int i=regionstart;i<regionend;i++)
mean += xMatrix[n,i];
mean /= regionlength;
for(int i=regionstart;i<regionend;i++)
xMatrix[n,i] -= mean;
}
break;
case 1: // Detrending of order 1 - subtract linear regression line
for(int n=0;n<xMatrix.Rows;n++)
{
QuickLinearRegression regression = new QuickLinearRegression();
for(int i=regionstart;i<regionend;i++)
regression.Add(i,xMatrix[n,i]);
double a0 = regression.GetA0();
double a1 = regression.GetA1();
for(int i=regionstart;i<regionend;i++)
xMatrix[n,i] -= (a1*i+a0);
}
break;
case 2: // Detrending of order 2 - subtract quadratic regression line
for(int n=0;n<xMatrix.Rows;n++)
{
QuickQuadraticRegression regression = new QuickQuadraticRegression();
for(int i=regionstart;i<regionend;i++)
regression.Add(i,xMatrix[n,i]);
double a0 = regression.GetA0();
double a1 = regression.GetA1();
double a2 = regression.GetA2();
for(int i=regionstart;i<regionend;i++)
xMatrix[n,i] -= (((a2*i)+a1)*i+a0);
}
break;
default:
throw new NotImplementedException(string.Format("Detrending of order {0} is not implemented yet",_order));
}
}
/// <summary>
/// Processes the spectra in matrix xMatrix.
/// </summary>
/// <param name="xMatrix">The matrix of spectra. Each spectrum is a row of the matrix.</param>
/// <param name="xMean">Output: On return, contains the ensemble mean of the spectra.</param>
/// <param name="xScale">Not used.</param>
/// <param name="regionstart">Starting index of the region to process.</param>
/// <param name="regionend">End index of the region to process.</param>
void ProcessForPrediction(IMatrix xMatrix, IROVector xMean, IROVector xScale, int regionstart, int regionend)
{
int regionlength = regionend - regionstart;
for(int n=0;n<xMatrix.Rows;n++)
{
// 2.) Do linear regression of the current spectrum versus the mean spectrum
QuickLinearRegression regression = new QuickLinearRegression();
for(int i=regionstart;i<regionend;i++)
regression.Add(xMean[i],xMatrix[n,i]);
double intercept = regression.GetA0();
double slope = regression.GetA1();
// 3.) Subtract intercept and divide by slope
for(int i=regionstart;i<regionend;i++)
xMatrix[n,i] = (xMatrix[n,i]-intercept)/slope;
}
}
/// <summary>
/// Processes the spectra in matrix xMatrix for prediction.
/// </summary>
/// <param name="xMatrix">The matrix of spectra. Each spectrum is a row of the matrix.</param>
/// <param name="xMean">Not used.</param>
/// <param name="xScale">Not used.</param>
/// <param name="regionstart">Starting index of the region.</param>
/// <param name="regionend">End index of the region (one behind the last region element).</param>
public void Process(IMatrix xMatrix, IROVector xMean, IROVector xScale, int regionstart, int regionend)
{
int regionlength = regionend-regionstart;
int currentorder = Math.Min(_order,regionlength);
switch(currentorder)
{
case 0: // Detrending of order 0 - subtract mean
for(int n=0;n<xMatrix.Rows;n++)
{
// 1.) Get the mean response of a spectrum
double mean = 0;
for(int i=regionstart;i<regionend;i++)
mean += xMatrix[n,i];
mean /= regionlength;
for(int i=regionstart;i<regionend;i++)
xMatrix[n,i] -= mean;
}
break;
case 1: // Detrending of order 1 - subtract linear regression line
for(int n=0;n<xMatrix.Rows;n++)
{
QuickLinearRegression regression = new QuickLinearRegression();
for(int i=regionstart;i<regionend;i++)
regression.Add(i,xMatrix[n,i]);
double a0 = regression.GetA0();
double a1 = regression.GetA1();
for(int i=regionstart;i<regionend;i++)
xMatrix[n,i] -= (a1*i+a0);
}
break;
case 2: // Detrending of order 2 - subtract quadratic regression line
for(int n=0;n<xMatrix.Rows;n++)
{
QuickQuadraticRegression regression = new QuickQuadraticRegression();
for(int i=regionstart;i<regionend;i++)
regression.Add(i,xMatrix[n,i]);
double a0 = regression.GetA0();
double a1 = regression.GetA1();
double a2 = regression.GetA2();
for(int i=regionstart;i<regionend;i++)
xMatrix[n,i] -= (((a2*i)+a1)*i+a0);
}
break;
default:
throw new NotImplementedException(string.Format("Detrending of order {0} is not implemented yet",_order));
}
}
/// <summary>
/// Processes the spectra in matrix xMatrix.
/// </summary>
/// <param name="xMatrix">The matrix of spectra. Each spectrum is a row of the matrix.</param>
/// <param name="xMean">Output: On return, contains the ensemble mean of the spectra.</param>
/// <param name="xScale">Not used.</param>
/// <param name="regionstart">Starting index of the region to process.</param>
/// <param name="regionend">End index of the region to process.</param>
void ProcessForPrediction(IMatrix xMatrix, IROVector xMean, IROVector xScale, int regionstart, int regionend)
{
int regionlength = regionend - regionstart;
for(int n=0;n<xMatrix.Rows;n++)
{
// 2.) Do linear regression of the current spectrum versus the mean spectrum
QuickLinearRegression regression = new QuickLinearRegression();
for(int i=regionstart;i<regionend;i++)
regression.Add(xMean[i],xMatrix[n,i]);
double intercept = regression.GetA0();
double slope = regression.GetA1();
// 3.) Subtract intercept and divide by slope
for(int i=regionstart;i<regionend;i++)
xMatrix[n,i] = (xMatrix[n,i]-intercept)/slope;
}
}
