/// <summary>
/// Creates an analyis from preprocessed spectra and preprocessed concentrations.
/// </summary>
/// <param name="matrixX">The spectral matrix (each spectrum is a row in the matrix). They must at least be centered.</param>
/// <param name="matrixY">The matrix of concentrations (each experiment is a row in the matrix). They must at least be centered.</param>
/// <param name="maxFactors">Maximum number of factors for analysis.</param>
/// <returns>A regression object, which holds all the loads and weights neccessary for further calculations.</returns>
protected override void AnalyzeFromPreprocessedWithoutReset(IROMatrix matrixX, IROMatrix matrixY, int maxFactors)
{
int numberOfFactors = _calib.NumberOfFactors = Math.Min(matrixX.Columns, maxFactors);
IMatrix helperY = new MatrixMath.BEMatrix(matrixY.Rows, 1);
_PRESS = null;
for (int i = 0; i < matrixY.Columns; i++)
{
MatrixMath.Submatrix(matrixY, helperY, 0, i);
PLS2Regression r = PLS2Regression.CreateFromPreprocessed(matrixX, helperY, maxFactors);
IPLS2CalibrationModel cal = r.CalibrationModel;
_calib.NumberOfFactors = Math.Min(_calib.NumberOfFactors, cal.NumberOfFactors);
_calib.XLoads[i] = cal.XLoads;
_calib.YLoads[i] = cal.YLoads;
_calib.XWeights[i] = cal.XWeights;
_calib.CrossProduct[i] = cal.CrossProduct;
if (_PRESS == null)
{
_PRESS = VectorMath.CreateExtensibleVector(r.PRESS.Length);
}
VectorMath.Add(_PRESS, r.PRESS, _PRESS);
}
}
/// <summary>
/// Creates an analyis from preprocessed spectra and preprocessed concentrations.
/// </summary>
/// <param name="matrixX">The spectral matrix (each spectrum is a row in the matrix). They must at least be centered.</param>
/// <param name="matrixY">The matrix of concentrations (each experiment is a row in the matrix). They must at least be centered.</param>
/// <param name="maxFactors">Maximum number of factors for analysis.</param>
/// <returns>A regression object, which holds all the loads and weights neccessary for further calculations.</returns>
public static PLS2Regression CreateFromPreprocessed(IROMatrix matrixX, IROMatrix matrixY, int maxFactors)
{
PLS2Regression result = new PLS2Regression();
result.AnalyzeFromPreprocessed(matrixX, matrixY, maxFactors);
return(result);
}
protected override void InternalGetXLeverageFromPreprocessed(IROMatrix matrixX, int numFactors, IMatrix xLeverage)
{
for (int i = 0; i < _calib.NumberOfY; i++)
{
PLS2Regression.CalculateXLeverageFromPreprocessed(matrixX, _calib.XWeights[i], numFactors, xLeverage, i);
}
}
/// <summary>
/// Calculates the prediction scores (for use withthe preprocessed spectra).
/// </summary>
/// <param name="numFactors">Number of factors used to calculate the prediction scores.</param>
/// <param name="predictionScores">Supplied matrix for holding the prediction scores.</param>
protected override void InternalGetPredictionScores(int numFactors, IMatrix predictionScores)
{
IMatrix pred = new MatrixMath.BEMatrix(predictionScores.Rows, 1);
for (int i = 0; i < _calib.NumberOfY; i++)
{
PLS2Regression.GetPredictionScoreMatrix(_calib.XLoads[i], _calib.YLoads[i], _calib.XWeights[i], _calib.CrossProduct[i], numFactors, pred);
MatrixMath.SetColumn(pred, predictionScores, i);
}
}
/// <summary>
/// This predicts concentrations of unknown spectra.
/// </summary>
/// <param name="XU">Matrix of unknown spectra (preprocessed the same way as the calibration spectra).</param>
/// <param name="numFactors">Number of factors used for prediction.</param>
/// <param name="predictedY">On return, holds the predicted y values. (They are centered).</param>
/// <param name="spectralResiduals">On return, holds the spectral residual values.</param>
public override void PredictedYAndSpectralResidualsFromPreprocessed(
IROMatrix XU, // unknown spectrum or spectra, horizontal oriented
int numFactors, // number of factors to use for prediction
IMatrix predictedY, // Matrix of predicted y-values, must be same number of rows as spectra
IMatrix spectralResiduals // Matrix of spectral residuals, n rows x 1 column, can be zero
)
{
if (numFactors > NumberOfFactors)
{
throw new ArgumentOutOfRangeException(string.Format("Required numFactors (={0}) is higher than numFactors of analysis (={1})", numFactors, NumberOfFactors));
}
IMatrix helperY = predictedY == null ? null : new MatrixMath.BEMatrix(XU.Rows, 1);
IMatrix helperS = spectralResiduals == null ? null : new MatrixMath.BEMatrix(XU.Rows, 1);
for (int i = 0; i < _calib.NumberOfY; i++)
{
PLS2Regression.Predict(
XU, // unknown spectrum or spectra, horizontal oriented
_calib.XLoads[i], // x-loads matrix
_calib.YLoads[i], // y-loads matrix
_calib.XWeights[i], // weighting matrix
_calib.CrossProduct[i], // Cross product vector
numFactors, // number of factors to use for prediction
helperY, // Matrix of predicted y-values, must be same number of rows as spectra
helperS // Matrix of spectral residuals, n rows x 1 column, can be zero
);
if (null != predictedY)
{
MatrixMath.Copy(helperY, predictedY, 0, i);
}
if (null != spectralResiduals)
{
MatrixMath.Copy(helperS, spectralResiduals, 0, i);
}
}
}
protected override void InternalGetXLeverageFromPreprocessed(IROMatrix matrixX, int numFactors, IMatrix xLeverage)
{
PLS2Regression.CalculateXLeverageFromPreprocessed(matrixX, _calib.XWeights, numFactors, xLeverage, 0);
}
/// <summary>
/// Creates an analyis from preprocessed spectra and preprocessed concentrations.
/// </summary>
/// <param name="matrixX">The spectral matrix (each spectrum is a row in the matrix). They must at least be centered.</param>
/// <param name="matrixY">The matrix of concentrations (each experiment is a row in the matrix). They must at least be centered.</param>
/// <param name="maxFactors">Maximum number of factors for analysis.</param>
/// <returns>A regression object, which holds all the loads and weights neccessary for further calculations.</returns>
public static PLS2Regression CreateFromPreprocessed(IROMatrix matrixX, IROMatrix matrixY, int maxFactors)
{
PLS2Regression result = new PLS2Regression();
result.AnalyzeFromPreprocessed(matrixX,matrixY,maxFactors);
return result;
}