public CrossPRESSEvaluator(
int[] spectralRegions,
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression analysis
)
: base(spectralRegions,numFactors,groupingStrategy,preprocessOptions,analysis)
{
}
public CrossValidationWorker(
int[] spectralRegions,
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression analysis
)
{
_spectralRegions = spectralRegions;
_numFactors = numFactors;
_groupingStrategy = groupingStrategy;
_preprocessOptions = preprocessOptions;
_analysis = analysis;
}
public CrossPredictedYEvaluator(
int[] spectralRegions,
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression analysis,
IMatrix YCrossValidationPrediction
)
: base(spectralRegions,numFactors,groupingStrategy,preprocessOptions,analysis)
{
_YCrossValidationPrediction = YCrossValidationPrediction;
}
public CrossPredictedXResidualsEvaluator(
int numberOfPoints,
int[] spectralRegions,
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression analysis
)
: base(spectralRegions,numFactors,groupingStrategy,preprocessOptions,analysis)
{
_numberOfPoints = numberOfPoints;
}
/// <summary>
/// Calculates the spectral residuals obtained from cross validation.
/// </summary>
/// <param name="xOfX">The spectral wavelength values corresponding to the spectral bins.</param>
/// <param name="X">Matrix of spectra (a spectrum = a row in the matrix).</param>
/// <param name="Y">Matrix of y values (e.g. concentrations).</param>
/// <param name="numFactors">Number of factors used for calculation.</param>
/// <param name="groupingStrategy">The strategy how to group the spectra for cross prediction.</param>
/// <param name="preprocessOptions">Information how to preprocess the data.</param>
/// <param name="regress">The type of regression (e.g. PCR, PLS1, PLS2) provided as an empty regression object.</param>
/// <param name="crossXResiduals">Returns the matrix of spectral residuals</param>
/// <returns>Mean number of spectra used for prediction.</returns>
public static double GetCrossXResiduals(
IROVector xOfX,
IROMatrix X, // matrix of spectra (a spectra is a row of this matrix)
IROMatrix Y, // matrix of concentrations (a mixture is a row of this matrix)
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression regress,
out IROMatrix crossXResiduals
)
{
return GetCrossXResiduals(
SpectralPreprocessingOptions.IdentifyRegions(xOfX),
X, // matrix of spectra (a spectra is a row of this matrix)
Y, // matrix of concentrations (a mixture is a row of this matrix)
numFactors,
groupingStrategy,
preprocessOptions,
regress,
out crossXResiduals);
}
/// <summary>
/// Calculates the spectral residuals obtained from cross validation.
/// </summary>
/// <param name="spectralRegions">Array of ascending indices representing the starting indices of spectral regions.</param>
/// <param name="X">Matrix of spectra (a spectrum = a row in the matrix).</param>
/// <param name="Y">Matrix of y values (e.g. concentrations).</param>
/// <param name="numFactors">Number of factors used for calculation.</param>
/// <param name="groupingStrategy">The strategy how to group the spectra for cross prediction.</param>
/// <param name="preprocessOptions">Information how to preprocess the data.</param>
/// <param name="regress">The type of regression (e.g. PCR, PLS1, PLS2) provided as an empty regression object.</param>
/// <param name="crossXResiduals">Returns the matrix of spectral residuals</param>
/// <returns>Mean number of spectra used for prediction.</returns>
public static double GetCrossXResiduals(
int[] spectralRegions,
IROMatrix X, // matrix of spectra (a spectra is a row of this matrix)
IROMatrix Y, // matrix of concentrations (a mixture is a row of this matrix)
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression regress,
out IROMatrix crossXResiduals
)
{
CrossPredictedXResidualsEvaluator worker = new CrossPredictedXResidualsEvaluator(X.Rows,spectralRegions,numFactors,groupingStrategy,preprocessOptions,regress);
double result = CrossValidationIteration(X,Y,groupingStrategy,new CrossValidationIterationFunction(worker.EhCrossValidationWorker));
crossXResiduals = worker.XCrossResiduals;
return result;
}
/// <summary>
/// Calculates the cross predicted y values.
/// </summary>
/// <param name="xOfX">The spectral wavelength values corresponding to the spectral bins.</param>
/// <param name="X">Matrix of spectra (a spectrum = a row in the matrix).</param>
/// <param name="Y">Matrix of y values (e.g. concentrations).</param>
/// <param name="numFactors">Number of factors used for calculation.</param>
/// <param name="groupingStrategy">The strategy how to group the spectra for cross prediction.</param>
/// <param name="preprocessOptions">Information how to preprocess the data.</param>
/// <param name="regress">The type of regression (e.g. PCR, PLS1, PLS2) provided as an empty regression object.</param>
/// <param name="yCrossPredicted">Matrix of cross predicted y values. Must be of same dimension as the Y matrix.</param>
/// <returns>Mean number of spectra used for cross prediction.</returns>
public static double GetCrossYPredicted(
IROVector xOfX,
IROMatrix X, // matrix of spectra (a spectra is a row of this matrix)
IROMatrix Y, // matrix of concentrations (a mixture is a row of this matrix)
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression regress,
IMatrix yCrossPredicted // vertical value of PRESS values for the cross validation
)
{
return GetCrossYPredicted(
SpectralPreprocessingOptions.IdentifyRegions(xOfX),
X, // matrix of spectra (a spectra is a row of this matrix)
Y, // matrix of concentrations (a mixture is a row of this matrix)
numFactors,
groupingStrategy,
preprocessOptions,
regress,
yCrossPredicted );
}
/// <summary>
/// Calculates the cross predicted y values.
/// </summary>
/// <param name="spectralRegions">Array of ascending indices representing the starting indices of spectral regions.</param>
/// <param name="X">Matrix of spectra (a spectrum = a row in the matrix).</param>
/// <param name="Y">Matrix of y values (e.g. concentrations).</param>
/// <param name="numFactors">Number of factors used for calculation.</param>
/// <param name="groupingStrategy">The strategy how to group the spectra for cross prediction.</param>
/// <param name="preprocessOptions">Information how to preprocess the data.</param>
/// <param name="regress">The type of regression (e.g. PCR, PLS1, PLS2) provided as an empty regression object.</param>
/// <param name="yCrossPredicted">Matrix of cross predicted y values. Must be of same dimension as the Y matrix.</param>
/// <returns>Mean number of spectra used for cross prediction.</returns>
public static double GetCrossYPredicted(
int[] spectralRegions,
IROMatrix X, // matrix of spectra (a spectra is a row of this matrix)
IROMatrix Y, // matrix of concentrations (a mixture is a row of this matrix)
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression regress,
IMatrix yCrossPredicted // vertical value of PRESS values for the cross validation
)
{
CrossPredictedYEvaluator worker = new CrossPredictedYEvaluator(spectralRegions,numFactors,groupingStrategy,preprocessOptions,regress,yCrossPredicted);
double result = CrossValidationIteration(X,Y,groupingStrategy,new CrossValidationIterationFunction(worker.EhYCrossPredicted));
return result;
}
/// <summary>
/// Get the cross predicted error sum of squares for the number of factors=0...numFactors.
/// </summary>
/// <param name="spectralRegions">Array of ascending indices representing the starting indices of spectral regions.</param>
/// <param name="X">Matrix of spectra (a spectrum = a row in the matrix).</param>
/// <param name="Y">Matrix of y values (e.g. concentrations).</param>
/// <param name="numFactors">Maximum number of factors to calculate the cross PRESS for.</param>
/// <param name="groupingStrategy">The strategy how to group the spectra for cross prediction.</param>
/// <param name="preprocessOptions">Information how to preprocess the data.</param>
/// <param name="regress">The type of regression (e.g. PCR, PLS1, PLS2) provided as an empty regression object.</param>
/// <param name="crossPRESS">The vector of CROSS press values. Note that this vector has the length numFactor+1.</param>
/// <returns>The mean number of spectra used for prediction.</returns>
public static double GetCrossPRESS(
int[] spectralRegions,
IROMatrix X, // matrix of spectra (a spectra is a row of this matrix)
IROMatrix Y, // matrix of concentrations (a mixture is a row of this matrix)
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression regress,
out IROVector crossPRESS // vertical value of PRESS values for the cross validation
)
{
CrossPRESSEvaluator worker = new CrossPRESSEvaluator(spectralRegions,numFactors,groupingStrategy,preprocessOptions,regress);
double result = CrossValidationIteration(X,Y,groupingStrategy,new CrossValidationIterationFunction(worker.EhCrossPRESS));
crossPRESS = VectorMath.ToROVector(worker.CrossPRESS,worker.NumberOfFactors+1);
return result;
}