/// <summary>
/// Copies all settings from another instance.
/// </summary>
/// <param name="from">The other instance to copy the data from.</param>
public void CopyFrom(SpectralPreprocessingOptions from)
{
this._method = from._method;
this._detrendingOrder = from._detrendingOrder;
this._ensembleScale = from._ensembleScale;
this._regions = (int[])from._regions.Clone();
}
public CrossPRESSEvaluator(
int[] spectralRegions,
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression analysis
)
: base(spectralRegions,numFactors,groupingStrategy,preprocessOptions,analysis)
{
}
public CrossPRESSEvaluator(
int[] spectralRegions,
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression analysis
)
: base(spectralRegions, numFactors, groupingStrategy, preprocessOptions, analysis)
{
}
/// <summary>
/// Copies all settings from another instance.
/// </summary>
/// <param name="from">The other instance to copy the data from.</param>
public void CopyFrom(SpectralPreprocessingOptions from)
{
if (object.ReferenceEquals(this, from))
{
return;
}
_method = from._method;
_detrendingOrder = from._detrendingOrder;
_ensembleScale = from._ensembleScale;
_regions = (int[])from._regions.Clone();
}
public CrossPredictedXResidualsEvaluator(
int numberOfPoints,
int[] spectralRegions,
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression analysis
)
: base(spectralRegions, numFactors, groupingStrategy, preprocessOptions, analysis)
{
_numberOfPoints = numberOfPoints;
}
public CrossPredictedYEvaluator(
int[] spectralRegions,
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression analysis,
IMatrix YCrossValidationPrediction
)
: base(spectralRegions, numFactors, groupingStrategy, preprocessOptions, analysis)
{
_YCrossValidationPrediction = YCrossValidationPrediction;
}
public CrossValidationWorker(
int[] spectralRegions,
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression analysis
)
{
_spectralRegions = spectralRegions;
_numFactors = numFactors;
_groupingStrategy = groupingStrategy;
_preprocessOptions = preprocessOptions;
_analysis = analysis;
}
public CrossValidationWorker(
int[] spectralRegions,
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression analysis
)
{
_spectralRegions = spectralRegions;
_numFactors = numFactors;
_groupingStrategy = groupingStrategy;
_preprocessOptions = preprocessOptions;
_analysis = analysis;
}
/// <summary>
/// Makes a PLS (a partial least squares) analysis of the table or the selected columns / rows and stores the results in a newly created table.
/// </summary>
/// <param name="mainDocument">The main document of the application.</param>
/// <param name="srctable">The table where the data come from.</param>
/// <param name="selectedColumns">The selected columns.</param>
/// <param name="selectedRows">The selected rows.</param>
/// <param name="selectedPropertyColumns">The selected property column(s).</param>
/// <param name="bHorizontalOrientedSpectrum">True if a spectrum is a single row, False if a spectrum is a single column.</param>
/// <param name="plsOptions">Provides information about the max number of factors and the calculation of cross PRESS value.</param>
/// <param name="preprocessOptions">Provides information about how to preprocess the spectra.</param>
/// <returns></returns>
public virtual string ExecuteAnalysis(
Altaxo.AltaxoDocument mainDocument,
Altaxo.Data.DataTable srctable,
IAscendingIntegerCollection selectedColumns,
IAscendingIntegerCollection selectedRows,
IAscendingIntegerCollection selectedPropertyColumns,
bool bHorizontalOrientedSpectrum,
MultivariateAnalysisOptions plsOptions,
SpectralPreprocessingOptions preprocessOptions
)
{
IMatrix matrixX, matrixY;
IROVector xOfX;
var plsContent = new MultivariateContentMemento();
plsContent.Analysis = this;
// now we have to create a new table where to place the calculated factors and loads
// we will do that in a vertical oriented manner, i.e. even if the loads are
// here in horizontal vectors: in our table they are stored in (vertical) columns
string newName = this.AnalysisName + " of " + Main.ProjectFolder.GetNamePart(srctable.Name);
newName = Main.ProjectFolder.CreateFullName(srctable.Name, newName);
Altaxo.Data.DataTable table = new Altaxo.Data.DataTable(newName);
// Fill the Table
using (var suspendToken = table.SuspendGetToken())
{
table.SetTableProperty("Content", plsContent);
plsContent.OriginalDataTableName = srctable.Name;
// Get matrices
GetXYMatrices(
srctable,
selectedColumns,
selectedRows,
selectedPropertyColumns,
bHorizontalOrientedSpectrum,
plsContent,
out matrixX, out matrixY, out xOfX);
StoreXOfX(xOfX, table);
// Preprocess
plsContent.SpectralPreprocessing = preprocessOptions;
IVector meanX, scaleX, meanY, scaleY;
MultivariateRegression.PreprocessForAnalysis(preprocessOptions, xOfX, matrixX, matrixY,
out meanX, out scaleX, out meanY, out scaleY);
StorePreprocessedData(meanX, scaleX, meanY, scaleY, table);
// Analyze and Store
IROVector press;
ExecuteAnalysis(
matrixX,
matrixY,
plsOptions,
plsContent,
table, out press);
this.StorePRESSData(press, table);
if (plsOptions.CrossPRESSCalculation != CrossPRESSCalculationType.None)
CalculateCrossPRESS(xOfX, matrixX, matrixY, plsOptions, plsContent, table);
StoreFRatioData(table, plsContent);
StoreOriginalY(table, plsContent);
suspendToken.Dispose();
}
Current.Project.DataTableCollection.Add(table);
// create a new worksheet without any columns
Current.ProjectService.CreateNewWorksheet(table);
return null;
}
/// <summary>
/// Creates an analyis from the raw spectra and raw concentrations.
/// </summary>
/// <param name="preprocessOptions">Information how to preprocess the data.</param>
/// <param name="xOfX">The spectral x variable (e.g. frequencies, wavelength).</param>
/// <param name="matrixX">Matrix of preprocessed spectra (number of observations, number of wavelengths).</param>
/// <param name="matrixY">Matrix of preprocessed y values (number of observations, number of y).</param>
/// <param name="maxFactors">Maximum number of factors to calculate.</param>
public void AnalyzeFromRaw(
SpectralPreprocessingOptions preprocessOptions,
IROVector xOfX,
IMatrix matrixX,
IMatrix matrixY,
int maxFactors)
{
AnalyzeFromRaw(preprocessOptions,
SpectralPreprocessingOptions.IdentifyRegions(xOfX),
matrixX,
matrixY,
maxFactors);
}
/// <summary>
/// Copies all settings from another instance.
/// </summary>
/// <param name="from">The other instance to copy the data from.</param>
public void CopyFrom(SpectralPreprocessingOptions from)
{
this._method = from._method;
this._detrendingOrder = from._detrendingOrder;
this._ensembleScale = from._ensembleScale;
this._regions = (int[])from._regions.Clone();
}
public CrossPredictedYEvaluator(
int[] spectralRegions,
int numFactors,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
MultivariateRegression analysis,
IMatrix YCrossValidationPrediction
)
: base(spectralRegions,numFactors,groupingStrategy,preprocessOptions,analysis)
{
_YCrossValidationPrediction = YCrossValidationPrediction;
}
/// <summary>
/// Asks the user for the maximum number of factors and the cross validation calculation.
/// </summary>
/// <param name="options">The PLS options to ask for. On return, this is the user's choice.</param>
/// <param name="preprocessOptions">The spectral preprocessing options to ask for (output).</param>
/// <returns>True if the user has made his choice, false if the user pressed the Cancel button.</returns>
public static bool QuestPLSAnalysisOptions(out MultivariateAnalysisOptions options, out SpectralPreprocessingOptions preprocessOptions)
{
options = new MultivariateAnalysisOptions();
options.MaxNumberOfFactors =20;
options.CrossPRESSCalculation = CrossPRESSCalculationType.ExcludeGroupsOfSimilarMeasurements;
PLSStartAnalysisController ctrlAA = new PLSStartAnalysisController(options);
PLSStartAnalysisControl viewAA = new PLSStartAnalysisControl();
ctrlAA.View = viewAA;
preprocessOptions = new SpectralPreprocessingOptions();
SpectralPreprocessingController ctrlBB = new SpectralPreprocessingController(preprocessOptions);
SpectralPreprocessingControl viewBB = new SpectralPreprocessingControl();
ctrlBB.View = viewBB;
TabbedElementController tabController = new TabbedElementController();
tabController.AddTab("Factors",ctrlAA,viewAA);
tabController.AddTab("Preprocessing", ctrlBB, viewBB);
if (Current.Gui.ShowDialog(tabController, "Enter analysis parameters", false))
{
options = ctrlAA.Doc;
return true;
}
/*
TabbedDialogController dialogctrl = new TabbedDialogController("PLS Analysis",false);
dialogctrl.AddTab("Factors",ctrlAA,viewAA);
dialogctrl.AddTab("Preprocessing",ctrlBB,viewBB);
TabbedDialogView dialogview = new TabbedDialogView();
dialogctrl.View = dialogview;
if(dialogctrl.ShowDialog(Current.MainWindow))
{
options = ctrlAA.Doc;
return true;
}
*/
return false;
}
/// <summary>
/// This will convert the raw spectra (horizontally in matrixX) to preprocessed spectra according to the calibration model.
/// </summary>
/// <param name="preprocessOptions">Information how to preprocess the spectra.</param>
/// <param name="matrixX">Matrix of raw spectra. On return, this matrix contains the preprocessed spectra.</param>
/// <param name="meanX">Mean spectrum.</param>
/// <param name="scaleX">Scale spectrum.</param>
public static void PreprocessSpectraForPrediction(
SpectralPreprocessingOptions preprocessOptions,
IMatrix matrixX,
IROVector meanX,
IROVector scaleX)
{
preprocessOptions.ProcessForPrediction(matrixX,meanX,scaleX);
}
/// <summary>
/// This will convert the raw spectra (horizontally in matrixX) to preprocessed spectra according to the calibration model.
/// </summary>
/// <param name="calib">The calibration model containing the instructions to process the spectra.</param>
/// <param name="preprocessOptions">Contains the information how to preprocess the spectra.</param>
/// <param name="matrixX">The matrix of spectra. Each spectrum is a row of the matrix.</param>
public static void PreprocessSpectraForPrediction(
IMultivariateCalibrationModel calib,
SpectralPreprocessingOptions preprocessOptions,
IMatrix matrixX)
{
preprocessOptions.ProcessForPrediction(matrixX,calib.PreprocessingModel.XMean,calib.PreprocessingModel.XScale);
}
/// <summary>
/// This will process the spectra before analysis in multivariate calibration.
/// </summary>
/// <param name="preprocessOptions">Contains the information how to preprocess the spectra.</param>
/// <param name="xOfX"></param>
/// <param name="matrixX">The matrix of spectra. Each spectrum is a row of the matrix.</param>
/// <param name="meanX"></param>
/// <param name="scaleX"></param>
public static void PreprocessSpectraForAnalysis(
SpectralPreprocessingOptions preprocessOptions,
IROVector xOfX,
IMatrix matrixX,
out IVector meanX, out IVector scaleX
)
{
// Before we can apply PLS, we have to center the x and y matrices
meanX = new MatrixMath.HorizontalVector(matrixX.Columns);
scaleX = new MatrixMath.HorizontalVector(matrixX.Columns);
// MatrixMath.HorizontalVector scaleX = new MatrixMath.HorizontalVector(matrixX.Cols);
preprocessOptions.SetRegionsByIdentification(xOfX);
preprocessOptions.Process(matrixX,meanX,scaleX);
}
/// <summary>
/// Preprocesses the x and y matrices before usage in multivariate calibrations.
/// </summary>
/// <param name="preprocessOptions">Information how to preprocess the data.</param>
/// <param name="xOfX">Spectral wavelength values.</param>
/// <param name="matrixX">Matrix of spectra.</param>
/// <param name="matrixY">Matrix of concentrations.</param>
/// <returns>The collected data about proprocessing.</returns>
public static MultivariatePreprocessingModel PreprocessForAnalysis(
SpectralPreprocessingOptions preprocessOptions,
IROVector xOfX,
IMatrix matrixX,
IMatrix matrixY)
{
MultivariatePreprocessingModel result= PreprocessForAnalysis(
preprocessOptions,
SpectralPreprocessingOptions.IdentifyRegions(xOfX),
matrixX,
matrixY);
result.XOfX = xOfX;
return result;
}
/// <summary>
/// Preprocesses the x and y matrices before usage in multivariate calibrations.
/// </summary>
/// <param name="preprocessOptions">Information how to preprocess the data.</param>
/// <param name="spectralRegions">Array of ascending indices representing the starting indices of spectral regions.</param>
/// <param name="matrixX">Matrix of spectra.</param>
/// <param name="matrixY">Matrix of concentrations.</param>
/// <returns>The collected data about proprocessing.</returns>
public static MultivariatePreprocessingModel PreprocessForAnalysis(
SpectralPreprocessingOptions preprocessOptions,
int[] spectralRegions,
IMatrix matrixX,
IMatrix matrixY)
{
MultivariatePreprocessingModel data = new MultivariatePreprocessingModel();
data.PreprocessOptions = (SpectralPreprocessingOptions)preprocessOptions.Clone();
data.SpectralRegions = spectralRegions;
IVector meanX, scaleX;
PreprocessSpectraForAnalysis(preprocessOptions,spectralRegions,matrixX,out meanX, out scaleX);
data.XMean = meanX;
data.XScale = scaleX;
IVector meanY, scaleY;
PreprocessYForAnalysis(matrixY,out meanY, out scaleY);
data.YMean = meanY;
data.YScale = scaleY;
return data;
}
/// <summary>
/// Preprocesses the x and y matrices before usage in multivariate calibrations.
/// </summary>
/// <param name="preprocessOptions">Information how to preprocess the data.</param>
/// <param name="xOfX"></param>
/// <param name="matrixX"></param>
/// <param name="matrixY"></param>
/// <param name="meanX"></param>
/// <param name="scaleX"></param>
/// <param name="meanY"></param>
/// <param name="scaleY"></param>
public static void PreprocessForAnalysis(
SpectralPreprocessingOptions preprocessOptions,
IROVector xOfX,
IMatrix matrixX,
IMatrix matrixY,
out IVector meanX, out IVector scaleX,
out IVector meanY, out IVector scaleY)
{
PreprocessSpectraForAnalysis(preprocessOptions,xOfX,matrixX,out meanX, out scaleX);
PreprocessYForAnalysis(matrixY,out meanY, out scaleY);
}
/// <summary>
///
/// </summary>
/// <param name="mcalib"></param>
/// <param name="groupingStrategy"></param>
/// <param name="preprocessOptions"></param>
/// <param name="xOfX"></param>
/// <param name="matrixX">Matrix of horizontal spectra, centered and preprocessed.</param>
/// <param name="matrixY">Matrix of concentrations, centered.</param>
/// <param name="numberOfFactors"></param>
/// <param name="predictedY"></param>
/// <param name="spectralResiduals"></param>
public virtual void CalculateCrossPredictedY(
IMultivariateCalibrationModel mcalib,
ICrossValidationGroupingStrategy groupingStrategy,
SpectralPreprocessingOptions preprocessOptions,
IROVector xOfX,
IMatrix matrixX,
IMatrix matrixY,
int numberOfFactors,
IMatrix predictedY,
IMatrix spectralResiduals)
{
MultivariateRegression.GetCrossYPredicted(xOfX,
matrixX, matrixY, numberOfFactors, groupingStrategy, preprocessOptions,
this.CreateNewRegressionObject(),
predictedY);
}
/// <summary>
/// For a given set of spectra, predicts the y-values and stores them in the matrix <c>predictedY</c>
/// </summary>
/// <param name="mcalib">The calibration model of the analysis.</param>
/// <param name="preprocessOptions">The information how to preprocess the spectra.</param>
/// <param name="matrixX">The matrix of spectra to predict. Each spectrum is a row in the matrix.</param>
/// <param name="numberOfFactors">The number of factors used for prediction.</param>
/// <param name="predictedY">On return, this matrix holds the predicted y-values. Each row in this matrix corresponds to the same row (spectrum) in matrixX.</param>
/// <param name="spectralResiduals">If you set this parameter to a appropriate matrix, the spectral residuals will be stored in this matrix. Set this parameter to null if you don't need the residuals.</param>
public virtual void CalculatePredictedY(
IMultivariateCalibrationModel mcalib,
SpectralPreprocessingOptions preprocessOptions,
IMatrix matrixX,
int numberOfFactors,
MatrixMath.BEMatrix predictedY,
IMatrix spectralResiduals)
{
MultivariateRegression.PreprocessSpectraForPrediction(mcalib, preprocessOptions, matrixX);
MultivariateRegression regress = this.CreateNewRegressionObject();
regress.SetCalibrationModel(mcalib);
regress.PredictedYAndSpectralResidualsFromPreprocessed(matrixX, numberOfFactors, predictedY, spectralResiduals);
MultivariateRegression.PostprocessY(mcalib.PreprocessingModel, predictedY);
}
/// <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;
}
/// <summary>
/// Constructor. Supply a document to control here.
/// </summary>
/// <param name="doc">The instance of option to set-up.</param>
public SpectralPreprocessingController(SpectralPreprocessingOptions doc)
{
_doc = doc;
}
/// <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;
}
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 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>
/// Copy constructor.
/// </summary>
/// <param name="from"></param>
public SpectralPreprocessingOptions(SpectralPreprocessingOptions from)
{
CopyFrom(from);
}
/// <summary>
/// Asks the user for the maximum number of factors and the cross validation calculation.
/// </summary>
/// <param name="options">The PLS options to ask for. On return, this is the user's choice.</param>
/// <param name="preprocessOptions">The spectral preprocessing options to ask for (output).</param>
/// <returns>True if the user has made his choice, false if the user pressed the Cancel button.</returns>
public static bool QuestPLSAnalysisOptions(out MultivariateAnalysisOptions options, out SpectralPreprocessingOptions preprocessOptions)
{
options = new MultivariateAnalysisOptions();
options.MaxNumberOfFactors = 20;
options.CrossPRESSCalculation = CrossPRESSCalculationType.ExcludeGroupsOfSimilarMeasurements;
PLSStartAnalysisController ctrlAA = new PLSStartAnalysisController(options);
Current.Gui.FindAndAttachControlTo(ctrlAA);
preprocessOptions = new SpectralPreprocessingOptions();
SpectralPreprocessingController ctrlBB = new SpectralPreprocessingController(preprocessOptions);
Current.Gui.FindAndAttachControlTo(ctrlBB);
TabbedElementController tabController = new TabbedElementController();
tabController.AddTab("Factors", ctrlAA, ctrlAA.ViewObject);
tabController.AddTab("Preprocessing", ctrlBB, ctrlBB.ViewObject);
if (Current.Gui.ShowDialog(tabController, "Enter analysis parameters", false))
{
options = ctrlAA.Doc;
return true;
}
return false;
}
/// <summary>
/// Copy constructor.
/// </summary>
/// <param name="from"></param>
public SpectralPreprocessingOptions(SpectralPreprocessingOptions from)
{
CopyFrom(from);
}
/// <summary>
/// Calculates the spectral leverage from raw spectra.
/// </summary>
/// <param name="preprocessOptions"></param>
/// <param name="matrixX">Matrix of spectra (a spectrum = a row in the matrix).</param>
/// <param name="numFactors">Number of factors used for calculation.</param>
/// <returns>Matrix of spectral leverages. Normally, this is a (NumberOfPoints,1) matrix, with exception of PLS1, where it is a (NumberOfPoints,NumberOfY) matrix.</returns>
public virtual IROMatrix GetXLeverageFromRaw(SpectralPreprocessingOptions preprocessOptions, IMatrix matrixX, int numFactors)
{
MultivariateRegression.PreprocessSpectraForPrediction(InternalCalibrationModel,preprocessOptions,matrixX);
return GetXLeverageFromPreprocessed(matrixX,numFactors);
}
/// <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>
/// Creates an analyis from the raw spectra and raw concentrations.
/// </summary>
/// <param name="preprocessOptions">Information how to preprocess the data.</param>
/// <param name="spectralRegions">Array of ascending indices representing the starting indices of spectral regions.</param>
/// <param name="matrixX">Matrix of preprocessed spectra (number of observations, number of wavelengths).</param>
/// <param name="matrixY">Matrix of preprocessed y values (number of observations, number of y).</param>
/// <param name="maxFactors">Maximum number of factors to calculate.</param>
public void AnalyzeFromRaw(
SpectralPreprocessingOptions preprocessOptions,
int[] spectralRegions,
IMatrix matrixX,
IMatrix matrixY,
int maxFactors)
{
Reset();
InternalCalibrationModel.SetPreprocessingModel( PreprocessForAnalysis(preprocessOptions,spectralRegions,matrixX,matrixY) );
InternalCalibrationModel.NumberOfX = matrixX.Columns;
InternalCalibrationModel.NumberOfY = matrixY.Columns;
AnalyzeFromPreprocessedWithoutReset(matrixX, matrixY, maxFactors);
}
/// <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>
/// Copies all settings from another instance.
/// </summary>
/// <param name="from">The other instance to copy the data from.</param>
public void CopyFrom(SpectralPreprocessingOptions from)
{
if (object.ReferenceEquals(this, from))
return;
this._method = from._method;
this._detrendingOrder = from._detrendingOrder;
this._ensembleScale = from._ensembleScale;
this._regions = (int[])from._regions.Clone();
}
