public override void SetCalibrationModel(IMultivariateCalibrationModel calib)
{
if(calib is PLS2CalibrationModel)
_calib = (PLS2CalibrationModel) calib;
else
throw new ArgumentException("Expecting argument of type PLS2CalibrationModel, but actual type is " + calib.GetType().ToString());
}
public override void Import(
IMultivariateCalibrationModel calibrationSet,
DataTable table)
{
PLS1CalibrationModel calib = (PLS1CalibrationModel)calibrationSet;
for (int yn = 0; yn < calib.NumberOfY; yn++)
{
// store the x-loads - careful - they are horizontal in the matrix
for (int i = 0; i < calib.XLoads[yn].Rows; i++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < calib.XLoads[yn].Columns; j++)
{
col[j] = calib.XLoads[yn][i, j];
}
table.DataColumns.Add(col, GetXLoad_ColumnName(yn, i), Altaxo.Data.ColumnKind.V, 0);
}
// now store the y-loads - careful - they are horizontal in the matrix
for (int i = 0; i < calib.YLoads[yn].Rows; i++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < calib.YLoads[yn].Columns; j++)
{
col[j] = calib.YLoads[yn][i, j];
}
table.DataColumns.Add(col, GetYLoad_ColumnName(yn, i), Altaxo.Data.ColumnKind.V, 1);
}
// now store the weights - careful - they are horizontal in the matrix
for (int i = 0; i < calib.XWeights[yn].Rows; i++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < calib.XWeights[yn].Columns; j++)
{
col[j] = calib.XWeights[yn][i, j];
}
table.DataColumns.Add(col, GetXWeight_ColumnName(yn, i), Altaxo.Data.ColumnKind.V, 0);
}
// now store the cross product vector - it is a horizontal vector
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < calib.CrossProduct[yn].Columns; j++)
{
col[j] = calib.CrossProduct[yn][0, j];
}
table.DataColumns.Add(col, GetCrossProduct_ColumnName(yn), Altaxo.Data.ColumnKind.V, 3);
}
} // for all y (constituents)
}
public override void Import(
IMultivariateCalibrationModel calibrationSet,
DataTable table)
{
var calib = (PCRCalibrationModel)calibrationSet;
int numFactors = calib.NumberOfFactors;
int numberOfY = calib.NumberOfY;
int numberOfPoints = calib.XLoads.RowCount;
// store the x-loads - careful - they are horizontal
for (int i = 0; i < numFactors; i++)
{
var col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < calib.XLoads.ColumnCount; j++)
{
col[j] = calib.XLoads[i, j];
}
table.DataColumns.Add(col, GetXLoad_ColumnName(i), Altaxo.Data.ColumnKind.V, 0);
}
// now store the scores - careful - they are vertical in the matrix
for (int i = 0; i < numFactors; i++)
{
var col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < calib.XScores.RowCount; j++)
{
col[j] = calib.XScores[j, i];
}
table.DataColumns.Add(col, GetXScore_ColumnName(i), Altaxo.Data.ColumnKind.V, 0);
}
// now store the y-loads (this are the preprocessed y in this case
for (int cn = 0; cn < numberOfY; cn++)
{
var col = new Altaxo.Data.DoubleColumn();
for (int i = 0; i < numberOfPoints; i++)
{
col[i] = calib.YLoads[i, cn];
}
table.DataColumns.Add(col, GetYLoad_ColumnName(cn), Altaxo.Data.ColumnKind.V, 0);
}
// now store the cross product vector - it is a horizontal vector
{
var col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < numFactors; j++)
{
col[j] = calib.CrossProduct[j];
}
table.DataColumns.Add(col, GetCrossProduct_ColumnName(), Altaxo.Data.ColumnKind.V, 3);
}
}
public override void Import(
IMultivariateCalibrationModel calibrationSet,
DataTable table)
{
PCRCalibrationModel calib = (PCRCalibrationModel)calibrationSet;
int numFactors = calib.NumberOfFactors;
int numberOfY = calib.NumberOfY;
int numberOfPoints = calib.XLoads.Rows;
// store the x-loads - careful - they are horizontal
for(int i=0;i<numFactors;i++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for(int j=0;j<calib.XLoads.Columns;j++)
col[j] = calib.XLoads[i,j];
table.DataColumns.Add(col,GetXLoad_ColumnName(i),Altaxo.Data.ColumnKind.V,0);
}
// now store the scores - careful - they are vertical in the matrix
for(int i=0;i<numFactors;i++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for(int j=0;j<calib.XScores.Rows;j++)
col[j] = calib.XScores[j,i];
table.DataColumns.Add(col,GetXScore_ColumnName(i),Altaxo.Data.ColumnKind.V,0);
}
// now store the y-loads (this are the preprocessed y in this case
for(int cn=0;cn<numberOfY;cn++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for(int i=0;i<numberOfPoints;i++)
col[i] = calib.YLoads[i,cn];
table.DataColumns.Add(col,GetYLoad_ColumnName(cn),Altaxo.Data.ColumnKind.V,0);
}
// now store the cross product vector - it is a horizontal vector
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for(int j=0;j<numFactors;j++)
col[j] = calib.CrossProduct[j];
table.DataColumns.Add(col,GetCrossProduct_ColumnName(),Altaxo.Data.ColumnKind.V,3);
}
}
public override void Import(
IMultivariateCalibrationModel calibrationSet,
DataTable table)
{
var calib = (PLS2CalibrationModel)calibrationSet;
// store the x-loads - careful - they are horizontal in the matrix
for (int i = 0; i < calib.XLoads.RowCount; i++)
{
var col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < calib.XLoads.ColumnCount; j++)
{
col[j] = calib.XLoads[i, j];
}
table.DataColumns.Add(col, GetXLoad_ColumnName(i), Altaxo.Data.ColumnKind.V, 0);
}
// now store the y-loads - careful - they are horizontal in the matrix
for (int i = 0; i < calib.YLoads.RowCount; i++)
{
var col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < calib.YLoads.ColumnCount; j++)
{
col[j] = calib.YLoads[i, j];
}
table.DataColumns.Add(col, GetYLoad_ColumnName(i), Altaxo.Data.ColumnKind.V, 1);
}
// now store the weights - careful - they are horizontal in the matrix
for (int i = 0; i < calib.XWeights.RowCount; i++)
{
var col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < calib.XWeights.ColumnCount; j++)
{
col[j] = calib.XWeights[i, j];
}
table.DataColumns.Add(col, GetXWeight_ColumnName(i), Altaxo.Data.ColumnKind.V, 0);
}
// now store the cross product vector - it is a horizontal vector
{
var col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < calib.CrossProduct.ColumnCount; j++)
{
col[j] = calib.CrossProduct[0, j];
}
table.DataColumns.Add(col, GetCrossProduct_ColumnName(), Altaxo.Data.ColumnKind.V, 3);
}
}
public override void Import(
IMultivariateCalibrationModel calibrationSet,
DataTable table)
{
PLS1CalibrationModel calib = (PLS1CalibrationModel)calibrationSet;
for(int yn=0;yn<calib.NumberOfY;yn++)
{
// store the x-loads - careful - they are horizontal in the matrix
for(int i=0;i<calib.XLoads[yn].Rows;i++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for(int j=0;j<calib.XLoads[yn].Columns;j++)
col[j] = calib.XLoads[yn][i,j];
table.DataColumns.Add(col,GetXLoad_ColumnName(yn,i),Altaxo.Data.ColumnKind.V,0);
}
// now store the y-loads - careful - they are horizontal in the matrix
for(int i=0;i<calib.YLoads[yn].Rows;i++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for(int j=0;j<calib.YLoads[yn].Columns;j++)
col[j] = calib.YLoads[yn][i,j];
table.DataColumns.Add(col,GetYLoad_ColumnName(yn,i),Altaxo.Data.ColumnKind.V,1);
}
// now store the weights - careful - they are horizontal in the matrix
for(int i=0;i<calib.XWeights[yn].Rows;i++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for(int j=0;j<calib.XWeights[yn].Columns;j++)
col[j] = calib.XWeights[yn][i,j];
table.DataColumns.Add(col,GetXWeight_ColumnName(yn,i),Altaxo.Data.ColumnKind.V,0);
}
// now store the cross product vector - it is a horizontal vector
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for(int j=0;j<calib.CrossProduct[yn].Columns;j++)
col[j] = calib.CrossProduct[yn][0,j];
table.DataColumns.Add(col,GetCrossProduct_ColumnName(yn),Altaxo.Data.ColumnKind.V,3);
}
} // for all y (constituents)
}
public override void SetCalibrationModel(IMultivariateCalibrationModel calib)
{
if (calib is PLS2CalibrationModel)
{
_calib = (PLS2CalibrationModel)calib;
}
else
{
throw new ArgumentException("Expecting argument of type PLS2CalibrationModel, but actual type is " + calib.GetType().ToString());
}
}
/// <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>
///
/// </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> /// Stores the calibrationSet into the data table table. /// </summary> /// <param name="calibrationSet">The data source.</param> /// <param name="table">The table where to store the data of the calibrationSet.</param> public abstract void Import(IMultivariateCalibrationModel calibrationSet, DataTable table);
/// <summary> /// This sets the calibration model data of the analysis to the provided data. This can be used to set back previously stored /// calibration data for use in the prediction functions. /// </summary> /// <param name="calib">The calibration set for use in the analysis. The provided calibration model have to correspond to /// the type of analysis.</param> public abstract void SetCalibrationModel(IMultivariateCalibrationModel calib);
/// <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);
}