private (List <string> allVaryingParameterNames, int[] indicesOfThisFitElementsVaryingParametersInAllVaryingParameters) CreateCachedMembers()
{
var fitElement = _fitDocument.FitEnsemble[_fitElementIndex];
_cachedFitFunction = fitElement.FitFunction;
_cachedParameters = _fitDocument.GetParametersForFitElement(_fitElementIndex);
_cachedParametersForJacobianEvaluation = _fitDocument.GetParametersForFitElement(_fitElementIndex);
_cachedJacobian = new double[_cachedParameters.Length];
_functionValues = new double[_cachedFitFunction.NumberOfDependentVariables];
// CovarianceMatrix: we have to pick exactly the varying parameters of this fitelement!
// next line retrieves all varying parameters of all fitelements, this corresponds to the rows of the provided covariance matrix
var allVaryingParameterNames = new List <string>(_fitDocument.CurrentParameters.Where(x => x.Vary).Select(x => x.Name));
// Indices of the varying parameters of this fit element (with respect to the parameter set of this fitelement)
_cachedIndicesOfVaryingParametersOfThisFitElement = Enumerable.Range(0, fitElement.NumberOfParameters).Where(i => allVaryingParameterNames.IndexOf(fitElement.ParameterName(i)) >= 0).ToArray();
// Indices in allVaryingParameters of the parameters in this fitlement, which are varying
var indicesOfThisFitElementsVaryingParametersInAllVaryingParameters = Enumerable.Range(0, _cachedIndicesOfVaryingParametersOfThisFitElement.Length).Select(i => allVaryingParameterNames.IndexOf(fitElement.ParameterName(_cachedIndicesOfVaryingParametersOfThisFitElement[i]))).ToArray();
// now we are able to pick the values out of the covariance matrix
if (!(_numberOfFitPoints > _cachedIndicesOfVaryingParametersOfThisFitElement.Length))
{
_cachedQuantileOfStudentsDistribution = double.NaN; // 0 degrees of freedom
}
else
{
// for a given confidence interval, e.g. 0.95, we need to make 0.975 out of it
_cachedQuantileOfStudentsDistribution = Altaxo.Calc.Probability.StudentsTDistribution.Quantile(1 - (0.5 * (1 - _confidenceLevel)), _numberOfFitPoints - _cachedIndicesOfVaryingParametersOfThisFitElement.Length);
}
// for convenience, we let this covariance matrix to be of dimension NumberOfParameters x NumberOfParameters, but we
// don't set the elements corresponding to the non-varying parameters, thus they remain zero
_covarianceMatrix = new double[fitElement.NumberOfParameters, fitElement.NumberOfParameters];
return(allVaryingParameterNames, indicesOfThisFitElementsVaryingParametersInAllVaryingParameters);
}
public void OnAfterFittingStep()
{
if (_view != null)
{
_view.SetChiSquare(this._chiSquare);
}
if (_doc.FitContext is Altaxo.Graph.GUI.GraphController)
{
// for every dependent variable in the FitEnsemble, create a function graph
Altaxo.Graph.GUI.GraphController graph = _doc.FitContext as Altaxo.Graph.GUI.GraphController;
int funcNumber = 0;
for (int i = 0; i < _doc.FitEnsemble.Count; i++)
{
FitElement fitEle = _doc.FitEnsemble[i];
for (int k = 0; k < fitEle.NumberOfDependentVariables; k++, funcNumber++)
{
if (funcNumber < _functionPlotItems.Count && _functionPlotItems[funcNumber] != null)
{
XYFunctionPlotItem plotItem = (XYFunctionPlotItem)_functionPlotItems[funcNumber];
FitFunctionToScalarFunctionDDWrapper wrapper = (FitFunctionToScalarFunctionDDWrapper)plotItem.Data.Function;
wrapper.Initialize(fitEle.FitFunction, k, 0, _doc.GetParametersForFitElement(i));
}
else
{
FitFunctionToScalarFunctionDDWrapper wrapper = new FitFunctionToScalarFunctionDDWrapper(fitEle.FitFunction, k, _doc.GetParametersForFitElement(i));
XYFunctionPlotData plotdata = new XYFunctionPlotData(wrapper);
XYFunctionPlotItem plotItem = new XYFunctionPlotItem(plotdata, new G2DPlotStyleCollection(LineScatterPlotStyleKind.Line));
graph.ActiveLayer.PlotItems.Add(plotItem);
_functionPlotItems.Add(plotItem);
}
}
}
// if there are more elements in _functionPlotItems, remove them from the graph
for (int i = _functionPlotItems.Count - 1; i >= funcNumber; --i)
{
if (_functionPlotItems[i] != null)
{
graph.ActiveLayer.PlotItems.Remove((IGPlotItem)_functionPlotItems[i]);
_functionPlotItems.RemoveAt(i);
}
}
graph.RefreshGraph();
}
}
/// <summary>
/// Initializes a new instance of the <see cref="XYNonlinearFitFunctionPlotData"/> class.
/// </summary>
/// <param name="fitDocumentIdentifier">The fit document identifier.</param>
/// <param name="fitDocument">The fit document. The document will be cloned before stored in this instance.</param>
/// <param name="fitElementIndex">Index of the fit element.</param>
/// <param name="dependentVariableIndex">Index of the dependent variable of the fit element.</param>
/// <param name="dependentVariableTransformation">Transformation, which is applied to the result of the fit function to be then shown in the plot. Can be null.</param>
/// <param name="independentVariableIndex">Index of the independent variable of the fit element.</param>
/// <param name="independentVariableTransformation">Transformation, which is applied to the x value before it is applied to the fit function. Can be null.</param>
public XYNonlinearFitFunctionPlotData(string fitDocumentIdentifier, NonlinearFitDocument fitDocument, int fitElementIndex, int dependentVariableIndex, IVariantToVariantTransformation dependentVariableTransformation, int independentVariableIndex, IVariantToVariantTransformation independentVariableTransformation)
{
if (null == fitDocumentIdentifier)
{
throw new ArgumentNullException(nameof(fitDocumentIdentifier));
}
if (null == fitDocument)
{
throw new ArgumentNullException(nameof(fitDocument));
}
ChildCloneToMember(ref _fitDocument, fitDocument); // clone here, because we want to have a local copy which can not change.
_fitDocumentIdentifier = fitDocumentIdentifier;
_fitElementIndex = fitElementIndex;
_dependentVariableIndex = dependentVariableIndex;
Function = new FitFunctionToScalarFunctionDDWrapper(_fitDocument.FitEnsemble[fitElementIndex].FitFunction, dependentVariableIndex, dependentVariableTransformation, independentVariableIndex, independentVariableTransformation, _fitDocument.GetParametersForFitElement(fitElementIndex));
}
