protected override System.Collections.Generic.IEnumerable <Main.DocumentNodeAndName> GetDocumentNodeChildrenWithName()
{
if (null != _fitDocument)
{
yield return(new Main.DocumentNodeAndName(_fitDocument, () => _fitDocument = null, "FitDocument"));
}
}
public static string ShowFitDialog(Altaxo.Gui.Graph.Gdi.Viewing.IGraphController ctrl)
{
var tuple = SelectFitDocument(ctrl);
if (!string.IsNullOrEmpty(tuple.Item1))
{
return(tuple.Item1);
}
var fitDocument = tuple.Item2;
var fitDocumentIdentifier = tuple.Item3;
var activeLayer = tuple.Item4;
// we assume we have a fit document by now
if (null == tuple.Item2)
{
throw new InvalidProgramException("At this place, fit document should always be != null");
}
if (!string.IsNullOrEmpty(fitDocumentIdentifier))
{
var answer = Current.Gui.YesNoCancelMessageBox(
"At least one fit function plot item was found in the document from which the fit document could be retrieved.\r\n" +
"When changing the fit or the parameters, these fit function plot items would be changed, too.\r\n" +
"Sometimes, you might want to keep the previous fit function plot items, e.g. in order to compare them with the new ones.\r\n" +
"\r\n" +
"Do you want to keep the previous fit function plot item(s) ?",
"Keep previous fit function plot items?", false);
if (null == answer)
{
return(null);
}
if (true == answer)
{
fitDocumentIdentifier = null; // by setting the identifier to null, we will keep the old fit functions
}
}
var fitController = (Gui.IMVCANController)Current.Gui.GetControllerAndControl(new object[] { fitDocument, fitDocumentIdentifier, activeLayer }, typeof(Gui.IMVCANController));
// before showing the fit dialog, deselect all objects selected
if (!ctrl.SelectedObjects.IsReadOnly) // with some graph tools, this is a read-only collection, which can not be cleared
{
ctrl.SelectedObjects.Clear();
}
if (true == Current.Gui.ShowDialog(fitController, "Non-linear fitting"))
{
var localdoc = fitController.ModelObject as NonlinearFitDocument;
// store the fit document in the graphs property
ctrl.Doc.SetGraphProperty(FitDocumentPropertyName, localdoc);
_lastFitDocument = (Altaxo.Calc.Regression.Nonlinear.NonlinearFitDocument)localdoc.Clone();
}
return(null);
}
public NonlinearFitController(NonlinearFitDocument doc)
{
_doc = doc;
_parameterController = (IMVCAController)Current.Gui.GetControllerAndControl(new object[] { _doc.CurrentParameters }, typeof(IMVCAController));
_fitEnsembleController = (IFitEnsembleController)Current.Gui.GetControllerAndControl(new object[] { _doc.FitEnsemble }, typeof(IFitEnsembleController));
_funcselController = new FitFunctionSelectionController(_doc.FitEnsemble.Count == 0 ? null : _doc.FitEnsemble[0].FitFunction);
Current.Gui.FindAndAttachControlTo(_funcselController);
_doc.FitEnsemble.Changed += new EventHandler(EhFitEnsemble_Changed);
}
/// <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));
}
/// <summary>
/// Initializes a new instance of the <see cref="XYNonlinearFitFunctionPlotItem"/> 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="ps">The ps.</param>
public XYNonlinearFitFunctionPlotItem(string fitDocumentIdentifier, NonlinearFitDocument fitDocument, int fitElementIndex, int dependentVariableIndex, IVariantToVariantTransformation dependentVariableTransformation, int independentVariableIndex, IVariantToVariantTransformation independentVariableTransformation, G2DPlotStyleCollection ps)
: base()
{
if (null == fitDocumentIdentifier)
{
throw new ArgumentNullException(nameof(fitDocumentIdentifier));
}
if (null == fitDocument)
{
throw new ArgumentNullException(nameof(fitDocument));
}
if (null == ps)
{
throw new ArgumentNullException(nameof(ps));
}
ChildSetMember(ref _plotData, new XYNonlinearFitFunctionPlotData(fitDocumentIdentifier, fitDocument, fitElementIndex, dependentVariableIndex, dependentVariableTransformation, independentVariableIndex, independentVariableTransformation));
Style = ps;
}
/// <summary>
/// Initializes a new instance of the <see cref="XYNonlinearFitFunctionConfidenceBandPlotData"/> class.
/// </summary>
/// <param name="isPredictionBand">If true, the prediction band is displayed instead of the confidence band. The prediction band is a little wider, because the sigma of the data points give an additional contribution.</param>
/// <param name="isLowerBand">True if this data present the lower confidence (or prediction) band; true if the data represent the upper confidence (or prediction) band.</param>
/// <param name="confidenceLevel">A number greater than 0 and less than 1 representing the confidence level. Usual values are e.g. 0.95, 0.99, 0.999.</param>
/// <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>
/// <param name="numberOfFittedPoints">Number of points that were used for fitting. Needed to calculate the Student's distribution quantile.</param>
/// <param name="sigmaSquare">Mean square difference between data points and fitting curve = sumChiSquare/(n-r).</param>
/// <param name="covarianceMatrixTimesSigmaSquare">A matrix, representing sigma²(A*At)^-1, which are the covariances of the parameter.</param>
public XYNonlinearFitFunctionConfidenceBandPlotData(
bool isPredictionBand,
bool isLowerBand,
double confidenceLevel,
string fitDocumentIdentifier,
NonlinearFitDocument fitDocument,
int fitElementIndex,
int dependentVariableIndex,
IVariantToVariantTransformation dependentVariableTransformation,
int independentVariableIndex,
IVariantToVariantTransformation independentVariableTransformation,
int numberOfFittedPoints,
double sigmaSquare,
double[] covarianceMatrixTimesSigmaSquare)
{
if (null == fitDocumentIdentifier)
{
throw new ArgumentNullException(nameof(fitDocumentIdentifier));
}
if (null == fitDocument)
{
throw new ArgumentNullException(nameof(fitDocument));
}
if (!(confidenceLevel > 0 && confidenceLevel < 1))
{
throw new ArgumentOutOfRangeException("Confidence level must be > 0 and < 1", nameof(confidenceLevel));
}
if (!(sigmaSquare >= 0))
{
throw new ArgumentOutOfRangeException("SigmaSquare must be >=0", nameof(sigmaSquare));
}
IsLowerBand = isLowerBand;
_confidenceLevel = confidenceLevel;
_sigmaSquare = sigmaSquare;
ChildCloneToMember(ref _fitDocument, fitDocument); // clone here, because we want to have a local copy which can not change.
_fitDocumentIdentifier = fitDocumentIdentifier;
_fitElementIndex = fitElementIndex;
_dependentVariableIndex = dependentVariableIndex;
_dependentVariableTransformation = dependentVariableTransformation;
_independentVariableTransformation = independentVariableTransformation;
_numberOfFitPoints = numberOfFittedPoints;
var(allVaryingParameterNames, indicesOfThisFitElementsVaryingParametersInAllVaryingParameters) = CreateCachedMembers();
// the covariance matrix should have the dimensions of allVaryingParameters.Count x allVaryingParameters.Count
if (!(covarianceMatrixTimesSigmaSquare.Length == allVaryingParameterNames.Count * allVaryingParameterNames.Count))
{
throw new InvalidProgramException("Covariance matrix dimension does not match with number of varying parameters");
}
for (int i = 0; i < _cachedIndicesOfVaryingParametersOfThisFitElement.Length; ++i)
{
int iRowOriginalCovMat = indicesOfThisFitElementsVaryingParametersInAllVaryingParameters[i];
int iRowThisCovMat = _cachedIndicesOfVaryingParametersOfThisFitElement[i];
for (int j = 0; j < _cachedIndicesOfVaryingParametersOfThisFitElement.Length; ++j)
{
int jColOriginalCovMat = indicesOfThisFitElementsVaryingParametersInAllVaryingParameters[j];
int jColThisCovMat = _cachedIndicesOfVaryingParametersOfThisFitElement[j];
_covarianceMatrix[iRowThisCovMat, jColThisCovMat] =
covarianceMatrixTimesSigmaSquare[iRowOriginalCovMat * allVaryingParameterNames.Count + jColOriginalCovMat]; // TODO is covariance matrix column major or row major
}
}
}
