public MultivariateLinearRegressionController(MultivariateLinearFitParameters param)
{
_param = param;
string[] names = new string[param.SelectedDataColumns.Count];
for(int i=0;i<names.Length;i++)
names[i] = param.Table[param.SelectedDataColumns[i]].Name;
_ctrl0 = new SingleChoiceController(names,0);
_ctrl1 = new BooleanValueController(_param.IncludeIntercept);
_ctrl2 = new BooleanValueController(_param.GenerateRegressionValues);
_ctrl3 = new BooleanValueController(_param.GenerateRegressionValues);
_ctrl0.DescriptionText = "Choose the dependent variable:";
_ctrl1.DescriptionText = "Include intercept";
_ctrl2.DescriptionText = "Generate prediction values";
_ctrl3.DescriptionText = "Generate residual values";
_elements[0] = new ControlViewElement(null,_ctrl0);
_elements[1] = new ControlViewElement(null, _ctrl1);
_elements[2] = new ControlViewElement(null, _ctrl2);
_elements[3] = new ControlViewElement(null, _ctrl3);
for(int i=0;i<_elements.Length;i++)
{
Current.Gui.FindAndAttachControlTo((IMVCController)_elements[i].Controller);
_elements[i].View = ((IMVCController)_elements[i].Controller).ViewObject;
}
base.Initialize(_elements,false);
}
public static LinearFitBySvd Regress(MultivariateLinearFitParameters parameters, out string[] paramNames)
{
DataColumnCollection table = parameters.Table;
IAscendingIntegerCollection selectedCols = parameters.SelectedDataColumns;
var selectedColsWODependent = new AscendingIntegerCollection(selectedCols);
selectedColsWODependent.RemoveAt(parameters.DependentColumnIndexIntoSelection);
IAscendingIntegerCollection validRows = DataTableWrapper.GetCollectionOfValidNumericRows(parameters.Table, selectedCols);
parameters.SelectedDataRows = validRows;
IROMatrix <double> xbase;
if (parameters.IncludeIntercept)
{
xbase = DataTableWrapper.ToROColumnMatrixWithIntercept(parameters.Table, selectedColsWODependent, validRows);
}
else
{
xbase = DataTableWrapper.ToROColumnMatrix(parameters.Table, selectedColsWODependent, validRows);
}
paramNames = new string[xbase.ColumnCount];
if (parameters.IncludeIntercept)
{
paramNames[0] = "Intercept";
for (int i = 0; i < selectedColsWODependent.Count; i++)
{
paramNames[i + 1] = table[selectedColsWODependent[i]].Name;
}
}
else
{
for (int i = 0; i < selectedColsWODependent.Count; i++)
{
paramNames[i] = table[selectedColsWODependent[i]].Name;
}
}
// Fill the y and the error array
double[] yarr = new double[validRows.Count];
double[] earr = new double[validRows.Count];
var ycol = (Altaxo.Data.INumericColumn)table[selectedCols[parameters.DependentColumnIndexIntoSelection]];
for (int i = 0; i < validRows.Count; i++)
{
yarr[i] = ycol[validRows[i]];
earr[i] = 1;
}
var fit =
new LinearFitBySvd(
xbase, yarr, earr, xbase.RowCount, xbase.ColumnCount, 1E-5);
return(fit);
}
public static LinearFitBySvd Regress(MultivariateLinearFitParameters parameters, bool outputResults)
{
LinearFitBySvd fit = Regress(parameters, out var paramNames);
if (outputResults)
{
OutputFitResults(fit, paramNames);
if (parameters.GenerateRegressionValues)
{
GenerateValues(parameters, fit);
}
}
return(fit);
}
public static void GenerateValues(MultivariateLinearFitParameters parameters, LinearFitBySvd fit)
{
DataColumn dependentColumn = parameters.Table[parameters.SelectedDataColumns[parameters.DependentColumnIndexIntoSelection]];
if (parameters.GenerateRegressionValues)
{
var col = new DoubleColumn();
VectorMath.Copy(VectorMath.ToROVector(fit.PredictedValues), DataColumnWrapper.ToVector(col, parameters.SelectedDataRows));
parameters.Table.Add(col, dependentColumn.Name + "(predicted)", ColumnKind.V, parameters.Table.GetColumnGroup(dependentColumn));
}
if (parameters.GenerateResidualValues)
{
var col = new DoubleColumn();
VectorMath.Copy(VectorMath.ToROVector(fit.ResidualValues), DataColumnWrapper.ToVector(col, parameters.SelectedDataRows));
parameters.Table.Add(col, dependentColumn.Name + "(residual)", ColumnKind.V, parameters.Table.GetColumnGroup(dependentColumn));
}
}
public static LinearFitBySvd ShowDialogAndRegress(DataColumnCollection table, IAscendingIntegerCollection selectedColumns)
{
if (selectedColumns.Count < 2)
{
return(null);
}
object paramobject = new MultivariateLinearFitParameters(table, selectedColumns);
if (!Current.Gui.ShowDialog(ref paramobject, "Multivariate linear fit"))
{
return(null);
}
var parameters = (MultivariateLinearFitParameters)paramobject;
LinearFitBySvd result = Regress(parameters, true);
return(result);
}
public static void GenerateValues(MultivariateLinearFitParameters parameters, LinearFitBySvd fit)
{
DataColumn dependentColumn = parameters.Table[parameters.SelectedDataColumns[parameters.DependentColumnIndexIntoSelection]];
if(parameters.GenerateRegressionValues)
{
DoubleColumn col = new DoubleColumn();
VectorMath.Copy(VectorMath.ToROVector(fit.PredictedValues),DataColumnWrapper.ToVector(col,parameters.SelectedDataRows));
parameters.Table.Add(col,dependentColumn.Name + "(predicted)",ColumnKind.V, parameters.Table.GetColumnGroup(dependentColumn));
}
if(parameters.GenerateResidualValues)
{
DoubleColumn col = new DoubleColumn();
VectorMath.Copy(VectorMath.ToROVector(fit.ResidualValues),DataColumnWrapper.ToVector(col,parameters.SelectedDataRows));
parameters.Table.Add(col,dependentColumn.Name + "(residual)",ColumnKind.V, parameters.Table.GetColumnGroup(dependentColumn));
}
}
public static LinearFitBySvd Regress(MultivariateLinearFitParameters parameters, bool outputResults)
{
string[] paramNames;
LinearFitBySvd fit = Regress(parameters, out paramNames);
if(outputResults)
{
OutputFitResults(fit,paramNames);
if(parameters.GenerateRegressionValues)
{
GenerateValues(parameters,fit);
}
}
return fit;
}
public static LinearFitBySvd Regress(MultivariateLinearFitParameters parameters, out string[] paramNames)
{
DataColumnCollection table = parameters.Table;
IAscendingIntegerCollection selectedCols = parameters.SelectedDataColumns;
AscendingIntegerCollection selectedColsWODependent = new AscendingIntegerCollection(selectedCols);
selectedColsWODependent.RemoveAt(parameters.DependentColumnIndexIntoSelection);
IAscendingIntegerCollection validRows = DataTableWrapper.GetCollectionOfValidNumericRows(parameters.Table,selectedCols);
parameters.SelectedDataRows = validRows;
IROMatrix xbase;
if(parameters.IncludeIntercept)
{
xbase = DataTableWrapper.ToROColumnMatrixWithIntercept(parameters.Table,selectedColsWODependent,validRows);
}
else
{
xbase = DataTableWrapper.ToROColumnMatrix(parameters.Table,selectedColsWODependent,validRows);
}
paramNames = new string[xbase.Columns];
if(parameters.IncludeIntercept)
{
paramNames[0] = "Intercept";
for(int i=0;i<selectedColsWODependent.Count;i++)
paramNames[i+1]=table[selectedColsWODependent[i]].Name;
}
else
{
for(int i=0;i<selectedColsWODependent.Count;i++)
paramNames[i]=table[selectedColsWODependent[i]].Name;
}
// Fill the y and the error array
double[] yarr = new double[validRows.Count];
double[] earr = new double[validRows.Count];
Altaxo.Data.INumericColumn ycol = (Altaxo.Data.INumericColumn)table[selectedCols[parameters.DependentColumnIndexIntoSelection]];
for(int i=0;i<validRows.Count;i++)
{
yarr[i] = ycol[validRows[i]];
earr[i] = 1;
}
LinearFitBySvd fit =
new LinearFitBySvd(
xbase,yarr,earr, xbase.Rows, xbase.Columns, 1E-5);
return fit;
}
public static LinearFitBySvd ShowDialogAndRegress(DataColumnCollection table, IAscendingIntegerCollection selectedColumns)
{
if(selectedColumns.Count<2)
return null;
object paramobject = new MultivariateLinearFitParameters(table,selectedColumns);
if(!Current.Gui.ShowDialog(ref paramobject,"Multivariate linear fit"))
return null;
MultivariateLinearFitParameters parameters = (MultivariateLinearFitParameters)paramobject;
LinearFitBySvd result = Regress(parameters, true);
return result;
}