public XYZMeshedColumnPlotData(Altaxo.Data.DataColumnCollection coll, IAscendingIntegerCollection selected)
{
m_XColumn = new Altaxo.Data.IndexerColumn();
m_YColumn = new Altaxo.Data.IndexerColumn();
int len = selected == null ? coll.ColumnCount : selected.Count;
m_DataColumns = new Altaxo.Data.IReadableColumn[len];
for (int i = 0; i < len; i++)
{
int idx = null == selected ? i : selected[i];
m_DataColumns[i] = coll[idx];
// set the event chain
if (m_DataColumns[i] is Altaxo.Data.DataColumn)
{
((Altaxo.Data.DataColumn)m_DataColumns[i]).Changed += new EventHandler(EhColumnDataChangedEventHandler);
}
}
this.SetXBoundsFromTemplate(new FiniteNumericalBoundaries());
this.SetYBoundsFromTemplate(new FiniteNumericalBoundaries());
this.SetVBoundsFromTemplate(new FiniteNumericalBoundaries());
}
/// <summary>
/// Copy constructor.
/// </summary>
/// <param name="from">The object to copy from.</param>
/// <remarks>Only clones the references to the data columns, not the columns itself.</remarks>
public XYZMeshedColumnPlotData(XYZMeshedColumnPlotData from)
{
if (from.m_XColumn is Altaxo.Data.DataColumn && ((Altaxo.Data.DataColumn)from.m_XColumn).ParentObject != null)
{
m_XColumn = from.m_XColumn;
}
else
{
m_XColumn = (Altaxo.Data.INumericColumn)from.m_XColumn.Clone();
}
if (from.m_YColumn is Altaxo.Data.DataColumn && ((Altaxo.Data.DataColumn)from.m_YColumn).ParentObject != null)
{
m_YColumn = from.m_YColumn;
}
else
{
m_YColumn = (Altaxo.Data.INumericColumn)from.m_YColumn.Clone();
}
int len = from.m_DataColumns.Length;
m_DataColumns = new Altaxo.Data.IReadableColumn[len];
for (int i = 0; i < len; i++)
{
m_DataColumns[i] = from.m_DataColumns[i]; // do not clone the data columns!
// set the event chain
if (m_DataColumns[i] is Altaxo.Data.DataColumn)
{
((Altaxo.Data.DataColumn)m_DataColumns[i]).Changed += new EventHandler(EhColumnDataChangedEventHandler);
}
}
this.SetXBoundsFromTemplate(new FiniteNumericalBoundaries());
this.SetYBoundsFromTemplate(new FiniteNumericalBoundaries());
this.SetVBoundsFromTemplate(new FiniteNumericalBoundaries());
}
/// <summary>
/// Multiplies selected columns to form a matrix.
/// </summary>
/// <param name="mainDocument"></param>
/// <param name="srctable"></param>
/// <param name="selectedColumns"></param>
/// <returns>Null if successful, else the description of the error.</returns>
/// <remarks>The user must select an even number of columns. All columns of the first half of the selection
/// must have the same number of rows, and all columns of the second half of selection must also have the same
/// number of rows. The first half of selected columns form a matrix of dimensions(firstrowcount,halfselected), and the second half
/// of selected columns form a matrix of dimension(halfselected, secondrowcount). The resulting matrix has dimensions (firstrowcount,secondrowcount) and is
/// stored in a separate worksheet.</remarks>
public static string MultiplyColumnsToMatrix(
Altaxo.AltaxoDocument mainDocument,
Altaxo.Data.DataTable srctable,
IAscendingIntegerCollection selectedColumns
)
{
// check that there are columns selected
if (0 == selectedColumns.Count)
{
return("You must select at least two columns to multiply!");
}
// selected columns must contain an even number of columns
if (0 != selectedColumns.Count % 2)
{
return("You selected an odd number of columns. Please select an even number of columns to multiply!");
}
// all selected columns must be numeric columns
for (int i = 0; i < selectedColumns.Count; i++)
{
if (!(srctable[selectedColumns[i]] is Altaxo.Data.INumericColumn))
{
return(string.Format("The column[{0}] (name:{1}) is not a numeric column!", selectedColumns[i], srctable[selectedColumns[i]].Name));
}
}
int halfselect = selectedColumns.Count / 2;
// check that all columns from the first half of selected colums contain the same
// number of rows
int rowsfirsthalf = int.MinValue;
for (int i = 0; i < halfselect; i++)
{
int idx = selectedColumns[i];
if (rowsfirsthalf < 0)
{
rowsfirsthalf = srctable[idx].Count;
}
else if (rowsfirsthalf != srctable[idx].Count)
{
return("The first half of selected columns have not all the same length!");
}
}
int rowssecondhalf = int.MinValue;
for (int i = halfselect; i < selectedColumns.Count; i++)
{
int idx = selectedColumns[i];
if (rowssecondhalf < 0)
{
rowssecondhalf = srctable[idx].Count;
}
else if (rowssecondhalf != srctable[idx].Count)
{
return("The second half of selected columns have not all the same length!");
}
}
// now create the matrices to multiply from the
MatrixMath.REMatrix firstMat = new MatrixMath.REMatrix(rowsfirsthalf, halfselect);
for (int i = 0; i < halfselect; i++)
{
Altaxo.Data.INumericColumn col = (Altaxo.Data.INumericColumn)srctable[selectedColumns[i]];
for (int j = 0; j < rowsfirsthalf; j++)
{
firstMat[j, i] = col[j];
}
}
MatrixMath.BEMatrix secondMat = new MatrixMath.BEMatrix(halfselect, rowssecondhalf);
for (int i = 0; i < halfselect; i++)
{
Altaxo.Data.INumericColumn col = (Altaxo.Data.INumericColumn)srctable[selectedColumns[i + halfselect]];
for (int j = 0; j < rowssecondhalf; j++)
{
secondMat[i, j] = col[j];
}
}
// now multiply the two matrices
MatrixMath.BEMatrix resultMat = new MatrixMath.BEMatrix(rowsfirsthalf, rowssecondhalf);
MatrixMath.Multiply(firstMat, secondMat, resultMat);
// and store the result in a new worksheet
Altaxo.Data.DataTable table = new Altaxo.Data.DataTable("ResultMatrix of " + srctable.Name);
table.Suspend();
// first store the factors
for (int i = 0; i < resultMat.Columns; i++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < resultMat.Rows; j++)
{
col[j] = resultMat[j, i];
}
table.DataColumns.Add(col, i.ToString());
}
table.Resume();
mainDocument.DataTableCollection.Add(table);
// create a new worksheet without any columns
Current.ProjectService.CreateNewWorksheet(table);
return(null);
}
/// <summary>
/// Makes a PCA (a principal component 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="bHorizontalOrientedSpectrum">True if a spectrum is a single row, False if a spectrum is a single column.</param>
/// <param name="maxNumberOfFactors">The maximum number of factors to calculate.</param>
/// <returns></returns>
public static string PrincipalComponentAnalysis(
Altaxo.AltaxoDocument mainDocument,
Altaxo.Data.DataTable srctable,
IAscendingIntegerCollection selectedColumns,
IAscendingIntegerCollection selectedRows,
bool bHorizontalOrientedSpectrum,
int maxNumberOfFactors
)
{
bool bUseSelectedColumns = (null != selectedColumns && 0 != selectedColumns.Count);
int prenumcols = bUseSelectedColumns ? selectedColumns.Count : srctable.DataColumns.ColumnCount;
// check for the number of numeric columns
int numcols = 0;
for (int i = 0; i < prenumcols; i++)
{
int idx = bUseSelectedColumns ? selectedColumns[i] : i;
if (srctable[i] is Altaxo.Data.INumericColumn)
{
numcols++;
}
}
// check the number of rows
bool bUseSelectedRows = (null != selectedRows && 0 != selectedRows.Count);
int numrows;
if (bUseSelectedRows)
{
numrows = selectedRows.Count;
}
else
{
numrows = 0;
for (int i = 0; i < numcols; i++)
{
int idx = bUseSelectedColumns ? selectedColumns[i] : i;
numrows = Math.Max(numrows, srctable[idx].Count);
}
}
// check that both dimensions are at least 2 - otherwise PCA is not possible
if (numrows < 2)
{
return("At least two rows are neccessary to do Principal Component Analysis!");
}
if (numcols < 2)
{
return("At least two numeric columns are neccessary to do Principal Component Analysis!");
}
// Create a matrix of appropriate dimensions and fill it
MatrixMath.BEMatrix matrixX;
if (bHorizontalOrientedSpectrum)
{
matrixX = new MatrixMath.BEMatrix(numrows, numcols);
int ccol = 0; // current column in the matrix
for (int i = 0; i < prenumcols; i++)
{
int colidx = bUseSelectedColumns ? selectedColumns[i] : i;
Altaxo.Data.INumericColumn col = srctable[colidx] as Altaxo.Data.INumericColumn;
if (null != col)
{
for (int j = 0; j < numrows; j++)
{
int rowidx = bUseSelectedRows ? selectedRows[j] : j;
matrixX[j, ccol] = col[rowidx];
}
++ccol;
}
}
} // end if it was a horizontal oriented spectrum
else // if it is a vertical oriented spectrum
{
matrixX = new MatrixMath.BEMatrix(numcols, numrows);
int ccol = 0; // current column in the matrix
for (int i = 0; i < prenumcols; i++)
{
int colidx = bUseSelectedColumns ? selectedColumns[i] : i;
Altaxo.Data.INumericColumn col = srctable[colidx] as Altaxo.Data.INumericColumn;
if (null != col)
{
for (int j = 0; j < numrows; j++)
{
int rowidx = bUseSelectedRows ? selectedRows[j] : j;
matrixX[ccol, j] = col[rowidx];
}
++ccol;
}
}
} // if it was a vertical oriented spectrum
// now do PCA with the matrix
MatrixMath.REMatrix factors = new MatrixMath.REMatrix(0, 0);
MatrixMath.BEMatrix loads = new MatrixMath.BEMatrix(0, 0);
MatrixMath.BEMatrix residualVariances = new MatrixMath.BEMatrix(0, 0);
MatrixMath.HorizontalVector meanX = new MatrixMath.HorizontalVector(matrixX.Columns);
// first, center the matrix
MatrixMath.ColumnsToZeroMean(matrixX, meanX);
MatrixMath.NIPALS_HO(matrixX, maxNumberOfFactors, 1E-9, factors, loads, residualVariances);
// 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
Altaxo.Data.DataTable table = new Altaxo.Data.DataTable("PCA of " + srctable.Name);
// Fill the Table
table.Suspend();
// first of all store the meanscore
{
double meanScore = MatrixMath.LengthOf(meanX);
MatrixMath.NormalizeRows(meanX);
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for (int i = 0; i < factors.Rows; i++)
{
col[i] = meanScore;
}
table.DataColumns.Add(col, "MeanFactor", Altaxo.Data.ColumnKind.V, 0);
}
// first store the factors
for (int i = 0; i < factors.Columns; i++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < factors.Rows; j++)
{
col[j] = factors[j, i];
}
table.DataColumns.Add(col, "Factor" + i.ToString(), Altaxo.Data.ColumnKind.V, 1);
}
// now store the mean of the matrix
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < meanX.Columns; j++)
{
col[j] = meanX[0, j];
}
table.DataColumns.Add(col, "MeanLoad", Altaxo.Data.ColumnKind.V, 2);
}
// now store the loads - careful - they are horizontal in the matrix
for (int i = 0; i < loads.Rows; i++)
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for (int j = 0; j < loads.Columns; j++)
{
col[j] = loads[i, j];
}
table.DataColumns.Add(col, "Load" + i.ToString(), Altaxo.Data.ColumnKind.V, 3);
}
// now store the residual variances, they are vertical in the vector
{
Altaxo.Data.DoubleColumn col = new Altaxo.Data.DoubleColumn();
for (int i = 0; i < residualVariances.Rows; i++)
{
col[i] = residualVariances[i, 0];
}
table.DataColumns.Add(col, "ResidualVariance", Altaxo.Data.ColumnKind.V, 4);
}
table.Resume();
mainDocument.DataTableCollection.Add(table);
// create a new worksheet without any columns
Current.ProjectService.CreateNewWorksheet(table);
return(null);
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="column">The <see cref="DataColumn" /> to wrap.</param>
/// <param name="nRows">The number of rows that are part of the vector. (Starting from index 0).</param>
public NumericColumnToROVectorWrapper(Altaxo.Data.INumericColumn column, int nRows)
{
_column = column;
_rows = nRows;
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="column">The <see cref="DataColumn" /> to wrap.</param>
/// <param name="nRows">The number of rows that are part of the vector. (Starting from index 0).</param>
public NumericColumnToROHorzMatrixWrapper(Altaxo.Data.INumericColumn column, int nRows)
{
_column = column;
_rows = nRows;
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="column">The <see cref="DataColumn" /> to wrap.</param>
/// <param name="selectedRows">The set of rows that are part of the vector. This collection is not cloned here, therefore it must not be subsequently changed!</param>
public NumericColumnSelectedRowsToROVectorWrapper(Altaxo.Data.INumericColumn column, IAscendingIntegerCollection selectedRows)
{
_column = column;
_rows = selectedRows;
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="column">The <see cref="DataColumn" /> to wrap.</param>
/// <param name="selectedRows">The set of rows that are part of the vector. This collection is not cloned here, therefore it must not be subsequently changed!</param>
public NumericColumnSelectedRowsToROVectorWrapper(Altaxo.Data.INumericColumn column, IAscendingIntegerCollection selectedRows)
{
_column = column;
_rows = selectedRows;
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="column">The <see cref="DataColumn" /> to wrap.</param>
/// <param name="start">Index of the element in <paramref name="column"/> that is used as first element of the wrapped vector.</param>
/// <param name="nRows">The number of rows that are part of the vector. (Starting from index 0).</param>
public NumericColumnToROVectorWrapper(Altaxo.Data.INumericColumn column, int start, int nRows)
{
_column = column;
_start = start;
_rows = nRows;
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="column">The <see cref="DataColumn" /> to wrap.</param>
/// <param name="nRows">The number of rows that are part of the vector. (Starting from index 0).</param>
public NumericColumnToROVertMatrixWrapper(Altaxo.Data.INumericColumn column, int nRows)
{
_column = column;
_rows = nRows;
}
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);
}