public GeneralMatrix[] decompose(int svCount, GeneralMatrix m)
{
SingularValueDecomposition s = new SingularValueDecomposition(m);
try
{
sv = svCount;
frameScale = m.RowDimension;
if (m.RowDimension > m.ColumnDimension)
{
frameScale = m.ColumnDimension;
}
else if (m.RowDimension < m.ColumnDimension)
{
frameScale = m.RowDimension;
}
// Make square matrix of data
if (m.RowDimension != m.ColumnDimension)
{
squareM = m.GetMatrix(0, frameScale - 1, 0, frameScale - 1);
}
else
{
squareM = m;
}
//perform the SVD here:
SingularValueDecomposition svd = new SingularValueDecomposition(squareM);
GeneralMatrix U = svd.GetU().GetMatrix(0, frameScale - 1, 0, sv - 1);
GeneralMatrix V = svd.GetV();
double[] D = svd.SingularValues;
GeneralMatrix dMat = makeDiagonalSquare(D, sv);
GeneralMatrix vMat = V.Transpose().GetMatrix(0, sv - 1, 0, frameScale - 1);
/*Console.WriteLine(U.RowDimension + "x" + U.ColumnDimension
+ " * " + dMat.RowDimension + "x" + dMat.ColumnDimension
+ " * " + vMat.RowDimension + "x" + vMat.ColumnDimension);
recomposition = U.Multiply(dMat).Multiply(vMat);
int[,] recompositionData = new int[recomposition.ColumnDimension, recomposition.RowDimension];
for (int i = 0; i < recomposition.ColumnDimension; i++)
{
for (int j = 0; j < recomposition.RowDimension; j++)
{
recompositionData[j, i] = (int)(recomposition.GetElement(j, i));
}
}*/
GeneralMatrix[] result = new GeneralMatrix[2];
result[0] = U;
result[1] = vMat;
return result;
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
return null;
}
/**
* Computes the Moore–Penrose pseudoinverse using the SVD method.
*
* Modified version of the original implementation by Kim van der Linde.
*/
public static GeneralMatrix pinv(GeneralMatrix x)
{
if (x.Rank() < 1)
return null;
if (x.ColumnDimension > x.RowDimension)
return pinv(x.Transpose()).Transpose();
SingularValueDecomposition svdX = new SingularValueDecomposition(x);
double[] singularValues = svdX.SingularValues;
double tol = Math.Max(x.ColumnDimension, x.RowDimension)
* singularValues[0] * 2E-16;
double[] singularValueReciprocals = new double[singularValues.Count()];
for (int i = 0; i < singularValues.Count(); i++)
singularValueReciprocals[i] = Math.Abs(singularValues[i]) < tol ? 0
: (1.0 / singularValues[i]);
double[][] u = svdX.GetU().Array;
double[][] v = svdX.GetV().Array;
int min = Math.Min(x.ColumnDimension, u[0].Count());
double[][] inverse = new double[x.ColumnDimension][];
for (int i = 0; i < x.ColumnDimension; i++) {
inverse[i] = new double[x.RowDimension];
for (int j = 0; j < u.Count(); j++)
for (int k = 0; k < min; k++)
inverse[i][j] += v[i][k] * singularValueReciprocals[k] * u[j][k];
}
return new GeneralMatrix(inverse);
}
