public static object SVD(double[,] Data)
{
DenseMatrix data = new DenseMatrix(Data);
int nRows = data.RowCount, nCols = data.ColumnCount;
MathNet.Numerics.LinearAlgebra.Double.Factorization.Svd svd = data.Svd(true);
Matrix <double> u = svd.U(); // m x m
Matrix <double> w = svd.W(); // m x n, diagonal
Matrix <double> vt = svd.VT(); // n x n, transpose
object[,] ret = new object[Math.Max(nRows, nCols), nRows + 1 + nCols + 1 + nCols];
for (int i = 0; i < ret.GetLength(0); ++i)
{
for (int j = 0; j < ret.GetLength(1); ++j)
{
ret[i, j] = "";
}
}
for (int i = 0; i < nRows; ++i)
{
for (int j = 0; j < nRows; ++j)
{
ret[i, j] = u[i, j];
}
}
for (int i = 0; i < nRows; ++i)
{
for (int j = 0; j < nCols; ++j)
{
ret[i, nRows + 1 + j] = w[i, j];
}
}
for (int i = 0; i < nCols; ++i)
{
for (int j = 0; j < nCols; ++j)
{
ret[i, nRows + 1 + nCols + 1 + j] = vt[j, i]; // Re-transpose, so that they're in the expected format.
}
}
return(ret);
}
public override void Estimate(List <Point3D> datas)
{
double sum_x = 0;
double sum_y = 0;
double sum_z = 0;
foreach (Point3D temp in datas)
{
sum_x += temp.x;
sum_y += temp.y;
sum_z += temp.z;
}
sum_x /= datas.Count;
sum_y /= datas.Count;
sum_z /= datas.Count;
DenseMatrix jacobian = new DenseMatrix(datas.Count, 3);
foreach (Point3D temp in datas)
{
Vector <double> gradient = new DenseVector(3);
gradient[0] = temp.x - sum_x;
gradient[1] = temp.y - sum_y;
gradient[2] = temp.z - sum_z;
jacobian.SetRow(datas.IndexOf(temp), gradient);
}
MathNet.Numerics.LinearAlgebra.Double.Factorization.Svd
svd = jacobian.Svd(true);
// get matrix of left singular vectors with first n columns of U
Matrix <double> U1 = svd.U().SubMatrix(0, datas.Count, 0, 3);
// get matrix of singular values
Matrix <double> S = new DiagonalMatrix(3, 3, svd.S().ToArray());
// get matrix of right singular vectors
Matrix <double> V = svd.VT().Transpose();
Vector <double> parameters = new DenseVector(3);
parameters = V.Column(2);
x = sum_x;
y = sum_y;
z = sum_z;
i = parameters[0];
j = parameters[1];
k = parameters[2];
}
