public PrincipalComponents(Matrix <double> data)
{
var dt = data.Clone();
dt.Transpose();
sampleCov = dt * data * (1.0 / data.RowCount);
//covDecomposition = new EigenvalueDecomposition(sampleCov);
covDecomposition = sampleCov.Evd();
covDecomposition.Solve(sampleCov);
var evs = new double[sampleCov.RowCount];
var indices = new int[sampleCov.RowCount];
for (var i = 0; i < sampleCov.RowCount; ++i)
{
evs[i] = covDecomposition.EigenValues[i].Real;
indices[i] = i;
}
Array.Sort(evs, indices);
var permutation = Matrix <double> .Build.Dense(sampleCov.RowCount, sampleCov.RowCount);
for (var i = 0; i < sampleCov.RowCount; ++i)
{
permutation[indices[i], i] = 1.0;
}
var v = covDecomposition.EigenVectors;
SortedComponents = v * permutation;
SortedEigenvalues = Vector <double> .Build.DenseOfArray(evs);
}
public PrincipalComponents(Matrix <double> covMatrix)
{
sampleCov = covMatrix;
// covDecomposition = new EigenvalueDecomposition(sampleCov);
covDecomposition = sampleCov.Evd();
covDecomposition.Solve(sampleCov);
var evs = new double[sampleCov.RowCount];
var indices = new int[sampleCov.RowCount];
for (int i = 0; i < sampleCov.RowCount; ++i)
{
evs[i] = covDecomposition.EigenValues[i].Real;
indices[i] = i;
}
Array.Sort(evs, indices);
var permutation = Matrix <double> .Build.Dense(sampleCov.RowCount, sampleCov.RowCount);
for (int i = 0; i < sampleCov.RowCount; ++i)
{
permutation[indices[i], i] = 1.0;
}
Matrix <double> v = covDecomposition.EigenVectors;
SortedComponents = v * permutation;
SortedEigenvalues = Vector <double> .Build.DenseOfArray(evs);
}