autoSVD TableOfReal_to_SVD (TableOfReal me) {
try {
autoSVD thee = SVD_create_d (my data, my numberOfRows, my numberOfColumns);
return thee;
} catch (MelderError) {
Melder_throw (me, U": no SVD created.");
}
}
SVD TableOfReal_to_SVD (I) {
iam (TableOfReal);
try {
autoSVD thee = SVD_create_d (my data, my numberOfRows, my numberOfColumns);
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, ": no SVD created.");
}
}
/*
* Generate n different cct's that have a common diagonalizer.
*/
autoCrossCorrelationTables CrossCorrelationTables_createTestSet (long dimension, long n, int firstPositiveDefinite, double sigma) {
try {
// Start with a square matrix with random gaussian elements and make its singular value decomposition UDV'
// The V matrix will be the common diagonalizer matrix that we use.
autoNUMmatrix<double> d (1, dimension, 1, dimension);
for (long i = 1; i <= dimension; i++) { // Generate the rotation matrix
for (long j = 1; j <= dimension; j++) {
d[i][j] = NUMrandomGauss (0, 1);
}
}
autoNUMmatrix<double> v (1, dimension, 1, dimension);
autoSVD svd = SVD_create_d (d.peek(), dimension, dimension);
autoCrossCorrelationTables me = CrossCorrelationTables_create ();
for (long i = 1; i <= dimension; i++) {
for (long j = 1; j <= dimension; j++) {
d[i][j] = 0;
}
}
// Start with a diagonal matrix D and calculate V'DV
for (long k = 1; k <= n; k++) {
autoCrossCorrelationTable ct = CrossCorrelationTable_create (dimension);
double low = k == 1 && firstPositiveDefinite ? 0.1 : -1;
for (long i = 1; i <= dimension; i++) {
d[i][i] = NUMrandomUniform (low, 1);
}
for (long i = 1; i <= dimension; i++) {
for (long j = 1; j <= dimension; j++) {
v[i][j] = NUMrandomGauss (svd -> v[i][j], sigma);
}
}
// we need V'DV, however our V has eigenvectors row-wise -> VDV'
NUMdmatrices_multiply_VCVp (ct -> data, v.peek(), dimension, dimension, d.peek(), 1);
Collection_addItem_move (me.peek(), ct.move());
}
return me;
} catch (MelderError) {
Melder_throw (U"CrossCorrelationTables test set not created.");
}
}
