TableOfReal CCA_and_TableOfReal_predict (CCA me, TableOfReal thee, long from)
{
TableOfReal him = NULL;
long ny = my y -> dimension, nx = my x -> dimension;
long i, j, k, ncols, nev = my y -> numberOfEigenvalues;
double *buf = NULL, **v = my y -> eigenvectors;
double *d = my y -> eigenvalues;
/*
We can only predict when we have the largest dimension as input
and the number of coefficients equals the dimension of the smallest.
*/
if (ny != nev) return Melder_errorp1 (L"There are not enough correlations present for prediction.");
if (from == 0) from = 1;
ncols = thy numberOfColumns - from + 1;
if (from < 1 || ncols != nx) return Melder_errorp3 (L"The number of columns to analyze must be equal to ",
Melder_integer (nx), L".");
/* ???? dimensions if nx .. ny ?? */
him = Eigen_and_TableOfReal_project (my x, thee, from, ny);
if (him == NULL) return NULL;
buf = NUMdvector (1, ny);
if (buf == NULL) goto end;
/*
u = V a -> a = V'u
*/
for (i = 1; i <= thy numberOfRows; i++)
{
NUMdvector_copyElements (his data[i], buf, 1, ny);
for (j = 1; j <= ny; j++)
{
double t = 0;
for (k = 1; k <= ny; k++)
{
t += sqrt (d[k]) * v[k][j] * buf[k];
}
his data [i][j] = t;
}
}
end:
NUMdvector_free (buf, 1);
if (Melder_hasError ()) forget (him);
return him;
}
TableOfReal CCA_and_TableOfReal_predict (CCA me, TableOfReal thee, long from) {
try {
long ny = my y -> dimension, nx = my x -> dimension;
long nev = my y -> numberOfEigenvalues;
/*
We can only predict when we have the largest dimension as input
and the number of coefficients equals the dimension of the smallest.
*/
if (ny != nev) {
Melder_throw (U"There are not enough correlations present for prediction.");
}
if (from == 0) {
from = 1;
}
long ncols = thy numberOfColumns - from + 1;
if (from < 1 || ncols != nx) {
Melder_throw (U"The number of columns to analyze must be equal to ", nx, U".");
}
// ???? dimensions if nx .. ny ??
autoTableOfReal him = Eigen_and_TableOfReal_project (my x, thee, from, ny);
autoNUMvector<double> buf (1, ny);
// u = V a -> a = V'u
double **v = my y -> eigenvectors;
double *d = my y -> eigenvalues;
for (long i = 1; i <= thy numberOfRows; i++) {
NUMvector_copyElements (his data[i], buf.peek(), 1, ny);
for (long j = 1; j <= ny; j++) {
double t = 0.0;
for (long k = 1; k <= ny; k++) {
t += sqrt (d[k]) * v[k][j] * buf[k];
}
his data [i][j] = t;
}
}
return him.transfer();
} catch (MelderError) {
Melder_throw (me, U": no predictions created.");
}
}