/*
raw r[j]: eigenvec[i][j]
unstandardized u[j]: sqrt(N-g) * r[j]
standardized s[j]: u[j] sqrt (w[i][i] / (N-g))
*/
autoTableOfReal Discriminant_extractCoefficients (Discriminant me, int choice) {
try {
int raw = choice == 0, standardized = choice == 2;
long nx = my eigen -> dimension, ny = my eigen -> numberOfEigenvalues;
SSCP total = my total.get();
autoTableOfReal thee = TableOfReal_create (ny, nx + 1);
NUMstrings_copyElements (my total -> columnLabels, thy columnLabels, 1, nx);
autoSSCP within;
if (standardized) {
within = Discriminant_extractPooledWithinGroupsSSCP (me);
}
TableOfReal_setColumnLabel (thee.get(), nx + 1, U"constant");
TableOfReal_setSequentialRowLabels (thee.get(), 1, ny, U"function_", 1, 1);
double scale = sqrt (total -> numberOfObservations - my numberOfGroups);
double *centroid = my total -> centroid;
for (long i = 1; i <= ny; i++) {
double u0 = 0.0, ui;
for (long j = 1; j <= nx; j++) {
if (standardized) {
scale = sqrt (within -> data[j][j]);
}
thy data[i][j] = ui = scale * my eigen -> eigenvectors[i][j];;
u0 += ui * centroid[j];
}
thy data[i][nx + 1] = raw ? 0.0 : -u0;
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": coefficients not extracted.");
}
}
TableOfReal CCA_and_Correlation_factorLoadings (CCA me, Correlation thee)
{
TableOfReal him;
long i, j, k, ny = my y -> dimension, nx = my x -> dimension;
double t, **evecy = my y -> eigenvectors, **evecx = my x -> eigenvectors;
if (ny + nx != thy numberOfColumns) return Melder_errorp1 (L"The number "
"of columns in the Correlation object must equal the sum of the "
"dimensions in the CCA object");
him = TableOfReal_create (2 * my numberOfCoefficients, thy numberOfColumns);
if (him == NULL) return NULL;
if (! NUMstrings_copyElements (thy columnLabels, his columnLabels,
1, thy numberOfColumns) ||
! TableOfReal_setSequentialRowLabels (him, 1, my numberOfCoefficients,
L"dv", 1, 1) ||
! TableOfReal_setSequentialRowLabels (him, my numberOfCoefficients + 1,
2 * my numberOfCoefficients, L"iv", 1, 1))
{
forget (him);
return NULL;
}
for (i = 1; i <= thy numberOfRows; i++)
{
for (j = 1; j <= my numberOfCoefficients; j++)
{
t = 0;
for (k = 1; k <= ny; k++)
{
t += thy data[i][k] * evecy[j][k];
}
his data[j][i] = t;
}
for (j = 1; j <= my numberOfCoefficients; j++)
{
t = 0;
for (k = 1; k <= nx; k++)
{
t += thy data[i][ny + k] * evecx[j][k];
}
his data[my numberOfCoefficients + j][i] = t;
}
}
return him;
}
class_methods_end
int Proximity_init (I, long numberOfPoints)
{
iam (Proximity);
return TableOfReal_init (me, numberOfPoints, numberOfPoints) &&
TableOfReal_setSequentialRowLabels (me, 0, 0, NULL, 1, 1) &&
TableOfReal_setSequentialColumnLabels (me, 0, 0, NULL, 1, 1);
}
autoConfiguration Configuration_create (long numberOfPoints, long numberOfDimensions) {
try {
autoConfiguration me = Thing_new (Configuration);
TableOfReal_init (me.peek(), numberOfPoints, numberOfDimensions);
my w = NUMvector<double> (1, numberOfDimensions);
TableOfReal_setSequentialRowLabels (me.peek(), 0, 0, nullptr, 1, 1);
TableOfReal_setSequentialColumnLabels (me.peek(), 0, 0, U"dimension ", 1, 1);
my metric = 2;
Configuration_setDefaultWeights (me.peek());
Configuration_randomize (me.peek());
return me;
} catch (MelderError) {
Melder_throw (U"Configuration not created.");
}
}
