double PCA_and_TableOfReal_getFractionVariance (PCA me, TableOfReal thee, long from, long to) {
try {
double fraction = NUMundefined;
if (from < 1 || from > to || to > thy numberOfColumns) {
return NUMundefined;
}
autoSSCP s = TableOfReal_to_SSCP (thee, 0, 0, 0, 0);
autoSSCP sp = Eigen_and_SSCP_project (me, s.get());
fraction = SSCP_getFractionVariation (sp.get(), from, to);
return fraction;
} catch (MelderError) {
return NUMundefined;
}
}
autoDiscriminant TableOfReal_to_Discriminant (TableOfReal me) {
try {
autoDiscriminant thee = Thing_new (Discriminant);
long dimension = my numberOfColumns;
if (! NUMdmatrix_hasFiniteElements(my data, 1, my numberOfRows, 1, my numberOfColumns)) {
Melder_throw (U"At least one of the table's elements is not finite or undefined.");
}
if (! TableOfReal_hasRowLabels (me)) {
Melder_throw (U"At least one of the rows has no label.");
}
autoTableOfReal mew = TableOfReal_sortOnlyByRowLabels (me);
if (! TableOfReal_hasColumnLabels (mew.get())) {
TableOfReal_setSequentialColumnLabels (mew.get(), 0, 0, U"c", 1, 1);
}
thy groups = TableOfReal_to_SSCPList_byLabel (mew.get());
thy total = TableOfReal_to_SSCP (mew.get(), 0, 0, 0, 0);
if ((thy numberOfGroups = thy groups -> size) < 2) {
Melder_throw (U"Number of groups must be greater than one.");
}
TableOfReal_centreColumns_byRowLabel (mew.get());
// Overall centroid and apriori probabilities and costs.
autoNUMvector<double> centroid (1, dimension);
autoNUMmatrix<double> between (1, thy numberOfGroups, 1, dimension);
thy aprioriProbabilities = NUMvector<double> (1, thy numberOfGroups);
thy costs = NUMmatrix<double> (1, thy numberOfGroups, 1, thy numberOfGroups);
double sum = 0, scale;
for (long k = 1; k <= thy numberOfGroups; k ++) {
SSCP m = thy groups->at [k];
sum += scale = SSCP_getNumberOfObservations (m);
for (long j = 1; j <= dimension; j ++) {
centroid [j] += scale * m -> centroid [j];
}
}
for (long j = 1; j <= dimension; j ++) {
centroid [j] /= sum;
}
for (long k = 1; k <= thy numberOfGroups; k ++) {
SSCP m = thy groups->at [k];
scale = SSCP_getNumberOfObservations (m);
thy aprioriProbabilities[k] = scale / my numberOfRows;
for (long j = 1; j <= dimension; j ++) {
between [k] [j] = sqrt (scale) * (m -> centroid [j] - centroid [j]);
}
}
// We need to solve B'B.x = lambda W'W.x, where B'B and W'W are the between and within covariance matrices.
// We do not calculate these covariance matrices directly from the data but instead use the GSVD to solve for
// the eigenvalues and eigenvectors of the equation.
thy eigen = Thing_new (Eigen);
Eigen_initFromSquareRootPair (thy eigen.get(), between.peek(), thy numberOfGroups, dimension, mew -> data, my numberOfRows);
// Default priors and costs
for (long igroup = 1; igroup <= thy numberOfGroups; igroup ++) {
for (long jgroup = igroup + 1; jgroup <= thy numberOfGroups; jgroup ++) {
thy costs [igroup] [jgroup] = thy costs [jgroup] [igroup] = 1.0;
}
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": Discriminant not created.");
}
}