Categories FFNet_Activation_to_Categories (FFNet me, Activation activation, int labeling)
{
Categories thee = NULL, categories = my outputCategories;
long i, (*labelingFunction) (I, const double act[]);
if (! my outputCategories)
{
(void) Melder_error1 (L"FFNet & Activation: To Categories\n");
return Melder_errorp1 (L"The neural net has no Categories (has the FFNet been trained yet?).");
}
if (my nOutputs != activation->nx)
{
(void) Melder_error1 (L"FFNet & Activation: To Categories\n");
return Melder_errorp1 (L"The number of columns in the Activation must equal the number of outputs of FFNet.");
}
thee = Categories_create ();
if (thee == NULL) return NULL;
labelingFunction = labeling == 2 ? stochastic : winnerTakesAll;
for (i = 1; i <= activation->ny; i++)
{
long index = labelingFunction (me, activation->z[i]);
Data item = Data_copy (categories->item[index]);
if (item == NULL || ! Collection_addItem (thee, item))
{
forget (thee);
return Melder_errorp3 (L"FFNet & Activation: To Categories\n\nError creating label ",
Melder_integer (i), L".");
}
}
return thee;
}
autoCategories FFNet_Activation_to_Categories (FFNet me, Activation activation, int labeling) {
try {
long (*labelingFunction) (FFNet me, const double act[]);
if (! my outputCategories) {
Melder_throw (U"No Categories (has the FFNet been trained yet?).");
}
if (my nOutputs != activation -> nx) {
Melder_throw (U"Number of columns and number of outputs must be equal.");
}
autoCategories thee = Categories_create ();
labelingFunction = labeling == 2 ? stochastic : winnerTakesAll;
for (long i = 1; i <= activation->ny; i++) {
long index = labelingFunction (me, activation -> z[i]);
autoSimpleString item = Data_copy (my outputCategories->at [index]);
thy addItem_move (item.move());
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": no Categories created.");
}
}
