autoCategories FFNet_Pattern_to_Categories (FFNet me, Pattern thee, int labeling) {
try {
if (! my outputCategories) {
Melder_throw (U"The FFNet has no output categories.");
}
if (my nInputs != thy nx) {
Melder_throw (U"The number of colums in the Pattern (", thy nx, U") should equal the number of inputs in the FFNet (", my nInputs, U").");
}
if (! _Pattern_checkElements (thee)) {
Melder_throw (U"All Pattern elements must be in the interval [0, 1].\nYou could use \"Formula...\" to scale the Pattern values first.");
}
autoCategories him = Categories_create ();
for (long k = 1; k <= thy ny; k++) {
FFNet_propagate (me, thy z[k], nullptr);
long index = FFNet_getWinningUnit (me, labeling);
autoDaata item = Data_copy ((Daata) my outputCategories -> item[index]);
Collection_addItem_move (him.peek(), item.move());
}
return him;
} catch (MelderError) {
Melder_throw (me, U": no Categories created.");
}
}
Categories FFNet_Pattern_to_Categories (FFNet me, Pattern thee, int labeling) {
try {
if (my outputCategories == 0) {
Melder_throw (U"The FFNet has no output categories.");
}
if (my nInputs != thy nx) Melder_throw (U"The number of colums in the Pattern (", thy nx,
U") should equal the number of inputs in the FFNet (", my nInputs, U").");
if (! _Pattern_checkElements (thee)) Melder_throw
(U"The elements in the Pattern are not all in the interval [0, 1].\n"
U"The input of the neural net can only process values that are between 0 and 1.\n"
U"You could use \"Formula...\" to scale the Pattern values first.");
autoCategories him = Categories_create ();
for (long k = 1; k <= thy ny; k++) {
FFNet_propagate (me, thy z[k], 0);
long index = FFNet_getWinningUnit (me, labeling);
autoDaata item = Data_copy ( (Daata) my outputCategories -> item[index]);
Collection_addItem (him.peek(), item.transfer());
}
return him.transfer();
} catch (MelderError) {
Melder_throw (me, U": no Categories created.");
}
}
static void _FFNet_Pattern_Categories_checkDimensions (FFNet me, Pattern p, Categories c) {
if (my nInputs != p -> nx) Melder_throw (U"The Pattern and the FFNet do not match.\n"
U"The number of colums in the Pattern must equal the number of inputs in the FFNet.");
if (p -> ny != c -> size) Melder_throw (U"The Pattern and the categories do not match.\n"
U"The number of rows in the Pattern must equal the number of categories.");
if (! _Pattern_checkElements (p)) Melder_throw (U"The elements in the Pattern are not all "
U"in the interval [0, 1].\nThe input of the neural net can only process values that are between 0 "
U"and 1.\nYou could use \"Formula...\" to scale the Pattern values first.");
}
static void _FFNet_Pattern_Categories_checkDimensions (FFNet me, Pattern p, Categories c) {
if (my nInputs != p -> nx) {
Melder_throw (U"The Pattern and the FFNet do not match.\nThe number of colums in the Pattern must equal the number of inputs in the FFNet.");
}
if (p -> ny != c -> size) {
Melder_throw (U"The Pattern and the categories do not match.\nThe number of rows in the Pattern must equal the number of categories.");
}
if (! _Pattern_checkElements (p)) {
Melder_throw (U"All Pattern elements must be in the interval [0, 1].\nYou could use \"Formula...\" to scale the Pattern values first.");
}
}
static void _FFNet_Pattern_Activation_checkDimensions (FFNet me, Pattern p, Activation a) {
if (my nInputs != p -> nx) Melder_throw (U"The Pattern and the FFNet do not match.\n"
"The number of colums in the Pattern must equal the number of inputs in the FFNet.");
if (my nOutputs != a -> nx) Melder_throw (U"The Activation and the FFNet do not match.\n"
"The number of colums in the Activation must equal the number of outputs in the FFNet.");
if (p -> ny != a -> ny) Melder_throw (U"The Pattern and the Activation do not match.\n"
"The number of rows in the Pattern must equal the number of rows in the Activation.");
if (! _Pattern_checkElements (p)) Melder_throw (U"The elements in the Pattern are not all "
"in the interval [0, 1].\nThe input of the neural net can only process values that are between 0 "
"and 1.\nYou could use a \"Formula...\" to scale the Pattern values first.");
if (! _Activation_checkElements (a)) Melder_throw (U"The elements in the Activation are not "
"all in the interval [0, 1].\nThe output of the neural net can only process values that are "
"between 0 and 1.\nYou could use \"Formula...\" to scale the Activation values first.");
}
Activation FFNet_Pattern_to_Activation (FFNet me, Pattern p, long layer) {
try {
if (layer < 1 || layer > my nLayers) {
layer = my nLayers;
}
if (my nInputs != p -> nx) Melder_throw (U"The Pattern and the FFNet do not match. "
U"The number of colums in the Pattern (", p -> nx, U") should equal the number of inputs "
U"in the FFNet (", my nInputs, U").");
if (! _Pattern_checkElements (p)) Melder_throw
(U"The elements in the Activation are not all in the interval [0, 1].\n"
U"The output units of the neural net can only process values that are between 0 and 1.\n"
U"You could use \"Formula...\" to scale the Activation values first.");
long nPatterns = p -> ny;
autoActivation thee = Activation_create (nPatterns, my nUnitsInLayer[layer]);
for (long i = 1; i <= nPatterns; i++) {
FFNet_propagateToLayer (me, p -> z[i], thy z[i], layer);
}
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, U": no Activation created.");
}
}
