void FFNet_PatternList_drawActivation (FFNet me, PatternList pattern, Graphics g, long index) {
if (index < 1 || index > pattern->ny) {
return;
}
FFNet_propagate (me, pattern->z[index], nullptr);
FFNet_drawActivation (me, g);
}
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.");
}
}
void FFNet_propagateToLayer (FFNet me, const double input[], double activity[], long layer)
{
long i, k = 0;
Melder_assert (activity);
FFNet_propagate (me, input, NULL);
for (i = 0; i < layer; i++) k += my nUnitsInLayer[i] + 1;
for (i = 1; i <= my nUnitsInLayer[layer]; i++) activity[i] = my activity[k+i];
}
void FFNet_propagateToLayer (FFNet me, const double input[], double activity[], long layer) {
Melder_assert (activity);
long k = 0;
FFNet_propagate (me, input, nullptr);
for (long i = 0; i < layer; i++) {
k += my nUnitsInLayer[i] + 1;
}
for (long i = 1; i <= my nUnitsInLayer[layer]; i++) {
activity[i] = my activity[k + i];
}
}
double FFNet_Pattern_Activation_getCosts_total (FFNet me, Pattern p, Activation a, int costFunctionType) {
try {
_FFNet_Pattern_Activation_checkDimensions (me, p, a);
FFNet_setCostFunction (me, costFunctionType);
double cost = 0;
for (long i = 1; i <= p -> ny; i++) {
FFNet_propagate (me, p -> z[i], NULL);
cost += FFNet_computeError (me, a -> z[i]);
}
return cost;
} catch (MelderError) {
return NUMundefined;
}
}
static double func (Daata object, const double p[]) {
FFNet me = (FFNet) object;
Minimizer thee = my minimizer;
double fp = 0;
for (long j = 1, k = 1; k <= my nWeights; k++) {
my dw[k] = 0.0;
if (my wSelected[k]) {
my w[k] = p[j++];
}
}
for (long i = 1; i <= my nPatterns; i++) {
FFNet_propagate (me, my inputPattern[i], NULL);
fp += FFNet_computeError (me, my targetActivation[i]);
FFNet_computeDerivative (me);
/* derivative (cumulative) */
for (long k = 1; k <= my nWeights; k++) {
my dw[k] += my dwi[k];
}
}
thy funcCalls++;
return fp;
}
