Signal correlacion(const Signal &imagen, const Signal &filtro) {
int filas = imagen.getRows();
int columnas = imagen.getCols();
Signal Iout(filas, columnas);
for (int i = 0; i < filas; ++i) {
for (int j = 0; j < columnas; ++j) {
Iout.set(i, j, 0.0);
}
}
int mitadF = (filtro.getRows()-1)/2;
int mitadC = (filtro.getCols()-1)/2;
int fila_inicio = mitadF;
int fila_fin = filas-mitadF;
int col_inicio = mitadC;
int col_fin = columnas-mitadC;
for (int i = fila_inicio; i < fila_fin; ++i) {
for (int j = col_inicio; j < col_fin; ++j) {
double suma = 0.0;
for (int k = -mitadF; k <= mitadF; ++k) {
for (int l = -mitadC; l <= mitadC; ++l) {
suma += imagen.get(i+k, j+l)*filtro.get(k+mitadF, l+mitadC);
}
}
Iout.set(i, j, suma);
}
}
return Iout;
}
//returns whether the connection was propagated successfully
bool Connection::send(Signal& S)
{
//weigh the signal down by this connection's weight
S.weigh(weight);
activation = S.get();
if(!connectionEstablished())
return false;
return allNeurons[to] -> receive(S);
}