/* A Hopfield network will be implemented as
a fully recurrent network with 0 inputs and 'num_neurons'
outputs! */
FANN_EXTERNAL struct fann *FANN_API fann_create_hopfield(
unsigned int num_neurons)
{
struct fann *ann = NULL;
assert(num_neurons > 0);
ann = (struct fann *)fann_create_fully_recurrent(
num_neurons, 0, num_neurons);
return ann;
}
int main(void)
{
struct fann *ann = NULL;
unsigned int i = 0;
unsigned int num_neurons = 0;
unsigned int num_inputs = 0;
unsigned int num_outputs = 0;
fann_seed_rand();
/* GENERATE RANDOM RECURRENT NETWORKS */
for (i=0; i<NUM_TESTS; i++)
{
num_inputs = rand()%5;
num_neurons = rand()%10 + 1;
// Number of outputs must be <= number of neurons
do { num_outputs = rand()%10; } while (num_outputs > num_neurons);
ann = fann_create_fully_recurrent(num_neurons, num_inputs, num_outputs);
printf("\n\nCreated recurrent network (neurons=%d, inputs=%d, outputs=%d)\n",
num_neurons, num_inputs, num_outputs);
printf("\n-------------------------------------------\n");
printf("NET %d - CONNECTION MATRIX:\n", i);
printf(" (I=input, b=bias, O=output, #=internal)\n");
printf("-------------------------------------------\n");
fann_print_connections(ann);
printf("\n\n-------------------------------------------\n");
printf("NET %d - ANN PARAMETERS:\n", i);
printf("-------------------------------------------\n");
fann_print_parameters(ann);
/* CLEAN UP */
fann_destroy(ann);
}
return 0;
}
