int main() {
size_t i, j;
size_t nencode = 3, ninput = pow(2, nencode); //Set the number of vectors
size_t iter_times = 50000; // Complex network requires more iteration times
double rate = 0.5, momentum = 0.5; // Learning parameters;
size_t hidden[1] = { nencode };
char *ch = "sigmoid";
char *chs[1] = {ch};
double **input = alloc_2d(ninput, ninput), **output = alloc_2d(ninput, ninput);
for (i = 0; i < ninput; ++i) {
for (j = 0; j < ninput; ++j) {
if (i == j)
input[i][j] = output[i][j] = 1;
else
input[i][j] = output[i][j] = 0;
}
}
/* Set the configuration of neural network */
struct nn_config config = {
.dim_h = hidden,
.ahfunc = chs,
.n_hidden = 1,
.dim_i = ninput,
.dim_o = ninput,
.aofunc = ch
};
struct neural_network* nn = create_nn(&config);
train(nn, input, output, ninput, rate, momentum, iter_times);
double *res = alloc_1d(ninput);
for (i = 0; i < ninput; ++i) {
for (j = 0; j < ninput; ++j)
printf("%d ", (int)input[i][j]);
printf("=> ");
predict_hidden(nn, input[i], res, 0);
for (j = 0; j < nencode; ++j) {
printf("%.3f ", res[j]); // Actually value
// printf("%d ", (int)(res[j]+0.5)); // 0-1 value
}
predict(nn, input[i], res);
printf("=> ");
for (j = 0; j < ninput; ++j)
printf("%.3f ", res[j]);
printf("\n");
}
free_2d(input, ninput);
free_2d(output, ninput);
free_1d(res);
destroy_nn(nn);
return 0;
}
mlp_all_inputs_spaceship_controller::mlp_all_inputs_spaceship_controller(size_t num_layers, size_t per_hidden, size_t num_outputs):
mlp_spaceship_controller(num_layers, per_hidden, num_outputs)
{
create_nn(NumNNInputs);
}
