void NeuralNetwork::build_weight_matrix(synapse& weights,
const int n_from,
const int n_to)
{
for (int i = 0; i < n_from; i++) {
weights.push_back(nerve());
for (int j = 0; j < n_to; j++) {
weights[i].push_back(rzero(2.0*rand() - 1.0));
}
}
}
void NeuralNetwork::build_feedback_matrix(synapse& feedback,
const int n_from,
const int n_to)
{
for (int i = 0; i < n_from; i++) {
feedback.push_back(nerve());
for (int j = 0; j < n_to; j++) {
feedback[i].push_back(0.0);
}
}
}
void identity_matrix(NeuralNetwork& neural_network,
const int n_inputs, const int n_outputs)
{
double node;
synapse input_grid;
synapse output_grid;
// Set up the inputs and outputs
for (int i = 0; i < n_inputs; i++) {
input_grid.push_back(nerve());
output_grid.push_back(nerve());
for (int j = 0; j < n_outputs; j++) {
node = (j == i) ? 1.0 : 0.0;
input_grid[i].push_back(node);
output_grid[i].push_back(node);
}
}
// Set up the network structure, train it, and show test results
neural_network.train(input_grid, output_grid, THINKER_MAX_CYCLES, 0.5);
neural_network.test(input_grid, output_grid);
}
