void NeuralNetwork::RTRL_update_gradients()
{
// for every neuron
for (unsigned int k = m_num_inputs; k < m_neurons.size(); k++)
{
// for all possible connections
for (unsigned int i = m_num_inputs; i < m_neurons.size(); i++)
// to
for (unsigned int j = 0; j < m_neurons.size(); j++) // from
{
int t_idx = ConnectionExists(i, j);
if (t_idx != -1)
{
//double t_derivative = unsigned_sigmoid_derivative( m_neurons[k].m_activation );
double t_derivative = 0;
if (m_neurons[k].m_activation_function_type
== NEAT::UNSIGNED_SIGMOID)
{
t_derivative = unsigned_sigmoid_derivative(
m_neurons[k].m_activation);
}
else if (m_neurons[k].m_activation_function_type
== NEAT::TANH)
{
t_derivative = tanh_derivative(
m_neurons[k].m_activation);
}
double t_sum = 0;
// calculate the other sum
for (unsigned int l = 0; l < m_neurons.size(); l++)
{
int t_l_idx = ConnectionExists(k, l);
if (t_l_idx != -1)
{
t_sum += m_connections[t_l_idx].m_weight
* m_neurons[l].m_sensitivity_matrix[i][j];
}
}
if (i == k)
{
t_sum += m_neurons[j].m_activation;
}
m_neurons[k].m_sensitivity_matrix[i][j] = t_derivative
* t_sum;
}
else
{
m_neurons[k].m_sensitivity_matrix[i][j] = 0;
}
}
}
}
// Выход из чата.
void OnUserDisconnect(BYTE *InBuffer) {
String name;
try {
//name
GetStreamString(&InBuffer, &name);
RemoveUserFromChannels(name);
// Проверяем, является ли пользователь виртуальным. Если да, то проверяем список отслеживаний каналов.
LogicalConnections::iterator connection = FindConnectionByUser(name);
if (ConnectionExists(connection)) {
UnsetConnectionUser(connection);
}
RemoveOnlineUser(name);
MapList params;
params.insert(pair<String, String>("user", name));
String json_object = SetParametersObject(params);
SendNotification("user_disconnect", json_object, GetConnectionsRef(true));
} catch (Exception *E) {
throw(Format(e_user_disconnect, ARRAYOFCONST((E->Message))));
}
}
/** Display dialog to get connection info from user. */
void Add::operator()(const com_ptr<IDataObject>&, const com_ptr<IBindCtx>&)
const
{
host_info info = add_host(m_hwnd);
if (ConnectionExists(info.name))
BOOST_THROW_EXCEPTION(com_error(E_FAIL));
AddConnectionToRegistry(
info.name, info.host, info.port, info.user, info.path);
notify_shell(m_folder_pidl);
}
// please pay attention. notice here only one output is assumed
void NeuralNetwork::RTRL_update_error(double a_target)
{
// add to total error
m_total_error = (a_target - Output()[0]);
// adjust each weight
for (unsigned int i = 0; i < m_neurons.size(); i++) // to
{
for (unsigned int j = 0; j < m_neurons.size(); j++) // from
{
int t_idx = ConnectionExists(i, j);
if (t_idx != -1)
{
// we know the first output's index is m_num_inputs
double t_delta = m_total_error
* m_neurons[m_num_inputs].m_sensitivity_matrix[i][j];
m_total_weight_change[t_idx] += t_delta * LEARNING_RATE;
}
}
}
}
// Инициализация соединения.
String meth::CheckConnectionID(String conn_id) {
return (ConnectionExists(conn_id)) ? "true" : "false";
}
