// Methods
// NOTE: ANN is special since it supports both regression and classification, we therefore override these methods
void ann::train()
{
const data_type data_type = get_data_type();
GRT::UINT numSamples = data_type == LABELLED_CLASSIFICATION ? classification_data.getNumSamples() : regression_data.getNumSamples();
if (numSamples == 0)
{
flext::error("no observations added, use 'add' to add training data");
return;
}
bool success = false;
if (data_type == LABELLED_CLASSIFICATION)
{
grt_ann.init(
classification_data.getNumDimensions(),
num_hidden_neurons,
classification_data.getNumClasses(),
input_activation_function,
hidden_activation_function,
output_activation_function
);
success = grt_ann.train(classification_data);
}
else if (data_type == LABELLED_REGRESSION)
{
grt_ann.init(
regression_data.getNumInputDimensions(),
num_hidden_neurons,
regression_data.getNumTargetDimensions(),
input_activation_function,
hidden_activation_function,
output_activation_function
);
success = grt_ann.train(regression_data);
}
if (!success)
{
flext::error("training failed");
}
t_atom a_success;
SetInt(a_success, success);
ToOutAnything(1, get_s_train(), 1, &a_success);
}
