internal TrainingData(training_data other)
{
InternalData = other;
}
internal TrainingData(training_data other)
{
InternalData = other;
}
public void cascadetrain_on_data(training_data data, uint max_neurons, uint neurons_between_reports, float desired_error)
{
fanndoublePINVOKE.neural_net_cascadetrain_on_data(swigCPtr, training_data.getCPtr(data), max_neurons, neurons_between_reports, desired_error);
if (fanndoublePINVOKE.SWIGPendingException.Pending) throw fanndoublePINVOKE.SWIGPendingException.Retrieve();
}
public float train_epoch(training_data data)
{
float ret = fanndoublePINVOKE.neural_net_train_epoch(swigCPtr, training_data.getCPtr(data));
if (fanndoublePINVOKE.SWIGPendingException.Pending) throw fanndoublePINVOKE.SWIGPendingException.Retrieve();
return ret;
}
public bool set_scaling_params(training_data data, float new_input_min, float new_input_max, float new_output_min, float new_output_max)
{
bool ret = fanndoublePINVOKE.neural_net_set_scaling_params(swigCPtr, training_data.getCPtr(data), new_input_min, new_input_max, new_output_min, new_output_max);
if (fanndoublePINVOKE.SWIGPendingException.Pending) throw fanndoublePINVOKE.SWIGPendingException.Retrieve();
return ret;
}
public void scale_train(training_data data)
{
fanndoublePINVOKE.neural_net_scale_train(swigCPtr, training_data.getCPtr(data));
if (fanndoublePINVOKE.SWIGPendingException.Pending) throw fanndoublePINVOKE.SWIGPendingException.Retrieve();
}
