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(); }