public static float train_epoch_quickprop_parallel(SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, uint threadnumb, floatVectorVector predicted_outputs) { float ret = fannfloatPINVOKE.train_epoch_quickprop_parallel__SWIG_1(SWIGTYPE_p_fann.getCPtr(ann), SWIGTYPE_p_fann_train_data.getCPtr(data), threadnumb, floatVectorVector.getCPtr(predicted_outputs)); if (fannfloatPINVOKE.SWIGPendingException.Pending) throw fannfloatPINVOKE.SWIGPendingException.Retrieve(); return ret; }
/* Method: TrainEpochSarpropParallel Parameters: data - the data to train on threadNumb - the thread to do training on predictedOutputs - the predicted outputs */ public float TrainEpochSarpropParallel(TrainingData data, uint threadNumb, List<List<float>> predictedOutputs) { using (floatVectorVector predicted_out = new floatVectorVector(predictedOutputs.Count)) { for (int i = 0; i < predictedOutputs.Count; i++) { predicted_out[i] = new floatVector(predictedOutputs[i].Count); } float result = fannfloat.train_epoch_sarprop_parallel(net.to_fann(), data.ToFannTrainData(), threadNumb, predicted_out); predictedOutputs.Clear(); for (int i = 0; i < predicted_out.Count; i++) { List<float> list = new List<float>(); for (int j = 0; j < predicted_out[i].Count; j++) { list.Add(predicted_out[i][j]); } predictedOutputs.Add(list); } return result; } }
public static float train_epoch_incremental_mod(SWIGTYPE_p_fann ann, SWIGTYPE_p_fann_train_data data, floatVectorVector predicted_outputs) { float ret = fannfloatPINVOKE.train_epoch_incremental_mod__SWIG_1(SWIGTYPE_p_fann.getCPtr(ann), SWIGTYPE_p_fann_train_data.getCPtr(data), floatVectorVector.getCPtr(predicted_outputs)); if (fannfloatPINVOKE.SWIGPendingException.Pending) throw fannfloatPINVOKE.SWIGPendingException.Retrieve(); return ret; }
/* Method: TrainEpochIncrementalMod Parameters: data - the data to train on predictedOutputs - the predicted outputs */ public float TrainEpochIncrementalMod(TrainingData data, List<List<float>> predictedOutputs) { using (floatVectorVector predicted_out = new floatVectorVector(predictedOutputs.Count)) { for (int i = 0; i < predictedOutputs.Count; i++) { predicted_out[i] = new floatVector(predictedOutputs[i].Count); } float result = fannfloat.train_epoch_incremental_mod(net.to_fann(), data.ToFannTrainData(), predicted_out); predictedOutputs.Clear(); for (int i = 0; i < predicted_out.Count; i++) { List<float> list = new List<float>(); for (int j = 0; j < predicted_out[i].Count; j++) { list.Add(predicted_out[i][j]); } predictedOutputs.Add(list); } return result; } }