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/* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
* This file is part of the LibreOffice project.
*
* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/.
*
* This file incorporates work covered by the following license notice:
*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed
* with this work for additional information regarding copyright
* ownership. The ASF licenses this file to you under the Apache
* License, Version 2.0 (the "License"); you may not use this file
* except in compliance with the License. You may obtain a copy of
* the License at http://www.apache.org/licenses/LICENSE-2.0 .
*/
#include "neuralnetworkfann.hxx"
NeuralNetworkFann::NeuralNetworkFann(sal_uInt32 nLayers, const sal_uInt32* nLayer) :
ann(fann_create_standard_array(nLayers, nLayer), fann_destroy),
data(nullptr, fann_destroy_train)
{
}
NeuralNetworkFann::NeuralNetworkFann(const OUString& file) :
ann(fann_create_from_file(OUStringToOString(file, RTL_TEXTENCODING_ASCII_US).pData->buffer), fann_destroy),
data(nullptr, fann_destroy_train)
{
}
void NeuralNetworkFann::SetActivationFunction(ActivationFunction function)
{
fann_activationfunc_enum func;
switch (function)
{
case ActivationFunction::SIGMOID:
{
func = FANN_SIGMOID_SYMMETRIC;
break;
}
default:
{
return;
}
}
fann_set_activation_function_hidden(ann.get(), func);
fann_set_activation_function_output(ann.get(), func);
}
void NeuralNetworkFann::SetTrainingAlgorithm(TrainingAlgorithm algorithm)
{
fann_train_enum algo;
switch (algorithm)
{
case TrainingAlgorithm::INCREMENTAL:
{
algo = FANN_TRAIN_INCREMENTAL;
break;
}
default:
{
return;
}
}
fann_set_training_algorithm(ann.get(), algo);
}
void NeuralNetworkFann::SetLearningRate(float rate)
{
fann_set_learning_rate(ann.get(), rate);
}
#include <iostream>
void NeuralNetworkFann::InitTraining(sal_uInt32 nExamples)
{
data.reset(fann_create_train(nExamples, fann_get_num_input(ann.get()), fann_get_num_output(ann.get())));
}
void NeuralNetworkFann::Save(const OUString& file)
{
OString o = OUStringToOString(file, RTL_TEXTENCODING_ASCII_US);
fann_save(ann.get(), o.pData->buffer);
}
sal_uInt32 NeuralNetworkFann::GetNumInput()
{
return fann_get_num_input(ann.get());
}
float* NeuralNetworkFann::GetInput(sal_uInt32 nIeme)
{
return &data->input[nIeme][0];
}
sal_uInt32 NeuralNetworkFann::GetNumOutput()
{
return fann_get_num_output(ann.get());
}
float* NeuralNetworkFann::GetOutput(sal_uInt32 nIeme)
{
return &data->output[nIeme][0];
}
float* NeuralNetworkFann::Run(float *data_input)
{
return fann_run(ann.get(), data_input);
}
void NeuralNetworkFann::Train(sal_uInt32 nEpochs, float error)
{
fann_train_on_data(ann.get(), data.get(), nEpochs, 1, error);
}
/* vim:set shiftwidth=4 softtabstop=4 expandtab: */
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