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diff --git a/cui/source/factory/neuralnetworkinternal.hxx b/cui/source/factory/neuralnetworkinternal.hxx
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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 .
+ */
+#ifndef INCLUDED_CUI_SOURCE_FACTORY_NEURALNETWORKINTERNAL_HXX
+#define INCLUDED_CUI_SOURCE_FACTORY_NEURALNETWORKINTERNAL_HXX
+
+#include "neuralnetwork.hxx"
+
+#include <fann.h>
+#include <vector>
+
+class NeuralNetworkInternal : public AbstractNeuralNetwork
+{
+public:
+    NeuralNetworkInternal(sal_uInt32 nLayers, const sal_uInt32* nLayer);
+    NeuralNetworkInternal(const OUString& file);
+
+    void SetActivationFunction(ActivationFunction function) override;
+    void SetTrainingAlgorithm(TrainingAlgorithm algorithm) override;
+    void SetLearningRate(float rate) override;
+
+    void InitTraining(sal_uInt32 nExamples) override;
+    sal_uInt32 GetNumInput() override;
+    float* GetInput(sal_uInt32 nIeme) override;
+    sal_uInt32 GetNumOutput() override;
+    float* GetOutput(sal_uInt32 nIeme) override;
+
+    void Train(sal_uInt32 nEpochs, float error) override;
+    void Run(float *data_input, float* result) override;
+    void Save(const OUString& file) override;
+
+    virtual ~NeuralNetworkInternal(){}
+
+    virtual void * GetTrain(){return nullptr;}
+
+private:
+    enum class FunctionTrans
+    {
+        Sigmoid,
+        SigmoidSymmetric
+    };
+    struct Neuron
+    {
+        // value of neuron.
+        // fann: value
+        // 0: input.
+        // last: output.
+        float a;
+        // weight
+        // fann: weights[first_con to last_con-2]
+        // Useless for n[0]
+        std::vector<float> w; // Number of neuron of the next layer without biais.
+        std::vector<float> dw; // Number of neuron of the next layer without biais.
+        // biais.
+        // fann: weights[last_con-1]
+        // function
+        // Useless for n[0]
+        FunctionTrans f;
+        // Steepness.
+        float stp;
+
+        // temporary field.
+        // gradient
+        float s;
+        float sum;
+
+        // b : between [-.1;.1]
+        Neuron() : a(0.), w(), f(FunctionTrans::Sigmoid), stp(1.f), sum(0) {}
+    };
+    std::vector<std::vector<Neuron>> n; // One neuron is biais.
+    float learning_rate;
+    float learning_rate_alpha;
+    std::vector<std::vector<float>> learning_input;
+    std::vector<std::vector<float>> learning_output;
+};
+
+#endif
+
+/* vim:set shiftwidth=4 softtabstop=4 expandtab: */