neuralNetwork.h

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#ifndef neuralNetwork_h
#define neuralNetwork_h
#include <vector>
#include "baseModel.h"

#ifndef EMSCRIPTEN
#include "../dependencies/json/json.h"
#endif

#define LEARNING_RATE 0.3
#define MOMENTUM 0.2
#define NUM_EPOCHS 500

template<typename T>
class neuralNetwork : public baseModel<T> {
    
public:
    neuralNetwork(const int &num_inputs,
                  const std::vector<int> &which_inputs,
                  const int &num_hidden_layers,
                  const int &num_hidden_nodes,
                  const std::vector<T> &weights,
                  const std::vector<T> &wHiddenOutput,
                  const std::vector<T> &inRanges,
                  const std::vector<T> &inBases,
                  const T &outRange,
                  const T &outBase);
    
    neuralNetwork(const int &num_inputs,
                  const std::vector<int> &which_inputs,
                  const int &num_hidden_layer,
                  const int &num_hidden_nodes);
    
    ~neuralNetwork();
    
    T run(const std::vector<T> &inputVector);
    
    void reset();
    
    int getNumInputs() const;
    std::vector<int> getWhichInputs() const;
    
    int getNumHiddenLayers() const;
    void setNumHiddenLayers(int num_hidden_layers);
    
    int getNumHiddenNodes() const;
    
    void setEpochs(const int &epochs);
    
    std::vector<T> getWeights() const;
    std::vector<T> getWHiddenOutput() const;
    
    std::vector<T> getInRanges() const;
    std::vector<T> getInBases() const;
    T getOutRange() const;
    T getOutBase() const;
    
#ifndef EMSCRIPTEN
    void getJSONDescription(Json::Value &currentModel);
#endif
    
    
private:
    int numInputs;
    std::vector<int> whichInputs;
    int numHiddenLayers;
    int numHiddenNodes;
    
    std::vector<T> inputNeurons;
    std::vector<std::vector<T> > hiddenNeurons;
    T outputNeuron;
    
    std::vector<std::vector<std::vector<T> > > weights;
    std::vector<T> wHiddenOutput;
    
    std::vector<T> inRanges;
    std::vector<T> inBases;
    T outRange;
    T outBase;
    
    inline T activationFunction(T);
    
    
public:
    void train(const std::vector<trainingExampleTemplate<T> > &trainingSet);
    
private:
    T learningRate;
    T momentum;
    int numEpochs;
    
    std::vector<std::vector< std::vector<T> > > deltaWeights;
    std::vector<T> deltaHiddenOutput;
    
    T outputErrorGradient;
    inline T getHiddenErrorGradient(int layer, int neuron);
    
    void initTrainer();
    
    void backpropagate(const T &desiredOutput);
    
    void updateWeights();
};

#endif