{

private:

double* inputNodeValues;

double* hiddenNodeValues;

double* outputNodeValues;

double* desiredOutputNodeValues;

int noOfInputs;

int noOfHidden;

int noOfOutputs;

double** inputToHiddenWeights;

double** newInputToHiddenWeights;

double** hiddenToOutputWeights;

double** newHiddenToOutputWeights;

double biasInputToHidden;

double biasHiddenToOutput;

double learningRate;

double totalNetworkError;

int learningEpoch;

boolean learning;

boolean verbose;

void _backPropagation();

public:

Neural(int inputs, int hidden, int outputs);

void calculateOutput();

void setLearningRate(double bias);

double getLearningRate();

void setBiasInputToHidden(double bias);

double getBiasInputToHidden();

void setBiasHiddenToOutput(double bias);

double getBiasHiddenToOutput();

void turnLearningOn();

void turnLearningOff();

void setInputNode(int node, double value);

double getInputNode(int node);

void setOutputNodeDesired(int node, double value);

double getOutputNodeDesired(int node);

int getNoOfInputNodes();

int getNoOfHiddenNodes();

int getNoOfOutputNodes();

double getOutputNode(int node);

void displayInputToHiddenWeightsCurrent();

void displayHiddenToOutputWeightsCurrent();

void turnVerboseOn();

void turnVerboseOff();

double getTotalNetworkError();

int getEpoch();

void setInputToHiddenWeight(int input, int hidden, double value);

double getInputToHiddenWeight(int input, int hidden);

void setHiddenToOutputWeight(int hidden, int output, double value);

double getHiddenToOutputWeight(int hidden, int output);

boolean getLearningStatus();

void displayInputNodes();

void displayHiddenNodes();

void displayOutputNodes();

/*

void seed(int x) {

randomSeed(x);

}

*/