#include "CNeuralNet.h"



//*************************** methods for Neuron **********************
//
//---------------------------------------------------------------------
SNeuron::SNeuron(int NumInputs): m_NumInputs(NumInputs+1)
											
{
	//we need an additional weight for the bias hence the +1
	for (int i=0; i<NumInputs+1; ++i)
	{
		//set up the weights with an initial random value
		m_vecWeight.push_back(RandomClamped());
	}
}




//************************ methods for NeuronLayer **********************

//-----------------------------------------------------------------------
//	ctor creates a layer of neurons of the required size by calling the 
//	SNeuron ctor the rqd number of times
//-----------------------------------------------------------------------
SNeuronLayer::SNeuronLayer(int NumNeurons, 
                           int NumInputsPerNeuron):	m_NumNeurons(NumNeurons)
{
	for (int i=0; i<NumNeurons; ++i)

		m_vecNeurons.push_back(SNeuron(NumInputsPerNeuron));
}




//************************ methods forCNeuralNet ************************

//------------------------------default ctor ----------------------------
//
//	creates a ANN based on the default values in defines.h
//-----------------------------------------------------------------------
CNeuralNet::CNeuralNet() 
{
	m_NumInputs	          =	CParams::iNumInputs;
	m_NumOutputs		      =	CParams::iNumOutputs;
	m_NumHiddenLayers	    =	CParams::iNumHidden;
	m_NeuronsPerHiddenLyr =	CParams::iNeuronsPerHiddenLayer;

	CreateNet();

}


//------------------------------createNet()------------------------------
//
//	this method builds the ANN. The weights are all initially set to 
//	random values -1 < w < 1
//------------------------------------------------------------------------
void CNeuralNet::CreateNet()
{
	//create the layers of the network
	if (m_NumHiddenLayers > 0)
	{
		//create first hidden layer
	  m_vecLayers.push_back(SNeuronLayer(m_NeuronsPerHiddenLyr, m_NumInputs));
    
    for (int i=0; i<m_NumHiddenLayers-1; ++i)
    {

			m_vecLayers.push_back(SNeuronLayer(m_NeuronsPerHiddenLyr,
                                         m_NeuronsPerHiddenLyr));
    }

    //create output layer
	  m_vecLayers.push_back(SNeuronLayer(m_NumOutputs, m_NeuronsPerHiddenLyr));
	}

  else
  {
	  //create output layer
	  m_vecLayers.push_back(SNeuronLayer(m_NumOutputs, m_NumInputs));
  }
}

//---------------------------------GetWeights-----------------------------
//
//	returns a vector containing the weights
//
//------------------------------------------------------------------------
vector<double> CNeuralNet::GetWeights() const
{
	//this will hold the weights
	vector<double> weights;
	
	//for each layer
	for (int i=0; i<m_NumHiddenLayers + 1; ++i)
	{

		//for each neuron
		for (int j=0; j<m_vecLayers[i].m_NumNeurons; ++j)
		{
			//for each weight
			for (int k=0; k<m_vecLayers[i].m_vecNeurons[j].m_NumInputs; ++k)
			{
				weights.push_back(m_vecLayers[i].m_vecNeurons[j].m_vecWeight[k]);
			}
		}
	}

	return weights;
}

//-----------------------------------PutWeights---------------------------
//
//	given a vector of doubles this function replaces the weights in the NN
//  with the new values
//
//------------------------------------------------------------------------
void CNeuralNet::PutWeights(vector<double> &weights)
{
	int cWeight = 0;
	
	//for each layer
	for (int i=0; i<m_NumHiddenLayers + 1; ++i)
	{

		//for each neuron
		for (int j=0; j<m_vecLayers[i].m_NumNeurons; ++j)
		{
			//for each weight
			for (int k=0; k<m_vecLayers[i].m_vecNeurons[j].m_NumInputs; ++k)
			{
				m_vecLayers[i].m_vecNeurons[j].m_vecWeight[k] = weights[cWeight++];
			}
		}
	}

	return;
}

//---------------------------------GetNumberOfWeights---------------------
//
//	returns the total number of weights needed for the net
//
//------------------------------------------------------------------------
int CNeuralNet::GetNumberOfWeights() const
{
	int weights = 0;
	
	//for each layer
	for (int i=0; i<m_NumHiddenLayers + 1; ++i)
	{

		//for each neuron
		for (int j=0; j<m_vecLayers[i].m_NumNeurons; ++j)
		{
			//for each weight
			for (int k=0; k<m_vecLayers[i].m_vecNeurons[j].m_NumInputs; ++k)
			
				weights++;			
		}
	}

	return weights;
}

//--------------------------- CalculateSplitPoints -----------------------
//
//  this method calculates all points in the vector of weights which 
//  represent the start and end points of individual neurons
//------------------------------------------------------------------------
vector<int> CNeuralNet::CalculateSplitPoints() const
{
	vector<int> SplitPoints;

  int WeightCounter = 0;
	
	//for each layer
	for (int i=0; i<m_NumHiddenLayers + 1; ++i)
	{
		//for each neuron
		for (int j=0; j<m_vecLayers[i].m_NumNeurons; ++j)
		{
			//for each weight
			for (int k=0; k<m_vecLayers[i].m_vecNeurons[j].m_NumInputs; ++k)
      {
				++WeightCounter;			
      }

      SplitPoints.push_back(WeightCounter - 1);
		}
	}

	return SplitPoints;
}

//-------------------------------Update-----------------------------------
//
//	given an input vector this function calculates the output vector
//
//------------------------------------------------------------------------
vector<double> CNeuralNet::Update(vector<double> &inputs)
{
	//stores the resultant outputs from each layer
	vector<double> outputs;

	int cWeight = 0;
	
	//first check that we have the correct amount of inputs
	if (inputs.size() != m_NumInputs)
  {
		//just return an empty vector if incorrect.
		return outputs;
  }
	
	//For each layer....
	for (int i=0; i<m_NumHiddenLayers + 1; ++i)
	{
		if ( i > 0 )
    {
			inputs = outputs;
    }

		outputs.clear();
		
		cWeight = 0;

		//for each neuron sum the (inputs * corresponding weights).Throw 
		//the total at our sigmoid function to get the output.
		for (int j=0; j<m_vecLayers[i].m_NumNeurons; ++j)
		{

			double netinput = 0.0f;

			int	NumInputs = m_vecLayers[i].m_vecNeurons[j].m_NumInputs;
			
			//for each weight
			for (int k=0; k<NumInputs - 1; ++k)
			{
				//sum the weights x inputs
				netinput += m_vecLayers[i].m_vecNeurons[j].m_vecWeight[k] * 
                    inputs[cWeight++];
			}

			//add in the bias
			netinput += m_vecLayers[i].m_vecNeurons[j].m_vecWeight[NumInputs-1] * 
                  CParams::dBias;

			//we can store the outputs from each layer as we generate them. 
      //The combined activation is first filtered through the sigmoid 
      //function
			outputs.push_back(Sigmoid(netinput, CParams::dActivationResponse));

			cWeight = 0;
		}
	}

	return outputs;
}

//-------------------------------Sigmoid function-------------------------
//
//------------------------------------------------------------------------
double CNeuralNet::Sigmoid(double netinput, double response)
{
	return ( 1 / ( 1 + exp(-netinput / response)));
}