Neural Network Classifier

#pragma once
#include <afxtempl.h>
#include "Layer.h"
#include "Synapse.h"
#include "Neuron.h"
#include <math.h>
class Perceptron
{
public:
	~Perceptron(void){};	
	CList<Layer*>	layers;
	CList<Samples>	inputSamples;//Samples are array of double
	CList<Samples>	outputSamples;
	Layer*	inputLayer;
	Layer*	outputLayer;
	double	error;
	//CMatrix<double> *iWeights[4];//initial wights (4 : input, hid1, hid2, hid3)
	Perceptron(int i,int o)
	{
		//iWeights = NULL;
		layers.RemoveAll();
		inputSamples.RemoveAll();
		outputSamples.RemoveAll();
		inputLayer      = new Layer(_T("I"),i+1); // plus the bias
		outputLayer     = new Layer(_T("O"),o);
		layers.AddTail(inputLayer);
		layers.AddTail(outputLayer);
		error = 0.0;
	}
	void addLayer(int n,CString name)
	{
		POSITION tailPos = layers.GetTailPosition();
		Layer* layer = new Layer(name,n);
		layers.InsertBefore(tailPos,layer);
	}
	Layer* getLayer(int i)
	{
		int      j=0;
		bool     found=false;
		POSITION pos = layers.GetHeadPosition();
		Layer* layer = layers.GetAt(pos);
		for (int j = 0; j < layers.GetCount(); j++) 
		{
			layer = layers.GetNext(pos);
			if (i==j)
			{
				found = true;
				break;
			} 
		}

		if (found == false) 
			layer = NULL;
		return layer;
	}

	void connect(int sourceLayer,int sourceNeuron,
		int destLayer,int destNeuron, double w = MAXWORD)
	{
		new Synapse(getLayer(sourceLayer)->getNeuron(sourceNeuron),
			getLayer(destLayer)->getNeuron(destNeuron), w);
	}
	void biasConnect(int destLayer,int destNeuron, double w = MAXWORD)
	{
		new Synapse(inputLayer->getNeuron(inputLayer->size-1),
			getLayer(destLayer)->getNeuron(destNeuron), w);
	}
	void removeSamples()
	{
		inputSamples.RemoveAll();
		outputSamples.RemoveAll();
	}
	void addSample(Samples inputs,Samples outputs)
	{
		ASSERT( (inputs.nLenght > 0) && (outputs.nLenght > 0) );
		ASSERT( (inputs.samples) && (outputs.samples) );
		inputSamples.AddTail(inputs);
		outputSamples.AddTail(outputs);
	}
	CString printSamples()
	{
		//System.out.println(inputSamples+"->"+outputSamples);
		CString str = _T("\ninputs : ");
		POSITION pos = inputSamples.GetHeadPosition();
		double* pSamples = inputSamples.GetAt(pos).samples;
		int len = inputSamples.GetAt(pos).nLenght;
		Samples S;
		for (int i = 0; i < inputSamples.GetCount(); i++) 
		{
			S = inputSamples.GetNext(pos);
			pSamples = S.samples;
			len = S.nLenght;
			str.AppendFormat(_T("\nInput Sample %03d\n"), i);
			for (int j = 0; j < len; j++) 
			{
				str.AppendFormat(_T("%.f ,"), pSamples[j]);					
			}
		}
		
		pos = outputSamples.GetHeadPosition();
		for (int i = 0; i < outputSamples.GetCount(); i++) 
		{
			S = outputSamples.GetNext(pos);
			pSamples = S.samples;
			len = S.nLenght;
			str.AppendFormat(_T("\nOutput Sample %03d\n"), i);
			for (int j = 0; j < len; j++) 
			{
				str.AppendFormat(_T("%.f ,"), pSamples[j]);					
			}
		}
		OutputDebugString(str);
	}

	Samples recognize(Samples iS)
	{
		initInputs(iS);
		propagate();
		Samples oS = getOutput();
		return oS;
	}
	void learn(int iterations)
	{
		int i=0,j=0;
		for(i = 0; i<iterations; i++)
		{
			POSITION pos1 = inputSamples.GetHeadPosition();
			POSITION pos2 = outputSamples.GetHeadPosition();
			Samples pattern = inputSamples.GetAt(pos1);
			Samples target = outputSamples.GetAt(pos2);
			// accumulate total error over each epoch
			error = 0.0;
			for (j = 0; j < inputSamples.GetCount(); j++) 
			{			
				pattern = inputSamples.GetNext(pos1);
				target = outputSamples.GetNext(pos2);
				learnPattern(pattern, target);
				error += computeError(target);
			}
			error /= (inputSamples.GetCount()*outputLayer->neurons.GetCount());
			error = sqrt(error);
		}
	}
	void learnPattern(Samples iS, Samples oS)
	{
		initInputs(iS);
		propagate();
		bpAdjustWeights(oS);
	}
	void initInputs(Samples iS)
	{
		POSITION pos = inputLayer->neurons.GetHeadPosition();
		Neuron* neuron = (Neuron*)inputLayer->neurons.GetAt(pos);
		for (int i = 0; i < iS.nLenght; i++) 
		{
			neuron = (Neuron*)inputLayer->neurons.GetNext(pos);
			neuron->output = iS.samples[i];
		}
		neuron = (Neuron*)inputLayer->neurons.GetNext(pos);// bias;
		neuron->output = 1.0;
	}
	void propagate()
	{
		POSITION pos = layers.GetHeadPosition();
		Layer* layer = (Layer*)layers.GetAt(pos);
		layer = (Layer*)layers.GetNext(pos);// skip the input layer
		for (int j = 0; j < layers.GetCount()-1; j++) 
		{
			layer = (Layer*)layers.GetNext(pos);
			layer->computeOutputs();
		}
	}
	Samples getOutput()
	{
		double sum=0.0;
		double tmp;
		POSITION pos = outputLayer->neurons.GetHeadPosition();
		Samples oS = {0,outputLayer->neurons.GetCount()};
		//oS.samples = new double[outputLayer->neurons.GetCount()];
		Neuron* neuron = (Neuron*)outputLayer->neurons.GetAt(pos);
		for (int j = 0; j < outputLayer->neurons.GetCount(); j++) 
		{
			neuron = (Neuron*)outputLayer->neurons.GetNext(pos);
			oS.samples[j] = neuron->getOutput();
		}
		return oS;
	}
	double computeError(Samples oS)
	{
		double sum=0.0;
		double tmp;
		POSITION pos = outputLayer->neurons.GetHeadPosition();
		Neuron* neuron = (Neuron*)outputLayer->neurons.GetAt(pos);
		for (int i = 0; i < outputLayer->neurons.GetCount(); i++) 
		{
			neuron = (Neuron*)outputLayer->neurons.GetNext(pos);
			tmp = oS.samples[i] - neuron->getOutput();
			sum += tmp * tmp;
		}
		return sum;//sum/2.0;
	}
	double currentError() {
		return error;
	}
	void bpAdjustWeights(Samples oS)
	{
		outputLayer->computeBackpropDeltas(oS);
		POSITION pos = layers.GetHeadPosition();
		
		for(int i=layers.GetCount()-2; i>=1; i--)
		{
			layers.GetNext(pos);
			((Layer*)layers.GetAt(pos))->computeBackpropDeltas();
		}
		outputLayer->computeWeights();
		pos = layers.GetHeadPosition();
		for(int i=layers.GetCount()-2; i>=1; i--)
		{
			layers.GetNext(pos);
			((Layer*)layers.GetAt(pos))->computeWeights();
		}
	}
	CString print()
	{
		CString str = _T("");
		POSITION pos = layers.GetHeadPosition();
		Layer* layer;
		for (int i = 0; i < layers.GetCount(); i++) 
		{
			layer = layers.GetNext(pos);
			str +=layer->print();
		}
		return str;
	}

	int SaveNetwork(CString FileName)
	{
		FILE *netFile = _wfopen(FileName, _T("wt"));
		CTime time = CTime::GetCurrentTime();
		CString str = _T(":::In the name of Allah:::\n:::MultiLayer Perceptron Information\n:::Producer : Hossein Khosravi\n");
		str += time.Format(_T(":::Creation Time : %A, %B %d, %Y (%H:%M)\n"));
		fprintf(netFile, "%S", str);
		int nLayers = layers.GetCount();
		POSITION pos = layers.GetHeadPosition();
		Layer *inLayer = layers.GetNext(pos);
		Layer *hid1,*hid2,*hid3;
		if (nLayers == 2)//no hidden layer
		{
			fprintf(netFile, "Inputs : %3d\nOutputs : %3d\nHid1 : %3d\nHid2 : %3d\nHid3 : %3d\n", 
				layers.GetNext(pos)->neurons.GetCount(),
				layers.GetTail()->neurons.GetCount(),0,0,0);
		}
		else if (nLayers == 3)//one hidden layer
		{
			hid1 = layers.GetNext(pos);
			fprintf(netFile, "Inputs : %3d\nOutputs : %3d\nHid1 : %3d\nHid2 : %3d\nHid3 : %3d\n", 
				inLayer->neurons.GetCount(),
				layers.GetTail()->neurons.GetCount(),
				hid1->neurons.GetCount(),0,0);
		}
		else if (nLayers == 4)//two hidden layer
		{
			hid1 = layers.GetNext(pos);
			hid2 = layers.GetNext(pos);
			fprintf(netFile, "Inputs : %3d\nOutputs : %3d\nHid1 : %3d\nHid2 : %3d\nHid3 : %3d\n", 
				inLayer->neurons.GetCount(),
				layers.GetTail()->neurons.GetCount(),
				hid1->neurons.GetCount(),
				hid2->neurons.GetCount(),0);
		}
		else if (nLayers == 5)//three hidden layer
		{
			hid1 = layers.GetNext(pos);
			hid2 = layers.GetNext(pos);
			hid3 = layers.GetNext(pos);
			fprintf(netFile, "Inputs : %3d\nOutputs : %3d\nHid1 : %3d\nHid2 : %3d\nHid3 : %3d\n", 
				inLayer->neurons.GetCount(),
				layers.GetTail()->neurons.GetCount(),
				hid1->neurons.GetCount(),
				hid2->neurons.GetCount(),
				hid3->neurons.GetCount());
		}
		fprintf(netFile, "%S", print());

		fclose(netFile);
		return 0;
	}

	int LoadNet(CString FileName)
	{
		int in = 0, out = 0, h1 = 0, h2 = 0, h3 = 0;
		int i = 0, j = 0;
		CStdioFile netFile(FileName, CFile::modeRead|CFile::typeText);
		CString str;
		netFile.ReadString(str);
		while (str[0] == ':')
		{
			netFile.ReadString(str);
		}
		in = _wtoi(str.Right(3));
		netFile.ReadString(str);
		out = _wtoi(str.Right(3));
		netFile.ReadString(str);
		h1 = _wtoi(str.Right(3));
		netFile.ReadString(str);
		h2 = _wtoi(str.Right(3));
		netFile.ReadString(str);
		h3 = _wtoi(str.Right(3));
		netFile.ReadString(str);//read blank line

		POSITION pos = layers.GetHeadPosition();
		int strPos = 0;
		CString strRes;
		Layer* layer = NULL;
		double weights[1024];
		int k = 0;
		for (i = 0; i < layers.GetCount()-1; i++) 
		{
			layer = layers.GetNext(pos);
			for (j = 0; j < layer->neurons.GetCount(); j++) 
			{
				strPos = 0;
				k = 0;
				netFile.ReadString(str);//skip header line
				netFile.ReadString(str);
				strRes = str.Tokenize(_T("| ()"), strPos);
				while (strRes != "")
				{
					strRes= str.Tokenize(_T("\ ()"),strPos);
					if(strRes.Find('.') != -1)//it is a weight
					{
						weights[k] = _wtof(strRes);
						k++;
					}
				};
				layer->getNeuron(j)->SetWeights(weights);
			}
		}
		return 0;
	}
};