allnet.cpp

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#include "NeuralNetwork.h"
#include <iostream>
#include <vector>
#include <fstream>
#include <string>
#include "CNeuralConfig.h"
#include "CUniformRNG.h"
using namespace std;

int main()
{
        CNeuralConfig Configuration;
        ttypeVector typeVector;
        ttypeArray typeArray;
        ifstream exfile,eyfile,hxfile,hyfile;
        ofstream exoutput,eyoutput,exepsfile,eyepsfile;
        
        double currentex, currentey, currenthx, currenthy;
        
        string exfilename,eyfilename,hxinfilename,hyinfilename,exoutfilename,eyoutfilename,hxoutfilename,hyoutfilename;
        string exepsfilename, eyepsfilename;
        
        Configuration.GetData("neural.conf");
        const int seglength = Configuration.seglength;
        const double maxinit = Configuration.maxinit;
        const int maxit = Configuration.maxit;
        const int hlayers = Configuration.hiddenlayers;
        vector<double> ex(seglength,0),ey(seglength,0), hx(seglength,0), hy(seglength,0);
        
        cout << "Datafile 1: ";
        cin >> exfilename;
        cout << "Datafile 2: ";
        cin >> eyfilename;
        cout << "Datafile 3: ";
        cin >> hxoutfilename;
        cout << "Datafile 4: ";
        cin >> hyoutfilename;
        cout << "Referencefile 1:";
        cin >> hxinfilename;
        cout << "Referencefile 2:";
        cin >> hyinfilename;
        exoutfilename = exfilename+".clean";
        eyoutfilename = eyfilename+".clean";
        exepsfilename = exfilename+".eps";
        eyepsfilename = eyfilename+".eps";
        exfile.open(exfilename.c_str());
        eyfile.open(eyfilename.c_str());
        exepsfile.open(exepsfilename.c_str());
        eyepsfile.open(eyepsfilename.c_str());
        hxfile.open(hxfilename.c_str());
        hyfile.open(hyfilename.c_str());
        exoutput.open(exoutfilename.c_str());
        //eyoutput.open(eyoutfilename.c_str());
        
        typeVector.assign(seglength*2,bipolar);
        for (int i = 0; i < hlayers; ++i)
        {
                exoutput << 0 << endl;
                //eyoutput << 0 << endl;
                typeArray.push_back(typeVector);
        }
        typeVector.assign(4,identity);
        typeArray.push_back(typeVector);
        
        NeuralNetwork Network;
        Network.Input.assign(seglength*2,0);
        //Network.Input.assign(seglength,0);
        Network.SetLayers(typeArray);
        Network.InitWeights(maxinit,maxinit);
        Network.mu = Configuration.mu;
        for (int i = 0; i < seglength; ++i)
        {
                exfile >> currentex;
                eyfile >> currentey;
                hxfile >> currenthx;
                hyfile >> currenthy;
                ex.at(i) = currentex;
                ey.at(i) = currentey;
                hx.at(i) = currenthx;
                hy.at(i) = currenthy;
        }
        for (int iterations = 0; iterations < maxit; ++iterations)
        {
        /*cout << "Input: ";
        for (int i = 0; i < Network.Input.size(); ++i)
                cout << Network.Input.at(i) << " ";
        cout << endl;
        cout << "Desired: ";
        for (int i = 0; i < Network.Desired.size(); ++i)
                cout << Network.Desired.at(i) << " ";
        cout << endl;
        cout << "Weights: " << endl;
        for (int i = 0; i < typeArray.size(); ++i)
        {
                cout << "Layer: " << i << endl;
                for (int j = 0; j < typeArray.at(i).size(); ++j)
                {
                        cout << "Neuron: " << j << endl;
                        cout << "Weights: " << endl;
                        for (int k = 0; k < Network.Layers.at(i).at(j)->Combiner->Weights.size(); ++k)
                                cout << Network.Layers.at(i).at(j)->Combiner->Weights.at(k) << " ";
                        cout << endl;
                        cout << "Bias: " << Network.Layers.at(i).at(j)->Combiner->Bias << endl;
                }
                cout << endl;
        }*/
                for (int i = 0; i < seglength; ++i)
                {
                        Network.Input.at(i) = hx.at(i);
                        Network.Input.at(i+seglength) = hy.at(i);
                }
                Network.Desired.at(0) =  ex.at(0);
                Network.Desired.at(1) =  ey.at(0);
                Network.CalcOutput();
                Network.AdaptWeights();
                
                exfile >> currentex;
                eyfile >> currentey;
                hxfile >> currenthx;
                hyfile >> currenthy;
                
                for (int i = 1; i < seglength; ++i)
                {
                        ex.at(i-1) = ex.at(i);
                        ey.at(i-1) = ey.at(i);
                        hx.at(i-1) =  hx.at(i);
                        hy.at(i-1) = hy.at(i);
                }
                ex.back() = currentex;
                ey.back() = currentey;
                hx.back() = currenthx;
                hy.back() = currenthy;
        /*cout << "Output: ";
        for (int i = 0; i < Network.Output.size(); ++i)
                cout << Network.Output.at(i) << " ";
        cout << endl << endl;*/
        
        /*cout << "Delta: " << endl;
        for (int i = 0; i < typeArray.size(); ++i)
        {
                cout << "Layer: " << i << endl;
                for (int j = 0; j < typeArray.at(i).size(); ++j)
                {
                        cout << "Neuron: " << j << endl;
                        cout << "Delta: " << Network.Layers.at(i).at(j)->Delta << endl;
                        cout << "Net: " << Network.Layers.at(i).at(j)->Net << endl;
                        cout << "Output: " << Network.Layers.at(i).at(j)->Output << endl;
                }
                cout << endl;
        }*/
                exoutput << Network.Output.at(0) <<endl ;
                //eyoutput << Network.Output.at(1) <<endl;
                exepsfile << (Network.Desired.at(0) - Network.Output.at(0)) << endl;
                //eyepsfile << (Network.Desired.at(1) - Network.Output.at(1)) << endl;
        }
        hxfile.close();
        exfile.close();
        eyfile.close();
        hyfile.close();
        exepsfile.close();
        eyepsfile.close();
        exoutput.close();
        //eyoutput.close();
}