nettest3.cpp

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#include "NeuralNetwork.h"
#include <iostream>
#include <vector>
#include <string>
#include <algorithm>
#include <iterator>
#include <cmath>
#include <fstream>
#include "UniformRNG.h"
#include "NetCDFTools.h"
#include "ApplyFilter.h"
using namespace std;

int main()
{
        UniformRNG Random;
        NeuralNetwork::ttypeVector typeVector;
        NeuralNetwork::ttypeArray typeArray;
                
        const int seglength = 5;
        const double maxinit = 0.5;
        const int maxit = 50000;
        const int hlayers = 2;
        const int deslength = 1;
        const double mu = 0.1;
        const double alpha = 0.0;
        const double period = 35.2;
        const double PI = acos(-1.0);
        const double noiselevel = 0.2;
        TimeSeriesComponent input, desired;
        
        typeVector.assign(seglength,SigmoidalNeuron::bipolar);
        for (int i = 0; i < hlayers; ++i)
        {
                typeArray.push_back(typeVector);
        }
        typeVector.assign(1,SigmoidalNeuron::bipolar);
        typeArray.push_back(typeVector);
        
        NeuralNetwork Network(seglength,deslength,mu,typeArray,maxinit);
        Network.SetAlpha(alpha);
        //Network.SetInput(input);
        //Network.SetLayers(typeArray);
        //Network.InitWeights(maxinit,maxinit);
        //Network.SetMu(mu);
        
        ApplyFilter Filter(Network,true);
        Filter.SetWeightSaveIntervall(100);
        for (int i = 0; i < maxit; ++i)
        {
                        input.GetData().push_back(sin( 2.0 * PI * i/period));
                        desired.GetData().push_back(0.5*cos( 2.0 * PI * i/period)+Random.GetNumber(-noiselevel,noiselevel));
        }
        Filter.AddInputChannel(input);
        Filter.AddReferenceChannel(desired);
        cout << "Weights: " << endl;
        Network.PrintWeights(cout);
        cout << endl;
        Filter.FilterData();
        cout << "Weights: " << endl;
        Network.PrintWeights(cout);
        cout << endl;
        ofstream cleanfile("test.clean");
        for (int i = 0; i < maxit; ++i)
                cleanfile << i << " " << Filter.GetOutChannels().front()->GetData().at(i) << " " << desired.GetData().at(i) << " " 
                                         << 0.5*cos( 2.0 * PI * i/period) << endl;
        ofstream difffile("test.diff");
        for (int i = 0; i < maxit; ++i)
                difffile << i << " " << Filter.GetOutChannels().front()->GetData().at(i) -0.5*cos( 2.0 * PI * i/period)  
                          << " " << desired.GetData().at(i) - 0.5* cos( 2.0 * PI * i/period) << endl;
        ofstream epsfile("test.eps");
        copy(Filter.GetEpsValues().front().begin(),Filter.GetEpsValues().front().end(),ostream_iterator<double>(epsfile,"\n")); 
        Network.PrintTopology("test.dot");                       
        WriteMatrixAsNetCDF("test.weights.nc",Filter.GetWeightHistory());
}

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