simplelp.cpp

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#include <iostream>
#include <fstream>
#include <algorithm>
#include <iterator>
#include "WienerInterpolator.h"
#include "UniformRNG.h"
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/vector_proxy.hpp>
using namespace boost::numeric::ublas;
int main()
{
        std::string filename;
        std::ifstream infile;
        UniformRNG Random;
        /*std::cout << "Infile name: ";
        std::cin >> filename;
        std::vector<double> read;
        infile.open(filename.c_str());
        std::copy(std::istream_iterator<double>(infile),std::istream_iterator<double>(),std::back_inserter(read));*/
        
        
        const int datasize = 500;
        const int filterlength = 20;
        const int inputlength = 100;
        gplib::rvec Data(datasize);
        for (int i = 0; i < datasize; ++i)
                Data(i) = sin(static_cast<double>(i)/0.9)+cos(static_cast<double>(i)/2.7)+ (0.5 - Random.GetNumber())*0.1;
        //copy(read.begin(),read.end(),Data.begin());
        WienerInterpolator Wiener(filterlength);
        gplib::rvec Input(inputlength);
        gplib::rvec Prediction(datasize);
        for (int i = 0; i < inputlength; ++i)
        {
                Input(i) = Data(i);
                Prediction(i) = Data(i);
        }
        Wiener.AdaptFilter(Input,Input);
        
        for (int i = inputlength-filterlength; i < datasize - filterlength; ++i)
        {
                vector_range<vector<double> > vr(Prediction, range(i,i+filterlength));
                Prediction(i+filterlength) = inner_prod(Wiener.GetWeights(),vr);
        }
        std::ofstream outfile("out");
        for (int i = 0; i < datasize; ++i)
           outfile << i << " " << Data(i) << " " << Prediction(i) <<  std::endl;
         //copy(Wiener.GetWeights().begin(),Wiener.GetWeights().end(),std::ostream_iterator<double>(std::cout,"\n")); 
}

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