Signal Processing/perftest.cpp

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//testing the performance of the LMS-Canceller to see difference between std::vector and ublas

#include <iostream>
#include <vector>
#include <fstream>
#include "LMSCanceller.h"

using namespace std;

int main()
{
        const int iterations = 100; 
        const int filterlength = 206; // set the length of the filter
        const double mu = 0.5; // the stepsize for adaptation, a negative value indicates automatic calculation
        const double secfactor = 5; // the factor between the value calculated and the value used
        const int shift=0; // the shift in index between the first point in the segment and the value used as reference
        
        vector<double> input, desired;//declare vectors that hold the input data, the reference data
        // the filter output and the difference to the original time series
        string inputfilename, reffilename, outputfilename; // the names of the input and output files
        ifstream inputfile, reffile; // the file objects for reading
        ofstream outfile, weightfile,epsfile; // and writingy
        double currentnumber; // we have to read in a bunch of numbers and use this a temporary storage
        
        cout << "Inputfile: "; 
        cin >> inputfilename; // read in inputfilename 
        cout << "Reference Filename: ";
        cin >> reffilename; // and reference filename
        
        inputfile.open(inputfilename.c_str()); //open inputfile stream for reading
        reffile.open(reffilename.c_str()); // same for reference file stream
        outfile.open((reffilename+".clean").c_str()); //open outputfile with an automatically generated name
        weightfile.open((reffilename+".weight").c_str());
        epsfile.open((reffilename+".eps").c_str());
        while (inputfile.good()) // while no errors occur
        {
                inputfile >> currentnumber; // read in a number from the input file
                input.push_back(currentnumber); // add it to the end of the input vector
        }
        
        while (reffile.good()) // while no errors occur
        {
                reffile >> currentnumber; // read in a number from the reference file
                desired.push_back(currentnumber);// add it to the end of the reference vector
        }
        inputfile.close(); //close both files
        reffile.close();
        vector<double> output(input.size()), epsilon(input.size()); 
        LMSCanceller Canceller(filterlength); // declare a new object of type LMSCanceller
        Canceller.SetMu(mu);
        for (int a = 0; a < iterations; ++a)
        {
                gplib::rvec currentinput(filterlength), currdesired(1);
                copy(input.begin(),input.begin()+filterlength,currentinput.begin());
                currdesired(0) = desired.at(filterlength+1); // so far we try to predict
                for (size_t i = 1; i < input.size()-filterlength-1; ++i)
                {
                        // needs to be fixed output.at(i+filterlength) = Canceller.CalcOutput(currentinput,currdesired)(0);
                        // Canceller.AdaptFilter(currentinput);
                        rotate(currentinput.begin(),currentinput.begin()+1,currentinput.end());
                        currdesired(0) = desired.at(filterlength+i+1);
                        currentinput(filterlength-1) = input.at(i+filterlength);
                        // Canceller.GetFilterOutput().front();
                        epsilon.at(i+filterlength) = Canceller.GetEpsilon()(0);
                }
        }
        for (size_t i = shift; i < output.size(); ++i) //output the output data
                outfile << output.at(i) << endl; //to outputfile
        for (size_t i = shift; i < epsilon.size(); ++i) //output the difference data
                epsfile << epsilon.at(i) << endl; //to difference file
        //for (int i = 0; i < Canceller.Weights.size(); ++i)
        //      weightfile << Canceller.Weights.at(i) << endl;
        outfile.close(); // close file
        weightfile.close();
        epsfile.close();
}

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