WienerInterpolator.h

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#ifndef WIENERINTERPOLATOR_H_
#define WIENERINTERPOLATOR_H_
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/vector.hpp>
#include "LSSOFilter.h"

/** \addtogroup sigproc Signal processing methods */
/* @{ */

/*!*********************************************
 * WienerInterpolator implements a linear prediction filter
 * as described in Numerical Recipes, p. 566 , it uses the autocorrelation
 * structure of the input data to predict the next sample
 * Although it  does not work exactly like the adaptive filters it is part of
 * the class hierachy for now. The prediction coefficients are stored as Weights, but
 * in opposite order to NR memcof, so they can be immediately used for prediction
 */
 
class WienerInterpolator: public LSSOFilter
{
private:
    //! The prediction error within the window used to calculate the filter coefficients
        double xms;
public:
   //! Return the prediction error
        double GetXms() const {return xms;}
        //! Calculate the prediction coefficients, the contents of Desired are ignored, this function has to be implemented in the filter class hieraychy
        virtual void AdaptFilter(const gplib::rvec &Input, const gplib::rvec &Desired) ;
        //! A more convenient method to get the prediction coefficients, but unique to this class
        gplib::rvec CalcCoefficients(const gplib::rvec &Input){AdaptFilter(Input,Input); return GetWeights();}
    //! The constructor needs to know the filterlength
        WienerInterpolator(const int filterlength);
        virtual ~WienerInterpolator();
};
/* @} */
#endif /*WIENERINTERPOLATOR_H_*/

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