source: trunk/src/RegressionNaive.h @ 235

// $Id: RegressionNaive.h 235 2005-02-21 14:53:48Z peter $

#ifndef _theplu_statistics_regression_naive_
#define _theplu_statistics_regression_naive_

// C++ tools include
/////////////////////
#include "Averager.h"
#include "Regression.h"
#include "vector.h"
#include "WeightedAverager.h"
// Standard C++ includes
////////////////////////
//#include <gsl/gsl_fit.h>
#include <iostream>
#include <utility>



namespace theplu {
namespace statistics {  
  
  ///
  /// Class for Regression.   
  ///
  
  class RegressionNaive : public Regression 
  {
  
  public:
    ///
    /// Default Constructor.
    ///
    RegressionNaive(void);

    ///
    /// Copy Constructor. (not implemented)
    ///
    RegressionNaive(const RegressionNaive&);

    ///
    /// Destructor 
    ///
    virtual ~RegressionNaive(void) {};
         
    ///
    /// This function computes the best-fit for the naive model \f$ y
    /// = m \f$ from vectors \a x and \a y, by minimizing \f$
    /// \sum{(y_i-m)^2} \f$. This function is the same as using the
    /// weighted version with unity weights.
    ///
    void fit(const gslapi::vector& x, const gslapi::vector& y);

    ///
    /// This function computes the best-fit for the naive model \f$ y
    /// = m \f$ from vectors \a x and \a y, by minimizing \f$ \sum
    /// w_i(y_i-m)^2 \f$. The weight \f$ w_i \f$ is proportional to
    /// the inverse of the variance for \f$ y_i \f$
    ///
    void fit(const gslapi::vector& x,
             const gslapi::vector& y,
             const gslapi::vector& w);

    ///
    /// Function predicting value using the naive model. \a y_err is
    /// the expected deviation from the line for a new data point. The
    /// weight for the new point can be specified. A smaller weight
    /// means larger error. The error has two components: the variance
    /// of point and error in estimation of m_.
    ///
    void predict(const double x, double& y, double& y_err, 
                 const double w) ;

    ///
    /// @return prediction value and parameters
    ///
    std::ostream& print(std::ostream&) const;
             
    ///
    /// @return header for print()
    ///
    std::ostream& print_header(std::ostream&) const;
             
         
  private:
    double s2_; // noise level - the typical variance for a point with
                // weight w is s2/w
    double m_;
    double m_err_; // error of estimation of mean m_

  };

}} // of namespace statistics and namespace theplu

#endif