source: trunk/lib/statistics/Naive.h @ 389

// $Id: Naive.h 389 2005-08-15 11:37:07Z peter $

#ifndef _theplu_statistics_regression_naive_
#define _theplu_statistics_regression_naive_

#include <c++_tools/statistics/OneDimensional.h>

#include <c++_tools/gslapi/vector.h>
#include <c++_tools/statistics/Averager.h>
#include <c++_tools/statistics/AveragerWeighted.h>

#include <iostream>
#include <utility>


namespace theplu {
namespace statistics {
namespace regression {

  ///
  /// @bief naive fitting.
  ///
  /// @todo document
  ///
  class Naive : public OneDimensional 
  {
  
  public:
    ///
    /// Default Constructor.
    ///
    inline Naive(void) : OneDimensional(), m_(0.0), m_err_(0.0) {}

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

    ///
    /// Destructor 
    ///
    virtual ~Naive(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=1) ;

    ///
    /// @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 namespaces regression, statisitcs and thep

#endif