source: trunk/lib/statistics/Naive.h @ 429

// $Id: Naive.h 429 2005-12-08 19:50:11Z peter $

#ifndef _theplu_statistics_regression_naive_
#define _theplu_statistics_regression_naive_

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

#include <c++_tools/gslapi/vector.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);

    ///
    /// Function predicting value using the naive model. \a y_err is
    /// the expected deviation from the line for a new data point. The
    /// error has two components: the variance of point and error in
    /// estimation of the mean.
    ///
    void predict(const double x, double& y, double& y_err) ;

    ///
    /// @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