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

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

#ifndef _theplu_statistics_regression_onedimensioanl_
#define _theplu_statistics_regression_onedimensioanl_

#include <ostream>

namespace theplu {
namespace gslapi {
  class vector;
}

namespace statistics {
namespace regression {
  
  ///
  /// Abstract Base Class for One Dimensional fitting.   
  ///
  /// @todo document
  ///
  class OneDimensional 
  {
  
  public:
    ///
    /// Default Constructor.
    ///
    inline OneDimensional(void) : x_(0.0), y_(0.0), y_err_(0.0) {}

    ///
    /// Destructor
    ///
    virtual ~OneDimensional(void) {};
         
    ///
    /// This function computes the best-fit given a model (see
    /// specific class for details) by minimizing \f$
    /// \sum{(\hat{y_i}-y_i)^2} \f$, where \f$ \hat{y} \f$ is the fitted value.
    ///
    virtual void fit(const gslapi::vector& x, const gslapi::vector& y)=0; 
    
    ///
    /// This function computes the best-fit given a model (see
    /// specific class for details) by minimizing \f$
    /// \sum{w_i(\hat{y_i}-y_i)^2} \f$, where \f$ \hat{y} \f$ is the
    /// fitted value. The weight \f$ w_i \f$ is should be proportional
    /// to the inverse of the variance for \f$ y_i \f$
    ///
    virtual void fit(const gslapi::vector& x, const gslapi::vector& y, 
                    const gslapi::vector& w)=0;
    ///
    /// function predicting in one point
    ///
    virtual void predict(const double x, double& y, double& y_err, 
                         const double w=1) =0;
    ///
    /// @return prediction value and parameters
    ///
    virtual std::ostream& print(std::ostream&) const=0;
             
    ///
    /// @return header for print()
    ///
    virtual std::ostream& print_header(std::ostream& s) const=0;
             
  protected:
    ///
    /// x for predicted point
    ///
    double x_;
    ///
    /// y for predicted point
    ///
    double y_;
    ///
    /// estimated error of predicted point (in y).
    ///
    double y_err_;
  };

}}} // of namespaces regression, statisitcs and thep

#endif