linear_regression.h

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// \file mygsl/linear_regression.h

#ifndef MYGSL_LINEAR_REGRESSION_H
#define MYGSL_LINEAR_REGRESSION_H 1

// Standard library:
#include <vector>

// Third party:
// - Boost:
#include <boost/serialization/base_object.hpp>
#include <boost/serialization/nvp.hpp>
// - Bayeux/datatools:
#include <datatools/i_serializable.ipp>

// This project:
#include <mygsl/i_unary_function.h>
#include <mygsl/datapoint.h>

namespace mygsl {

  class linear_regression
  {
  public:

    class fit_data : public datatools::i_serializable
    {

    public:

      fit_data();

      virtual ~fit_data();

      bool is_valid() const;

      bool is_weighted() const;

      bool has_constant() const;

      void reset();

      double get_slope() const;

      double get_constant() const;

      size_t get_ndof() const;

      double get_chisq() const;

      double get_sumsq() const;

      double get_slope_err() const;

      double get_constant_err() const;

    public:

      uint32_t  n;          
      int32_t   status;     
      bool   weighted;      
      bool   with_constant; 
      double c1;            
      double c0;            
      double cov00;         
      double cov01;         
      double cov11;         
      double sumsq;         
      double chisq;         

      DATATOOLS_SERIALIZATION_DECLARATION()

    };

    class function : public i_unary_function {

    public:

      function(const fit_data &);

      void set_fit_data(const fit_data &);

      const fit_data & get_fit_data() const;

      void eval_err(double x_, double & y_, double & yerr_) const;

    protected:

      virtual double _eval(double x_) const;

    private:

      fit_data _fit_data_; 

    };

  public:

    static const unsigned int MINIMUM_NUMBER_OF_DATA_POINTS = 2;

    bool is_initialized() const;

    bool can_weighted() const;

    const fit_data & get_fit_data() const;

    linear_regression();

    linear_regression(const std::vector<datapoint> & p_);

    linear_regression(size_t npoints_,
                      const double * x_,
                      const double * y_,
                      const double * w_ = 0);

    linear_regression(const std::vector<double> & x_,
                      const std::vector<double> & y_,
                      const std::vector<double> & w_);

    linear_regression(const std::vector<double> & x_,
                      const std::vector<double> & y_);

    virtual ~linear_regression();

    void init(const std::vector<datapoint> & p_);

    void init(size_t npoints_,
              const double * x_,
              const double * y_,
              const double * w_ = 0);

    void init(const std::vector<double> & x_,
              const std::vector<double> & y_,
              const std::vector<double> & w_);

    void init(const std::vector<double> & x_,
              const std::vector<double> & y_);

    void reset();

    bool fit_linear();

    bool fit_weighted_linear();

    bool fit_linear_no_constant();

    bool fit_weighted_linear_no_constant();

  private:

    bool        _initialized_; 
    fit_data    _fit_data_; 
    bool        _delete_; 
    double    * _x_; 
    double    * _y_; 
    double    * _w_; 
    bool        _can_weighted_; 

  };

}

#endif // MYGSL_LINEAR_REGRESSION_H

/* Local Variables: */
/* mode: c++        */
/* coding: utf-8    */
/* End:             */