Changeset 2842
 Timestamp:
 Sep 17, 2012, 6:52:20 AM (9 years ago)
 Location:
 trunk
 Files:

 3 edited
Legend:
 Unmodified
 Added
 Removed

trunk/build_support/yatconfig.in
r2828 r2842 2 2 3 3 # Copyright (C) 2008 Jari Häkkinen, Peter Johansson 4 # Copyright (C) 2009, 2010, 2011 Peter Johansson4 # Copyright (C) 2009, 2010, 2011, 2012 Peter Johansson 5 5 # 6 6 # This file is part of the yat library, http://dev.thep.lu.se/yat 
trunk/yat/regression/LinearWeighted.h
r2119 r2842 35 35 36 36 /// 37 /// @brief linear regression. 37 /// @brief linear regression. 38 38 /// 39 /// @todo document 40 /// 41 class LinearWeighted : public OneDimensionalWeighted 39 class LinearWeighted : public OneDimensionalWeighted 42 40 { 43 41 44 42 public: 45 43 /// … … 52 50 /// 53 51 virtual ~LinearWeighted(void); 54 52 55 53 /** 56 54 \f$ alpha \f$ is estimated as \f$ \frac{\sum w_iy_i}{\sum w_i} \f$ 57 55 58 56 @return the parameter \f$ \alpha \f$ 59 57 */ … … 72 70 \f$ beta \f$ is estimated as \f$ \frac{\sum 73 71 w_i(y_im_y)(x_im_x)}{\sum w_i(x_im_x)^2} \f$ 74 72 75 73 @return the parameter \f$ \beta \f$ 76 74 */ … … 85 83 */ 86 84 double beta_var(void) const; 87 85 88 86 /** 89 87 This function computes the bestfit linear regression … … 97 95 void fit(const utility::VectorBase& x, const utility::VectorBase& y, 98 96 const utility::VectorBase& w); 99 97 100 98 /// 101 /// Function predicting value using the linear model: 99 /// Function predicting value using the linear model: 102 100 /// \f$ y =\alpha + \beta (x  m) \f$ 103 101 /// … … 126 124 double syy(void) const; 127 125 double sxy(void) const; 128 126 129 127 double alpha_; 130 128 double alpha_var_; 
trunk/yat/regression/NaiveWeighted.h
r2119 r2842 39 39 /// @brief naive fitting. 40 40 /// 41 /// @todo document 42 /// 43 class NaiveWeighted : public OneDimensionalWeighted 41 class NaiveWeighted : public OneDimensionalWeighted 44 42 { 45 43 46 44 public: 47 45 /// … … 54 52 /// 55 53 virtual ~NaiveWeighted(void); 56 54 57 55 /** 58 56 This function computes the bestfit for the naive model \f$ y … … 67 65 /// 68 66 /// Function predicting value using the naive model, i.e. a 69 /// weighted average. 67 /// weighted average. 70 68 /// 71 69 double predict(const double x) const;
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