# Changeset 695 for trunk/yat/regression/OneDimensional.h

Ignore:
Timestamp:
Oct 25, 2006, 11:20:17 AM (16 years ago)
Message:

References #81 improved documentation

File:
1 edited

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Unmodified
 r682 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. /// /** 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 utility::vector& x, const utility::vector& y)=0; /// /// function predicting in one point /// @return expected value in @a x accrding to the fitted model /// virtual double predict(const double x) const=0; /// /// @return expected prediction error for a new data point in @a x /// /** The prediction error is defined as the square root of the expected squared deviation a new data point will have from value the model provides. The expected squared deviation is defined as \f$E(Y|x - \hat{y}(x))^2 \f$ and is typically divided into two terms \f$E(Y|x - E(Y|x))^2 \f$ and \f$E(E(Y|x) - \hat{y}(x))^2 \f$, which is the conditional variance in \f$x \f$ and the squared standard error (see standard_error()) of the model estimation in \f$x \f$, respectively. @return expected prediction error for a new data point in @a x */ virtual double prediction_error(const double x) const=0; /// /// @brief print output to @a os /// @brief print output to ostream @a os /// /// Printing estimated model to @a os in the points defined by @a /// min, @a max, and @a n. The values printed for each point is /// the x-value, the estimated y-value, and the estimated standard /// deviation of a new data poiunt will have from the y-value /// given the x-value (see prediction_error()). /// /// @param n number of points printed /// @param min smallest x-value for which the model is printed /// @param max largest x-value for which the model is printed /// std::ostream& print(std::ostream& os,const double min, double max, const u_int n) const; /// /// @return error of model value in @a x /// /** The standard error is defined as \f$\sqrt{E(Y|x - \hat{y}(x))^2 }\f$ @return error of model value in @a x */ virtual double standard_error(const double x) const=0;