Changeset 1115 for trunk/yat

Ignore:
Timestamp:
Feb 21, 2008, 8:20:59 PM (15 years ago)
Message:

Fixes #254 and #295

Location:
trunk/yat
Files:
5 edited

Unmodified
Removed
• trunk/yat/classifier/KNN.h

 r1112 /// @brief Class for Nearest Neigbor Classification. /// /// The template argument Distance should be a class implementing /// The template argument Distance should be a class modelling /// the concept \ref concept_distance. /// The template argument NeigborWeighting should be a class implementing /// The template argument NeigborWeighting should be a class modelling /// the concept \ref concept_neighbor_weighting.
• trunk/yat/classifier/NCC.h

 r1112 /// @brief Class for Nearest Centroid Classification. /// /// The template argument Distance should be a class implementing /// The template argument Distance should be a class modelling /// the concept \ref concept_distance. ///
• trunk/yat/statistics/EuclideanDistance.h

 r1093 /// /// @brief Calculates the Euclidean distance between two points /// stored in 1-dimensional containers. Implements the concept \ref /// concept_distance. /// given by elements of ranges. /// /// This class is modelling the concept \ref concept_distance. /// /// { /** \brief Calculates the Euclidean distance between two ranges. \brief Calculates the Euclidean distance between elements of two ranges. If both ranges are unweighted the distance is calculated as \f$\sqrt{\sum (x_i-y_i)^2 } \f$ If elements of both ranges are unweighted the distance is calculated as \f$\sqrt{\sum (x_i-y_i)^2 } \f$, where \f$x_i \f$ and \f$y_i \f$ are elements of the first and second range, respectively. Else distance is calculated as \f$N \frac{\sum w_xw_y(x-y)^2}{\sum w_xw_y} \f$ If elements of one or both of ranges have weights the distance is calculated as \f$\sqrt{N \sum w_{x,i}w_{y,i}(x_i-y_i)^2/\sum w_{x,i}w_{y,i}} \f$, where \f$N \f$ is the number of elements in the two ranges and \f$w_x \f$ and \f$w_y \f$ are weights for the elements of the first and the second range, respectively. If the elements of one of the two ranges are unweighted, the weights for these elements are set to unity. */ template
• trunk/yat/statistics/PearsonDistance.h

 r1092 /// /// @brief Calculates the %Pearson correlation distance between two points stored in 1-dimensional containers. Implements the concept \ref concept_distance. /// @brief Calculates the %Pearson correlation distance between two points given by elements of ranges. /// /// This class is modelling the concept \ref concept_distance. /// struct PearsonDistance { /// /// @brief Calculates the %Pearson correlation distance between two ranges. /// /** \brief Calculates the %Pearson correlation distance between elements of two ranges. If elements of both ranges are unweighted the distance is calculated as \f$1-\mbox{C}(x,y) \f$, where \f$x \f$ and \f$y \f$ are the two points and C is the %Pearson correlation. If elements of one or both of ranges have weights the distance is calculated as \f$1-[\sum w_{x,i}w_{y,i}(x_i-y_i)^2/(\sum w_{x,i}w_{y,i}(x_i-m_x)^2\sum w_{x,i}w_{y,i}(y_i-m_y)^2)] \f$, where and \f$w_x \f$ and \f$w_y \f$ are weights for the elements of the first and the second range, respectively, and \f$m_x=\sum w_{x,i}w_{y,i}x_i/\sum w_{x,i}w_{y,i} \f$ and correspondingly for \f$m_y \f$.  If the elements of one of the two ranges are unweighted, the weights for these elements are set to unity. */ template double operator()
• trunk/yat/utility/iterator_traits.h

 r1092 /** Struct to be used to make compile-time decision that Iterator is weighted. Some algorithms come in also in a weighted version in weighted. Some algorithms come also in a weighted version and this tag could be used to decide on using them (rather than unweighted cousin). the corresponding unweighted algorithm). */ struct weighted_iterator_tag {}; /** \brief traits to make unweighted iterator work in as a weighted \brief traits to make unweighted iterator work as in a weighted This class must be implemented for every iterator that can be weighted.
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