# Changeset 1115 for trunk/yat/statistics

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

Fixes #254 and #295

Location:
trunk/yat/statistics
Files:
2 edited

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Unmodified
 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
 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()