Ignore:
Timestamp:
Feb 26, 2008, 11:09:04 PM (14 years ago)
Author:
Peter
Message:

refs #343 moving data to inherited classes and using SmartPtr?.

File:
1 edited

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  • trunk/yat/classifier/NBC.h

    r1160 r1169  
    8989
    9090       \f$ P_j = \frac{1}{Z}\prod_i{\frac{1}{\sqrt{2\pi\sigma_i^2}}}
    91        \exp(\frac{w_i(x_i-\mu_i)^2}{\sigma_i^2})\f$, where \f$ \mu_i
     91       \exp(\frac{(x_i-\mu_i)^2}{\sigma_i^2})\f$, where \f$ \mu_i
    9292       \f$ and \f$ \sigma_i^2 \f$ are the estimated mean and variance,
    9393       respectively. If a \f$ \sigma_i \f$ could not be estimated
     
    9595       words, that feature is ignored for the prediction of that
    9696       particular class. Z is chosen such that total probability, \f$
    97        \sum P_j \f$, equals unity. If \a data is a MatrixLookup is
    98        equivalent to using all weight equal to unity.
     97       \sum P_j \f$, equals unity.
    9998    */
    10099    void predict(const MatrixLookup& data, utility::Matrix& res) const;
    101100
    102101    /**
    103        @see above
     102       Each sample (column) in \a data is predicted and predictions
     103       are returned in the corresponding column in passed \a res. Each
     104       row in \a res corresponds to a class. The prediction is the
     105       estimated probability that sample belong to class \f$ j \f$
    104106     */
    105107    void predict(const MatrixLookupWeighted& data, utility::Matrix& res) const;
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