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object FANBayes0

The FANBayes0 object is the companion object for the FANBayes0 class.

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  4. def apply(xy: MatriI, fn: Strings, k: Int, cn: Strings, me: Double = me_default, vc: Array[Int] = null): FANBayes0

    Create a FANBayes0 object, passing 'x' and 'y' together in one matrix.

    Create a FANBayes0 object, passing 'x' and 'y' together in one matrix.

    xy

    the data vectors along with their classifications stored as rows of a matrix

    fn

    the names of the features

    k

    the number of classes

    cn

    the class names

    me

    use m-estimates (me == 0 => regular MLE estimates)

    vc

    the value count (number of distinct values) for each feature

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  15. def smoothP(k: Int, n: Int, fset: Array[Boolean], parent: VectorI, vc: Array[Int], vcp: Array[Int], trainSize: Double, nu_y: VectorI, nu_X: HMatrix2[Int], nu_Xy: HMatrix3[Int], p_XyP: HMatrix4[Double]): Unit

    Perform smoothing operations on the learned parameters by using Dirichlet priors to compute the posterior probabilities of the parameters given the training dataset.

    Perform smoothing operations on the learned parameters by using Dirichlet priors to compute the posterior probabilities of the parameters given the training dataset.

    k

    the number of class values/labels for y

    n

    the total number of features/x-variables

    fset

    the selected features

    parent

    the parent for each feature

    vc

    the value count

    vcp

    the value count for parent

    trainSize

    the size of the training dataset

    nu_y

    the frequqncy of class y

    nu_X

    the frequency of each feature X = [x_0, ... x_n-1]

    nu_Xy

    the joint frequency of X and y

    p_XyP

    the conditional probability of X given y and P(arent) - to be updated

    See also

    citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.178.8884&rep=rep1&type=pdf

    www.cs.technion.ac.il/~dang/journal_papers/friedman1997Bayesian.pdf

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