object TANBayes0
The TANBayes0 object is the companion object for the TANBayes0 class.
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- def apply(xy: MatriI, fn: Strings, k: Int, cn: Strings, me: Double = me_default, vc: Array[Int] = null): TANBayes0
Create a
TANBayes0object, passing 'x' and 'y' together in one matrix.Create a
TANBayes0object, 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
- 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