GetLikelihoods: GetLikelihoods

In PDEnaiveBayes: Plausible Naive Bayes Classifier Using PDE

GetLikelihoods

Description

Yields the likelihoods per feauture and class as values of distribution either defined by Gaussian or estimated form the data using pareto density estimation.

Usage

GetLikelihoods(Data,Cls,...)

Arguments

Data	[1:n,1:d] matrix of training data. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features.
Cls	[1:n] numerical vector with n numbers defining the classification. It has k unique numbers representing the arbitrary labels of the classification.
...	Further arguements for defineOrEstimateDistribution Robust=TRUE: robustly estimated gaussians na.rm=TRUE: remove NaNs Threshold: threshold for which the standard deviation cannot be smaller (defaul 0.0001)

Details

Due to pareto density estimation per class and feature, usually the number of rows in each element of c_Kernels_list and ListOfLikelihoods varies and does not equal the number of rows of data n.

Value

c_Kernels_list	List of d numeric matrices, one per feature, each matrix with 1:k columns containing the kernels of class 1:k
ListOfLikelihoods	List of d numeric matrices, one per feature, each matrix with 1:k columns containing distribution values (likelihood) of class 1:k
Thetas	If Gaussian=TRUE: List of d numeric matrices, one per feauture, each matrix with 1:k rows containing the mean in the first column and the standard deviation in teh seconf columd of class 1:k Otherwise: NULL
ParetoRadiusPerFeauture	Numeric vector with estimated pareto radius per feature.

Author(s)

Michael Thrun

Examples

if(requireNamespace("FCPS")){
data(Hepta)
Data=Hepta$Data
Cls=Hepta$Cls
Priors=getPriors(Cls)
}

PDEnaiveBayes documentation built on Nov. 17, 2025, 5:07 p.m.