PDEnaiveBayes (version 0.2.8)

GetLikelihoods: 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,...)

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.

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)

Author

Michael Thrun

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.

Examples

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