## Contents

- Description of demo_multiclass_KDE.m
- usage of generative Gaussian model
- usage of generative RBF kernel density estimation model
- usage of generative polynomial kernel density estimation model

## Description of demo_multiclass_KDE.m

Demonstrates generative kernel density estimation with RBF and polynomial kernels, with a Gaussian maximum likelihood fit as baseline

    clear all
    close all

    generateData_clustersXonly

## usage of generative Gaussian model

    options_gs = [];
    model_gs = ml_generative_Gaussian(Xtrain, ytrain, options_gs);
    figure;
    plotPDF(Xtrain, model_gs);
    title('Generative Gaussian Model');

## usage of generative RBF kernel density estimation model

    options_kde = [];
    options_kde.kernelOptions = struct('sigma',.75);
    model_kde = ml_generative_KDE(Xtrain, ytrain, options_kde);
    figure;
    title('Generative RBF Kernel Density Estimation Model');
    plotPDF(Xtrain, model_kde);

## usage of generative polynomial kernel density estimation model

    options_kde_poly = [];
    options_kde_poly.kernelOptions = struct('order',3,'bias',1);
    options_kde_poly.kernelOptions.kernelFunc = @ml_kernel_poly;
    model_kde_poly = ml_generative_KDE(Xtrain, ytrain, options_kde_poly);
    figure;
    title('Generative Poly Kernel Density Estimation Model');
    plotPDF(Xtrain, model_kde_poly);