NaiveBayesLearning Class

[SerializableAttribute]
public class NaiveBayesLearning : NaiveBayesLearningBase<NaiveBayes, GeneralDiscreteDistribution, int, IndependentOptions<GeneralDiscreteOptions>, GeneralDiscreteOptions>, 
	ISupervisedLearning<NaiveBayes, int[], double[]>, ISupervisedLearning<NaiveBayes, int[], int>

<SerializableAttribute>
Public Class NaiveBayesLearning
	Inherits NaiveBayesLearningBase(Of NaiveBayes, GeneralDiscreteDistribution, Integer, IndependentOptions(Of GeneralDiscreteOptions), GeneralDiscreteOptions)
	Implements ISupervisedLearning(Of NaiveBayes, Integer(), Double()), 
	ISupervisedLearning(Of NaiveBayes, Integer(), Integer)

// To test the effectiveness of the Laplace rule for when
// an example of a symbol is not present in the training set,
// lets create dataset where the second column could contain
// values 0, 1 or 2 but only actually contains examples with
// containing 1 and 2:

int[][] inputs =
{
    //      input     output
    new [] { 0, 1 }, //  0 
    new [] { 0, 2 }, //  0
    new [] { 0, 1 }, //  0
    new [] { 1, 2 }, //  1
    new [] { 0, 2 }, //  1
    new [] { 0, 2 }, //  1
    new [] { 1, 1 }, //  2
    new [] { 0, 1 }, //  2
    new [] { 1, 1 }, //  2
};

int[] outputs = // those are the class labels
{
    0, 0, 0, 1, 1, 1, 2, 2, 2,
};

// Since the data is not enough to determine which symbols we are
// expecting in our model, we will have to specify the model by
// hand. The first column can assume 2 different values, whereas
// the third column can assume 3:
var bayes = new NaiveBayes(classes: 3, symbols: new[] { 2, 3 });

// Now we can create a learning algorithm
var learning = new NaiveBayesLearning()
{
    Model = bayes
};

// Enable the use of the Laplace rule
learning.Options.InnerOption.UseLaplaceRule = true;

// Learn the Naive Bayes model
learning.Learn(inputs, outputs);

// Estimate a sample with 0 in the second col
int answer = bayes.Decide(new int[] { 0, 1 });