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Showing content from http://accord-framework.net/docs/html/T_Accord_MachineLearning_Bayes_NaiveBayesLearning_3.htm below:

double[][] inputs =
{
    
    new double[] { 0, 1, 1, 0 }, 
    new double[] { 0, 1, 0, 0 }, 
    new double[] { 0, 0, 1, 0 }, 
    new double[] { 0, 1, 1, 0 }, 
    new double[] { 0, 1, 0, 0 }, 
    new double[] { 1, 0, 0, 0 }, 
    new double[] { 1, 0, 0, 0 }, 
    new double[] { 1, 0, 0, 1 }, 
    new double[] { 0, 0, 0, 1 }, 
    new double[] { 0, 0, 0, 1 }, 
    new double[] { 1, 1, 1, 1 }, 
    new double[] { 1, 0, 1, 1 }, 
    new double[] { 1, 1, 0, 1 }, 
    new double[] { 0, 1, 1, 1 }, 
    new double[] { 1, 1, 1, 1 }, 
};

int[] outputs = 
{
    0, 0, 0, 0, 0,
    1, 1, 1, 1, 1,
    2, 2, 2, 2, 2,
};


var teacher = new NaiveBayesLearning<NormalDistribution, NormalOptions>();


teacher.Options.InnerOption.Regularization = 1e-5; 


NaiveBayes<NormalDistribution> bayes = teacher.Learn(inputs, outputs);


int[] predicted = bayes.Decide(inputs);


double error = new ZeroOneLoss(outputs).Loss(predicted);


int answer = bayes.Decide(new double[] { 1, 0, 0, 1 }); 

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