Accord.Tests.MachineLearning.NaiveBayesGenericTest.learn_test_with_options C# (CSharp) Method

        public void learn_test_with_options()
        {
            #region doc_learn_options
            // Let's say we have the following data to be classified
            // into three possible classes. Those are the samples:
            //
            double[][] inputs =
            {
                //               input         output
                new double[] { 0, 1, 1, 0 }, //  0 
                new double[] { 0, 1, 0, 0 }, //  0
                new double[] { 0, 0, 1, 0 }, //  0
                new double[] { 0, 1, 1, 0 }, //  0
                new double[] { 0, 1, 0, 0 }, //  0
                new double[] { 1, 0, 0, 0 }, //  1
                new double[] { 1, 0, 0, 0 }, //  1
                new double[] { 1, 0, 0, 1 }, //  1
                new double[] { 0, 0, 0, 1 }, //  1
                new double[] { 0, 0, 0, 1 }, //  1
                new double[] { 1, 1, 1, 1 }, //  2
                new double[] { 1, 0, 1, 1 }, //  2
                new double[] { 1, 1, 0, 1 }, //  2
                new double[] { 0, 1, 1, 1 }, //  2
                new double[] { 1, 1, 1, 1 }, //  2
            };

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

            // Create a new Gaussian distribution naive Bayes learner
            var teacher = new NaiveBayesLearning<NormalDistribution, NormalOptions>();

            // Set options for the component distributions
            teacher.Options.InnerOption.Regularization = 1e-5; // to avoid zero variances

            // Learn the naive Bayes model
            NaiveBayes<NormalDistribution> bayes = teacher.Learn(inputs, outputs);

            // Use the model to predict class labels
            int[] predicted = bayes.Decide(inputs);

            // Estimate the model error. The error should be zero:
            double error = new ZeroOneLoss(outputs).Loss(predicted);

            // Now, let's test  the model output for the first input sample:
            int answer = bayes.Decide(new double[] { 1, 0, 0, 1 }); // should be 1
            #endregion

            Assert.AreEqual(0, error);
            Assert.AreEqual(1, answer);
            Assert.IsTrue(predicted.IsEqual(outputs));
        }