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

        public void ComputeTest()
        {
            DataTable data = new DataTable("Mitchell's Tennis Example");

            data.Columns.Add("Day", "Outlook", "Temperature", "Humidity", "Wind", "PlayTennis");

            data.Rows.Add("D1", "Sunny", "Hot", "High", "Weak", "No");
            data.Rows.Add("D2", "Sunny", "Hot", "High", "Strong", "No");
            data.Rows.Add("D3", "Overcast", "Hot", "High", "Weak", "Yes");
            data.Rows.Add("D4", "Rain", "Mild", "High", "Weak", "Yes");
            data.Rows.Add("D5", "Rain", "Cool", "Normal", "Weak", "Yes");
            data.Rows.Add("D6", "Rain", "Cool", "Normal", "Strong", "No");
            data.Rows.Add("D7", "Overcast", "Cool", "Normal", "Strong", "Yes");
            data.Rows.Add("D8", "Sunny", "Mild", "High", "Weak", "No");
            data.Rows.Add("D9", "Sunny", "Cool", "Normal", "Weak", "Yes");
            data.Rows.Add("D10", "Rain", "Mild", "Normal", "Weak", "Yes");
            data.Rows.Add("D11", "Sunny", "Mild", "Normal", "Strong", "Yes");
            data.Rows.Add("D12", "Overcast", "Mild", "High", "Strong", "Yes");
            data.Rows.Add("D13", "Overcast", "Hot", "Normal", "Weak", "Yes");
            data.Rows.Add("D14", "Rain", "Mild", "High", "Strong", "No");

            // Create a new codification codebook to
            // convert strings into discrete symbols
            Codification codebook = new Codification(data,
                "Outlook", "Temperature", "Humidity", "Wind", "PlayTennis");

            int classCount = codebook["PlayTennis"].Symbols; // 2 possible values (yes, no)
            int inputCount = 4; // 4 variables (Outlook, Temperature, Humidity, Wind)

            GeneralDiscreteDistribution[] priors =
            {
                new GeneralDiscreteDistribution(codebook["Outlook"].Symbols),     // 3 possible values (Sunny, overcast, rain)
                new GeneralDiscreteDistribution(codebook["Temperature"].Symbols), // 3 possible values (Hot, mild, cool)
                new GeneralDiscreteDistribution(codebook["Humidity"].Symbols),    // 2 possible values (High, normal)
                new GeneralDiscreteDistribution(codebook["Wind"].Symbols)         // 2 possible values (Weak, strong)
            };

            // Create a new Naive Bayes classifiers for the two classes
            var target = new NaiveBayes<GeneralDiscreteDistribution>(classCount, inputCount, priors);

            // Extract symbols from data and train the classifier
            DataTable symbols = codebook.Apply(data);
            double[][] inputs = symbols.ToArray("Outlook", "Temperature", "Humidity", "Wind");
            int[] outputs = symbols.ToArray<int>("PlayTennis");

            // Compute the Naive Bayes model
            target.Estimate(inputs, outputs);


            double logLikelihood;
            double[] responses;

            // Compute the result for a sunny, cool, humid and windy day:
            double[] instance = codebook.Translate("Sunny", "Cool", "High", "Strong").ToDouble();

            int c = target.Compute(instance, out logLikelihood, out responses);

            string result = codebook.Translate("PlayTennis", c);

            Assert.AreEqual("No", result);
            Assert.AreEqual(0, c);
            Assert.AreEqual(0.795, responses[0], 1e-3);
            Assert.AreEqual(1, responses.Sum(), 1e-10);
            Assert.IsFalse(double.IsNaN(responses[0]));
            Assert.AreEqual(2, responses.Length);
        }