Accord.Tests.MachineLearning.NaiveBayesGenericTest.ComputeTest2 C# (CSharp) Method

        public void ComputeTest2()
        {
            DataTable data = new DataTable("Mitchell's Tennis Example");

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

            // We will set Temperature and Humidity to be continuous
            data.Columns["Temperature"].DataType = typeof(double);
            data.Columns["Humidity"].DataType = typeof(double);

            data.Rows.Add("D1", "Sunny", 38.0, 96.0, "Weak", "No");
            data.Rows.Add("D2", "Sunny", 39.0, 90.0, "Strong", "No");
            data.Rows.Add("D3", "Overcast", 38.0, 75.0, "Weak", "Yes");
            data.Rows.Add("D4", "Rain", 25.0, 87.0, "Weak", "Yes");
            data.Rows.Add("D5", "Rain", 12.0, 30.0, "Weak", "Yes");
            data.Rows.Add("D6", "Rain", 11.0, 35.0, "Strong", "No");
            data.Rows.Add("D7", "Overcast", 10.0, 40.0, "Strong", "Yes");
            data.Rows.Add("D8", "Sunny", 24.0, 90.0, "Weak", "No");
            data.Rows.Add("D9", "Sunny", 12.0, 26.0, "Weak", "Yes");
            data.Rows.Add("D10", "Rain", 25, 30.0, "Weak", "Yes");
            data.Rows.Add("D11", "Sunny", 26.0, 40.0, "Strong", "Yes");
            data.Rows.Add("D12", "Overcast", 27.0, 97.0, "Strong", "Yes");
            data.Rows.Add("D13", "Overcast", 39.0, 41.0, "Weak", "Yes");
            data.Rows.Add("D14", "Rain", 23.0, 98.0, "Strong", "No");

            // Create a new codification codebook to
            // convert strings into discrete symbols
            Codification codebook = new Codification(data);

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

            IUnivariateFittableDistribution[] priors =
            {
                new GeneralDiscreteDistribution(codebook["Outlook"].Symbols),   // 3 possible values (Sunny, overcast, rain)
                new NormalDistribution(),                                       // Continuous value (Celsius)
                new NormalDistribution(),                                       // Continuous value (percentage)
                new GeneralDiscreteDistribution(codebook["Wind"].Symbols)       // 2 possible values (Weak, strong)
            };

            // Create a new Naive Bayes classifiers for the two classes
            var target = new NaiveBayes<IUnivariateFittableDistribution>(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 = new double[] 
            {
                codebook.Translate(columnName:"Outlook", value:"Sunny"), 
                12.0, 
                90.0,
                codebook.Translate(columnName:"Wind", value:"Strong")
            };

            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(0.840, responses[0], 1e-3);
            Assert.AreEqual(1, responses.Sum(), 1e-10);
            Assert.IsFalse(double.IsNaN(responses[0]));
            Assert.AreEqual(2, responses.Length);

            int c2 = target.Compute(instance, out logLikelihood);

            Assert.AreEqual(c, c2);
        }