Accord.Tests.MachineLearning.ID3LearningTest.ConstantDiscreteVariableTest C# (CSharp) Method

        public void ConstantDiscreteVariableTest()
        {
            DecisionTree tree;
            int[][] inputs;
            int[] outputs;

            DataTable data = new DataTable("Degenerated 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", "Hot", "High", "Weak", "Yes");
            data.Rows.Add("D5", "Rain", "Hot", "Normal", "Weak", "Yes");
            data.Rows.Add("D6", "Rain", "Hot", "Normal", "Strong", "No");
            data.Rows.Add("D7", "Overcast", "Hot", "Normal", "Strong", "Yes");
            data.Rows.Add("D8", "Sunny", "Hot", "High", "Weak", "No");
            data.Rows.Add("D9", "Sunny", "Hot", "Normal", "Weak", "Yes");
            data.Rows.Add("D10", "Rain", "Hot", "Normal", "Weak", "Yes");
            data.Rows.Add("D11", "Sunny", "Hot", "Normal", "Strong", "Yes");
            data.Rows.Add("D12", "Overcast", "Hot", "High", "Strong", "Yes");
            data.Rows.Add("D13", "Overcast", "Hot", "Normal", "Weak", "Yes");
            data.Rows.Add("D14", "Rain", "Hot", "High", "Strong", "No");

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

            DecisionVariable[] attributes =
            {
               new DecisionVariable("Outlook",     codebook["Outlook"].Symbols),     // 3 possible values (Sunny, overcast, rain)
               new DecisionVariable("Temperature", codebook["Temperature"].Symbols), // 1 constant value (Hot)
               new DecisionVariable("Humidity",    codebook["Humidity"].Symbols),    // 2 possible values (High, normal)
               new DecisionVariable("Wind",        codebook["Wind"].Symbols)         // 2 possible values (Weak, strong)
            };

            int classCount = codebook["PlayTennis"].Symbols; // 2 possible values (yes, no)


            bool thrown = false;
            try
            {
                tree = new DecisionTree(attributes, classCount);
            }
            catch
            {
                thrown = true;
            }

            Assert.IsTrue(thrown);


            attributes[1] = new DecisionVariable("Temperature", 2);
            tree = new DecisionTree(attributes, classCount);
            ID3Learning id3 = new ID3Learning(tree);

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

            double error = id3.Run(inputs, outputs);

            Assert.AreEqual(0, error);

            for (int i = 0; i < inputs.Length; i++)
            {
                int y = tree.Compute(inputs[i]);
                Assert.AreEqual(outputs[i], y);
            }
        }