Accord.Tests.MachineLearning.C45LearningTest.AttributeReuseTest1 C# (CSharp) Method

        public void AttributeReuseTest1()
        {
            string[][] text = Resources.iris_data.Split(
                new[] { '\n' }, StringSplitOptions.RemoveEmptyEntries)
                .Apply(x => x.Split(','));

            double[][] inputs = new double[text.Length][];
            for (int i = 0; i < inputs.Length; i++)
                inputs[i] = text[i].First(4).Convert(s => Double.Parse(s, System.Globalization.CultureInfo.InvariantCulture));

            string[] labels = text.GetColumn(4);

            Codification codebook = new Codification("Label", labels);

            int[] outputs = codebook.Translate("Label", labels);


            DecisionVariable[] features =
            {
                new DecisionVariable("sepal length", DecisionVariableKind.Continuous), 
                new DecisionVariable("sepal width", DecisionVariableKind.Continuous), 
                new DecisionVariable("petal length", DecisionVariableKind.Continuous), 
                new DecisionVariable("petal width", DecisionVariableKind.Continuous), 
            };


            DecisionTree tree = new DecisionTree(features, codebook.Columns[0].Symbols);

            C45Learning teacher = new C45Learning(tree);

            teacher.Join = 3;

            double error = teacher.Run(inputs, outputs);
            Assert.AreEqual(0.02, error, 1e-10);

            DecisionSet rules = tree.ToRules();

            double newError = ComputeError(rules, inputs, outputs);
            Assert.AreEqual(0.02, newError, 1e-10);

            string ruleText = rules.ToString(codebook,
                System.Globalization.CultureInfo.InvariantCulture);

            // TODO: implement this assertion properly, actually checking
            // the text contents once the feature is completely finished.
            Assert.AreEqual(600, ruleText.Length);
        }