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

        public void ComputeTest()
        {
            #region doc_mitchell
            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");
            #endregion

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

            // Extract input and output pairs to train
            DataTable symbols = codebook.Apply(data);
            int[][] inputs = symbols.ToArray<int>("Outlook", "Temperature", "Humidity", "Wind");
            int[] outputs = symbols.ToArray<int>("PlayTennis");
            #endregion

            #region doc_learn
            // Create a new Naive Bayes learning
            var learner = new NaiveBayesLearning();

            // Learn a Naive Bayes model from the examples
            NaiveBayes nb = learner.Learn(inputs, outputs);
            #endregion


            #region doc_test
            // Consider we would like to know whether one should play tennis at a
            // sunny, cool, humid and windy day. Let us first encode this instance
            int[] instance = codebook.Translate("Sunny", "Cool", "High", "Strong");

            // Let us obtain the numeric output that represents the answer
            int c = nb.Decide(instance); // answer will be 0

            // Now let us convert the numeric output to an actual "Yes" or "No" answer
            string result = codebook.Translate("PlayTennis", c); // answer will be "No"

            // We can also extract the probabilities for each possible answer
            double[] probs = nb.Probabilities(instance); // { 0.795, 0.205 }
            #endregion

            Assert.AreEqual("No", result);
            Assert.AreEqual(0, c);
            Assert.AreEqual(0.795, probs[0], 1e-3);
            Assert.AreEqual(0.205, probs[1], 1e-3);
            Assert.AreEqual(1, probs.Sum(), 1e-10);
            Assert.IsFalse(double.IsNaN(probs[0]));
            Assert.AreEqual(2, probs.Length);
        }