Accord.Tests.Neuro.LevenbergMarquardtLearningTest.MulticlassTest1 C# (CSharp) 메소드

        public void MulticlassTest1()
        {
            Accord.Math.Tools.SetupGenerator(0);
            // Neuron.RandGenerator = new ThreadSafeRandom(0);


            int numberOfInputs = 3;
            int numberOfClasses = 4;
            int hiddenNeurons = 5;

            double[][] input = 
            {
                new double[] { -1, -1, -1 }, // 0
                new double[] { -1,  1, -1 }, // 1
                new double[] {  1, -1, -1 }, // 1
                new double[] {  1,  1, -1 }, // 0
                new double[] { -1, -1,  1 }, // 2
                new double[] { -1,  1,  1 }, // 3
                new double[] {  1, -1,  1 }, // 3
                new double[] {  1,  1,  1 }  // 2
            };

            int[] labels =
            {
                0,
                1,
                1,
                0,
                2,
                3,
                3,
                2,
            };

            double[][] outputs = Accord.Statistics.Tools
                .Expand(labels, numberOfClasses, -1, 1);

            var function = new BipolarSigmoidFunction(2);
            var network = new ActivationNetwork(function,
                numberOfInputs, hiddenNeurons, numberOfClasses);

            new NguyenWidrow(network).Randomize();

            var teacher = new LevenbergMarquardtLearning(network);

            double error = Double.PositiveInfinity;
            for (int i = 0; i < 10; i++)
                error = teacher.RunEpoch(input, outputs);

            for (int i = 0; i < input.Length; i++)
            {
                int answer;
                double[] output = network.Compute(input[i]);
                double response = output.Max(out answer);

                int expected = labels[i];
                Assert.AreEqual(expected, answer);
            }
        }