using System;
using Accord.MachineLearning.VectorMachines;
using Accord.MachineLearning.VectorMachines.Learning;
using Accord.MachineLearning.VectorMachines.Learning.Generics;
using Accord.MachineLearning.VectorMachines.Learning.Multiclass;
using Accord.Statistics.Kernels;

class Program
{
    static void Main()
    {
        // Create the training data
        double[][] inputs =
        {
            new double[] { 2.0, 2.0 },
            new double[] { 1.0, 3.0 },
            new double[] { 3.0, 3.0 },
            new double[] { 4.0, 2.0 }
        };

        // Create the labels for the training data
        int[] labels = { 1, 1, -1, -1 };

        // Create the Support Vector Machine with a linear kernel
        var teacher = new MulticlassSupportVectorLearning<Gaussian>()
        {
            Learner = (param) => new SupportVectorMachine<Gaussian>()
            {
                Kernel = param
            }
        };

        // Train the Support Vector Machine
        var svm = teacher.Learn(inputs, labels);

        // Create the test data
        double[][] testData =
        {
            new double[] { 2.5, 2.5 },
            new double[] { 1.5, 2.0 },
            new double[] { 3.5, 3.5 },
            new double[] { 4.5, 1.5 }
        };

        // Predict the labels for the test data
        int[] predictedLabels = svm.Decide(testData);

        // Print the predicted labels
        for (int i = 0; i < predictedLabels.Length; i++)
        {
            Console.WriteLine("Predicted label: " + predictedLabels[i]);
        }
    }
}
