Accord.Tests.MachineLearning.SequentialMinimalOptimizationTest.learn_linear C# (CSharp) Method

        public void learn_linear()
        {
            #region doc_xor_linear
            // As an example, we will try to learn a linear machine  that can 
            // replicate the "exclusive-or" logical function. However, since we
            // will be using a linear SVM, we will not be able to solve this 
            // problem perfectly as the XOR is a non-linear classification problem:
            double[][] inputs =
            {
                new double[] { 0, 0 }, // the XOR function takes two booleans
                new double[] { 0, 1 }, // and computes their exclusive or: the
                new double[] { 1, 0 }, // output is true only if the two booleans
                new double[] { 1, 1 }  // are different
            };

            int[] xor = // this is the output of the xor function
            {
                0, // 0 xor 0 = 0 (inputs are equal)
                1, // 0 xor 1 = 1 (inputs are different)
                1, // 1 xor 0 = 1 (inputs are different)
                0, // 1 xor 1 = 0 (inputs are equal)
            };

            // Now, we can create the sequential minimal optimization teacher
            var learn = new SequentialMinimalOptimization()
            {
                UseComplexityHeuristic = true,
                UseKernelEstimation = false
            };

            // And then we can obtain a trained SVM by calling its Learn method
            SupportVectorMachine svm = learn.Learn(inputs, xor);

            // Finally, we can obtain the decisions predicted by the machine:
            bool[] prediction = svm.Decide(inputs);
            #endregion

            Assert.AreEqual(prediction[0], false);
            Assert.AreEqual(prediction[1], false);
            Assert.AreEqual(prediction[2], false);
            Assert.AreEqual(prediction[3], false);


            int[] or = // this is the output of the xor function
            {
                0, // 0 or 0 = 0 (inputs are equal)
                1, // 0 or 1 = 1 (inputs are different)
                1, // 1 or 0 = 1 (inputs are different)
                1, // 1 or 1 = 1 (inputs are equal)
            };


            learn = new SequentialMinimalOptimization()
            {
                Complexity = 1e+8,
                UseKernelEstimation = false
            };

            svm = learn.Learn(inputs, or);

            prediction = svm.Decide(inputs);

            Assert.AreEqual(prediction[0], false);
            Assert.AreEqual(prediction[1], true);
            Assert.AreEqual(prediction[2], true);
            Assert.AreEqual(prediction[3], true);
        }