Accord.Tests.Statistics.LinearDiscriminantAnalysisTest.new_method C# (CSharp) Method

        public void new_method()
        {
            #region doc_learn
            // Create some sample input data instances. This is the same
            // data used in the Gutierrez-Osuna's example available on:
            // http://research.cs.tamu.edu/prism/lectures/pr/pr_l10.pdf

            double[][] inputs = 
            {
                // Class 0
                new double[] {  4,  1 }, 
                new double[] {  2,  4 },
                new double[] {  2,  3 },
                new double[] {  3,  6 },
                new double[] {  4,  4 },

                // Class 1
                new double[] {  9, 10 },
                new double[] {  6,  8 },
                new double[] {  9,  5 },
                new double[] {  8,  7 },
                new double[] { 10,  8 }
            };

            int[] output = 
            {
                0, 0, 0, 0, 0, // The first five are from class 0
                1, 1, 1, 1, 1  // The last five are from class 1
            };

            // We will create a LDA object for the data
            var lda = new LinearDiscriminantAnalysis();

            // Compute the analysis and create a classifier
            var classifier = lda.Learn(inputs, output);

            // Now we can project the data into LDA space:
            double[][] projection = lda.Transform(inputs);

            // Or perform classification using:
            int[] results = classifier.Decide(inputs);
            #endregion

            double[][] classifierProjection = classifier.First.Transform(inputs);
            Assert.IsTrue(projection.IsEqual(classifierProjection));

            double[][] expected = new double[][] 
            {
                new double[] { 4.42732558139535, 1.96296296296296 },
                new double[] { 3.7093023255814, -2.51851851851852 },
                new double[] { 3.28197674418605, -1.51851851851852 },
                new double[] { 5.56395348837209, -3.77777777777778 },
                new double[] { 5.7093023255814, -1.03703703703704 },
                new double[] { 13.2732558139535, -3.33333333333333 },
                new double[] { 9.41860465116279, -3.55555555555556 },
                new double[] { 11.1366279069767, 1.66666666666667 },
                new double[] { 10.9912790697674, -1.07407407407407 },
                new double[] { 13.4186046511628, -0.592592592592593 } 
            };

            Assert.IsTrue(expected.IsEqual(projection, 1e-6));

            // Test the classify method
            for (int i = 0; i < 5; i++)
            {
                int actual = results[i];
                Assert.AreEqual(0, actual);
            }

            for (int i = 5; i < 10; i++)
            {
                int actual = results[i];
                Assert.AreEqual(1, actual);
            }
        }