Accord.IO.SparseReader.ReadSparseToEnd C# (CSharp) Method

        public Tuple<Sparse<double>[], double[]> ReadSparseToEnd()
        {
            return ReadSparse(-1);
        }

        public void SparseLinearTest()
        {
            MulticlassSupportVectorMachine<Linear> svm1;
            MulticlassSupportVectorMachine<Linear, Sparse<double>> svm2;

            {
                Accord.Math.Random.Generator.Seed = 0;
                MemoryStream file = new MemoryStream(
                    Encoding.Default.GetBytes(Resources.iris_scale));
                var reader = new SparseReader(file, Encoding.Default);

                var samples = reader.ReadDenseToEnd();
                double[][] x = samples.Item1;
                int[] y = samples.Item2.ToMulticlass();

                var learner = new MulticlassSupportVectorLearning<Linear>()
                {
                    Learner = (p) => new LinearDualCoordinateDescent<Linear>()
                };

                svm1 = learner.Learn(x, y);
            }

            {
                Accord.Math.Random.Generator.Seed = 0;
                MemoryStream file = new MemoryStream(
                    Encoding.Default.GetBytes(Resources.iris_scale));

                // Create a new Sparse Sample Reader to read any given file,
                //  passing the correct dense sample size in the constructor
                var reader = new SparseReader(file, Encoding.Default);

                var samples = reader.ReadSparseToEnd();
                Sparse<double>[] x = samples.Item1;
                int[] y = samples.Item2.ToMulticlass();

                var learner = new MulticlassSupportVectorLearning<Linear, Sparse<double>>()
                {
                    Learner = (p) => new LinearDualCoordinateDescent<Linear, Sparse<double>>()
                };

                svm2 = learner.Learn(x, y);
            }

            Assert.AreEqual(svm1.Models.Length, svm2.Models.Length);
            for (int i = 0; i < svm1.Models.Length; i++)
            {
                var ma = svm1[i].Value;
                var mb = svm2[i].Value;

                Assert.IsTrue(ma.Weights.IsEqual(mb.Weights));
                Assert.AreEqual(ma.SupportVectors.Length, mb.SupportVectors.Length);
                for (int j = 0; j < ma.SupportVectors.Length; j++)
                {
                    double[] expected = ma.SupportVectors[j];
                    double[] actual = mb.SupportVectors[j].ToDense(4);
                    Assert.IsTrue(expected.IsEqual(actual, 1e-5));
                }
            }
        }