Accord.Tests.MachineLearning.CrossvalidationTest.FittingTest C# (CSharp) Method

        public void FittingTest()
        {

            int[] folds = CrossValidation.Splittings(100, 10);

            int[] samples = Matrix.Indices(0, 100);

            CrossValidation val = new CrossValidation(folds, 10);

            val.RunInParallel = false;

            int current = 0;
            val.Fitting = (k, trainingSamples, validationSamples) =>
            {
                Assert.AreEqual(current, k);
                Assert.AreEqual(90, trainingSamples.Length);
                Assert.AreEqual(10, validationSamples.Length);

                int[] trainingSet = samples.Submatrix(trainingSamples);
                int[] validationSet = samples.Submatrix(validationSamples);

                for (int i = 0; i < trainingSet.Length; i++)
                    Assert.AreEqual(samples[trainingSamples[i]], trainingSet[i]);

                for (int i = 0; i < validationSet.Length; i++)
                    Assert.AreEqual(samples[validationSamples[i]], validationSet[i]);

                current++;

                return new CrossValidationValues(k, 2 * k);
            };

            var result = val.Compute();

            Assert.AreEqual(10, current);
            Assert.AreEqual(4.5, result.Training.Mean);
            Assert.AreEqual(9.0, result.Validation.Mean);
            Assert.AreEqual(
                2 * result.Training.StandardDeviation,
                result.Validation.StandardDeviation);

            Assert.AreEqual(val.Folds.Length, result.Training.Sizes.Length);
            Assert.AreEqual(val.Folds.Length, result.Validation.Sizes.Length);

            for (int i = 0; i < result.Training.Sizes.Length; i++)
                Assert.AreEqual(90, result.Training.Sizes[i]);

            for (int i = 0; i < result.Validation.Sizes.Length; i++)
                Assert.AreEqual(10, result.Validation.Sizes[i]);
        }