Accord.Tests.Neuro.LevenbergMarquardtLearningTest.JacobianByChainRuleTest4 C# (CSharp) Method

        public void JacobianByChainRuleTest4()
        {
            // Network with no hidden layers: 3-1

            double[][] input = 
            {
                new double[] {-1, -1 },
                new double[] {-1,  1 },
                new double[] { 1, -1 },
                new double[] { 1,  1 }
            };

            double[][] output =
            {
                new double[] {-1 },
                new double[] { 1 },
                new double[] { 1 },
                new double[] {-1 }
            };

            // Neuron.RandGenerator = new ThreadSafeRandom(0);
            Accord.Math.Random.Generator.Seed = 0;

            ActivationNetwork network = new ActivationNetwork(
                   new BipolarSigmoidFunction(2), 2, 1);

            var teacher1 = new LevenbergMarquardtLearning(network,
                false, JacobianMethod.ByFiniteDifferences);

            var teacher2 = new LevenbergMarquardtLearning(network,
                false, JacobianMethod.ByBackpropagation);

            // Set lambda to lambda max so no iterations are performed
            teacher1.LearningRate = 1e30f;
            teacher2.LearningRate = 1e30f;

            teacher1.RunEpoch(input, output);
            teacher2.RunEpoch(input, output);

            var jacobian1 = teacher1.Jacobian;
            var jacobian2 = teacher2.Jacobian;


            for (int i = 0; i < jacobian1.Length; i++)
            {
                for (int j = 0; j < jacobian1[i].Length; j++)
                {
                    double j1 = jacobian1[i][j];
                    double j2 = jacobian2[i][j];

                    Assert.AreEqual(j1, j2, 1e-5);

                    Assert.IsFalse(Double.IsNaN(j1));
                    Assert.IsFalse(Double.IsNaN(j2));
                }
            }
        }