Accord.Tests.Statistics.Models.Fields.NormalQuasiNewtonHiddenLearningTest.RunTest C# (CSharp) Method

        public void RunTest()
        {
            var hmm = MarkovContinuousFunctionTest.CreateModel1();
            var function = new MarkovContinuousFunction(hmm);

            var model = new HiddenConditionalRandomField<double>(function);
            var target = new HiddenQuasiNewtonLearning<double>(model);

            double[] actual = new double[inputs.Length];
            double[] expected = new double[inputs.Length];

            for (int i = 0; i < inputs.Length; i++)
            {
                actual[i] = model.Compute(inputs[i]);
                expected[i] = outputs[i];
            }

            for (int i = 0; i < inputs.Length; i++)
                Assert.AreEqual(expected[i], actual[i]);

            double llm = hmm.LogLikelihood(inputs, outputs);
            double ll0 = model.LogLikelihood(inputs, outputs);
            Assert.AreEqual(llm, ll0, 1e-10);
            Assert.IsFalse(Double.IsNaN(llm));
            Assert.IsFalse(Double.IsNaN(ll0));

            double error = target.Run(inputs, outputs);
            double ll1 = model.LogLikelihood(inputs, outputs);
            Assert.AreEqual(-ll1, error, 1e-10);
            Assert.IsFalse(Double.IsNaN(ll1));
            Assert.IsFalse(Double.IsNaN(error));

            for (int i = 0; i < inputs.Length; i++)
            {
                actual[i] = model.Compute(inputs[i]);
                expected[i] = outputs[i];
            }

            Assert.AreEqual(-0.0000041736023099758768, ll0, 1e-10);
            
            for (int i = 0; i < inputs.Length; i++)
                Assert.AreEqual(expected[i], actual[i]);

            Assert.IsTrue(ll1 > ll0);
        }