Accord.Tests.Statistics.LogisticRegressionAnalysisTest.learn1 C# (CSharp) Method

        public void learn1()
        {
            double[][] inputs = training.Submatrix(null, 0, 3);
            double[] outputs = training.GetColumn(4);

            var lra = new LogisticRegressionAnalysis()
            {
                ComputeInnerModels = true
            };

            var regression = lra.Learn(inputs, outputs);

            double[] actual = regression.Score(inputs);

            double[] expected = 
            {
                0.000012, 0.892611, 0.991369, 0.001513, 0.904055,
                0.001446, 0.998673, 0.001260, 0.629312, 0.004475,
                0.505362, 0.999791, 0.000050, 1.000000, 0.990362,
                0.985265, 1.000000, 1.000000, 0.001319, 0.000001,
                0.000001, 0.000050, 0.702488, 0.003049, 0.000046,
                0.000419, 0.026276, 0.036813, 0.000713, 0.001484,
                0.000008, 0.000009, 0.278950, 0.001402, 0.025764,
                0.002464, 0.000219, 0.007328, 0.000106, 0.002619,
                0.002913, 0.000002,
            };

            for (int i = 0; i < expected.Length; i++)
                Assert.AreEqual(expected[i], actual[i], 1e-6);

            Assert.AreEqual(5, lra.LikelihoodRatioTests.Length);
            Assert.IsNull(lra.LikelihoodRatioTests[0]);
            Assert.AreEqual(0.99999995244237416, lra.LikelihoodRatioTests[1].PValue, 1e-7);
            Assert.AreEqual(0.99999993274336052, lra.LikelihoodRatioTests[2].PValue, 1e-7);
            Assert.AreEqual(0.99999992480479116, lra.LikelihoodRatioTests[3].PValue, 1e-7);
            Assert.AreEqual(0.0000000059730130622305527, lra.LikelihoodRatioTests[4].PValue, 1e-20);
        }