Accord.Tests.Statistics.LogisticRegressionTest.RegressTest C# (CSharp) Method

        public void RegressTest()
        {

            double[,] inputGrouped =
            {
                { 1, 4, 5 }, // product 1 has four occurrences of class 1 and five  of class 0
                { 2, 1, 3 }, // product 2 has one  occurrence  of class 1 and three of class 0
            };

            double[,] inputGroupProb =
            {
                { 1, 4.0 / (4 + 5) }, // product 1 has 0.44 probability of belonging to class 1
                { 2, 1.0 / (1 + 3) }, // product 2 has 0.25 probability of belonging to class 1
            };


            double[,] inputExtended =
            {
                { 1, 1 }, // observation of product 1 in class 1
                { 1, 1 }, // observation of product 1 in class 1
                { 1, 1 }, // observation of product 1 in class 1
                { 1, 1 }, // observation of product 1 in class 1
                { 1, 0 }, // observation of product 1 in class 0
                { 1, 0 }, // observation of product 1 in class 0
                { 1, 0 }, // observation of product 1 in class 0
                { 1, 0 }, // observation of product 1 in class 0
                { 1, 0 }, // observation of product 1 in class 0
                { 2, 1 }, // observation of product 2 in class 1
                { 2, 0 }, // observation of product 2 in class 0
                { 2, 0 }, // observation of product 2 in class 0
                { 2, 0 }, // observation of product 2 in class 0
            };


            // Fit using extended data
            double[][] inputs = Matrix.ColumnVector(inputExtended.GetColumn(0)).ToJagged();
            double[] outputs = inputExtended.GetColumn(1);
            LogisticRegression target = new LogisticRegression(1);
            IterativeReweightedLeastSquares irls = new IterativeReweightedLeastSquares(target);
            irls.Run(inputs, outputs);

            // Fit using grouped data
            double[][] inputs2 = Matrix.ColumnVector(inputGroupProb.GetColumn(0)).ToJagged();
            double[] outputs2 = inputGroupProb.GetColumn(1);
            LogisticRegression target2 = new LogisticRegression(1);
            IterativeReweightedLeastSquares irls2 = new IterativeReweightedLeastSquares(target2);
            irls2.Run(inputs2, outputs2);


            Assert.IsTrue(Matrix.IsEqual(target.Coefficients, target2.Coefficients, 0.000001));



            double[,] data = new double[,]
            {
                {  1, 0 },
                {  2, 0 },
                {  3, 0 },
                {  4, 0 },
                {  5, 1 },
                {  6, 0 },
                {  7, 1 },
                {  8, 0 },
                {  9, 1 },
                { 10, 1 }
            };


            double[][] inputs3 = Matrix.ColumnVector(data.GetColumn(0)).ToJagged();
            double[] outputs3 = data.GetColumn(1);
            LogisticRegressionAnalysis analysis = new LogisticRegressionAnalysis(inputs3, outputs3);

            analysis.Compute();

            Assert.IsFalse(double.IsNaN(analysis.Deviance));
            Assert.IsFalse(double.IsNaN(analysis.ChiSquare.PValue));

            Assert.AreEqual(analysis.Deviance, 8.6202, 0.0005);
            Assert.AreEqual(analysis.ChiSquare.PValue, 0.0278, 0.0005);

            // Check intercept
            Assert.IsFalse(double.IsNaN(analysis.Coefficients[0].Value));
            Assert.AreEqual(analysis.Coefficients[0].Value, -4.3578, 0.0005);

            // Check coefficients
            Assert.IsFalse(double.IsNaN(analysis.Coefficients[1].Value));
            Assert.AreEqual(analysis.Coefficients[1].Value, 0.6622, 0.0005);

            // Check statistics
            Assert.AreEqual(analysis.Coefficients[1].StandardError, 0.4001, 0.0005);
            Assert.AreEqual(analysis.Coefficients[1].Wald.PValue, 0.0979, 0.0005);

            Assert.AreEqual(analysis.Coefficients[1].OddsRatio, 1.9391, 0.0005);

            Assert.AreEqual(analysis.Coefficients[1].ConfidenceLower, 0.8852, 0.0005);
            Assert.AreEqual(analysis.Coefficients[1].ConfidenceUpper, 4.2478, 0.0005);


            Assert.IsFalse(double.IsNaN(analysis.Coefficients[1].Wald.PValue));
            Assert.IsFalse(double.IsNaN(analysis.Coefficients[1].StandardError));
            Assert.IsFalse(double.IsNaN(analysis.Coefficients[1].OddsRatio));
            Assert.IsFalse(double.IsNaN(analysis.Coefficients[1].ConfidenceLower));
            Assert.IsFalse(double.IsNaN(analysis.Coefficients[1].ConfidenceUpper));
        }