Accord.Tests.Statistics.LogisticGradientDescentTest.run_batch_mode_new_method C# (CSharp) Метод

        public void run_batch_mode_new_method()
        {
            // Suppose we have the following data about some patients.
            // The first variable is continuous and represent patient
            // age. The second variable is dichotomic and give whether
            // they smoke or not (This is completely fictional data).
            double[][] input =
            {
                new double[] { 55, 0 }, // 0 - no cancer
                new double[] { 28, 0 }, // 0
                new double[] { 65, 1 }, // 0
                new double[] { 46, 0 }, // 1 - have cancer
                new double[] { 86, 1 }, // 1
                new double[] { 56, 1 }, // 1
                new double[] { 85, 0 }, // 0
                new double[] { 33, 0 }, // 0
                new double[] { 21, 1 }, // 0
                new double[] { 42, 1 }, // 1
            };

            // We also know if they have had lung cancer or not, and 
            // we would like to know whether smoking has any connection
            // with lung cancer (This is completely fictional data).
            double[] output =
            {
                0, 0, 0, 1, 1, 1, 0, 0, 0, 1
            };


            // To verify this hypothesis, we are going to create a logistic
            // regression model for those two inputs (age and smoking) using
            // gradient descent.

            var teacher = new LogisticGradientDescent()
            {
                Stochastic = false,
                LearningRate = 1e-4,
                Iterations = 0,
                Tolerance = 1e-10
            };

            // Now, we can proceed and estimate our model.
            var regression = teacher.Learn(input, output);

            // At this point, we can compute the odds ratio of our variables.
            // In the model, the variable at 0 is always the intercept term, 
            // with the other following in the sequence. Index 1 is the age
            // and index 2 is whether the patient smokes or not.

            // For the age variable, we have that individuals with
            //   higher age have 1.021 greater odds of getting lung
            //   cancer controlling for cigarette smoking.
            double ageOdds = regression.GetOddsRatio(1); // 1.0208597028836701

            // For the smoking/non smoking category variable, however, we
            //   have that individuals who smoke have 5.858 greater odds
            //   of developing lung cancer compared to those who do not 
            //   smoke, controlling for age (remember, this is completely
            //   fictional and for demonstration purposes only).
            double smokeOdds = regression.GetOddsRatio(2); // 5.8584748789881331

            Assert.AreEqual(1.0208597028836701, ageOdds, 1e-3);
            Assert.AreEqual(5.8584748789881331, smokeOdds, 1e-3);
        }