Generate() public méthode

Generate Logistic Regression model based on a set of examples.
public Generate ( Matrix X, Vector y ) : IModel
X Matrix The Matrix to process.
y numl.Math.LinearAlgebra.Vector The Vector to process.
Résultat IModel
LogisticRegressionGenerator Class Documentation
Exemple #1
0
        public void Save_And_Load_LogisticRegression()
        {
            Matrix m = new[,] {
                {  0.0512670,   0.6995600 },
                { -0.0927420,   0.6849400 },
                { -0.2137100,   0.6922500 },
                { -0.3750000,   0.5021900 },
                { -0.5132500,   0.4656400 },
                { -0.5247700,   0.2098000 },
                { -0.3980400,   0.0343570 },
                { -0.3058800,  -0.1922500 },
                {  0.0167050,  -0.4042400 },
                {  0.1319100,  -0.5138900 },
                { -0.6111800,  -0.0679820 },
                { -0.6630200,  -0.2141800 },
                { -0.5996500,  -0.4188600 },
                { -0.7263800,  -0.0826020 },
                { -0.8300700,   0.3121300 },
                { -0.7206200,   0.5387400 },
                { -0.5938900,   0.4948800 },
                { -0.4844500,   0.9992700 },
                { -0.0063364,   0.9992700 },
                {  0.6326500,  -0.0306120 },
            };

            Vector y = new Vector(new double[] {
                1,
                1,
                1,
                1,
                1,
                1,
                1,
                1,
                1,
                1,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            });

            var generator = new LogisticRegressionGenerator() { Lambda = 1, LearningRate = 0.01, PolynomialFeatures = 6, MaxIterations = 400 };

            var model = generator.Generate(m, y) as LogisticRegressionModel;

            Serialize(model);

            var lmodel = Deserialize<LogisticRegressionModel>();
            Assert.AreEqual(model.Theta, lmodel.Theta);
            Assert.AreEqual(model.PolynomialFeatures, lmodel.PolynomialFeatures);
            Assert.AreEqual(model.LogisticFunction.GetType(), lmodel.LogisticFunction.GetType());
        }
Exemple #2
0
        public void Logistic_Regression_Test_Generator()
        {
            Matrix m = new[,] {
                {  0.0512670,   0.6995600 },
                { -0.0927420,   0.6849400 },
                { -0.2137100,   0.6922500 },
                { -0.3750000,   0.5021900 },
                { -0.5132500,   0.4656400 },
                { -0.5247700,   0.2098000 },
                { -0.3980400,   0.0343570 },
                { -0.3058800,  -0.1922500 },
                {  0.0167050,  -0.4042400 },
                {  0.1319100,  -0.5138900 },
                { -0.6111800,  -0.0679820 },
                { -0.6630200,  -0.2141800 },
                { -0.5996500,  -0.4188600 },
                { -0.7263800,  -0.0826020 },
                { -0.8300700,   0.3121300 },
                { -0.7206200,   0.5387400 },
                { -0.5938900,   0.4948800 },
                { -0.4844500,   0.9992700 },
                { -0.0063364,   0.9992700 },
                {  0.6326500,  -0.0306120 },
            };

            Vector y = new Vector(new double[] {
                1,
                1,
                1,
                1,
                1,
                1,
                1,
                1,
                1,
                1,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            });

            Vector test = new Vector(new double[] { 0.1319100, -0.513890 });

            var generator = new LogisticRegressionGenerator() { Lambda = 1, LearningRate = 0.1, PolynomialFeatures = 6, MaxIterations = 400, NormalizeFeatures = true };

            var model2 = generator.Generate(m, y);
            double p = model2.Predict(test);

            Assert.Equal(1d, p);
        }