public void TestTrain()
        {
            var linearPerceptron          = new LinearPerceptron();
            var linearPerceptronParameter = new LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100);

            linearPerceptron.Train(iris.GetInstanceList(), linearPerceptronParameter);
            Assert.AreEqual(8.67, 100 * linearPerceptron.Test(iris.GetInstanceList()).GetErrorRate(), 0.01);
            linearPerceptronParameter = new LinearPerceptronParameter(1, 0.001, 0.99, 0.2, 100);
            linearPerceptron.Train(bupa.GetInstanceList(), linearPerceptronParameter);
            Assert.AreEqual(31.88, 100 * linearPerceptron.Test(bupa.GetInstanceList()).GetErrorRate(), 0.01);
            linearPerceptronParameter = new LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100);
            linearPerceptron.Train(dermatology.GetInstanceList(), linearPerceptronParameter);
            Assert.AreEqual(3.28, 100 * linearPerceptron.Test(dermatology.GetInstanceList()).GetErrorRate(), 0.01);
        }
Exemple #2
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        public void TestLinearPerceptron()
        {
            var linearPerceptron          = new LinearPerceptron();
            var linearPerceptronParameter = new LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100);
            var discreteToIndexed         = new DiscreteToIndexed(car);

            discreteToIndexed.Convert();
            linearPerceptron.Train(car.GetInstanceList(), linearPerceptronParameter);
            Assert.AreEqual(8.80, 100 * linearPerceptron.Test(car.GetInstanceList()).GetErrorRate(), 0.01);
            discreteToIndexed = new DiscreteToIndexed(tictactoe);
            discreteToIndexed.Convert();
            linearPerceptron.Train(tictactoe.GetInstanceList(), linearPerceptronParameter);
            Assert.AreEqual(1.67, 100 * linearPerceptron.Test(tictactoe.GetInstanceList()).GetErrorRate(), 0.01);
        }
        public void TestLinearPerceptron()
        {
            var linearPerceptron          = new LinearPerceptron();
            var linearPerceptronParameter = new LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100);
            var normalize = new Normalize(iris);

            normalize.Convert();
            linearPerceptron.Train(iris.GetInstanceList(), linearPerceptronParameter);
            Assert.AreEqual(2.67, 100 * linearPerceptron.Test(iris.GetInstanceList()).GetErrorRate(), 0.01);
            normalize = new Normalize(bupa);
            normalize.Convert();
            linearPerceptron.Train(bupa.GetInstanceList(), linearPerceptronParameter);
            Assert.AreEqual(31.59, 100 * linearPerceptron.Test(bupa.GetInstanceList()).GetErrorRate(), 0.01);
            normalize = new Normalize(dermatology);
            normalize.Convert();
            linearPerceptron.Train(dermatology.GetInstanceList(), linearPerceptronParameter);
            Assert.AreEqual(1.09, 100 * linearPerceptron.Test(dermatology.GetInstanceList()).GetErrorRate(), 0.01);
        }
        public void TestLinearPerceptron()
        {
            var linearPerceptron          = new LinearPerceptron();
            var linearPerceptronParameter = new LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100);
            var pca = new Pca(iris);

            pca.Convert();
            linearPerceptron.Train(iris.GetInstanceList(), linearPerceptronParameter);
            Assert.AreEqual(8.67, 100 * linearPerceptron.Test(iris.GetInstanceList()).GetErrorRate(), 0.01);
            linearPerceptronParameter = new LinearPerceptronParameter(1, 0.01, 0.99, 0.2, 100);
            pca = new Pca(bupa);
            pca.Convert();
            linearPerceptron.Train(bupa.GetInstanceList(), linearPerceptronParameter);
            Assert.AreEqual(42.03, 100 * linearPerceptron.Test(bupa.GetInstanceList()).GetErrorRate(), 0.01);
            pca = new Pca(dermatology);
            pca.Convert();
            linearPerceptron.Train(dermatology.GetInstanceList(), linearPerceptronParameter);
            Assert.AreEqual(1.64, 100 * linearPerceptron.Test(dermatology.GetInstanceList()).GetErrorRate(), 0.01);
        }