public void RegressionRandomModelSelectingEnsembleLearner_Learn_Start_With_3_Models()
        {
            var learners = new IIndexedLearner <double>[]
            {
                new RegressionDecisionTreeLearner(2),
                new RegressionDecisionTreeLearner(5),
                new RegressionDecisionTreeLearner(7),
                new RegressionDecisionTreeLearner(9),
                new RegressionDecisionTreeLearner(11),
                new RegressionDecisionTreeLearner(21),
                new RegressionDecisionTreeLearner(23),
                new RegressionDecisionTreeLearner(1),
                new RegressionDecisionTreeLearner(14),
                new RegressionDecisionTreeLearner(17),
                new RegressionDecisionTreeLearner(19),
                new RegressionDecisionTreeLearner(33)
            };

            var metric = new MeanSquaredErrorRegressionMetric();

            var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5,
                                                                        new RandomCrossValidation <double>(5, 42), new MeanRegressionEnsembleStrategy(), metric, 3, false);

            var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();

            var model       = sut.Learn(observations, targets);
            var predictions = model.Predict(observations);

            var actual = metric.Error(targets, predictions);

            Assert.AreEqual(0.0090143589987671056, actual, 0.0001);
        }
        public void RegressionRandomModelSelectingEnsembleLearner_Learn()
        {
            var learners = new IIndexedLearner <double>[]
            {
                new RegressionDecisionTreeLearner(2),
                new RegressionDecisionTreeLearner(5),
                new RegressionDecisionTreeLearner(7),
                new RegressionDecisionTreeLearner(9),
                new RegressionDecisionTreeLearner(11),
                new RegressionDecisionTreeLearner(21),
                new RegressionDecisionTreeLearner(23),
                new RegressionDecisionTreeLearner(1),
                new RegressionDecisionTreeLearner(14),
                new RegressionDecisionTreeLearner(17),
                new RegressionDecisionTreeLearner(19),
                new RegressionDecisionTreeLearner(33)
            };

            var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5);

            var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();

            var model       = sut.Learn(observations, targets);
            var predictions = model.Predict(observations);

            var metric = new MeanSquaredErrorRegressionMetric();
            var actual = metric.Error(targets, predictions);

            Assert.AreEqual(0.017238327841614508, actual, 0.0001);
        }
Beispiel #3
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        public void RegressionRandomModelSelectingEnsembleLearner_Learn_Start_With_3_Models()
        {
            var learners = new IIndexedLearner <double>[]
            {
                new RegressionDecisionTreeLearner(2),
                new RegressionDecisionTreeLearner(5),
                new RegressionDecisionTreeLearner(7),
                new RegressionDecisionTreeLearner(9),
                new RegressionDecisionTreeLearner(11),
                new RegressionDecisionTreeLearner(21),
                new RegressionDecisionTreeLearner(23),
                new RegressionDecisionTreeLearner(1),
                new RegressionDecisionTreeLearner(14),
                new RegressionDecisionTreeLearner(17),
                new RegressionDecisionTreeLearner(19),
                new RegressionDecisionTreeLearner(33)
            };

            var metric = new MeanSquaredErrorRegressionMetric();

            var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5,
                                                                        new RandomCrossValidation <double>(5, 42), new MeanRegressionEnsembleStrategy(), metric, 3, false);

            var parser       = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
            var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
            var targets      = parser.EnumerateRows("T").ToF64Vector();

            var model       = sut.Learn(observations, targets);
            var predictions = model.Predict(observations);

            var actual = metric.Error(targets, predictions);

            Assert.AreEqual(0.0090143589987671056, actual, 0.0001);
        }
        public void RegressionRandomModelSelectingEnsembleLearner_Learn_Indexed()
        {
            var learners = new IIndexedLearner <double>[]
            {
                new RegressionDecisionTreeLearner(2),
                new RegressionDecisionTreeLearner(5),
                new RegressionDecisionTreeLearner(7),
                new RegressionDecisionTreeLearner(9),
                new RegressionDecisionTreeLearner(11),
                new RegressionDecisionTreeLearner(21),
                new RegressionDecisionTreeLearner(23),
                new RegressionDecisionTreeLearner(1),
                new RegressionDecisionTreeLearner(14),
                new RegressionDecisionTreeLearner(17),
                new RegressionDecisionTreeLearner(19),
                new RegressionDecisionTreeLearner(33)
            };

            var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5);

            var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();

            var indices = Enumerable.Range(0, 25).ToArray();

            var model       = sut.Learn(observations, targets, indices);
            var predictions = model.Predict(observations);

            var metric = new MeanSquaredErrorRegressionMetric();
            var actual = metric.Error(targets, predictions);

            Assert.AreEqual(0.13601421174394385, actual, 0.0001);
        }
Beispiel #5
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        public void RegressionRandomModelSelectingEnsembleLearner_Learn()
        {
            var learners = new IIndexedLearner <double>[]
            {
                new RegressionDecisionTreeLearner(2),
                new RegressionDecisionTreeLearner(5),
                new RegressionDecisionTreeLearner(7),
                new RegressionDecisionTreeLearner(9),
                new RegressionDecisionTreeLearner(11),
                new RegressionDecisionTreeLearner(21),
                new RegressionDecisionTreeLearner(23),
                new RegressionDecisionTreeLearner(1),
                new RegressionDecisionTreeLearner(14),
                new RegressionDecisionTreeLearner(17),
                new RegressionDecisionTreeLearner(19),
                new RegressionDecisionTreeLearner(33)
            };

            var sut = new RegressionRandomModelSelectingEnsembleLearner(learners, 5);

            var parser       = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
            var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
            var targets      = parser.EnumerateRows("T").ToF64Vector();

            var model       = sut.Learn(observations, targets);
            var predictions = model.Predict(observations);

            var metric = new MeanSquaredErrorRegressionMetric();
            var actual = metric.Error(targets, predictions);

            Assert.AreEqual(0.017238327841614508, actual, 0.0001);
        }