predict() public method

public predict ( Example e ) : String
e AIMA.Core.Learning.Framework.Example
return String
DecisionTreeLearner Class Documentation
コード例 #1
0
ファイル: LearnerTests.cs プロジェクト: PaulMineau/AIMA.Net 
        public void testDefaultUsedWhenTrainingDataSetHasNoExamples()
        {
            // tests RecursionBaseCase#1
            DataSet ds = DataSetFactory.getRestaurantDataSet();
            DecisionTreeLearner learner = new DecisionTreeLearner();

            DataSet ds2 = ds.emptyDataSet();
            Assert.AreEqual(0, ds2.size());

            learner.train(ds2);
            Assert.AreEqual("Unable To Classify", learner.predict(ds
                    .getExample(0)));
        }
コード例 #2
0
ファイル: LearnerTests.cs プロジェクト: PaulMineau/AIMA.Net 
        public void testClassificationReturnedWhenAllExamplesHaveTheSameClassification()
        {
            // tests RecursionBaseCase#2
            DataSet ds = DataSetFactory.getRestaurantDataSet();
            DecisionTreeLearner learner = new DecisionTreeLearner();

            DataSet ds2 = ds.emptyDataSet();

            // all 3 examples have the same classification (willWait = yes)
            ds2.add(ds.getExample(0));
            ds2.add(ds.getExample(2));
            ds2.add(ds.getExample(3));

            learner.train(ds2);
            Assert.AreEqual("Yes", learner.predict(ds.getExample(0)));
        }
コード例 #3
0
ファイル: LearnerTests.cs プロジェクト: PaulMineau/AIMA.Net 
        public void testMajorityReturnedWhenAttributesToExamineIsEmpty()
        {
            // tests RecursionBaseCase#2
            DataSet ds = DataSetFactory.getRestaurantDataSet();
            DecisionTreeLearner learner = new DecisionTreeLearner();

            DataSet ds2 = ds.emptyDataSet();

            // 3 examples have classification = "yes" and one ,"no"
            ds2.add(ds.getExample(0));
            ds2.add(ds.getExample(1));// "no"
            ds2.add(ds.getExample(2));
            ds2.add(ds.getExample(3));
            ds2.setSpecification(new MockDataSetSpecification("will_wait"));

            learner.train(ds2);
            Assert.AreEqual("Yes", learner.predict(ds.getExample(1)));
        }