public addNode ( String attributeValue, |
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attributeValue | String | |
tree | ||
Résultat | void |
private static DecisionTree createActualRestaurantDecisionTree() { // from AIMA 2nd ED // Fig 18.2 // raining node DecisionTree raining = new DecisionTree("raining"); raining.addLeaf(Util.YES, Util.YES); raining.addLeaf(Util.NO, Util.NO); // bar node DecisionTree bar = new DecisionTree("bar"); bar.addLeaf(Util.YES, Util.YES); bar.addLeaf(Util.NO, Util.NO); // friday saturday node DecisionTree frisat = new DecisionTree("fri/sat"); frisat.addLeaf(Util.YES, Util.YES); frisat.addLeaf(Util.NO, Util.NO); // second alternate node to the right of the diagram below hungry DecisionTree alternate2 = new DecisionTree("alternate"); alternate2.addNode(Util.YES, raining); alternate2.addLeaf(Util.NO, Util.YES); // reservation node DecisionTree reservation = new DecisionTree("reservation"); frisat.addNode(Util.NO, bar); frisat.addLeaf(Util.YES, Util.YES); // first alternate node to the left of the diagram below waitestimate DecisionTree alternate1 = new DecisionTree("alternate"); alternate1.addNode(Util.NO, reservation); alternate1.addNode(Util.YES, frisat); // hungry node DecisionTree hungry = new DecisionTree("hungry"); hungry.addLeaf(Util.NO, Util.YES); hungry.addNode(Util.YES, alternate2); // wait estimate node DecisionTree waitEstimate = new DecisionTree("wait_estimate"); waitEstimate.addLeaf(">60", Util.NO); waitEstimate.addNode("30-60", alternate1); waitEstimate.addNode("10-30", hungry); waitEstimate.addLeaf("0-10", Util.YES); // patrons node DecisionTree patrons = new DecisionTree("patrons"); patrons.addLeaf("None", Util.NO); patrons.addLeaf("Some", Util.YES); patrons.addNode("Full", waitEstimate); return patrons; }
// // PRIVATE METHODS // private static DecisionTree createInducedRestaurantDecisionTree() { // from AIMA 2nd ED // Fig 18.6 // friday saturday node DecisionTree frisat = new DecisionTree("fri/sat"); frisat.addLeaf(Util.YES, Util.YES); frisat.addLeaf(Util.NO, Util.NO); // type node DecisionTree type = new DecisionTree("type"); type.addLeaf("French", Util.YES); type.addLeaf("Italian", Util.NO); type.addNode("Thai", frisat); type.addLeaf("Burger", Util.YES); // hungry node DecisionTree hungry = new DecisionTree("hungry"); hungry.addLeaf(Util.NO, Util.NO); hungry.addNode(Util.YES, type); // patrons node DecisionTree patrons = new DecisionTree("patrons"); patrons.addLeaf("None", Util.NO); patrons.addLeaf("Some", Util.YES); patrons.addNode("Full", hungry); return patrons; }
// // PRIVATE METHODS // private DecisionTree decisionTreeLearning(DataSet ds, List<String> attributeNames, ConstantDecisonTree defaultTree) { if (ds.size() == 0) { return defaultTree; } if (allExamplesHaveSameClassification(ds)) { return new ConstantDecisonTree(ds.getExample(0).targetValue()); } if (attributeNames.Count == 0) { return majorityValue(ds); } String chosenAttribute = chooseAttribute(ds, attributeNames); DecisionTree tree = new DecisionTree(chosenAttribute); ConstantDecisonTree m = majorityValue(ds); List<String> values = ds.getPossibleAttributeValues(chosenAttribute); foreach (String v in values) { DataSet filtered = ds.matchingDataSet(chosenAttribute, v); List<String> newAttribs = Util.removeFrom(attributeNames, chosenAttribute); DecisionTree subTree = decisionTreeLearning(filtered, newAttribs, m); tree.addNode(v, subTree); } return tree; }