|
public getPossibleAttributeValues ( String attributeName ) : List |
||
| attributeName | String | |
| return | List |
|
public static List<DecisionTree> getStumpsFor(DataSet ds,
String returnValueIfMatched, String returnValueIfUnmatched)
{
List<String> attributes = ds.getNonTargetAttributes();
List<DecisionTree> trees = new List<DecisionTree>();
foreach (String attribute in attributes)
{
List<String> values = ds.getPossibleAttributeValues(attribute);
foreach (String value in values)
{
List<String> unmatchedValues = Util.removeFrom(ds
.getPossibleAttributeValues(attribute), value);
DecisionTree tree = getStumpFor(ds, attribute, value,
returnValueIfMatched, unmatchedValues,
returnValueIfUnmatched);
trees.Add(tree);
}
}
return trees;
}
//
// 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;
}
