Java Reference
In-Depth Information
private static double information(Map items){
Map frequencies # new HashMap();
// compute number of occurrences of classes
Iterator it # items.keySet().iterator();
while (it.hasNext()){
Item item # (Item);
String category # (String)items.get(item);
Long num_occur # (Long)frequencies.get(category);
if (num_occur ## null )
frequencies.put(category, new Long(1));
new Long(num_occur.longValue() ! 1));
// compute information
double info # 0;
double numItems # items.size();
it # frequencies.values().iterator();
while (it.hasNext()){
Long num_occurr # (Long);
double freq # num_occurr.doubleValue() / numItems;
info !# freq * Math.log(freq) / Math.log(2.0);
return -info;
Finally, we define a constructor for the DecisionTree class that invokes the
buildDecisionTree() method to build the decision tree; the parameters of the
constructor must be the same as those of the method.
public DecisionTree(Map items, Map features) {
root # buildDecisionTree(items,features);
To test the decision tree construction algorithm we need to:
define a training set of items;
instantiate the decision tree based on this set; and
check if it classifies correctly some items.
If the training algorithm works correctly then a decision tree equivalent to
the one used in previous testing should be generated, therefore the same test
items can be used to check if the tree is correct. We will use the training set
of items described in Table 4.2.
The test method testExample() is made up the following steps:
create the set of features ( “AC” and “ABS” both of type yn );
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