Java Reference
In-Depth Information
an array the number of elements must be known in advance, while the number of
elements in a collection is not defined a priori .
Filling a set with two strings:
Collection items # new Vector();
items.add( new String("first"));
items.add( new String("second"));
Using an array the same population code can be written as follows:
String [] items # new String[2];
items[0] # new String("first");
items[1] # new String("first");
The use of arrays poses several problems: the number of elements must be
known in advance, an index must be used to address elements.
Another important interface is Map . It represents the association of a set of keys
to a set of values. The main methods of interface Map are void put(Object key,
Object value) and Object get(Object key) . The former stores an association between
the key and the value, the latter retrieves the value associated with a key (or null
if no value is present).
We will have a method ( buildDecisionTree() ) that implements the main fea-
tures of the algorithm, and several auxiliary methods that implement the
details. The root node of the decision tree is computed according to
Algorithm 4.2. Since the algorithm is generic and does not require accessing
any extra information in addition to its arguments, we decide to implement
is using static methods. The method buildDecisionTree is as close an imple-
mentation of the original algorithm as possible.
The method receives two parameters; the first is a map that associates
each item with its category; the second is a map that associates the names of
the features with their types. The collection of items can be obtained getting
the key set from the map items (using the method keySet() ).
//Input: a training set of items T, a set of attributes A
//Output: a decision tree (the root of it)
private static Node buildDecisionTree
(Map items, Map features){
// 0) if the attribute set is empty or the items
// belong to a single class
if (features.size() ## 0 || information(items) ## 0.0){
// a) create a leaf node labelled according to the class
// of the item
return new Node(findCategory(items));
// 1) select the "best" split attribute s in A
String splitFeature # selectSplit(items,features);
// 2) create a node n with label
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