Java Reference
In-Depth Information
String repres # node.toString();
assertTrue(repres ! " doesn't include " ! label,
public void testSimpleTree(){
String leftLabel # "left";
String rightLable # "right";
Node leftLeaf # new Node(leftLabel);
Node rightLeaf # new Node(rightLable);
String toLeft # "to the left";
String toRight # "to the right";
String rootLabel # "root";
Node root # new Node(rootLabel);
String repres # root.toString();
int index # repres.indexOf("root");
assertTrue("root not found",index>0);
int indexToL # repres.indexOf(toLeft,index);
int indexToR # repres.indexOf(toRight,index);
assertTrue("left arc not found",indexToL>index);
assertTrue("right arc not found",indexToR>index);
int indexL # repres.indexOf("left",indexToL);
int indexR # repres.indexOf("right",indexToR);
assertTrue("left node not found",indexL>index);
assertTrue("right node not found",indexR>index);
Prototype 3: Training
In this iteration the decision tree construction algorithm is used to build a
classifier starting from a training set of items.
The class structure remains basically unchanged; the increment is essen-
tially algorithmic. We have to implement Algorithm 4.2 that takes as input a
set of features that are already assigned to categories and builds a decision
tree that captures the criteria adopted to assign the categories.
The main problem in the design of this prototype lies in the definition of the
data structures used for the implementation of the training algorithm
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