RapidMiner im akademischen Einsatz - Documentation - Rapid

2. Die Anwendungsfälle
1 public class MyLearner extends AbstractLearner {
2
3 public s t a t i c void S t r i n g PARAMETER ALPHA = ” alpha ” ;
4
5 /** Constructor called via reflection . */
6 public MyLearner ( O p e r a t o r D e s c r i p t i o n d e s c r i p t i o n ) {
7 super ( d e s c r i p t i o n ) ;
8 }
9
10 /** Main method generating the prediction model . */
11 @Override
12 public Model l e a r n ( ExampleSet data ) throws OperatorException {
13 // Obtain user - specified parameter
14 int alpha = getParameterAsInt (PARAMETER ALPHA) ;
15 MyPredictionModel model ;
16 // use data to create prediction model here
17 return model ;
18 }
19
20 /** Define user - configurable parameters here . */
21 @Override
22 public L i s t getParameterTypes ( ) {
23 L i s t parameterTypes = super . getParameterTypes ( ) ;
24 parameterTypes . add (new ParameterTypeInt ( ” alpha ” ,
25 ”The parameter alpha ” , 0 , 100 , 10) ) ;
26 return parameterTypes ;
27 }
28
29 /** Tell the user what kind of input the algorithm supports . */
30 @Override
31 public boolean s u p p o r t s C a p a b i l i t y ( O p e r a t o r C a p a b i l i t y c a p a b i l i t y ) {
32 switch ( c a p a b i l i t y ) {
33 case NUMERICAL ATTRIBUTES:
34 case BINOMINAL LABEL:
35 return true ;
36 }
37 return f a l s e ;
38 }
39 }
Abbildung 2.3: Eine beispielhafte Implementierung eines Lernverfahrens in
RapidMiner
18