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-rw-r--r--src/predictor/PredictorFactory.java98
1 files changed, 98 insertions, 0 deletions
diff --git a/src/predictor/PredictorFactory.java b/src/predictor/PredictorFactory.java
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+package predictor;
+
+import org.apache.log4j.*;
+
+import weka.classifiers.bayes.*;
+import weka.classifiers.trees.*;
+import weka.classifiers.rules.*;
+import weka.classifiers.functions.*;
+import weka.classifiers.functions.LibSVM;
+import weka.classifiers.lazy.*;
+
+import predictor.PredictorInterface;
+
+public class PredictorFactory {
+ public static Logger logger = Logger.getLogger(PredictorFactory.class);
+
+ public static enum PredictionTechnique {
+ NAIVE_BAYES ("NBC", "Naive Bayes Classifier"),
+ BAYES_NET ("BN", "Bayesian Network"),
+ M5P ("M5P", "M5P Decision Tree"),
+ J48 ("J48", "C4.5 Decision Tree"),
+ DT ("DT", "Decision Table"),
+ ZEROR ("ZEROR", "ZeroR"),
+ REPTREE ("REPTREE", "REPTree"),
+ SMO ("SMO", "Sequential Minimal Optimization"),
+ RBFN ("RBFN", "RBF Network"),
+ MP ("MP", "Multilayer Perceptron"),
+ SLR ("SLR", "Simple Linear Regression"),
+ SL ("SL", "Simple Logistic"),
+ SVM ("SVM", "Support Vector Machine"),
+ LOG ("LOG", "Logistic"),
+ SGD ("SGD", "Stochastic Gradient Descent"),
+ VP ("VP", "VotedPerceptron"),
+ SMOR ("SMOR", "Sequential Minimal Optimization Regression"),
+ KSTAR ("KSTAR", "KStar"),
+ LWL ("LWL", "Locally weighted learning"),
+ RF ("RF", "Random Forest"),
+ NBM ("NBM", "Naive Bayes Multinomial"),
+ IBK ("IBK", "Instance-based Learning"),
+ JRIP ("JRIP", "JRip"),
+ M5R ("M5R", "M5Rules"),
+ ONER ("ONER", "OneR"),
+ PART ("PART", "PART"),
+ ;
+
+ private final String shortName;
+ private final String name;
+
+ PredictionTechnique(String shortName, String name) {
+ this.shortName = shortName;
+ this.name = name;
+ }
+
+ public String getShortName() {
+ return this.shortName;
+ }
+
+ public String getName() {
+ return this.name;
+ }
+ }
+
+ public static PredictorInterface createPredictor(PredictionTechnique pTechnique) {
+ String name = pTechnique.getName();
+ logger.debug("Creating predictor: " + name);
+
+ switch(pTechnique) {
+ case NAIVE_BAYES: return new ClassifierAdapter(new NaiveBayes(),name);
+ case BAYES_NET: return new ClassifierAdapter(new BayesNet(),name);
+ case M5P: return new ClassifierAdapter(new M5P(), name);
+ case J48: return new ClassifierAdapter(new J48(), name);
+ case DT: return new ClassifierAdapter(new DecisionTable(), name);
+ case ZEROR: return new ClassifierAdapter(new ZeroR(), name);
+ case REPTREE: return new ClassifierAdapter(new REPTree(), name);
+ case SMO: return new ClassifierAdapter(new SMO(), name);
+ //case RBFN: return new ClassifierAdapter(new RBFNetwork(), name);
+ case MP: return new ClassifierAdapter(new MultilayerPerceptron(), name);
+ case SLR: return new ClassifierAdapter(new SimpleLinearRegression(), name);
+ case SL: return new ClassifierAdapter(new SimpleLogistic(), name);
+ case SVM: return new ClassifierAdapter(new LibSVM(), name);
+ case LOG: return new ClassifierAdapter(new Logistic(), name);
+ //case SGD: return new ClassifierAdapter(new SGD(), name);
+ case VP: return new ClassifierAdapter(new VotedPerceptron(), name);
+ case SMOR: return new ClassifierAdapter(new SMOreg(), name);
+ case KSTAR: return new ClassifierAdapter(new KStar(), name);
+ case LWL: return new ClassifierAdapter(new LWL(), name);
+ case RF: return new ClassifierAdapter(new RandomForest(), name);
+ case NBM: return new ClassifierAdapter(new NaiveBayesMultinomial(), name);
+ case IBK: return new ClassifierAdapter(new IBk(), name);
+ case JRIP: return new ClassifierAdapter(new JRip(), name);
+ case M5R: return new ClassifierAdapter(new M5Rules(), name);
+ case ONER: return new ClassifierAdapter(new OneR(), name);
+ case PART: return new ClassifierAdapter(new PART(), name);
+ default: return new ClassifierAdapter(new NaiveBayes(),name);
+ }
+ }
+}
+