Add predictorfactory to predictor

JIRA:PREDICTION-41

Change-Id: I5f969d34042267fe4668b9e60eb1813c1d8a30cd

1 files changed, 98 insertions, 0 deletions

diff --git a/src/predictor/PredictorFactory.java b/src/predictor/PredictorFactory.java
new file mode 100644
index 0000000..af21b10
--- /dev/null
+++ b/src/predictor/PredictorFactory.java
@@ -0,0 +1,98 @@
+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);
+		}
+	}
+}
+