Changeset 184 for trunk/EventBenchConsole/src/de/ugoe/cs/eventbench/commands/CMDtrainPPM.java

Timestamp:

09/14/11 20:17:10 (13 years ago)

Author:

sherbold

Message:

• refactored commands for the training of models (trainDFA, trainMarkovModel, trainPPM) by extracting one common abstract superclass

+ added command updateModel for model reinforcement

File:

• trunk/EventBenchConsole/src/de/ugoe/cs/eventbench/commands/CMDtrainPPM.java (modified) (4 diffs)

trunk/EventBenchConsole/src/de/ugoe/cs/eventbench/commands/CMDtrainPPM.java

@@ -1 +1 @@
 package de.ugoe.cs.eventbench.commands;
 
-import java.security.InvalidParameterException;
 import java.util.List;
 import java.util.Random;
 
-import de.ugoe.cs.eventbench.data.Event;
-import de.ugoe.cs.eventbench.data.GlobalDataContainer;
 import de.ugoe.cs.eventbench.models.PredictionByPartialMatch;
-import de.ugoe.cs.util.console.Command;
+import de.ugoe.cs.eventbench.models.TrieBasedModel;
 import de.ugoe.cs.util.console.Console;
 
@@ -17 +14 @@
  * 
  * @author Steffen Herbold
- * @version 1.0
+ * @version 2.0
  */
-public class CMDtrainPPM implements Command {
+public class CMDtrainPPM extends AbstractTrainCommand {
+
+        /**
+         * <p>
+         * Escape probability of the PPM model.
+         * </p>
+         */
+        double probEscape;
+
+        /**
+         * <p>
+         * Maximal Markov order of the PPM model.
+         * </p>
+         */
+        int maxOrder;
+
+        /**
+         * <p>
+         * Minimal Markov order of the PPM model. Default: 0
+         * </p>
+         */
+        int minOrder = 0;
 
         /*
@@ -28 +46 @@
         @Override
         public void help() {
-                Console.println("Usage: trainPPM <modelName> <probEscape> <maxOrder> {<minOrder>}");
+                Console.println("Usage: trainPPM <modelName> <sequencesName> <probEscape> <maxOrder> {<minOrder>}");
+        }
+
+        /**
+         * <p>
+         * Handles the parameters probEscape, maxOrder, and minOrder.
+         * </p>
+         * 
+         * @see de.ugoe.cs.eventbench.commands.AbstractTrainCommand#handleOptionalParameters(java.util.List)
+         */
+        @Override
+        void handleAdditionalParameters(List<Object> parameters) throws Exception {
+                probEscape = Double.parseDouble((String) parameters.get(2));
+                maxOrder = Integer.parseInt((String) parameters.get(3));
+                if (parameters.size() == 5) {
+                        minOrder = Integer.parseInt((String) parameters.get(4));
+                }
         }
 
@@ -34 +68 @@
          * (non-Javadoc)
          * 
-         * @see de.ugoe.cs.util.console.Command#run(java.util.List)
+         * @see de.ugoe.cs.eventbench.commands.AbstractTrainCommand#createModel()
          */
-        @SuppressWarnings("unchecked")
         @Override
-        public void run(List<Object> parameters) {
-                String modelname;
-                double probEscape;
-                int maxOrder;
-                int minOrder = 0;
-                try {
-                        modelname = (String) parameters.get(0);
-                        probEscape = Double.parseDouble((String) parameters.get(1));
-                        maxOrder = Integer.parseInt((String) parameters.get(2));
-                        if (parameters.size() == 4) {
-                                minOrder = Integer.parseInt((String) parameters.get(3));
-                        }
-                } catch (Exception e) {
-                        throw new InvalidParameterException();
-                }
-
-                List<List<Event<?>>> sequences = null;
-                Object dataObject = GlobalDataContainer.getInstance().getData(
-                                "sequences");
-
-                try {
-                        sequences = (List<List<Event<?>>>) dataObject;
-                        if (sequences.size() > 0) {
-                                if (sequences.get(0).get(0) instanceof Event) {
-                                        PredictionByPartialMatch model = new PredictionByPartialMatch(
-                                                        maxOrder, minOrder, new Random(), probEscape);
-                                        model.train(sequences);
-                                        if (GlobalDataContainer.getInstance().addData(modelname,
-                                                        model)) {
-                                                Console.traceln("Old data \"" + modelname
-                                                                + "\" overwritten");
-                                        }
-                                } else {
-                                        Console.traceln("Illegal use of \"sequences\" parameter in the GlobalDataContainer.");
-                                        Console.traceln("The parameter should always be of type List<List<Event>>!");
-                                }
-                        }
-                } catch (ClassCastException e) {
-                        Console.println("Sequences need to be loaded first using parseXML");
-                }
+        TrieBasedModel createModel() {
+                return new PredictionByPartialMatch(maxOrder, minOrder, new Random(),
+                                probEscape);
         }