Changeset 18 for trunk/EventBenchConsole/src/de/ugoe/cs/eventbench/commands/CMDtrainMarkovModel.java

Timestamp:

04/14/11 11:12:06 (13 years ago)

Author:

sherbold

Message:

• removed all reference to de.ugoe.cs.eventbench.MarkovModel? and replaced them with de.ugoe.cs.eventbench.models.FirstOrderMarkovModel? and de.ugoe.cs.eventbench.models.IStochasticProcess

File:

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

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

@@ -4 +4 @@
 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.markov.MarkovModel;
+import de.ugoe.cs.eventbench.models.FirstOrderMarkovModel;
+import de.ugoe.cs.eventbench.models.HighOrderMarkovModel;
 import de.ugoe.cs.util.console.Command;
 import de.ugoe.cs.util.console.Console;
@@ -15 +17 @@
         @Override
         public void help() {
-                Console.println("Usage: trainMarkovModel <modelName>");
+                Console.println("Usage: trainMarkovModel <modelName> {<order>}");
         }
 
@@ -22 +24 @@
         public void run(List<Object> parameters) {
                 String modelname;
+                int order = 1;
                 try {
                         modelname = (String) parameters.get(0);
+                        if( parameters.size()==2 ) {
+                                order = Integer.parseInt((String) parameters.get(0));
+                        }
                 } catch (Exception e) {
                         throw new InvalidParameterException();
@@ -35 +41 @@
                         if( sequences.size()>0 ) {
                                 if( sequences.get(0).get(0) instanceof Event ) {
-                                        MarkovModel model = new MarkovModel();
+                                        HighOrderMarkovModel model;
+                                        if( order==1 ) {
+                                                model = new FirstOrderMarkovModel(new Random());
+                                        } else {
+                                                model = new HighOrderMarkovModel(order, new Random());
+                                        }
                                         model.train(sequences);
                                         if( GlobalDataContainer.getInstance().addData(modelname, model) ) {