Experimental data.

jvia · Apr 12, 2012 · 42678c8 · 42678c8
1 parent bd63012
commit 42678c8
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Showing 5 changed files with 14 additions and 14 deletions.
diff --git a/cast_configs/10x1chain/10x1chain.cast b/cast_configs/10x1chain/10x1chain.cast
@@ -24,4 +24,4 @@ JAVA MG node9 experiments.Node --start --delay "gaussian 100 10"
 
 INCLUDE ../includes/aucom.cast
 INCLUDE ../includes/counter.cast
-#INCLUDE ../includes/conductor.cast
+INCLUDE ../includes/conductor.cast
diff --git a/cast_configs/10x1chain/10x1chain_fault.cast b/cast_configs/10x1chain/10x1chain_fault.cast
@@ -11,7 +11,7 @@ HOST localhost
 SUBARCHITECTURE 10x1.sa
 JAVA WM cast.architecture.SubarchitectureWorkingMemory
 JAVA TM cast.architecture.AlwaysPositiveTaskManager
-JAVA MG node0 experiments.Node --start --delay "gaussian 100 10" --fault "2500"
+JAVA MG node0 experiments.Node --start --delay "gaussian 100 10" --fault "10000"
 JAVA MG node1 experiments.Node --start --delay "gaussian 100 10"
 JAVA MG node2 experiments.Node --start --delay "gaussian 100 10"
 JAVA MG node3 experiments.Node --start --delay "gaussian 100 10"
@@ -24,4 +24,4 @@ JAVA MG node9 experiments.Node --start --delay "gaussian 100 10"
 
 INCLUDE ../includes/aucom.cast
 INCLUDE ../includes/counter.cast
-#INCLUDE ../includes/conductor.cast
+INCLUDE ../includes/conductor.cast
diff --git a/cast_configs/4x4chain/4x4chain_fault.cast b/cast_configs/4x4chain/4x4chain_fault.cast
@@ -5,7 +5,7 @@ SUBARCHITECTURE 4x4.sa
 JAVA WM cast.architecture.SubarchitectureWorkingMemory
 JAVA TM cast.architecture.AlwaysPositiveTaskManager
 
-JAVA MG node0 experiments.Node  --start             --delay "gaussian 100 10" --fault "13000"
+JAVA MG node0 experiments.Node  --start             --delay "gaussian 100 10" --fault "3000"
 JAVA MG node1 experiments.Node  --follows "node0"   --delay "gaussian 100 10"
 JAVA MG node2 experiments.Node  --follows "node1"   --delay "gaussian 100 10"
 JAVA MG node3 experiments.Node  --follows "node2"   --delay "gaussian 100 10" --end

diff --git a/cast_configs/three_chain/three_chain_fault.cast b/cast_configs/three_chain/three_chain_fault.cast
@@ -7,7 +7,7 @@ HOST localhost
 SUBARCHITECTURE three.sa
 JAVA WM cast.architecture.SubarchitectureWorkingMemory
 JAVA TM cast.architecture.AlwaysPositiveTaskManager
-JAVA MG node0 experiments.Node --start          --delay "gaussian 100 10"  --fault "2000"
+JAVA MG node0 experiments.Node --start          --delay "gaussian 100 10"  --fault "1000"
 JAVA MG node1 experiments.Node --follows node0  --delay "gaussian 100 10"
 JAVA MG node2 experiments.Node --follows node1  --delay "gaussian 100 10"
 JAVA MG node3 experiments.Node --follows node2  --delay "gaussian 100 10" --end

diff --git a/src/main/java/org/bham/app/experiment/CastExperiment.java b/src/main/java/org/bham/app/experiment/CastExperiment.java
@@ -159,7 +159,7 @@ public void process() {
             }
 
 
-            errorTime = cast.getObservation(error).getTimestamp();
+            errorTime = 0;//cast.getObservation(error).getTimestamp();
             faultDetector.stop();
             cast.disconnect();
         } catch (ActionFailedException e) {
@@ -258,20 +258,20 @@ public void modelTrainerStatusChanged(StatusChangedEvent evt) {
      */
     private T2GramDetector createDetector(T2GramModelI model) {
         // load the fault detector
-        MeanVarianceResult r = new MeanVarianceResult(0.0, 0.0);
-        try {
-            r = (MeanVarianceResult) new Replay(observation, model).call();
-        } catch (Exception e) {
-            e.printStackTrace();
-        }
+//        MeanVarianceResult r = new MeanVarianceResult(0.0, 0.0);
+//        try {
+//            r = (MeanVarianceResult) new Replay(observation, model).call();
+//        } catch (Exception e) {
+//            e.printStackTrace();
+//        }
 
         T2GramDetector detector = new T2GramDetector();
         detector.setModel(model);
 
 
         // Create a new classifier with a mean that is a s
-        detector.setClassificator(new StatisticalAnomalyClassifier(r.getMean(), r.getVariance()));
-        detector.setSlidingWindow(new SlidingWindow(800, 400));
+        detector.setClassificator(new StatisticalAnomalyClassifier(0.2, 0.001));//new StatisticalAnomalyClassifier(r.getMean(), r.getVariance()));
+        detector.setSlidingWindow(new SlidingWindow(200, 100));
 
         System.out.printf("Classifier: %s", detector.getClassificator().getAttributes());
         return detector;