Changeset 21 for trunk/CrossPare/src/de/ugoe

Timestamp:

11/05/14 11:56:16 (10 years ago)

Author:

sherbold

Message:

• bug fixes for local trainers

Location:

trunk/CrossPare/src/de/ugoe/cs/cpdp/training

Files:

• WekaClusterTraining2.java (modified) (2 diffs)
• WekaLocalTraining2.java (modified) (3 diffs)

trunk/CrossPare/src/de/ugoe/cs/cpdp/training/WekaClusterTraining2.java

@@ -5 +5 @@
 import java.util.HashSet;
 import java.util.Iterator;
+import java.util.Map.Entry;
 import java.util.Set;
 import java.util.logging.Level;
@@ -146 +147 @@
                         cclassifier = new HashMap<Integer, Classifier>();
                         ctraindata = new HashMap<Integer, Instances>();
-                        
-                        // 3. cluster data
-                        // use standard params for now
-                        clusterer = new EM();
-                        // we can set options like so:
-                        //String[] params = {"-N", "100"};
-                        //clusterer.setOptions(params);
-                        
-                        // set max num of clusters to train data size (although we do not want that)
-                        clusterer.setMaximumNumberOfClusters(train.size());
                                                 
-                        // build clusterer
-                        clusterer.buildClusterer(train);
-                        
-                        Instances ctrain = new Instances(train);
-                        
-                        // get train data per cluster
-                        int cnumber;
-                        for ( int j=0; j < ctrain.numInstances(); j++ ) {
-                                cnumber = clusterer.clusterInstance(ctrain.get(j));
-                                
-                                // add training data to list of instances for this cluster number
-                                if ( !ctraindata.containsKey(cnumber) ) {
-                                        ctraindata.put(cnumber, new Instances(traindata));
-                                        ctraindata.get(cnumber).delete();
+                        Instances ctrain;
+                        int maxNumClusters = train.size();
+                        boolean sufficientInstancesInEachCluster;
+                        do { // while(onlyTarget)
+                                sufficientInstancesInEachCluster = true;
+                                clusterer = new EM();
+                                clusterer.setMaximumNumberOfClusters(maxNumClusters);
+                                clusterer.buildClusterer(train);
+                                
+                                // 4. get cluster membership of our traindata
+                                //AddCluster cfilter = new AddCluster();
+                                //cfilter.setClusterer(clusterer);
+                                //cfilter.setInputFormat(train);
+                                //Instances ctrain = Filter.useFilter(train, cfilter);
+                                
+                                ctrain = new Instances(train);
+                                ctraindata = new HashMap<>();
+                                
+                                // get traindata per cluster
+                                for ( int j=0; j < ctrain.numInstances(); j++ ) {
+                                        // get the cluster number from the attributes, subract 1 because if we clusterInstance we get 0-n, and this is 1-n
+                                        //cnumber = Integer.parseInt(ctrain.get(j).stringValue(ctrain.get(j).numAttributes()-1).replace("cluster", "")) - 1;
+                                        
+                                        int cnumber = clusterer.clusterInstance(ctrain.get(j));
+                                        // add training data to list of instances for this cluster number
+                                        if ( !ctraindata.containsKey(cnumber) ) {
+                                                ctraindata.put(cnumber, new Instances(traindata));
+                                                ctraindata.get(cnumber).delete();
+                                        }
+                                        ctraindata.get(cnumber).add(traindata.get(j));
                                 }
-                                ctraindata.get(cnumber).add(traindata.get(j));
-                        }
+                                
+                                for( Entry<Integer,Instances> entry : ctraindata.entrySet() ) {
+                                        Instances instances = entry.getValue();
+                                        int[] counts = instances.attributeStats(instances.classIndex()).nominalCounts;
+                                        for( int count : counts ) {
+                                                sufficientInstancesInEachCluster &= count>0;
+                                        }
+                                        sufficientInstancesInEachCluster &= instances.numInstances()>=5;
+                                }
+                                maxNumClusters = clusterer.numberOfClusters()-1;
+                        } while(!sufficientInstancesInEachCluster);
                         
                         // train one classifier per cluster, we get the cluster number from the training data
                         Iterator<Integer> clusternumber = ctraindata.keySet().iterator();
                         while ( clusternumber.hasNext() ) {
-                                cnumber = clusternumber.next();                 
+                                int cnumber = clusternumber.next();                     
                                 cclassifier.put(cnumber,setupClassifier());
                                 cclassifier.get(cnumber).buildClassifier(ctraindata.get(cnumber));

trunk/CrossPare/src/de/ugoe/cs/cpdp/training/WekaLocalTraining2.java

@@ -271 +271 @@
                                 dist = new EuclideanDistance(traindata2);
                                 if( !this.ctraindata.containsKey(found_cnumber) ) { 
-                                        double min_distance = 99999999;
+                                        double min_distance = Double.MAX_VALUE;
                                         clusternumber = ctraindata.keySet().iterator();
                                         while ( clusternumber.hasNext() ) {
@@ -353 +353 @@
                         // we need these for the sizes of the quadrants
                         double[] big = {0,0};
-                        double[] small = {9999999,99999999};
+                        double[] small = {Double.MAX_VALUE,Double.MAX_VALUE};
                         
                         // set quadtree payload values and get max and min x and y values for size
@@ -581 +581 @@
                  */
                 private int findFarthest(int index) {
-                        double furthest = -1000000;
+                        double furthest = Double.MIN_VALUE;
                         int ret = 0;