Changeset 15 for trunk/CrossPare/src/de/ugoe/cs/cpdp

Timestamp:

08/31/14 19:31:50 (10 years ago)

Author:

atrautsch

Message:

upd

File:

• trunk/CrossPare/src/de/ugoe/cs/cpdp/training/WekaLocalTraining2.java (modified) (4 diffs)

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

@@ -53 +53 @@
         private static Fastmap FMAP;
         private static EuclideanDistance DIST;
+        private static Instances TRAIN;
         
         // cluster
@@ -134 +135 @@
                                 Instance clusterInstance = createInstance(traindata, instance);
                                 
-                                // build temp dist matrix
-                                double[][] distmat = new double[3][3];
-                                
+                                // build temp dist matrix (2 Pivot per dimension + 1 instance we want to classify)
+                                double[][] distmat = new double[2*FMAP.target_dims+1][2*FMAP.target_dims+1];
+                                
+                                // vector of instances of pivots + 1 (for the instance we want to classify)
+                                int[] tmp = new int[FMAP.PA.length+1];
+                                
+                                Instance tmpi;
+                                Instance tmpj;
+                                for(int i=0; i < tmp.length; i++) {
+                                        for(int j=0; j < tmp.length; j++) {
+                                                if(i==0) {
+                                                        tmpi = instance; 
+                                                }else{
+                                                        tmpi = TRAIN.get(i);
+                                                }
+                                                
+                                                if(j == 0) {
+                                                        tmpj = instance;
+                                                }else {
+                                                        tmpj = TRAIN.get(j);
+                                                }
+                                                
+                                                distmat[i][j] = DIST.distance(tmpi, tmpj);
+                                        }
+                                }
+                                
+                                // this is the projection vector for our instance
+                                double[] proj = FMAP.addInstance(distmat);
+                                
+                                
+                                // jetzt suchen wir den cluster in dem wir uns befinden mit den 2 projektionen
                                 
                                 
                                 // get distance of this instance to every other instance
                                 // if the distance is minimal apply the classifier of the current cluster
+                                
+                                int cnumber;
+                                Iterator<Integer> clusternumber = ctraindata.keySet().iterator();
+                                while ( clusternumber.hasNext() ) {
+                                        cnumber = clusternumber.next();
+
+                                        for(int i=0; i < ctraindata.get(cnumber).size(); i++) {
+                                        }
+                                }
+                                
                                 
                                 /*
@@ -190 +229 @@
                         //train = Filter.useFilter(train, filter);
                         
-                        
+                        TRAIN = train;
                         // 3. calculate distance matrix (needed for Fastmap because it starts at dimension 1)
                         DIST = new EuclideanDistance(train);
@@ -843 +882 @@
                 }
                 
+                
+                /**
+                 * todo
+                 */
+                public boolean isInside(double x, double y) {
+                        boolean is_inside_x = false;
+                        boolean is_inside_y = false;
+                        double[][] our_size = this.getSize();
+                        
+                        
+                        if(our_size[0][0] <= x && our_size[0][1] >= x) {
+                                is_inside_x = true;
+                        }
+                        
+                        if(our_size[1][0] <= y && our_size[1][1] >= y) {
+                                is_inside_y = true;
+                        }
+                        
+                        
+                        if(is_inside_y && is_inside_x && this.verbose) {
+                                System.out.println(this + " contains: " + x + ", "+ y);
+                        }
+                        
+                        return is_inside_x && is_inside_y;
+                }
+                
+                
                 /**
                  * Perform Pruning and clustering of the quadtree