Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/training/WekaLocalTraining2.java =================================================================== --- trunk/CrossPare/src/de/ugoe/cs/cpdp/training/WekaLocalTraining2.java (revision 15) +++ trunk/CrossPare/src/de/ugoe/cs/cpdp/training/WekaLocalTraining2.java (revision 16) @@ -33,7 +33,9 @@ * 3) We cluster the QuadTree nodes together if they have similar density (50%) * 4) We save the clusters and their training data - * 5) We train a Weka classifier for each cluster with the clusters training data - * 5) We classify single instances to a cluster and then classify them using the classifier of the cluster - * + * 5) We only use clusters with > ALPHA instances (currently Math.sqrt(SIZE)), rest is discarded + * 6) We train a Weka classifier for each cluster with the clusters training data + * 7) We recalculate Fastmap distances for a single instance and then try to find a cluster containing the coords of the instance. + * 7.1.) If we can not find a cluster (due to coords outside of all clusters) we find the nearest cluster. + * 8) We classifiy the Instance with the classifier and traindata from the Cluster we found in 7. */ public class WekaLocalTraining2 extends WekaBaseTraining2 implements ITrainingStrategy { @@ -41,13 +43,13 @@ private final TraindatasetCluster classifier = new TraindatasetCluster(); - // these values are set later when we have all the information we need - + // these values are set later when we have all the information we need (size) /*Stopping rule for tree recursion (Math.sqrt(Instances)*/ public static double ALPHA = 0; + /*size of the complete set (used for density function)*/ + public static int SIZE = 0; /*Stopping rule for clustering*/ public static double DELTA = 0.5; - /*size of the complete set (used for density function)*/ - public static int SIZE = 0; - + + // we need these references later in the testing private static QuadTree TREE; private static Fastmap FMAP; @@ -55,6 +57,9 @@ private static Instances TRAIN; - // cluster + // cluster payloads private static ArrayList>> cluster = new ArrayList>>(); + + // cluster sizes (index is cluster number, arraylist is list of boxes (x0,y0,x1,y1) + private static HashMap> CSIZE = new HashMap>(); @Override @@ -118,4 +123,5 @@ * * TODO: class attribute filter raus + * TODO: werden auf die übergebene Instance ebenfalls die preprocessors angewendet? müsste eigentlich */ @Override @@ -163,50 +169,56 @@ // 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 clusternumber = ctraindata.keySet().iterator(); - while ( clusternumber.hasNext() ) { - cnumber = clusternumber.next(); - - for(int i=0; i < ctraindata.get(cnumber).size(); i++) { - } - } - - - /* - int cnumber; - int min_cluster = -1; - double min_distance = 99999999; - EuclideanDistance d; - Iterator clusternumber = ctraindata.keySet().iterator(); + int found_cnumber = -1; + Iterator clusternumber = CSIZE.keySet().iterator(); while ( clusternumber.hasNext() ) { cnumber = clusternumber.next(); - d = new EuclideanDistance(ctraindata.get(cnumber)); - for(int i=0; i < ctraindata.get(cnumber).size(); i++) { - if(d.distance(clusterInstance, ctraindata.get(cnumber).get(i)) <= min_distance) { - min_cluster = cnumber; - min_distance = d.distance(clusterInstance, ctraindata.get(cnumber).get(i)); + // jetzt iterieren wir über die boxen und hoffen wir finden was (cluster könnte auch entfernt worden sein) + for ( int box=0; box < CSIZE.get(cnumber).size(); box++ ) { + Double[][] current = CSIZE.get(cnumber).get(box); + if(proj[0] <= current[0][0] && proj[0] >= current[0][1] && // x + proj[1] <= current[1][0] && proj[1] >= current[1][1]) { // y + found_cnumber = cnumber; } } } + // wenn wir keinen cluster finden, liegen wir außerhalb des bereichs + // kann das vorkommen mit fastmap? + + // ja das kann vorkommen wir suchen also weiterhin den nächsten + // müssten mal durchzählen wie oft das vorkommt + if ( found_cnumber == -1 ) { + //Console.traceln(Level.INFO, String.format("ERROR matching instance to cluster!")); + //throw new RuntimeException("no cluster for test instance found!"); + } + + // jetzt kann es vorkommen das der cluster gelöscht wurde (weil zuwenig instanzen), jetzt müssen wir den + // finden der am nächsten dran ist + if( !this.ctraindata.containsKey(found_cnumber) ) { + double min_distance = 99999999; + clusternumber = ctraindata.keySet().iterator(); + while ( clusternumber.hasNext() ) { + cnumber = clusternumber.next(); + for(int i=0; i < ctraindata.get(cnumber).size(); i++) { + if(DIST.distance(clusterInstance, ctraindata.get(cnumber).get(i)) <= min_distance) { + found_cnumber = cnumber; + min_distance = DIST.distance(clusterInstance, ctraindata.get(cnumber).get(i)); + } + } + } + } + // here we have the cluster where an instance has the minimum distance between itself the // instance we want to classify - if(min_cluster == -1) { + if( found_cnumber == -1 ) { // this is an error condition + Console.traceln(Level.INFO, String.format("ERROR matching instance to cluster with full search!")); throw new RuntimeException("min_cluster not found"); } - */ // classify the passed instance with the cluster we found - ret = cclassifier.get(min_cluster).classifyInstance(classInstance); + ret = cclassifier.get(found_cnumber).classifyInstance(classInstance); }catch( Exception e ) { @@ -301,4 +313,6 @@ } } + + } @@ -625,4 +639,17 @@ public double[][] getSize() { return new double[][] {this.x, this.y}; + } + + public Double[][] getSizeDouble() { + Double[] tmpX = new Double[2]; + Double[] tmpY = new Double[2]; + + tmpX[0] = this.x[0]; + tmpX[1] = this.x[1]; + + tmpY[0] = this.y[0]; + tmpY[1] = this.y[1]; + + return new Double[][] {tmpX, tmpY}; } @@ -950,4 +977,7 @@ remove.add(list.size()-1); //System.out.println("removing "+biggest.getDensity() + " from list"); + + ArrayList tmpSize = new ArrayList(); + tmpSize.add(biggest.getSizeDouble()); // check the items for their density @@ -964,4 +994,7 @@ // wir können hier nicht removen weil wir sonst den index verschieben remove.add(i); + + // außerdem brauchen wir die größe + tmpSize.add(current.getSizeDouble()); } } @@ -975,4 +1008,11 @@ cluster.add(current_cluster); + // 5. add size of our current (biggest) + // we need that to classify test instances to a cluster + Integer cnumber = new Integer(cluster.size()-1); + if(CSIZE.containsKey(cnumber) == false) { + CSIZE.put(cnumber, tmpSize); + } + // recurse //System.out.println("restlist " + list.size());