| 39 | } |
| 40 | |
| 41 | @Override |
| 42 | public CategoricalResults classify(DataPoint data) |
| 43 | { |
| 44 | CategoricalResults cr = new CategoricalResults(predicting.getNumOfCategories()); |
| 45 | |
| 46 | //Use a priority que so that we always pick the two lowest value class labels, makes indexing into the oneVsOne array simple |
| 47 | PriorityQueue<Integer> options = new PriorityQueue<Integer>(predicting.getNumOfCategories()); |
| 48 | for(int i = 0; i < cr.size(); i++) |
| 49 | options.add(i); |
| 50 | |
| 51 | |
| 52 | CategoricalResults subRes; |
| 53 | int c1, c2; |
| 54 | //We will now loop through and repeatedly pick two combinations, and eliminate the loser, until there is one winer |
| 55 | while(options.size() > 1) |
| 56 | { |
| 57 | c1 = options.poll(); |
| 58 | c2 = options.poll(); |
| 59 | |
| 60 | subRes = oneVone[c1][c2-c1-1].classify(data); |
| 61 | |
| 62 | if(subRes.mostLikely() == 0)//c1 wins, c2 no longer a candidate |
| 63 | options.add(c1); |
| 64 | else//c2 wins, c1 no onger a candidate |
| 65 | options.add(c2); |
| 66 | } |
| 67 | |
| 68 | cr.setProb(options.peek(), 1.0); |
| 69 | |
| 70 | |
| 71 | return cr; |
| 72 | } |
| 73 | |
| 74 | @Override |
| 75 | public DDAG clone() |