(DataPoint data)
| 284 | } |
| 285 | |
| 286 | @Override |
| 287 | public CategoricalResults classify(DataPoint data) |
| 288 | { |
| 289 | Vec x = data.getNumericalValues(); |
| 290 | if(ws.length == 1) |
| 291 | return ((LossC)loss).getClassification(ws[0].dot(x)+bs[0]); |
| 292 | else |
| 293 | { |
| 294 | Vec pred = new DenseVector(ws.length); |
| 295 | for(int i = 0; i < ws.length; i++) |
| 296 | pred.set(i, ws[i].dot(x)+bs[i]); |
| 297 | ((LossMC)loss).process(pred, pred); |
| 298 | return ((LossMC)loss).getClassification(pred); |
| 299 | } |
| 300 | } |
| 301 | |
| 302 | @Override |
| 303 | public double regress(DataPoint data) |