@Override public int numCategories() { return models.get(0).numCategories(); }
@Override public int numCategories() { return models.get(0).numCategories(); }
@Override public int numCategories() { return models.get(0).numCategories(); }
public CrossFoldLearner copy() { CrossFoldLearner r = new CrossFoldLearner(models.size(), numCategories(), numFeatures, prior); r.models.clear(); for (OnlineLogisticRegression model : models) { model.close(); OnlineLogisticRegression newModel = new OnlineLogisticRegression(model.numCategories(), model.numFeatures(), model.prior); newModel.copyFrom(model); r.models.add(newModel); } return r; }
public CrossFoldLearner copy() { CrossFoldLearner r = new CrossFoldLearner(models.size(), numCategories(), numFeatures, prior); r.models.clear(); for (OnlineLogisticRegression model : models) { model.close(); OnlineLogisticRegression newModel = new OnlineLogisticRegression(model.numCategories(), model.numFeatures(), model.prior); newModel.copyFrom(model); r.models.add(newModel); } return r; }
public CrossFoldLearner copy() { CrossFoldLearner r = new CrossFoldLearner(models.size(), numCategories(), numFeatures, prior); r.models.clear(); for (OnlineLogisticRegression model : models) { model.close(); OnlineLogisticRegression newModel = new OnlineLogisticRegression(model.numCategories(), model.numFeatures(), model.prior); newModel.copyFrom(model); r.models.add(newModel); } return r; }
public OnlineLogisticRegression copy() { close(); OnlineLogisticRegression r = new OnlineLogisticRegression(numCategories(), numFeatures(), prior); r.copyFrom(this); return r; }
public OnlineLogisticRegression copy() { close(); OnlineLogisticRegression r = new OnlineLogisticRegression(numCategories(), numFeatures(), prior); r.copyFrom(this); return r; }
public OnlineLogisticRegression copy() { close(); OnlineLogisticRegression r = new OnlineLogisticRegression(numCategories(), numFeatures(), prior); r.copyFrom(this); return r; }
System.out.println("no of categories = " + learningAlgo.numCategories()); System.out.println("no of features = " + learningAlgo.numFeatures()); System.out.println("Probability of cluster 0 = " + (1.0d - r.get(0)));
System.out.println("no of categories = " + learningAlgo.numCategories()); System.out.println("no of features = " + learningAlgo.numFeatures()); System.out.println("Probability of cluster 0 = " + r.get(0));