@Override public void close() { for (OnlineLogisticRegression m : models) { m.close(); } }
@Override public void close() { for (OnlineLogisticRegression m : models) { m.close(); } }
@Override public void close() { for (OnlineLogisticRegression m : models) { m.close(); } }
static void remove(String model) { LogisticModel existing = models.remove(model); existing.learn.close(); }
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; }
learningAlgo.close();
learningAlgo.close();
model.close();
.decayExponent(-0.02); lr.close();
@Test public void onlineLogisticRegressionRoundTrip() throws IOException { OnlineLogisticRegression olr = new OnlineLogisticRegression(2, 5, new L1()); train(olr, 100); OnlineLogisticRegression olr3 = roundTrip(olr, OnlineLogisticRegression.class); assertEquals(0, olr.getBeta().minus(olr3.getBeta()).aggregate(Functions.MAX, Functions.IDENTITY), 1.0e-6); train(olr, 100); train(olr3, 100); assertEquals(0, olr.getBeta().minus(olr3.getBeta()).aggregate(Functions.MAX, Functions.IDENTITY), 1.0e-6); olr.close(); olr3.close(); }