public CrossFoldLearner learningRate(double x) { for (OnlineLogisticRegression model : models) { model.learningRate(x); } return this; }
public CrossFoldLearner learningRate(double x) { for (OnlineLogisticRegression model : models) { model.learningRate(x); } return this; }
public CrossFoldLearner learningRate(double x) { for (OnlineLogisticRegression model : models) { model.learningRate(x); } return this; }
model.learningRate(Double.parseDouble(options.get("learningRate"))); options.remove("learningRate");
learningAlgo = new OnlineLogisticRegression(2, 3, new L1()); learningAlgo.lambda(0.1); learningAlgo.learningRate(10);
.decayExponent(0.9) .lambda(3.0e-5) .learningRate(20);
.stepOffset(11) .alpha(0.01) .learningRate(50) .decayExponent(-0.02);
@Test public void testTrain() throws Exception { Vector target = readStandardData(); // lambda here needs to be relatively small to avoid swamping the actual signal, but can be // larger than usual because the data are dense. The learning rate doesn't matter too much // for this example, but should generally be < 1 // --passes 1 --rate 50 --lambda 0.001 --input sgd-y.csv --features 21 --output model --noBias // --target y --categories 2 --predictors V2 V3 V4 V5 V6 V7 --types n OnlineLogisticRegression lr = new OnlineLogisticRegression(2, 8, new L1()) .lambda(1 * 1.0e-3) .learningRate(50); train(getInput(), target, lr); test(getInput(), target, lr, 0.05, 0.3); }