public CrossFoldLearner decayExponent(double x) { for (OnlineLogisticRegression model : models) { model.decayExponent(x); } return this; }
public CrossFoldLearner decayExponent(double x) { for (OnlineLogisticRegression model : models) { model.decayExponent(x); } return this; }
public CrossFoldLearner decayExponent(double x) { for (OnlineLogisticRegression model : models) { model.decayExponent(x); } return this; }
public CrossFoldLearner(int folds, int numCategories, int numFeatures, PriorFunction prior) { this.numFeatures = numFeatures; this.prior = prior; for (int i = 0; i < folds; i++) { OnlineLogisticRegression model = new OnlineLogisticRegression(numCategories, numFeatures, prior); model.alpha(1).stepOffset(0).decayExponent(0); models.add(model); } }
public CrossFoldLearner(int folds, int numCategories, int numFeatures, PriorFunction prior) { this.numFeatures = numFeatures; this.prior = prior; for (int i = 0; i < folds; i++) { OnlineLogisticRegression model = new OnlineLogisticRegression(numCategories, numFeatures, prior); model.alpha(1).stepOffset(0).decayExponent(0); models.add(model); } }
public CrossFoldLearner(int folds, int numCategories, int numFeatures, PriorFunction prior) { this.numFeatures = numFeatures; this.prior = prior; for (int i = 0; i < folds; i++) { OnlineLogisticRegression model = new OnlineLogisticRegression(numCategories, numFeatures, prior); model.alpha(1).stepOffset(0).decayExponent(0); models.add(model); } }
model.decayExponent(Double.parseDouble(options.get("decayExponent"))); options.remove("decayExponent");
20, FEATURES, new L1()) .alpha(1).stepOffset(1000) .decayExponent(0.9) .lambda(3.0e-5) .learningRate(20);
.alpha(0.01) .learningRate(50) .decayExponent(-0.02);