/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.classifier.sgd;
import org.apache.mahout.classifier.AbstractVectorClassifier;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.function.Functions;
/**
* Implements the basic logistic training law.
*/
public class DefaultGradient implements Gradient {
/**
* Provides a default gradient computation useful for logistic regression.
*
* @param groupKey A grouping key to allow per-something AUC loss to be used for training.
* @param actual The target variable value.
* @param instance The current feature vector to use for gradient computation
* @param classifier The classifier that can compute scores
* @return The gradient to be applied to beta
*/
@Override
public final Vector apply(String groupKey, int actual, Vector instance, AbstractVectorClassifier classifier) {
// what does the current model say?
Vector v = classifier.classify(instance);
Vector r = v.like();
if (actual != 0) {
r.setQuick(actual - 1, 1);
}
r.assign(v, Functions.MINUS);
return r;
}
}