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* 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,
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* See the License for the specific language governing permissions and
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*/
package org.apache.mahout.classifier.discriminative;
import org.apache.mahout.math.Vector;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* This class implements training according to the winnow update algorithm.
*/
@Deprecated
public class WinnowTrainer extends LinearTrainer {
private static final Logger log = LoggerFactory.getLogger(WinnowTrainer.class);
/** Promotion step to multiply weights with on update. */
private final double promotionStep;
public WinnowTrainer(int dimension, double promotionStep, double threshold, double init, double initBias) {
super(dimension, threshold, init, initBias);
this.promotionStep = promotionStep;
}
public WinnowTrainer(int dimension, double promotionStep) {
this(dimension, promotionStep, 0.5, 1, 0);
}
/**
* Initializes with dimension and promotionStep of 2.
*
* @param dimension
* number of features.
*/
public WinnowTrainer(int dimension) {
this(dimension, 2);
}
/**
* {@inheritDoc} Winnow update works such that in case the predicted label
* does not match the real label, the weight vector is updated as follows: In
* case the prediction was positive but should have been negative, all entries
* in the weight vector that correspond to non null features in the example
* are doubled.
*
* In case the prediction was negative but should have been positive, all
* entries in the weight vector that correspond to non null features in the
* example are halved.
*
* NOTE: case 1 and case 2 below look _exactly_ the same. Is this broken?
*/
@Override
protected void update(double label, Vector dataPoint, LinearModel model) {
if (label > 0) {
// case one
Vector updateVector = dataPoint.times(1 / this.promotionStep);
log.info("Winnow update positive: {}", updateVector);
for (Vector.Element element : updateVector.nonZeroes()) {
if (element.get() != 0) {
model.timesDelta(element.index(), element.get());
}
}
} else {
// case two
Vector updateVector = dataPoint.times(1 / this.promotionStep);
log.info("Winnow update negative: {}", updateVector);
for (Vector.Element element : updateVector.nonZeroes()) {
if (element.get() != 0) {
model.timesDelta(element.index(), element.get());
}
}
}
log.info(model.toString());
}
}