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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;
/**
* Implements training according to the perceptron update rule.
*/
public class PerceptronTrainer extends LinearTrainer {
private static final Logger log = LoggerFactory.getLogger(PerceptronTrainer.class);
/** Rate the model is to be updated with at each step. */
private final double learningRate;
public PerceptronTrainer(int dimension, double threshold,
double learningRate, double init, double initBias) {
super(dimension, threshold, init, initBias);
this.learningRate = learningRate;
}
/**
* {@inheritDoc} Perceptron 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, the weight vector
* is set to the sum of weight vector and example (multiplied by the learning rate).
*
* In case the prediction was negative but should have been positive, the example
* vector (multiplied by the learning rate) is subtracted from the weight vector.
*/
@Override
protected void update(double label, Vector dataPoint, LinearModel model) {
double factor = 1.0;
if (label == 0.0) {
factor = -1.0;
}
Vector updateVector = dataPoint.times(factor).times(this.learningRate);
log.debug("Updatevec: {}", updateVector);
model.addDelta(updateVector);
model.shiftBias(factor * this.learningRate);
log.debug("{}", model);
}
}