edu.snu.dolphin.bsp.examples.ml.loss.LogisticLoss.java Source code

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/*
 * Copyright (C) 2015 Seoul National University
 *
 * Licensed 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 edu.snu.dolphin.bsp.examples.ml.loss;

import org.apache.mahout.math.Vector;

import javax.inject.Inject;

/**
 * Represents the regularize function for logistic regression.
 */
public final class LogisticLoss implements Loss {

    @Inject
    public LogisticLoss() {
    }

    @Override
    public double loss(final double predict, final double output) {
        return Math.log(1 + Math.exp(-predict * output));
    }

    @Override
    public Vector gradient(final Vector feature, final double predict, final double output) {

        // http://lingpipe-blog.com/2012/02/16/howprevent-overflow-underflow-logistic-regression/
        final double exponent = -predict * output;
        final double maxExponent = Math.max(exponent, 0);
        final double logSumExp = maxExponent + Math.log(Math.exp(-maxExponent) + Math.exp(exponent - maxExponent));
        return feature.times(output * (Math.exp(-logSumExp) - 1));
    }
}