/**
* Copyright (c) 2009/09-2012/08, Regents of the University of Colorado
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/**
* Copyright 2012/09-2013/04, 2013/11-Present, University of Massachusetts Amherst
* Copyright 2013/05-2013/10, IPSoft Inc.
*
* 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 com.clearnlp.classification.algorithm;
import java.util.Collections;
import java.util.List;
import com.clearnlp.classification.instance.IntInstance;
import com.clearnlp.classification.model.StringModelAD;
import com.clearnlp.classification.prediction.IntPrediction;
import com.clearnlp.classification.vector.SparseFeatureVector;
import com.clearnlp.util.UTMath;
/**
* AdaGrad algorithm using hinge loss.
* @since 1.3.0
* @author Jinho D. Choi ({@code jdchoi77@gmail.com})
*/
public class AdaGradOnlineHingeLoss extends AbstractAdaGrad
{
/**
* @param alpha the learning rate.
* @param rho the smoothing denominator.
*/
public AdaGradOnlineHingeLoss(double alpha, double rho, boolean average)
{
super(alpha, rho, average);
}
@Override
protected boolean update(StringModelAD model, IntInstance instance, int averageCount)
{
IntPrediction max = getPrediction(model, instance);
if (max.label != instance.getLabel())
{
updateCounts (model, instance, instance.getLabel(), max.label);
updateWeights(model, instance, instance.getLabel(), max.label, averageCount);
return true;
}
return false;
}
protected IntPrediction getPrediction(StringModelAD model, IntInstance instance)
{
List<IntPrediction> ps = model.getIntPredictions(instance.getFeatureVector());
ps.get(instance.getLabel()).score -= 1d;
return Collections.max(ps);
}
private void updateCounts(StringModelAD model, IntInstance instance, int yp, int yn)
{
SparseFeatureVector x = instance.getFeatureVector();
int i, xi, len = x.size();
double vi;
for (i=0; i<len; i++)
{
xi = x.getIndex(i);
vi = UTMath.sq(x.getWeight(i));
d_gradients[model.getWeightIndex(yp, xi)] += vi;
d_gradients[model.getWeightIndex(yn, xi)] += vi;
}
}
private void updateWeights(StringModelAD model, IntInstance instance, int yp, int yn, int averageCount)
{
SparseFeatureVector x = instance.getFeatureVector();
int i, xi, len = x.size();
double vi;
for (i=0; i<len; i++)
{
xi = x.getIndex(i);
vi = x.getWeight(i);
updateWeight(model, yp, xi, vi, averageCount);
updateWeight(model, yn, xi, -vi, averageCount);
}
}
}