/*
* Copyright 2013 Red Hat, Inc. and/or its affiliates.
*
* 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 org.optaplanner.core.api.score.buildin.hardsoftdouble;
import org.kie.api.runtime.rule.RuleContext;
import org.optaplanner.core.api.score.Score;
import org.optaplanner.core.api.score.buildin.hardsoftbigdecimal.HardSoftBigDecimalScoreHolder;
import org.optaplanner.core.api.score.holder.AbstractScoreHolder;
/**
* WARNING: NOT RECOMMENDED TO USE DUE TO ROUNDING ERRORS THAT CAUSE SCORE CORRUPTION.
* Use {@link HardSoftBigDecimalScoreHolder} instead.
* @see HardSoftDoubleScore
*/
public class HardSoftDoubleScoreHolder extends AbstractScoreHolder {
protected double hardScore;
protected double softScore;
public HardSoftDoubleScoreHolder(boolean constraintMatchEnabled) {
super(constraintMatchEnabled, HardSoftDoubleScore.ZERO);
}
public double getHardScore() {
return hardScore;
}
public double getSoftScore() {
return softScore;
}
// ************************************************************************
// Worker methods
// ************************************************************************
/**
* @param kcontext never null, the magic variable in DRL
* @param hardWeight higher is better, negative for a penalty, positive for a reward
*/
public void addHardConstraintMatch(RuleContext kcontext, double hardWeight) {
hardScore += hardWeight;
registerConstraintMatch(kcontext,
() -> hardScore -= hardWeight,
() -> HardSoftDoubleScore.valueOf(hardWeight, 0.0));
}
/**
* @param kcontext never null, the magic variable in DRL
* @param softWeight higher is better, negative for a penalty, positive for a reward
*/
public void addSoftConstraintMatch(RuleContext kcontext, double softWeight) {
softScore += softWeight;
registerConstraintMatch(kcontext,
() -> softScore -= softWeight,
() -> HardSoftDoubleScore.valueOf(0.0, softWeight));
}
/**
* @param kcontext never null, the magic variable in DRL
* @param hardWeight higher is better, negative for a penalty, positive for a reward
* @param softWeight higher is better, negative for a penalty, positive for a reward
*/
public void addMultiConstraintMatch(RuleContext kcontext, double hardWeight, double softWeight) {
hardScore += hardWeight;
softScore += softWeight;
registerConstraintMatch(kcontext,
() -> {
hardScore -= hardWeight;
softScore -= softWeight;
},
() -> HardSoftDoubleScore.valueOf(hardWeight, softWeight));
}
@Override
public Score extractScore(int initScore) {
return HardSoftDoubleScore.valueOfUninitialized(initScore, hardScore, softScore);
}
}