Package org.optaplanner.core.api.score.buildin.hardsoftlong

Interface HardSoftLongScoreHolder

All Superinterfaces:

ScoreHolder<HardSoftLongScore>

All Known Implementing Classes:

HardSoftLongScoreHolderImpl

public interface HardSoftLongScoreHolder
extends ScoreHolder<HardSoftLongScore>

See Also:

HardSoftLongScore

Method Summary

Modifier and Type	Method	Description
void	addHardConstraintMatch(org.kie.api.runtime.rule.RuleContext kcontext, long hardWeight)
void	addMultiConstraintMatch(org.kie.api.runtime.rule.RuleContext kcontext, long hardWeight, long softWeight)
void	addSoftConstraintMatch(org.kie.api.runtime.rule.RuleContext kcontext, long softWeight)
void	impactScore(org.kie.api.runtime.rule.RuleContext kcontext, long weightMultiplier)
void	penalize(org.kie.api.runtime.rule.RuleContext kcontext, long weightMultiplier)	Penalize a match by the ConstraintWeight negated and multiplied with the weightMultiplier for all score levels.
void	penalize(org.kie.api.runtime.rule.RuleContext kcontext, long hardWeightMultiplier, long softWeightMultiplier)	Penalize a match by the ConstraintWeight negated and multiplied with the specific weightMultiplier per score level.
void	reward(org.kie.api.runtime.rule.RuleContext kcontext, long weightMultiplier)	Reward a match by the ConstraintWeight multiplied with the weightMultiplier for all score levels.
void	reward(org.kie.api.runtime.rule.RuleContext kcontext, long hardWeightMultiplier, long softWeightMultiplier)	Reward a match by the ConstraintWeight multiplied with the specific weightMultiplier per score level.

Methods inherited from interface org.optaplanner.core.api.score.holder.ScoreHolder

penalize, reward

Method Detail

penalize

void penalize(org.kie.api.runtime.rule.RuleContext kcontext,
              long weightMultiplier)

Penalize a match by the ConstraintWeight negated and multiplied with the weightMultiplier for all score levels.

Parameters:

kcontext - never null, the magic variable in DRL

weightMultiplier - at least 0

penalize

void penalize(org.kie.api.runtime.rule.RuleContext kcontext,
              long hardWeightMultiplier,
              long softWeightMultiplier)

Penalize a match by the ConstraintWeight negated and multiplied with the specific weightMultiplier per score level. Slower than penalize(RuleContext, long).

Parameters:

kcontext - never null, the magic variable in DRL

hardWeightMultiplier - at least 0

softWeightMultiplier - at least 0

reward

void reward(org.kie.api.runtime.rule.RuleContext kcontext,
            long weightMultiplier)

Reward a match by the ConstraintWeight multiplied with the weightMultiplier for all score levels.

Parameters:

kcontext - never null, the magic variable in DRL

weightMultiplier - at least 0

reward

void reward(org.kie.api.runtime.rule.RuleContext kcontext,
            long hardWeightMultiplier,
            long softWeightMultiplier)

Reward a match by the ConstraintWeight multiplied with the specific weightMultiplier per score level. Slower than reward(RuleContext, long).

Parameters:

kcontext - never null, the magic variable in DRL

hardWeightMultiplier - at least 0

softWeightMultiplier - at least 0

impactScore

void impactScore(org.kie.api.runtime.rule.RuleContext kcontext,
                 long weightMultiplier)

addHardConstraintMatch

void addHardConstraintMatch(org.kie.api.runtime.rule.RuleContext kcontext,
                            long hardWeight)

Parameters:

kcontext - never null, the magic variable in DRL

hardWeight - higher is better, negative for a penalty, positive for a reward

addSoftConstraintMatch

void addSoftConstraintMatch(org.kie.api.runtime.rule.RuleContext kcontext,
                            long softWeight)

Parameters:

kcontext - never null, the magic variable in DRL

softWeight - higher is better, negative for a penalty, positive for a reward

addMultiConstraintMatch

void addMultiConstraintMatch(org.kie.api.runtime.rule.RuleContext kcontext,
                             long hardWeight,
                             long softWeight)

Parameters:

kcontext - never null, the magic variable in DRL

softWeight - higher is better, negative for a penalty, positive for a reward