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

Interface HardSoftScoreHolder

All Superinterfaces:

ScoreHolder<HardSoftScore>

All Known Implementing Classes:

HardSoftScoreHolderImpl

public interface HardSoftScoreHolder
extends ScoreHolder<HardSoftScore>

See Also:

HardSoftScore

Method Summary

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

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

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,
            int 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,
            int hardWeightMultiplier,
            int softWeightMultiplier)

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

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,
                 int weightMultiplier)

addHardConstraintMatch

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

Parameters:

kcontext - never null, the magic variable in DRL

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

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