object
ranking
Type Members
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type
Ranking[M[_], I] = Kleisli[M, Vector[I], Vector[Lazy[Int]]]
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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def
hierarchicalRanking[M[_], I](fitness: Fitness[I, Vector[Double]])(implicit arg0: Monad[M]): Ranking[M, I]
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def
hitCountRanking[M[_], I, C](cell: (I) ⇒ C)(implicit arg0: Monad[M], hm: HitMapper[M, C]): Ranking[M, I]
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def
hyperVolumeRanking[M[_], I](referencePoint: Seq[Double], fitness: Fitness[I, Seq[Double]])(implicit arg0: Monad[M]): Ranking[M, I]
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final
def
isInstanceOf[T0]: Boolean
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def
monoObjectiveRanking[M[_], I](fitness: Fitness[I, Double])(implicit arg0: Monad[M]): Ranking[M, I]
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
paretoRanking[M[_], I](fitness: Fitness[I, Vector[Double]], dominance: Dominance = nonStrictDominance)(implicit arg0: Monad[M]): Ranking[M, I]
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def
paretoRankingMinAndCrowdingDiversity[M[_], I](fitness: (I) ⇒ Vector[Double])(implicit arg0: Monad[M], arg1: RandomGen[M]): Kleisli[M, Vector[I], Vector[(Lazy[Int], Lazy[Double])]]
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def
profileRanking[M[_], I](niche: Niche[I, Int], fitness: Fitness[I, Double])(implicit arg0: Monad[M]): Ranking[M, I]
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def
rank[M[_], I, K](ranking: Kleisli[M, Vector[I], Vector[K]])(implicit arg0: Monad[M]): Kleisli[M, Vector[I], Vector[(I, K)]]
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def
rankAndDiversity[M[_], I](ranking: Ranking[M, I], diversity: Diversity[M, I])(implicit arg0: Monad[M]): Kleisli[M, Vector[I], Vector[(Lazy[Int], Lazy[Double])]]
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def
reversedRanking[M[_], I](ranking: Ranking[M, I])(implicit arg0: Monad[M]): Ranking[M, I]
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
Inherited from AnyRef
Inherited from Any
