mgo

Value Members

1. object EvolutionData extends Serializable

   

   Definition Classes

   DefaultContext

2. object RunResult extends Serializable

   

3. object ToHistoryOps

   

4. def afterDuration[M[_], I](d: Duration)(implicit arg0: Monad[M], mStartTime: StartTime[M]): StopCondition[M, I]

   

   Definition Classes

   Imports

5. def afterGeneration[M[_], I](g: Long)(implicit arg0: Monad[M], mGeneration: Generational[M]): StopCondition[M, I]

   

   Definition Classes

   Imports

6. package algorithm

   

7. def andO[M[_], I](o1: Elitism[M, I], o2: Elitism[M, I])(implicit arg0: Monad[M]): Elitism[M, I]

   

8. def anyReaches[M[_], I](goalReached: (I) ⇒ Boolean)(population: Vector[I])(implicit arg0: Monad[M]): (Vector[I]) ⇒ M[Boolean]

   

   ** Stop conditions ***

9. def array2ToVectorLens[A](implicit arg0: Manifest[A]): Lens[Array[Array[A]], Vector[Vector[A]]]

   

10. def arrayToVectorLens[A](implicit arg0: Manifest[A]): Lens[Array[A], Vector[A]]

    

11. def asB[M[_], I, I1, G1, G](itoi1: (I) ⇒ I1, g1tog: (G1) ⇒ G, breeding: Breeding[M, I1, G1])(implicit arg0: Monad[M]): Breeding[M, I, G]

    

12. def asE[G, G1, P](gtog1: (G) ⇒ G1, express: Expression[G1, P]): Expression[G, P]

    

13. def bindB[M[_], I, G1, G2](b1: Breeding[M, I, G1], b2: (Vector[G1]) ⇒ Breeding[M, I, G2])(implicit arg0: Monad[M]): Breeding[M, I, G2]

    

    ** Breeding ***

14. def bindE[G, P1, P2](e1: Expression[G, P1], e2: (P1) ⇒ Expression[G, P2]): Expression[G, P2]

    

    ** Expression ***

15. def bindO[M[_], I](o1: Elitism[M, I], o2: (Vector[I]) ⇒ Elitism[M, I])(implicit arg0: Monad[M]): Elitism[M, I]

    

    ** Objectives ***

16. object breeding

    

17. def byNicheB[I, N, M[_], G](niche: (I) ⇒ N)(breeding: Breeding[M, I, G])(implicit arg0: Monad[M]): Breeding[M, I, G]

    

18. object contexts

    

19. object diversity

    

    Layer of the cake that compute a diversity metric for a set of values

20. object dominance

    

21. implicit def double2Scalable(d: Double): AnyRef { ... /* 2 definitions in type refinement */ }

    

22. object elitism

    

23. implicit def evolutionStartTime[S]: StartTime[[β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β]]

    

    Definition Classes

    DefaultContext

24. implicit def evolutionStateGenerational[S]: Generational[[β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β]]

    

    Definition Classes

    DefaultContext

25. implicit def evolutionStateMonad[S]: Monad[[β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β]]

    

    Definition Classes

    DefaultContext

26. implicit def evolutionStateMonadState[S]: MonadState[[β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β], EvolutionData[S]]

    

    Definition Classes

    DefaultContext

27. implicit def evolutionStateMonadTrans[S]: MonadTrans[[α[_$1], γ]IndexedStateT[α, EvolutionData[S], EvolutionData[S], γ]]

    

    Definition Classes

    DefaultContext

28. implicit def evolutionStateUseParallelRG[S]: ParallelRandomGen[[β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β]]

    

    Definition Classes

    DefaultContext

29. implicit def evolutionStateUseRG[S]: RandomGen[[β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β]]

    

    Definition Classes

    DefaultContext

30. object fitness

    

31. def flatMapB[M[_], I, G](op: (I) ⇒ M[Vector[G]])(implicit arg0: Monad[M]): Breeding[M, I, G]

    

32. def flatMapPureB[M[_], I, G](op: (I) ⇒ Vector[G])(implicit arg0: Monad[M]): Breeding[M, I, G]

    

33. def keepNiches[M[_], I, N](niche: (I) ⇒ N, objective: Elitism[M, I])(implicit arg0: Monad[M]): Elitism[M, I]

    

34. implicit def lensToField[A, S](l: Lens[S, A]): Field[[β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β], A]

    

    Definition Classes

    DefaultContext

35. def liftIOValue[S, A](mio: EvolutionState[S, IO[A]]): EvolutionState[S, A]

    

    Definition Classes

    DefaultContext

36. def mapB[M[_], I, G](op: (I) ⇒ M[G])(implicit arg0: Monad[M]): Breeding[M, I, G]

    

37. def mapPureB[M[_], I, G](op: (I) ⇒ G)(implicit arg0: Monad[M]): Breeding[M, I, G]

    

38. def muCommaLambda[I](parents: Vector[I], offsprings: Vector[I]): Vector[I]

    

39. def muPlusLambda[M[_], I](p2: Vector[I])(implicit arg0: Monad[M]): Kleisli[M, Vector[I], Vector[I]]

    

    ** Replacement strategies ***

40. def never[M[_], I](implicit arg0: Monad[M]): StopCondition[M, I]

    

    Definition Classes

    Imports

41. def newRNG(seed: Long): Random

    

42. object niche

    

43. def noTrace[S, I]: Kleisli[[β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β], Vector[I], Unit]

    

44. object openmole

    

45. def orO[M[_], I](o1: Elitism[M, I], o2: Elitism[M, I])(implicit arg0: Monad[M]): Elitism[M, I]

    

46. def productB[M[_], I, G1, G2](b1: Breeding[M, I, G1], b2: (G1) ⇒ Breeding[M, I, G2])(implicit arg0: Monad[M]): Breeding[M, I, G2]

    

47. def productWithB[M[_], I, G1, G2, G3](f: (G1, G2) ⇒ G3)(b1: Breeding[M, I, G1], b2: (G1) ⇒ Breeding[M, I, G2])(implicit arg0: Monad[M]): Breeding[M, I, G3]

    

48. object ranking

    

49. def run[T, I, G, S](t: T)(implicit algo: Algorithm[T, [β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β], I, G, EvolutionData[S]]): RunResult[T, I, G, S]

    

50. def runEA[M[_], I](stepFunction: Kleisli[M, Vector[I], Vector[I]])(implicit arg0: Monad[M]): Kleisli[M, Vector[I], Vector[I]]

    

51. def runEAUntil[M[_], I](stopCondition: Kleisli[M, Vector[I], Boolean], stepFunction: Kleisli[M, Vector[I], Vector[I]])(implicit arg0: Monad[M]): Kleisli[M, Vector[I], Vector[I]]

    

    Non-tail recursive.

    Non-tail recursive. Will break for long runs.

52. def runEAUntilStackless[S, I](stopCondition: StopCondition[[β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β], I], stepFunction: Kleisli[[β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β], Vector[I], Vector[I]]): Kleisli[[β]IndexedStateT[IO, EvolutionData[S], EvolutionData[S], β], Vector[I], Vector[I]]

    

    Definition Classes

    DefaultContext

53. def stepEA[M[_], I, G](preStep: (Vector[I]) ⇒ M[Unit], breeding: Breeding[M, I, G], expression: Expression[G, I], objective: Elitism[M, I], replacementStrategy: (Vector[I], Vector[I]) ⇒ Vector[I])(implicit arg0: Monad[M]): Kleisli[M, Vector[I], Vector[I]]

    

    ** Running the EA ***

54. object stop

    

55. package test

    

56. def thenK[M[_], R, A, B](k: Kleisli[M, A, B])(a: A)(implicit arg0: Monad[M]): Kleisli[M, R, B]

    

    This function helps chaining Kleisli arrows in for comprehensions.

    This function helps chaining Kleisli arrows in for comprehensions. For example, given two Kleisli arrows k of type Kleisli[M,A,B] and l of type Kleisli[M,B,C], the following are equivalent:

    k >=> l

    for { a <- k b <- thenK(l)(a) } yield b

57. def thenO[M[_], I](o1: Elitism[M, I], o2: Elitism[M, I])(implicit arg0: Monad[M]): Elitism[M, I]

    

58. package tools

    

59. def unwrap[S, T](x: EvolutionState[S, T], s: EvolutionData[S]): (EvolutionData[S], T)

    

    Definition Classes

    DefaultContext

60. def withE[G, P](f: (G) ⇒ P): Expression[G, P]

    

61. def withGenomeE[G, P](express: Expression[G, P]): Expression[G, (P, G)]

    

62. def withRandomGenB[M[_], G](implicit MM: Monad[M], MR: ParallelRandomGen[M]): Breeding[M, G, (Random, G)]

    

    Breed a genome for subsequent stochastic expression

63. def wrap[S, T](x: (EvolutionData[S], T)): EvolutionState[S, T]

    

    Definition Classes

    DefaultContext

64. def write[M[_]](s: String, writeFunction: (String) ⇒ IO[Unit] = IO.putStrLn)(implicit arg0: Monad[M]): M[IO[Unit]]

    

    ** Pre-step functions ***

65. def writeGen[M[_]](writeFunction: (Long) ⇒ IO[Unit] = ...)(implicit MM: Monad[M], MG: Generational[M]): M[IO[Unit]]

    

66. def zipB[M[_], I, G1, G2](b1: Breeding[M, I, G1], b2: Breeding[M, I, G2])(implicit arg0: Monad[M]): Breeding[M, I, (G1, G2)]

    

67. def zipE[G, P1, P2](e1: Expression[G, P1], e2: Expression[G, P2]): Expression[G, (P1, P2)]

    

68. def zipWithB[M[_], I, G1, G2, G3](f: (G1, G2) ⇒ G3)(b1: Breeding[M, I, G1], b2: Breeding[M, I, G2])(implicit arg0: Monad[M]): Breeding[M, I, G3]

    

69. def zipWithE[G, P1, P2, P3](f: (P1, P2) ⇒ P3)(e1: Expression[G, P1], e2: Expression[G, P2]): Expression[G, P3]