object Util
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- Util.scala
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!=(arg0: Any): Boolean
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##(): Int
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==(arg0: Any): Boolean
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def
aggToFold[A, B, C](agg: MonoidAggregator[A, B, C]): Fold[A, C]
And a helper function that will let me test this out with monoid aggregators, like the ones I wrote to walk trajectories.
- def allMaxBy[A, B](as: Set[A])(f: (A) ⇒ B)(implicit arg0: Ordering[B]): Set[A]
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- def confine[A](a: A, min: A, max: A)(implicit ord: Ordering[A]): A
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def
diff[A](as: TraversableOnce[A], lf: (A) ⇒ Double, rf: (A) ⇒ Double, combine: (Double, Double) ⇒ Double): Double
Accumulates differences between the two for every A in the supplied sequence.
Accumulates differences between the two for every A in the supplied sequence. The combine function is used to aggregate the differences.
I recommend using max or +.
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eq(arg0: AnyRef): Boolean
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finalize(): Unit
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def
foldUntilM[M[_], A, B, C](init: A, fold: Fold[B, C])(f: (A) ⇒ M[(A, B)])(p: (A) ⇒ Boolean)(implicit M: Monad[M]): M[(A, C)]
A version of iterateUntilM that uses a Fold to store the auxiliary results kicked out by the step function.
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def
idToMonad[M[_]](implicit M: Monad[M]): FunctionK[Id, M]
Cats helpers.
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def
isInstanceOf[T0]: Boolean
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- def iterateM[M[_], A](n: Int)(a: A)(f: (A) ⇒ M[A])(implicit M: Monad[M]): M[A]
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def
iterateUntilM[M[_], A, B, C, D](init: A, agg: MonoidAggregator[B, C, D])(f: (A) ⇒ M[(A, B)])(p: (A) ⇒ Boolean)(implicit M: Monad[M]): M[(A, D)]
A version of iterateUntilM that uses an aggregator to store the auxiliary results kicked out by the step function.
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def
iterateWhileM[M[_], A, B, C, D](init: A, agg: MonoidAggregator[B, C, D])(f: (A) ⇒ M[(A, B)])(p: (A) ⇒ Boolean)(implicit arg0: Monad[M]): M[(A, D)]
A version of iterateWhileM that uses an aggregator to store the auxiliary results kicked out by the step function.
- def makeMap[K, V](keys: Set[K])(f: (K) ⇒ V): Map[K, V]
- def makeMapUnsafe[K, V](keys: TraversableOnce[K])(f: (K) ⇒ V): Map[K, V]
- def maxKeys[A, B](m: Map[A, B])(implicit arg0: Ordering[B]): Set[A]
- def mergeV[K, V](m: Map[K, V], k: K, delta: V)(implicit arg0: Semigroup[V]): Map[K, V]
- def mfk[M[_], N[_]](implicit M: Comonad[M], N: Monad[N]): FunctionK[M, N]
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notifyAll(): Unit
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def
runUntilM[M[_], S, A, B, C](state: StateT[M, S, A], agg: MonoidAggregator[A, B, C])(p: (S) ⇒ Boolean)(implicit arg0: Monad[M]): StateT[M, S, C]
Unused for now...
Unused for now... TODO try this out, get the interface going in state monad style!
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def
toString(): String
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def
updateWith[K, V](m: Map[K, V], k: K)(f: (Option[V]) ⇒ V): Map[K, V]
Update the key in the supplied map using the function - the function handles both cases, when the item is there and when it's not.
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wait(): Unit
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wait(arg0: Long): Unit
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ScalaRL
This is the API documentation for the ScalaRL functional reinforcement learning library.
Further documentation for ScalaRL can be found at the documentation site.
Check out the ScalaRL package list for all the goods.