object FiniteDistributionLearner
A combinator for learning systems with state finite distributions on vertices. Systems are built from components labeled by elements of a set M. The state also has weights for these components. The components are built from: moves (partial functions), partial combinations and islands.
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Type Members
- case class Atom[V](x: V) extends AdjDiffbleFunction[Double, FiniteDistribution[V]] with Product with Serializable
An atom for a finite distribution
- case class CombinationFn[V](f: (V, V) => Option[V], firstFilter: (V) => Boolean = (a: V) => true) extends AdjDiffbleFunction[FiniteDistribution[V], FiniteDistribution[V]] with Product with Serializable
- case class Evaluate[V](x: V) extends AdjDiffbleFunction[FiniteDistribution[V], Double] with Product with Serializable
Evaluation at a point for a finite distribution
- case class ExtendM[M, X](fn: AdjDiffbleFunction[(FiniteDistribution[M], X), X]) extends AdjDiffbleFunction[(FiniteDistribution[M], X), (FiniteDistribution[M], X)] with Product with Serializable
- case class MoveFn[V, W](f: (V) => Option[W]) extends AdjDiffbleFunction[FiniteDistribution[V], FiniteDistribution[W]] with Product with Serializable
smooth function applying move wherever applicable
- case class NewVertex[V](v: V) extends AdjDiffbleFunction[(Double, FiniteDistribution[V]), FiniteDistribution[V]] with Product with Serializable
Add a new vertex, mainly for lambdas
- case class NormalizeFD[V]() extends AdjDiffbleFunction[FiniteDistribution[V], FiniteDistribution[V]] with Product with Serializable
Normalizing a finite distribution.
- case class ProjectV[M, X]() extends AdjDiffbleFunction[(FiniteDistribution[M], X), X] with Product with Serializable
- case class PtwiseProd[V](sc: (V) => Double) extends AdjDiffbleFunction[FiniteDistribution[V], FiniteDistribution[V]] with Product with Serializable
- case class Sample[X](N: Double) extends FormalExtension[FiniteDistribution[X]] with Product with Serializable
Value Members
- def extendM[M, X](fn: AdjDiffbleFunction[(FiniteDistribution[M], X), X]): AdjDiffbleFunction[(FiniteDistribution[M], X), (FiniteDistribution[M], X)]
Extend differentiable function by identity on M.
- def projectV[M, X]: AdjDiffbleFunction[(FiniteDistribution[M], X), X]
- def purgeFD[V](size: Int)(fd: FiniteDistribution[V]): FiniteDistribution[V]
purging (randomly) a finite distribution.
purging (randomly) a finite distribution.
- size
upper bound on the expected size of the support.
- def sample[X](N: Double): AdjDiffbleFunction[FiniteDistribution[X], FiniteDistribution[X]]
- def sampleV[M, V](N: Double): AdjDiffbleFunction[(FiniteDistribution[M], FiniteDistribution[V]), (FiniteDistribution[M], FiniteDistribution[V])]
- def weightedDyn[M, X](implicit arg0: LinearStructure[X], arg1: InnerProduct[X]): (M, AdjDiffbleFunction[X, X]) => AdjDiffbleFunction[(FiniteDistribution[M], X), X]
Returns a smooth function (FD[M], X) => X, given a parameter index m : M and a dynamical system f: X => X; the system f should correspond to m.
Returns a smooth function (FD[M], X) => X, given a parameter index m : M and a dynamical system f: X => X; the system f should correspond to m. For a distribution p in FD[M], if p(m) denotes the value at m, the smooth function being defined is p(m)f.