Type Members

1.  case class Atom[X](value: X, output: RandomVar[X]) extends GeneratorNode[X] with Product with Serializable

   atomic value taken by a random variable

   atomic value taken by a random variable

   X

   the scala type of the random variable

   value

   the value

2.  case class BaseThenCondition[O, Y](gen: GeneratorNode[O], output: RandomVar[Y], condition: Sort[O, Y]) extends GeneratorNode[Y] with ThenCondition[O, Y] with Product with Serializable
3.  case class ConditionFunc[O, D, S >: O, T](base: GeneratorNode[O], conditionFamily: (D) => Sort[S, T], outputFamily: RandomVarFamily[::[D, HNil], T]) extends (::[D, HNil]) => BaseThenCondition[O, T] with Product with Serializable
4.  case class FiberProductMap[X1, X2, Z, Y](quot: (X1) => Z, fiberVar: (Z) => RandomVar[X2], f: (X1, X2) => Y, baseInput: RandomVar[X1], output: RandomVar[Y]) extends GeneratorNode[Y] with Product with Serializable
5.  case class FlatMap[X, Y](baseInput: RandomVar[X], fiberNode: (X) => GeneratorNode[Y], output: RandomVar[Y]) extends GeneratorNode[Y] with Product with Serializable

   flat-maps a family of generator nodes, such as lambda islands corresponding to different types.

   flat-maps a family of generator nodes, such as lambda islands corresponding to different types.

   X

   scala type of the parametrizing random variable

   Y

   scala type of the output random variable

   baseInput

   the random variable for the parameter of the family

   fiberNode

   the node for each parameter

   output

   the output random variable

6.  case class FlatMapOpt[X, Y](baseInput: RandomVar[X], fiberNodeOpt: (X) => Option[GeneratorNode[Y]], output: RandomVar[Y]) extends GeneratorNode[Y] with Product with Serializable
7.  case class Idty[A]() extends (A) => A with Product with Serializable

   Wrapper for identity to allow equality and toString to work.

8.  case class Init[X](input: RandomVar[X]) extends GeneratorNode[X] with Product with Serializable

   generator node for simply using the initial distribution.

   generator node for simply using the initial distribution.

   input

   the random variable whose distribution we extract.

9.  case class Island[Y, InitState, O, Boat](output: RandomVar[Y], islandOutput: (Boat) => RandomVar[O], initMap: (InitState) => (Double) => (InitState, Boat), export: (Boat, O) => Y, finalMap: (Boat, InitState) => InitState) extends RecursiveGeneratorNode[InitState, Y] with Product with Serializable

   An island, i.e., a full map from initial state to all required distributions, which is used in recursive definitions where we recurse on not just the final state but also the dynamics.

   An island, i.e., a full map from initial state to all required distributions, which is used in recursive definitions where we recurse on not just the final state but also the dynamics. One enters the island using a boat which is returned, and must exit by the same boat.

   Y

   the scala type of the island output

   InitState

   the initial state

   O

   the scala type of the final output

   Boat

   scala type of the boat

   output

   the final output, as seen from the outside

   islandOutput

   the variable whose distribution from the island is used

   initMap

   the map from the initial state on the outside to the initial state in the island

   export

   the object (e.g. term) level map applied when mapping a distribution obtained in the island to the outside; this is specific to the island

10.  case class Map[X, Y](f: (X) => Y, input: RandomVar[X], output: RandomVar[Y]) extends GeneratorNode[Y] with Product with Serializable

    generator node for mapping

    generator node for mapping

    X

    scala type of the input random variable

    Y

    scala type of the output random variable

    f

    the function

    input

    the input random variable

    output

    the output random variable

11.  case class MapOpt[X, Y](f: (X) => Option[Y], input: RandomVar[X], output: RandomVar[Y]) extends GeneratorNode[Y] with Product with Serializable

    generator node for optionally mapping, to be conditioned by non-trivial outputs (if any)

    generator node for optionally mapping, to be conditioned by non-trivial outputs (if any)

    X

    scala type of the input random variable

    Y

    scala type of the output random variable

    f

    the optional function

    input

    the input random variable

    output

    the output random variable

12.  case class RecursiveThenCondition[State, O, Y](gen: RecursiveGeneratorNode[State, O], output: RandomVar[Y], condition: Sort[O, Y]) extends RecursiveGeneratorNode[State, Y] with ThenCondition[O, Y] with Product with Serializable
13.  sealed trait ThenCondition[O, Y] extends GeneratorNode[Y]

    compose a generator node with conditioning

    compose a generator node with conditioning

    O

    scala type of the original output

    Y

    scala type of the final output

14.  case class ZipFlatMap[X1, X2, Y](baseInput: RandomVar[X1], fiberVar: (X1) => RandomVar[X2], f: (X1, X2) => Y, output: RandomVar[Y]) extends GeneratorNode[Y] with Product with Serializable
15.  case class ZipMap[X1, X2, Y](f: (X1, X2) => Y, input1: RandomVar[X1], input2: RandomVar[X2], output: RandomVar[Y]) extends GeneratorNode[Y] with Product with Serializable

    generator node for combining using a binary operation

    generator node for combining using a binary operation

    X1

    scala type of the first input random variable

    X2

    scala type of the second input random variable

    Y

    scala type of the output random variable

    f

    the binary operation

    input1

    the first input random variable

    input2

    the second input random variable

    output

    the output random variable

16.  case class ZipMapOpt[X1, X2, Y](f: (X1, X2) => Option[Y], input1: RandomVar[X1], input2: RandomVar[X2], output: RandomVar[Y]) extends GeneratorNode[Y] with Product with Serializable

    generator node for combining using an optional binary operation, should be conditioned on output being non-trivial

    generator node for combining using an optional binary operation, should be conditioned on output being non-trivial

    X1

    scala type of the first input random variable

    X2

    scala type of the second input random variable

    Y

    scala type of the output random variable

    f

    the optional binary operation

    input1

    the first input random variable

    input2

    the second input random variable

    output

    the output random variable