DecisionPolicyNN

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. def apply[T, U](nnIndex: Index[T, U], kNN: Double)(implicit arg0: (T) ⇒ Distance[T]): DecisionPolicy[T, U]

   

   Create an approximate decision policy from an index, using the supplied kNN.

   Create an approximate decision policy from an index, using the supplied kNN. This uses the default combination function (unweighted maximum).

5. def apply[T, U](policy: Map[(T, U), DecisionSample], kNN: Double = .01)(implicit arg0: (T) ⇒ Distance[T]): DecisionPolicy[T, U]

   

   Create an approximate decision policy from a Map of (parent, decision) tuples to a DecisionSample, using the supplied kNN.

   Create an approximate decision policy from a Map of (parent, decision) tuples to a DecisionSample, using the supplied kNN. This uses the default combination function (unweighted maximum) and index (VP-Tree).

6. def apply[T, U](Alg: DecisionAlgorithm[T, U], kNN: Double)(implicit arg0: (T) ⇒ Distance[T]): DecisionPolicy[T, U]

   

   Create an approximate decision policy from a DecisionAlgorithm, using the supplied kNN.

   Create an approximate decision policy from a DecisionAlgorithm, using the supplied kNN. This uses the default combination function (unweighted maximum) and index (VP-Tree).

7. def apply[T, U](nnIndex: Index[T, U], combineFcn: (List[(Double, U, DecisionSample)]) ⇒ (U, Double), kNN: Double)(implicit arg0: (T) ⇒ Distance[T]): DecisionPolicyNN[T, U]

   

   Create an approximate decision policy from an index, using the supplied combination function and kNN.

8. def apply[T, U](policy: Map[(T, U), DecisionSample], combineFcn: (List[(Double, U, DecisionSample)]) ⇒ (U, Double), kNN: Double)(implicit arg0: (T) ⇒ Distance[T]): DecisionPolicyNN[T, U]

   

   Create an approximate decision policy from a Map of (parent, decision) tuples to a DecisionSample, using the supplied combination function and kNN.

   Create an approximate decision policy from a Map of (parent, decision) tuples to a DecisionSample, using the supplied combination function and kNN. This uses the default index (VP-Tree).

9. def apply[T, U](Alg: DecisionAlgorithm[T, U], combineFcn: (List[(Double, U, DecisionSample)]) ⇒ (U, Double), kNN: Double)(implicit arg0: (T) ⇒ Distance[T]): DecisionPolicyNN[T, U]

   

   Create an approximate decision policy from a DecisionAlgorithm, using the supplied combination function and kNN.

   Create an approximate decision policy from a DecisionAlgorithm, using the supplied combination function and kNN. This uses the default index (VP-Tree).

10. final def asInstanceOf[T0]: T0

    

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24. final def wait(arg0: Long, arg1: Int): Unit

    

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