DecisionMetropolisHastings

Instance Constructors

new DecisionMetropolisHastings(universe: Universe, proposalScheme: ProposalScheme, burnIn: Int, interval: Int, utilityNodes: List[Element[_]], decisionTarget: Decision[T, U])

Type Members

type LastUpdate[T] = (T, Int)

Attributes
protected
Definition Classes
BaseUnweightedSampler
type Sample = Map[Element[_], Any]

A sample is a map from elements to their values.
A sample is a map from elements to their values.

Definition Classes
BaseUnweightedSampler
type TimesSeen[T] = Map[T, Int]

Attributes
protected
Definition Classes
BaseUnweightedSampler
type WeightSeen[T] = (Element[T], Map[T, Double])

Attributes
protected

Abstract Value Members

abstract def doKill(): Unit

Attributes
protected
Definition Classes
Algorithm
abstract def doResume(): Unit

Attributes
protected
Definition Classes
Algorithm
abstract def doStart(): Unit

Attributes
protected
Definition Classes
Algorithm
abstract def doStop(): Unit

Attributes
protected
Definition Classes
Algorithm

Concrete Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def accept(state: State): Unit

Attributes
protected
Definition Classes
MetropolisHastings
def acceptRejectRatio: Double

Get the acceptance ratio for the sampler.
Get the acceptance ratio for the sampler.

Definition Classes
MetropolisHastings
var accepts: Int

Attributes
protected
Definition Classes
MetropolisHastings
var active: Boolean

Attributes
protected
Definition Classes
Algorithm
var allLastUpdates: Map[Element[_], LastUpdate[_]]

Attributes
protected
Definition Classes
BaseUnweightedSampler
var allTimesSeen: Map[Element[_], TimesSeen[_]]

Attributes
protected
Definition Classes
BaseUnweightedSampler
final def asInstanceOf[T0]: T0

Definition Classes
Any
def cleanUp(): Unit

Called when the algorithm is killed.
Called when the algorithm is killed. By default, does nothing. Can be overridden.

Definition Classes
Algorithm
def cleanup(): Unit

Cleans up the temporary elements created during sampling
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def computeScores(): Double

Attributes
protected
Definition Classes
MetropolisHastings
def computeUtility(): Map[(T, U), DecisionSample]

Compute the utility of each parent/decision tuple and return a DecisionSample.
Compute the utility of each parent/decision tuple and return a DecisionSample. Each decision algorithm must define how this is done since it is used to set the policy for a decision. For sampling algorithms, this will me a map of parent/decision tuples to a utility and a weight for that combination. For factored algorithms, the DecisionSample will contain the exact expected utility with a weight of 1.0.

Definition Classes
DecisionMetropolisHastings → DecisionAlgorithm
var debug: Boolean

Set this flag to true to obtain debugging information.
Set this flag to true to obtain debugging information.

Definition Classes
MetropolisHastings
def decideToAccept(newState: State): Boolean

Attributes
protected
Definition Classes
MetropolisHastings
var dissatisfied: Set[Element[_]]

Attributes
protected
Definition Classes
MetropolisHastings
def doInitialize(): Unit

Attributes
protected
Definition Classes
MetropolisHastings
final def doSample(): Unit

Attributes
protected
Definition Classes
DecisionMetropolisHastings → MetropolisHastings → BaseUnweightedSampler → Sampler
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getDissatisfied: Set[Element[_]]

Attributes
protected
Definition Classes
MetropolisHastings
def getSampleCount: Int

Number of samples taken.
Number of samples taken.

Definition Classes
BaseUnweightedSampler
def getUtility(p: T, d: U): DecisionSample

Get the total utility and weight for a specific value of a parent and decision.
Get the total utility and weight for a specific value of a parent and decision.

Definition Classes
DecisionAlgorithm
def getUtility(): Map[(T, U), DecisionSample]

Get the total utility and weight for all sampled values of the parent and decision.
Get the total utility and weight for all sampled values of the parent and decision.

Definition Classes
DecisionAlgorithm
def hashCode(): Int

Definition Classes
AnyRef → Any
def initConstrainedValues(): Unit

Attributes
protected
Definition Classes
MetropolisHastings
def initUpdates(): Unit

Attributes
protected
Definition Classes
BaseUnweightedSampler
def initialize(): Unit

Called when the algorithm is started before running any steps.
Called when the algorithm is started before running any steps. By default, does nothing. Can be overridden.

Definition Classes
Algorithm
def isActive: Boolean

Definition Classes
Algorithm
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def kill(): Unit

Kill the algorithm so that it is inactive.
Kill the algorithm so that it is inactive. It will no longer be able to provide answers.Throws AlgorithmInactiveException if the algorithm is not active.

Definition Classes
Algorithm
def mhStep(): State

Attributes
protected
Definition Classes
MetropolisHastings
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def newLastUpdate[T](target: Element[T]): LastUpdate[T]

Attributes
protected
Definition Classes
BaseUnweightedSampler
def newTimesSeen[T](target: Element[T]): TimesSeen[T]

Attributes
protected
Definition Classes
BaseUnweightedSampler
def newWeightSeen[T](target: Element[T]): WeightSeen[T]

Attributes
protected
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def proposeAndUpdate(): State

Attributes
protected
Definition Classes
MetropolisHastings
lazy val queryTargets: List[Element[_]]

Definition Classes
BaseUnweightedSampler
var rejects: Int

Attributes
protected
Definition Classes
MetropolisHastings
def resetCounts(): Unit

Attributes
protected
Definition Classes
DecisionMetropolisHastings → BaseUnweightedSampler → Sampler
def resume(): Unit

Resume the computation of the algorithm, if it has been stopped.
Resume the computation of the algorithm, if it has been stopped. Throws AlgorithmInactiveException if the algorithm is not active.

Definition Classes
Algorithm
def runScheme(): State

Attributes
protected
Definition Classes
MetropolisHastings
def sample(): (Boolean, Sample)

Produce a single sample.
Produce a single sample. In decision MH, we always update the target (parent and decision) since the utilities mights have changed

Definition Classes
DecisionMetropolisHastings → MetropolisHastings → BaseUnweightedSampler
var sampleCount: Int

Attributes
protected
Definition Classes
BaseUnweightedSampler
def setPolicy(e: Decision[T, U]): Unit

Sets the policy for the given decision.
Sets the policy for the given decision. This will get the computed utility of the algorithm and call setPolicy on the decision. Note there is no error checking here, so the decision in the argument must match the target decision in the algorithm.

Definition Classes
DecisionAlgorithm
def start(): Unit

Start the algorithm and make it active.
Start the algorithm and make it active. After it returns, the algorithm must be ready to provide answers. Throws AlgorithmActiveException if the algorithm is already active.

Definition Classes
Algorithm
def stop(): Unit

Stop the algorithm from computing.
Stop the algorithm from computing. The algorithm is still ready to provide answers after it returns. Throws AlgorithmInactiveException if the algorithm is not active.

Definition Classes
Algorithm
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def test(numSamples: Int, predicates: Seq[Predicate[_]], elementsToTrack: Seq[Element[_]]): (Double, Map[Predicate[_], Double], Map[Element[_], Double])

Test Metropolis-Hastings by repeatedly running a single step from the same initial state.
Test Metropolis-Hastings by repeatedly running a single step from the same initial state. For each of a set of predicates, the fraction of times the predicate is satisfied by the resulting state is returned. By the resulting state, we mean the new state if it is accepted and the original state if not.

Definition Classes
MetropolisHastings
def toString(): String

Definition Classes
AnyRef → Any
def undo(state: State): Unit

Attributes
protected
Definition Classes
MetropolisHastings
val universe: Universe

Definition Classes
BaseUnweightedSampler
def update(): Unit

Attributes
protected
Definition Classes
DecisionMetropolisHastings → BaseUnweightedSampler → Sampler
def updateMany[T](state: State, toUpdate: Set[Element[_]]): State

Attributes
protected
Definition Classes
MetropolisHastings
def updateTimesSeenForTarget[T](elem: Element[T], newValue: T): Unit

Attributes
protected
Definition Classes
BaseUnweightedSampler
def updateTimesSeenWithValue[T](value: T, timesSeen: TimesSeen[T], seen: Int): Unit

Attributes
protected
Definition Classes
BaseUnweightedSampler
def updateWeightSeenForTarget[T](sample: (Double, Map[Element[_], Any]), weightSeen: WeightSeen[T]): Unit

Attributes
protected
def updateWeightSeenWithValue[T](value: T, weight: Double, weightSeen: WeightSeen[T]): Unit

Attributes
protected
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

abstract class DecisionMetropolisHastings[T, U] extends MetropolisHastings with DecisionAlgorithm[T, U]

Instance Constructors

new DecisionMetropolisHastings(universe: Universe, proposalScheme: ProposalScheme, burnIn: Int, interval: Int, utilityNodes: List[Element[_]], decisionTarget: Decision[T, U])

Type Members

type LastUpdate[T] = (T, Int)

type Sample = Map[Element[_], Any]

type TimesSeen[T] = Map[T, Int]

type WeightSeen[T] = (Element[T], Map[T, Double])

Abstract Value Members

abstract def doKill(): Unit

abstract def doResume(): Unit

abstract def doStart(): Unit

abstract def doStop(): Unit

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def accept(state: State): Unit

def acceptRejectRatio: Double

var accepts: Int

var active: Boolean

var allLastUpdates: Map[Element[_], LastUpdate[_]]

var allTimesSeen: Map[Element[_], TimesSeen[_]]

final def asInstanceOf[T0]: T0

def cleanUp(): Unit

def cleanup(): Unit

def clone(): AnyRef

def computeScores(): Double

def computeUtility(): Map[(T, U), DecisionSample]

var debug: Boolean

def decideToAccept(newState: State): Boolean

var dissatisfied: Set[Element[_]]

def doInitialize(): Unit

final def doSample(): Unit

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getDissatisfied: Set[Element[_]]

def getSampleCount: Int

def getUtility(p: T, d: U): DecisionSample

def getUtility(): Map[(T, U), DecisionSample]

def hashCode(): Int

def initConstrainedValues(): Unit

def initUpdates(): Unit

def initialize(): Unit

def isActive: Boolean

final def isInstanceOf[T0]: Boolean

def kill(): Unit

def mhStep(): State

final def ne(arg0: AnyRef): Boolean

def newLastUpdate[T](target: Element[T]): LastUpdate[T]

def newTimesSeen[T](target: Element[T]): TimesSeen[T]

def newWeightSeen[T](target: Element[T]): WeightSeen[T]

final def notify(): Unit

final def notifyAll(): Unit

def proposeAndUpdate(): State

lazy val queryTargets: List[Element[_]]

var rejects: Int

def resetCounts(): Unit

def resume(): Unit

def runScheme(): State

def sample(): (Boolean, Sample)

var sampleCount: Int

def setPolicy(e: Decision[T, U]): Unit

def start(): Unit

def stop(): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def test(numSamples: Int, predicates: Seq[Predicate[_]], elementsToTrack: Seq[Element[_]]): (Double, Map[Predicate[_], Double], Map[Element[_], Double])

def toString(): String

def undo(state: State): Unit

val universe: Universe

def update(): Unit

def updateMany[T](state: State, toUpdate: Set[Element[_]]): State

def updateTimesSeenForTarget[T](elem: Element[T], newValue: T): Unit

def updateTimesSeenWithValue[T](value: T, timesSeen: TimesSeen[T], seen: Int): Unit

def updateWeightSeenForTarget[T](sample: (Double, Map[Element[_], Any]), weightSeen: WeightSeen[T]): Unit

def updateWeightSeenWithValue[T](value: T, weight: Double, weightSeen: WeightSeen[T]): Unit

final def wait(): Unit