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

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

Attributes
protected

Abstract Value Members

abstract def doDistribution[T](target: Element[T]): Stream[(Double, T)]

Attributes
protected
Definition Classes
ProbQueryAlgorithm
abstract def doExpectation[T](target: Element[T], function: (T) ⇒ Double): Double

Attributes
protected
Definition Classes
ProbQueryAlgorithm
abstract def doKill(): Unit

Attributes
protected
Definition Classes
Algorithm
abstract def doProbability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

Attributes
protected
Definition Classes
ProbQueryAlgorithm
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: AnyRef): Boolean

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

Definition Classes
Any
final def ##(): Int

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

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

Definition Classes
Any
var active: Boolean

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

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

Attributes
protected
Definition Classes
UnweightedSampler
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 computeDistribution[T](target: Element[T]): Stream[(Double, T)]

Return an estimate of the marginal probability distribution over the target that lists each element with its probability.
Return an estimate of the marginal probability distribution over the target that lists each element with its probability.

Definition Classes
UnweightedSampler → ProbQueryAlgorithm
def computeExpectation[T](target: Element[T], function: (T) ⇒ Double): Double

Return an estimate of the expectation of the function under the marginal probability distribution of the target.
Return an estimate of the expectation of the function under the marginal probability distribution of the target.

Definition Classes
UnweightedSampler → ProbQueryAlgorithm
def computeProbability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

Return an estimate of the probability of the predicate under the marginal probability distribution of the target.
Return an estimate of the probability of the predicate under the marginal probability distribution of the target.

Definition Classes
ProbQueryAlgorithm
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
def distribution[T](target: Element[T]): Stream[(Double, T)]

Return an estimate of the marginal probability distribution over the target that lists each element with its probability.
Return an estimate of the marginal probability distribution over the target that lists each element with its probability. The result is a lazy stream. It is up to the algorithm how the stream is ordered. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

Definition Classes
ProbQueryAlgorithm
def doInitialize(): Unit

Attributes
protected
final def doSample(): Unit

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

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

Definition Classes
AnyRef → Any
def expectation[T](target: Element[T], function: (T) ⇒ Double): Double

Return an estimate of the expectation of the function under the marginal probability distribution of the target.
Return an estimate of the expectation of the function under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

Definition Classes
ProbQueryAlgorithm
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 getSampleCount: Int

Number of samples taken.
Number of samples taken.

Definition Classes
UnweightedSampler
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 initUpdates(): Unit

Attributes
protected
Definition Classes
UnweightedSampler
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 mean(target: Element[Double]): Double

Return the mean of the probability density function for the given continuous element
Return the mean of the probability density function for the given continuous element

Definition Classes
ProbQueryAlgorithm
final def ne(arg0: AnyRef): Boolean

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

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

Attributes
protected
Definition Classes
UnweightedSampler
def newWeightSeen[T](target: Element[T]): (Element[T], Map[T, Double])

Attributes
protected
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def posteriorElement[T](target: Element[T], universe: Universe = Universe.universe): Element[T]

Return an element representing the posterior probability distribution of the given element
Return an element representing the posterior probability distribution of the given element

Definition Classes
ProbQueryAlgorithm
def probability[T](target: Element[T], value: T): Double

Return an estimate of the probability that the target produces the value.
Return an estimate of the probability that the target produces the value. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

Definition Classes
ProbQueryAlgorithm
def probability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

Return an estimate of the probability of the predicate under the marginal probability distribution of the target.
Return an estimate of the probability of the predicate under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

Definition Classes
ProbQueryAlgorithm
lazy val queryTargets: List[Element[_]]

Definition Classes
UnweightedSampler → ProbQueryAlgorithm
def resetCounts(): Unit

Attributes
protected
Definition Classes
DecisionMetropolisHastings → UnweightedSampler → 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
final def sample(): (Boolean, Sample)

Produce a single sample.
Produce a single sample.

Definition Classes
DecisionMetropolisHastings → UnweightedSampler
var sampleCount: Int

Attributes
protected
Definition Classes
UnweightedSampler
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[_]]): (Map[Predicate[_], Double], Map[Element[_], Double])

Test Metropolis-Hastings Decisions by repeatedly running a single step from the same initial state.
Test Metropolis-Hastings Decisions 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.
def toString(): String

Definition Classes
AnyRef → Any
def update(): Unit

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

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

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

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

Attributes
protected
def variance(target: Element[Double]): Double

Return the variance of the probability density function for the given continuous element
Return the variance of the probability density function for the given continuous element

Definition Classes
ProbQueryAlgorithm
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 UnweightedSampler 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 doDistribution[T](target: Element[T]): Stream[(Double, T)]

abstract def doExpectation[T](target: Element[T], function: (T) ⇒ Double): Double

abstract def doKill(): Unit

abstract def doProbability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

abstract def doResume(): Unit

abstract def doStart(): Unit

abstract def doStop(): Unit

Concrete Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

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 computeDistribution[T](target: Element[T]): Stream[(Double, T)]

def computeExpectation[T](target: Element[T], function: (T) ⇒ Double): Double

def computeProbability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

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

var debug: Boolean

def distribution[T](target: Element[T]): Stream[(Double, T)]

def doInitialize(): Unit

final def doSample(): Unit

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def expectation[T](target: Element[T], function: (T) ⇒ Double): Double

def finalize(): Unit

final def getClass(): Class[_]

def getSampleCount: Int

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

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

def hashCode(): Int

def initUpdates(): Unit

def initialize(): Unit

def isActive: Boolean

final def isInstanceOf[T0]: Boolean

def kill(): Unit

def mean(target: Element[Double]): Double

final def ne(arg0: AnyRef): Boolean

def newLastUpdate[T](target: Element[T]): (T, Int)

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

def newWeightSeen[T](target: Element[T]): (Element[T], Map[T, Double])

final def notify(): Unit

final def notifyAll(): Unit

def posteriorElement[T](target: Element[T], universe: Universe = Universe.universe): Element[T]

def probability[T](target: Element[T], value: T): Double

def probability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

lazy val queryTargets: List[Element[_]]

def resetCounts(): Unit

def resume(): Unit

final 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[_]]): (Map[Predicate[_], Double], Map[Element[_], Double])

def toString(): String

def update(): Unit

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: (Element[T], Map[T, Double])): Unit

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

def variance(target: Element[Double]): Double

final def wait(): Unit