OneTimeMetropolisHastings

Instance Constructors

new OneTimeMetropolisHastings(universe: Universe, myNumSamples: Int, scheme: ProposalScheme, burnIn: Int, interval: Int, targets: Element[_]*)

burnIn
The number of iterations to run before samples are collected
interval
The number of iterations to perform between collecting samples

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

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 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 computeScores(): Double

Attributes
protected
Definition Classes
MetropolisHastings
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 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 doDistribution[T](target: Element[T]): Stream[(Double, T)]

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

Attributes
protected
Definition Classes
OneTimeProbQuery → ProbQueryAlgorithm
def doInitialize(): Unit

Attributes
protected
Definition Classes
MetropolisHastings
def doKill(): Unit

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

Attributes
protected
Definition Classes
OneTimeProbQuery → ProbQueryAlgorithm
def doResume(): Unit

Attributes
protected
Definition Classes
OneTime → Algorithm
def doSample(): Unit

Attributes
protected
Definition Classes
MetropolisHastings → BaseUnweightedSampler → Sampler
def doStart(): Unit

Attributes
protected
Definition Classes
OneTime → Algorithm
def doStop(): Unit

Attributes
protected
Definition Classes
OneTime → Algorithm
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 getDissatisfied: Set[Element[_]]

Attributes
protected
Definition Classes
MetropolisHastings
def getSampleCount: Int

Number of samples taken.
Number of samples taken.

Definition Classes
BaseUnweightedSampler
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 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
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
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numSamples: Int

The number of samples to collect from the model.
The number of samples to collect from the model.

Definition Classes
OneTimeMetropolisHastings → OneTimeSampler
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
def projection[T](target: Element[T]): List[(T, Double)]

Attributes
protected
Definition Classes
UnweightedSampler
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
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 run(): Unit

Run the algorithm, performing its computation to completion.
Run the algorithm, performing its computation to completion.

Definition Classes
OneTimeMetropolisHastings → OneTimeSampler → OneTime
def runScheme(): State

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

Produce a single sample.
Produce a single sample.

Definition Classes
MetropolisHastings → BaseUnweightedSampler
var sampleCount: Int

Attributes
protected
Definition Classes
BaseUnweightedSampler
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
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 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( ... )

class OneTimeMetropolisHastings extends MetropolisHastings with UnweightedSampler with OneTimeProbQuerySampler

Instance Constructors

new OneTimeMetropolisHastings(universe: Universe, myNumSamples: Int, scheme: ProposalScheme, burnIn: Int, interval: Int, targets: Element[_]*)

Type Members

type LastUpdate[T] = (T, Int)

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

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

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 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 computeScores(): Double

var debug: Boolean

def decideToAccept(newState: State): Boolean

var dissatisfied: Set[Element[_]]

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

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

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

def doInitialize(): Unit

def doKill(): Unit

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

def doResume(): Unit

def doSample(): Unit

def doStart(): Unit

def doStop(): 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 getDissatisfied: Set[Element[_]]

def getSampleCount: Int

def hashCode(): Int

def initConstrainedValues(): Unit

def initUpdates(): Unit

def initialize(): Unit

def isActive: Boolean

final def isInstanceOf[T0]: Boolean

def kill(): Unit

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

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]

final def notify(): Unit

final def notifyAll(): Unit

val numSamples: Int

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

def projection[T](target: Element[T]): List[(T, Double)]

def proposeAndUpdate(): State

lazy val queryTargets: List[Element[_]]

var rejects: Int

def resetCounts(): Unit

def resume(): Unit

def run(): Unit

def runScheme(): State

def sample(): (Boolean, Sample)

var sampleCount: Int

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