c

com.cra.figaro.experimental.marginalmap

AnytimeProbEvidenceMarginalMAP

class AnytimeProbEvidenceMarginalMAP extends ProbEvidenceMarginalMAP with AnytimeSampler with AnytimeMarginalMAP

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Inherited
  1. AnytimeProbEvidenceMarginalMAP
  2. AnytimeMarginalMAP
  3. AnytimeSampler
  4. Anytime
  5. ProbEvidenceMarginalMAP
  6. MarginalMAPAlgorithm
  7. MetropolisHastings
  8. BaseUnweightedSampler
  9. Sampler
  10. Algorithm
  11. AnyRef
  12. Any
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Visibility
  1. Public
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Instance Constructors

  1. new AnytimeProbEvidenceMarginalMAP(universe: Universe, tolerance: Double, samplesPerIteration: Int, maxRuns: Int, proposalScheme: ProposalScheme, schedule: Schedule, mapElements: List[Element[_]])

Type Members

  1. class Runner extends Actor

    A class representing the actor running the algorithm.

    A class representing the actor running the algorithm.

    Definition Classes
    Anytime
  2. case class ComputeMostLikelyValue [T](target: Element[T]) extends Service with Product with Serializable

    A message instructing the handler to compute the most likely value of the target element.

    A message instructing the handler to compute the most likely value of the target element.

    Definition Classes
    AnytimeMarginalMAP
  3. case class MostLikelyValue [T](value: T) extends Response with Product with Serializable

    A message from the handler containing the most likely value of the previously requested element.

    A message from the handler containing the most likely value of the previously requested element.

    Definition Classes
    AnytimeMarginalMAP
  4. type LastUpdate[T] = (T, Int)
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  5. 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
  6. type TimesSeen[T] = Map[T, Int]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  7. class NotATargetException [T] extends AlgorithmException
    Definition Classes
    MarginalMAPAlgorithm
  8. class MMAPProbEvidenceSampler extends ProbEvidenceSampler with OneTimeProbEvidenceSampler with OnlineLogStatistics

    Special probability of evidence sampler used for marginal MAP.

    Special probability of evidence sampler used for marginal MAP. Unlike a regular probability of evidence sampler, this records its own variance. It does so in an online fashion, and computes it in log space to prevent underflow. Additionally, this algorithm may be run multiple times. The rolling mean and variance computation incorporates the samples taken from all runs.

    Definition Classes
    ProbEvidenceMarginalMAP

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def accept(state: State): Unit
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  5. var acceptProbability: Double
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  6. def acceptRejectRatio: Double

    Get the acceptance ratio for the sampler.

    Get the acceptance ratio for the sampler.

    Definition Classes
    MetropolisHastings
  7. var accepts: Int
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  8. val active: Boolean
    Attributes
    protected
    Definition Classes
    Algorithm
  9. var allLastUpdates: Map[Element[_], LastUpdate[_]]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  10. var allTimesSeen: Map[Element[_], TimesSeen[_]]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  11. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  12. def awaitResponse(response: Future[Any], duration: Duration): Response
    Attributes
    protected
    Definition Classes
    Anytime
  13. val blockSize: Int

    Number of samples that should be taken in a single step of the algorithm.

    Number of samples that should be taken in a single step of the algorithm.

    Definition Classes
    AnytimeSampler
  14. var chainCache: Cache
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  15. def check[T](target: Element[T]): Unit
    Attributes
    protected
    Definition Classes
    MarginalMAPAlgorithm
  16. def cleanUp(): Unit

    Clean up the sampler, freeing memory.

    Clean up the sampler, freeing memory.

    Definition Classes
    ProbEvidenceMarginalMAPMetropolisHastingsAlgorithm
  17. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  18. final def compareMeans(oldSampler: MMAPProbEvidenceSampler, newSampler: MMAPProbEvidenceSampler, logConstant: Double, runs: Int): Boolean

    Decides whether or not the mean of the old sampler, multiplied by the constant given, is likely to be less than the mean of the new sampler.

    Decides whether or not the mean of the old sampler, multiplied by the constant given, is likely to be less than the mean of the new sampler. Computes in log space to avoid underflow. This may mutate the state of the universe. This does not take into account conditions and constraints on the MAP elements directly; these should be incorporated in the log constant provided.

    oldSampler

    Probability of evidence sampler for the previous state of the MAP elements.

    newSampler

    Probability of evidence sampler for the next state of the MAP elements.

    logConstant

    Log of a multiplicative constant, by which we multiply the mean of the old sampler.

    runs

    Maximum allowed additional runs of probability of evidence sampling before this method should return a best guess. This is a kill switch to avoid taking an absurd number of samples when the difference between the means is negligible. Must be >= 0. Setting this to 0 is equivalent to performing no hypothesis test at all and just comparing the values.

    returns

    A decision to accept based on a one-sided t-test of the weights sampled from the two samplers.

    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAP
    Annotations
    @tailrec()
  19. def computeMostLikelyValue[T](target: Element[T]): T
  20. def computeScores(): Double
    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAPMetropolisHastings
  21. var constraintsBound: Boolean

    Set this flag to true when constraints are bound between 0 and 1 to enable early rejection of states with constrained elements.

    Set this flag to true when constraints are bound between 0 and 1 to enable early rejection of states with constrained elements.

    Definition Classes
    MetropolisHastings
  22. var constraintsSum: Double
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  23. val currentConstraintValues: Map[Element[_], Double]
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  24. def currentMAPValues: List[ElemVal[_]]

    Record the current values of all MAP elements.

    Record the current values of all MAP elements.

    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAP
  25. val customConf: Config

    The actor running the algorithm.

    The actor running the algorithm.

    Definition Classes
    Anytime
  26. var debug: Boolean

    Set this flag to true to obtain debugging information.

    Set this flag to true to obtain debugging information.

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

    Decide whether or not to accept the new (unconstrained) state, first taking into account conditions on the MAP elements.

    Decide whether or not to accept the new (unconstrained) state, first taking into account conditions on the MAP elements. Does not change the state of the universe. Updates the temperature, preserved elements, and probability of evidence sampler accordingly. Incorporates constraints on the MAP elements.

    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAPMetropolisHastings
  28. def decideToAcceptSatisfied(): Boolean

    Like decideToAccept, but assume all conditions on the MAP elements are satisfied.

    Like decideToAccept, but assume all conditions on the MAP elements are satisfied.

    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAP
  29. var dissatisfied: Set[Element[_]]
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  30. def doInitialize(): Unit
    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAPMetropolisHastings
  31. def doKill(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    AnytimeAlgorithm
  32. def doMostLikelyValue[T](target: Element[T]): T
    Attributes
    protected
    Definition Classes
    AnytimeMarginalMAPMarginalMAPAlgorithm
  33. def doResume(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    AnytimeAlgorithm
  34. def doSample(): Unit
    Attributes
    protected
    Definition Classes
    MetropolisHastingsBaseUnweightedSamplerSampler
  35. def doStart(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    AnytimeAlgorithm
  36. def doStop(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    AnytimeAlgorithm
  37. var elementsUsedBy: Map[Element[_], Set[Element[_]]]
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  38. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  39. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  40. val fastTargets: Set[Element[_]]
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  41. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  42. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  43. def getDissatisfied: Set[Element[_]]
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  44. def getSampleCount: Int

    Number of samples taken

    Number of samples taken

    Definition Classes
    BaseUnweightedSampler
  45. def getTemperature: Double

    Get the current temperature.

    Get the current temperature. Used for debugging.

    Definition Classes
    ProbEvidenceMarginalMAP
  46. def handle(service: Service): Response

    A handler of services provided by the algorithm.

    A handler of services provided by the algorithm.

    Definition Classes
    AnytimeMarginalMAPAnytime
  47. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  48. def initConstrainedValues(): Unit
    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAPMetropolisHastings
  49. def initUpdates(): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  50. def initialize(): Unit

    Initialize the algorithm.

    Initialize the algorithm.

    Definition Classes
    AnytimeProbEvidenceMarginalMAPAnytimeSamplerAlgorithm
  51. def isActive: Boolean
    Definition Classes
    Algorithm
  52. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  53. 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
  54. val mapElements: List[Element[_]]
  55. implicit val messageTimeout: Timeout

    default message timeout.

    default message timeout. Increase if queries to the algorithm fail due to timeout

    Definition Classes
    Anytime
  56. def mhStep(): State
    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAPMetropolisHastings
  57. def mostLikelyValue[T](target: Element[T]): T

    Returns an estimate of the max a posteriori value of the target.

    Returns an estimate of the max a posteriori value of the target.

    Definition Classes
    MarginalMAPAlgorithm
    Exceptions thrown

    AlgorithmInactiveException if the algorithm is inactive.

    NotATargetException if called on a target that is not in the list of MAP elements.

  58. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  59. def newLastUpdate[T](target: Element[T]): LastUpdate[T]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  60. def newTimesSeen[T](target: Element[T]): TimesSeen[T]
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  61. final def notify(): Unit
    Definition Classes
    AnyRef
  62. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  63. var oldModelProb: Double
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  64. var oldPropProb: Double
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  65. var preserve: Set[Element[_]]
    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAP
  66. var probEvidenceSampler: MMAPProbEvidenceSampler
    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAP
  67. def proposeAndUpdate(): State
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  68. var proposedElementsSortedUpdates: Map[Iterable[Element[_]], List[Element[_]]]
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  69. lazy val queryTargets: List[Element[_]]
    Definition Classes
    BaseUnweightedSampler
  70. var rejects: Int
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  71. def resetCounts(): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSamplerSampler
  72. 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
  73. def runScheme(): State
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  74. def runStep(): Unit

    Run a single step of the algorithm.

    Run a single step of the algorithm. The algorithm must be able to provide answers after each step.

    Definition Classes
    AnytimeSamplerAnytime
  75. val runner: ActorRef
    Definition Classes
    Anytime
  76. val running: Boolean
    Definition Classes
    Anytime
  77. def sample(): (Boolean, Sample)

    Produce a single sample.

    Produce a single sample.

    Definition Classes
    ProbEvidenceMarginalMAPMetropolisHastingsBaseUnweightedSampler
  78. var sampleCount: Int
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  79. def shutdown: Unit

    Release all resources from this anytime algorithm.

    Release all resources from this anytime algorithm.

    Definition Classes
    Anytime
  80. 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
  81. 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
  82. def stopUpdate(): Unit

    Override the stopUpdate function in anytime to call the sampler update function

    Override the stopUpdate function in anytime to call the sampler update function

    Definition Classes
    AnytimeSamplerAnytime
  83. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  84. val system: ActorSystem
    Definition Classes
    Anytime
  85. var temperature: Double
    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAP
  86. 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
  87. def toString(): String
    Definition Classes
    AnyRef → Any
  88. def undo(state: State): Unit
    Attributes
    protected
    Definition Classes
    MetropolisHastings
  89. val universe: Universe
    Definition Classes
    BaseUnweightedSampler
  90. def update(): Unit
  91. def updateTimesSeenForTarget[T](elem: Element[T], newValue: T): Unit
    Attributes
    protected
    Definition Classes
    ProbEvidenceMarginalMAPBaseUnweightedSampler
  92. def updateTimesSeenWithValue[T](value: T, timesSeen: TimesSeen[T], seen: Int): Unit
    Attributes
    protected
    Definition Classes
    BaseUnweightedSampler
  93. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  94. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  95. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from AnytimeMarginalMAP

Inherited from AnytimeSampler

Inherited from Anytime

Inherited from ProbEvidenceMarginalMAP

Inherited from MarginalMAPAlgorithm

Inherited from MetropolisHastings

Inherited from BaseUnweightedSampler

Inherited from Sampler

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped