Merge pull request #508 from p2t2/DEV3.3

Dev3.3

Figaro/META-INF/MANIFEST.MF

@@ -2,7 +2,7 @@ Manifest-Version: 1.0
 Bundle-ManifestVersion: 2
 Bundle-Name: Figaro
 Bundle-SymbolicName: com.cra.figaro
-Bundle-Version: 3.2.1
+Bundle-Version: 3.3.0
 Export-Package: com.cra.figaro.algorithm,
  com.cra.figaro.algorithm.decision,
  com.cra.figaro.algorithm.decision.index,

Figaro/figaro_build.properties

@@ -1 +1 @@
-version=3.2.1.1
+version=3.3.0.0

Figaro/src/main/scala/com/cra/figaro/algorithm/decision/DecisionImportance.scala

@@ -75,6 +75,7 @@ abstract class DecisionImportance[T, U] private (override val universe: Universe
   // override doSample so can update the local utilities
   override protected def doSample(): Unit = {
     val s = sample()
+    universe.clearTemporaries()
     totalWeight = logSum(s._1, totalWeight)
     allWeightsSeen foreach (updateWeightSeenForTarget(s, _))
     allUtilitiesSeen foreach (updateWeightSeenForTargetNoLog((math.exp(s._1) * utilitySum, s._2), _))

Figaro/src/main/scala/com/cra/figaro/algorithm/factored/VariableElimination.scala

@@ -21,6 +21,7 @@ import com.cra.figaro.util._
 import annotation.tailrec
 import scala.collection.mutable.{ Map, Set }
 import scala.language.postfixOps
+import scala.util.control.TailCalls._
 
 /**
  * Trait of algorithms that perform variable elimination.
@@ -123,18 +124,32 @@ trait VariableElimination[T] extends FactoredAlgorithm[T] with OneTime {
     }
   }
 
+  // Wraps the TailRec class and returns the result
   protected def eliminateInOrder(
     order: List[Variable[_]],
     factors: MultiSet[Factor[T]],
-    map: FactorMap[T]): MultiSet[Factor[T]] =
+    map: FactorMap[T]): MultiSet[Factor[T]] = {
+    callEliminateInOrder(order, factors, map).result
+  }
+
+  /*
+   *  TailRec class turns a tail-recursive method into a while loop
+   *  The result needs to be extracted explicitly
+   */
+  private def callEliminateInOrder(
+    order: List[Variable[_]],
+    factors: MultiSet[Factor[T]],
+    map: FactorMap[T]): TailRec[MultiSet[Factor[T]]] = {
     order match {
       case Nil =>
-        factors
+        done(factors)
       case first :: rest =>
         eliminate(first, factors, map)
-        eliminateInOrder(rest, factors, map)
+        tailcall(callEliminateInOrder(rest, factors, map))
     }
-
+  }
+
+
   private[figaro] def ve(): Unit = {
     //expand()
     val (neededElements, _) = getNeededElements(starterElements, Int.MaxValue)
@@ -236,7 +251,7 @@ class ProbQueryVariableElimination(override val universe: Universe, targets: Ele
     dist.toStream
   }
 
- /**
+  /**
    * Computes the expectation of a given function for single target element.
    */
   def computeExpectation[T](target: Element[T], function: T => Double): Double = {

Figaro/src/main/scala/com/cra/figaro/algorithm/filtering/ParFiltering.scala

@@ -1,11 +1,11 @@
 /*
- * Filtering.scala
- * Filtering algorithms.
+ * ParFiltering.scala
+ * A parallel version of Filtering.
  * 
- * Created By:      Avi Pfeffer (apfeffer@cra.com)
- * Creation Date:   Jan 1, 2009
+ * Created By:      Lee Kellogg (lkellogg@cra.com)
+ * Creation Date:   Jun 2, 2015
  * 
- * Copyright 2013 Avrom J. Pfeffer and Charles River Analytics, Inc.
+ * Copyright 2015 Avrom J. Pfeffer and Charles River Analytics, Inc.
  * See http://www.cra.com or email figaro@cra.com for information.
  * 
  * See http://www.github.com/p2t2/figaro for a copy of the software license.

Figaro/src/main/scala/com/cra/figaro/algorithm/filtering/ParParticleFilter.scala

@@ -1,8 +1,24 @@
+/*
+ * ParParticleFilter.scala
+ * A parallel one-time particle filter.
+ * 
+ * Created By:      Lee Kellogg (lkellogg@cra.com)
+ * Creation Date:   Jun 2, 2015
+ * 
+ * Copyright 2015 Avrom J. Pfeffer and Charles River Analytics, Inc.
+ * See http://www.cra.com or email figaro@cra.com for information.
+ * 
+ * See http://www.github.com/p2t2/figaro for a copy of the software license.
+ */
+
 package com.cra.figaro.algorithm.filtering
 
 import com.cra.figaro.language._
 import scala.collection.parallel.ParSeq
 import com.cra.figaro.algorithm.filtering.ParticleFilter.WeightedParticle
+import com.cra.figaro.library.cache.PermanentCache
+import com.cra.figaro.library.cache.Cache
+import com.cra.figaro.algorithm.sampling.LikelihoodWeighter
 
 /**
  * A parallel one-time particle filter. Distributes the work of generating particles at each time step over a specified
@@ -42,7 +58,7 @@ class ParOneTimeParticleFilter(static: () => Universe, initial: () => Universe,
    * @param windows the UniverseWindows to sample from
    * @param weightedParticleCreator a function that generates a WeightedParticle, given a UniverseWindow and an index
    */
-  private def genParticles(windows: Seq[UniverseWindow], weightedParticleCreator: (UniverseWindow, Int) => WeightedParticle): Seq[WeightedParticle] = {
+  private def genParticles(windows: Seq[(UniverseWindow, LikelihoodWeighter)], weightedParticleCreator: ((UniverseWindow, LikelihoodWeighter), Int) => WeightedParticle): Seq[WeightedParticle] = {
     val parWindows = windows.par
     val particles = parWindows zip indices flatMap { case(window, (start, end)) =>
       (start to end) map { i => weightedParticleCreator(window, i) }
@@ -58,13 +74,15 @@ class ParOneTimeParticleFilter(static: () => Universe, initial: () => Universe,
 
   def run(): Unit = {
     windows = genInitialWindows()
-    val particles = genParticles(windows, (w, _) => initialWeightedParticle(w.static, w.current))
+    val windowWithCaches = windows.map(w => (w, new LikelihoodWeighter(w.current, new PermanentCache(w.current))))
+    val particles = genParticles(windowWithCaches, (w, _) => initialWeightedParticle(w._1.static, w._1.current, w._2))
     doTimeStep(particles)
   }
 
   def advanceTime(evidence: Seq[NamedEvidence[_]] = List()): Unit = {
     val newWindows = advanceUniverseWindows(windows)
-    val particles = genParticles(newWindows, (w, i) => addWeightedParticle(evidence, i, w))
+    val newWindowsWithCaches = newWindows.map(w => (w, new LikelihoodWeighter(w.current, new PermanentCache(w.current))))
+    val particles = genParticles(newWindowsWithCaches, (w, i) => addWeightedParticle(evidence, i, w._1, w._2))
     doTimeStep(particles)
     windows = newWindows
   }

Figaro/src/main/scala/com/cra/figaro/algorithm/filtering/ParticleFilter.scala

@@ -16,6 +16,9 @@ package com.cra.figaro.algorithm.filtering
 import com.cra.figaro.algorithm.sampling._
 import com.cra.figaro.language._
 import com.cra.figaro.util._
+import com.cra.figaro.library.cache.PermanentCache
+import com.cra.figaro.library.cache.Cache
+import com.cra.figaro.algorithm.sampling.LikelihoodWeighter
 
 /**
  * An abstract class of particle filters.
@@ -35,7 +38,7 @@ import com.cra.figaro.util._
  * @param transition The transition function that returns a new universe from a static and previous universe, respectively.
  */
 trait ParticleFilter {
-  
+
   val numParticles: Int
 
   /** The belief about the state of the system at the current point in time. */
@@ -76,22 +79,18 @@ trait ParticleFilter {
    
    * TODO: previous state could be replaced by the static universe (or a universe window)
    */
-  protected def makeWeightedParticle(previousState: State, currentUniverse: Universe): ParticleFilter.WeightedParticle = {
-    Forward(currentUniverse)
-    // avoiding recursion
-
-    // satisfied if all conditioned elements are satisfied
-    val satisfied = currentUniverse.conditionedElements.forall { x => x.conditionSatisfied }
-
-    //multiply weights together in log space if satisfied
-    val weight =
-      if (satisfied) {
-        math.exp(currentUniverse.constrainedElements.foldLeft(0.0)((b,a) => b + a.constraintValue))
-      } else 0.0
+  protected def makeWeightedParticle(previousState: State, currentUniverse: Universe, lw: LikelihoodWeighter): ParticleFilter.WeightedParticle = {
+
+    val weight = try {
+      math.exp(lw.computeWeight(currentUniverse.activeElements))
+    } catch {
+      case Importance.Reject => 0.0
+    }   
 
     val snapshot = new Snapshot
     snapshot.store(currentUniverse)
     val state = new State(snapshot, previousState.static)
+    currentUniverse.clearTemporaries
     (weight, state)
   }
 
@@ -118,21 +117,21 @@ trait ParticleFilter {
     logProbEvidence = logProbEvidence + scala.math.log(sum / numParticles)
   }
 
-  protected def addWeightedParticle(evidence: Seq[NamedEvidence[_]], index: Int, universes: UniverseWindow): ParticleFilter.WeightedParticle = {
+  protected def addWeightedParticle(evidence: Seq[NamedEvidence[_]], index: Int, universes: UniverseWindow, lw: LikelihoodWeighter): ParticleFilter.WeightedParticle = {
     val previousState = beliefState(index)
     previousState.dynamic.restore(universes.previous)
     previousState.static.restore(universes.static)
     universes.current.assertEvidence(evidence)
-    val result = makeWeightedParticle(previousState, universes.current)
+    val result = makeWeightedParticle(previousState, universes.current, lw)
     result
   }
 
-  protected def initialWeightedParticle(static: Universe, current: Universe): ParticleFilter.WeightedParticle = {
+  protected def initialWeightedParticle(static: Universe, current: Universe, lw: LikelihoodWeighter): ParticleFilter.WeightedParticle = {
     Forward(static)
     val staticSnapshot = new Snapshot
     staticSnapshot.store(static)
     val state = new State(new Snapshot, staticSnapshot)
-    makeWeightedParticle(state, current)
+    makeWeightedParticle(state, current, lw)
   }
 
   /*
@@ -159,7 +158,7 @@ trait ParticleFilter {
     logProbEvidence
   }
 
-   /**
+  /**
    * The computed probability of evidence.
    */
   def probEvidence(): Double = {
@@ -179,10 +178,10 @@ trait ParticleFilter {
  */
 class OneTimeParticleFilter(static: Universe = new Universe(), initial: Universe, transition: (Universe, Universe) => Universe, val numParticles: Int)
   extends Filtering(static, initial, transition) with ParticleFilter with OneTimeFiltering {
-  
+
   var currentUniverse: Universe = initial
   var previousUniverse: Universe = _
-  
+
   private def doTimeStep(weightedParticleCreator: Int => ParticleFilter.WeightedParticle) {
     val weightedParticles = for { i <- 0 until numParticles } yield weightedParticleCreator(i)
 
@@ -196,17 +195,19 @@ class OneTimeParticleFilter(static: Universe = new Universe(), initial: Universe
    * Begin the particle filter, determining the initial distribution.
    */
   def run(): Unit = {
-    doTimeStep((i: Int) => initialWeightedParticle(static, currentUniverse))
+    val lw = new LikelihoodWeighter(currentUniverse, new PermanentCache(currentUniverse))
+    doTimeStep((i: Int) => initialWeightedParticle(static, currentUniverse, lw))
   }
 
   /**
    * Advance the filtering one time step, conditioning on the given evidence at the new time point.
    */
   def advanceTime(evidence: Seq[NamedEvidence[_]] = List()): Unit = {
-    
+
     val currentWindow = new UniverseWindow(previousUniverse, currentUniverse, static)
     val newWindow = advanceUniverse(currentWindow, transition)
-    doTimeStep((i: Int) => addWeightedParticle(evidence, i, newWindow))
+    val lw = new LikelihoodWeighter(newWindow.current, new PermanentCache(newWindow.current))
+    doTimeStep((i: Int) => addWeightedParticle(evidence, i, newWindow, lw))
     previousUniverse = newWindow.previous
     currentUniverse = newWindow.current
   }
@@ -256,14 +257,14 @@ object ParticleFilter {
 
   /** Weighted particles, consisting of a weight and a state. */
   type WeightedParticle = (Double, State)
-  
+
   /** Reference to parallel implementation. */
   def par = ParParticleFilter
 
 }
 
 /**
- * A class representing a single window in time, with a current universe, a previous universe, 
+ * A class representing a single window in time, with a current universe, a previous universe,
  * and a static universe.
  */
 class UniverseWindow(val previous: Universe, val current: Universe, val static: Universe)

Figaro/src/main/scala/com/cra/figaro/algorithm/sampling/ElementSampler.scala

@@ -77,6 +77,7 @@ class OneTimeElementSampler(target: Element[_], myNumSamples: Int)
     doInitialize()
     super.run()
     update
+    universe.clearTemporaries
   }
 }