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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1 @@ | ||
| version=2.2.0.0 | ||
| version=2.2.1.0 |
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149 changes: 149 additions & 0 deletions
149
Figaro/src/main/scala/com/cra/figaro/algorithm/learning/GeneralizedEM.scala
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| /* | ||
| * GeneralizedEM.scala | ||
| * Expectation maximization algorithm using any ProbQueryAlgorithm as the inference algorithm. | ||
| * | ||
| * Created By: Michael Howard (mhoward@cra.com) | ||
| * Creation Date: Jun 1, 2013 | ||
| * | ||
| * Copyright 2013 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. | ||
| */ | ||
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| package com.cra.figaro.algorithm.learning | ||
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| import com.cra.figaro.language._ | ||
| import com.cra.figaro.algorithm.{Algorithm, ParameterLearner, ProbQueryAlgorithm, OneTime} | ||
| import com.cra.figaro.algorithm.factored.beliefpropagation.BeliefPropagation | ||
| import com.cra.figaro.algorithm.sampling.{Importance, MetropolisHastings, ProposalScheme} | ||
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| /* | ||
| * @param inferenceAlgorithmConstructor | ||
| */ | ||
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| class GeneralizedEM(inferenceAlgorithmConstructor: Seq[Element[_]] => Universe => ProbQueryAlgorithm with OneTime, universe: Universe, targetParameters: Parameter[_]*)(val numberOfIterations: Int) extends Algorithm with ParameterLearner { | ||
| /* | ||
| * Start the algorithm. After it returns, the algorithm must be ready to provide answers. | ||
| */ | ||
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| protected def doStart(): Unit = { | ||
| em() | ||
| } | ||
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| protected def em(): Unit = { | ||
| for (iteration <- 1 to numberOfIterations) { | ||
| /* | ||
| * Obtain an estimate of sufficient statistics from expectation step | ||
| */ | ||
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| val sufficientStatistics = doExpectationStep() | ||
| doMaximizationStep(sufficientStatistics) | ||
| } | ||
| } | ||
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| protected def doExpectationStep(): Map[Parameter[_], Seq[Double]] = { | ||
| val inferenceTargets = | ||
| universe.activeElements.filter(_.isInstanceOf[Parameterized[_]]).map(_.asInstanceOf[Parameterized[_]]) | ||
| val algorithm = inferenceAlgorithmConstructor(inferenceTargets)(universe) | ||
| algorithm.start() | ||
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| var result: Map[Parameter[_], Seq[Double]] = Map() | ||
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| for { parameter <- targetParameters } { | ||
| var stats = parameter.zeroSufficientStatistics | ||
| for { | ||
| target <- inferenceTargets | ||
| if target.parameter == parameter | ||
| } { | ||
| val t: Parameterized[target.Value] = target.asInstanceOf[Parameterized[target.Value]] | ||
| val distribution: Stream[(Double, target.Value)] = algorithm.distribution(t) | ||
| val newStats = t.distributionToStatistics(distribution) | ||
| stats = (stats.zip(newStats)).map(pair => pair._1 + pair._2) | ||
| } | ||
| result += parameter -> stats | ||
| } | ||
| algorithm.kill() | ||
| result | ||
| } | ||
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| protected def doMaximizationStep(parameterMapping: Map[Parameter[_], Seq[Double]]): Unit = { | ||
| for (p <- targetParameters) yield { | ||
| p.maximize(parameterMapping(p)) | ||
| } | ||
| } | ||
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| /* | ||
| * Stop the algorithm from computing. The algorithm is still ready to provide answers after it returns. | ||
| */ | ||
| protected def doStop(): Unit = {} | ||
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| /* | ||
| * Resume the computation of the algorithm, if it has been stopped. | ||
| */ | ||
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| protected def doResume(): Unit = {} | ||
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| /* | ||
| * Kill the algorithm so that it is inactive. It will no longer be able to provide answers. | ||
| */ | ||
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| protected def doKill(): Unit = {} | ||
| } | ||
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| object EMWithBP { | ||
| private def makeBP(numIterations: Int, targets: Seq[Element[_]])(universe: Universe) = { | ||
| BeliefPropagation(numIterations, targets:_*)(universe) | ||
| } | ||
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| /** | ||
| * An expectation maximization algorithm which will run for the default of 10 iterations | ||
| */ | ||
| def apply(p: Parameter[_]*)(implicit universe: Universe) = | ||
| new GeneralizedEM((targets: Seq[Element[_]]) => (universe: Universe) => makeBP(10, targets)(universe), universe, p: _*)(10) | ||
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| /** | ||
| * An expectation maximization algorithm which will run for the number of iterations specified | ||
| */ | ||
| def apply(emIterations: Int, bpIterations: Int, p: Parameter[_]*)(implicit universe: Universe) = | ||
| new GeneralizedEM((targets: Seq[Element[_]]) => (universe: Universe) => makeBP(bpIterations, targets)(universe), universe, p: _*)(emIterations) | ||
| } | ||
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| object EMWithImportance { | ||
| private def makeImportance(numParticles: Int, targets: Seq[Element[_]])(universe: Universe) = { | ||
| Importance(numParticles, targets:_*)(universe) | ||
| } | ||
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| /** | ||
| * An expectation maximization algorithm using importance sampling for inference | ||
| * | ||
| * @param emIterations number of iterations of the EM algorithm | ||
| * @param importanceParticles number of particles of the importance sampling algorithm | ||
| */ | ||
| def apply(emIterations: Int, importanceParticles: Int, p: Parameter[_]*)(implicit universe: Universe) = | ||
| new GeneralizedEM((targets: Seq[Element[_]]) => (universe: Universe) => makeImportance(importanceParticles, targets)(universe), universe, p: _*)(emIterations) | ||
| } | ||
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| object EMWithMH { | ||
| private def makeMH(numParticles: Int, proposalScheme: ProposalScheme, targets: Seq[Element[_]])(universe: Universe) = { | ||
| MetropolisHastings(numParticles, proposalScheme, targets:_*)(universe) | ||
| } | ||
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| /** | ||
| * An expectation maximization algorithm using Metropolis Hastings for inference. | ||
| * | ||
| * @param emIterations number of iterations of the EM algorithm | ||
| * @param mhParticles number of particles of the MH algorithm | ||
| */ | ||
| def apply(emIterations: Int, mhParticles: Int, p: Parameter[_]*)(implicit universe: Universe) = | ||
| new GeneralizedEM((targets: Seq[Element[_]]) => (universe: Universe) => makeMH(mhParticles, ProposalScheme.default(universe), targets)(universe), universe, p: _*)(emIterations) | ||
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| /** | ||
| * An expectation maximization algorithm using Metropolis Hastings for inference. | ||
| * | ||
| * @param emIterations number of iterations of the EM algorithm | ||
| * @param mhParticles number of particles of the MH algorithm | ||
| */ | ||
| def apply(emIterations: Int, mhParticles: Int, proposalScheme: ProposalScheme, p: Parameter[_]*)(implicit universe: Universe) = | ||
| new GeneralizedEM((targets: Seq[Element[_]]) => (universe: Universe) => makeMH(mhParticles, proposalScheme, targets)(universe), universe, p: _*)(emIterations) | ||
| } |
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