io.improbable.keanu.algorithms.mcmc

Class SimulatedAnnealing

java.lang.Object

io.improbable.keanu.algorithms.mcmc.SimulatedAnnealing

public class SimulatedAnnealing
extends java.lang.Object

Simulated Annealing is a modified version of Metropolis Hastings that causes the MCMC random walk to tend towards the Maximum A Posteriori (MAP)

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	SimulatedAnnealing.AnnealingSchedule An annealing schedule determines how T (temperature) changes as a function of the current iteration number (i.e.
static class	SimulatedAnnealing.SimulatedAnnealingBuilder

Constructor Summary

Constructors

Constructor and Description
SimulatedAnnealing()

Method Summary

Modifier and Type	Method and Description
static SimulatedAnnealing.SimulatedAnnealingBuilder	builder()
static SimulatedAnnealing.AnnealingSchedule	exponentialSchedule(int iterations, double startT, double endT)
NetworkState	getMaxAPosteriori(ProbabilisticModel model, int sampleCount)
NetworkState	getMaxAPosteriori(ProbabilisticModel model, int sampleCount, SimulatedAnnealing.AnnealingSchedule annealingSchedule) Finds the MAP using the default annealing schedule, which is an exponential decay schedule.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SimulatedAnnealing

public SimulatedAnnealing()

Method Detail

builder

public static SimulatedAnnealing.SimulatedAnnealingBuilder builder()

getMaxAPosteriori

public NetworkState getMaxAPosteriori(ProbabilisticModel model,
                                      int sampleCount)

getMaxAPosteriori

public NetworkState getMaxAPosteriori(ProbabilisticModel model,
                                      int sampleCount,
                                      SimulatedAnnealing.AnnealingSchedule annealingSchedule)

Finds the MAP using the default annealing schedule, which is an exponential decay schedule.

Parameters:

model - a probabilistic model containing latent variables

sampleCount - the number of samples to take

annealingSchedule - the schedule to update T (temperature) as a function of sample number.

Returns:

the NetworkState that represents the Max A Posteriori

exponentialSchedule

public static SimulatedAnnealing.AnnealingSchedule exponentialSchedule(int iterations,
                                                                       double startT,
                                                                       double endT)

Parameters:

iterations - the number of iterations annealing over

startT - the value of T at iteration 0

endT - the value of T at the last iteration

Returns:

the annealing schedule