io.improbable.keanu.algorithms.variational.optimizer.nongradient

Class NonGradientOptimizer

java.lang.Object

io.improbable.keanu.algorithms.variational.optimizer.nongradient.NonGradientOptimizer

All Implemented Interfaces:

Optimizer

public class NonGradientOptimizer
extends java.lang.Object
implements Optimizer

This class can be used to construct a BOBYQA non-gradient optimizer. This will use a quadratic approximation of the gradient to perform optimization without derivatives.

See Also:

BOBYQA Optimizer

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	NonGradientOptimizer.NonGradientOptimizerBuilder

Constructor Summary

Constructors

Constructor and Description
NonGradientOptimizer()

Method Summary

Modifier and Type	Method and Description
void	addFitnessCalculationHandler(java.util.function.BiConsumer<java.util.Map<VariableReference,DoubleTensor>,java.lang.Double> fitnessCalculationHandler) Adds a callback to be called whenever the optimizer evaluates the fitness of a point.
static NonGradientOptimizer.NonGradientOptimizerBuilder	builder()
OptimizedResult	maxAPosteriori() This will use MAP estimation to optimize the values of latent vertices such that the probability of the whole Bayesian network is maximised.
OptimizedResult	maxLikelihood() This method will use Maximum Likelihood estimation to optimize the values of latent vertices such that the probability of the observed vertices is maximised.
void	removeFitnessCalculationHandler(java.util.function.BiConsumer<java.util.Map<VariableReference,DoubleTensor>,java.lang.Double> fitnessCalculationHandler) Removes a callback function that previously would have been called whenever the optimizer evaluated the fitness of a point.

Methods inherited from class java.lang.Object

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

Methods inherited from interface io.improbable.keanu.algorithms.variational.optimizer.Optimizer

convertFromPoint, convertToArrayPoint, convertToMapPoint, createFitnessStatusBar, getAsDoubleTensors, numDimensions, toDoubleTensorVariable, totalNumberOfLatentDimensions

Constructor Detail

NonGradientOptimizer

public NonGradientOptimizer()

Method Detail

builder

public static NonGradientOptimizer.NonGradientOptimizerBuilder builder()

addFitnessCalculationHandler

public void addFitnessCalculationHandler(java.util.function.BiConsumer<java.util.Map<VariableReference,DoubleTensor>,java.lang.Double> fitnessCalculationHandler)

Description copied from interface: Optimizer

Adds a callback to be called whenever the optimizer evaluates the fitness of a point. E.g. for logging.

Specified by:

addFitnessCalculationHandler in interface Optimizer

Parameters:

fitnessCalculationHandler - a function to be called whenever the optimizer evaluates the fitness of a point. The first argument to the handler represents the point being evaluated. The second argument to the handler represents the fitness of that point.

removeFitnessCalculationHandler

public void removeFitnessCalculationHandler(java.util.function.BiConsumer<java.util.Map<VariableReference,DoubleTensor>,java.lang.Double> fitnessCalculationHandler)

Description copied from interface: Optimizer

Removes a callback function that previously would have been called whenever the optimizer evaluated the fitness of a point. If the callback is not registered then this function will do nothing.

Specified by:

removeFitnessCalculationHandler in interface Optimizer

Parameters:

fitnessCalculationHandler - the function to be removed from the list of fitness evaluation callbacks

maxAPosteriori

public OptimizedResult maxAPosteriori()

Description copied from interface: Optimizer

This will use MAP estimation to optimize the values of latent vertices such that the probability of the whole Bayesian network is maximised. This method will modify in place the Bayesian network that was used to create this object.

Specified by:

maxAPosteriori in interface Optimizer

Returns:

the natural logarithm of the Maximum a posteriori (MAP)

maxLikelihood

public OptimizedResult maxLikelihood()

Description copied from interface: Optimizer

This method will use Maximum Likelihood estimation to optimize the values of latent vertices such that the probability of the observed vertices is maximised. This method will modify in place the Bayesian network that was used to create this object.

Specified by:

maxLikelihood in interface Optimizer

Returns:

the natural logarithm of the maximum likelihood (MLE)