package decision
 Alphabetic
 Public
 All
Type Members

class
AnytimeDecisionMetropolisHastings
[T, U] extends DecisionMetropolisHastings[T, U] with UnweightedSampler with AnytimeProbQuerySampler
Anytime Decision MetropolisHastings sampler.

trait
DecisionAlgorithm
[T, U] extends Algorithm
Trait that defines some common interface functions for decision algorithms.
Trait that defines some common interface functions for decision algorithms. Every decision algorithm must define the function computeUtility().

abstract
class
DecisionImportance
[T, U] extends Importance with DecisionAlgorithm[T, U]
Importance sampling for decisions.
Importance sampling for decisions. Almost the exact same as normal importance sampling except that it keeps track of utilities and probabilities (to compute expected utility) and it implements DecisionAlgorithm trait.

abstract
class
DecisionMetropolisHastings
[T, U] extends MetropolisHastings with DecisionAlgorithm[T, U]
MetropolisHastings Decision sampler.
MetropolisHastings Decision sampler. Almost the exact same as normal MH except that it keeps track of utilities and probabilities (to compute expected utility) and it implements DecisionAlgorithm trait

trait
DecisionPolicy
[T, U] extends AnyRef
Abstract base class for all Decision Policies.
Abstract base class for all Decision Policies. Must define two functions: toFcn: T => Element[U]  this is the function that is called to compute the decision for a parent value.
toUtility: T => Element[Double]  this returns the expected utility of the decision for a parent value. Used in backward induction algorithm.
 T
The parent value type
 U
The decision type

class
DecisionPolicyExact
[T, U] extends DecisionPolicy[T, U]
An exact decision policy.
An exact decision policy. This policy is exact because every possible value of the parent must have a defined policy. This makes it suitable for variable elimination algorithms or sampling algorithms if the range of the parent is small and enough samples are generated.

class
DecisionPolicyNN
[T, U] extends DecisionPolicy[T, U]
A nearest neighbor decision policy.
A nearest neighbor decision policy. This policy computes an approximate decision from a sampling algorithm. The input to the class is an index (which holds (parent, decision) samples) a function that will combine a set of (decision, utility) samples into a single decision, and numNNSamples, the number of samples to use in a nearest neighbor algorithm. By default, this uses a VPtree to store the samples.

abstract
class
MultiDecisionAlgorithm
extends OneTime
Abstract class common to all multidecision algorithms.
Abstract class common to all multidecision algorithms. Multidecision algorithms implement backward induction by 1) determining the order in which decisions can be computed 2) Implementing a single decision algorithm on each decision (in the proper order).
Note: Only OneTime algorithms are supported in multidecision algorithms.

class
MultiDecisionVariableElimination
extends MultiDecisionAlgorithm
A multidecision algorithm that uses Variable Elimination for each decision.

class
OneTimeDecisionMetropolisHastings
[T, U] extends DecisionMetropolisHastings[T, U] with UnweightedSampler with OneTimeProbQuerySampler
Onetime Decision MetropolisHastings sampler.

class
OneTimeMultiDecisionImportance
extends MultiDecisionAlgorithm
A OneTime multidecision algorithm that uses Importance sampling for each decision.

class
OneTimeMultiDecisionMetropolisHastings
extends MultiDecisionAlgorithm
A OneTime multidecision algorithm that uses MetropolisHastings sampling for each decision.
A OneTime multidecision algorithm that uses MetropolisHastings sampling for each decision. A user must supple an instance of a ProposalMakerType, which indicates how to create a proposal scheme for each decision.

trait
OneTimeProbQueryDecision
[T, U] extends OneTimeProbQuery with DecisionAlgorithm[T, U]
Trait for one time Decision Algorithms.

class
ProbQueryVariableEliminationDecision
[T, U] extends OneTimeProbQuery with ProbabilisticVariableEliminationDecision with DecisionAlgorithm[T, U]
Decision VariableElimination algorithm that computes the expected utility of decision elements using the default elimination order.

trait
ProbabilisticVariableEliminationDecision
extends VariableElimination[(Double, Double)]
Trait for Decision based Variable Elimination.
Trait for Decision based Variable Elimination. This implementation is hardcoded to use. Double utilities.
Value Members
 object DecisionImportance
 object DecisionMetropolisHastings
 object DecisionPolicy
 object DecisionPolicyExact
 object DecisionPolicyNN
 object DecisionVariableElimination
 object MultiDecisionImportance
 object MultiDecisionMetropolisHastings
 object MultiDecisionVariableElimination