Uses of Interface
de.up.ling.irtg.sampling.RuleWeighting

Packages that use RuleWeighting

Package	Description
de.up.ling.irtg.sampling	This package contains tools to implement approximate inference via sampling.
de.up.ling.irtg.sampling.rule_weighting	This package contains automata with adaptable sampling heuristics.

Uses of RuleWeighting in de.up.ling.irtg.sampling

Methods in de.up.ling.irtg.sampling with parameters of type RuleWeighting

Modifier and Type	Method and Description
List<TreeSample<Rule>>	AdaptiveImportanceSampler.adaSample(int rounds, int populationSize, RuleWeighting rw, boolean deterministic, boolean reset) Runs the sampler for the given number of rounds with the given population size and adapting the given rule weights.
TreeSample<Rule>	AdaptiveImportanceSampler.adaSampleMinimal(int rounds, int populationSize, RuleWeighting rw, boolean deterministic, boolean reset) Runs the sampler for the given number of rounds with the given population size and adapting the given rule weights.
double	ProposalSumComputer.computeInside(Tree<Rule> input, int state, RuleWeighting weights) Computes the proposal probability for the given tree assuming the given rule weights and assuming that we start at the given state.
double	ProposalSumComputer.computeInside(Tree<Rule> input, RuleWeighting weights) Computes the proposal probability for the given tree assuming the given rule weights.
void	ProposalSumComputer.fillInsides(TreeSample<Rule> sample, RuleWeighting rw) Computes the proposal probability for each tree in the sample assuming the given rule weightings and sets it as the log sum weight of the sample.
TreeSample<Integer>	Proposal.getRawTreeSample(RuleWeighting guide, int sampleSize) Get a sample of trees with label IDs for the rules sampled.
TreeSample<String>	Proposal.getStringTreeSample(RuleWeighting guide, int sampleSize) Returns a sample of string trees by transforming sampled rule trees.
<Type> TreeSample<Type>	Proposal.getTreeSample(BiFunction<Rule,TreeAutomaton,Type> mapping, RuleWeighting guide, int numberOfSamples) This method samples a number of rule trees and transforms them into the desired type of tree by applying the mapping to the tree and the automaton the guide returns.
TreeSample<Rule>	Proposal.getTreeSample(RuleWeighting guide, int sampleSize) Returns a sample of rule trees.

Constructor parameters in de.up.ling.irtg.sampling with type arguments of type RuleWeighting

Constructor and Description
Configuration(Function<TreeAutomaton,RuleWeighting> rwSource) Creates a new instance with default parameters and based on the given RuleWeightings

Uses of RuleWeighting in de.up.ling.irtg.sampling.rule_weighting

Classes in de.up.ling.irtg.sampling.rule_weighting that implement RuleWeighting

Modifier and Type	Class and Description
class	AutomatonWeighted This class simply assigns each tree the weight of the multiplication of its rule weights.
class	RegularizedKLRuleWeighting Implements adaption by minimizing the KL between the target distribution and the proposal distribution as described in our adaptive importance sampling paper.