Uses of Class
de.up.ling.irtg.sampling.TreeSample

Packages that use TreeSample

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 TreeSample in de.up.ling.irtg.sampling

Methods in de.up.ling.irtg.sampling that return TreeSample

Modifier and Type	Method and Description
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.
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.

Methods in de.up.ling.irtg.sampling that return types with arguments of type TreeSample

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.
List<TreeSample<Rule>>	AdaptiveImportanceSampler.Configuration.run(TreeAutomaton ta) Runs an importance sampler once for the given configuration.

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

Modifier and Type	Method and Description
void	RuleWeighting.adapt(TreeSample<Rule> treSamp, boolean deterministic) Adapts the proposal distribution with the assumption that treSamp is an importance sample generated from this proposal distribution.
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.

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

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

Modifier and Type	Method and Description
void	RegularizedKLRuleWeighting.adapt(TreeSample<Rule> treSamp, boolean deterministic)