de.up.ling.irtg.sampling

Interface RuleWeighting

All Known Implementing Classes:

AutomatonWeighted, RegularizedKLRuleWeighting

public interface RuleWeighting

This interface represents the target distribution for sampling and the auxiliary distribution. All probability are only computed when the implementing class is told that they will be needed. The interface imposes some order on the start states and the rules for a state. This makes it possible to address them both by numbers.

Author:

christoph

Method Summary

Modifier and Type	Method and Description
void	adapt(TreeSample<Rule> treSamp, boolean deterministic) Adapts the proposal distribution with the assumption that treSamp is an importance sample generated from this proposal distribution.
TreeAutomaton	getAutomaton() Returns the underlying tree automaton from which the rules and start states are drawn.
double	getLogProbability(int state, int number) Returns the log of the probability of choosing the rule identified by the number given the state.
double	getLogProbability(Rule r) Returns the proposal probability of the given rule given its parent.
double	getLogTargetProbability(Tree<Rule> sample) Returns the unnormalized probability of the given tree in the target distribution.
int	getNumberOfStartStates() Returns the overall number of start states available.
Rule	getRuleByNumber(int state, int number) Returns the rule identified by the given state and number.
int	getRuleNumber(int state, double choicePoint) Returns the number of the first rule such that the cumulative probability of the earlier rules plus this one is larger than choicePoint.
int	getStartStateByNumber(int number) Returns the start state with the given number.
int	getStartStateNumber(double choicePoint) Returns the start first start state such that the cumulative probability of earlier start states plus this one is larger than choicePoint.
double	getStateStartLogProbability(int number) Returns the log of the proposal probability of the given start state.
void	prepareProbability(int state) Tells the class to recompute the proposal probabilities for the rules of the given state.
void	prepareStartProbability() Recomputes the proposal probabilities for the start states.
void	reset() Resets any adaption of the proposal distribution.

Method Detail

getLogProbability

double getLogProbability(int state,
                         int number)

Returns the log of the probability of choosing the rule identified by the number given the state. The value should be those computed by the last prepareProbability call.

Parameters:

state -

number -

Returns:

prepareProbability

void prepareProbability(int state)

Tells the class to recompute the proposal probabilities for the rules of the given state. If it is certain that they have not changed, then it is acceptable to do nothing.

Parameters:

state -

getRuleNumber

int getRuleNumber(int state,
                  double choicePoint)

Returns the number of the first rule such that the cumulative probability of the earlier rules plus this one is larger than choicePoint. May throw an error if there is no such rule.

Parameters:

state -

choicePoint -

Returns:

getRuleByNumber

Rule getRuleByNumber(int state,
                     int number)

Returns the rule identified by the given state and number. May throw an error if there is no such rule.

Parameters:

state -

number -

Returns:

getStateStartLogProbability

double getStateStartLogProbability(int number)

Returns the log of the proposal probability of the given start state. May throw an error if there is no such state. The value is always the last one computed by prepareStartProbability.

Parameters:

number -

Returns:

getStartStateByNumber

int getStartStateByNumber(int number)

Returns the start state with the given number. May throw an error if there is no such state.

Parameters:

number -

Returns:

getStartStateNumber

int getStartStateNumber(double choicePoint)

Returns the start first start state such that the cumulative probability of earlier start states plus this one is larger than choicePoint. May throw an error if there is no such state.

Parameters:

choicePoint -

Returns:

prepareStartProbability

void prepareStartProbability()

Recomputes the proposal probabilities for the start states. If they are guaranteed to be unchanged, then this step can be skipped by the implementing class.

getNumberOfStartStates

int getNumberOfStartStates()

Returns the overall number of start states available.

Returns:

reset

void reset()

Resets any adaption of the proposal distribution.

adapt

void adapt(TreeSample<Rule> treSamp,
           boolean deterministic)

Adapts the proposal distribution with the assumption that treSamp is an importance sample generated from this proposal distribution.

Parameters:

treSamp -

deterministic - indicates whether we can assume the underlying automaton to be unambiguous.

getAutomaton

TreeAutomaton getAutomaton()

Returns the underlying tree automaton from which the rules and start states are drawn.

Returns:

getLogTargetProbability

double getLogTargetProbability(Tree<Rule> sample)

Returns the unnormalized probability of the given tree in the target distribution.

Parameters:

sample -

Returns:

getLogProbability

double getLogProbability(Rule r)

Returns the proposal probability of the given rule given its parent.

Parameters:

r -

Returns: