com.cra.figaro.algorithm.sampling
The schedule that determines how to anneal the model
The number of iterations to run before annealing starts
The number of iterations to perform before recording the annealing state
A message instructing the handler to compute the most likely value of the target element.
A message from the handler containing the most likely value of the previously requested element.
A class representing the actor running the algorithm.
Number of samples that should be taken in a single step of the algorithm.
Number of samples that should be taken in a single step of the algorithm.
Clean up the annealer, freeing memory.
Clean up the annealer, freeing memory.
The actor running the algorithm.
The actor running the algorithm.
Set this flag to true to obtain debugging information
Set this flag to true to obtain debugging information
Return the best energy computed by the annealer.
Return the best energy computed by the annealer.
Return the current energy of the annealer.
Return the current energy of the annealer.
Number of samples taken.
Number of samples taken.
The current temperature of the model.
The current temperature of the model.
A handler of services provided by the algorithm.
A handler of services provided by the algorithm.
Initialize the annealer.
Initialize the annealer.
Kill the algorithm so that it is inactive.
Kill the algorithm so that it is inactive. It will no longer be able to provide answers.Throws AlgorithmInactiveException if the algorithm is not active.
The last computed transition probability.
The last computed transition probability.
Returns the most likely value for the target element.
Returns the most likely value for the target element.
Resume the computation of the algorithm, if it has been stopped.
Resume the computation of the algorithm, if it has been stopped. Throws AlgorithmInactiveException if the algorithm is not active.
Run a single step of the algorithm.
Run a single step of the algorithm. The algorithm must be able to provide answers after each step.
Produce a single sample.
Produce a single sample.
Start the algorithm and make it active.
Start the algorithm and make it active. After it returns, the algorithm must be ready to provide answers. Throws AlgorithmActiveException if the algorithm is already active.
Stop the algorithm from computing.
Stop the algorithm from computing. The algorithm is still ready to provide answers after it returns. Throws AlgorithmInactiveException if the algorithm is not active.
Override the stopUpdate function in anytime to call the sampler update function
Override the stopUpdate function in anytime to call the sampler update function
Test Metropolis-Hastings by repeatedly running a single step from the same initial state.
Test Metropolis-Hastings by repeatedly running a single step from the same initial state. For each of a set of predicates, the fraction of times the predicate is satisfied by the resulting state is returned. By the resulting state, we mean the new state if it is accepted and the original state if not.
Anytime Metropolis-Hastings annealer.