com.cra.figaro.algorithm.decision
ProbQueryVariableEliminationDecision
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package decision
Called when the algorithm is killed.
Called when the algorithm is killed. By default, does nothing. Can be overridden.
Some variable elimination algorithms, such as computing the most probable explanation, record values of variables as they are eliminated.
Some variable elimination algorithms, such as computing the most probable explanation, record values of variables as they are eliminated. Such values are stored in a factor that maps values of the other variables to a value of the eliminated variable. This factor is produced by finding the value of the variable that "maximizes" the entry associated with the value in the product factor resulting from eliminating this variable, for some maximization function. The recordingFunction determines which of two entries is greater according to the maximization function. It returns true iff the second entry is greater. The recording function is an option so that variable elimination algorithms that do not use it can ignore it.
Returns distribution of the target, ignoring utilities.
Returns distribution of the target, ignoring utilities.
Returns expectation of the target, ignoring utilities
Returns expectation of the target, ignoring utilities
Return an estimate of the probability of the predicate under the marginal probability distribution of the target.
Return an estimate of the probability of the predicate under the marginal probability distribution of the target.
Returns the computed utility of all parent/decision tuple values.
Returns the computed utility of all parent/decision tuple values. For VE, these are not samples but the actual computed expected utility for all combinations of the parent and decision.
By default, implementations that inherit this trait have no debug information.
By default, implementations that inherit this trait have no debug information. Override this if you want a debugging option.
The algorithm to compute probability of specified evidence in a dependent universe.
The algorithm to compute probability of specified evidence in a dependent universe. We use () => Double to represent this algorithm instead of an instance of ProbEvidenceAlgorithm. Typical usage is to return the result of ProbEvidenceAlgorithm.computeProbEvidence when invoked.
A list of universes that depend on this universe such that evidence on those universes should be taken into account in this universe.
A list of universes that depend on this universe such that evidence on those universes should be taken into account in this universe.
Return an estimate of the marginal probability distribution over the target that lists each element with its probability.
Return an estimate of the marginal probability distribution over the target that lists each element with its probability. The result is a lazy stream. It is up to the algorithm how the stream is ordered. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.
Return an estimate of the expectation of the function under the marginal probability distribution of the target.
Return an estimate of the expectation of the function under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.
All implementation of variable elimination must specify what to do after variables have been eliminated.
All implementation of variable elimination must specify what to do after variables have been eliminated.
Create the factors for decision factors.
Create the factors for decision factors. Each factor is hardcoded as a tuple of (Double, Double), where the first value is the probability and the second is the utility.
Get the elements that are needed by the query target variables and the evidence variables.
Get the elements that are needed by the query target variables and the evidence variables. Also compute the values of those variables to the given depth. Only get factors for elements that are actually used by the target variables. This is more efficient. Also, it avoids problems when values of unused elements have not been computed.
In addition to getting all the needed elements, it determines if any of the conditioned, constrained, or dependent universe parent elements has * in its range. If any of these elements has * in its range, the lower and upper bounds of factors will be different, so we need to compute both. If they don't, we don't need to compute bounds.
Get the total utility and weight for a specific value of a parent and decision.
Get the total utility and weight for a specific value of a parent and decision.
Get the total utility and weight for all sampled values of the parent and decision.
Get the total utility and weight for all sampled values of the parent and decision.
Retrieve utility nodes in the model
Retrieve utility nodes in the model
Called when the algorithm is started before running any steps.
Called when the algorithm is started before running any steps. By default, does nothing. Can be overridden.
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.
Makes a utility factor an element designated as a utility.
Makes a utility factor an element designated as a utility. This is factor of a tuple (Double, Double) where the first value is 1.0 and the second is a possible utility of the element.
Return the mean of the probability density function for the given continuous element.
Return the mean of the probability density function for the given continuous element.
Return an element representing the posterior probability distribution of the given element.
Return an element representing the posterior probability distribution of the given element.
Return an estimate of the probability that the target produces the value.
Return an estimate of the probability that the target produces the value. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.
Return an estimate of the probability of the predicate under the marginal probability distribution of the target.
Return an estimate of the probability of the predicate under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.
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 the algorithm, performing its computation to completion.
Run the algorithm, performing its computation to completion.
Semiring for Decisions uses a sum-product-utility semiring.
Semiring for Decisions uses a sum-product-utility semiring.
Sets the policy for the given decision.
Sets the policy for the given decision. This will get the computed utility of the algorithm and call setPolicy on the decision. Note there is no error checking here, so the decision in the argument must match the target decision in the algorithm.
Flag indicating whether the run time of each step should be displayed.
Flag indicating whether the run time of each step should be displayed.
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.
Elements towards which queries are directed.
Elements towards which queries are directed. By default, these are the target elements. This is overridden by DecisionVariableElimination, where it also includes utility variables.
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.
The variable elimination eliminates all variables except on all decision nodes and their parents.
The variable elimination eliminates all variables except on all decision nodes and their parents. Thus the target elements is both the decision element and the parent element.
The universe on which this variable elimination algorithm should be applied.
The universe on which this variable elimination algorithm should be applied.
Return the variance of the probability density function for the given continuous element.
Return the variance of the probability density function for the given continuous element.
Decision VariableElimination algorithm that computes the expected utility of decision elements using the default elimination order.