learning

Type Members

1. abstract class EMTerminationCriteria extends AnyRef

   

   Termination criteria for EM algorithms.

   Termination criteria for EM algorithms. A termination criteria can be passed as an argument to the EM apply method.

2. abstract class ExpectationMaximization extends Algorithm with ParameterLearner

   

   Base class of Expectation Maximization algorithms.

   Base class of Expectation Maximization algorithms. This class also implements the outer EM loop and checks against termination criteria.

3. class ExpectationMaximizationWithFactors extends ExpectationMaximization

   

   Expectation maximization iteratively produces an estimate of sufficient statistics for learnable parameters, then maximizes the parameters according to the estimate.

   Expectation maximization iteratively produces an estimate of sufficient statistics for learnable parameters, then maximizes the parameters according to the estimate. It uses an factored inference algorithm, SufficientStatisticsVariableElimination, to produce the estimate of the sufficient statistics. This class can be extended with a different expectation or maximization algorithm; see the code for details.

4. class GeneralizedEM extends ExpectationMaximization

   

5. class MaxIterations extends EMTerminationCriteria

   

   Terminate when the maximum number of iterations has been reached

6. class SufficientStatisticsFactor extends AnyRef

   

   Methods for creating probabilistic factors associated with elements and their sufficient statistics.

7. class SufficientStatisticsMagnitudes extends EMTerminationCriteria

   

   Terminate when the magnitude of sufficient statistics does not exhibit a change greater than the specified tolerance.