## Uses of Class
org.apache.mahout.ep.State

Packages that use State
org.apache.mahout.classifier.sgd: Implements a variety of on-line logistric regression classifiers using SGD-based algorithms.
org.apache.mahout.ep: Provides basic evolutionary optimization using recorded-step mutation.

Uses of State in org.apache.mahout.classifier.sgd

Methods in org.apache.mahout.classifier.sgd that return State
`State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner>` `AdaptiveLogisticRegression.getBest()`
`State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner>` `AdaptiveLogisticRegression.getSeed()`

Methods in org.apache.mahout.classifier.sgd with parameters of type State
`static void` `AdaptiveLogisticRegression.Wrapper.freeze(State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> s)`
`void` `AdaptiveLogisticRegression.setBest(State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> best)`
`static void` `AdaptiveLogisticRegression.Wrapper.setMappings(State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> x)`
`void` `AdaptiveLogisticRegression.setSeed(State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> seed)`

Uses of State in org.apache.mahout.ep

Methods in org.apache.mahout.ep that return State
`State<T,U>` `State.copy()` Deep copies a state, useful in mutation.
`State<T,U>` `State.mutate()` Clones this state with a random change in position.
`State<T,U>` `EvolutionaryProcess.parallelDo(EvolutionaryProcess.Function<Payload<U>> fn)` Execute an operation on all of the members of the population with many threads.

Methods in org.apache.mahout.ep that return types with arguments of type State
`List<State<T,U>>` `EvolutionaryProcess.getPopulation()`

Methods in org.apache.mahout.ep with parameters of type State
`void` `EvolutionaryProcess.add(State<T,U> value)`
`int` `State.compareTo(State<T,U> other)` Natural order is to sort in descending order of score.

Constructors in org.apache.mahout.ep with parameters of type State
`EvolutionaryProcess(int threadCount, int populationSize, State<T,U> seed)` Creates an evolutionary optimization framework with specified threadiness, population size and initial state.

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