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Packages that use State | |
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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 |
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Methods in org.apache.mahout.classifier.sgd that return State | |
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State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> |
AdaptiveLogisticRegression.getBest()
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State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> |
AdaptiveLogisticRegression.getSeed()
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Methods in org.apache.mahout.classifier.sgd with parameters of type State | |
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static void |
AdaptiveLogisticRegression.Wrapper.freeze(State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> s)
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void |
AdaptiveLogisticRegression.setBest(State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> best)
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static void |
AdaptiveLogisticRegression.Wrapper.setMappings(State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> x)
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void |
AdaptiveLogisticRegression.setSeed(State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> seed)
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Uses of State in org.apache.mahout.ep |
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Methods in org.apache.mahout.ep that return State | |
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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 | |
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List<State<T,U>> |
EvolutionaryProcess.getPopulation()
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Methods in org.apache.mahout.ep with parameters of type State | |
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void |
EvolutionaryProcess.add(State<T,U> value)
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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 | |
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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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