Package | Description |
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org.apache.mahout.classifier.sgd |
Implements a variety of on-line logistric regression classifiers using SGD-based algorithms.
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org.apache.mahout.ep |
Provides basic evolutionary optimization using recorded-step
mutation.
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Modifier and Type | Method and Description |
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State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> |
AdaptiveLogisticRegression.getBest() |
State<AdaptiveLogisticRegression.Wrapper,CrossFoldLearner> |
AdaptiveLogisticRegression.getSeed() |
Modifier and Type | Method and Description |
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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) |
Modifier and Type | Method and Description |
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State<T,U> |
State.copy()
Deep copies a state, useful in mutation.
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State<T,U> |
State.mutate()
Clones this state with a random change in position.
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State<T,U> |
EvolutionaryProcess.parallelDo(EvolutionaryProcess.Function<Payload<U>> fn)
Execute an operation on all of the members of the population with many threads.
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Modifier and Type | Method and Description |
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List<State<T,U>> |
EvolutionaryProcess.getPopulation() |
Modifier and Type | Method and Description |
---|---|
void |
EvolutionaryProcess.add(State<T,U> value) |
int |
State.compareTo(State<T,U> other)
Natural order is to sort in descending order of score.
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Constructor and Description |
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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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