Package org.databene.benerator.sample

Provides Generators that are based on collections of samples.

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          Description

Class Summary
AbstractSampleGenerator<E> Generates values from a list of samples.
AttachedWeightSampleGenerator<E> Generates values from a weighted or non-weighted set of samples.
ConstantGenerator<E> Generator implementation that always returns the same value.
IndividualWeightSampleGenerator<E> Maps an IndividualWeight distribution to an AbstractWeightFunction and uses its capabilities for providing distribution features based on the IndividualWeight's characteristics.
NonNullSampleGenerator<E> Generates data from a base of non-null sample values.
OneShotGenerator<E> Returns a value only once and then becomes unavailable immediately.
SampleGenerator<E> Generates values from a non-weighted list of samples, applying an explicitly defined distribution.
SampleGeneratorUtil Provides utility methods for sample-based generators.
SeedGenerator<E> Generates value sequences derived from seed sequences.
SeedManager<E> Counts frequencies of atoms and provides random atoms with the same frequency.
SequencedCSVSampleGenerator<E> Sample Generator for values that are read from a CSV file.
SequenceGenerator<E> Creates a predefined sequence of objects.
StateGenerator<E> Generates states as configured by a state machine.
StateTransitionGenerator<E> Generates state transitions of a state machine.
WeightedCSVSampleGenerator<E> Sample Generator for values that are read from a CSV file.
WeigthedLiteralGenerator<E> Generates values defined by a weighted or non-weighted value list literal, like "'A'^3,'B'^2", supporting weighted random generation and uniqueness.
 

Package org.databene.benerator.sample Description

Provides Generators that are based on collections of samples. Samples may have individual weights.



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