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Interface Summary | |
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InstanceHandler | Whether the generator can handle instances directly for setting the parameters. |
Mean | A interface indicating that a class expects the mean and standard deviation to be set. |
NominalAttributeGenerator | Used to indicate this generator can be used to generate artificial instances for nominal attributes. |
NumericAttributeGenerator | Used to indicate this generator can be used to generate artificial instances for numeric attributes. |
Ranged | An interface indicating that this generator expect to be given a range of values to operate within. |
Class Summary | |
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DiscreteGenerator | An artificial data generator that uses discrete buckets for values. In this discrete generator, values are ranked according to how often they appear. |
DiscreteUniformGenerator | An artificial data generator that uses discrete buckets for values. In this discrete uniform generator, all buckets are given the same probability, regardless of how many values fall into each bucket. |
EMGenerator | A generator that uses EM as an underlying model. |
GaussianGenerator | An artificial data generator that uses a single Gaussian distribution. If a mixture of Gaussians is required, use the EM Generator. |
Generator | An artificial data generator. |
MixedGaussianGenerator | A mixed Gaussian artificial data generator. This generator only has two Gaussians, each sitting 3 standard deviations (by default) away from the mean of the main distribution. |
NominalGenerator | A generator for nominal attributes. Generates artificial data for nominal attributes. |
RandomizableDistributionGenerator | An abstract superclass for randomizable generators that make use of mean and standard deviation. |
RandomizableGenerator | An abstract superclass for generators that use a seeded internal random number generator. |
RandomizableRangedGenerator | Abstract superclass for generators that take ranges and use a seeded random number generator internally |
UniformDataGenerator | A uniform artificial data generator. This generator uses a uniform data model - all values have the same probability, and generated values must fall within the range given to the generator. |
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