org.apache.tinkerpop.gremlin.algorithm.generator

Interface Distribution

All Known Implementing Classes:

CopyDistribution, NormalDistribution, PowerLawDistribution

public interface Distribution

Interface for a distribution over discrete values.

Used, for instance, by DistributionGenerator to define the in- and out-degree distributions and by CommunityGenerator to define the community size distribution.

Author:

Matthias Broecheler (me@matthiasb.com)

Method Summary

Modifier and Type	Method and Description
Distribution	initialize(int invocations, int expectedTotal) Initializes the distribution such that expectedTotal is equal to the expected sum of generated values after the given number of invocatiosn.
int	nextConditionalValue(Random random, int otherValue) Returns the next value conditional on another given value.
int	nextValue(Random random) Returns the next value.

Method Detail

initialize

Distribution initialize(int invocations,
                        int expectedTotal)

Initializes the distribution such that expectedTotal is equal to the expected sum of generated values after the given number of invocatiosn.

Since most distributions have an element of randomness, these values are the expected values.

Returns:

A new distribution configured to match the expected total for the number of invocations.

nextValue

int nextValue(Random random)

Returns the next value. If this value is randomly generated, the randomness must be drawn from the provided random generator.

DO NOT use your own internal random generator as this makes the generated values non-reproducible and leads to faulty behavior.

Parameters:

random - random generator to use for randomness

Returns:

next value

nextConditionalValue

int nextConditionalValue(Random random,
                         int otherValue)

Returns the next value conditional on another given value.

This can be used, for instance, to define conditional degree distributions where the in-degree is conditional on the out-degree.

If this value is randomly generated, the randomness must be drawn from the provided random generator. DO NOT use your own internal random generator as this makes the generated values non-reproducible and leads to faulty behavior.

Parameters:

random - random generator to use for randomness

otherValue - The prior value

Returns:

next value