Package org.apache.tinkerpop.gremlin.algorithm.generator

Class NormalDistribution

java.lang.Object
org.apache.tinkerpop.gremlin.algorithm.generator.NormalDistribution
All Implemented Interfaces:
Distribution
public class NormalDistribution extends Object implements Distribution
Generates values according to a normal distribution with the configured standard deviation.
Author:
Matthias Broecheler (me@matthiasb.com)

  • Constructor Details

    • NormalDistribution

      public NormalDistribution(double stdDeviation)
      Constructs a NormalDistribution with the given standard deviation.

      Setting the standard deviation to 0 makes this a constant distribution.

      Parameters:
      stdDeviation - Simple deviation of the distribution. Must be non-negative.

  • Method Details

    • initialize

      public Distribution initialize(int invocations, int expectedTotal)
      Description copied from interface: Distribution
      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.

      Specified by:
      initialize in interface Distribution
      Returns:
      A new distribution configured to match the expected total for the number of invocations.

    • nextValue

      public int nextValue(Random random)
      Description copied from interface: Distribution
      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.

      Specified by:
      nextValue in interface Distribution
      Parameters:
      random - random generator to use for randomness
      Returns:
      next value

    • nextConditionalValue

      public int nextConditionalValue(Random random, int otherValue)
      Description copied from interface: Distribution
      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.

      Specified by:
      nextConditionalValue in interface Distribution
      Parameters:
      random - random generator to use for randomness
      otherValue - The prior value
      Returns:
      next value

    • toString

      public String toString()
      Overrides:
      toString in class Object