random: Creates uniformly distributed random values
cumulated: Creates random values with a bell-shape probability distribution
step: (Parameter: increment)
Depending on the settings of 'increment', it starts with the min or max value of the
specified range. With each further invocation, the 'increment' value is added. If
addition makes the current value exceed the specified number range, the Sequence
becomes unavailable. So the numbers provided are unique. Example: increment =
-2, range=1..7: 7, 5, 3, 1
randomWalk: (Parameters: minimum step, maximum step)
Depending on the settings of minimum step and maximum step, it starts with the min,
max or medium value of the specified range. With each further invocation, a random
value is added, which is between minimum step and maximum step.
shuffle: (Parameter: increment)
Covers a large number range avoiding duplicate values. It starts from an offset of
0 and iterates the number range with a fix increment. After the range is covered, it
increases the offset by one and reiterates the range. When the offset reaches the
same value as the increment, it is set back to 0 again. For an increment of 3 in a
range 1..7, the generated numbers would be 1, 4, 7, 2, 5, 3, 6, 1, 4, ...
wedge: Starting with first the lowest, then the highest available number, this alternatively
provides increasing small numbers and decreasing large numbers until they
converge in the middle and the Sequence becomes unavailable. So generation is
unique. For a number range 1..7, the generated numbers would be: 1, 7, 2, 6, 3, 5, 4.
bitreverse: Creates numbers by continually increasing an internal counter and providing its value
in bit-reversed order. This stops when each available number has been generated
once, thus providing unique number generation.

WeightFunctions

ConstantFunction

Returns a constant value, independent of the argument

Full class name: org.databene.benerator.primitive.number.distribution.ConstantFunction

GaussianFunction

This implements the well-known Gaussian Function.

Full class name: org.databene.benerator.primitive.number.distribution.GaussianFunction

Parameters: average [, deviation]

Example:

<attribute name="price" type="big_decimal"  min="0.1" max="99.90" precision="0.1" 
    distribution="org.databene.benerator.primitive.number.distribution.GaussianFunction(50,20)"/>

ExponentialFunction

The Exponential Function.

Full class name: org.databene.benerator.primitive.number.distribution.ExponentialFunction

Parameters: [scale,] frequency

Example:

<attribute name="category" type="char" values="A,B,C" 
    distribution="org.databene.benerator.primitive.number.distribution.ExponentialFunction(0.5)"/>

DiscreteFunction

Discrete Function that specifies an explicit weight for each possible value

Full class name: org.databene.benerator.primitive.number.distribution.DiscreteFunction

Parameters: weight1 [, weight2 [, weight3 ...]]

Example:

<attribute name="rating" type="int" min="1", max="3" 
    distribution="org.databene.benerator.primitive.number.distribution.DiscreteFunction(1,2,1)"/>