Discrete Uniform

class stats_arrays.DiscreteUniform

Bases: stats_arrays.distributions.base.UncertaintyBase

The discrete uniform distribution includes all integer values from the minimum up to, but excluding the maximum.

See https://en.wikipedia.org/wiki/Uniform_distribution_(discrete).

bounded_random_variables(params, size, seeded_random=None, maximum_iterations=50)

Generate random variables repeatedly until all varaibles are within the bounds of each distribution. Raise MaximumIterationsError if this takes more that maximum_iterations. Uses random_variables for random number generation.

Inputs

  • params : A Parameter array.
  • size : Integer. The number of values to draw from each distribution in params.
  • seeded_random : Integer. Optional. Random seed to get repeatable samples.
  • maximum_iterations : Integer. Optional. Maximum iterations to try to fit the given bounds before an error is raised.

Output

An array of random values, with dimensions params rows by size.

check_2d_inputs(params, vector)

Convert vector to 2 dimensions if not already, and raise stats_arrays.InvalidParamsError if vector and params dimensions don’t match.

check_bounds_reasonableness(params, *args, **kwargs)

Test if there is at least a threshold percent chance of generating random numbers within the provided bounds.

Doesn’t return anything. Raises stats_arrays.UnreasonableBoundsError if this condition is not met.

Inputs

  • params : A one-row Parameter array.
  • threshold : A percentage between 0 and 1. The minimum loc of the distribution covered by the bounds before an error is raised.
from_dicts(*dicts)

Construct a Heterogeneous parameter array from parameter dictionaries.

Dictionary keys are the normal parameter array columns. Each distribution defines which columns are required and which are optional.

Example:

>>> from stats_arrays import UncertaintyBase
>>> import numpy as np
>>> UncertaintyBase.from_dicts(
...     {'loc': 2, 'scale': 3, 'uncertainty_type': 3},
...     {'loc': 5, 'minimum': 3, 'maximum': 10, 'uncertainty_type': 5}
...     )
array([(2.0, 3.0, nan, nan, nan, False, 3),
       (5.0, nan, nan, 3.0, 10.0, False, 5)],
       dtype=[('loc', '<f8'), ('scale', '<f8'), ('shape', '<f8'),
              ('minimum', '<f8'), ('maximum', '<f8'), ('negative', '?'),
              ('uncertainty_type', 'u1')])
Args:
One of more dictionaries.
Returns:
A Heterogeneous parameter array
from_tuples(*data)

Construct a Heterogeneous parameter array from parameter tuples.

The order of the parameters is:

  1. loc
  2. scale
  3. shape
  4. minimum
  5. maximum
  6. negative
  7. uncertainty_type

Each input tuple must have a length of exactly 7. For more flexibility, use from_dicts.

Example:

>>> from stats_arrays import UncertaintyBase
>>> import numpy as np
>>> UncertaintyBase.from_tuples(
...     (2, 3, np.NaN, np.NaN, np.NaN, False, 3),
...     (5, np.NaN, np.NaN, 3, 10, False, 5)
...     )
array([(2.0, 3.0, nan, nan, nan, False, 3),
       (5.0, nan, nan, 3.0, 10.0, False, 5)],
       dtype=[('loc', '<f8'), ('scale', '<f8'), ('shape', '<f8'),
              ('minimum', '<f8'), ('maximum', '<f8'), ('negative', '?'),
              ('uncertainty_type', 'u1')])
Args:
One of more tuples of length 7.
Returns:
A Heterogeneous parameter array