Triangular

class stats_arrays.TriangularUncertainty

Bases: stats_arrays.distributions.base.BoundedUncertaintyBase

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

No bounds checking because the bounds do not exclude any of the distribution.

classmethod 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.

classmethod check_bounds_reasonableness(params, *args, **kwargs)

Always true because the bounds do not exclude any of the distribution.

classmethod 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

classmethod 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

classmethod rescale(params)

Rescale params to a (0,1) interval. Return adjusted_means and scale. Needed because SciPy assumes a (0,1) interval for many distributions.