Bernoulli¶
-
class
stats_arrays.
BernoulliUncertainty
¶ 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 raisestats_arrays.InvalidParamsError
ifvector
andparams
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:
loc
scale
shape
minimum
maximum
negative
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
pdf
(params, *args, **kwargs)¶ Provide a standard interface to calculate the probability distribution function of a uncertainty distribution. Default is cls.default_number_points_in_pdf points between min to max range if bounds are present, or cls.standard_deviations_in_default_range standard distributions.
Inputs
- params : A one-row Parameter array.
- xs : Optional. A one-dimensional numpy array of input values.
Output
Important
The output format for PDF is different than CDF or PPF.
A tuple of a vactor x values and a vector of y values. Y values are a one-dimensional array of probability densities, bounded on (0,1), with length xs, if provided, or cls.default_number_points_in_pdf.
-
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.
-
classmethod
statistics
(params, *args, **kwargs)¶ Build a dictionary of mean, mode, median, and 95% confidence interval upper and lower values.
Inputs
- params : A one-row Parameter array.
Output
{‘mean’: mean value, ‘mode’: mode value, ‘median’: median value, ‘upper’: upper limit value, ‘lower’: lower limit value}. All values should be floats (not single-element arrays). Parameters that are not defined should be returned None, not omitted.
-
classmethod