bucky.util.distributions

Provide probability distributions used by the model that aren’t in numpy/cupy.

Module Contents

Functions

approx_betaincinv(alp1, alp2, u)	Approximate betaincinv using Kumaraswamy after converting the params s.t. means and modes are equal.
approx_mPERT(mu, a=0.0, b=1.0, gamma=4.0)	Approximate sample from an mPERT distribution that uses a Kumaraswamy distrib in place of the incbeta.
generic_distribution(base_func, params: dict, interp: partial, clip: partial)	Return value sampled from basic distribution, with additional interpolation and clipping.
kumaraswamy_invcdf(a, b, u)	Inverse CDF of the Kumaraswamy distribution.
mPERT(mu, a=0.0, b=1.0, gamma=4.0, var=None)	Provide a vectorized Modified PERT distribution.
truncnorm(loc=0.0, scale=1.0, size=None, a_min=None, a_max=None)	Provide a vectorized truncnorm implementation that is compatible with cupy.
truncnorm_from_CI(CI, size=1, a_min=None, a_max=None)	Truncnorm implementation that first derives mean and standard deviation from a 95% confidence interval.

bucky.util.distributions.approx_betaincinv(alp1, alp2, u)

Approximate betaincinv using Kumaraswamy after converting the params s.t. means and modes are equal.

bucky.util.distributions.approx_mPERT(mu, a=0.0, b=1.0, gamma=4.0)

Approximate sample from an mPERT distribution that uses a Kumaraswamy distrib in place of the incbeta.

Supports Cupy.

bucky.util.distributions.generic_distribution(base_func, params: dict, interp: partial, clip: partial)

Return value sampled from basic distribution, with additional interpolation and clipping.

bucky.util.distributions.kumaraswamy_invcdf(a, b, u)

Inverse CDF of the Kumaraswamy distribution.

bucky.util.distributions.mPERT(mu, a=0.0, b=1.0, gamma=4.0, var=None)

Provide a vectorized Modified PERT distribution.

Parameters

• mu (float or numpy.ndarray or cupy.ndarray if using CuPy) – Mean value for the PERT distribution.

• a (float or numpy.ndarray or cupy.ndarray if using CuPy) – Lower bound for the distribution.

• b (float or numpy.ndarray or cupy.ndarray if using CuPy) – Upper bound for the distribution.

• gamma (float or numpy.ndarray or cupy.ndarray if using CuPy) – Shape paramter.

• var (float, numpy.ndarray or cupy.ndarray if using CuPy or None) – Variance of the distribution. If var != None, gamma will be calcuated to meet the desired variance.

Returns

out – Samples drawn from the specified mPERT distribution. Shape is the broadcasted shape of the the input parameters.

Return type

float or numpy.ndarray or cupy.ndarray if using CuPy

bucky.util.distributions.truncnorm(loc=0.0, scale=1.0, size=None, a_min=None, a_max=None)

Provide a vectorized truncnorm implementation that is compatible with cupy.

The output is calculated by using the numpy/cupy random.normal() and truncted via rejection sampling. The interface is intended to mirror the scipy implementation of truncnorm.

bucky.util.distributions.truncnorm_from_CI(CI, size=1, a_min=None, a_max=None)

Truncnorm implementation that first derives mean and standard deviation from a 95% confidence interval.

bucky.util.cached_prop bucky.util.extrapolate