"""Truncated exponential distribution."""
import numpy
from scipy import special
from ..baseclass import SimpleDistribution, ShiftScaleDistribution
class truncexpon(SimpleDistribution):
"""Truncated exponential distribution."""
def __init__(self, b):
super(truncexpon, self).__init__(dict(b=b))
def _pdf(self, x, b):
return numpy.exp(-x) / (1 - numpy.exp(-b))
def _cdf(self, x, b):
return (1.0 - numpy.exp(-x)) / (1 - numpy.exp(-b))
def _ppf(self, q, b):
return -numpy.log(1 - q + q * numpy.exp(-b))
def _lower(self, b):
return 0.0
def _upper(self, b):
return b
[docs]class TruncExponential(ShiftScaleDistribution):
"""
Truncated exponential distribution.
Args:
upper (float, Distribution):
Location of upper threshold
scale (float, Distribution):
Scaling parameter in the exponential distribution
shift (float, Distribution):
Location parameter
Examples:
>>> distribution = chaospy.TruncExponential(1.5)
>>> distribution
TruncExponential(1.5)
>>> uloc = numpy.linspace(0, 1, 6)
>>> uloc
array([0. , 0.2, 0.4, 0.6, 0.8, 1. ])
>>> xloc = distribution.inv(uloc)
>>> xloc.round(3)
array([0. , 0.169, 0.372, 0.628, 0.972, 1.5 ])
>>> numpy.allclose(distribution.fwd(xloc), uloc)
True
>>> distribution.pdf(xloc).round(3)
array([1.287, 1.087, 0.887, 0.687, 0.487, 0.287])
>>> distribution.sample(4).round(3)
array([0.709, 0.094, 1.34 , 0.469])
"""
[docs] def __init__(self, upper=1, scale=1, shift=0):
super(TruncExponential, self).__init__(
dist=truncexpon((upper - shift) * 1.0 / scale),
scale=scale,
shift=shift,
repr_args=[upper],
)