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refactor kumaraswamy #4706

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merged 6 commits into from
May 17, 2021
Merged

refactor kumaraswamy #4706

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a904fb2
refactor kumaraswamy
farhanreynaldo May 17, 2021
0b29957
change dtype to floatX
farhanreynaldo May 17, 2021
84a5ff7
create logcdf function
farhanreynaldo May 17, 2021
dd5e6e6
constraint on Kumaraswamy logcdf
farhanreynaldo May 17, 2021
4978b71
update release notes on Kumaraswamy logcdf method
farhanreynaldo May 17, 2021
368ebd8
Add more numerically stable cdf expression
ricardoV94 May 17, 2021
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18 changes: 18 additions & 0 deletions pymc3/distributions/continuous.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1359,6 +1359,24 @@ def logp(value, a, b):

return bound(logp, value >= 0, value <= 1, a > 0, b > 0)

def logcdf(value, a, b):
r"""
Compute the log of cumulative distribution function for the Kumaraswamy distribution
at the specified value.

Parameters
----------
value: numeric or np.ndarray or aesara.tensor
Value(s) for which log CDF is calculated. If the log CDF for
multiple values are desired the values must be provided in a numpy
array or Aesara tensor.

Returns
-------
TensorVariable
"""
return bound(at.log1p(-((1 - value ** a) ** b)), value >= 0, value <= 1)
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@ricardoV94 ricardoV94 May 17, 2021 •
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Value above one should give a logcdf of 0, so we need a switch: at.switch(value < 1, normal expression, 0), and to remove value <= 1 as a bound condition. We should still keep the a > 0 and b > 0 conditions as in the logp

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does tt equal to at? Because I assume tt is Theano tensor and it is not available.

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@ricardoV94 ricardoV94 May 17, 2021 •
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Yes of course. It was a mistake, I am still used to writing with theano's abbreviation :p

Fixed the comment



class Exponential(PositiveContinuous):
r"""
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4 changes: 4 additions & 0 deletions pymc3/tests/test_distributions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1117,7 +1117,11 @@ def scipy_log_pdf(value, a, b):
np.log(a) + np.log(b) + (a - 1) * np.log(value) + (b - 1) * np.log(1 - value ** a)
)

def scipy_log_cdf(value, a, b):
return np.log1p(-((1 - value ** a) ** b))

self.check_logp(Kumaraswamy, Unit, {"a": Rplus, "b": Rplus}, scipy_log_pdf)
self.check_logcdf(Kumaraswamy, Unit, {"a": Rplus, "b": Rplus}, scipy_log_cdf)

def test_exponential(self):
self.check_logp(
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