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fix: determine points for mean computation based on inputs #38

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merged 2 commits into from
Oct 9, 2023
Merged

fix: determine points for mean computation based on inputs #38

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954a983
fix: determine points for mean computation based on inputs
Solosneros Oct 6, 2023
c36388b
style: create upper log space simpler
Solosneros Oct 9, 2023
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6 changes: 5 additions & 1 deletion prv_accountant/privacy_random_variables/abstract_privacy_random_variable.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,11 @@ def __init__(self, prv, t_min: float, t_max: float) -> None:
self.remaining_mass = self.prv.cdf(t_max) - self.prv.cdf(t_min)

def mean(self) -> float:
points = [self.t_min, -1e-1, -1e-2, -1e-3, -1e-4, -1e-5, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, self.t_max]
# determine points based on t_min and t_max
lower_exponent = int(np.log10(np.abs(self.t_min)))
upper_exponent = int(np.log10(self.t_max))
points = np.concatenate([[self.t_min], -np.logspace(start=lower_exponent, stop=-5, num=10), [0],
np.logspace(start=upper_exponent, stop=-5, num=10)[::-1], [self.t_max]])
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Swapping start and stop should be equivalent to [::-1] and is a bit simpler. Is there an edge case where they are not equivalent?

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Yes, you are right. Thanks!

I fixed it with another commit.

m = 0.0
for L, R in zip(points[:-1], points[1:]):
I, err = integrate.quad(self.cdf, L, R, limit=500)
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