Hi Julien, Thank you for your reply. However, shouldn't the quantiles of continuous unbounded distributions at p=0 and p=1 be undefined and return NaN (or -/+inf if we consider the limits)? What is the purpose of real-numbered boundary values in these cases? If this is a coding convenience, then why does ot.Normal().computeQuantile() not return quantiles that are symmetric about 0 for ot.SpecFunc.MinScalar and 1-ot.SpecFunc.Precision?
Phil From: [email protected] [mailto:[email protected]] On Behalf Of Julien Schueller | Phimeca Sent: Monday, September 04, 2017 9:37 AM To: [email protected] Subject: [External] Re: [ot-users] Nonsensical output from computeQuantile Maybe we don't want to throw, but I'm not sure. We never throw, but there are some other cases where the quantile function is not really continuous at the bounds, because in DistributionImplementation::computeQuantile(L2063) we filter according to the values defined in the range attribute: import openturns as ot import traceback factories = ot.DistributionFactory.GetUniVariateFactories() for factory in factories: dist = factory.build() try: q0 = dist.computeQuantile(0.0) qm1 = dist.computeQuantile(-1.0) if q0 != qm1: print(dist.getName(), '<0', q0, qm1) q1 = dist.computeQuantile(1.0) q0p = dist.computeQuantile(ot.SpecFunc.MinScalar) if q0 != q0p: print(dist.getName(), '0+', q0, q0p) except: print(' exc for', dist.getName()) traceback.print_exc() pass try: q1 = dist.computeQuantile(1.0) q2 = dist.computeQuantile(2.0) if q1 != q2: print(dist.getName(), '>1', q1, q2) q1m = dist.computeQuantile(1.0-ot.SpecFunc.ScalarEpsilon) if q1m != q1: print(dist.getName(),'1-', q1m, q1) except: print(' exc for', dist.getName()) traceback.print_exc() pass ________________________________ De : [email protected]<mailto:[email protected]> <[email protected]<mailto:[email protected]>> de la part de Julien Schueller | Phimeca <[email protected]<mailto:[email protected]>> Envoyé : lundi 4 septembre 2017 16:40:25 À : [email protected]<mailto:[email protected]> Objet : Re: [ot-users] Nonsensical output from computeQuantile Hello Phil, - Calling computeQuantile(x) with p outside of (0,1) does not make sense as x is a probability, and it should throw an exception, maybe we should fix that. - Otherwise the boundary values at x=0 and x=1 seem perfectly fine, (and should not be -/+inf) regarding the continuity of the fonction: In [32]: ot.Normal().computeQuantile(ot.SpecFunc.MinScalar) Out[32]: class=Point name=Unnamed dimension=1 values=[-37.5016] In [42]: ot.Normal().computeQuantile(1-ot.SpecFunc.Precision) Out[42]: class=Point name=Unnamed dimension=1 values=[8.12589] j ________________________________ De : [email protected]<mailto:[email protected]> <[email protected]<mailto:[email protected]>> de la part de Phil Fernandes <[email protected]<mailto:[email protected]>> Envoyé : mercredi 23 août 2017 22:37:37 À : [email protected]<mailto:[email protected]> Objet : [ot-users] Nonsensical output from computeQuantile Hello, I've noticed that the computeQuantile function, at least for continuous unbounded distributions, yields a nonsensical numerical result with inputs <=0 and >=1. For example, ot.Normal().computeQuantile(x) when x = 0 the output is -37.5194. If I enter x < 0, the output is -7.65063. At the moment I am handling this by subclassing the distribution and overriding the computeQuantile method with an if-else statement that returns -np.inf or np.inf if x = 0 or 1, but this is cumbersome. Would it be possible to incorporate this fix into the source code? Thank you, Phil
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