Thank you Régis and Julien!
Unfortunately the code with the bug is proprietary and I am unable to share it.
However, I was able to generate the distribution using Julien's code for the
SciPyDistribution class, except that I had to modify computeQuantile() to
accept two arguments, as in the PythonDistribution example, i.e.,
computeQuantile(p, tail=False).
Unfortunately FORM still fails to run. When I try to run
algo = ot.FORM(optimizer, event, dists.getMean())
it throws the error
File "C:\Anaconda3\lib\site-packages\openturns\analytical.py", line 1034, in
__init__
this = _analytical.new_FORM(*args)
RuntimeError: NotYetImplementedException : In
DistributionImplementation::getParameter()
I tried adding a getParameter() method to SciPyDistribution as
def getParameter(self):
params = self._dist.args
return [params]
but this doesn't work either. Do you have any suggestions?
Thank you,
Phil
From: regis lebrun [mailto:[email protected]]
Sent: Saturday, November 11, 2017 2:22 AM
To: [email protected]; Phil Fernandes
Subject: Re: [ot-users] [External] Re: Custom distribution in FORM
Is it possible to have a script reproducing the bug? As the distribution
algebra involves quite different numerical algorithms it is a complex task to
check everything. Any help is GREATLY appreciated on this part of the code.
Many thanks
Régis
Le samedi 11 novembre 2017 à 00:31:02 UTC+1, Phil Fernandes
<[email protected]<mailto:[email protected]>> a écrit :
I tried implementing via distribution algebra, but for some reason my program
just hangs, so I decided to try implementing the distribution as a subclass of
PythonDistribution as per the example
https://github.com/openturns/openturns/blob/master/python/test/t_Distribution_python.py.
<https://github.com/openturns/openturns/blob/master/python/test/t_Distribution_python.py.%20>
Unfortunately there seem to be inconsistencies in the argument types that are
accepted by the methods of Distribution objects and the example
PythonDistribution. What are the required object types for the outputs of
computeXXX(), e.g., computeQuantile() in order for the distribution to work in
FORM? Would it suffice to output a list?
For example
a = ot.Normal()
x = np.linspace(0,1,5)[:,None]
a.computePDF(x)
returns
class=Sample name=Unnamed implementation=class=SampleImplementation
name=Unnamed size=5 dimension=1
data=[[0.398942],[0.386668],[0.352065],[0.301137],[0.241971]]
However
b=UniformNdPy()
b.computePDF(x)
returns 1.0.
Thank you.
-----Original Message-----
From: regis lebrun
[mailto:[email protected]<mailto:[email protected]>]
Sent: Friday, November 10, 2017 12:12 PM
To: [email protected]<mailto:[email protected]>; Phil Fernandes
Subject: [External] Re: [ot-users] Custom distribution in FORM
Hi,
You can easily implement this distribution using OpenTURNS unique feature
regarding distribution algebra (see
https://en.wikipedia.org/wiki/Johnson%27s_SU-distribution):
import openturns as ot
lamb = 1.5
xi = 1.1
delta = 2.0
gamma = 1.0
distJU = ((ot.Normal() - gamma) / delta).sinh() * lamb + xi print("distJU=",
distJU)
ot.Show(distJU.drawPDF())
You will get:
distJU= RandomMixture(1.1 + 1.5 *
CompositeDistribution=f(RandomMixture(Normal(mu = -0.5, sigma = 0.5))) with
f=[x]->[sinh(x)])
and a graph similar to the one given on the wikipedia page.
Your script contains some bugs (computeQuantile, getMean, getStandardDeviation
should return the result as a float sequence of size 1) and a missing method,
namely getRange(). To get a running distribution you must implement getRange()
and computeCDF(), all the other methods have a generic implementation, but
these generic algorithms may be slow or inaccurate in difficult situations, so
the more methods you provide the most efficient your distribution is.
The online documentation
(http://secure-web.cisco.com/1_QlR51NTnci244KawP-NQBpuYV0mfhSTp4JXBwTpGJAqWfkDBxUY1JGCpr_XGFfQIZiEjVXqGphin3yoL6fV0Ro5q1JtmYip6xNs_iSbp14Iqwp3nL4GrrNfOhdGLYYpkwqSHmNCB0HmQ9TV4e4AAXGci3esSntXt8UjGeLuSqsWVIAgSfMQHC-yrAm6_JuU7HLDfmDuKjh0tAEtKH4Exm1PXrpvRwVwODuNBTOUej_7Q49C1pP-1sswRmGgOGm3NLSy3q3ZTJfNSogVMSuyZ4wcTzp0YH2CW-VW6edm4x21iG0omiNXB3pYDqQmrnNqz_uUj9AvsTr72Dh6iW8jGw/http%3A%2F%2Fopenturns.github.io%2Fopenturns%2Fmaster%2Fuser_manual%2F_generated%2Fopenturns.PythonDistribution.html%3Fhighlight%3Dpythondistribution)
is very poor and will be updated. You can have a look at
https://github.com/openturns/openturns/blob/master/python/test/t_Distribution_python.py
<https://github.com/openturns/openturns/blob/master/python/test/t_Distribution_python.py%20>
for an example of a custom Python distribution.
You could also have used the SciPyDistribution wrapper of scipy distributions
(see
http://secure-web.cisco.com/1B_pKGvPkYtC86WGBDSnrF--4exrvoUqVoGHUYGIgi2rsg8OcFilo_WAXcF13h2kI6KqhmosBqsMMIjA_MZ9-V-t5GJ0wnk2hxZCqcogsNY4Q-1P7-gw4Jaj5Q1Y4l_n0WzDuI9V0YpSEvzmwpq65VeVL9VfSWO3Ec7QcmzW3i2NaK5oYsp4X5rrLp03MjlZiJscbJaKKstp9LrMGohAul3vBWC30HVxArmix5guZPggdaAavQcloR4ZVn0HF0ZGPR1LI6wJzM4LA4ZLfRh_EnO3yzEybS_kpyPYtyPUHVZY4xpUX8ojw-cDz54hEAWAx3a4pqsF5QN57pa9gdsppCw/http%3A%2F%2Fopenturns.github.io%2Fopenturns%2Fmaster%2Fuser_manual%2F_generated%2Fopenturns.SciPyDistribution.html
and
https://github.com/openturns/openturns/blob/master/python/test/t_Distribution_scipy.py<http://secure-web.cisco.com/1B_pKGvPkYtC86WGBDSnrF--4exrvoUqVoGHUYGIgi2rsg8OcFilo_WAXcF13h2kI6KqhmosBqsMMIjA_MZ9-V-t5GJ0wnk2hxZCqcogsNY4Q-1P7-gw4Jaj5Q1Y4l_n0WzDuI9V0YpSEvzmwpq65VeVL9VfSWO3Ec7QcmzW3i2NaK5oYsp4X5rrLp03MjlZiJscbJaKKstp9LrMGohAul3vBWC30HVxArmix5guZPggdaAavQcloR4ZVn0HF0ZGPR1LI6wJzM4LA4ZLfRh_EnO3yzEybS_kpyPYtyPUHVZY4xpUX8ojw-cDz54hEAWAx3a4pqsF5QN57pa9gdsppCw/http%3A%2F%2Fopenturns.github.io%2Fopenturns%2Fmaster%2Fuser_manual%2F_generated%2Fopenturns.SciPyDistribution.html
and
https:/github.com/openturns/openturns/blob/master/python/test/t_Distribution_scipy.py>):
import openturns as ot
import scipy.stats as st
lamb = 1.5
xi = 1.1
delta = 2.0
gamma = 1.0
distJU = ot.Distribution(ot.ScyPyDistribution(st.johnsonsu(gamma, delta,
loc=xi, scale=lamb)))
but unfortunately this wrapper has a bug for unbounded distributions, resulting
in a wrong range and a boggus computeQuantile() method.
Thanks for the question, it raised a lot of problems in OT!
Best regards,
Régis
Le vendredi 10 novembre 2017 à 18:42:19 UTC+1, Phil Fernandes
<[email protected]<mailto:[email protected]>> a écrit :
Hello,
I am attempting to use a custom continuous probability distribution for a
probability of failure calculation via FORM, but when I try to create a
ComposedDistribution with my custom dist, the program fails with
NotImplementedError: Wrong number or type of arguments for overloaded function
'new_ComposedDistribution'.
The custom dist is defined as
class JohnsonSU(ot.PythonDistribution):
def __init__(self, gamma=1, xi=0, delta=0.5, lam=1):
super(JohnsonSU, self).__init__(1)
if np.any(delta <= 0):
raise ValueError('Delta must be >0.')
if np.any(lam <= 0):
raise ValueError('Lambda must be >0.')
self.gamma = gamma # shape 1
self.xi = xi # location
self.delta = delta # shape 2, >0
self.lam = lam # scale, >0
self.scipy_dist = st.johnsonsu(self.gamma, self.delta, loc=self.xi,
scale=self.lam)
def computeCDF(self, x):
return self.scipy_dist.cdf(x)
def computePDF(self, x):
return self.scipy_dist.pdf(x)
def computeQuantile(self, p):
return self.scipy_dist.ppf(p)
def getMean(self):
return self.scipy_dist.mean()
def getStandardDeviation(self):
return self.scipy_dist.std()
Are there additional functions that must be defined in order for the
PythonDistribution to be compatible with existing OpenTurns Distributions? Or,
is there a straightforward way that I could add arbitrary distributions to the
OpenTurns source code?
Many thanks!
Phil Fernandes P.Eng, MASc
Engineer, Reliability Assessment
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TEL: 780-420-8210 | FAX: 780-420-5234
7045 Enbridge Centre, 10175 101 Street NW, Edmonton, AB, T5J 0H3
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