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#! /usr/bin/env python
import openturns as ot
ot.TESTPREAMBLE()
class UniformNdPy(ot.PythonDistribution):
def __init__(self, a=[-1.0], b=[1.0]):
# https://github.com/uqfoundation/dill/issues/300
# super(UniformNdPy, self).__init__(len(a))
ot.PythonDistribution.__init__(self, len(a))
if len(a) != len(b):
raise ValueError("Invalid bounds")
for i in range(len(a)):
if a[i] > b[i]:
raise ValueError("Invalid bounds")
self.a = a
self.b = b
self.factor = 1.0
for i in range(len(a)):
self.factor *= b[i] - a[i]
def getRange(self):
return ot.Interval(self.a, self.b, [True] * len(self.a), [True] * len(self.a))
def getRealization(self):
X = []
for i in range(len(self.a)):
X.append(
self.a[i] + (self.b[i] - self.a[i]) * ot.RandomGenerator.Generate()
)
return X
def getSample(self, size):
X = []
for i in range(size):
X.append(self.getRealization())
return X
def computeCDF(self, X):
prod = 1.0
for i in range(len(self.a)):
if X[i] < self.a[i]:
return 0.0
prod *= min(self.b[i], X[i]) - self.a[i]
return prod / self.factor
def computePDF(self, X):
for i in range(len(self.a)):
if X[i] < self.a[i]:
return 0.0
if X[i] > self.b[i]:
return 0.0
return 1.0 / self.factor
def getMean(self):
mu = []
for i in range(len(self.a)):
mu.append(0.5 * (self.a[i] + self.b[i]))
return mu
d = ot.Distribution(UniformNdPy())
print(d.getKurtosis())
print(d.computePDF([0.5]))
print(d.computeCDF([0.5]))
# save
study = ot.Study()
study.setStorageManager(ot.XMLStorageManager("pyd.xml"))
study.add("d", d)
study.save()
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