# %%
# We create the distributions associated to the input random variables :
X1 = ot.Exponential(1.5)
X2 = ot.Normal(4.0, 1.0)
# %%
# We define an offset `a0` :
a0 = 2.0
# %%
# We create the `weights` :
weight = [5.0, 1.0]
# %%
# We create the affine combination :math:`Y` :
distribution = ot.RandomMixture([X1, X2], weight, a0)
print(distribution)
# %%
# We get its mean :
mean = distribution.getMean()[0]
print("Mean : %.3f" % mean)
# %%
# its variance :
variance = distribution.getCovariance()[0, 0]
print("Variance : %.3f" % variance)
# %%
# the 90% quantile :
quantile = distribution.computeQuantile(0.9)[0]
distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
elif ot.LogNormal().__class__.__name__ == 'KernelMixture':
kernel = ot.Uniform()
sample = ot.Normal().getSample(5)
bandwith = [1.0]
distribution = ot.KernelMixture(kernel, bandwith, sample)
elif ot.LogNormal().__class__.__name__ == 'MaximumDistribution':
coll = [ot.Uniform(2.5, 3.5), ot.LogUniform(1.0, 1.2), ot.Triangular(2.0, 3.0, 4.0)]
distribution = ot.MaximumDistribution(coll)
elif ot.LogNormal().__class__.__name__ == 'Multinomial':
distribution = ot.Multinomial(5, [0.2])
elif ot.LogNormal().__class__.__name__ == 'RandomMixture':
coll = [ot.Triangular(0.0, 1.0, 5.0), ot.Uniform(-2.0, 2.0)]
weights = [0.8, 0.2]
cst = 3.0
distribution = ot.RandomMixture(coll, weights, cst)
elif ot.LogNormal().__class__.__name__ == 'TruncatedDistribution':
distribution = ot.TruncatedDistribution(ot.Normal(2.0, 1.5), 1.0, 4.0)
elif ot.LogNormal().__class__.__name__ == 'UserDefined':
distribution = ot.UserDefined([[0.0], [1.0], [2.0]], [0.2, 0.7, 0.1])
elif ot.LogNormal().__class__.__name__ == 'ZipfMandelbrot':
distribution = ot.ZipfMandelbrot(10, 2.5, 0.3)
else:
distribution = ot.LogNormal()
dimension = distribution.getDimension()
title = str(distribution)[:100].split('\n')[0]
if dimension == 1:
distribution.setDescription(['$x$'])
pdf_graph = distribution.drawPDF()
cdf_graph = distribution.drawCDF()
fig = plt.figure(figsize=(10, 4))
# Create a collection of test-cases and the associated references
numberOfTests = 3
testCases = list()
references = list()
testCases.append([ot.Uniform(-1.0, 3.0)] * 2)
references.append(ot.Triangular(-2.0, 2.0, 6.0))
testCases.append([ot.Normal(), ot.Normal(1.0, 2.0), ot.Normal(-2.0, 2.0)])
references.append(ot.Normal(-1.0, 3.0))
testCases.append([ot.Exponential()] * 3)
references.append(ot.Gamma(3.0, 1.0, 0.0))
print("testCases=", testCases)
print("references=", references)
for testIndex in range(len(testCases)):
# Instanciate one distribution object
distribution = ot.RandomMixture(testCases[testIndex])
distribution.setBlockMin(5)
distribution.setBlockMax(20)
distributionReference = references[testIndex]
print("Distribution ", repr(distribution))
print("Distribution ", distribution)
# Is this distribution elliptical ?
print("Elliptical = ", distribution.isElliptical())
# Is this distribution continuous ?
print("Continuous = ", distribution.isContinuous())
# Test for realization of distribution
oneRealization = distribution.getRealization()
print("oneRealization=", oneRealization)
