coll.add(rm)
weights.add(-2.5)
coll.add(ot.Gamma(3.0, 4.0, -2.0))
weights.add(2.5)
distribution = ot.RandomMixture(coll, weights)
print("distribution=", repr(distribution))
print("distribution=", distribution)
mu = distribution.getMean()[0]
sigma = distribution.getStandardDeviation()[0]
for i in range(10):
x = mu + (-3.0 + 6.0 * i / 9.0) * sigma
print("pdf( %.6f )=%.6f" % (x, distribution.computePDF(x)))
# Tests of the projection mechanism
collFactories = [ot.UniformFactory(), ot.NormalFactory(
), ot.TriangularFactory(), ot.ExponentialFactory(), ot.GammaFactory()]
# , TrapezoidalFactory()
result, norms = distribution.project(collFactories)
print("projections=", result)
print("norms=", norms)
# ------------------------------ Multivariate tests ------------------------------#
# 2D RandomMixture
collection = [ot.Normal(0.0, 1.0)] * 3
weightMatrix = ot.Matrix(2, 3)
weightMatrix[0, 0] = 1.0
weightMatrix[0, 1] = -2.0
weightMatrix[0, 2] = 1.0
weightMatrix[1, 0] = 1.0
weightMatrix[1, 1] = 1.0
weightMatrix[1, 2] = -3.0
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
ot.RandomGenerator.SetSeed(0)
factory = ot.TriangularFactory()
ref = factory.build()
dimension = ref.getDimension()
if dimension <= 2:
sample = ref.getSample(50)
distribution = factory.build(sample)
if dimension == 1:
distribution.setDescription(['$t$'])
pdf_graph = distribution.drawPDF(256)
cloud = ot.Cloud(sample, ot.NumericalSample(sample.getSize(), 1))
cloud.setColor('blue')
cloud.setPointStyle('fcircle')
pdf_graph.add(cloud)
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
pdf_axis = fig.add_subplot(111)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)
else:
sample = ref.getSample(500)
distribution.setDescription(['$t_0$', '$t_1$'])
pdf_graph = distribution.drawPDF([256]*2)
cloud = ot.Cloud(sample)
cloud.setColor('red')
cloud.setPointStyle('fcircle')
pdf_graph.add(cloud)
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
from matplotlib import pylab as plt
ot.Log.Show(ot.Log.NONE)
# %%
# Create a sample from a continuous distribution
distribution = ot.Beta(2.0, 2.0, 0.0, 1.)
sample = distribution.getSample(1000)
graph = ot.UserDefined(sample).drawCDF()
view = viewer.View(graph)
# %%
# **1. Specify the model only**
# %%
# Create the list of distribution estimators
factories = [ot.BetaFactory(), ot.TriangularFactory()]
# %%
# Rank the continuous models by the Lilliefors p-values:
estimated_distribution, test_result = ot.FittingTest.BestModelLilliefors(
sample, factories)
test_result
# %%
# Rank the continuous models wrt the BIC criteria (no test result):
ot.FittingTest.BestModelBIC(sample, factories)
# %%
# Rank the continuous models wrt the AIC criteria (no test result)
ot.FittingTest.BestModelAIC(sample, factories)
