#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
ot.PlatformInfo.SetNumericalPrecision(3)
distribution = ot.Beta(2.3, 2.2, -1.0, 1.0)
print('distribution=', distribution)
sample = distribution.getSample(1000)
factory = ot.MaximumLikelihoodFactory(ot.Beta())
inf_distribution = factory.build(sample)
print('estimated distribution=', inf_distribution)
# set (a,b) out of (r, t, a, b)
factory.setKnownParameter([-1.0, 1.0], [2, 3])
inf_distribution = factory.build(sample)
print('estimated distribution with bounds=', inf_distribution)
factory = ot.MaximumLikelihoodFactory(ot.Exponential())
factory.setKnownParameter([0.1], [1])
print(factory.build())
print(factory.build([3, 0]))
bounds = ot.Interval(lowerBound, upperBound, finiteLowerBound,
finiteUpperBound)
# %%
# Create the starting point of the research:
#
# - for :math:`\mu` : the first point,
# - for :math:`\sigma` : a value evaluated from the two first points.
mu0 = sample[0][0]
sigma0 = m.sqrt((sample[1][0] - sample[0][0]) * (sample[1][0] - sample[0][0]))
startingPoint = [mu0, sigma0]
# %%
# Create the estimator from a parametric PDF.
pdf = ot.Normal()
factory = ot.MaximumLikelihoodFactory(pdf)
factory.setOptimizationBounds(bounds)
# %%
# Set the starting point via the solver.
solver = factory.getOptimizationAlgorithm()
solver.setStartingPoint(startingPoint)
factory.setOptimizationAlgorithm(solver)
# %%
# Estimate the parametric model.
distribution = factory.build(sample)
str(distribution)
# %%
# Retrieve the estimated parameters.
#! /usr/bin/env python from __future__ import print_function import openturns as ot ot.PlatformInfo.SetNumericalPrecision(3) distribution = ot.Normal() sample = distribution.getSample(1000) factory = ot.MaximumLikelihoodFactory(distribution) distribution = factory.build(sample) print(distribution)
#! /usr/bin/env python from __future__ import print_function import openturns as ot ot.PlatformInfo.SetNumericalPrecision(3) inner_factory = ot.NormalFactory() sample = inner_factory.build().getSample(1000) factory = ot.MaximumLikelihoodFactory(inner_factory) distribution = factory.build(sample) print(distribution)
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
ot.PlatformInfo.SetNumericalPrecision(3)
distribution = ot.Beta(2.3, 2.2, -1.0, 1.0)
print('distribution=', distribution)
sample = distribution.getSample(1000)
factory = ot.MaximumLikelihoodFactory(ot.Beta())
inf_distribution = factory.build(sample)
print('estimated distribution=', inf_distribution)
# set (a,b) out of (r, t, a, b)
factory.setKnownParameter([-1.0, 1.0], [2, 3])
inf_distribution = factory.build(sample)
print('estimated distribution with bounds=', inf_distribution)
