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"""
Fit a parametric distribution
=============================
"""
# %%
# In this example we estimate the parameters of a distribution from a given sample.
# Once we are settled on a good candidate, we use the corresponding factory to fit
# the distribution. Each distribution factory has one or several estimators available.
# They are all derived from either the Maximum Likelihood method or from the method of moments (see :ref:`parametric_estimation`).
#
# %%
import openturns as ot
import openturns.viewer as otv
# %%
# The Normal distribution
# -----------------------
#
# The parameters are estimated by the method of moments.
#
# %%
# We consider a sample, here created from a standard :class:`~openturns.Normal` distribution :
sample = ot.Normal().getSample(1000)
# %%
# We can estimate a Normal distribution with :class:`~openturns.NormalFactory` :
distribution = ot.NormalFactory().build(sample)
# %%
# We take a look at the estimated parameters with the `getParameter` method :
print(distribution.getParameter())
# %%
# We draw the fitted distribution
graph = distribution.drawPDF()
graph.setTitle("Fitted Normal distribution")
view = otv.View(graph)
# %%
# The Student distribution
# ------------------------
#
# The parameters of the Student law are estimated by a mixed method of moments and reduces MLE.
#
# %%
# We generate a sample from a :class:`~openturns.Student` distribution with parameters :math:`\nu=5.0`, :math:`\mu = -0.5` and a scale parameter :math:`\sigma=2.0`.
sample = ot.Student(5.0, -0.5, 2.0).getSample(1000)
# %%
# We use the factory to build an estimated distribution :
distribution = ot.StudentFactory().build(sample)
# %%
# We can obtain the estimated parameters with the `getParameter` method :
print(distribution.getParameter())
# %%
# Draw fitted distribution
graph = distribution.drawPDF()
graph.setTitle("Fitted Student distribution")
view = otv.View(graph)
# %%
# The Pareto distribution
# -----------------------
#
# By default the parameters of the :class:`~openturns.Pareto` distribution are estimated by least squares.
#
# %%
# We use a sample from a Pareto distribution with a scale parameter :math:`\beta=1.0`, a shape parameter :math:`\alpha > 1.0` and a location parameter :math:`\gamma = 0.0`.
sample = ot.Pareto(1.0, 1.0, 0.0).getSample(1000)
# %%
# Draw fitted distribution
distribution = ot.ParetoFactory().build(sample)
print(distribution.getParameter())
graph = distribution.drawPDF()
graph.setTitle("Fitted Pareto distribution")
view = otv.View(graph)
# %%
# Display all graphs
otv.View.ShowAll()
|
