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#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
ot.TESTPREAMBLE()
ot.RandomGenerator.SetSeed(0)
mu = [0.0] * 4
sigma = [1.0] * 4
a = [-4., -1., 1., 3.]
b = [4., 4., 2., 6.]
for i in range(4):
ot.PlatformInfo.SetNumericalPrecision(1 if i==2 else 2)
distribution = ot.TruncatedNormal(mu[i], sigma[i], a[i], b[i])
size = 10000
sample = distribution.getSample(size)
factory = ot.TruncatedNormalFactory()
estimatedDistribution = factory.build(sample)
print("distribution=", repr(distribution))
print("Estimated distribution=", repr(estimatedDistribution))
estimatedDistribution = factory.build()
print("Default distribution=", estimatedDistribution)
estimatedDistribution = factory.build(
distribution.getParameter())
print("Distribution from parameters=", estimatedDistribution)
estimatedTruncatedNormal = factory.buildAsTruncatedNormal(sample)
print("TruncatedNormal =", distribution)
print("Estimated TruncatedNormal=", estimatedTruncatedNormal)
estimatedTruncatedNormal = factory.buildAsTruncatedNormal()
print("Default TruncatedNormal=", estimatedTruncatedNormal)
estimatedTruncatedNormal = factory.buildAsTruncatedNormal(
distribution.getParameter())
print("TruncatedNormal from parameters=", estimatedTruncatedNormal)
sample = [[0.0]]*size
estimatedDistribution = factory.build(sample)
print("Estimated distribution=", repr(estimatedDistribution))
sample = [[1.0]]*size
estimatedDistribution = factory.build(sample)
print("Estimated distribution=", repr(estimatedDistribution))
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