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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
dim = 3
mean = NumericalPoint(dim)
sigma = NumericalPoint(dim)
R = CorrelationMatrix(dim)
for i in range(dim):
mean[i] = i + 0.5
sigma[i] = 2 * i + 1.0
for j in range(i):
R[i, j] = 0.5 * (1.0 + i) / dim
distribution = Normal(mean, sigma, R)
size = 10000
sample = distribution.getSample(size)
factory = NormalFactory()
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)
estimatedNormal = factory.buildAsNormal(sample)
print("Normal =", distribution)
print("Estimated normal=", estimatedNormal)
estimatedNormal = factory.buildAsNormal()
print("Default normal=", estimatedNormal)
estimatedNormal = factory.buildAsNormal(
distribution.getParameter())
print("Normal from parameters=", estimatedNormal)
except:
import sys
print("t_NormalFactory_std.py", sys.exc_info()[0], sys.exc_info()[1])
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