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#! /usr/bin/env python
import openturns as ot
import openturns.testing as ott
distParams = []
distParams.append(ot.ArcsineMuSigma(8.4, 2.25))
distParams.append(ot.BetaMuSigma(0.2, 0.6, -1, 2))
distParams.append(ot.GammaMuSigma(1.5, 2.5, -0.5))
distParams.append(ot.GumbelLambdaGamma(0.6, 6.0))
distParams.append(ot.GumbelMuSigma(1.5, 1.3))
distParams.append(ot.LogNormalMuErrorFactor(0.63, 1.5, -0.5))
distParams.append(ot.LogNormalMuSigma(0.63, 3.3, -0.5))
distParams.append(ot.LogNormalMuSigmaOverMu(0.63, 5.24, -0.5))
distParams.append(ot.UniformMuSigma(8.4, 2.25))
distParams.append(ot.WeibullMaxMuSigma(1.3, 1.23, 3.1))
distParams.append(ot.WeibullMinMuSigma(1.3, 1.23, -0.5))
# defaults
distParams.append(ot.ArcsineMuSigma())
distParams.append(ot.BetaMuSigma())
distParams.append(ot.GammaMuSigma())
distParams.append(ot.GumbelLambdaGamma())
distParams.append(ot.GumbelMuSigma())
distParams.append(ot.LogNormalMuErrorFactor())
distParams.append(ot.LogNormalMuSigma())
distParams.append(ot.LogNormalMuSigmaOverMu())
distParams.append(ot.UniformMuSigma())
distParams.append(ot.WeibullMaxMuSigma())
distParams.append(ot.WeibullMinMuSigma())
for distParam in distParams:
print("Distribution Parameters ", repr(distParam))
print("Distribution Parameters ", distParam)
p = distParam.getValues()
desc = distParam.getDescription()
print("p=", p, desc)
p_native = distParam.evaluate()
print("native=", p_native)
p_roundtrip = distParam.inverse(p_native)
print("roundtrip=", p_roundtrip)
ott.assert_almost_equal(p_roundtrip, p)
dist = distParam.getDistribution()
print("built dist=", dist)
assert dist.getParameter() == p_native
# derivative of the native parameters with regards the parameters of the
# distribution
grad = distParam.gradient()
print(grad)
# by the finite difference technique
eps = 1e-5
dim = len(p)
grad_fd = ot.SquareMatrix(ot.IdentityMatrix(dim))
for i in range(dim):
for j in range(dim):
xp = list(p)
xp[i] += eps
xm = list(p)
xm[i] -= eps
grad_fd[i, j] = 0.5 * (distParam(xp)[j] - distParam(xm)[j]) / eps
print(grad_fd)
ott.assert_almost_equal(grad, grad_fd)
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