def setUpClass(cls):
# attributes to compare a one-dimensional InverseWishart to the
# equivalent InverseGamma distribution
U = ot.Uniform(0., 1.)
scale = 10. * U.getRealization()[0]
DoF = 3. + U.getRealization()[0] # Degrees of Freedom
cls.k, cls.beta = 0.5 * DoF, 0.5 * scale
cls.one_dimensional_inverse_wishart \
= ot.InverseWishart(ot.CovarianceMatrix([[scale]]), DoF)
cls.inverse_gamma = ot.InverseGamma(cls.k, 1. / cls.beta)
# attributes to test a multi-dimensional InverseWishart
cls.dimension = 5
cls.DoF = cls.dimension + 3 + U.getRealization()[0]
cls.L = ot.TriangularMatrix(cls.dimension)
diagL = ot.Uniform(0.5, 1.).getSample(cls.dimension)
cls.determinant = 1.
for i in range(cls.dimension):
cls.determinant *= diagL[i, 0]
cls.L[i, i] = diagL[i, 0]
for j in range(i):
cls.L[i, j] = U.getRealization()[0]
cls.determinant *= cls.determinant
cls.Scale = ot.CovarianceMatrix(cls.L * cls.L.transpose())
#! /usr/bin/env python
import openturns as ot
ref_values = [[1.0, 0.0], [0.0, 0.5]]
mats = [ot.Matrix(ref_values),
ot.SquareMatrix(ref_values),
ot.TriangularMatrix(ref_values),
ot.SymmetricMatrix(ref_values),
ot.CovarianceMatrix(ref_values),
ot.CorrelationMatrix(ref_values)]
mats.extend([
ot.ComplexMatrix(ref_values),
ot.HermitianMatrix(ref_values),
ot.TriangularComplexMatrix(ref_values),
ot.SquareComplexMatrix(ref_values)])
for a in mats:
# try conversion
ref = ot.Matrix([[1.0, 0.0], [0.0, 0.5]])
iname = a.__class__.__name__
print('a=', a)
# try scalar mul
try:
s = 5.
ats = a * s
print('a*s=', ats)
sta = s * a
a0 = np.array(((1., 2.), (3., 4.), (5., 6.)))
m0 = ot.Matrix(a0)
print("array", a0, "=> Matrix", m0)
a1 = np.array(m0)
print("Matrix", m0, "=> array", a1)
m0 = ot.Matrix(a0.transpose())
print("with transpose, array", a0.transpose(), "=> Matrix", m0)
a0 = np.array(((1., 2.), (3., 4.)))
m0 = ot.SquareMatrix(a0)
print("array", a0, "=> SquareMatrix", m0)
a1 = np.array(m0)
print("SquareMatrix", m0, "=> array", a1)
a0 = np.array(((1., 2.), (0., 4.)))
m0 = ot.TriangularMatrix(a0)
print("array", a0, "=> TriangularMatrix", m0)
a1 = np.array(m0)
print("TriangularMatrix", m0, "=> array", a1)
a0 = np.array(((1., 2.), (2., 4.)))
m0 = ot.SymmetricMatrix(a0)
print("array", a0, "=> SymmetricMatrix", m0)
m0[1, 0] = 3.0
a1 = np.array(m0)
print("SymmetricMatrix", m0, "=> array", a1)
a0 = np.array(
(((1., 2.), (3., 4.), (5., 6.)), ((7., 8.), (9., 10.), (11., 12.)),
((13., 14.), (15., 16.), (17., 18.)), ((19., 20.), (21., 22.), (23.,
24.))))