ot.SoizeGhanemFactory(ot.ComposedDistribution(marginals, copula), False),
ot.SoizeGhanemFactory(ot.ComposedDistribution(marginals, copula), True)
]
x = [0.5] * 2
kMax = 5
ot.ResourceMap.SetAsUnsignedInteger("IteratedQuadrature-MaximumSubIntervals",
2048)
ot.ResourceMap.SetAsScalar("IteratedQuadrature-MaximumError", 1.0e-6)
for soize in factories:
distribution = soize.getMeasure()
print('SoizeGhanem=', soize)
functions = list()
for k in range(kMax):
functions.append(soize.build(k))
print('SoizeGhanem(', k, ')=', functions[k].getEvaluation())
print('SoizeGhanem(', k, ')(', x, '=', functions[k](x))
M = ot.SymmetricMatrix(kMax)
for m in range(kMax):
for n in range(m + 1):
def wrapper(x):
return functions[m](x) * functions[n](
x)[0] * distribution.computePDF(x)
kernel = ot.PythonFunction(distribution.getDimension(), 1, wrapper)
value = ot.IteratedQuadrature().integrate(
kernel, distribution.getRange())[0]
if abs(value) >= 1.0e-6:
M[m, n] = value
print('M=\n', M)
print("isSPD=", isSPD)
matrix2 = matrix1.computeCholesky()
print("matrix2=" + repr(matrix2))
b = ot.Matrix(2, 3)
b[0, 0] = 5.0
b[1, 0] = 0.0
b[0, 1] = 10.0
b[1, 1] = 1.0
b[0, 2] = 15.0
b[1, 2] = 2.0
result2 = matrix1.solveLinearSystem(b, True)
print("result2=" + repr(result2))
matrix3 = ot.CovarianceMatrix(3)
matrix3[1, 0] = float('nan')
try:
print("ev=", matrix3.computeSingularValues())
except:
print("ok")
# from SymmetricMatrix
sym = ot.SymmetricMatrix(3)
sym[0, 0] = 1.0e-02
sym[1, 1] = 1.0e-02
sym[2, 2] = 1.0e-02
sym[0, 1] = 7.0e-04
ot.CovarianceMatrix(sym)
print("ok")
ot.Beta(0.5, 1.0, -2.0, 3.0),
ot.Gamma(1.0, 3.0),
ot.Arcsine()
]
for n in range(len(distributionCollection)):
distribution = distributionCollection[n]
name = distribution.getClassName()
polynomialFactory = ot.StandardDistributionPolynomialFactory(
ot.AdaptiveStieltjesAlgorithm(distribution))
print("polynomialFactory(", name, "=", polynomialFactory, ")")
for i in range(iMax):
print(name, " polynomial(", i, ")=", clean(polynomialFactory.build(i)))
roots = polynomialFactory.getRoots(iMax - 1)
print(name, " polynomial(", iMax - 1, ") roots=", roots)
nodes, weights = polynomialFactory.getNodesAndWeights(iMax - 1)
print(name, " polynomial(", iMax - 1, ") nodes=", nodes, " and weights=",
weights)
M = ot.SymmetricMatrix(iMax)
for i in range(iMax):
pI = polynomialFactory.build(i)
for j in range(i + 1):
pJ = polynomialFactory.build(j)
def kernel(x):
return [pI(x[0]) * pJ(x[0]) * distribution.computePDF(x)]
M[i,
j] = ot.GaussKronrod().integrate(ot.PythonFunction(1, 1, kernel),
distribution.getRange())[0]
print("M=\n", M.clean(1.0e-6))
#! /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
print(sample.getDescription())
sample.setDescription(('y0', 'y1', 'y2'))
print(sample.getDescription())
sample.setDescription(np.array(('z0', 'z1', 'z2')))
print(sample.getDescription())
# Check Matrix tuple constructor
t0 = (1., 2., 3., 4.)
m0 = ot.Matrix(2, 2, t0)
print("tuple", t0, "=> Matrix", m0)
m0 = ot.SquareMatrix(2, t0)
print("tuple", t0, "=> SquareMatrix", m0)
m0 = ot.SymmetricMatrix(2, t0)
print("tuple", t0, "=> SymmetricMatrix", m0)
m0 = ot.Tensor(2, 2, 1, t0)
print("tuple", t0, "=> Tensor", m0)
m0 = ot.SymmetricTensor(2, 1, t0)
print("tuple", t0, "=> SymmetricTensor", m0)
m0 = ot.CorrelationMatrix(2, t0)
print("tuple", t0, "=> CorrelationMatrix", m0)
m0 = ot.CovarianceMatrix(2, t0)
print("tuple", t0, "=> CovarianceMatrix", m0)
t0c = (1. + 3.j, 2. - 5.j, 3. + 7.j, 4. - 9.j)
