def __init__(self, beta_coeff, idx_set, jpdf):
self.beta_coeff = beta_coeff
self.idx_set = idx_set
self.jpdf = jpdf
self.N = jpdf.getDimension()
# get the distribution type of each random variable
dist_types = []
for i in range(self.N):
dist_type = self.jpdf.getMarginal(i).getName()
dist_types.append(dist_type)
# create orthogonal univariate bases
poly_collection = ot.PolynomialFamilyCollection(self.N)
for i in range(self.N):
pdf = jpdf.getDistributionCollection()[i]
algo = ot.AdaptiveStieltjesAlgorithm(pdf)
poly_collection[i] = ot.StandardDistributionPolynomialFactory(algo)
# create multivariate basis
multivariate_basis = ot.OrthogonalProductPolynomialFactory(
poly_collection, ot.EnumerateFunction(self.N))
# get enumerate function (multi-index handling)
enum_func = multivariate_basis.getEnumerateFunction()
# get epansion
self.expansion = multivariate_basis.getSubBasis(
transform_multi_index_set(idx_set, enum_func))
# create openturns surrogate model
sur_model = ot.FunctionCollection()
for i in range(len(self.expansion)):
multi = str(beta_coeff[i]) + '*x'
help_function = ot.SymbolicFunction(['x'], [multi])
sur_model.add(ot.ComposedFunction(help_function,
self.expansion[i]))
self.surrogate_model = np.sum(sur_model)
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
basisSize = 3
sampleSize = 3
X = ot.Sample(sampleSize, 1)
for i in range(sampleSize):
X[i, 0] = i + 1.0
Y = ot.Sample(sampleSize, 1)
phis = []
for j in range(basisSize):
phis.append(ot.SymbolicFunction(['x'], ['x^' + str(j + 1)]))
basis = ot.Basis(phis)
for i in range(basisSize):
print(ot.FunctionCollection(basis)[i](X))
proxy = ot.DesignProxy(X, basis)
full = range(basisSize)
design = proxy.computeDesign(full)
print(design)
proxy.setWeight([0.5] * sampleSize)
design = proxy.computeDesign(full)
print(design)