PolynomialChaosExpansion

class pyapprox.multivariate_polynomials.PolynomialChaosExpansion

Bases: object

Methods Summary

__call__(samples)	Call self as a function.
basis_matrix(samples[, opts])
canonical_basis_matrix(canonical_samples[, opts])
configure(opts)
covariance()	Compute the covariance between each quantity of interest of the polynomial chaos expansion
get_coefficients()
get_indices()
jacobian(sample)
mean()	Compute the mean of the polynomial chaos expansion
num_terms()
num_vars()
set_coefficients(coefficients)
set_indices(indices)
update_recursion_coefficients(…)
value(samples)
variance()	Compute the variance of the polynomial chaos expansion

Methods Documentation

__call__(samples)

Call self as a function.

basis_matrix(samples, opts={})

canonical_basis_matrix(canonical_samples, opts={})

configure(opts)

covariance()

Compute the covariance between each quantity of interest of the polynomial chaos expansion

Returns

covarnp.ndarray (nqoi)

The covariance between each quantitity of interest

get_coefficients()

get_indices()

jacobian(sample)

mean()

Compute the mean of the polynomial chaos expansion

Returns

meannp.ndarray (nqoi)

The mean of each quantitity of interest

num_terms()

num_vars()

set_coefficients(coefficients)

set_indices(indices)

update_recursion_coefficients(num_coefs_per_var, opts)

value(samples)

variance()

Compute the variance of the polynomial chaos expansion

Returns

varnp.ndarray (nqoi)

The variance of each quantitity of interest