def test_orthogonals():
dist = cp.Iid(cp.Normal(), dim)
cp.orth_gs(order, dist)
cp.orth_ttr(order, dist)
cp.orth_chol(order, dist)
_=plt.xticks([])
poly = cp.orth_chol(polynomial_order, n, normed=True)
print('Cholesky decomposition {}'.format(poly))
ax = plt.subplot(222)
ax.set_title('Cholesky decomposition')
_=plt.plot(x, poly(x).T)
_=plt.xticks([])
poly = cp.orth_ttr(polynomial_order, n, normed=True)
print('Discretized Stieltjes / Thre terms reccursion {}'.format(poly))
ax = plt.subplot(223)
ax.set_title('Discretized Stieltjes ')
_=plt.plot(x, poly(x).T)
poly = cp.orth_gs(polynomial_order, n, normed=True)
print('Modified Gram-Schmidt {}'.format(poly))
ax = plt.subplot(224)
ax.set_title('Modified Gram-Schmidt')
_=plt.plot(x, poly(x).T)
# end example orthogonalization schemes
# _Linear Regression_
# linear regression in chaospy
cp.fit_regression?
# end linear regression in chaospy
# example linear regression
# 1. define marginal and joint distributions
u1 = cp.Uniform(0,1)