Return a random correlation matrix, given a vector of eigenvalues.
The eigs keyword specifies the eigenvalues of the correlation matrix, and implies the dimension.
Generates a random correlation matrix following a numerically stable algorithm spelled out by Davies & Higham. This algorithm uses a single O(N) similarity transformation to construct a symmetric positive semi-definite matrix, and applies a series of Givens rotations to scale it to have ones on the diagonal.
Eigenvalues of correlation matrix
If seed is None (or np.random), the numpy.random.RandomState singleton is used. If seed is an int, a new RandomState instance is used, seeded with seed. If seed is already a Generator or RandomState instance then that instance is used.
Tolerance for input parameter checks
Tolerance for deviation of the diagonal of the resulting matrix. Default: 1e-7
Floating point error prevented generating a valid correlation matrix.
Random size N-dimensional matrices, dimension (size, dim, dim), each having eigenvalues eigs.
A random correlation matrix.
import numpy as np
from scipy.stats import random_correlation
rng = np.random.default_rng()
x = random_correlation.rvs((.5, .8, 1.2, 1.5), random_state=rng)
x
import scipy.linalg
e, v = scipy.linalg.eigh(x)
e
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