scipy.stats.random_correlation¶
- scipy.stats.random_correlation = <scipy.stats._multivariate.random_correlation_gen object at 0x2b23194db5d0>[source]¶
A random correlation matrix.
Return a random correlation matrix, given a vector of eigenvalues.
The eigs keyword specifies the eigenvalues of the correlation matrix, and implies the dimension.
Parameters: eigs : 1d ndarray
Eigenvalues of correlation matrix.
Notes
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.
References
[R628] Davies, Philip I; Higham, Nicholas J; “Numerically stable generation of correlation matrices and their factors”, BIT 2000, Vol. 40, No. 4, pp. 640 651 Examples
>>> from scipy.stats import random_correlation >>> np.random.seed(514) >>> x = random_correlation.rvs((.5, .8, 1.2, 1.5)) >>> x array([[ 1. , -0.20387311, 0.18366501, -0.04953711], [-0.20387311, 1. , -0.24351129, 0.06703474], [ 0.18366501, -0.24351129, 1. , 0.38530195], [-0.04953711, 0.06703474, 0.38530195, 1. ]])
>>> import scipy.linalg >>> e, v = scipy.linalg.eigh(x) >>> e array([ 0.5, 0.8, 1.2, 1.5])
Methods
rvs(eigs=None, random_state=None) Draw random correlation matrices, all with eigenvalues eigs.