scipy.stats.random_correlation#
- scipy.stats.random_correlation = <scipy.stats._multivariate.random_correlation_gen object>[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:
- eigs1d ndarray
Eigenvalues of correlation matrix
- seed{None, int,
numpy.random.Generator
,numpy.random.RandomState
}, optional If seed is None (or np.random), the
numpy.random.RandomState
singleton is used. If seed is an int, a newRandomState
instance is used, seeded with seed. If seed is already aGenerator
orRandomState
instance then that instance is used.- tolfloat, optional
Tolerance for input parameter checks
- diag_tolfloat, optional
Tolerance for deviation of the diagonal of the resulting matrix. Default: 1e-7
Methods
rvs(eigs=None, random_state=None)
Draw random correlation matrices, all with eigenvalues eigs.
- Returns:
- rvsndarray or scalar
Random size N-dimensional matrices, dimension (size, dim, dim), each having eigenvalues eigs.
- Raises:
- RuntimeError
Floating point error prevented generating a valid 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
[1]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
>>> 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 array([[ 1. , -0.02423399, 0.03130519, 0.4946965 ], [-0.02423399, 1. , 0.20334736, 0.04039817], [ 0.03130519, 0.20334736, 1. , 0.02694275], [ 0.4946965 , 0.04039817, 0.02694275, 1. ]]) >>> import scipy.linalg >>> e, v = scipy.linalg.eigh(x) >>> e array([ 0.5, 0.8, 1.2, 1.5])