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.RandomStatesingleton is used. If seed is an int, a new- RandomStateinstance is used, seeded with seed. If seed is already a- Generatoror- RandomStateinstance 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 
 
- 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]) - Methods - rvs(eigs=None, random_state=None) - Draw random correlation matrices, all with eigenvalues eigs.