Covariance — SciPy v1.15.3 Manual (original) (raw)

scipy.stats.

class scipy.stats.Covariance[source]#

Representation of a covariance matrix

Calculations involving covariance matrices (e.g. data whitening, multivariate normal function evaluation) are often performed more efficiently using a decomposition of the covariance matrix instead of the covariance matrix itself. This class allows the user to construct an object representing a covariance matrix using any of several decompositions and perform calculations using a common interface.

Examples

The Covariance class is used by calling one of its factory methods to create a Covariance object, then pass that representation of the Covariance matrix as a shape parameter of a multivariate distribution.

For instance, the multivariate normal distribution can accept an array representing a covariance matrix:

from scipy import stats import numpy as np d = [1, 2, 3] A = np.diag(d) # a diagonal covariance matrix x = [4, -2, 5] # a point of interest dist = stats.multivariate_normal(mean=[0, 0, 0], cov=A) dist.pdf(x) 4.9595685102808205e-08

but the calculations are performed in a very generic way that does not take advantage of any special properties of the covariance matrix. Because our covariance matrix is diagonal, we can use Covariance.from_diagonalto create an object representing the covariance matrix, andmultivariate_normal can use this to compute the probability density function more efficiently.

cov = stats.Covariance.from_diagonal(d) dist = stats.multivariate_normal(mean=[0, 0, 0], cov=cov) dist.pdf(x) 4.9595685102808205e-08

Attributes:

covariance

Explicit representation of the covariance matrix

log_pdet

Log of the pseudo-determinant of the covariance matrix

rank

Rank of the covariance matrix

shape

Shape of the covariance array

Methods