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bundles / scipy latest / scipy / stats / _covariance / Covariance

class

scipy.stats._covariance:Covariance

source: /scipy/stats/_covariance.py :12

Signature

class   Covariance ( )

Members

Summary

Representation of a covariance matrix

Extended Summary

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)
✗
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_diagonal`` to create an object representing the covariance matrix, and `multivariate_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)
✗

Aliases

  • scipy.stats.Covariance

Referenced by

This package