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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.
.. note::
The `Covariance` class cannot be instantiated directly. Instead, use
one of the factory methods (e.g. `Covariance.from_diagonal`).
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_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)
4.9595685102808205e-08
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