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scipy.stats.

# Covariance

class scipy.stats.Covariance

  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`).

  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

  `colorize`(x) | Perform a colorizing transformation on data.
  `from_cholesky`(cholesky) | Representation of a covariance provided via the (lower) Cholesky factor.
  `from_diagonal`(diagonal) | Return a representation of a covariance matrix from its diagonal.
  `from_eigendecomposition`(eigendecomposition) | Representation of a covariance provided via eigendecomposition.
  `from_precision`(precision[, covariance]) | Return a representation of a covariance from its precision matrix.
  `whiten`(x) | Perform a whitening transformation on data.

  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