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scipy.stats.Covariance.

# colorize

Covariance.colorize(x)

  Perform a colorizing transformation on data.

  “Colorizing” (“color” as in “colored noise”, in which different frequencies may have different magnitudes) transforms a set of uncorrelated random variables into a new set of random variables with the desired covariance. When a coloring transform is applied to a sample of points distributed according to a multivariate normal distribution with identity covariance and zero mean, the covariance of the transformed sample is approximately the covariance matrix used in the coloring transform.

  Parameters:
    xarray_like

      An array of points. The last dimension must correspond with the dimensionality of the space, i.e., the number of columns in the covariance matrix.

  Returns:
    x_array_like

      The transformed array of points.

  References

  [1]

  “Whitening Transformation”. Wikipedia. https://en.wikipedia.org/wiki/Whitening_transformation

  [2]

  Novak, Lukas, and Miroslav Vorechovsky. “Generalization of coloring linear transformation”. Transactions of VSB 18.2 (2018): 31-35. DOI:10.31490/tces-2018-0013

  Examples

      >>> import numpy as np
      >>> from scipy import stats
      >>> rng = np.random.default_rng()
      >>> n = 3
      >>> A = rng.random(size=(n, n))
      >>> cov_array = A @ A.T  # make matrix symmetric positive definite
      >>> cholesky = np.linalg.cholesky(cov_array)
      >>> cov_object = stats.Covariance.from_cholesky(cholesky)
      >>> x = rng.multivariate_normal(np.zeros(n), np.eye(n), size=(10000))
      >>> x_ = cov_object.colorize(x)
      >>> cov_data = np.cov(x_, rowvar=False)
      >>> np.allclose(cov_data, cov_array, rtol=3e-2)
      True