mlpy.stats.shrink_cov¶
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mlpy.stats.
shrink_cov
(x, return_lambda=False, return_estimate=False)[source]¶ Covariance shrinkage estimation.
Ledoit-Wolf optimal shrinkage estimator for cov(X) using the diagonal variance ‘target’ t=np.diag(s) with the unbiased sample cov s as the unconstrained estimate.
Parameters: x : array_like, shape (n, dim)
The data, where n is the number of data points and dim is the dimensionality of each data point.
return_lambda : bool
Whether to return lambda or not.
return_estimate : bool
Whether to return the unbiased estimate or not.
Returns: C : array
The shrunk final estimate
lambda_ : float, optional
Lambda
estimate : array, optional
Unbiased estimate.
Examples
>>> shrink_cov()