class_docstr: Covariance estimator with shrinkage
  
  Read more in the :ref:`User Guide <shrunk_covariance>`.
  
  Parameters
  ----------
  store_precision : boolean, default True
      Specify if the estimated precision is stored
  
  shrinkage : float, 0 <= shrinkage <= 1, default 0.1
      Coefficient in the convex combination used for the computation
      of the shrunk estimate.
  
  assume_centered : boolean, default False
      If True, data are not centered before computation.
      Useful when working with data whose mean is almost, but not exactly
      zero.
      If False, data are centered before computation.
  
  Attributes
  ----------
  covariance_ : array-like, shape (n_features, n_features)
      Estimated covariance matrix
  
  precision_ : array-like, shape (n_features, n_features)
      Estimated pseudo inverse matrix.
      (stored only if store_precision is True)
  
  `shrinkage` : float, 0 <= shrinkage <= 1
      Coefficient in the convex combination used for the computation
      of the shrunk estimate.
  
  Notes
  -----
  The regularized covariance is given by
  
  (1 - shrinkage)*cov
    + shrinkage*mu*np.identity(n_features)
  
  where mu = trace(cov) / n_features
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code: def __init__(
      self, store_precision=True, assume_centered=False, shrinkage=0.1
  ):
      super(ShrunkCovariance, self).__init__(
          store_precision=store_precision,
          assume_centered=assume_centered,
      )
      self.shrinkage = shrinkage
  
imports: from __future__ import division
  import warnings
  import numpy
  from .empirical_covariance_ import empirical_covariance, EmpiricalCovariance
  from ..externals.six.moves import xrange
  from ..utils import check_array
label_desc: Use image node_id 1 to create a new ShrunkCovariance object from inherited base classes: EmpiricalCovariance with example: obj = ShrunkCovariance(store_precision, assume_centered, shrinkage)