class_docstr: LedoitWolf Estimator
  
  Ledoit-Wolf is a particular form of shrinkage, where the shrinkage
  coefficient is computed using O. Ledoit and M. Wolf's formula as
  described in "A Well-Conditioned Estimator for Large-Dimensional
  Covariance Matrices", Ledoit and Wolf, Journal of Multivariate
  Analysis, Volume 88, Issue 2, February 2004, pages 365-411.
  
  Read more in the :ref:`User Guide <shrunk_covariance>`.
  
  Parameters
  ----------
  store_precision : bool, default=True
      Specify if the estimated precision is stored.
  
  assume_centered : bool, 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 (default), data are centered before computation.
  
  block_size : int, default=1000
      Size of the blocks into which the covariance matrix will be split
      during its Ledoit-Wolf estimation. This is purely a memory
      optimization and does not affect results.
  
  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 regularised covariance is::
  
      (1 - shrinkage)*cov
              + shrinkage*mu*np.identity(n_features)
  
  where mu = trace(cov) / n_features
  and shrinkage is given by the Ledoit and Wolf formula (see References)
  
  References
  ----------
  "A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices",
  Ledoit and Wolf, Journal of Multivariate Analysis, Volume 88, Issue 2,
  February 2004, pages 365-411.
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code: def __init__(
      self, store_precision=True, assume_centered=False, block_size=1000
  ):
      super(LedoitWolf, self).__init__(
          store_precision=store_precision,
          assume_centered=assume_centered,
      )
      self.block_size = block_size
  
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 LedoitWolf object from inherited base classes: EmpiricalCovariance with example: obj = LedoitWolf(store_precision, assume_centered, block_size)