def fit(self, X, y=None):
    """
    X : data matrix, (n x d)
    y : unused
    """
    X = self._prepare_inputs(X, ensure_min_samples=2)
    M = np.cov(X, rowvar = False)
    if M.ndim == 0:
      M = 1./M
    else:
      M = np.linalg.inv(M)

    self.transformer_ = transformer_from_metric(np.atleast_2d(M))
    return self
def fit ( self , X , y = None ) : X = self . _prepare_inputs ( X , ensure_min_samples = 2 ) M = np . cov ( X , rowvar = False ) if M . ndim == 0 : M = 1. / M else : M = np . linalg . inv ( M ) self . transformer_ = transformer_from_metric ( np . atleast_2d ( M ) ) return self
X : data matrix, (n x d)
    y : unused