class_docstr: Factor Analysis (FA)
  
  A simple linear generative model with Gaussian latent variables.
  
  The observations are assumed to be caused by a linear transformation of
  lower dimensional latent factors and added Gaussian noise.
  Without loss of generality the factors are distributed according to a
  Gaussian with zero mean and unit covariance. The noise is also zero mean
  and has an arbitrary diagonal covariance matrix.
  
  If we would restrict the model further, by assuming that the Gaussian
  noise is even isotropic (all diagonal entries are the same) we would obtain
  :class:`PPCA`.
  
  FactorAnalysis performs a maximum likelihood estimate of the so-called
  `loading` matrix, the transformation of the latent variables to the
  observed ones, using expectation-maximization (EM).
  
  Read more in the :ref:`User Guide <FA>`.
  
  Parameters
  ----------
  n_components : int | None
      Dimensionality of latent space, the number of components
      of ``X`` that are obtained after ``transform``.
      If None, n_components is set to the number of features.
  
  tol : float
      Stopping tolerance for EM algorithm.
  
  copy : bool
      Whether to make a copy of X. If ``False``, the input X gets overwritten
      during fitting.
  
  max_iter : int
      Maximum number of iterations.
  
  noise_variance_init : None | array, shape=(n_features,)
      The initial guess of the noise variance for each feature.
      If None, it defaults to np.ones(n_features)
  
  svd_method : {'lapack', 'randomized'}
      Which SVD method to use. If 'lapack' use standard SVD from
      scipy.linalg, if 'randomized' use fast ``randomized_svd`` function.
      Defaults to 'randomized'. For most applications 'randomized' will
      be sufficiently precise while providing significant speed gains.
      Accuracy can also be improved by setting higher values for
      `iterated_power`. If this is not sufficient, for maximum precision
      you should choose 'lapack'.
  
  iterated_power : int, optional
      Number of iterations for the power method. 3 by default. Only used
      if ``svd_method`` equals 'randomized'
  
  random_state : int or RandomState
      Pseudo number generator state used for random sampling. Only used
      if ``svd_method`` equals 'randomized'
  
  Attributes
  ----------
  components_ : array, [n_components, n_features]
      Components with maximum variance.
  
  loglike_ : list, [n_iterations]
      The log likelihood at each iteration.
  
  noise_variance_ : array, shape=(n_features,)
      The estimated noise variance for each feature.
  
  n_iter_ : int
      Number of iterations run.
  
  References
  ----------
  .. David Barber, Bayesian Reasoning and Machine Learning,
      Algorithm 21.1
  
  .. Christopher M. Bishop: Pattern Recognition and Machine Learning,
      Chapter 12.2.4
  
  See also
  --------
  PCA: Principal component analysis is also a latent linear variable model
      which however assumes equal noise variance for each feature.
      This extra assumption makes probabilistic PCA faster as it can be
      computed in closed form.
  FastICA: Independent component analysis, a latent variable model with
      non-Gaussian latent variables.
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docstr: Compute the average log-likelihood of the samples
  
  Parameters
  ----------
  X: array, shape (n_samples, n_features)
      The data
  
  Returns
  -------
  ll: float
      Average log-likelihood of the samples under the current model
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code: def score(self, X, y=None):
      """Compute the average log-likelihood of the samples
  
      Parameters
      ----------
      X: array, shape (n_samples, n_features)
          The data
  
      Returns
      -------
      ll: float
          Average log-likelihood of the samples under the current model
      """
      return np.mean(self.score_samples(X))
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/decomposition/factor_analysis.py
imports: import warnings
  from math import sqrt, log
  import numpy
  from scipy import linalg
  from ..base import BaseEstimator, TransformerMixin
  from ..externals.six.moves import xrange
  from ..utils import check_array, check_random_state
  from ..utils.extmath import fast_logdet, fast_dot, randomized_svd, squared_norm
  from ..utils.validation import check_is_fitted
  from ..exceptions import ConvergenceWarning
label_desc: Use image node_id 7 for calling the FactorAnalysis obj's underlying member method code with example usage: obj.score(X, y) and returns: np