class_docstr: Dimensionality reduction using truncated SVD (aka LSA).
  
  This transformer performs linear dimensionality reduction by means of
  truncated singular value decomposition (SVD). Contrary to PCA, this
  estimator does not center the data before computing the singular value
  decomposition. This means it can work with scipy.sparse matrices
  efficiently.
  
  In particular, truncated SVD works on term count/tf-idf matrices as
  returned by the vectorizers in sklearn.feature_extraction.text. In that
  context, it is known as latent semantic analysis (LSA).
  
  This estimator supports two algorithms: a fast randomized SVD solver, and
  a "naive" algorithm that uses ARPACK as an eigensolver on (X * X.T) or
  (X.T * X), whichever is more efficient.
  
  Read more in the :ref:`User Guide <LSA>`.
  
  Parameters
  ----------
  n_components : int, default = 2
      Desired dimensionality of output data.
      Must be strictly less than the number of features.
      The default value is useful for visualisation. For LSA, a value of
      100 is recommended.
  
  algorithm : string, default = "randomized"
      SVD solver to use. Either "arpack" for the ARPACK wrapper in SciPy
      (scipy.sparse.linalg.svds), or "randomized" for the randomized
      algorithm due to Halko (2009).
  
  n_iter : int, optional (default 5)
      Number of iterations for randomized SVD solver. Not used by ARPACK.
      The default is larger than the default in `randomized_svd` to handle
      sparse matrices that may have large slowly decaying spectrum.
  
  random_state : int or RandomState, optional
      (Seed for) pseudo-random number generator. If not given, the
      numpy.random singleton is used.
  
  tol : float, optional
      Tolerance for ARPACK. 0 means machine precision. Ignored by randomized
      SVD solver.
  
  Attributes
  ----------
  components_ : array, shape (n_components, n_features)
  
  explained_variance_ratio_ : array, [n_components]
      Percentage of variance explained by each of the selected components.
  
  explained_variance_ : array, [n_components]
      The variance of the training samples transformed by a projection to
      each component.
  
  Examples
  --------
  >>> from sklearn.decomposition import TruncatedSVD
  >>> from sklearn.random_projection import sparse_random_matrix
  >>> X = sparse_random_matrix(100, 100, density=0.01, random_state=42)
  >>> svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
  >>> svd.fit(X) # doctest: +NORMALIZE_WHITESPACE
  TruncatedSVD(algorithm='randomized', n_components=5, n_iter=7,
          random_state=42, tol=0.0)
  >>> print(svd.explained_variance_ratio_) # doctest: +ELLIPSIS
  [ 0.0606... 0.0584... 0.0497... 0.0434... 0.0372...]
  >>> print(svd.explained_variance_ratio_.sum()) # doctest: +ELLIPSIS
  0.249...
  
  See also
  --------
  PCA
  RandomizedPCA
  
  References
  ----------
  Finding structure with randomness: Stochastic algorithms for constructing
  approximate matrix decompositions
  Halko, et al., 2009 (arXiv:909) http://arxiv.org/pdf/0909.4061
  
  Notes
  -----
  SVD suffers from a problem called "sign indeterminancy", which means the
  sign of the ``components_`` and the output from transform depend on the
  algorithm and random state. To work around this, fit instances of this
  class to data once, then keep the instance around to do transformations.
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docstr: Fit LSI model on training data X.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape (n_samples, n_features)
      Training data.
  
  Returns
  -------
  self : object
      Returns the transformer object.
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code: def fit(self, X, y=None):
      """Fit LSI model on training data X.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape (n_samples, n_features)
          Training data.
  
      Returns
      -------
      self : object
          Returns the transformer object.
      """
      self.fit_transform(X)
      return self
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/decomposition/truncated_svd.py
imports: import numpy
  import scipy.sparse
  from ..base import BaseEstimator, TransformerMixin
  from ..utils import check_array, as_float_array, check_random_state
  from ..utils.extmath import randomized_svd, safe_sparse_dot, svd_flip
  from ..utils.sparsefuncs import mean_variance_axis
label_desc: Use image node_id 2 for calling the TruncatedSVD obj's underlying member method code with example usage: obj.fit(X, y) and returns: self