class_docstr: Principal component analysis (PCA) using randomized SVD
  
  .. deprecated:: 0.18
      This class will be removed in 0.20.
      Use :class:`PCA` with parameter svd_solver 'randomized' instead.
      The new implementation DOES NOT store whiten ``components_``.
      Apply transform to get them.
  
  Linear dimensionality reduction using approximated Singular Value
  Decomposition of the data and keeping only the most significant
  singular vectors to project the data to a lower dimensional space.
  
  Read more in the :ref:`User Guide <RandomizedPCA>`.
  
  Parameters
  ----------
  n_components : int, optional
      Maximum number of components to keep. When not given or None, this
      is set to n_features (the second dimension of the training data).
  
  copy : bool
      If False, data passed to fit are overwritten and running
      fit(X).transform(X) will not yield the expected results,
      use fit_transform(X) instead.
  
  iterated_power : int, default=2
      Number of iterations for the power method.
  
      .. versionchanged:: 0.18
  
  whiten : bool, optional
      When True (False by default) the `components_` vectors are multiplied by
      the square root of (n_samples) and divided by the singular values to
      ensure uncorrelated outputs with unit component-wise variances.
  
      Whitening will remove some information from the transformed signal
      (the relative variance scales of the components) but can sometime
      improve the predictive accuracy of the downstream estimators by
      making their data respect some hard-wired assumptions.
  
  random_state : int or RandomState instance or None (default)
      Pseudo Random Number generator seed control. If None, use the
      numpy.random singleton.
  
  Attributes
  ----------
  components_ : array, [n_components, n_features]
      Components with maximum variance.
  
  explained_variance_ratio_ : array, [n_components]
      Percentage of variance explained by each of the selected components.
      k is not set then all components are stored and the sum of explained
      variances is equal to 1.0
  
  mean_ : array, [n_features]
      Per-feature empirical mean, estimated from the training set.
  
  Examples
  --------
  >>> import numpy as np
  >>> from sklearn.decomposition import RandomizedPCA
  >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
  >>> pca = RandomizedPCA(n_components=2)
  >>> pca.fit(X)                 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  RandomizedPCA(copy=True, iterated_power=2, n_components=2,
         random_state=None, whiten=False)
  >>> print(pca.explained_variance_ratio_) # doctest: +ELLIPSIS
  [ 0.99244...  0.00755...]
  
  See also
  --------
  PCA
  TruncatedSVD
  
  References
  ----------
  
  .. [Halko2009] `Finding structure with randomness: Stochastic algorithms
    for constructing approximate matrix decompositions Halko, et al., 2009
    (arXiv:909)`
  
  .. [MRT] `A randomized algorithm for the decomposition of matrices
    Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert`
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docstr: Apply dimensionality reduction on X.
  
  X is projected on the first principal components previous extracted
  from a training set.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
      New data, where n_samples in the number of samples
      and n_features is the number of features.
  
  Returns
  -------
  X_new : array-like, shape (n_samples, n_components)
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code: def transform(self, X, y=None):
      """Apply dimensionality reduction on X.
  
      X is projected on the first principal components previous extracted
      from a training set.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
          New data, where n_samples in the number of samples
          and n_features is the number of features.
  
      Returns
      -------
      X_new : array-like, shape (n_samples, n_components)
  
      """
      check_is_fitted(self, "mean_")
  
      X = check_array(X)
      if self.mean_ is not None:
          X = X - self.mean_
  
      X = fast_dot(X, self.components_.T)
      return X
  
imports: from math import log, sqrt
  import numpy
  from scipy import linalg
  from scipy.special import gammaln
  from scipy.sparse import issparse
  from ..externals import six
  from .base import _BasePCA
  from ..base import BaseEstimator, TransformerMixin
  from ..utils import deprecated
  from ..utils import check_random_state, as_float_array
  from ..utils import check_array
  from ..utils.extmath import fast_dot, fast_logdet, randomized_svd, svd_flip
  from ..utils.validation import check_is_fitted
  from ..utils.arpack import svds
label_desc: Use image node_id 4 for calling the RandomizedPCA obj's underlying member method code with example usage: obj.transform(X, y) and returns: X