class_docstr: Reduce dimensionality through Gaussian random projection
  
  The components of the random matrix are drawn from N(0, 1 / n_components).
  
  Read more in the :ref:`User Guide <gaussian_random_matrix>`.
  
  Parameters
  ----------
  n_components : int or 'auto', optional (default = 'auto')
      Dimensionality of the target projection space.
  
      n_components can be automatically adjusted according to the
      number of samples in the dataset and the bound given by the
      Johnson-Lindenstrauss lemma. In that case the quality of the
      embedding is controlled by the ``eps`` parameter.
  
      It should be noted that Johnson-Lindenstrauss lemma can yield
      very conservative estimated of the required number of components
      as it makes no assumption on the structure of the dataset.
  
  eps : strictly positive float, optional (default=0.1)
      Parameter to control the quality of the embedding according to
      the Johnson-Lindenstrauss lemma when n_components is set to
      'auto'.
  
      Smaller values lead to better embedding and higher number of
      dimensions (n_components) in the target projection space.
  
  random_state : integer, RandomState instance or None (default=None)
      Control the pseudo random number generator used to generate the
      matrix at fit time.
  
  Attributes
  ----------
  n_component_ : int
      Concrete number of components computed when n_components="auto".
  
  components_ : numpy array of shape [n_components, n_features]
      Random matrix used for the projection.
  
  See Also
  --------
  SparseRandomProjection
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docstr: Generate the random projection matrix
  
  Parameters
  ----------
  n_components : int,
      Dimensionality of the target projection space.
  
  n_features : int,
      Dimensionality of the original source space.
  
  Returns
  -------
  components : numpy array or CSR matrix [n_components, n_features]
      The generated random matrix.
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code: def _make_random_matrix(self, n_components, n_features):
      """Generate the random projection matrix
  
      Parameters
      ----------
      n_components : int,
          Dimensionality of the target projection space.
  
      n_features : int,
          Dimensionality of the original source space.
  
      Returns
      -------
      components : numpy array or CSR matrix [n_components, n_features]
          The generated random matrix.
  
      """
      random_state = check_random_state(self.random_state)
      return gaussian_random_matrix(
          n_components, n_features, random_state=random_state
      )
  
imports: from __future__ import division
  import warnings
  from abc import ABCMeta, abstractmethod
  import numpy
  from numpy.testing import assert_equal
  import scipy.sparse
  from .base import BaseEstimator, TransformerMixin
  from .externals import six
  from .externals.six.moves import xrange
  from .utils import check_random_state
  from .utils.extmath import safe_sparse_dot
  from .utils.random import sample_without_replacement
  from .utils.validation import check_array
  from .exceptions import DataDimensionalityWarning
  from .exceptions import NotFittedError
label_desc: Use image node_id 2 for calling the GaussianRandomProjection obj's underlying member method code with example usage: obj._make_random_matrix(n_components, n_features) and returns: gaussian_random_matrix