class_docstr: Reduce dimensionality through sparse random projection
  
  Sparse random matrix is an alternative to dense random
  projection matrix that guarantees similar embedding quality while being
  much more memory efficient and allowing faster computation of the
  projected data.
  
  If we note `s = 1 / density` the components of the random matrix are
  drawn from:
  
    - -sqrt(s) / sqrt(n_components)   with probability 1 / 2s
    -  0                              with probability 1 - 1 / s
    - +sqrt(s) / sqrt(n_components)   with probability 1 / 2s
  
  Read more in the :ref:`User Guide <sparse_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.
  
  density : float in range ]0, 1], optional (default='auto')
      Ratio of non-zero component in the random projection matrix.
  
      If density = 'auto', the value is set to the minimum density
      as recommended by Ping Li et al.: 1 / sqrt(n_features).
  
      Use density = 1 / 3.0 if you want to reproduce the results from
      Achlioptas, 2001.
  
  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.
  
  dense_output : boolean, optional (default=False)
      If True, ensure that the output of the random projection is a
      dense numpy array even if the input and random projection matrix
      are both sparse. In practice, if the number of components is
      small the number of zero components in the projected data will
      be very small and it will be more CPU and memory efficient to
      use a dense representation.
  
      If False, the projected data uses a sparse representation if
      the input is sparse.
  
  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_ : CSR matrix with shape [n_components, n_features]
      Random matrix used for the projection.
  
  density_ : float in range 0.0 - 1.0
      Concrete density computed from when density = "auto".
  
  See Also
  --------
  GaussianRandomProjection
  
  References
  ----------
  
  .. [1] Ping Li, T. Hastie and K. W. Church, 2006,
         "Very Sparse Random Projections".
         http://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf
  
  .. [2] D. Achlioptas, 2001, "Database-friendly random projections",
         https://users.soe.ucsc.edu/~optas/papers/jl.pdf
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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.
docstr_tok: ["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","."]
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)
      self.density_ = _check_density(self.density, n_features)
      return sparse_random_matrix(
          n_components,
          n_features,
          density=self.density_,
          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 SparseRandomProjection obj's underlying member method code with example usage: obj._make_random_matrix(n_components, n_features) and returns: sparse_random_matrix