class_docstr: Base class for random projections.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
class_docstr_tok: ["Base","class","for","random","projections",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
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(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.
  
      """
  
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 BaseRandomProjection obj's underlying member method code with example usage: obj._make_random_matrix(n_components, n_features) without return types