code: def make_sparse_uncorrelated(
      n_samples=100, n_features=10, random_state=None
  ):
      """Generate a random regression problem with sparse uncorrelated design
  
      This dataset is described in Celeux et al [1]. as::
  
          X ~ N(0, 1)
          y(X) = X[:, 0] + 2 * X[:, 1] - 2 * X[:, 2] - 1.5 * X[:, 3]
  
      Only the first 4 features are informative. The remaining features are
      useless.
  
      Read more in the :ref:`User Guide <sample_generators>`.
  
      Parameters
      ----------
      n_samples : int, optional (default=100)
          The number of samples.
  
      n_features : int, optional (default=10)
          The number of features.
  
      random_state : int, RandomState instance or None, optional (default=None)
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
      Returns
      -------
      X : array of shape [n_samples, n_features]
          The input samples.
  
      y : array of shape [n_samples]
          The output values.
  
      References
      ----------
      .. [1] G. Celeux, M. El Anbari, J.-M. Marin, C. P. Robert,
             "Regularization in regression: comparing Bayesian and frequentist
             methods in a poorly informative situation", 2009.
      """
      generator = check_random_state(random_state)
  
      X = generator.normal(loc=0, scale=1, size=(n_samples, n_features))
      y = generator.normal(
          loc=(X[:, 0] + 2 * X[:, 1] - 2 * X[:, 2] - 1.5 * X[:, 3]),
          scale=np.ones(n_samples),
      )
  
      return X, y
  
imports: import numbers
  import array
  import numpy
  from scipy import linalg
  import scipy.sparse
  from ..preprocessing import MultiLabelBinarizer
  from ..utils import check_array, check_random_state
  from ..utils import shuffle
  from ..utils.fixes import astype
  from ..utils.random import sample_without_replacement
  from ..externals import six
label_desc: Use image node_id 14 for calling a global function with example usage: make_sparse_uncorrelated(n_samples, n_features, random_state) and returns: X,  y