parameters: (n_samples=100, n_features=10, random_state=None)
return_statement: return X, y
docstring: 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 (default)
          Determines random number generation for dataset creation. Pass an int
          for reproducible output across multiple function calls.
          See :term:`Glossary <random_state>`.
  
      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.
docstring_summary: Generate a random regression problem with sparse uncorrelated design
docstring_tokens: Generate a random regression problem with sparse uncorrelated design
function: 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 (default)
          Determines random number generation for dataset creation. Pass an int
          for reproducible output across multiple function calls.
          See :term:`Glossary <random_state>`.
  
      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
function_tokens: def make_sparse_uncorrelated ( n_samples = 100 , n_features = 10 , random_state = None ) : 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