parameters: (n_samples=100, n_features=10, noise=0.0, random_state=None)
return_statement: return X, y
docstring: Generate the "Friedman #1" regression problem
  
      This dataset is described in Friedman [1] and Breiman [2].
  
      Inputs `X` are independent features uniformly distributed on the interval
      [0, 1]. The output `y` is created according to the formula::
  
          y(X) = 10 * sin(pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - 0.5) ** 2 \
  + 10 * X[:, 3] + 5 * X[:, 4] + noise * N(0, 1).
  
      Out of the `n_features` features, only 5 are actually used to compute
      `y`. The remaining features are independent of `y`.
  
      The number of features has to be >= 5.
  
      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. Should be at least 5.
  
      noise : float, optional (default=0.0)
          The standard deviation of the gaussian noise applied to the output.
  
      random_state : int, RandomState instance or None (default)
          Determines random number generation for dataset noise. 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] J. Friedman, "Multivariate adaptive regression splines", The Annals
             of Statistics 19 (1), pages 1-67, 1991.
  
      .. [2] L. Breiman, "Bagging predictors", Machine Learning 24,
             pages 123-140, 1996.
docstring_summary: Generate the "Friedman #1" regression problem
docstring_tokens: Generate the Friedman #1 regression problem
function: def make_friedman1(n_samples=100, n_features=10, noise=0.0, random_state=None):
      """Generate the "Friedman #1" regression problem
  
      This dataset is described in Friedman [1] and Breiman [2].
  
      Inputs `X` are independent features uniformly distributed on the interval
      [0, 1]. The output `y` is created according to the formula::
  
          y(X) = 10 * sin(pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - 0.5) ** 2 \
  + 10 * X[:, 3] + 5 * X[:, 4] + noise * N(0, 1).
  
      Out of the `n_features` features, only 5 are actually used to compute
      `y`. The remaining features are independent of `y`.
  
      The number of features has to be >= 5.
  
      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. Should be at least 5.
  
      noise : float, optional (default=0.0)
          The standard deviation of the gaussian noise applied to the output.
  
      random_state : int, RandomState instance or None (default)
          Determines random number generation for dataset noise. 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] J. Friedman, "Multivariate adaptive regression splines", The Annals
             of Statistics 19 (1), pages 1-67, 1991.
  
      .. [2] L. Breiman, "Bagging predictors", Machine Learning 24,
             pages 123-140, 1996.
      """
      if n_features < 5:
          raise ValueError("n_features must be at least five.")
  
      generator = check_random_state(random_state)
  
      X = generator.rand(n_samples, n_features)
      y = 10 * np.sin(np.pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - 0.5) ** 2 \
          + 10 * X[:, 3] + 5 * X[:, 4] + noise * generator.randn(n_samples)
  
      return X, y
function_tokens: def make_friedman1 ( n_samples = 100 , n_features = 10 , noise = 0.0 , random_state = None ) : if n_features < 5 : raise ValueError ( "n_features must be at least five." ) generator = check_random_state ( random_state ) X = generator . rand ( n_samples , n_features ) y = 10 * np . sin ( np . pi * X [ : , 0 ] * X [ : , 1 ] ) + 20 * ( X [ : , 2 ] - 0.5 ) ** 2 + 10 * X [ : , 3 ] + 5 * X [ : , 4 ] + noise * generator . randn ( n_samples ) return X , y