parameters: (n_samples=100, shuffle=True, noise=None, random_state=None,
                   factor=.8)
return_statement: return X, y
docstring: Make a large circle containing a smaller circle in 2d.
  
      A simple toy dataset to visualize clustering and classification
      algorithms.
  
      Read more in the :ref:`User Guide <sample_generators>`.
  
      Parameters
      ----------
      n_samples : int, optional (default=100)
          The total number of points generated.
  
      shuffle: bool, optional (default=True)
          Whether to shuffle the samples.
  
      noise : double or None (default=None)
          Standard deviation of Gaussian noise added to the data.
  
      factor : double < 1 (default=.8)
          Scale factor between inner and outer circle.
  
      Returns
      -------
      X : array of shape [n_samples, 2]
          The generated samples.
  
      y : array of shape [n_samples]
          The integer labels (0 or 1) for class membership of each sample.
docstring_summary: Make a large circle containing a smaller circle in 2d.
docstring_tokens: Make a large circle containing a smaller circle in 2d .
function: def make_circles(n_samples=100, shuffle=True, noise=None, random_state=None,
                   factor=.8):
      """Make a large circle containing a smaller circle in 2d.
  
      A simple toy dataset to visualize clustering and classification
      algorithms.
  
      Read more in the :ref:`User Guide <sample_generators>`.
  
      Parameters
      ----------
      n_samples : int, optional (default=100)
          The total number of points generated.
  
      shuffle: bool, optional (default=True)
          Whether to shuffle the samples.
  
      noise : double or None (default=None)
          Standard deviation of Gaussian noise added to the data.
  
      factor : double < 1 (default=.8)
          Scale factor between inner and outer circle.
  
      Returns
      -------
      X : array of shape [n_samples, 2]
          The generated samples.
  
      y : array of shape [n_samples]
          The integer labels (0 or 1) for class membership of each sample.
      """
  
      if factor > 1 or factor < 0:
          raise ValueError("'factor' has to be between 0 and 1.")
  
      generator = check_random_state(random_state)
      # so as not to have the first point = last point, we add one and then
      # remove it.
      linspace = np.linspace(0, 2 * np.pi, n_samples // 2 + 1)[:-1]
      outer_circ_x = np.cos(linspace)
      outer_circ_y = np.sin(linspace)
      inner_circ_x = outer_circ_x * factor
      inner_circ_y = outer_circ_y * factor
  
      X = np.vstack((np.append(outer_circ_x, inner_circ_x),
                     np.append(outer_circ_y, inner_circ_y))).T
      y = np.hstack([np.zeros(n_samples // 2, dtype=np.intp),
                     np.ones(n_samples // 2, dtype=np.intp)])
      if shuffle:
          X, y = util_shuffle(X, y, random_state=generator)
  
      if noise is not None:
          X += generator.normal(scale=noise, size=X.shape)
  
      return X, y
function_tokens: def make_circles ( n_samples = 100 , shuffle = True , noise = None , random_state = None , factor = .8 ) : if factor > 1 or factor < 0 : raise ValueError ( "'factor' has to be between 0 and 1." ) generator = check_random_state ( random_state ) # so as not to have the first point = last point, we add one and then # remove it. linspace = np . linspace ( 0 , 2 * np . pi , n_samples // 2 + 1 ) [ : - 1 ] outer_circ_x = np . cos ( linspace ) outer_circ_y = np . sin ( linspace ) inner_circ_x = outer_circ_x * factor inner_circ_y = outer_circ_y * factor X = np . vstack ( ( np . append ( outer_circ_x , inner_circ_x ) , np . append ( outer_circ_y , inner_circ_y ) ) ) . T y = np . hstack ( [ np . zeros ( n_samples // 2 , dtype = np . intp ) , np . ones ( n_samples // 2 , dtype = np . intp ) ] ) if shuffle : X , y = util_shuffle ( X , y , random_state = generator ) if noise is not None : X += generator . normal ( scale = noise , size = X . shape ) return X , y