class_docstr: Perceptron
  
  Read more in the :ref:`User Guide <perceptron>`.
  
  Parameters
  ----------
  
  penalty : None, 'l2' or 'l1' or 'elasticnet'
      The penalty (aka regularization term) to be used. Defaults to None.
  
  alpha : float
      Constant that multiplies the regularization term if regularization is
      used. Defaults to 0.0001
  
  fit_intercept : bool
      Whether the intercept should be estimated or not. If False, the
      data is assumed to be already centered. Defaults to True.
  
  n_iter : int, optional
      The number of passes over the training data (aka epochs).
      Defaults to 5.
  
  shuffle : bool, optional, default True
      Whether or not the training data should be shuffled after each epoch.
  
  random_state : int seed, RandomState instance, or None (default)
      The seed of the pseudo random number generator to use when
      shuffling the data.
  
  verbose : integer, optional
      The verbosity level
  
  n_jobs : integer, optional
      The number of CPUs to use to do the OVA (One Versus All, for
      multi-class problems) computation. -1 means 'all CPUs'. Defaults
      to 1.
  
  eta0 : double
      Constant by which the updates are multiplied. Defaults to 1.
  
  class_weight : dict, {class_label: weight} or "balanced" or None, optional
      Preset for the class_weight fit parameter.
  
      Weights associated with classes. If not given, all classes
      are supposed to have weight one.
  
      The "balanced" mode uses the values of y to automatically adjust
      weights inversely proportional to class frequencies in the input data
      as ``n_samples / (n_classes * np.bincount(y))``
  
  warm_start : bool, optional
      When set to True, reuse the solution of the previous call to fit as
      initialization, otherwise, just erase the previous solution.
  
  Attributes
  ----------
  coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes,            n_features]
      Weights assigned to the features.
  
  intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
      Constants in decision function.
  
  Notes
  -----
  
  `Perceptron` and `SGDClassifier` share the same underlying implementation.
  In fact, `Perceptron()` is equivalent to `SGDClassifier(loss="perceptron",
  eta0=1, learning_rate="constant", penalty=None)`.
  
  See also
  --------
  
  SGDClassifier
  
  References
  ----------
  
  https://en.wikipedia.org/wiki/Perceptron and references therein.
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code: def __init__(
      self,
      penalty=None,
      alpha=0.0001,
      fit_intercept=True,
      n_iter=5,
      shuffle=True,
      verbose=0,
      eta0=1.0,
      n_jobs=1,
      random_state=0,
      class_weight=None,
      warm_start=False,
  ):
      super(Perceptron, self).__init__(
          loss="perceptron",
          penalty=penalty,
          alpha=alpha,
          l1_ratio=0,
          fit_intercept=fit_intercept,
          n_iter=n_iter,
          shuffle=shuffle,
          verbose=verbose,
          random_state=random_state,
          learning_rate="constant",
          eta0=eta0,
          power_t=0.5,
          warm_start=warm_start,
          class_weight=class_weight,
          n_jobs=n_jobs,
      )
  
imports: from .stochastic_gradient import BaseSGDClassifier
  from ..feature_selection.from_model import _LearntSelectorMixin
label_desc: Use image node_id 1 to create a new Perceptron object from inherited base classes: BaseSGDClassifier, _LearntSelectorMixin with example: obj = Perceptron(penalty, alpha, fit_intercept, n_iter, shuffle, verbose, eta0, n_jobs, random_state, class_weight, warm_start)