class_docstr: Perceptron classifier.
  
  Note that this implementation of the Perceptron expects binary class labels
  in {0, 1}.
  
  Parameters
  ------------
  eta : float (default: 0.1)
      Learning rate (between 0.0 and 1.0)
  epochs : int (default: 50)
      Number of passes over the training dataset.
      Prior to each epoch, the dataset is shuffled to prevent cycles.
  random_seed : int
      Random state for initializing random weights and shuffling.
  print_progress : int (default: 0)
      Prints progress in fitting to stderr.
      0: No output
      1: Epochs elapsed and cost
      2: 1 plus time elapsed
      3: 2 plus estimated time until completion
  
  Attributes
  -----------
  w_ : 2d-array, shape={n_features, 1}
    Model weights after fitting.
  b_ : 1d-array, shape={1,}
    Bias unit after fitting.
  cost_ : list
      Number of misclassifications in every epoch.
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/classifier/Perceptron/
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code: def _predict(self, X):
      return np.where(self._net_input(X) < 0.0, 0, 1)
  
file_path: mlxtend/mlxtend/classifier/perceptron.py
imports: from time import time
  import numpy
  from .._base import _BaseModel, _Classifier, _IterativeModel
label_desc: Use image node_id 5 for calling the Perceptron obj's underlying member method code with example usage: obj._predict(X) and returns: np