class_docstr: ADAptive LInear NEuron classifier.
  
  Note that this implementation of Adaline expects binary class labels
  in {0, 1}.
  
  Parameters
  ------------
  eta : float (default: 0.01)
      solver rate (between 0.0 and 1.0)
  epochs : int (default: 50)
      Passes over the training dataset.
      Prior to each epoch, the dataset is shuffled
      if `minibatches > 1` to prevent cycles in stochastic gradient descent.
  minibatches : int (default: None)
      The number of minibatches for gradient-based optimization.
      If None: Normal Equations (closed-form solution)
      If 1: Gradient Descent learning
      If len(y): Stochastic Gradient Descent (SGD) online learning
      If 1 < minibatches < len(y): SGD Minibatch learning
  random_seed : int (default: None)
      Set random state for shuffling and initializing the weights.
  print_progress : int (default: 0)
      Prints progress in fitting to stderr if not solver='normal equation'
      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
    Sum of squared errors after each epoch.
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/classifier/Adaline/
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docstr: Compute the linear net input.
docstr_tok: ["Compute","the","linear","net","input","."]
code: def _net_input(self, X):
      """Compute the linear net input."""
      return (np.dot(X, self.w_) + self.b_).flatten()
  
file_path: mlxtend/mlxtend/classifier/adaline.py
imports: from time import time
  import numpy
  from .._base import _BaseModel, _Classifier, _IterativeModel
label_desc: Use image node_id 5 for calling the Adaline obj's underlying member method code with example usage: obj._net_input(X) and returns: unknown