!!! Overview
[{$pagename}] ([MLP]) is a class of [Artificial Neuron] that takes takes several [binary] inputs, x1,x2,…x1,x2,…, and produces a single [binary] output based on the [bias].

[{$pagename}] are sometimes colloquially referred to as "vanilla" [Artificial Neural networks], especially when they have a single [Hidden layer]

[{$pagename}]s are not in common usage in [Artificial Neural networks] as the use of more sophisticated functions.

[{$pagename}] in the context of a [binary] [classification] task where we refer to our two [classifications] as 1 (positive class) and -1 (negative class) for simplicity. We can then define a [Mapping function]:
__Z__ = __w__(transform)T __x__


[Perceptron/perceptron.jpg]


!! [{$pagename}] in [Python]

%%prettify 
{{{
import numpy as np

class Perceptron(object):
    """Perceptron classifier.

    Parameters
    ------------
    eta : float
      Learning rate (between 0.0 and 1.0)
    n_iter : int
      Passes over the training dataset.
    random_state : int
      Random number generator seed for random weight
      initialization.

    Attributes
    -----------
    w_ : 1d-array
      Weights after fitting.
    errors_ : list
      Number of misclassifications (updates) in each epoch.

    """
    def __init__(self, eta=0.01, n_iter=50, random_state=1):
        self.eta = eta
        self.n_iter = n_iter
        self.random_state = random_state

    def fit(self, X, y):
        """Fit training data.

        Parameters
        ----------
        X : {array-like}, shape = [n_samples, n_features]
          Training vectors, where n_samples is the number of 
          samples and
          n_features is the number of features.
        y : array-like, shape = [n_samples]
          Target values.

        Returns
        -------
        self : object

        """
        rgen = np.random.RandomState(self.random_state)
        self.w_ = rgen.normal(loc=0.0, scale=0.01, 
                              size=1 + X.shape[1])
        self.errors_ = []

        for _ in range(self.n_iter):
            errors = 0
            for xi, target in zip(X, y):
                update = self.eta * (target - self.predict(xi))
                self.w_[1:] += update * xi
                self.w_[0] += update
                errors += int(update != 0.0)
            self.errors_.append(errors)
        return self

    def net_input(self, X):
        """Calculate net input"""
        return np.dot(X, self.w_[1:]) + self.w_[0]

    def predict(self, X):
        """Return class label after unit step"""
        return np.where(self.net_input(X) >= 0.0, 1, -1)
}}} 
/%

!! More Information
There might be more information for this subject on one of the following:
[{ReferringPagesPlugin before='*' after='\n' }]
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* [#1] - [Multilayer_perceptron|Wikipedia:Multilayer_perceptron|target='_blank'] - based on information obtained 2017-11-24-