watex.base.AdalineGradientDescent#

class watex.base.AdalineGradientDescent(eta=0.01, n_iter=50, random_state=42)[source]#

Adaptative Linear Neuron Classifier

ADAptative LInear NEuron (Adaline) was published by Bernard Widrow and his doctoral studentTeed Hoff only a few uears after Rosenblatt’s perceptron algorithm. It can be considered as impovrment of the latter Windrow and al., 1960.

Adaline illustrates the key concepts of defining and minimizing continuous cost function. This lays the groundwork for understanding more advanced machine learning algorithm for classification, such as Logistic Regression, Support Vector Machines,and Regression models.

The key difference between Adaline rule (also know as the WIdrow-Hoff rule) and Rosenblatt’s perceptron is that the weights are updated based on linear activation function rather than unit step function like in the perceptron. In Adaline, this linear activation function \(\phi(z)\) is simply the identifu function of the net input so that:

\[\phi (w^Tx)= w^Tx\]

while the linear activation function is used for learning the weights.

Parameters:

  • eta (float,) – Learning rate between (0. and 1.)

  • n_iter (int ,) – number of iteration passes over the training set

  • random_state (int, default is 42) – random number generator seed for random weight initialization.

w_#

Weight after fitting

Type:

Array-like,

cost_#

Sum of squares cost function (updates ) in each epoch

Type:

list

References

[1]

Windrow and al., 1960. An Adaptative “Adeline” Neuron Using Chemical “Memistors”, Technical reports Number, 1553-2,B Windrow and al., standford Electron labs, Standford, CA,October 1960.