watex.base.AdalineStochasticGradientDescent#

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

Adaptative Linear Neuron Classifier with batch (stochastic) gradient descent

A stochastic gradient descent is a popular alternative algorithm which is sometimes also called iterative or online gradient descent [1]. It updates the weights based on the sum of accumulated errors over all training examples \(x^{(i)}\):

\[\delta w: \sum{i} (y^{(i)} -\phi( z^{(i)}))x^(i)\]

the weights are updated incremetally for each training examples:

\[\eta(y^{(i)} - \phi(z^{(i)})) x^{(i)}\]

Parameters:

eta (float,) – Learning rate between (0. and 1.)
n_iter (int,) – number of iteration passes over the training set
suffle (bool,) – shuffle training data every epoch if True to prevent cycles.
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