/
adaline_gd.py
67 lines (50 loc) · 1.55 KB
/
adaline_gd.py
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import numpy as np
class AdalineGD(object):
"""Adaptive Linear Neuron classifer.
Parameters
-----------
eta : float
Learning rate (0.0 - 1.0)
n_iter : int
Passes over the training set
Attributes
-----------
w_ : 1d-array
Weights after filtering.
errors_ : list
Number of misclassifications in every epoch.
"""
def __init__(self, eta=0.01, n_iter=10):
self.eta = eta
self.n_iter = n_iter
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
"""
self.w_ = np.zeroes(1 + X.shape[1])
self.cost_ = []
for __ in range(self.n_iter):
output =self.net_input(X)
errors = (y - output)
self.w_[1:] += self.eta * X.T.dot(errors)
self.w_[0] += self.eta * errors.sum()
cost = (errors ** 2).sum()/2.0
self.cost_.append(cost)
return self
def net_input(self, X):
"""Calculate net input"""
return np.dot(X, self.w_[1:]) + self.w_[0]
def activation(self, X):
"""Compute linear activation"""
return self.net_input(X)
def predict(self, X):
"""Return class label after unit step"""
return np.where(self.net_input(X) >= 0.0, 1, -1)