In [1]:

    

import numpy as np


    

In [2]:

    

learning_rate = 0.2
iteration = 100


    

In [3]:

    

X = np.array([
        [1, 0, 0],
        [1, 1, 0],
        [1, 0, 1],
        [1, 1, 1]
    ])


    

In [4]:

    

t = np.array([0,1,1,1])


    

In [5]:

    

m, n = X.shape


    

In [6]:

    

y = lambda x: 1 if x > 0 else 0


    

In [7]:

    

errors = []
weights = np.random.rand(n).reshape(3,1).T
for i in range(iteration):
    random_row = np.random.choice(m)
    X_row = X[random_row].reshape(3,1)
    weights = weights + learning_rate * (t[random_row] - y(np.dot(weights, X_row))) * X[random_row]
    error = t[random_row] - y(np.dot(weights, X_row)) # cost function
    errors.append(error)


    

In [8]:

    

list(map(y, np.dot(X, weights.T)))


    

    
Out[8]:





[0, 1, 1, 1]

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