Logistic Regression

logress.py

import math
import random

def train(w, D, T):
    for t in range(1, T+1):
        x, y = random.choice(D)
        a = sum([w[i] * x[i] for i in range(len(w))])             # a(x) = w \cdot x
        g = y - (1. / (1. + math.exp(-a))) if -100. < a else y    # g = y - p
        eta = 1. / t
        for i in range(len(w)):                                   # w ← w + eta * g * x
            w[i] += eta * g * x[i]                                #

def prob(x):
    a = sum([w[i] * x[i] for i in range(len(w))])                 # a(x) = w \cdot x
    return 1. / (1 + math.exp(-a)) if -100. < a else 0.           # p = 1/(1 + exp(a))

if __name__ == '__main__':
    w = [0.] * 9
    D = (
        ((1, 1, 1, 1, 1, 1, 0, 0, 0), 1),
        ((1, 0, 0, 0, 1, 1, 1, 1, 1), 0),
    )
    train(w, D, 10000)
    print prob(D[0][0]), prob(D[1][0])
    print w