# -*- coding:utf-8 -*-
# /usr/bin/python
import numpy as np
import math
class EM:
def __init__(self, prob):
self.pro_A, self.pro_B, self.pro_C = prob
# e_step
def pmf(self, i):
pro_1 = self.pro_A * math.pow(self.pro_B, data[i]) * math.pow((1 - self.pro_B), 1 - data[i])
pro_2 = (1 - self.pro_A) * math.pow(self.pro_C, data[i]) * math.pow((1 - self.pro_C), 1 - data[i])
return pro_1 / (pro_1 + pro_2)
# m_step
def fit(self, data):
count = len(data)
print('init prob:{}, {}, {}'.format(self.pro_A, self.pro_B, self.pro_C))
for d in range(count):
_ = yield
_pmf = [self.pmf(k) for k in range(count)]
pro_A = 1 / count * sum(_pmf)
pro_B = sum([_pmf[k] * data[k] for k in range(count)]) / sum([_pmf[k] for k in range(count)])
pro_C = sum([(1 - _pmf[k]) * data[k] for k in range(count)]) / sum([(1 - _pmf[k]) for k in range(count)])
print('{}/{} pro_a:{:.3f}, pro_b:{:.3f}, pro_c:{:.3f}'.format(d + 1, count, pro_A, pro_B, pro_C))
self.pro_A = pro_A
self.pro_B = pro_B
self.pro_C = pro_C
data=[1,1,0,1,0,0,1,0,1,1]
em = EM(prob=[0.5, 0.5, 0.5])
f = em.fit(data)
next(f)
f.send(1)
f.send(2)
f.send(9)