第09章 EM算法及其推广 - M step: - 实现EM类

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