潜在狄利克雷分配 LDA 吉布斯抽样法算法

import numpy as np
import jieba

class LDA:
    def __init__(self,text_list,k):
        self.k = k
        self.text_list = text_list
        self.text_num = len(text_list)
        self.get_X()
        self.NKV = np.zeros((self.k,self.word_num))
        self.NMK = np.zeros((self.text_num,self.k))
        self.nm = np.zeros(self.text_num)
        self.nk = np.zeros(self.k)
        self.zmn = [[] for i in range(self.text_num)]
        self.alpha = np.random.randint(1,self.k,size=k)
        self.beta = np.random.randint(1,self.word_num, size=self.word_num)


    def get_X(self):
        self.cuted_text = [jieba.lcut(text,cut_all=True) for text in self.text_list]
        self.word_all = []
        for i in self.cuted_text:
            self.word_all.extend(i)
        self.word_set = list(set(self.word_all))
        self.word_num = len(self.word_set)
        self.word_dict = {}
        for index,word in enumerate(self.word_set):
            self.word_dict[word] = index

    def initial_K(self):
        for doc_num in range(self.text_num):
            for word in self.cuted_text[doc_num]:
                k = np.random.choice(self.k, 1)[0]
                self.zmn[doc_num].append(k)
                v = self.word_dict[word]
                self.NMK[doc_num,k] += 1
                self.nm[doc_num] += 1
                self.NKV[k,v] += 1
                self.nk[k] += 1

    def iter_jbs(self):
        for doc_num in range(self.text_num):
            for word_index in range(len(self.cuted_text[doc_num])):
                v = self.word_dict[self.cuted_text[doc_num][word_index]]
                k = self.zmn[doc_num][word_index]
                self.NMK[doc_num,k] -= 1
                self.nm[doc_num] -= 1
                self.NKV[k,v] -= 1
                self.nk[k] -= 1
                p_klist = (self.NKV[:,v]+self.beta[v])/np.sum(self.NKV[:,v]+self.beta[v])*(self.NMK[doc_num]+self.alpha[k])/np.sum(self.NMK[doc_num]+self.alpha[k])
                p_klist = p_klist/np.sum(p_klist)
                k_choice = np.random.choice(self.k,p = p_klist)
                self.zmn[doc_num][word_index] = k_choice

                self.NMK[doc_num,k_choice] += 1
                self.nm[doc_num] += 1
                self.NKV[k_choice,v] += 1
                self.nk[k_choice] += 1

    def get_sita_y(self):
        self.sita_mk  = np.zeros((self.text_num,self.k))
        self.yta_kv = np.zeros((self.k,self.word_num))
        for i in range(self.text_num):
            self.sita_mk[i] = (self.NMK[i]+self.alpha)/np.sum(self.NMK[i])
        for j in range(self.k):
            self.yta_kv[j] = (self.NKV[j]+self.beta)/np.sum(self.NKV[j])

    def fit(self,max_iter = 100):
        self.initial_K()
        for iter in range(max_iter):
            print(iter)
            self.iter_jbs()
        self.get_sita_y()

def main():
    text_list = [
    '一个月前，足协杯十六进八的比赛，辽足费尽周折对调主客场，目的只是为了葫芦岛体育场的启用仪式。那场球辽足5比0痛宰“主力休息”的天津泰达。几天后中超联赛辽足客场对天津，轮到辽足“全替补”，\
    1比3输球，甘为天津泰达保级的祭品。那时，辽足以“联赛保级问题不大，足协杯拼一拼”作为主力和外援联赛全部缺阵的理由。',
    '被一脚踹进“忘恩负义”坑里的孙杨，刚刚爬出来，又有手伸出来，要把孙杨再往坑里推。即使是陪伴孙杨参加世锦赛的张亚东(微博)教练，\
    也没敢大义凛然地伸出援手，“孙杨愿意回去我不拦”，球又踢给了孙杨。张亚东教练怕什么呢？',
    '孙杨成绩的利益分配，以及荣誉的分享，圈里人都知道，拿了世界冠军和全运冠军，运动员都会有相应的高额奖金，那么主管教练也会得到与之对应的丰厚奖励，\
    所以孙杨获得的荣誉，也会惠及主管教练。']
    k = 2
    lda = LDA(text_list,k)
    lda.fit()
    print(lda.sita_mk)
    print(lda.yta_kv)

if __name__ == '__main__':
    main()

#result--------------------------

[[0.20689655 0.81034483]
 [0.7        0.32222222]
 [0.50666667 0.52      ]]
 
[[1.2295082  0.12295082 0.58196721 1.21311475 1.08196721 0.06557377
  1.12295082 0.18032787 0.98360656 0.16393443 0.78688525 1.01639344
  0.7704918  1.12295082 1.01639344 0.43442623 1.00819672 0.72131148
  0.70491803 0.21311475 0.78688525 0.14754098 0.6147541  0.53278689
  0.59836066 1.20491803 0.6557377  0.01639344 1.05737705 0.53278689
  1.22131148 0.71311475 1.29508197 1.23770492 0.59016393 1.20491803
  0.13114754 0.04918033 0.99180328 0.93442623 1.27868852 1.1557377
  0.90983607 0.66393443 1.08196721 1.07377049 0.57377049 0.08196721
  0.17213115 0.54098361 1.14754098 0.98360656 0.17213115 0.26229508
  0.6557377  1.12295082 0.80327869 0.77868852 1.10655738 0.81967213
  0.79508197 0.41803279 0.63934426 0.36065574 1.29508197 0.74590164
  0.99180328 1.14754098 0.67213115 0.33606557 0.40163934 0.73770492
  0.67213115 0.86885246 0.18852459 0.17213115 0.75409836 0.33606557
  0.07377049 1.13114754 0.40163934 0.63934426 0.36885246 1.27868852
  1.19672131 0.35245902 1.10655738 0.21311475 1.19672131 0.71311475
  0.29508197 0.67213115 1.02459016 0.87704918 0.81147541 1.04918033
  0.1147541  1.1147541  0.40163934 1.05737705 0.31147541 0.40983607
  0.31147541 0.59016393 0.74590164 1.18852459 1.32786885 0.74590164
  0.48360656 0.42622951 0.8442623  1.22131148 0.95901639 0.69672131
  0.09836066 1.26229508 1.1147541  0.63934426 1.1557377  0.14754098
  1.18032787 0.1557377  0.93442623 0.63114754 0.45901639 0.52459016
  1.28688525 1.13114754 0.91803279 1.27868852 0.82786885 0.31147541
  0.33606557 0.41803279 1.30327869 0.99180328 1.31147541 1.17213115
  0.97540984 1.19672131 0.24590164 0.90983607 0.59016393 0.49180328
  0.87704918 1.08196721 0.42622951 0.27868852 0.49180328 0.69672131
  0.08196721 0.48360656 0.5        0.7704918  0.95081967 1.
  0.52459016 0.16393443 1.1147541  0.18852459 0.82786885 1.09016393
  0.1147541  0.93442623]
 [0.99371069 0.10691824 0.44025157 0.93710692 0.83647799 0.05660377
  0.88679245 0.14465409 0.76100629 0.1509434  0.61006289 0.78616352
  0.59119497 0.87421384 0.77987421 0.35220126 0.77358491 0.55974843
  0.53459119 0.17610063 0.60377358 0.11949686 0.47798742 0.40880503
  0.45283019 0.93081761 0.50314465 0.00628931 0.81761006 0.41509434
  0.93081761 0.55345912 0.98742138 0.94339623 0.44654088 0.91823899
  0.10691824 0.03144654 0.7672956  0.72327044 0.98742138 0.88050314
  0.71069182 0.51572327 0.83647799 0.81761006 0.44654088 0.05660377
  0.12578616 0.40880503 0.87421384 0.74842767 0.12578616 0.19496855
  0.50943396 0.85534591 0.62264151 0.60377358 0.8427673  0.66037736
  0.6163522  0.32704403 0.48427673 0.27044025 1.01257862 0.56603774
  0.74213836 0.86792453 0.50943396 0.25157233 0.31446541 0.57232704
  0.52830189 0.65408805 0.1509434  0.12578616 0.58490566 0.26415094
  0.05660377 0.8490566  0.31446541 0.50314465 0.27044025 0.98742138
  0.9245283  0.27672956 0.8427673  0.16981132 0.9245283  0.54716981
  0.2327044  0.52201258 0.77987421 0.67924528 0.63522013 0.79874214
  0.08805031 0.8490566  0.29559748 0.81761006 0.24528302 0.32075472
  0.25157233 0.4591195  0.57861635 0.90566038 1.02515723 0.56603774
  0.36477987 0.32075472 0.65408805 0.93710692 0.72955975 0.52830189
  0.08176101 0.97484277 0.8490566  0.49685535 0.89308176 0.11949686
  0.89937107 0.11320755 0.70440252 0.49056604 0.33962264 0.40880503
  0.98113208 0.86163522 0.69811321 0.97484277 0.66037736 0.24528302
  0.25157233 0.32075472 0.99371069 0.75471698 1.01257862 0.89308176
  0.75471698 0.93081761 0.18238994 0.6918239  0.4591195  0.36477987
  0.66666667 0.80503145 0.32075472 0.22012579 0.3836478  0.52830189
  0.05660377 0.37735849 0.37735849 0.58490566 0.74213836 0.77358491
  0.40251572 0.13207547 0.8490566  0.1509434  0.64150943 0.83647799
  0.09433962 0.72327044]]