统计学习方法第十七章作业：LSA潜在语义分析算法 代码实现

LSA潜在语义分析算法

import numpy as np
import jieba
import collectionsclass LSA:def __init__(self,text_list):self.text_list = text_listself.text_num = len(text_list)self.get_X()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] = indexself.X = np.zeros((self.word_num,self.text_num))self.word_IDF = {word:0 for word in self.word_set}for i in self.cuted_text:for word in set(i):self.word_IDF[word] += 1for i in range(self.text_num):count_ = collections.Counter(self.cuted_text[i])tf_sum = len(self.cuted_text[i])for k, v in count_.items():self.X[self.word_dict[k],i] = v/tf_sum * np.log(self.text_num/self.word_IDF[k])def norm_w(self):norm = 1 / np.array([np.sqrt(np.sum(self.W[:, x] ** 2)) for x in range(self.k)])for i in range(self.k):self.W[:, i] = self.W[:, i] * norm[i]def SVD(self,k):u,s,v = np.linalg.svd(self.X)return u[:,:k],s[:k].dot(v[:,:k].T)def nonegetive(self,k,max_iter,way='MES'):self.k = kself.W = np.random.random((self.word_num, self.k))self.H = np.random.random((self.k, self.text_num))if way == 'MES':for iter in range(max_iter):self.norm_w()n_w = (self.X.dot(self.H.T))m_w = (self.W.dot(self.H).dot(self.H.T))n_h = (self.W.T.dot(self.X))m_h = self.W.T.dot(self.W).dot(self.H)for j in range(self.k):for i in range(self.word_num):self.W[i,j] = self.W[i,j] * n_w[i,j]/m_w[i,j]for i in range(self.text_num):self.H[j,i] = self.H[j,i] * n_h[j,i]/m_h[j,i]self.norm_w()if way == 'DIV':for iter in range(max_iter):self.norm_w()W = self.Wfor k_ in range(self.k):for i in range(self.word_num):self.W[i,k_] = self.W[i,k_]*np.sum(self.H[k_].dot(self.X[i])\/(self.W.dot(self.H)[i]))/np.sum(self.H[k_])for k_ in range(self.k):for j in range(self.text_num):self.H[k_,j] = self.H[k_,j]*np.sum(W[:,k_].dot(self.X[:,j])\/(W.dot(self.H)[:,j]))/np.sum(W[:,k_])self.norm_w()def main():text_list = [……]lsa = LSA(text_list)lsa.nonegetive(2,100,way='MES')print(lsa.W)print(lsa.H)if __name__ == '__main__':main()

散度损失非负矩阵分解算法推导

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