简单的文本相似性测量（Python实现）

一、数据集与测试集

数据集：

测试集：

imaging databases

二、Python代码

# -*- coding: utf-8 -*-
"""
Created on Mon Apr 13 09:49:25 2015

@author: Administrator
"""
import numpy
import sys
import scipy as sp 
import os
import nltk.stem as  stm
english_stemmer = stm.SnowballStemmer('english')
from sklearn.feature_extraction.text import CountVectorizer
class StemmedCountVectorizer(CountVectorizer):
    def build_analyzer(self):
        analyzer=super(StemmedCountVectorizer,self).build_analyzer()
        return lambda doc:(english_stemmer.stem(w) for w in analyzer(doc))
    

from sklearn.feature_extraction.text import CountVectorizer


def dist_raw(v1,v2):
    delta=v1-v2
    return sp.linalg.norm(delta.toarray())


def dist_norm(v1,v2):
    v1_normalized=v1/sp.linalg.norm(v1.toarray())
    v2_normalized=v2/sp.linalg.norm(v2.toarray())
    delta=v1_normalized-v2_normalized
    return sp.linalg.norm(delta.toarray())
    
    


#content=["how to format my hard disk","Hard disk format problems"]
#X=vectorizer.fit_transform(content)
dir="E:data"
posts=[open(os.path.join(dir,f)).read() for f in os.listdir(dir)]
#vectorizer=CountVectorizer(min_df=1)
vectorizer=StemmedCountVectorizer(min_df=1,stop_words='english')
x_train=vectorizer.fit_transform(posts)
num_samples,num_features=x_train.shape

"""
print("#samples: %d,#features: %d" % (num_samples,num_features))
print(vectorizer.get_feature_names())
"""

new_post="imaging databases"
new_post_vec=vectorizer.transform([new_post])


best_doc=None
best_dist=sys.maxint
best_i=None
for i in range(0,num_samples):
    post=posts[i]
    
    if(post==new_post):
        continue
    post_vec=x_train.getrow(i)
    #d=dist_raw(post_vec,new_post_vec)
    d=dist_norm(post_vec,new_post_vec)
    print"=== Post %i with dis=%.2f: %s" %(i,d,post)
    if d<best_dist:
        best_dist=d
        best_i=i
print("Best post is %i with dist=%.2f" %(best_i,best_dist))
    

3、结果 === Post 0 with dis=1.41: This is a toy post about machine learning. Actually,it contains not much interesting stuff === Post 1 with dis=0.61: Imaging databases can get huge. === Post 2 with dis=0.63: Most imaging databases safe images permanently === Post 3 with dis=0.52: Imaging databases store images. === Post 4 with dis=0.52: Imaging databases store images. Imaging databases store images. Imaging databases store images. Best post is 3 with dist=0.52