def simpleTest(self):
"""
LSA最简单的例子，这里的文本数据都保存在一个list中
"""
docs = [
"The Neatest Little Guide to Stock Market Investing",
"Investing For Dummies, 4th Edition",
"The Little Book of Common Sense Investing: The Only Way to Guarantee Your Fair Share of Stock Market Returns",
"The Little Book of Value Investing",
"Value Investing: From Graham to Buffett and Beyond",
"Rich Dad's Guide to Investing: What the Rich Invest in, That the Poor and the Middle Class Do Not!",
"Investing in Real Estate, 5th Edition",
"Stock Investing For Dummies",
"Rich Dad's Advisors: The ABC's of Real Estate Investing: The Secrets of Finding Hidden Profits Most Investors Miss"]
stopwordsfile = open('lib/stopwords.txt', "r")
for word in stopwordsfile: # a stop word in each line
word = word.replace("\n", '')
word = word.replace("\r\n", '')
self.STOP_WORDS_SET.append(word)
lsa = LSA(self.STOP_WORDS_SET, self.PUNCTUATION)
## 解析英文文档，注意这里9个文档中每个文档都是一个字符串
for t in docs:
lsa.parseEnglish(t)
## 生成一个TDM矩阵,行为词汇，列为文档
lsa.buildTDM()
## 输出词频权重
lsa.printA()
lsa.TFIDF()
## 输出TF-IDF权重
lsa.printA()
print '计算两个词汇estate与market的Spearman Rank Corralation相关性'
w1 = lsa.A[3]
w2 = lsa.A[6]
print spearmanr(w1, w2)[0]
print '----------------------'
## 计算SVD
u, s, vt = lsa.calc()
print "奇异值矩阵为"
print s
n90 = lsa.maxWeight(s, 0.9)
print "前项目占据了奇异值信息量的90%:", n90
Sig3 = mat([[s[0], 0, 0, 0, 0], [0, s[1], 0, 0, 0], [0, 0, s[2], 0, 0], [0, 0, 0, s[3], 0], [0, 0, 0, 0, s[4]]])
#print Sig3
print "模拟原始矩阵"
newMat = mat(u[:, 0:5]) * Sig3 * mat(vt[0:5, :])
print newMat
print '两个矩阵之差'
t = mat(lsa.A) - newMat
print t
w1 = asarray(newMat[3, :])[0]
w2 = asarray(newMat[6, :])[0]
print spearmanr(w1, w2)[0]
print "对比w1和w2的前后spearmanr系数，可知其相关性提升了"
def englishCorpusTest(self):
courses = [line.strip() for line in file('data/LSA/coursera_corpus')]
courses_name = [course.split('\t')[0] for course in courses]
print courses_name[0:2]
stopwords = ['and', 'edition', 'for', 'in', 'little', 'of', 'the', 'to', 'a', '1', '2', '3', '4']
ignorechars = ''',:'!()&-'''
lsa = LSA(stopwords, ignorechars)
for t in courses_name:
lsa.parseEnglish(t)
lsa.buildTDM()
## 输出词频权重
lsa.printA()
lsa.TFIDF()
## 输出TF-IDF权重
lsa.printA()
## 计算SVD
u, s, vt = lsa.calc()
print "奇异值矩阵为"
print u.shape, s.shape, vt.shape
print s
n90 = lsa.maxWeight(s, 0.9)
print "前项目占据了奇异值信息量的90%:", n90
emptyMat = zeros(shape=(n90, n90))
i = 0
for a in emptyMat:
a[i] = s[i]
i = i + 1
def weiboTest(self):
# 以下三行代码是将数据从csv转化为txt
#from hmgis.TextMining.TextTools.parseFile import *
#p = parseCSV('lib/chinesestopwords.txt', 'lib/userdict')
#p.parse('data/LSA/jiangbt.csv', 'data/LSA/wb_clean.txt')
lsa = LSA([], [])
lsa.parseChinese('data/LSA/wb_clean.txt')
lsa.buildTDM()
lsa.printA()
lsa.TFIDF()
lsa.printA()
u, s, vt = lsa.calc()
print "奇异值矩阵为"
print u.shape, s.shape, vt.shape
print s
n90 = lsa.maxWeight(s, 0.4)
print "前项目占据了奇异值信息量的90%:", n90
n90 = lsa.maxWeight(s, 0.5)
print "前项目占据了奇异值信息量的90%:", n90
n90 = lsa.maxWeight(s, 0.6)
print "前项目占据了奇异值信息量的90%:", n90
