sample.py

# http://www.puffinwarellc.com/index.php/news-and-articles/articles/33.html
# https://github.com/bbbbear/LSA-Example/blob/master/lsa_example.ipynb
import numpy as np
from scipy.linalg import svd

# data
titles = ["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"
		]

stopwords = ['and','edition','for','in','little','of','the','to']
ignorechars = ''',:'!'''

class LSA(object):
	def __init__(self, stopwords, ignorechars):
		self.stopwords = stopwords
		self.ignorechars = ignorechars
		self.wdict = {}
		self.dcount = 0

	def parse(self, doc):
		words = doc.split();
		for w in words:
			w = w.lower().translate(self.ignorechars)
			if w in self.stopwords:
				continue
			elif w in self.wdict:
				self.wdict[w].append(self.dcount)
			else:
				self.wdict[w] = [self.dcount]
		self.dcount += 1
	
	# rows -> keywords (occur more than twice), cols -> documentID
	def build(self):
		self.keys = [k for k in self.wdict.keys() if len(self.wdict[k]) > 1]
		self.keys.sort()
		self.A = np.zeros([len(self.keys), self.dcount])
		for i, k in enumerate(self.keys):
			for d in self.wdict[k]:
				self.A[i,d] += 1
	
	def calc(self):
		self.U, self.S, self.Vt = svd(self.A)
	
	def TFIDF(self):
		WordsPerDoc = sum(self.A, axis=0)        
		DocsPerWord = sum(np.asarray(self.A > 0, 'i'), axis=1)
		rows, cols = self.A.shape
		for i in range(rows):
			for j in range(cols):
				self.A[i,j] = (self.A[i,j] / WordsPerDoc[j]) * np.log(float(cols) / DocsPerWord[i])
	
	def printA(self):
		print('Here is the count matrix')
		print(self.A)
	
	def printSVD(self):
		print('Here are the singular values')
		print(self.S)
		print('Here are the first 3 columns of the U matrix')
		print(-1*self.U[:, 0:3])
		print('Here are the first 3 rows of the Vt matrix')
		print(-1*self.Vt[0:3, :])

	def TFIDF(self):
		WordsPerDoc = sum(self.A, axis=0) 
		DocsPerWord = sum(np.asarray(self.A > 0, 'i'), axis=1) 
		rows, cols = self.A.shape 
		for i in range(rows):
			for j in range(cols):
				self.A[i,j] = (self.A[i,j] / WordsPerDoc[j]) * np.log(float(cols) / DocsPerWord[i])

if __name__ == '__main__':
    mylsa = LSA(stopwords, ignorechars)
    for t in titles:
        mylsa.parse(t)

    mylsa.build()
    mylsa.printA()
    mylsa.calc()
    mylsa.printSVD()