#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
frqanalysis.py
Open a text file, read text, tokenize it and generate a
frequency profile.
"""
from corpus import getTextFromFile, tokenize, makeFrequencyProfile, removeJunk
# mytext = getTextFromFile("pg873.txt")
# read text from file to memory and return a list of tokens
mytokens = tokenize(getTextFromFile("pg873.txt"))
mydict = makeFrequencyProfile(mytokens)
junk = " ,;:-+=()[]'\"?!$%.<>"
removeJunk(mydict, junk)
if "" in mydict:
del mydict[""]
# generate a nice output
total = sum(mydict.values())
for token in mydict:
print(token, mydict[token], mydict[token] / total, sep='\t')
#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
from math import log
#import corpus
from corpus import getTextFromFile, makeFrequencyProfile, tokenize, relativizeFP
mydict = makeFrequencyProfile( tokenize( getTextFromFile("pg873.txt") ) )
relativizeFP(mydict)
#for key in mydict:
# print(key, mydict[key], sep="\t")
mysportsdict = makeFrequencyProfile( tokenize( getTextFromFile("sports.txt") ) )
relativizeFP(mysportsdict)
unktokens = tokenize("""
The young King was eating pomegranates and talking about his soul and other emotional issues.
""")
probpomeg = 0.0
probsports = 0.0
for token in unktokens:
probpomeg += log(mydict.get(token, 0.00000000000001))
probsports += log(mysportsdict.get(token, 0.00000000000001))
if probpomeg > probsports:
print("This text is probably House of Pomeg.")
else:
#!/usr/bin/env python3
from corpus import getTextFromFile, tokenize, makeFrequencyProfile, removeJunk, prettyPrintFRP
for x in range (1,6):
loadSpam.split_data( x , 5, spamPath)
for file in spamList:
mytokens = tokenize(getTextFromFile(file) )
mydict = makeFrequencyProfile(mytokens)
junk = " ,;:-+=()[]'\"?!%.<>"
removeJunk(mydict, junk)
if "" in mydict:
del mydict[""]
prettyPrintFRP (mydict)