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backup.py
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backup.py
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from __future__ import print_function
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import io
import redis
from ngram import NGram
import nltk
import math as mat
r = redis.Redis("localhost")
def Split_Sentence(sentence):
verb=sentence.split()
array=[]
for i in range(0,len(verb)):
if i+1<len(verb):
verbs = verb[i]+" "+verb[i+1]
array.insert(i,verbs)
return array
def HowMamyVerbinText2(text):
hm=0
text = Split_Sentence(text)
print (text)
for i in range(0,len(text)):
for j in range(0,len(text)):
if text[i]==text[j]:
hm+=1
r.set(text[i], hm)
hm = 0
def HowMamyVerbinText(text):
hm=0
text = nltk.word_tokenize(text)
print (text)
for i in range(0,len(text)):
for j in range(0,len(text)):
if text[i]==text[j]:
hm+=1
r.set(text[i], hm)
hm = 0
def FixedText(textt):
word={}
word_count_index=[]
Value=[0.5]
a=0
fix=""
maxvalue=Value[0]
for key in r.scan_iter():
x=NGram.compare(textt,key,N=1)
if x>= 0.5:
a=float(r.get(key))
b=100*x+float(a)
if b>maxvalue:
maxvalue=b
fix=key
word[fix]=b
else:
continue
for key in word.keys():
if key is None:
pass
else:
word_count_index.append(r.get(key))
return word_count_index,word.values(),word.keys()
import matplotlib.pyplot as plt
degress={}
textt="thiz"
counts,accuracy,keys=FixedText(textt)
counts2,accuracy2,keys2=FixedText("hit")
for i in range(len(counts)):
x=mat.tan(float(counts[i])/float(accuracy[i]))
degress['aci{}'.format(i)]=(1.61977519054-x)
degress['kelime{}'.format(i)]=counts[i]
##UTKUTOSPONTUS YONTEMI.<3
print(keys)
print (degress)
plt.plot(counts,accuracy)
plt.plot(counts2,accuracy2)
plt.show()
#FixedText(str_read)