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Skrypt.py
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#!/usr/bin/env python
# coding: utf-8
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
import string
from stop_words import get_stop_words
stop_words = get_stop_words('polish')
inp1 = open('dev-0/in.tsv', 'r', encoding="utf-8")
out1 = open("dev-0/out.tsv", "w")
linia1 = inp1.readlines()
inp1.close()
prep=[]
for x in linia1:
temp = ""
for y in x.split():
y = y.strip().replace(",", "")
if y not in stop_words:
temp = temp + " " + y
prep.append(temp)
vectorizer1 = TfidfVectorizer()
vectorizer1 = vectorizer1.fit_transform(prep)
predict1 = KMeans(n_clusters=25, max_iter=1000).fit_predict(vectorizer1)
print(predict1)
for x in predict1:
out1.write(str(x) + '\n')
out1.close()
inp2 = open('test-A/in.tsv', 'r', encoding="utf-8")
out2 = open("test-A/out.tsv", "w")
linia2 = inp2.readlines()
inp2.close()
prep2=[]
for x2 in linia2:
temp2 = ""
for y2 in x2.split():
y2 = y2.strip().replace(",", "")
if y2 not in stop_words:
temp2 = temp2 + " " + y2
prep2.append(temp2)
vectorizer2 = TfidfVectorizer()
vectorizer2 = vectorizer2.fit_transform(prep)
predict2 = KMeans(n_clusters=25, max_iter=1000).fit_predict(vectorizer2)
print(predict2)
for y in predict2:
out2.write(str(y) + '\n')
out2.close()

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