Kosinus o‘xshashligining python dasturlash tiliga tatbig‘i

Tabiiy tilni qayta ishlashda so‘zlar orasidagi masofani aniqlash algoritmlaridan foydalanish
81
to‘plam hosil qilamiz
rvector = X_set.union(Y_set)
for w in rvector:
if w in X_set: l1.append(1) # maxsus vektor 
yaratamiz
else: l1.append(0)
if w in Y_set: l2.append(1)
else: l2.append(0)
c = 0
# kosinus formulasi
for i in range(len(rvector)):
c+= l1[i]*l2[i]
cosine = c / float((sum(l1)*sum(l2))**0.5)
print(“kosinus o‘xshashligi: “, cosine)
 
Natijada:  kosinus o‘xshashligi 0.4 
Xulosa
So‘zlar orasidagi masofa qiymatini tavsiflovchi bir qancha 
qiymatlar mavjud. Ulardan Hamming, Levenshteyin masofalari 
va Kosinus o‘xshashligini yuqorida ko‘rib chiqqan holda shuni 
xulosa qilish mumkinki, har bir nazariyada kelib chiqadigan natija 
hamda ularning samaradorligi orqali o‘zining ishlatilish o‘rnini 
aniqlash mumkin. Aynan bir xil belgilar bilan boshlanuvchi so‘zlarni 
tekshirgan vaziyatda, Hamming masofasi boshqalarga nisbatan 
tabiiy tilni qayta ishlash jarayonida soddaroq hamda samaraliroq 
bo‘ladi. Biroq, satrlar hajmi ortishi hamda farq qiluvchi nuqtalar 
o‘zgarishi bilan Levenshteyin masofasi samaradorlik darajasi 
yuqoriga ko‘tariladi. Bundan tashqari, yuqorida berilgan nazariyalar 
orasida, katta hajmdagi matn yoki hujjatlar bilan ishlagan vaziyatda 
Kosinus o‘xshashligi eng munosib tanlov bo‘ladi. Demak, Hamming 
masofasini boshlang‘ich asos deb qaraladigan bo‘lsa, Levenshteyin 
masofasini uning so‘zlarga nisbatan mukammalroq vaziyati, shu 
bilan birgalikda, Kosinus o‘xshashligi esa so‘zlar jamlanmasi bo‘lgan 
matnlar yoki hujjatlar bilan ishlash uchun eng munosib nazariya 
sifatida qaraladi. 
Adabiyotlar:
Waggener Bill. Pulse Code Modulation Techniques. Springer. p. 206. ISBN. 
Retrieved 13 June, 2020.
Robinson, Derek J. S. (2003). An Introduction to Abstract Algebra. Walter 
de Gruyter. pp. 255–257.ISBN.

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Nizomaddin XUDAYBERGANOV, Shaxboz HASANOV 
Levenshteyin, Vladimir I. (February 1966). “Binary codes capable of 
correcting deletions, insertions, and reversals”. Soviet Physics 
Doklady.
Levenshteyin Distance Computation by Sergey Grashchenko November 16, 2022. 
https://www.baeldung.com/cs/Levenshteyin-distance-
computation
Cosine similarity https://en.wikipedia.org/wiki/Cosine_similarity
Connor, Richard (2016). A Tale of Four Metrics. Similarity Search and 
Applications. Tokyo: Springer.
Cosine distance, cosine similarity, angular cosine distance, angular cosine similarity. 
https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/
cosdist.htm.
Understanding cosine similarity and its application. Richmond Alake 
Sep 15,2020. Connor, Richard (2016). A Tale of Four Metrics. 
Similarity Search and Applications. Tokyo: Springer.
Sidorov, Grigori; Velasquez, Francisco; Stamatatos, Efstathios; Gelbukh, 
Alexander; Chanona-Hernández, Liliana (2013). Advances 
in Computational Intelligence. Lecture Notes in Computer 
Science. Vol.7630. LNAI 7630. pp.1–11.

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