42 lines
2.1 KiB
Python
42 lines
2.1 KiB
Python
# bag of words (bow) algorithm implementation
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import numpy as np
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.metrics.pairwise import cosine_similarity, manhattan_distances, paired_distances, pairwise_distances
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# متون ورودی
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text0 = "قانون خانواده و جوانی جمعیت"
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text1 = "قانون اصلاح موادی از قانون خانواده و جوانی جمعیت ایران"
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text2 = "خانواده و جمعیت جوانی مهم است"
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# ایجاد یک نمونه از CountVectorizer برای تبدیل متن به بردارهای ویژگی
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vectorizer = CountVectorizer()
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# تبدیل متون به بردارهای ویژگی
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X = vectorizer.fit_transform([text0,text1, text2])
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# Y = vectorizer.fit_transform([text1,text3])
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# Z = vectorizer.fit_transform([text2,text2])
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# تبدیل ماتریس sparse به آرایه numpy
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X_array = X.toarray()
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# محاسبه شباهت کسینوسی بین دو بردار
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similarity = cosine_similarity(X_array)
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manhattan_distance = manhattan_distances(X_array)
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pairwise_distance = pairwise_distances(X_array)
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#paired_distance = paired_distances(X_array)
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# نمایش شباهت کسینوسی
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print(f"Cosine Similarity between text0 and text1: {similarity[0, 1]}")
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print(f"Cosine Similarity between text0 and text2: {similarity[0, 2]}")
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print(f"Cosine Similarity between text1 and text2: {similarity[2, 1]}")
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print(f"manhattan_distance between text0 and text1: {manhattan_distance[0, 1]}")
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print(f"manhattan_distance between text0 and text2: {manhattan_distance[0, 2]}")
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print(f"manhattan_distance between text1 and text2: {manhattan_distance[2, 1]}")
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#print(f"paired_distance between text0 and text1: {paired_distance[0, 1]}")
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#print(f"paired_distance between text0 and text2: {paired_distance[0, 2]}")
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#print(f"paired_distance between text1 and text2: {paired_distance[2, 1]}")
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print(f"pairwise_distance between text0 and text1: {pairwise_distance[0, 1]}")
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print(f"pairwise_distance between text0 and text2: {pairwise_distance[0, 2]}")
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print(f"pairwise_distance between text1 and text2: {pairwise_distance[2, 1]}") |