回答編集履歴
2
修正
test
CHANGED
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@@ -124,9 +124,9 @@
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124
124
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common_titles = person1_reviews.keys() & person2_reviews.keys()
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-
if
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if not common_titles:
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return 0 #
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return 0 # 共通のタイトルが存在しない場合
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@@ -136,10 +136,6 @@
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scores2 = [person2_reviews[title] for title in common_titles]
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-
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# 確認用
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-
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for title, score1, score2 in zip(common_titles, scores1, scores2):
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print(f"title: {title} {person1}: {score1}点, {person2}: {score2}点")
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@@ -148,7 +144,7 @@
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# コサイン類似度 cosΘ = <a, b>/(|a| |b|)
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cos_similarity = np.dot(scores1, scores2) / np.linalg.norm(scores1) * np.linalg.norm(scores2)
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cos_similarity = np.dot(scores1, scores2) / (np.linalg.norm(scores1) * np.linalg.norm(scores2))
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1
修正
test
CHANGED
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@@ -124,9 +124,9 @@
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124
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125
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common_titles = person1_reviews.keys() & person2_reviews.keys()
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127
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if n
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127
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+
if len(common_titles) < 2:
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129
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return 0 # 共通のタイトルが
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129
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return 0 # 最低2つの共通のタイトルが必要
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