22 lines
574 B
Python
22 lines
574 B
Python
import shadowtube.preprocess as prep
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import shadowtube.recommend as rec
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raw_history = prep.parse_database("./short.db")
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# print(raw_history[0]["title"] + ": " + str(prep.relevancy(raw_history[0], raw_history)))
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# print(
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# raw_history[28]["title"] + ": " + str(prep.relevancy(raw_history[28], raw_history))
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# )
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# for i in range(0, 10):
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# print(prep.get_similarity(raw_history[i], raw_history))
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history = prep.sort_history(raw_history)
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print(len(history))
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# print(recommend(history))
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recommendations = rec.recommend(history, verbose=False)
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print(recommendations)
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