homework_04 #1267
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| # быстрее, чем python2, при этом циклы for для больших | ||
| # массивов стали работать практически | ||
| # со скоростью list comprehension, в то время как в python2 | ||
| # разница для больших списков была почти двукратной |
| # --- | ||
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| # Что и требовалось доказать, мемоизация в данном случае | ||
| # делает только хуже |
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мемоизация здесь просто не нужна
но нужна. если делать много вызовов
| # cProfile строит свою статистику на основе измерений вызовов | ||
| # всех функций. Очевидно, при таком подходе чаще всего | ||
| # вызывается revers_2(0). При усреднении это приводит | ||
| # к занижению результатов по времени. |
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Остается неясным, почему cProfile и timeit
cProfile - 1 запуск
timeit - number запусков
| # 0.00952054699882865 | ||
| # --- | ||
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| # Здесь самым быстрым является алгоритм №3, |
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здесь решается в одну строку - ф-ция max
посмотрите пример
| # 42.7186805739766 | ||
| # 104729 | ||
| # 0.1595245740027167 |
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нет аналитики, где Решето эффективнее и почему
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