London | 25-SDC-Nov | Aida Eslamimoghadam | Sprint 1 | Analyse and refactor functions#141
London | 25-SDC-Nov | Aida Eslamimoghadam | Sprint 1 | Analyse and refactor functions#141aydaeslami wants to merge 10 commits intoCodeYourFuture:mainfrom
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Code is good.
Can you also show the time complexity of the original implementation?
| * Time Complexity: | ||
| * Space Complexity: | ||
| * ANSWER: O(n + m) | ||
| Because the second array is converted into a Set once, and the first array is looped through once. | ||
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I think "Time Complexity" here refers to the time complexity of the original implementation, and "Optimal Time Complexity" refers to the time complexity of the refactored version. (Otherwise they will always have the same complexity)
| if i in second_sequence_set and i not in common_items: | ||
| common_items.append(i) |
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Can also consider using a built-in set operation for better performance and simpler code.
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Thanks for your feedback @cjyuan !I’ve made the suggested changes, including using the built-in set intersection and updating the complexity comments. Please let me know if anything else needs improvement.
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Can you also show the time complexity of the original implementation (in each file)?
| Time Complexity: | ||
| Space Complexity: | ||
| Optimal time complexity: | ||
| Time Complexity: O(n + m) process both sequences |
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The complexity of the original implementation is not O(n+m).
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Refactored functions to reduce time complexity by removing nested loops and using sets for faster lookups, improving overall performance.