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The complexity analysis on the JS implementation looks good.
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| Time Complexity:O(nm) | ||
| Space Complexity:O(n+m) | ||
| Optimal time complexity:O(n+m) | ||
| which is the same as JavaScript const findCommonItems = (firstArray, secondArray) | ||
| """ | ||
| common_items: List[ItemType] = [] | ||
| for i in first_sequence: | ||
| for j in second_sequence: | ||
| if i == j and i not in common_items: | ||
| common_items.append(i) | ||
| return common_items |
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The complexity of this Python implementation is not O(nm).
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| // For two loops, we have visit each element of array in order to get the result | ||
| // so time complexity is O (n) which means linear. | ||
| // Regarding space complexity, we only get one result | ||
| // for sum and product no matter how long the array is.so it is O(1)(constant) | ||
| // While time complexity is O(n), optimal time complexity should be O(n) because loop | ||
| // has to go each single element in array. |
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Note: While the complexity remains
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Self checklist
Task code
CYF-1173
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did prep and get general idea about JavaScript and Python. Also learn from test and give explanation about refactor.