Spruce - Emily C #40
Spruce - Emily C #40
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anselrognlie
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anselrognlie
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✨ Your implementations look good, Emily! I left some comments on your implementation below.
Because of the importance of thinking about complexity for this project, I've evaluated this as a yellow due to the missing complexities for both waves. A yellow is a passing score so resubmission is not required, but you are free to resubmit with the time and space complexity filled out for a green score.
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| Time Complexity: ? | ||
| Space Complexity: ? |
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👀 Implementation looks good, but what are the time and space complexity for this approach?
| if num == 0: | ||
| raise ValueError() |
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We should raise this error for any value below the valid starting point of the sequence:
if num <= 0:
raise ValueError()|
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| # return ' '.join(base) | ||
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| f = [0, 1, 1] |
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✨ Nice use of a buffer slot to account for the 1-based calculation.
| r = f[f[i-1]]+f[i-f[i-1]] | ||
| f.append(r) | ||
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| return ' '.join([str(j) for j in f[1:]]) No newline at end of file |
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✨ Nice use of a list comprehension to convert the numeric values to strings.
Another approach would be to use the map function:
return ' '.join(map(str, f[1:]))| max_til_now = nums[0] | ||
| max_ending = 0 | ||
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| for i in range(len(nums)): |
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👀 Implementation looks good, but what are the time and space complexity for this approach?
How would this compare to a "naïve" approach? Though this might not look like what we would think of as a dynamic programming approach, this article has a fairly good explanation of why it is. The main reason we look for dynamic programming approaches is to significantly improve the time complexity of an otherwise nasty algorithm.
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