Leaves - Morgan #21
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Leaves - Morgan #21
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CheezItMan
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Mar 18, 2020
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| # Time Complexity: I think this is O(n)? It touches every item in the list | ||
| # Space Complexity: O(n)? - creating new sorted array | ||
| def heap_sort(list) |
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For time complexity it does touch every item, but removing and adding an item to a heap is O(log n), so this algorithm is O(n log n). Otherwise well done.
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| # Time Complexity: I think O(log N) | ||
| # Space Complexity: O(1) - I think this is for all space regardless of operation because it's not necessarily | ||
| #growing all that much in size? | ||
| def add(key, value = key) |
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Since heap_up is recursive you need to account for the system stack in the space complexity. So Space complexity is O(log n). Otherwise well done.
| def empty? | ||
| raise NotImplementedError, "Method not implemented yet..." | ||
| return true if @store.empty? |
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Suggested change
| return true if @store.empty? | |
| return @store.empty? |
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| # Time complexity: O(1) | ||
| # Space complexity: O(1) | ||
| def empty? |
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| # Time complexity: O(log n) | ||
| # Space complexity: O(1) | ||
| def heap_up(index) |
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See above regarding space complexity (add).
| end | ||
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| # This helper method takes an index and | ||
| # moves it up the heap if it's smaller | ||
| # than it's parent node. | ||
| def heap_down(index) |
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Heaps Practice
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Comprehension Questions
heap_up&heap_downmethods useful? Why?