Leaves - Hallie Johnson #17
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CheezItMan
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Mar 18, 2020
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| # then again to remove the sorted elements and output them as a list O(2n), drop the constant. | ||
| # Space Complexity: O(1); I think this is constant, because the heap is created as the array is | ||
| # reduced so there is not a growth in memory space (except that a heap may take more space?). | ||
| def heapsort(list) |
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Good insight on space complexity, but the array doesn't actually shrink when you do .pop just the amount of spaces used inside it. Otherwise this works.
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| # Time Complexity: O(log n) | ||
| # Space Complexity: O(1) | ||
| def add(key, value = key) |
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| # Time Complexity: O(log n) | ||
| # Space Complexity: O(1) It only stores the result variable, which is not altered by | ||
| # the size of the input. | ||
| def remove() |
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| # Time complexity: O(1) - arrays keep track of their length so that this can be contant time retrieval. | ||
| # Space complexity: O(1) | ||
| def empty? |
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| # Time complexity: O(log n); it compares with parent nodes, not all nodes. | ||
| # Space complexity: O(1); swaps are happening in-place. | ||
| def heap_up(index) |
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Since this is a recursive method the space complexity is O(log n) because of the system stack!
| # This helper method takes an index and | ||
| # moves it up the heap if it's smaller | ||
| # than it's parent node. | ||
| # ===> Aren't we taking the new root and comparing it to its child and moving it DOWN? | ||
| def heap_down(index) |
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Heaps Practice
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Comprehension Questions
heap_up&heap_downmethods useful? Why?