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kyra-patton
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kyra-patton
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✨ Nice work, Mac! I left a few comments on complexity below. Also technically you could create a heap with a linked list, it's just, as you said, much more convoluted and therefore not advisable.
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| from heapq import heappush, heappop | ||
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| def heap_sort(list): |
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✨ Time and space complexity? It would also be great to see you implement heap_sort using the MinHeap you implement below!
| the heap property is reestablished. | ||
| """ | ||
| pass | ||
| left_index = (index * 2) + 1 |
| Time complexity: ? | ||
| Space complexity: ? | ||
| Time complexity: O(1) | ||
| Space complexity: O(n) |
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✨ However time and space complexity are O(log n) here because of the recursive call. heap_up will be called (log n) times because each time you are halving the index
| Time complexity: ? | ||
| Space complexity: ? | ||
| Time complexity: O(1) | ||
| Space complexity: O(1) |
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| self.heap_up(len(self.store) - 1) | ||
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| def remove(self): |
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✨ However space complexity will be O(log n) because of the recursive call stack of heap_down
| """ Adds a HeapNode instance to the heap | ||
| If value == None the new node's value is set to key | ||
| Time Complexity: O(log n) | ||
| Space Complexity: O(n) |
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✨ However space complexity will just be O(log n) here because of the recursive call stack of heap_up. You aren't creating any other significant data structures.
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Heaps Practice
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Comprehension Questions
heap_up&heap_downmethods useful? Why?