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CheezItMan
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Nice work Yolotzin, You hit the learning goals here. Do take a look at my notes on space complexity.
The space complexity for add and remove is O(log n) because the system stack is going to be log n levels due to the height of the tree.
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| # Time Complexity: O(n log n) | ||
| # Space Complexity: O(n) | ||
| def heap_sort(list) |
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| # Time Complexity: O(log n) | ||
| # Space Complexity: O(n) | ||
| def add(key, value = key) |
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👍 , However your space complexity due to the tree structure and recursion is O(log n)
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| # Time Complexity: O(log n) | ||
| # Space Complexity: O(n) | ||
| def remove() |
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| # Time complexity: O(log n) | ||
| # Space complexity: O(n) | ||
| def heap_up(index) |
| # moves it up the heap if it's smaller | ||
| # moves it down the heap if it's larger | ||
| # 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?