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CheezItMan
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Nice work Lee, you hit the learning goals here. Well done.
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| # Time Complexity: O(n log n), where n is the number of nodes and because this method adds the elements to a heap, then places them back in the array in order | ||
| # Space Complexity: O(log n), due to recursive calls of the heap_up and heap_down methods within the add and remove methods | ||
| def heapsort(list) |
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👍 Since you are building a heap, the space complexity is O(n) (a heap of n elements).
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| # Time Complexity: O(log n), where n is the number of nodes in a heap (a complete binary tree has log n levels) | ||
| # Space Complexity: O(log n), due to recursive calls of the heap_up method | ||
| def add(key, value = key) |
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| # Time Complexity: O(log n), where n is the number of nodes in a heap (a complete binary tree has log n levels) | ||
| # Space Complexity: O(log n), due to recursive calls of the heap_down method | ||
| def remove() |
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| # Time complexity: O(n), where n is the number of nodes | ||
| # Space complexity: O(1) | ||
| def empty? |
| @@ -58,14 +65,35 @@ def empty? | |||
| # Time complexity: ? | |||
| # Space complexity: ? | |||
| def heap_up(index) | |||
| # 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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Nice, very clever way to handle checking if the right and left children are beyond the array.
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Heaps Practice
Congratulations! You're submitting your assignment!
Comprehension Questions
heap_up&heap_downmethods useful? Why?