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CheezItMan
reviewed
Nov 8, 2019
| # Time Complexity: ? | ||
| # Space Complexity: ? | ||
| # Time complexity: O(log(n)) | ||
| # Space complexity: O(1) | ||
| def remove() |
| # Time complexity: ? | ||
| # Space complexity: ? | ||
| # Time complexity: O(log(n)) | ||
| # Space complexity: O(1) | ||
| def heap_up(index) |
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| # Time Complexity: O(n) => this is based of the math proof on geeks for geeks, not sure if I 100 % understand ... yet | ||
| # Space Complexity: O(n) <= can optimize by using iterative approach over recursion |
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Correct, you can optimize it a bit further using iteration.
| # Time Complexity: O(nlogn) | ||
| # Space Complexity: O(n) |
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Correct by how you're using recursion to transform list into a heap.
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Heaps Practice
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Comprehension Questions
CQs answered on previous pull request (closed)
heap_up&heap_downmethods useful? Why?Removed conflict =) For heap sort I wanted to try in place, otherwise I would have used the min heap, added each element to it then removed each element and pushed in to new list or index exisiting list 0 .. end to add elements.