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CheezItMan
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Well done Erika, thanks for getting this in. Take a look at my notes regarding space/time complexity and let me know if it's confusing.
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| # Time Complexity: O(n log n) | ||
| # Space Complexity: O(n) | ||
| def heapsort(list) |
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(1) | ||
| def add(key, value = key) |
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The Binary tree nature of the heap and heap_up method mean the time complexity is O(log n) and the recursion causes a space complexity of O(log n) as well.
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(1) | ||
| def remove() |
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The Binary tree nature of the heap and heap_down method mean the time complexity is O(log n) and the recursion causes a space complexity of O(log n) as well.
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| # Time complexity: O(1) | ||
| # Space complexity: O(1) | ||
| def empty? |
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| # Time complexity: O(n) | ||
| # Space complexity: O(1) | ||
| def heap_up(index) |
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👍 The code is well done here
See above for time/space complexity notes
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| # 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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Heaps Practice
Congratulations! You're submitting your assignment!
Comprehension Questions
heap_up&heap_downmethods useful? Why?