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Nice work Jessica, you hit the learning goals here. Well done.
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| # Time Complexity: O(nlogn) - loops n times based on length of list, and then logn adding or removing | ||
| # Space Complexity: O(nlogn) | ||
| def heapsort(list) |
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👍 However your space complexity is O(n) because you add n elements into the heap.
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| def find_parent(index) | ||
| return (index - 1) / 2 | ||
| end | ||
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| # helper method to find left child | ||
| def left_child(index) | ||
| return 2 * index + 1 | ||
| end | ||
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| # helper method to find right child | ||
| def right_child(index) | ||
| return 2 * index + 2 | ||
| end |
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| # Time Complexity: O(logn) depending on the height of the heap | ||
| # Space Complexity: O(logn) to hold the recursive stack | ||
| def add(key, value = key) |
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| # Time Complexity: O(logn) depending on the height of the heap | ||
| # Space Complexity: O(logn) to hold the recursive stack | ||
| def remove |
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| # Time complexity: O(logn) | ||
| # Space complexity: O(logn) | ||
| def heap_up(index) |
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| if !@store[left] | ||
| smallest = right |
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Just a note, that if left is not in the array, then right is not either.
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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?