Angele Z. #29
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Angele Z. #29
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CheezItMan
reviewed
Mar 19, 2020
lib/min_heap.rb
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| # Time Complexity: O(h) - height of the heap | ||
| # Space Complexity: O(h) | ||
| def add(key, value = key) |
lib/min_heap.rb
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| # Time Complexity: O(h) | ||
| # Space Complexity: O(h) | ||
| def remove() |
lib/min_heap.rb
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| # Time complexity: o(1) | ||
| # Space complexity: o(1) | ||
| def heap_up(index) |
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O(1)?? How? This is a recursive method so it's going to have time/space complexity.
lib/min_heap.rb
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| end | ||
| swap(index, index_with_smaller_key) | ||
| end | ||
| heap_down(index_with_smaller_key) |
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This might lead you to heaping down, even when you didn't do a swap... It won't cause bugs, but might lead to extra recursive calls.
CheezItMan
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Mar 23, 2020
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| # Time Complexity: o((n * log n) + (n * log n)) => o(n log n) | it depends on the length of the list, but adding and removing from the list takes log(n), and the removal of each takes log(n) as well, and has to happen n times (the length of the list) | ||
| # Space Complexity: also o(n log n) | ||
| def heapsort(list) |
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The method here works, great! However the space complexity is O(n) because you're making a heap of n elements, not n log n elements.
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| # Time Complexity: O(log n) - height of the heap is a logarithmic relationship to the number of nodes | ||
| # Space Complexity: O(log n) | ||
| def add(key, value = key) |
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| # Time Complexity: O(log n) | ||
| # Space Complexity: O(log n) | ||
| def remove() |
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| # Time complexity: o(1) - the access time complexity of an array | ||
| # Space complexity: o(1) - the access space complexity of an array | ||
| def empty? |
| def heap_up(index) | ||
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| return if index == 0 | ||
| parent_index = (index % 2 != 0) ? (index / 2) : (index / 2 - 1) |
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Could this just be:
Suggested change
| parent_index = (index % 2 != 0) ? (index / 2) : (index / 2 - 1) | |
| parent_index =(index - 1) / 2 |
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| # Time complexity: o(h) - the time it takes to run depends on the height of the heap; in the worst case scenario it will touch each level of the heap | ||
| # Space complexity: o(h) - the space complexity of heap_up depends on the number of recursive calls that will be held in memory, up to the height of the heap. | ||
| def heap_up(index) |
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👍 And h = log n, where n is the number of nodes.
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| # moves it down the heap if it's bigger | ||
| # than its child 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?