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Heaps Practice

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Comprehension Questions

Question Answer
How is a Heap different from a Binary Search Tree? The BST is an ordered data structure, however, the Heap is not.
Could you build a heap with linked nodes? Yes, but building a heap using an array has many more benefits
Why is adding a node to a heap an O(log n) operation? adding a value to the end of the array is O(1), but we still need to sort the nodes. Since a heap is built on a complete binary tree, looking at each level of the heap will take O(log n).
Were the heap_up & heap_down methods useful? Why? Yes, these helper methods made it easy to implement add and remove method in a recursive way to traverse each level up or down.

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Nice work Mona, you hit the learning goals here. Well done. I had a few minor comments , but this is solid work!

Comment on lines +4 to +6
# Time Complexity: O(n log n)
# Space Complexity: O(n)
def heapsort(list)

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👍

Comment on lines +17 to 19
# Time Complexity: O(log n)
# Space Complexity: O(log n) - n is the number of elemnts in the heap
def add(key, value = key)

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👍

Comment on lines +28 to 30
# Time Complexity: O(log n)
# Space Complexity: O(log n)
def remove()

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👍

Comment on lines +54 to 56
# Time complexity: O(1)
# Space complexity: O(1)
def empty?

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👍

Comment on lines +65 to 67
# Time complexity: O(log n)
# Space complexity: O(log n)
def heap_up(index)

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👍

Comment on lines +89 to +95
if left_child < @store.length && @store[left_child].key < @store[min_child].key
swap(min_child, left_child)
end

if right_child < @store.length && @store[right_child].key < @store[min_child].key
swap(min_child, right_child)
end

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This works, but you could end up doing more swaps than needed.

Comment on lines 79 to 82
# 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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👍

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2 participants