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Heaps Practice

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Comprehension Questions

Question Answer
How is a Heap different from a Binary Search Tree?
Could you build a heap with linked nodes?
Why is adding a node to a heap an O(log n) operation?
Were the heap_up & heap_down methods useful? Why?

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@CheezItMan CheezItMan left a comment

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Nice work Araceli, you hit the learning goals here. Really nice work with the in-place solution to HeapSort.

I left a few notes on time/space complexity.

Comment on lines +3 to 7
def heap_sort(arr):
""" This method uses a heap to sort an array.
Time Complexity: ?
Space Complexity: ?
Time Complexity: O (log n)
Space Complexity: O(1)
"""

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👍 Awesome work an O(1) heapsort solution. Nice work.

Comment on lines 19 to 24
def add(self, key, value = None):
""" This method adds a HeapNode instance to the heap
If value == None the new node's value should be set to key
Time Complexity: ?
Space Complexity: ?
Time Complexity: O(log n)
Space Complexity: O(1)
"""

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👍 However this is O(log n) for space complexity because heap_up is recursive and you have the call stack.

Comment on lines 35 to +39
def remove(self):
""" This method removes and returns an element from the heap
maintaining the heap structure
Time Complexity: ?
Space Complexity: ?
Time Complexity: O(log n)
Space Complexity: O(1)

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👍 However space complexity is O(log n) due to the recursive call stack.

return f"[{', '.join([str(element) for element in self.store])}]"


def empty(self):

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👍


return self.store

def heap_up(self, index):

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👍 However time/space complexity are both O(log n) due to recursion.

Comment on lines 88 to 93
def heap_down(self, index):
""" This helper method takes an index and
moves the corresponding element down the heap if it's
larger than either of its children and continues until
the heap property is reestablished.
"""

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👍

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2 participants