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20 changes: 15 additions & 5 deletions heaps/heap_sort.py
Original file line number Diff line number Diff line change
@@ -1,8 +1,18 @@
from heaps.min_heap import MinHeap


def heap_sort(list):
def heap_sort(arr):
""" This method uses a heap to sort an array.
Time Complexity: ?
Space Complexity: ?
Time Complexity: o(n^2)

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✨ However time complexity is actually O(n log n) because remove and add are O(log n) operations instead of O(n). Because you are creating a heap of size n, space complexity will be O(n)

Space Complexity: o(1)
"""
pass
heap = MinHeap()

for x in arr:
heap.add(x) # o(n) | o(1)

i = 0
while not heap.empty():
arr[i] = heap.remove()

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👀 remove is actually an O(log n) operation - see additional comments in your remove implementation

i += 1

return arr
59 changes: 46 additions & 13 deletions heaps/min_heap.py
Original file line number Diff line number Diff line change
Expand Up @@ -19,18 +19,36 @@ def __init__(self):
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(n)

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✨ However space complexity is O(log n). See comment below ⬇️

Space Complexity: o(1)
"""
pass
if value == None:
value = key
node = HeapNode(key, value)

self.store.append(node)

index = len(self.store) -1
while index != None and index != 0:

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Interesting to do this iteratively! Because you are halving the index with each call to heap_up, this loop should actually run O(log n) times

index = self.heap_up(index)



def remove(self):
""" This method removes and returns an element from the heap
maintaining the heap structure
Time Complexity: ?
Space Complexity: ?
Time Complexity: o(n)
Space Complexity: o(1)

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✨ Because of the recursive heap_down operation, space and time complexity will actually be O(log n) here.

"""
pass
if len(self.store) == 0:
return

self.swap(0, len(self.store) - 1)
root = self.store.pop().value
self.heap_down(0)

return root




Expand All @@ -44,10 +62,10 @@ def __str__(self):

def empty(self):
""" This method returns true if the heap is empty
Time complexity: ?
Space complexity: ?
Time complexity: o(1)
Space complexity: o(0-1)

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✨ Space complexity would be O(1)

"""
pass
return len(self.store) == 0


def heap_up(self, index):
Expand All @@ -57,18 +75,33 @@ def heap_up(self, index):
property is reestablished.

This could be **very** helpful for the add method.
Time complexity: ?
Space complexity: ?
Time complexity: o(1)
Space complexity: o(1)

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✨ Creative to use this in conjunction with iteration in your add function. For consistency style wise, I would recommend sticking to either an iterative or recursive solution between heap_up and heap_down. Technically the specification does say heap_up should perform its operation "until the Heap property is reestablished" which indicates recursion, but I'm not too concerned with that.

"""
pass

if self.store[index].key < self.store[(index-1)//2].key:
self.swap((index-1)//2, index)
return (index-1)//2


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.
"""
pass
left = index * 2 + 1

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right = index * 2 + 2

if left < len(self.store):
if right< len(self.store) and self.store[left].key >= self.store[right].key:
swap_for = right
else:
swap_for = left

if self.store[index].key > self.store[swap_for].key:
self.swap(index, swap_for)
self.heap_down(swap_for)


def swap(self, index_1, index_2):
Expand Down