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Whit - Paper #39
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Whit - Paper #39
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Well done Whit, you hit the learning goals and more with BFS. Nice work. I left a little feedback on space complexity due to the recursive stack.
# Time Complexity: O(log n) if balanced | ||
# Space Complexity: O(1) | ||
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def add(self, key, value = None): |
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👍 However the space complexity is O(log n) due to the recursive stack
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# Time Complexity: | ||
# Space Complexity: | ||
def add_iterative(self, key, value = None): |
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Nice iterative solution
# Time Complexity: O(log n) if balanced | ||
# Space Complexity: O(1) | ||
def find(self, key): |
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👍 However the space complexity is O(log n)
# Time Complexity: O(n) | ||
# Space Complexity: O(n) | ||
def inorder(self): |
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👍
# Time Complexity: O(n) | ||
# Space Complexity: O(n) | ||
def preorder(self): |
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👍
# Time Complexity: O(n) | ||
# Space Complexity: O(n) | ||
def postorder(self): |
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👍
# Time Complexity: O(n) | ||
# Space Complexity: O(1) | ||
def height(self): |
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👍 however the space complexity is O(log n) for a balanced tree.
# # Time Complexity: O(n) | ||
# # Space Complexity: O(n) | ||
def bfs(self): |
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Nice BFS solution!
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