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CheezItMan
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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.
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| # 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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| # 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)
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| # 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.
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| # # Time Complexity: O(n) | ||
| # # Space Complexity: O(n) | ||
| def bfs(self): |
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