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CheezItMan
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CheezItMan
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Well done Lin, you hit the learning goals here. Well done. I did leave some minor comments. Take a look and let me know what questions you have.
| # Time Complexity: O(log(n))*assumes all balanced tree | ||
| # Space Complexity: O(1) | ||
| def add(self, key, value=None): |
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👍 However due to the recursive call stack this is O(log n) for space complexity (if the tree is balanced and O(n) otherwise).
| # Time Complexity: O(log(n)) | ||
| # Space Complexity: O(1) | ||
| def find(self, value): |
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👍 Similar issues with space complexity.
| # Time Complexity: O(logn) | ||
| # Space Complexity: O(n) | ||
| def inorder(self): |
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👍 Since you hit each node and make a list the of all the nodes in the tree the time and space complexity is O(n)
| # Time Complexity: O(logn) | ||
| # Space Complexity: O(n) | ||
| def preorder(self): |
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👍 Similar issues to time/space complexity
| # Time Complexity: O(logn) | ||
| # Space Complexity: O(n) | ||
| def postorder(self): |
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👍 Similar issues to time/space complexity
| # Time Complexity: O(logn) | ||
| # Space Complexity: O(1) | ||
| def height(self): |
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👍 However the space complexity is O(log n) due to the recursive call stack.
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