Kristy, Branches#35
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CheezItMan
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Feb 27, 2020
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Nice work, you hit the basic learning goals, with some issues on space/time complexity. Check out my comments and let me know if you have any questions.
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| def add(key, value) | ||
| raise NotImplementedError |
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The space/time complexity is O(log n) if the tree is balanced and O(n) if it's not.
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| # Time Complexity: O(log n) | ||
| # Space Complexity: O(log n) | ||
| def find(key) |
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| def inorder |
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| def preorder |
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| def postorder |
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| def height |
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Since you're visiting each node, the time complexity is right, but the space complexity is O(log n) for a balanced tree since the call stack never gets greater than height of the tree.
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