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CheezItMan
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Nice work, you hit the learning goals here Ross. Let me know if you have questions about my comments.
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| # Time Complexity: o(log n) if it's balanced; worst case is o(n) | ||
| # Space Complexity: o(n) | ||
| def add(key, value) |
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👍 Space & Time complexities are both O(log n) unless the tree is unbalanced, in which case they are O(n).
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| # Time Complexity: o(log n) if it's balanced; worst case is o(n) | ||
| # Space Complexity: o(n) | ||
| def find(key) |
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👍 Space & Time complexities are both O(log n) unless the tree is unbalanced, in which case they are O(n).
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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(log n) | ||
| # Space Complexity: o(n) | ||
| def height |
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👍 The time complexity is O(n) since you visit each node, and space complexity is O(log n) if the tree is balanced and O(n) if it's unbalanced.
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