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Awesome work Blaine, this hits the learning goals.
I did have comments on time/space complexity. Let me know if you have questions.
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| # Time Complexity: O(log n) | ||
| # Space Complexity: [O(1)-if it's a loop] - why do I wanna say O(n)?? help me. | ||
| # Try with a while loop | ||
| def add(key, value = nil) |
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👍 The time complexity is O(n) if the tree is balanced and O(log n) if it is balanced.
Since you're doing recursion in add_helper the space complexity is the same as the time.
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| # Time Complexity: O(log n) | ||
| # Space Complexity: O(1) | ||
| def find(key) |
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| # left-root-right | ||
| def inorder |
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| # root-left-right | ||
| def preorder |
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| # left-right-root | ||
| def postorder |
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| # Time Complexity: O(n)^2 | ||
| # Space Complexity: O(n) | ||
| def height |
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👍 The time complexity is O(n) (not n^2)
The space complexity is O(log n) if the tree is balanced or O(n) if it's not. The space complexity is basically the height of the tree.
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