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Nice work Beauttie, you hit the learning goals and got BFS working. Interesting recursive solution.
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| # Time Complexity: O(log n) if balanced; O(n) if not | ||
| # Space Complexity: O(log n) if balanced; O(n) if not | ||
| def add(key, value = nil) |
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👍 Correct on time/space given the assumptions.
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| # Time Complexity: O(log n) if balanced; O(n) if not | ||
| # Space Complexity: O(log n) if balanced; O(n) if not | ||
| def find(key) |
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👍 Correct on time/space given the assumptions.
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| # Time Complexity: visit all nodes => O(n) | ||
| # Space Complexity: array contains all nodes => O(n) | ||
| def inorder |
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👍 Correct on time/space given the assumptions.
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| def preorder |
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👍 Correct on time/space given the assumptions.
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| def postorder |
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👍 Correct on time/space given the assumptions.
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| # Time Complexity: visit all nodes => O(n) | ||
| # Space Complexity: O(log n) if balanced; O(n) if not | ||
| def height |
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👍 Correct on time/space given the assumptions.
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| # Time Complexity: O(n^2) | ||
| # Space Complexity: O(n) | ||
| def bfs |
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👍 Actually I think this is O(n) for both space/time
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