Fire-Kalki-Tree-Practice #17
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CheezItMan
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Apr 7, 2021
CheezItMan
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CheezItMan
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Nice work Kalki, you hit the learning goals here. Well done. I had a few comments on space/time complexity, otherwise outstanding work.
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| # Time Complexity: O(log n) | ||
| # Space Complexity: O(log n) | ||
| def add(key, value = nil) |
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👍 Good use of default parameters. The space/time complexities are O(n) if the tree is unbalanced and O(log n) if the tree is balanced.
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| # Time Complexity: O(log n) | ||
| # Space Complexity: O(l) | ||
| def find(key) |
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👍 The space/time complexities are O(n) if the tree is unbalanced and O(log n) if the tree is balanced. I'm uncertain what O(l) is?
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| # Time Complexity: O(n) going through each node | ||
| # Space Complexity: O(n) creating an array | ||
| 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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The space complexity is O(n) if the tree is unbalanced and O(log n) if the tree is balanced.
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