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CheezItMan
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Sep 5, 2019
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Well done, you hit the learning goals here. Do look at my comments on Big-O. I liked the helper methods you wrote and how you did the height methods. Great work!
| # Time Complexity: | ||
| # Space Complexity: | ||
| # Time Complexity: O(log(n)) where n is the number of nodes | ||
| # Space Complexity: O(1) |
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Remember since you're doing this recursively, the system stack will allocate space for each recursive call.
So if the tree is balanced the space complexity is O(log n), if it's widely unbalanced O(n). The same also applies to time complexity.
| return 0 if !node | ||
| right = longest_tree_path(node.right) | ||
| left = longest_tree_path(node.left) | ||
| return [right, left].max + 1 |
| # Time Complexity: | ||
| # Space Complexity: | ||
| # Time Complexity: O(n) where n is the number of nodes | ||
| # Space Complexity: O(n) |
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The space complexity will be O(n) if the tree is unbalanced (due to the system stack) or O(log n) if the tree is balanced.
| # Space Complexity: | ||
| # Time Complexity: O(n) where n is the number of nodes | ||
| # Space Complexity: O(n) "" | ||
| def bfs |
| end | ||
| end | ||
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| describe "hieght" do |
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Nice, I will use these for C12, if that's ok.
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