Scissors - Araceli #56
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CheezItMan
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Jan 24, 2022
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(1) | ||
| def add(self, key, value = None): |
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(1) | ||
| def find(self, key): |
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👍 Time complexity is O(n) if the tree is unbalanced and O(log n) if it is balanced.
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(1) | ||
| def inorder(self): |
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👍 However the space complexity is O(n) because you're building a list of all the nodes.
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(1) | ||
| def preorder(self): |
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👍 However the space complexity is O(n) because you're building a list of all the nodes.
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(1) | ||
| def postorder(self): |
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👍 However the space complexity is O(n) because you're building a list of all the nodes.
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(1) | ||
| def height(self): |
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👍 Because of the recursion the space complexity is O(h) where h is the height of the tree (which will be log n if the tree is balanced and it can approach n if the tree is very unbalanced).
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