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CheezItMan
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Sep 22, 2020
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Well done, you hit all the learning goals here. Take a look at my comment about reverse. Otherwise nice work!
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| # Time complexity: O(n) | ||
| # Space complexity: O(1) | ||
| def length(array) |
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| # Time complexity: O(n) | ||
| # Space complexity: O(1) | ||
| def print_array(array) |
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| # Time complexity: | ||
| # sorted: O(n) | ||
| # unsorted: O(n) | ||
| # Space complexity: O(1) | ||
| def search(array, length, value_to_find) |
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| # Time complexity: | ||
| # sorted: O(1) | ||
| # unsorted: O(n) | ||
| # Space complexity: O(1) | ||
| def find_largest(array, length) |
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👍 Actually this algorithm is always O(n) in time complexity
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| # Time complexity: | ||
| # sorted: O(1) | ||
| # unsorted: O(n) | ||
| # Space complexity: O(1) | ||
| def find_smallest(array, length) |
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| # Time complexity: O(n) | ||
| # Space complexity: O(1) | ||
| def reverse(array, length) |
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👍 , this works but since you're making an auxiliary array it has O(n) space complexity.
Can you think of a way to do this without the other array?
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| # Time complexity: ? | ||
| # Space complexity: ? | ||
| def binary_search(array, length, value_to_find) |
Author
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Thank you Chris |
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Restricted arrays solution