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CheezItMan
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Well done, you hit the learning goals here. Nice work.
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| # Time complexity: O(n^1), where n is the number of elements in the array. | ||
| # Space complexity: O(1) since the additional storage needed does not change based on input array size | ||
| def length(array) |
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👍 You could just say O(n) instead of O(n^1)
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| # Time complexity: O(n^1) , where n is the number of elements in the array. | ||
| # Space complexity: O(1) | ||
| def print_array(array) |
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| # Time complexity: O(n^2), where n is the number of elements in the array. | ||
| # Space complexity: O(1) since the additional storage needed does not depend on input array size | ||
| def search(array, length, value_to_find) |
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👍 , why do you think the time complexity is O(n^2)?
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| # Time complexity: O(n^2), where n is the number of elements in the array. | ||
| # Space complexity: O(1) since the additional storage needed does not depend on input array size | ||
| def find_largest(array, length) |
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👍 , why do you think the time complexity is O(n^2)?
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| # Time complexity: O(n^2), where n is the number of elements in the array. | ||
| # Space complexity: O(1) since the additional storage needed does not depend on input array size | ||
| def find_smallest(array, length) |
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👍 , why do you think the time complexity is O(n^2)?
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| # Time complexity: O(n^2) where n is the number of elements in the array. | ||
| # Space complexity: O(n^1) | ||
| def reverse(array, length) |
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👍 , why do you think the time complexity is O(n^2)?
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