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Find Duplicate: sort, set, floyd
(version: 0)
Find the fastest method of finding duplicates
Comparing performance of:
sort vs Set vs floyd
Created:
6 years ago
by:
Guest
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Tests:
sort
const dupearray1 = [1,8,4,5,9,3,7,4,6,2]; let sortResult = 0; function sortMethod(array){ let prev = 0; array .sort() .forEach(element =>{ if(element === prev) { sortResult = element; } if (element !== prev){ prev = element; } }); } sortMethod(dupearray1);
Set
const dupearray2 = [1,8,4,5,9,3,7,4,6,2]; let setResult = 0; function setMethod(array){ let seen = new Set(); return array.forEach(element => { if(!seen.has(element)){ seen.add(element); } else{ setResult = element; return setResult; } }); } setMethod(dupearray2);
floyd
const dupearray3 = [1,8,4,5,9,3,7,4,6,2]; let floydResult = 0; function floyd(array){ let tortoise = array[0]; let hare = array[array[0]]; while(hare !== tortoise){ tortoise = array[tortoise]; hare = array[array[hare]]; } hare = 0; while (tortoise !== hare){ tortoise = array[tortoise]; hare = array[hare]; } floydResult = tortoise; return tortoise; } floyd(dupearray3);
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Suite status:
<idle, ready to run>
Run tests (3)
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Test case name
Result
sort
Set
floyd
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Measuring the performance of different approaches to finding duplicates in an array is a classic benchmarking problem. The provided benchmark definition tests three approaches: sorting, using a Set data structure, and Floyd's cycle-finding algorithm (also known as the "tortoise and hare" algorithm). **Sorting Approach** In this approach, the code sorts the input array and then iterates through it to find duplicates. The `sortMethod` function uses JavaScript's built-in `sort()` method to sort the array in ascending order. Pros: * Easy to implement * Widely supported by most browsers * Simple to understand Cons: * Time complexity is O(n log n) due to the sorting step, which can be slow for large arrays * Requires extra memory to store the sorted array **Set Approach** In this approach, the code creates a Set data structure and iterates through the input array, adding each element to the set. If an element is already in the set, it means we've found a duplicate. Pros: * Time complexity is O(n) because Set lookups are fast * Requires minimal extra memory Cons: * May not work correctly for duplicate values (e.g., 1 and 1 are treated as distinct) * Not all browsers support Set data structures **Floyd's Algorithm** In this approach, the code uses Floyd's cycle-finding algorithm to find duplicates. The basic idea is to treat the array as a linked list and use two pointers that move at different speeds through the list. Pros: * Time complexity is O(n) because we only need to iterate through the array once * Does not require extra memory Cons: * More complex to understand and implement * May not work correctly for all types of input data (e.g., arrays with non-numeric values) The other alternatives that could be considered are: 1. **Hash Table Approach**: This approach would involve creating a hash table to store the elements in the array and then iterating through it to find duplicates. 2. **Binary Search Approach**: This approach would involve using binary search to find the first occurrence of each element in the array and then checking for duplicates. These alternative approaches may offer different trade-offs between time complexity, memory usage, and implementation simplicity, but they are not currently being tested by MeasuringThat.net. As a software engineer, understanding these different approaches can help you choose the best solution for your specific use case. When working with performance-critical code, considering factors like time complexity, memory usage, and implementation simplicity can help ensure that your code is efficient and scalable.
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Lodash#uniq + Native v2
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