Benchmark: Phone Numbers - MeasureThat.net

Tests:

Theirs
const L = {'2':"abc",'3':"def",'4':"ghi",'5':"jkl", '6':"mno",'7':"pqrs",'8':"tuv",'9':"wxyz"} var letterCombinations = function(D) { let len = D.length, ans = [] if (!len) return [] const dfs = (pos, str) => { if (pos === len) ans.push(str) else { let letters = L[D[pos]] for (let i = 0; i < letters.length; i++) dfs(pos+1,str+letters[i]) } } dfs(0,"") return ans };
Mine
const phonePad = { 0: [''], 1: [''], 2: ['a', 'b', 'c'], 3: ['d', 'e', 'f'], 4: ['g', 'h', 'i'], 5: ['j', 'k', 'l'], 6: ['m', 'n', 'o'], 7: ['p', 'q', 'r', 's'], 8: ['t', 'u', 'v'], 9: ['w', 'x', 'y', 'z'] }; var letterCombinations = function(...nums) { const first = nums.shift(); const options = nums.reduce((acc, cur) => { let strings = []; for (const str of acc) { for (const letter of phonePad[cur]) { const s = `${str}${letter}`; strings.push(s); } } return strings; }, phonePad[first]); return options; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Theirs
Mine

Fastest: N/A

Slowest: N/A

Autogenerated LLM Sumamry (model llama3.2:3b, generated 3 days ago):

LLMs can make mistakes. Check important info.

Let's break down the benchmark definition and test cases to understand what's being tested.

**Benchmark Definition**

The benchmark definition is empty, which means that the script preparation code and HTML preparation code are not provided. This might be due to the fact that the script itself contains everything needed to execute the benchmark.

**Individual Test Cases**

There are two test cases:

1. **"Theirs"`**
This test case defines a `letterCombinations` function using a nested recursive approach with depth-first search (DFS). The function takes a string `D` as input and returns an array of letter combinations.

Here's the code snippet:
```javascript
const L = {'2':\"abc\",'3':\"def\",'4':\"ghi\",'5':\"jkl\",\r\n     '6':\"mno\",'7':\"pqrs\",'8':\"tuv\",'9\":\"wxyz\"}\r\n\r\nvar letterCombinations = function(D) {\r\n    let len = D.length, ans = []\r\n    if (!len) return []\r\n    const dfs = (pos, str) => {\r\n        if (pos === len) ans.push(str)\r\n        else {\r\n            let letters = L[D[pos]]\r\n            for (let i = 0; i < letters.length; i++)\r\n                dfs(pos+1,str+letters[i])\r\n        }\r\n    }\r\n    dfs(0,\"\")\r\n    return ans\r\n};
```
In this implementation:

* The `L` object is a mapping of digit strings to letter arrays.
* The `letterCombinations` function takes a string `D` as input and initializes an array `ans` to store the result.
* If the length of `D` is 0, the function returns an empty array.
* Otherwise, it defines a recursive DFS function `dfs` that takes two arguments: `pos` (the current position in the string) and `str` (the current combination).
* In the `dfs` function:
	+ If the current position `pos` is equal to the length of `D`, the function adds the current combination `str` to the result array `ans`.
	+ Otherwise, it retrieves the letter array for the current digit from the `L` object and iterates over each letter.
	+ For each letter, it recursively calls the `dfs` function with the next position and the updated string by appending the letter to the current combination.

2. **"Mine"`**
This test case defines a similar `letterCombinations` function but uses a different approach:

Here's the code snippet:
```javascript
const phonePad = {\r\n    0: [''],\r\n    1: [''],\r\n    2: ['a', 'b', 'c'],\r\n    3: ['d', 'e', 'f'],\r\n    4: ['g', 'h', 'i'],\r\n    5: ['j', 'k', 'l'],\r\n    6: ['m', 'n', 'o'],\r\n    7: ['p', 'q', 'r', 's'],\r\n    8: ['t', 'u', 'v'],\r\n    9: ['w', 'x', 'y', 'z']\r\n};\r\n\r\nvar letterCombinations = function(...nums) {\r\n    const first = nums.shift();\r\n    const options = nums.reduce((acc, cur) => {\r\n        let strings = [];\r\n        for (const str of acc) {\r\n            for (const letter of phonePad[cur]) {\r\n                const s = `${str}${letter}`;\r\n                strings.push(s);\r\n            }\r\n        }\r\n        \r\n        return strings;\r\n    }, phonePad[first]);\r\n    return options;\r\n};
```
In this implementation:

* The `phonePad` object is a mapping of digit strings to letter arrays.
* The `letterCombinations` function takes a variable number of arguments (`nums`) and uses the `shift()` method to extract the first argument as the input string `D`.
* It then iterates over each remaining argument in `nums` using the `reduce()` method, generating all possible combinations by concatenating the current input string with each letter in the `phonePad` object.
* The function returns an array of these generated combinations.

**Comparison**

Both implementations have a similar structure but differ in their approach:

* "Theirs"` uses a recursive DFS approach to generate combinations.
* "Mine"` uses a more iterative approach with a mapping-based data structure (`phonePad`) to generate combinations.

In terms of performance, the "Mine"` implementation might be slightly faster due to its more efficient use of iteration and array operations. However, the "Theirs"` implementation provides a clear example of a recursive DFS algorithm, which can be useful for understanding the underlying mechanics of combinatorial problems.

**Library**

Neither test case uses an external library, so it's likely that these implementations are meant to demonstrate basic problem-solving strategies rather than relying on specialized libraries or frameworks.

Let me know if you have any further questions!

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36

Browser/OS: Chrome 114 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Theirs	833612500.0 Ops/sec
Mine	861583100.0 Ops/sec

Related benchmarks:

Phone Numbers (version: 0)

Comparing performance of: Theirs vs Mine

Created: one year ago by: Registered User

Jump to the latest result

Theirs

Mine

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Sumamry (model llama3.2:3b, generated 3 days ago):