Benchmark: Power set of string

Script Preparation code:

var str = 'abcdefghi'

Tests:

recursive
const powerSetRec = ([head, ...tail]) => head == undefined ? [[]] : powerSetRec(tail).flatMap((subsets) => [subsets, subsets.concat(head)]) const stringPowerSet = (str) => powerSetRec([...str]) .map((subset) => subset.sort().join('')) .sort() .sort((a, b) => a.length - b.length) stringPowerSet(str)
reduce
const powerSet = (arr) => { return arr.reduce( (subsetsSoFar, elem) => subsetsSoFar.concat(subsetsSoFar.map((subset) => subset.concat(elem))), [[]] ) } const stringPowerSet = (str) => powerSet([...str]) .map((subset) => subset.sort().join('')) .sort() .sort((a, b) => a.length - b.length) stringPowerSet(str)
misc from random gist
var powerSet = function(str) { var set = []; var hash = {}; if (!str) { str = ''; } str = str.split('').sort(); // remove duplicates for (var i = 1; i < str.length; i++) { if (str[i - 1] === str[i]) { str.splice(i, 1); i--; } } // recursive through the sub sets var recurse = function(strSet) { var joined = strSet.join(''); // check if we have visited this combo if (hash[joined]) { return; } hash[joined] = true; set.push(joined); // don't recurse to empty set - add it once at the end if (strSet.length === 1) { return; } // recurse all subsets for (var i = 0; i < strSet.length; i++) { var subSet = strSet.slice(0, i).concat(strSet.slice(i + 1)); recurse(subSet); } }; recurse(str); set.push(''); // the power set, by definition, includes the empty set return set; }; powerSet(str)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
recursive
reduce
misc from random gist

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark is testing the performance of three different implementations of generating the power set of a string.

**Options Compared**

The options compared are:

1. Recursive (powerSetRec function)
2. Reduce (powerSet function with reduce method)

These two approaches are used to generate the power set, which is a set of all possible subsets of a given string.

**Pros and Cons of Each Approach**

**Recursive Approach:**

Pros:

* Easy to understand and implement
* Can be parallelized more easily

Cons:

* May lead to stack overflow errors for large input strings
* Can be slower due to the overhead of recursive function calls

**Reduce Approach:**

Pros:

* Often faster than recursive approach, as it avoids the overhead of recursive function calls
* More efficient memory usage, as it uses a accumulator array to build the power set

Cons:

* May require more complex implementation logic
* Less intuitive for developers without prior experience with reduce method

**Miscellaneous Approach (from random gist)**

This approach uses a combination of iterative and recursive techniques. It first removes duplicates from the string by iterating through it, then recursively generates all subsets.

Pros:

* May be faster than reduce approach due to reduced overhead
* More flexible in terms of input handling

Cons:

* More complex implementation logic
* May lead to performance issues if not implemented correctly

**Library Usage**

None of the benchmark definitions use any external libraries. However, some of the implementations may rely on built-in JavaScript functions like `split()`, `join()`, and `sort()`.

**Special JS Features/Syntax**

The recursive approach uses a special syntax for defining arrow functions (`=>`), which is supported by modern browsers. The reduce approach uses the `reduce()` method, which is also widely supported. The miscellaneous approach uses some ES6 features like template literals (`''`) and destructuring (`const { ... } = strSet`).

**Other Alternatives**

If you're interested in exploring other alternatives, here are a few options:

* Using a library like Lodash or Ramda for power set generation
* Implementing the power set using a different data structure, such as a trie or a suffix tree
* Using parallel processing techniques to speed up the benchmark
* Optimizing the implementation for specific use cases or input types

Keep in mind that these alternatives may not be directly comparable to the provided benchmark, as they may introduce additional dependencies or complexity.

Related benchmarks:

Power set of string (version: 0)

Comparing performance of: recursive vs reduce vs misc from random gist

Created: 4 years ago by: Registered User

Jump to the latest result

recursive

reduce

misc from random gist

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):