Benchmark: Selection Algorithm Comparison 2

Script Preparation code:

function gaussian(mean, variance) {
    return Math.sqrt(-2 * variance * Math.log(Math.random())) *
        Math.cos(2 * Math.PI * Math.random()) + mean;
}

Tests:

Algorithm 1
const n = 2; const columns = 4*n; const rows = 3*n; const cellWidth = 640/columns; const matrix = {}; let population = rows*columns; const numParents = Math.ceil(0.2*rows*columns); for (let i = 0; i < rows; i++) { matrix[i] = {}; for (let j = 0; j < columns; j++) { matrix[i][j] = 0; } } let p = 0; while (p < numParents) { try { for (let i = 0; i < rows; i++) { for (let j = 0; j < columns; j++) { if (p < numParents) { if (matrix[i][j] === 1) { continue; } if (Math.abs(gaussian(0,columns) > j)) { matrix[i][j] = 1; ++p; } } else { throw ""; } } } } catch (e) {} }
Algorithm 2
const n = 2; const columns = 4*n; const rows = 3*n; const cellWidth = 640/columns; const matrix = {}; for (let i = 0; i < rows; i++) { matrix[i] = {} for (let j = 0; j < columns; j++) { matrix[i][j] = 0 } } let population = rows*columns; let numParents = Math.ceil(0.2*rows*columns); for (let i = 0; i < numParents; i++) { let success = false; while (!success) { let columnIndex = null; while (columnIndex === null) { const rand = Math.floor(Math.abs(gaussian(0, columns))); console.log(rand); if (rand < columns) { columnIndex = rand } } const rowIndex = Math.floor(Math.random()*rows); if (matrix[rowIndex][columnIndex] === 0) { matrix[rowIndex][columnIndex] = 1; success = true; } } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Algorithm 1
Algorithm 2

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The benchmark in question compares two selection algorithms for generating random numbers.

**Benchmark Definition JSON Analysis**

The provided Benchmark Definition JSON defines two test cases, "Algorithm 1" and "Algorithm 2". Both tests use a simulated population of cells in a matrix, where each cell is initialized to 0. A parent cell is randomly selected, and with a probability of 20% (based on the Gaussian distribution), the cell is set to 1.

**Options Compared**

The two algorithms being compared are:

1. **Algorithm 1**: This algorithm generates random column indices using `Math.random()` and selects a row index based on that value.
2. **Algorithm 2**: This algorithm uses a while loop to generate a random number between 0 and the total number of columns, effectively creating a uniform distribution.

**Pros and Cons**

* **Algorithm 1**:
	+ Pros: Simple and efficient implementation.
	+ Cons: May not produce a truly uniform distribution due to the limitations of `Math.random()`.
* **Algorithm 2**:
	+ Pros: Produces a more accurate uniform distribution by generating random numbers within the range of the total number of columns.
	+ Cons: More complex implementation, potentially slower.

**Library and Purpose**

The Gaussian function used in both algorithms is a common mathematical formula for generating random numbers from a normal distribution. In this context, it's used to simulate the natural variability of real-world data.

**Special JavaScript Feature/ Syntax**

None mentioned in the provided Benchmark Definition JSON.

**Other Considerations**

When selecting a random number generator, it's essential to consider factors such as:

* **Uniformity**: How evenly is the distribution of generated numbers?
* **Speed**: How quickly can the algorithm generate random numbers?
* **Consistency**: Does the algorithm produce consistent results across multiple runs?

**Alternatives**

If you're looking for alternative JavaScript libraries or implementations, some popular options include:

* **Crypto-Random**: A built-in JavaScript API for generating cryptographically secure random numbers.
* **Random.org**: A widely used online random number generator that provides a uniform distribution.

Keep in mind that the choice of library or implementation ultimately depends on your specific use case and requirements.

Related benchmarks:

Selection Algorithm Comparison 2 (version: 0)

Comparing performance of: Algorithm 1 vs Algorithm 2

Created: 4 years ago by: Guest

Jump to the latest result

Algorithm 1

Algorithm 2

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):