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;
}

AخA
 
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) {}
}

​x
 
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;
    }
  }
}

 
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

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

Test case name	Result
Algorithm 1
Algorithm 2

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36

Browser/OS: Chrome 91 on Windows

View result in a separate tab

Test name	Executions per second
Algorithm 1	19323.2 Ops/sec
Algorithm 2	13451.1 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 7 months ago):

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:

Algorithm 1: This algorithm generates random column indices using Math.random() and selects a row index based on that value.
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.

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**

**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**

**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: 3 years ago by: Guest

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