How to test the accuracy of a random number generator in Golang?

The steps to test the accuracy of a random number generator in Go include generating a large number of random numbers and counting the number of occurrences in each range to ensure an even distribution. Counts the number of occurrences in each range for a specified mean and standard deviation to ensure a normal distribution.

How to test the accuracy of a random number generator in Golang?

Testing the accuracy of random number generators in Golang is crucial as it ensures that the random numbers in your application are predictable and unguessable.

Preparation

To test the random number generator, you need to create it and instantiate it. In this example we will use the Rand type from the math/rand package:

import (
    "math/rand"
    "time"
)

// 随机数生成器
rng := rand.New(rand.NewSource(time.Now().UnixNano()))

Uniform distribution

Uniformly distributed random numbers should Appears randomly within the specified range. To test this:

1. Generate a large number of random numbers (e.g. 1 million)
2. Count the number of occurrences of the random number in each bucket (a specific interval within the range)
3. Ensure that the number of occurrences of random numbers in each bucket is approximately even

// 均匀分布测试
桶数 := 10
范围 := 0.0
for i := 0; i < 桶数; i++ {
    范围 += 1.0 / float64(桶数)
}

桶计数 := make([]int, 桶数)
for i := 0; i < 1000000; i++ {
    n := rng.Float64()
    for j := 0; j < 桶数; j++ {
        if n < 范围 {
            桶计数[j]++
            break
        } else {
            范围 += 1.0 / float64(桶数)
        }
    }
}

// 检查桶数是否大致均匀

Normal distribution

Normally distributed random numbers should be clustered around the specified mean and standard deviation. To test this:

1. Generate a large number of random numbers (e.g. 1 million)
2. Count the number of occurrences of the random number in each bucket (a specific interval around the mean)
3. Ensure that the number of occurrences of random numbers in each bucket is consistent with the normal distribution

// 正态分布测试
平均值 := 0.0
标准差 := 1.0
桶数 := 10
范围 := 默认计算桶范围

桶计数 := make([]int, 桶数)
for i := 0; i < 1000000; i++ {
    n := rng.NormFloat64(平均值, 标准差)
    for j := 0; j < 桶数; j++ {
        if n < 范围 {
            桶计数[j]++
            break
        } else {
            范围 += 默认计算桶范围
        }
    }
}

// 检查桶数是否与正态分布相符

Practical case

Suppose you have a function that generates passwords:

func 生成密码(长度 int) string {
    密码 := ""
    for i := 0; i < 长度; i++ {
        密码 += 字符(rng.Intn(26) + 'a')
    }
    return 密码
}

To make sure the password is secure, you test for:

• Uniform distribution: Ensure that each character in the password appears with approximately equal probability
• Normal Distribution: Ensure that password lengths cluster around expected lengths without creating outliers

By testing the accuracy of your random number generator, you can ensure that your application relies on a secure and reliable of randomness.

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