A deep dive into how random number generators work in numpy

In-depth understanding of the random number generator in NumPy

Introduction:
NumPy (Numerical Python) is one of the important scientific computing libraries in Python, providing A large number of numerical operations and matrix operation functions. Among them, random number generation is an important part of the NumPy library. It provides powerful support for simulations, experiments, and data analysis in scientific computing, and can help us generate random numbers that obey various distributions. This article will provide an in-depth understanding of the random number generator in the NumPy library and provide specific code examples.

1. Introduction to the random number generator in NumPy
The random number generator in NumPy is located in the sub-module random. Various types of random numbers can be generated by calling the corresponding functions. A random number generator is a program based on a specific algorithm that produces random outputs from certain inputs. In scientific computing, we often use random numbers to simulate experiments, generate sample data, perform probability statistics, etc.

2. Types of random number generators
2.1 Uniformly distributed random number generator
We first introduce the uniformly distributed random number generator. In NumPy, we can use the function random() of the random module to generate uniformly distributed random numbers between [0,1). The specific code is as follows:

import numpy as np

# 生成一个[0,1)之间的随机数
random_num = np.random.random()
print(random_num)

This function returns a random floating point number. Multiple random numbers can be generated by setting the parameters of the random() function.

2.2 Normal distribution random number generator
The normal distribution is the distribution form of many phenomena in nature and is also one of the most common distributions in statistics. In NumPy, we can use the function normal()` of the random module to generate normally distributed random numbers that conform to the specified mean and standard deviation. The specific code is as follows:

import numpy as np

# 生成均值为0，标准差为1的正态分布随机数
random_num = np.random.normal(0, 1)
print(random_num)

This function returns a random number that conforms to the specified mean and standard deviation.

2.3 Other distributed random number generators
In addition to the uniform distribution and normal distribution, NumPy also provides many other distributed random number generators. For example, the binomial distribution can be generated with the binomial() function, the Poisson distribution can be generated with the poisson() function, and so on. Specific code examples are as follows:

import numpy as np

# 生成满足二项分布的随机数
random_num = np.random.binomial(10, 0.5, size=100)
print(random_num)

# 生成满足泊松分布的随机数
random_num = np.random.poisson(5, size=100)
print(random_num)

3. Repeatability and seeds for generating random numbers
In scientific computing, we often need to generate random numbers with a certain degree of repeatability. NumPy's random number generator can be implemented by setting a random number seed. The random number seed is a parameter that determines the generated random number sequence. The same seed will generate the same random number sequence. The specific code is as follows:

import numpy as np

# 设置随机数种子
np.random.seed(0)

# 生成随机数
random_num = np.random.random()
print(random_num)

By setting the same seed, we can ensure that the generated random number sequence is repeated.

Conclusion:
This article provides a detailed introduction to the random number generator in the NumPy library and provides specific code examples. By in-depth understanding of NumPy's random number generator, we can better apply this function to simulate experiments, generate sample data, perform probability statistics, etc. At the same time, through seed parameters, we can achieve repeatable random number generation to ensure the repeatability of experiments and the consistency of results. I hope this article will be helpful to readers in understanding the random number generator in NumPy.

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