Random number generation techniques and examples in numpy

Random number generation skills and examples in numpy

Introduction:
In the field of data science and machine learning, the generation and processing of random numbers is very important . Numpy is a commonly used scientific computing library in Python. It provides a wealth of random number generation functions, as well as some flexible methods to process generated random numbers. This article will introduce several commonly used numpy random number generation techniques and give corresponding code examples.

1. Generate random numbers from a specific distribution:

1. Normal distribution (Gaussian distribution) random numbers:
   In numpy, we can use numpy.random The .normal() function generates random numbers that conform to the normal distribution. This function requires the mean and standard deviation parameters to be specified.
   Sample code:

import numpy as np
mean = 0
std = 1
np.random.normal(mean, std, 10)

2. Uniformly distributed random numbers:
   numpy.random.uniform()The function can generate random numbers that conform to a uniform distribution. We need to specify the lower and upper bounds of the range of random numbers.
   Sample code:

import numpy as np
low = 0
high = 1
np.random.uniform(low, high, 10)

3. Binomial distribution random number:
   numpy.random.binomial()The function can generate random numbers that conform to the binomial distribution number. We need to specify the number of trials and the probability of success.
   Sample code:

import numpy as np
n = 10
p = 0.5
np.random.binomial(n, p, 10)

2. Settings of random number generator:
In numpy, we can set the seed (seed) of the random number generator to obtain repeatable randomness number sequence. This is very important for the reproducibility of the experiment.
Sample code:

import numpy as np
np.random.seed(0)
np.random.random(10)

3. Arrangement and sampling of random numbers:

1. Randomly arrange the array:
   numpy.random.shuffle()The function can randomly rearrange the array.
   Sample code:

import numpy as np
x = np.array([1, 2, 3, 4, 5])
np.random.shuffle(x)

2. Random sampling:
   numpy.random.choice()The function can randomly draw a specified number from a given array Elements.
   Sample code:

import numpy as np
x = np.array([1, 2, 3, 4, 5])
np.random.choice(x, 3)

The above are just some commonly used random number generation techniques and examples. Numpy also provides more rich random number generation functions and functions. In practical applications, we can choose the appropriate method to generate and process random numbers according to the specific situation.

Conclusion:
This article introduces random number generation techniques and examples in numpy, including generating random numbers of a specific distribution, setting the seed of the random number generator, and using random numbers for permutations and sampling. These techniques are useful for working with random numbers in data science and machine learning. I hope this article will be helpful to readers on generating and processing random numbers in numpy.

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