Python's numpy can generate random numbers with a certain probability distribution, but if you need more specific probability densities, cumulative probabilities, use. Used for statistical analysis, statistical tools and the probability of the random process, the random number generator for each random process can be found in it. This article describes the principles and examples of using generating random numbers in python.
1. Normal stepwise format
# Generate specified distributions (loc=have expectations, scale=(statistics) standard deviation, size=Number of random numbers generated) #Generate a specified number of random numbers from a Poisson distribution.
2. Instructions for use
The loc and scale parameters allow you to specify the offset and scaling parameters of the random variable, which here correspond to the expectation and standard deviation of a normal distribution.
size gets the shape parameter of the array of random numbers.
3. Example of use: generating random numbers
#1. random number #(loc=0.0, scale=1.0, size=None) rv_unif = (size=10) print(rv_unif) rv_norm=(loc = 5,scale = 1,size =(2,2)) print(rv_norm) rv_beta=(size=10,a=4,b=2) print(rv_beta)
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