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This article mainly introduces "how to use numpy.random.uniform to achieve uniform distribution". In daily operation, I believe many people have doubts about how to achieve uniform distribution with numpy.random.uniform. The editor consulted all kinds of data and sorted out simple and easy-to-use methods of operation. I hope it will be helpful for you to answer the doubt of "how to achieve uniform distribution with numpy.random.uniform"! Next, please follow the editor to study!

Numpy.random.uniform introduction:

1. Function prototype: numpy.random.uniform (low,high,size)

Function: random sampling from a uniformly distributed [low,high]. Note that the definition domain is left closed and right open, that is, it contains low and does not contain high.

Parameter description:

Low: sampling lower bound, float type. Default is 0.

High: sampling upper bound, float type. Default is 1.

Size: output the number of samples, which is of int or tuple type, for example, size=, m*n*k samples, and 1 value by default.

Return value: ndarray type, whose shape is the same as that described in the parameter size.

By the way, the ndarray type represents an N-dimensional array object with a shape (table dimension size) and dtype (an object that describes the array data type). You can use the zeros and ones functions to create an array with all-zero or all-one data. Prototype:

Numpy.ones (shape,dtype=None,order='C')

Among them, the shape table array shape (dtype n), the dtype table type, whether the order table stores data in C or fortran form.

two。 Similar to uniform, there are the following random number generating functions:

A. randint: prototype: numpy.random.randint (low, high=None, size=None, dtype='l') to generate random integers

B. random_integers: prototype: numpy.random.random_integers (low, high=None, size=None), which generates random integers on closed intervals

C. random_sample: prototype: numpy.random.random_sample (size=None), random sampling on [0. 0. 0. 0. 0)

D. random: prototype: numpy.random.random (size=None), like random_sample, is an alias for random_sample

E. rand: prototype: numpy.random.rand (d0, D1,..., dn), the number of float types that produce the shape of d0-D1 -...-dn and are uniformly distributed on [0Magne1).

F. Randn: archetype: numpy.random.randn (d0djue dlce.dm DN), which produces the number of float types of standard normal distributions of d0-d1 -..-dn shapes.

At this point, the study of "how to achieve uniform distribution with numpy.random.uniform" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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