Updated April 12, 2023
Introduction to Numpy Random Seed ()
Numpy. random. seed * () function is used in the Python coding language which is functionality present under the random() function. This aids in saving the current state of the random function. This is done so that function is capable of generating the exactly same random number while the code is executed multiple times on either same machine it was developed in or a different machine where it is being run (referring to the specified seed value). By defining the seed value we mean in a general term the previously generated value or numbers that were processed when the code was run. It must be noted that for the time when the code is being executed first, and there is no previously processed value, the function makes utilization of the system time at the current moment.
Syntax and Parameters
Following is the syntax used to utilize the NumPy. random. seed* () while writing codes in the Python programming language:
Following are the parameters used for the NumPy. random. seed () function written in the Python programming language. The random seed method is called by the system initialized the RandomState. It can further be called in order for the generator to be seeded again
|Parameter||Description of the Parameter|
|seed: int or 1-d array_like, optional
This represents the input data that is being fed to the machine, this can be either integer kind of data or one dimensional array-like objects, although it is not necessary for the user or coder to define the data type. This is an optional parameter which can be used.
This parameter can be used to generate any integer ranging between 0 and infinite possibilities (up to 232 inclusive of the number), the data being generated can be an array (or other similar sequences) of integers, or the parameter can be set at None (which is the default parameter criteria).
If seed parameter is set at None (unless specified otherwise in the code), then Random State class would be trying to read the available from the Windows analogue or the dev/urandom, in case available or otherwise it will seed clock otherwise
|Note||It should be noted that as a best practice it is advised not to take re-seeding the Bit generator as an option, but rather recreation of an entirely new one is recommended. Please note that legacy reasons are the core principle behind such recommendations. The example can be used in order to demonstrate the best practice to be included:|
How can the Numpy Random Seed be utilized?
The NumPy random seed function can be used for the generation of an encryption key or pattern (which is pseudo-randomized). These will be playing a very vital role in the development in the field of data and computer security. These encryption keys would provide to be a solution to not having unauthorized access to personal devices or access over the internet in various forms.
The NumPy random seed function enables the coder to optimize codes very easily wherein random numbers can be used for testing the utility and efficiency. The output which is generated on executing the code completely depends on the random data variables that were used by the system, and hence are input dependent. This can make usage of random number for checking the correctness of the testing code-based algorithm to be a complex procedure. As the NumPy random seed function can be used in the process of generating the same sequences of random numbers on a constant basis and can be recalled time and again, this holistically simplifies the entire process of testing using the testing algorithm by implementing the usage of NumPy random seed method.
Let us discuss examples of Numpy Random Seed ().
# Python program explaining the use of NumPy.random.seed function import random. import random for i in range(10): # Any number or integer value can be used instead of using '0'. random.seed(0) # Generation of random values will be between 1 to 100. print(random.randint(1, 100))
# Python program explaining the use of NumPy.random.seed function import random. import random random.seed(3) # print a random number between 1 and 1000. print(random.randint(1000, 8000)) # the code is written in order to repeat the same random number multiple times random.seed(3) print(random.randint(1000, 8000)) # If seed function is not used # The program is being used to generate unpridictible output and genrate totally random values print(random.randint(1000, 8000))
The NumPy. Random. seed() function is very essential in use, as it readily makes possible for a systemic generation of an encryption key or pattern (which is pseudo-randomized). These will be playing a very vital role in the development in the field of data and computer security. These encryption keys would provide to be a solution to not having unauthorized access to personal devices or access over the internet in various forms. In a general essence, it helps in reducing the verbosity of the code which enhances the turnaround speed for the program that is being run.
This is a guide to Numpy Random Seed (). Here we also discuss the Introduction of Numpy Random Seed (), How can the Numpy Random Seed be utilized? along with different examples. You may also have a look at the following articles to learn more –