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How to normalize a vector in python

Hello guys. How are you all? I hope you all fine. In this tutorial we will learn about How to normalize a vector in python. so without wasting time lets learn about of this.

How to normalize a vector in python

  1. normalize a vector in python

    to normalize a vector in python just Use mathematical formula. By using mathematical formula you can normalize a vector in python. It is very easy to use. Lets learn about of this by given below example: import numpy as np var1 = np.random.rand(3) result1 = var1 / np.sqrt(np.sum(var1**2)) print(result1) Output : [0.04908173 0.01589104 0.99866834]

  2. How to normalize a vector in python

    to normalize a vector in python just Use list comprehension. By using list comprehension you can normalize a vector in python. It is very easy to use. Lets learn about of this by given below example: mylist = [[18,22,19,44]] result = [m / sum(n) for n in mylist for m in n] print(result) Output : [0.17475728155339806, 0.21359223300970873, 0.18446601941747573, 0.42718446601941745]

  3. python normalize vector

    To normalize a vector in python just Use list comprehension. By using list comprehension you can normalize a vector in python. It is very easy to use. Lets learn about of this by given below example: mylist = [[18,22,19,44]] result = [m / sum(n) for n in mylist for m in n] print(result) Output : [0.17475728155339806, 0.21359223300970873, 0.18446601941747573, 0.42718446601941745]

Method 1: Use mathematical formula

By using mathematical formula you can normalize. It is very easy to use. Lets learn about of this by given below example:

import numpy as np
var1 = np.random.rand(3)
result1 = var1 / np.sqrt(np.sum(var1**2))
print(result1)

Output :

[0.04908173 0.01589104 0.99866834]

Method 2: Use list comprehension

By using list comprehension you can normalize a vector. It is very easy to use. Lets learn about of this by given below example:

mylist = [[18,22,19,44]]
result = [m / sum(n) for n in mylist for m in n]
print(result)

Output :

[0.17475728155339806, 0.21359223300970873, 0.18446601941747573, 0.42718446601941745]

Conclusion

It’s all About this Tutorial. Hope all methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which method worked for you?

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