Min Max Normalization on Arbitrary Values


Difficulty: 1 | Problem written by mesakarghm
Problem reported in interviews at


Data Normalization / Scaling is an important part of data preprocessing. To rescale the data using Min Max Normalization between a set of arbitary values [a,b], we can use the formula: 

\(x' = a + {(x - min(x)) \over max(x) - min(x)} * (b-a)\)

Write a function minMax(x,a,b) which scales the input array (1D list) between the values a and b. 

Sample Input:
<class 'list'>
x: [100, 50, 10, 9]
<class 'int'>
a: 0
<class 'int'>
b: 5

Expected Output:
<class 'list'>
[5.0, 2.25, 0.05, 0.0]

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numpy has been already imported as np (import numpy as np)