min-max-feature-scaling — AWS case

AWS-inspired monitoring model combines numeric signals that arrive on very different scales. Before training, the team needs every feature mapped to a fixed interval, so you need to implement min-max scaling and handle the stated boundary cases correctly.

Map the values linearly so the minimum becomes 0 and the maximum becomes 1. If all values are equal (the range is zero) return all zeros.

Implement solve(x).

Returns. Return a float NumPy vector in $[0,1][0,1]$.

Examples

Example 1

Input:

solve([10.0, 20.0, 30.0])

Output:

[0.0, 0.5, 1.0]

Example 2

Input:

solve([5.0, 5.0, 5.0])

Output:

[0.0, 0.0, 0.0]

Example 3

Input:

solve([-1.0, 0.0, 3.0])

Output:

[0.0, 0.25, 1.0]