Min-Max Normalization

Features measured on different scales (age in years, income in dollars) confuse many models. Min-max normalization rescales a feature to the range 0 to 1:

x' = (x - min) / (max - min)

If all values are equal (max = min), return 0 for every value.

Implement min_max_normalize(values).

Input

values: The values of one feature.

Output

Return the normalized values in the same order.

Vincoli

1 ≤ length ≤ 10,000

-1,000,000 ≤ each value ≤ 1,000,000

Esempi

Input:
values = [10.0, 20.0, 15.0, 30.0]

Output atteso:
expected = [0.0, 0.5, 0.25, 1.0]

Input:
std::vector<double> values = {10.0, 20.0, 15.0, 30.0};

Output atteso:
std::vector<double> expected = {0.0, 0.5, 0.25, 1.0};

Input:
double[] values = new double[] {10.0, 20.0, 15.0, 30.0};

Output atteso:
double[] expected = new double[] {0.0, 0.5, 0.25, 1.0};

Input:
values = [5.0, 5.0, 5.0]

Output atteso:
expected = [0.0, 0.0, 0.0]

Input:
std::vector<double> values = {5.0, 5.0, 5.0};

Output atteso:
std::vector<double> expected = {0.0, 0.0, 0.0};

Input:
double[] values = new double[] {5.0, 5.0, 5.0};

Output atteso:
double[] expected = new double[] {0.0, 0.0, 0.0};

Riferimenti

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• scikit-learn, Preprocessing data