​​https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html​​

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 2019-01-20

@author: brucelau
"""

import numpy as np
from sklearn.preprocessing import MinMaxScaler

def info():
    '''
    Transforms features by scaling each feature to a given range.
    This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.

    The transformation is given by:
    X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
    X_scaled = X_std * (max - min) + min
    '''
    pass

print(info.__doc__)

data_1 = np.arange(1,10).reshape((-3,3))
data_2 = np.array([[10,11,12]])
scaler = MinMaxScaler().fit(data_1)
x2 = scaler.transform(data_1)
x3 = scaler.transform(data_2)

print('data_1:\n',data_1)
print('data_2:\n',data_2)


print('x2:\n',x2)
print('x3:\n',x3)