数据归一化

数据归一化

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

data = np.arange(36)
data = data.reshape(6,6)
data = pd.DataFrame(data)
data

	0	1	2	3	4	5
0	0	1	2	3	4	5
1	6	7	8	9	10	11
2	12	13	14	15	16	17
3	18	19	20	21	22	23
4	24	25	26	27	28	29
5	30	31	32	33	34	35

sclar = MinMaxScaler(feature_range=(0,1)).fit(data)
result = sclar.transform(data)
result

array([[0. , 0. , 0. , 0. , 0. , 0. ],
[0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
[0.4, 0.4, 0.4, 0.4, 0.4, 0.4],
[0.6, 0.6, 0.6, 0.6, 0.6, 0.6],
[0.8, 0.8, 0.8, 0.8, 0.8, 0.8],
[1. , 1. , 1. , 1. , 1. , 1. ]])

sclar_ = sclar.inverse_transform(result)
sclar_

array([[ 0., 1., 2., 3., 4., 5.],
[ 6., 7., 8., 9., 10., 11.],
[12., 13., 14., 15., 16., 17.],
[18., 19., 20., 21., 22., 23.],
[24., 25., 26., 27., 28., 29.],
[30., 31., 32., 33., 34., 35.]])

使用 np 实现数据归一化

data = np.arange(36)
data = data.reshape(6,6)
data = pd.DataFrame(data)
data

	0	1	2	3	4	5
0	0	1	2	3	4	5
1	6	7	8	9	10	11
2	12	13	14	15	16	17
3	18	19	20	21	22	23
4	24	25	26	27	28	29
5	30	31	32	33	34	35

x_nor = (data - data.min(axis=0)) / (data.max(axis=0) - data.min(axis=0))
x_nor

	0	1	2	3	4	5
0	0.0	0.0	0.0	0.0	0.0	0.0
1	0.2	0.2	0.2	0.2	0.2	0.2
2	0.4	0.4	0.4	0.4	0.4	0.4
3	0.6	0.6	0.6	0.6	0.6	0.6
4	0.8	0.8	0.8	0.8	0.8	0.8
5	1.0	1.0	1.0	1.0	1.0	1.0

x_nor_inverse = x_nor * (data.max(axis=0) - data.min(axis=0)) + data.min(axis=0)
x_nor_inverse

	0	1	2	3	4	5
0	0.0	1.0	2.0	3.0	4.0	5.0
1	6.0	7.0	8.0	9.0	10.0	11.0
2	12.0	13.0	14.0	15.0	16.0	17.0
3	18.0	19.0	20.0	21.0	22.0	23.0
4	24.0	25.0	26.0	27.0	28.0	29.0
5	30.0	31.0	32.0	33.0	34.0	35.0

数据标准化

data = np.arange(36)
data = data.reshape(6,6)
data = pd.DataFrame(data)
data

	0	1	2	3	4	5
0	0	1	2	3	4	5
1	6	7	8	9	10	11
2	12	13	14	15	16	17
3	18	19	20	21	22	23
4	24	25	26	27	28	29
5	30	31	32	33	34	35

from sklearn.preprocessing import StandardScaler
std = StandardScaler().fit(data)
result1 = std.transform(data)
result1

array([[-1.46385011, -1.46385011, -1.46385011, -1.46385011, -1.46385011,
-1.46385011],
[-0.87831007, -0.87831007, -0.87831007, -0.87831007, -0.87831007,
-0.87831007],
[-0.29277002, -0.29277002, -0.29277002, -0.29277002, -0.29277002,
-0.29277002],
[ 0.29277002, 0.29277002, 0.29277002, 0.29277002, 0.29277002,
0.29277002],
[ 0.87831007, 0.87831007, 0.87831007, 0.87831007, 0.87831007,
0.87831007],
[ 1.46385011, 1.46385011, 1.46385011, 1.46385011, 1.46385011,
1.46385011]])

result1.mean()

-3.700743415417188e-17

result1.var()

1.0000000000000002

std.inverse_transform(result1)

array([[ 0., 1., 2., 3., 4., 5.],
[ 6., 7., 8., 9., 10., 11.],
[12., 13., 14., 15., 16., 17.],
[18., 19., 20., 21., 22., 23.],
[24., 25., 26., 27., 28., 29.],
[30., 31., 32., 33., 34., 35.]])