使用sklearn进行量纲缩放的程序
作者：互联网
使用sklearn进行量纲缩放的程序
# -*- coding: utf-8 -*-
"""
演示内容：量纲的特征缩放
（两种方法：标准化缩放法和区间缩放法。每种方法举了两个例子：简单二维矩阵和iris数据集）
"""
#方法1：标准化缩放法 例1：对简单示例二维矩阵的列数据进行
from sklearn import preprocessing
import numpy as np
#采用numpy的array表示，因为要用到其mean等函数，而list没有这些函数
X = np.array([[0, 0],
[0, 0],
[100, 1],
[1, 1]])
# calculate mean
X_mean = X.mean(axis=0)
# calculate variance
X_std = X.std(axis=0)
#print (X_std)
# standardize X
X1 = (X-X_mean)/X_std
print (X1)
print ("")
# we can also use function preprocessing.scale to standardize X
X_scale = preprocessing.scale(X)
print (X_scale)
[[-0.58504784 -1. ]
[-0.58504784 -1. ]
[ 1.73197332 1. ]
[-0.56187763 1. ]]
[[-0.58504784 -1. ]
[-0.58504784 -1. ]
[ 1.73197332 1. ]
[-0.56187763 1. ]]
#方法1： 标准化缩放法 例2：对iris数据二维矩阵的列数据进行。这次采用一个集成的方法StandardScaler
from sklearn import datasets
iris = datasets.load_iris()
X_scale = preprocessing.scale(iris.data)
print (X_scale)
[[-9.00681170e-01 1.01900435e+00 -1.34022653e+00 -1.31544430e+00]
[-1.14301691e+00 -1.31979479e-01 -1.34022653e+00 -1.31544430e+00]
[-1.38535265e+00 3.28414053e-01 -1.39706395e+00 -1.31544430e+00]
[-1.50652052e+00 9.82172869e-02 -1.28338910e+00 -1.31544430e+00]
[-1.02184904e+00 1.24920112e+00 -1.34022653e+00 -1.31544430e+00]
[-5.37177559e-01 1.93979142e+00 -1.16971425e+00 -1.05217993e+00]
[-1.50652052e+00 7.88807586e-01 -1.34022653e+00 -1.18381211e+00]
[-1.02184904e+00 7.88807586e-01 -1.28338910e+00 -1.31544430e+00]
[-1.74885626e+00 -3.62176246e-01 -1.34022653e+00 -1.31544430e+00]
[-1.14301691e+00 9.82172869e-02 -1.28338910e+00 -1.44707648e+00]
[-5.37177559e-01 1.47939788e+00 -1.28338910e+00 -1.31544430e+00]
[-1.26418478e+00 7.88807586e-01 -1.22655167e+00 -1.31544430e+00]
[-1.26418478e+00 -1.31979479e-01 -1.34022653e+00 -1.44707648e+00]
[-1.87002413e+00 -1.31979479e-01 -1.51073881e+00 -1.44707648e+00]
[-5.25060772e-02 2.16998818e+00 -1.45390138e+00 -1.31544430e+00]
[-1.73673948e-01 3.09077525e+00 -1.28338910e+00 -1.05217993e+00]
[-5.37177559e-01 1.93979142e+00 -1.39706395e+00 -1.05217993e+00]
[-9.00681170e-01 1.01900435e+00 -1.34022653e+00 -1.18381211e+00]
[-1.73673948e-01 1.70959465e+00 -1.16971425e+00 -1.18381211e+00]
[-9.00681170e-01 1.70959465e+00 -1.28338910e+00 -1.18381211e+00]
[-5.37177559e-01 7.88807586e-01 -1.16971425e+00 -1.31544430e+00]
[-9.00681170e-01 1.47939788e+00 -1.28338910e+00 -1.05217993e+00]
[-1.50652052e+00 1.24920112e+00 -1.56757623e+00 -1.31544430e+00]
[-9.00681170e-01 5.58610819e-01 -1.16971425e+00 -9.20547742e-01]
[-1.26418478e+00 7.88807586e-01 -1.05603939e+00 -1.31544430e+00]
[-1.02184904e+00 -1.31979479e-01 -1.22655167e+00 -1.31544430e+00]
[-1.02184904e+00 7.88807586e-01 -1.22655167e+00 -1.05217993e+00]
[-7.79513300e-01 1.01900435e+00 -1.28338910e+00 -1.31544430e+00]
[-7.79513300e-01 7.88807586e-01 -1.34022653e+00 -1.31544430e+00]
[-1.38535265e+00 3.28414053e-01 -1.22655167e+00 -1.31544430e+00]
[-1.26418478e+00 9.82172869e-02 -1.22655167e+00 -1.31544430e+00]
[-5.37177559e-01 7.88807586e-01 -1.28338910e+00 -1.05217993e+00]
[-7.79513300e-01 2.40018495e+00 -1.28338910e+00 -1.44707648e+00]
[-4.16009689e-01 2.63038172e+00 -1.34022653e+00 -1.31544430e+00]
[-1.14301691e+00 9.82172869e-02 -1.28338910e+00 -1.31544430e+00]
[-1.02184904e+00 3.28414053e-01 -1.45390138e+00 -1.31544430e+00]
[-4.16009689e-01 1.01900435e+00 -1.39706395e+00 -1.31544430e+00]
[-1.14301691e+00 1.24920112e+00 -1.34022653e+00 -1.44707648e+00]
[-1.74885626e+00 -1.31979479e-01 -1.39706395e+00 -1.31544430e+00]
[-9.00681170e-01 7.88807586e-01 -1.28338910e+00 -1.31544430e+00]
[-1.02184904e+00 1.01900435e+00 -1.39706395e+00 -1.18381211e+00]
[-1.62768839e+00 -1.74335684e+00 -1.39706395e+00 -1.18381211e+00]
[-1.74885626e+00 3.28414053e-01 -1.39706395e+00 -1.31544430e+00]
[-1.02184904e+00 1.01900435e+00 -1.22655167e+00 -7.88915558e-01]
[-9.00681170e-01 1.70959465e+00 -1.05603939e+00 -1.05217993e+00]
[-1.26418478e+00 -1.31979479e-01 -1.34022653e+00 -1.18381211e+00]
[-9.00681170e-01 1.70959465e+00 -1.22655167e+00 -1.31544430e+00]
[-1.50652052e+00 3.28414053e-01 -1.34022653e+00 -1.31544430e+00]
[-6.58345429e-01 1.47939788e+00 -1.28338910e+00 -1.31544430e+00]
[-1.02184904e+00 5.58610819e-01 -1.34022653e+00 -1.31544430e+00]
[ 1.40150837e+00 3.28414053e-01 5.35408562e-01 2.64141916e-01]
[ 6.74501145e-01 3.28414053e-01 4.21733708e-01 3.95774101e-01]
[ 1.28034050e+00 9.82172869e-02 6.49083415e-01 3.95774101e-01]
[-4.16009689e-01 -1.74335684e+00 1.37546573e-01 1.32509732e-01]
[ 7.95669016e-01 -5.92373012e-01 4.78571135e-01 3.95774101e-01]
[-1.73673948e-01 -5.92373012e-01 4.21733708e-01 1.32509732e-01]
[ 5.53333275e-01 5.58610819e-01 5.35408562e-01 5.27406285e-01]
[-1.14301691e+00 -1.51316008e+00 -2.60315415e-01 -2.62386821e-01]
[ 9.16836886e-01 -3.62176246e-01 4.78571135e-01 1.32509732e-01]
[-7.79513300e-01 -8.22569778e-01 8.07091462e-02 2.64141916e-01]
[-1.02184904e+00 -2.43394714e+00 -1.46640561e-01 -2.62386821e-01]
[ 6.86617933e-02 -1.31979479e-01 2.51221427e-01 3.95774101e-01]
[ 1.89829664e-01 -1.97355361e+00 1.37546573e-01 -2.62386821e-01]
[ 3.10997534e-01 -3.62176246e-01 5.35408562e-01 2.64141916e-01]
[-2.94841818e-01 -3.62176246e-01 -8.98031345e-02 1.32509732e-01]
[ 1.03800476e+00 9.82172869e-02 3.64896281e-01 2.64141916e-01]
[-2.94841818e-01 -1.31979479e-01 4.21733708e-01 3.95774101e-01]
[-5.25060772e-02 -8.22569778e-01 1.94384000e-01 -2.62386821e-01]
[ 4.32165405e-01 -1.97355361e+00 4.21733708e-01 3.95774101e-01]
[-2.94841818e-01 -1.28296331e+00 8.07091462e-02 -1.30754636e-01]
[ 6.86617933e-02 3.28414053e-01 5.92245988e-01 7.90670654e-01]
[ 3.10997534e-01 -5.92373012e-01 1.37546573e-01 1.32509732e-01]
[ 5.53333275e-01 -1.28296331e+00 6.49083415e-01 3.95774101e-01]
[ 3.10997534e-01 -5.92373012e-01 5.35408562e-01 8.77547895e-04]
[ 6.74501145e-01 -3.62176246e-01 3.08058854e-01 1.32509732e-01]
[ 9.16836886e-01 -1.31979479e-01 3.64896281e-01 2.64141916e-01]
[ 1.15917263e+00 -5.92373012e-01 5.92245988e-01 2.64141916e-01]
[ 1.03800476e+00 -1.31979479e-01 7.05920842e-01 6.59038469e-01]
[ 1.89829664e-01 -3.62176246e-01 4.21733708e-01 3.95774101e-01]
[-1.73673948e-01 -1.05276654e+00 -1.46640561e-01 -2.62386821e-01]
[-4.16009689e-01 -1.51316008e+00 2.38717193e-02 -1.30754636e-01]
[-4.16009689e-01 -1.51316008e+00 -3.29657076e-02 -2.62386821e-01]
[-5.25060772e-02 -8.22569778e-01 8.07091462e-02 8.77547895e-04]
[ 1.89829664e-01 -8.22569778e-01 7.62758269e-01 5.27406285e-01]
[-5.37177559e-01 -1.31979479e-01 4.21733708e-01 3.95774101e-01]
[ 1.89829664e-01 7.88807586e-01 4.21733708e-01 5.27406285e-01]
[ 1.03800476e+00 9.82172869e-02 5.35408562e-01 3.95774101e-01]
[ 5.53333275e-01 -1.74335684e+00 3.64896281e-01 1.32509732e-01]
[-2.94841818e-01 -1.31979479e-01 1.94384000e-01 1.32509732e-01]
[-4.16009689e-01 -1.28296331e+00 1.37546573e-01 1.32509732e-01]
[-4.16009689e-01 -1.05276654e+00 3.64896281e-01 8.77547895e-04]
[ 3.10997534e-01 -1.31979479e-01 4.78571135e-01 2.64141916e-01]
[-5.25060772e-02 -1.05276654e+00 1.37546573e-01 8.77547895e-04]
[-1.02184904e+00 -1.74335684e+00 -2.60315415e-01 -2.62386821e-01]
[-2.94841818e-01 -8.22569778e-01 2.51221427e-01 1.32509732e-01]
[-1.73673948e-01 -1.31979479e-01 2.51221427e-01 8.77547895e-04]
[-1.73673948e-01 -3.62176246e-01 2.51221427e-01 1.32509732e-01]
[ 4.32165405e-01 -3.62176246e-01 3.08058854e-01 1.32509732e-01]
[-9.00681170e-01 -1.28296331e+00 -4.30827696e-01 -1.30754636e-01]
[-1.73673948e-01 -5.92373012e-01 1.94384000e-01 1.32509732e-01]
[ 5.53333275e-01 5.58610819e-01 1.27429511e+00 1.71209594e+00]
[-5.25060772e-02 -8.22569778e-01 7.62758269e-01 9.22302838e-01]
[ 1.52267624e+00 -1.31979479e-01 1.21745768e+00 1.18556721e+00]
[ 5.53333275e-01 -3.62176246e-01 1.04694540e+00 7.90670654e-01]
[ 7.95669016e-01 -1.31979479e-01 1.16062026e+00 1.31719939e+00]
[ 2.12851559e+00 -1.31979479e-01 1.61531967e+00 1.18556721e+00]
[-1.14301691e+00 -1.28296331e+00 4.21733708e-01 6.59038469e-01]
[ 1.76501198e+00 -3.62176246e-01 1.44480739e+00 7.90670654e-01]
[ 1.03800476e+00 -1.28296331e+00 1.16062026e+00 7.90670654e-01]
[ 1.64384411e+00 1.24920112e+00 1.33113254e+00 1.71209594e+00]
[ 7.95669016e-01 3.28414053e-01 7.62758269e-01 1.05393502e+00]
[ 6.74501145e-01 -8.22569778e-01 8.76433123e-01 9.22302838e-01]
[ 1.15917263e+00 -1.31979479e-01 9.90107977e-01 1.18556721e+00]
[-1.73673948e-01 -1.28296331e+00 7.05920842e-01 1.05393502e+00]
[-5.25060772e-02 -5.92373012e-01 7.62758269e-01 1.58046376e+00]
[ 6.74501145e-01 3.28414053e-01 8.76433123e-01 1.44883158e+00]
[ 7.95669016e-01 -1.31979479e-01 9.90107977e-01 7.90670654e-01]
[ 2.24968346e+00 1.70959465e+00 1.67215710e+00 1.31719939e+00]
[ 2.24968346e+00 -1.05276654e+00 1.78583195e+00 1.44883158e+00]
[ 1.89829664e-01 -1.97355361e+00 7.05920842e-01 3.95774101e-01]
[ 1.28034050e+00 3.28414053e-01 1.10378283e+00 1.44883158e+00]
[-2.94841818e-01 -5.92373012e-01 6.49083415e-01 1.05393502e+00]
[ 2.24968346e+00 -5.92373012e-01 1.67215710e+00 1.05393502e+00]
[ 5.53333275e-01 -8.22569778e-01 6.49083415e-01 7.90670654e-01]
[ 1.03800476e+00 5.58610819e-01 1.10378283e+00 1.18556721e+00]
[ 1.64384411e+00 3.28414053e-01 1.27429511e+00 7.90670654e-01]
[ 4.32165405e-01 -5.92373012e-01 5.92245988e-01 7.90670654e-01]
[ 3.10997534e-01 -1.31979479e-01 6.49083415e-01 7.90670654e-01]
[ 6.74501145e-01 -5.92373012e-01 1.04694540e+00 1.18556721e+00]
[ 1.64384411e+00 -1.31979479e-01 1.16062026e+00 5.27406285e-01]
[ 1.88617985e+00 -5.92373012e-01 1.33113254e+00 9.22302838e-01]
[ 2.49201920e+00 1.70959465e+00 1.50164482e+00 1.05393502e+00]
[ 6.74501145e-01 -5.92373012e-01 1.04694540e+00 1.31719939e+00]
[ 5.53333275e-01 -5.92373012e-01 7.62758269e-01 3.95774101e-01]
[ 3.10997534e-01 -1.05276654e+00 1.04694540e+00 2.64141916e-01]
[ 2.24968346e+00 -1.31979479e-01 1.33113254e+00 1.44883158e+00]
[ 5.53333275e-01 7.88807586e-01 1.04694540e+00 1.58046376e+00]
[ 6.74501145e-01 9.82172869e-02 9.90107977e-01 7.90670654e-01]
[ 1.89829664e-01 -1.31979479e-01 5.92245988e-01 7.90670654e-01]
[ 1.28034050e+00 9.82172869e-02 9.33270550e-01 1.18556721e+00]
[ 1.03800476e+00 9.82172869e-02 1.04694540e+00 1.58046376e+00]
[ 1.28034050e+00 9.82172869e-02 7.62758269e-01 1.44883158e+00]
[-5.25060772e-02 -8.22569778e-01 7.62758269e-01 9.22302838e-01]
[ 1.15917263e+00 3.28414053e-01 1.21745768e+00 1.44883158e+00]
[ 1.03800476e+00 5.58610819e-01 1.10378283e+00 1.71209594e+00]
[ 1.03800476e+00 -1.31979479e-01 8.19595696e-01 1.44883158e+00]
[ 5.53333275e-01 -1.28296331e+00 7.05920842e-01 9.22302838e-01]
[ 7.95669016e-01 -1.31979479e-01 8.19595696e-01 1.05393502e+00]
[ 4.32165405e-01 7.88807586e-01 9.33270550e-01 1.44883158e+00]
[ 6.86617933e-02 -1.31979479e-01 7.62758269e-01 7.90670654e-01]]
#方法2： 区间缩放法 例3：对简单示例二维矩阵的列数据进行
from sklearn.preprocessing import MinMaxScaler
data = [[0, 0],
[0, 0],
[100, 1],
[1, 1]]
scaler = MinMaxScaler()
print(scaler.fit(data))
print(scaler.transform(data))
MinMaxScaler(copy=True, feature_range=(0, 1))
[[0. 0. ]
[0. 0. ]
[1. 1. ]
[0.01 1. ]]
#方法2： 区间缩放法 例4：对iris数据二维矩阵的列数据进行
from sklearn.preprocessing import MinMaxScaler
data = iris.data
scaler = MinMaxScaler()
print(scaler.fit(data))
print(scaler.transform(data))
MinMaxScaler(copy=True, feature_range=(0, 1))
[[0.22222222 0.625 0.06779661 0.04166667]
[0.16666667 0.41666667 0.06779661 0.04166667]
[0.11111111 0.5 0.05084746 0.04166667]
[0.08333333 0.45833333 0.08474576 0.04166667]
[0.19444444 0.66666667 0.06779661 0.04166667]
[0.30555556 0.79166667 0.11864407 0.125 ]
[0.08333333 0.58333333 0.06779661 0.08333333]
[0.19444444 0.58333333 0.08474576 0.04166667]
[0.02777778 0.375 0.06779661 0.04166667]
[0.16666667 0.45833333 0.08474576 0. ]
[0.30555556 0.70833333 0.08474576 0.04166667]
[0.13888889 0.58333333 0.10169492 0.04166667]
[0.13888889 0.41666667 0.06779661 0. ]
[0. 0.41666667 0.01694915 0. ]
[0.41666667 0.83333333 0.03389831 0.04166667]
[0.38888889 1. 0.08474576 0.125 ]
[0.30555556 0.79166667 0.05084746 0.125 ]
[0.22222222 0.625 0.06779661 0.08333333]
[0.38888889 0.75 0.11864407 0.08333333]
[0.22222222 0.75 0.08474576 0.08333333]
[0.30555556 0.58333333 0.11864407 0.04166667]
[0.22222222 0.70833333 0.08474576 0.125 ]
[0.08333333 0.66666667 0. 0.04166667]
[0.22222222 0.54166667 0.11864407 0.16666667]
[0.13888889 0.58333333 0.15254237 0.04166667]
[0.19444444 0.41666667 0.10169492 0.04166667]
[0.19444444 0.58333333 0.10169492 0.125 ]
[0.25 0.625 0.08474576 0.04166667]
[0.25 0.58333333 0.06779661 0.04166667]
[0.11111111 0.5 0.10169492 0.04166667]
[0.13888889 0.45833333 0.10169492 0.04166667]
[0.30555556 0.58333333 0.08474576 0.125 ]
[0.25 0.875 0.08474576 0. ]
[0.33333333 0.91666667 0.06779661 0.04166667]
[0.16666667 0.45833333 0.08474576 0.04166667]
[0.19444444 0.5 0.03389831 0.04166667]
[0.33333333 0.625 0.05084746 0.04166667]
[0.16666667 0.66666667 0.06779661 0. ]
[0.02777778 0.41666667 0.05084746 0.04166667]
[0.22222222 0.58333333 0.08474576 0.04166667]
[0.19444444 0.625 0.05084746 0.08333333]
[0.05555556 0.125 0.05084746 0.08333333]
[0.02777778 0.5 0.05084746 0.04166667]
[0.19444444 0.625 0.10169492 0.20833333]
[0.22222222 0.75 0.15254237 0.125 ]
[0.13888889 0.41666667 0.06779661 0.08333333]
[0.22222222 0.75 0.10169492 0.04166667]
[0.08333333 0.5 0.06779661 0.04166667]
[0.27777778 0.70833333 0.08474576 0.04166667]
[0.19444444 0.54166667 0.06779661 0.04166667]
[0.75 0.5 0.62711864 0.54166667]
[0.58333333 0.5 0.59322034 0.58333333]
[0.72222222 0.45833333 0.66101695 0.58333333]
[0.33333333 0.125 0.50847458 0.5 ]
[0.61111111 0.33333333 0.61016949 0.58333333]
[0.38888889 0.33333333 0.59322034 0.5 ]
[0.55555556 0.54166667 0.62711864 0.625 ]
[0.16666667 0.16666667 0.38983051 0.375 ]
[0.63888889 0.375 0.61016949 0.5 ]
[0.25 0.29166667 0.49152542 0.54166667]
[0.19444444 0. 0.42372881 0.375 ]
[0.44444444 0.41666667 0.54237288 0.58333333]
[0.47222222 0.08333333 0.50847458 0.375 ]
[0.5 0.375 0.62711864 0.54166667]
[0.36111111 0.375 0.44067797 0.5 ]
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[0.58333333 0.33333333 0.77966102 0.875 ]
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[0.5 0.25 0.77966102 0.54166667]
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[0.69444444 0.5 0.83050847 0.91666667]
[0.66666667 0.54166667 0.79661017 1. ]
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[0.55555556 0.20833333 0.6779661 0.75 ]
[0.61111111 0.41666667 0.71186441 0.79166667]
[0.52777778 0.58333333 0.74576271 0.91666667]
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标签：0.04166667,00,01,缩放,0.5,0.41666667,0.58333333,量纲,sklearn 来源： https://blog.csdn.net/qq_39827677/article/details/118942655