# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import numpy as np
import sklearn.preprocessing as sp
raw_samples = np.array([
[3, -1.5, 2, -5.4],
[0, 4, -0.3, 2.1],
[1, 3.3, -1.9, -4.3]])
print(raw_samples)
print(raw_samples.mean(axis=0))
print(raw_samples.std(axis=0))
std_samples = raw_samples.copy()
for col in std_samples.T:
col_mean = col.mean()
col_std = col.std()
col -= col_mean
col /= col_std
print(std_samples)
print(std_samples.mean(axis=0))
print(std_samples.std(axis=0))
std_samples = sp.scale(raw_samples)
#scale函数保持均值为0,方差为1
print(std_samples)
print(std_samples.mean(axis=0))
print(std_samples.std(axis=0))
ML7: Sklearn scale mean std
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转载自blog.csdn.net/weixin_38246633/article/details/80584247
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