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  • 最大值最小值归一化

     1 # -*- coding: utf-8 -*-
     2 """
     3 Created on Fri Sep  7 16:28:20 2018
     4 
     5 @author: zhen
     6 """
     7 # 最大值最小值归一化:(X-Xmin)/(Xmax-Xmin)
     8 import numpy as np
     9 import matplotlib.pyplot as plt
    10 
    11 x = np.random.rand(100)
    12 x = x.reshape(-1, 1)
    13 
    14 rand = np.random.rand(100) * np.random.randint(10)
    15 rand = rand.reshape(-1, 1)
    16 # 获取Xmax,Xmin
    17 x_max = np.max(rand)
    18 x_min = np.min(rand)
    19 
    20 result = []
    21 # 查找数组的索引:np.where(rand == i)
    22 for i in rand:
    23     result.append((float(i[0]) - x_min)/(x_max - x_min))
    24     
    25 result = np.array(result, dtype = float)
    26 result = result.reshape(-1, 1)
    27 # 可视化
    28 plt.plot(x, rand, "r.", label="native")
    29 plt.plot(x, result, "b.", linewidth=2, label="normalized")
    30 
    31 plt.legend(loc="upper left")
    32 plt.grid()
    33 plt.show()

    结果:

    分析:可知,数据的离散性大大降低,数据之间的内聚性增加,数据更加密集!

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  • 原文地址:https://www.cnblogs.com/yszd/p/9606284.html
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