Python实现归一化算法详情

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# 1.最小最大标准化
Data = np.array([[0.2,0.9,29],
                [0.9,0.1,100],
                [0.5,0.5,30]]) #最小－最大归一化算法
#　1.1数据转化
def MinMax(data):
    min = 0
    max = 1
    C = data[:,2]
    min = np.min(C)
    max = np.max(C)
    for one in data:
        one[2] = (one[2]-min) / (max-min)
    print('转化后的矩阵：\n',data)
    return data


# 1.2可视化
def ShowData(Data,ShowD1):
    length = len(Data)
    X = np.ones(Data.shape[0])
    plt.figure(1)
    plt.subplot(121)
    for i in range(length):
        plt.scatter(X*(i+1),Data[:,i])
    plt.subplot(122)
    for i in range(length):
        plt.scatter(X*(i+1),ShowD1[:,i])
    plt.show()
ShowData(Data,MinMax(Data.copy()))

def Zscore(data):
    x_mean = np.mean(data[:2])
    length = len(data[:,2])
    vari = np.sqrt((np.sum((data[:2]-x_mean)**2))/length)
    print('方差:',vari)
    data[:,2] = (data[:,2]-x_mean)/vari
    print('Z-score标准化后的矩阵是',data)
    return data

ShowData(Data,Zscore(Data.copy()))

# 小数定标归一化算法
def Decimals(data):
    C = np.abs(data[:,2])
    max = int(np.sort(C)[-1]) # 按从小到大排序，取最后一位，及最大值
    k = len(str(max))
    print('绝对值最大的位数：\n',k)
    data[:2] = data[:,2] /(10**k)
    print('小数点定标准化后的矩阵：\n',data)
    return data
ShowData(Data,Decimals(Data.copy()))