4.5 PCA.py

# -*- coding: utf-8 -*-
"""
Created on Fri Jan 15 09:32:31 2021

@author: mashituo
"""
import matplotlib.pyplot as plt
import sklearn.decomposition as dp
from sklearn.datasets import load_iris

x,y=load_iris(return_X_y=True) #加载数据，x表示数据集中的属性数据，y表示数据标签
pca=dp.PCA(n_components=0.99) #加载pca算法，设置降维后主成分数目为2
reduced_x=pca.fit_transform(x,y) #对原始数据进行降维，保存在reduced_x中
red_x,red_y=[],[]
blue_x,blue_y=[],[]
green_x,green_y=[],[]
for i in range(len(reduced_x)): #按鸢尾花的类别将降维后的数据点保存在不同的表表中
    if y[i]==0:
        red_x.append(reduced_x[i][0])
        red_y.append(reduced_x[i][1])
    elif y[i]==1:
        blue_x.append(reduced_x[i][0])
        blue_y.append(reduced_x[i][1])
    else:
        green_x.append(reduced_x[i][0])
        green_y.append(reduced_x[i][1])
plt.scatter(red_x,red_y,c='r',marker='x')
plt.scatter(blue_x,blue_y,c='b',marker='D')
plt.scatter(green_x,green_y,c='g',marker='.')
plt.show()