数学建模——主成分分析算法详解Python代码
import matplotlib.pyplot as plt #加载matplotlib用于数据的可视化from sklearn.decomposition import PCA #加载PCA算法包from sklearn.datasets import load_iris data=load_iris()y=data.targetx=data.datapca=PCA(n_components=2) #加载PCA算法，设置降维后主成分数目为2reduced_x=pca.fit_transform(x)#对样本进行降维red_x,red_y=[],[]blue_x,blue_y=[],[]green_x,green_y=[],[] for i in range(len(reduced_x)): 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])#可视化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()