鸢尾花主成分分析 jupyter实现

import matplotlib.pyplot as pltfrom sklearn.decomposition import PCAfrom sklearn.datasets import load_iris data = load_iris() #字典的形式#print(data,‘!!!!!!‘)y = data.target#print(y)X = data.data#print(X)pca = PCA(n_components=2) #n_components：指定主成分的个数，即降维后数据的维度#print(pca)#PCA(copy=True, iterated_power=‘auto‘, n_components=2, random_state=None, #特征值分解 svd_solver=‘auto‘, tol=0.0, whiten=False)reduced_X = pca.fit_transform(X)#用PCA处理的数据是X#print(reduced_X,‘!!!!‘) #此时的X是2维了#print(reduced_X.shape) #输出矩阵维度#建立三类数据集red_x, red_y = [], []blue_x, blue_y = [], []green_x, green_y = [], [] for i in range(len(reduced_X)): #len（reduce_x）有150个 if y[i] == 0:# 以字典的方式检索，y = data.target，target 就是0，1，2 red_x.append(reduced_X[i][0])#不断地添加红色的样本点的个数 red_y.append(reduced_X[i][1]) #print(len(red_x),‘!!!‘) #一个一个添加，一直有50个 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‘)#c 是color#print(plt.scatter)plt.scatter(blue_x, blue_y, c=‘b‘, marker=‘D‘)plt.scatter(green_x, green_y, c=‘g‘, marker=‘+‘) #marker是标记点的形状plt.show()