1 将XX中的数据进行零均值化，即每一列都减去其均值。2 计算协方差矩阵C=1mXTXC=1mXTX3 求出CC的特征值和特征向量4 将特征向量按对应特征值大小从上到下按行排列成矩阵，取前k行组成矩阵P5 Y=XPY=XP就是降维到k维后的数据。

# coding=utf-8import matplotlib.pyplot as pltfrom sklearn.decomposition import PCAfrom sklearn.datasets import load_irisdata = load_iris()y = data.targetX = data.dataprint data.feature_namesprint data.data;pca = PCA(n_components=2)reduced_X = pca.fit_transform(X)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])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()