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<>主成分分析法代码实现
之间我介绍过主成分份分析法，这里给出代码实现
from sklearn.decomposition import PCA import pandas as pd import os path=
"C:/Users/Administrator/Desktop/o25mso/homework/AMZN.csv"#存放文件路径，这个文件在我的资源上传里 df
=pd.read_csv(path)#读取文件 pca=PCA()#创建对象 df=(df.iloc[:,2:]-df.iloc[:,2:].mean())/
df.iloc[:,2:].std()#对数据进行中心化处理 #print(df) pca.fit(df) print(pca.components_)
#返回模型的各个特征向量 print(pca.explained_variance_ratio_)#返回各个成分各自的方差百分比 pca=PCA(2)
#设置转化主成分个数两个 pca.fit(df) low_d=pca.transform(df) print(low_d)#返回降维后的数据
运行结果：
上图的结果分别为特征向量，和主成分所占的方差百分比，可以发现第一个和第二个主成分占的方差百分比比较多，其他几个特别小，所以这里我们取两个主成分进行降维，对应上诉代码。
好的，代码很简单，原理并没那么简单，如果不了解原理可以多看看这方面的理论知识。
