如何获得PCA的重量

Question

我正在尝试使用短剧学习来查找PCA的权重。但是，这些方法都不起作用。

代码：

import pandas as pd
import numpy as np
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
# load dataset into Pandas DataFrame
df = pd.read_csv(url, names=['sepal length','sepal width','petal length','petal width','target'])
from sklearn.preprocessing import StandardScaler
features = ['sepal length', 'sepal width', 'petal length', 'petal width']
# Separating out the features
x = df.loc[:, features].values
# Standardizing the features
x = StandardScaler().fit_transform(x)
from sklearn.decomposition import PCA
pca = PCA(n_components=1)
principalComponents = pca.fit_transform(x)

查找权重

方法 1

weights = pca.components_*np.sqrt(pca.explained_variance_)
# recovering original data
pca_recovered = np.dot(weights, x)
### This output is not matching with PCA

方法 2

# Standardising the weights then recovering
weights1 = weights/np.sum(weights)
pca_recovered = np.dot(weights1, x)
### This output is not matching with PCA

如果我在这里做错了什么，请帮助。或者，包装中缺少某些内容。

Answer 1

使用

weight = pca.components_

但输出

x.dot(pca.components_.T)

不同于

pca.fit_transform(x)

因为 PCA 输出是标准化的。 用

tmp = x.dot(pca.components_.T)
tmp-tmp.mean(axis=0)

您将获得相同的输出

Answer 2

而不是

weights = pca.components_*np.sqrt(pca.explained_variance_)

如果我简单地使用

weights = pca.components_

可能是第一次，当我尝试时，有计算错误。