pca.explained_variance_ratio_()代码怎么写
时间: 2023-07-16 19:11:27 浏览: 137
假设已经使用PCA对数据进行了降维,可以使用`explained_variance_ratio_()`方法获取每个主成分解释的方差比例,代码如下:
```python
from sklearn.decomposition import PCA
# 假设已经使用PCA对数据进行了降维,存储在pca对象中
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
# 获取每个主成分解释的方差比例
var_ratio = pca.explained_variance_ratio_
print(var_ratio)
```
`var_ratio`将包含每个主成分解释的方差比例,它们按照降序排列。例如,如果有5个主成分,则`var_ratio`可能是`[0.4, 0.3, 0.2, 0.05, 0.05]`,表示第一个主成分解释了总方差的40%,第二个解释了30%,以此类推。
相关问题
explained_variance_ratio = pca.explained_variance_ratio_
This variable stores the explained variance ratio of each principal component in the PCA analysis. It is an array of length equal to the number of principal components computed in the analysis. The value of each element represents the proportion of the total variance in the data that is explained by the corresponding principal component. The sum of all elements in the array is equal to 1.0. This information can be used to determine the number of principal components needed to capture a certain percentage of the total variance in the data. For example, if the first two principal components have an explained variance ratio of 0.5 and 0.3, respectively, then these two components capture 80% of the total variance in the data.
explained_variance_ratio = pca.explained_variance_ratio_ print(explained_variance_ratio)将这个的输出结果行列互换
要将 `explained_variance_ratio` 输出结果的行列互换,您可以使用 NumPy 库中的 `transpose()` 函数。请按照以下步骤进行修改:
1. 导入所需的库:
```python
import numpy as np
```
2. 对数据进行转置:
```python
explained_variance_ratio_transposed = np.transpose(explained_variance_ratio)
```
这将对 `explained_variance_ratio` 进行转置操作,将行列互换。
3. 打印结果:
```python
print(explained_variance_ratio_transposed)
```
这将打印转置后的结果,其中行列已经互换。
请确保已正确导入所需的库,并在代码中添加转置操作。执行完上述代码后,将获得已经行列互换的输出结果 `explained_variance_ratio_transposed`。
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