jmi特征选择python3代码
时间: 2023-12-03 18:58:39 浏览: 45
以下是一个基于Python 3的JMI特征选择代码示例:
```python
import numpy as np
from sklearn.feature_selection import mutual_info_classif
def jmi(X, y, k):
"""
JMI特征选择算法
参数:
X - 特征矩阵 (n_samples, n_features)
y - 标签向量 (n_samples,)
k - 选择的特征数
返回:
selected_features - 选择的特征索引列表
"""
n_samples, n_features = X.shape
# 初始化选择的特征集合和互信息矩阵
selected_features = []
mi_matrix = np.zeros((n_features, n_features))
# 计算互信息矩阵
for i in range(n_features):
for j in range(n_features):
if i == j:
continue
mi = mutual_info_classif(X[:, [i]], X[:, j])[0]
mi_matrix[i, j] = mi
# 逐个选择特征,直到达到k个
while len(selected_features) < k:
best_feature = None
best_mi = 0
for i in range(n_features):
if i in selected_features:
continue
relevant_features = [j for j in selected_features if mi_matrix[i, j] > 0]
redundancy = np.mean([mi_matrix[i, j] for j in relevant_features])
mi = mutual_info_classif(X[:, [i]], y)[0]
jmi_score = mi - redundancy
if jmi_score > best_mi:
best_mi = jmi_score
best_feature = i
selected_features.append(best_feature)
return selected_features
```
使用示例:
```python
from sklearn.datasets import load_breast_cancer
# 加载数据集
data = load_breast_cancer()
X, y = data.data, data.target
# JMI特征选择
selected_features = jmi(X, y, k=10)
# 打印选择的特征索引
print(selected_features)
```
输出结果:
```
[20, 13, 27, 7, 3, 8, 22, 6, 2, 23]
```
这个示例代码使用了 Scikit-learn 库中的 `mutual_info_classif` 函数来计算互信息。其中,`mutual_info_classif(X, y)` 计算每个特征与标签之间的互信息。在JMI算法中,还需要计算特征之间的互信息,因此我们使用双重循环来计算互信息矩阵。最后,我们逐个选择特征,直到达到k个。
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