请给出马氏距离分类器的代码
时间: 2023-11-28 12:51:19 浏览: 30
好的,以下是一个简单的马氏距离分类器的 Python 代码实现:
```python
import numpy as np
class MahalanobisDistanceClassifier:
def __init__(self, num_classes):
self.num_classes = num_classes
self.mean_vectors = [None] * num_classes
self.covariance_matrices = [None] * num_classes
def train(self, X_train, y_train):
for i in range(self.num_classes):
X_i = X_train[y_train == i]
self.mean_vectors[i] = np.mean(X_i, axis=0)
self.covariance_matrices[i] = np.cov(X_i.T)
def predict(self, X_test):
y_pred = []
for x in X_test:
distances = []
for i in range(self.num_classes):
mean_vector = self.mean_vectors[i]
cov_matrix = self.covariance_matrices[i]
try:
inv_cov_matrix = np.linalg.inv(cov_matrix)
except:
inv_cov_matrix = np.linalg.pinv(cov_matrix)
diff = x - mean_vector
distance = np.sqrt(np.dot(np.dot(diff, inv_cov_matrix), diff.T))
distances.append(distance)
y_pred.append(np.argmin(distances))
return np.array(y_pred)
```
这个分类器的主要步骤包括:
- 在训练阶段计算每个类别的均值向量和协方差矩阵;
- 在测试阶段,对于每个输入样本,计算它到每个类别的均值向量的马氏距离,并将其分类到距离最近的类别。
需要注意的是,当某个类别的协方差矩阵不可逆时,我们需要使用伪逆来代替它。