更新 function.py

function.py

@@ -61,10 +61,20 @@ def classify0(inX, dataSet, labels, k=3):
     covMat = np.cov(dataSet, rowvar=False)
     meanVec = np.mean(dataSet, axis=0)
     
-    # 计算待分类样本与训练集中每个样本的马氏距离
+    # 计算协方差矩阵的逆
     invCovMat = np.linalg.inv(covMat)
-    diffVecs = dataSet - meanVec
-    dist = np.sqrt(np.sum((inX - meanVec).dot(invCovMat) * (inX - meanVec), axis=1))
+    
+    # 初始化距离数组
+    dist = []
+    
+    # 计算待分类样本与训练集中每个样本的马氏距离
+    for i in range(dataSet.shape[0]):
+        diffVec = dataSet[i, :] - inX
+        # 避免除以零的情况
+        if np.linalg.det(covMat) == 0:
+            dist.append(np.linalg.norm(diffVec))
+        else:
+            dist.append(np.sqrt(diffVec.dot(invCovMat).dot(diffVec.T)))
     
     # 获取距离最近的 k 个样本的索引
     kNearestIndices = np.argsort(dist)[:k]