From c5a7ee51c4030846504c0861d65816040e32dae3 Mon Sep 17 00:00:00 2001
From: cy <939601402@qq.com>
Date: Wed, 23 Oct 2024 18:02:32 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 26 +++++++++-----------------
 1 file changed, 9 insertions(+), 17 deletions(-)

diff --git a/function.py b/function.py
index 7ebb0c9..61879b6 100644
--- a/function.py
+++ b/function.py
@@ -29,25 +29,17 @@ name = "程悦"
 
 
 
-def classify(input_vector, dataSet, labels, k=9):
-    """
-    KNN 分类算法，添加了数据归一化处理。
-
-    Args:
-    - input_vector (numpy array): 输入待分类的样本。
-    - dataSet (numpy array): 训练数据集。
-    - labels (list or numpy array): 训练集对应的标签。
-    - k (int): kNN 算法参数。
-
-    Returns:
-    - int: 预测的分类标签。
-    """
+def classify(input_vector, dataSet, labels, k=2):
     dataSet_size = dataSet.shape[0]
-    # 计算欧氏距离
+    # 计算马氏距离
+    cov_matrix = np.cov(dataSet.T)
+    inv_cov_matrix = np.linalg.pinv(cov_matrix)
     diff_mat = np.tile(input_vector, (dataSet_size, 1)) - dataSet
-    sq_diff_mat = diff_mat ** 2
-    sq_distances = sq_diff_mat.sum(axis=1)
-    distances = np.sqrt(sq_distances)
+    distances = []
+    for i in range(dataSet_size):
+        distance = np.sqrt(np.dot(np.dot(diff_mat[i], inv_cov_matrix), diff_mat[i].T))
+        distances.append(distance)
+    distances = np.array(distances)
     # 对距离进行排序
     sorted_indices = distances.argsort()
     class_count = {}