完成作业X

import numpy as nddd.py

@@ -0,0 +1,21 @@
+import numpy as np
+
+# 假设这是从图像中提取的2个特征图
+feature_map1 = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
+feature_map2 = np.array([[1, 1, 1], [1, 0, 0], [1, 0, 0]])
+
+# 补全代码：将特征图展平为向量
+vector1 = feature_map1.flatten()  # 展平
+vector2 = feature_map2.flatten()  # 展平
+
+print("vector1:", vector1)
+print("vector2:", vector2)
+print("-" * 40)
+
+# 1. 计算欧几里得距离
+euclidean_dist = np.linalg.norm(vector1 - vector2)
+print("欧几里得距离：", euclidean_dist)
+
+# 2. 计算余弦相似度
+cos_sim = np.dot(vector1, vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2))
+print("余弦相似度：", cos_sim)