完成作业一：3-1-3

0416 3.py

@@ -0,0 +1,24 @@
+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]])
+
+# 1) 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 vector1 和 vector2 的欧几里得距离
+euclidean_dist = np.linalg.norm(vector1 - vector2)
+print(f"欧几里得距离: {euclidean_dist:.4f}")
+
+# 2. 计算 vector1 和 vector2 的余弦相似度
+dot_product = np.dot(vector1, vector2)
+norm_a = np.linalg.norm(vector1)
+norm_b = np.linalg.norm(vector2)
+cos_similarity = dot_product / (norm_a * norm_b)
+print(f"余弦相似度: {cos_similarity:.4f}")