24 lines
800 B
Python
24 lines
800 B
Python
import numpy as np
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# 假设这是从图像中提取的2个特征图
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feature_map1 = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
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feature_map2 = np.array([[1, 1, 1], [1, 0, 0], [1, 0, 0]])
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# 1) 全代码：将特征图展平为向量
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vector1 = feature_map1.flatten() # 展平
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vector2 = feature_map2.flatten() # 展平
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print("vector1:", vector1)
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print("vector2:", vector2)
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print("-" * 40)
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# 1. 计算 vector1 和 vector2 的欧几里得距离
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euclidean_dist = np.linalg.norm(vector1 - vector2)
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print(f"欧几里得距离: {euclidean_dist:.4f}")
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# 2. 计算 vector1 和 vector2 的余弦相似度
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dot_product = np.dot(vector1, vector2)
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norm_a = np.linalg.norm(vector1)
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norm_b = np.linalg.norm(vector2)
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cos_similarity = dot_product / (norm_a * norm_b)
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print(f"余弦相似度: {cos_similarity:.4f}") |