From 64a2de73d8ef2a3160757570a946abeb46c2936a Mon Sep 17 00:00:00 2001
From: 2509165045 <2509165045@student.edu.cn>
Date: Tue, 21 Apr 2026 11:30:19 +0800
Subject: [PATCH] =?UTF-8?q?=E4=BD=9C=E4=B8=9A10?=
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diff --git a/1 b/1
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+#第一题
+for c in "Hello":
+    print(f"{c}:{ord(c)}")
+#第二题
+print(chr(65))
+assert chr(65) =='A'
+#思考题:“图像为数值化结构数据，适配计算架构；文本为语义化符号数据，需认知理解，超出当前AI本质能力。”
+#第三题
+import numpy as np
+A=np.array([3,4])
+B=np.array([1,2])
+c=A+B
+d=A*2
+print(f"A+B={c}")
+print(f"2*A={d}")
+length = np.linalg.norm(A)
+print(f"A的长度为{length}")
+#第四题
+
+C = np.array([1, 2, 3])
+D = np.array([4, 5, 6])
+dot = np.dot(C, D)
+print(f"点积 = {dot}")  
+import numpy as np
+
+def cosine_similarity(C, D):
+    norm_a = np.linalg.norm(C)  # 向量a的长度
+    norm_b = np.linalg.norm(D)  # 向量b的长度
+    return dot / (norm_a * norm_b)
+print(f"相似度 = {cosine_similarity(C, D):.3f}")
+ 
+E=np.array([1,0])
+F=np.array([0,1])
+def cosine_similarity(E, F):
+    norm_C = np.linalg.norm(E)  # 向量a的长度
+    norm_D = np.linalg.norm(F)  # 向量b的长度
+    return dot / (norm_C * norm_D)
+print(f"相似度 = {cosine_similarity(E, F):.3f}")
+ 
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