From 0d833c7817d08704674f42f1cf7c4a905704e615 Mon Sep 17 00:00:00 2001
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 <2509165011@student.example.com>
Date: Tue, 21 Apr 2026 11:29:52 +0800
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?=
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---
 yzz.py  | 14 ++++++++++++++
 yzz1.py | 32 ++++++++++++++++++++++++++++++++
 yzz2.py | 27 +++++++++++++++++++++++++++
 3 files changed, 73 insertions(+)
 create mode 100644 yzz.py
 create mode 100644 yzz1.py
 create mode 100644 yzz2.py

diff --git a/yzz.py b/yzz.py
new file mode 100644
index 0000000..f698028
--- /dev/null
+++ b/yzz.py
@@ -0,0 +1,14 @@
+# 方式1：直接用字符串表示 "Hello"
+str1 = "Hello"
+# 方式2：用单引号包裹表示 "Hello"
+str2 = 'Hello'
+
+print("方式1（双引号）：", str1)
+print("方式2（单引号）：", str2)
+
+print("\n--- Hello 每个字符的ASCII码 ---")
+for char in "Hello":
+    print(f"字符 '{char}' 的ASCII码：{ord(char)}")
+
+print("\n--- chr() 函数验证 ---")
+print(f"ASCII码 65 对应的字符：{chr(65)}")
\ No newline at end of file
diff --git a/yzz1.py b/yzz1.py
new file mode 100644
index 0000000..195a487
--- /dev/null
+++ b/yzz1.py
@@ -0,0 +1,32 @@
+# 定义向量 A 和 B
+A = [3, 4]
+B = [1, 2]
+
+# ======================
+# 方法1：纯Python手动计算（适合理解原理）
+# ======================
+print("==== 纯Python计算结果 ====")
+# 1. 向量加法 A + B
+add_result = [A[0]+B[0], A[1]+B[1]]
+print("A + B =", add_result)
+
+# 2. 数乘 2×A
+mul_result = [2*A[0], 2*A[1]]
+print("2 × A =", mul_result)
+
+# 3. 向量A的长度（模）：勾股定理 √(x²+y²)
+import math
+norm_A = math.sqrt(A[0]**2 + A[1]**2)
+print("A 的长度（模）= %.2f" % norm_A)
+
+# ======================
+# 方法2：NumPy库（工业界标准写法）
+# ======================
+print("\n==== NumPy计算结果 ====")
+import numpy as np
+A_np = np.array([3, 4])
+B_np = np.array([1, 2])
+
+print("A + B =", A_np + B_np)       # 加法
+print("2 × A =", 2 * A_np)           # 数乘
+print("A 的长度（模）= %.2f" % np.linalg.norm(A_np))  # 模长
\ No newline at end of file
diff --git a/yzz2.py b/yzz2.py
new file mode 100644
index 0000000..092d306
--- /dev/null
+++ b/yzz2.py
@@ -0,0 +1,27 @@
+import math
+
+# 向量 A, B
+A = [1, 2, 3]
+B = [4, 5, 6]
+
+# ========== 1. 计算点积 ==========
+dot = A[0]*B[0] + A[1]*B[1] + A[2]*B[2]
+print("A · B =", dot)
+
+# ========== 2. 计算余弦相似度 ==========
+# 模长
+norm_A = math.sqrt(A[0]**2 + A[1]**2 + A[2]**2)
+norm_B = math.sqrt(B[0]**2 + B[1]**2 + B[2]**2)
+
+# 余弦相似度
+cos_sim = dot / (norm_A * norm_B)
+print("余弦相似度 =", round(cos_sim, 4))
+
+# ========== 3. A=[1,0], B=[0,1] 的余弦相似度 ==========
+A2 = [1, 0]
+B2 = [0, 1]
+dot2 = A2[0]*B2[0] + A2[1]*B2[1]
+norm_A2 = math.sqrt(A2[0]**2 + A2[1]**2)
+norm_B2 = math.sqrt(B2[0]**2 + B2[1]**2)
+cos_sim2 = dot2 / (norm_A2 * norm_B2)
+print("\nA=[1,0], B=[0,1] 余弦相似度 =", cos_sim2)
\ No newline at end of file