From c39c151794204b8bea3ff058418fab1345c4a32d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=BB=84=E6=A5=A0=E6=A5=A0?=
 <2509165032@student.example.com>
Date: Tue, 21 Apr 2026 11:23:58 +0800
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?=
 =?UTF-8?q?=20/?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 hh.py | 64 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 hh.py

diff --git a/hh.py b/hh.py
new file mode 100644
index 0000000..d423b34
--- /dev/null
+++ b/hh.py
@@ -0,0 +1,64 @@
+# 文本在计算机中的存储方式
+text = "Hello"
+
+# 如果我们看它的"数字形式"：
+print([ord(c) for c in text])
+# 输出: [72, 101, 108, 108, 111]
+# 72='H', 101='e', 108='l', 111='o'
+
+# 中文例子
+text_cn = "你好"
+print([ord(c) for c in text_cn])
+# 输出: [20320, 22909]
+# 20320='你', 22909='好'
+import numpy as np
+
+# 一维向量
+v1 = np.array([3])          # 只有1个数字
+print(f"v1 = {v1}")
+
+# 二维向量
+v2 = np.array([2, 3])       # 2个数字，表示平面上的一个点
+print(f"v2 = {v2}")
+
+# 三维向量
+v3 = np.array([1, 2, 3])    # 3个数字，表示立体空间的一个点
+print(f"v3 = {v3}")
+
+# 更多维向量（机器学习中常用）
+v100 = np.array([0.1, 0.5, -0.3, 0.8, ...])  # 100维！
+print(f"v100有 {len(v100)} 个元素")
+import numpy as np
+
+# 向量加法：对应位置相加
+a = np.array([1, 2, 3])
+b = np.array([4, 5, 6])
+c = a + b  # [1+4, 2+5, 3+6] = [5, 7, 9]
+
+print(f"a + b = {c}")  # [5, 7, 9]
+
+# 直观理解：
+# a = [1, 2, 3] 从原点出发走1步、再走2步、再走3步
+# b = [4, 5, 6] 从原点出发走4步、再走5步、再走6步
+# a + b = 从原点走完a再走b = [5, 7, 9]
+# 向量乘以一个数字（标量）
+v = np.array([1, 2, 3])
+result = v * 2  # [1*2, 2*2, 3*2] = [2, 4, 6]
+
+print(f"v * 2 = {result}")  # [2, 4, 6]
+
+# 直观理解：
+# v = [1, 2, 3] 表示"方向"
+# v * 2 = [2, 4, 6] 方向不变，长度变成2倍
+# 两个向量对应位置相乘，然后加起来
+a = np.array([1, 2, 3])
+b = np.array([4, 5, 6])
+
+# 点积计算过程：
+# 1*4 + 2*5 + 3*6 = 4 + 10 + 18 = 32
+
+dot = np.dot(a, b)
+print(f"点积 = {dot}")  # 32
+
+# 或者用 @ 运算符
+print(f"a @ b = {a @ b}")  # 32