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| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "id": "f04dd603-a2d1-48ce-8c17-9f1dba8de1ee", |
| 6 | + "metadata": {}, |
| 7 | + "source": [ |
| 8 | + "Chapter 01\n", |
| 9 | + "\n", |
| 10 | + "# 标量乘法\n", |
| 11 | + "《线性代数》 | 鸢尾花书:数学不难" |
| 12 | + ] |
| 13 | + }, |
| 14 | + { |
| 15 | + "cell_type": "markdown", |
| 16 | + "id": "ccafb456-2453-4c82-8a65-b1963a370cb2", |
| 17 | + "metadata": {}, |
| 18 | + "source": [ |
| 19 | + "这段代码的主要目的是演示**标量乘法**(scalar multiplication)在向量上的作用。具体来说,它定义了一个二维向量,并将其与标量相乘,以观察向量的变化情况。\n", |
| 20 | + "\n", |
| 21 | + "---\n", |
| 22 | + "\n", |
| 23 | + "### **数学描述**\n", |
| 24 | + "\n", |
| 25 | + "1. **初始化向量** \n", |
| 26 | + " 代码定义了一个二维向量 $a$:\n", |
| 27 | + " $$\n", |
| 28 | + " \\mathbf{a} = \\begin{bmatrix} 2 \\\\ 1 \\end{bmatrix}\n", |
| 29 | + " $$\n", |
| 30 | + " 这个向量在二维平面上指向 $(2,1)$ 这个点。\n", |
| 31 | + "\n", |
| 32 | + "2. **标量乘法** \n", |
| 33 | + " 代码定义了一个标量 $k = -2$,然后计算向量 $\\mathbf{a}$ 与标量 $k$ 的乘积:\n", |
| 34 | + " $$\n", |
| 35 | + " k \\mathbf{a} = (-2) \\cdot \\begin{bmatrix} 2 \\\\ 1 \\end{bmatrix}\n", |
| 36 | + " = \\begin{bmatrix} -4 \\\\ -2 \\end{bmatrix}\n", |
| 37 | + " $$\n", |
| 38 | + " 这个运算的几何意义是**将原向量沿其方向缩放,并翻转方向**:\n", |
| 39 | + " - 由于 $|k| = 2$,新向量的长度是原向量的 $2$ 倍,即:\n", |
| 40 | + " $$\n", |
| 41 | + " \\| k\\mathbf{a} \\| = |k| \\cdot \\|\\mathbf{a}\\| = 2 \\|\\mathbf{a}\\|\n", |
| 42 | + " $$\n", |
| 43 | + " - 由于 $k$ 是负数,新向量的方向与 $\\mathbf{a}$ **相反**。\n", |
| 44 | + "\n", |
| 45 | + "3. **结论** \n", |
| 46 | + " 计算结果是向量:\n", |
| 47 | + " $$\n", |
| 48 | + " \\mathbf{a'} = \\begin{bmatrix} -4 \\\\ -2 \\end{bmatrix}\n", |
| 49 | + " $$\n", |
| 50 | + " 这个向量与原向量 $\\mathbf{a}$ **共线**(collinear),但方向相反,且长度是原向量的两倍。\n", |
| 51 | + "\n", |
| 52 | + "---\n", |
| 53 | + "\n", |
| 54 | + "### **总结**\n", |
| 55 | + "- 标量乘法会**改变向量的长度**(当 $|k| \\neq 1$ 时)。\n", |
| 56 | + "- 若 $k > 0$,向量方向不变;若 $k < 0$,向量方向翻转。\n", |
| 57 | + "- 计算后的向量始终与原向量**共线**。" |
| 58 | + ] |
| 59 | + }, |
| 60 | + { |
| 61 | + "cell_type": "markdown", |
| 62 | + "id": "27f1d742-511b-4ddb-beca-ff9ced2a9a0e", |
| 63 | + "metadata": {}, |
| 64 | + "source": [ |
| 65 | + "## 初始化" |
| 66 | + ] |
| 67 | + }, |
| 68 | + { |
| 69 | + "cell_type": "code", |
| 70 | + "execution_count": 3, |
| 71 | + "id": "6abc03f3-e5ad-44cc-a780-3e91b400e6c6", |
| 72 | + "metadata": {}, |
| 73 | + "outputs": [], |
| 74 | + "source": [ |
| 75 | + "import numpy as np" |
| 76 | + ] |
| 77 | + }, |
| 78 | + { |
| 79 | + "cell_type": "markdown", |
| 80 | + "id": "ad5dd6c9-99ef-4c96-afdd-91d97e5a6243", |
| 81 | + "metadata": {}, |
| 82 | + "source": [ |
| 83 | + "## 定义向量" |
| 84 | + ] |
| 85 | + }, |
| 86 | + { |
| 87 | + "cell_type": "code", |
| 88 | + "execution_count": 9, |
| 89 | + "id": "ebb78725-794d-408e-b63d-ca88e55ffb45", |
| 90 | + "metadata": {}, |
| 91 | + "outputs": [], |
| 92 | + "source": [ |
| 93 | + "a_vec = np.array([3, 4])" |
| 94 | + ] |
| 95 | + }, |
| 96 | + { |
| 97 | + "cell_type": "code", |
| 98 | + "execution_count": 10, |
| 99 | + "id": "adbe91b7-d5ec-492e-89de-2bbf2b5ccfda", |
| 100 | + "metadata": {}, |
| 101 | + "outputs": [ |
| 102 | + { |
| 103 | + "data": { |
| 104 | + "text/plain": [ |
| 105 | + "5.0" |
| 106 | + ] |
| 107 | + }, |
| 108 | + "execution_count": 10, |
| 109 | + "metadata": {}, |
| 110 | + "output_type": "execute_result" |
| 111 | + } |
| 112 | + ], |
| 113 | + "source": [ |
| 114 | + "np.linalg.norm(a_vec)" |
| 115 | + ] |
| 116 | + }, |
| 117 | + { |
| 118 | + "cell_type": "markdown", |
| 119 | + "id": "1431be92-8111-499f-9c85-2caf2dc1a351", |
| 120 | + "metadata": {}, |
| 121 | + "source": [ |
| 122 | + "## 标量乘法" |
| 123 | + ] |
| 124 | + }, |
| 125 | + { |
| 126 | + "cell_type": "code", |
| 127 | + "execution_count": 11, |
| 128 | + "id": "ff697021-6f3b-42f2-be08-6d5ac68b8ac5", |
| 129 | + "metadata": {}, |
| 130 | + "outputs": [ |
| 131 | + { |
| 132 | + "data": { |
| 133 | + "text/plain": [ |
| 134 | + "array([-6, -8])" |
| 135 | + ] |
| 136 | + }, |
| 137 | + "execution_count": 11, |
| 138 | + "metadata": {}, |
| 139 | + "output_type": "execute_result" |
| 140 | + } |
| 141 | + ], |
| 142 | + "source": [ |
| 143 | + "k = -2\n", |
| 144 | + "k_a_vec = k * a_vec\n", |
| 145 | + "k_a_vec" |
| 146 | + ] |
| 147 | + }, |
| 148 | + { |
| 149 | + "cell_type": "code", |
| 150 | + "execution_count": 12, |
| 151 | + "id": "954b7525-dcef-47cb-af13-40c741ca3891", |
| 152 | + "metadata": {}, |
| 153 | + "outputs": [ |
| 154 | + { |
| 155 | + "data": { |
| 156 | + "text/plain": [ |
| 157 | + "10.0" |
| 158 | + ] |
| 159 | + }, |
| 160 | + "execution_count": 12, |
| 161 | + "metadata": {}, |
| 162 | + "output_type": "execute_result" |
| 163 | + } |
| 164 | + ], |
| 165 | + "source": [ |
| 166 | + "np.linalg.norm(k_a_vec)" |
| 167 | + ] |
| 168 | + }, |
| 169 | + { |
| 170 | + "cell_type": "markdown", |
| 171 | + "id": "58ded0f4-b0b4-41ad-8a8a-521956853916", |
| 172 | + "metadata": {}, |
| 173 | + "source": [ |
| 174 | + "作者\t**生姜DrGinger** \n", |
| 175 | + "脚本\t**生姜DrGinger** \n", |
| 176 | + "视频\t**崔崔CuiCui** \n", |
| 177 | + "开源资源\t[**GitHub**](https://github.com/Visualize-ML) \n", |
| 178 | + "平台\t[**油管**](https://www.youtube.com/@DrGinger_Jiang)\t\t\n", |
| 179 | + "\t\t[**iris小课堂**](https://space.bilibili.com/3546865719052873)\t\t\n", |
| 180 | + "\t\t[**生姜DrGinger**](https://space.bilibili.com/513194466) " |
| 181 | + ] |
| 182 | + } |
| 183 | + ], |
| 184 | + "metadata": { |
| 185 | + "kernelspec": { |
| 186 | + "display_name": "Python [conda env:base] *", |
| 187 | + "language": "python", |
| 188 | + "name": "conda-base-py" |
| 189 | + }, |
| 190 | + "language_info": { |
| 191 | + "codemirror_mode": { |
| 192 | + "name": "ipython", |
| 193 | + "version": 3 |
| 194 | + }, |
| 195 | + "file_extension": ".py", |
| 196 | + "mimetype": "text/x-python", |
| 197 | + "name": "python", |
| 198 | + "nbconvert_exporter": "python", |
| 199 | + "pygments_lexer": "ipython3", |
| 200 | + "version": "3.12.7" |
| 201 | + } |
| 202 | + }, |
| 203 | + "nbformat": 4, |
| 204 | + "nbformat_minor": 5 |
| 205 | +} |
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