{ "cells": [ { "cell_type": "markdown", "id": "73bd968b-d970-4a05-94ef-4e7abf990827", "metadata": {}, "source": [ "Chapter 02\n", "\n", "# L2范数\n", "Book_4《矩阵力量》 | 鸢尾花书:从加减乘除到机器学习 (第二版)" ] }, { "cell_type": "markdown", "id": "ab88dee3-1a2e-4357-b6b5-136abf3073c5", "metadata": {}, "source": [ "此代码定义了两个二维列向量 `a` 和 `b`,并计算它们的L2范数。L2范数也称为欧几里得范数,用于测量向量的长度。公式如下:\n", "\n", "$$\n", "\\| a \\|_2 = \\sqrt{a_1^2 + a_2^2}\n", "$$ \n", "\n", "对于向量 `a` 和 `b`,L2范数分别为:\n", "\n", "$$\n", "\\| a \\|_2 = \\sqrt{4^2 + 3^2} = 5\n", "$$\n", "\n", "$$\n", "\\| b \\|_2 = \\sqrt{(-3)^2 + 4^2} = 5\n", "$$" ] }, { "cell_type": "markdown", "id": "2a36cd49-a4e6-4321-a7e8-7f9a34c6aaef", "metadata": {}, "source": [ "## 导入所需库" ] }, { "cell_type": "code", "execution_count": 1, "id": "01a128eb-3998-4172-bd8a-ec95a23bbc83", "metadata": {}, "outputs": [], "source": [ "import numpy as np # 导入NumPy库,用于数值计算" ] }, { "cell_type": "markdown", "id": "8ad8b4b0-856b-47a7-adb8-b020180d0c30", "metadata": {}, "source": [ "## 定义两个列向量" ] }, { "cell_type": "code", "execution_count": 2, "id": "212d3a51-f2c6-4785-8c9a-40fa93ae0566", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[4],\n", " [3]])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.array([[4], [3]]) # 定义向量a,值为[4, 3]\n", "a" ] }, { "cell_type": "code", "execution_count": 3, "id": "8d5f6350-fddc-4633-946e-f21797b6f5e8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-3],\n", " [ 4]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = np.array([[-3], [4]]) # 定义向量b,值为[-3, 4]\n", "b" ] }, { "cell_type": "markdown", "id": "a3ba8142-26a8-43d6-b777-685207499ca9", "metadata": {}, "source": [ "## 计算L2范数" ] }, { "cell_type": "code", "execution_count": 4, "id": "1954434f-cb3e-44d3-b985-45b73af8a815", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a_L2_norm = np.linalg.norm(a) # 计算向量a的L2范数\n", "a_L2_norm" ] }, { "cell_type": "code", "execution_count": 5, "id": "8110a0d6-c941-4825-bf60-bc989324f592", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b_L2_norm = np.linalg.norm(b) # 计算向量b的L2范数\n", "b_L2_norm" ] }, { "cell_type": "code", "execution_count": null, "id": "85a80909-2aac-49ed-bb7a-f8cc6b80ee7d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "ecd322f4-f919-4be2-adc3-69d28ef25e69", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }