{ "cells": [ { "cell_type": "markdown", "id": "73bd968b-d970-4a05-94ef-4e7abf990827", "metadata": {}, "source": [ "Chapter 02\n", "\n", "# 矩阵乘积\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": "98f9659f-671c-40ba-bf01-26b02f299f65", "metadata": {}, "source": [ "\n", "\n", "此代码定义了两个 $2 \\times 2$ 的矩阵 $A$ 和 $B$,并计算它们的矩阵乘积。矩阵 $A$ 和 $B$ 的定义分别为:\n", "\n", "$$\n", "A = \\begin{bmatrix} 2 & 3 \\\\ 3 & 4 \\end{bmatrix}, \\quad B = \\begin{bmatrix} 3 & 4 \\\\ 5 & 6 \\end{bmatrix}\n", "$$\n", "\n", "矩阵乘积 $A @ B$ 的计算公式是:\n", "\n", "$$\n", "A @ B = \\begin{bmatrix} 2 \\cdot 3 + 3 \\cdot 5 & 2 \\cdot 4 + 3 \\cdot 6 \\\\ 3 \\cdot 3 + 4 \\cdot 5 & 3 \\cdot 4 + 4 \\cdot 6 \\end{bmatrix} = \\begin{bmatrix} 21 & 26 \\\\ 29 & 38 \\end{bmatrix}\n", "$$\n" ] }, { "cell_type": "markdown", "id": "b7d099d5-b19e-4dc1-bcbc-c6f27ff10d63", "metadata": {}, "source": [ "## 导入所需库" ] }, { "cell_type": "code", "execution_count": 1, "id": "fe40fe29-6b65-47eb-a82d-c9739d4f17b1", "metadata": {}, "outputs": [], "source": [ "import numpy as np # 导入NumPy库,用于数值计算" ] }, { "cell_type": "markdown", "id": "972ceb2f-dfc9-406b-aa4d-759d8a3c5ceb", "metadata": {}, "source": [ "## 定义两个矩阵" ] }, { "cell_type": "code", "execution_count": 2, "id": "022dfa07-a9e4-4ba6-9456-f7dd6b861bcd", "metadata": {}, "outputs": [], "source": [ "A = np.array([[2, 3], # 定义矩阵A\n", " [3, 4]])" ] }, { "cell_type": "code", "execution_count": 3, "id": "eba046cd-9051-403b-86a4-5586226e9c86", "metadata": {}, "outputs": [], "source": [ "B = np.array([[3, 4], # 定义矩阵B\n", " [5, 6]])" ] }, { "cell_type": "markdown", "id": "97150b17-9793-47aa-b9c1-aa61baf34bde", "metadata": {}, "source": [ "## 计算矩阵点积" ] }, { "cell_type": "code", "execution_count": 4, "id": "dec0b7d8-6d24-4d13-8bf2-0786a4d1e8e6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[21, 26],\n", " [29, 36]])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A_dot_B = np.dot(A, B) # 使用np.dot计算A和B的矩阵乘积\n", "A_dot_B" ] }, { "cell_type": "code", "execution_count": 5, "id": "85a80909-2aac-49ed-bb7a-f8cc6b80ee7d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[21, 26],\n", " [29, 36]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 可以直接用A @ B作为矩阵乘法的简写\n", "A @ B" ] }, { "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 }