{ "cells": [ { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[11 1]\n", " [15 20]\n", " [ 2 24]\n", " [11 18]] \n", "\n", "[[ 7 13]\n", " [19 8]\n", " [10 8]\n", " [11 17]] \n", "\n", "[[503 466]\n", " [825 671]]\n" ] } ], "source": [ "import random\n", "import numpy as np\n", "\n", "num = random.randint(1,25)\n", "nums1 = []\n", "nums2 = []\n", "\n", "# generate random numbers\n", "for i in range(0,8):\n", " nums1.append(random.randint(1,25))\n", " nums2.append(random.randint(1,25))\n", "\n", "# populate matrices\n", "m1 = np.array([[nums1[0],nums1[1]],\n", " [nums1[2],nums1[3]],\n", " [nums1[4],nums1[5]],\n", " [nums1[6],nums1[7]]])\n", "\n", "m2 = np.array([[nums2[0],nums2[1]],\n", " [nums2[2],nums2[3]],\n", " [nums2[4],nums2[5]],\n", " [nums1[6],nums2[7]]])\n", "\n", "print(m1,\"\\n\")\n", "print(m2,\"\\n\")\n", "\n", "res = []\n", "\n", "# compute dot products between columns\n", "for i in range(0,2):\n", " row = []\n", " for j in range(0,2):\n", " row.append(m1.transpose()[i].dot(m2.transpose()[j]))\n", " res.append(row)\n", "\n", "# print result\n", "res = np.array(res)\n", "print(res)" ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.9.12" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }