Mat/Wrzodak_Koszarek_Zadania.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Zadanie 4.6"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sympy import symbols, Matrix\n",
"from numpy.linalg import eig\n",
"\n",
"A=np.matrix(QQ,5,3,[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 31])\n",
"print(A.transpose()*A)\n",
"print((A.transpose()*A)^(-1))\n",
"mm=(A.transpose()*A)^(-1)\n",
"mm=(A.transpose()*A)^(-1)*A.transpose()\n",
"print(mm)\n",
"\n",
"b1=np.vector([-1,0,1,0,1])\n",
"mm1=mm*b1\n",
"print(mm1)\n",
"print((b1-A*mm1))\n",
"b2=np.vector([1,1,1,1,1])\n",
"mm2=mm*b2\n",
"print(mm2)\n",
"print((b2-A*mm2))\n",
"\n",
"b2 in (m.transpose()).image()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Zadanie 4.7\n"
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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"source": [
"zb1=[(1,1),(2,3),(4,5)]\n",
"zb2=[(1,1),(2,3),(3,4),(4,5),(5,7),(6,9)]\n",
"\n",
"m1=matrix(3,2,[1,exp(1.0),1,exp(2.0),1,exp(4.0)])\n",
"m2=matrix(6,2,[1,exp(1.0),1,exp(2.0),1,exp(3.0),1,exp(4.0),1,exp(5.0),1,exp(6.0)])\n",
"\n",
"a,b,t=var('a,b,t')\n",
"\n",
"m1*vector([a,b])-vector([1,3,5])\n",
"m2*vector([a,b])-vector([1,3,4,5,7,9])\n",
"\n",
"\n",
"\n",
"M1=m1.transpose()*m1\n",
"M1.det()\n",
"\n",
"\n",
"\n",
"M2=m2.transpose()*m2\n",
"M2.det()\n",
"\n",
"\n",
"\n",
"M1^(-1)*m1.transpose()*vector([1,3,5])\n",
"\n",
"M2^(-1)*m2.transpose()*vector([1,3,4,5,7,9])\n",
"\n",
"\n",
"plot(1.64148598265947+ 0.0629860338045423*exp(t),(t,0,4))+sum([point(x) for x in zb1])\n",
"\n",
"plot(3.10041190358990+ 0.0163320609303546*exp(t),(t,0,6))+sum([point(x) for x in zb2])"
]
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},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Zadanie 4.9"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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"source": [
"m=matrix(3,3,[1,1,0,1,2,2,0,2,3])\n",
"\n",
"eigenvalues = np.m.eigvals(matrix)\n",
"\n",
"eigen=m.right_eigenvectors()\n",
"e1=eigen[0][1][0]\n",
"e2=eigen[1][1][0]\n",
"print(e1.dot_product(e2))\n",
"e3=eigen[2][1][0]\n",
"print(e3.dot_product(e1))\n",
"print(e2.dot_product(e3))"
]
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}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.10.9"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}