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スタンフォード ベクトル・行列からはじめる最適化数学の第9章
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{
"cells": [
{
"cell_type": "markdown",
"id": "03733849",
"metadata": {},
"source": [
"# Introduction to APPLIED LINEAR ALGEBRA\n",
"\n",
"邦訳『スタンフォード ベクトル・行列からはじめる最適化数学』 の第9章の演習問題 2 で出題された産出額の線形動的モデルの時系列グラフを描画してみる。"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "941de13d",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# 時不変な状態遷移行列\n",
"B = np.array(\n",
" [[0.10, 0.06, 0.05, 0.70],\n",
" [0.48, 0.44, 0.10, 0.04],\n",
" [0.00, 0.55, 0.52, 0.04],\n",
" [0.04, 0.01, 0.42, 0.51]])\n",
"\n",
"# 第1期の産出額\n",
"a1 = np.array([0.6, 0.9, 1.3, 0.5])\n",
"\n",
"# 第2期以降の産出額を計算する。\n",
"a2 = np.dot(B, a1)\n",
"a3 = np.dot(B, a2)\n",
"a4 = np.dot(B, a3)\n",
"a5 = np.dot(B, a4)\n",
"a6 = np.dot(B, a5)\n",
"a7 = np.dot(B, a6)\n",
"a8 = np.dot(B, a7)\n",
"a9 = np.dot(B, a8)\n",
"a10 = np.dot(B, a9)\n",
"a11 = np.dot(B, a10)\n",
"a12 = np.dot(B, a11)\n",
"a13 = np.dot(B, a12)\n",
"a14 = np.dot(B, a13)\n",
"a15 = np.dot(B, a14)\n",
"a16 = np.dot(B, a15)\n",
"a17 = np.dot(B, a16)\n",
"a18 = np.dot(B, a17)\n",
"a19 = np.dot(B, a18)\n",
"a20 = np.dot(B, a19)\n",
"\n",
"# 全期間の総産出額の推移を計算する。\n",
"total = np.array([\n",
" a1.sum(), a2.sum(), a3.sum(), a4.sum(), a5.sum(),\n",
" a6.sum(), a7.sum(), a8.sum(), a9.sum(), a10.sum(),\n",
" a11.sum(), a12.sum(), a13.sum(), a14.sum(), a15.sum(),\n",
" a16.sum(), a17.sum(), a18.sum(), a19.sum(), a20.sum()\n",
"])\n",
"\n",
"# 第1期から第20期までの第1セクタから第4セクタの産出額の推移を計算する。\n",
"z = list(zip(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20))\n",
"the_1_sector = np.array(z[0])\n",
"the_2_sector = np.array(z[1])\n",
"the_3_sector = np.array(z[2])\n",
"the_4_sector = np.array(z[3])\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "617e0f00",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# X 軸のデータ。1 から 20 まで。\n",
"x = list(range(1, len(total) + 1))\n",
"\n",
"plt.figure(figsize=(15, 8))\n",
"\n",
"plt.plot(x, the_1_sector, label='the 1 sector', color='red')\n",
"plt.plot(x, the_2_sector, label='the 2 sector', color='blue')\n",
"plt.plot(x, the_3_sector, label='the 3 sector', color='green', linestyle='dotted')\n",
"plt.plot(x, the_4_sector, label='the 4 sector', color='grey', linestyle='dashed')\n",
"plt.plot(x, total, label='total', color='black')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f752b40",
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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