{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 計算機演習小テスト\n",
    "\n",
    "以下の4問を解いて、各問の後のセルに解答のコードを書いてください。小テストの間には、過去の授業資料を参考にしても良いです。他の方と検討してはいけません。\n",
    "\n",
    "\n",
    "2021年5月24日"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 問1: 与えられる三つの整数の最小値を求める関数my_minを作成してください。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "#解答\n",
    "def my_min(x,y,z):\n",
    "    min_value = x\n",
    "    if y < min_value:\n",
    "        min_value = y\n",
    "    if z < min_value:\n",
    "        min_value = z\n",
    "    \n",
    "    return min_value \n",
    "\n",
    "# コードの確認\n",
    "# 作成した関数が正しいかどうかを確認するために、以下のコードを実行してみてください。\n",
    "print(my_min(13,14,11))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 問2: 与えられるリストxの逆順を返す関数 my_reverse を作成してください。\n",
    "\n",
    "例えば、x = [2,3,5,3,1,6] の場合、my_reverse(x)が [6,1,3,5,3,2] を返すことが必要です。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 1, 3, 5, 3, 2]\n"
     ]
    }
   ],
   "source": [
    "#解答\n",
    "def my_reverse(x):\n",
    "    n = len(x)\n",
    "    reverse_x = []\n",
    "    for idx in range(n):\n",
    "        reverse_x.append(x[n-idx-1])\n",
    "        \n",
    "    return reverse_x\n",
    "\n",
    "# コードの確認\n",
    "# 作成した関数が正しいかどうかを確認するために、以下のコードを実行してみてください。\n",
    "x = [2,3,5,3,1,6]\n",
    "print(my_reverse(x)) #上のｘに対して、my_reverseが[6,1,3,5,3,2]というリストを返すべきです。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 問3: 以下の関数に対するPythonの関数my_fを作成して、区間[1,2]における関数のグラフを描いてください。\n",
    "\n",
    "$$\n",
    "f(x)=\\sin(x^3)\\cos(x)+1, \\quad x \\in [1,2]\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#解答\n",
    "x_list = []\n",
    "y_list = []\n",
    "\n",
    "def my_f(x):\n",
    "    import math\n",
    "    y = math.sin(x*x*x)*math.cos(x)+1\n",
    "    return y\n",
    "\n",
    "for k in range(0,101):\n",
    "    x = 1+0.01*k\n",
    "    x_list.append(x)\n",
    "    y_list.append(my_f(x))\n",
    "    \n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x_list,y_list)\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 問4: 以下の条件に従って数列を作成し、数列の第1項から第20項までを出力してください。\n",
    "\n",
    "- 1) 発散する有界数列$\\{ a_n \\}$\n",
    "- 2) 無限大に発散する数列$\\{ b_n \\}$\n",
    "- 3) 1に収束する数列$\\{ c_n \\}$\n",
    "- 4) 次の式を満たす数列$\\{ d_n \\}$: \n",
    "\n",
    "$$\n",
    "d_1=0, \\quad d_2 = 10,  \\quad  d_n = 0.2 d_{n-1} + 0.8 d_{n-2}~(n=3,4,5,\\cdots)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a list is:\n",
      "[0.0, 0.8414709848078965, 0.9092974268256817, 0.1411200080598672, -0.7568024953079282, -0.9589242746631385, -0.27941549819892586, 0.6569865987187891, 0.9893582466233818, 0.4121184852417566, -0.5440211108893698, -0.9999902065507035, -0.5365729180004349, 0.4201670368266409, 0.9906073556948704, 0.6502878401571168, -0.2879033166650653, -0.9613974918795568, -0.750987246771676, 0.14987720966295234]\n",
      "b list is:\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
      "c list is:\n",
      "[11.0, 6.0, 4.333333333333334, 3.5, 3.0, 2.666666666666667, 2.428571428571429, 2.25, 2.111111111111111, 2.0, 1.9090909090909092, 1.8333333333333335, 1.7692307692307692, 1.7142857142857144, 1.6666666666666665, 1.625, 1.5882352941176472, 1.5555555555555556, 1.526315789473684, 1.5]\n",
      "d list is:\n",
      "[0, 10, 2.0, 8.4, 3.2800000000000002, 7.376000000000001, 4.099200000000001, 6.720640000000001, 4.623488000000001, 6.301209600000002, 4.959032320000001, 6.032774144000002, 5.173780684800001, 5.860975452160003, 5.311219638272002, 5.7510242893824035, 5.399180568494082, 5.68065554520474, 5.455475563836215, 5.635619548931035]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#解答\n",
    "a_list = []\n",
    "b_list = []\n",
    "c_list = []\n",
    "d_list = [0,10]\n",
    "\n",
    "import math\n",
    "\n",
    "#1,2,3,4,5,...\n",
    "for k in range(0,20):\n",
    "    a_list.append(math.sin(k))\n",
    "    b_list.append(k+1)\n",
    "    c_list.append(1+10/(k+1))\n",
    "for k in range(2,20):\n",
    "    d_list.append(d_list[k-1]*0.2+d_list[k-2]*0.8)\n",
    "    \n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(a_list,'-o',label=\"a_n\")\n",
    "plt.plot(b_list,'-o',label=\"b_n\")\n",
    "plt.plot(c_list,'-o',label=\"c_n\")\n",
    "plt.plot(d_list,'-o',label=\"d_n\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "print(\"a list is:\")\n",
    "print(a_list)\n",
    "print(\"b list is:\")\n",
    "print(b_list)\n",
    "print(\"c list is:\")\n",
    "print(c_list)\n",
    "print(\"d list is:\")\n",
    "print(d_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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