machine_learning_01.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMlu7mS94QmRVm4n5RzqiB8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/matsunagalab/lecture_ML/blob/main/machine_learning_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pythonの基礎"
      ],
      "metadata": {
        "id": "Y3pXof4n2o2j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 変数とデータ型"
      ],
      "metadata": {
        "id": "rlRGUD8b287r"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "PqShvXJgzJ7D"
      },
      "outputs": [],
      "source": [
        "# 変数\n",
        "x = 5\n",
        "name = \"John\"\n",
        "is_student = True\n",
        "\n",
        "# データ型\n",
        "integer_example = 42\n",
        "float_example = 3.14\n",
        "string_example = \"Hello, World!\"\n",
        "boolean_example = False"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 基本的な算術演算"
      ],
      "metadata": {
        "id": "KLdzUrZr3C8k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 足し算\n",
        "result = 3 + 5\n",
        "print(result)  # Output: 8\n",
        "\n",
        "# 引き算\n",
        "result = 10 - 6\n",
        "print(result)  # Output: 4\n",
        "\n",
        "# 掛け算\n",
        "result = 7 * 2\n",
        "print(result)  # Output: 14\n",
        "\n",
        "# 割り算\n",
        "result = 9 / 3\n",
        "print(result)  # Output: 3.0\n",
        "\n",
        "# 剰余\n",
        "result = 10 % 3\n",
        "print(result)  # Output: 1"
      ],
      "metadata": {
        "id": "skeefhxM2Jmt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b3043de-3280-4005-c28b-ecc5764a15eb"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8\n",
            "4\n",
            "14\n",
            "3.0\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## リスト"
      ],
      "metadata": {
        "id": "sAYCuQZk3TyM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# リストの作成\n",
        "fruits = [\"apple\", \"banana\", \"cherry\"]\n",
        "\n",
        "# リストの要素へのアクセス\n",
        "print(fruits[1])  # Output: \"banana\"\n",
        "\n",
        "# リストへ要素を追加\n",
        "fruits.append(\"orange\")\n",
        "\n",
        "# リストから要素を削除\n",
        "fruits.remove(\"banana\")"
      ],
      "metadata": {
        "id": "3LJW2CX22M96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfe18fbc-e89b-4fcb-cb01-345b2a86f12e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "banana\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 辞書"
      ],
      "metadata": {
        "id": "IMZZzvah3Vje"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 辞書の作成\n",
        "student = {\"name\": \"John\", \"age\": 20, \"major\": \"Computer Science\"}\n",
        "\n",
        "# 辞書の要素へアクセス\n",
        "print(student[\"name\"])  # Output: \"John\"\n",
        "\n",
        "# 辞書へkey-valueペアを追加\n",
        "student[\"GPA\"] = 3.5\n",
        "\n",
        "# 辞書からkey-valueペアを削除\n",
        "del student[\"age\"]"
      ],
      "metadata": {
        "id": "sQb97mb22O6M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "802a04f1-a3bc-4c49-c511-086a8b351aab"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "John\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 制御構文"
      ],
      "metadata": {
        "id": "Ng3YKGck3XZ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# If文の例\n",
        "x = 10\n",
        "\n",
        "if x > 5:\n",
        "    print(\"x is greater than 5\")\n",
        "else:\n",
        "    print(\"x is less than or equal to 5\")"
      ],
      "metadata": {
        "id": "TWWawONM2Qvy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d618264e-16e8-4037-8202-ceb051321419"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x is greater than 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ループ"
      ],
      "metadata": {
        "id": "avnDXDAT3dzF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ループの例\n",
        "fruits = [\"apple\", \"banana\", \"cherry\"]\n",
        "\n",
        "for fruit in fruits:\n",
        "    print(fruit)"
      ],
      "metadata": {
        "id": "m3HPK4Wk2S0K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a970d1af-c635-4dbd-922f-a2ae22a78d72"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "apple\n",
            "banana\n",
            "cherry\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 関数"
      ],
      "metadata": {
        "id": "g5ON4bdS3g2K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 関数の定義\n",
        "def greet(name):\n",
        "    return f\"Hello, {name}!\"\n",
        "\n",
        "# Calling a function\n",
        "print(greet(\"John\"))  # Output: \"Hello, John!\""
      ],
      "metadata": {
        "id": "g7-MoVQo2UXZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b37d888-549d-4c98-9bc8-e55e93d11d70"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello, John!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 関数の応用"
      ],
      "metadata": {
        "id": "vmgGuSDH3iu_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 受け取ったリストが持つ偶数の和を出力する関数\n",
        "def sum_even_numbers(numbers):\n",
        "    even_sum = 0\n",
        "    for num in numbers:\n",
        "        if num % 2 == 0:\n",
        "            even_sum += num\n",
        "    return even_sum\n",
        "\n",
        "# Test the function\n",
        "print(sum_even_numbers([1, 2, 3, 4, 5, 6]))  # Output: 12"
      ],
      "metadata": {
        "id": "hy5menXH2V5c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "897258be-c6ab-472c-ae84-27194df204f5"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# グラフの描画"
      ],
      "metadata": {
        "id": "TVzK7mvV2wzY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## matplotlibのインポート"
      ],
      "metadata": {
        "id": "6sfTogRO3pV6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# matplotlibのインポート\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "nttIU69A2YMX"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 折れ線グラフの描画"
      ],
      "metadata": {
        "id": "zL8ol98A3vY4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# サンプルデータ\n",
        "x = [1, 2, 3, 4, 5]\n",
        "y = [2, 4, 6, 8, 10]\n",
        "\n",
        "# 折れ線グラフの描画\n",
        "plt.plot(x, y)\n",
        "\n",
        "# ラベルとタイトルの追加\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"Y\")\n",
        "\n",
        "# グラフの表示\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "tZothniO2beo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "ddf6a920-931f-4f34-9b70-87bc72519adf"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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HGfVRFkcKnAT5+/BM/44MvDzW7FjiQVRkRETE47jcBq9/u5U3vtuKYUDbqDpMS02hTVSY2dHEw6jIiIiIRznkKGHU7Ex+3nEcgDsub8KEmzsSHOBrcjLxRCoyIiLiMZZvOcKDH2VxrKiUkABfnrulI7ckNzE7lngwH7MDnI/L5eLJJ5+kRYsWBAcH06pVK5555hkMwzA7moiIVKFyl5uXFm1i2IxVHCsqJa5RGF+M6KYSIxfk0WdkXnjhBd58800++OAD4uPjWbNmDXfffTd2u52RI0eaHU9ERKrAgfxTjJyVyepdJwBI7dyUp27qQJC/RklyYR5dZH766Sf69+9P3759AWjevDmzZs1i1apVJicTEZGq8P2mw4yZk8WJ4jLqBPox8dZO9EuMMTuWWIhHj5a6du3Kt99+y5YtWwDIzs5mxYoV9OnTp8LnOJ1OHA7HGTcREfEsZS43E7/eyN3vr+ZEcRkdG4fz5YhuKjFy0Tz6jMzYsWNxOBzExcXh6+uLy+XiueeeY+jQoRU+Z+LEiUyYMKEGU4qIyMXYe6KYEbMyydxzEoBhXZrxeN/2BPpplCQXz6OLzJw5c5g5cybp6enEx8eTlZXF6NGjiYmJYdiwYed8zrhx4xgzZszp+w6Hg9hYvXmSiIgn+Gb9QR75eB35p8oIC/LjpdsTuKFjtNmxxMJshgf/ClBsbCxjx44lLS3t9GPPPvssH374IZs2barUn+FwOLDb7eTn5xMers/kEBExQ2m5m4kLNjLjx10AJDaxMzU1hdiIEHODiceq7Ou3R5+RKS4uxsfnzMt4fH19cbvdJiUSEZGLtedYMcNnZbBubz4A93ZrwWM3xBHg59GXaYpFeHSR6devH8899xxNmzYlPj6ezMxMJk+ezD333GN2NBERqYQFOQd49ON1FDjLsQf78/LARK7rEGV2LPEiHj1aKigo4Mknn2TevHkcPnyYmJgYhgwZwlNPPUVAQECl/gyNlkREal5JmYvnv97Iv1buBiClaV3eSE2hcd1gk5OJVVT29duji0xVUJEREalZO48WMTw9g/X7//P2F/f3aMnDvdvh76tRklSeV1wjIyIi1vJ59n4e/zSHQmc5EaEBvHJHIte0izQ7lngxFRkREfndSspcTPhiA7NW7QHgyuYRTBmSTCN7kMnJxNupyIiIyO+y7XAhw9Mz2HSwAJsNhl/TmlE92+CnUZLUABUZERG5ZJ9m7OWJ+bkUl7poUCeAVwcl0b1NQ7NjSS2iIiMiIhetuLSc8Z+tZ+7avQB0aVmf1wcnERmuUZLULBUZERG5KFsOFZA2M4Othwux2WBUzzaMuLYNvj42s6NJLaQiIyIilWIYBnPX7OWpz3MpKXPTMCyQ1wcn0bVVA7OjSS2mIiMiIhdU5Cznifm5zMvcB0D3Ng14dVASDeoEmpxMajsVGREROa+NBxykzcxgx9EifGzwUO92PNCjFT4aJYkHUJEREZFzMgyD9FV7mPDFBkrL3TQKD2LKkGSubBFhdjSR01RkRETkNwpKyhj3aQ5frjsAwDXtGvLKHUlEhFbuc+5EaoqKjIiInCF3Xz7D0zPYdawYPx8bj1zfjr90b6lRkngkFRkREQH+M0r618rdPPfVRkpdbhrXDWbKkGQua1bP7GgiFVKRERER8k+VMfaTdSzIPQhAr/ZRvDwwgbohGiWJZ1ORERGp5bLyTjI8PYO9J07h72tjbJ/23HNVc2w2jZLE86nIiIjUUoZh8N6KnbywcBNlLoPYiGCmDkkhMbau2dFEKk1FRkSkFjpZXMrDc9exZOMhAPp0bMSk2xKwB/ubnEzk4qjIiIjUMmt3n2BEegb780sI8PXhiZva8+c/NNMoSSxJRUZEpJZwuw3e/mEHLy3ajMtt0Lx+CFNTU+jY2G52NJFLpiIjIlILHC8qZcycLJZuPgJAv8QYnr+lI2FBGiWJtanIiIh4uVU7jzNyViYHHSUE+vkwvl88Q66M1ShJvIKKjIiIl3K7Df6xdBuTF2/BbUDLhqFMS02hfXS42dFEqoyKjIiIFzpS4GTMnCx+2HoUgFuTG/PMgI6EBurHvngX/R8tIuJlftp2lFEfZXGkwEmQvw9/79+RgZc10ShJvJKKjIiIl3C5DaZ8u5Up323FMKBNZB2mDU2hbVSY2dFEqo2KjIiIFzjsKGHU7CxW7jgGwB2XN2HCzR0JDvA1OZlI9VKRERGxuB+2HuHBj7I4WlhKSIAvz93SkVuSm5gdS6RGqMiIiFhUucvNa0u2Mm3pNgwD4hqFMTU1hdaRdcyOJlJjVGRERCzoQP4pRs3KYtWu4wCkdm7KUzd1IMhfoySpXVRkREQs5vtNhxkzJ4sTxWXUCfTj+Vs7cXNijNmxREyhIiMiYhFlLjcvL9rM9OU7AIiPCWdaagrNG4SanEzEPD5mB7iQ5s2bY7PZfnNLS0szO5qISI3Zd/IUg6avPF1ihnVpxicPdFWJkVrP48/IrF69GpfLdfp+bm4u1113HQMHDjQxlYhIzVm84RAPz80m/1QZYUF+vHhbAn06RZsdS8QjeHyRadiw4Rn3J02aRKtWrejRo4dJiUREakZpuZsXFm7ivRU7AUhsYmdqagqxESEmJxPxHB5fZH6ttLSUDz/8kDFjxlT4VttOpxOn03n6vsPhqKl4IiJVJu94McPTM8jemw/Avd1a8NgNcQT4efwVASI1ylJFZv78+Zw8eZK77rqrwmMmTpzIhAkTai6UiEgVW5h7gEc+XkdBSTn2YH9eHpjIdR2izI4l4pFshmEYZoeorOuvv56AgAC++OKLCo851xmZ2NhY8vPzCQ/XR9eLiOcqKXMx8euNfLByNwApTesyZUgyTepplCS1j8PhwG63X/D12zJnZHbv3s2SJUv49NNPz3tcYGAggYGBNZRKRKRq7DpaRFp6Buv3/2ccfn+Pljzcux3+vholiZyPZYrMjBkziIyMpG/fvmZHERGpUl9k72fcpzkUOsupF+LP5DuSuCYu0uxYIpZgiSLjdruZMWMGw4YNw8/PEpFFRC6opMzF37/cQPovewC4onk9pgxJJtoebHIyEeuwRCtYsmQJe/bs4Z577jE7iohIldh+pJC0mRlsOliAzQZpV7dmdK82+GmUJHJRLFFkevfujYWuSRYROa95mXv527xciktdNKgTwKuDkujepuGFnygiv2GJIiMi4g1OlboY/3kuc9bsBaBLy/q8PjiJyPAgk5OJWJeKjIhIDdh6qIC/zsxg6+FCbDYY1bMNI65tg6/Pud/cU0QqR0VGRKQaGYbB3LV7eeqzXErK3DQMC+T1wUl0bdXA7GgiXkFFRkSkmhQ5y3lyfi6fZu4DoHubBky+I4mGYXqvK5GqoiIjIlINNh5wkJaewY4jRfjY4KHe7XigRyt8NEoSqVIqMiIiVcgwDGatymPCF+txlrtpFB7ElCHJXNkiwuxoIl5JRUZEpIoUlJTx+LxcvsjeD8DV7Roy+Y4kIkIDTE4m4r1UZEREqkDuvnyGp2ew61gxvj42Hr2+HX/p3lKjJJFqpiIjIvI7GIbBv3/ezbNfbqTU5aZx3WCmDEnmsmb1zI4mUiuoyIiIXKL8U2WM+3QdX+ccBKBX+yheHphA3RCNkkRqioqMiMglyM47yfBZGeQdP4W/r42xfdpzz1XNsdk0ShKpSSoyIiIXwTAM/vnjLiYt2EiZy6BJvWCmpaaQGFvX7GgitZKKjIhIJZ0sLuWRj9exeMMhAG6Ib8QLtydgD/Y3OZlI7aUiIyJSCRl7TjAiPZN9J08R4OvDEze1589/aKZRkojJVGRERM7D7TZ454cdvLRoM+Vug2b1Q5iWmkLHxnazo4kIKjIiIhU6XlTKQ3Oy+H7zEQBuSohm4q2dCAvSKEnEU6jIiIicw6qdxxk5K5ODjhIC/Hx4ul88Q66M1ShJxMOoyIiI/IrbbfDmsu1MXrwFl9ugZcNQpqWm0D463OxoInIOKjIiIv/naKGTBz/K4oetRwG4Jbkxzw7oSGigflSKeCp9d4qIACu3H2PU7EwOFzgJ8vfh7/07MvCyJholiXg4FRkRqdVcboM3vtvKlG+34jagTWQdpg1NoW1UmNnRRKQSVGREpNY6XFDC6NlZ/LT9GAADL2vChP7xhAToR6OIVei7VURqpRVbjzL6o0yOFpYSEuDLswM6cmtKE7NjichFUpERkVql3OXmtSVbmbZ0G4YBcY3CmJqaQuvIOmZHE5FLoCIjIrXGwfwSRs7KZNWu4wCkdm7KUzd1IMjf1+RkInKpVGREpFb4fvNhHpqTzfGiUuoE+vH8rZ24OTHG7Fgi8jupyIiIVytzuXn5m81MX7YDgPiYcKamptCiQajJyUSkKqjIiIjX2nfyFCNnZbJ29wkA7uzSjMdvbK9RkogXUZEREa+0ZMMhHpqbTf6pMsKC/HjxtgT6dIo2O5aIVDEVGRHxKqXlbl5cuIl3V+wEILGJnTeGpNC0fojJyUSkOqjIiIjXyDtezPBZmWTnnQTgnqtaMLZPHAF+PuYGE5FqU+nv7v3791dnjgrt27ePP/3pT9SvX5/g4GA6derEmjVrTMkiIp5rYe4BbpzyA9l5JwkP8uPtP1/GU/06qMSIeLlKf4fHx8eTnp5enVl+48SJE1x11VX4+/uzYMECNmzYwCuvvEK9evVqNIeIeC5nuYvxn+Xyvx9mUFBSTnLTunw9qju94xuZHU1EakClR0vPPfcc999/P/PmzWP69OlERERUZy4AXnjhBWJjY5kxY8bpx1q0aFHt/14RsYZdR4sYPiuD3H0OAO7v0ZKHe7fD31dnYURqi0p/t//1r39l3bp1HDt2jA4dOvDFF19UZy4APv/8cy6//HIGDhxIZGQkycnJvPPOO+d9jtPpxOFwnHETEe/z5br93PTGCnL3OagX4s+Mu65gXJ/2KjEitYzNMAzjYp80depUHnzwQdq3b4+f35kndTIyMqosXFBQEABjxoxh4MCBrF69mlGjRvHWW28xbNiwcz7n6aefZsKECb95PD8/n/Dw8CrLJiLmKClz8fcvN5D+yx4ArmhejylDkom2B5ucTESqksPhwG63X/D1+6KLzO7du7n77rvJzc3l/vvv/02RGT9+/KUlPoeAgAAuv/xyfvrpp9OPjRw5ktWrV7Ny5cpzPsfpdOJ0Ok/fdzgcxMbGqsiIeIHtRwpJm5nBpoMF2Gzw16tb8WCvtvjpLIyI16lskbmoX79+5513eOihh+jVqxfr16+nYcOGvzvo+URHR9OhQ4czHmvfvj2ffPJJhc8JDAwkMDCwWnOJSM2bn7mPx+flUFzqon5oAK8OSuKPbav3Z5CIeL5KF5kbbriBVatWMXXqVO68887qzHTaVVddxebNm894bMuWLTRr1qxG/v0iYr5TpS6e/nw9H63JA+APLSOYMjiZyPAgk5OJiCeodJFxuVysW7eOJk2aVGeeMzz44IN07dqV559/njvuuINVq1bx9ttv8/bbb9dYBhExz9ZDBaSlZ7DlUCE2G4y8tg0je7bB18dmdjQR8RCXdLFvTfryyy8ZN24cW7dupUWLFowZM4a//OUvlX5+ZWdsIuJZ5q7J46nP1nOqzEXDsEBeH5RE19YNzI4lIjWk2i72tRoVGRFrKXKW8+RnuXyasQ+Abq0b8OqgJBqG6do3kdqkWi72FRGpTpsOOkibmcH2I0X42GDMdW3569Wt8dEoSUQqoCIjIqYzDIPZq/N4+vP1OMvdRIUHMmVwMp1b1jc7moh4OBUZETFVobOcxz/N4fPs/3ww7dXtGvLKwETq19EoSUQuTEVGREyTuy+f4ekZ7DpWjK+PjUeub8f/dG+pUZKIVJqKjIjUOMMw+PDn3Tzz5UZKXW5i7EG8kZrMZc2q/8NoRcS7qMiISI1ylJQx9pN1fJ1zEIBe7SN5eWAidUMCTE4mIlakIiMiNWbd3pOkpWeQd/wU/r42Hrshjnu7tcBm0yhJRC6NioyIVDvDMJjx4y4mLthImcugSb1gpqamkBRb1+xoImJxKjIiUq3yi8t45ONsvtlwCIAb4hvxwu0J2IP9TU4mIt5ARUZEqk3GnhOMSM9k38lTBPj68Le+7bmzSzONkkSkyqjIiEiVc7sN3l2xgxcXbqbcbdCsfghTh6TQqYnd7Ggi4mVUZESkSp0oKuWhudl8t+kwADclRDPx1k6EBWmUJCJVT0VGRKrM6l3HGTkrkwP5JQT4+TC+XwdSr2yqUZKIVBsVGRH53dxugzeXbWfy4i243AYtG4QyNTWFDjH6xHkRqV4qMiLyuxwtdPLgR1n8sPUoALckN+bZAR0JDdSPFxGpfvpJIyKXbOX2Y4yancnhAidB/j78/eaODLy8iUZJIlJjVGRE5KK53AZTv9vG699uwW1A68g6/GNoCm2jwsyOJiK1jIqMiFyUwwUljJ6dxU/bjwEw8LImTOgfT0iAfpyISM3TTx4RqbQVW48y+qMsjhY6Cfb35blbOnJrShOzY4lILaYiIyIXVO5y8/q3W5n6/TYMA+IahTE1NYXWkXXMjiYitZyKjIic18H8EkbOzmTVzuMADLkylvH94gny9zU5mYiIioyInMfSzYcZMyeb40WlhAb48vytneif1NjsWCIip6nIiMhvlLncTF68hTeXbgegQ3Q404am0KJBqMnJRETOpCIjImfYf/IUI2Zlsnb3CQDu7NKMx29sr1GSiHgkFRkROW3JhkM8/HE2J4vLCAv044XbE7ixU7TZsUREKqQiIyKUlrt5ceEm3l2xE4CEJnamDkmhaf0Qk5OJiJyfioxILZd3vJjhszLJzjsJwD1XteCxPu0I9NMoSUQ8n4qMSC22MPcgj36cjaOknPAgP14emEjv+EZmxxIRqTQVGZFayFnuYuLXm3j/p10AJDetyxtDkmlST6MkEbEWFRmRWmb3sSKGp2eSsy8fgP/5Y0seub4d/r4+JicTEbl4KjIitchX6w4w9pN1FDjLqRfizyt3JHJtXJTZsURELplH/xXs6aefxmaznXGLi4szO5aI5ZSUuXhifg5p6RkUOMu5onk9vh7VXSVGRCzP48/IxMfHs2TJktP3/fw8PrKIR9lxpJC09Ew2HnAA8NerWzHmurb4aZQkIl7A41uBn58fjRrptyhELsVnWft4/NMcikpd1A8NYPKgJHq0bWh2LBGRKuPxRWbr1q3ExMQQFBREly5dmDhxIk2bNq3weKfTidPpPH3f4XDUREwRj3Kq1MXTn6/nozV5APyhZQSvD04mKjzI5GQiIlXLo88td+7cmffff5+FCxfy5ptvsnPnTrp3705BQUGFz5k4cSJ2u/30LTY2tgYTi5hv2+ECBkz7kY/W5GGzwciebZh53x9UYkTEK9kMwzDMDlFZJ0+epFmzZkyePJl77733nMec64xMbGws+fn5hIeH11RUEVN8vHYvT87P5VSZiwZ1ApkyOImurRuYHUtE5KI5HA7sdvsFX789frT0a3Xr1qVt27Zs27atwmMCAwMJDAyswVQi5isuLeeJ+bl8mrEPgG6tG/DqoCQahul7QUS8m0ePls5WWFjI9u3biY7Wp/GK/Nemgw76vbGCTzP24WODh65rywf3XKkSIyK1gkefkXn44Yfp168fzZo1Y//+/YwfPx5fX1+GDBlidjQR0xmGwUer8xj/+Xqc5W6iwgN5fXAyf2hZ3+xoIiI1xqOLzN69exkyZAjHjh2jYcOGdOvWjZ9//pmGDfXro1K7FTrL+du8HD7L2g9Aj7YNmXxHIvXr6CyMiNQuHl1kZs+ebXYEEY+zfn8+w9Mz2Xm0CF8fGw/3bsf9f2yJj4/N7GgiIjXOo4uMiPx/hmHw4S97eObLDZSWu4mxB/FGajKXNYswO5qIiGlUZEQswFFSxrhPcvgq5wAAvdpH8tLtidQLDTA5mYiIuVRkRDzcur0nGZ6eyZ7jxfj52BjbJ457u7XAZtMoSURERUbEQxmGwfs/7eL5rzdS5jJoXDeYqanJJDetZ3Y0ERGPoSIj4oHyi8t45ONsvtlwCIDr46N48bZE7CH+JicTEfEsKjIiHiZzzwmGp2ey7+QpAnx9ePzGOIZ1ba5RkojIOajIiHgIwzB494edvLBwE+Vug6YRIUxLTaFTE7vZ0UREPJaKjIgHOFFUysNzs/l202EA+iZEM/HWToQHaZQkInI+KjIiJluz6zgjZmVyIL+EAD8fnrqpA0M7N9UoSUSkElRkREzidhu8tXw7r3yzBZfboEWDUKamJhMfo1GSiEhlqciImOBYoZMxc7JZtuUIAP2TYnjulk7UCdS3pIjIxdBPTZEa9vOOY4yanckhh5Mgfx8m3BzPHZfHapQkInIJVGREaojLbTDt+228tmQLbgNaR9ZhWmoK7RqFmR1NRMSyVGREasDhghIe/CiLH7cdA+D2y5rw9/7xhAToW1BE5PfQT1GRavbjtqOMmp3F0UInwf6+PDugI7dd1sTsWCIiXkFFRqSauNwGry/Zwhvfb8MwoF1UGNOGJtM6UqMkEZGqoiIjUg0OOUoYOSuTX3YeB2DIlbGM7xdPkL+vyclERLyLioxIFVu25QgPfpTF8aJSQgN8ef7WTvRPamx2LBERr6QiI1JFyl1uXlm8hTeXbgegfXQ401KTadmwjsnJRES8l4qMSBXYf/IUI2dlsmb3CQD+/Idm/K1ve42SRESqmYqMyO/03aZDjJmTzcniMsIC/Zh0WwJ9E6LNjiUiUiuoyIhcojKXmxcXbuKdH3YC0KmxnampyTSrH2pyMhGR2kNFRuQS5B0vZsSsTLLyTgJw91XNGdsnjkA/jZJERGqSiozIRVq0/iCPzM3GUVJOeJAfLw1M5Pr4RmbHEhGplVRkRCrJWe5i4tebeP+nXQAkxdbljSHJxEaEmBtMRKQWU5ERqYTdx4oYnp5Jzr58AP7SvQWPXB9HgJ+PyclERGo3FRmRC/hq3QHGfrKOAmc5dUP8eWVgIj3bR5kdS0REUJERqVBJmYtnv9rAhz/vAeDyZvWYMiSZmLrBJicTEZH/UpEROYedR4tIm5nBhgMOAP56dSsevK4t/r4aJYmIeBIVGZGzfJa1j8c/zaGo1EVEaACvDkqiR9uGZscSEZFzUJER+T8lZS6e/nw9s1fnAdC5RQRThiQTFR5kcjIREamIpc6TT5o0CZvNxujRo82OIl5m2+EC+k/9kdmr87DZYGTPNsy8r7NKjIiIh7PMGZnVq1czffp0EhISzI4iXuaTtXt5Yn4up8pcNKgTyOuDk7iqdQOzY4mISCVY4oxMYWEhQ4cO5Z133qFevXpmxxEvUVxazsNzs3lobjanylxc1bo+X4/qphIjImIhligyaWlp9O3bl169el3wWKfTicPhOOMmcrbNBwu4eeqPfLx2Lz42GHNdW/51T2ciwzRKEhGxEo8fLc2ePZuMjAxWr15dqeMnTpzIhAkTqjmVWJVhGMxZk8dTn63HWe4mMiyQKUOS+UPL+mZHExGRS+DRZ2Ty8vIYNWoUM2fOJCiocn9THjduHPn5+adveXl51ZxSrKLQWc6DH2Xx2Cc5OMvd/LFtQxaM6q4SIyJiYTbDMAyzQ1Rk/vz53HLLLfj6+p5+zOVyYbPZ8PHxwel0nvG1c3E4HNjtdvLz8wkPD6/uyOKhNux3MDw9gx1Hi/D1sfFQ77b87x9b4eNjMzuaiIicQ2Vfvz16tNSzZ09ycnLOeOzuu+8mLi6Oxx577IIlRsQwDGb+soe/f7mB0nI30fYg3hiSzOXNI8yOJiIiVcCji0xYWBgdO3Y847HQ0FDq16//m8dFzuYoKWPcpzl8te4AAD3jInl5YCL1QgNMTiYiIlXFo4uMyKXK2ZvP8FkZ7D5WjJ+PjcduiOO+7i2w2TRKEhHxJpYrMkuXLjU7gngwwzD44KddPP/1JkpdbhrXDeaN1GRSmur9h0REvJHlioxIRfKLy3j0k2wWrT8EQO8OUbx0eyL2EH+Tk4mISHVRkRGvkJV3kuHpGew9cQp/XxuP39ieu7o21yhJRMTLqciIpRmGwXsrdjJpwSbK3QZNI0KYmppMQpO6ZkcTEZEaoCIjlnWyuJSH52azZONhAPp2imbibZ0ID9IoSUSktlCREUtau/s4I9Iz2Z9fQoCfD0/e1IE/dW6qUZKISC2jIiOW4nYbTF++g5e/2YzLbdCiQShTU5OJj7GbHU1EREygIiOWcazQyZg52SzbcgSA/kkxPHdLJ+oE6n9jEZHaSq8AYgm/7DjGyNmZHHI4CfTzYcLN8Qy6IlajJBGRWk5FRjyay23wj++38eqSLbgNaNUwlGlDU4hrpA8AFRERFRnxYEcKnDz4URYrth0F4LaUJjwzIJ6QAP1vKyIi/6FXBPFIP207ysjZWRwtdBLs78szAzpy+2VNzI4lIiIeRkVGPIrLbfD6t1t547utGAa0iwpjamoybaLCzI4mIiIeSEVGPMYhRwmjZmfy847jAAy+Ipbx/eIJDvA1OZmIiHgqFRnxCMu3HOHBj7I4VlRKaIAvz9/aif5Jjc2OJSIiHk5FRkxV7nIzefEW/rF0OwDto8OZlppMy4Z1TE4mIiJWoCIjpjmQf4qRszJZvesEAH/6Q1Oe6NuBIH+NkkREpHJUZMQU3286zJg5WZwoLqNOoB+TbuvETQkxZscSERGLUZGRGlXmcvPyos1MX74DgE6N7UxNTaZZ/VCTk4mIiBWpyEiN2XuimBGzMsnccxKAu7o2Z9yNcQT6aZQkIiKXRkVGasQ36w/y8NxsHCXlhAf58eLtidzQsZHZsURExOJUZKRalZa7mbhgIzN+3AVAYmxdpg5JJjYixNxgIiLiFVRkpNrsOVbM8FkZrNubD8BfurfgkevjCPDzMTmZiIh4CxUZqRZf5xzgsY/XUeAsp26IPy/fnkivDlFmxxIRES+jIiNVqqTMxXNfbeTfP+8G4LJm9XhjSDIxdYNNTiYiIt5IRUaqzM6jRQxPz2D9fgcAD1zdijHXtcXfV6MkERGpHioyUiU+z97PuE/WUVTqIiI0gMl3JHJ1u0izY4mIiJdTkZHfpaTMxYQvNjBr1R4ArmwRwZTByTSyB5mcTEREagMVGblk2w4XMjw9g00HC7DZYMQ1rRnZsw1+GiWJiEgNUZGRS/Jpxl6emJ9LcamLBnUCeW1QEt3aNDA7loiI1DIqMnJRikvLGf/Zeuau3QtA11b1eW1wEpFhGiWJiEjNU5GRSttyqIC0mRlsPVyIjw1G92pL2jWt8fWxmR1NRERqKY++mOHNN98kISGB8PBwwsPD6dKlCwsWLDA7Vq1jGAZzVudx89QVbD1cSGRYIDPv+wMje7ZRiREREVN59BmZJk2aMGnSJNq0aYNhGHzwwQf079+fzMxM4uPjzY5XKxQ5y/nbvBzmZ+0HoHubBrw6KIkGdQJNTiYiIgI2wzAMs0NcjIiICF566SXuvffeSh3vcDiw2+3k5+cTHh5ezem8y4b9DoanZ7DjaBG+PjYe6t2W//1jK3x0FkZERKpZZV+/PfqMzK+5XC7mzp1LUVERXbp0qfA4p9OJ0+k8fd/hcNREPK9iGAbpq/Yw4YsNlJa7ibYHMWVIMlc0jzA7moiIyBk8vsjk5OTQpUsXSkpKqFOnDvPmzaNDhw4VHj9x4kQmTJhQgwm9S0FJGeM+zeHLdQcAuDYukpcHJhIRGmByMhERkd/y+NFSaWkpe/bsIT8/n48//ph3332XZcuWVVhmznVGJjY2VqOlSsjdl09aega7jxXj52PjsRviuLdbC42SRESkxlV2tOTxReZsvXr1olWrVkyfPr1Sx+samQszDIN/rdzNc19tpNTlpnHdYN5ITSalaT2zo4mISC3lddfI/Jfb7T7jjIv8Pvmnynjs43UsXH8QgN4donjp9kTsIf4mJxMREbkwjy4y48aNo0+fPjRt2pSCggLS09NZunQpixYtMjuaV8jKO8nw9Az2njiFv6+Nx29sz11dm2OzaZQkIiLW4NFF5vDhw9x5550cOHAAu91OQkICixYt4rrrrjM7mqUZhsF7K3bywsJNlLkMmkaEMDU1mYQmdc2OJiIiclE8usi89957ZkfwOieLS3l47jqWbDwEwI2dGjHptgTCgzRKEhER6/HoIiNVa+3uE4xIz2B/fgkBfj48eVMH/tS5qUZJIiJiWSoytYDbbfD2Dzt4adFmXG6DFg1CmZqaTHyM3exoIiIiv4uKjJc7VujkobnZLN18BICbE2N4/tZO1AnU1ouIiPXp1cyLrdp5nBGzMjjkcBLo58OEm+MZdEWsRkkiIuI1VGS8kNtt8I+l25i8eAtuA1o1DGXa0BTiGukNAUVExLuoyHiZIwVOxszJ4oetRwG4NaUxz/TvSKhGSSIi4oX06uZFftp2lFEfZXGkwEmwvy9/7x/PwMtjzY4lIiJSbVRkvIDLbTDl261M+W4rhgFto+owLTWFNlFhZkcTERGpVioyFnfYUcKo2Vms3HEMgEGXx/L0zfEEB/ianExERKT6qchY2A9bj/DgR1kcLSwlJMCX52/pxIDkxmbHEhERqTEqMhZU7nLz2pKtTFu6DcOA9tHhTEtNpmXDOmZHExERqVEqMhZzIP8Uo2ZlsWrXcQCGdm7Kkzd1IMhfoyQREal9VGQs5PtNhxkzJ4sTxWXUCfRj0m2duCkhxuxYIiIiplGRsYAyl5uXF21m+vIdAHRsHM601BSa1Q81OZmIiIi5VGQ83L6TpxiRnkHGnpMA3NW1OeNujCPQT6MkERERFRkPtnjDIR6em03+qTLCgvx46fYEbugYbXYsERERj6Ei44FKy91MWrCJf/64E4DE2LpMHZJMbESIyclEREQ8i4qMh8k7Xszw9Ayy9+YDcF+3Fjx6QxwBfj4mJxMREfE8KjIeZGHuAR75eB0FJeXYg/15ZWAivTpEmR1LRETEY6nIeICSMhcTv97IByt3A3BZs3pMGZJM47rBJicTERHxbCoyJtt1tIi09AzW73cA8L89WvFQ77b4+2qUJCIiciEqMib6Ins/4z7NodBZTkRoAJPvSOTqdpFmxxIREbEMFRkTlJS5+PuXG0j/ZQ8AV7aIYMrgZBrZg0xOJiIiYi0qMjVs+5FC0mZmsOlgATYbDL+mNaN6tsFPoyQREZGLpiJTg+Zl7uVv83IpLnXRoE4Arw1KplubBmbHEhERsSwVmRpwqtTF+M9zmbNmLwBdW9XntUFJRIZrlCQiIvJ7qMhUs62HCvjrzAy2Hi7ExwajerZl+LWt8fWxmR1NRETE8lRkqolhGMxdu5enPsulpMxNZFggrw9Opkur+mZHExER8RoqMtWgyFnOk/Nz+TRzHwDd2zTg1UFJNKgTaHIyERER76IiU8U2HnCQlp7BjiNF+PrYGHNdWx7o0QofjZJERESqnIpMFTEMg1mr8pjwxXqc5W4ahQfxRmoyVzSPMDuaiIiI1/LoNy+ZOHEiV1xxBWFhYURGRjJgwAA2b95sdqzfKCgpY+TsLB6fl4Oz3M21cZF8Paq7SoyIiEg18+gis2zZMtLS0vj5559ZvHgxZWVl9O7dm6KiIrOjnZa7L59+b6zgi+z9+PnYePzGON6983IiQgPMjiYiIuL1bIZhGGaHqKwjR44QGRnJsmXL+OMf/1ip5zgcDux2O/n5+YSHh1dZFsMw+PfPu3n2y42Uutw0rhvMG6nJpDStV2X/DhERkdqqsq/flrpGJj8/H4CIiIpHNk6nE6fTefq+w+Go8hyGYfDgR1nMz9oPwHUdonjp9gTqhugsjIiISE3y6NHSr7ndbkaPHs1VV11Fx44dKzxu4sSJ2O3207fY2Ngqz2Kz2UhuWg9/XxtP3dSBt/98mUqMiIiICSwzWnrggQdYsGABK1asoEmTJhUed64zMrGxsdUyWtp5tIiWDetU2Z8pIiIi/+FVo6Xhw4fz5Zdfsnz58vOWGIDAwEACA6v/jedsNptKjIiIiMk8usgYhsGIESOYN28eS5cupUWLFmZHEhEREQ/i0UUmLS2N9PR0PvvsM8LCwjh48CAAdrud4OBgk9OJiIiI2Tz6Ghmb7dxv6z9jxgzuuuuuSv0Z1fXr1yIiIlJ9vOIaGQ/uWCIiIuIBLPPr1yIiIiJnU5ERERERy1KREREREctSkRERERHLUpERERERy1KREREREctSkRERERHLUpERERERy1KREREREcvy6Hf2rQr/fXdgh8NhchIRERGprP++bl/oXf69vsgUFBQAEBsba3ISERERuVgFBQXY7fYKv+7RHxpZFdxuN/v37ycsLKzCD6G8FA6Hg9jYWPLy8rz2wyi9fY3evj7w/jVqfdbn7WvU+i6dYRgUFBQQExODj0/FV8J4/RkZHx8fmjRpUm1/fnh4uFf+z/lr3r5Gb18feP8atT7r8/Y1an2X5nxnYv5LF/uKiIiIZanIiIiIiGWpyFyiwMBAxo8fT2BgoNlRqo23r9Hb1wfev0atz/q8fY1aX/Xz+ot9RURExHvpjIyIiIhYloqMiIiIWJaKjIiIiFiWioyIiIhYlopMBZYvX06/fv2IiYnBZrMxf/78Cz5n6dKlpKSkEBgYSOvWrXn//ferPeelutj1LV26FJvN9pvbwYMHaybwRZo4cSJXXHEFYWFhREZGMmDAADZv3nzB582dO5e4uDiCgoLo1KkTX3/9dQ2kvTSXssb333//N3sYFBRUQ4kvzptvvklCQsLpN9rq0qULCxYsOO9zrLR/F7s+K+3duUyaNAmbzcbo0aPPe5yV9vBslVmjlfbx6aef/k3WuLi48z7HjP1TkalAUVERiYmJTJs2rVLH79y5k759+3LNNdeQlZXF6NGjue+++1i0aFE1J700F7u+/9q8eTMHDhw4fYuMjKymhL/PsmXLSEtL4+eff2bx4sWUlZXRu3dvioqKKnzOTz/9xJAhQ7j33nvJzMxkwIABDBgwgNzc3BpMXnmXskb4zztw/noPd+/eXUOJL06TJk2YNGkSa9euZc2aNVx77bX079+f9evXn/N4q+3fxa4PrLN3Z1u9ejXTp08nISHhvMdZbQ9/rbJrBGvtY3x8/BlZV6xYUeGxpu2fIRcEGPPmzTvvMY8++qgRHx9/xmODBg0yrr/++mpMVjUqs77vv//eAIwTJ07USKaqdvjwYQMwli1bVuExd9xxh9G3b98zHuvcubNx//33V3e8KlGZNc6YMcOw2+01F6qK1atXz3j33XfP+TWr759hnH99Vt27goICo02bNsbixYuNHj16GKNGjarwWKvu4cWs0Ur7OH78eCMxMbHSx5u1fzojU0VWrlxJr169znjs+uuvZ+XKlSYlqh5JSUlER0dz3XXX8eOPP5odp9Ly8/MBiIiIqPAYq+9hZdYIUFhYSLNmzYiNjb3gGQBP4XK5mD17NkVFRXTp0uWcx1h5/yqzPrDm3qWlpdG3b9/f7M25WHUPL2aNYK193Lp1KzExMbRs2ZKhQ4eyZ8+eCo81a/+8/kMja8rBgweJioo647GoqCgcDgenTp0iODjYpGRVIzo6mrfeeovLL78cp9PJu+++y9VXX80vv/xCSkqK2fHOy+12M3r0aK666io6duxY4XEV7aGnXgf0a5VdY7t27fjnP/9JQkIC+fn5vPzyy3Tt2pX169dX64erXqqcnBy6dOlCSUkJderUYd68eXTo0OGcx1px/y5mfVbbO4DZs2eTkZHB6tWrK3W8FffwYtdopX3s3Lkz77//Pu3atePAgQNMmDCB7t27k5ubS1hY2G+ON2v/VGSkUtq1a0e7du1O3+/atSvbt2/n1Vdf5d///reJyS4sLS2N3Nzc8852ra6ya+zSpcsZf+Pv2rUr7du3Z/r06TzzzDPVHfOitWvXjqysLPLz8/n4448ZNmwYy5Ytq/DF3mouZn1W27u8vDxGjRrF4sWLPfZi1t/rUtZopX3s06fP6X9OSEigc+fONGvWjDlz5nDvvfeamOxMKjJVpFGjRhw6dOiMxw4dOkR4eLjlz8ZU5Morr/T4cjB8+HC+/PJLli9ffsG/7VS0h40aNarOiL/bxazxbP7+/iQnJ7Nt27ZqSvf7BAQE0Lp1awAuu+wyVq9ezeuvv8706dN/c6wV9+9i1nc2T9+7tWvXcvjw4TPO2LpcLpYvX87UqVNxOp34+vqe8Ryr7eGlrPFsnr6Pv1a3bl3atm1bYVaz9k/XyFSRLl268O23357x2OLFi88777a6rKwsoqOjzY5xToZhMHz4cObNm8d3331HixYtLvgcq+3hpazxbC6Xi5ycHI/dx7O53W6cTuc5v2a1/TuX863vbJ6+dz179iQnJ4esrKzTt8svv5yhQ4eSlZV1zhd4q+3hpazxbJ6+j79WWFjI9u3bK8xq2v5V66XEFlZQUGBkZmYamZmZBmBMnjzZyMzMNHbv3m0YhmGMHTvW+POf/3z6+B07dhghISHGI488YmzcuNGYNm2a4evrayxcuNCsJZzXxa7v1VdfNebPn29s3brVyMnJMUaNGmX4+PgYS5YsMWsJ5/XAAw8YdrvdWLp0qXHgwIHTt+Li4tPH/PnPfzbGjh17+v6PP/5o+Pn5GS+//LKxceNGY/z48Ya/v7+Rk5NjxhIu6FLWOGHCBGPRokXG9u3bjbVr1xqDBw82goKCjPXr15uxhPMaO3assWzZMmPnzp3GunXrjLFjxxo2m8345ptvDMOw/v5d7PqstHcVOfs3eqy+h+dyoTVaaR8feughY+nSpcbOnTuNH3/80ejVq5fRoEED4/Dhw4ZheM7+qchU4L+/bnz2bdiwYYZhGMawYcOMHj16/OY5SUlJRkBAgNGyZUtjxowZNZ67si52fS+88ILRqlUrIygoyIiIiDCuvvpq47vvvjMnfCWca23AGXvSo0eP0+v9rzlz5hht27Y1AgICjPj4eOOrr76q2eAX4VLWOHr0aKNp06ZGQECAERUVZdx4441GRkZGzYevhHvuucdo1qyZERAQYDRs2NDo2bPn6Rd5w7D+/l3s+qy0dxU5+0Xe6nt4Lhdao5X2cdCgQUZ0dLQREBBgNG7c2Bg0aJCxbdu201/3lP2zGYZhVO85HxEREZHqoWtkRERExLJUZERERMSyVGRERETEslRkRERExLJUZERERMSyVGRERETEslRkRERExLJUZERERMSyVGRERETEslRkRMRSXC4XXbt25dZbbz3j8fz8fGJjY/nb3/5mUjIRMYM+okBELGfLli0kJSXxzjvvMHToUADuvPNOsrOzWb16NQEBASYnFJGaoiIjIpY0ZcoUnn76adavX8+qVasYOHAgq1evJjEx0exoIlKDVGRExJIMw+Daa6/F19eXnJwcRowYwRNPPGF2LBGpYSoyImJZmzZton379nTq1ImMjAz8/PzMjiQiNUwX+4qIZf3zn/8kJCSEnTt3snfvXrPjiIgJdEZGRCzpp59+okePHnzzzTc8++yzACxZsgSbzWZyMhGpSTojIyKWU1xczF133cUDDzzANddcw3vvvceqVat46623zI4mIjVMZ2RExHJGjRrF119/TXZ2NiEhIQBMnz6dhx9+mJycHJo3b25uQBGpMSoyImIpy5Yto2fPnixdupRu3bqd8bXrr7+e8vJyjZhEahEVGREREbEsXSMjIiIilqUiIyIiIpalIiMiIiKWpSIjIiIilqUiIyIiIpalIiMiIiKWpSIjIiIilqUiIyIiIpalIiMiIiKWpSIjIiIilqUiIyIiIpb1/wBSBOvpB/vlOAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 散布図の描画"
      ],
      "metadata": {
        "id": "s6PFV7Dy31mS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# サンプルデータ\n",
        "x = [1, 2, 3, 4, 5]\n",
        "y = [2, 4, 6, 8, 10]\n",
        "\n",
        "# 散布図の描画\n",
        "plt.scatter(x, y)\n",
        "\n",
        "# ラベルとタイトルの追加\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"Y\")\n",
        "\n",
        "# グラフの表示\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "bVBIxN6R2dZs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "6b451ef2-8886-45c8-c26a-77c3e82d9dbf"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## グラフのカスタマイズ"
      ],
      "metadata": {
        "id": "QkneVJiX37Ut"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# サンプルデータ\n",
        "x = [1, 2, 3, 4, 5]\n",
        "y = [2, 4, 6, 8, 10]\n",
        "\n",
        "# 折れ線グラフのカスタマイズ\n",
        "plt.plot(x, y, linestyle='--', marker='o', color='red')\n",
        "\n",
        "# ラベルとタイトルの追加\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"Y\")\n",
        "\n",
        "# グラフの表示\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "d0eCrsxO2ibl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "8953c342-405a-4d0c-aec5-fd9f0058e6fc"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# scikit-learnの典型的な使い方\n",
        "\n"
      ],
      "metadata": {
        "id": "TeMCpzxOjqui"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## scikit-learnのインポートとデータセットの生成"
      ],
      "metadata": {
        "id": "xHZNZFfxj7Z2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 必要なライブラリのインポート\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# データセットの生成（例として、単純な線形関係にノイズを加えたデータ）\n",
        "np.random.seed(0)\n",
        "X = 2 * np.random.rand(100, 1)  # 説明変数\n",
        "y = 4 + 3 * X + np.random.randn(100, 1)  # 目的変数\n",
        "\n",
        "# データセットを訓練用とテスト用に分割\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "7jzWKUH0j5Ro"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## モデルの作成と訓練 (fit)"
      ],
      "metadata": {
        "id": "4puAsnE7kDl8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 線形回帰モデルの作成\n",
        "model = LinearRegression()\n",
        "\n",
        "# モデルの訓練\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "J8oWMO_3kDLH",
        "outputId": "69d9d05a-ece0-4714-f987-0e7390847db2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## モデルを用いた予測 (predict)\n"
      ],
      "metadata": {
        "id": "0nzkL72LkMew"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# テストデータに対する予測\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# 予測結果の可視化\n",
        "plt.scatter(X_test, y_test, color='blue', label='test data')\n",
        "plt.plot(X_test, y_pred, color='red', label='prediction')\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('Y')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "g5mE6tk9kLtD",
        "outputId": "a6191830-0e0f-4a3b-d487-008bcdc9df24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 更なる学習"
      ],
      "metadata": {
        "id": "Vou8C_tP4A4b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pythonを学習するためのコンテンツはネットに多くあるので自分に合うものを見つけて学習してみてください。Google Colabで体験できる教材が良いと思います。思います。おすすめは例えば東大の数理・情報教育センターが公開している以下の教材です。\n",
        "\n",
        "Pythonプログラミング入門\n",
        "\n",
        "https://utokyo-ipp.github.io\n",
        "\n"
      ],
      "metadata": {
        "id": "jodMqh-54DN7"
      }
    }
  ]
}