![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Google Colaboratoryの使い方\n",
- "\n",
- ":::{note}\n",
- "以下の内容は、Google Colaboratoryや関連サービスのアップデートに伴い、数ヶ月経つとところどころ文言が変更されている場合があります。適宜対応するものに置き換えて読んでいただけると助かります。\n",
- ":::\n",
- ":::{note}\n",
- "Macユーザーの人は適宜下記のコントロールキー`Ctrl`を`command`(`⌘`)キーに読み替えてください。\n",
- ":::\n",
- "\n",
- "Google Colaboratory上でノートブックを開くと、下記のような画面が開く。(上のOpen in ColabまたはロケットマークからColabを開いてみよう!)\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "この画面が開いたときに、右上にお使いのGoogleのアカウントのアイコンが表示されている人はそのまま以下へ、\"ログイン\"というボタンが表示されている人はまずそちらからご自身のアカウントにログインしてください。 \n",
- "授業ではプログラミングの強力さを体感してもらうために、**Google Driveの中に入っているcsv/excelファイルを読み出して自動で作画をするプログラム**を書いたりする。\n",
- "その際、教員やTAと受講者間でdriveを見ながら相談することもあり得る。プライベートなアカウントとは別に、**大学の授業で使う専用のアカウントを新規作成する**のを強く推奨します。\n",
- "\n",
- "\n",
- "Google Colaboratory(あるいはJupyterNotebook)環境ではユーザーは\n",
- "- **テキストセル**\n",
- "- **コードセル**\n",
- "\n",
- "の２つを駆使して、自分だけのノートブックを作成したり、他者が作成した`.ipynb`形式のファイルを開いて編集したりプログラムを実行することができる。\n",
- "\n",
- "\n",
- "## テキストセル\n",
- "[+テキスト]というボタンを押すとテキストセルを追加することができます。\n",
- "\n",
- "\n",
- "### テキスト部分への数式の挿入\n",
- "\n",
- "Jupyter Nootebookでは$\\LaTeX$形式の数式をサポートしていて、ドルマークで囲むと数式を表示させることもできます。 \n",
- "$f(x) = \\frac{1}{2}\\exp{(-2x^2 + 3x + 5)}$\n",
- "\n",
- "\n",
- "## コードセル\n",
- "[+コード]というボタンを押すか、特定のショートカット`Ctrl + M B`でコードセルを追加することができる。\n",
- "\n",
- "コードセルに記述したPythonコードは、コードセルにマウスオーバーした際に現れる再生ボタンのようなアイコンをクリックするか、セル内で`Ctrl+Enter`を押すことで実行ができる。\n",
- "実行のたびにいちいちマウスに手をやるのは面倒なので、是非ショートカットを使おう。\n",
- "この例に限らず、手の移動を最小限にすることで作業を楽にしたり押し間違いを防ぐこともできる。\n",
- "アプリケーションにあまり依らない共通のショートカット(`Ctrl+S`(`⌘+S`)は保存,`Ctrl+W`(`⌘+W`)はタブを閉じる, などなど)ので少しずつショートカットを覚えるようにしよう。\n",
- "\n",
- "\n",
- "**コードセルにはコメントを除いて、全角文字は使わない**ように意識しよう。\n",
- "**全角スペースなどが気づかずに混入して、エラーの原因になること**がよくある。\n",
- "\n",
- "## 重要な注\n",
- "\n",
- "ブックから開いたノートブック\n",
- "```{margin}\n",
- "ノートブックの実体(ソースファイル)は[GitHub上](https://github.com/SotaYoshida/Lecture_DataScience)にあります\n",
- "```\n",
- "は、皆さんがコピーをするなどしない限り、実行はできても保存はできません。\n",
- "```{warning}\n",
- "**必ず、[ドライブにコピーを保存]ボタンか、[ファイル]->[ドライブにコピーを保存]を押し、\"ご自身のファイル\"を編集・保存するようにしてください**\n",
- "\n",
- "```\n",
- "\n",
- "上の画像のように、ノートブックのファイル名の横にGitHubのロゴ(タコ足猫)が表示されている場合、\n",
- "皆さんが開いているのはGitHubからインポートされたノートブックであって、皆さん自身のファイルではありません。 \n",
- "途中でうっかりタブやブラウザを閉じてしまうと作業内容が消えてしまいます。\n",
- "\n",
- "私も一連の講義資料作成時にGitHubから開いたノートブックを直接編集して数時間分の作業内容を消してしまったことが何度もあります...。辛いです。\n",
- "\n",
- "コピーを編集している場合は、左上のノートブック名の隣がGoogle driveのロゴになっているはず。 \n",
- "この場合、編集されたものはGoogle Driveに一定時間で自動でバックアップされるので安心ですし、いつでもどの端末からでも開いて作業を再開することができます。 \n",
- "\n",
- "\n",
- "\n",
- "```{margin}\n",
- "MS Officeなど、他の多くのソフトでも共通のショートカットなので、こまめに保存する手クセを付けておきましょう。\n",
- "保存!保存!!保存!!!\n",
- "```\n",
- "Windowsなら`Ctrl+ S`, Macなら`Command + S`を押すことで随時Google Driveにバックアップを保存することができます。 \n",
- "誤った内容に編集してしまった場合もGoogle Colaboratoryの\"最終編集: X月Y日\"といったところやGoogle Drive上でファイルの上で右クリック→\"版を管理\"から復元することができます。\n",
- "\n",
- "## なんか挙動が変だな、と思ったら。\n",
- "\n",
- "複数のセルにまたがるコードを実行していくうちに変数に意図しないものが入っていたりなんか変だな、挙動が怪しいな、と思ったら[ランタイム]や[編集]などから下記の操作を行ってみましょう。\n",
- "\n",
- "* セルの実行を停止したい場合: [ランタイム]→[実行の中断]で中断 (or [セッションの管理]から不要なセッションを削除する)\n",
- "* 再起動したい場合: [ランタイム]→[ランタイムを再起動]\n",
- "* 出力をいったん全部消したい: [編集]→[出力をすべて消去]\n",
- "\n",
- "## Google Driveからファイルのインポート\n",
- "\n",
- "Google Colaboratoryでは、Google Driveに保存されたファイルの内容を読み込んだり、逆にコードでテキストファイル,エクセルファイルや画像,音声などを作成しGoogle Driveに保存することができます。\n",
- "\n",
- "お使いのアカウントのDriveをGoogle colab.から操作する方法は主に2通りあります。\n",
- "\n",
- "1つめ: \n",
- "以下を実行し、出てくるURLに遷移 \n",
- "authorization codeをコピーし枠内に貼り付けてEnterを押す \n",
- "(Google colabからgoogle driveのファイルにアクセスできるようになります)\n",
- "\n",
- "```Python\n",
- "from google.colab import drive\n",
- "drive.mount('/content/drive/') ##google driveを/content/という場所にマウント (２番めと整合するようにパスを選びましたが、マウントする場所は自由に選べます)\n",
- "```\n",
- "\n",
- "たとえばgoogle drive直下にあるXXX.pngというファイルを指定したい場合は\n",
- "```Python\n",
- "filename = \"/content/drive/My Drive/XXX.png\"\n",
- "```\n",
- "とすれば良い。\n",
- "\n",
- "2つめ: \n",
- "Google Colabの左側にあるフォルダマークをクリックし、(「ファイルのブラウジングを有効にするには、ランタイムに接続してください。」と出る場合は、少し待ってください) \n",
- "次に、Google driveのロゴがついたフォルダをクリックする(画像の、[ファイル]というところの右下にある、driveのロゴがついたグレーがかったフォルダマーク) \n",
- "\n",
- "\n",
- "この場合は自動で```/content/drive```という場所にマウントされるので(仕様が変更になる可能性あり) \n",
- "google drive直下にあるXXX.pngというファイルを指定したい場合はやはり\n",
- "```Python\n",
- "filename = \"/content/drive/My Drive/XXX.png\"\n",
- "```\n",
- "などとすれば良い。 \n",
- "また、driveを一度マウントした後でパスがわからなくなったときには左の[ファイル]からdriveに相当するフォルダにマウスオーバーして縦3点$\\vdots$から[パスをコピー]で、パスをクリップボードにコピーすることができます。\n",
- "\n",
- "\n",
- "## シェルコマンドとドライブ内のファイルのパス\n",
- "\n",
- "Google Colab.では先頭にエクスクラメーションマークをつけることで\n",
- "* ls (リスト,ファイル等表示)\n",
- "* mkdir (ディレクトリ作成)\n",
- "\n",
- "などのLinux/Unixで用いられるコマンドを実行することもできる。 \n",
- "たとえばGoogle Driveをマウントした後にマイドライブ直下にあるpng画像のリストを表示させたければ\n",
- "```bash\n",
- "!ls /content/drive/MyDrive/*.png \n",
- "```\n",
- "とすればよい。\n",
- "なお、半角のアスタリスクはワイルドカード記号「対象を任意に」という命令になっている。\n",
- "\n",
- "\n",
- "\n"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "include_colab_link": true,
- "name": "Python_chapter0_HowToUse.ipynb",
- "provenance": []
- },
- "interpreter": {
- "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
- },
- "kernelspec": {
- "display_name": "Python 3.9.10 64-bit",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "name": "python",
- "version": "3.9.13"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_chapter1_Introduction.ipynb b/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_chapter1_Introduction.ipynb
deleted file mode 100644
index 6b040732..00000000
--- a/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_chapter1_Introduction.ipynb
+++ /dev/null
@@ -1,1010 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "colab_type": "text",
- "id": "view-in-github"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "rk52fX27r7hj"
- },
- "source": [
- "# Pythonの基本 その１:\n",
- "\n",
- "授業の冒頭で説明するように、プログラミングによるデータ分析は、複雑なデータの取り扱いを簡単にしてくれたり、系統的な作業を可能にしミス(精度)をコントロールすることが出来たりと利点が多い。しかし、その恩恵を受けるためには基本的な事項をまず学修しなくてはならない。\n",
- "\n",
- ":::{margin}\n",
- "さらにいうと英語で検索できればウェブ上で収集できる情報は何倍にも膨れ上がる.\n",
- ":::\n",
- "この授業で重視するのは**Pythonの文法や作法、テクニックではない**。というのも、自分が実現したい操作・作業をきちんと**言語化**することさえできれば、授業で扱う程度の内容であればググればだいたいのことは解決できるし、インターフェース(見た目)が違ったとしても別のプログラミング言語やデータ解析ツールにも応用が効く。 \n",
- "\n",
- "したがって、以降では「いったいなんのためにホニャララなんてものを導入(定義)するのか？」を意識しながら学習していくことにしよう。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9f041YDhyHKb"
- },
- "source": [
- "## 変数の定義と簡単な演算\n",
- "\n",
- "**「\"値\"に固有の名前（識別子）を付したもの」** を**変数**と定義する。\n",
- "\n",
- "どのようなものが\"値\"として扱えるかは後で見ていくことにして、まずは代表的な値である**実数値**,**整数**を変数として定義してみよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "_emEY3U-SGBk"
- },
- "outputs": [],
- "source": [
- "a = 2.0 \n",
- "b = 5"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CUJ8eEf8x2bs"
- },
- "source": [
- "これで変数```a```と変数```b```に値が代入され\"定義\"された。 \n",
- "プログラムでは通常、等号記号(`=`)は代入に使用される。値の代入は、等号の左に変数(値を入れたいものの名前)、等号の右に値を書くことで行う。\n",
- "\n",
- "注: よく書籍などでは変数を箱と見立てて箱の中に数値や文字列を入れる説明もよく見られるが、変数は値が保管されるメモリ上の住所を示すものというのが正確。関連した話題としては、次の章の**リスト操作の注意点**を参照.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "kSahHBeguqRX"
- },
- "source": [
- "## 簡単な演算\n",
- "次に、上で定義された変数を使って四則演算をしてみよう。\n",
- "\n",
- "足し算"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
- },
- "id": "PLwBPo2OyCvc",
- "outputId": "e803fa31-9566-4d87-a253-a3a99ee33753"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "7.0"
- ]
- },
- "execution_count": 6,
- "metadata": {
- "tags": []
- },
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a+b "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "1ntUEuJWyBoC"
- },
- "source": [
- "引き算"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
- },
- "id": "1IpzKL9TyW2k",
- "outputId": "08807a99-86e7-40a3-f556-bd67d6cf2338"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "-3.0"
- ]
- },
- "execution_count": 7,
- "metadata": {
- "tags": []
- },
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a-b"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "S4-CasuLyiJM"
- },
- "source": [
- "掛け算 掛け算記号は`*`(アスタリスク)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
- },
- "id": "ZQCmuN-IyiZQ",
- "outputId": "80bc16fe-5012-4c2e-db6a-001609a5d640"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "10.0"
- ]
- },
- "execution_count": 8,
- "metadata": {
- "tags": []
- },
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a*b"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "割り算"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "a/b"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "uYolSMVnyiiz"
- },
- "source": [
- "演算した結果を別の変数`c`として定義したり代入して保存しておくこともできる"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "8Z5Q-TN0yirc"
- },
- "outputs": [],
- "source": [
- "c=a*b "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "_eyM3WdUyiyP"
- },
- "source": [
- "`c`の値が後で知りたいと思ったときは、以下のように`print`関数というものを使う"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
- },
- "id": "dS6W58sfyi8w",
- "outputId": "0b78e406-7d9d-4f8c-d584-4cd1195f3680"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "10.0\n"
- ]
- }
- ],
- "source": [
- "print(c)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "SvfvgAEu2hAq"
- },
- "source": [
- "変数の値を確認することは、プログラムが正しく動いているかを確認する最も単純かつ強力な方法である。 \n",
- "慣れないうちは「値はなんだっけな？」「何かおかしい、意図した動きと違うな」と思ったら`print`してみよう。\n",
- "\n",
- "またコード部分に`#`(半角シャープ記号)をつけるとその行の`#`以降に書かれたことは無視されるのでコメント(注釈)を書いたりすることができる。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "MArtGR067qrB"
- },
- "outputs": [],
- "source": [
- "print(\"Hello\") # Helloとプリントする(ここは読まれない)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "なれないうちは**どういった計算をするのか**、**その行をいつ編集したのか**などコメントを逐一書いておくのも良い。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "uNWje7ZmSPQh"
- },
- "outputs": [],
- "source": [
- "## 10月1日に編集\n",
- "print(b//a) # 切り捨て除算 \n",
- "print(b%a) # bをaで割った余り\n",
- "print(a**b) # aのb乗"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "上の例で、`#`を忘れると"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 135
- },
- "id": "CVrAtuQS7zqd",
- "outputId": "a02ec808-9b4b-4ce3-cbd6-5d75b0561b7f"
- },
- "outputs": [
- {
- "ename": "SyntaxError",
- "evalue": "ignored",
- "output_type": "error",
- "traceback": [
- "\u001b[0;36m File \u001b[0;32m\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9f041YDhyHKb"
- },
- "source": [
- "# Pythonの基本 その２: \n",
- "\n",
- "\n",
- "[この章の目的]\n",
- "プログラミングで非常に重要な概念である配列や繰り返し処理について学ぶ。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "FxEjmIHu3StG"
- },
- "source": [
- "## リスト\n",
- "\n",
- "実際にいろんなデータを扱う際には、値や変数をまとめて処理したくなります。\n",
- "こうしたときに必要になるのが以下で扱う**リスト**型を始めとする**配列**です。\n",
- "\n",
- "リストは、値や変数などを括弧[ ]で括り、カンマで区切ることで作ることができます。\n",
- "例: ```[1.0, 2.0, 3.0]``` \n",
- "また好きな名前の変数にリストを代入する(リストに名前を割り振る)こともできます"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "dxpkCjfvjsDD"
- },
- "outputs": [],
- "source": [
- "heights=[178.0, 180.0, 153.0]\n",
- "\n",
- "# 変数heightsをprint\n",
- "print(heights)\n",
- "\n",
- "# 変数heightsの型をprint\n",
- "print(\"変数aの型(type)は\", type(heights)) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CI7AqILkzWYY"
- },
- "source": [
- "リストの要素にできるのは数値だけではなく、文字列のリストも作ることができます。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "a7c5bw5O36CC"
- },
- "outputs": [],
- "source": [
- "names = [ \"Aさん\", \"Bさん\", \"宇大太郎さん\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "SkfATN0Jj_Oo"
- },
- "source": [
- "リストに入っている要素の数は、`len()`関数(lengthの略)で見ることができます。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "nexyb9lAkJC0"
- },
- "outputs": [],
- "source": [
- "print(\"リストnamesは\", names)\n",
- "print(\"長さは\", len(names))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7NWIj0tWzxGa"
- },
- "source": [
- "当然、長さに名前をつけて適当な変数として定義することもできます。\n",
- "**関数の出力の結果を何度も後で再利用する場合**などは、このように変数に代入しておくと便利です。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "yQWDGLFCmszW"
- },
- "outputs": [],
- "source": [
- "ln_a = len(names)\n",
- "print(\"リストaの長さは\", len(names), \"で、型は\", type(ln_a)) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gjwXE2cJz05q"
- },
- "source": [
- "文字列と値を組み合わせたようなリストも作ることができます。 \n",
- "例：名前、身長、体重 => ```[\"Aさん\", 178, 66]```\n",
- "\n",
- "また、これを拡張して、入れ子に(リストのリストを作成)することもできます。\n",
- "``` [ [\"Aさん\", 178,66], [\"Bさん\", 180, 70] ]```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8kVQe42tkK-D"
- },
- "source": [
- "### リストの要素にアクセスする, インデックス(index)\n",
- "\n",
- "リストの中の要素にアクセスするときは、半角括弧を使って[整数]といった形で\"番地\"を指定します。 \n",
- "このときの番地(あるいは座標といっても良いですが)を指定する整数のことをインデックス(index)と呼びます。\n",
- "\n",
- "**【重要】Pythonでは要素にアクセスするためのインデックスは１からではなく、0からカウントします!**\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "kRMlRuwhj2Rc"
- },
- "outputs": [],
- "source": [
- "a = [\"Aさん\", 178, 66]\n",
- "print(a[0])\n",
- "print(a[1])\n",
- "print(a[2])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Ci65y6MBoSvU"
- },
- "source": [
- "ですので、`a[3]`にアクセスしようとすると、\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "mkON3qP1occT"
- },
- "outputs": [],
- "source": [
- "print(a[3])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "zHlSjTaJn9UU"
- },
- "source": [
- "list index out of range (リストのインデックスが用意された範囲(range)を逸脱している)というエラーが出ます。 \n",
- "慣れないうちは0からカウントするのを変に思うかもしれませんが、これはプログラミング言語の仕様によるもので、Pythonの他にもC/C++なども0からカウントします。(ちなみにFORTRAN, Juliaなどは1からカウントします。)\n",
- "\n",
- "こうした0-based indexingと呼ばれる言語の利点はいくつかありますが、代表的なものは負のインデックスが自然に使えることでしょうか？\n",
- "\n",
- "理工系分野では、教科書などで行列やベクトルの要素を指定するのに1-based indexingを使うことも多いため、混乱するかもしれません。慣れるまでは注意が必要です。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "D_hxwmblo4GK"
- },
- "outputs": [],
- "source": [
- "a = [\"Aさん\", 178, 66]\n",
- "print(a)\n",
- "print(a[-1])\n",
- "print(a[-2])\n",
- "print(a[-3])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "--1OHCHKo9An"
- },
- "source": [
- "`[-1]`でアクセスすれば末尾の要素に、`[-2]`番目の要素は後ろから二番目といった具合です。\n",
- "\n",
- "長いリストを作って「えっとこれ前から何番目の要素だったっけ...？」というときに、 \n",
- "後ろからX番目の要素といったように、サクッと取得できるのが便利なときもあります。\n",
- "\n",
- "入れ子にしたリストの要素を見たり、使いたいときには、少々慣れが必要です。 \n",
- "```a = [ [[1,2], [3,4]], 5, 6]``` \n",
- "このような場合では、一番外側の括弧から数えて、 \n",
- "何番目の階層になっているかを考えることが必要になります。 \n",
- "練習してみましょう。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "_yXPKxkk1XYh"
- },
- "source": [
- "**練習**\n",
- "\n",
- ":::{margin} 変数名の規則\n",
- "授業の資料のように、一時的な変数を使う場合は`a`や`tmp`といった意味のない変数名で十分なのですが\n",
- "自身でいろんなコードを書くときは**後で自分や他人が見たときになんの変数のつもりか分かる**ような変数名にしましょう。\n",
- "5日後の自分も他人のようなものです。\n",
- ":::\n",
- "1. [+コード]でセルを以下に追加し、下のリストを作成する \n",
- " ```tmp = [ [\"Aさん\", 178,66], [\"Bさん\", 180, 70] ]```\n",
- "2. ```tmp[i][j]```のi,jに可能な整数を入れて、Aさんの身長、Bさんの体重などをprint関数で表示してみましょう。\n",
- "3. 2.と同じことを、負のインデックスを駆使してやってみましょう。\n",
- "4. ```a = [ [[1,2], [3,4]], 5, 6]```を作成して、\n",
- " len(a[0]) #0番目の要素の数をprintしてみましょう。\n",
- "5. ```print(a[0][0][1])```を実行してみましょう。\n",
- "6. 4.5.の挙動から推測して、aのリストの中から４を取り出したい場合どうすればいいのかprint関数を使いながら考えてみましょう。\n",
- "\n",
- "\n",
- ":::{note}\n",
- "テキストセルの内容をコードセルなどにコピー&ペーストしたければ、対象をドラッグして、`Ctrl+C`でコピー,`Ctrl+V`でペースト出来ます(Macの場合は`Ctrl`を`⌘`に置き換えてください)\n",
- ":::"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ZB9epIx_VAQ6"
- },
- "source": [
- "### リストの結合\n",
- "\n",
- "２つのリストを結合することもできます。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "YUsuP81s3_BG"
- },
- "outputs": [],
- "source": [
- "a=[1,3]\n",
- "b=[2,4]\n",
- "c= a+b\n",
- "print(c)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hfEDbcvd6bFE"
- },
- "source": [
- "\n",
- "上のリストを\"座標\"だと思えば、要素ごとの和になってほしいような気もしますが、リストの足し算は要素ごとの和ではなく\"リストの結合\"を意味します。\n",
- "\n",
- "> 補足: 要素ごとの和のような、数学などで必要な演算は以降で扱う```numpy```というモジュールを使って`ndarray`型と呼ばれるものにすれば簡単に実行できます\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CDhimmq_0x-q"
- },
- "source": [
- "長さや階層の異なる2つのリストでも可能です."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "b3DFZtE602hP"
- },
- "outputs": [],
- "source": [
- "c=[1,2,\"Aさん\"]\n",
- "d=[[5,6],[7,8]]\n",
- "c+d"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ovAjU_321ROI"
- },
- "source": [
- "### リストに要素を加える"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "s568X9r-4c6J"
- },
- "source": [
- "リストにあとから要素を加えたくなるときもあります. \n",
- "そんなときは ```append```関数か```+=```を使います。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "b_1nWHpd4ptJ"
- },
- "outputs": [],
- "source": [
- "A = [\"Aさん\", 178,66] \n",
- "A.append(\"O型\")\n",
- "print(\"A\", A)\n",
- "\n",
- "B = [ \"Bさん\", 180,70] \n",
- "B += [\"A型\"]\n",
- "print(B)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "qpkIcDzA8S4X"
- },
- "source": [
- "厳密には両者は違うのですが、この授業では見た目がスッキリするので後者をよく使います。 \n",
- "入れ子のリストを作るときは"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "lqfalNz788Rx"
- },
- "outputs": [],
- "source": [
- "data = [ ]\n",
- "data += [ [\"Aさん\",178,66] ]\n",
- "data += [ [\"Bさん\",180,70] ]\n",
- "print(\"data\", data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "wT8k9-LB9Y74"
- },
- "source": [
- "などとする。空リストに既存(新規)のリストを追加したいときに、上のように`［ ］`が二重に必要な理由はリスト同士の和の演算が結合となることから理解できる。\n",
- "実際に1重の括弧で試してみよう:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "GRI40dgS1U-u"
- },
- "outputs": [],
- "source": [
- "data2 = [ ]\n",
- "data2 += [\"Aさん\", 178,66] # １重カッコ これだと右辺のリストを空リストに結合することになる\n",
- "data2 += [ \"Bさん\",180,70] # これも同様\n",
- "print(data2)\n",
- "print(\"data2\", data2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "u3Q7Qi8mnE4l"
- },
- "source": [
- "こうしてしまう(リスト同士の単なる結合)と、人ごとにデータが区切られていないので扱うのに不便となる。\n",
- "\n",
- "リストに格納する情報に血液型を加えたりする際は以下のようにすればよい"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "_l9GJWZ98srB"
- },
- "outputs": [],
- "source": [
- "### 名前,身長,体重,血液型,住んでる市区町村のリストを\n",
- "a = [ \"Aさん\", 178, 66, \"A型\"]\n",
- "print(a)\n",
- "a += [\"宇都宮市\"]\n",
- "print(\"住所追加後\", a)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "cdCiouyW2oqJ"
- },
- "source": [
- "リストの要素は後から更新することもできます.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "XIATihMK9Lh6"
- },
- "outputs": [],
- "source": [
- "data = [ [\"Aさん\", 178,66],[\"Bさん\",180,70] ]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vdRIu-li9Oqe"
- },
- "source": [
- "というリストがあったとして、Aさんの体重を修正したければ、"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "fZ0mjwjn2uXd"
- },
- "outputs": [],
- "source": [
- "data[0][2] = 58 #Aさんの体重を更新\n",
- "print(data) #リストを表示"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "血液型を追加したければ以下のようにすればよい。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "data[0] += [ \"A型\"]\n",
- "data[1] += [ \"B型\"]\n",
- "\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "a0gQkWzeUqgj"
- },
- "source": [
- "### インデックスの取得"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "V_MaDugdUs2T"
- },
- "source": [
- "\n",
- "```index```関数を使ってリスト内の、[興味のある要素]のインデックスを取得することができます。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "DsvZFrmmU4iz"
- },
- "outputs": [],
- "source": [
- "tlist = [ \"いちご\", \"りんご\", \"ぶどう\"]\n",
- "tlist.index(\"りんご\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ZIyt7579U7bR"
- },
- "source": [
- "重複する要素がある場合、初めにヒットしたインデックスを返します。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "WD1qkBNvU8fH"
- },
- "outputs": [],
- "source": [
- "tlist2 = [ \"いちご\", \"りんご\", \"ぶどう\",\"メロン\",\"りんご\"]\n",
- "tlist2.index(\"りんご\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "97rfD_PvVKU2"
- },
- "source": [
- "複雑なデータを扱う際に「あれ、あの要素ってどこの番地にあるんだっけ？」といった状況に便利な関数です。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "rYMz3ClOhp9D"
- },
- "source": [
- "### スライスを用いた部分リストの取得"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "GSRzB_IPiPCO"
- },
- "source": [
- "以下の```a```のようなリストがあったとき、"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "mkyfjA4ZiPtu"
- },
- "outputs": [],
- "source": [
- "a= [ \"years\", 1990, 1995, 2000, 2005, 2010, 2015, 2020]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0WmC8_qwiZPi"
- },
- "source": [
- "始点,コロン(`:`),終点でインデックスの範囲を指定して、部分的に取り出すことが出来ます。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "z2fish5zixSf"
- },
- "outputs": [],
- "source": [
- "a[2:5]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "COx3lOQZjCFo"
- },
- "source": [
- "終点のインデックスに相当する要素は取り出されないことに注意しましょう。 "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "XasfLIqRjHbe"
- },
- "outputs": [],
- "source": [
- "a[1:]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "sG4YvJTQjIE0"
- },
- "source": [
- "とすると、最後の要素まで含まれます。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "LWya_HtMjKXw"
- },
- "source": [
- "負のインデックスを使用することもできます。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "MKJ5-cJPi_xy"
- },
- "outputs": [],
- "source": [
- "a[1:-1]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "BsLWAvmb3JDd"
- },
- "source": [
- "### $\\clubsuit$ リスト操作の注意点 \n",
- "\n",
- "授業で扱う程度の内容のプログラミングに関する疑問は、ググれば自分でだいたい解決することができます。 \n",
- "ただしこの項目で述べることは(初学者にとって)「直感に反するのだけど、どこがおかしいのか分からないので**そもそもどうググっていいかが分からない**」という点で、少し事情が異なります。 \n",
- "\n",
- "例を見せるために、以下の2種類のリストを用意します.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "IUJ1-lAY3T0n"
- },
- "outputs": [],
- "source": [
- "data1 = [[ \"Aさん\", 178,66], [ \"Bさん\",180,70] ]\n",
- "tmp = [\"Aさん\", 178,66]\n",
- "data2 =[ tmp, tmp] \n",
- "\n",
- "print(\"data1\", data1)\n",
- "print(\"data2\", data2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "t2FS4d0U3-rq"
- },
- "source": [
- "`data2`のようにまず雛形のリスト`tmp`を作って人数分の長さを持つリストを作ってから中身を編集しようと考えた場合、"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "w-uX1Alb4OOi"
- },
- "outputs": [],
- "source": [
- "data2[1][0]=\"Bさん\"\n",
- "data2[1][1]=180\n",
- "data2[1][2]=70"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "iQwPT_WN4WG_"
- },
- "source": [
- "という操作を思いつきます。このとき、`data2`の2つ目(０から数えて1番目)の要素だけを編集したつもりでも`data2`を`print`すると\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "rkK0uOpz4huX"
- },
- "outputs": [],
- "source": [
- "print(data2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "rVVjGsPu4jhj"
- },
- "source": [
- "data2の最初の要素(Aさんのままであってほしいリスト)まで上書きされてしまっています。 \n",
- "これは直感に反しているという点で、初学者が陥りやすい落とし穴です。\n",
- "\n",
- "※C言語などを学習していれば、参照渡し(ポインタ渡し)などである程度馴染みがあるかもしれません。\n",
- "\n",
- "「値を格納する箱というよりは値が保管される住所を示すもの」と1章の[変数の定義と簡単な演算]のところで書きました。 \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "xt1Q_B0f5QYL"
- },
- "outputs": [],
- "source": [
- "tmp = [\"Aさん\", 178,66]\n",
- "data2 =[ tmp, tmp] \n",
- "print(id(data2[0]), id(data2[1])) #それぞれのidを調べてprint\n",
- "print(\"idが等しいか\", id(data2[0])== id(data2[1])) #id()は変数のidを確認する関数"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HktuZ7f85_FY"
- },
- "source": [
- "今の場合、`data2`を最初に作ったときには0番目と1番目の要素(リスト)は同一の`id`を持つ`tmp`というリストです。 \n",
- "したがって`tmp`の中身を書き換える操作(`data2[1][0]=\"Bさん\"`)は、`tmp`の更新を通して`data2`の要素いずれもに影響します。\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "LU95twWnrFGG"
- },
- "source": [
- "このように、(特に)リストを入れ子にする際には、注意が必要です。なんかへんだな？と思ったときは要素の`id`に気を配ってみるのも重要です. \n",
- "上のコードで、意図したものと違う挙動を起こした原因は、リスト`tmp`を参照する形で`data2`を作ったことでした。 \n",
- "これは`copy`モジュールの`copy`関数を用いて配列のコピーを作成することで回避できます。(モジュールについては4章で説明します)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Bor6WGZzrIXO"
- },
- "outputs": [],
- "source": [
- "import copy #copyというモジュールをインポートする\n",
- "tmp=[\"Aさん\",178,66]\n",
- "data2 = [ copy.copy(tmp), copy.copy(tmp)]\n",
- "print(id(data2[0]) == id(data2[1])) # ← data2の0番目と1番目のidが同じ(参照元が同じ)だと困るのでFalseであってほしい"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "EZQPPyoaryQS"
- },
- "source": [
- " また、リストのリスト(ネストされたリストといったりします)それ自体をcopyしたいときは、\n",
- "```copy.deepcopy()```を使います。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "WkRQ4_klncVY"
- },
- "outputs": [],
- "source": [
- "import copy\n",
- "data = [[ \"Aさん\", 178,66], [\"Bさん\",180,70] ]\n",
- "\n",
- "copydata = copy.copy(data)\n",
- "deepcopydata = copy.deepcopy(data)\n",
- "\n",
- "\n",
- "print(id(data), id(copydata),id(deepcopydata))\n",
- "print(id(data[0]), id(copydata[0]), id(deepcopydata[0]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "di-HvKQhpV4c"
- },
- "source": [
- "上を見ると```data```というリストと```copydata```という\"リスト同士\"が異なるidを持っていても、それぞれの0番目の要素のidを見てみると、同じものを参照していることがわかります。 \n",
- "このように、ネストされたリストをコピーして別々に扱いたい場合は特に注意が必要で、```deepcopy```のような作業が必要となります。(私も初めてプログラミングで配列を使ったときに、この点に気が付かずに時間をかなり溶かしました)\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "66r8pzgrfwOi"
- },
- "source": [
- "## 条件分岐 (if文)\n",
- "\n",
- "`if`文では、書かれた条件が成立した場合、後に続くブロックを実行します。 \n",
- "`if`文を使って命題の真偽を判定することで、条件分岐を表現することができます。\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "JOz7cyvQf2NX"
- },
- "outputs": [],
- "source": [
- "a=3.0; b =-0.3\n",
- "if a > b: \n",
- " print(\"aはbよりも大きい\")\n",
- "if b > a:\n",
- " print(\"bはaよりも大きい\") ##これは呼び出されない"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QF6tLC7agluM"
- },
- "source": [
- "条件を満たすときはA,満たさないときはBをしたい、という場合には```else```を使う。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "SacEU4f0gsmv"
- },
- "outputs": [],
- "source": [
- "if a< b:\n",
- " print(\"aはbよりも小さい\")\n",
- "else:\n",
- " print(\"a>=b\") "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "t0pZV6Ghg31K"
- },
- "source": [
- "```elif```(else ifの略)を用いれば、もう少し複雑な条件を表現することができる。\n",
- "\n",
- "たとえば、もし条件1が満たされればAを実行、条件1は満たされないが条件2が満たされればBを、さらに1も２も満たされない場合はCを実行するという場合は下記のようなコードになる:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "a68DTqL-hak5"
- },
- "outputs": [],
- "source": [
- "if a < b: #条件1\n",
- " print(\"a"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7Id5T1JiYLyY"
- },
- "source": [
- "# 関数\n",
- "\n",
- "[この章の目的]プログラム内での関数と戻り値,スコープの概念を獲得する。\n",
- "\n",
- "ここでは、Pythonにおける関数の定義と関数が返す値[戻り値(返り値とも呼ぶ)]、ややテクニカルですが重要な点である変数のスコープについて説明します。 "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "39hwOTPKHdBK"
- },
- "source": [
- "## 関数の定義"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8YqBoNnXHjDe"
- },
- "source": [
- "既に現れた```print```や```len```などはPythonに備え付けられた\"組み込み関数\"と呼ばれるものの一種です。以下に示すように、組み込み関数とは別にユーザーが独自の関数を定義することができます。\n",
- "\n",
- "たとえば"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "oeG9EoBPYflr"
- },
- "outputs": [],
- "source": [
- "p1 = [2.0, 4.0, -5.0]\n",
- "p2 = [1.0, 3.0, -4.0]\n",
- "#...中略\n",
- "p100 = [5.5,-2.0, 3.0]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "f-1OoOC3ZFfW"
- },
- "source": [
- "といったようなリスト(ある点の$x,y,z$座標だと思ってください)がたくさん(たとえば100個)あったときに、任意の２つの点の距離を求める操作が必要だったとします。\n",
- "そんなとき${}_{100}C_2=4950$通りに対して毎回"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "lrWeMpk5ZGy6"
- },
- "outputs": [],
- "source": [
- "d_1_2 = ( (p1[0] - p2[0])**2 + (p1[1] - p2[1])**2 + (p1[2] - p2[2])**2 ) ** 0.5\n",
- "d_1_100 = ( (p1[0] - p100[0])**2 + (p1[1] - p100[1])**2 + (p1[2] - p100[2])**2 ) ** 0.5"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "lyuVwV40Ytk8"
- },
- "source": [
- "などと書くのは面倒です。このように、何回も必要になる処理は、**関数**として定義しておけば呼び出して簡単に使うことができます。 \n",
- "\n",
- "例として「任意の長さが3の数値リストに対して3次元空間での距離を計算する関数」を自作してみましょう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "f0iYOWb5Zfcp",
- "outputId": "b42f1ff6-dd82-4cb7-e8a3-4f4a29676c14"
- },
- "outputs": [],
- "source": [
- "def calc_d(l1,l2): \n",
- " return ( (l1[0] - l2[0])**2 + (l1[1] - l2[1])**2 + (l1[2] - l2[2])**2 ) ** 0.5\n",
- "\n",
- "t = calc_d(p1,p2) \n",
- "print(\"点1\",p1, \"と点2\", p2, \"の距離は\", t, \"です\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "r6JbFf61Zv3l"
- },
- "source": [
- "`calc_d`は自作した関数の名前で、関数名の後ろのカッコには**引数**(ひきすう)を指定します。\n",
- "\n",
- "リスト(```l1```)とリスト(```l2```)を突っ込んだときに距離を計算して```return```する(返す)という一連の操作を```def```(defineの略)した、というのが上のコードで行っていることです。\n",
- "コロン`:`は「以下で関数の中身を記述するブロックが開始する」ことを意味していて、インデントによってどこまでが関数のブロックかわかるようになっています。(ブロックについては```if```や```for```を思い出してください)\n",
- "\n",
- "定義した関数を使用する際には、この関数```calc_d()```に必要な引数(変数,今の場合リスト)```l1,l2```を代入して使います。\n",
- "関数に入れる引数は代名詞のようなもので、関数に入れるものを`l1`,`l2`という名前にあわせて定義しておく必要はありません。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "vfn_uW7jsk1x",
- "outputId": "fdbad61a-ab09-423b-daae-9dca5c621215"
- },
- "outputs": [],
- "source": [
- "print(calc_d(p1,p100)) #←これでも使えるし\n",
- "print(calc_d([20.0, 1.0,-5.0], [-2.0, 3.0,5.5])) #←などとして名前をつけなくても使える"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "JYEIUOkRx34k"
- },
- "source": [
- "上の例のように100個の点の3次元座標に対応するリストがある場合,\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "rQeQdGnqytj6",
- "outputId": "57e68e0c-4e6c-49c9-de91-51e56a20adea"
- },
- "outputs": [],
- "source": [
- "import random \n",
- "lists = [ [ random.gauss(0,1) for n in range(3)] for i in range(100)] #3次元の座標点をランダムに100個作っている n,iはダミー変数(特に使ってない)\n",
- "hit = 0\n",
- "for j in range(100):\n",
- " for i in range(j+1,100): # i>j\n",
- " distance = calc_d( lists[j], lists[i])\n",
- " #print(j,i, distance) # 4950回文の計算結果をprintすると邪魔なのでコメントアウトした\n",
- " hit += 1 \n",
- "print(hit) #回数だけ表示しよう\n",
- "#上のjのループ内で、iはj+1から99までを回る。 j+1= 100つまり j=99のとき range(j+1,100)はちゃんと空になる\n",
- "#つまり、長さ100のリストにindex=100でアクセス(範囲外参照)したりすることはない。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "YsUvuSp0hHta"
- },
- "source": [
- "などとすれば、全組み合わせ(${}_{100}C_2$)に対して距離を計算することが出来る。 \n",
- "上で使われているインデックス($j,i$)の模式図: \n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "OCa-xyVjiadQ"
- },
- "source": [
- "# ライブラリ/パッケージ/モジュールとデータの可視化(Matplotlib)\n",
- "\n",
- "[この章の目的]\n",
- "* [ライブラリ]という概念の獲得\n",
- "* Pythonを用いた各種グラフの描画\n",
- "\n",
- "\n",
- ":::{margin} ライブラリ/パッケージ/モジュール\n",
- "授業では呼び方が混在しているかと思います。正確な定義としてはライブラリ/パッケージ/モジュールの順に、より上位の集合を指しますが境界はあいまいな印象です。\n",
- ":::\n",
- "Pythonでは(他のプログラミング言語と同様)特定の作業がパッケージ化されたプログラム群が用意されていてこれらをライブラリ/パッケージ/モジュールなどと呼ぶ\n",
- "Pythonのライブラリ/パッケージの多くはGitHub上で開発・公開されていて、簡単にインストールしたりインポートして使うことができる。 \n",
- "\n",
- "データ分析, AI・機械学習, エクセル等の操作, Webスクレイピングなど、非常に多岐にわたるライブラリが存在していて誰でも気軽に使うことができますし、幾つかのライブラリ/モジュールは標準で組み込まれているため、インストール作業をすることなく、以下のように簡単にインポートして使うことができます。\n",
- "\n",
- "たとえば`math`という名前のモジュールをインポートする際には\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "hGBW_qTNkvRq"
- },
- "outputs": [],
- "source": [
- "import math"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "It2WtKEtkwNT"
- },
- "source": [
- "とします。簡単ですね。\n",
- " \n",
- "上のコードを実行し`math`モジュールを一度`import`してやれば、数学で多用される円周率```pi```,対数関数```log(),log10()```,指数関数```exp```,三角関数```sin,cos```などを使うことができます。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "KbRzvsm95XU_"
- },
- "source": [
- "モジュール内に用意されている関数を利用するときは通常 \n",
- "```モジュール名.関数```や```モジュール名.関数(引数)```といった形で使います。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "9BbEO3a8k0VH"
- },
- "outputs": [],
- "source": [
- "print(math.log(2.0)) #自然対数 \n",
- "print(math.log10(2.0)) #常用(底が10)対数\n",
- "print(math.exp(2.0)) #指数関数\n",
- "print(math.pi) #円周率pi (引数なし)\n",
- "print(math.sin(math.pi)) # sin(pi)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "cGKN5FijmR8v"
- },
- "source": [
- "注:sin(π)は厳密に0になるべきですが1章で述べたように実数が有限の精度なので微小な量0からずれています"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "69y4fMDWSB11"
- },
- "source": [
- "## Numpy"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "FIsrOtnTR4qZ"
- },
- "source": [
- "データ分析などで非常によく使われるNumpyというライブラリがあります。 \n",
- "Numpyの大きな特徴に、数値を要素に持つ配列の計算・演算を高速に行うことが可能という点があります。\n",
- "\n",
- "簡単な作業ならリストでも代用できるので必須ではありませんが、コードを大幅に簡略化することもできるため、今後ノートブックでもたびたびNumpyが用いられます。\n",
- "詳しくは[こちらのノートブック](https://colab.research.google.com/github/SotaYoshida/Lecture_DataScience/blob/main/notebooks/Python_misc_numpy.ipynb)を参照してください."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "_RjUF-6603dn"
- },
- "outputs": [],
- "source": [
- "#先程のmathもnumpyで置き換え可\n",
- "import numpy as np #numpyをnpという名前で使う\n",
- "print(np.log(2.0))\n",
- "print(np.log10(2.0))\n",
- "print(np.exp(2.0))\n",
- "print(np.pi)\n",
- "print(np.sin(np.pi))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "以降、数値を要素に持つリストを使って演算やグラフを描画するときは、`numpy`の`ndarray`型というものに変更して使う事が多いです。 \n",
- "リストを`ndarray`型に変換するには、numpyの`array`関数を用います。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "\n",
- "list_a = [ 1.23, 9341.22, -32.33] \n",
- "list_a = np.array(list_a)\n",
- "print(list_a, type(list_a))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "XD-dP853zCmO"
- },
- "source": [
- "## Matplotlibを用いた作図\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "SGAFbBQJnplQ"
- },
- "source": [
- "\n",
- "以降ではMatplotlibと呼ばれるライブラリを使って、各種のグラフを作成する方法を見ていきましょう.\n",
- "\n",
- "Matplotlibは様々なグラフが描ける一方で少々テクニカルな部分が多いので**細かい部分は分からなくても心配は不要です** \n",
- "「こういうおまじないを唱える(書くと)こうなる」というざっくりとした理解でまずは大丈夫です。 \n",
- "\n",
- ":::{margin} ライブラリの使い方を調べる\n",
- "Pythonのライブラリの細かな使い方を調べる時、真っ先に思いつくのが**ググる**ことだ。\n",
- "最近だと日本語で書かれた情報に気軽にアクセスできるのは良いことだが、(この授業資料同様)不正確な記述が含まれていてかえって理解を遠ざける危険性もある。\n",
- "なにか困ったときに一番頼りになるのは大元のライブラリの公式ドキュメント以外ありえない。なぜなら開発者が作ってんだから。\n",
- ":::\n",
- "はじめはネットに転がっている誰かが書いたコードを少しずつ流用し、自分の目的のためにどうすれば良いか類推しながら改良していって、 \n",
- "自分の描きたいグラフに近づけていくのが良いでしょう。(私もよく過去の自分が作成したコードを流用して作図しています)\n",
- "\n",
- "慣れてくるといろんな図を作ったり、細かいところにこだわったりしたくなります。 そんなときは以下のチートシートが役に立ちます. \n",
- "https://github.com/matplotlib/cheatsheets \n",
- "\n",
- "\n",
- "まず、以下のコードを実行して、`matplotlib`内の`pyplot`モジュールをインポートします. \n",
- "`matplot.pyplot`だと名前が長いので```plt```という名前で使えるように```import XXX as YY``` などとします.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "id": "oevjB6tvA97n"
- },
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt \n",
- "\n",
- "from matplotlib import pyplot as plt #でも同じ"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-KUcnUBIg2W2"
- },
- "source": [
- "いくつかのライブラリにはディレクトリのように\n",
- "```\n",
- "LibraryA \n",
- "├ module1.py \n",
- "│ └─ sub_module1_1.py \n",
- "│ └─ sub_module1_2.py \n",
- "├ module2.py \n",
- "︙ \n",
- "```\n",
- "という階層構造があり\"子\"はピリオドで指定します. \n",
- "上の```matplotlib.pyplot```はその一例です."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Ycksu5i7BsWp"
- },
- "source": [
- "次に、Matplotlibは標準だと日本語が文字化けして豆腐のように表示されてしまうので、 \n",
- "日本語フォントを使うためのライブラリをGoogleのサーバにインストールします"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Qwb9pJyCAcfC",
- "outputId": "29634cc6-f768-4511-fd59-4ebc352de8dd"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Collecting japanize-matplotlib\n",
- " Downloading japanize-matplotlib-1.1.3.tar.gz (4.1 MB)\n",
- "\u001b[K |████████████████████████████████| 4.1 MB 5.1 MB/s \n",
- "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from japanize-matplotlib) (3.2.2)\n",
- "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (1.19.5)\n",
- "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (2.4.7)\n",
- "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (0.10.0)\n",
- "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (2.8.2)\n",
- "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (1.3.2)\n",
- "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->japanize-matplotlib) (1.15.0)\n",
- "Building wheels for collected packages: japanize-matplotlib\n",
- " Building wheel for japanize-matplotlib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
- " Created wheel for japanize-matplotlib: filename=japanize_matplotlib-1.1.3-py3-none-any.whl size=4120274 sha256=f4b79909d06560d80efbd01142270456e2403c260cad143b4647081c91d3a36c\n",
- " Stored in directory: /root/.cache/pip/wheels/83/97/6b/e9e0cde099cc40f972b8dd23367308f7705ae06cd6d4714658\n",
- "Successfully built japanize-matplotlib\n",
- "Installing collected packages: japanize-matplotlib\n",
- "Successfully installed japanize-matplotlib-1.1.3\n"
- ]
- }
- ],
- "source": [
- "!pip install japanize-matplotlib #!から始めることでLinuxコマンドを使うことができる。\n",
- "import japanize_matplotlib\n",
- "import numpy as np #ついでにコレもインポート"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QPB383yO9SFq"
- },
- "source": [
- "Pythonでグラフを作る際の基本は、**データ(描画したいもの)が入ったリストを作ること**です。\n",
- "\n",
- "リストに対して、線や棒、いろんな形のシンボルを描いたりして、どんどんグラフを構成する要素を足しながら、 \n",
- "それらの色や線種、透過度や太さ,背景色などを変えつつ徐々に望むようなグラフにしていきます。\n",
- "\n",
- "以下では代表的なグラフを例に、Matplotlibを使った作画を試してみましょう。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "88mhl_c22S4-"
- },
- "source": [
- "### 棒グラフ"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "j98rkXdk03-h"
- },
- "source": [
- "Aさんの共通テストの得点をリスト(括弧[ ]内に、数字カンマで区切る)に格納します。 \n",
- "順番(各科目の名前(ラベル))は、国語,英語,数IA,数IIB,化学,物理,世界史として... "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "WWBmSr7L0_6s"
- },
- "outputs": [],
- "source": [
- "data = [152, 170, 82, 85, 79, 92, 88]\n",
- "label = [\"国語\",\"英語\", \"数IA\", \"数IIB\", \"化学\", \"物理\", \"世界史\"]\n",
- "total = sum(data)\n",
- "#print(\"合計得点は\", total, \"点で、得点率は\"+str(total/900)+\"%です\")\n",
- "print(\"合計得点は\", total, \"点で、得点率は\"+str(\"%6.1f\" % (100* total/900))+\"%です\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4gaNmr3CefUn"
- },
- "source": [
- "Aさん優秀ですね。これを棒グラフにでもしてみましょう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "S0rBmtTL3YsB"
- },
- "outputs": [],
- "source": [
- "plt.figure( figsize=(10,2) ) \n",
- "plt.bar(label,data,align='center',width=0.5,color=\"red\") \n",
- "plt.xlabel(\"各科目\"); plt.ylabel(\"Aさんの得点\") \n",
- "plt.show() \n",
- "plt.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4SV4bCn9nxqV"
- },
- "source": [
- "棒グラフが描けたでしょうか。それぞれの行の意味を順に説明します。 \n",
- "\n",
- "* 1行目: \n",
- "```plt.figure(figsize=(10,2)) ``` \n",
- "ここでは、`matplotlib.pyplot`内の`figure`という関数を読んでいます。 \n",
- "上で`matplotlib`モジュール内の`pyplot`というモジュールを \n",
- "`plt`という名前でインポートしたので`plt.figure`というのは、 \n",
- "`pyplot`内の`figure`という関数を使うことを意味します。 \n",
- "役割としては、図を描くキャンバスを用意しているようなイメージです。 \n",
- "ここでは```figsize=(10,2)```という引数を指定しました。 \n",
- "(指定しなければ自動で図のサイズが決まります) \n",
- "figsize=(横,縦)で大きさが指定できます、figsizeを変えて試してみましょう \n",
- "\n",
- "* 2行目: \n",
- "```plt.bar(label,data,align='center',width=0.5,color=\"red\")``` \n",
- "`pyplot`内の`bar`という関数(棒グラフを描く関数)を使っています。 \n",
- "1つめの引数はx軸上の値で、2つめはy軸に対応する値です。 \n",
- "x軸用のリストが数値以外のときは整数値を割り当ててプロットしてくれます \n",
- "(７個データがあれば、x=0,1,...,6に割り当てられる)\n",
- "\n",
- "* 3行目: \n",
- "```plt.xlabel(\"各科目\"); plt.ylabel(\"Aさんの得点\") ``` \n",
- "ここではx軸とy軸のデータの種類(ラベル/labelと呼ぶ)を指定しています。 \n",
- "\n",
- "* 4行目: \n",
- "```plt.show()``` \n",
- "それまでに指定した条件で絵を描いて表示する\n",
- "\n",
- "* 5行目: \n",
- "```plt.close() ```キャンバスを閉じる。 \n",
- "とくに一つのプログラムで複数絵を描くときはこれを書きましょう。 \n",
- "(closeしないと、どこまでがどのグラフのための指示かわからず \n",
- "意図しない絵になることがあります)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QJU3ua-h6JS0"
- },
- "source": [
- "#### **練習** \n",
- "国語と英語の得点を100点満点に換算し、 \n",
- "いくつかの得点や棒グラフの色を変えてプロットしてみましょう。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "nmcYXrZACP4n"
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "EJpfw2KwCPdV"
- },
- "source": [
- "\n",
- "\n",
- "---\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "BzWX1S4I3ERW"
- },
- "source": [
- "### 円グラフ"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "lH6qwevuz3Wo"
- },
- "source": [
- "世の中には多くの円グラフの誤用が溢れています。 \n",
- "ワイドショーなどで総和が100%になっていなかったり、 \n",
- "円に対して占める割合が明らかに間違っていたりと、ひどい有様です。 \n",
- "\n",
- "他にも、奥行方向に傾いた円グラフも誤った印象を与えます。 \n",
- "このように、円グラフというのは作成する際に注意が必要ですし、 \n",
- "作成した円グラフがおかしいと「私は教養のない人です」 \n",
- "と言っているようなもので大変恥ずかしい思いをします。 \n",
- "\n",
- "そもそも円グラフにしなきゃいけないデータってあるんでしょうか？ \n",
- "帯グラフなどのほうがよっぽど量の比較にも向いています。\n",
- "\n",
- "あれ...無くない？ということで円グラフはオマケとします。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "onNa3DTcgq-y"
- },
- "outputs": [],
- "source": [
- "#どうしても円グラフが描きたい方へ (下のコードを適当にいじって遊んでみてください)\n",
- "tlabels = [\"Aと答えた人\",\"Bと答えた人\",\"Cと答えた人\"]\n",
- "ratio = [513,142, 249]\n",
- "\n",
- "plt.figure(figsize=(4,4)) \n",
- "plt.pie(ratio,labels=tlabels,counterclock=False, startangle=90,autopct=\"%.1f%%\")\n",
- "plt.show()\n",
- "plt.close() \n",
- "# labels=tlabelsは、 pie()関数の中のlabelsという引数にtlabelsを突っ込むという意味\n",
- "# label(ラベル)とは、凡例(legend)のこと。それぞれのオブジェクトが何を指すのか\n",
- "# counterclockは反時計回りのオプションをFalse(偽)とするのでつまり時計回り(回りくどいな...)\n",
- "# startangleは円グラフの始点を、x軸から見て何度のところにとるかで、今は90度(つまり12時の方向)と設定.\n",
- "# autopctは「値を%表記に直せ」というオプションで、 %.1f%%は小数点以下1桁まで出力しなさいという命令."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "efNzOCmBlOyj"
- },
- "outputs": [],
- "source": [
- "tlabels = [\"Aと答えた人\",\"Bと答えた人\",\"Cと答えた人\"]\n",
- "ratio = [513,142, 249]\n",
- "\n",
- "plt.figure(figsize=(10,3)) \n",
- "plt.ylim([-1,1]) #plt.ylim(-1,1)でも可\n",
- "xs = 0.0\n",
- "for i in range(len(tlabels)):\n",
- " tx = ratio[i]\n",
- " ty = 0.0\n",
- " plt.barh([ty],width=tx,left=xs,label=tlabels[i]+\"(\"+str(ratio[i])+\"人, \"+str(\"%4.1f\" % (100*ratio[i]/sum(ratio)))+\"%)\")\n",
- " xs += ratio[i]\n",
- "plt.legend()\n",
- "plt.show()\n",
- "plt.close()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Bvlp1LLbpLut"
- },
- "source": [
- "こっちの棒グラフのほうが良い気がする"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6iN_Tl4millJ"
- },
- "source": [
- "### plot: 線の描画\n",
- "\n",
- "説明変数$x$と目的変数$y$があって、 \n",
- "とくに**$x$に対する$y$の振る舞い**に興味がある場合には```plot```を使います。\n",
- "\n",
- "たとえば、日付($x$)に対するコロナウイルス感染者($y$)の推移をプロットしてみましょう。\n",
- "日付は、WHOのSituation Reportが初めて発表された2019年1月20日を起点(ゼロ)とする経過日数として表すことにして...\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 539
- },
- "id": "N0LR-xQii7b3",
- "outputId": "0254ee47-8280-4fec-fb06-81463204cafc"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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fdbxevXoZ+poVK1bkhx9+oGjRolx//fXs3bv3rFCZqHz58kyZMoWXX36ZLl268MADD/Bmsnkg3333XXr06EGTJk38F0guWrTIfwEiwMSJEwkKCqJhw4aEhYURHR2dYtaP5Dp37uwPzB999BFlypRJdWq25s2b07x587Pa3333XX+vKkCpUqUYMGAAPXr0SHHe6NGj/b3niT7//HOOHTvGhx9+mGptyf32228MGjSIPXv2MG/evDTHMp/L2rVrmTdvHl999RUAJUuWpEyZMgwYMIDrrrsuxbmdO3emSpUqLFmyxP+JQbt27QgICGDkyJEMGjSIAgUK8O677zJgwAAWLlzI3Llz+frrr1m7di2HDx+md+/efPLJJ1SoUIFx48bx6aefMnDgQDZv3kxwcDDdunXj4Ycfpk6dOjz11FNccskl/q+/bNkydu3a5b8fFRWVor4hQ4bQrl07duzYQXh4OPv372fRokW8+OKLtG3blvHjx/uHA4mISDYUHQ1TprjwvGiRm/u5enXo3x/uuw+uvtrrCnM8k1MX8ggLC7NnzvCwYcOGLJv67FzCw8PPOf3d/Pnz+fe//8348eP943YbN27s730G99F68t7a1IwbN47KlSuzePFiVq1axbPPPgu4ixnPFaSTs9Zy8uRJCp9j6pqtW7dSpUqVFLOKHD9+nEKFCvHBBx8QFhbG1KlTuf/++6lduzbDhw/3j5FOnMnkXJ5++ukU8xl7Zd26dezevds/Q8q5xMTEsHTpUpo0aXLWMWttulb4S895GzZs4JNPPmHIkCG0bduWgwcPEhgYyOHDhxk0aBCVK1cmISGBEydOMGfOHJ5++mmKFy/OmDFjCA4O5t5778Vay/Tp06lbt67//ZY4HCj5tHgtW7b0T20XFRXFM888Q/PmzWnXrh1Lly5l2rRpDB8+nMKFCzNnzhyCgoJo1KhRqjVnh/+HIiJ51vTp8J//wLx5EBcHl1/ugnOHDlCzphsHLelmjFllrQ1L9ZiCdNY7efIkgYGB/gsJc4PY2Fj/eG/J23LK/0MRkVzHWrfS4GuvQeXKbshGhw5Qr57C8z9wriCtoR0eSH7hX26hEC0iIuKhhAR46ikYORI6d4YxY9zcz3JRaYlwERERkZwsNhYeeMCF6GefhXHjFKKziHqkRURERHKq48fdfM/z5sG//w19+2oYRxZSj/RFdOzYMXbu3Mn//vc/Zs2aleLY2LFjiYuLY8iQIR5VJyIiIjnaX39B48Ywf767uLBfP4XoLKYe6Uy2fft23njjDYwx7Nixg/Lly3P77bcTGhpKXFwcBQoUYMOGDWzZsoVZs2Yxb968FNOOFSxYkEGDBp31vMlnVIiIiKBEiRL+eZGHDx9O0aJFCQkJYdu2bfTu3Ztu3boxfvz4LHnNIiIiksX27oW77oKtW93iKu3aeV1RnqQgnYkiIyNTrPi2b98+Dh486F8Fb+DAgbz//vtERERQt25dBg4cSEBAAJGRkf7HjBs3LtXnW7duHeHh4QQGBlK8eHG++OIL/3lHjhwhPj6eP/74g1q1arFlyxYOHjzoD95169b1L8ghIiIiOdymTXDnnW6e6LlzIZWpSCVrKEhnojp16jB58mS+//57ABYuXEiRIkVo0KABAI0aNaJ06dJ06dKFIUOG8PLLLzNs2DBat24NwNy5c/2ryx05coTg4GB/r3K3bt3o168fr7/+Oq+++ipr166lcuXKLF26lO+//541a9YQGhrKd999R+PGjSlTpgyLFi1i9erVjBo1SkFaREQkN1i5Epo1c0M4IiLgjMW+JGvl2iD99NOQrKM3U9SpA8OHn/ucDRs2sHDhQu677z5KlChB0aJFKVOmDJMmTaJ8+fIcPXqUjRs3EhISQnBwMFu2bGHq1KmAW+I60aZNm1iyZAngFv/Ytm0bCxYsoHv37mzbtg1w0+i1bt2azZs3ExISQlxcHI0aNWLOnDn07duX2NhYrrrqKq655prM/UaIiIhI1lu4ENq2hdKl4bvv4KqrvK4oz8u1QdpL3333HRs2bGD//v3kz5+fBQsWsGfPHjp16sTRo0fp1q0b69atY9euXbRr144KFSr4Hztz5kwSEhK44YYbCAsLY/LkybzwwgtYa1m+fDnLly+nUKFCzJkzB3DjpZs0aUJ0dDRdunShcOHCREZGsnDhQkqVKuXvDRcREZEc7JtvoFMnuPJKN0OHPmnOFnJtkD5fz/HFUqJECVq3bs3VV1/NDz/8QOHChalTpw6bNm2iRIkSNGzYkPXr13PttddSvHhx3nnnHWrWrJniOfr06cO8efP48MMPKVeuHO3bt6dr167+448//rh///fff2fQoEGcOHGCefPmERsby8SJExkxYgTbt29PMWZbREREcqDRo6FnT7jxRpgxA0qV8roi8bmgIG2MeRW4w1obboypDYwACgIHgc7W2mhjTDAwFqgA5AMetdZGGmMMMAC43feYIdbaz33P2x54znd+hLW2T+a8vKw3duxYIiMjWbNmDfv27SN//vxs3LjRf2y4L+GvXbuWZcuWMWnSJJYsWULZsmWpUqUKEyZMoECBAoSHh3PXXXcRERHBuHHjWL9+vf9rJI6jBujYsSPz58+nVatWrF69mg4dOlCzZk1uuOEGxo0bR2xsLAULFszab4KIiIj8c9bCgAHwyituXPQ330Dhwl5XJcmkO0gbY8KAqr59A0wE7veF5MeBN4BewBBcGH7fGFML+BSoC3QErgQaAMWAn4wxi4BA4E3gBuAoMNEYc4+1dnImvcYsNTxZV/iYMWMIDg7m3nvvPeu8a6+9lhMnTvDpp5/Sq1cvBg8ezJNPPsknn3wCkCL8Llu2jF27dvnvJ58uD2DIkCG0a9eOHTt2EB4ezv79+1m0aBEvvvgibdu2Zfz48ZQrVy6zX6qIiIhcLAkJbpXC995zQzq0WmG2lK4gbYwpBAwD7gG+Aq4Coq21iZfzjQE24oJ0c6A3gLV2rTHmb2PM5UBLYLS11gJHjTHf+M7ND0y21h7xfa2Pga5AjgvSs2bNSrHASmKP9MiRI/1tzz//PACnT5+maNGi9OjRg8GDB/Pss88SHh7OdalcffvYY4/x3HPP+e+3bNnSvx8VFcUzzzzDww8/TLt27Vi6dCnTpk1j/PjxFC5cmIIFC/Lbb78pSIuIiOQUcXHw8MMwYQL07g3DhkGA1tDLjozLtec5yZiRuF7mb4wxEcBLwHPW2nbJztltrQ01xhyw1pZL1j4JGA687nvMWl/7Y0AwYIBj1toRvvbqwHBr7V2p1NED6AEQGhpaL3kvLbgZM6pXr34hr98TsbGx5M+fn4CAABISEjDGYLQSkeQSOeX/oYhItnTiBLRvD7NmwZtvwssva7VCjxljVllrw1I7dt4/b4wxdwElrbXfJGs+AIQkO6cgEOu7e9J3P1F53/kpHpOO9rNYa0dba8OstWFly5Y9X+nZVmBgIAG+vywDAgIUokVERMQtsNKkCcyeDR9+6MZGKyNka+n5nKAlUNYYM9UYMxWoCbwGFDXGJE438SAwx7c/Ezc0I7F3uZi1djswDejmay8MtPM9ZjZwtzGmmO/xD/vOzZD09LCLyMWh/38iIhn0++9w663w888waZKbpUOyvfOOkbbW9kp+3xgTYa3tbIypA/zHGJMAHAIe8p3yKvCpMeYhwOKCMbgxzzcaY1b62gdaa/f5nnMAsMQYEwsszeiFhkFBQRw6dIjSpUurl1cki1lrOXToEEFBQV6XIiKSs2zZ4pb8PnjQ9UY3bux1RZJO6RojnR2FhYXZlStXpmiLi4tj7969xMTEeFSVSN4WFBREpUqVKKAry0VE0mfNGmja1M3SMXs2XH+91xXJGc41RjpXLchSoEABqlat6nUZIiIiIucXEQGtW0NwsFvyu1o1ryuSC6S5VERERESy2tSprie6UiX44QeF6BxKQVpEREQkK40dC/fcA3XqwNKlLkxLjqQgLSIiIpIVrIVBg6B7d3dB4YIFULq011XJP6AgLSIiInKxJSTA889Dv35w330wYwYULep1VfIPKUiLiIiIXCwxMW4oR+3aMHQoPP44fP45BAZ6XZlkglw1a4eIiIhItnDggFud8IMP3PzQtWrBZ59Bp05arTAXUZAWERERySzr1sGwYa7XOTYWWrSAZ56B229XgM6FFKRFRERE/omEBJg71wXoBQugUCHo1g2eegquvtrr6uQiUpAWERERyYgTJ9xwjeHDYeNGuOQSGDAAevTQbBx5hIK0iIiIyIX4/XcYNQo++gj++gvq1YMJE+Bf/9JFhHmMgrSIiIhIeqxZ44ZvTJwI8fHQpg08+yw0bKjxz3mUgrSIiIhIWk6fhpkzXYBevBiKFIHHHoPeveHyy72uTjymIC0iIiJypmPHYPx4eO892LoVQkNhyBC3KmFwsNfVSTahIC0iIiKSaM8eeP99+M9/4PBhaNAA3n4b2rWD/IpNkpLeESIiIiIrVrjhG19/DdbCPfe4+Z9vvNHryiQbU5AWERGRvMlamD7dDdn43/+geHF4+mno1QsuvdTr6iQHUJAWERGRvGflSjfjxtKlULWqmwv64YehWDGvK5McREFaRERE8o69e+Hll+G//4WyZd1c0N26afyzZIjeNSIiIpL7HT8Ogwe7YRynT0PfvvDSS244h0gGKUiLiIhI7pWQ4HqfX3oJ9u2D++6Df//bDecQ+YcCvC5ARERE5KKIiICwMOjaFSpXdhcUTpyoEC2ZRkFaREREcpctW+Duu6FRIzh0CL74An78EW66yevKJJdRkBYREZHcITrazf1cowYsWAADBsDGjXD//RCgyCOZT2OkRUREJGeLi4MPP4TXX3erEXbrBm+8AeXLe12Z5HL680xERERyJmthxgyoWROeegrq1oU1a2D0aIVoyRIK0iIiIpLzREZC48bQujUYAzNnwvz5UKuW15VJHqIgLSIiIjnHvn3QvTtcd50L0++/D+vWQYsWLlCLZCGNkRYREZHs78QJePddGDgQYmPd8t4vvwwlS3pdmeRhCtIiIiKSfSUkwJdfQr9+bnnvdu3cCoWXX+51ZSIa2iEiIiLZ1LJl0KABPPAAlCsHixfD5MkK0ZJtKEiLiIhI9rJ1K7RvD7fcAlFR8OmnsGIF3Hqr15WJpKAgLSIiItnD77/DY49B9eowaxb07w+bN0PnzlpQRbIljZEWERERb0VHu3HP773nFld59FF45RXNBS3ZnoK0iIiIeOPECTd93cCBcOQIdOzoVifUGGjJIfQ5iYiIiGStuDj46CO44go3G8fNN7sVCSdMUIiWHCVdQdoY84Ix5gdjzBpjzCfGmEBjTKgxZq6vPcIYc6nv3EBjzFhf+2pjTONkz9PbGLPCGBNpjHkuWXsjY8yPvmOfGWMCM/+lioiIiKcSp7KrXt2Nhb7sMli61K1KWLu219WJXLDzBmljTBmgBHCztbYuUBhoA4wFRllrbwIGAyN9D3keOOxrbwV8aIwpaIy5GbgfaAjcALQ1xoQZY4oC44B/WWtvAPYBvTLzRYqIiIiHrIU5c9xqhB07QuHCLjwvXQoNG3pdnUiGnTdIW2v/tNa+bK21vtBbHPgNqGatneE7ZzZQ09eT3BL42NceBfyIC88tgXHW2lhrbSzwCS6Q3wz8YK3d6/uSHwFtM/NFioiIiEd++AHCw6F5czh61A3fiIzUkt6SK6R7jLQx5nNgB/A9cBg4eMYpfwClfdv+ZO37gJAMtKdWQw9jzEpjzMqDB8/88iIiIpJtrF8Pbdq48c+bNsGoUbBxI3TqpKnsJNdI9zvZWtsJuBRoALTABeDkygJ/AgdIGYTL+9outD21GkZba8OstWFly5ZNb+kiIiKSVXbscPM+16oFERHw9tuwbRs8/jgE6hIoyV3SM0a6jjHmIQBr7QlgM26c9DpjTFPfOY2BX621ccA0oLuvvRwueP/P197ZGFPAGJMPeAiY7jtW3xhTwfclu/nOFRERkZziwAHo3Ruuvhq+/hqeew62b4eXXoIiRbyuTuSiSM880puAx4wxvYCTwF7gLWAKMN4Y8ypwCujqO38EMNYYsxwwwBPW2lPASmPMdGAFEA9MtNauBDDGPAbMNMacArYCb2TWCyoG9roAACAASURBVBQREZGL6MgReOcdGDYMYmKgWzf4v/+DihW9rkzkojPWWq9ryJCwsDC7cuVKr8sQERHJm06ehA8+gAED4K+/oH17ePNNuOoqrysTyVTGmFXW2rDUjmm0v4iIiKRffDyMHesC83PPQVgYrFwJkyYpREueoyAtIiIi52ctTJ0KNWtC9+5u6MaiRTBvHtSr53V1Ip5QkBYREZFz27EDWraEu+92U9dNmQI//giNGnldmYinFKRFREQkdbGx8O9/wzXXwOLFMHQorF0LbdtqMRUR0jdrh4iIiOQ1ixfDY4/Bhg3Qrh0MHw6VK3tdlUi2oh5pERERSXLwIHTp4pb1PnkSZs6EyZMVokVSoSAtIiIikJAAY8a4BVU+/xxefBF+/RVatPC6MpFsS0M7RERE8rq1a6FnT3cB4a23uvmhr7nG66pEsj31SIuIiORVx47B88/DddfBli0wfjxERChEi6STeqRFRETyomnToFcv2LPHzQs9cCCULu11VSI5inqkRURE8pJdu6B1azeFXXAwLFsG//mPQrRIBihIi4iI5AVxcTB4MNSoAQsXwpAhsGoV3Hyz15WJ5Fga2iEiIpLbLV3q5oT+9VfXE/3eexAa6nVVIjmeeqRFRERyqz//hG7d3Ewcf//txkVPmaIQLZJJFKRFRERym4QE+OQTqFYN/vtf6NsXfvvNjY0WkUyjoR0iIiK5yfr1bhjHsmXQsCF8+CHUrOl1VSK5knqkRUREcoPjx13Pc926rvd57FhYvFghWuQiUo+0iIhITjdjBjz5JOzeDQ8/DIMGQZkyXlclkuupR1pERCSnOnAA2rd3Y5+LFYMlS1xPtEK0SJZQkBYREclprHUXEVav7mbieOstWL0abrnF68pE8hQN7RAREclJdu2CRx+FefPgpptcD3S1al5XJZInqUdaREQkJ0hIgFGj3MWDy5bBiBFuoRWFaBHPqEdaREQku9u0Cbp3dwH6zjvh44+hShWvqxLJ89QjLSIikl3FxcHAgVC7tlvee/x4mDtXIVokm1CPtIiISHa0Zo1b3nvNGrjnHhg5EsqX97oqEUlGPdIiIiLZSUwMvPwyXH89/P47fPON2xSiRbId9UiLiIhkF//7n+uF3rQJunSBoUOhVCmvqxKRNKhHWkRExGt//w29erl5oGNi3NR248YpRItkcwrSIiIiXpo3z01pN2qUW+Z7/Xo3M4eIZHsK0iIiIl746y83fKNpUyhUyM0JPWIEFC3qdWUikk4K0iIiIllt8mSoUQMmTHAXFkZGws03e12ViFwgXWwoIiKSVfbtc8M3vv0W6tZ1c0LXqeN1VSKSQeqRFhERudisdYup1KgBs2a5RVZWrFCIFsnh1CMtIiJyMe3cCT16wPz50LAhjBkDV1/tdVUikgnUIy0iInIxnD4N77/vZuT48Uc3K8fixQrRIrmIeqRFREQy26JF0LcvrFzpZuX46CO49FKvqxKRTJauHmljTHtjzI/GmKXGmK+MMYWNMbWNMYuNMT8ZY2YYY0r6zg02xkw2xvxgjFlujKnjazfGmH/72iKNMZ3OeP4VxphVxpihF+elioiIXGRr1sBdd8Edd8CBA/DZZzB7tkK0SC513iBtjCkFvADcbq29BdgFPAJMBJ6y1jYA5gBv+B4yBIiw1t7kO2+cr70jcCXQALgVeNkYU8EYcynwJtAECAMqGWPuyaTXJyIicvFt2wYdO8J117le6Hfegc2b4YEHwBivqxORi+S8Qdpa+xfQ0Fp70teUH4gBoq21kb62MUAL335z332stWuBv40xlwMtgdHWOQp84zu3KTDZWnvEWmuBj4G2mfLqRERELqYDB9x0dtWqwdSp8NJLLlT36QNBQV5XJyIXWbrGSFtrY4wxQcAgoCCwHtif7HisMSbxufInC90A+4AQoHTyxyRrN2m0n8UY0wPoARAaGpqe0kVERDLf0aMwdKjbYmKge3f4v/+DSy7xujIRyULpHSNdCZgCzLXW9sQF35BkxwsCsb67J333E5UHDvi2kAtoP4u1drS1NsxaG1a2bNn0lC4iIpJ5Tp2C996Dyy+HN96AZs3g11/dxYQK0SJ5TnrGSAcB44Ee1to5ANbabUBRY0xN32kP4sZJA8wEuvoeWx0oZq3dDkwDuvnaCwPtfI+ZDdxtjCnme/zDvnNFRESyh4QEt5x3tWrw9NNw7bVuQZWvv9Z0diJ5WHqGdjQGqgOfmaQLJhYBXYD/GGMSgEPAQ75jrwKfGmMeAiwuGANMBm40xqz0tQ+01u4DMMYMAJYYY2KBpdbayf/0hYmIiPxj1sKcOfDii7B2rVuJcO5cuPNOXUQoIhh3fV/OExYWZleuXOl1GSIiklv99BP06+cWUbnsMnjrLbjvPgjQWmYieYkxZpW1Niy1Y/ppICIiktzGjdCuHdx4I2zY4FYn3LAB7r9fIVpEUtBPBBEREYCoKHjkEbjmGpg/H15/3U1l9+STEBjodXUikg1piXAREcnboqNh4EAYMQJOn3bB+eWXISTVmVhFRPwUpEVEJG86edKF54ED4cgR6NTJTWlXtarXlYlIDqGhHSIikrfEx8OYMXDlle5iwhtvhDVr4LPPFKJF5IIoSIuISN4QF+fC8rXXurHQlSpBRATMng21a3tdnYjkQArSIiKSu5044WbeuOIK6NwZ8uWDb7+FH3+E227zujoRycE0RlpERHKnv/6CUaPcOOg//4SbbnL3mzfXNHYikikUpEVEJHfZuxeGDYOPP4bjx6FFCzcWumFDrysTkVxGQVpERHKHTZtg8GA3DjohATp0gBdegFq1vK5MRHIpBWkREcnZfv7ZTWE3ZQoULOguJOzTxy3rLSJyESlIi4hIzmMtLFzoAvTChVCiBLz4IvTuDeXKeV2diOQRCtIiIpJznD7tep4HDoRVq6B8eTec49FHoXhxr6sTkTxGQVpERLK/U6fc2OfBg2HLFjeV3ejR8OCDEBTkdXUikkcpSIuISPb1998uML/7Lvz+O9StC199Be3aufmgRUQ8pCAtIiLZz8GDbv7nkSPh8GG4/XYYPx4aNwZjvK5ORARQkBYRkexk504YOhTGjoWTJ+Huu6FvX6hf3+vKRETOoiAtIiLeW7sWhgyBL790Pc4PPgjPPw/Vq3tdmYhImhSkRUTEG6dPw/TpbghHRAQUKeKmr3vmGahc2evqRETOS0FaRESyVnS0G7oxciTs2gWhoTBoEHTvDqVKeV2diEi6KUiLiEjW+O031/v82Wdw4gTcequbjaN1a8ivX0cikvPoJ5eIiFw8CQkweza89x4sWOCW8O7UCXr1gjp1vK5OROQfUZAWEZHMd+SIm67u/fdh2zaoWBHefhseeQTKlvW6OhGRTKEgLSIimWfzZheex4+HY8fgpptcgG7XDgoU8Lo6EZFMpSAtIiL/TEICfPedG/88Z44LzB06uBk4wsK8rk5E5KJRkBYRkYw5dgw+/dT1QG/aBOXKQf/+8OijUL6819WJiFx0CtIiInJhtm93U9eNHQtHj8L118OECfCvf0FgoNfViYhkGQVpERE5P2th0SI3fGPGDMiXzwXn3r3d8t3GeF2hiEiWU5AWEZG0nTjheptHjIBff3Uzbrz8MvTs6WbiEBHJwxSkRUQkpYQEWLIEvvgCvv4aDh92cz6PG+cuIgwK8rpCEZFsQUFaRETc0I01a1x4njgRoqKgSBFo29ZdPNiwoYZviIicQUFaRCQv27rVhecvvnAzbxQoAM2awdCh0KoVFC7sdYUiItmWgrSISF6zfz9MmuTC84oVrqf5ttugTx+45x4oVcrrCkVEcgQFaRGRvODIEfj2WxeeFy1y46Dr1oUhQ+C++6ByZa8rFBHJcRSkRURyq5gYmD3bheeZM+HUKbj8cjfrxv33Q/XqXlcoIpKjKUiLiOQmp0/D99+78Dx5slswpVw5N11dx45u8RRdNCgikinOG6SNMfcC7YEG1tpQX1soMBooDsQCD1lrdxljAoEPgepAEPCCtXaB7zG9gQeAQGCCtfYdX3sjYACQD9gEdLPWxmbqqxQRyc2shZUr4fPP3djn/fuhWDE33rljR2jUCPKr30REJLOl5yfrQeBxYH2ytrHACGvtDGNMc2Ak0Ap4Hjhsrb3JGFMRiDDG1ATCgPuBhr7HLzLGRAAbgXFAQ2vtXmPMYKAXMPSfvzQRkVxu06akGTe2bnXLc7ds6cJz8+ZQqJDXFYqI5GrnDdLW2sUAxvdRoDGmMFDNWjvDd3y2MWaUrze6JfCQrz3KGPMjLjw3BsYl9jQbYz4B2gClgR+stXt9X+4j4FPSCNLGmB5AD4DQ0NCMvF4RkZxtxw745hs31/Pq1W6Yxu23w0svwd13Q3Cw1xWKiOQZGfmsLxjXS53cH7hQXBrYn6x9HxDia//xjPb65zg/Vdba0bghJYSFhdkM1C4ikvPs2OFWGPz6azeEA9xY52HD3IwbFSp4W5+ISB6VkSD9Jy4AJ1fW134AF4SP+trL+9oS20lnu4hI3rZ9e1J4XrXKtV1/PQweDPfeC1WrelufiIgQcKEP8A3PWGeMaQpgjGkM/GqtjQOmAd197eWABsD/fO2djTEFjDH5cMM/pvuO1TfGJHandPOdKyKS92zbBgMHQr16bpq6fv0gXz431/OOHW7xlOefV4gWEckmMnoZ9xPAeGPMq8ApoKuvfQQw1hizHDDAE9baU8BKY8x0YAUQD0y01q4EMMY8Bsw0xpwCtgJvZPjViIjkNNu2JfU8r17t2m64wYXne++FKlU8LU9ERNJmrM2ZQ43DwsLsysSxgiIiOcnWrUnhec0a11a/PvzrXy48X3qpt/WJiIifMWaVtTYstWOaWFREJCts2ZIUniMjXVuDBjB0qAvPmolIRCRN1kJ8PBQo4HUlKSlIi4hcLJs3J4XnX35xbQrPIiJpSkiAnTthw4azt1degT59vK4wJQVpEZHMlFp4vvFGePddF54rV/a2PhGRbCA21n1Qd2ZY3rgRYmKSzgsJgerVoUMHqFPHu3rToiAtIvJPxMfDTz/B7NkwcyasW+fab7rJzfN8zz0KzyKSZx075sLxmYF561Y4fTrpvEsvdYG5USN3m7iVPnPC5WxGQVpE5EIdPAhz57rwPG8eREe7aepuvlnhWUTypEOH4Lffzg7Mu3cnnZM/P1xxBdSo4T6gSwzLV18NRYp4V/s/oSAtInI+CQluarrZs922YoW78qVcOWjTBpo3hyZNtDy3iORqx465GTu3bk3aNm92gflgsjWvCxeGatXglltS9i5fcUX2u1jwn1KQFhFJzeHDMH++C85z5sCBA2CMm+O5f38Xnq+7DgIueF0rEZFs6/Dhs8Ny4rZ/f8pzy5Vz4bhNm5SBOTQ07/xoVJAWEQHXw/zrr0m9zsuWuQF8JUvCXXe54Ny0KZQt63WlIiIZZq0bhpFaUN62Df78M+X5FSu6sNyihbtN3C6/HIoV8+Y1ZCcK0iKSdx0/DosWJYXnxMF8tWvDCy+43xz167uBfSIiOYS17kO01MLy1q1w5EjSuca4HuQrrnCXdyQPy5dd5oZpSNr020FE8patW5OCc0QEnDrlrnJp0sRNUtqsGVSq5HWVIpKHWAsnTsDff7txyKndXuixuLik58+XD6pUceG4QYOUYblqVShY0LOXnuMpSItI7hYTA0uXJoXnzZtd+9VXw+OPu17nhg31m0QkD/r7b3cd8aFDbibL06fd7cXaj4tLOxBbm76aCxaEokXdsIrE2xIl3BCM5G2JQzKuuMJNLZfbLvLLLhSkRSR3OXYMfvgBlixx2/Llbub/ggXdBKW9erle58sv97pSEclCcXGwfr2bdCdx++03NylPRuXP73p78+dP336BAi7kJl6kd2YgTr6f1jEF4uxFQVpEcrbDh92FgUuWwOLFsGqV6wLKlw/q1YPevSE83IVoDfYTyROsdctML1+eFJpXr4aTJ93x0qXdBDz33ONuK1ZMfxhO3A8IcOOLJW9TkBaRnOXgwaTe5iVL3DLc1kJgoPuN2K8f3HqrW5Zbl5SL5AmHDsHPP6cMzomzTwQFuZkqH33U/YioX9+NC1YIlsygIC0i2VtUVFJoXrzYzfwPUKiQW4a7f38XnOvXd20ikqudPAmRkUmBeflyN20buHBcowa0auVC8w03wLXXajiEXDwK0iKSfSR+Hrt4cVJ4TvwNWayYuyiwc2e47TY3bCMw0NNyReTiSkiAjRtTjmv+5Rd34R64IRn168Mjj7jQXK8eFC/ubc2StyhIi4h3rIVNm1L2OO/d646VKuV6mp94wt3Wrq35nEVyKWvhjz/cB06J2/r1sHKlm9UC3N/S118Pzz/vQvP117sgLeIl/VYSkax18KBbcnvWLDeP8x9/uPZy5VxP8623utsaNfLOGrMiecTp0+5Dpw0bXE9zYmjeuBGio5POK1LELTX9wAOux/mGG9yMlfqRINmNgrSIXFzWwrp1MHOm2376ybWVLw933pkUnq+8Ulf/iOQSJ0/Cli0pe5g3bnTTuMfEJJ0XEuIC8333QbVqbr96dbcmkn4cSE6gIC0ime/kSfj++6TwvGePaw8Lg9deg5YtoW5ddS+J5HDR0SmDcuL+jh1JC4wY42bJqFbNLSCaGJarVXMjuERyMgVpEckcUVFu5cAZM2DBAhemE5fefu01aN4cKlTwukoRuQCnTsHvv7v/3nv3ututW5OC84EDSecWLAhXXeX+Xn7wwaQe5quu0oQ6knspSItIxiQkuMVPEnudV6927ZdeCt26uV7n225zk7iKSLZiretNjopK2hKDcvItcS7m5IKDXUBu0SLlcIwqVdxCJSJ5iYK0iKTf33+73uaZM93FggcOuOEZN94IAwe68FyjhgY3ingoLg727Tt/SE4+VjlR2bJufHLlytCggZsV48wtOFj/xUUSKUiLyLnt2JHU6xwRAbGxUKIENG3qgnPTplCmjNdViuRK1sLx4/DXX271vr/+StqS3//zz6SA/McfSeOTEwUGJgXh66+Htm3PDsgVKrjhGSKSfgrSIpJSfDz8+GNSeP7tN9d+9dXQq5cLzzffrKXCRC5AYiA+VxhO61hcXNrPW6iQu2CvdGm45BJ3DW/Fiq5XOXlILl1avcgiF4OCtEheZC3s3+96m7dvd7eJ2y+/uMGT+fO7Mc6PPOIGQ155pddVi2Qbp065KdEPHnS9wYn7Z27Jw/G5AnHhwi4QJ4bi6tWT7ie2nXm/ZEldxCfiNQVpkdzq8OGUATl5aN658+wBkhUquDmq7r7bDde48043hEMkl0vsLU4rDKcWlBNX2ztTQIAb6VSmjBtvXKNG6iE4+f1SpXRNrkhOpSAtklPFxMCuXWeH5MQt+TJh4EJx1apJl9tXrZq0Vamiri3Jlqx1PbkxMalvp05d+LGTJ1OG4z//TP3CO3Bji8uWTdouvzzl/cTAnLiVLKnp0UXyEgVpkezq5Em3kEnidmbP8u+/pzw/MNAF4qpV3Zq6yYPyZZe53/AiF1F8vOupPXrU3SbfT63tzOMnTqQehs+8cO5CBQS4vxODgpK2xDHFtWunHYrLloWiRTW2WETSpiAt4oX4eDc/1Z49sHt3UlhO3N+9++wJXI1xVw1ddplb5OTMoFyhgrrCJN2sdcH1+HE4dszdprWf3iCcVq/umQoWhOLFoVgxtxUv7laML1IkZdg911awYPrPya/fdCJykejHi0hms9ZdYZRWQN6zx/Umnz6d8nHFi7vJW0ND3dJgifuVKydtmpsqV0scxnDqlJtlMPntmfsxMecPwOfaP378wmorXDgp9CYG4EqVUobh9O5rwhcRyS0UpEXSKyEBjhxxPcWJ24EDZ4flPXvcsIzkAgOTwnCjRkn7yYOyLuy7aOLjXQiNjXVB9Vy3//SctIJvevf/CWNcr27Rou42cb9YMdfje2Z7auemta9eXRGRs+lHo+RNiZfpJw/FqW2JVyL9+afrZT6zFxlceqlQwYXi2rWhVauzg3LZshp2cR5xcWf3mKbVk5pWW1rt8fEXr+58+VwPa2Cguy1Y0O0nv03cL1o09fZz7ad1PCjo7MBbqJDG84qIZCUFacm5Tp9OO0FFR58/HJ86lfrz5svnrkRKnMOqWrWkq5AS25LPb3XJJfqsOpmEBDfz3h9/JG0HD6a8n9h29GjSP9m55thNTaFCKUNk4lapUuoBMzAwKeyeuZ/W7fnOKVDAvV1ERCRvUpCWrHHihFsA5Fxdhxd6P71XNgUHJ4XgypXd0l+pBeLE/RIl1HucTGLnfWpB+My2P/5wf6Ok1QNcurT7VoeEuPl1S5RIGYJTC8aptRcurH8iERHxXrYJ0saY9sBzQD4gwlrbx+OSJDNNmwYdO57/vAIFUk9PpUu7YRKpJa3U2kqWdKG4VKlc0VtsrQunycfhnjlW91xtF/K4xIUpkofjtP5mKVYsKRhXqQI33OD2Q0KS2hO30qVzxT+FiIiIX7YI0saYS4E3gRuAo8BEY8w91trJ3lZ2hh494KefXKqx1n2Gnd79Czk3cX/hQmjQwOtXnTkaNMCOG8/pQkWJL1SM00FFiC9YhNNBRZL2CxYm3hTg9GkXGlO7Pdex+Hg4fRji/4SEHa4tISHlbWpt6b1N61h8fMoaztzP6LEz9y+WgICkYQyBgW7sbWL4rVEj7WBctqzWcBERkbwtWwRpoCkw2Vp7BMAY8zHQFchWQbrnqkf4eXdf8F/MY1LcgEm2f+b9NM7Nl9q57v4nfxlqZ1LtXpu0oiodulb1uox0CQhwW758ad8m7gcEJI2TzZ/fbcn3E+8nzmV7vvPS2s+XL2XYPXMcb1pt6bmvMb4iIiIZk12CdGlgf7L7+4CQM08yxvQAegCEhoZmTWXJlG56PZeszbqvF5gzcme6XHMNvPZaymCYPDCe6/ZCz028f74QnNqtMZr1QERERNInuwTpA0Dy2Fje15aCtXY0MBogLCzsHy4ae+Hefjurv2LuUbOm20RERERyi+xy3fts4G5jTDHf/YeBaR7WIyIiIiJyTtmiR9pau88YMwBYYoyJBZZmuwsNRURERESSyRZBGsBa+znwudd1iIiIiIikR3YZ2iEiIiIikqMoSIuIiIiIZICCtIiIiIhIBihIi4iIiIhkgIK0iIiIiEgGGGuzfF2TTGGMOQjs8uBLlwH+9ODrSvak94OcSe8JSU7vB0lO74ec6VJrbdnUDuTYIO0VY8xKa22Y13VI9qD3g5xJ7wlJTu8HSU7vh9xHQztERERERDJAQVpEREREJAMUpC/caK8LkGxF7wc5k94TkpzeD5Kc3g+5jMZIi4iIiIhkgHqkRUREREQyQEFaRERERCQDFKTTyRjT3hizwhizyhgz1Ot6xHvGmPHGmJ+MMRG+rbXXNUnWMcbca4z5yhizO1lbqDFmrjHmB9974lIva5SslcZ7ItwYszPZz4kPvKxRspYvO/xojFnqe28UNsbUNsYs9v3+mGGMKel1nZJxGiOdDr5fht8BNwBHgYnAV9bayZ4WJp4yxiwCmltrY7yuRbKeMeY24FdgvbW2vK9tPjDCWjvDGNMceMxa28rLOiXrpPGe6AoUsNbqIrM8xhhTCpcdbrHWnjTGDAH2Aj2B+621kcaYx4Hq1tpeXtYqGace6fRpCky21h6x7i+Pj4G2Htck3gsGPjLGLDHGjDTGFPa6IMk61trF1lr/CmW+f/9q1toZvuOzgZrGmECvapSsdeZ7wqcKEG6M+d73aUUdD0oTD1hr/wIaWmtP+pryAzFAtLU20tc2BmjhRX2SORSk06c0sD/Z/X1AiEe1SPaxEnjVWnsrcBB41eN6xFvBuPdBcn/gfn5I3rUTmGqtbQQ8A0wyxuTztiTJKtbaGGNMkDHmPaAQsJ5kecJaG4sL2JJDKUinzwFSBufyvjbJw6y1Pay1e3x3v8YN/ZG860/ODs1lfe2SR1lrx1lrv/LtbwCOAJd4W5VkFWNMJWAKMNda2xMXokOSHS8IxHpUnmQCBen0mQ3cbYwp5rv/MDDNw3rEY8aYQsaYN5N9bN8MWO1lTeItX8/SOmNMUwBjTGPgV2ttnLeViZeMMY8YY2r59i/FfXKxz9uqJCsYY4KA8UAPa+0cAGvtNqCoMaam77QHgTneVCiZQR8npIO1dp8xZgCwxBgTCyzVhYZ5m+/CkT+BFcaYI0AU8KjHZYn3ngDGG2NeBU4BXT2uR7y3AhhljAkAEoDO1tp4j2uSrNEYqA58ZoxJbFsEdAH+Y4xJAA4BD3lSnWQKzdohIiIiIpIBGtohIiIiIpIBCtIiIiIiIhmgIC0iIiIikgEK0iIiIiIiGaAgLSKSyxhjChpjKvm2gr62Z0yyqQMy+Lx9MqdCEZHcQdPfiYhcAGNMOPAJsD1ZcxWgu7U24h88bz1gaBqHO1hr9/vOexWYgVvY4bC1NsbX/r1v9TyARsBbuOWIpxhj5gKPA22MMVcD7a21Sy+gtgDgTqC8b/7bZ4Hbga24RWfmWmv7GmNKAl8A7YEiuLnVN/uepjzQM/F7lJ7X65uHtwtQCZgHXOm7fdJa+2J66xcRuVjUIy0ikgpjzL3GmK+MMbtTOfxfa23jxA236ELyx1YyxswzxkQYY34wxjTwtQcaY8b62lb7Fm1JVBKIsNaGJ9+AvUBQsvOCcEsNXw98Y4wpkMZL+Ai30MPPwDvAJuANYDmw7Ix6yxhjBhhjZhpjvjPGTDDG1E92SmNgJPA50AfoDmwAOuNWeR0PYK2NBj4AXvY9bjouwE/l7AWLzvt6fX8kXAnkw60c+gtwLS7Qt/RtJdN4/SIiF516pEVEUncQFwLXp3KsszGmYbL7VUgZTt8F3rbWLjHGXAN8BlzH/7d3byFWlWEYx/8PqYFkpEYKqWkhkpVlRlhIQaiZlBUJRUoUlVhKF6KiYGgYhBUlCYGHUHK6iszKvBCjwuhA5qELja2u5gAABHNJREFUo4zKMwXhKRRPvV28367FcszZk403zw82zF57rW+tNRczz3p59/fBDLKKfKuky4FPJF0bEceauK4TQKeI+EBSX2ACtSBfcSNZEX6RXFHvI+D2qCwgIGkAsBSYRYbivcB6csGIlohYHRHrJG0CXgCmR8SfkuYCm4HFZelrAMp1rQF6lU2NAHy0iXtsXNtUYBhwC/AtcA/5ez4IPFDuaQuwv9mxzczOBQdpM7NWRMSnAGdoK34zIuY13kiaU/v8kUbLBfl3thEi76asYhYReyR9AYwgA25bnShjEhGvK3UDDtf26wqcAj4HxpLBdijwoKQ7gSURsQN4jWxL+UXSJGB9ROyX9DjwMVlNBlgOXA9Mk/RkGW8rcLOkwRGxrSyFPRjYRobun4GbygtgEhmM22oF0A/4AzgArAXmlPsZDeyMiN1NjGdmdk45SJuZNacTudTzGVX6lscB08k+X4CeZG9zwz7gssr7R0sPdtXVZHhs+DtIS+obEbtK68g3teOOANvJ4DoRCGAysKucd3dpi1AJ0QJuIFtAiIiDkrqW84wEHgZ6ADuBuWQLRgu55PE0SS+V8/Yq4xwmK91PAyeBt4FLa9d4tvsdWcaYXV5XkBXoUWRgX4OZ2XnkIG1m1pyLgAmSbqts60+ltaOE0gVk4B5dqU7/SgbnQ+V977KtYUW10l3Gaqmd/wTQR9Ii4HCphs8C3qntdwkZmC8E3gU2AuOBlxtVXEkXAI22khHA1xFxsnzWvfLZxvJ5Z7K6vpUMyEPIlovvI2IfsE/SEbKHGrIP+kB59eZ0Z7vfK8nf43FgA/lwMAZ4FTgaEXtbGdPMrMM4SJuZNWcg8HxErGxsaKW1Yw7wQ0Qsq21/jwyZsyT1AoaT7Q5tUmbcGAV0IfutfyKrwu+TLRbdImJB2f06sgr9G3AvGd57AcMkHQNmRMRmST0lXQU8B0wp5+lGzkyysIw1gAyzABeTs3SskrQ6Iu5r5VIbX2Q/BOwhw3Y/Tq9I/6uIeEXSWPJB4SlgZkT8LmkHMEiSqv3eZmYdzUHazKw595OzVVTVG6mnAt9JmljZNprsR35D0lflmCm1LxqerdWhB//MvDGvjDk/ItaWKerekjSw7LuhEuRnAkhaSKUiXTxDtk8sA3ZIWkrOCLIoIpaXfYaTfc9DgMWc4X9HqcTPIMP2qojYRJmto4T1z2qHtKWV5Qngw/Jzf0l3lfHXAS2SJkdEvT/czKxDyA/zZmZtU0Lf1IgYX94/C4wjK62jIuLH/zB2T6B7fYzyBb7tEXG0tn0YsCUiTrUy1higT70ifoYgXT92EFlNr87s0Y0M/ksi4qHSflFv1ZgBXEO2vqwE5pOtGZ3LsV3IuaUbwf+s9ytpKFmNbiEfHu4gZyKZHRGnJD0GfFmdNcTMrCM5SJuZNaG0T7gC2gpJXSLieGvbgYiIE+fhsszM/jcO0mZmZmZm7eCVDc3MzMzM2sFB2szMzMysHRykzczMzMzawUHazMzMzKwdHKTNzMzMzNrhLxVonPvUAsJTAAAAAElFTkSuQmCC",
- "text/plain": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 確率と疑似乱数\n",
- "\n",
- "[この章の目的]\n",
- "コンピュータで確率・乱数を扱う概要を学ぶ。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "OgC7tgxYLNx4"
- },
- "source": [
- "この章では、確率と擬似乱数について扱います。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "4rzt6Nw35wtJ"
- },
- "outputs": [],
- "source": [
- "#ライブラリをインポートしておきましょう\n",
- "import random\n",
- "import numpy as np\n",
- "from matplotlib import pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8Pc1kC3ELQF3"
- },
- "source": [
- "## 疑似乱数について\n",
- "\n",
- "コンピュータで何かの処理を実現したいときや、自然科学や統計学などで様々なことをシミュレーションしたいとき、[確率的な事象]を考えたくなることがよくあります。 \n",
- "\n",
- "たとえば人◯ゲームや◯鉄のようなゲームを作るときにもサイコロの出目が必要になりますし、技が急所に当たる確率や色違いのポ◯モンが出現する確率などを設定しなければなりません.\n",
- "\n",
- "真に理想的なサイコロならば1から6の目が出る確率は等しく1/6ですが、このような\"理想的な\"サイコロは、現実世界でも、コンピュータの上でも実現することはできず、我々人間が乱数をシミュレーションして扱う際には、何らかの確定的な方法で乱数列(**十分ランダムだとみなせる数の集合**)を生成することになります。\n",
- "\n",
- "真の意味での乱数と区別する意味で、我々が普段ゲームなどで扱う乱数は擬似乱数と呼ばれるべきものです。以下ではめんどくさいので、単に乱数と呼ぶことにします。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "EDPi_vGwQ3lb"
- },
- "source": [
- "**$\\clubsuit$ 細かな注**\n",
- "\n",
- "乱数を生成する方法はいくつもあるが代表的なものはメルセンヌツイスタと呼ばれる方法で、多くのプログラミング言語でも採用されている。\n",
- "```{margin}\n",
- "ちなみにモンテカルロはカジノで有名なモナコの地名Monte Carloに由来。\n",
- "```\n",
- "また[モンテカルロ法]と検索すると(主に学術的な分野で)乱数がどのように活用されているか、雰囲気を味わうことができます。 \n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "DeY7-UhTPrpm"
- },
- "source": [
- "## Pythonでの乱数生成\n",
- "\n",
- "Pythonでは`random`モジュールを使えば簡単に乱数を使用することができる。 "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Jz-0jgJnLG3L",
- "outputId": "6b61c6b6-c249-4413-8223-49fa276e3826"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "2"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "random.randint(1,6)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "b1m6sjIoLJTu"
- },
- "source": [
- "のように`random.randint(最小値,最大値)`とすると指定した閉区間の整数値をランダムに生成することができる。 \n",
- "上のコードセルを実行するたびに答えが変わること(ランダム性)も確かめよう。\n",
- "\n",
- "今の場合、最小値に1、最大値に6を採用したことで、この乱数をサイコロの出目とみなすことができる。 \n",
- "(```range```などと違い、最大値の6も含まれていることに注意! 紛らわしい...。)\n",
- "\n",
- "100個のサイコロの出目を保持しておきたければ、２章で学習したリスト内包表記を用いて"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "zc0Q1VTiLoRU",
- "outputId": "ffaced8e-d9de-42f0-d871-2865312c788c"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1, 4, 5, 3, 4, 2, 2, 5, 5, 6, 1, 2, 6, 3, 2, 4, 3, 6, 6, 5, 5, 6, 4, 4, 2, 3, 3, 5, 4, 6, 6, 6, 6, 4, 3, 2, 1, 5, 6, 4, 3, 2, 5, 2, 6, 4, 3, 6, 1, 3, 5, 2, 6, 3, 2, 3, 3, 3, 1, 4, 1, 5, 2, 6, 4, 4, 4, 3, 2, 3, 6, 6, 3, 1, 2, 6, 1, 3, 1, 6, 2, 5, 2, 6, 5, 2, 1, 1, 4, 1, 2, 3, 1, 2, 5, 2, 3, 6, 2, 3]\n"
- ]
- }
- ],
- "source": [
- "a = [random.randint(1,6) for i in range(100)]\n",
- "print(a)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Y7fYRqZyM6R5"
- },
- "source": [
- "などとすればよい。$10^p$回 ($p=1,2,...,6)$回サイコロを振った場合の出目をそれぞれヒストグラムにしてみると... \n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 353
- },
- "id": "wXlYsFMLSRBo",
- "outputId": "d8defd9e-3de9-433f-8a90-84684107b1c1"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Vysbhio4dRuQ"
- },
- "source": [
- "# 相関・回帰分析\n",
- "\n",
- "*相関関係は因果関係を含意しない (Correlation does not imply causation)*\n",
- "\n",
- "[この章の目的]\n",
- "初歩的な相関分析と回帰分析がPythonで出来るようになる。\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "mm1vDuyut69e"
- },
- "source": [
- "今回使用するライブラリをインポートしておきましょう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "vd39tZH4t6UZ"
- },
- "outputs": [],
- "source": [
- "from matplotlib import pyplot as plt \n",
- "!pip install japanize-matplotlib \n",
- "import japanize_matplotlib \n",
- "import numpy as np "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7k8KOlPCIuaF"
- },
- "source": [
- "## 相関分析 (復習)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Fm6stZb_IwM1"
- },
- "source": [
- "1年次の必修科目、データサイエンス入門でも相関分析を学習したことかと思います。\n",
- "\n",
- "解析したいデータが2種類だけなら、プログラムを使うご利益はそれほど感じられないが\n",
- "「多くのデータ間の相関関係を系統的に調べたい」「複数年度に渡るデータを解析したい」 \n",
- "あるいは「その結果をベクタ画像として出力したい」となると \n",
- "これまで学習してきた繰り返し操作や作図が役に立つ。\n",
- "\n",
- "\n",
- "まずは簡単な例から初めよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "HPWcU6_ylxcu"
- },
- "outputs": [],
- "source": [
- "x= [3.1, 4.3, 6.6, 13.2, 19.1, 20.9, 26.4, 25.1, 21.9, 15.7, 9.6, 3.8]\n",
- "y= [568, 572, 804, 833, 930, 965, 1213, 1120, 835, 540, 451, 502]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "aK_TO1InnQ2x"
- },
- "source": [
- "上に示したのは、2017年の宇都宮市における月別の平均気温$x$と \n",
- "世帯ごとのアイスクリーム・シャーベットの平均消費金額$y$で、 \n",
- "散布図にすると↓こんな感じ"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "T0uJSEOQmE47"
- },
- "outputs": [],
- "source": [
- "plt.figure(figsize=(6,6)) \n",
- "plt.title(\"宇都宮市\") \n",
- "plt.xlabel(\"平均気温 (℃)\")\n",
- "plt.ylabel(\"世帯あたりのアイスクリム・シャーベットの消費金額 (円)\")\n",
- "plt.scatter(x,y)\n",
- "plt.show()\n",
- "plt.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "DHPfNSoymCxz"
- },
- "source": [
- "「平均気温とアイスの消費には相関がありそう」という直感の通り、正の相関があることが見て取れる。\n",
- "\n",
- "では\"どれほどの\"相関を持つかを表す量として相関係数を算出してみよう。 \n",
- "相関係数$r$は以下のように定義され\n",
- "$r = \\frac{ \\sum^n_i (x_i-\\bar{x})(y_i-\\bar{y})}{ \\sqrt{\\sum^n_i (x_i-\\bar{x})^2 \\sum^n_i (y_i-\\bar{y})^2} }$ \n",
- "$\\bar{x},\\bar{y}$はそれぞれ$x,y$の平均値で$\\bar{x} = \\frac{1}{n} \\sum^n_i x_i $, $\\bar{y} = \\frac{1}{n} \\sum^n_i y_i $ \n",
- "と書ける。\n",
- "\n",
- "下付き添字$i$は$x$の$i$番目の要素であることを表し(つまり$x$をn次元ベクトルとみなしたときの第$i$成分が$x_i$) \n",
- "今考えているデータの場合、$\\sum$の和記号は$i$は1から12までの値を取り、対応する値を足し上げることを意味する。 \n",
- "(\"$i$の和が1から12までを走る\"と言ったりもする)\n",
- "\n",
- "$r$は必ず-1から1までの値を取り1.0(-1.0)に近づくにつれ強い正(負)の相関を示す。 \n",
- "(強いというのは曖昧な表現で絶対的な線引がある訳では無いことに注意)\n",
- ">$|r|\\leq1$は、コーシーシュワルツの不等式を用いるか \n",
- "上の$r$の定義と$n$次元ベクトル同士の内積の定義とを見比べると示せる(暇があればやってみよう)。 \n",
- "\n",
- "次に`x`と`y`、２つのリストを引数に持ち、相関係数$r$を返す関数を作成してみよう。\n",
- "\n",
- "にらめっこするために式を再掲:\n",
- "$r= \\frac{ \\sum^n_i (x_i-\\bar{x})(y_i-\\bar{y})}{ \\sqrt{\\sum^n_i (x_i-\\bar{x})^2 \\sum^n_i (y_i-\\bar{y})^2} }$ "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Sqr9IFdzoT7A"
- },
- "outputs": [],
- "source": [
- "### ライブラリを一切使わない方法\n",
- "def cor_coeff(x,y):\n",
- " # xとyの長さが違う場合や長さ0の場合はエラーを出す\n",
- " if len(x) != len(y) or len(x)==len(y)==0:\n",
- " raise ValueError(\"Error: x&y must satisfy len(x) = len(y) != 0\")\n",
- " n = len(x) \n",
- " ## 平均を計算\n",
- " xbar = sum(x)/n; ybar = sum(y)/n \n",
- "\n",
- " ##分子(numerator)の和を計算 (初期値を0に)\n",
- " s_n = 0.0 \n",
- " for i in range(n):\n",
- " s_n += (x[i]-xbar)*(y[i]-ybar)\n",
- "\n",
- " ##分母(denominator)の計算 (和を先に計算して積を取り、最後にsquare rootをとる)\n",
- " s_x = 0.0; s_y = 0.0\n",
- " for i in range(n):\n",
- " s_x += (x[i]-xbar)**2 \n",
- " s_y += (y[i]-ybar)**2\n",
- " s_d = (s_x * s_y)**0.5\n",
- " # 一行で書くなら\n",
- " #s_d = ( sum([(x[i]-xbar)**2 for i in range(n)]) * sum([(y[i]-ybar)**2 for i in range(n)]) )**0.5\n",
- "\n",
- " return s_n/s_d # 分子/分母の値を返す\n",
- "\n",
- "cor_coeff(x,y)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9IM8Ebhopmre"
- },
- "source": [
- "という風に、$r$が約0.83で、非常に強い正の相関を示すことが分かる。\n",
- "\n",
- "少しずつ自作関数に慣れてきたら、上のように意図しない引数を入れたときの挙動なども設定すると \n",
- "より**安全な**コードを作る事ができる。\n",
- "\n",
- "`x`と`y`の長さが違う場合(上の`raise`文でエラーが生じさせる場合)を試しておこう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cor_coeff(x,y[1:])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "相関係数の計算は、numpyライブラリを使うと実はもう少しシンプルに書ける"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "mG4Fie5epxP_"
- },
- "outputs": [],
- "source": [
- "def cor_coeff_np(x,y):\n",
- " xbar = np.mean(x); ybar=np.mean(y)\n",
- " return np.dot(x - xbar,y-ybar) / np.sqrt( np.dot(x-xbar,x-xbar) * np.dot(y-ybar,y-ybar) ) \n",
- "\n",
- "cor_coeff_np(x,y) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "criDMWxYrJUo"
- },
- "source": [
- "とすると、関数自体は3行で書けてしまう。\n",
- "さらに$\\bar{x},\\bar{y}$をいちいち定義しないように書き換えれば、関数の中身自体は一行でかけてしまいます。\n",
- "\n",
- "上のコードを少し補足しておくと...分子や分母に現れる \n",
- "$\\sum^n_i (x_i-\\bar{x})(y_i-\\bar{y})$や$\\sum^n_i (x_i-\\bar{x})^2 $といった項は、 \n",
- "$i$番目の成分に$x_i-\\bar{x}$を持つベクトル$\\tilde{x}$と \n",
- "$i$番目の成分に$y_i-\\bar{y}$を持つベクトル$\\tilde{y}$を定義しておくと、 \n",
- "$\\tilde{x}\\cdot\\tilde{y}$, $\\tilde{x}\\cdot\\tilde{x}$, $\\tilde{y}\\cdot\\tilde{y}$といったように\n",
- "ベクトルの内積の形でいずれも表すことができる。\n",
- "\n",
- "`numpy`にはブロードキャスト機能(Numpyのノートを参照)やベクトル積を計算する関数```dot```が備わっているので、 \n",
- "それらを活用することで相関係数の計算を短く実装することができた。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "eDhJHIZesEMQ"
- },
- "source": [
- "更に言うと実は`numpy`には相関係数を計算する関数```corrcoef()```が予め用意されていて\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "DZ8RiA7ssJUB"
- },
- "outputs": [],
- "source": [
- "print(np.corrcoef(x,y))\n",
- "print(\"r(x,y)=\", np.corrcoef(x,y)[0,1])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "n9KiMZZJsNZU"
- },
- "source": [
- "を使えば \n",
- "[ xとxの相関(=1.0), xとyの相関; \n",
- "yとxの相関, yとyの相関(=1.0)] \n",
- "といった2行2列の相関行列を取得することが出来る。 \n",
- "確かに上の相関行列の[0,1]成分は、さっき計算した$r$の値と一致している。\n",
- "\n",
- "「初めからそれを教えろ！」と思うかもしれないが \n",
- "**考えたい量を数式として定義してそれをプログラムに変換し、値が正しいことを確認する作業**は \n",
- "**式(考え方)とプログラミング双方の理解を深める上で非常に重要**である "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-P5hjR0O3AOe"
- },
- "source": [
- "### 相関分析と因果関係"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "NatgqwbX7Fua"
- },
- "source": [
- "以下では、ある一つのグラフの例を見ながら、冒頭の \n",
- "*相関関係は因果関係を含意しない (Correlation does not imply causation)* \n",
- "に関して説明する。\n",
- "\n",
- "下の図は、2017年の家計調査・気候データから作成した散布図で、 \n",
- "千葉市での平均気温と、しめじの消費支出の間の相関を示している。\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "W7cNmbENdx0R"
- },
- "source": [
- "# 最適化問題の基礎\n",
- "\n",
- "\n",
- "[この章の目的]\n",
- "最適化問題のイメージを掴み、一次元の単峰的な関数の最小値を探索できるようになる。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "r244nZzmtIZ0"
- },
- "source": [
- "種々のデータ解析や学術的分野での計算をはじめ世の中の多くの問題は**最適化問題**に帰着される. \n",
- "最適化問題とは、大雑把に言えば「ある量の最小値/最大値とそれを与える変数/パラメータの値を知ること」と言い換えられる. \n",
- "\n",
- "「人生も、何らかの目的関数$f(x)$(一般に$x$は多次元),たとえば幸福感(不幸感)を最大化(最小化)すること \n",
- "という意味では、最適化問題を考えていることに相当する」というと少し大げさでしょうか。\n",
- "\n",
- "\n",
- "この章では、最適化の基礎について学んでいくことにしよう。 \n",
- "授業では実際に最適化で必要な数学的な操作をするコードを作ったりする訳ではないが \n",
- "「ライブラリに入れてポンッ」ではなく、背後にあるモチベーションや概念を理解しておくことは \n",
- "自分が興味のある問題を最適化問題に帰着させて解くためには不可欠だ。\n",
- "\n",
- "※高校で微分を勉強していないという方に向けて末尾に数学的準備の項を設けてあるのでチェックしてください. \n",
- "(よく知っているという方もさっと目を通してみてください) \n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "loZUoKQD9uYU"
- },
- "source": [
- "## 考えたい問題のイメージ"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "L1wUnmJn9xoF"
- },
- "source": [
- "以下で考えたい問題のポンチ絵\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "JXf9XKfop6wz"
- },
- "source": [
- "# ファイル・文字列操作\n",
- "\n",
- "[この章の目的]\n",
- "text,csvやxlsx形式のデータをプログラムでサクッと扱えるようになる。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "LfaRUw0tp8wv"
- },
- "source": [
- "この章では、テキストファイルやcsvファイル(excelファイルはおまけ$\\clubsuit$)をPythonで操作する簡単な方法を学習する。 \n",
- "\n",
- "これまでの章では、データはリストとして既に与えられた状態から解析を行ったが、実際にデータを扱う際は \n",
- "既に誰かが作成した何らかのファイルをプログラムで読み込んで操作する場合も多い。 \n",
- "この章の内容は、データ解析というよりは、Pythonでデータ解析をするための下準備に相当する。 \n",
- "\n",
- "愚直にコードを書いている事もあり少々泥臭い部分が多いが、この章のような操作のエッセンスを抑えておけば \n",
- "普通にやると膨大な時間がかかる様々な処理を高速化・自動化することができるので、頑張って学習しよう。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Y0pkvGuu6B4t"
- },
- "source": [
- "## 授業で使うファイルの準備\n",
- "\n",
- "予め以下のリンクをクリックして、ファイルをダウンロードし、 \n",
- "ご自身のGoogle Driveにアップロードしておいてください。\n",
- "\n",
- "* [test.txt](https://drive.google.com/file/d/1U2uvrN18713ylN4OQiI2fsfX5gudL45w/view?usp=sharing) (テキストファイル)\n",
- "\n",
- "* [python_handling_test.csv](https://drive.google.com/file/d/1bYJNWdtujcQWfSBAa1UeXi2ZzJRJktil/view?usp=sharing) (csv, カンマ区切りのテキストファイル)\n",
- "\n",
- "* [kakei.xlsx](https://drive.google.com/file/d/1gJMVHivmP7R9Qf4LdqRhdPVc3x0IzD8v/view?usp=sharing) (エクセルファイル)\n",
- "\n",
- "本章では、ファイルの場所を指定する**パス**という概念がたびたび登場する。\n",
- "以下のコードをそのまま使いたいという方は、マイドライブ直下に`AdDS`というフォルダを作り、さらにその中に```chapter8_data```というフォルダを作成し、ファイルをいれてください。\n",
- "\n",
- "パスについては後の節で詳しく説明します(今気になる方は末尾をチェック)。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "GT7Cl8yb8iL8"
- },
- "source": [
- "## テキストファイルの操作"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5LKmHodPF_n0"
- },
- "source": [
- "膨大な行のテキストファイルに対して、人間が手で操作をするというのは時として非現実的です。 \n",
- "\n",
- "誤変換を置換するくらいなら、どのテキスト/メモ帳アプリやwordでもできますが、\n",
- "全行(数千とか数万)に対して、決まった操作が必要な場合、プログラムにしてしまったほうが遥かに便利です。\n",
- "\n",
- "以下ではGoogle Driveのマイドライブの```AdDS```の下に作った```chapter8_data```というフォルダにファイルを保存したと仮定して話を進めますので、**適宜皆さんの場合に置き換えて使用してください**\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "B6oBjOGQ8k1K"
- },
- "source": [
- "まずはgoogle driveに保存した```test.txt```という名前のファイルを読み込んでみましょう。 \n",
- "既に何回かやったようにgoogle driveをマウントします。\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "oKJPB3hafCDd",
- "outputId": "7b4ad307-91cd-4e63-e951-19fe78efb362"
- },
- "outputs": [],
- "source": [
- "from google.colab import drive\n",
- "drive.mount('/content/drive')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "FGYId3osJfo9"
- },
- "source": [
- "**注意** 以後のコードは、google driveの中にあるファイルを読み書きしたりといった操作を行うため \n",
- "上でGoogle Driveをマウントした状態でなければ実行しても多くがエラーとなる。\n",
- "\n",
- "---\n",
- "\n",
- "Google Driveのマウントができたら、先程のファイルがあるかlsコマンドで確かめてみよう。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Nx4kIQw48w7y",
- "outputId": "b1b93834-5bd0-4954-a465-680acf6a9e11"
- },
- "outputs": [],
- "source": [
- "!ls /content/drive/MyDrive/AdDS/chapter8_data/*"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "UCzT9lFC0x93"
- },
- "source": [
- "*はワイルドカード記号で、対象を任意とする命令に相当します。 \n",
- "*.拡張子 などとして使うのも便利です。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "lBNc6YKaJ-tO"
- },
- "source": [
- "ファイルが見つからない場合は\n",
- "> No such file or directory\n",
- "\n",
- "などと表示される。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9ypPj31PKJKY"
- },
- "source": [
- "> $\\clubsuit$ 上のファイルをGoogle Driveに保存する方法としては \n",
- "一度ローカルに保存したファイルをブラウザなどからアップロードする方法はもちろん \n",
- "全てを(Linux)コマンドで行うこともできる。\n",
- "```\n",
- "!git clone https://github.com/SotaYoshida/Lecture_DataScience\n",
- "!mkdir /content/drive/MyDrive/AdDS/\n",
- "!mv Lecture_DataScience/Chapter8_data /content/drive/MyDrive/AdDS/chapter8_data\n",
- "!ls /content/drive/MyDrive/AdDS/chapter8_data\n",
- "```\n",
- "1つめの行ではまず授業資料のGitHubレポジトリをColab環境で間借りしているgoogleのサーバー上にクローン(≒コピー)する。2行目でマイドライブの下にAdDSというフォルダの作成を試み、3行目でダウンロードしてきたレポジトリにある`Chapter8_data`をさっき作ったAdDSというフォルダの中に別名(先頭が小文字になっている)で移動する。\n",
- "最後に、どんなファイルがあるかをlsコマンドで確認している。 \n",
- " 重複する作業も多いのでこれらのコードはコードセルには書かなかったが、うまくアップロードできなかった場合やコマンドラインによるファイルの移動などをやってみたければ上の4行のコードをコードセルに貼って試してみよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Ayz1B8fz9wGb",
- "outputId": "40f02a5b-6069-4c88-d4bd-a4271afbcbe0"
- },
- "outputs": [],
- "source": [
- "!ls /content/drive/MyDrive/AdDS/chapter8_data/*txt "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "uYhMU-Od-s4Z"
- },
- "source": [
- "とするとマイドライブ/AdDS/chapter8_data/にある`.txt`形式のファイル一覧を表示させることができる。 \n",
- "`test.txt`が見つかったでしょうか？(DriveにアップロードしてColabから読み込みできるまでに少し時間がかかる場合がある)\n",
- "\n",
- "\n",
- "では次に、このファイルに書かれているテキストを取得してみよう。 \n",
- "方法は幾つかあるが、最も標準的なものとして、ファイルを開いてテキストを取得する方法を試してみよう。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "YCF5xUQ_3r_3"
- },
- "source": [
- "### テキストファイルを開いて内容を読み出す"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "4qbsm40WpqK1"
- },
- "outputs": [],
- "source": [
- "filename = \"/content/drive/My Drive/AdDS/chapter8_data/test.txt\" \n",
- "inp = open(filename,\"r\")\n",
- "lines = inp.readlines()\n",
- "inp.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "aZxGX1R4_QUN"
- },
- "source": [
- "1行目でファイル名(正確にはファイルのパス)を指定し```filename```という変数にした。 \n",
- "\n",
- "2行目では、指定したパスにあるファイルを開いている。 \n",
- "今はファイルに書き込むのではなく、既にあるファイルを開いて読み込むので`\"r\"`というオプションを指定している。 \n",
- "他には`\"w\"`(書き出し,上書き), `\"a\"`(書き出し,追記)などがあり、新しく上書きでファイルを作成したい場合は`\"w\"`,すでにあるファイルの内容は消さずに追記したい場合は`\"a\"`を指定して使う。\n",
- "\n",
- "3行目では、`inp`(ファイルを`open`して得たオブジェクト)に対して```readlines```という操作を適用している。\n",
- "これは、ファイルに書かれているテキストを(可能なら)全行に渡って読み込みメモリにストアする関数になっている。 "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "TMHIZJPTAvd7",
- "outputId": "2aae100d-5f48-461b-b699-74985a929dca"
- },
- "outputs": [],
- "source": [
- "print(lines)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QSbIzGcdAyWH"
- },
- "source": [
- "とすると、全ての行が読み込まれ、変数```lines```に格納されていることがわかる。ここで```\\n```は改行記号を意味する。\n",
- "\n",
- "ループを回して一行ずつ表示させると"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "dPfMZpjUA_ZQ",
- "outputId": "001554b4-6212-4ef7-ec3e-f255f8d5223b"
- },
- "outputs": [],
- "source": [
- "for line in lines:\n",
- " print(line)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "KS1P7UYYBFX2"
- },
- "source": [
- "といった感じ(行ごとにスペースが生じている理由については後で説明します)。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "W4CNTbH5Avlf"
- },
- "source": [
- "必要な行番号が分かっている場合は、`nlines = lines[2:10]`などとして、要らないところは捨てても良い。(リストのスライスについては2章を参照)\n",
- "\n",
- "次に、もう少し具体的なテキスト操作をしてみよう。 \n",
- "まず、上の1行ずつ表示するコードでは、改行コードを明示的に含む文字列を一行ずつ表示したため、改めて`print`すると余分なスペースが空いてしまう。 \n",
- "(`print`関数はデフォルトで末尾に改行```\\n```を挿入するのでファイルにある改行記号とあわせて2回改行してしまう→[参考リンク](https://docs.python.org/ja/3/library/functions.html#print))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "olRZ5IDD3yuV"
- },
- "source": [
- "### strip関数\n",
- "\n",
- "たとえば```strip()```関数を使うと、文字列に含まれる空白、タブや改行コードを消去することができる。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "fWB00WIvzOlz",
- "outputId": "867eb590-a09a-4d51-baf6-57009d8a72a1"
- },
- "outputs": [],
- "source": [
- "a = \"test character\\t\"\n",
- "b = \"test2 \\n\"\n",
- "print(\"a\", a, \"←タブが隠れている\")\n",
- "print(\"b\", b, \"←改行される\")\n",
- "### strip関数をもちいて...\n",
- "print(\"a.strip()\", a.strip(),\"b.strip()\",b.strip())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "先程のforループでstrip関数を適用してやると..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "bwCxUBMdBOb2",
- "outputId": "d964e851-62c0-4ded-9370-1f279c4a9dd7"
- },
- "outputs": [],
- "source": [
- "for line in lines:\n",
- " print(line.strip())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "IpOm3O9rBlSH"
- },
- "source": [
- "文字列の右側に空白や改行コードが入っていることが明確な場合は \n",
- "`strip`の代わりに`rstrip`を使ってもOK(`rstrip`のrはrightの意味)。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "zVlMYYFtBnlD",
- "outputId": "2b14197a-506d-4207-bbc4-2937db656ed5"
- },
- "outputs": [],
- "source": [
- "for line in lines:\n",
- " print(line.rstrip())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "E-iAh4w7BrbU"
- },
- "source": [
- "\n",
- "ファイルによってはインデントをするために左側にタブ```\\t```が含まれる場合もあります(PythonのコードをテキストとしてPythonから読むときなどがこれに該当)。そのような場合に左側にある空白やタブのみを取り除きたければ```lstrip()```を使って取り除くことができる。\n",
- "\n",
- "もちろんPythonではインデントが文法なので、インデントを一律で消す、といった操作は必要ないが、特定の状況では、`lstrip`も使えると便利だ。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "V1Uk2u1Z2haB"
- },
- "source": [
- "上のファイルの文字列で`#`記号を含む行以降だけが必要な場合はどうすればいいでしょうか？\n",
- "\n",
- "最も単純(？)な実装は"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "o9ePmRrlCQV3",
- "outputId": "abd39a60-4d5b-40ae-8e81-e3d17f90e735"
- },
- "outputs": [],
- "source": [
- "hit = 0 #\n",
- "for line in lines:\n",
- " if \"###\" in line:\n",
- " hit += 1 \n",
- " continue\n",
- " if hit == 0 :\n",
- " continue #hitが0の状態では何もしない\n",
- " print(line.rstrip())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "S4pLj4K-CQhm"
- },
- "source": [
- "といった愚直な例が考えられる。\n",
- "つまり、`#`を含む行に到達するまでの行は無視して、それ以降の行だけをリストに格納するというもの。\n",
- "もちろん`#`を含む行が複数あるようなケースでは、自分が実現したい操作にあわせてコードを書き換える必要がある。\n",
- "\n",
- "以下では、`###data`までの行が必要ないので、必要なところまでを別のリストに格納してしまおう。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "eYsStscDDXDa",
- "outputId": "e5334884-190b-4a4b-d2a6-e3705becf3f0"
- },
- "outputs": [],
- "source": [
- "hit = 0 #\n",
- "nlines = []\n",
- "for line in lines:\n",
- " if \"###\" in line:\n",
- " hit += 1 \n",
- " continue\n",
- " if hit == 0 :\n",
- " continue #hitが0の状態では何もしない\n",
- " nlines += [line]\n",
- "print(nlines)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "p7_aA_O0p7tt"
- },
- "source": [
- "### split関数"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "RDD1fCO3DNm6"
- },
- "source": [
- "また、1,2,3,4,5,6といったコンマやスペースで区切られたものをリストに格納したい場合には、```split```関数が便利。`split`関数は引数に何も指定しなければ、スペースや改行もしくはタブごとに文字列を区切ったリストを返す。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "sample_text = \"This is a\\nsample\\ttext.\"\n",
- "sample_text.split()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "1P4mcqMkCzbf",
- "outputId": "6bbaadf5-f756-4990-c57c-e452f7ddd7d8"
- },
- "outputs": [],
- "source": [
- "for line in nlines:\n",
- " print(line.split())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0QGZ-qxWDF6L"
- },
- "source": [
- "カンマがあるときはカンマで分割する、という約束を表現したければ"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "R5Wk_P3gDJ2C",
- "outputId": "9589e77b-476a-44e7-cb1c-61a45f67ca11"
- },
- "outputs": [],
- "source": [
- "for line in nlines:\n",
- " if \",\" in line :\n",
- " print(line.rstrip().split(\",\"))\n",
- " else :\n",
- " print(line.rstrip().split())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QaOMvxGUDx7g"
- },
- "source": [
- "などとすれば良い。これを利用すれば、空のリストにファイルから読んだ要素を詰めていくといった操作も実現できる。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ocRjQCaGEF07",
- "outputId": "8f26d228-b132-4002-efbb-694216794090"
- },
- "outputs": [],
- "source": [
- "# 数字とプロフィールの空リストを作り、そこに読み込んだものを詰めていく\n",
- "# その際に、数字のリストとプロフィールのリストを分けたいとする\n",
- "nums = [] \n",
- "profs = [] \n",
- "\n",
- "for line in nlines:\n",
- " if \",\" in line :\n",
- " nums += [ line.rstrip().split(\",\") ]\n",
- " else :\n",
- " profs += [ line.rstrip().split()]\n",
- "print(\"nums\", nums)\n",
- "print(\"profs\", profs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pu5-guZDEcIo"
- },
- "source": [
- "上の`nums`の様に、予め全ての要素が整数だと分かっていて整数に対する演算(四則演算など)を後でするのなら、`str`(文字列)型ではなく`int`型にしておくほうが良いこともあるだろう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "J85gGgidEq6j",
- "outputId": "87947845-dd2e-4574-e710-05b6c7e7819c"
- },
- "outputs": [],
- "source": [
- "##リスト内包表記を使った実装\n",
- "nums = []\n",
- "for line in nlines:\n",
- " if \",\" in line : \n",
- " tl = line.rstrip().split(\",\")\n",
- " nums += [ [ int(tmp) for tmp in tl] ]\n",
- "print(\"方法1:\", nums)\n",
- "\n",
- "## map関数(後述)を使った実装\n",
- "nums = []\n",
- "for line in nlines:\n",
- " if \",\" in line : \n",
- " nums += [ list(map(int, line.rstrip().split(\",\") )) ]\n",
- "print(\"方法2:\", nums)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ageWDCqlpxPy"
- },
- "source": [
- "### replace関数"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-ppyblOIq8PL"
- },
- "source": [
- "`replace`関数で文字の置換が可能です"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "B78Kb1QKqA5P",
- "outputId": "1048156c-f018-4f8a-87ff-3d01d047b562"
- },
- "outputs": [],
- "source": [
- "text = \"abcdあいうえお\"\n",
- "text = text.replace(\"abcd\", \"1234\")\n",
- "print(\"置換や→\",text)\n",
- "print(\"除去にも→\", text.replace(\"4\", \"\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "JHXIca-kE39k"
- },
- "source": [
- "### $\\clubsuit$ map関数\n",
- "\n",
- "`map`関数は`map(操作,対象)`という風に使って、対象の各要素に対して一括で操作を適用することができます。 \n",
- "今の場合、`['1', ' 2', ' 3', ' 4', ' 5', ' 6']`などの文字列のリストに対して、 \n",
- "整数型に変換する```int```関数を作用させるという操作を一度に行います。\n",
- "\n",
- ">注: `map`関数の返り値はmap objectと呼ばれるものなので、 \n",
- "元のようなリストの形で使いたい場合は```list()```を使ってリストに変換するステップが必要です。\n",
- "\n",
- "世の中には、アンケート結果や産業データなどがcsv(カンマ区切りのテキスト)ファイルで公開されている場合が多いですが、 \n",
- "その場合は**上で説明したような手順でリストなどに格納すれば今まで行ったような解析やグラフ描画が実行できる**といったわけです。 \n",
- "(もちろんcsvを読むのに便利なライブラリもありますが \n",
- "いろんな形式のファイルをプログラムで読み込む場合には \n",
- "上のような基本的な操作を組み合わせることも必要になります。)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7Zteg1NaYXuR"
- },
- "source": [
- "### テキストファイルの書き出し"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "d1feYXb-4J-s"
- },
- "source": [
- "次に、テキストファイルを書き込んで保存してみます。 \n",
- "上の文字列で、敬称を\"さん\"から\"様\"に置換したテキストを作成して、それを別ファイルとして保存してみましょう。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "J1fOkXTcYuQm"
- },
- "outputs": [],
- "source": [
- "filename = \"/content/drive/My Drive/AdDS/chapter8_data/test_replace.txt\" \n",
- "oup = open(filename,\"w\") ## oup は\"output\"の気持ち...\n",
- "for line in lines:\n",
- " print(line.rstrip().replace(\"さん\",\"様\"), file=oup) # file=[openしたファイル]にすることで、printする先をファイルに指定できます。\n",
- "oup.close() #ファイルはきちんと閉じる."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "BhsUEsIh6tC6"
- },
- "source": [
- "Google Driveで、作成されたファイルをチェックしてみましょう。\n",
- "\n",
- "なお、filenameに元ファイルと同じものを指定すると```open(filename,\"w\")```を実行した時点で \n",
- "ファイルが上書きされて空ファイルになるので注意しましょう。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "navysabZ4enm"
- },
- "source": [
- "今の例ではもちろん、手で置き換えたりするほうが遥かに速いですがこうしたPythonによるファイル操作を覚えておくと\n",
- "\n",
- "* ファイル自体が大量にあり、同じ操作を繰り返す場合\n",
- "* 単一のテキストファイルに大量の行に渡って情報がある場合\n",
- "\n",
- "など、手作業が非現実的な様々な状況でも、楽ちんに作業を終わらせることができる(かもしれません)。\n",
- "\n",
- "上の内容や、これまでに学習したループ処理を駆使すると、 \n",
- "数万人のデータが1行ずつ記載されたテキストファイルから条件にヒットする人の \n",
- "情報だけを抽出して小さなファイルにまとめるといったことも可能です。\n",
- "\n",
- "**プログラミングを用いたファイル操作をする発想があるかどうか**がきっとこの先 \n",
- "皆さんの生き方や働き方に大きな影響を与えると私は考えています。\n",
- "\n",
- "> **文字コードに関連した余談** \n",
- "Windows環境で作成されたテキストファイルを扱う際は読み込みで、文字コードによるエラーが出るかもしれない。最近ではメモ帳でもUTF-8(世界標準)を採用しているよう(→[MicrosoftのWindows blogの記事](https://blogs.windows.com/japan/2020/02/20/about-windows-and-japanese-text/))だが、古いテキストファイルだとShift-JISになっているかも。そういうときは、```open(file, \"r\", encoding = \"shift_jis\")```など、ファイルを開くときにencodingを明示的に指定する必要がある。明示的にUTF-8で保存したいときは```open(file, \"w\", encoding = \"utf-8\")```などとする。 \n",
- "参考: [公式ドキュメント](https://docs.python.org/ja/3/howto/unicode.html#reading-and-writing-unicode-data) \n",
- "ここまで勉強してきた皆さんには「そんなの、パソコンに存在するShift-JISで書かれたテキストファイルを全てUTF-8に変換するPythonスクリプト書けばいいんじゃね？」という発想があることを期待しています。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "J5sIptAmd1ni"
- },
- "source": [
- "## csv,エクセルファイルの操作"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ovU-6Bd-D9e1"
- },
- "source": [
- "### アンケート分析\n",
- "\n",
- "冒頭の二番目のファイル[python_handling_test.csv](https://drive.google.com/file/d/1bYJNWdtujcQWfSBAa1UeXi2ZzJRJktil/view?usp=sharing)はあるアンケート結果をまとめたファイルになっています。\n",
- "\n",
- "これは、Google フォームで作成したアンケーで、国数英社理(中学の５科目)に対する得意/苦手意識の調査を想定した疑似アンケートです。\n",
- "\n",
- "このようなアンケート調査は事務作業や卒業研究などで頻繁に見られ、会社や大学など所属コミュニティで何らかの意思決定に用いられることも多いことでしょう。こうしたアンケート分析を行っていると、\n",
- "* 各回答項目同士の関係が知りたい\n",
- "* 明示的な項目以外の情報も抽出したい\n",
- "\n",
- "といった要望が出てきます。今の場合でいうと、\n",
- "* 各科目ごとの得意・苦手意識の相関を調べたい\n",
- "* 夜中(あるいは日中)にアンケートを回答した夜型(昼型)の人に見られる特徴がなにかないか？\n",
- "\n",
- "といったイメージです。そんなとき、\n",
- "\n",
- "> 国語が得意(どちらかというと得意)と回答した方に質問です。 \n",
- "英語についてはどうでしょうか？\n",
- "\n",
- "などと新たに設問を増やしてアンケートをやり直すというのは得策では有りません。 \n",
- "すでに得られた情報からさらなる情報を引き出すことを考えてみましょう。 \n",
- "まずは、csvファイルに記載された情報を整理してプログラムで扱いやすくすることを考えます。\n",
- "\n",
- "> 余談: このcsvファイルをExcelで開こうとするとお使いの環境によって文字化けを起こすかと思います。これはgoogleフォームで作成されたcsvファイルの文字コードが世界標準のutf-8を使用しているのに対し、ExcelがShift-JISという時代遅れな文字コードでcsvファイルを開こうとするためです。Googleのスプレッドシートや、Mac標準のNumbersで開くと文字化けしません。\n",
- "\n",
- "> 2000件の回答は、もちろん私が手作業で入力したわけでも誰かに協力してもらったわけでもなく、一定のルール(傾向)を勝手に設定した上でランダムに回答を作成しフォームから自動回答するPythonスクリプトを書きました。 \n",
- "時間に余裕があれば、こうしたWeb操作を自動化する方法も授業で扱います。 c.f. ブラウザ操作, Webスクレイピング"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "zF6wdCGff4qd"
- },
- "outputs": [],
- "source": [
- "filename = \"/content/drive/My Drive/AdDS/chapter8_data/python_handling_test.csv\" #読み込むファイルのパスの指定"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vq3T9r6wgBmg"
- },
- "source": [
- "とりあえずファイルの中身を数行表示してみる。\n",
- "\n",
- "csvファイル(コンマ区切りのテキスト)なので、テキストファイルと同じ方法をとる(他の方法ももちろんある)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "4yG6dZUngpnA",
- "outputId": "eb5da226-418d-4a75-e4fd-f680959f03ed"
- },
- "outputs": [],
- "source": [
- "inp=open(filename,\"r\")\n",
- "csv_lines=inp.readlines() \n",
- "inp.close()\n",
- "print(\"行数は\",len(csv_lines))\n",
- "for i in range(5):\n",
- " print(csv_lines[i].rstrip())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "mfdbVE1Bhrhi"
- },
- "source": [
- "ちなみに...```pandas```ライブラリを使うとcsvをサクッと読み込むことができる"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "fgDQrP-1hA0D",
- "outputId": "8a51c3dc-77f5-4909-95b0-3e209da09a55"
- },
- "outputs": [],
- "source": [
- "import pandas as pd \n",
- "df = pd.read_csv(filename)\n",
- "print(df)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "bMWTdHs9ilWM"
- },
- "source": [
- "さて、```csv_lines```に格納したデータをもう少し扱いやすいように変更しよう。 \n",
- "最初の0行目はどういうデータが入っているか(データの項目)を表している。 \n",
- "1-2000行目には2000人分の回答が詰まっている。 \n",
- "\n",
- "これによると、 \n",
- "> 0列目: 回答した時刻 \n",
- "> 1列目: 性別 \n",
- "> 2列目: 国語 \n",
- "> 3列目: 数学 \n",
- "> 4列目: 英語 \n",
- "> 5列目: 社会 \n",
- "> 6列目: 理科 \n",
- "\n",
- "らしい。いろいろなデータの整理方法があると思うがここでは、\n",
- "* 処理A 0列目の時刻を24時間表記にして表示する \n",
- "* 処理B 2-6列目の各科目の得意・苦手意識を、文字列を除去して数値[-2,-1,0,1,2]として扱う\n",
- "\n",
- "をまずやってみよう。\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Kso8FZs6nQa0",
- "outputId": "3e2c5835-19b6-41de-ba7c-bda96bcc60d5"
- },
- "outputs": [],
- "source": [
- "#処理Aのための関数\n",
- "#input_strが、\"年月日 時刻(h:m:s) 午前/午後 GMT+9\" という文字列である、という文字列の[構造]を知った上での実装になっていることに注意\n",
- "def make_time_24h(input_str): \n",
- " time = input_str.split()[1]\n",
- " AMPM = input_str.split()[2]\n",
- " hms = time.split(\":\")\n",
- " h = int(hms[0])\n",
- " if AMPM == \"午前\":\n",
- " output_str = time \n",
- " else :\n",
- " if h != 12:\n",
- " output_str = str(h +12)+\":\"+hms[1]+\":\"+hms[2]\n",
- " else:\n",
- " output_str = str(h)+\":\"+hms[1]+\":\"+hms[2] # 12時xx分だけは別の取り扱いが必要\n",
- " return output_str\n",
- "\n",
- "nlines=[] #整理したものをリストとしてまとめるための空のリスト\n",
- "for nth,line in enumerate(csv_lines[1:]): \n",
- " nline = line.rstrip().replace('\"','').split(\",\") # 改行文字の除去、ダブルクォーテーションの除去, カンマで分割 \n",
- " # この時点でnlineは0:時刻 1:性別, ...のリストとなっているはず print()でcheckしてみよう\n",
- " # 処理A)\n",
- " time = make_time_24h(nline[0])\n",
- " #print(\"nline[0]\", nline[0], \"time\", time)\n",
- " M_or_F = nline[1] #性別\n",
- "\n",
- " # 処理B)\n",
- " points = [ int(nline[k].split()[0]) for k in range(2,7)] #各科目の値だけのリスト(points)を作成\n",
- " # 上記をmap関数にしてみよう。\n",
- "\n",
- " nline = [time, M_or_F]+points #リストを連結(時刻,性別と各科目の値を同じ階層で結合)して、nlineという名前で上書き\n",
- " nlines += [ nline ]\n",
- "\n",
- " # うまく編集できたか400行おきほどでprintしてチェックしてみる\n",
- " if nth % 400 == 0 :\n",
- " print(\"編集前\", line.rstrip())\n",
- " print(\"編集後\", nline)\n",
- " print(\"\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VVxC-_FanOp2"
- },
- "source": [
- "最後に、各項目の得点を適当なリスト(あるいはnp.array)に整形しておけば、種々の分析を行うことができます。\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "38OyKcL4DVtR",
- "outputId": "a78ecf75-7448-4011-ee0c-00cd6acc7470"
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "points = [ [] for i in range(5)]\n",
- "for tmp in nlines:\n",
- " for i in range(5):\n",
- " points[i]+=[tmp[2+i]]\n",
- "print(\"points\", np.array(points))\n",
- "print(\"各科目の平均スコア:\", [np.mean(points[i]) for i in range(5)])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gplM2Js9DWwP"
- },
- "source": [
- "相関分析は以降の章で扱うので具体例は省略します。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "2_SWNlqrhMLY"
- },
- "source": [
- "## $\\clubsuit$ 複雑なエクセルファイルの操作\n",
- "\n",
- "```kakei.xlsx```はエクセルファイルで以降では、2020年度前期のデータサイエンス入門(一部学科を除く)の \n",
- "相関分析で使用されたエクセルファイル、[kakei.xlsx](https://drive.google.com/file/d/1gJMVHivmP7R9Qf4LdqRhdPVc3x0IzD8v/view?usp=sharing)を使用します。 \n",
- "\n",
- "\n",
- "以下では、上と同じディレクトリに`kakei.xlsx`を置いたと仮定して \n",
- "処理を行いますので、適宜ご自身の環境にパスを置き換えてください。\n",
- "\n",
- "※もともとはxlrdというライブラリを使って実装していましたが.xlsx形式をサポートしなくなるとのことで、pandasライブラリを用いた実装に変更しました。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "FXWuDrYahMXf"
- },
- "outputs": [],
- "source": [
- "filename = \"/content/drive/My Drive/AdDS/chapter8_data/kakei.xlsx\" #読み込むファイルのパスの指定"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CcnorJJvmAR6"
- },
- "source": [
- "まずはxlsxファイルをPythonで読み込んで、どんな\"シート\"があるのかを確認してみましょう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "vsLXQKlG8WYL",
- "outputId": "7bf4f36d-c5f5-496e-d2ec-a93c7b854a2f"
- },
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "input_file = pd.ExcelFile(filename)\n",
- "sheet_names = input_file.sheet_names\n",
- "print(\"pandas: シート名\",sheet_names)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "SRyFwkbDqGjP"
- },
- "source": [
- "たくさんシートがあることが分かります。 \n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "X1HOxhl6edOF"
- },
- "source": [
- "Sheet1の中身をのぞいてみましょう。まずは行と列の数を取得してみます。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "0YivHM_Yed3Y",
- "outputId": "f86c467c-0249-4f97-dabc-8d0cee8f51e9"
- },
- "outputs": [],
- "source": [
- "Sheet1 = pd.read_excel(filename, sheet_name=\"Sheet1\")\n",
- "print(\"行,列の数\", Sheet1.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "X5mDZqBWgXSD"
- },
- "source": [
- "0-5番目の行にはどんな値がセルに入っているのかな...と思ったら"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "nu6LskLOf7zz",
- "outputId": "8cdbd47d-1f90-460f-e825-74c8bcf45d44"
- },
- "outputs": [],
- "source": [
- "for i in range(5):\n",
- " print( list(Sheet1.iloc[i]) )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "xkzCNbhDpbOT"
- },
- "source": [
- "などとする。このように、扱いたいファイルの\"構造\"を知ることが \n",
- "やりたい操作を系統的に実行するための第一歩です。 \n",
- "このエクセルを実際に開くとSheet1からSheet12までが複数都市の家計調査のデータで \n",
- "S1からS12までが気候データになっていて \n",
- "1-12までの数字が2017年の1月から12月までに対応していることが分かります。\n",
- "\n",
- "実際のデータを触っていると「2006年までとそれ以降とでデータファイル(.xlsx)の\"構造\"が違う」 \n",
- "といったことも出てきます。 \n",
- "最初は特定のものに合わせたコードを作り、徐々に\"汎用性の高い\"コードにしていくのがよいでしょう。\n",
- "\n",
- "このエクセルを使って実際に作業をするには、[細かいライブラリの使い方]などを説明することになるため \n",
- "授業ではやらず、以下の\"おまけ\"にいれておきます。この作業や実践DSに限らず\n",
- "* 自分がやりたい操作をきちんと言語化する\n",
- "* 公式ドキュメントやWebから情報を探す\n",
- "* とにかく試してみる\n",
- "\n",
- "という意識が重要です。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HJJ1Rd9XkITb"
- },
- "source": [
- "### $\\clubsuit$$\\clubsuit$おまけ\n",
- "\n",
- "以下のコードは、プログラミングの\"ありがたみ\"を感じてもらうためのお試し用です。 \n",
- "(昔書いたかなり読みにくいコードなのであまり真剣に読まないでください.)\n",
- "\n",
- "**大量の画像ファイルをドライブに生成するので、以下を読んだ上で実行してください**\n",
- "\n",
- "以下のコードたちを何もいじらずに実行すると、 \n",
- "全都市の月別平均気温と全品目の世帯平均支出のうち、 \n",
- "相関係数の絶対値が0.9以上のもの(291通り)をプロットして画像として保存します。 \n",
- "```pthre```の値を小さくすると、生成される画像の数がとんでもなく増えるのでやらないでください。\n",
- "\n",
- "(0.9 → 291通り, 0.8 → 1234通り, 0.7 → 2871通り, \n",
- " 0.6 → 5233通り, 0.5 → 8375通り, 0.0 → 32876通り)\n",
- "\n",
- "Google Colab上で実行して291枚の画像が生成されるまでに80~150秒程度かかるようです。\n",
- "\n",
- "この時間未満でエクセルで操作をして同様の処理を完了出来るという方は...おそらく地球上にいないでしょう(要出典)\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "ALOn2XR5lfT-"
- },
- "outputs": [],
- "source": [
- "# 画像がいっぱい生成されると面倒なので画像を保存するフォルダを作成しておく\n",
- "!mkdir /content/drive/MyDrive/AdDS/chapter8_data/kakei_cor_pic "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "dsPxfNhrr5e2",
- "outputId": "6d37c43f-3c92-4ff2-84f2-609ba697704d"
- },
- "outputs": [],
- "source": [
- "!pip install japanize_matplotlib "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "rZHYbCjWmXgG",
- "outputId": "81a4fc87-c666-4255-f658-d01ed18d0e7e"
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import pandas as pd\n",
- "from matplotlib import pyplot as plt\n",
- "import japanize_matplotlib\n",
- "import time\n",
- "\n",
- "class ebook:\n",
- " def __init__(self,inpf):\n",
- " self.input_file = pd.ExcelFile(filename)\n",
- " sheet_names = input_file.sheet_names\n",
- " self.sname = sheet_names\n",
- " self.ns = len(sheet_names)\n",
- " print(\"pandas: シート名\",sheet_names)\n",
- " print(\"self.ns\", self.ns)\n",
- "\n",
- " s_kikou=[]; s_kakei=[]\n",
- " for i, sheetname in enumerate(self.sname):\n",
- " if \"Sheet\" in sheetname :\n",
- " s_kakei += [ i ]\n",
- " elif \"S\" in sheetname :\n",
- " s_kikou += [ i ]\n",
- " self.s_kakei,self.s_kikou = s_kakei,s_kikou\n",
- " def indices(self):\n",
- " return self.s_kakei, self.s_kikou\n",
- " def readkakei(self,ikakei) :\n",
- " ws = self.input_file.parse(sheet_name=self.sname[ikakei])\n",
- " nr = ws.shape[0]\n",
- " premode = True\n",
- " items = []\n",
- " for ii in range(nr): \n",
- " trow = list(ws.iloc[ii])\n",
- " hit = 0\n",
- " if premode == True:\n",
- " for jj,tmp in enumerate(trow):\n",
- " if type(tmp) is str:\n",
- " if \"市\" in tmp:\n",
- " hit += 1\n",
- " if hit > 5:\n",
- " premode=False\n",
- " i_kakei=[];p_kakei=[]\n",
- " for jj,tmp in enumerate(trow):\n",
- " if type(tmp) is str:\n",
- " if \"市\" in tmp:\n",
- " i_kakei += [jj]\n",
- " p_kakei +=[ tmp ] \n",
- " v_kakei = [ ]\n",
- " else: \n",
- " if ii >= 22:\n",
- " if type(trow[8]) is str and trow[8] != \"\":\n",
- " v_kakei += [ [trow[jj+1] for jj in i_kakei] ]\n",
- " items += [trow[8]] \n",
- " return i_kakei, p_kakei, v_kakei,items\n",
- " def readkikou(self,ikikou):\n",
- " ws = self.input_file.parse(sheet_name=self.sname[ikikou], header=None)\n",
- " nr = ws.shape[0]\n",
- " quantities = [];v_kikou=[]\n",
- " premode=True\n",
- " for ii in range(nr): \n",
- " trow = list(ws.iloc[ii])\n",
- " if premode :\n",
- " if any([\"市\" in str(tmp) for tmp in trow]):\n",
- " Tplaces = trow[1:]\n",
- " premode=False\n",
- " else:\n",
- " quantities += [ trow[0] ]\n",
- " v_kikou += [ trow[1:] ]\n",
- " return Tplaces, v_kikou,quantities\n",
- "\n",
- "def seasoncolor(month):\n",
- " if month <= 2 or month ==12:\n",
- " return \"blue\"\n",
- " elif 3 <= month <=5:\n",
- " return \"green\"\n",
- " elif 6 <= month <=8:\n",
- " return \"red\"\n",
- " elif 9<= month <=11:\n",
- " return \"orange\"\n",
- " return tcol\n",
- "\n",
- "def plot_cor(x,y,item,quantity,place,corrcoef): \n",
- " fig = plt.figure(figsize=(4,4))\n",
- " ax = fig.add_subplot(1,1,1)\n",
- " ax.set_facecolor(\"#e0e0e0\")\n",
- " ax.set_title(place+\" r=\"+str(\"%8.2f\" % corrcoef).strip())\n",
- " ax.set_xlabel(item);ax.set_ylabel(quantity)\n",
- " ax.grid(True,axis=\"both\",color=\"w\", linestyle=\"dotted\", linewidth=0.8)\n",
- " for i in range(len(x)):\n",
- " tcol=seasoncolor(i+1)\n",
- " ax.scatter(x[i],y[i],marker=\"o\",s=5,color=tcol,zorder=20000,alpha=0.7)\n",
- " ax.text(x[i],y[i],str(i+1)+\"月\",color=\"k\",fontsize=8)\n",
- " plt.savefig(oupdir + \"corr_\"+item+\"vs\"+quantity+\"_at_\"+place+\".png\",dpi=300) \n",
- " plt.close()\n",
- "\n",
- "def calcor(date,places,items, Vs_kakei,Tplaces,quantities,Vs_kikou):\n",
- " hit = 0; num_pic=0\n",
- " Vs = [] \n",
- " for j_K,place in enumerate(places):\n",
- " for j_T, Tplace in enumerate(Tplaces):\n",
- " if place != Tplace :\n",
- " continue\n",
- " for ik,item in enumerate(items):\n",
- " kvalue = np.array([ Vs_kakei[i][ik][j_K] for i in range(len(Vs_kakei))])\n",
- " quantity=quantities[iT]\n",
- " Tvalue = np.array([ Vs_kikou[i][iT][j_T] for i in range(len(Vs_kikou))])\n",
- " if all(Tvalue) == 0.0: ## missing value in climate data\n",
- " continue\n",
- " if printlog:\n",
- " print(\"@\", place,\" \",item,kvalue,\" VS \",quantity, \",\",Tvalue)\n",
- " corrcoef=np.corrcoef(kvalue,Tvalue)[0][1]\n",
- " Vs += [ [ corrcoef, item, quantity, place] ]\n",
- " if abs(corrcoef) > pthre:\n",
- " hit += 1\n",
- " if pltmode==True:\n",
- " plot_cor(kvalue,Tvalue,item,quantity,place,corrcoef) \n",
- " num_pic += 1\n",
- " print(\"hit:\",hit, \" number of picture\", num_pic)\n",
- "\n",
- "if __name__ == \"__main__\":\n",
- " ti=time.time()\n",
- " T=True; F=False\n",
- "\n",
- " inpf = \"/content/drive/My Drive/AdDS/chapter8_data/kakei.xlsx\"\n",
- " oupdir = \"/content/drive/My Drive/AdDS/chapter8_data/kakei_cor_pic/\" #適宜置き換える\n",
- " iT = 6 # iT=6: 日平均気温\n",
- " printlog= F #条件にhitした都市の品目と気候データを逐次printするかどうか. (Fを推奨)\n",
- " pthre= 0.90 ## corrplotを描く相関係数のthreshold \n",
- " pltmode = T ## T:plotする F:計算のみ ** 画像をいちいちplotして保存する必要がない場合Fを推奨\n",
- " year=\"2017\" \n",
- "\n",
- " wb=ebook(inpf)\n",
- " s_kakei,s_kikou=wb.indices() \n",
- " Vs_kakei=[]; Vs_kikou=[];dates=[]\n",
- " for i,ind_kakei in enumerate(s_kakei):\n",
- " i_places,places, v_kakei,items = wb.readkakei(ind_kakei)\n",
- " Tplaces, v_kikou, quantities = wb.readkikou(s_kikou[i])\n",
- " if i+1 < 10:\n",
- " date=year+\"0\"+str(i+1)\n",
- " else:\n",
- " date=year+str(i+1)\n",
- " dates += [date]\n",
- " Vs_kakei += [ v_kakei ]\n",
- " Vs_kikou += [ v_kikou ]\n",
- " calcor(dates,places,items,Vs_kakei,Tplaces,quantities,Vs_kikou) \n",
- "\n",
- " tf=time.time()\n",
- " print(\"Elapced time[sec]:\", tf-ti)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "riw7f35TRWsD"
- },
- "source": [
- "## 余談: 電子ファイルのフォーマット\n",
- "\n",
- "プログラムでデータを機械的に読み出して活用することで、人間が到底出来ないような作業効率を実現することができる場合も多い。\n",
- "そんな光の側面ばかりなら良いが、実際にはそう上手くは行かないことも多い。\n",
- "\n",
- "業務のデジタル化・デジタルトランスフォーメーションなどといった標語とは裏腹に、世の中にあふれるcsv,スプレッドシートなどは、\n",
- "csvと謳っておいて、実際にはカンマ区切りではなくタブ区切りであったり、機械判読を全く想定していないデータの書き方・並べ方となっているものが多く、プログラムを書ける人にとっては苦痛な状況も多い。 \n",
- "\n",
- "総務省統計局は令和2年2月に、政府統計(e-Stat)に関して[統計表における機械判読可能なデータの表記方法の統一ルールの策定](https://www.soumu.go.jp/menu_news/s-news/01toukatsu01_02000186.html)というものを出している。\n",
- "これが最適な提案かはさておき、データの記述に法則性と機械判読性をもたせる意識を全員が持つことが重要なように思う。\n",
- "\n",
- "お掃除ロボットが床を綺麗にするためには、まずお掃除ロボットが走れるよう掃除する(床に物が散乱していない)という条件が求められる、という話だ(そうなの？)。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "jH_bE8PSMP89"
- },
- "source": [
- "## パスの指定\n",
- "\n",
- "ファイルがコンピュータ上でどこにあるかを指し示す文字列はファイルパス(path)と呼ばれる。 \n",
- "```\"/content/drive/My Drive/XXX.png\"```もファイルパスの一例になっている。\n",
- "\n",
- "たとえば... \n",
- ">[Sota]というユーザの[ドキュメント] (あるいは[書類])フォルダに \n",
- "[csv_file]というフォルダがあり[test.csv]というcsvファイルが入っている\n",
- "\n",
- "とするとそのファイルを指し示すパスは \n",
- "Windowsの場合→ ```C:\\Users\\Sota\\Douments\\csv_file\\test.csv``` \n",
- "macOSの場合→ ```/Users/Sota/Documents/csv_file/test.csv```\n",
- "となる。\n",
- "\n",
- "注: \n",
- "* Windowsの場合→\"C\"の部分は皆さんのディスク環境に依存\n",
- "* Google Colab.環境では、Unix(Mac)やLinuxと同様の方式(スラッシュを使用) \n",
- "* バックスラッシュ\\はWindowsの日本語環境では¥円記号で表示される \n",
- "(プログラムなどを書く人には厄介な仕様だったりする) \n",
- "\n",
- "コンピュータには、ホームディレクトリというものが指定されておりWindowsなら ```C:\\Users\\ユーザー名```,Macなら ```/Users/ユーザー名```に通常設定されていて、ユーザーがよく使うデスクトップや写真・ドキュメントなどのフォルダはホームディレクトリ直下に配置されている。また、ホームディレクトリは```~/```で簡略化して指定することもできる。\n",
- "OSにもよるが...ライトユーザーはホームディレクトリより上の階層をあまり触らないことが推奨されている(と思う)。理由は、システムファイルが入っていることが多いため。\n",
- "\n",
- "パスの指定の仕方にはその他にも方法があり、ピリオドやスラッシュを駆使して現在のディレクトリからの[相対パス]で指定する事もできる。たとえば...\n",
- "\n",
- "Home \n",
- "├ Documents \n",
- "│└─ AdDS2021 \n",
- "││ └─ Report1 \n",
- "│└─ AdDS2020 \n",
- "││ └─ Report1 \n",
- "││ │ └─ StudentA \n",
- "││ │ └─ StudentB \n",
- "││ └─ Report2 \n",
- "│└─ AdDS2019 \n",
- "├ Picures \n",
- "︙\n",
- "\n",
- "こういう階層構造になっていて、現在```Home/Documents/AdDS2020/Report1```という\n",
- "ディレクトリにいるとすると、そこから\n",
- "* StudentAへの相対パスは ```./StudentA```\n",
- "* Report2への相対パスは ```../Report2```\n",
- "* AdDS2019への相対パスは ```../../AdDS2019```\n",
- "* Pictureへの相対パスは```../../../Pictures```\n",
- "\n",
- "といった感じ。前述のように愚直にReport1フォルダを指定するときは```/Users/Sota/Documents/AdDS2020/Report1```といった感じで、これを相対パスと対比させて絶対パスと呼んだりする。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "kkrEMQUfhqzA"
- },
- "source": [
- "### 余談: ファイル名に使用すべきでない文字\n",
- "\n",
- "授業で公開しているノートブックの名前は基本的に半角英数字とアンダースコアのみで構成されている。これは別に作成者(吉田)がイキってる訳ではない。\n",
- "\n",
- "* 半角スペース(以下␣という記号で表現する)\n",
- "* 各種括弧 (),{},[]\n",
- "* カンマ ,\n",
- "* ピリオド .\n",
- "* ハイフン -\n",
- "* スラッシュ /\n",
- "* エクスクラメーションマーク !\n",
- "* 円記号(バックスラッシュ) ¥\n",
- "* その他、機種依存文字などはもちろん、全角記号等\n",
- "\n",
- "などは、(プログラムで扱う予定がある)ファイルの名前には使用しないことが推奨される。その理由は色々あるが\n",
- "\n",
- "1. 機械の解釈にambiguity(あいまいさ)が生じる\n",
- "2. (1.により人間側の操作が増えて)面倒\n",
- "\n",
- "というところに尽きる。例を示そう。 \n",
- "Google Colab.上では冒頭に!を付けることで、以下に例を示すようなLinuxコマンドを実行できる。\n",
- "\n",
- "```!ls hogehoge.pdf``` #← lsコマンド リスト(該当ファイル等)を表示 \n",
- "```!mkdir hogehoge``` #← make directoryコマンド \n",
- "```!rm hogehoge``` #←remove(削除)コマンド "
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "id": "i7-Lssixk1ig"
- },
- "source": [
- "たとえば半角スペースが入った```test␣.pdf```というファイルがあったとする。 \n",
- "これをlsコマンドで表示させようとして\n",
- "\n",
- "```\n",
- "!ls test .pdf\n",
- "```\n",
- "\n",
- "という命令を行うと、```test␣.pdf```という指定ではなく```test```と```.pdf```という2つのファイルが存在するかどうかを尋ねる命令になってしまう。 \n",
- "この場合、```test␣.pdf```の有無を調べたければ、別途バックスラッシュを入れて「記号としての空白です」と機械に教えなくてはならない。 \n",
- "```\n",
- "!ls test\\ .pdf\n",
- "```\n",
- "\n",
- "といった具合に、人間側の手間が必要になってしまう。 \n",
- "人間が目で見るフォルダ名と機械に与えるべきパスが異なるというのは...やっぱり色んな場面で不便が生じる。\n",
- "上記の記号や２バイト以上の文字はファイル(フォルダ)名に使わないのがコンピューターにとっては無難なのだ。\n",
- "こういうことは小中高や大学でも理由付きで教えてくれなかったりするので、プログラミングをやって初めて気がつく(気にするようになった)という人も多いかもしれない。\n"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "Python_chapter8_handling_files.ipynb",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3.9.13 64-bit",
- "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": "3.9.13"
- },
- "vscode": {
- "interpreter": {
- "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_chapter_ArtificialNeuralNetwork.ipynb b/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_chapter_ArtificialNeuralNetwork.ipynb
deleted file mode 100644
index 8614dbdc..00000000
--- a/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_chapter_ArtificialNeuralNetwork.ipynb
+++ /dev/null
@@ -1,1168 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "colab_type": "text",
- "id": "view-in-github"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "sKor3X3ievgT"
- },
- "source": [
- "# 機械学習: ニューラルネットワークによる回帰"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4qZBy99AnBTn"
- },
- "source": [
- "\n",
- "\n",
- "この章では、最も単純な、入力層・隠れ層・出力層からなるニューラルネットワークを使って、データから尤もらしい予測を与える関数を構築してみましょう。\n",
- "\n",
- "対象とする(疑似)データは、多項式回帰の際に用いたデータと同じsin関数＋ノイズで生成することにします。\n",
- "\n",
- "\n",
- "* すすんだ注: このノートブックでは「ニューラルネットワークをPythonで表現してみる」ことに重きをおくため、使用するデータを訓練データ,検証データ,テストデータに分けることはせず、データは全てニューラルネットワークの訓練データとして使うこととします。\n",
- "授業で説明するとおり、一般に[教師あり学習]の文脈でニューラルネットワークを考える際は、本来データを上の様に複数用途に分けながら、モデル選択を行ったり、汎化性能の評価に使ったりします。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 135,
- "metadata": {
- "id": "nme6EtxcerdN",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "def create_toy_data(sample_size, std):\n",
- " np.random.seed(1234) #毎回同じデータになるように乱数の種を固定しておく \n",
- " x = np.linspace(0, 0.5, sample_size)\n",
- " t = np.sin(2*np.pi*x) + np.random.normal(scale=std, size=x.shape) \n",
- " return x, t\n",
- "\n",
- "xt,yt = create_toy_data(40,5.e-2) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 136,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 279
- },
- "id": "kZDRP8GFCSl1",
- "outputId": "b2d6c33e-26ad-45d1-ed2a-30b914602522",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "zy7aOwXne87b"
- },
- "source": [
- "# ベイズ最適化による実験計画法"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "WSCRPDv9bBpz"
- },
- "source": [
- "以下では、ベイズ最適化を用いた実験計画法を見てみよう。\n",
- "\n",
- "数学的部分やコードの詳細よりも「なんとなくこのあたりを探索しようかな」といった \n",
- "人間の経験に依る部分を客観的な方法で置き換えた実験計画の方法論の強力さを感じることが目的なので難しいところはスキップしても構わない。\n",
- "\n",
- "ガウス過程の基本や詳細は[補足ノート](https://github.com/SotaYoshida/Lecture_DataScience/blob/main/notebooks/GPnote.pdf)に譲る.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "id": "XWIv70U6e6In",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "#使うライブラリのインポート\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import copy\n",
- "from scipy import special\n",
- "\n",
- "## データの生成用関数\n",
- "def f(x):\n",
- " return np.sin(x) + 0.2 * x\n",
- "\n",
- "## ガウス過程のカーネル(共分散行列)の設計\n",
- "def Mat52(Thetas,r):\n",
- " tau,sigma = Thetas\n",
- " thetar = r * np.sqrt(5.0)/sigma\n",
- " return tau * (1.0 + thetar + (thetar**2) /3.0) * np.exp(-thetar)\n",
- "def KernelMat(Thetas,xt,xp):\n",
- " lt = len(xt); lp=len(xp)\n",
- " Ktt = np.zeros((lt,lt)); Kpt = np.zeros((lp,lt)); Kpp = np.zeros((lp,lp))\n",
- " for j in range(lt):\n",
- " for i in range(j,lt):\n",
- " r = abs(xt[i]-xt[j])\n",
- " tmp = Mat52(Thetas,r) \n",
- " Ktt[i,j] = tmp; Ktt[j,i] = tmp \n",
- " for i in range(lp):\n",
- " r= abs(xp[i]-xt[j])\n",
- " Kpt[i,j] = Mat52(Thetas,r)\n",
- " for j in range(lp):\n",
- " for i in range(j,lp):\n",
- " r= abs(xp[i]-xp[j])\n",
- " tmp = Mat52(Thetas,r)\n",
- " Kpp[i,j] = tmp; Kpp[j,i] = tmp\n",
- " return Ktt,Kpt,Kpp\n",
- "\n",
- "## 事後共分散行列の計算\n",
- "def calcSj(cLinv,Kpt,Kpp,yt,mu_yt,mu_yp):\n",
- " tKtp= np.dot(cLinv,Kpt.T)\n",
- " return mu_yp + np.dot(Kpt,np.dot(cLinv.T,np.dot(cLinv,yt-mu_yt))), Kpp - np.dot(tKtp.T,tKtp)\n",
- "\n",
- "## Cholesky分解\n",
- "def Mchole(tmpA,ln) :\n",
- " cLL = np.linalg.cholesky(tmpA)\n",
- " logLii=0.0\n",
- " for i in range(ln):\n",
- " logLii += np.log(cLL[i,i])\n",
- " return np.linalg.inv(cLL), 2.0*logLii\n",
- "\n",
- "## 獲得関数を計算, 次点の計算点を決める\n",
- "def calcEI(xp,mujoint,sigmaj,xbest,ybest):\n",
- " EIs = [ (mujoint[i]-ybest) * Phi((mujoint[i]-ybest)/sigmaj[i]) +\n",
- " sigmaj[i]* np.exp(-0.5* ((mujoint[i]-ybest)/sigmaj[i])**2) for i in range(len(xp))]\n",
- " xnew,ynew,ind=xybest(xp,EIs)\n",
- " ynew= np.sin(xnew) + 0.2*xnew #+ 0.01 * (0.5-np.random.rand())\n",
- " return xnew,ynew,EIs,ind\n",
- "def Phi(z):\n",
- " return 0.5 * special.erfc(-(z/(2**0.5)) )\n",
- "\n",
- "def xybest(xt,yt):\n",
- " ind = np.argmax(yt)\n",
- " return xt[ind],yt[ind],ind\n",
- "\n",
- "## お絵かき\n",
- "def plotGP0(xt,yt,xp,ytrue):\n",
- " fig = plt.figure(figsize=(8,4))\n",
- " axT = fig.add_subplot(1,1,1)\n",
- " axT.set_xlabel(\"x\"); axT.set_ylabel(\"y\")\n",
- " axT.set_xlim(-2.0,12); axT.set_ylim(-2.0,5.0)\n",
- " axT.scatter(xt,yt,marker=\"o\",color=\"black\",label=\"Data\")\n",
- " axT.plot(xp,ytrue,color=\"red\",label=\"True\",linestyle=\"dotted\")\n",
- " axT.legend(loc=\"upper right\")\n",
- " plt.show()\n",
- " #plt.savefig(\"BayesOpt_initial.pdf\",bbox_inches=\"tight\", pad_inches=0.1)\n",
- " plt.close()\n",
- "\n",
- "def plotGP(nxt,nyt,nxp,xp,ytrue,mujoint,sigmaj,ysamples,EIs):\n",
- " fig = plt.figure(figsize=(16,4))\n",
- " axT = fig.add_subplot(121)\n",
- " axB = fig.add_subplot(122)\n",
- " axT.set_xlabel(\"x\"); axT.set_ylabel(\"y\")\n",
- " axB.set_xlabel(\"x\"); axB.set_ylabel(\"Acquisition function\")\n",
- " axT.set_xlim(-2.0,12); axT.set_ylim(-2.0,5.0)\n",
- " axB.set_xlim(-2.0,12)\n",
- " axT.scatter(nxt,nyt,marker=\"o\",color=\"black\",label=\"Data\")\n",
- " for i in range(len(ysamples)):\n",
- " axT.plot(nxp,ysamples[i],alpha=0.1)\n",
- " axT.plot(nxp,mujoint,label=\"GP mean\",linestyle=\"dashed\",color=\"blue\")\n",
- " axB.plot(nxp,EIs,color=\"green\")\n",
- " axB.set_yticklabels([]) \n",
- " axT.fill_between(nxp,mujoint-sigmaj,mujoint+sigmaj,color=\"blue\", alpha=0.3)\n",
- " axT.plot(xp,ytrue,color=\"red\",label=\"True\",linestyle=\"dotted\")\n",
- " axT.legend(loc=\"upper right\")\n",
- " plt.show()\n",
- " plt.close()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 283
- },
- "id": "jajF8gfhfJOo",
- "outputId": "ce535d2e-ce2a-4a28-fc5c-f280bd89958a",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Qlp8TMCwqzju"
- },
- "source": [
- "# ベイズ線形回帰"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "qDs_Ob0fwnVN"
- },
- "source": [
- "## 線形回帰 (多項式回帰の例)\n",
- "\n",
- "\n",
- "線形回帰モデル: \n",
- "$y({\\bf x},{\\bf w})= \\sum^{M-1}_{j=0} w_j \\phi_j({\\bf x}) = {\\bf w}^T\\boldsymbol{\\phi}({\\bf x})$を考える。 \n",
- "\n",
- "ここで$\\boldsymbol{\\phi}$は基底関数のベクトルで、\n",
- "$p$次の多項式回帰の場合は$\\boldsymbol{\\phi}=(1, x, x^2, \\ldots, x^p)$といった感じ。\n",
- "\n",
- "実際この場合にベクトルの内積を書き下すと\n",
- "${\\bf w}^T\\boldsymbol{\\phi}=w_0 + w_1x+ w_2x^2+ \\cdots + w_{p}x^p$となり、\n",
- "$p$次多項式になっている。\n",
- "\n",
- "\n",
- "さて目標変数$t$が、決定論的な関数$y({\\bf x},{\\bf w})$と平均0,精度$\\beta\\equiv 1/\\sigma^2$の誤差$\\epsilon$の和: $t=y({\\bf x},{\\bf w})+\\epsilon$で与えられていると仮定する。 \n",
- "\n",
- "つまり、$t$の確率分布が\n",
- "$p(t|{\\bf x},{\\bf w},\\beta) = \\mathcal{N}(t|y({\\bf x},{\\bf w}),\\beta^{-1})$で与えられるとする。\n",
- "\n",
- "\n",
- "入力$\\boldsymbol{\\mathrm{X}}=\\{{\\bf x_1},{\\bf x_2},\\ldots, {\\bf x_N}\\}$と、\n",
- "対応する目標値を$\\boldsymbol{\\mathrm{t}}=\\{t_1,t_2,\\ldots, t_N\\}$と表すことにしておこう。\n",
- "\n",
- "これらのデータ点が上の分布$p(t|{\\bf x},{\\bf w},\\beta)$から独立に生成されたと仮定すると、 \n",
- "データの目標変数についての確率分布は$p(\\boldsymbol{\\mathrm{t}}|\\boldsymbol{\\mathrm{X}},{\\bf w},\\beta) = \\prod^N_{n=1} \\mathcal{N}(t_n|{\\bf w}^T \\boldsymbol{\\phi}({\\bf x_n}) ,\\beta^{-1})$で与えられる。 \n",
- "(以下では、確率分布の条件部分にある${\\bf x}$などを適宜省略する)\n",
- "\n",
- "このとき、対数尤度は\n",
- "$\\ln p(\\boldsymbol{\\mathrm{t}}|{\\bf w},\\beta) = \\sum^N_{n=1} \\ln \\mathcal{N}(t_n|{\\bf w}^T\\boldsymbol{\\phi}({\\bf x_n}) ,\\beta^{-1}) = \\frac{N}{2}\\ln\\beta - \\frac{N}{2}\\ln(2\\pi) -\\beta E_D({\\bf w})$となる。\n",
- "\n",
- "なお$E_D$は二乗和誤差関数:\n",
- "$E_D({\\bf w}) \\equiv \\frac{1}{2}\\sum^N_{n=1} \\{ t_n- {\\bf w}^T \\boldsymbol{\\phi}({\\bf x_n}) \\}$\n",
- "である。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "FjSnT2V6wk08"
- },
- "source": [
- "対数尤度の勾配を書き下して、最尤推定を考えるとすると$\\nabla \\ln p(\\boldsymbol{\\mathrm{t}}|{\\bf w},\\beta) = \\beta \\sum^N_{n=1} \\{ t_n -{\\bf w}^T \\boldsymbol{\\phi}({\\bf x_n}) \\} \\boldsymbol{\\phi}({\\bf x_n})^T $の根が必要。\n",
- "\n",
- "つまり$0= \\sum^N_{n=1} t_n \\boldsymbol{\\phi}({\\bf x_n})^T - {\\bf w}^T(\\sum^N_{n=1}\\boldsymbol{\\phi}({\\bf x_n})\\boldsymbol{\\phi}({\\bf x_n})^T)$をときたい。\n",
- "\n",
- "上の式を変形すると${\\bf w} = ( \\boldsymbol{\\Phi}^T\\boldsymbol{\\Phi} )^{-1} \\boldsymbol{\\Phi}^T \\boldsymbol{\\mathrm{t}}$が根であることがわかる。 \n",
- "つまり、最尤推定を与えるパラメータ${\\bf w}_{ML}$は、計画行列$\\boldsymbol{\\Phi}$を用いて閉じた形で与えられる。\n",
- "\n",
- "なお、計画行列の要素は\n",
- "$\\boldsymbol{\\Phi}_{nj} = \\phi_j({\\bf x_n})$で与えられる。\n",
- "\n",
- "\n",
- "\n",
- "---\n",
- "\n",
- "次の１次元擬似データの回帰を考えてみる。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 320
- },
- "id": "Had64uyx6ieC",
- "outputId": "1dcbff3d-7d29-4bb7-84a5-4c65b3715e49",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hZr60g6XehqN"
- },
- "source": [
- "# Web操作・スクレイピング"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "y6bjRQEEPOur"
- },
- "source": [
- "Webから情報を抽出・整形・解析したり、 \n",
- "ブラウザ上での特定の操作を自動化する、といったことも \n",
- "Pythonでは比較的容易に実行することができる。\n",
- "\n",
- "Web上にある情報にアクセスしたりする方法は色々あるが、大まかには２つに分類され\n",
- "\n",
- "1. プログラムでWebページにアクセスして中身(HTML)を読み、そこから情報を抽出する方法\n",
- "2. ブラウザをプログラムに操作させて特定の作業を実行する方法\n",
- "\n",
- "この章では、とくに1.のWebから情報を抽出すること(スクレイピング)に絞ってそのエッセンスを紹介する。 \n",
- "HTMLとは、Webページを作るための言語のことで、 \n",
- "みなさんが普段からアクセスするほぼ全てのWebページは、 \n",
- "HTMLで記述されています。\n",
- "\n",
- "\n",
- "Webから情報を抽出したりする際、共通して言える注意点を述べておく:\n",
- "* **対象とするページの利用規約を必ず確認する** \n",
- "規約でスクレイピングを禁止しているページがある (例: Amazon, Twitter, Instagram, facebook, 金融系などなど) \n",
- " 禁止している場合でも、APIが提供されている場合があります \n",
- " ※APIはApplication Programming Interfaceの略です。 \n",
- " 今の場合、大雑把にはデータ提供用の窓口とでも思ってください.\n",
- "\n",
- "* **サーバーに負荷をかけない** \n",
- " 規約で特にスクレイピングを禁止していない場合でも、過度なアクセスをしてはいけません。 \n",
- " (どこかの大学の教務ポータルみたいに落ちてしまったら大変です) \n",
- " 過度なアクセスは、悪意のあるDos攻撃とみなされてアクセスを制限されたり、 \n",
- " 最悪の場合、偽計業務妨害罪などの罪に問われる可能性があります。\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4F9VwZUYTv-k"
- },
- "source": [
- "\n",
- "## 東京都の新型コロナウイルス関連情報の取得\n",
- "\n",
- "スクレイピングを可能とするライブラリは多数存在する。 \n",
- "代表的なものは```requests```や```urllib```。以下では```requests```を使います。 \n",
- "\n",
- "JavaScriptの実行などがないページならこれでだいたい十分かと思います. \n",
- "また```BeutifulSoup4```というライブラリを使うと、HTMLを構文解析して、 \n",
- "タグなどを取り除くといった操作が比較的容易に行えます。\n",
- "\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "1つ目の例として、[東京都 新型コロナウイルス感染症対策サイト](https://stopcovid19.metro.tokyo.lg.jp/)の公開データを取得して \n",
- "それをグラフにして可視化してみましょう。まずは必要なライブラリをインポートしておきます。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "up8cPse8AetS",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "import requests\n",
- "import json\n",
- "import datetime\n",
- "import matplotlib.pyplot as plt\n",
- "!pip install japanize-matplotlib \n",
- "import japanize_matplotlib "
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "id": "BnQLXHKjFjBq"
- },
- "source": [
- "[東京都 新型コロナウイルス感染症対策サイト](https://stopcovid19.metro.tokyo.lg.jp/)の[レポジトリ](https://github.com/tokyo-metropolitan-gov/covid19)で公開されているデータ(json形式)を読み出す。\n",
- "\n",
- "指定したurlにリクエストを送り、レスポンスをjson形式で取得する"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "T_1P0zx8AkK2",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "url = \"https://raw.githubusercontent.com/tokyo-metropolitan-gov/covid19/development/data/data.json\"\n",
- "response = requests.get(url)\n",
- "data = response.json()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "SKHu3BaJGHV7"
- },
- "source": [
- "上の`data`を直接`print`すると表示が冗長になるため \n",
- "代わりに、jsonのキーとして何があるかを見てみよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "pYuurgg8GQt0",
- "outputId": "daada50f-ae8c-41fb-ae69-8173e3c676b9",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "dict_keys(['contacts', 'querents', 'patients_summary', 'inspections_summary', 'lastUpdate', 'main_summary'])"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "data.keys()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "MauYUeNZGSUt"
- },
- "source": [
- "それぞれ\n",
- "* contacts: 新型コロナコールセンター相談件数\n",
- "* querents: 帰国者・接触者電話相談センター相談件数\n",
- "* patients_summary: 陽性患者数\n",
- "* inspections_summary: 検査実施日別状況\n",
- "* lastUpdate: 最終更新日時\n",
- "* main_summary: (これはよくわからない)\n",
- "\n",
- "に対応している。\n",
- "\n",
- "陽性患者数のデータの構造を調べつつ結果を可視化してみよう。 \n",
- "まずは型を調べてみる。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "NkQkByDHH4d_",
- "outputId": "a19e1dc9-5625-4cb3-8df2-20eb4305894d",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4_5p-ivElTgJ"
- },
- "source": [
- "# よくあるエラー集"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "JvTtKsM5pmDk"
- },
- "source": [
- "このノートブックにはPythonを触っていてよく起こりがちなエラーの例をまとめます. \n",
- "**皆さんの手元で起こったエラーの例も共有したいので、ぜひ教えてください**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "eYKyKUoYmDRE"
- },
- "source": [
- "\n",
- "エラーには、いろいろな分類の仕方がありますが代表的なものは以下のとおりです:\n",
- "\n",
- "* 構文エラー (syntax error)\n",
- "* ランタイムエラー (runtime error) または 例外 (exception)\n",
- "* 論理エラー (logic error)\n",
- "\n",
- "これらの代表的な例を以下で紹介します。\n",
- "\n",
- "pythonのようなインタプリタ言語では、実行時にコードの有効性が検証されるため、構文エラーはランタイムエラーとも言えますが、エラーメッセージに従って分類することにします。\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "SpDDGwnLnKqK"
- },
- "source": [
- "## 構文エラー\n",
- "\n",
- "構文エラーとは、プログラミング言語の文法・構文規則に違反していることによるエラーです。 \n",
- "代表的なものは\n",
- "\n",
- "* 括弧や引用符の閉じ忘れ\n",
- "* コロンのつけ忘れ\n",
- "* ピリオドとカンマの間違い\n",
- "* 全角記号の使用\n",
- "* ```=```(代入)と```==```(等号)の混同\n",
- "* 不適切なインデント\n",
- "\n",
- "などがあります。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "aZyMaLbLoQK-"
- },
- "source": [
- "**引用符のつけ忘れ**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 134
- },
- "id": "t_Q-dc0IoO0r",
- "outputId": "5c62d651-f031-4ea7-b479-6304fc6a9bbb"
- },
- "outputs": [
- {
- "ename": "SyntaxError",
- "evalue": "ignored",
- "output_type": "error",
- "traceback": [
- "\u001b[0;36m File \u001b[0;32m\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "aLf3PqMgkFNC"
- },
- "source": [
- "# ニュートン法によるN次元多項式の求根\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0ZIFvSR9kOo9"
- },
- "source": [
- "$n$次元多項式$f(x) = c_0 + c_1 x + c_2 x^2 + \\ldots + c_nx^n$が与えられた時、 \n",
- "$f(x)=0$となるような解(根)をどのように求めたら良いだろうか？\n",
- "\n",
- "$n=2$までの場合については、中学までで習うようによく知られた公式がある。 \n",
- "$n=3,4$の場合についてもそれぞれ、[カルダノの公式]や[フェラリの解法]として知られる \n",
- "代数的な求根法が存在する。 \n",
- "なお、5次以上の代数方程式には代数的な解法※が存在しないことが知られている(アーベル-ルフィニの定理) \n",
- "(※方程式の係数の有限回の四則演算および冪根操作で解を表示すること)\n",
- "\n",
- "\n",
- "方程式の解や積分値などが代数的/解析的に解けない場合は、もちろん数値計算の出番となる。 \n",
- "\n",
- "このノートブックでは、ニュートン法(あるいはニュートン・ラフソン法)として知られる求根アルゴリズムを紹介する。 "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "tPW10YYsnOHs"
- },
- "source": [
- "## ニュートン法"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ZnosTDdTnPFL"
- },
- "source": [
- "ニュートン法による関数$f(x)=0$の求根アルゴリズム自体は非常にシンプルで\n",
- "\n",
- "1. 初期値$x \\in \\mathbb{R} $ (または$x \\in \\mathbb{C})$を決める\n",
- "2. 上の初期値を$x_0$とでも呼ぶことにして、 \n",
- " $ x_{t+1} = x_t - f(x_t) / f'(x_t) $ と$x$を更新し \n",
- " $x$の値が収束するまで更新を繰り返す\n",
- "\n",
- "というものである。 \n",
- "\n",
- "$\\clubsuit$ 進んだ注 \n",
- "高次元のベクトル$\\boldsymbol{x}$に拡張することもできる。 \n",
- "$ \\boldsymbol{x}_{t+1} = \\boldsymbol{x}_{t} - \\partial f(\\boldsymbol{x}_t)^{-1} f(\\boldsymbol{x}_t)$\n",
- "$\\partial f(\\boldsymbol{x}_t)$はヤコビ行列になるが、 \n",
- "数値計算上の困難が有り、実際に解く場合には様々な工夫が必要となる。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "lP5pa0eSpqfz"
- },
- "source": [
- "### ニュートン法のアルゴリズムの実装例\n",
- "\n",
- "$n=5$の多項式の例を用いて、ニュートン法のアルゴリズムを実装してみよう。 \n",
- "実数係数の多項式の場合、非ゼロの係数を持つ最大の$n$が奇数次ならば、 \n",
- "関数$f(x)$は必ず実軸を横切る(つまり、$f(x)=0$の解が存在する。 \n",
- "\n",
- "まず係数を適当に...\n",
- "$c_0 = 1.0, c_1 = 1.0, c_2 = 2.0, c_3 = 3.0, c_4 = 4.0, c_5 = 5.0$\n",
- "とでもして、プロットしてみよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 276
- },
- "id": "mDpCwZF0kEnj",
- "outputId": "c5eb065f-87bf-42d0-fc38-1c57f88b8848"
- },
- "outputs": [
- {
- "data": {
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SJElqnEvAJEmSJEmSGmcCSJIkSZIkqXEmgCRJkiRJkhpnAkiSJEmSJKlxJoAkSZIkSZIaZwJIkiRJkiSpcSaAJEmSJEmSGvcTIpMUKhx0SIAAAAAASUVORK5CYII=",
- "text/plain": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "wVdYiDLFHEEL"
- },
- "source": [
- "# 常微分方程式の数値解法"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "MO6-C742mcJ-"
- },
- "outputs": [],
- "source": [
- "#使用するモジュールのimport\n",
- "import numpy as np\n",
- "from matplotlib import pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "JxV3JSWOd-W8"
- },
- "source": [
- "## 常微分方程式"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "AGBSpCwiHIgY"
- },
- "source": [
- "多くの自然現象や社会現象は微分方程式として定式化される。\n",
- "\n",
- "身近な例としてはニュートンの運動方程式にはじまり、\n",
- "金融や群集動態、感染症のモデルなどにも当てはまる。\n",
- "\n",
- "このノートでは、微分方程式のうちとくに一変数関数の場合である \n",
- "常微分方程式(ordinary differential equation, ODE)について \n",
- "いくつかの代表的な数値解法を解説するとともに、 \n",
- "アルゴリズムを自分で実装してみよう。\n",
- "\n",
- "例えば1階の常微分方程式は、一般に以下の形で書くことが出来る: \n",
- "$dx(t)/dt = f(x,t)$ (Eq. 1)\n",
- "\n",
- "これは、ある質点の時刻$t$での座標を$x(t)$と呼ぶことにすると、 \n",
- "質点の位置の変化が$f(x,t)$の関数として書けるような状況に相当する。\n",
- "\n",
- "注) 別に$t$は時間である必要はないし、 \n",
- "$x(t)$は座標以外の量(金融商品の価格など)何でも構わない。\n",
- "\n",
- "\n",
- "一般に常微分方程式が与えられた時、まずはその解が変数分離などを用いて \n",
- "閉じた形で書き下せないかと考えるわけだが、 \n",
- "実際の多くの問題ではそうは上手く行かず、数値計算が必要となる。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "duBNxeB_cUMU"
- },
- "source": [
- "以下では、主として$dx/dt = f(x,t)$を考えるが、 \n",
- "高階の微分を含む場合も、\n",
- "\n",
- "$$\n",
- "\\left[\n",
- "\\begin{matrix} \n",
- "x_1 \\\\ \n",
- "x_2 \\\\\n",
- "\\vdots \\\\\n",
- "x_r\n",
- "\\end{matrix}\n",
- "\\right]\n",
- "= \n",
- "\\left[\n",
- "\\begin{matrix} \n",
- "x \\\\ \n",
- "dx/dt \\\\\n",
- "\\vdots \\\\\n",
- "d^{r-1}x/dt^{r-1}\n",
- "\\end{matrix} \\right]\n",
- "$$\n",
- "\n",
- "と置き換えれば、連立1階常微分方程式:\n",
- "\n",
- "$$\n",
- "\\left[\n",
- "\\begin{matrix} \n",
- "x_2 \\\\ \n",
- "x_3 \\\\\n",
- "\\vdots \\\\\n",
- "x_{r}\n",
- "\\end{matrix}\n",
- "\\right]\n",
- "= \n",
- "\\frac{d}{dx}\n",
- "\\left[\n",
- "\\begin{matrix} \n",
- "x_1 \\\\ \n",
- "x_2 \\\\\n",
- "\\vdots \\\\\n",
- "x_{r-1} \n",
- "\\end{matrix} \\right]\n",
- "$$\n",
- "とみなすことが出来る。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "brvU1jS2W0NK"
- },
- "source": [
- "\n",
- "常微分方程式の解を求めるためには、 \n",
- "適切な条件(初期条件や境界条件)を設定しなければならない。\n",
- "\n",
- "そのうち、変数$t$の最初の値(初期値,初期条件)$t_0$と \n",
- "$x(t=t_0)$の値が与えられた問題を初期値問題と呼ぶ。 \n",
- "※$t_0$は問題によって0だったり、$-\\infty$だったりする。\n",
- "\n",
- "\n",
- "解の存在性や下記のリプシッツ条件など適切な条件の元での \n",
- "解の一意性の証明等については教科書に譲る。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "o2JwiqJAd2LL"
- },
- "source": [
- "###リプシッツ条件"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ipc62VyEeFXo"
- },
- "source": [
- "関数$f$が$U$(たとえば$R^n$の部分集合) 上でリプシッツ(Lipshitz)連続であるとは、 \n",
- "正実数$K$が存在し、任意の$x,y \\in U$に対し、 \n",
- "$||f(x)-f(y)|| \\leq K ||x −y||$が成立することである。 \n",
- "以下では$||\\cdot||$はユークリッドノルムとし、$K$をリプシッツ定数と呼ぶ。\n",
- "\n",
- "この$K$が存在しているとき、 \n",
- "前述の常微分方程式の初期値問題は一意の解を持つ。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "3BMukvRtgdZI"
- },
- "source": [
- "## 代表定期な数値解法"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-pwbBdUBgjMr"
- },
- "source": [
- "### オイラー(Euler)法"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "JrC4_IHYgu8-"
- },
- "source": [
- "1階の常微分方程式$dx(t)/dt=f(x,y)$を数値的に解く上で \n",
- "最も基本的な考え方は時刻(便宜上そう呼ぶ)を \n",
- "細かく分割した上で、$x$の任意時刻$t$での近似値を \n",
- "初期値から初めて逐次的に求めていくことである。 \n",
- "\n",
- "以下では、その最も単純な例であるオイラー法を説明する。\n",
- "\n",
- "微分の定義:\n",
- "$dx(t)/dt = \\lim_{h\\to 0} \\frac{x(t+h) - x(t)}{h}$から \n",
- "微小変分$h$を十分小さく取れば、 \n",
- "$x_{i+1} = x_{i} + h f(x,t_i)$という近似式が得られる。\n",
- "\n",
- "つまり、$i=0$の初期値$(x_0,t_0=0)$から順番に上の漸化式を用いて、 $i$番目の時刻$t_i$での座標$x_i$を推定していく。\n",
- "\n",
- "※一般に微小変分$h$は各ステップ$i$に依存しても良いが、簡単のため共通とした。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VgupCOzCjPwO"
- },
- "source": [
- "例: $dx/dt = -2x^2t$, 初期条件$x(t=0)=1.0$のもとで \n",
- "$0 < t \\leq 1.0$での$x$の値を予想してみよう:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 225
- },
- "id": "lI-2SaBkHCXa",
- "outputId": "be62334d-2414-425b-a2a3-29a2efee59ff"
- },
- "outputs": [
- {
- "data": {
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",
- "text/plain": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "dG3tCL-_aQHA"
- },
- "source": [
- "# 主成分分析"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "bzqUoCdAaRxG"
- },
- "source": [
- "このノートブックでは主成分分析(principal component analysis, PCA)を説明する。 \n",
- "基礎の理解に重点を置くため、scikit-learnなど既存のライブラリにあるPCA関数を利用するだけではなく、 \n",
- "定式化とnumpyを用いた実装を行う。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "70ilJPZVd6LC"
- },
- "outputs": [],
- "source": [
- "from matplotlib import pyplot as plt\n",
- "import numpy as np"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "WEJasJ_KbKVJ"
- },
- "source": [
- "## 主成分分析とは？"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "U6D19ywlbNFz"
- },
- "source": [
- "まず主成分分析とはなんだろうか？ \n",
- "\n",
- "Wikipediaには\n",
- ">相関のある多数の変数から相関のない少数で全体のばらつきを最もよく表す \n",
- "主成分と呼ばれる変数を合成する多変量解析の一手法\n",
- "\n",
- "とある。\n",
- "\n",
- "具体例を考えてみよう。 \n",
- "ある集団200人の身長($x$)と体重($y$)のデータが下のように \n",
- "得られているとしよう。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 283
- },
- "id": "YZT6a_-cd5lG",
- "outputId": "ce4dfd59-4bc4-427d-c990-fb89f88afc40"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "| \n", - " | sepal length (cm) | \n", - "sepal width (cm) | \n", - "petal length (cm) | \n", - "petal width (cm) | \n", - "target | \n", - "
|---|---|---|---|---|---|
| 0 | \n", - "5.1 | \n", - "3.5 | \n", - "1.4 | \n", - "0.2 | \n", - "setosa | \n", - "
| 1 | \n", - "4.9 | \n", - "3.0 | \n", - "1.4 | \n", - "0.2 | \n", - "setosa | \n", - "
| 2 | \n", - "4.7 | \n", - "3.2 | \n", - "1.3 | \n", - "0.2 | \n", - "setosa | \n", - "
| 3 | \n", - "4.6 | \n", - "3.1 | \n", - "1.5 | \n", - "0.2 | \n", - "setosa | \n", - "
| 4 | \n", - "5.0 | \n", - "3.6 | \n", - "1.4 | \n", - "0.2 | \n", - "setosa | \n", - "
| \n", - " | sepal length (cm) | \n", - "sepal width (cm) | \n", - "petal length (cm) | \n", - "petal width (cm) | \n", - "
|---|---|---|---|---|
| 0 | \n", - "5.1 | \n", - "3.5 | \n", - "1.4 | \n", - "0.2 | \n", - "
| 1 | \n", - "4.9 | \n", - "3.0 | \n", - "1.4 | \n", - "0.2 | \n", - "
| 2 | \n", - "4.7 | \n", - "3.2 | \n", - "1.3 | \n", - "0.2 | \n", - "
| 3 | \n", - "4.6 | \n", - "3.1 | \n", - "1.5 | \n", - "0.2 | \n", - "
| 4 | \n", - "5.0 | \n", - "3.6 | \n", - "1.4 | \n", - "0.2 | \n", - "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "g05kSZSuyD8r"
- },
- "source": [
- "# Pandasの使い方 (基礎)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "NnDUb09nyHXf"
- },
- "source": [
- "```Pandas```は、データ分析のためのライブラリで \n",
- "統計量を計算・表示したり、それらをグラフとして可視化出来たり \n",
- "前処理などの地道だが重要な作業を比較的簡単に行うことができます。\n",
- "\n",
- "\n",
- "まずはインポートしましょう。```pd```という名前で使うのが慣例です。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "G0LxApMex5f6",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "import pandas as pd "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "q-qC7xkDhU5A"
- },
- "source": [
- "pandasでは主に```Series```と```DataFrame```の２つのオブジェクトを扱います。 \n",
- "SeriesはDataFrameの特殊な場合とみなせるので、以下ではDataFrameのみ説明することにします。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-1ut22WgzFeQ"
- },
- "source": [
- "## DataFrame型"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "A-RriyHt1r-p"
- },
- "source": [
- "DataFrameはExcelシートのような二次元のデータを表現するのに利用され \n",
- "各種データ分析などで非常に役にたちます。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Ubw2JSMmzCkX",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "from pandas import DataFrame"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "oeBpZK4l7knI"
- },
- "source": [
- "以下の辞書型をDataFrame型のオブジェクトに変換してみましょう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "HKPVuPjz7jmb",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "data = { '名前': [\"Aさん\", \"Bさん\", \"Cさん\", \"Dさん\", \"Eさん\"],\n",
- " '出身都道府県':['Tokyo', 'Tochigi', 'Hokkaido','Kyoto','Tochigi'],\n",
- " '生年': [ 1998, 1993,2000,1989,2002],\n",
- " '身長': [172, 156, 162, 180,158]}\n",
- "df = DataFrame(data)\n",
- "print(\"dataの型\", type(data))\n",
- "print(\"dfの型\",type(df))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5F90_vjr8ukA"
- },
- "source": [
- "jupyter環境でDataFrameを読むと、\"いい感じ\"に表示してくれる"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "8VZZ6asj8qie",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "BkqFef0A83To"
- },
- "source": [
- "printだとちょっと無機質な感じに。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "xiIqLRFG82BW",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "print(df)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vn8s-8VR7b6Q"
- },
- "source": [
- "```info()```関数を作用させると、詳細な情報が得られる。 \n",
- "列ごとにどんな種類のデータが格納されているのかや、\n",
- "メモリ使用量など表示することができる。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "2KsTF1d17anv",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df.info()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-TsUcNTsBIIi"
- },
- "source": [
- "## DataFrameの要素を確認・指定する方法"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6ChkvnngBLb_"
- },
- "source": [
- "index: 行方向のデータ項目(おもに整数値(行番号),ID,名前など) \n",
- "columns: 列方向のデータの項目(おもにデータの種類) \n",
- "をそれぞれ表示してみよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "YkQCL0ay59o8",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df.index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "WNqLrkQ35-4O",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df.columns"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-MtegVlt60qr"
- },
- "source": [
- "行方向を、整数値(行数)ではなく名前にしたければ"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "aE51PtdW6pKE",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "data1 = {'出身都道府県':['Tokyo', 'Tochigi', 'Hokkaido','Kyoto','Tochigi'],\n",
- " '生年': [ 1998, 1993,2000,1989,2002],\n",
- " '身長': [172, 156, 162, 180,158]}\n",
- "df1 = DataFrame(data1)\n",
- "df1.index =[\"Aさん\", \"Bさん\", \"Cさん\", \"Dさん\", \"Eさん\"]\n",
- "df1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "LaFJVeL27NqY"
- },
- "source": [
- "などとしてもよい。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "_uFFuzQM8FoQ"
- },
- "source": [
- "### 特定の列を取得したい場合"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "oQ9hZkVx8K-p",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df[\"身長\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "zn3WJ4_5946D"
- },
- "source": [
- "とする。 \n",
- "以下の方法は非推奨とする。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "3VyUAqQs9596",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df.身長"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "uK8zkK3h8cpY"
- },
- "source": [
- "値のリスト(正確にはnumpy.ndarray型)として取得したければ"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "tozKlT028hfi",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df[\"身長\"].values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "87TOA6-e8j8v",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df[\"出身都道府県\"].values"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Yh97e7y58yHY"
- },
- "source": [
- "などとすればよい。\n",
- "\n",
- "慣れ親しんだ形に変換したければ、リストに変換すればよい\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "2Uh6YGih5FjE",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "list(df[\"出身都道府県\"].values)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "WfpujEkA5BSD"
- },
- "source": [
- "ある列が特定のものに一致するもののみを抽出するのも簡単にできる"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "qlFGoUdc8zPn",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df[df[\"出身都道府県\"]==\"Tochigi\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ItbqbBdn9HG0"
- },
- "source": [
- "これは"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Ubs3Zbjj9KA9",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df[\"出身都道府県\"]==\"Tochigi\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "bUB1Puao9LqO"
- },
- "source": [
- "が条件に合致するかどうかTrue/Falseの配列になっていて、 \n",
- "df[ [True/Falseの配列] ]とすると、Trueに対応する要素のみを返す \n",
- "フィルターのような役割になっている。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4xcTwpmcf3WK"
- },
- "source": [
- "### 列の追加"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Hznr0hZMghcK",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "#スカラー値の場合\"初期化\"のような振る舞いをする\n",
- "df[\"血液型\"] = \"A\"\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "1ved-4XAgMvq",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "#リストで追加\n",
- "df[\"血液型\"] = [ \"A\", \"O\",\"AB\",\"B\",\"A\"]\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "S_I81w5L-UOE"
- },
- "source": [
- "### 特定の行を取得したい場合"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "he5uTk3H-kMs"
- },
- "source": [
- "たとえば、行番号がわかっているなら、```iloc```関数を使えば良い"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "kbxrpQ1E-k8F",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df.iloc[3]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "2998C5BK_Wut"
- },
- "source": [
- "値のみ取得したければ先程と同様"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "6HoJX4h__Uj3",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df.iloc[3].values"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4gwiSube_ax9"
- },
- "source": [
- "また、以下のような使い方もできるが"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "4oabG7uA-TyS",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df[1:4] #1から3行目まで"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hbFfX303_eVc"
- },
- "source": [
- "```df[1]```といった使い方は出来ない。 "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "oH1A89pw_s6b"
- },
- "source": [
- "### より複雑な行・列の抽出"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vxd7k3f3AJjj"
- },
- "source": [
- "上にならって、2000年より前に生まれた人だけを抽出し"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "rDrnJUg9_w1Q",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df[ df[\"生年\"] < 2000 ]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "iZrMq16v__fV"
- },
- "source": [
- "さらにこのうち身長が170cm以上の人だけがほしければ"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "d6q2DR7nAYEv",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df[(df[\"生年\"] < 2000) & (df[\"身長\"]>170)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-b36txVaA7fQ"
- },
- "source": [
- "などとすればよい。 \n",
- "他にも```iloc```,```loc```などを用いれば \n",
- "特定の行・列を抽出することができる\n",
- "\n",
- "* ```iloc```は番号の指定のみに対応\n",
- "* ```loc```は名前のみ\n",
- "\n",
- "**欲しい要素の数値もしくは項目名のリスト**を \n",
- "行・列の２つついて指定してやればよい。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "xZhc6nlDBXJH",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df.iloc[[0], [0]] #0行目,0列目"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "i-W-XTw9Bz5-",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "#スライスで指定することもできる\n",
- "df.iloc[1:4, :3] #1-3行目かつ0-2列目 (スライスの終点は含まれないことに注意)\n",
- "\n",
- "#スライスの場合は、 1:4が[1,2,3]と同じ働きをするので、括弧[]はいらない"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "acMQBcZw5hmv"
- },
- "source": [
- "```loc```を使う場合は、indexの代わりに項目名で指定する。\n",
- "\n",
- "※今の場合、行を指定する項目名が既に整数値なので \n",
- "インデックスと見分けが付きづらいことに注意"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "JLlD6iUvCH-q",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df.loc[1:4,[\"名前\",\"身長\"]] "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "y3rZAq4rCTSh",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df.loc[[1,2,3,4],\"名前\":\"生年\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "WzV2r0oPA82E"
- },
- "source": [
- "といった具合。\n",
- "\n",
- "```loc```を使う場合、1:4や[1,2,3,4]は \n",
- "indexのスライスではなく、項目名を意味し \n",
- "Eさんのデータも含まれている事がわかる。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "py5Dt6wY10pB"
- },
- "source": [
- "## Webページにある表をDataFrameとして取得する"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "s7XShK0B15-t"
- },
- "source": [
- "```pandas```内の```read_html```関数を用いれば、 \n",
- "Webページの中にある表をDataFrame形式で取得することもできます。\n",
- "\n",
- "以下では例としてWikipediaの[ノーベル物理学賞](https://ja.wikipedia.org/wiki/%e3%83%8e%e3%83%bc%e3%83%99%e3%83%ab%e7%89%a9%e7%90%86%e5%ad%a6%e8%b3%9e)のページにある、受賞者一覧を取得してみましょう"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "WQG0tt0q2Hj-",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "url = \"https://ja.wikipedia.org/wiki/%e3%83%8e%e3%83%bc%e3%83%99%e3%83%ab%e7%89%a9%e7%90%86%e5%ad%a6%e8%b3%9e\"\n",
- "tables = pd.read_html(url)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "xLVtylYB2Uow",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "print(len(tables))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "yBghdSUU2XdQ"
- },
- "source": [
- "ページ内に、21個もの表があることがわかります。 \n",
- "(ほとんどはwikipediaのテンプレート等)\n",
- "\n",
- "たとえば、2010年代の受賞者のみに興味がある場合は"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "nf2JwszO2nBM",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df = tables[12]\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "O6bVMFaNKeUT"
- },
- "source": [
- "## Pandasで複雑なエクセルファイルを操作する"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "KBgNqsmGKjvm"
- },
- "source": [
- "Pandasにはread_excel()という関数が用意されていて、 \n",
- "多数のシートを含むようなエクセルファイルを開くことも出来る。\n",
- "\n",
- "まずは必要なモジュールをインポートしよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "hBKVdR2zKxe_",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "from pandas import DataFrame"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "34iZ93suY1nz"
- },
- "source": [
- "今まではGoogle Driveにいれたファイルを読み出していたが、 \n",
- "Webから直接xlsxファイルを読み込んでみよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "2yR7aEeImkAl",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "url = \"https://www.mext.go.jp/content/20201225-mxt_kagsei-mext_01110_012.xlsx\"\n",
- "input_file = pd.ExcelFile(url)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "YEsLWG0mY8z-"
- },
- "source": [
- "ブック内のシートの一覧は以下のように取得できる。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "VJv-enRZTxUt",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "sheet_names = input_file.sheet_names\n",
- "print(\"pandas: シート名\",sheet_names)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Tn6Lq7UhT1ZX"
- },
- "source": [
- "シートを指定するのは、インデックスかシート名の文字列で行う。\n",
- "\n",
- "\"1 穀類\"を使うことにして、 \n",
- "pandasにあるread_excel関数を使ってみよう。 \n",
- "read_excel関数の最初の引数にはパスの他に、urlも取れる。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "07xxSw7nVdK2",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df = pd.read_excel(url,sheet_name=\"1穀類\")\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "xew_ouFxVnLl"
- },
- "source": [
- "同じものが得られている。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pPBxyiV2oVKV"
- },
- "source": [
- "### データの整形\n",
- "\n",
- "次に、今取得したデータフレームのままでは少々扱い辛いので\"整形\"を考える。 \n",
- "というのも前から4行ほど表示してみると..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "NKcdOLCMV3qM",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df[0:4] "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CULET-99WJ3D"
- },
- "source": [
- "最初の4行ほどに栄養素等の情報が入っているのだが、 \n",
- "セルが結合されたりしているため、所々にNaNが入っていたりして見辛い。\n",
- "\n",
- "(碁盤目の構造を破壊してしまうため「セルの結合」は機械的な処理と \n",
- "やや相性が悪く、プログラミングを用いたデータ分析では嫌われる)\n",
- "\n",
- "各省庁の公開データのフォーマットの統一化は今後に期待することにして... \n",
- "まず以下の項目に該当する列だけを抽出する事を考える。\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Jhdp9z7cZm97",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "targets = [\"食品名\", \"エネルギー\",\"たんぱく質\", \"脂質\", \"炭水化物\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "zGrqpS-6jhaK"
- },
- "source": [
- "該当するデータがどの行・列に格納されているかをコードで指定するのは、 \n",
- "前述のファイル構造の事情からやや面倒くさい。 \n",
- "\n",
- "以下では、その場しのぎ的ではあるが、 \n",
- "興味のある量が何番目かを指定してまとめてみることにしよう。\n",
- "\n",
- "そのために、1-2行目の要素を表示してみよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "yZ-IVG2Ma30o",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "#1-2行目(エクセルだと2行目)の要素から\n",
- " #半角空白, 全角空白(\\u3000)や改行コード\\nを取り除いたリストを作って表示してみる\n",
- "\n",
- "for idx in range(1,3):\n",
- " tmp = df.iloc[idx].values\n",
- " tlist = list(map( lambda s: str(s).replace(\"\\u3000\",\"\").replace(\"\\n\",\"\").replace(\" \",\"\"),tmp))\n",
- " print(tlist)\n",
- "# for target in targets:\n",
- "# tlist.index(target)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "KEaiS1EAiWDY"
- },
- "source": [
- "セルの結合により、興味のあるデータがどの列に記述されているかは注意が必要。 \n",
- "\n",
- "実際、[エネルギー]という文字列は1行目の6列目(それぞれインデックスでいうと0,5)で取得できるが、 \n",
- "kJ単位になっていて、kcal単位でほしければ、7列目に格納された値が必要になる。 \n",
- "\n",
- "また、エクセルファイルを見るとわかるように、たんぱく質・脂質・炭水化物はさらに細分化されており、 \n",
- "O列R列など、細かい列の分割が挿入されている. ~~これは大変困る~~\n",
- "\n",
- "単純にたんぱく質・脂質・炭水化物と表記されている列のインデックスはそれぞれ9,12,20となる。 \n",
- "食品名が格納されている列(3)、エネルギー[kJ単位] (6)と合わせて確認してみよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "U-Hzne6VgWf3",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "targets = [3,6,9,12,20]\n",
- "df.iloc[:,targets]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "LNk4oIypmpTe"
- },
- "source": [
- "もう少し整形したいので、新しいデータフレームのコラムを書き換える。\n",
- "\n",
- "食品名等が記載されているのは10行目以降なので、それを使い \n",
- "columnを指定する。\n",
- "\n",
- "さらに、食品名に含まれる余分な文字コードも削除しておこう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "ZWkaRBZynATr",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "ndf = df.iloc[:,targets] \n",
- "ndf = ndf.iloc[10:,:]\n",
- "ndf.columns=[\"食品名\",\"エネルギー(kcal)\",\"たんぱく質(g)\",\"脂質(g)\",\"炭水化物(g)\"]\n",
- "ndf[\"食品名\"] = ndf[\"食品名\"].str.replace(\"\\u3000\",\" \") # 食品名の中にある余分な全角空白(\\u3000)を半角スペースに置き換える\n",
- "ndf"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "B5FN5MFcmECG"
- },
- "source": [
- "次に、食品名の一覧を取得した後、興味のあるもの(日常的に馴染みのあるもの)だけを \n",
- "ピックアップしてみよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "_SI2LaqPmOK3",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "print(list(ndf[\"食品名\"]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "bT1akYL_pBXs"
- },
- "source": [
- "この中から...\n",
- "* こむぎ［パン類］食パンリッチタイプ\n",
- "* こむぎ［パン類］フランスパン\n",
- "* こめ［水稲軟めし］精白米\n",
- "* そばそばゆで\n",
- "* こむぎ［うどん・そうめん類］うどんゆで\n",
- "\n",
- "のみに興味があれば"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "F2ouiW-jqpjm",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "tshokuhin = [\"こむぎ ［パン類］ 食パン リッチタイプ\",\"こむぎ ［パン類］ フランスパン\",\"こめ ［水稲軟めし］ 精白米\", \"そば そば ゆで\", \"こむぎ ［うどん・そうめん類］ うどん ゆで\"]\n",
- "ndf[ ndf[\"食品名\"].isin(tshokuhin)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Jt2aLH3jrWSY"
- },
- "source": [
- "などとする。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "So7B0ZauwuyT"
- },
- "source": [
- "'6野菜類'でも同様に..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "aYwheZeiw0Oe",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df6 = pd.read_excel(url,sheet_name=\"6野菜類\")\n",
- "df6.iloc[:,[3,6,9,12,20]]\n",
- "ndf6 = df6.iloc[:,[3,6,9,12,20]] \n",
- "ndf6 = ndf6.iloc[10:,:]\n",
- "ndf6.columns=[\"食品名\",\"エネルギー(kcal)\",\"たんぱく質(g)\",\"脂質(g)\",\"炭水化物(g)\"]\n",
- "ndf6[\"食品名\"] = ndf6[\"食品名\"].str.replace(\"\\u3000\",\" \") \n",
- "ndf6"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "di6-R2kIx88j"
- },
- "source": [
- "特定のキーワードを含むものを全て取得して、 \n",
- "食品名を細かく指定したり、対応する行番号のインデックスを取得できたりする"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "XDN2orG9xP_j",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "kyabetu = ndf6[ndf6[\"食品名\"].str.contains('キャベツ')]\n",
- "kyabetu"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "QboGKX8bx0yB",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "tomato = ndf6[ndf6[\"食品名\"].str.contains('トマト')]\n",
- "tomato"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ojENrlEZGepm"
- },
- "source": [
- "DataFrame同士を結合してまとめるなどして \n",
- "扱いやすいデータに整形していく.\n",
- "\n",
- "縦方向の結合はpandasのconcat(concatenateの略)を使う。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "SKoSAqvNGkrt",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "tdf = pd.concat([kyabetu, tomato])\n",
- "tdf"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "U6LFpNLN2sZh"
- },
- "source": [
- "## DataFrameのcsv/Excelファイルへの書き出し"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "tbZn9qyB2xTS"
- },
- "source": [
- "DataFrameオブジェクトは、```pandas```内の関数を用いれば、 \n",
- "簡単にcsvやExcelファイルとして書き出すことができます。\n",
- "\n",
- "先程の、２０１０年代のノーベル物理学賞受賞者のデータを、 \n",
- "Google Driveにファイルとして書き出してみましょう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "CKxWkUsW3Hi2",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "from google.colab import drive\n",
- "drive.mount('/content/drive')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gsr83KhA3Hww"
- },
- "source": [
- "**csvとして書き出す場合**\n",
- "\n",
- "適当にパスを指定して、DataFrameオブジェクトに \n",
- "```to_csv```関数を作用させます。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "jLS-3W8F27Xe",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df.to_csv(\"/content/drive/My Drive/AdDS2021/pd_write_test.csv\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "k6XnW1V93mik"
- },
- "source": [
- "**Excelファイルとして書き出す場合**\n",
- "\n",
- "この場合も同様で、```to_excel```関数を用います。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "A4A69Qb83vkk",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "df.to_excel(\"/content/drive/My Drive/AdDS2021/pd_write_test.xlsx\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "jrfPQzrq4GJK"
- },
- "source": [
- "上記の関数内で文字コードを指定することもできます。 \n",
- "例: ```encoding=\"utf-8_sig\"```, ```encoding=\"shift_jis\"```"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "Python_misc_Pandas.ipynb",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_misc_SingularValueDecomposition.ipynb b/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_misc_SingularValueDecomposition.ipynb
deleted file mode 100644
index ed3f4900..00000000
--- a/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_misc_SingularValueDecomposition.ipynb
+++ /dev/null
@@ -1,479 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HLZRySda6U-g"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "LGVnPDHJw9_I"
- },
- "source": [
- "# 特異値分解と情報削減\n",
- "\n",
- "このノートブックでは、行列の特異値分解と、その応用例として画像データの\"低ランク近似\"について説明する。\n",
- "\n",
- "関連する線形代数の知識、とくに諸定理の証明については、適宜線形代数の教科書等を参照すること。\n",
- "\n",
- "*純然たる*(≒形式的な？)線形代数の教科書には意外とSVDは載っていなかったりするので、工学であるとか数値計算など、応用を意識した教科書を参照するのが良い。\n",
- "\n",
- "Webで無料のものが良ければ、Mathematics for Machine Learningもいいかもしれない \n",
- "※[著者らのページのリンク](https://mml-book.com)からPDFがダウンロードできる。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "aakEipnQ6lik"
- },
- "source": [
- "## 特異値分解 (Singular Value Decomposition, SVD)\n",
- "\n",
- "特異値分解は、線形代数の中でも応用上非常に重要である。 \n",
- "本ノートで説明するような画像処理や情報削減の方法として用いられるだけでなく、量子多体系(物性や素粒子・原子核などの物理学,量子化学などが対象とする系)の計算手法である密度行列くりこみ群(DMRG)法など、自然科学分野の理論計算などの根幹にもなっている汎用性の高い手法である。\n",
- "\n",
- "\n",
- "**特異値分解定理**: \n",
- "任意のランク$r=[0,\\min(m,n)]$の$m \\times n$行列$A$は\n",
- "- $m \\times m$のユニタリ行列 $U$\n",
- "- $m \\times n$の対角行列$\\Sigma$ \n",
- "- $n \\times n$のユニタリ行列 $V$\n",
- "\n",
- "を用いて$\n",
- "A = U \\Sigma V^\\dagger\n",
- "$\n",
- "と分解できる。 \n",
- "上は$A$が複素行列の場合の表記で、実行列の場合は、$U,V$は直交行列であり、随伴操作(複素共役かつ転置)$\\dagger$は転置$T$となり、対角行列$\\Sigma$の各成分は非負となる。 \n",
- "\n",
- "以下では、特に断らない限り実行列の場合のみを考える。 \n",
- "$\\Sigma$の各対角成分を**特異値**と呼び、慣例的に$\\sigma_1 \\geq \\sigma_2 \\geq ... \\geq \\sigma_r \\geq 0$となるよう並べられる。 \n",
- "※一般の$m \\neq n$の場合は長方行列となるので、上での\"対角行列\"は正確には、対角ブロックとゼロ行列ブロックを含むような行列を指す。\n",
- "\n",
- "$m![](\"https://github.com/SotaYoshida/Lecture_DataScience/blob/main/notebooks/pic_for_notebook/pic_vscode_1.png?raw=true\")
\n",
- "\n",
- "2. 起動すると、日本語化を提案してくれるので日本語で使いたければ、インストールして再起動をクリック\n",
- "\n",
- " ![](\"https://github.com/SotaYoshida/Lecture_DataScience/blob/main/notebooks/pic_for_notebook/pic_vscode_2.png?raw=true\")
\n",
- "\n",
- "3. ターミナルから新しいターミナルを開くと規定のターミナルが開く(Windowsの場合だとpowershell?)\n",
- " ∨記号から、Ubuntu(WSL)を選択すると、Ubuntu(Linux)ターミナルを起動することが出来る\n",
- "\n",
- " ![](\"https://github.com/SotaYoshida/Lecture_DataScience/blob/main/notebooks/pic_for_notebook/pic_vscode_3.png?raw=true\")
\n",
- "\n",
- " ![](\"https://github.com/SotaYoshida/Lecture_DataScience/blob/main/notebooks/pic_for_notebook/pic_vscode_4.png?raw=true\")
\n",
- " ![](\"https://github.com/SotaYoshida/Lecture_DataScience/blob/main/notebooks/pic_for_notebook/pic_vscode_5.png?raw=true\")
\n",
- "\n",
- "\n",
- "### Pythonコードの編集\n",
- "\n",
- "1. 適当なPythonファイルを作成してみよう。[ファイル]→[新しいファイル]で新規ファイルを作成する。 \n",
- " その際**ファイルの種類を入力するか、ファイル名を入力してください**などという表示がでるのでpythonと打ち、Pythonファイルを作成する。 \n",
- " Untitled-1などというファイルが作成されるので、`Ctrl+S`で保存しようとすると、ファイル名を入力するように促される。 \n",
- " その際、適当な名前をつけてみよう。例えば`sample_code.py`などとする。\n",
- "2. ファイルを編集したら、保存(`Ctrl+S`)しよう。編集内容が残っている場合は(標準では)ファイル名の横に丸◯が表示されるので、わかりやすい。\n",
- " Pythonコードだけでなく、様々なファイルを編集したり閲覧したりすることができるので試してみよう。(例: pdfファイルをVS Codeにドラッグ&ドロップしてみよう)\n",
- "3. 拡張子に応じて、なんのプログラミング言語で書かれたソースコードなのかを推定して、色分けしてくれたりする。\n",
- "4. また、VS Codeでは、\"フォルダを開く\"ことで複数のソースコードを効率的に編集したりすることもできる。\n",
- "\n",
- "\n",
- "### ターミナルの起動\n",
- "\n",
- "VS Codeでは、ターミナルをVS Code内で起動することができる。 \n",
- "[ターミナル]タブから新しいターミナル(規定のターミナル)を起動することができ、画面の上下や左右などに分割して表示することができる。 \n",
- "したがって、ソースコードを編集しながら実行したりといった作業がしやすい。\n",
- "\n",
- "Windowsの場合は、WSL(Windows Subsystem for Linux)をインストールしておくと、Linuxのターミナルを使うことができる。 \n",
- "MacやLinuxの場合は言うまでもなく、規定のターミナルが開くので、Unix/Linuxコマンドを使えば良い。\n",
- "\n",
- "よく使うLinuxコマンドを下記の表にまとめる:\n",
- "\n",
- "|コマンド|説明|\n",
- "|:--|:--|\n",
- "|`ls`|カレントディレクトリのファイル一覧を表示する|\n",
- "|`cd`|ディレクトリを移動する|\n",
- "|`pwd`|カレントディレクトリのパスを表示する|\n",
- "|`mkdir`|ディレクトリを作成する|\n",
- "|`rm`|ファイルを削除する(ゴミ箱を経由しないので使用には注意すること)|\n",
- "|`cp`|ファイルをコピーする|\n",
- "|`mv`|ファイルを移動する|\n",
- "|`cat`|ファイルの中身を表示する|\n",
- "|`head`|ファイルの先頭を表示する|\n",
- "|`tail`|ファイルの末尾を表示する|\n",
- "|`grep`|ファイルの中から文字列を検索する|\n",
- "|`echo`|文字列を表示する|\n",
- "|`chmod`|ファイルのアクセス権を変更する|\n",
- "|`sudo`|root権限でコマンドを実行する|\n",
- "\n",
- "それぞれのコマンドの使い方やオプションについては網羅的に説明することはしないので、適宜調べて使い方を覚えてほしい。 \n",
- "Chat GPTなどに尋ねてみるのも良い。\n",
- "\n",
- "ターミナルからPythonコードを実行する際は、\n",
- "\n",
- "1. `python`コマンドでPythonの対話環境を起動して使う\n",
- "2. `python`コマンドに続けてファイル名を入力し、そのファイルを実行する\n",
- "\n",
- "の２つの方法がある。最初は1の方法でも良いが、コードで実現したい作業が大きくなるにつれ、\n",
- "作成したソースコードを後者の方法で実行することが多くなるはずだ。\n",
- "\n",
- "```bash\n",
- "python sample_code.py\n",
- "```\n",
- "\n",
- "※システムによっては`python3`などとバージョンを明示的に指定しないといけない場合もあるので注意すること。 \n",
- "Pythonをコマンドラインで使う際に毎回`python`などと入力するのが面倒なら、\n",
- "エイリアスを設定して別ののコマンド(著者は`py`としている)に置き換えてしまうのも良い。\n",
- "\n",
- "(Linuxコマンドやシェルについての知識が少しだけ必要になるが)詳しい方法については「python コマンド エイリアス」などで検索してみよう。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 授業資料(.ipynb)をVS codeで実行・編集する\n",
- "\n",
- "VS Codeを使うと、ローカル環境で.ipynbファイルを実行・編集することができる。\n",
- "著者も、この授業資料の作成にはVS Codeを使っている。\n",
- "\n",
- "以下で紹介するGitHub Copilotとの相性も良いので、今後プログラミング学習を継続するつもりだ、という方は、\n",
- "VS Codeでの.ipynbファイルの実行・編集も是非やってみてほしい。\n",
- "\n",
- "### 授業資料のダウンロード方法\n",
- "\n",
- "本授業資料の.ipynbファイルは、GitHubのレポジトリかBookの各ページからダウンロードすることができる。\n",
- "\n",
- "* 方法1: GitHubの[レポジトリ](https://github.com/SotaYoshida/Lecture_DataScience)からダウンロードする.\n",
- " `<>Code`という緑色のボタンをクリックし、`Download ZIP`をクリックすると、全てのファイルをダウンロードできる。\n",
- " 展開したフォルダ内の`notebooks`というディレクトリが、本授業資料の.ipynbファイルがある場所である。\n",
- " \n",
- " ![](\"https://github.com/SotaYoshida/Lecture_DataScience/blob/main/notebooks/pic_for_notebook/download_repozip.png?raw=true\")
\n",
- "\n",
- "* 方法2: Bookの各ページからダウンロードする. ブックの各章の上部にダウンロードボタン(下向き矢印)があるので、そこから`.ipynb`形式を選択し、ダウンロードすれば良い。\n",
- " \n",
- "\n",
- "### ipynb形式のファイルをVS Codeで開く&実行する\n",
- "\n",
- "実行には当然Python環境が必要なので、インストール済みであるとする。\n",
- "\n",
- "1. ターミナルから \n",
- "\n",
- " ```bash\n",
- " pip3 install jupyter\n",
- " ```\n",
- " などとして、Jupyterをインストールしておく。\n",
- "2. VS Codeを起動し、ダウンロードした`ipynb`ファイルを開く。\n",
- "3. コードセルを実行するには、`Shift+Enter`を押す。または、コードセルの左側にある`▶︎`をクリックする。このとき、複数のPython環境がインストールされている場合は、どのPython環境で実行するかを選択する必要がある。\n",
- "4. コードセルの実行結果が表示される。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## GitHub Copilot\n",
- "\n",
- "GitHub Copilotは、GitHubがOpenAIと共同で開発したコード補完ツールである。 \n",
- "背後には、GPT-3ベースのOpenAI Codexが動いており、文字通りcopilotとして様々なコードやスニペットを提案してくれる。\n",
- "\n",
- "GitHub Copilotは、VS Codeなどの拡張機能としても使用することができる。\n",
- "ただし、GitHub Copilotが生成するコードは完璧ではないため、使う際は、**提案されたコードを注意深く確認し、必要に応じて修正を行う必要がある**ことは言うまでもない。\n",
- "\n",
- "GitHub Copilotを使うには、まず、GitHub Copilotのベータ版に登録する必要がある。学生は、学生証などを提出することで、無料で登録・使用することができる。上手く使いこなせれば、かなりの生産性向上が期待できるツールであり、私も日々の研究において活用している。\n",
- "\n",
- "もちろん、自身の意図を100%反映したコードを生成してくれるわけではないが、コードの骨組みを作ってくれるため大幅に作業が楽になることも多い。ときに「こんな書き方もあるのか」という気づきを与えてくれたりもする。\n",
- "\n",
- "\n",
- "### VS Codeへの導入\n",
- "\n",
- "1. GitHub Copilotに登録する。この際、GitHubアカウントを作成する必要がある。\n",
- " 既にGitHubアカウントを持っている場合は、そのアカウントでログインする。 \n",
- " 「Github Copilot 学生申請」などで検索すると、学生向けの登録方法が見つかる。\n",
- " 認証が済むと、GitHubのアカウントがPROアカウントになり、Copilotを使うことができるようになる。\n",
- "2. 次に、[Copilotのページ](https://github.com/features/copilot/)から、Start my free trialをクリックし、指示に従う。\n",
- "\n",
- "3. VS Codeのアカウント(人型)のアイコンから、GitHubアカウントにログインする。\n",
- "\n",
- "4. VS Codeの拡張機能タブから[GitHub Copilot]などと検索したのち、拡張機能をインストールする(2.でインストールが始まるかも)。その後指示に従い、インストールを完了する。\n",
- "\n",
- "### 試してみよう\n",
- "\n",
- "試しに著者の環境(VS Code)で「適当な二次元データのヒートマップを描画するコードは以下のようになる」などと打つと... \n",
- "以下のように、自身で入力した際よりも薄い色でコード(場合によっては複数行)が提案される。\n",
- "\n",
- "![](\"https://github.com/SotaYoshida/Lecture_DataScience/blob/main/notebooks/pic_for_notebook/pic_copilot_1.jpeg?raw=true\")
\n",
- "\n",
- "Copilotの提案を受け入れるには`Tab`キーを押し、棄却する場合には`Esc`を押せば良い。複数行に渡る場合も同様である。\n",
- "こうした作業を繰り返して生成されたコードを実行してみよう。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
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",
- "text/plain": [
- "![](\"https://github.com/SotaYoshida/Lecture_DataScience/blob/main/notebooks/pic_for_notebook/pic_copilot_2.jpeg?raw=true\")
\n",
- "\n",
- "\n",
- "もちろん、ここでは具体的なファイル構造を示したりファイルを与えているわけではないので、正しく動く保証も全く無いわけだが、概ねもっともらしいコードを生成してくれている。\n",
- "仮に提案されたコードがうまく動かない/使えないとき、うまく行かない理由と関連していそうな情報を与えてやったりすることで、解決策を提示してきたり、補足知識を与えてくれたりするという例も見せよう。\n",
- "\n",
- "![](\"https://github.com/SotaYoshida/Lecture_DataScience/blob/main/notebooks/pic_for_notebook/pic_copilot_3.jpeg?raw=true\")
\n",
- "![](\"https://github.com/SotaYoshida/Lecture_DataScience/blob/main/notebooks/pic_for_notebook/pic_copilot_4.jpeg?raw=true\")
\n",
- "\n",
- "細かいことを言うと、2.の1つめの画像の提案(`encoding=\"SHIFT-JIS\"`)は、サポートされていないオプションの指定となりエラーになるが、(言語モデルはPythonの文法、とくにPythonの中での文字コードの指定に相当するコードを\"理解\"しているわけではないため)推論としては真っ当なものになっている。 \n",
- "\n",
- "実は上の画像で挙げた例は、実際にPythonでcsvやエクセルファイルを読み書きしようとする際に、よく遭遇するエラーの例になっていて、\n",
- "私はそのことを知っているため、上のようなある種\"誘導的な\"プロンプトを与えることで、より有用な回答を得られるよう仕向けている。\n",
- "2.の2つめの画像で、より適切なオプションを指定するためにもう少しヒントをあげているという訳だ。\n",
- "\n",
- "\n",
- "\"自身の理解やこれまでの思考・試行を開示した上で質問をする\"ことが、他人に質問する際に有効な回答を引き出すための近道であることと同じで、\n",
- "言語モデルを対話的に活用する上では、こうしたコツを抑えておくと良いかもしれない。\n",
- "\n",
- "また、大規模言語モデルの性質・特性やその背景にある基礎について理解を深めることも非常に重要な姿勢だと感じる。 \n",
- "理研AIPシンポジウムでの岡崎直観先生の特別講演[Youtubeへのリンク](https://youtu.be/PUuk4Cv-ycg)が非常に参考になる。ぜひ一度視聴してみてほしい。"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [
- "h2K-l3Iu0vHS",
- "c17cT0gy2fwF",
- "OtjKhaH451we",
- "N4vEf_Av9hjN"
- ],
- "include_colab_link": true,
- "name": "Python_misc_python_env_forWin11.ipynb",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3.9.13 64-bit",
- "language": "python",
- "name": "python3"
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- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
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- },
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diff --git a/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_misc_numpy.ipynb b/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_misc_numpy.ipynb
deleted file mode 100644
index c6fce304..00000000
--- a/Lecture_DataScience_dotfiles/_build/html/_sources/notebooks/Python_misc_numpy.ipynb
+++ /dev/null
@@ -1,889 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "colab_type": "text",
- "id": "view-in-github"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5ObsFxycyq-y"
- },
- "source": [
- "# Numpyについて"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "rFUjRPktyouP"
- },
- "source": [
- "この授業で使うノートブックや、巷に転がってるPythonのコードでは、たびたび`Numpy`というものが使われています。\n",
- "この補足ノートではその`Numpy`について説明します。\n",
- "\n",
- "`numpy`は数値演算ライブラリで、機械学習・画像音声処理などなど様々な場面で使用されるライブラリです。 \n",
- "その特徴としては色々ありますが、\n",
- "* 配列を用いた計算が楽かつ高速にできる \n",
- "* その他数学的な処理がパッケージ化されている\n",
- "\n",
- "といったところでしょうか。\n",
- "\n",
- "1番目の点ですが、一般にPythonのような動的型付け言語に分類される言語は、Cなどの静的言語と呼ばれる言語に比べて、計算時間が長くかかってしまうことが多いです。\n",
- "とくに、コンピュータを用いたシミュレーションの至るところに現れる配列の操作や、行列やベクトルに対する演算でその差が顕著なものとなります。\n",
- "\n",
- "一方、Pythonで読み出せる`numpy`ライブラリは、中身はC言語(およびFortran)で実装されていて、かつBLAS(行列演算ライブラリ)など最適化された線形計算ライブラリを使用できるためPythonの書きやすさを維持したまま、高速な計算を実現することができます。\n",
- "\n",
- "参考: NASAのModeling Gruによる数値演算速度の比較 \n",
- "https://modelingguru.nasa.gov/docs/DOC-1762 \n",
- "https://modelingguru.nasa.gov/docs/DOC-2783\n",
- "\n",
- "2番目の点 \n",
- "numpyでは数学で用いる種々の関数(sin,cos,log,log10, etc.)が定義されていて簡単に読み出すことができます。また、乱数を生成してサイコロを振ったりすることも簡単です。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4yAkLESw18J3"
- },
- "source": [
- "## numpy.array"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "n7PehTnq2FO7"
- },
- "source": [
- "それでは`numpy`を`import`して、使ってみましょう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "1GqJqy6E2Fx0",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "import numpy as np #numpyとかくと長いのでnpという名前で使う\n",
- "\n",
- "a = [ i for i in range(1,5001) ]\n",
- "b = np.array(a)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vMRwzNrg2x7c"
- },
- "source": [
- "まずは、1から5000までの整数が並んだリスト```a```と、 \n",
- "それを```np.array()```関数に突っ込んだ```b```を定義してみました。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "NfUOTAtO3BUl",
- "outputId": "d56e79f4-0cb4-4a30-b0cc-5ccaaffd94bf",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "b [ 1 2 3 ... 4998 4999 5000]\n"
- ]
- }
- ],
- "source": [
- "print(\"a\", a)\n",
- "print(\"b\", b)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "FMzFJCkV3FPc"
- },
- "source": [
- "中身は同じものなのですが、```b```は途中を省略して表示してくれています。気が利いていますね。 \n",
- "では`type`を調べてみましょう"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "qsO-w49n3My5",
- "outputId": "685c671b-f927-42df-dbb8-555dd61ce432",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "aの型 "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 練習帳\n",
- "\n",
- "このノートブックでは、1-8章で抑えておくべき基本事項を確認するための練習問題を与える。\n",
- "\n",
- "CLで収集する授業のリアクションシートには、\n",
- "* 練習帳のURL\n",
- "* 質問: 授業や資料でわからなかったこと (なければ所感のみでOK)\n",
- "* 所感: その回で理解した/できるようになったことを完結にまとめる\n",
- "* 適当な雑談: 最近ハマっていること\n",
- "\n",
- "を設定された〆切までに提出すること。これをもって出席とみなす。\n",
- "なお、(この授業に限らず)、2/3以上の出席がない場合、履修規程に従い自動的に不可となるため、必ず提出すること。\n",
- "\n",
- "この練習帳の目的は、皆さんが自分自身の理解・疑問点を確認するためであって\n",
- "**練習帳で書いているコードや記述が正しいかどうかは一切授業の評点には関係がない**。\n",
- "\n",
- "したがって友人に見せてもらったり相談をして体裁を整える必要はまったく無い。\n",
- "とにかく、自分の理解を確認するためにコードをどんどん書いてどんどん失敗しよう。\n",
- "\n",
- "質問やその返答を円滑にするための連絡帳のようなものだと思ってもらえれば良い。\n",
- "\n",
- "\n",
- "また、教員に対する質問の見落としを防ぐため、リアクションシート(CL)の方で\n",
- ">練習帳に詳細な質問とコードを書きました\n",
- "\n",
- "などと教えてもらえるとスムーズな対応が可能になります。\n",
- "リアクションシートの提出や質問の際は、必ず練習帳の共有リンクを添えてください。\n",
- "\n",
- "\n",
- "慣れないうちは、エラーの原因がわからない場合は、とにかく教員に聞く。\n",
- "慣れてくると、自分でエラーメッセージをWebで検索したり、[よくあるエラー集](https://sotayoshida.github.io/Lecture_DataScience/notebooks/Python_misc_Error.html)などを見て、解決ができるようになる。\n",
- "Chat GPTなどに尋ねてみるのも、**疑問や問題を言語化し要約する訓練**になるので良いかもしれない。\n",
- "それでもわからない場合は、エラーメッセージを添えて教員に尋ねよう。\n",
- "\n",
- "\n",
- "## 練習帳のノートブックの共有の仕方\n",
- "\n",
- "以下の手順にならってください。1-3は1度やれば再び行う必要はありません。\n",
- "\n",
- "1. まずノートブックのコピーを作成し、ファイル名を適当に編集する: 例: 練習帳_氏名_123456X.ipynb (123456Xは学籍番号のつもり)\n",
- "2. 右上の共有ボタンを押し、「制限付き」を「リンクを知っている全員」に変更\n",
- " (共有ボタンが見えない場合、編集権限がないつまり「ノートブックのコピーをつくる」というお約束を忘れていることを意味する)\n",
- "3. 「リンクを知っている全員」の右にある「閲覧者」を「編集者」に変更する\n",
- "4. 最後に、ブックマークするか、リンクをコピーしどこかに保存しておこう(2回目以降はCLで一度提出した過去のURLをコピーするのが手っ取り早い)\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "元の問題文さえ残っていれば、レイアウトは好きに改変してOK.\n",
- "コードセル・テキストセルともに自由に追加して構わない。\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 第1章:Pythonの基礎\n",
- "\n",
- "練習問題: \n",
- "1. 身長と体重に相当する変数を適当に定義し、BMI(体重kg ÷ 身長mの二乗)を計算した上でprintせよ (自身の身長体重を用いる必要はない)\n",
- "2. 上で計算したBMIに対応する変数とstr関数を用いて文字列を連結し、\"AさんのBMIは22.0です\"などと表示させよ。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# コードを以下に記載\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**１章の振り返り**\n",
- "\n",
- "これらの点をチェックし、必要ならコードセルを追加して練習しよう:\n",
- "\n",
- "* 変数の定義や四則演算の方法が分かる\n",
- "* `print`や`str`などの基本的な関数の使い方がわかる\n",
- "* プログラムの実行順序と、セルを跨いで実行する際の注意点がわかる\n",
- "* 基本的な変数の型とその調べ方が分かる"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 第2章:Pythonの基礎2\n",
- "\n",
- "練習問題: \n",
- "1. 要素に身長(単位:cm)と体重(単位:kg)を持つ適当なリストを定義し、5人のデータ(リスト)を要素にもつ**入れ子構造のリスト**を作成せよ。ただし3人目の身長と体重は必ず175,60とすること(それ以外は適当で構わない)。\n",
- "2. 上のリストから、5人の平均身長と平均体重をそれぞれ計算するコードを作成せよ。算術平均を取る際は、数字の5などを使うのではなく、リストの長さを使ったり、汎用性の高いコードにすること。\n",
- "3. 上のリストについて、`for`文を用いて全員のBMI(体重kg ÷ 身長mの二乗)を計算し、一人ずつBMIを表示させよ。(単位に注意)\n",
- "4. `for`文のブロック内で`if`と`break`を用いて、「BMIが20.0以下なら、値を表示したあとにループを終了する」という処理を実現せよ。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# コードを以下に記載\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**2章の振り返り**\n",
- "\n",
- "**とくに２章は重要な概念が盛りだくさんなので、必ず練習したり、疑問があれば質問すること!**\n",
- "\n",
- "これらの点をチェックし、必要ならコードセルを追加して練習しよう:\n",
- "\n",
- "* リストの定義や要素へのアクセスの方法(インデックスやスライス)が分かる\n",
- "* リストに要素を加える方法が分かる\n",
- "* `if`文による条件分岐が分かる\n",
- "* `for`や`while`によるループ処理がわかる"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 第3章:関数\n",
- "\n",
- "練習問題:\n",
- "1. 要素に実数値を持つ適当なリストを2つ以上定義し、任意の長さの実数値のリストについて平均と分散を返り値とする自作関数を作成せよ。\n",
- "2. 1.で作った自作関数を1つの変数で受け取り(例`ret_value=myfunction(mylist)`)その返り値の型を調べよ。\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# コードを以下に記載\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**3章の振り返り**\n",
- "\n",
- "これらの点をチェックし、必要ならコードセルを追加して練習しよう:\n",
- "\n",
- "* 自作関数の定義の仕方が分かる\n",
- "* 引数(インプット)や返り値(アウトプット)の扱い方が分かる"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 第4章:ライブラリ\n",
- "\n",
- "練習問題:\n",
- "1. 授業で扱った`math`,`numpy`の何れかを使って、任意の半径`r`について円の面積と球の体積を計算する自作関数を作成せよ。\n",
- "2. `matplotlib`を用いて、好きな図を作成させノートブック上に表示させよ。その際、`matplotlib`(とくに`matplotlib.pyplot`)の使い方を調べて\n",
- " - 色をカラーコードで指定する\n",
- " - グラフを構成するオブジェクトの透過度を設定する\n",
- "\n",
- " など、授業で指定していないオプションを試してみよう。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# コードを以下に記載\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**4章の振り返り**\n",
- "\n",
- "これらの点をチェックし、必要ならコードセルを追加して練習しよう:\n",
- "\n",
- "* ライブラリのインストールやインポートの仕方が分かる\n",
- "* matplotlibの簡単な使い方が分かる\n",
- "* Webの情報や公式ドキュメントを読んだりして、使い方を調べることができる"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 第5章:確率と擬似乱数\n",
- "\n",
- "練習問題:\n",
- "1. 0からn-1(nは適当な整数)までのn個の整数から、重複なくランダムにn個選ぶ(つまり0からn-1の無作為な並べかえをする)コードを作成せよ。(`numpy.random.choice`を使うとよい)\n",
- "2. 1.を用いて、任意の文字列のリスト(例: 名前のリスト `[\"Aさん\",\"Bさん\",...]`)をランダムに並び替えて出力するコードを作成せよ。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# コードを以下に記載\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**5章の振り返り**\n",
- "\n",
- "これらの点をチェックし、必要ならコードセルを追加して練習しよう:\n",
- "\n",
- "* `random`や`numpy.random`モジュールを用いて、簡単な確率的事象を表現する方法がわかる\n",
- "* 適当な区間内でランダムな整数を生成することができる\n",
- "* 適当な実数の乱数(一様乱数・正規乱数)を生成することができる"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 第6章:相関・回帰分析\n",
- "\n",
- "練習問題:\n",
- "1. 以下のデータ`x`(宇都宮市の月別平均気温)と`y`(アイスクリーム・シャーベットの消費量)のうち、8月のデータ(気温か消費量のいずれか)をランダムな値に変更し相関係数がどうなるか5通りほどで示せ。なお、値を変更する際は手でリストを書き換えるのではなく、リストの要素を書き換えるコードにすること。\n",
- "2. 疑似相関について調べ例をあげよ。(できれば自身の興味に近いものや日本の事例などを調べてみること)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# コードを以下に記載(追記)\n",
- "x= [3.1, 4.3, 6.6, 13.2, 19.1, 20.9, 26.4, 25.1, 21.9, 15.7, 9.6, 3.8]\n",
- "y= [568, 572, 804, 833, 930, 965, 1213, 1120, 835, 540, 451, 502]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**6章の振り返り**\n",
- "\n",
- "これらの点をチェックし、必要ならコードセルを追加して練習しよう:\n",
- "\n",
- "* 相関分析の意味を、友人等に説明できる\n",
- "* 長さの等しい２つのリストについて、相関係数を計算することができる\n",
- "* 簡単な場合について自作関数とライブラリの出力が同じであることを確認することができる"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 第7章:最適化問題\n",
- "\n",
- "練習問題:\n",
- "1. 以下に示したデータ(`x`:年, `y`:男子100m走の世界記録(秒))について、6章で出てきたpolyfitを使って1-5次式までの多項式でフィッティングしてみよう。\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# コードを以下に記載(追記)\n",
- "y = [ 10.06, 10.03,10.02, 9.95,9.93, 9.92,9.9, 9.86,9.85, 9.84, 9.79, 9.78, 9.77, 9.74,9.72,9.69,9.58 ]\n",
- "x = [1964, 1968,1968,1968,1983,1988,1991,1991,1994,1996,1999,2002,2005,2007,2008,2008,2009 ]\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**7章の振り返り**\n",
- "\n",
- "これらの点をチェックし、必要ならコードセルを追加して練習しよう:\n",
- "\n",
- "* 多項式回帰の意味を、友人等に説明できる \n",
- "* ライブラリを用いて多項式回帰(係数の最適化)を行うことができる\n",
- "* 係数の最適化の結果から、グラフの描画点を生成し、可視化することができる"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 第8章:ファイル操作\n",
- "\n",
- "練習問題:\n",
- "1. Google Colaboratoryから授業で使ったノートを参照し、Google Driveをマウントしtest.txtファイルの中身を表示せよ。その際、Unix/Linuxコマンドである`cat`を用いれば良い。\n",
- "2. matplotlibを用いて適当なグラフを描きGoogleドライブに保存せよ。\n",
- "\n",
- "この問題はDriveのマウント・パスの指定などの一連の作業が理解できているかを確認するものなので、エラーが出た場合はそのログや自身が試した工程について、スクリーンショットなども活用しながらできるだけ詳細に報告すること。特に問題なく作業ができた場合は、2の図を送るなどはしなくて構わない。 \n",
- "\n",
- "また、プライベートなGoogleアカウントを使用している(※授業では非推奨)場合、相談の際にはプログラムの出力結果やスクリーンショットなどに他人に見られて困るものが映っていないか配慮すること。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# コードを以下に記載(追記)\n",
- "#1.のコードのヒント\n",
- "!cat path_to_yourfile/test.text\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**8章の振り返り**\n",
- "\n",
- "これらの点をチェックし、必要ならコードセルを追加して練習しよう:\n",
- "\n",
- "* Google ColaboratoryでGoogle Driveをマウントすることができる\n",
- "* Google Drive上にあるcsvなどのファイルを読み込むことができる\n",
- "* 読み込んだファイルの内容から、必要なデータを取り出してprintしたり、配列を作り、それをグラフとして描いたりすることができる"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 最終課題\n",
- "\n",
- "授業の後半で取り組む最終課題については、自由な発想で課題を設計し、取り組んでもらって構わない。\n",
- "条件としては、以下のようなものが挙げられる:\n",
- "\n",
- "- 1人(ないし２名ペア)で取り組むこと\n",
- "- ペアの場合は、それぞれの寄与を明確にし、一人の寄与が十分に認められること\n",
- "- 都度、教員に相談しながら、計画的に進めること\n",
- "- 事前に教員に確認すべき課題の例\n",
- " - 個人情報(例えば自営業をしている実家のデータを分析するなど)を含む場合は、それをマスクする処理が可能であること\n",
- " - 特定のサービス・ソフトウェア・アプリに関する分析(ゲームのデータなどは著作権等の理由から多くの場合不可)\n",
- " - アカウントの作成やライセンス等の購入が必要な外部サービスを使用する課題でないこと(≒教員が特段の手続きを経ずとも、課題作成者のサポートや採点・評価が可能なものであること)\n",
- "\n",
- "その他、教員が不適切と判断した課題については、課題の変更を求めることがある。\n",
- "\n",
- "何もないところから課題を設計するのは難しい。以下の例を参考に、計画をたてよう:\n",
- "\n",
- "**公開データを元に、栃木県のデータを可視化・分析したい**とする。\n",
- "その際に必要な工程や、分析を行うために学修すべき事項を列挙しながら、課題を設計していく。\n",
- "\n",
- "- データの収集: 興味のあるデータがオープンデータとして公開されているかを調べる。\n",
- " - 例1: [e-Stat](https://www.e-stat.go.jp/)\n",
- " - 例2: [オープンデータ・ベリーとちぎ](https://odcs.bodik.jp/090000/)\n",
- "- 地図を描き、市町村ごとに特定のデータをカラーマップとして表示する事を考える。\n",
- " - そのためには、市町村の境界線のデータが必要になる。例えば、[国土数値情報ダウンロードサービス](https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-N03-v2_3.html)からデータを収集するとする。\n",
- " - 得られたデータを地図上で可視化するために、使えそうなライブラリを探す。例えば、[geopandas](https://geopandas.org/)や[folium](https://python-visualization.github.io/folium/)が使えそうである。\n",
- " - 簡単なデータから初めて、ライブラリの使い方を学びながら、地図を描くことを目指す。\n",
- " - その他、地図上に関連する(例えば...特定の商業施設やLRTの停留所など)ピンを打ってみる\n",
- "- 実際のデータを元に、分析を行う\n",
- "- 問題点が生じた場合、その原因を特定し、解決するために必要な知識を学ぶ。(以下試行錯誤...)\n",
- "\n",
- "といった具合だ。あくまで、例であるので、自身の興味に合わせて課題を設計していくこと。\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- }
- ],
- "metadata": {
- "colab": {
- "include_colab_link": true,
- "name": "Python_practice.ipynb",
- "provenance": []
- },
- "interpreter": {
- "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
- },
- "kernelspec": {
- "display_name": "Python 3.9.10 64-bit",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "name": "python",
- "version": "3.9.13"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
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diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/check-solid.svg b/Lecture_DataScience_dotfiles/_build/html/_static/check-solid.svg
deleted file mode 100644
index 92fad4b5..00000000
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+++ /dev/null
@@ -1,4 +0,0 @@
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diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/clipboard.min.js b/Lecture_DataScience_dotfiles/_build/html/_static/clipboard.min.js
deleted file mode 100644
index 54b3c463..00000000
--- a/Lecture_DataScience_dotfiles/_build/html/_static/clipboard.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
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- *
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- */
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- */ - .o-tooltip--left { - position: relative; - } - - .o-tooltip--left:after { - opacity: 0; - visibility: hidden; - position: absolute; - content: attr(data-tooltip); - padding: .2em; - font-size: .8em; - left: -.2em; - background: grey; - color: white; - white-space: nowrap; - z-index: 2; - border-radius: 2px; - transform: translateX(-102%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); -} - -.o-tooltip--left:hover:after { - display: block; - opacity: 1; - visibility: visible; - transform: translateX(-100%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); - transition-delay: .5s; -} diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/copybutton.js b/Lecture_DataScience_dotfiles/_build/html/_static/copybutton.js deleted file mode 100644 index 482bda03..00000000 --- a/Lecture_DataScience_dotfiles/_build/html/_static/copybutton.js +++ /dev/null @@ -1,197 +0,0 @@ -// Localization support -const messages = { - 'en': { - 'copy': 'Copy', - 'copy_to_clipboard': 'Copy to clipboard', - 'copy_success': 'Copied!', - 'copy_failure': 'Failed to copy', - }, - 'es' : { - 'copy': 'Copiar', - 'copy_to_clipboard': 'Copiar al portapapeles', - 'copy_success': '¡Copiado!', - 'copy_failure': 'Error al copiar', - }, - 'de' : { - 'copy': 'Kopieren', - 'copy_to_clipboard': 'In die Zwischenablage kopieren', - 'copy_success': 'Kopiert!', - 'copy_failure': 'Fehler beim Kopieren', - }, - 'fr' : { - 'copy': 'Copier', - 'copy_to_clipboard': 'Copié dans le presse-papier', - 'copy_success': 'Copié !', - 'copy_failure': 'Échec de la copie', - }, - 'ru': { - 'copy': 'Скопировать', - 'copy_to_clipboard': 'Скопировать в буфер', - 'copy_success': 'Скопировано!', - 'copy_failure': 'Не удалось скопировать', - }, - 'zh-CN': { - 'copy': '复制', - 'copy_to_clipboard': '复制到剪贴板', - 'copy_success': '复制成功!', - 'copy_failure': '复制失败', - } -} - -let locale = 'en' -if( document.documentElement.lang !== undefined - && messages[document.documentElement.lang] !== undefined ) { - locale = document.documentElement.lang -} - -let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; -if (doc_url_root == '#') { - doc_url_root = ''; -} - -const path_static = `${doc_url_root}_static/`; - -/** - * Set up copy/paste for code blocks - */ - -const runWhenDOMLoaded = cb => { - if (document.readyState != 'loading') { - cb() - } else if (document.addEventListener) { - document.addEventListener('DOMContentLoaded', cb) - } else { - document.attachEvent('onreadystatechange', function() { - if (document.readyState == 'complete') cb() - }) - } -} - -const codeCellId = index => `codecell${index}` - -// Clears selected text since ClipboardJS will select the text when copying -const clearSelection = () => { - if (window.getSelection) { - window.getSelection().removeAllRanges() - } else if (document.selection) { - document.selection.empty() - } -} - -// Changes tooltip text for two seconds, then changes it back -const temporarilyChangeTooltip = (el, oldText, newText) => { - el.setAttribute('data-tooltip', newText) - el.classList.add('success') - setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000) - setTimeout(() => el.classList.remove('success'), 2000) -} - -// Changes the copy button icon for two seconds, then changes it back -const temporarilyChangeIcon = (el) => { - img = el.querySelector("img"); - img.setAttribute('src', `${path_static}check-solid.svg`) - setTimeout(() => img.setAttribute('src', `${path_static}copy-button.svg`), 2000) -} - -const addCopyButtonToCodeCells = () => { - // If ClipboardJS hasn't loaded, wait a bit and try again. This - // happens because we load ClipboardJS asynchronously. - if (window.ClipboardJS === undefined) { - setTimeout(addCopyButtonToCodeCells, 250) - return - } - - // Add copybuttons to all of our code cells - const codeCells = document.querySelectorAll('div.highlight pre') - codeCells.forEach((codeCell, index) => { - const id = codeCellId(index) - codeCell.setAttribute('id', id) - - const clipboardButton = id => - `` - codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) - }) - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} - - -var copyTargetText = (trigger) => { - var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - return formatCopyText(target.innerText, '', false, true, true, true, '', '') -} - - // Initialize with a callback so we can modify the text before copy - const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) - - // Update UI with error/success messages - clipboard.on('success', event => { - clearSelection() - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) - temporarilyChangeIcon(event.trigger) - }) - - clipboard.on('error', event => { - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) - }) -} - -runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/copybutton_funcs.js b/Lecture_DataScience_dotfiles/_build/html/_static/copybutton_funcs.js deleted file mode 100644 index b9168c55..00000000 --- a/Lecture_DataScience_dotfiles/_build/html/_static/copybutton_funcs.js +++ /dev/null @@ -1,58 +0,0 @@ -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/css/blank.css b/Lecture_DataScience_dotfiles/_build/html/_static/css/blank.css deleted file mode 100644 index 8a686ec7..00000000 --- a/Lecture_DataScience_dotfiles/_build/html/_static/css/blank.css +++ /dev/null @@ -1,2 +0,0 @@ -/* This file is intentionally left blank to override the stylesheet of the -parent theme via theme.conf. 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/***************************************************************************** - * Theme config - **/ - --pst-header-height: 60px; - - /***************************************************************************** - * Font size - **/ - --pst-font-size-base: 15px; /* base font size - applied at body / html level */ - - /* heading font sizes */ - --pst-font-size-h1: 36px; - --pst-font-size-h2: 32px; - --pst-font-size-h3: 26px; - --pst-font-size-h4: 21px; - --pst-font-size-h5: 18px; - --pst-font-size-h6: 16px; - - /* smaller then heading font sizes*/ - --pst-font-size-milli: 12px; - - --pst-sidebar-font-size: .9em; - --pst-sidebar-caption-font-size: .9em; - - /***************************************************************************** - * Font family - **/ - /* These are adapted from https://systemfontstack.com/ */ - --pst-font-family-base-system: -apple-system, BlinkMacSystemFont, Segoe UI, "Helvetica Neue", - Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol; - --pst-font-family-monospace-system: "SFMono-Regular", Menlo, Consolas, Monaco, - Liberation Mono, Lucida Console, monospace; - - --pst-font-family-base: var(--pst-font-family-base-system); - --pst-font-family-heading: var(--pst-font-family-base); - --pst-font-family-monospace: var(--pst-font-family-monospace-system); - - /***************************************************************************** - * Color - * - * Colors are defined in rgb string way, "red, green, blue" - **/ - --pst-color-primary: 19, 6, 84; - --pst-color-success: 40, 167, 69; - --pst-color-info: 0, 123, 255; /*23, 162, 184;*/ - --pst-color-warning: 255, 193, 7; - --pst-color-danger: 220, 53, 69; - --pst-color-text-base: 51, 51, 51; - - --pst-color-h1: var(--pst-color-primary); - --pst-color-h2: var(--pst-color-primary); - --pst-color-h3: var(--pst-color-text-base); - --pst-color-h4: var(--pst-color-text-base); - --pst-color-h5: var(--pst-color-text-base); - --pst-color-h6: var(--pst-color-text-base); - --pst-color-paragraph: var(--pst-color-text-base); - --pst-color-link: 0, 91, 129; - --pst-color-link-hover: 227, 46, 0; - --pst-color-headerlink: 198, 15, 15; - --pst-color-headerlink-hover: 255, 255, 255; - --pst-color-preformatted-text: 34, 34, 34; - --pst-color-preformatted-background: 250, 250, 250; - --pst-color-inline-code: 232, 62, 140; - - --pst-color-active-navigation: 19, 6, 84; - --pst-color-navbar-link: 77, 77, 77; - --pst-color-navbar-link-hover: var(--pst-color-active-navigation); - --pst-color-navbar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-link: 77, 77, 77; - --pst-color-sidebar-link-hover: var(--pst-color-active-navigation); - --pst-color-sidebar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-expander-background-hover: 244, 244, 244; - --pst-color-sidebar-caption: 77, 77, 77; - --pst-color-toc-link: 119, 117, 122; - --pst-color-toc-link-hover: var(--pst-color-active-navigation); - --pst-color-toc-link-active: var(--pst-color-active-navigation); - - /***************************************************************************** - * Icon - **/ - - /* font awesome icons*/ - --pst-icon-check-circle: '\f058'; - --pst-icon-info-circle: '\f05a'; - --pst-icon-exclamation-triangle: '\f071'; - --pst-icon-exclamation-circle: '\f06a'; - --pst-icon-times-circle: '\f057'; - --pst-icon-lightbulb: '\f0eb'; - - /***************************************************************************** - * Admonitions - **/ - - --pst-color-admonition-default: var(--pst-color-info); - --pst-color-admonition-note: var(--pst-color-info); - --pst-color-admonition-attention: var(--pst-color-warning); - --pst-color-admonition-caution: var(--pst-color-warning); - --pst-color-admonition-warning: var(--pst-color-warning); - --pst-color-admonition-danger: var(--pst-color-danger); - --pst-color-admonition-error: var(--pst-color-danger); - --pst-color-admonition-hint: var(--pst-color-success); - --pst-color-admonition-tip: var(--pst-color-success); - --pst-color-admonition-important: var(--pst-color-success); - - --pst-icon-admonition-default: var(--pst-icon-info-circle); - --pst-icon-admonition-note: var(--pst-icon-info-circle); - --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle); - --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-error: var(--pst-icon-times-circle); - --pst-icon-admonition-hint: var(--pst-icon-lightbulb); - --pst-icon-admonition-tip: var(--pst-icon-lightbulb); - --pst-icon-admonition-important: var(--pst-icon-exclamation-circle); - -} diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/doctools.js b/Lecture_DataScience_dotfiles/_build/html/_static/doctools.js deleted file mode 100644 index 8cbf1b16..00000000 --- a/Lecture_DataScience_dotfiles/_build/html/_static/doctools.js +++ /dev/null @@ -1,323 +0,0 @@ -/* - * doctools.js - * ~~~~~~~~~~~ - * - * Sphinx JavaScript utilities for all documentation. - * - * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - -/** - * make the code below compatible with browsers without - * an installed firebug like debugger -if (!window.console || !console.firebug) { - var names = ["log", "debug", "info", "warn", "error", "assert", "dir", - "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", - "profile", "profileEnd"]; - window.console = {}; - for (var i = 0; i < names.length; ++i) - window.console[names[i]] = function() {}; -} - */ - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x - } - return decodeURIComponent(x.replace(/\+/g, ' ')); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; - -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - var bbox = node.parentElement.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } - } - } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } - } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; -}; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} - -/** - * Small JavaScript module for the documentation. - */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { - this.initOnKeyListeners(); - } - }, - - /** - * i18n support - */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; - }, - - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; - }, - - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; - }, - - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); - }, - - /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 - */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); - }, - - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); - } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('') - .appendTo($('#searchbox')); - } - }, - - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - }, - - /** - * make the url absolute - */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, - - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); - }, - - initOnKeyListeners: function() { - $(document).keydown(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box, textarea, dropdown or button - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' - && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey - && !event.shiftKey) { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; - } - break; - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; - } - break; - } - } - }); - } -}; - -// quick alias for translations -_ = Documentation.gettext; - -$(document).ready(function() { - Documentation.init(); -}); diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/documentation_options.js b/Lecture_DataScience_dotfiles/_build/html/_static/documentation_options.js deleted file mode 100644 index 93b7c24d..00000000 --- a/Lecture_DataScience_dotfiles/_build/html/_static/documentation_options.js +++ /dev/null @@ -1,12 +0,0 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '', - LANGUAGE: 'None', - COLLAPSE_INDEX: false, - BUILDER: 'html', - FILE_SUFFIX: '.html', - LINK_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '', - NAVIGATION_WITH_KEYS: true -}; \ No newline at end of file diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/file.png b/Lecture_DataScience_dotfiles/_build/html/_static/file.png deleted file mode 100644 index a858a410..00000000 Binary files a/Lecture_DataScience_dotfiles/_build/html/_static/file.png and /dev/null differ diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/images/logo_binder.svg b/Lecture_DataScience_dotfiles/_build/html/_static/images/logo_binder.svg deleted file mode 100644 index 45fecf75..00000000 --- a/Lecture_DataScience_dotfiles/_build/html/_static/images/logo_binder.svg +++ /dev/null @@ -1,19 +0,0 @@ - - - diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/images/logo_colab.png b/Lecture_DataScience_dotfiles/_build/html/_static/images/logo_colab.png deleted file mode 100644 index b7560ec2..00000000 Binary files a/Lecture_DataScience_dotfiles/_build/html/_static/images/logo_colab.png and /dev/null differ diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/images/logo_jupyterhub.svg b/Lecture_DataScience_dotfiles/_build/html/_static/images/logo_jupyterhub.svg deleted file mode 100644 index 60cfe9f2..00000000 --- a/Lecture_DataScience_dotfiles/_build/html/_static/images/logo_jupyterhub.svg +++ /dev/null @@ -1 +0,0 @@ - diff --git a/Lecture_DataScience_dotfiles/_build/html/_static/jquery-3.5.1.js b/Lecture_DataScience_dotfiles/_build/html/_static/jquery-3.5.1.js deleted file mode 100644 index 50937333..00000000 --- a/Lecture_DataScience_dotfiles/_build/html/_static/jquery-3.5.1.js +++ /dev/null @@ -1,10872 +0,0 @@ -/*! - * jQuery JavaScript Library v3.5.1 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2020-05-04T22:49Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML