05-nakai_roc_curve.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LmTNQjqi4mJ3"
   },
   "source": [
    "# 第5章 ロジスティック回帰とROC曲線：学習モデルの評価方法(自習書き込み用）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4epk53Iv4mJ5"
   },
   "source": [
    "## 「05-roc_curve」の解説"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "e9x6HQlm4mJ5"
   },
   "source": [
    "ITエンジニアための機械学習理論入門「第5章 ロジスティック回帰とROC曲線:学習モデルの評価方法」で使用しているサンプルコード「05-roc_curve.py」の解説です。\n",
    "\n",
    "※ 解説用にコードの内容は少し変更しています。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1EYTbX3r4mJ6"
   },
   "source": [
    "はじめに必要なモジュールをインポートしておきます。\n",
    "\n",
    "関数 multivariate_normal は、多次元の正規分布に従う乱数を生成するために利用します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wjJ5cQp34mJ7"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "from numpy.random import multivariate_normal\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.size'] = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WTyhlOs84mKA"
   },
   "source": [
    "トレーニング用データを生成する関数を用意します。\n",
    "\n",
    "平面上の○☓の2種類のデータについて、それぞれの「個数、中心座標、分散」を引数で指定します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ns7Ec__S4mKB"
   },
   "outputs": [],
   "source": [
    "def prepare_dataset(n1, mu1, variance1, n2, mu2, variance2):\n",
    "    df1 = DataFrame(multivariate_normal(mu1, np.eye(2)*variance1 ,n1),\n",
    "                    columns=['x','y'])\n",
    "    df1['type'] = 1\n",
    "    df2 = DataFrame(multivariate_normal(mu2, np.eye(2)*variance2, n2),\n",
    "                    columns=['x','y'])\n",
    "    df2['type'] = -1\n",
    "    df = pd.concat([df1,df2], ignore_index=True)\n",
    "    df = df.reindex(np.random.permutation(df.index)).reset_index(drop=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UTAK1g_b4mKE"
   },
   "source": [
    "ロジスティック回帰で分割線を決定する関数を用意します。\n",
    "\n",
    "ここでは、得られた結果を用いて、トレーニングセットの各データに対して確率の値を付与したデータフレームも返却するようにしています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0K6JJbzD4mKF"
   },
   "outputs": [],
   "source": [
    "# ロジスティック回帰\n",
    "def run_logistic(train_set):\n",
    "    pd.options.mode.chained_assignment = None\n",
    "    #ベクトルwを生成する。とりあえずの値に初期化する\n",
    "    w = np.array([[0], [0.1], [0.1]])\n",
    "    #ベクトルφを作る。train_setにはtypeが存在し、邪魔なのでtrain_setからtypeを消去したものをφとする\n",
    "    phi = train_set[['x', 'y']]\n",
    "    #bias項が必要のため作成する。初期値は1（初期値に特に意味はない)。\n",
    "    phi['bias'] = 1\n",
    "    #DataFrameからnumpyの配列に変換する\n",
    "    phi = phi[['bias', 'x', 'y']].values\n",
    "    #どちらに分類されるか(直線の上か、下か)を示すデータ(1 or 0)を作成する\n",
    "    t = (train_set[['type']] + 1)*0.5 # type = 1, -1 を type = 1, 0 に変換（別の問題のトレーニングセット生成関数を流用したためこれが必要）\n",
    "    #numpyの配列に変換する\n",
    "    t = t.values\n",
    "    #ちなみに、tは縦ベクトルになっている（注意）\n",
    "    print(\"=== PRINT T =====\")\n",
    "    print(t)\n",
    "\n",
    "    # 最大100回のIterationを実施\n",
    "    for i in range(100):\n",
    "        # IRLS法によるパラメータの修正\n",
    "        ### [START] 対角行列Rを作る ###\n",
    "        y = np.array([])\n",
    "        for line in phi:\n",
    "            a = np.dot(line, w)\n",
    "            y = np.append(y, [1.0/(1.0+np.exp(-a))]) #ロジスティック関数からそのテストデータline(bias,x,y)の属性が1の確率を求め、znの配列をつくる\n",
    "        r = np.diag(y*(1-y)) #求めたznの配列から対角行列を一気に作る（numpyすごい)\n",
    "        ### [END] 対角行列Rを作る ###\n",
    "        print(\"=====DEBUG=====\")\n",
    "        print(y)\n",
    "        print(\"-----\")\n",
    "        #後でtと減算をするために、横ベクトルを転置して縦ベクトルにする\n",
    "        y = y[np.newaxis,:].T\n",
    "        print(y)\n",
    "        print(\"=====DEBUG=====\")\n",
    "        #IRLS法の計算をする（ニユートンラフソン更新）\n",
    "        tmp1 = np.linalg.inv(np.dot(np.dot(phi.T, r),phi))\n",
    "        tmp2 = np.dot(phi.T, (y-t))\n",
    "        w_new = w - np.dot(tmp1, tmp2)\n",
    "        # パラメータの変化が 0.1% 未満になったら終了\n",
    "        #ノルムの二乗なので、それぞれのベクトルの内積を計算している。\n",
    "        print(\"==== DEBUG2 ====\")\n",
    "        print(\"numpyではベクトルの内積を求める際には、横ベクトル×縦ベクトルである必要がある\")\n",
    "        print(\"-> w_new-w\")\n",
    "        print(w_new-w)\n",
    "        print(\"-> (w_new-w).T\")\n",
    "        print((w_new-w).T)\n",
    "        print(\"-> w\")\n",
    "        print(w)\n",
    "        print(\"#計算結果は1行1列の行列になる\")\n",
    "        print(np.dot((w_new-w).T, (w_new-w)))\n",
    "        print(\"#こちらはエラーになる\")\n",
    "        try:\n",
    "            print(np.dot((w_new-w).T, (w_new-w).T))\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "            \n",
    "        print(\"#内積の計算結果(1行1列)はスカラーとして扱うことができるらしい\")\n",
    "        print(np.dot((w_new-w).T, (w_new-w)))\n",
    "        print(np.dot((w_new-w).T, (w_new-w)) < 10)\n",
    "        \n",
    "        if np.dot((w_new-w).T, (w_new-w)) < 0.001 * np.dot(w.T, w):\n",
    "            w = w_new\n",
    "            break\n",
    "        w = w_new\n",
    "        \n",
    "    # 分類誤差の計算\n",
    "    w0, w1, w2 = w[0], w[1], w[2]\n",
    "    err = 0\n",
    "    for index, line in train_set.iterrows():\n",
    "        a = np.dot(np.array([1, line.x, line.y]), w)\n",
    "        p = 1.0/(1.0+np.exp(-a))\n",
    "        train_set.loc[index, 'probability'] = p\n",
    "        if (p-0.5)*line.type < 0: #誤って分類されている場合はこの計算結果は負になる。ロジスティック関数の0.5より上は正。下は負になるため。\n",
    "            err += 1\n",
    "    err_rate = err * 100 / len(train_set)\n",
    "    result = train_set.sort_values(by=['probability'], ascending=[False]).reset_index()\n",
    "    \n",
    "    print(\" ==== PRINT RESULT ===\")\n",
    "    print(result)\n",
    "    \n",
    "    return w0, w1, w2, err_rate, result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kjB72H8E4mKI"
   },
   "source": [
    "結果をグラフ、および、ROC曲線として表示する関数を用意します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "975xN3Ng4mKJ"
   },
   "outputs": [],
   "source": [
    "# 結果の表示\n",
    "def show_result(subplot, train_set, w0, w1, w2, err_rate):\n",
    "    train_set1 = train_set[train_set['type']==1]\n",
    "    train_set2 = train_set[train_set['type']==-1]\n",
    "    ymin, ymax = train_set.y.min()-5, train_set.y.max()+10\n",
    "    xmin, xmax = train_set.x.min()-5, train_set.x.max()+10\n",
    "\n",
    "    subplot.set_ylim([ymin-1, ymax+1])\n",
    "    subplot.set_xlim([xmin-1, xmax+1])\n",
    "    subplot.scatter(train_set1.x, train_set1.y, marker='o', label=None)\n",
    "    subplot.scatter(train_set2.x, train_set2.y, marker='x', label=None)\n",
    "\n",
    "    linex = np.arange(xmin-5, xmax+5)\n",
    "    liney = - linex * w1 / w2 - w0 / w2\n",
    "    label = \"ERR %.2f%%\" % err_rate\n",
    "    subplot.plot(linex, liney, label=label, color='red')\n",
    "    subplot.legend(loc=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "es7zn3ZK4mKM"
   },
   "outputs": [],
   "source": [
    "# ROC曲線の表示\n",
    "def draw_roc(subplot, result):\n",
    "    positives = len(result[result['type']==1])\n",
    "    negatives = len(result[result['type']==-1])\n",
    "    tp = [0.0] * len(result)\n",
    "    fp = [0.0] * len(result)\n",
    "    for index, line in result.iterrows():\n",
    "        for c in np.arange(0, len(result)):\n",
    "            if index < c:\n",
    "                if line.type == 1:\n",
    "                    tp[c] += 1\n",
    "                else:\n",
    "                    fp[c] += 1\n",
    "    tp_rate = np.array(tp) / positives\n",
    "    fp_rate = np.array(fp) / negatives\n",
    "    \n",
    "    print(\"=== DEBUG tp_rate and fp_rate\")\n",
    "    print(\"TP RATE\")\n",
    "    print(tp_rate)\n",
    "    print(\"FP RATE\")\n",
    "    print(fp_rate)\n",
    "\n",
    "    subplot.set_ylim([0, 1])\n",
    "    subplot.set_xlim([0, 1])\n",
    "    subplot.set_xlabel(\"False positive rate\")\n",
    "    subplot.set_ylabel(\"True positive rate\")\n",
    "    subplot.plot(fp_rate, tp_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hpjiZe904mKP"
   },
   "source": [
    "比較的分散が小さくて、分類が容易なトレーニングセットを用意します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9WlpWi0a4mKQ"
   },
   "outputs": [],
   "source": [
    "train_set = prepare_dataset(80, [9, 9], 50, 200, [-3, -3], 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_QSCi3054mKT"
   },
   "source": [
    "ロジスティック回帰を適用した結果を表示します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 714
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3046,
     "status": "ok",
     "timestamp": 1587542466395,
     "user": {
      "displayName": "Etsuji Nakai",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhetRRD1sS-9biTr-QKIKpeab3l7NjR8gRdtuQWcg=s64",
      "userId": "13606123633698401153"
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     "user_tz": -540
    },
    "id": "xuatDrRe4mKU",
    "outputId": "3089ec2f-88b4-46f2-d41f-d451bc32c11d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== PRINT T =====\n",
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      " [0.]]\n",
      "=====DEBUG=====\n",
      "[0.49653084 0.87445532 0.19177318 0.19007415 0.58942467 0.40125063\n",
      " 0.42564413 0.48125692 0.36828745 0.72736199 0.10124072 0.52755768\n",
      " 0.16020666 0.58120463 0.72850361 0.90339265 0.20298198 0.19471199\n",
      " 0.90243806 0.52243473 0.81307488 0.6436517  0.59870751 0.73708311\n",
      " 0.63969211 0.19252315 0.80146937 0.29522444 0.23998053 0.24433474\n",
      " 0.20164821 0.02028655 0.22118349 0.5308753  0.61716065 0.11784813\n",
      " 0.57426425 0.96047498 0.66341379 0.15921788 0.33968275 0.28792758\n",
      " 0.88054032 0.08012881 0.4761619  0.42728005 0.75178293 0.28221632\n",
      " 0.48359382 0.88832368 0.9654662  0.75936605 0.54558498 0.40280187\n",
      " 0.31632713 0.79297298 0.09298196 0.89402315 0.23474021 0.29787202\n",
      " 0.95943525 0.72008439 0.97505906 0.17456659 0.18997928 0.83283915\n",
      " 0.80610335 0.86011071 0.93539132 0.44753293 0.92552528 0.8425286\n",
      " 0.60388051 0.37048337 0.28155921 0.13858144 0.88665752 0.79908266\n",
      " 0.8695061  0.2426583  0.41536427 0.32442323 0.7008344  0.8530558\n",
      " 0.94589039 0.931275   0.96432979 0.17463321 0.16204602 0.06297957\n",
      " 0.4251269  0.60112867 0.49467378 0.40212228 0.69767729 0.96445462\n",
      " 0.08763036 0.88309066 0.23001839 0.15442285 0.66395936 0.71546201\n",
      " 0.15855745 0.21381182 0.3082549  0.75467116 0.17520864 0.64257278\n",
      " 0.23827804 0.31838721 0.32715035 0.1508177  0.30768001 0.36971994\n",
      " 0.73178314 0.55271691 0.82835232 0.06249126 0.79227109 0.19591855\n",
      " 0.96391845 0.32619985 0.27357799 0.19855274 0.21072295 0.93656333\n",
      " 0.11955661 0.48133013 0.96080424 0.61005501 0.76906169 0.41241565\n",
      " 0.4337913  0.34714971 0.95957463 0.90302088 0.71356201 0.52272402\n",
      " 0.25699318 0.44944832 0.57529569 0.51987109 0.64254295 0.4239757\n",
      " 0.61874979 0.90004564 0.15619783 0.83543285 0.52646257 0.5535761\n",
      " 0.82804434 0.32392194 0.10483424 0.93132593 0.9215582  0.48348784\n",
      " 0.56403856 0.4121633  0.9723031  0.06062037 0.50302652 0.78438615\n",
      " 0.72343702 0.36585147 0.73947961 0.16297189 0.28764309 0.40208885\n",
      " 0.09059514 0.86484229 0.53890372 0.49648218 0.25905498 0.25458792\n",
      " 0.8905108  0.71608102 0.77436196 0.6223776  0.88300558 0.19464213\n",
      " 0.08316364 0.53322372 0.63590131 0.8300564  0.44750864 0.68868642\n",
      " 0.73966119 0.68864827 0.52071422 0.14203949 0.35633771 0.16522168\n",
      " 0.86435506 0.2291127  0.21795327 0.5098597  0.45821477 0.36492263\n",
      " 0.19359807 0.37903726 0.4991683  0.33578151 0.35409585 0.45030986\n",
      " 0.38248626 0.67772495 0.83390078 0.74306499 0.44274986 0.113664\n",
      " 0.60477647 0.52100128 0.86384285 0.14317837 0.25020142 0.40091116\n",
      " 0.53996118 0.43852281 0.23332838 0.21989303 0.50874319 0.47358737\n",
      " 0.09820025 0.11467662 0.12913319 0.73290331 0.2833983  0.42971429\n",
      " 0.82307141 0.51442283 0.75977493 0.23129374 0.77687068 0.54726624\n",
      " 0.06146534 0.86104463 0.56073198 0.12906996 0.4172969  0.61342738\n",
      " 0.91106548 0.48878202 0.75339881 0.42742683 0.51924393 0.22386295\n",
      " 0.69973704 0.09508526 0.69174942 0.69541419 0.56331094 0.43480608\n",
      " 0.03043617 0.5270938  0.24736994 0.67727159 0.18719009 0.28345169\n",
      " 0.70259801 0.33730216 0.25587019 0.16861269 0.81727222 0.37989571\n",
      " 0.18572605 0.407024   0.26520754 0.96391193 0.5731246  0.20329071\n",
      " 0.4066005  0.07069653 0.50049294 0.41681631 0.33603039 0.33185035\n",
      " 0.44652596 0.30227594 0.53268101 0.29197272]\n",
      "-----\n",
      "[[0.49653084]\n",
      " [0.87445532]\n",
      " [0.19177318]\n",
      " [0.19007415]\n",
      " [0.58942467]\n",
      " [0.40125063]\n",
      " [0.42564413]\n",
      " [0.48125692]\n",
      " [0.36828745]\n",
      " [0.72736199]\n",
      " [0.10124072]\n",
      " [0.52755768]\n",
      " [0.16020666]\n",
      " [0.58120463]\n",
      " [0.72850361]\n",
      " [0.90339265]\n",
      " [0.20298198]\n",
      " [0.19471199]\n",
      " [0.90243806]\n",
      " [0.52243473]\n",
      " [0.81307488]\n",
      " [0.6436517 ]\n",
      " [0.59870751]\n",
      " [0.73708311]\n",
      " [0.63969211]\n",
      " [0.19252315]\n",
      " [0.80146937]\n",
      " [0.29522444]\n",
      " [0.23998053]\n",
      " [0.24433474]\n",
      " [0.20164821]\n",
      " [0.02028655]\n",
      " [0.22118349]\n",
      " [0.5308753 ]\n",
      " [0.61716065]\n",
      " [0.11784813]\n",
      " [0.57426425]\n",
      " [0.96047498]\n",
      " [0.66341379]\n",
      " [0.15921788]\n",
      " [0.33968275]\n",
      " [0.28792758]\n",
      " [0.88054032]\n",
      " [0.08012881]\n",
      " [0.4761619 ]\n",
      " [0.42728005]\n",
      " [0.75178293]\n",
      " [0.28221632]\n",
      " [0.48359382]\n",
      " [0.88832368]\n",
      " [0.9654662 ]\n",
      " [0.75936605]\n",
      " [0.54558498]\n",
      " [0.40280187]\n",
      " [0.31632713]\n",
      " [0.79297298]\n",
      " [0.09298196]\n",
      " [0.89402315]\n",
      " [0.23474021]\n",
      " [0.29787202]\n",
      " [0.95943525]\n",
      " [0.72008439]\n",
      " [0.97505906]\n",
      " [0.17456659]\n",
      " [0.18997928]\n",
      " [0.83283915]\n",
      " [0.80610335]\n",
      " [0.86011071]\n",
      " [0.93539132]\n",
      " [0.44753293]\n",
      " [0.92552528]\n",
      " [0.8425286 ]\n",
      " [0.60388051]\n",
      " [0.37048337]\n",
      " [0.28155921]\n",
      " [0.13858144]\n",
      " [0.88665752]\n",
      " [0.79908266]\n",
      " [0.8695061 ]\n",
      " [0.2426583 ]\n",
      " [0.41536427]\n",
      " [0.32442323]\n",
      " [0.7008344 ]\n",
      " [0.8530558 ]\n",
      " [0.94589039]\n",
      " [0.931275  ]\n",
      " [0.96432979]\n",
      " [0.17463321]\n",
      " [0.16204602]\n",
      " [0.06297957]\n",
      " [0.4251269 ]\n",
      " [0.60112867]\n",
      " [0.49467378]\n",
      " [0.40212228]\n",
      " [0.69767729]\n",
      " [0.96445462]\n",
      " [0.08763036]\n",
      " [0.88309066]\n",
      " [0.23001839]\n",
      " [0.15442285]\n",
      " [0.66395936]\n",
      " [0.71546201]\n",
      " [0.15855745]\n",
      " [0.21381182]\n",
      " [0.3082549 ]\n",
      " [0.75467116]\n",
      " [0.17520864]\n",
      " [0.64257278]\n",
      " [0.23827804]\n",
      " [0.31838721]\n",
      " [0.32715035]\n",
      " [0.1508177 ]\n",
      " [0.30768001]\n",
      " [0.36971994]\n",
      " [0.73178314]\n",
      " [0.55271691]\n",
      " [0.82835232]\n",
      " [0.06249126]\n",
      " [0.79227109]\n",
      " [0.19591855]\n",
      " [0.96391845]\n",
      " [0.32619985]\n",
      " [0.27357799]\n",
      " [0.19855274]\n",
      " [0.21072295]\n",
      " [0.93656333]\n",
      " [0.11955661]\n",
      " [0.48133013]\n",
      " [0.96080424]\n",
      " [0.61005501]\n",
      " [0.76906169]\n",
      " [0.41241565]\n",
      " [0.4337913 ]\n",
      " [0.34714971]\n",
      " [0.95957463]\n",
      " [0.90302088]\n",
      " [0.71356201]\n",
      " [0.52272402]\n",
      " [0.25699318]\n",
      " [0.44944832]\n",
      " [0.57529569]\n",
      " [0.51987109]\n",
      " [0.64254295]\n",
      " [0.4239757 ]\n",
      " [0.61874979]\n",
      " [0.90004564]\n",
      " [0.15619783]\n",
      " [0.83543285]\n",
      " [0.52646257]\n",
      " [0.5535761 ]\n",
      " [0.82804434]\n",
      " [0.32392194]\n",
      " [0.10483424]\n",
      " [0.93132593]\n",
      " [0.9215582 ]\n",
      " [0.48348784]\n",
      " [0.56403856]\n",
      " [0.4121633 ]\n",
      " [0.9723031 ]\n",
      " [0.06062037]\n",
      " [0.50302652]\n",
      " [0.78438615]\n",
      " [0.72343702]\n",
      " [0.36585147]\n",
      " [0.73947961]\n",
      " [0.16297189]\n",
      " [0.28764309]\n",
      " [0.40208885]\n",
      " [0.09059514]\n",
      " [0.86484229]\n",
      " [0.53890372]\n",
      " [0.49648218]\n",
      " [0.25905498]\n",
      " [0.25458792]\n",
      " [0.8905108 ]\n",
      " [0.71608102]\n",
      " [0.77436196]\n",
      " [0.6223776 ]\n",
      " [0.88300558]\n",
      " [0.19464213]\n",
      " [0.08316364]\n",
      " [0.53322372]\n",
      " [0.63590131]\n",
      " [0.8300564 ]\n",
      " [0.44750864]\n",
      " [0.68868642]\n",
      " [0.73966119]\n",
      " [0.68864827]\n",
      " [0.52071422]\n",
      " [0.14203949]\n",
      " [0.35633771]\n",
      " [0.16522168]\n",
      " [0.86435506]\n",
      " [0.2291127 ]\n",
      " [0.21795327]\n",
      " [0.5098597 ]\n",
      " [0.45821477]\n",
      " [0.36492263]\n",
      " [0.19359807]\n",
      " [0.37903726]\n",
      " [0.4991683 ]\n",
      " [0.33578151]\n",
      " [0.35409585]\n",
      " [0.45030986]\n",
      " [0.38248626]\n",
      " [0.67772495]\n",
      " [0.83390078]\n",
      " [0.74306499]\n",
      " [0.44274986]\n",
      " [0.113664  ]\n",
      " [0.60477647]\n",
      " [0.52100128]\n",
      " [0.86384285]\n",
      " [0.14317837]\n",
      " [0.25020142]\n",
      " [0.40091116]\n",
      " [0.53996118]\n",
      " [0.43852281]\n",
      " [0.23332838]\n",
      " [0.21989303]\n",
      " [0.50874319]\n",
      " [0.47358737]\n",
      " [0.09820025]\n",
      " [0.11467662]\n",
      " [0.12913319]\n",
      " [0.73290331]\n",
      " [0.2833983 ]\n",
      " [0.42971429]\n",
      " [0.82307141]\n",
      " [0.51442283]\n",
      " [0.75977493]\n",
      " [0.23129374]\n",
      " [0.77687068]\n",
      " [0.54726624]\n",
      " [0.06146534]\n",
      " [0.86104463]\n",
      " [0.56073198]\n",
      " [0.12906996]\n",
      " [0.4172969 ]\n",
      " [0.61342738]\n",
      " [0.91106548]\n",
      " [0.48878202]\n",
      " [0.75339881]\n",
      " [0.42742683]\n",
      " [0.51924393]\n",
      " [0.22386295]\n",
      " [0.69973704]\n",
      " [0.09508526]\n",
      " [0.69174942]\n",
      " [0.69541419]\n",
      " [0.56331094]\n",
      " [0.43480608]\n",
      " [0.03043617]\n",
      " [0.5270938 ]\n",
      " [0.24736994]\n",
      " [0.67727159]\n",
      " [0.18719009]\n",
      " [0.28345169]\n",
      " [0.70259801]\n",
      " [0.33730216]\n",
      " [0.25587019]\n",
      " [0.16861269]\n",
      " [0.81727222]\n",
      " [0.37989571]\n",
      " [0.18572605]\n",
      " [0.407024  ]\n",
      " [0.26520754]\n",
      " [0.96391193]\n",
      " [0.5731246 ]\n",
      " [0.20329071]\n",
      " [0.4066005 ]\n",
      " [0.07069653]\n",
      " [0.50049294]\n",
      " [0.41681631]\n",
      " [0.33603039]\n",
      " [0.33185035]\n",
      " [0.44652596]\n",
      " [0.30227594]\n",
      " [0.53268101]\n",
      " [0.29197272]]\n",
      "=====DEBUG=====\n",
      "==== DEBUG2 ====\n",
      "numpyではベクトルの内積を求める際には、横ベクトル×縦ベクトルである必要がある\n",
      "-> w_new-w\n",
      "[[-1.12551679]\n",
      " [ 0.01817398]\n",
      " [ 0.04906108]]\n",
      "-> (w_new-w).T\n",
      "[[-1.12551679  0.01817398  0.04906108]]\n",
      "-> w\n",
      "[[0. ]\n",
      " [0.1]\n",
      " [0.1]]\n",
      "#計算結果は1行1列の行列になる\n",
      "[[1.26952533]]\n",
      "#こちらはエラーになる\n",
      "shapes (1,3) and (1,3) not aligned: 3 (dim 1) != 1 (dim 0)\n",
      "#内積の計算結果(1行1列)はスカラーとして扱うことができるらしい\n",
      "[[1.26952533]]\n",
      "[[ True]]\n",
      "=====DEBUG=====\n",
      "[0.23122882 0.86126445 0.0458001  0.0408623  0.29024681 0.14090793\n",
      " 0.17588075 0.25382264 0.12074388 0.57845955 0.01603616 0.27052891\n",
      " 0.04209044 0.35075679 0.61531316 0.85578118 0.05483758 0.04820506\n",
      " 0.83051798 0.25627962 0.73339888 0.4330404  0.32098294 0.54960265\n",
      " 0.45076354 0.03761272 0.63715505 0.09243334 0.0672285  0.08532241\n",
      " 0.04476488 0.00168889 0.05153589 0.23546326 0.38186607 0.01811059\n",
      " 0.36356632 0.96156961 0.41705971 0.04564483 0.10721307 0.09109248\n",
      " 0.7973467  0.01413989 0.23284287 0.17267124 0.53857101 0.08646063\n",
      " 0.18207139 0.79582233 0.96098521 0.59412121 0.27601331 0.16410063\n",
      " 0.11374317 0.63079816 0.01093557 0.87997275 0.06819394 0.07991564\n",
      " 0.9489136  0.53239524 0.97534966 0.04593663 0.04812803 0.71845405\n",
      " 0.70556129 0.74710571 0.93359082 0.1469431  0.90164621 0.73262218\n",
      " 0.35058703 0.12455235 0.09480122 0.02970887 0.83109631 0.6388276\n",
      " 0.83365279 0.08591798 0.22077776 0.08542307 0.46337536 0.77637514\n",
      " 0.91695496 0.89250535 0.96145923 0.0420107  0.0390447  0.00708718\n",
      " 0.17531063 0.39815172 0.25635106 0.16300883 0.55104105 0.96740733\n",
      " 0.01401344 0.84298549 0.06323223 0.02980512 0.46145933 0.44725881\n",
      " 0.03420064 0.0691807  0.08884699 0.61873095 0.04032743 0.44147866\n",
      " 0.05660008 0.10732591 0.11296957 0.0327907  0.07635    0.13648931\n",
      " 0.64043553 0.26391529 0.76049694 0.00835469 0.65450823 0.04243253\n",
      " 0.96607243 0.08706857 0.10080943 0.04049913 0.0572628  0.9302171\n",
      " 0.01902621 0.20467222 0.96501905 0.45069486 0.60819585 0.16332357\n",
      " 0.17740345 0.14946104 0.95591638 0.82383745 0.50485102 0.24513508\n",
      " 0.07896068 0.2120136  0.32629136 0.31265374 0.39556934 0.20527749\n",
      " 0.4280978  0.89353664 0.0447165  0.7563928  0.25940845 0.28738966\n",
      " 0.68066701 0.10038001 0.01684509 0.92844153 0.9149054  0.19604856\n",
      " 0.29262789 0.16032415 0.97209823 0.01015253 0.27780145 0.74436554\n",
      " 0.62296046 0.10112    0.55858779 0.04255447 0.07605555 0.16422956\n",
      " 0.0161663  0.77683198 0.2938142  0.24937769 0.08043772 0.07012958\n",
      " 0.80230782 0.4707794  0.59421291 0.43367709 0.82487769 0.052337\n",
      " 0.01440199 0.27683249 0.41792171 0.67504484 0.22561069 0.50379107\n",
      " 0.59487491 0.41885789 0.22248567 0.03398674 0.15342895 0.03384947\n",
      " 0.77190643 0.05677253 0.04427171 0.29281962 0.20819971 0.12243853\n",
      " 0.03941962 0.13572179 0.23556852 0.08563033 0.15566951 0.2064941\n",
      " 0.17592585 0.47811352 0.76918945 0.53497592 0.18529167 0.02248254\n",
      " 0.38366878 0.2428052  0.86416298 0.02987178 0.0774517  0.20336084\n",
      " 0.33482644 0.18706566 0.06017471 0.05775549 0.22324071 0.21633748\n",
      " 0.01637577 0.01649903 0.02621033 0.54101544 0.10943218 0.17643604\n",
      " 0.68897605 0.24043409 0.54314955 0.06419098 0.62086635 0.28519251\n",
      " 0.00850717 0.82466331 0.33453506 0.02249005 0.13397678 0.36029173\n",
      " 0.86505002 0.20187854 0.60260363 0.16079199 0.20337298 0.06409352\n",
      " 0.47206145 0.01377951 0.52121598 0.4608568  0.35235283 0.15126312\n",
      " 0.00268725 0.23850137 0.06406185 0.45147386 0.0443937  0.08304695\n",
      " 0.43679478 0.11032449 0.0617263  0.03611596 0.6910438  0.13200283\n",
      " 0.05904679 0.16241984 0.08112844 0.95756809 0.35827034 0.0432331\n",
      " 0.15514061 0.01021404 0.24844747 0.19184395 0.10124189 0.10265829\n",
      " 0.18382725 0.09633279 0.2801314  0.09338274]\n",
      "-----\n",
      "[[0.23122882]\n",
      " [0.86126445]\n",
      " [0.0458001 ]\n",
      " [0.0408623 ]\n",
      " [0.29024681]\n",
      " [0.14090793]\n",
      " [0.17588075]\n",
      " [0.25382264]\n",
      " [0.12074388]\n",
      " [0.57845955]\n",
      " [0.01603616]\n",
      " [0.27052891]\n",
      " [0.04209044]\n",
      " [0.35075679]\n",
      " [0.61531316]\n",
      " [0.85578118]\n",
      " [0.05483758]\n",
      " [0.04820506]\n",
      " [0.83051798]\n",
      " [0.25627962]\n",
      " [0.73339888]\n",
      " [0.4330404 ]\n",
      " [0.32098294]\n",
      " [0.54960265]\n",
      " [0.45076354]\n",
      " [0.03761272]\n",
      " [0.63715505]\n",
      " [0.09243334]\n",
      " [0.0672285 ]\n",
      " [0.08532241]\n",
      " [0.04476488]\n",
      " [0.00168889]\n",
      " [0.05153589]\n",
      " [0.23546326]\n",
      " [0.38186607]\n",
      " [0.01811059]\n",
      " [0.36356632]\n",
      " [0.96156961]\n",
      " [0.41705971]\n",
      " [0.04564483]\n",
      " [0.10721307]\n",
      " [0.09109248]\n",
      " [0.7973467 ]\n",
      " [0.01413989]\n",
      " [0.23284287]\n",
      " [0.17267124]\n",
      " [0.53857101]\n",
      " [0.08646063]\n",
      " [0.18207139]\n",
      " [0.79582233]\n",
      " [0.96098521]\n",
      " [0.59412121]\n",
      " [0.27601331]\n",
      " [0.16410063]\n",
      " [0.11374317]\n",
      " [0.63079816]\n",
      " [0.01093557]\n",
      " [0.87997275]\n",
      " [0.06819394]\n",
      " [0.07991564]\n",
      " [0.9489136 ]\n",
      " [0.53239524]\n",
      " [0.97534966]\n",
      " [0.04593663]\n",
      " [0.04812803]\n",
      " [0.71845405]\n",
      " [0.70556129]\n",
      " [0.74710571]\n",
      " [0.93359082]\n",
      " [0.1469431 ]\n",
      " [0.90164621]\n",
      " [0.73262218]\n",
      " [0.35058703]\n",
      " [0.12455235]\n",
      " [0.09480122]\n",
      " [0.02970887]\n",
      " [0.83109631]\n",
      " [0.6388276 ]\n",
      " [0.83365279]\n",
      " [0.08591798]\n",
      " [0.22077776]\n",
      " [0.08542307]\n",
      " [0.46337536]\n",
      " [0.77637514]\n",
      " [0.91695496]\n",
      " [0.89250535]\n",
      " [0.96145923]\n",
      " [0.0420107 ]\n",
      " [0.0390447 ]\n",
      " [0.00708718]\n",
      " [0.17531063]\n",
      " [0.39815172]\n",
      " [0.25635106]\n",
      " [0.16300883]\n",
      " [0.55104105]\n",
      " [0.96740733]\n",
      " [0.01401344]\n",
      " [0.84298549]\n",
      " [0.06323223]\n",
      " [0.02980512]\n",
      " [0.46145933]\n",
      " [0.44725881]\n",
      " [0.03420064]\n",
      " [0.0691807 ]\n",
      " [0.08884699]\n",
      " [0.61873095]\n",
      " [0.04032743]\n",
      " [0.44147866]\n",
      " [0.05660008]\n",
      " [0.10732591]\n",
      " [0.11296957]\n",
      " [0.0327907 ]\n",
      " [0.07635   ]\n",
      " [0.13648931]\n",
      " [0.64043553]\n",
      " [0.26391529]\n",
      " [0.76049694]\n",
      " [0.00835469]\n",
      " [0.65450823]\n",
      " [0.04243253]\n",
      " [0.96607243]\n",
      " [0.08706857]\n",
      " [0.10080943]\n",
      " [0.04049913]\n",
      " [0.0572628 ]\n",
      " [0.9302171 ]\n",
      " [0.01902621]\n",
      " [0.20467222]\n",
      " [0.96501905]\n",
      " [0.45069486]\n",
      " [0.60819585]\n",
      " [0.16332357]\n",
      " [0.17740345]\n",
      " [0.14946104]\n",
      " [0.95591638]\n",
      " [0.82383745]\n",
      " [0.50485102]\n",
      " [0.24513508]\n",
      " [0.07896068]\n",
      " [0.2120136 ]\n",
      " [0.32629136]\n",
      " [0.31265374]\n",
      " [0.39556934]\n",
      " [0.20527749]\n",
      " [0.4280978 ]\n",
      " [0.89353664]\n",
      " [0.0447165 ]\n",
      " [0.7563928 ]\n",
      " [0.25940845]\n",
      " [0.28738966]\n",
      " [0.68066701]\n",
      " [0.10038001]\n",
      " [0.01684509]\n",
      " [0.92844153]\n",
      " [0.9149054 ]\n",
      " [0.19604856]\n",
      " [0.29262789]\n",
      " [0.16032415]\n",
      " [0.97209823]\n",
      " [0.01015253]\n",
      " [0.27780145]\n",
      " [0.74436554]\n",
      " [0.62296046]\n",
      " [0.10112   ]\n",
      " [0.55858779]\n",
      " [0.04255447]\n",
      " [0.07605555]\n",
      " [0.16422956]\n",
      " [0.0161663 ]\n",
      " [0.77683198]\n",
      " [0.2938142 ]\n",
      " [0.24937769]\n",
      " [0.08043772]\n",
      " [0.07012958]\n",
      " [0.80230782]\n",
      " [0.4707794 ]\n",
      " [0.59421291]\n",
      " [0.43367709]\n",
      " [0.82487769]\n",
      " [0.052337  ]\n",
      " [0.01440199]\n",
      " [0.27683249]\n",
      " [0.41792171]\n",
      " [0.67504484]\n",
      " [0.22561069]\n",
      " [0.50379107]\n",
      " [0.59487491]\n",
      " [0.41885789]\n",
      " [0.22248567]\n",
      " [0.03398674]\n",
      " [0.15342895]\n",
      " [0.03384947]\n",
      " [0.77190643]\n",
      " [0.05677253]\n",
      " [0.04427171]\n",
      " [0.29281962]\n",
      " [0.20819971]\n",
      " [0.12243853]\n",
      " [0.03941962]\n",
      " [0.13572179]\n",
      " [0.23556852]\n",
      " [0.08563033]\n",
      " [0.15566951]\n",
      " [0.2064941 ]\n",
      " [0.17592585]\n",
      " [0.47811352]\n",
      " [0.76918945]\n",
      " [0.53497592]\n",
      " [0.18529167]\n",
      " [0.02248254]\n",
      " [0.38366878]\n",
      " [0.2428052 ]\n",
      " [0.86416298]\n",
      " [0.02987178]\n",
      " [0.0774517 ]\n",
      " [0.20336084]\n",
      " [0.33482644]\n",
      " [0.18706566]\n",
      " [0.06017471]\n",
      " [0.05775549]\n",
      " [0.22324071]\n",
      " [0.21633748]\n",
      " [0.01637577]\n",
      " [0.01649903]\n",
      " [0.02621033]\n",
      " [0.54101544]\n",
      " [0.10943218]\n",
      " [0.17643604]\n",
      " [0.68897605]\n",
      " [0.24043409]\n",
      " [0.54314955]\n",
      " [0.06419098]\n",
      " [0.62086635]\n",
      " [0.28519251]\n",
      " [0.00850717]\n",
      " [0.82466331]\n",
      " [0.33453506]\n",
      " [0.02249005]\n",
      " [0.13397678]\n",
      " [0.36029173]\n",
      " [0.86505002]\n",
      " [0.20187854]\n",
      " [0.60260363]\n",
      " [0.16079199]\n",
      " [0.20337298]\n",
      " [0.06409352]\n",
      " [0.47206145]\n",
      " [0.01377951]\n",
      " [0.52121598]\n",
      " [0.4608568 ]\n",
      " [0.35235283]\n",
      " [0.15126312]\n",
      " [0.00268725]\n",
      " [0.23850137]\n",
      " [0.06406185]\n",
      " [0.45147386]\n",
      " [0.0443937 ]\n",
      " [0.08304695]\n",
      " [0.43679478]\n",
      " [0.11032449]\n",
      " [0.0617263 ]\n",
      " [0.03611596]\n",
      " [0.6910438 ]\n",
      " [0.13200283]\n",
      " [0.05904679]\n",
      " [0.16241984]\n",
      " [0.08112844]\n",
      " [0.95756809]\n",
      " [0.35827034]\n",
      " [0.0432331 ]\n",
      " [0.15514061]\n",
      " [0.01021404]\n",
      " [0.24844747]\n",
      " [0.19184395]\n",
      " [0.10124189]\n",
      " [0.10265829]\n",
      " [0.18382725]\n",
      " [0.09633279]\n",
      " [0.2801314 ]\n",
      " [0.09338274]]\n",
      "=====DEBUG=====\n",
      "==== DEBUG2 ====\n",
      "numpyではベクトルの内積を求める際には、横ベクトル×縦ベクトルである必要がある\n",
      "-> w_new-w\n",
      "[[-0.53185289]\n",
      " [ 0.04844807]\n",
      " [ 0.0624725 ]]\n",
      "-> (w_new-w).T\n",
      "[[-0.53185289  0.04844807  0.0624725 ]]\n",
      "-> w\n",
      "[[-1.12551679]\n",
      " [ 0.11817398]\n",
      " [ 0.14906108]]\n",
      "#計算結果は1行1列の行列になる\n",
      "[[0.28911753]]\n",
      "#こちらはエラーになる\n",
      "shapes (1,3) and (1,3) not aligned: 3 (dim 1) != 1 (dim 0)\n",
      "#内積の計算結果(1行1列)はスカラーとして扱うことができるらしい\n",
      "[[0.28911753]]\n",
      "[[ True]]\n",
      "=====DEBUG=====\n",
      "[1.45920269e-01 9.26419151e-01 1.26018273e-02 1.06231479e-02\n",
      " 2.07536695e-01 6.73233513e-02 9.52634438e-02 1.70110193e-01\n",
      " 5.31720076e-02 5.95852812e-01 2.75102107e-03 1.87111235e-01\n",
      " 1.12249642e-02 2.82283817e-01 6.48111064e-01 9.20703780e-01\n",
      " 1.64762607e-02 1.35981616e-02 8.97912091e-01 1.71557528e-01\n",
      " 7.98028846e-01 3.90928173e-01 2.43956119e-01 5.53529972e-01\n",
      " 4.16292683e-01 9.37514678e-03 6.73747413e-01 3.56761533e-02\n",
      " 2.22044221e-02 3.19471728e-02 1.21367335e-02 1.11810780e-04\n",
      " 1.49164566e-02 1.49349660e-01 3.21692155e-01 3.26261837e-03\n",
      " 2.99317718e-01 9.88954606e-01 3.67477729e-01 1.26838940e-02\n",
      " 4.44427970e-02 3.49420123e-02 8.66097646e-01 2.31639661e-03\n",
      " 1.48104534e-01 9.24754961e-02 5.36497434e-01 3.22857445e-02\n",
      " 9.95778431e-02 8.64037785e-01 9.88620574e-01 6.16179519e-01\n",
      " 1.92707636e-01 8.56821064e-02 4.89892343e-02 6.65541419e-01\n",
      " 1.57442925e-03 9.40745445e-01 2.27258803e-02 2.84951474e-02\n",
      " 9.83090966e-01 5.29628492e-01 9.94154606e-01 1.27335362e-02\n",
      " 1.35866866e-02 7.78630597e-01 7.64173242e-01 8.11481137e-01\n",
      " 9.75477311e-01 7.12796343e-02 9.55621208e-01 7.95347090e-01\n",
      " 2.81074301e-01 5.57976075e-02 3.72229278e-02 6.72832083e-03\n",
      " 8.99252774e-01 6.76228113e-01 9.02379713e-01 3.23058599e-02\n",
      " 1.37434288e-01 3.13729440e-02 4.30808812e-01 8.45192823e-01\n",
      " 9.65186430e-01 9.48877429e-01 9.88846030e-01 1.11335179e-02\n",
      " 1.00222940e-02 8.52390249e-04 9.47804820e-02 3.44580199e-01\n",
      " 1.72459453e-01 8.47887880e-02 5.58119318e-01 9.91313525e-01\n",
      " 2.27440424e-03 9.10320884e-01 2.02836617e-02 6.71658273e-03\n",
      " 4.30235952e-01 4.06913382e-01 8.23216579e-03 2.33721041e-02\n",
      " 3.34577582e-02 6.51277446e-01 1.04689308e-02 4.02922595e-01\n",
      " 1.71035152e-02 4.47298913e-02 4.83513972e-02 7.75454090e-03\n",
      " 2.65058823e-02 6.44405957e-02 6.82712966e-01 1.78978842e-01\n",
      " 8.28810630e-01 1.08501105e-03 6.98143885e-01 1.12198888e-02\n",
      " 9.90785305e-01 3.22956538e-02 4.10208466e-02 1.04526412e-02\n",
      " 1.75496021e-02 9.73531931e-01 3.50610532e-03 1.20196803e-01\n",
      " 9.90397992e-01 4.17796554e-01 6.35508425e-01 8.48765426e-02\n",
      " 9.64326268e-02 7.47257768e-02 9.86450042e-01 8.91478633e-01\n",
      " 4.89966413e-01 1.59632740e-01 2.82648832e-02 1.27954815e-01\n",
      " 2.51464541e-01 2.36788243e-01 3.38985649e-01 1.22056960e-01\n",
      " 3.85290822e-01 9.50642169e-01 1.23123401e-02 8.23756646e-01\n",
      " 1.74841527e-01 2.05472633e-01 7.30624964e-01 4.02628052e-02\n",
      " 2.95204645e-03 9.72617950e-01 9.64576449e-01 1.12117448e-01\n",
      " 2.11209003e-01 8.24161827e-02 9.93013987e-01 1.44621285e-03\n",
      " 1.96134439e-01 8.12446928e-01 6.59087408e-01 4.03479027e-02\n",
      " 5.66399864e-01 1.13992534e-02 2.64778265e-02 8.58013185e-02\n",
      " 2.80181284e-03 8.45030306e-01 2.13486712e-01 1.64881574e-01\n",
      " 2.90645140e-02 2.35881775e-02 8.70661318e-01 4.40496767e-01\n",
      " 6.15276226e-01 3.92963866e-01 8.93530604e-01 1.53949805e-02\n",
      " 2.37486016e-03 1.94040850e-01 3.70143971e-01 7.23045461e-01\n",
      " 1.41478813e-01 4.89971765e-01 6.18545525e-01 3.68580987e-01\n",
      " 1.36520424e-01 8.21014464e-03 7.77393627e-02 8.08574176e-03\n",
      " 8.39615149e-01 1.72276134e-02 1.18759645e-02 2.13334184e-01\n",
      " 1.24071962e-01 5.43843730e-02 1.00540521e-02 6.37469306e-02\n",
      " 1.50300563e-01 3.14087392e-02 7.95832893e-02 1.22619913e-01\n",
      " 9.61111050e-02 4.53408592e-01 8.38274110e-01 5.31864778e-01\n",
      " 1.03202890e-01 4.50115027e-03 3.24712828e-01 1.57231819e-01\n",
      " 9.29129330e-01 6.76999784e-03 2.74907280e-02 1.20757393e-01\n",
      " 2.63614400e-01 1.04873061e-01 1.87944534e-02 1.77296349e-02\n",
      " 1.37565343e-01 1.31601973e-01 2.84231486e-03 2.84811619e-03\n",
      " 5.60850587e-03 5.41330890e-01 4.64867703e-02 9.56690688e-02\n",
      " 7.41843543e-01 1.54934245e-01 5.42597275e-01 2.07457188e-02\n",
      " 6.52803261e-01 2.03146764e-01 1.11516285e-03 8.94300305e-01\n",
      " 2.62397933e-01 4.46972270e-03 6.19755398e-02 2.93254291e-01\n",
      " 9.28205782e-01 1.17408368e-01 6.28636957e-01 8.25510761e-02\n",
      " 1.18146048e-01 2.07425021e-02 4.43357535e-01 2.20842864e-03\n",
      " 5.15134431e-01 4.27527710e-01 2.85126563e-01 7.48137354e-02\n",
      " 2.15252992e-04 1.52582331e-01 2.05892615e-02 4.15189772e-01\n",
      " 1.20428424e-02 3.03356780e-02 3.92889059e-01 4.64921448e-02\n",
      " 1.94139346e-02 8.89659284e-03 7.44915796e-01 6.10082796e-02\n",
      " 1.85337676e-02 8.42204584e-02 2.94013097e-02 9.87126393e-01\n",
      " 2.92413193e-01 1.15119180e-02 7.83123519e-02 1.44664937e-03\n",
      " 1.63773298e-01 1.09647450e-01 4.06889257e-02 4.16151359e-02\n",
      " 1.01827839e-01 3.79661713e-02 1.97819669e-01 3.62725494e-02]\n",
      "-----\n",
      "[[1.45920269e-01]\n",
      " [9.26419151e-01]\n",
      " [1.26018273e-02]\n",
      " [1.06231479e-02]\n",
      " [2.07536695e-01]\n",
      " [6.73233513e-02]\n",
      " [9.52634438e-02]\n",
      " [1.70110193e-01]\n",
      " [5.31720076e-02]\n",
      " [5.95852812e-01]\n",
      " [2.75102107e-03]\n",
      " [1.87111235e-01]\n",
      " [1.12249642e-02]\n",
      " [2.82283817e-01]\n",
      " [6.48111064e-01]\n",
      " [9.20703780e-01]\n",
      " [1.64762607e-02]\n",
      " [1.35981616e-02]\n",
      " [8.97912091e-01]\n",
      " [1.71557528e-01]\n",
      " [7.98028846e-01]\n",
      " [3.90928173e-01]\n",
      " [2.43956119e-01]\n",
      " [5.53529972e-01]\n",
      " [4.16292683e-01]\n",
      " [9.37514678e-03]\n",
      " [6.73747413e-01]\n",
      " [3.56761533e-02]\n",
      " [2.22044221e-02]\n",
      " [3.19471728e-02]\n",
      " [1.21367335e-02]\n",
      " [1.11810780e-04]\n",
      " [1.49164566e-02]\n",
      " [1.49349660e-01]\n",
      " [3.21692155e-01]\n",
      " [3.26261837e-03]\n",
      " [2.99317718e-01]\n",
      " [9.88954606e-01]\n",
      " [3.67477729e-01]\n",
      " [1.26838940e-02]\n",
      " [4.44427970e-02]\n",
      " [3.49420123e-02]\n",
      " [8.66097646e-01]\n",
      " [2.31639661e-03]\n",
      " [1.48104534e-01]\n",
      " [9.24754961e-02]\n",
      " [5.36497434e-01]\n",
      " [3.22857445e-02]\n",
      " [9.95778431e-02]\n",
      " [8.64037785e-01]\n",
      " [9.88620574e-01]\n",
      " [6.16179519e-01]\n",
      " [1.92707636e-01]\n",
      " [8.56821064e-02]\n",
      " [4.89892343e-02]\n",
      " [6.65541419e-01]\n",
      " [1.57442925e-03]\n",
      " [9.40745445e-01]\n",
      " [2.27258803e-02]\n",
      " [2.84951474e-02]\n",
      " [9.83090966e-01]\n",
      " [5.29628492e-01]\n",
      " [9.94154606e-01]\n",
      " [1.27335362e-02]\n",
      " [1.35866866e-02]\n",
      " [7.78630597e-01]\n",
      " [7.64173242e-01]\n",
      " [8.11481137e-01]\n",
      " [9.75477311e-01]\n",
      " [7.12796343e-02]\n",
      " [9.55621208e-01]\n",
      " [7.95347090e-01]\n",
      " [2.81074301e-01]\n",
      " [5.57976075e-02]\n",
      " [3.72229278e-02]\n",
      " [6.72832083e-03]\n",
      " [8.99252774e-01]\n",
      " [6.76228113e-01]\n",
      " [9.02379713e-01]\n",
      " [3.23058599e-02]\n",
      " [1.37434288e-01]\n",
      " [3.13729440e-02]\n",
      " [4.30808812e-01]\n",
      " [8.45192823e-01]\n",
      " [9.65186430e-01]\n",
      " [9.48877429e-01]\n",
      " [9.88846030e-01]\n",
      " [1.11335179e-02]\n",
      " [1.00222940e-02]\n",
      " [8.52390249e-04]\n",
      " [9.47804820e-02]\n",
      " [3.44580199e-01]\n",
      " [1.72459453e-01]\n",
      " [8.47887880e-02]\n",
      " [5.58119318e-01]\n",
      " [9.91313525e-01]\n",
      " [2.27440424e-03]\n",
      " [9.10320884e-01]\n",
      " [2.02836617e-02]\n",
      " [6.71658273e-03]\n",
      " [4.30235952e-01]\n",
      " [4.06913382e-01]\n",
      " [8.23216579e-03]\n",
      " [2.33721041e-02]\n",
      " [3.34577582e-02]\n",
      " [6.51277446e-01]\n",
      " [1.04689308e-02]\n",
      " [4.02922595e-01]\n",
      " [1.71035152e-02]\n",
      " [4.47298913e-02]\n",
      " [4.83513972e-02]\n",
      " [7.75454090e-03]\n",
      " [2.65058823e-02]\n",
      " [6.44405957e-02]\n",
      " [6.82712966e-01]\n",
      " [1.78978842e-01]\n",
      " [8.28810630e-01]\n",
      " [1.08501105e-03]\n",
      " [6.98143885e-01]\n",
      " [1.12198888e-02]\n",
      " [9.90785305e-01]\n",
      " [3.22956538e-02]\n",
      " [4.10208466e-02]\n",
      " [1.04526412e-02]\n",
      " [1.75496021e-02]\n",
      " [9.73531931e-01]\n",
      " [3.50610532e-03]\n",
      " [1.20196803e-01]\n",
      " [9.90397992e-01]\n",
      " [4.17796554e-01]\n",
      " [6.35508425e-01]\n",
      " [8.48765426e-02]\n",
      " [9.64326268e-02]\n",
      " [7.47257768e-02]\n",
      " [9.86450042e-01]\n",
      " [8.91478633e-01]\n",
      " [4.89966413e-01]\n",
      " [1.59632740e-01]\n",
      " [2.82648832e-02]\n",
      " [1.27954815e-01]\n",
      " [2.51464541e-01]\n",
      " [2.36788243e-01]\n",
      " [3.38985649e-01]\n",
      " [1.22056960e-01]\n",
      " [3.85290822e-01]\n",
      " [9.50642169e-01]\n",
      " [1.23123401e-02]\n",
      " [8.23756646e-01]\n",
      " [1.74841527e-01]\n",
      " [2.05472633e-01]\n",
      " [7.30624964e-01]\n",
      " [4.02628052e-02]\n",
      " [2.95204645e-03]\n",
      " [9.72617950e-01]\n",
      " [9.64576449e-01]\n",
      " [1.12117448e-01]\n",
      " [2.11209003e-01]\n",
      " [8.24161827e-02]\n",
      " [9.93013987e-01]\n",
      " [1.44621285e-03]\n",
      " [1.96134439e-01]\n",
      " [8.12446928e-01]\n",
      " [6.59087408e-01]\n",
      " [4.03479027e-02]\n",
      " [5.66399864e-01]\n",
      " [1.13992534e-02]\n",
      " [2.64778265e-02]\n",
      " [8.58013185e-02]\n",
      " [2.80181284e-03]\n",
      " [8.45030306e-01]\n",
      " [2.13486712e-01]\n",
      " [1.64881574e-01]\n",
      " [2.90645140e-02]\n",
      " [2.35881775e-02]\n",
      " [8.70661318e-01]\n",
      " [4.40496767e-01]\n",
      " [6.15276226e-01]\n",
      " [3.92963866e-01]\n",
      " [8.93530604e-01]\n",
      " [1.53949805e-02]\n",
      " [2.37486016e-03]\n",
      " [1.94040850e-01]\n",
      " [3.70143971e-01]\n",
      " [7.23045461e-01]\n",
      " [1.41478813e-01]\n",
      " [4.89971765e-01]\n",
      " [6.18545525e-01]\n",
      " [3.68580987e-01]\n",
      " [1.36520424e-01]\n",
      " [8.21014464e-03]\n",
      " [7.77393627e-02]\n",
      " [8.08574176e-03]\n",
      " [8.39615149e-01]\n",
      " [1.72276134e-02]\n",
      " [1.18759645e-02]\n",
      " [2.13334184e-01]\n",
      " [1.24071962e-01]\n",
      " [5.43843730e-02]\n",
      " [1.00540521e-02]\n",
      " [6.37469306e-02]\n",
      " [1.50300563e-01]\n",
      " [3.14087392e-02]\n",
      " [7.95832893e-02]\n",
      " [1.22619913e-01]\n",
      " [9.61111050e-02]\n",
      " [4.53408592e-01]\n",
      " [8.38274110e-01]\n",
      " [5.31864778e-01]\n",
      " [1.03202890e-01]\n",
      " [4.50115027e-03]\n",
      " [3.24712828e-01]\n",
      " [1.57231819e-01]\n",
      " [9.29129330e-01]\n",
      " [6.76999784e-03]\n",
      " [2.74907280e-02]\n",
      " [1.20757393e-01]\n",
      " [2.63614400e-01]\n",
      " [1.04873061e-01]\n",
      " [1.87944534e-02]\n",
      " [1.77296349e-02]\n",
      " [1.37565343e-01]\n",
      " [1.31601973e-01]\n",
      " [2.84231486e-03]\n",
      " [2.84811619e-03]\n",
      " [5.60850587e-03]\n",
      " [5.41330890e-01]\n",
      " [4.64867703e-02]\n",
      " [9.56690688e-02]\n",
      " [7.41843543e-01]\n",
      " [1.54934245e-01]\n",
      " [5.42597275e-01]\n",
      " [2.07457188e-02]\n",
      " [6.52803261e-01]\n",
      " [2.03146764e-01]\n",
      " [1.11516285e-03]\n",
      " [8.94300305e-01]\n",
      " [2.62397933e-01]\n",
      " [4.46972270e-03]\n",
      " [6.19755398e-02]\n",
      " [2.93254291e-01]\n",
      " [9.28205782e-01]\n",
      " [1.17408368e-01]\n",
      " [6.28636957e-01]\n",
      " [8.25510761e-02]\n",
      " [1.18146048e-01]\n",
      " [2.07425021e-02]\n",
      " [4.43357535e-01]\n",
      " [2.20842864e-03]\n",
      " [5.15134431e-01]\n",
      " [4.27527710e-01]\n",
      " [2.85126563e-01]\n",
      " [7.48137354e-02]\n",
      " [2.15252992e-04]\n",
      " [1.52582331e-01]\n",
      " [2.05892615e-02]\n",
      " [4.15189772e-01]\n",
      " [1.20428424e-02]\n",
      " [3.03356780e-02]\n",
      " [3.92889059e-01]\n",
      " [4.64921448e-02]\n",
      " [1.94139346e-02]\n",
      " [8.89659284e-03]\n",
      " [7.44915796e-01]\n",
      " [6.10082796e-02]\n",
      " [1.85337676e-02]\n",
      " [8.42204584e-02]\n",
      " [2.94013097e-02]\n",
      " [9.87126393e-01]\n",
      " [2.92413193e-01]\n",
      " [1.15119180e-02]\n",
      " [7.83123519e-02]\n",
      " [1.44664937e-03]\n",
      " [1.63773298e-01]\n",
      " [1.09647450e-01]\n",
      " [4.06889257e-02]\n",
      " [4.16151359e-02]\n",
      " [1.01827839e-01]\n",
      " [3.79661713e-02]\n",
      " [1.97819669e-01]\n",
      " [3.62725494e-02]]\n",
      "=====DEBUG=====\n",
      "==== DEBUG2 ====\n",
      "numpyではベクトルの内積を求める際には、横ベクトル×縦ベクトルである必要がある\n",
      "-> w_new-w\n",
      "[[-0.32486223]\n",
      " [ 0.03293904]\n",
      " [ 0.04166448]]\n",
      "-> (w_new-w).T\n",
      "[[-0.32486223  0.03293904  0.04166448]]\n",
      "-> w\n",
      "[[-1.65736969]\n",
      " [ 0.16662205]\n",
      " [ 0.21153358]]\n",
      "#計算結果は1行1列の行列になる\n",
      "[[0.10835637]]\n",
      "#こちらはエラーになる\n",
      "shapes (1,3) and (1,3) not aligned: 3 (dim 1) != 1 (dim 0)\n",
      "#内積の計算結果(1行1列)はスカラーとして扱うことができるらしい\n",
      "[[0.10835637]]\n",
      "[[ True]]\n",
      "=====DEBUG=====\n",
      "[1.07820719e-01 9.54051347e-01 5.38056463e-03 4.38133441e-03\n",
      " 1.67847814e-01 4.13137966e-02 6.33941421e-02 1.30556800e-01\n",
      " 3.09194283e-02 6.14516990e-01 8.63898823e-04 1.47228082e-01\n",
      " 4.67588725e-03 2.46853709e-01 6.75274213e-01 9.49699720e-01\n",
      " 7.43341693e-03 5.89711163e-03 9.31272959e-01 1.32055405e-01\n",
      " 8.38409636e-01 3.70733762e-01 2.05631694e-01 5.64559299e-01\n",
      " 4.00522300e-01 3.77083946e-03 7.04979935e-01 1.89785763e-02\n",
      " 1.06680803e-02 1.65739610e-02 5.14441668e-03 1.86220475e-05\n",
      " 6.59805626e-03 1.11068946e-01 2.90883340e-01 1.06054022e-03\n",
      " 2.65619421e-01 9.95427432e-01 3.43536594e-01 5.41584703e-03\n",
      " 2.48241024e-02 1.85015651e-02 9.03615411e-01 7.02236426e-04\n",
      " 1.09792992e-01 6.11112685e-02 5.44404159e-01 1.68056518e-02\n",
      " 6.70473575e-02 9.01802432e-01 9.95264436e-01 6.38528764e-01\n",
      " 1.52820248e-01 5.55875886e-02 2.79391772e-02 6.95619680e-01\n",
      " 4.42938275e-04 9.64811709e-01 1.09698061e-02 1.44502918e-02\n",
      " 9.92364452e-01 5.35987836e-01 9.97875413e-01 5.44475238e-03\n",
      " 5.89010992e-03 8.18808988e-01 8.03660639e-01 8.52039653e-01\n",
      " 9.88005379e-01 4.43611193e-02 9.75335085e-01 8.35874101e-01\n",
      " 2.45627611e-01 3.27983088e-02 1.99757906e-02 2.52676350e-03\n",
      " 9.32329023e-01 7.07763371e-01 9.34863667e-01 1.67991593e-02\n",
      " 9.98923713e-02 1.62491135e-02 4.18069145e-01 8.84348392e-01\n",
      " 9.81677762e-01 9.70701550e-01 9.95376131e-01 4.63284059e-03\n",
      " 4.08081365e-03 2.12212231e-04 6.29963401e-02 3.16825123e-01\n",
      " 1.32852015e-01 5.48727759e-02 5.69738181e-01 9.96575911e-01\n",
      " 6.87416382e-04 9.41388542e-01 9.56072052e-03 2.52328840e-03\n",
      " 4.17155386e-01 3.89939344e-01 3.22155446e-03 1.13403243e-02\n",
      " 1.75611665e-02 6.79076041e-01 4.30258613e-03 3.84793431e-01\n",
      " 7.78487598e-03 2.50063415e-02 2.75033384e-02 2.99754871e-03\n",
      " 1.32412216e-02 3.91253079e-02 7.14627931e-01 1.39326949e-01\n",
      " 8.68722631e-01 2.83136174e-04 7.32257213e-01 4.67981037e-03\n",
      " 9.96324061e-01 1.68314432e-02 2.24769670e-02 4.29827639e-03\n",
      " 8.02318145e-03 9.86845960e-01 1.15607024e-03 8.46649816e-02\n",
      " 9.96135832e-01 4.02124757e-01 6.61030024e-01 5.49553969e-02\n",
      " 6.43689617e-02 4.69055088e-02 9.94152408e-01 9.25867276e-01\n",
      " 4.88595792e-01 1.20679911e-01 1.42926087e-02 9.14514992e-02\n",
      " 2.13511753e-01 1.97789873e-01 3.10646045e-01 8.61926582e-02\n",
      " 3.64003860e-01 9.71868713e-01 5.22490031e-03 8.63885760e-01\n",
      " 1.35237098e-01 1.65642138e-01 7.68115799e-01 2.20017891e-02\n",
      " 9.40178009e-04 9.86287458e-01 9.81245569e-01 7.76610888e-02\n",
      " 1.71497219e-01 5.29976687e-02 9.97367490e-01 3.99113682e-04\n",
      " 1.56106372e-01 8.52615595e-01 6.87809039e-01 2.20811510e-02\n",
      " 5.79845489e-01 4.76385847e-03 1.32171580e-02 5.56821274e-02\n",
      " 8.82403386e-04 8.84253211e-01 1.73737624e-01 1.25594722e-01\n",
      " 1.47848811e-02 1.14818707e-02 9.07736533e-01 4.29663504e-01\n",
      " 6.37585794e-01 3.72995125e-01 9.27521475e-01 6.84760874e-03\n",
      " 7.23656479e-04 1.54109697e-01 3.46472534e-01 7.59910512e-01\n",
      " 1.03639434e-01 4.88433748e-01 6.41147949e-01 3.44963121e-01\n",
      " 9.92806803e-02 3.20886228e-03 4.92549393e-02 3.15377725e-03\n",
      " 8.79172525e-01 7.85076365e-03 5.01456083e-03 1.73487077e-01\n",
      " 8.80285980e-02 3.17809809e-02 4.10136600e-03 3.86177799e-02\n",
      " 1.11880626e-01 1.62762711e-02 5.06949034e-02 8.67352478e-02\n",
      " 6.40313070e-02 4.44759254e-01 8.77762572e-01 5.38818929e-01\n",
      " 7.00210365e-02 1.55870520e-03 2.94235467e-01 1.18412242e-01\n",
      " 9.56100951e-01 2.54609405e-03 1.38170496e-02 8.50114531e-02\n",
      " 2.26391064e-01 7.14209314e-02 8.72099683e-03 8.12502226e-03\n",
      " 1.00203773e-01 9.47544251e-02 8.98141624e-04 9.01222158e-04\n",
      " 2.03037401e-03 5.50021179e-01 2.61849303e-02 6.37336821e-02\n",
      " 7.80144415e-01 1.16239457e-01 5.51728029e-01 9.82471242e-03\n",
      " 6.80976598e-01 1.63274349e-01 2.92536665e-04 9.28095766e-01\n",
      " 2.25164197e-01 1.54694603e-03 3.73388819e-02 2.59076772e-01\n",
      " 9.55512518e-01 8.22448156e-02 6.52967676e-01 5.31224983e-02\n",
      " 8.29396832e-02 9.82053928e-03 4.32951346e-01 6.63971186e-04\n",
      " 5.18501974e-01 4.14147448e-01 2.49895076e-01 4.70673728e-02\n",
      " 4.08158451e-05 1.14069247e-01 9.74014360e-03 3.99448668e-01\n",
      " 5.09393211e-03 1.55830400e-02 3.73385558e-01 2.62251729e-02\n",
      " 9.07526758e-03 3.53765325e-03 7.83387730e-01 3.65947455e-02\n",
      " 8.56136153e-03 5.44254847e-02 1.49954358e-02 9.94505518e-01\n",
      " 2.57965097e-01 4.82789422e-03 4.97595202e-02 3.99630634e-04\n",
      " 1.24549958e-01 7.54377813e-02 2.22922964e-02 2.29091828e-02\n",
      " 6.88731896e-02 2.04731946e-02 1.57879577e-01 1.93630825e-02]\n",
      "-----\n",
      "[[1.07820719e-01]\n",
      " [9.54051347e-01]\n",
      " [5.38056463e-03]\n",
      " [4.38133441e-03]\n",
      " [1.67847814e-01]\n",
      " [4.13137966e-02]\n",
      " [6.33941421e-02]\n",
      " [1.30556800e-01]\n",
      " [3.09194283e-02]\n",
      " [6.14516990e-01]\n",
      " [8.63898823e-04]\n",
      " [1.47228082e-01]\n",
      " [4.67588725e-03]\n",
      " [2.46853709e-01]\n",
      " [6.75274213e-01]\n",
      " [9.49699720e-01]\n",
      " [7.43341693e-03]\n",
      " [5.89711163e-03]\n",
      " [9.31272959e-01]\n",
      " [1.32055405e-01]\n",
      " [8.38409636e-01]\n",
      " [3.70733762e-01]\n",
      " [2.05631694e-01]\n",
      " [5.64559299e-01]\n",
      " [4.00522300e-01]\n",
      " [3.77083946e-03]\n",
      " [7.04979935e-01]\n",
      " [1.89785763e-02]\n",
      " [1.06680803e-02]\n",
      " [1.65739610e-02]\n",
      " [5.14441668e-03]\n",
      " [1.86220475e-05]\n",
      " [6.59805626e-03]\n",
      " [1.11068946e-01]\n",
      " [2.90883340e-01]\n",
      " [1.06054022e-03]\n",
      " [2.65619421e-01]\n",
      " [9.95427432e-01]\n",
      " [3.43536594e-01]\n",
      " [5.41584703e-03]\n",
      " [2.48241024e-02]\n",
      " [1.85015651e-02]\n",
      " [9.03615411e-01]\n",
      " [7.02236426e-04]\n",
      " [1.09792992e-01]\n",
      " [6.11112685e-02]\n",
      " [5.44404159e-01]\n",
      " [1.68056518e-02]\n",
      " [6.70473575e-02]\n",
      " [9.01802432e-01]\n",
      " [9.95264436e-01]\n",
      " [6.38528764e-01]\n",
      " [1.52820248e-01]\n",
      " [5.55875886e-02]\n",
      " [2.79391772e-02]\n",
      " [6.95619680e-01]\n",
      " [4.42938275e-04]\n",
      " [9.64811709e-01]\n",
      " [1.09698061e-02]\n",
      " [1.44502918e-02]\n",
      " [9.92364452e-01]\n",
      " [5.35987836e-01]\n",
      " [9.97875413e-01]\n",
      " [5.44475238e-03]\n",
      " [5.89010992e-03]\n",
      " [8.18808988e-01]\n",
      " [8.03660639e-01]\n",
      " [8.52039653e-01]\n",
      " [9.88005379e-01]\n",
      " [4.43611193e-02]\n",
      " [9.75335085e-01]\n",
      " [8.35874101e-01]\n",
      " [2.45627611e-01]\n",
      " [3.27983088e-02]\n",
      " [1.99757906e-02]\n",
      " [2.52676350e-03]\n",
      " [9.32329023e-01]\n",
      " [7.07763371e-01]\n",
      " [9.34863667e-01]\n",
      " [1.67991593e-02]\n",
      " [9.98923713e-02]\n",
      " [1.62491135e-02]\n",
      " [4.18069145e-01]\n",
      " [8.84348392e-01]\n",
      " [9.81677762e-01]\n",
      " [9.70701550e-01]\n",
      " [9.95376131e-01]\n",
      " [4.63284059e-03]\n",
      " [4.08081365e-03]\n",
      " [2.12212231e-04]\n",
      " [6.29963401e-02]\n",
      " [3.16825123e-01]\n",
      " [1.32852015e-01]\n",
      " [5.48727759e-02]\n",
      " [5.69738181e-01]\n",
      " [9.96575911e-01]\n",
      " [6.87416382e-04]\n",
      " [9.41388542e-01]\n",
      " [9.56072052e-03]\n",
      " [2.52328840e-03]\n",
      " [4.17155386e-01]\n",
      " [3.89939344e-01]\n",
      " [3.22155446e-03]\n",
      " [1.13403243e-02]\n",
      " [1.75611665e-02]\n",
      " [6.79076041e-01]\n",
      " [4.30258613e-03]\n",
      " [3.84793431e-01]\n",
      " [7.78487598e-03]\n",
      " [2.50063415e-02]\n",
      " [2.75033384e-02]\n",
      " [2.99754871e-03]\n",
      " [1.32412216e-02]\n",
      " [3.91253079e-02]\n",
      " [7.14627931e-01]\n",
      " [1.39326949e-01]\n",
      " [8.68722631e-01]\n",
      " [2.83136174e-04]\n",
      " [7.32257213e-01]\n",
      " [4.67981037e-03]\n",
      " [9.96324061e-01]\n",
      " [1.68314432e-02]\n",
      " [2.24769670e-02]\n",
      " [4.29827639e-03]\n",
      " [8.02318145e-03]\n",
      " [9.86845960e-01]\n",
      " [1.15607024e-03]\n",
      " [8.46649816e-02]\n",
      " [9.96135832e-01]\n",
      " [4.02124757e-01]\n",
      " [6.61030024e-01]\n",
      " [5.49553969e-02]\n",
      " [6.43689617e-02]\n",
      " [4.69055088e-02]\n",
      " [9.94152408e-01]\n",
      " [9.25867276e-01]\n",
      " [4.88595792e-01]\n",
      " [1.20679911e-01]\n",
      " [1.42926087e-02]\n",
      " [9.14514992e-02]\n",
      " [2.13511753e-01]\n",
      " [1.97789873e-01]\n",
      " [3.10646045e-01]\n",
      " [8.61926582e-02]\n",
      " [3.64003860e-01]\n",
      " [9.71868713e-01]\n",
      " [5.22490031e-03]\n",
      " [8.63885760e-01]\n",
      " [1.35237098e-01]\n",
      " [1.65642138e-01]\n",
      " [7.68115799e-01]\n",
      " [2.20017891e-02]\n",
      " [9.40178009e-04]\n",
      " [9.86287458e-01]\n",
      " [9.81245569e-01]\n",
      " [7.76610888e-02]\n",
      " [1.71497219e-01]\n",
      " [5.29976687e-02]\n",
      " [9.97367490e-01]\n",
      " [3.99113682e-04]\n",
      " [1.56106372e-01]\n",
      " [8.52615595e-01]\n",
      " [6.87809039e-01]\n",
      " [2.20811510e-02]\n",
      " [5.79845489e-01]\n",
      " [4.76385847e-03]\n",
      " [1.32171580e-02]\n",
      " [5.56821274e-02]\n",
      " [8.82403386e-04]\n",
      " [8.84253211e-01]\n",
      " [1.73737624e-01]\n",
      " [1.25594722e-01]\n",
      " [1.47848811e-02]\n",
      " [1.14818707e-02]\n",
      " [9.07736533e-01]\n",
      " [4.29663504e-01]\n",
      " [6.37585794e-01]\n",
      " [3.72995125e-01]\n",
      " [9.27521475e-01]\n",
      " [6.84760874e-03]\n",
      " [7.23656479e-04]\n",
      " [1.54109697e-01]\n",
      " [3.46472534e-01]\n",
      " [7.59910512e-01]\n",
      " [1.03639434e-01]\n",
      " [4.88433748e-01]\n",
      " [6.41147949e-01]\n",
      " [3.44963121e-01]\n",
      " [9.92806803e-02]\n",
      " [3.20886228e-03]\n",
      " [4.92549393e-02]\n",
      " [3.15377725e-03]\n",
      " [8.79172525e-01]\n",
      " [7.85076365e-03]\n",
      " [5.01456083e-03]\n",
      " [1.73487077e-01]\n",
      " [8.80285980e-02]\n",
      " [3.17809809e-02]\n",
      " [4.10136600e-03]\n",
      " [3.86177799e-02]\n",
      " [1.11880626e-01]\n",
      " [1.62762711e-02]\n",
      " [5.06949034e-02]\n",
      " [8.67352478e-02]\n",
      " [6.40313070e-02]\n",
      " [4.44759254e-01]\n",
      " [8.77762572e-01]\n",
      " [5.38818929e-01]\n",
      " [7.00210365e-02]\n",
      " [1.55870520e-03]\n",
      " [2.94235467e-01]\n",
      " [1.18412242e-01]\n",
      " [9.56100951e-01]\n",
      " [2.54609405e-03]\n",
      " [1.38170496e-02]\n",
      " [8.50114531e-02]\n",
      " [2.26391064e-01]\n",
      " [7.14209314e-02]\n",
      " [8.72099683e-03]\n",
      " [8.12502226e-03]\n",
      " [1.00203773e-01]\n",
      " [9.47544251e-02]\n",
      " [8.98141624e-04]\n",
      " [9.01222158e-04]\n",
      " [2.03037401e-03]\n",
      " [5.50021179e-01]\n",
      " [2.61849303e-02]\n",
      " [6.37336821e-02]\n",
      " [7.80144415e-01]\n",
      " [1.16239457e-01]\n",
      " [5.51728029e-01]\n",
      " [9.82471242e-03]\n",
      " [6.80976598e-01]\n",
      " [1.63274349e-01]\n",
      " [2.92536665e-04]\n",
      " [9.28095766e-01]\n",
      " [2.25164197e-01]\n",
      " [1.54694603e-03]\n",
      " [3.73388819e-02]\n",
      " [2.59076772e-01]\n",
      " [9.55512518e-01]\n",
      " [8.22448156e-02]\n",
      " [6.52967676e-01]\n",
      " [5.31224983e-02]\n",
      " [8.29396832e-02]\n",
      " [9.82053928e-03]\n",
      " [4.32951346e-01]\n",
      " [6.63971186e-04]\n",
      " [5.18501974e-01]\n",
      " [4.14147448e-01]\n",
      " [2.49895076e-01]\n",
      " [4.70673728e-02]\n",
      " [4.08158451e-05]\n",
      " [1.14069247e-01]\n",
      " [9.74014360e-03]\n",
      " [3.99448668e-01]\n",
      " [5.09393211e-03]\n",
      " [1.55830400e-02]\n",
      " [3.73385558e-01]\n",
      " [2.62251729e-02]\n",
      " [9.07526758e-03]\n",
      " [3.53765325e-03]\n",
      " [7.83387730e-01]\n",
      " [3.65947455e-02]\n",
      " [8.56136153e-03]\n",
      " [5.44254847e-02]\n",
      " [1.49954358e-02]\n",
      " [9.94505518e-01]\n",
      " [2.57965097e-01]\n",
      " [4.82789422e-03]\n",
      " [4.97595202e-02]\n",
      " [3.99630634e-04]\n",
      " [1.24549958e-01]\n",
      " [7.54377813e-02]\n",
      " [2.22922964e-02]\n",
      " [2.29091828e-02]\n",
      " [6.88731896e-02]\n",
      " [2.04731946e-02]\n",
      " [1.57879577e-01]\n",
      " [1.93630825e-02]]\n",
      "=====DEBUG=====\n",
      "==== DEBUG2 ====\n",
      "numpyではベクトルの内積を求める際には、横ベクトル×縦ベクトルである必要がある\n",
      "-> w_new-w\n",
      "[[-0.09680672]\n",
      " [ 0.00999053]\n",
      " [ 0.01282026]]\n",
      "-> (w_new-w).T\n",
      "[[-0.09680672  0.00999053  0.01282026]]\n",
      "-> w\n",
      "[[-1.98223191]\n",
      " [ 0.19956109]\n",
      " [ 0.25319806]]\n",
      "#計算結果は1行1列の行列になる\n",
      "[[0.00963571]]\n",
      "#こちらはエラーになる\n",
      "shapes (1,3) and (1,3) not aligned: 3 (dim 1) != 1 (dim 0)\n",
      "#内積の計算結果(1行1列)はスカラーとして扱うことができるらしい\n",
      "[[0.00963571]]\n",
      "[[ True]]\n",
      "=====DEBUG=====\n",
      "[9.82485268e-02 9.60477041e-01 4.15437385e-03 3.34577056e-03\n",
      " 1.57149269e-01 3.55597819e-02 5.59574941e-02 1.20479099e-01\n",
      " 2.61849212e-02 6.20915430e-01 6.07357819e-04 1.36814839e-01\n",
      " 3.58722322e-03 2.37161607e-01 6.84218239e-01 9.56432208e-01\n",
      " 5.83869010e-03 4.57538086e-03 9.39330367e-01 1.21857584e-01\n",
      " 8.49817266e-01 3.65309686e-01 1.95110185e-01 5.68470593e-01\n",
      " 3.96544894e-01 2.85618125e-03 7.14501859e-01 1.56582364e-02\n",
      " 8.53691109e-03 1.35893479e-02 3.96109552e-03 1.07875045e-05\n",
      " 5.14606106e-03 1.01333842e-01 2.82335287e-01 7.53071219e-04\n",
      " 2.56459193e-01 9.96521632e-01 3.36793582e-01 4.18814800e-03\n",
      " 2.07728000e-02 1.52456755e-02 9.13185036e-01 4.88985371e-04\n",
      " 1.00201545e-01 5.38199988e-02 5.47182290e-01 1.37757327e-02\n",
      " 5.93153873e-02 9.11413510e-01 9.96388118e-01 6.45791879e-01\n",
      " 1.42312055e-01 4.86997789e-02 2.35515685e-02 7.04886931e-01\n",
      " 3.00732129e-04 9.70177408e-01 8.79320598e-03 1.17408577e-02\n",
      " 9.94028800e-01 5.38547720e-01 9.98444921e-01 4.20922247e-03\n",
      " 4.57039968e-03 8.30194627e-01 8.15144772e-01 8.63073288e-01\n",
      " 9.90409562e-01 3.83063959e-02 9.79480124e-01 8.47157153e-01\n",
      " 2.35814702e-01 2.78699422e-02 1.65344696e-02 1.87721316e-03\n",
      " 9.40370327e-01 7.17391338e-01 9.42785434e-01 1.37850663e-02\n",
      " 9.07266056e-02 1.32803579e-02 4.14605311e-01 8.94721521e-01\n",
      " 9.84984847e-01 9.75371504e-01 9.96478479e-01 3.55045699e-03\n",
      " 3.10760802e-03 1.38898610e-04 5.55855719e-02 3.09304020e-01\n",
      " 1.22705011e-01 4.80366646e-02 5.74166134e-01 9.97433974e-01\n",
      " 4.77874238e-04 9.48816532e-01 7.60710054e-03 1.87321831e-03\n",
      " 4.13867765e-01 3.85002678e-01 2.42265913e-03 9.11270377e-03\n",
      " 1.44209911e-02 6.88002978e-01 3.28418105e-03 3.80036856e-01\n",
      " 6.12342580e-03 2.09446815e-02 2.31571971e-02 2.24622959e-03\n",
      " 1.07037430e-02 3.35914394e-02 7.24874341e-01 1.28944419e-01\n",
      " 8.79606949e-01 1.88174063e-04 7.42643888e-01 3.58569136e-03\n",
      " 9.97234950e-01 1.37828429e-02 1.87333792e-02 3.27801148e-03\n",
      " 6.32661231e-03 9.89427521e-01 8.24600796e-04 7.59978670e-02\n",
      " 9.97086896e-01 3.98389322e-01 6.69168031e-01 4.81028628e-02\n",
      " 5.68614204e-02 4.07265966e-02 9.95492764e-01 9.34247938e-01\n",
      " 4.88709465e-01 1.10710682e-01 1.16182552e-02 8.25411513e-02\n",
      " 2.03179919e-01 1.87445993e-01 3.02686663e-01 7.75451530e-02\n",
      " 3.58403108e-01 9.76458650e-01 4.03314802e-03 8.74842635e-01\n",
      " 1.24977885e-01 1.55051433e-01 7.79061836e-01 1.82918585e-02\n",
      " 6.63819595e-04 9.88958126e-01 9.84642809e-01 6.93328100e-02\n",
      " 1.60870777e-01 4.62881337e-02 9.98052084e-01 2.70159484e-04\n",
      " 1.45690273e-01 8.63973565e-01 6.97235536e-01 1.83435963e-02\n",
      " 5.84504908e-01 3.65798722e-03 1.06886628e-02 4.87883536e-02\n",
      " 6.21475164e-04 8.94579886e-01 1.63187231e-01 1.15575589e-01\n",
      " 1.20403974e-02 9.22156015e-03 9.17088043e-01 4.26689536e-01\n",
      " 6.44704652e-01 3.67790541e-01 9.35873149e-01 5.35609653e-03\n",
      " 5.04593776e-04 1.43625147e-01 3.40001375e-01 7.70690923e-01\n",
      " 9.42902546e-02 4.88698435e-01 6.48652790e-01 3.38142436e-01\n",
      " 8.99487730e-02 2.41427976e-03 4.28825907e-02 2.36836429e-03\n",
      " 8.89646142e-01 6.17978022e-03 3.85375119e-03 1.63021777e-01\n",
      " 7.92472209e-02 2.69594937e-02 3.12048321e-03 3.31234025e-02\n",
      " 1.02184810e-01 1.33001712e-02 4.42147052e-02 7.80226140e-02\n",
      " 5.66059655e-02 4.42801269e-01 8.88400922e-01 5.41369421e-01\n",
      " 6.21634489e-02 1.12994032e-03 2.85860163e-01 1.08500803e-01\n",
      " 9.62360999e-01 1.89195933e-03 1.12127160e-02 7.64524644e-02\n",
      " 2.16408161e-01 6.34892282e-02 6.90343600e-03 6.40947015e-03\n",
      " 9.08599126e-02 8.56732464e-02 6.32850160e-04 6.34544061e-04\n",
      " 1.49157260e-03 5.53220083e-01 2.20089661e-02 5.62708278e-02\n",
      " 7.91308199e-01 1.06396112e-01 5.54825868e-01 7.82853748e-03\n",
      " 6.89830932e-01 1.52707397e-01 1.94776118e-04 9.36475856e-01\n",
      " 2.15081595e-01 1.12009356e-03 3.19366373e-02 2.49567341e-01\n",
      " 9.61731446e-01 7.36804930e-02 6.60880957e-01 4.63888238e-02\n",
      " 7.42970575e-02 7.82679480e-03 4.30249316e-01 4.60505572e-04\n",
      " 5.20331641e-01 4.10520558e-01 2.40350264e-01 4.07977434e-02\n",
      " 2.45886393e-05 1.04257465e-01 7.75381538e-03 3.95203515e-01\n",
      " 3.92207452e-03 1.27193080e-02 3.67748954e-01 2.20154981e-02\n",
      " 7.19485212e-03 2.67270569e-03 7.94628473e-01 3.12886798e-02\n",
      " 6.78066520e-03 4.76166059e-02 1.22194258e-02 9.95776524e-01\n",
      " 2.48593487e-01 3.70445788e-03 4.33007917e-02 2.70288738e-04\n",
      " 1.14543397e-01 6.73174143e-02 1.85418269e-02 1.90864054e-02\n",
      " 6.10774623e-02 1.69607533e-02 1.47374371e-01 1.59948647e-02]\n",
      "-----\n",
      "[[9.82485268e-02]\n",
      " [9.60477041e-01]\n",
      " [4.15437385e-03]\n",
      " [3.34577056e-03]\n",
      " [1.57149269e-01]\n",
      " [3.55597819e-02]\n",
      " [5.59574941e-02]\n",
      " [1.20479099e-01]\n",
      " [2.61849212e-02]\n",
      " [6.20915430e-01]\n",
      " [6.07357819e-04]\n",
      " [1.36814839e-01]\n",
      " [3.58722322e-03]\n",
      " [2.37161607e-01]\n",
      " [6.84218239e-01]\n",
      " [9.56432208e-01]\n",
      " [5.83869010e-03]\n",
      " [4.57538086e-03]\n",
      " [9.39330367e-01]\n",
      " [1.21857584e-01]\n",
      " [8.49817266e-01]\n",
      " [3.65309686e-01]\n",
      " [1.95110185e-01]\n",
      " [5.68470593e-01]\n",
      " [3.96544894e-01]\n",
      " [2.85618125e-03]\n",
      " [7.14501859e-01]\n",
      " [1.56582364e-02]\n",
      " [8.53691109e-03]\n",
      " [1.35893479e-02]\n",
      " [3.96109552e-03]\n",
      " [1.07875045e-05]\n",
      " [5.14606106e-03]\n",
      " [1.01333842e-01]\n",
      " [2.82335287e-01]\n",
      " [7.53071219e-04]\n",
      " [2.56459193e-01]\n",
      " [9.96521632e-01]\n",
      " [3.36793582e-01]\n",
      " [4.18814800e-03]\n",
      " [2.07728000e-02]\n",
      " [1.52456755e-02]\n",
      " [9.13185036e-01]\n",
      " [4.88985371e-04]\n",
      " [1.00201545e-01]\n",
      " [5.38199988e-02]\n",
      " [5.47182290e-01]\n",
      " [1.37757327e-02]\n",
      " [5.93153873e-02]\n",
      " [9.11413510e-01]\n",
      " [9.96388118e-01]\n",
      " [6.45791879e-01]\n",
      " [1.42312055e-01]\n",
      " [4.86997789e-02]\n",
      " [2.35515685e-02]\n",
      " [7.04886931e-01]\n",
      " [3.00732129e-04]\n",
      " [9.70177408e-01]\n",
      " [8.79320598e-03]\n",
      " [1.17408577e-02]\n",
      " [9.94028800e-01]\n",
      " [5.38547720e-01]\n",
      " [9.98444921e-01]\n",
      " [4.20922247e-03]\n",
      " [4.57039968e-03]\n",
      " [8.30194627e-01]\n",
      " [8.15144772e-01]\n",
      " [8.63073288e-01]\n",
      " [9.90409562e-01]\n",
      " [3.83063959e-02]\n",
      " [9.79480124e-01]\n",
      " [8.47157153e-01]\n",
      " [2.35814702e-01]\n",
      " [2.78699422e-02]\n",
      " [1.65344696e-02]\n",
      " [1.87721316e-03]\n",
      " [9.40370327e-01]\n",
      " [7.17391338e-01]\n",
      " [9.42785434e-01]\n",
      " [1.37850663e-02]\n",
      " [9.07266056e-02]\n",
      " [1.32803579e-02]\n",
      " [4.14605311e-01]\n",
      " [8.94721521e-01]\n",
      " [9.84984847e-01]\n",
      " [9.75371504e-01]\n",
      " [9.96478479e-01]\n",
      " [3.55045699e-03]\n",
      " [3.10760802e-03]\n",
      " [1.38898610e-04]\n",
      " [5.55855719e-02]\n",
      " [3.09304020e-01]\n",
      " [1.22705011e-01]\n",
      " [4.80366646e-02]\n",
      " [5.74166134e-01]\n",
      " [9.97433974e-01]\n",
      " [4.77874238e-04]\n",
      " [9.48816532e-01]\n",
      " [7.60710054e-03]\n",
      " [1.87321831e-03]\n",
      " [4.13867765e-01]\n",
      " [3.85002678e-01]\n",
      " [2.42265913e-03]\n",
      " [9.11270377e-03]\n",
      " [1.44209911e-02]\n",
      " [6.88002978e-01]\n",
      " [3.28418105e-03]\n",
      " [3.80036856e-01]\n",
      " [6.12342580e-03]\n",
      " [2.09446815e-02]\n",
      " [2.31571971e-02]\n",
      " [2.24622959e-03]\n",
      " [1.07037430e-02]\n",
      " [3.35914394e-02]\n",
      " [7.24874341e-01]\n",
      " [1.28944419e-01]\n",
      " [8.79606949e-01]\n",
      " [1.88174063e-04]\n",
      " [7.42643888e-01]\n",
      " [3.58569136e-03]\n",
      " [9.97234950e-01]\n",
      " [1.37828429e-02]\n",
      " [1.87333792e-02]\n",
      " [3.27801148e-03]\n",
      " [6.32661231e-03]\n",
      " [9.89427521e-01]\n",
      " [8.24600796e-04]\n",
      " [7.59978670e-02]\n",
      " [9.97086896e-01]\n",
      " [3.98389322e-01]\n",
      " [6.69168031e-01]\n",
      " [4.81028628e-02]\n",
      " [5.68614204e-02]\n",
      " [4.07265966e-02]\n",
      " [9.95492764e-01]\n",
      " [9.34247938e-01]\n",
      " [4.88709465e-01]\n",
      " [1.10710682e-01]\n",
      " [1.16182552e-02]\n",
      " [8.25411513e-02]\n",
      " [2.03179919e-01]\n",
      " [1.87445993e-01]\n",
      " [3.02686663e-01]\n",
      " [7.75451530e-02]\n",
      " [3.58403108e-01]\n",
      " [9.76458650e-01]\n",
      " [4.03314802e-03]\n",
      " [8.74842635e-01]\n",
      " [1.24977885e-01]\n",
      " [1.55051433e-01]\n",
      " [7.79061836e-01]\n",
      " [1.82918585e-02]\n",
      " [6.63819595e-04]\n",
      " [9.88958126e-01]\n",
      " [9.84642809e-01]\n",
      " [6.93328100e-02]\n",
      " [1.60870777e-01]\n",
      " [4.62881337e-02]\n",
      " [9.98052084e-01]\n",
      " [2.70159484e-04]\n",
      " [1.45690273e-01]\n",
      " [8.63973565e-01]\n",
      " [6.97235536e-01]\n",
      " [1.83435963e-02]\n",
      " [5.84504908e-01]\n",
      " [3.65798722e-03]\n",
      " [1.06886628e-02]\n",
      " [4.87883536e-02]\n",
      " [6.21475164e-04]\n",
      " [8.94579886e-01]\n",
      " [1.63187231e-01]\n",
      " [1.15575589e-01]\n",
      " [1.20403974e-02]\n",
      " [9.22156015e-03]\n",
      " [9.17088043e-01]\n",
      " [4.26689536e-01]\n",
      " [6.44704652e-01]\n",
      " [3.67790541e-01]\n",
      " [9.35873149e-01]\n",
      " [5.35609653e-03]\n",
      " [5.04593776e-04]\n",
      " [1.43625147e-01]\n",
      " [3.40001375e-01]\n",
      " [7.70690923e-01]\n",
      " [9.42902546e-02]\n",
      " [4.88698435e-01]\n",
      " [6.48652790e-01]\n",
      " [3.38142436e-01]\n",
      " [8.99487730e-02]\n",
      " [2.41427976e-03]\n",
      " [4.28825907e-02]\n",
      " [2.36836429e-03]\n",
      " [8.89646142e-01]\n",
      " [6.17978022e-03]\n",
      " [3.85375119e-03]\n",
      " [1.63021777e-01]\n",
      " [7.92472209e-02]\n",
      " [2.69594937e-02]\n",
      " [3.12048321e-03]\n",
      " [3.31234025e-02]\n",
      " [1.02184810e-01]\n",
      " [1.33001712e-02]\n",
      " [4.42147052e-02]\n",
      " [7.80226140e-02]\n",
      " [5.66059655e-02]\n",
      " [4.42801269e-01]\n",
      " [8.88400922e-01]\n",
      " [5.41369421e-01]\n",
      " [6.21634489e-02]\n",
      " [1.12994032e-03]\n",
      " [2.85860163e-01]\n",
      " [1.08500803e-01]\n",
      " [9.62360999e-01]\n",
      " [1.89195933e-03]\n",
      " [1.12127160e-02]\n",
      " [7.64524644e-02]\n",
      " [2.16408161e-01]\n",
      " [6.34892282e-02]\n",
      " [6.90343600e-03]\n",
      " [6.40947015e-03]\n",
      " [9.08599126e-02]\n",
      " [8.56732464e-02]\n",
      " [6.32850160e-04]\n",
      " [6.34544061e-04]\n",
      " [1.49157260e-03]\n",
      " [5.53220083e-01]\n",
      " [2.20089661e-02]\n",
      " [5.62708278e-02]\n",
      " [7.91308199e-01]\n",
      " [1.06396112e-01]\n",
      " [5.54825868e-01]\n",
      " [7.82853748e-03]\n",
      " [6.89830932e-01]\n",
      " [1.52707397e-01]\n",
      " [1.94776118e-04]\n",
      " [9.36475856e-01]\n",
      " [2.15081595e-01]\n",
      " [1.12009356e-03]\n",
      " [3.19366373e-02]\n",
      " [2.49567341e-01]\n",
      " [9.61731446e-01]\n",
      " [7.36804930e-02]\n",
      " [6.60880957e-01]\n",
      " [4.63888238e-02]\n",
      " [7.42970575e-02]\n",
      " [7.82679480e-03]\n",
      " [4.30249316e-01]\n",
      " [4.60505572e-04]\n",
      " [5.20331641e-01]\n",
      " [4.10520558e-01]\n",
      " [2.40350264e-01]\n",
      " [4.07977434e-02]\n",
      " [2.45886393e-05]\n",
      " [1.04257465e-01]\n",
      " [7.75381538e-03]\n",
      " [3.95203515e-01]\n",
      " [3.92207452e-03]\n",
      " [1.27193080e-02]\n",
      " [3.67748954e-01]\n",
      " [2.20154981e-02]\n",
      " [7.19485212e-03]\n",
      " [2.67270569e-03]\n",
      " [7.94628473e-01]\n",
      " [3.12886798e-02]\n",
      " [6.78066520e-03]\n",
      " [4.76166059e-02]\n",
      " [1.22194258e-02]\n",
      " [9.95776524e-01]\n",
      " [2.48593487e-01]\n",
      " [3.70445788e-03]\n",
      " [4.33007917e-02]\n",
      " [2.70288738e-04]\n",
      " [1.14543397e-01]\n",
      " [6.73174143e-02]\n",
      " [1.85418269e-02]\n",
      " [1.90864054e-02]\n",
      " [6.10774623e-02]\n",
      " [1.69607533e-02]\n",
      " [1.47374371e-01]\n",
      " [1.59948647e-02]]\n",
      "=====DEBUG=====\n",
      "==== DEBUG2 ====\n",
      "numpyではベクトルの内積を求める際には、横ベクトル×縦ベクトルである必要がある\n",
      "-> w_new-w\n",
      "[[-0.00663974]\n",
      " [ 0.00068258]\n",
      " [ 0.0008926 ]]\n",
      "-> (w_new-w).T\n",
      "[[-0.00663974  0.00068258  0.0008926 ]]\n",
      "-> w\n",
      "[[-2.07903863]\n",
      " [ 0.20955162]\n",
      " [ 0.26601832]]\n",
      "#計算結果は1行1列の行列になる\n",
      "[[4.53487337e-05]]\n",
      "#こちらはエラーになる\n",
      "shapes (1,3) and (1,3) not aligned: 3 (dim 1) != 1 (dim 0)\n",
      "#内積の計算結果(1行1列)はスカラーとして扱うことができるらしい\n",
      "[[4.53487337e-05]]\n",
      "[[ True]]\n",
      " ==== PRINT RESULT ===\n",
      "     index          x          y  type  probability\n",
      "0       62  21.401772  15.258103     1     0.998478\n",
      "1      158  20.326509  15.256962     1     0.998092\n",
      "2       95  13.082755  19.924770     1     0.997485\n",
      "3      120  13.677698  19.174554     1     0.997289\n",
      "4      128  10.551460  21.440560     1     0.997144\n",
      "..     ...        ...        ...   ...          ...\n",
      "275    234 -13.934305 -13.324159    -1     0.000189\n",
      "276    117 -12.492145 -14.589849    -1     0.000183\n",
      "277     89  -6.722999 -20.275948    -1     0.000135\n",
      "278    252 -11.923881 -22.688266    -1     0.000024\n",
      "279     31 -16.934962 -21.838060    -1     0.000010\n",
      "\n",
      "[280 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== DEBUG tp_rate and fp_rate\n",
      "TP RATE\n",
      "[0.     0.0125 0.025  0.0375 0.05   0.0625 0.075  0.0875 0.1    0.1125\n",
      " 0.125  0.1375 0.15   0.1625 0.175  0.1875 0.2    0.2125 0.225  0.2375\n",
      " 0.25   0.2625 0.275  0.2875 0.3    0.3125 0.325  0.3375 0.35   0.3625\n",
      " 0.375  0.3875 0.4    0.4125 0.425  0.4375 0.4375 0.45   0.4625 0.475\n",
      " 0.475  0.4875 0.5    0.5    0.5125 0.525  0.5375 0.5375 0.55   0.5625\n",
      " 0.575  0.5875 0.6    0.6    0.6125 0.625  0.6375 0.6375 0.65   0.6625\n",
      " 0.675  0.675  0.6875 0.7    0.7125 0.7125 0.7125 0.725  0.725  0.7375\n",
      " 0.7375 0.7375 0.75   0.7625 0.775  0.775  0.7875 0.7875 0.8    0.8125\n",
      " 0.8125 0.825  0.825  0.825  0.8375 0.8375 0.8375 0.8375 0.8375 0.8375\n",
      " 0.8375 0.8375 0.8375 0.8375 0.85   0.8625 0.875  0.875  0.875  0.8875\n",
      " 0.9    0.9    0.9    0.9    0.9    0.9    0.9    0.9    0.9    0.9\n",
      " 0.9    0.9125 0.9125 0.9125 0.9125 0.9125 0.9125 0.9125 0.9125 0.9125\n",
      " 0.9125 0.9125 0.9125 0.9125 0.9125 0.9125 0.9125 0.925  0.925  0.925\n",
      " 0.925  0.925  0.9375 0.9375 0.9375 0.95   0.95   0.95   0.95   0.9625\n",
      " 0.9625 0.9625 0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975\n",
      " 0.975  0.9875 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875\n",
      " 0.9875 0.9875 0.9875 1.     1.     1.     1.     1.     1.     1.\n",
      " 1.     1.     1.     1.     1.     1.     1.     1.     1.     1.\n",
      " 1.     1.     1.     1.     1.     1.     1.     1.     1.     1.\n",
      " 1.     1.     1.     1.     1.     1.     1.     1.     1.     1.\n",
      " 1.     1.     1.     1.     1.     1.     1.     1.     1.     1.\n",
      " 1.     1.     1.     1.     1.     1.     1.     1.     1.     1.\n",
      " 1.     1.     1.     1.     1.     1.     1.     1.     1.     1.\n",
      " 1.     1.     1.     1.     1.     1.     1.     1.     1.     1.\n",
      " 1.     1.     1.     1.     1.     1.     1.     1.     1.     1.\n",
      " 1.     1.     1.     1.     1.     1.     1.     1.     1.     1.\n",
      " 1.     1.     1.     1.     1.     1.     1.     1.     1.     1.\n",
      " 1.     1.     1.     1.     1.     1.     1.     1.     1.     1.    ]\n",
      "FP RATE\n",
      "[0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      " 0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      " 0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      " 0.005 0.005 0.005 0.005 0.01  0.01  0.01  0.015 0.015 0.015 0.015 0.02\n",
      " 0.02  0.02  0.02  0.02  0.02  0.025 0.025 0.025 0.025 0.03  0.03  0.03\n",
      " 0.03  0.035 0.035 0.035 0.035 0.04  0.045 0.045 0.05  0.05  0.055 0.06\n",
      " 0.06  0.06  0.06  0.065 0.065 0.07  0.07  0.07  0.075 0.075 0.08  0.085\n",
      " 0.085 0.09  0.095 0.1   0.105 0.11  0.115 0.12  0.125 0.13  0.13  0.13\n",
      " 0.13  0.135 0.14  0.14  0.14  0.145 0.15  0.155 0.16  0.165 0.17  0.175\n",
      " 0.18  0.185 0.19  0.19  0.195 0.2   0.205 0.21  0.215 0.22  0.225 0.23\n",
      " 0.235 0.24  0.245 0.25  0.255 0.26  0.265 0.265 0.27  0.275 0.28  0.285\n",
      " 0.285 0.29  0.295 0.295 0.3   0.305 0.31  0.31  0.315 0.32  0.32  0.325\n",
      " 0.33  0.335 0.34  0.345 0.35  0.355 0.36  0.36  0.365 0.37  0.375 0.38\n",
      " 0.385 0.39  0.395 0.4   0.405 0.41  0.415 0.415 0.42  0.425 0.43  0.435\n",
      " 0.44  0.445 0.45  0.455 0.46  0.465 0.47  0.475 0.48  0.485 0.49  0.495\n",
      " 0.5   0.505 0.51  0.515 0.52  0.525 0.53  0.535 0.54  0.545 0.55  0.555\n",
      " 0.56  0.565 0.57  0.575 0.58  0.585 0.59  0.595 0.6   0.605 0.61  0.615\n",
      " 0.62  0.625 0.63  0.635 0.64  0.645 0.65  0.655 0.66  0.665 0.67  0.675\n",
      " 0.68  0.685 0.69  0.695 0.7   0.705 0.71  0.715 0.72  0.725 0.73  0.735\n",
      " 0.74  0.745 0.75  0.755 0.76  0.765 0.77  0.775 0.78  0.785 0.79  0.795\n",
      " 0.8   0.805 0.81  0.815 0.82  0.825 0.83  0.835 0.84  0.845 0.85  0.855\n",
      " 0.86  0.865 0.87  0.875 0.88  0.885 0.89  0.895 0.9   0.905 0.91  0.915\n",
      " 0.92  0.925 0.93  0.935 0.94  0.945 0.95  0.955 0.96  0.965 0.97  0.975\n",
      " 0.98  0.985 0.99  0.995]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w0, w1, w2, err_rate, result = run_logistic(train_set)\n",
    "fig = plt.figure(figsize=(6, 12))\n",
    "subplot = fig.add_subplot(2, 1, 1)\n",
    "show_result(subplot, train_set, w0, w1, w2, err_rate)\n",
    "subplot = fig.add_subplot(2, 1, 2)\n",
    "draw_roc(subplot, result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rbqphpBR4mKY"
   },
   "source": [
    "分散が大きくて、分類が困難なトレーニングセットを用意します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NXl2wPqW4mKZ"
   },
   "outputs": [],
   "source": [
    "train_set = prepare_dataset(80, [9, 9], 150, 200, [-3, -3], 150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Rxghxt9b4mKb"
   },
   "source": [
    "ロジスティック回帰を適用した結果を表示します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 717
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4135,
     "status": "ok",
     "timestamp": 1587542467513,
     "user": {
      "displayName": "Etsuji Nakai",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhetRRD1sS-9biTr-QKIKpeab3l7NjR8gRdtuQWcg=s64",
      "userId": "13606123633698401153"
     },
     "user_tz": -540
    },
    "id": "3T-vpuw_4mKb",
    "outputId": "1722e5ee-9238-4aad-e062-6bccf6b48255"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== PRINT T =====\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
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      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
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      " [0.]\n",
      " [0.]\n",
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      " [1.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
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      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
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      " [0.]\n",
      " [1.]\n",
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      " [1.]\n",
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      " [1.]\n",
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      " [1.]\n",
      " [1.]\n",
      " [1.]\n",
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      " [1.]\n",
      " [0.]\n",
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      " [0.]\n",
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      " [0.]\n",
      " [1.]\n",
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      " [1.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]]\n",
      "=====DEBUG=====\n",
      "[0.78857418 0.81153413 0.51297113 0.8913175  0.85038744 0.86465108\n",
      " 0.95153286 0.32812497 0.15041672 0.79364727 0.37979229 0.82106825\n",
      " 0.59320161 0.0746832  0.90092631 0.9204351  0.07703405 0.9661054\n",
      " 0.97875641 0.93248044 0.84787059 0.85716127 0.64163753 0.72792766\n",
      " 0.25343949 0.36965755 0.39138902 0.88327604 0.8188652  0.09511377\n",
      " 0.03552298 0.70631744 0.7381147  0.67510663 0.91138034 0.54429494\n",
      " 0.88941697 0.22944765 0.72583293 0.89024142 0.84146514 0.0760521\n",
      " 0.26877262 0.07440501 0.41491663 0.43355996 0.95091869 0.9971233\n",
      " 0.49229924 0.91094496 0.94771475 0.83187446 0.37642901 0.00492356\n",
      " 0.2799081  0.04490317 0.21020298 0.669005   0.99295099 0.05233934\n",
      " 0.98461568 0.69482979 0.35950917 0.11871249 0.52503617 0.02110568\n",
      " 0.27225427 0.85757414 0.84678893 0.28692746 0.12417051 0.29259264\n",
      " 0.53527176 0.69009374 0.55357194 0.60612377 0.71693892 0.48864059\n",
      " 0.62550335 0.66733956 0.70943543 0.04881861 0.11696235 0.1702011\n",
      " 0.84844141 0.182162   0.87238403 0.71029613 0.51012097 0.96358588\n",
      " 0.84848394 0.40500737 0.40764371 0.947823   0.56922369 0.61879959\n",
      " 0.11708594 0.22821098 0.4212815  0.09589231 0.69557342 0.89406474\n",
      " 0.67374486 0.13820489 0.45927669 0.29677317 0.18979728 0.85108709\n",
      " 0.43444421 0.70196533 0.65950088 0.73669138 0.61789396 0.09044166\n",
      " 0.22520163 0.12371197 0.8145251  0.87245756 0.96014085 0.91372105\n",
      " 0.75986586 0.18866294 0.08953608 0.14838828 0.62442513 0.65094645\n",
      " 0.91084068 0.22286613 0.72370866 0.38445632 0.2069572  0.07377139\n",
      " 0.23602229 0.8617196  0.6801164  0.04678489 0.37362638 0.12424873\n",
      " 0.28502495 0.08563238 0.1934924  0.57905002 0.87721001 0.29111956\n",
      " 0.70150748 0.64176345 0.4219391  0.79210746 0.90186574 0.62105258\n",
      " 0.69687667 0.98346142 0.94592315 0.27622576 0.9448587  0.07661298\n",
      " 0.11202225 0.98903889 0.29154798 0.69772201 0.57886382 0.42887379\n",
      " 0.10496584 0.77566477 0.79625883 0.22687522 0.05444744 0.80876836\n",
      " 0.08189884 0.23707012 0.07172807 0.73582859 0.1681188  0.38017939\n",
      " 0.67199004 0.14733653 0.80882605 0.88801681 0.62724385 0.5436765\n",
      " 0.88281804 0.09025723 0.85409551 0.11154874 0.33400341 0.18611853\n",
      " 0.0353036  0.31435486 0.87035722 0.89419486 0.51078867 0.88426776\n",
      " 0.31083045 0.70602521 0.0945732  0.75020657 0.95362459 0.89227866\n",
      " 0.51358553 0.13525901 0.16959414 0.88630153 0.99408318 0.64681393\n",
      " 0.88392296 0.79598762 0.16359    0.97015168 0.89866251 0.22519113\n",
      " 0.66043673 0.85715403 0.87272228 0.41889896 0.02619699 0.34839438\n",
      " 0.98700493 0.16576946 0.48239867 0.89535554 0.10234675 0.35552502\n",
      " 0.93287526 0.36124107 0.48718712 0.36728427 0.34065668 0.29641409\n",
      " 0.62377572 0.26845005 0.66941862 0.72358805 0.3043145  0.5581535\n",
      " 0.32594276 0.39385107 0.67789164 0.75013391 0.80249148 0.19519388\n",
      " 0.55145624 0.58670082 0.33828393 0.59541089 0.89064109 0.37745397\n",
      " 0.99341872 0.93298568 0.53690917 0.2048082  0.76483579 0.12745353\n",
      " 0.91978801 0.1723547  0.23056978 0.34679239 0.04400075 0.91947365\n",
      " 0.45524117 0.04587833 0.08216098 0.17498817 0.07940994 0.175915\n",
      " 0.51353575 0.83411572 0.47825037 0.47625403 0.10775794 0.44255494\n",
      " 0.03449727 0.29696367 0.898171   0.19356655 0.46808896 0.84198921\n",
      " 0.82322276 0.87460315 0.91714763 0.11148532]\n",
      "-----\n",
      "[[0.78857418]\n",
      " [0.81153413]\n",
      " [0.51297113]\n",
      " [0.8913175 ]\n",
      " [0.85038744]\n",
      " [0.86465108]\n",
      " [0.95153286]\n",
      " [0.32812497]\n",
      " [0.15041672]\n",
      " [0.79364727]\n",
      " [0.37979229]\n",
      " [0.82106825]\n",
      " [0.59320161]\n",
      " [0.0746832 ]\n",
      " [0.90092631]\n",
      " [0.9204351 ]\n",
      " [0.07703405]\n",
      " [0.9661054 ]\n",
      " [0.97875641]\n",
      " [0.93248044]\n",
      " [0.84787059]\n",
      " [0.85716127]\n",
      " [0.64163753]\n",
      " [0.72792766]\n",
      " [0.25343949]\n",
      " [0.36965755]\n",
      " [0.39138902]\n",
      " [0.88327604]\n",
      " [0.8188652 ]\n",
      " [0.09511377]\n",
      " [0.03552298]\n",
      " [0.70631744]\n",
      " [0.7381147 ]\n",
      " [0.67510663]\n",
      " [0.91138034]\n",
      " [0.54429494]\n",
      " [0.88941697]\n",
      " [0.22944765]\n",
      " [0.72583293]\n",
      " [0.89024142]\n",
      " [0.84146514]\n",
      " [0.0760521 ]\n",
      " [0.26877262]\n",
      " [0.07440501]\n",
      " [0.41491663]\n",
      " [0.43355996]\n",
      " [0.95091869]\n",
      " [0.9971233 ]\n",
      " [0.49229924]\n",
      " [0.91094496]\n",
      " [0.94771475]\n",
      " [0.83187446]\n",
      " [0.37642901]\n",
      " [0.00492356]\n",
      " [0.2799081 ]\n",
      " [0.04490317]\n",
      " [0.21020298]\n",
      " [0.669005  ]\n",
      " [0.99295099]\n",
      " [0.05233934]\n",
      " [0.98461568]\n",
      " [0.69482979]\n",
      " [0.35950917]\n",
      " [0.11871249]\n",
      " [0.52503617]\n",
      " [0.02110568]\n",
      " [0.27225427]\n",
      " [0.85757414]\n",
      " [0.84678893]\n",
      " [0.28692746]\n",
      " [0.12417051]\n",
      " [0.29259264]\n",
      " [0.53527176]\n",
      " [0.69009374]\n",
      " [0.55357194]\n",
      " [0.60612377]\n",
      " [0.71693892]\n",
      " [0.48864059]\n",
      " [0.62550335]\n",
      " [0.66733956]\n",
      " [0.70943543]\n",
      " [0.04881861]\n",
      " [0.11696235]\n",
      " [0.1702011 ]\n",
      " [0.84844141]\n",
      " [0.182162  ]\n",
      " [0.87238403]\n",
      " [0.71029613]\n",
      " [0.51012097]\n",
      " [0.96358588]\n",
      " [0.84848394]\n",
      " [0.40500737]\n",
      " [0.40764371]\n",
      " [0.947823  ]\n",
      " [0.56922369]\n",
      " [0.61879959]\n",
      " [0.11708594]\n",
      " [0.22821098]\n",
      " [0.4212815 ]\n",
      " [0.09589231]\n",
      " [0.69557342]\n",
      " [0.89406474]\n",
      " [0.67374486]\n",
      " [0.13820489]\n",
      " [0.45927669]\n",
      " [0.29677317]\n",
      " [0.18979728]\n",
      " [0.85108709]\n",
      " [0.43444421]\n",
      " [0.70196533]\n",
      " [0.65950088]\n",
      " [0.73669138]\n",
      " [0.61789396]\n",
      " [0.09044166]\n",
      " [0.22520163]\n",
      " [0.12371197]\n",
      " [0.8145251 ]\n",
      " [0.87245756]\n",
      " [0.96014085]\n",
      " [0.91372105]\n",
      " [0.75986586]\n",
      " [0.18866294]\n",
      " [0.08953608]\n",
      " [0.14838828]\n",
      " [0.62442513]\n",
      " [0.65094645]\n",
      " [0.91084068]\n",
      " [0.22286613]\n",
      " [0.72370866]\n",
      " [0.38445632]\n",
      " [0.2069572 ]\n",
      " [0.07377139]\n",
      " [0.23602229]\n",
      " [0.8617196 ]\n",
      " [0.6801164 ]\n",
      " [0.04678489]\n",
      " [0.37362638]\n",
      " [0.12424873]\n",
      " [0.28502495]\n",
      " [0.08563238]\n",
      " [0.1934924 ]\n",
      " [0.57905002]\n",
      " [0.87721001]\n",
      " [0.29111956]\n",
      " [0.70150748]\n",
      " [0.64176345]\n",
      " [0.4219391 ]\n",
      " [0.79210746]\n",
      " [0.90186574]\n",
      " [0.62105258]\n",
      " [0.69687667]\n",
      " [0.98346142]\n",
      " [0.94592315]\n",
      " [0.27622576]\n",
      " [0.9448587 ]\n",
      " [0.07661298]\n",
      " [0.11202225]\n",
      " [0.98903889]\n",
      " [0.29154798]\n",
      " [0.69772201]\n",
      " [0.57886382]\n",
      " [0.42887379]\n",
      " [0.10496584]\n",
      " [0.77566477]\n",
      " [0.79625883]\n",
      " [0.22687522]\n",
      " [0.05444744]\n",
      " [0.80876836]\n",
      " [0.08189884]\n",
      " [0.23707012]\n",
      " [0.07172807]\n",
      " [0.73582859]\n",
      " [0.1681188 ]\n",
      " [0.38017939]\n",
      " [0.67199004]\n",
      " [0.14733653]\n",
      " [0.80882605]\n",
      " [0.88801681]\n",
      " [0.62724385]\n",
      " [0.5436765 ]\n",
      " [0.88281804]\n",
      " [0.09025723]\n",
      " [0.85409551]\n",
      " [0.11154874]\n",
      " [0.33400341]\n",
      " [0.18611853]\n",
      " [0.0353036 ]\n",
      " [0.31435486]\n",
      " [0.87035722]\n",
      " [0.89419486]\n",
      " [0.51078867]\n",
      " [0.88426776]\n",
      " [0.31083045]\n",
      " [0.70602521]\n",
      " [0.0945732 ]\n",
      " [0.75020657]\n",
      " [0.95362459]\n",
      " [0.89227866]\n",
      " [0.51358553]\n",
      " [0.13525901]\n",
      " [0.16959414]\n",
      " [0.88630153]\n",
      " [0.99408318]\n",
      " [0.64681393]\n",
      " [0.88392296]\n",
      " [0.79598762]\n",
      " [0.16359   ]\n",
      " [0.97015168]\n",
      " [0.89866251]\n",
      " [0.22519113]\n",
      " [0.66043673]\n",
      " [0.85715403]\n",
      " [0.87272228]\n",
      " [0.41889896]\n",
      " [0.02619699]\n",
      " [0.34839438]\n",
      " [0.98700493]\n",
      " [0.16576946]\n",
      " [0.48239867]\n",
      " [0.89535554]\n",
      " [0.10234675]\n",
      " [0.35552502]\n",
      " [0.93287526]\n",
      " [0.36124107]\n",
      " [0.48718712]\n",
      " [0.36728427]\n",
      " [0.34065668]\n",
      " [0.29641409]\n",
      " [0.62377572]\n",
      " [0.26845005]\n",
      " [0.66941862]\n",
      " [0.72358805]\n",
      " [0.3043145 ]\n",
      " [0.5581535 ]\n",
      " [0.32594276]\n",
      " [0.39385107]\n",
      " [0.67789164]\n",
      " [0.75013391]\n",
      " [0.80249148]\n",
      " [0.19519388]\n",
      " [0.55145624]\n",
      " [0.58670082]\n",
      " [0.33828393]\n",
      " [0.59541089]\n",
      " [0.89064109]\n",
      " [0.37745397]\n",
      " [0.99341872]\n",
      " [0.93298568]\n",
      " [0.53690917]\n",
      " [0.2048082 ]\n",
      " [0.76483579]\n",
      " [0.12745353]\n",
      " [0.91978801]\n",
      " [0.1723547 ]\n",
      " [0.23056978]\n",
      " [0.34679239]\n",
      " [0.04400075]\n",
      " [0.91947365]\n",
      " [0.45524117]\n",
      " [0.04587833]\n",
      " [0.08216098]\n",
      " [0.17498817]\n",
      " [0.07940994]\n",
      " [0.175915  ]\n",
      " [0.51353575]\n",
      " [0.83411572]\n",
      " [0.47825037]\n",
      " [0.47625403]\n",
      " [0.10775794]\n",
      " [0.44255494]\n",
      " [0.03449727]\n",
      " [0.29696367]\n",
      " [0.898171  ]\n",
      " [0.19356655]\n",
      " [0.46808896]\n",
      " [0.84198921]\n",
      " [0.82322276]\n",
      " [0.87460315]\n",
      " [0.91714763]\n",
      " [0.11148532]]\n",
      "=====DEBUG=====\n",
      "==== DEBUG2 ====\n",
      "numpyではベクトルの内積を求める際には、横ベクトル×縦ベクトルである必要がある\n",
      "-> w_new-w\n",
      "[[-1.51644841]\n",
      " [-0.04202957]\n",
      " [-0.05902486]]\n",
      "-> (w_new-w).T\n",
      "[[-1.51644841 -0.04202957 -0.05902486]]\n",
      "-> w\n",
      "[[0. ]\n",
      " [0.1]\n",
      " [0.1]]\n",
      "#計算結果は1行1列の行列になる\n",
      "[[2.3048662]]\n",
      "#こちらはエラーになる\n",
      "shapes (1,3) and (1,3) not aligned: 3 (dim 1) != 1 (dim 0)\n",
      "#内積の計算結果(1行1列)はスカラーとして扱うことができるらしい\n",
      "[[2.3048662]]\n",
      "[[ True]]\n",
      "=====DEBUG=====\n",
      "[0.34411487 0.26192507 0.1827928  0.4095841  0.37697671 0.35525976\n",
      " 0.47017095 0.13384721 0.10463104 0.30225125 0.15212401 0.28479992\n",
      " 0.19339069 0.06609488 0.37343608 0.3558879  0.06630308 0.4809288\n",
      " 0.53663372 0.40674976 0.32035262 0.29964122 0.27919594 0.26605228\n",
      " 0.10797656 0.16916432 0.1622986  0.33266463 0.28959618 0.09009773\n",
      " 0.03416906 0.30941074 0.31537944 0.24387106 0.43414758 0.18497173\n",
      " 0.40799347 0.12638226 0.24435163 0.37375023 0.36773271 0.04973801\n",
      " 0.10279024 0.05627251 0.16624172 0.14623936 0.46629568 0.79386905\n",
      " 0.17276495 0.33424677 0.48060245 0.32921607 0.1344382  0.01485714\n",
      " 0.13949756 0.0505268  0.09175473 0.2367146  0.7201296  0.04035879\n",
      " 0.60014531 0.24614149 0.1545696  0.12303654 0.20524628 0.03657913\n",
      " 0.119228   0.35895562 0.32522479 0.13165231 0.07236548 0.10501344\n",
      " 0.19126662 0.17467579 0.19924336 0.20326629 0.23414358 0.14274597\n",
      " 0.22723862 0.23653848 0.22622353 0.05207888 0.06861682 0.09394936\n",
      " 0.37581005 0.1099398  0.38472733 0.25517529 0.20137869 0.54308264\n",
      " 0.3369541  0.19078428 0.15052927 0.42738841 0.18113867 0.2993261\n",
      " 0.08056109 0.12320381 0.15684535 0.06165081 0.22323245 0.39346175\n",
      " 0.27296013 0.07381234 0.18190177 0.1221743  0.10748037 0.32426855\n",
      " 0.16285451 0.24953096 0.25503594 0.27552177 0.2002275  0.06278136\n",
      " 0.11258854 0.06932591 0.2939614  0.30981323 0.48709483 0.41655604\n",
      " 0.26421552 0.08668378 0.05820989 0.06815255 0.20686762 0.21755433\n",
      " 0.47290656 0.10464376 0.2217641  0.12293775 0.09289197 0.06104497\n",
      " 0.11089839 0.31124657 0.25752119 0.04271894 0.12942781 0.07790163\n",
      " 0.11964568 0.05466238 0.09546266 0.17847393 0.35848914 0.11648692\n",
      " 0.23454543 0.25798051 0.1720372  0.34373319 0.34534291 0.21433325\n",
      " 0.31107278 0.58514856 0.55035404 0.13968951 0.42025118 0.05814467\n",
      " 0.06659093 0.64581134 0.10626943 0.24855186 0.22972328 0.15376084\n",
      " 0.06613803 0.32646121 0.35091798 0.11247769 0.05406648 0.29061988\n",
      " 0.04943188 0.10639802 0.05706981 0.22774017 0.09988685 0.14850491\n",
      " 0.19353897 0.08745368 0.33709327 0.37703311 0.19559359 0.21783289\n",
      " 0.37021109 0.06403463 0.37072001 0.07588418 0.1289353  0.08792825\n",
      " 0.05140569 0.1172325  0.40179751 0.36544312 0.22112541 0.38512255\n",
      " 0.11830972 0.23885626 0.059186   0.25958266 0.52047875 0.47219648\n",
      " 0.17449502 0.07492918 0.08099518 0.36591413 0.73127536 0.24111284\n",
      " 0.37480673 0.26424118 0.07367644 0.47973216 0.37241176 0.11789138\n",
      " 0.23757061 0.3442277  0.42917456 0.16786011 0.03481551 0.1143845\n",
      " 0.65486824 0.06444467 0.17648511 0.39755728 0.06569993 0.14069043\n",
      " 0.42861182 0.11379158 0.1628626  0.143217   0.12935319 0.11610334\n",
      " 0.21624213 0.12429384 0.17942176 0.28722746 0.13144614 0.18938844\n",
      " 0.18003121 0.13321986 0.23572827 0.23855239 0.32652426 0.08931034\n",
      " 0.18562172 0.21791938 0.14549283 0.21950028 0.34818003 0.14479256\n",
      " 0.69578664 0.44079914 0.15393733 0.09922138 0.26323316 0.07785727\n",
      " 0.44881646 0.09168238 0.11243774 0.1686495  0.04406684 0.41645408\n",
      " 0.19410538 0.05191617 0.0657183  0.07940823 0.07359094 0.09225792\n",
      " 0.18085423 0.34557846 0.17293503 0.18150896 0.07305507 0.16778104\n",
      " 0.03966983 0.11184435 0.33327626 0.07645205 0.2121297  0.30371452\n",
      " 0.34589737 0.39570711 0.41919054 0.06680814]\n",
      "-----\n",
      "[[0.34411487]\n",
      " [0.26192507]\n",
      " [0.1827928 ]\n",
      " [0.4095841 ]\n",
      " [0.37697671]\n",
      " [0.35525976]\n",
      " [0.47017095]\n",
      " [0.13384721]\n",
      " [0.10463104]\n",
      " [0.30225125]\n",
      " [0.15212401]\n",
      " [0.28479992]\n",
      " [0.19339069]\n",
      " [0.06609488]\n",
      " [0.37343608]\n",
      " [0.3558879 ]\n",
      " [0.06630308]\n",
      " [0.4809288 ]\n",
      " [0.53663372]\n",
      " [0.40674976]\n",
      " [0.32035262]\n",
      " [0.29964122]\n",
      " [0.27919594]\n",
      " [0.26605228]\n",
      " [0.10797656]\n",
      " [0.16916432]\n",
      " [0.1622986 ]\n",
      " [0.33266463]\n",
      " [0.28959618]\n",
      " [0.09009773]\n",
      " [0.03416906]\n",
      " [0.30941074]\n",
      " [0.31537944]\n",
      " [0.24387106]\n",
      " [0.43414758]\n",
      " [0.18497173]\n",
      " [0.40799347]\n",
      " [0.12638226]\n",
      " [0.24435163]\n",
      " [0.37375023]\n",
      " [0.36773271]\n",
      " [0.04973801]\n",
      " [0.10279024]\n",
      " [0.05627251]\n",
      " [0.16624172]\n",
      " [0.14623936]\n",
      " [0.46629568]\n",
      " [0.79386905]\n",
      " [0.17276495]\n",
      " [0.33424677]\n",
      " [0.48060245]\n",
      " [0.32921607]\n",
      " [0.1344382 ]\n",
      " [0.01485714]\n",
      " [0.13949756]\n",
      " [0.0505268 ]\n",
      " [0.09175473]\n",
      " [0.2367146 ]\n",
      " [0.7201296 ]\n",
      " [0.04035879]\n",
      " [0.60014531]\n",
      " [0.24614149]\n",
      " [0.1545696 ]\n",
      " [0.12303654]\n",
      " [0.20524628]\n",
      " [0.03657913]\n",
      " [0.119228  ]\n",
      " [0.35895562]\n",
      " [0.32522479]\n",
      " [0.13165231]\n",
      " [0.07236548]\n",
      " [0.10501344]\n",
      " [0.19126662]\n",
      " [0.17467579]\n",
      " [0.19924336]\n",
      " [0.20326629]\n",
      " [0.23414358]\n",
      " [0.14274597]\n",
      " [0.22723862]\n",
      " [0.23653848]\n",
      " [0.22622353]\n",
      " [0.05207888]\n",
      " [0.06861682]\n",
      " [0.09394936]\n",
      " [0.37581005]\n",
      " [0.1099398 ]\n",
      " [0.38472733]\n",
      " [0.25517529]\n",
      " [0.20137869]\n",
      " [0.54308264]\n",
      " [0.3369541 ]\n",
      " [0.19078428]\n",
      " [0.15052927]\n",
      " [0.42738841]\n",
      " [0.18113867]\n",
      " [0.2993261 ]\n",
      " [0.08056109]\n",
      " [0.12320381]\n",
      " [0.15684535]\n",
      " [0.06165081]\n",
      " [0.22323245]\n",
      " [0.39346175]\n",
      " [0.27296013]\n",
      " [0.07381234]\n",
      " [0.18190177]\n",
      " [0.1221743 ]\n",
      " [0.10748037]\n",
      " [0.32426855]\n",
      " [0.16285451]\n",
      " [0.24953096]\n",
      " [0.25503594]\n",
      " [0.27552177]\n",
      " [0.2002275 ]\n",
      " [0.06278136]\n",
      " [0.11258854]\n",
      " [0.06932591]\n",
      " [0.2939614 ]\n",
      " [0.30981323]\n",
      " [0.48709483]\n",
      " [0.41655604]\n",
      " [0.26421552]\n",
      " [0.08668378]\n",
      " [0.05820989]\n",
      " [0.06815255]\n",
      " [0.20686762]\n",
      " [0.21755433]\n",
      " [0.47290656]\n",
      " [0.10464376]\n",
      " [0.2217641 ]\n",
      " [0.12293775]\n",
      " [0.09289197]\n",
      " [0.06104497]\n",
      " [0.11089839]\n",
      " [0.31124657]\n",
      " [0.25752119]\n",
      " [0.04271894]\n",
      " [0.12942781]\n",
      " [0.07790163]\n",
      " [0.11964568]\n",
      " [0.05466238]\n",
      " [0.09546266]\n",
      " [0.17847393]\n",
      " [0.35848914]\n",
      " [0.11648692]\n",
      " [0.23454543]\n",
      " [0.25798051]\n",
      " [0.1720372 ]\n",
      " [0.34373319]\n",
      " [0.34534291]\n",
      " [0.21433325]\n",
      " [0.31107278]\n",
      " [0.58514856]\n",
      " [0.55035404]\n",
      " [0.13968951]\n",
      " [0.42025118]\n",
      " [0.05814467]\n",
      " [0.06659093]\n",
      " [0.64581134]\n",
      " [0.10626943]\n",
      " [0.24855186]\n",
      " [0.22972328]\n",
      " [0.15376084]\n",
      " [0.06613803]\n",
      " [0.32646121]\n",
      " [0.35091798]\n",
      " [0.11247769]\n",
      " [0.05406648]\n",
      " [0.29061988]\n",
      " [0.04943188]\n",
      " [0.10639802]\n",
      " [0.05706981]\n",
      " [0.22774017]\n",
      " [0.09988685]\n",
      " [0.14850491]\n",
      " [0.19353897]\n",
      " [0.08745368]\n",
      " [0.33709327]\n",
      " [0.37703311]\n",
      " [0.19559359]\n",
      " [0.21783289]\n",
      " [0.37021109]\n",
      " [0.06403463]\n",
      " [0.37072001]\n",
      " [0.07588418]\n",
      " [0.1289353 ]\n",
      " [0.08792825]\n",
      " [0.05140569]\n",
      " [0.1172325 ]\n",
      " [0.40179751]\n",
      " [0.36544312]\n",
      " [0.22112541]\n",
      " [0.38512255]\n",
      " [0.11830972]\n",
      " [0.23885626]\n",
      " [0.059186  ]\n",
      " [0.25958266]\n",
      " [0.52047875]\n",
      " [0.47219648]\n",
      " [0.17449502]\n",
      " [0.07492918]\n",
      " [0.08099518]\n",
      " [0.36591413]\n",
      " [0.73127536]\n",
      " [0.24111284]\n",
      " [0.37480673]\n",
      " [0.26424118]\n",
      " [0.07367644]\n",
      " [0.47973216]\n",
      " [0.37241176]\n",
      " [0.11789138]\n",
      " [0.23757061]\n",
      " [0.3442277 ]\n",
      " [0.42917456]\n",
      " [0.16786011]\n",
      " [0.03481551]\n",
      " [0.1143845 ]\n",
      " [0.65486824]\n",
      " [0.06444467]\n",
      " [0.17648511]\n",
      " [0.39755728]\n",
      " [0.06569993]\n",
      " [0.14069043]\n",
      " [0.42861182]\n",
      " [0.11379158]\n",
      " [0.1628626 ]\n",
      " [0.143217  ]\n",
      " [0.12935319]\n",
      " [0.11610334]\n",
      " [0.21624213]\n",
      " [0.12429384]\n",
      " [0.17942176]\n",
      " [0.28722746]\n",
      " [0.13144614]\n",
      " [0.18938844]\n",
      " [0.18003121]\n",
      " [0.13321986]\n",
      " [0.23572827]\n",
      " [0.23855239]\n",
      " [0.32652426]\n",
      " [0.08931034]\n",
      " [0.18562172]\n",
      " [0.21791938]\n",
      " [0.14549283]\n",
      " [0.21950028]\n",
      " [0.34818003]\n",
      " [0.14479256]\n",
      " [0.69578664]\n",
      " [0.44079914]\n",
      " [0.15393733]\n",
      " [0.09922138]\n",
      " [0.26323316]\n",
      " [0.07785727]\n",
      " [0.44881646]\n",
      " [0.09168238]\n",
      " [0.11243774]\n",
      " [0.1686495 ]\n",
      " [0.04406684]\n",
      " [0.41645408]\n",
      " [0.19410538]\n",
      " [0.05191617]\n",
      " [0.0657183 ]\n",
      " [0.07940823]\n",
      " [0.07359094]\n",
      " [0.09225792]\n",
      " [0.18085423]\n",
      " [0.34557846]\n",
      " [0.17293503]\n",
      " [0.18150896]\n",
      " [0.07305507]\n",
      " [0.16778104]\n",
      " [0.03966983]\n",
      " [0.11184435]\n",
      " [0.33327626]\n",
      " [0.07645205]\n",
      " [0.2121297 ]\n",
      " [0.30371452]\n",
      " [0.34589737]\n",
      " [0.39570711]\n",
      " [0.41919054]\n",
      " [0.06680814]]\n",
      "=====DEBUG=====\n",
      "==== DEBUG2 ====\n",
      "numpyではベクトルの内積を求める際には、横ベクトル×縦ベクトルである必要がある\n",
      "-> w_new-w\n",
      "[[0.065684  ]\n",
      " [0.0344945 ]\n",
      " [0.03646282]]\n",
      "-> (w_new-w).T\n",
      "[[0.065684   0.0344945  0.03646282]]\n",
      "-> w\n",
      "[[-1.51644841]\n",
      " [ 0.05797043]\n",
      " [ 0.04097514]]\n",
      "#計算結果は1行1列の行列になる\n",
      "[[0.0068338]]\n",
      "#こちらはエラーになる\n",
      "shapes (1,3) and (1,3) not aligned: 3 (dim 1) != 1 (dim 0)\n",
      "#内積の計算結果(1行1列)はスカラーとして扱うことができるらしい\n",
      "[[0.0068338]]\n",
      "[[ True]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=====DEBUG=====\n",
      "[0.46560319 0.39548789 0.19581488 0.6067946  0.54038569 0.53177718\n",
      " 0.73327128 0.11341047 0.06171289 0.42688907 0.13810665 0.42647982\n",
      " 0.22842479 0.02963601 0.58472149 0.59252195 0.03012291 0.76906884\n",
      " 0.83177041 0.65435683 0.48324958 0.46948646 0.32992546 0.35397304\n",
      " 0.0814981  0.14968145 0.14893043 0.52710081 0.43004634 0.04381876\n",
      " 0.01183193 0.38741197 0.4089598  0.30808749 0.64935317 0.20619713\n",
      " 0.60348696 0.08959598 0.33027032 0.57352586 0.52465613 0.02303736\n",
      " 0.08031644 0.02553482 0.15745121 0.14428193 0.72949601 0.97046666\n",
      " 0.1813229  0.55946876 0.73406852 0.4799264  0.12297758 0.0024575\n",
      " 0.10831435 0.01864275 0.06402465 0.29835397 0.94073307 0.01621931\n",
      " 0.87684788 0.31852709 0.13583438 0.06461182 0.21999941 0.01011831\n",
      " 0.09251812 0.52924792 0.48731146 0.10404783 0.04029378 0.08562196\n",
      " 0.20973461 0.23993269 0.22248786 0.2422991  0.31544274 0.15268177\n",
      " 0.27273881 0.29752618 0.30376465 0.01985709 0.03736108 0.05915426\n",
      " 0.53773589 0.07057153 0.56635948 0.3345861  0.21225267 0.80101837\n",
      " 0.50036866 0.17566076 0.14258444 0.69556495 0.20931995 0.34021986\n",
      " 0.04330356 0.08728874 0.15085791 0.0310126  0.29484856 0.59540347\n",
      " 0.33686216 0.04307581 0.18144261 0.09893942 0.0704628  0.48979929\n",
      " 0.15893653 0.32508241 0.31317595 0.36800349 0.24298702 0.0307296\n",
      " 0.079836   0.0386793  0.43169688 0.49355332 0.7605217  0.63752605\n",
      " 0.36800061 0.05769541 0.02855303 0.04138815 0.25185994 0.27192701\n",
      " 0.67957047 0.07428226 0.30509455 0.11494093 0.06425856 0.02740632\n",
      " 0.08057953 0.48543348 0.32401898 0.01627988 0.11835051 0.04312123\n",
      " 0.09504179 0.02644009 0.06377832 0.20941683 0.54631499 0.09379538\n",
      " 0.30924406 0.30920855 0.16397326 0.46741787 0.55937098 0.25828076\n",
      " 0.38463511 0.86734519 0.77695923 0.10772218 0.68489557 0.02664577\n",
      " 0.03565531 0.90691395 0.08638223 0.32224687 0.25949248 0.14983751\n",
      " 0.03442015 0.4405134  0.47701048 0.08005378 0.02152564 0.42440093\n",
      " 0.02366202 0.07775834 0.02545773 0.31702068 0.06222589 0.13523033\n",
      " 0.25420961 0.05181134 0.47165388 0.57427902 0.24125959 0.23720666\n",
      " 0.56277618 0.03126226 0.53752861 0.04011131 0.1107121  0.05807297\n",
      " 0.0171131  0.09828872 0.58001241 0.56995531 0.23050665 0.5779453\n",
      " 0.09850235 0.31569462 0.029697   0.35812352 0.7700263  0.65990267\n",
      " 0.18821425 0.04324348 0.05155674 0.56221607 0.94718713 0.29433901\n",
      " 0.56813356 0.38832165 0.04649885 0.77780688 0.58126451 0.0832788\n",
      " 0.29591889 0.51464139 0.60616768 0.15972425 0.01058845 0.10177763\n",
      " 0.90384953 0.04143453 0.18228859 0.60040235 0.03382991 0.12400271\n",
      " 0.67267883 0.10342948 0.17100084 0.12833953 0.11225244 0.09441056\n",
      " 0.2611195  0.09540775 0.23805096 0.37355204 0.10714743 0.21429188\n",
      " 0.14805911 0.12526359 0.30083405 0.33556023 0.45702352 0.06026441\n",
      " 0.20878216 0.25060029 0.12435823 0.25495017 0.54980797 0.13164555\n",
      " 0.93637122 0.6824181  0.17426636 0.06790231 0.36959086 0.04359653\n",
      " 0.67120997 0.05818483 0.08066543 0.14413781 0.0163156  0.64457847\n",
      " 0.1911427  0.01928554 0.03072645 0.0513891  0.03359623 0.05908427\n",
      " 0.19415251 0.49775615 0.17806139 0.18530117 0.03814541 0.16506459\n",
      " 0.01337991 0.09135993 0.54341341 0.05210487 0.21035517 0.46155899\n",
      " 0.49016268 0.57837115 0.64392955 0.03568476]\n",
      "-----\n",
      "[[0.46560319]\n",
      " [0.39548789]\n",
      " [0.19581488]\n",
      " [0.6067946 ]\n",
      " [0.54038569]\n",
      " [0.53177718]\n",
      " [0.73327128]\n",
      " [0.11341047]\n",
      " [0.06171289]\n",
      " [0.42688907]\n",
      " [0.13810665]\n",
      " [0.42647982]\n",
      " [0.22842479]\n",
      " [0.02963601]\n",
      " [0.58472149]\n",
      " [0.59252195]\n",
      " [0.03012291]\n",
      " [0.76906884]\n",
      " [0.83177041]\n",
      " [0.65435683]\n",
      " [0.48324958]\n",
      " [0.46948646]\n",
      " [0.32992546]\n",
      " [0.35397304]\n",
      " [0.0814981 ]\n",
      " [0.14968145]\n",
      " [0.14893043]\n",
      " [0.52710081]\n",
      " [0.43004634]\n",
      " [0.04381876]\n",
      " [0.01183193]\n",
      " [0.38741197]\n",
      " [0.4089598 ]\n",
      " [0.30808749]\n",
      " [0.64935317]\n",
      " [0.20619713]\n",
      " [0.60348696]\n",
      " [0.08959598]\n",
      " [0.33027032]\n",
      " [0.57352586]\n",
      " [0.52465613]\n",
      " [0.02303736]\n",
      " [0.08031644]\n",
      " [0.02553482]\n",
      " [0.15745121]\n",
      " [0.14428193]\n",
      " [0.72949601]\n",
      " [0.97046666]\n",
      " [0.1813229 ]\n",
      " [0.55946876]\n",
      " [0.73406852]\n",
      " [0.4799264 ]\n",
      " [0.12297758]\n",
      " [0.0024575 ]\n",
      " [0.10831435]\n",
      " [0.01864275]\n",
      " [0.06402465]\n",
      " [0.29835397]\n",
      " [0.94073307]\n",
      " [0.01621931]\n",
      " [0.87684788]\n",
      " [0.31852709]\n",
      " [0.13583438]\n",
      " [0.06461182]\n",
      " [0.21999941]\n",
      " [0.01011831]\n",
      " [0.09251812]\n",
      " [0.52924792]\n",
      " [0.48731146]\n",
      " [0.10404783]\n",
      " [0.04029378]\n",
      " [0.08562196]\n",
      " [0.20973461]\n",
      " [0.23993269]\n",
      " [0.22248786]\n",
      " [0.2422991 ]\n",
      " [0.31544274]\n",
      " [0.15268177]\n",
      " [0.27273881]\n",
      " [0.29752618]\n",
      " [0.30376465]\n",
      " [0.01985709]\n",
      " [0.03736108]\n",
      " [0.05915426]\n",
      " [0.53773589]\n",
      " [0.07057153]\n",
      " [0.56635948]\n",
      " [0.3345861 ]\n",
      " [0.21225267]\n",
      " [0.80101837]\n",
      " [0.50036866]\n",
      " [0.17566076]\n",
      " [0.14258444]\n",
      " [0.69556495]\n",
      " [0.20931995]\n",
      " [0.34021986]\n",
      " [0.04330356]\n",
      " [0.08728874]\n",
      " [0.15085791]\n",
      " [0.0310126 ]\n",
      " [0.29484856]\n",
      " [0.59540347]\n",
      " [0.33686216]\n",
      " [0.04307581]\n",
      " [0.18144261]\n",
      " [0.09893942]\n",
      " [0.0704628 ]\n",
      " [0.48979929]\n",
      " [0.15893653]\n",
      " [0.32508241]\n",
      " [0.31317595]\n",
      " [0.36800349]\n",
      " [0.24298702]\n",
      " [0.0307296 ]\n",
      " [0.079836  ]\n",
      " [0.0386793 ]\n",
      " [0.43169688]\n",
      " [0.49355332]\n",
      " [0.7605217 ]\n",
      " [0.63752605]\n",
      " [0.36800061]\n",
      " [0.05769541]\n",
      " [0.02855303]\n",
      " [0.04138815]\n",
      " [0.25185994]\n",
      " [0.27192701]\n",
      " [0.67957047]\n",
      " [0.07428226]\n",
      " [0.30509455]\n",
      " [0.11494093]\n",
      " [0.06425856]\n",
      " [0.02740632]\n",
      " [0.08057953]\n",
      " [0.48543348]\n",
      " [0.32401898]\n",
      " [0.01627988]\n",
      " [0.11835051]\n",
      " [0.04312123]\n",
      " [0.09504179]\n",
      " [0.02644009]\n",
      " [0.06377832]\n",
      " [0.20941683]\n",
      " [0.54631499]\n",
      " [0.09379538]\n",
      " [0.30924406]\n",
      " [0.30920855]\n",
      " [0.16397326]\n",
      " [0.46741787]\n",
      " [0.55937098]\n",
      " [0.25828076]\n",
      " [0.38463511]\n",
      " [0.86734519]\n",
      " [0.77695923]\n",
      " [0.10772218]\n",
      " [0.68489557]\n",
      " [0.02664577]\n",
      " [0.03565531]\n",
      " [0.90691395]\n",
      " [0.08638223]\n",
      " [0.32224687]\n",
      " [0.25949248]\n",
      " [0.14983751]\n",
      " [0.03442015]\n",
      " [0.4405134 ]\n",
      " [0.47701048]\n",
      " [0.08005378]\n",
      " [0.02152564]\n",
      " [0.42440093]\n",
      " [0.02366202]\n",
      " [0.07775834]\n",
      " [0.02545773]\n",
      " [0.31702068]\n",
      " [0.06222589]\n",
      " [0.13523033]\n",
      " [0.25420961]\n",
      " [0.05181134]\n",
      " [0.47165388]\n",
      " [0.57427902]\n",
      " [0.24125959]\n",
      " [0.23720666]\n",
      " [0.56277618]\n",
      " [0.03126226]\n",
      " [0.53752861]\n",
      " [0.04011131]\n",
      " [0.1107121 ]\n",
      " [0.05807297]\n",
      " [0.0171131 ]\n",
      " [0.09828872]\n",
      " [0.58001241]\n",
      " [0.56995531]\n",
      " [0.23050665]\n",
      " [0.5779453 ]\n",
      " [0.09850235]\n",
      " [0.31569462]\n",
      " [0.029697  ]\n",
      " [0.35812352]\n",
      " [0.7700263 ]\n",
      " [0.65990267]\n",
      " [0.18821425]\n",
      " [0.04324348]\n",
      " [0.05155674]\n",
      " [0.56221607]\n",
      " [0.94718713]\n",
      " [0.29433901]\n",
      " [0.56813356]\n",
      " [0.38832165]\n",
      " [0.04649885]\n",
      " [0.77780688]\n",
      " [0.58126451]\n",
      " [0.0832788 ]\n",
      " [0.29591889]\n",
      " [0.51464139]\n",
      " [0.60616768]\n",
      " [0.15972425]\n",
      " [0.01058845]\n",
      " [0.10177763]\n",
      " [0.90384953]\n",
      " [0.04143453]\n",
      " [0.18228859]\n",
      " [0.60040235]\n",
      " [0.03382991]\n",
      " [0.12400271]\n",
      " [0.67267883]\n",
      " [0.10342948]\n",
      " [0.17100084]\n",
      " [0.12833953]\n",
      " [0.11225244]\n",
      " [0.09441056]\n",
      " [0.2611195 ]\n",
      " [0.09540775]\n",
      " [0.23805096]\n",
      " [0.37355204]\n",
      " [0.10714743]\n",
      " [0.21429188]\n",
      " [0.14805911]\n",
      " [0.12526359]\n",
      " [0.30083405]\n",
      " [0.33556023]\n",
      " [0.45702352]\n",
      " [0.06026441]\n",
      " [0.20878216]\n",
      " [0.25060029]\n",
      " [0.12435823]\n",
      " [0.25495017]\n",
      " [0.54980797]\n",
      " [0.13164555]\n",
      " [0.93637122]\n",
      " [0.6824181 ]\n",
      " [0.17426636]\n",
      " [0.06790231]\n",
      " [0.36959086]\n",
      " [0.04359653]\n",
      " [0.67120997]\n",
      " [0.05818483]\n",
      " [0.08066543]\n",
      " [0.14413781]\n",
      " [0.0163156 ]\n",
      " [0.64457847]\n",
      " [0.1911427 ]\n",
      " [0.01928554]\n",
      " [0.03072645]\n",
      " [0.0513891 ]\n",
      " [0.03359623]\n",
      " [0.05908427]\n",
      " [0.19415251]\n",
      " [0.49775615]\n",
      " [0.17806139]\n",
      " [0.18530117]\n",
      " [0.03814541]\n",
      " [0.16506459]\n",
      " [0.01337991]\n",
      " [0.09135993]\n",
      " [0.54341341]\n",
      " [0.05210487]\n",
      " [0.21035517]\n",
      " [0.46155899]\n",
      " [0.49016268]\n",
      " [0.57837115]\n",
      " [0.64392955]\n",
      " [0.03568476]]\n",
      "=====DEBUG=====\n",
      "==== DEBUG2 ====\n",
      "numpyではベクトルの内積を求める際には、横ベクトル×縦ベクトルである必要がある\n",
      "-> w_new-w\n",
      "[[-0.09933462]\n",
      " [ 0.01015299]\n",
      " [ 0.00916536]]\n",
      "-> (w_new-w).T\n",
      "[[-0.09933462  0.01015299  0.00916536]]\n",
      "-> w\n",
      "[[-1.45076441]\n",
      " [ 0.09246493]\n",
      " [ 0.07743796]]\n",
      "#計算結果は1行1列の行列になる\n",
      "[[0.01005445]]\n",
      "#こちらはエラーになる\n",
      "shapes (1,3) and (1,3) not aligned: 3 (dim 1) != 1 (dim 0)\n",
      "#内積の計算結果(1行1列)はスカラーとして扱うことができるらしい\n",
      "[[0.01005445]]\n",
      "[[ True]]\n",
      "=====DEBUG=====\n",
      "[0.47572052 0.40211376 0.18132817 0.63277841 0.55955951 0.55162685\n",
      " 0.76765367 0.09755281 0.0485688  0.43462748 0.12180722 0.43595805\n",
      " 0.2165581  0.02136218 0.61079798 0.62123906 0.02176874 0.80450416\n",
      " 0.86475958 0.68637311 0.49865148 0.48470792 0.32402554 0.35319274\n",
      " 0.06727157 0.13273296 0.13244047 0.54838418 0.43962907 0.03288479\n",
      " 0.00773352 0.387808   0.41235888 0.30228794 0.67867799 0.19257839\n",
      " 0.62915857 0.07417043 0.32789301 0.59797943 0.54220922 0.01631231\n",
      " 0.06641731 0.01820055 0.14117704 0.12875042 0.76388359 0.9812229\n",
      " 0.16634947 0.5855265  0.76784868 0.49389254 0.10734004 0.00132998\n",
      " 0.09202764 0.01271006 0.05134147 0.29180045 0.95867117 0.01101914\n",
      " 0.90529111 0.31410783 0.11925949 0.05050261 0.20607412 0.00640127\n",
      " 0.07745826 0.5483513  0.50289887 0.08821465 0.03040092 0.07149664\n",
      " 0.19592904 0.23144199 0.20937791 0.23121477 0.31177703 0.1379776\n",
      " 0.26341228 0.29086649 0.29910775 0.01364795 0.02795062 0.04666293\n",
      " 0.55660362 0.05664869 0.58877978 0.33178276 0.19782806 0.8339287\n",
      " 0.51694442 0.15878441 0.12662745 0.7300285  0.19639112 0.33401447\n",
      " 0.03276714 0.07208384 0.13489225 0.02267612 0.28913999 0.62121113\n",
      " 0.33264365 0.03282653 0.16572093 0.08367732 0.05666409 0.50589289\n",
      " 0.14302329 0.3213462  0.30704572 0.3685633  0.23228478 0.02239612\n",
      " 0.06540036 0.02908677 0.44110565 0.51172409 0.79530192 0.66684627\n",
      " 0.36972867 0.04564387 0.02068693 0.03154673 0.24167173 0.2634369\n",
      " 0.70929139 0.06046091 0.30111538 0.09987964 0.05150494 0.01962976\n",
      " 0.06621349 0.50211885 0.31931224 0.01101072 0.10292202 0.03270548\n",
      " 0.07994059 0.01901012 0.05092333 0.19674833 0.56811407 0.07889335\n",
      " 0.30456936 0.30219359 0.14768999 0.4778758  0.58436406 0.24822403\n",
      " 0.38441272 0.89693506 0.80887466 0.09141968 0.71893865 0.01907638\n",
      " 0.02652865 0.9312256  0.07217015 0.31816446 0.24814211 0.13404883\n",
      " 0.0254675  0.44813407 0.4883773  0.06561739 0.01495072 0.43291418\n",
      " 0.01684163 0.06372254 0.01811289 0.31428272 0.04925721 0.11906228\n",
      " 0.24568201 0.04017361 0.48346574 0.59854724 0.23078829 0.22414537\n",
      " 0.58597485 0.02281166 0.55683354 0.03010777 0.09513091 0.04593651\n",
      " 0.01147911 0.08333876 0.60292453 0.59464177 0.2163771  0.60202284\n",
      " 0.08348386 0.31157874 0.02163073 0.35869534 0.80331327 0.68753366\n",
      " 0.17371977 0.03293177 0.04022285 0.58572371 0.96378279 0.28675531\n",
      " 0.59170631 0.39347416 0.0359249  0.81354132 0.60694534 0.06845909\n",
      " 0.28891718 0.5328229  0.63042924 0.14347458 0.00678045 0.08704565\n",
      " 0.92827724 0.031749   0.16708623 0.62654194 0.02497011 0.10795683\n",
      " 0.70494302 0.08876194 0.15598988 0.11226792 0.09667301 0.07953016\n",
      " 0.25126096 0.08002526 0.22880252 0.37384657 0.09135099 0.20115381\n",
      " 0.13027657 0.10979911 0.29475251 0.33465187 0.46742086 0.04792008\n",
      " 0.19538208 0.23919218 0.10799357 0.24395802 0.57310495 0.11559186\n",
      " 0.95547555 0.71474271 0.16031455 0.05464017 0.3716879  0.03313204\n",
      " 0.70182664 0.04586479 0.06621303 0.1269491  0.01100806 0.67473672\n",
      " 0.17527162 0.01319248 0.02230008 0.04014119 0.02451131 0.04667568\n",
      " 0.17967227 0.51306651 0.16279225 0.16994439 0.02847832 0.14919245\n",
      " 0.00881051 0.07675831 0.56716234 0.04094195 0.19483112 0.47502434\n",
      " 0.5041935  0.60157805 0.67379641 0.02654653]\n",
      "-----\n",
      "[[0.47572052]\n",
      " [0.40211376]\n",
      " [0.18132817]\n",
      " [0.63277841]\n",
      " [0.55955951]\n",
      " [0.55162685]\n",
      " [0.76765367]\n",
      " [0.09755281]\n",
      " [0.0485688 ]\n",
      " [0.43462748]\n",
      " [0.12180722]\n",
      " [0.43595805]\n",
      " [0.2165581 ]\n",
      " [0.02136218]\n",
      " [0.61079798]\n",
      " [0.62123906]\n",
      " [0.02176874]\n",
      " [0.80450416]\n",
      " [0.86475958]\n",
      " [0.68637311]\n",
      " [0.49865148]\n",
      " [0.48470792]\n",
      " [0.32402554]\n",
      " [0.35319274]\n",
      " [0.06727157]\n",
      " [0.13273296]\n",
      " [0.13244047]\n",
      " [0.54838418]\n",
      " [0.43962907]\n",
      " [0.03288479]\n",
      " [0.00773352]\n",
      " [0.387808  ]\n",
      " [0.41235888]\n",
      " [0.30228794]\n",
      " [0.67867799]\n",
      " [0.19257839]\n",
      " [0.62915857]\n",
      " [0.07417043]\n",
      " [0.32789301]\n",
      " [0.59797943]\n",
      " [0.54220922]\n",
      " [0.01631231]\n",
      " [0.06641731]\n",
      " [0.01820055]\n",
      " [0.14117704]\n",
      " [0.12875042]\n",
      " [0.76388359]\n",
      " [0.9812229 ]\n",
      " [0.16634947]\n",
      " [0.5855265 ]\n",
      " [0.76784868]\n",
      " [0.49389254]\n",
      " [0.10734004]\n",
      " [0.00132998]\n",
      " [0.09202764]\n",
      " [0.01271006]\n",
      " [0.05134147]\n",
      " [0.29180045]\n",
      " [0.95867117]\n",
      " [0.01101914]\n",
      " [0.90529111]\n",
      " [0.31410783]\n",
      " [0.11925949]\n",
      " [0.05050261]\n",
      " [0.20607412]\n",
      " [0.00640127]\n",
      " [0.07745826]\n",
      " [0.5483513 ]\n",
      " [0.50289887]\n",
      " [0.08821465]\n",
      " [0.03040092]\n",
      " [0.07149664]\n",
      " [0.19592904]\n",
      " [0.23144199]\n",
      " [0.20937791]\n",
      " [0.23121477]\n",
      " [0.31177703]\n",
      " [0.1379776 ]\n",
      " [0.26341228]\n",
      " [0.29086649]\n",
      " [0.29910775]\n",
      " [0.01364795]\n",
      " [0.02795062]\n",
      " [0.04666293]\n",
      " [0.55660362]\n",
      " [0.05664869]\n",
      " [0.58877978]\n",
      " [0.33178276]\n",
      " [0.19782806]\n",
      " [0.8339287 ]\n",
      " [0.51694442]\n",
      " [0.15878441]\n",
      " [0.12662745]\n",
      " [0.7300285 ]\n",
      " [0.19639112]\n",
      " [0.33401447]\n",
      " [0.03276714]\n",
      " [0.07208384]\n",
      " [0.13489225]\n",
      " [0.02267612]\n",
      " [0.28913999]\n",
      " [0.62121113]\n",
      " [0.33264365]\n",
      " [0.03282653]\n",
      " [0.16572093]\n",
      " [0.08367732]\n",
      " [0.05666409]\n",
      " [0.50589289]\n",
      " [0.14302329]\n",
      " [0.3213462 ]\n",
      " [0.30704572]\n",
      " [0.3685633 ]\n",
      " [0.23228478]\n",
      " [0.02239612]\n",
      " [0.06540036]\n",
      " [0.02908677]\n",
      " [0.44110565]\n",
      " [0.51172409]\n",
      " [0.79530192]\n",
      " [0.66684627]\n",
      " [0.36972867]\n",
      " [0.04564387]\n",
      " [0.02068693]\n",
      " [0.03154673]\n",
      " [0.24167173]\n",
      " [0.2634369 ]\n",
      " [0.70929139]\n",
      " [0.06046091]\n",
      " [0.30111538]\n",
      " [0.09987964]\n",
      " [0.05150494]\n",
      " [0.01962976]\n",
      " [0.06621349]\n",
      " [0.50211885]\n",
      " [0.31931224]\n",
      " [0.01101072]\n",
      " [0.10292202]\n",
      " [0.03270548]\n",
      " [0.07994059]\n",
      " [0.01901012]\n",
      " [0.05092333]\n",
      " [0.19674833]\n",
      " [0.56811407]\n",
      " [0.07889335]\n",
      " [0.30456936]\n",
      " [0.30219359]\n",
      " [0.14768999]\n",
      " [0.4778758 ]\n",
      " [0.58436406]\n",
      " [0.24822403]\n",
      " [0.38441272]\n",
      " [0.89693506]\n",
      " [0.80887466]\n",
      " [0.09141968]\n",
      " [0.71893865]\n",
      " [0.01907638]\n",
      " [0.02652865]\n",
      " [0.9312256 ]\n",
      " [0.07217015]\n",
      " [0.31816446]\n",
      " [0.24814211]\n",
      " [0.13404883]\n",
      " [0.0254675 ]\n",
      " [0.44813407]\n",
      " [0.4883773 ]\n",
      " [0.06561739]\n",
      " [0.01495072]\n",
      " [0.43291418]\n",
      " [0.01684163]\n",
      " [0.06372254]\n",
      " [0.01811289]\n",
      " [0.31428272]\n",
      " [0.04925721]\n",
      " [0.11906228]\n",
      " [0.24568201]\n",
      " [0.04017361]\n",
      " [0.48346574]\n",
      " [0.59854724]\n",
      " [0.23078829]\n",
      " [0.22414537]\n",
      " [0.58597485]\n",
      " [0.02281166]\n",
      " [0.55683354]\n",
      " [0.03010777]\n",
      " [0.09513091]\n",
      " [0.04593651]\n",
      " [0.01147911]\n",
      " [0.08333876]\n",
      " [0.60292453]\n",
      " [0.59464177]\n",
      " [0.2163771 ]\n",
      " [0.60202284]\n",
      " [0.08348386]\n",
      " [0.31157874]\n",
      " [0.02163073]\n",
      " [0.35869534]\n",
      " [0.80331327]\n",
      " [0.68753366]\n",
      " [0.17371977]\n",
      " [0.03293177]\n",
      " [0.04022285]\n",
      " [0.58572371]\n",
      " [0.96378279]\n",
      " [0.28675531]\n",
      " [0.59170631]\n",
      " [0.39347416]\n",
      " [0.0359249 ]\n",
      " [0.81354132]\n",
      " [0.60694534]\n",
      " [0.06845909]\n",
      " [0.28891718]\n",
      " [0.5328229 ]\n",
      " [0.63042924]\n",
      " [0.14347458]\n",
      " [0.00678045]\n",
      " [0.08704565]\n",
      " [0.92827724]\n",
      " [0.031749  ]\n",
      " [0.16708623]\n",
      " [0.62654194]\n",
      " [0.02497011]\n",
      " [0.10795683]\n",
      " [0.70494302]\n",
      " [0.08876194]\n",
      " [0.15598988]\n",
      " [0.11226792]\n",
      " [0.09667301]\n",
      " [0.07953016]\n",
      " [0.25126096]\n",
      " [0.08002526]\n",
      " [0.22880252]\n",
      " [0.37384657]\n",
      " [0.09135099]\n",
      " [0.20115381]\n",
      " [0.13027657]\n",
      " [0.10979911]\n",
      " [0.29475251]\n",
      " [0.33465187]\n",
      " [0.46742086]\n",
      " [0.04792008]\n",
      " [0.19538208]\n",
      " [0.23919218]\n",
      " [0.10799357]\n",
      " [0.24395802]\n",
      " [0.57310495]\n",
      " [0.11559186]\n",
      " [0.95547555]\n",
      " [0.71474271]\n",
      " [0.16031455]\n",
      " [0.05464017]\n",
      " [0.3716879 ]\n",
      " [0.03313204]\n",
      " [0.70182664]\n",
      " [0.04586479]\n",
      " [0.06621303]\n",
      " [0.1269491 ]\n",
      " [0.01100806]\n",
      " [0.67473672]\n",
      " [0.17527162]\n",
      " [0.01319248]\n",
      " [0.02230008]\n",
      " [0.04014119]\n",
      " [0.02451131]\n",
      " [0.04667568]\n",
      " [0.17967227]\n",
      " [0.51306651]\n",
      " [0.16279225]\n",
      " [0.16994439]\n",
      " [0.02847832]\n",
      " [0.14919245]\n",
      " [0.00881051]\n",
      " [0.07675831]\n",
      " [0.56716234]\n",
      " [0.04094195]\n",
      " [0.19483112]\n",
      " [0.47502434]\n",
      " [0.5041935 ]\n",
      " [0.60157805]\n",
      " [0.67379641]\n",
      " [0.02654653]]\n",
      "=====DEBUG=====\n",
      "==== DEBUG2 ====\n",
      "numpyではベクトルの内積を求める際には、横ベクトル×縦ベクトルである必要がある\n",
      "-> w_new-w\n",
      "[[-0.01129401]\n",
      " [ 0.00093197]\n",
      " [ 0.0008209 ]]\n",
      "-> (w_new-w).T\n",
      "[[-0.01129401  0.00093197  0.0008209 ]]\n",
      "-> w\n",
      "[[-1.55009903]\n",
      " [ 0.10261792]\n",
      " [ 0.08660332]]\n",
      "#計算結果は1行1列の行列になる\n",
      "[[0.0001291]]\n",
      "#こちらはエラーになる\n",
      "shapes (1,3) and (1,3) not aligned: 3 (dim 1) != 1 (dim 0)\n",
      "#内積の計算結果(1行1列)はスカラーとして扱うことができるらしい\n",
      "[[0.0001291]]\n",
      "[[ True]]\n",
      " ==== PRINT RESULT ===\n",
      "     index          x          y  type  probability\n",
      "0       47  27.568456  30.913861     1     0.981942\n",
      "1      202  24.593615  26.646611     1     0.964934\n",
      "2       58  25.547502  23.930439     1     0.959927\n",
      "3      246  16.950034  33.219196     1     0.956808\n",
      "4      157  16.020289  29.003510     1     0.932963\n",
      "..     ...        ...        ...   ...          ...\n",
      "275    270 -17.948540 -15.369148    -1     0.008463\n",
      "276     30 -27.799701  -5.214360    -1     0.007422\n",
      "277    214 -19.083229 -17.072417    -1     0.006496\n",
      "278     65 -10.732165 -27.636649    -1     0.006128\n",
      "279     53 -29.571393 -23.516477    -1     0.001255\n",
      "\n",
      "[280 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== DEBUG tp_rate and fp_rate\n",
      "TP RATE\n",
      "[0.     0.0125 0.025  0.0375 0.05   0.0625 0.075  0.0875 0.1    0.1125\n",
      " 0.125  0.1375 0.15   0.15   0.1625 0.175  0.1875 0.1875 0.1875 0.2\n",
      " 0.2125 0.225  0.225  0.225  0.225  0.2375 0.25   0.2625 0.275  0.2875\n",
      " 0.3    0.3    0.3125 0.3125 0.325  0.325  0.325  0.3375 0.35   0.35\n",
      " 0.35   0.3625 0.3625 0.3625 0.3625 0.3625 0.375  0.375  0.375  0.375\n",
      " 0.3875 0.4    0.4125 0.425  0.4375 0.4375 0.45   0.45   0.4625 0.475\n",
      " 0.4875 0.5    0.5    0.5125 0.525  0.5375 0.55   0.5625 0.5625 0.5625\n",
      " 0.575  0.5875 0.6    0.6    0.6125 0.625  0.625  0.625  0.6375 0.65\n",
      " 0.6625 0.6625 0.6625 0.675  0.675  0.6875 0.7    0.7    0.7    0.7125\n",
      " 0.725  0.7375 0.7375 0.7375 0.75   0.75   0.75   0.7625 0.775  0.775\n",
      " 0.775  0.775  0.7875 0.7875 0.7875 0.7875 0.7875 0.7875 0.7875 0.7875\n",
      " 0.8    0.8    0.8    0.8    0.8125 0.8125 0.8125 0.825  0.825  0.825\n",
      " 0.825  0.825  0.8375 0.8375 0.8375 0.8375 0.8375 0.8375 0.8375 0.8375\n",
      " 0.8375 0.8375 0.85   0.8625 0.8625 0.8625 0.8625 0.8625 0.8625 0.8625\n",
      " 0.8625 0.8625 0.8625 0.8625 0.8625 0.875  0.8875 0.9    0.9    0.9\n",
      " 0.9    0.9    0.9    0.9    0.9125 0.9125 0.9125 0.9125 0.9125 0.9125\n",
      " 0.9125 0.9125 0.9125 0.925  0.925  0.925  0.925  0.925  0.925  0.9375\n",
      " 0.9375 0.9375 0.9375 0.95   0.95   0.95   0.95   0.95   0.95   0.95\n",
      " 0.95   0.95   0.95   0.95   0.95   0.95   0.95   0.95   0.95   0.9625\n",
      " 0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975\n",
      " 0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975\n",
      " 0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975\n",
      " 0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975\n",
      " 0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.975\n",
      " 0.975  0.975  0.975  0.975  0.975  0.975  0.975  0.9875 0.9875 0.9875\n",
      " 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875\n",
      " 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875 0.9875\n",
      " 0.9875 0.9875 0.9875 0.9875 0.9875 1.     1.     1.     1.     1.    ]\n",
      "FP RATE\n",
      "[0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      " 0.    0.005 0.005 0.005 0.005 0.01  0.015 0.015 0.015 0.015 0.02  0.025\n",
      " 0.03  0.03  0.03  0.03  0.03  0.03  0.03  0.035 0.035 0.04  0.04  0.045\n",
      " 0.05  0.05  0.05  0.055 0.06  0.06  0.065 0.07  0.075 0.08  0.08  0.085\n",
      " 0.09  0.095 0.095 0.095 0.095 0.095 0.095 0.1   0.1   0.105 0.105 0.105\n",
      " 0.105 0.105 0.11  0.11  0.11  0.11  0.11  0.11  0.115 0.12  0.12  0.12\n",
      " 0.12  0.125 0.125 0.125 0.13  0.135 0.135 0.135 0.135 0.14  0.145 0.145\n",
      " 0.15  0.15  0.15  0.155 0.16  0.16  0.16  0.16  0.165 0.17  0.17  0.175\n",
      " 0.18  0.18  0.18  0.185 0.19  0.195 0.195 0.2   0.205 0.21  0.215 0.22\n",
      " 0.225 0.23  0.23  0.235 0.24  0.245 0.245 0.25  0.255 0.255 0.26  0.265\n",
      " 0.27  0.275 0.275 0.28  0.285 0.29  0.295 0.3   0.305 0.31  0.315 0.32\n",
      " 0.32  0.32  0.325 0.33  0.335 0.34  0.345 0.35  0.355 0.36  0.365 0.37\n",
      " 0.375 0.375 0.375 0.375 0.38  0.385 0.39  0.395 0.4   0.405 0.405 0.41\n",
      " 0.415 0.42  0.425 0.43  0.435 0.44  0.445 0.445 0.45  0.455 0.46  0.465\n",
      " 0.47  0.47  0.475 0.48  0.485 0.485 0.49  0.495 0.5   0.505 0.51  0.515\n",
      " 0.52  0.525 0.53  0.535 0.54  0.545 0.55  0.555 0.56  0.56  0.56  0.565\n",
      " 0.57  0.575 0.58  0.585 0.59  0.595 0.6   0.605 0.61  0.615 0.62  0.625\n",
      " 0.63  0.635 0.64  0.645 0.65  0.655 0.66  0.665 0.67  0.675 0.68  0.685\n",
      " 0.69  0.695 0.7   0.705 0.71  0.715 0.72  0.725 0.73  0.735 0.74  0.745\n",
      " 0.75  0.755 0.76  0.765 0.77  0.775 0.78  0.785 0.79  0.795 0.8   0.805\n",
      " 0.81  0.815 0.82  0.825 0.83  0.835 0.84  0.84  0.845 0.85  0.855 0.86\n",
      " 0.865 0.87  0.875 0.88  0.885 0.89  0.895 0.9   0.905 0.91  0.915 0.92\n",
      " 0.925 0.93  0.935 0.94  0.945 0.95  0.955 0.96  0.965 0.97  0.975 0.975\n",
      " 0.98  0.985 0.99  0.995]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w0, w1, w2, err_rate, result = run_logistic(train_set)\n",
    "fig = plt.figure(figsize=(6, 12))\n",
    "subplot = fig.add_subplot(2, 1, 1)\n",
    "show_result(subplot, train_set, w0, w1, w2, err_rate)\n",
    "subplot = fig.add_subplot(2, 1, 2)\n",
    "draw_roc(subplot, result)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "05-roc_curve.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}