05_support_vector_machines.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5장 – 서포트 벡터 머신**\n",
    "\n",
    "_이 노트북은 5장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/handson-ml2/blob/master/05_support_vector_machines.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 몇 개의 모듈을 임포트합니다. 맷플롯립 그래프를 인라인으로 출력하도록 만들고 그림을 저장하는 함수를 준비합니다. 또한 파이썬 버전이 3.5 이상인지 확인합니다(파이썬 2.x에서도 동작하지만 곧 지원이 중단되므로 파이썬 3을 사용하는 것이 좋습니다). 사이킷런 버전이 0.20 이상인지도 확인합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:09.736930Z",
     "iopub.status.busy": "2021-10-23T11:53:09.735881Z",
     "iopub.status.idle": "2021-10-23T11:53:11.010739Z",
     "shell.execute_reply": "2021-10-23T11:53:11.011740Z"
    }
   },
   "outputs": [],
   "source": [
    "# 파이썬 ≥3.5 필수\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# 사이킷런 ≥0.20 필수\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# 공통 모듈 임포트\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 노트북 실행 결과를 동일하게 유지하기 위해\n",
    "np.random.seed(42)\n",
    "\n",
    "# 깔끔한 그래프 출력을 위해\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# 그림을 저장할 위치\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"svm\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"그림 저장:\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 선형 SVM 분류"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음 몇 개의 코드 셀은 5장 앞부분의 그래프를 만듭니다. 실제 코드 예제는 그 이후에 나옵니다.\n",
    "\n",
    "**<그림 5–1. 라지 마진 분류> 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:11.022833Z",
     "iopub.status.busy": "2021-10-23T11:53:11.021839Z",
     "iopub.status.idle": "2021-10-23T11:53:11.203722Z",
     "shell.execute_reply": "2021-10-23T11:53:11.204427Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, kernel='linear')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # 꽃잎 길이, 꽃잎 너비\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]\n",
    "\n",
    "# SVM 분류 모델\n",
    "svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:11.220518Z",
     "iopub.status.busy": "2021-10-23T11:53:11.219273Z",
     "iopub.status.idle": "2021-10-23T11:53:12.278094Z",
     "shell.execute_reply": "2021-10-23T11:53:12.279194Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: large_margin_classification_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 나쁜 모델\n",
    "x0 = np.linspace(0, 5.5, 200)\n",
    "pred_1 = 5*x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 + 0.5\n",
    "\n",
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    # 결정 경계에서 w0*x0 + w1*x1 + b = 0 이므로\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
    "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
    "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris versicolor\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris setosa\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"large_margin_classification_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<그림 5-2. 특성 스케일에 따른 민감성> 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:12.304407Z",
     "iopub.status.busy": "2021-10-23T11:53:12.283276Z",
     "iopub.status.idle": "2021-10-23T11:53:13.814361Z",
     "shell.execute_reply": "2021-10-23T11:53:13.815558Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: sensitivity_to_feature_scales_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
    "ys = np.array([0, 0, 1, 1])\n",
    "svm_clf = SVC(kernel=\"linear\", C=100)\n",
    "svm_clf.fit(Xs, ys)\n",
    "\n",
    "plt.figure(figsize=(9,2.7))\n",
    "plt.subplot(121)\n",
    "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
    "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, 0, 6)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x_1$    \", fontsize=20, rotation=0)\n",
    "plt.title(\"Unscaled\", fontsize=16)\n",
    "plt.axis([0, 6, 0, 90])\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(Xs)\n",
    "svm_clf.fit(X_scaled, ys)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n",
    "plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, -2, 2)\n",
    "plt.xlabel(\"$x'_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x'_1$  \", fontsize=20, rotation=0)\n",
    "plt.title(\"Scaled\", fontsize=16)\n",
    "plt.axis([-2, 2, -2, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_feature_scales_plot\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 소프트 마진 분류\n",
    "\n",
    "**<그림 5-3. 이상치에 민감한 하드 마진> 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:13.821571Z",
     "iopub.status.busy": "2021-10-23T11:53:13.819738Z",
     "iopub.status.idle": "2021-10-23T11:53:14.838445Z",
     "shell.execute_reply": "2021-10-23T11:53:14.839623Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: sensitivity_to_outliers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
    "y_outliers = np.array([0, 0])\n",
    "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
    "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
    "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
    "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
    "\n",
    "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
    "svm_clf2.fit(Xo2, yo2)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
    "plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n",
    "plt.text(0.3, 1.0, \"Impossible!\", fontsize=24, color=\"red\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[0][0], X_outliers[0][1]),\n",
    "             xytext=(2.5, 1.7),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n",
    "plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n",
    "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[1][0], X_outliers[1][1]),\n",
    "             xytext=(3.2, 0.08),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_outliers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**다음이 5장의 첫 번째 코드 예제입니다:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:14.845619Z",
     "iopub.status.busy": "2021-10-23T11:53:14.843840Z",
     "iopub.status.idle": "2021-10-23T11:53:14.884610Z",
     "shell.execute_reply": "2021-10-23T11:53:14.883058Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('linear_svc', LinearSVC(C=1, loss='hinge', random_state=42))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # 꽃잎 길이, 꽃잎 너비\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris virginica\n",
    "\n",
    "svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\", random_state=42)),\n",
    "    ])\n",
    "\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:14.893598Z",
     "iopub.status.busy": "2021-10-23T11:53:14.892615Z",
     "iopub.status.idle": "2021-10-23T11:53:14.900107Z",
     "shell.execute_reply": "2021-10-23T11:53:14.901442Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5, 1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<그림 5-4. 넓은 마진(왼쪽) 대 적은 마진 오류(오른쪽)> 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:14.915745Z",
     "iopub.status.busy": "2021-10-23T11:53:14.914749Z",
     "iopub.status.idle": "2021-10-23T11:53:14.947761Z",
     "shell.execute_reply": "2021-10-23T11:53:14.946481Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/handson-ml2/.env/lib/python3.7/site-packages/sklearn/svm/_base.py:1201: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  ConvergenceWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('linear_svc',\n",
       "                 LinearSVC(C=100, loss='hinge', random_state=42))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svm_clf1 = LinearSVC(C=1, loss=\"hinge\", random_state=42)\n",
    "svm_clf2 = LinearSVC(C=100, loss=\"hinge\", random_state=42)\n",
    "\n",
    "scaled_svm_clf1 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf1),\n",
    "    ])\n",
    "scaled_svm_clf2 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf2),\n",
    "    ])\n",
    "\n",
    "scaled_svm_clf1.fit(X, y)\n",
    "scaled_svm_clf2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:14.958489Z",
     "iopub.status.busy": "2021-10-23T11:53:14.957516Z",
     "iopub.status.idle": "2021-10-23T11:53:14.975890Z",
     "shell.execute_reply": "2021-10-23T11:53:14.974492Z"
    }
   },
   "outputs": [],
   "source": [
    "# 스케일되지 않은 파라미터로 변경\n",
    "b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "w1 = svm_clf1.coef_[0] / scaler.scale_\n",
    "w2 = svm_clf2.coef_[0] / scaler.scale_\n",
    "svm_clf1.intercept_ = np.array([b1])\n",
    "svm_clf2.intercept_ = np.array([b2])\n",
    "svm_clf1.coef_ = np.array([w1])\n",
    "svm_clf2.coef_ = np.array([w2])\n",
    "\n",
    "# 서포트 벡터 찾기 (libsvm과 달리 liblinear 라이브러리에서 제공하지 않기 때문에 \n",
    "# LinearSVC에는 서포트 벡터가 저장되어 있지 않습니다.)\n",
    "t = y * 2 - 1\n",
    "support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\n",
    "support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\n",
    "svm_clf1.support_vectors_ = X[support_vectors_idx1]\n",
    "svm_clf2.support_vectors_ = X[support_vectors_idx2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:14.985734Z",
     "iopub.status.busy": "2021-10-23T11:53:14.984762Z",
     "iopub.status.idle": "2021-10-23T11:53:16.390046Z",
     "shell.execute_reply": "2021-10-23T11:53:16.389208Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: regularization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris virginica\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris versicolor\")\n",
    "plot_svc_decision_boundary(svm_clf1, 4, 5.9)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf1.C), fontsize=16)\n",
    "plt.axis([4, 5.9, 0.8, 2.8])\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 5.99)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
    "plt.axis([4, 5.9, 0.8, 2.8])\n",
    "\n",
    "save_fig(\"regularization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 비선형 분류"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<그림 5-5. 특성을 추가하여 선형적으로 구분되는 데이터셋 만들기>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:16.406058Z",
     "iopub.status.busy": "2021-10-23T11:53:16.405064Z",
     "iopub.status.idle": "2021-10-23T11:53:17.176294Z",
     "shell.execute_reply": "2021-10-23T11:53:17.175269Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: higher_dimensions_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n",
    "X2D = np.c_[X1D, X1D**2]\n",
    "y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.plot(X1D[:, 0][y==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][y==1], np.zeros(5), \"g^\")\n",
    "plt.gca().get_yaxis().set_ticks([])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.2, 0.2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \"bs\")\n",
    "plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \"g^\")\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_2$  \", fontsize=20, rotation=0)\n",
    "plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n",
    "plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n",
    "plt.axis([-4.5, 4.5, -1, 17])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"higher_dimensions_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:17.194990Z",
     "iopub.status.busy": "2021-10-23T11:53:17.186203Z",
     "iopub.status.idle": "2021-10-23T11:53:17.538476Z",
     "shell.execute_reply": "2021-10-23T11:53:17.539242Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
    "\n",
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**다음이 5장의 두 번째 코드 예제입니다:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:17.549804Z",
     "iopub.status.busy": "2021-10-23T11:53:17.547657Z",
     "iopub.status.idle": "2021-10-23T11:53:17.579571Z",
     "shell.execute_reply": "2021-10-23T11:53:17.580253Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/handson-ml2/.env/lib/python3.7/site-packages/sklearn/svm/_base.py:1201: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  ConvergenceWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('poly_features', PolynomialFeatures(degree=3)),\n",
       "                ('scaler', StandardScaler()),\n",
       "                ('svm_clf', LinearSVC(C=10, loss='hinge', random_state=42))])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polynomial_svm_clf = Pipeline([\n",
    "        (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\", random_state=42))\n",
    "    ])\n",
    "\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<그림 5-6. 다항 특성을 사용한 선형 SVM 분류기> 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:17.592813Z",
     "iopub.status.busy": "2021-10-23T11:53:17.590998Z",
     "iopub.status.idle": "2021-10-23T11:53:18.746379Z",
     "shell.execute_reply": "2021-10-23T11:53:18.747570Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: moons_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "\n",
    "save_fig(\"moons_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 다항식 커널 \n",
    "\n",
    "**다음 코드 에제:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:18.753137Z",
     "iopub.status.busy": "2021-10-23T11:53:18.751501Z",
     "iopub.status.idle": "2021-10-23T11:53:18.768415Z",
     "shell.execute_reply": "2021-10-23T11:53:18.769698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=1, kernel='poly'))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "poly_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
    "    ])\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<그림 5-7. 다항식 커널을 사용한 SVM 분류기> 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:18.775484Z",
     "iopub.status.busy": "2021-10-23T11:53:18.773833Z",
     "iopub.status.idle": "2021-10-23T11:53:18.902600Z",
     "shell.execute_reply": "2021-10-23T11:53:18.903938Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=100, degree=10, kernel='poly'))])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly100_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
    "    ])\n",
    "poly100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:18.909927Z",
     "iopub.status.busy": "2021-10-23T11:53:18.908168Z",
     "iopub.status.idle": "2021-10-23T11:53:20.791999Z",
     "shell.execute_reply": "2021-10-23T11:53:20.793170Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: moons_kernelized_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(10.5, 4), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.4, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.4, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "save_fig(\"moons_kernelized_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 유사도 특성"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<그림 5-8. 가우시안 RBF를 사용한 유사도 특성> 생성 코드**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**식 5-1: 가우시안 RBF**\n",
    "\n",
    "$\n",
    "{\\displaystyle \\phi_{\\gamma}(\\mathbf{x}, \\boldsymbol{\\ell})} = {\\displaystyle \\exp({\\displaystyle -\\gamma \\left\\| \\mathbf{x} - \\boldsymbol{\\ell} \\right\\|^2})}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:20.804691Z",
     "iopub.status.busy": "2021-10-23T11:53:20.803553Z",
     "iopub.status.idle": "2021-10-23T11:53:22.594407Z",
     "shell.execute_reply": "2021-10-23T11:53:22.595563Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: kernel_method_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gaussian_rbf(x, landmark, gamma):\n",
    "    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n",
    "\n",
    "gamma = 0.3\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(10.5, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(r'$\\mathbf{x}$',\n",
    "             xy=(X1D[3, 0], 0),\n",
    "             xytext=(-0.5, 0.20),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "             xy=(XK[3, 0], XK[3, 1]),\n",
    "             xytext=(0.65, 0.50),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "    \n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"kernel_method_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:22.605740Z",
     "iopub.status.busy": "2021-10-23T11:53:22.604766Z",
     "iopub.status.idle": "2021-10-23T11:53:22.610723Z",
     "shell.execute_reply": "2021-10-23T11:53:22.611406Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi(-1.0, -2) = [0.74081822]\n",
      "Phi(-1.0, 1) = [0.30119421]\n"
     ]
    }
   ],
   "source": [
    "x1_example = X1D[3, 0]\n",
    "for landmark in (-2, 1):\n",
    "    k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\n",
    "    print(\"Phi({}, {}) = {}\".format(x1_example, landmark, k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가우시안 RBF 커널"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**다음 코드 예제:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:22.620341Z",
     "iopub.status.busy": "2021-10-23T11:53:22.619394Z",
     "iopub.status.idle": "2021-10-23T11:53:22.643594Z",
     "shell.execute_reply": "2021-10-23T11:53:22.644281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=0.001, gamma=5))])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
    "    ])\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<그림 5-9. RBF 커널을 사용한 SVM 분류기> 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:22.658613Z",
     "iopub.status.busy": "2021-10-23T11:53:22.657624Z",
     "iopub.status.idle": "2021-10-23T11:53:26.044651Z",
     "shell.execute_reply": "2021-10-23T11:53:26.045381Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: moons_rbf_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
    "        ])\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10.5, 7), sharex=True, sharey=True)\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.sca(axes[i // 2, i % 2])\n",
    "    plot_predictions(svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.45, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "    if i in (0, 1):\n",
    "        plt.xlabel(\"\")\n",
    "    if i in (1, 3):\n",
    "        plt.ylabel(\"\")\n",
    "\n",
    "save_fig(\"moons_rbf_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:26.054726Z",
     "iopub.status.busy": "2021-10-23T11:53:26.053255Z",
     "iopub.status.idle": "2021-10-23T11:53:26.056369Z",
     "shell.execute_reply": "2021-10-23T11:53:26.055540Z"
    }
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 50\n",
    "X = 2 * np.random.rand(m, 1)\n",
    "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**다음 코드 예제:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:26.065537Z",
     "iopub.status.busy": "2021-10-23T11:53:26.062701Z",
     "iopub.status.idle": "2021-10-23T11:53:26.069552Z",
     "shell.execute_reply": "2021-10-23T11:53:26.070221Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(epsilon=1.5, random_state=42)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<그림 5-10. SVM 회귀> 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:26.080375Z",
     "iopub.status.busy": "2021-10-23T11:53:26.079380Z",
     "iopub.status.idle": "2021-10-23T11:53:26.094044Z",
     "shell.execute_reply": "2021-10-23T11:53:26.094712Z"
    }
   },
   "outputs": [],
   "source": [
    "svm_reg1 = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\n",
    "svm_reg1.fit(X, y)\n",
    "svm_reg2.fit(X, y)\n",
    "\n",
    "def find_support_vectors(svm_reg, X, y):\n",
    "    y_pred = svm_reg.predict(X)\n",
    "    off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n",
    "    return np.argwhere(off_margin)\n",
    "\n",
    "svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n",
    "svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n",
    "\n",
    "eps_x1 = 1\n",
    "eps_y_pred = svm_reg1.predict([[eps_x1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:26.110377Z",
     "iopub.status.busy": "2021-10-23T11:53:26.109347Z",
     "iopub.status.idle": "2021-10-23T11:53:27.832608Z",
     "shell.execute_reply": "2021-10-23T11:53:27.833323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: svm_regression_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_svm_regression(svm_reg, X, y, axes):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n",
    "    y_pred = svm_reg.predict(x1s)\n",
    "    plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "    plt.plot(x1s, y_pred + svm_reg.epsilon, \"k--\")\n",
    "    plt.plot(x1s, y_pred - svm_reg.epsilon, \"k--\")\n",
    "    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(X, y, \"bo\")\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.legend(loc=\"upper left\", fontsize=18)\n",
    "    plt.axis(axes)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(9, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \"k-\", linewidth=2)\n",
    "plt.annotate(\n",
    "        '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
    "        xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n",
    "        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n",
    "    )\n",
    "plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n",
    "plt.sca(axes[1])\n",
    "plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_regression_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:27.841927Z",
     "iopub.status.busy": "2021-10-23T11:53:27.840946Z",
     "iopub.status.idle": "2021-10-23T11:53:27.844346Z",
     "shell.execute_reply": "2021-10-23T11:53:27.844986Z"
    }
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 100\n",
    "X = 2 * np.random.rand(m, 1) - 1\n",
    "y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**노트**: 향후 버전을 위해 사이킷런 0.22에서 기본값이 될 `gamma=\"scale\"`으로 지정했습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**다음 코드 예제:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:27.852802Z",
     "iopub.status.busy": "2021-10-23T11:53:27.851654Z",
     "iopub.status.idle": "2021-10-23T11:53:27.888177Z",
     "shell.execute_reply": "2021-10-23T11:53:27.888832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, degree=2, kernel='poly')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1, gamma=\"scale\")\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<그림 5-11. 2차 다항 커널을 사용한 SVM 회귀> 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:27.897975Z",
     "iopub.status.busy": "2021-10-23T11:53:27.897085Z",
     "iopub.status.idle": "2021-10-23T11:53:27.934688Z",
     "shell.execute_reply": "2021-10-23T11:53:27.935332Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.01, degree=2, kernel='poly')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg1 = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1, gamma=\"scale\")\n",
    "svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1, gamma=\"scale\")\n",
    "svm_poly_reg1.fit(X, y)\n",
    "svm_poly_reg2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:27.946573Z",
     "iopub.status.busy": "2021-10-23T11:53:27.945575Z",
     "iopub.status.idle": "2021-10-23T11:53:29.586586Z",
     "shell.execute_reply": "2021-10-23T11:53:29.587883Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: svm_with_polynomial_kernel_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(9, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "plt.sca(axes[1])\n",
    "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_with_polynomial_kernel_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM 이론\n",
    "\n",
    "## 결정 함수와 예측"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**식 5-2: 선형 SVM 분류기의 예측**\n",
    "\n",
    "$\n",
    "\\hat{y} = \\begin{cases}\n",
    " 0 & \\mathbf{w}^T \\mathbf{x} + b < 0 \\text{ 일 때}, \\\\\n",
    " 1 & \\mathbf{w}^T \\mathbf{x} + b \\geq 0 \\text{ 일 때}\n",
    "\\end{cases}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<그림 5-12. iris 데이터셋의 결정 함수> 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:29.594693Z",
     "iopub.status.busy": "2021-10-23T11:53:29.592813Z",
     "iopub.status.idle": "2021-10-23T11:53:29.602101Z",
     "shell.execute_reply": "2021-10-23T11:53:29.603256Z"
    }
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # 꽃잎 길이, 꽃잎 너비\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:29.609605Z",
     "iopub.status.busy": "2021-10-23T11:53:29.607621Z",
     "iopub.status.idle": "2021-10-23T11:53:31.674475Z",
     "shell.execute_reply": "2021-10-23T11:53:31.675687Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: iris_3D_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "def plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\n",
    "    x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\n",
    "    X_crop = X[x1_in_bounds]\n",
    "    y_crop = y[x1_in_bounds]\n",
    "    x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\n",
    "    x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    xs = np.c_[x1.ravel(), x2.ravel()]\n",
    "    df = (xs.dot(w) + b).reshape(x1.shape)\n",
    "    m = 1 / np.linalg.norm(w)\n",
    "    boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\n",
    "    margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\n",
    "    margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\n",
    "    ax.plot_surface(x1s, x2, np.zeros_like(x1),\n",
    "                    color=\"b\", alpha=0.2, cstride=100, rstride=100)\n",
    "    ax.plot(x1s, boundary_x2s, 0, \"k-\", linewidth=2, label=r\"$h=0$\")\n",
    "    ax.plot(x1s, margin_x2s_1, 0, \"k--\", linewidth=2, label=r\"$h=\\pm 1$\")\n",
    "    ax.plot(x1s, margin_x2s_2, 0, \"k--\", linewidth=2)\n",
    "    ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, \"g^\")\n",
    "    ax.plot_wireframe(x1, x2, df, alpha=0.3, color=\"k\")\n",
    "    ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, \"bs\")\n",
    "    ax.axis(x1_lim + x2_lim)\n",
    "    ax.text(4.5, 2.5, 3.8, \"Decision function $h$\", fontsize=16)\n",
    "    ax.set_xlabel(r\"Petal length\", fontsize=16, labelpad=10)\n",
    "    ax.set_ylabel(r\"Petal width\", fontsize=16, labelpad=10)\n",
    "    ax.set_zlabel(r\"$h = \\mathbf{w}^T \\mathbf{x} + b$\", fontsize=18, labelpad=5)\n",
    "    ax.legend(loc=\"upper left\", fontsize=16)\n",
    "\n",
    "fig = plt.figure(figsize=(11, 6))\n",
    "ax1 = fig.add_subplot(111, projection='3d')\n",
    "plot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\n",
    "\n",
    "save_fig(\"iris_3D_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<그림 5-13. 가중치 벡터가 작을수록 마진은 커집니다> 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:31.716467Z",
     "iopub.status.busy": "2021-10-23T11:53:31.705819Z",
     "iopub.status.idle": "2021-10-23T11:53:32.866688Z",
     "shell.execute_reply": "2021-10-23T11:53:32.867883Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: small_w_large_margin_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_2D_decision_function(w, b, ylabel=True, x1_lim=[-3, 3]):\n",
    "    x1 = np.linspace(x1_lim[0], x1_lim[1], 200)\n",
    "    y = w * x1 + b\n",
    "    m = 1 / w\n",
    "\n",
    "    plt.plot(x1, y)\n",
    "    plt.plot(x1_lim, [1, 1], \"k:\")\n",
    "    plt.plot(x1_lim, [-1, -1], \"k:\")\n",
    "    plt.axhline(y=0, color='k')\n",
    "    plt.axvline(x=0, color='k')\n",
    "    plt.plot([m, m], [0, 1], \"k--\")\n",
    "    plt.plot([-m, -m], [0, -1], \"k--\")\n",
    "    plt.plot([-m, m], [0, 0], \"k-o\", linewidth=3)\n",
    "    plt.axis(x1_lim + [-2, 2])\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=16)\n",
    "    if ylabel:\n",
    "        plt.ylabel(r\"$w_1 x_1$  \", rotation=0, fontsize=16)\n",
    "    plt.title(r\"$w_1 = {}$\".format(w), fontsize=16)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(9, 3.2), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_2D_decision_function(1, 0)\n",
    "plt.sca(axes[1])\n",
    "plot_2D_decision_function(0.5, 0, ylabel=False)\n",
    "save_fig(\"small_w_large_margin_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:32.880439Z",
     "iopub.status.busy": "2021-10-23T11:53:32.878510Z",
     "iopub.status.idle": "2021-10-23T11:53:32.902699Z",
     "shell.execute_reply": "2021-10-23T11:53:32.901429Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)] # 꽃잎 길이, 꽃잎 너비\n",
    "y = (iris[\"target\"] == 2).astype(np.float64) # Iris virginica\n",
    "\n",
    "svm_clf = SVC(kernel=\"linear\", C=1)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict([[5.3, 1.3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**식 5-3: 하드 마진 선형 SVM 분류기 목적 함수**\n",
    "\n",
    "$\n",
    "\\begin{split}\n",
    "&\\underset{\\mathbf{w}, b}{\\operatorname{minimize}}\\quad{\\frac{1}{2}\\mathbf{w}^T \\mathbf{w}} \\\\\n",
    "&\\text{subject to} \\quad t^{(i)}(\\mathbf{w}^T \\mathbf{x}^{(i)} + b) \\ge 1 \\quad \\text{for } i = 1, 2, \\dots, m\n",
    "\\end{split}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**식 5-4: 소프트 마진 선형 SVM 분류기 목적 함수**\n",
    "\n",
    "$\n",
    "\\begin{split}\n",
    "&\\underset{\\mathbf{w}, b, \\mathbf{\\zeta}}{\\operatorname{minimize}}\\quad{\\dfrac{1}{2}\\mathbf{w}^T \\mathbf{w} + C \\sum\\limits_{i=1}^m{\\zeta^{(i)}}}\\\\\n",
    "&\\text{subject to} \\quad t^{(i)}(\\mathbf{w}^T \\mathbf{x}^{(i)} + b) \\ge 1 - \\zeta^{(i)} \\quad \\text{and} \\quad \\zeta^{(i)} \\ge 0 \\quad \\text{for } i = 1, 2, \\dots, m\n",
    "\\end{split}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**식 5-8: 2차 다항식 매핑**\n",
    "\n",
    "$\n",
    "\\phi\\left(\\mathbf{x}\\right) = \\phi\\left( \\begin{pmatrix}\n",
    "  x_1 \\\\\n",
    "  x_2\n",
    "\\end{pmatrix} \\right) = \\begin{pmatrix}\n",
    "  {x_1}^2 \\\\\n",
    "  \\sqrt{2} \\, x_1 x_2 \\\\\n",
    "  {x_2}^2\n",
    "\\end{pmatrix}\n",
    "$\n",
    "\n",
    "\n",
    "**식 5-9: 2차 다항식 매핑을 위한 커널 트릭**\n",
    "\n",
    "$\n",
    "\\begin{split}\n",
    "\\phi(\\mathbf{a})^T \\phi(\\mathbf{b}) & \\quad = \\begin{pmatrix}\n",
    "  {a_1}^2 \\\\\n",
    "  \\sqrt{2} \\, a_1 a_2 \\\\\n",
    "  {a_2}^2\n",
    "  \\end{pmatrix}^T \\begin{pmatrix}\n",
    "  {b_1}^2 \\\\\n",
    "  \\sqrt{2} \\, b_1 b_2 \\\\\n",
    "  {b_2}^2\n",
    "\\end{pmatrix} = {a_1}^2 {b_1}^2 + 2 a_1 b_1 a_2 b_2 + {a_2}^2 {b_2}^2 \\\\\n",
    " & \\quad = \\left( a_1 b_1 + a_2 b_2 \\right)^2 = \\left( \\begin{pmatrix}\n",
    "  a_1 \\\\\n",
    "  a_2\n",
    "\\end{pmatrix}^T \\begin{pmatrix}\n",
    "    b_1 \\\\\n",
    "    b_2\n",
    "  \\end{pmatrix} \\right)^2 = (\\mathbf{a}^T \\mathbf{b})^2\n",
    "\\end{split}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**식 5-10: 일반적인 커널**\n",
    "\n",
    "$\n",
    "\\begin{split}\n",
    "\\text{선형:} & \\quad K(\\mathbf{a}, \\mathbf{b}) = \\mathbf{a}^T \\mathbf{b} \\\\\n",
    "\\text{다항식:} & \\quad K(\\mathbf{a}, \\mathbf{b}) = \\left(\\gamma \\mathbf{a}^T \\mathbf{b} + r \\right)^d \\\\\n",
    "\\text{가우시안 RBF:} & \\quad K(\\mathbf{a}, \\mathbf{b}) = \\exp({\\displaystyle -\\gamma \\left\\| \\mathbf{a} - \\mathbf{b} \\right\\|^2}) \\\\\n",
    "\\text{시그모이드:} & \\quad K(\\mathbf{a}, \\mathbf{b}) = \\tanh\\left(\\gamma \\mathbf{a}^T \\mathbf{b} + r\\right)\n",
    "\\end{split}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**식 5-13: 선형 SVM 분류기의 비용 함수**\n",
    "\n",
    "$\n",
    "J(\\mathbf{w}, b) = \\dfrac{1}{2} \\mathbf{w}^T \\mathbf{w} \\,+\\, C {\\displaystyle \\sum\\limits_{i=1}^{m}max\\left(0, t^{(i)} - (\\mathbf{w}^T \\mathbf{x}^{(i)} + b) \\right)}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**힌지 손실 그림 생성 코드**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:32.915417Z",
     "iopub.status.busy": "2021-10-23T11:53:32.912656Z",
     "iopub.status.idle": "2021-10-23T11:53:33.717480Z",
     "shell.execute_reply": "2021-10-23T11:53:33.716462Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: hinge_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x201.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(-2, 4, 200)\n",
    "h = np.where(1 - t < 0, 0, 1 - t)  # max(0, 1-t)\n",
    "\n",
    "plt.figure(figsize=(5,2.8))\n",
    "plt.plot(t, h, \"b-\", linewidth=2, label=\"$max(0, 1 - t)$\")\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.yticks(np.arange(-1, 2.5, 1))\n",
    "plt.xlabel(\"$t$\", fontsize=16)\n",
    "plt.axis([-2, 4, -1, 2.5])\n",
    "plt.legend(loc=\"upper right\", fontsize=16)\n",
    "save_fig(\"hinge_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 추가 내용"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 훈련 시간"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:33.725696Z",
     "iopub.status.busy": "2021-10-23T11:53:33.724755Z",
     "iopub.status.idle": "2021-10-23T11:53:33.986568Z",
     "shell.execute_reply": "2021-10-23T11:53:33.997291Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ef5e72a20>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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weCe/n8LROaMDhlOWOofE77dlvMdR3MMSYivx9d3r0RxrzvusnDPqYsxMtdQ/ueGUQLmFcrWVmLDPM0wNIlfFTA97eCABfHM/sHgoY9ZxohPKsy3A/+oLXJMnlGDVOYDtc3UK+SqJGHKamky/LdPn6x8psJJqmdE5iMvZg3jPqj3oWdwD6ytWaDNTLfVPbjglUG1CGSheMfltF9l5BqjCqQrgmO7X1W4Bf5TNwr1uh3sfIUMWi8aOjvmX9UBaM2BVyJd7bCXANlOY7mGlV6gqqoO4raktJ4zL1Zu4mMieUvkpSkHDKYFqRKeYxs5kSh4EWbKHEewmv0cgQpYXSKU0K42b+4FYNgY8lnKvBjYPA98cA37Tkx+dU6rkrHYL+MINc8rNiSrkS1wttFiC+LRUM4XqTC9mkuAVC19MnHwlHau1EtkTBaIEqhR7FmIqdxyGILZpX0LYw41KRl0FAObVgF8mcJT27N4EcNlzQNwxw1dNUCGEfCVLfPv5HQp1VvoJcq8Zc6Gz6agdq2GVUa1E9kSBKAGFanHiqrMQdJkdj2UlCnu4ugqwca4G/ExOhXYG06GWhnCuArKJYbZQryYzSSEQAX/w+cKclV6C3GvGXMxsOqxjtRhFZTonPwVUC9nAQRAloFBJf4Gztvv585kHYOp8MEFbciEVhT38nbs1FTkBLFEyQX0ygYPE6KvXwvO6qMdyOJ+d9tuwK6go7kfUk5Kpi8I7K/0EudeMuZjZtJdjVSd8TULemrRwww9uwNbh4MooqAKqhWzgIIgSqBJctd0DhiEWEu1jQv2+S9BEYQ8/c7n3+37nHsAnYSttW1CM/NrwwJegeqZ6zaJojGPKOD51qsBaV8o9DOqs9BLkXjPmYs05w+uGsb57PVriLQCAlngL+rr7MLxu2CV8vRRVYiiB5377HJLppPYcdASJ7InKZ1ANqwlRAtVGCLOLXZpA5xAsFtOqqCh798N7gBlHOYCZNuDhvZn/e517SKFtC4r+ff1oXqvxIUQY7lmKFaMt/IPmF5SiAKKfIPeaMRebaGXP3p3HHh0fdQlfp6Lq/+d+9G7rxej4KAaGBwAg93kQZRQksicqn0E1rCZECRgotNpo0VVKPcwuzrIUtgKIIqu4LDZvP8HrZXIKIbRHrBFsOrQJaU5j8PAgZi992u1gL3G4Z7HlPsIqlFJklvsJcq8Zc7GJVursXT326sdWuwS+U1ENvjCIp088jdWPrcZMyl2IrlhHb1RO62qJQCKOMJSBiC4E8CCA/wDgNQAbmPkfNNttBPBVAGpLyeuY+RWv/Xd3d/OhQ4ciG68XXg+v1yUrxfd02KuAKGb96rjicX2Bt1gsE/IJeB+zs1MvkDo7gVOfWA5cMuL+cPoC4Lu/Bs56aMp1hu9aXXkmKWbg3d9/N45MHMm+gYzfYaYN+PYrwNlF6OwEJia8C9k571XY+xrFfTFdS91xwx4vyGO/fPNyjIyPuN7vWtRVsnh3a9LC1d+5Gudnz2s/JxAY+YNvjjVrq456Ucw59O3uw4PDD+YpuZZ4C764/Iv43ke+V9B+Cvl+GIjoeWbu1n3WFPGxvgcgCaATQBeA3UQ0ysxHNNvuZOY7Ij5+wUQ1oy4XQcdqP+xeQiKIALEFpteKxlZMJhYtGnaP+yN9wIrNmRn9Ho8HIIDvobMzswrIKQBACUedzR2jkPvspdxsOq628Frv7cCjOwEUb5+p9O+xEolNeXkMDsGoE74AAiuAMILWmrRw+49vx85P7cSi9vx7GUU2sGk1cV/vfa7jlZrIzEFEdAGAWwDcx8xnmfkAgCcAfCaqY5SSSj9wtRKC6HWd/EwY4+OZJLicjT5gtI8fqnnsjscN84qmmdDHUE06JgWgnvNr76qNcgydnRqz5XwL9Lle0PzxijVZ8jOzmArLEQjWVyyM3TOmLUFtE0ZQe9nqo8gGrqbaQlH6BJYAmGXmY8p7owCWGrb/KBG9TkRHiGi9aadEtJaIDhHRoYmJiQiHWzxROmTrqfa8CWvSwootK+Zs9AEzkINeF2vSyl8FOAno+PVzytqon1uT0Si0cjA+rjk3R/5FqSdFtvP3xgdvDORoBvKFrxo51BxvNjqj7aiiMIK6HLb6aqotFKUSaAdwxvHemwDma7b9IYBrAXQA+BKA/0xEK3U7ZeYtzNzNzN0dHR0RDrd4Cn1QTM7jsPvzm62VW6n4OcH79/XDOmuBweCuAcy7YcA72iebHXzqXPAsz5jXTzqg47eQKJ/EUGElpgumPT+0cOFVRWRSF7giK7YkxHO/fQ7PvvpsIEez87i6FcPQiaFIBGs5soWrqbZQlErgLIAFjvcWAJh0bsjMR5l5jJlTzPwMgG8D+FSEYykZhQpW9XtRzbJOnfJOKKpUUTz1/HIKb76F7SMP594/n5x2RW40z5sF1l6fnxcQwLxiK58t/30IaWi8vRNLSlrnJ1eULcKcA196880Vn/5uArGrDuRqAYXdVyEKrJiSEHboJgAMjAxg/Ox4YMFoWjH0Lu4tWrDWUh+AqIhSCRwD0ERE71TeWwbAY32ew47hqEoKyR4uV9axV5Zz1F3JCtlPTiHc1J8RMDYxdgnsmfQMMN/KzwsIMTtNvdLjXgkwgHRzSfsCqxneOUq5Gmi3gOVz5gpd7HyofRWQNFdsSQh1AmDqGGaiXO0kbaR2UECY+RyAxwB8g4guIKL3Afg4gB3ObYno40T0f1CG9wD4cwA/jWoshVAtdYOipNiGJcUkiNmmgpwDeNnDbjU/25yz147dM5ZJJCO4W0gGFahXaFYCBODio5n9GVYVxShrIpS/xHRvAi3z5swVztj5xP5E8N9zgUlzhZpM7FWAep/SnM6tBoJQrCnFy4xVTbb6clGKPIEBAP8ewGkA/cz8D0T0xwD2MnN7drt/RCaXYB6AVwF8n5m/47f/cuYJBCHMzFg1z0TZLzjK3AOvfYbd1/pdfdj8/Gakn7sLIAa6H9Cu9ex47b7dfXjg2QczwnO2ObMCUIvNKXH+Rj7Sh9h7Nrv70862ZARbPDW3HzDwqdux8Oc7MXFcv8+qDBtut4C7rwaa9XH0AIDZNuD/U65Vu+U619y5GfIvmia6MPP3eqGqi+Vva2rDK3e/4hve2Le7D5sPbXYp6xjFcNeKu0oWJ+8aw/Oby3a8asArTyDSjGFmfp2ZP8HMFzDzFXaiGDM/bSuA7N8rmfkiZm5n5j8MogCqkTCrhFIKk6J6BJSC9uxsj9PA8gHgyp/pjX1WF6yvDWts6jP5piPAf3aaNWvoGpSjKTmnUOz99CaAxQdw69+HWOYXUMa6mDlW3u/L7nuwbrn72rhwXKtsJ7fXls1Va82ZETfpZ9WqAnDOnHUmk9n0bKDVwMFXD2r9NmlOB5ptq2MpxDFdLVm61YSUjSgCnT0+CNqOWwpOU4zf0r4c4XyhhF9vAtMzWZtvPAmAAee1ydYMOnVKL1S0vYW9zCs6swYDeOHWOTNTdj+tNw6g9YatAHkLAr8wyiAQ+d9vHfbK0f4NrH8kAVz+HDD/VEZJeqFeK0cnN5o/HjoHoH9fP4ZODGHDvg2wJi1sH92uTdjaf2K/USjbE5WRu4bzmgZ1PhDOnJOrCfXP/VixZQWePvF0KHt9I/UJCIoogQrgVa4AcEfXBElUigqdwkkMeQu/PCXYbgFdA3P1+WNpYOExTQnpudmqKQkIVlfwiqWmXsF/+FOXckimkr5VJV2CsojENuf9tpW8F+q9dUbTtDa1uvreqq+8a6Xp5BZm0mBNWnj4hUxU147DO7DhqQ2Ymp1CX3dfXnJWjGJY2rHUGC2Ud0xFmYYdiz2L33F4Ry7cOOiMvhEjf4IgSqBKsePtvapIRm320SmW0D1pexNAPEAavzJbHV43nN/nN2RHL9usMXbPmDs6qGnapRzSSPtWldSuAiLKA7DvXVCCRtPYs+0cQTu5edC/rx8pziiRFKewY3RHzpSy4akNueuY5jR+ePSH/maWIpSpOotXTUpBZ/SNGPkTBFECZSSs0C6lmSdo85z+ff2Yns3U+QvUk/aKIXeXLvWcZ9rmegerQt4pHC4ezTNBBYl26d/Xr3U42rPWnsU9WLNsTS7T1MZXEIQJo4yg/aV6TmGiaVy/F49ObkEq3aqrgNyxs+OYTc9i8PCgdgXneT19lKnJzu+cxasEndE3YuRPEEQJREwth5Q6sYWAXbUx0MN2sicTjWPCNIt2CodbVudMBrFYRtkMj43gbX/9doyOH9YqoSePPenabRpp7D+xP2dL3n1sd3hBECCMMnffo2x/CfcqwCZQbH2QTm4KdrOaXIe7z/YjldY7oWfSM7kVgm5sA8MDeSUhABiVqVeHMFspqKsOHUFm9H6hpdXQ4KUSiBKImCg6fBVFwJlokF4HqinAxvdh09nmVRwO3s5Ope6OKhwuPpJbFfz2zcy53PH4HXhz+k2s+vEq126tSQtnpp1VSzK1Y7ov7c7Zks/NnNPa062vDZsjqwx5AGoP4vFxmE0dunvicZ9UU98DTxYRTWMKpzV1eINjNaFTIgFJppJ5JSEAGJWpV4cwWynsOrZL7zdSjlfsjL4aGryYKKWCEiVQbxQwEzWZnXa/tNv1nt/D1vmT4TlH7jfH5jqJzbbNCd9Nw3nCM6/ujhPKNA5ZsXlFrjjckYkjOHzqcN5miaGEdmaaTCXx0DO7chm9JiXmaXpztNa0TUt7V+/N26ztQwZTh+6eBLlP7RYwvWDOfJZ92RE1vtE0J3vyfSxhS2d4KAs/bMVlC/POThiVqf17cnUI29efUwpvzbwF6ysWuhZ1aY9XbI+Dag8dLaWCEiUQgqK7hhVAKPNSRKWZgcxDcW7mXN57bU0ZQe71sDnDGltas7Xh55lXEAdfNUT2AEBTEoOHB/GL8V/kffzpH33avQ8dp5YCLedy+y82IqSzU/9AWpMWWOc3uHjUfU+c96lzVL8qMCgKX1+RnVOgHMOaHA+/OjX1lc72KDYJZAA5B72tdMfHzTkJe1bt0baTHDw8mDNH2ftxVhK1fT7FFl6r5tDRUisoUQIhCNrvNSx+ReACP7hFRLA4FZyuHk7Y8gBBw/FMJYJzx4V7hn/s9LG81YDdmNw18z3Z41plhH3ImTN9EHq29uK/j+jr9OhzHbK+Dec9cd6nT652C/sQCt0VFdSbAC57LpujgTyTS5TY901Xx99eCQRRul7tJO2GMc79BBWMQcwo1R46WmoFJUoApZ3hB5nJexWBC0yBhcBsXIpMs3QP25QjaDie+qAacwY02KsBa9LCis0rsOnQJrfQLPI81PM5cPKAtk6PKYEKTUmg42jePcklqhn8H3kVVD0UurEEuZ2nQZiL0mpKYtPzm3JK0zPSqoDoJq0CVPASXLkQZMBzH879BBWMQcwo1Rw6Wg4FJUoApZvhA8Fm8pEooQILgRlRTAGFFOkKE46nPqjqqmDsnjHEKW48xrHfvZxbtfzC+gVydbDU89aYNAotL5zmNI5MHHE9kGoClXqt1nevR0tzs+saOGe8OShbSrtzNCPIC1HohjyNdDqNZV9fhUWLvCcdbR8K71PyU9xeStfZTtK+hjpTk72foIIx6GqhmkNHy6GgIi0gV2pKVUAuaBPxQpvI+33Xi5xc8/t+wEbsOvwamuu2jypbWS1G5ixCtubxNdh+eLv5yzNtwH87CKz9vzJJYc7PDAXnmIMXh1t4lYXm/7gCp6dOawVdS7wFyZlUJv7eHs+f/Hmmz/DqD+vviRcM4LVrgIteys+3mG0BfvFF7z7MfsXl0gR8a8x1Tez76bwXU39jLtgXhdgYsUZw/Zbr8xrH27+B8clxrPhvK5DmtOt3EbTRezkbuZeK5ZuXY2R8xPV+WEd4ORvNC5UiRHatjlInsZkad6vJaPYMx35QddFJedg29/i0/jOP5vVe56Beo0//8M/x6K8s47bJVFLJyM2Op+NXmWP73JO2T/UhtczROJ0ALHwxfO0kQL8aTGcX+7F0pq+C5prY18J5L9o+lMDUo+7rpzMnWZMW/mznn4GI8Phtj/tWE7UmLbxv4H15CsA+bmJ/AvtP7HeZe+zfRZCZezU1ci+GcnQaE3NQCIrpOVCufgWF2nVLjSmiRpeMNjo+it5tvf4Pa1MS6Diij2WPoJ6/NWnh0V896nrfNlvkHKJKJJPWvm9g6iIPM4otG8OU0dCFYMbSeb4B07h09wJd+VFFXr4qXbtIL/r39eOt2bdc7ydTSex7ZV9er2inuSdIP4FCfVK6v+sdUQIhKMaBG4nz14BrfxFnrRaDNWnhxh/cmCstrT7MpmS01Y+txoGTB4ztAvlrnKkzxNAqgBhiaPtln1ZoxmLBVj3WpIWuzV3az+xZp6dDNEjp6+kFsL5izTXUyX0Xc+cVy5bj9lIottJ/eG++/+Nf1ruztw3juvTzd7uyg4Pank3tIr22V8tREAhrlq3J3dvmeLPrO+dnz2PDvg2u900U6pPS/V3viBJAbXQVCzrGXPZtBLkCYdE5uC9dmcCzrz6bK32gChZTMtrRiaOe3aZGrBFgxaZ8YamQRhrX3PyMVun6VXC16d/Xj9+d+13eezHEcMNlN+RyJbQOUXVV4HX9s4o6sT/hnSwHZEI9vRSKU+nbSuGKoeAdz655wnUtk6kk9p/Ybz5ulrDtIp3Kn8EYPDyY6xFwdOKo6zsM1pYFMRG0+5jTeVxUq84aRZQASjtLD0IQAR90jHkCxVTfJkJUwe+ysyvhis648dHxUVcyGgBcc9E1oOxU3SRM7nj8DoCy9hKnj2PWP6HND13hNCBzDqq5wxY0YWfdag7A1pGtGDqhEdYqsTSw2CCM8/IJBoAv3DjXSvNE71zmdrZ+v9as1DnicqzHKQ4CofuSbrSs6wXNH8/c5/kW6HO9ub4EXgXubLOeKkhHrBGts9/OEE4MJbQrAQB4a+atyIWyM9RUFwJc74gSqALCKCFTOKn9gD5wMD8Gve29c3ZdIJp8CFWZGB2s7RawdoU2XNH5sKm8ePrFvPLEztXAiDWSsRer5ZEVvDKTfWm30Lw2U6zMVBwNgGuG6FUSQTvrVhy4U+dTOLK7N7+UthMmYEwb2JHvDI4ngcueBa4bzF8JZruotX3IcF0+eYfrrVQ6BQbjoZFBzCx6WlsCw24IpCtwd372PG754S14+sTTuH7z9bnrdcfj7mPZ7D622zPcNGqhrHMeO0OAB4bNpq168R1EqgSI6EIiepyIzhHRCSJyV/rKbEdE9LdEdDr7+luiQoMoK0+pks10+/WMzNFEh0ydT+GS2xK+vQm8ViMFrZBu7gfmW+6y0sg8XC+/8XKgpDDnasBLiNjb23bfUA9pu4VLNq7A7KVPY/DwoOemTmE0Pg5ziQXnrNteHTlzADqV8hJOiIElmkgpZ4JgLJ1NElNaad7Un9mG0jlHr2sfFx91m9XUiKcYK2McyPNTmNpFAsDLb7wMBsM6a6F/X7/R1GNzbuYc9q7emysHsea6NZgXn5f7vJBEKa/fgF+Sm33MYhLRaoGoVwLfA5AE0AlgNYAHiGipZru1AD4BYBmA6wB8FMC6iMdSNvwavxSqDEKHYnrMRv325bUaCa3knM1MbBiZWj7fHMN73vGenFPUWXJAJc3pnF3aS4h0Lepy2X1DPaQ39+c6VTlXAS3xlryktWQqie8f3DpnIiEEjshq+1ACsWbHzNkuHeFQ4C3xlrmVQctZfW0hnS9B9UssG4TdkzinvNSx9iaAlMP8wpp92WOMK21D116Pvav35iX3tcT0ZcQHDw9iw1MbcqYe0jhznEXjBl8YdK0ywq4GvH4DQbLT7VLkTqq94FwYIlMCRHQBgFsA3MfMZ5n5AIAnAHxGs/kaAP+VmV9l5t8C+K8A7oxqLNWGnwCOrFF80NmoB7qZU+iM6t6Eu5kJkBEoHUeBmzbkHky/2VhzrBm9i3sBQGsvbom34NZ33YrjbxzPqyVkeki1K552C7jO7QOwSaaSbvOQ097vEZGlKtWr/+8h98y5KQlc+LK2vEVuZaBzDvuV7bbH2ZRff2fen26YG6upLacTNfwVyPw738qL2EkMJYzZ0ClOYXB0rgmNMz/AHt+uY7vybPLOa6XLBzDN9P0Etcl5rNavaom35H5/KtVccC4sUa4ElgCYZeZjynujAHQrgaXZz/y2AxGtJaJDRHRoYmIissFWE6VuFB+Gope4tonCpMTSTcCywdyDOXRiyHM2NpOeyT30prC/n774U1efAdND6lzxdHYiY7oih5BPx3Olr7XVMp3N3ANGZPUs7pkrkDfbMpcD8P9M5ZWKHrtnLM8Uog0VzSp9XdG9HI77MJuexey1GZ9B23u3wvrm3jnn9kbOZJgHhTJ9h+2ongd/8aDn5rpCgHGau85j94zh3Mw5Y2a2XVLCmQ9g+r0WIqiDlKSo9oJzYYlSCbQDcHb1eBPAfMO2bzq2a9f5BZh5CzN3M3N3R0dHZIMV8sk5lp8Nt8TV9hj2CndsmskzT/Qu7nX3Fv7mGDCTEYCtTa25uv3D64ZztfxtwTG8dhjTqUxki91nIExtmVN/eoPedEWpnB17wbwFueNpV1gBq7e6WiQawkhzDte0w2wUn87UFnJsbzJr6MxsakcwrWC0V5M6ZaBR7PY+vFYBXpiKwjnxEsZpTucVyFM/CyuogySZVXPBuUKIUgmcBbDA8d4CAJMBtl0A4CzXUiGjGsQ3RLQ3ASDczMlVIM9korC65prMZIVJMpXEwMiA4mxUolnicyYM5wOozvycjuJVP14V+CFNDGVLLhtMV0+8+IT/ysjk6NX0RjaWm9YI9qETQ+5tY5xxtjuUjE458tcYU1+dyjNzOP0vqmB0/TYe3pNTxH7kwlwDsLRjqUs52WPws9F7CeM0p40rQZvZ9KzvbzpIklk1F5wrhCiVwDEATUT0TuW9ZQCOaLY9kv3Mb7uqQ2e/LzdqxE6Y76imEBeOSJOCl7hOv4Qdo/7wXu0qIZlKzjkbc9EsAznbsxom6rTx7ns5v7wAkFkN/Oz4z7QP6f4T+3P241wJY0PCGQC8ce4MHjiYyXT+/rPZa+F0AGuqdra0ptD3SAJjZyxc8zdz9mqtkGtKagV7z+IeYFbjsCVolUyxJZOdZrL1j2gc2AZm07N4fep1rFm2Js+WbpfW6Fncg5F1I+hZ3IPuS7uNY1Bt9F5VRAF943m145zuWqumRRNBksyCJqLVCpEpAWY+B+AxAN8goguI6H0APg5gh2bz7QDuIaJ3ENGlAL4CYFtUYyklXvZ7v6SvyBzA0DQS8cE3rFMjoCNZ4vYmELvqAPoeSaDro+5VQpodtW2uG3QJVXs14LTx3vrordpDNsWatA9pz+IeHDh5INOucsuKuTIJsy3AxBJ34lksNdeYBZlrkVdqud0CrtvmCutMppL4/pPP4NKVCQwdP4BLbkug4+qMWWlk3chciYiZ1rmVkcMslOm25owisv+dMznZ1T9t5fjAoQdcrTfVfQaZweoSwLyYSc/AOmth8PBgnvllYGQAd//T3Rg6MYRbH73V2CtY5+xVTXD2KkZt6WkyHdmrAV3Dm7amNldLUAHRlpImogsBDAD49wBOA+hn5n8goj8GsJeZ27PbEYC/BfDF7Fd/AOCv/MxBpSolHYZKlJOOAufYXGMxlKLuWtSFkbvMMxw1Cc2FUtpYLQfct7sPm5/fjGsXXouXXn8pXyjYs10H11x0DU68eQLnZw1lkhVam1ox9dWpvPfUMslxxN1OSsNx8/YbzwiT86nM+dy69FY8NPoQYhRDmtOZstIHvwgM3Qvc9klg0TDQPJ0pMf3LWxG7fgeuXXgtjlgvZRSeWuHTLhU9dC/wqdsx8tffQdff35ApC60bW7ZUNk9mrucPfvGDnA9hacdS/LLvl77XyUTf7j5sPrTZVwm0xFuwculK7Dy6U3tfYoi59uEsC208/vObcdeKu3KVQ53vmUosEwhjXxnTlp0mED677LPY9oltPleg/vAqJV3X/QRMCVLF1MMvlRLQfbfsSuMjfcCKzeh7z115ddeDjFt7rT/SByx/EGiaq+d+b8+9OWFMIG2ooFPotcRb8M4L3+lSGM2xZqQ5jRSnQCCM3DWC6zqv045zzeNrsOPwDv3xNMfUQSAQUU7gz6Zm3YJydh7wwu1A10MAx7ICvjlbzdOchQwgpyzQtQNLL74WR6wXgaZZ/bZZpTH2yNz1zDuXh/YBH/gG8OhOdF6wKNTv3SRgdVzUdhEmk5OBu8H51fXX9ZdgZm3PCb++Auq+VEbvGjX+TuoVLyVQ12UjStkxrO5w1LMJ6wtw2pTHzlhovdHtY9jw1IbcMr453uzqxtW1qEtbyEyXYaxGujAYt/5Ibx5ylknWQsg4r20/xvQFrk0YnBt7MpXUR7LEp7MJWlDMXHMRUZ5QCvHrBwHKdDAzKgAgF6KaGEq4qn8CAG79NHDF08Da63HqXP69HLFG8Pa/ebvWbKSaYrwayS+5aIk2pDNOcTTH9LV/AH9fky6s0xTq6WfeMpmMTL+TRqWulUCliNL2X0qcjsCW1uiSX0xOSKfd2CkQTE63qa9O5cXDN8eaXa0nXzz9Ip565SlX8pCuZLWNGqfOm5SQz+YpYHhNfoSMU4fo7ilBL/CD3P8mTVKaDqZMMbjNmUqmrlBSAtD2xlw00U39eR/f8fgdrrwKO+lqw1Mb8lp9fva6z2qze5tjzcZ77BqPA6+6/s6wzoGRAQwMD2h/M34OWlO00YunX6zZmP5SIEogJEEqflbLSiNo1VC/mOog5+zM3DTN0nT9AwpJ4lFXASq3/PAWV5SMV4eyFKew7sl1ubEvvEpJ/HI6qb0EOSv/+gl8NUnMzpEYXgPMmstn5B0jlekQ1tkJ7Fm1x7MPMwjAssHcfckV4EMmmqZ7SzfGz44jMZTA0ycydZPUPJHdL+3WrqCOThz1TfQzYcr6VVeJ6raFlo6wQ2fzku6y9O/r13yjMRElEJJSlJ0uVT+DoMrIL64+yDk7QxR1szS/sL+wY9Tx5vSbeULMmrS0JatVdr+0Ozf2T393bkWEeMpd0G2mNVP/yImhqqnWAtWURNfHnpkzmcXSiF8/iJZ5Puen1gTq3oRTfBiXfrZfbw5Sx0EpXHJHPxYtcuRVMPD82PO45I5+PHBwa6ZuUnoukax/X7/x2hGRq+mPl/nIZmnHUmPW7+5ju11KJc1p39IRXiSGElpFZfcvEEQJlJ0wCsQzrr8QDEXOik1+CVpMq5j46iDFvlRUe7JWSDq2TXMaA8MD2Dq81fs48Wng30y4eweYIGSURi4stA34ZqbfgdPWrTtu16IufVkISgO3rALeudt/5ZFdDZxqc+RV2DkSy3bMmbCURL7Bw4PGa6cW9rPZs2pPXjim9RXLtw6P+ts5N3MOI+tGcEn7JSBQzl+kS4QLGpP/s+M/065kypHhWyulputaCdRCx7CyohQ5U69BUOFs8nX8wedLX0zLOUa/Wadt0ho6MeRro1a/41v6IMZA+++U/AEDM61zCXMnexQhOwv0JLTJTrbgdN4DYwezjqOZ6qJAvjlKB6WAWw0OUUq7cxLgbd/XCXSnUuv/537f0g2u3I8f3Zqr5mpva+pPHUTANsWajJ+VOsO3VkpN17USqHTHsKpCif5pe+9WjPw6/EXQmpfaLUxdU/5iWsPrhn0VQYpT6L6k27NUtUoaaaPJ6ZL2S3DhzpG55C5DLkNOgN+fzVPIZWJnhWnTDLA8EyUVtKevqgDXd6sdzGhOGanmKJ0iIACtv9efeAFBC6YMbPV38NDhhzzPUfedY6/P1Z+cTc/mlZZWf1dBBKzq/7BRFW0pM3xrqdR0XSuBSlGKFUjR+1QygiOdrZcq09iAOgMMUmZg10u7AvkSbNRqlQsfmmsAb01aeP3f3eZZHE876+xNuKOF4lN48sUnXbNsLxOcNWnhxh/cmGnorjaRiRmm/umsZFed0N9IY+yeMW9HchadMxXI5AU4M7C9SoJ7naOfn2cmPZNnkrJ/V0EFrK4BUbkKvdVSqWlRAiWgFCsQr336KoKo6gLp0BSMK2UxLdMMUFUIY/eMoXmsB3hgBKfP6EsTm1DH/tqy/owPAMjMlhe+6Fm//+U3Xna/eZmm/EMsk3PgtJ+bGqHbkTPP/vZZbStHF4Q55eAoaJcYSpjDUO08iY2Mzy//vDbe3+7z6xTEpkghAuXqBjnPMYifRzVJ6XJNvMJNdQ2IylHordZKTdd1xnCj4sreVTJ3bVriLYiPfhFTj7ozN00Z1cVkS0eBLptUV36gb3cfHvhfm4HT7wQWHkOuKb0Dr+xVa9LCpf/v5ZkIIRtNJrNX9uuiRcCpc3OlM1RiiKEp3oRkKqndjzVp4fYf346r3n4Vto9uR4xiwXIIdMy2AC+sRHPncSxZdtplIskcsCtXGnvhVRbOfsGdaaueM4NzpSqaY8340vVfyivxYGfy2tnev3rtV3llIJz07e7DA4ce8D0VNUvcRvdb8MsmLiWVPLaJhs0YrifCtHh0rhp0hduSqSSmLtLPiKolz8FJkCV2rjpoLJ2ZuRsUAOA9K+zf1+8u86DJZPaaVY6P5yfhqaSR9nWY2nH7asvLlngL1ixbM+fnmGmdS2ibadOHrzYlcdF7dyH1jgN5YZ12L9++7j7wpuHc7+XT3/Wu6//U8aewdWRrboY+k57JC8nVNW/3M90cfPWg8Tqq6PJDdL+FSpZ7rrVS07ISqBFKMQv32mdnZ3BlUEgtJnumu/NTOz2LianbO+vA+M4A7Zk7AxhZA/x0G4Bg12vh3y3E6anT+g/TMVd9Jed5fedD38Fdu+7C4d8dxlszb/keL0jNG5s4xRGjWEYI64rQ7ckf19iZ4PV4AP/aQUs7luLY6WN59n57NcBg1yxYd446gsygTWPrWtRVs6Wcy4GsBITgZHMJnPVmVMbOWOjZ2gtrcrxgX4fTtu8X8qdzIp6fPZ+X+ekKu1QjZpYNGts+Oo/tm2AWy/Q4uPHBG13jtc9r9WOr8exvn8VbM28FcsSqM8U/+HwC58+bTT95oZsxRyluR0nqzk7k5UrYjVW8VlV2uQgAWLNsjSvr9ujEUZfD167V72Xn97ONB5lB11st/2pAlECVUExiSRhTkS8eDdNtio1/1kV3+O1TJyAYjCdefCJvXMZoE0oBH7w70Plo9+NYPSRnk3j21WddsesDw5kmNKrd3cuW37WoK0+QWZMWpq4Z0MbtB8FuaGObdoZfslymG109noHhgTwl+PALDwPIZNZueGqDyyHtVGx2rX5nOKszyc0rUkYEfGUQJVAlFCNYI6uWGqRhenvx8c+upCJDLLiKM/rHFkJnps94d+6yIQDv/hEWXpm/b51CMiZnKdilDAaG51YEiaFEsOgdzAl/p4BLDLk7lYXBGb+vy5ienp12jVNt43n33rtziivFKewY3ZFXukH1UdhUm11eCI4ogQKJcvZtTc41eP/+wa2g+ePFzeYD4tkk3tQwvbe4+Ged41AXC+6FWhU0xSls2LcBwJyiMCaRESP+H7s8M1bt6pnOcgd2q0Rn4lkylVkR9O/rD9yNy6vD1dCJIXe9ooDY41Tj93VVRhnsGmcamTIQ1qSFR3/1qOszP5KpJB4afShQRViZ2VcXogQKJMpeBYmhuQbvTuFbykgdNYrIWf8/z75s1xzqHAW6iot/9is/7LdP1VRhs+PwDpfwMSmCU+dOeWasbh3ZitHxUbe5ZGQAXZu6MJvKr/FvC0i1RLYfs+lZXL/5etc5WpMWXp963d1bOCC2WWfr8NzKZtvHt7lMN3b5bF1dn7v33u3dd0FBNWWt716Pqdmpqk6KEvSIEqgwuZBGnfAtI1o7uK2QbD/BJ1cXnR0cNEHItE9dbwB1NWCzZ9UeY9arbf82KaTVj612Z7rOJvG7t36HWdY3eklxKrDwtHvyOs+xf18/rLNWwf4AIL/+UYpTuO3R27TXa+0Ta7UK8IljT7j2CQAL2xYaZ/SqL8TucVyIj8vvO7VSkK3WiEwJENGFRPQ4EZ0johNEtMpj241ENENEZ5XX1VGNpZbwFL5lRCucm5JY+pH9uXLHuPioNt9gy16zjdf54AYpBOdlNzb1Bnjy2JN5f3vZ5237t8lm/evXf+26J0GbrjtZcuGSXLbs2D1juPiCi3Of2auO3m29GB0fda1wXATQMWr9o2QqiRdPv6jd7sl/3Y3z590KcDo1rd3+srddZjxmYiiRU5oMxqofryrIx+X3nbARZUIwIssTIKJ/REapfAFAF4DdAN7LzK70RCLaCODfMrO7uIcH1ZQnEFXcvjEmW8ngZC4spj9o/L5XL+ZPPqjEbhvi0O0x6tA1DS8GrzjxPav25GL0b3jwBs+m9HZs/F/981/leg+r2bBqxmsTNeGtWf9YfxMEwvru9TibPIvth7fn3m+ONWPJRUvwq9d+lWlAr8vkRaaOj0k42+fibHrvS5DmNwjWE/iqb1/lGl9LrAXJdDJQY3l7P17Z4LrPv7H/G5H+tuqZkjeaJ6ILALwB4N3MfCz73g4Av2VmVwsfUQLh9+clqIutimo8druF1n5H0tJMG/DtV4Cz+Q+17pyDlnnwIkhSmVpiYcfhHbh24bWupvROWuItWPnulRmntGIuaY1nHL/nU2YFUgit8dZMX+KAKwrdOExN1lviLUilU8ZQVDuRiv5UKR+iKvSP9CH2ns24sPVCvDb1mvH7Ks5rblI8TiViup/O83EqHufnK9+9EjuP7Czqt9VIlCNZbAmAWVsBZBkFoMlhz/FRInqdiI4Q0XrTRkS0logOEdGhiYmJiIZbPFFXCvXbXxRF6UL3Pu4tzlQVRSXFIGYFZ2vEoxNHff0OyVQST7z4hEtwBuopUADnU+dDmZR047Cv4Yg1ggcOPZBnz1fLSthVUJ35B2oRwZzv6cp9wIpNuaYurn4Ga4dx/I3jrqb09n154sUnPFceTke/qTeAV8G1KCLKBDNRKYF2AGcc770JYL5h+x8CuBZAB4AvAfjPRLRStyEzb2Hmbmbu7ujoiGi4xRN1pdBy9D4IHWl0md5PgMv947yjqKQYpGSw7ZRUY9eb4805QWjXx1Gx6+U43we8ewqUE9047Gt4+49vN37PdJ0TQ+6S37lGM9n6Sjph6mxKr5a0TnMaZ6bPaBvRq/iVgPZrb1psRJngTSAlQEQ/JyI2vA4AOAtggeNrCwBM6vbHzEeZeYyZU8z8DIBvA/hUMScilIDNc07cXJesjZzzVXjh92AHIchKQtdDVhUKQyeG3E5eTuOp40+5SkOoJZ11OQF+wi4q4hTXlnEGMuGlJmevjSlxy1UGuykJtL2R11JSFabOpvSHTx1GYiiRV9I6SFSUnUOgloA+P3s+F9FlctBvG92G0fFRbB/dXlREmeBNICXAzB9gZjK83g/gGIAmInqn8rVlAPSeLs0hUFB/o9om0nIPFURnyoqqb7HXSkJdBThJcQrXb74e3Zd2u0oXtMRb0BRrCj37tFcY67uN1stc7HyQpusmvNo6BmmVqbvOw+uGM01yftMDfNPKKHNNxVH1Gjibstz6o1szTW2QHy3V1tSGNcvWuJLr1ByCt2beysulYHAuv8MZMWZf37dm3sLqx1ZjanYqr9/wBc0XBDpnIRhRRgc9goww/yIy0UF7YI4O+jiAIQC/B/B/AngcwH9i5oe8jlFNjuEoKHd9fk/7vwbV6VzusQapKGlHHnmZbxa2LdQ6O1ubWrXRQ12LumB9bRinPrEcuGRE+zmAQJUsvaqBzovNw3R6LqLGGQHkdAJvfn4zPvNHn8EjRx5xReK0NrXi+N3HfR2jaqTWvT334h3feodWgXYt6sLWj23F8i3LPfdn0xxrdiknXbVSHXcuuxNbP7E197cp2kgigorDyzFs7sIcnj4AAwB+B+A0gPW2AiCiPwawl5nbs9vent12HoBXAfytnwIQSku1VRQPspLQmXqATGz+yTMncX72fM7Z6Qw39Io4orugN3m1W1jw3eDlr70K2qkKAIBL6Nkz8nt77s3Z3wdfGIRu0mbnPXgJRac9/tzMOTTHm13XOE5x7F29Fzdvv9n3/Gx0qxN7/Az2VNK6/A6d6cduXL/z6E6kOY1Nz2/Cuu51uK7zusDjFPREpgSY+XUAnzB89jQyzmP7b60TWCgtXvkEpfxuIQSpL9OzuMcVCtoSb0FzvNnlS1AFpB1NdP3m6/GLdb8IFlrYbgFrV2Do+DguuS2RlythCtMNkh1twlZ4aiKWKQQ0zWlfU4jTv7Lr2C6jsN2wb4O+VaaDlngLVi5die2Ht7tWFHYhu5ffeNnzGtjtKhe1L/I07yVTSQy+MIh4LJ4751U/XoVf9v3Sd5yCN1I2ooEoJgKpHNFLYTGtFtQQUVO4IYNhnbVc5SaM3NQPzLcyvXsdZT1OndJnrw6vG8ZFbRcFPh/b8Ww7qPes2pNbBdi0xFpww2U3uEM5PZSmzr/y1sxbsL5i5foGqOw4vAPH7z4eKLN710u70BzPOLGdvoCexT2+kVZOP4yXwkhxKu9z21ktFIcoAaFklDqtX1elcn33+pxQsvFy+DqLz2lpt4BlDytNamZcuRKmfIbL33Z54POxZ8D2eNVVgE0ynalaqitAZ8IUqdW/r19bqiLFKdy9527Pch+2k/bczDmjwg2yElJNfAdfPRi4/pKNHboqFI60l6wgpcwCrgaiLhkRBL+yEjonpdM56XKCf3wN0LU9P34tTcCmEeB31+VlVheSvWpqnXnF267wDAf92JKP4ffTv8/zUej8HaZrclHbRcYWmvPi8zCTnvFtDu903hMIn132WWz7xDa/03bh1UL0ww9/WHsOBMLYV8YkW9gHaS9ZpVSjiSUqgiR6lQKvGvYmR61zNZDn53CuAmyIgVuys9AieyyYZupNsSZXeKvKE8eewNMnns47nm5F4jWLNzE9O53tbzEA+uKN6LjKff9M3d52H9MX+fPDK7fE2ePBpjneLPkBRSJKoMap1lyDKEpGRI3JPOEcn6qc1z+SAGIahywhU1X1Yu8eC0FMYibfhp9TFcgIXft4YRSvZytOlXgSuOxZvPYu9/1zdnuzk+vOzZwrSOn7RYSVolOZVCIVJVDzRNncJiqC1IKpxIPn1WzGJEgOvnrQvEMGcIt3j4UgtY+cwtQuPT311Snt7NdJkObxuvPyVDD2yieWzvx/+UBgpVKo0t+zak9OkajZ27bTuxSdyortl10PiBIQIidILZhKPXi2WcGuH+QssqaL8FnfvR4x3aMSY1CnvsfCM68+4zsz1x3PeW2COFdn0jO5jmJBazWZBCr+ZT2Q1pxrPGm8X1HUiQLKv3qslMmy2hAlIESO17K90g+e1/FNymnoxJCxAigRYWnHUlfJBKcPQtdS0nm8EWsEmw5tyhubKqy9wk29qo6G4gpDj+OY+X5FUScqKkUShmo0WVYCUQJC5AR1zlbiwTMd30s59CzuMe7PWbpa7VOsCjW7pWT/vn7j8e54/A5XmKi9be+2Xs8IGFPV0dD28pM9mV4DgKuTmel+RWGrj0KRhKESSqdaESUglI1KP3hex/dSTia/wMK2hdpkq9n0LFY/tlrreB08PKg93t17787rLOYc24GTB9C7uDcvH8Jp0irWXm5NWplEONu85YiIMgn2KGz1pXD6elFupVPNSJ5AjVNLuQZBisKVkjWPr8m1klSPv3LpSuw8ulMbn+6cfTu7pbU2teKN82+4jmUqUAcAa5atyXXFsiGQK1HK1EFLLcoWZVetSt+fcuKVT1KMo7laKVcBOaECVJug96Lcsz0nu1/ara1xs+ulXcZZoVP4Oe38Z6YzvZTUdpCqYNYlQD00+pCrX4CpXs6uY7tcKxS1KJtpnEFRk8sqfX/KST0K+kIRJSCUjUo+eNaklUuOcs6edbNCnfBzmpPUkg7JVDJnPrFLMhz//XFc9fartGYhv74A6ipANV/Z9fydJq37eu8raDWgOqd1fYT/bOefgYhyRd6qFVWZMbNvX2phDvEJCFVDKfMHvGz+QW3aXglWqmPW7oH79ImnsfvYbm2IJ4FycfDG4mzH3CsUvyigMNfQrtqZ5jQGht15AImhBJ777XN49tVnq95Wriozif0PhygBoWoo1cMblUM6TGlou+2i2rxdTfxSyx2YlNDlb7vcdTy/KKAw1zAxlMi1ibR7EtjYCsJmYMQ7WaySqJFWdr5Eo8f+h0GUgFA0UczgS5k/EFUkiCqsg7aPdDZZD6OITMpB99qzag9u+MENgQVgbhWQzX9II381oCoIe7xRKucoV33q/VVXSo0a7RMWUQJC0UQxgy9l/kAxDk+TsDIJaGeDelvYq03WbfzOM4ygtE03QQWgU8jbY7UVlqoggEw+RJjVgN/Yo1r1OZWr0ywnqwF/RAkIRRHFDL7U+QPFxLGHFVamVYeuk5efIgp6bNV0E1QA6rKg00hj/4n9WgVh7zPMdTCNPapVnzVpYcWWFUil9R3XAFkNBCESJUBEXyaiQ0Q0TUTbAmz/F0Q0TkRniGiAiOZFMQ6h/EQxg6/WxJ1ChJVp1XH52y73VUTq7DlsRVCd0Pa6hj2Le1yF6VriLei+pBvbR7dry2QEaWNpn4fX2KNa9SWGErDOWp6RVvUa4holUYWIjgG4H8AHAbR5bUhEHwTQD+DfZb/3OICvZ98TagjTDD5suGK1xqfrhJVfPL5fm0ev0EV19hw0FyAncDVC2+samq75rpd2YWp2Cn3dfQXnHnhdt6h+M/Z+AHNinxCMSDOGieh+AJcx850e2/wDgN8w83/K/n0TgIeZ2fcOSsZwdVHPGaZeXa4KFTZendbU47U2tYKZMZ2a9j12lPfAmQ1dyLn6Xbeoxqvup15+c6Wk2jqLLQUwqvw9CqCTiLQlEolobdbUdGhiYqIsAxSCUa0z+CiI2kQVxkSSTCVd5p1SFm/TjaFUpr0oxlvpGlT1RiUyhtsBvKn8bf9/PgBXw1Nm3gJgC5BZCZR8dEJg6jn1PmoFF8ZEoktI8yreFgXlMu0NrxsuehbvpWhkNRAeXyVARD8H0Gv4+H8y8/tDHvMsgAXK3/b/J0PuR6gy/GzetUSUCs5PwOqEWrlNHFEJVr/rFoWyqecVaCXwNQcx8weYmQyvsAoAAI4AWKb8vQzAKWZ2rQKE2kLS9fVEbSIpRXmNcgnWKMxspWgz2chEYg4ioqbsvuIA4kTUCmCWmWc1m28HsI2IHkYmOuheANuiGIdQOZw270ILmlWSUq1kgphIwqAq26hWCrricfa1iBKZxVchzFz0C8BGZPoQqa+N2c+uQMYEdIWy/T0ATgE4A2ArgHlBjrNixQoWqpP1u9ZzS6KFsRHckmjhvl19lR5SaNbvWs+xr8eqeuxjZ8a49f5WxkZw2/1tbE1aJTlOLVwLITgADrFBrkYSHcTMG9ltKtqY/ewkM7cz80ll+28xcyczL2DmzzHztHHnQtVTD9Eale59bI/Bz8xTjvac1XAthPIhZSOEoqnWjN8wVLr3sT0GnU/FVg7OvsWlUrbVcC2E8iFKQCiaWrfzVsNKxmv2bSsHXd/iqIV0NVwLobyIEhCKptajNaphJWOafavK4ejE0ZIr22q4FkJ5kfaSQsNT6ZWMV+y8KpSb480lzx2o9LUQyk+ktYNKjdQOEspJuZLfTPV07B7DUdYvEhqTaqsdJAhFU8p+xDblSn4zVvTU9BgW04wQNaIEhJqk1AK6nGGSYXoMi2lGiBpRAkLNUQ4BXQ1hkrXscC/HSk2IBlECQs1RagEtYZLFI3WkagdRAkJNUQ4BLWGSxSEZx7WFKAGhpiiHgJYwyeKoBlOaEBzJExBqinII6FqwuVcrUTWnEcqHKAGhphABXd1I16/aQ8xBgiBEhpjSag9ZCQiCEBmyUqs9ZCUgCILQwIgSEIQ6RpK2BD9ECQhCHSNJW4IfkSgBIvoyER0iomki2uaz7Z1ElCKis8rrA1GMQxCEOSRpSwhCVCuBMQD3AxgIuP3BbN9h+/XziMYhCEKWoElbYjJqbKJqNP8YM/8EwOko9icIQnGEKa8hJqPGplI+geVE9BoRHSOi+4jIGKpKRGuzpqZDExMT5RyjINQsQctriMlIqIQSGALwbgAXA7gFwEoAf2namJm3MHM3M3d3dHSUaYiCUNsETdqSOj+Cb3tJIvo5gF7Dx/+Tmd+vbHs/gMuY+c7AAyC6HcBfMvMKv22lvaQgRIc1aeHq71wt7SsbgKLaSzLzB5iZDK/3+30/AAyAItiPIAghkJLZAhBdiGgTEbUCiAOIE1Gryc5PRB8mos7s//8QwH0AfhrFOARBCI7U+RGA6GoH3Qvga8rfdwD4OoCNRHQFgKMA3sXMJwHcBGAbEbUDOAVgEMB/iWgcgiAEROr8CEAAn0A1IT4BQRCE8BTlExAEQRDqF1ECgiAIDYwoAUEQhAZGlIAgCEIDU1OOYSKaAHDCZ7OFAF4rw3CqBTnf+qfRzlnON3oWM7O25EJNKYEgENEhkxe8HpHzrX8a7ZzlfMuLmIMEQRAaGFECgiAIDUw9KoEtlR5AmZHzrX8a7ZzlfMtI3fkEBEEQhODU40pAEARBCIgoAUEQhAZGlIAgCEIDU3dKgIjmEdGDRHSCiCaJaISIPlzpcZUaIvpythfzNBFtq/R4ooaILiSix4noXPberqr0mEpJvd9PJ4343BLRIBFZRHQm22/9i5UYR1T9BKqJJgD/G5mWmCcB/AmAHxLRHzHzbyo5sBIzBuB+AB8E0FbhsZSC7wFIAugE0AVgNxGNMvORio6qdNT7/XTSiM/tXwP4AjNPZxts/ZyIhpn5+XIOou5WAsx8jpk3MvNvmDnNzLsAHAfg28O4lmHmx5j5JwBOV3osUUNEFwC4BcB9zHyWmQ8AeALAZyo7stJRz/dTRyM+t8x8hJmn7T+zrz8o9zjqTgk4ybayXAKgXmeMjcASALPMfEx5bxTA0gqNRygxjfLcEtH3iegtAP8KwAKwp9xjqGslQETNAB4G8BAz/2ulxyMUTDuAM4733gQwvwJjEUpMIz23zNyHzO/4jwE8BmDa+xvRU3NKgIh+TkRseB1QtosB2IGMHfnLFRtwBAQ95zrmLIAFjvcWAJiswFiEElJPz21QmDmVNXFeBmB9uY9fc45hZv6A3zZERAAeRMaJ+CfMPFPqcZWSIOdc5xwD0ERE72Tml7LvLUOdmwoajXp7bgugCeITiIwHAFwL4KPMPFXpwZQDImoiolYAcQBxImoloppT8jqY+RwyS+VvENEFRPQ+AB9HZsZYl9Tz/fSgYZ5bIrqYiG4nonYiihPRBwGsBPBU2QfDzHX1ArAYGS/7eWTMCPZrdaXHVuLz3oi5CAP7tbHS44rw/C4E8BMA55AJIVxV6THJ/Yz0fBvquQXQAWA/gN8j4+96AcCXKjEWKSAnCILQwNSrOUgQBEEIgCgBQRCEBkaUgCAIQgMjSkAQBKGBESUgCILQwIgSEARBaGBECQiCIDQwogQEQRAamP8fwLDToj1pUh0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_moons(n_samples=1000, noise=0.4, random_state=42)\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:53:34.008465Z",
     "iopub.status.busy": "2021-10-23T11:53:34.007433Z",
     "iopub.status.idle": "2021-10-23T11:54:09.182365Z",
     "shell.execute_reply": "2021-10-23T11:54:09.183520Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]......................................\n",
      "Warning: using -h 0 may be faster\n",
      "*.......................\n",
      "Warning: using -h 0 may be faster\n",
      "*..............................................................\n",
      "Warning: using -h 0 may be faster\n",
      "*...................................*.......................................................*\n",
      "optimization finished, #iter = 212105\n",
      "obj = -4447.997680, rho = 0.075931\n",
      "nSV = 449, nBSV = 441\n",
      "Total nSV = 449\n",
      "0 0.1 0.7381584644317627\n",
      "[LibSVM]................................................*..........................................................*..............*..............................................................*..................................................................*...........*\n",
      "optimization finished, #iter = 258151\n",
      "obj = -4448.479655, rho = 0.058653\n",
      "nSV = 446, nBSV = 441\n",
      "Total nSV = 446\n",
      "1 0.01 0.7186133861541748\n",
      "[LibSVM]...................................................*......*............................................................*..............................................................................*..............................................................................*...........................................................................*...................*..............................................................................................*\n",
      "optimization finished, #iter = 457597\n",
      "obj = -4448.486560, rho = 0.058757\n",
      "nSV = 448, nBSV = 442\n",
      "Total nSV = 448\n",
      "2 0.001 0.7357399463653564\n",
      "[LibSVM]..................................................................*.......*..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*.............................................*................................................................................................................................................................................................................................................................................................*.........................................................................*...................*........................................................................................................*\n",
      "optimization finished, #iter = 2065000\n",
      "obj = -4448.486967, rho = 0.058504\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "3 0.0001 1.5096161365509033\n",
      "[LibSVM]...................................................................*........*..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*...........................................\n",
      "Warning: using -h 0 may be faster\n",
      "*.............................................................*.............*...............................*...................................................*.................................................................................*\n",
      "optimization finished, #iter = 5714802\n",
      "obj = -4448.486967, rho = 0.058489\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "4 1e-05 2.542227029800415\n",
      "[LibSVM]....................................................................*.........*..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*..............................*........................................................................*..................................................................*.................................................................................*\n",
      "optimization finished, #iter = 5269415\n",
      "obj = -4448.486967, rho = 0.058480\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "5 1.0000000000000002e-06 2.4116880893707275\n",
      "[LibSVM]......................................................................*........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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      "optimization finished, #iter = 44838621\n",
      "obj = -4448.486967, rho = 0.058480\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "6 1.0000000000000002e-07 18.176522254943848\n",
      "[LibSVM].......................................................................*...........*..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*..............................*........................................................................*...................................................................*.................................................................................*\n",
      "optimization finished, #iter = 5795624\n",
      "obj = -4448.486967, rho = 0.058480\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "7 1.0000000000000002e-08 2.8188459873199463\n",
      "[LibSVM]........................................................................*............*..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*..............................*........................................................................*...................................................................*.................................................................................*\n",
      "optimization finished, #iter = 5798063\n",
      "obj = -4448.486967, rho = 0.058480\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "8 1.0000000000000003e-09 2.4928481578826904\n",
      "[LibSVM].........................................................................*.............*..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................*..............................*........................................................................*...................................................................*.................................................................................*\n",
      "optimization finished, #iter = 5800419\n",
      "obj = -4448.486967, rho = 0.058480\n",
      "nSV = 447, nBSV = 442\n",
      "Total nSV = 447\n",
      "9 1.0000000000000003e-10 2.57908296585083\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "tol = 0.1\n",
    "tols = []\n",
    "times = []\n",
    "for i in range(10):\n",
    "    svm_clf = SVC(kernel=\"poly\", gamma=3, C=10, tol=tol, verbose=1)\n",
    "    t1 = time.time()\n",
    "    svm_clf.fit(X, y)\n",
    "    t2 = time.time()\n",
    "    times.append(t2-t1)\n",
    "    tols.append(tol)\n",
    "    print(i, tol, t2-t1)\n",
    "    tol /= 10\n",
    "plt.semilogx(tols, times, \"bo-\")\n",
    "plt.xlabel(\"Tolerance\", fontsize=16)\n",
    "plt.ylabel(\"Time (seconds)\", fontsize=16)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 배치 경사 하강법을 사용한 선형 SVM 분류기 구현"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:54:09.192182Z",
     "iopub.status.busy": "2021-10-23T11:54:09.191193Z",
     "iopub.status.idle": "2021-10-23T11:54:09.196529Z",
     "shell.execute_reply": "2021-10-23T11:54:09.195244Z"
    }
   },
   "outputs": [],
   "source": [
    "# 훈련 세트\n",
    "X = iris[\"data\"][:, (2, 3)] # # 꽃잎 길이, 꽃잎 너비\n",
    "y = (iris[\"target\"] == 2).astype(np.float64).reshape(-1, 1) # Iris virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:54:09.207636Z",
     "iopub.status.busy": "2021-10-23T11:54:09.206583Z",
     "iopub.status.idle": "2021-10-23T11:54:15.778392Z",
     "shell.execute_reply": "2021-10-23T11:54:15.779554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class MyLinearSVC(BaseEstimator):\n",
    "    def __init__(self, C=1, eta0=1, eta_d=10000, n_epochs=1000, random_state=None):\n",
    "        self.C = C\n",
    "        self.eta0 = eta0\n",
    "        self.n_epochs = n_epochs\n",
    "        self.random_state = random_state\n",
    "        self.eta_d = eta_d\n",
    "\n",
    "    def eta(self, epoch):\n",
    "        return self.eta0 / (epoch + self.eta_d)\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        # Random initialization\n",
    "        if self.random_state:\n",
    "            np.random.seed(self.random_state)\n",
    "        w = np.random.randn(X.shape[1], 1) # n feature weights\n",
    "        b = 0\n",
    "\n",
    "        m = len(X)\n",
    "        t = y * 2 - 1  # -1 if y==0, +1 if y==1\n",
    "        X_t = X * t\n",
    "        self.Js=[]\n",
    "\n",
    "        # Training\n",
    "        for epoch in range(self.n_epochs):\n",
    "            support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "            X_t_sv = X_t[support_vectors_idx]\n",
    "            t_sv = t[support_vectors_idx]\n",
    "\n",
    "            J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))\n",
    "            self.Js.append(J)\n",
    "\n",
    "            w_gradient_vector = w - self.C * np.sum(X_t_sv, axis=0).reshape(-1, 1)\n",
    "            b_derivative = -self.C * np.sum(t_sv)\n",
    "                \n",
    "            w = w - self.eta(epoch) * w_gradient_vector\n",
    "            b = b - self.eta(epoch) * b_derivative\n",
    "            \n",
    "\n",
    "        self.intercept_ = np.array([b])\n",
    "        self.coef_ = np.array([w])\n",
    "        support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "        self.support_vectors_ = X[support_vectors_idx]\n",
    "        return self\n",
    "\n",
    "    def decision_function(self, X):\n",
    "        return X.dot(self.coef_[0]) + self.intercept_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return (self.decision_function(X) >= 0).astype(np.float64)\n",
    "\n",
    "C=2\n",
    "svm_clf = MyLinearSVC(C=C, eta0 = 10, eta_d = 1000, n_epochs=60000, random_state=2)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict(np.array([[5, 2], [4, 1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:54:15.793130Z",
     "iopub.status.busy": "2021-10-23T11:54:15.791862Z",
     "iopub.status.idle": "2021-10-23T11:54:16.098539Z",
     "shell.execute_reply": "2021-10-23T11:54:16.099280Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 60000.0, 0.0, 100.0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEACAYAAABPiSrXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYBElEQVR4nO3df5TddX3n8ed7ZpKZJJOBxPw0aAIpEDdYomTxR4vSIl1+aOWY7h6Eurptjcqy7qnHU7ELloIUaPdYT6Xq5iz+qIGq7QG0lWWtFq1QdA0KlNRIifyGQALkdzLJzHz2j/u9w53LhHzJvXfu5848H+fMyb2f74/P+zP33ry+v+53IqWEJElldLW7AElS5zA0JEmlGRqSpNIMDUlSaYaGJKk0Q0OSVJqhIUkqrVRoRMTFEbEhIgYj4kt1086IiE0RsTcibo+IpTXTeiPiCxGxMyK2RMRHmly/JGkCld3TeBL4JPCF2saImAfcBFwGzAU2AF+rmeVy4HhgKfBrwB9ExFmNlSxJapd4Od8Ij4hPAseklN5XPF8LvC+l9Obi+SxgG/C6lNKmiHiymP7tYvqVwPEppfObOwxJ0kToaXD5lcC91ScppT0RsRlYGRFPA4trpxePzxtvRUUArQXomjFwSs9RC8ZMf+2SoxosVZImt7vvvntbSml+K/toNDT6ga11bTuA2cW06vP6aS+SUloHrAPoXXx8WvzeT4+ZvuGacxssVZImt4h4pNV9NHr11G5goK5tANhVTKNuenVaKYuP6muoOElSczUaGhuBk6tPinMay4GNKaXngadqpxePN5ZdeTRYnCSpucpectsTEX1AN9AdEX0R0QPcDJwUEWuK6Z8A7kspbSoW/Svg0oiYExErgPcDXypb3IVvXHr4mSRJE6bsnsalwD7gEuC3i8eXppS2AmuAq4DngTcAtVdG/RGwGXgE+D7wZyml28oWd8ycGWVnlSRNgFInwlNKl1P5zsV4074DrDjEtEHgd4ofSVKH8zYikqTSDA1JUmlZh8ar5s4EYF7/9DZXIkmCzEPjlxZUvh/4gbcsb3MlkiTIPDSqwi9sSFIWsg4Ns0KS8pJ1aEiS8tIRofEy7t4uSWqhrEMjPJkhSVnJOjQkSXnpiNBIeHxKknKQdWh4cEqS8pJ1aEiS8tIRoeHVU5KUh6xDw4unJCkvWYeGJCkvHREaHp2SpDxkHRrh9VOSlJWsQ0OSlJeOCA2vnpKkPGQdGl49JUl5yTo0JEl56YjQ8N5TkpSHjggNSVIeDA1JUmkdERpePSVJecg6NLx6SpLyknVoSJLyYmhIkkrLOjS895Qk5SXr0JAk5aUjQiN5+ZQkZSHr0PDqKUnKS9ahIUnKS8OhERHLIuLWiHg+IrZExHUR0VNMWxURd0fE3uLfVUfSh0enJCkPzdjT+CzwDLAYWAW8FbgoIqYD3wDWA3OALwPfKNpL8eiUJOWlGaFxLPD1lNL+lNIW4DZgJXA60AN8OqU0mFL6Cyo58OtN6FOS1AbNCI1PA+dHxMyIWAKczQvBcV8ae+nTfUX7i0TE2ojYEBEb6qd5dEqS8tCM0PgnKkGwE3gc2ADcAvQDO+rm3QHMHm8lKaV1KaXVKaXV1bbw8ilJykpDoRERXVT2Km4CZgHzqJy/uBbYDQzULTIA7GqkT0lS+zS6pzEXeDVwXXHe4lngi8A5wEbgl2Ps7sIvF+0vi1dPSVIeGgqNlNI24CHgQxHRExFHA++lcu7ie8Aw8OGI6I2Ii4vF/rHs+j04JUl5acY5jXcBZwFbgQeBg8Dvp5QOAOcB/xnYDvwOcF7RLknqQD2NriCldA+Vy2vHm/ZT4JSG+/D6KUnKQta3EfHiKUnKS9ahIUnKS0eEhldPSVIesg4Nv9wnSXnJOjQkSXnpiNDw6JQk5aEjQkOSlAdDQ5JUWmeEhpdPSVIWsg8NL6CSpHxkHxqSpHx0RGh4cEqS8pB9aHh0SpLykX1oSJLy0RGh4cVTkpSH7EPD+09JUj6yDw1JUj46IjT8y32SlIfsQ8ODU5KUj2xDY8Wi2e0uQZJUJ9vQ6Ko5Ae7VU5KUh2xDo8qLpyQpH9mHhiQpHx0RGh6dkqQ8ZBsaZ7xmAQDh9VOSlI1sQ2Nad7alSdKU1RH/M3v1lCTlIf/Q8OiUJGUj/9CQJGWjI0LDe09JUh6yDw2PTklSPrIPDcAvakhSJrIPDW8jIkn5aFpoRMT5EfGziNgTEZsj4rSi/YyI2BQReyPi9ohYWmZ97z711c0qTZLUJE0JjYg4E7gW+C/AbOAtwC8iYh5wE3AZMBfYAHytzDpn9/WMPvbolCTloefws5Tyx8AVKaUfFs+fAIiItcDGlNLfFM8vB7ZFxIqU0qYyK/Y2IpKUj4b3NCKiG1gNzI+IByPi8Yi4LiJmACuBe6vzppT2AJuL9vr1rI2IDRGxodGaJEmt0YzDUwuBacBvAacBq4DXAZcC/cCOuvl3UDmENUZKaV1KaXVKafU405pQpiSpUc0IjX3Fv59JKT2VUtoGfAo4B9gNDNTNPwDsKrtyr56SpHw0HBoppeeBxxl7vrr6eCNwcrUxImYBy4t2SVKHadYlt18E/ltELIiIOcDvA38P3AycFBFrIqIP+ARwX9mT4FUenZKkPDQrNK4Efgw8APwM+ClwVUppK7AGuAp4HngDcP7LWbFHpyQpH00JjZTSwZTSRSmlo1NKi1JKH04p7S+mfSeltCKlNCOldHpK6eGXs+49B4b533c81IwyJUkNyv42IpKkfBgakqTSDA1JUmmGhiSptI4Jjf0Hh9tdgiRNeR0TGv/1hp+0uwRJmvI6JjS+u+mZdpcgSVNex4SGJKn9sg2N8W5UeHB4ZOILkSSNyjY0enu6AZg/u3e0bdNTpW+OK0lqgWxDo+rWD582+vgd193RxkokSVmGxqKBvtHH8/qnt7ESSVKtLEOj9pBU1J3cGBnxPumS1C5ZhsZL+fa/bml3CZI0ZXVcaHxwvV/yk6R26YjQuPH9b2h3CZIkOiQ03rx83pjnw57XkKS26IjQqLf8D29tdwmSNCV1TGis/10PUUlSu3VMaPzq8WMPUXmrdEmaeB0TGvVWXHZbu0uQpCmnY0NDkjTxOio0fv7Js9pdgiRNaR0VGtU730qS2qOjQgPgrJWL2l2CJE1ZHRcan3/PKaOPl13yrTZWIklTT8eFhiSpfToyNB6+5tzRx489t7eNlUjS1NKRoQHQ3VX5Oxun/entba5EkqaOjg2NzX9yzujjP7z5X9pYiSRNHR0bGgB//JsrAbjxR4/6F/0kaQJ0dGi8983LRh8f551vJanlOjo0AB66+oXDVG/71PfbWIkkTX4dHxoRwd9+8E0APPjMbv7hX59uc0WSNHk1LTQi4viI2B8R62vaLoiIRyJiT0TcEhFzm9VfrdXL5vKOk18JwPv/agNPbt/Xim4kacpr5p7GXwI/rj6JiJXA/wLeAywE9gKfbWJ/Y3zm3a8bffzma/6RZ3btb1VXkjRlNSU0IuJ8YDvw3ZrmC4G/Syn9U0ppN3AZ8K6ImN2MPsdT+6W/U6/6rsEhSU3WcGhExABwBfCRukkrgXurT1JKm4EDwAmHWM/aiNgQERu2bt16xPXUB4ffGJek5mnGnsaVwPUppcfr2vuBHXVtO4Bx9zRSSutSSqtTSqvnz5/fUEG1wXHan97Oe67/UUPrkyRVNBQaEbEKeBvw5+NM3g0M1LUNALsa6bOsh685l6NnTgPgB/+2zTviSlITNLqncTqwDHg0IrYAHwXWRMRPgI3AydUZI+I4oBd4oME+S7vnE7/BRacvH32+7JJvcf8T9Ts/kqSyIqUjv/1GRMxk7N7ER6mEyIeABcBdwLnAT6hcSdWTUjr/cOtdvXp12rBhwxHXVe+5PQd4/ZX/MKbtoavPISKa1ocktVtE3J1SWt3KPhra00gp7U0pban+UDkktT+ltDWltBH4IHAD8AyVcxkXNVzxEZg7a/qY8xwAx378Vo77uIesJOnlaGhPo1WavadRa//BYVZcdtuYtiVHz+DOS369Jf1J0kTJfk+jE/VN6+bha87l87/9wp+NfWL7PpZd8i1PlkvSYUy5PY1637z3ST781z99UfslZ6/gg29dPs4SkpSnidjTmPKhUfXYc3sP+VcAz1ixgOvf9+8ntB5JerkMjTZIKXHsx1/6b3P8v/9xBgtm901QRZJUjqHRZjv2HuTkK7592Pl+8Ae/xqvmzpyAiiTp0AyNjAwODXPipbcdfsbC1z/wJlYvncPzew8wd9Z0vxMiqeUMjYzd/8QO3v6ZOxpax+cufD1nnbTIQJHUFIZGB9k9OMRJf/R/m7rO1y45iivPO4nXLJ5Nb093U9ctafIxNDpcSokbfvQol95yf0vWv2LRbD529gp+aX4/r+ifzszpPS3pR1JnMDQmsZQS37z3Sf77V+9p6npXvnKAWdN7mNnbzey+yl1++3t7mDGtm+k9XTy9cz8PPL2LjU/uHF3mY2et4LVLjqJvWhe9Pd0Mp8QX73yIExbOZvn8fl41dwZLjp7BQN80IvBwmpSpiQgNN03bJCJ456olvHPVkpecL6XEL7bt4Yt3PsT6Hz562PV2RdDVBU/vHOTfnt5NVxfs3j/EnsFhhkZGGBlnG+Ha2zYd6TDa6sx/t5D7n9jBiYtms/ioGTzw9C4WH9XHq+bO5L7Ht9PT1cWcmdM4cdEAPV3B3gPD7D0wxPzZvUQE+w8O0zetm6HhEXbtH+L2nz/Dxid38qbjXsFdv3gWgJOWDHD/Ey8E7Lz+XlYsms2s3m5WL53L9n0HmDNzOnsGhwHom9ZFd1clVHfsO8gzOwcZSYkTF439MzKDQyNs2bGfY+bM4PsPbGXB7F72Hhjm1GPnjpkvIkgp0RXByDgbeNXp2/ceZM6s6QSQgANDI9z1i2d543Fz6e3p5it3PcyJi2Zz6rGvGF32rs3bePjZvbz71Fdz9yPP0Tetm5WvPIp7H9vO4qP7mN/fO7qBMDg0zI69B1kw0DfaB8DB4REODI0wq7eHroCUYOf+g0zr7mLGtG6uv+MhXr90DqtedTTjbWpEscyhNl237R5k8OAIx8yZMaZ938FhHntuLycsfOH3Wt2WGR5JPPzsHo6d108U7dWah0cSew8M09/74v/6qrWMt01UXX5waISerhh9jSdKtbf631MAu/YPsefAMAsHeiemFvc0ppaDwyPsGRxi2+5BtuwY5Kkd+1hy9Ax6ursYHBpm8OAID27dzTX/pzODRJrKHrn27R6e0tRT/56sbv3Vb5GmlMY8r26JBy9slVeXGym21odH0uiWZHV91S35oeFEIjGtu4uh4RfWXO1lWncXB4ZG6O4O0ghEF2P2AGrrLDvO6p5CRDAykugabwu2dn1R08dh+hlOie5isIlEjLOtX/291M4zNDJCT9eLb0vX3R0MDY8UZcTo8vVb5iMJumq2+nu6uqh9pYIg1RV/qEOewyNpdF219Q+Pvta166j8W329q3sDta9Hdbz1exXVecq8frW/szLzH2qeVFP/4eapTopxpsELr/Xc/l4PT2nqqf8PpPr0xf+vTPy5lVkTcwRAytaUu8utJOnIGRqSpNIMDUlSaYaGJKk0Q0OSVJqhIUkqzdCQJJVmaEiSSjM0JEmlGRqSpNIMDUlSaYaGJKk0Q0OSVJqhIUkqzdCQJJVmaEiSSjM0JEmlGRqSpNIMDUlSaQ2HRkT0RsT1EfFIROyKiHsi4uya6WdExKaI2BsRt0fE0kb7lCS1RzP2NHqAx4C3AkcBlwJfj4hlETEPuAm4DJgLbAC+1oQ+JUlt0NPoClJKe4DLa5r+PiIeAk4BXgFsTCn9DUBEXA5si4gVKaVNjfYtSZpYTT+nERELgROAjcBK4N7qtCJgNhft9cutjYgNEbFh69atzS5LktQETQ2NiJgG3AB8udiT6Ad21M22A5hdv2xKaV1KaXVKafX8+fObWZYkqUmaFhoR0QV8BTgAXFw07wYG6mYdAHY1q19J0sRpSmhERADXAwuBNSmlg8WkjcDJNfPNApYX7ZKkDtOsPY3PAa8B3pFS2lfTfjNwUkSsiYg+4BPAfZ4El6TO1IzvaSwFPgCsArZExO7i58KU0lZgDXAV8DzwBuD8RvuUJLVHMy65fQSIl5j+HWBFo/1IktrP24hIkkozNCRJpRkakqTSDA1JUmmGhiSpNENDklSaoSFJKs3QkCSVZmhIkkozNCRJpRkakqTSDA1JUmmGhiSpNENDklSaoSFJKs3QkCSVZmhIkkozNCRJpRkakqTSDA1JUmmGhiSpNENDklSaoSFJKs3QkCSVZmhIkkozNCRJpRkakqTSDA1JUmmGhiSpNENDklSaoSFJKs3QkCSVZmhIkkozNCRJpbU8NCJibkTcHBF7IuKRiLig1X1KklqjZwL6+EvgALAQWAV8KyLuTSltnIC+JUlN1NI9jYiYBawBLksp7U4p3QF8E3hPK/uVJLVGq/c0TgCGUkoP1LTdC7y1fsaIWAusLZ4ORsT9La6tneYB29pdRItM5rGB4+t0k318J7a6g1aHRj+ws65tBzC7fsaU0jpgHUBEbEgprW5xbW0zmcc3mccGjq/TTYXxtbqPVp8I3w0M1LUNALta3K8kqQVaHRoPAD0RcXxN28mAJ8ElqQO1NDRSSnuAm4ArImJWRPwK8E7gK4dZdF0r68rAZB7fZB4bOL5O5/gaFCml1nYQMRf4AnAm8CxwSUrpxpZ2KklqiZaHhiRp8vA2IpKk0gwNSVJpWYVG7vepioiLI2JDRAxGxJfqpp0REZsiYm9E3B4RS2um9UbEFyJiZ0RsiYiPNGvZJo6tNyKuL37vuyLinog4e7KMr+hrfUQ8VfT1QET83mQaX02fx0fE/ohYX9N2QfHa7omIW4pzjdVpL/m5a2TZJo/re8W4dhc/P59M4yv6Oz8iflb0tzkiTiva83l/ppSy+QH+GvgalS8F/iqVLwKubHddNfW9CzgP+BzwpZr2eUWt/xHoA/4M+GHN9KuBHwBzgNcAW4CzGl22yWObBVwOLKOyMfF2Kt+nWTYZxlf0tRLoLR6vKPo6ZbKMr6bPbxd9rq8Z9y7gLcVn60bgq2U+d40s24JxfQ/4vUO8rpNhfGcCjwBvpPIZXFL8ZPX+bNkb9wh+YbOo3NjwhJq2rwDXtLu2cWr9JGNDYy3wz3Vj2QesKJ4/CfxGzfQrq2/MRpadgHHeR+XeYZNufFRut/AU8J8m0/iA84GvU9kAqIbGnwA31syzvPiszT7c566RZVswtu8xfmhMlvH9M/C747Rn9f7M6fDUoe5TtbJN9bwcK6nUCox+P2UzsDIi5gCLa6czdlyNLNsyEbGQymuyscEasxpfRHw2IvYCm6iExq0N1pjN+CJiALgCqD/EUF/jZor/DDn8566RZVvh6ojYFhF3RsTpTagxi/FFRDewGpgfEQ9GxOMRcV1EzBinxra+P3MKjdL3qcpQP5Vaa1Vr7695Xj+t0WVbIiKmATcAX04pbWqwxqzGl1K6qFj/aVS+eDrYYI05je9K4PqU0uN17Yer8aU+d40s22wfA46jcshmHfB3EbG8wRpzGd9CYBrwW1Tem6uA1wGXlqgRJvD9mVNodPJ9ql6q9t01z+unNbps00VEF5Vd8APAxU2oMavxAaSUhlPlNv3HAB9qsMYsxhcRq4C3AX8+zuTD1fhSn7tGlm2qlNKPUkq7UkqDKaUvA3cC5zRYYy7j21f8+5mU0lMppW3Apyg3PpjA92dOodHJ96naSKVWYPTviCwHNqaUnqdyGOTkmvlrx9XIsk0VEQFcT2WrZ01K6WATasxmfOPoqdbSQI25jO90KhctPBoRW4CPAmsi4ifj1Hgc0EvlM3e4z10jy7ZaAqLBGrMYX/FeeZzKmEabD1Fje9+frTih08CJoK9SuVphFvAr5Hf1VA+VKxCuprI13le0zS9qXVO0XcvYKxSuAb5P5QqFFcULVb264YiXbcH4Pg/8EOiva+/48QELqJwk7ge6gf8A7AF+c5KMbyawqObnfwJ/W9S3ksphltOKz9Z6xl4hdMjPXSPLNnl8RxevWfUzd2Hx+p0wGcZX9HUF8OPivTqHylVNV+b2/mz6wBv8pc0FbineDI8CF7S7prr6LqeS/rU/lxfT3kbl5Oo+Kld5LKtZrpfK/bd2Ak8DH6lb7xEv28SxLS3Gs5/Kbmv158JJMr75xYdje9HXvwDvb0aNOYzvEO/V9TXPLyg+U3uAbwBza6a95OeukWWb/Pr9mMqhk+1UNm7OnCzjK/qaBny2GN8W4C+Avtzen957SpJUWk7nNCRJmTM0JEmlGRqSpNIMDUlSaYaGJKk0Q0OSVJqhIUkqzdCQJJX2/wEGW4aqH2501gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(svm_clf.n_epochs), svm_clf.Js)\n",
    "plt.axis([0, svm_clf.n_epochs, 0, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:54:16.106219Z",
     "iopub.status.busy": "2021-10-23T11:54:16.105080Z",
     "iopub.status.idle": "2021-10-23T11:54:16.108820Z",
     "shell.execute_reply": "2021-10-23T11:54:16.109477Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.56761653] [[[2.28120287]\n",
      "  [2.71621742]]]\n"
     ]
    }
   ],
   "source": [
    "print(svm_clf.intercept_, svm_clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:54:16.118168Z",
     "iopub.status.busy": "2021-10-23T11:54:16.117252Z",
     "iopub.status.idle": "2021-10-23T11:54:16.121394Z",
     "shell.execute_reply": "2021-10-23T11:54:16.120606Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.51721253] [[2.27128546 2.71287145]]\n"
     ]
    }
   ],
   "source": [
    "svm_clf2 = SVC(kernel=\"linear\", C=C)\n",
    "svm_clf2.fit(X, y.ravel())\n",
    "print(svm_clf2.intercept_, svm_clf2.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:54:16.134065Z",
     "iopub.status.busy": "2021-10-23T11:54:16.133044Z",
     "iopub.status.idle": "2021-10-23T11:54:16.750385Z",
     "shell.execute_reply": "2021-10-23T11:54:16.751168Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.0, 6.0, 0.8, 2.8)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr = y.ravel()\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(11, 3.2), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\", label=\"Iris virginica\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\", label=\"Not Iris virginica\")\n",
    "plot_svc_decision_boundary(svm_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"MyLinearSVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"SVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:54:16.764122Z",
     "iopub.status.busy": "2021-10-23T11:54:16.763100Z",
     "iopub.status.idle": "2021-10-23T11:54:17.282024Z",
     "shell.execute_reply": "2021-10-23T11:54:17.282779Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-12.52988101   1.94162342   1.84544824]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4.0, 6.0, 0.8, 2.8)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x230.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", alpha=0.017, max_iter=1000, tol=1e-3, random_state=42)\n",
    "sgd_clf.fit(X, y.ravel())\n",
    "\n",
    "m = len(X)\n",
    "t = y * 2 - 1  # y==0이면 -1, y==1이면 +1\n",
    "X_b = np.c_[np.ones((m, 1)), X]  # 편향 x0=1을 추가합니다\n",
    "X_b_t = X_b * t\n",
    "sgd_theta = np.r_[sgd_clf.intercept_[0], sgd_clf.coef_[0]]\n",
    "print(sgd_theta)\n",
    "support_vectors_idx = (X_b_t.dot(sgd_theta) < 1).ravel()\n",
    "sgd_clf.support_vectors_ = X[support_vectors_idx]\n",
    "sgd_clf.C = C\n",
    "\n",
    "plt.figure(figsize=(5.5,3.2))\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(sgd_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"SGDClassifier\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 연습문제 해답"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "부록 A 참조."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_문제: 선형적으로 분리되는 데이터셋에 `LinearSVC`를 훈련시켜보세요. 그런 다음 같은 데이터셋에 `SVC`와`SGDClassifier`를 적용해보세요. 거의 비슷한 모델이 만들어지는지 확인해보세요._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iris 데이터셋을 사용하겠습니다. Iris Setosa와 Iris Versicolor 클래스는 선형적으로 구분이 가능합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:54:17.293075Z",
     "iopub.status.busy": "2021-10-23T11:54:17.292099Z",
     "iopub.status.idle": "2021-10-23T11:54:17.295568Z",
     "shell.execute_reply": "2021-10-23T11:54:17.296254Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # 꽃잎 길이, 꽃잎 너비\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:54:17.306383Z",
     "iopub.status.busy": "2021-10-23T11:54:17.305426Z",
     "iopub.status.idle": "2021-10-23T11:54:17.329183Z",
     "shell.execute_reply": "2021-10-23T11:54:17.329805Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC:                    [0.28475098] [[1.05364854 1.09903804]]\n",
      "SVC:                          [0.31896852] [[1.1203284  1.02625193]]\n",
      "SGDClassifier(alpha=0.00200): [0.117] [[0.77714169 0.72981762]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "C = 5\n",
    "alpha = 1 / (C * len(X))\n",
    "\n",
    "lin_clf = LinearSVC(loss=\"hinge\", C=C, random_state=42)\n",
    "svm_clf = SVC(kernel=\"linear\", C=C)\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, alpha=alpha,\n",
    "                        max_iter=1000, tol=1e-3, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "lin_clf.fit(X_scaled, y)\n",
    "svm_clf.fit(X_scaled, y)\n",
    "sgd_clf.fit(X_scaled, y)\n",
    "\n",
    "print(\"LinearSVC:                   \", lin_clf.intercept_, lin_clf.coef_)\n",
    "print(\"SVC:                         \", svm_clf.intercept_, svm_clf.coef_)\n",
    "print(\"SGDClassifier(alpha={:.5f}):\".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 3개 모델의 결정 경계를 그려 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:54:17.339768Z",
     "iopub.status.busy": "2021-10-23T11:54:17.338736Z",
     "iopub.status.idle": "2021-10-23T11:54:17.650393Z",
     "shell.execute_reply": "2021-10-23T11:54:17.651194Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 각 결정 경계의 기울기와 편향을 계산합니다\n",
    "w1 = -lin_clf.coef_[0, 0]/lin_clf.coef_[0, 1]\n",
    "b1 = -lin_clf.intercept_[0]/lin_clf.coef_[0, 1]\n",
    "w2 = -svm_clf.coef_[0, 0]/svm_clf.coef_[0, 1]\n",
    "b2 = -svm_clf.intercept_[0]/svm_clf.coef_[0, 1]\n",
    "w3 = -sgd_clf.coef_[0, 0]/sgd_clf.coef_[0, 1]\n",
    "b3 = -sgd_clf.intercept_[0]/sgd_clf.coef_[0, 1]\n",
    "\n",
    "# 결정 경계를 원본 스케일로 변환합니다\n",
    "line1 = scaler.inverse_transform([[-10, -10 * w1 + b1], [10, 10 * w1 + b1]])\n",
    "line2 = scaler.inverse_transform([[-10, -10 * w2 + b2], [10, 10 * w2 + b2]])\n",
    "line3 = scaler.inverse_transform([[-10, -10 * w3 + b3], [10, 10 * w3 + b3]])\n",
    "\n",
    "# 세 개의 결정 경계를 모두 그립니다\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.plot(line1[:, 0], line1[:, 1], \"k:\", label=\"LinearSVC\")\n",
    "plt.plot(line2[:, 0], line2[:, 1], \"b--\", linewidth=2, label=\"SVC\")\n",
    "plt.plot(line3[:, 0], line3[:, 1], \"r-\", label=\"SGDClassifier\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\") # label=\"Iris versicolor\"\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\") # label=\"Iris setosa\"\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper center\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아주 비슷하네요!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_문제: MNIST 데이터셋에 SVM 분류기를 훈련시켜보세요. SVM 분류기는 이진 분류기라서 OvA 전략을 사용해 10개의 숫자를 분류해야 합니다. 처리 속도를 높이기 위해 작은 검증 세트로 하이퍼파라미터를 조정하는 것이 좋습니다. 어느 정도까지 정확도를 높일 수 있나요?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 데이터셋을 로드하고 훈련 세트와 테스트 세트로 나눕니다. `train_test_split()` 함수를 사용할 수 있지만 보통 처음 60,000개의 샘플을 훈련 세트로 사용하고 나머지는 10,000개를 테스트 세트로 사용합니다(이렇게 하면 다른 사람들의 모델과 성능을 비교하기 좋습니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:54:17.659204Z",
     "iopub.status.busy": "2021-10-23T11:54:17.658283Z",
     "iopub.status.idle": "2021-10-23T11:55:22.858058Z",
     "shell.execute_reply": "2021-10-23T11:55:22.859103Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1, cache=True)\n",
    "\n",
    "X = mnist[\"data\"]\n",
    "y = mnist[\"target\"].astype(np.uint8)\n",
    "\n",
    "X_train = X[:60000]\n",
    "y_train = y[:60000]\n",
    "X_test = X[60000:]\n",
    "y_test = y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "많은 훈련 알고리즘은 훈련 샘플의 순서에 민감하므로 먼저 이를 섞는 것이 좋은 습관입니다. 하지만 이 데이터셋은 이미 섞여있으므로 이렇게 할 필요가 없습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "선형 SVM 분류기부터 시작해보죠. 이 모델은 자동으로 OvA(또는 OvR) 전략을 사용하므로 특별히 처리해 줄 것이 없습니다. 간단하네요!\n",
    "\n",
    "**경고**: 이 작업은 하드웨어에 따라 몇 분이 걸릴 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T11:55:22.874520Z",
     "iopub.status.busy": "2021-10-23T11:55:22.865951Z",
     "iopub.status.idle": "2021-10-23T12:00:28.283267Z",
     "shell.execute_reply": "2021-10-23T12:00:28.282668Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/handson-ml2/.env/lib/python3.7/site-packages/sklearn/svm/_base.py:1201: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  ConvergenceWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(random_state=42)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "훈련 세트에 대한 예측을 만들어 정확도를 측정해 보겠습니다(최종 모델을 선택해 훈련시킨 것이 아니기 때문에 아직 테스트 세트를 사용해서는 안됩니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:00:28.295752Z",
     "iopub.status.busy": "2021-10-23T12:00:28.295023Z",
     "iopub.status.idle": "2021-10-23T12:00:28.451028Z",
     "shell.execute_reply": "2021-10-23T12:00:28.451914Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8348666666666666"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = lin_clf.predict(X_train)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MNIST에서 83.5% 정확도면 나쁜 성능입니다. 선형 모델이 MNIST 문제에 너무 단순하기 때문이지만 먼저 데이터의 스케일을 조정할 필요가 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:00:28.463144Z",
     "iopub.status.busy": "2021-10-23T12:00:28.461746Z",
     "iopub.status.idle": "2021-10-23T12:00:29.398935Z",
     "shell.execute_reply": "2021-10-23T12:00:29.397957Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float32))\n",
    "X_test_scaled = scaler.transform(X_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**경고**: 이 작업은 하드웨어에 따라 몇 분이 걸릴 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:00:29.403966Z",
     "iopub.status.busy": "2021-10-23T12:00:29.402722Z",
     "iopub.status.idle": "2021-10-23T12:11:33.747805Z",
     "shell.execute_reply": "2021-10-23T12:11:33.748283Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/handson-ml2/.env/lib/python3.7/site-packages/sklearn/svm/_base.py:1201: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  ConvergenceWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(random_state=42)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:11:33.752089Z",
     "iopub.status.busy": "2021-10-23T12:11:33.751417Z",
     "iopub.status.idle": "2021-10-23T12:11:34.047599Z",
     "shell.execute_reply": "2021-10-23T12:11:34.048545Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9214"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lin_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "훨씬 나아졌지만(에러율을 절반으로 줄였습니다) 여전히 MNIST에서 좋은 성능은 아닙니다. SVM을 사용한다면 커널 함수를 사용해야 합니다. RBF 커널(기본값)로 `SVC`를 적용해 보겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**노트**: 향후 버전을 위해 사이킷런 0.22에서 기본값인 `gamma=\"scale\"`을 지정합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:11:34.058736Z",
     "iopub.status.busy": "2021-10-23T12:11:34.057242Z",
     "iopub.status.idle": "2021-10-23T12:11:50.535893Z",
     "shell.execute_reply": "2021-10-23T12:11:50.535285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(gamma=\"scale\")\n",
    "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:11:50.540566Z",
     "iopub.status.busy": "2021-10-23T12:11:50.539890Z",
     "iopub.status.idle": "2021-10-23T12:15:40.873895Z",
     "shell.execute_reply": "2021-10-23T12:15:40.873214Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9455333333333333"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아주 좋네요 6배나 적은 데이터에서 모델을 훈련시켰지만 더 좋은 성능을 얻었습니다. 교차 검증을 사용한 랜덤 서치로 하이퍼파라미터 튜닝을 해보겠습니다. 진행을 빠르게 하기 위해 작은 데이터셋으로 작업하겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:15:40.886171Z",
     "iopub.status.busy": "2021-10-23T12:15:40.885045Z",
     "iopub.status.idle": "2021-10-23T12:15:50.188511Z",
     "shell.execute_reply": "2021-10-23T12:15:50.188950Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] END ....C=5.847490967837556, gamma=0.004375955271336425; total time=   0.3s\n",
      "[CV] END ....C=5.847490967837556, gamma=0.004375955271336425; total time=   0.3s\n",
      "[CV] END ....C=5.847490967837556, gamma=0.004375955271336425; total time=   0.3s\n",
      "[CV] END ....C=2.544266730893301, gamma=0.024987648190235304; total time=   0.3s\n",
      "[CV] END ....C=2.544266730893301, gamma=0.024987648190235304; total time=   0.3s\n",
      "[CV] END ....C=2.544266730893301, gamma=0.024987648190235304; total time=   0.3s\n",
      "[CV] END ....C=2.199505425963898, gamma=0.009340106304825553; total time=   0.3s\n",
      "[CV] END ....C=2.199505425963898, gamma=0.009340106304825553; total time=   0.3s\n",
      "[CV] END ....C=2.199505425963898, gamma=0.009340106304825553; total time=   0.3s\n",
      "[CV] END .....C=7.327377306009368, gamma=0.04329656504133618; total time=   0.3s\n",
      "[CV] END .....C=7.327377306009368, gamma=0.04329656504133618; total time=   0.3s\n",
      "[CV] END .....C=7.327377306009368, gamma=0.04329656504133618; total time=   0.3s\n",
      "[CV] END ....C=7.830259944094713, gamma=0.009933958471354695; total time=   0.3s\n",
      "[CV] END ....C=7.830259944094713, gamma=0.009933958471354695; total time=   0.3s\n",
      "[CV] END ....C=7.830259944094713, gamma=0.009933958471354695; total time=   0.3s\n",
      "[CV] END ....C=6.867969780001033, gamma=0.027511132256566175; total time=   0.3s\n",
      "[CV] END ....C=6.867969780001033, gamma=0.027511132256566175; total time=   0.3s\n",
      "[CV] END ....C=6.867969780001033, gamma=0.027511132256566175; total time=   0.3s\n",
      "[CV] END .....C=3.584980864373988, gamma=0.01237128009623357; total time=   0.3s\n",
      "[CV] END .....C=3.584980864373988, gamma=0.01237128009623357; total time=   0.3s\n",
      "[CV] END .....C=3.584980864373988, gamma=0.01237128009623357; total time=   0.3s\n",
      "[CV] END ....C=5.073078322899452, gamma=0.002259275783824143; total time=   0.3s\n",
      "[CV] END ....C=5.073078322899452, gamma=0.002259275783824143; total time=   0.3s\n",
      "[CV] END ....C=5.073078322899452, gamma=0.002259275783824143; total time=   0.3s\n",
      "[CV] END ..C=10.696324058267928, gamma=0.0039267813006514255; total time=   0.3s\n",
      "[CV] END ..C=10.696324058267928, gamma=0.0039267813006514255; total time=   0.3s\n",
      "[CV] END ..C=10.696324058267928, gamma=0.0039267813006514255; total time=   0.3s\n",
      "[CV] END ..C=3.8786881587000437, gamma=0.0017076019229344522; total time=   0.3s\n",
      "[CV] END ..C=3.8786881587000437, gamma=0.0017076019229344522; total time=   0.2s\n",
      "[CV] END ..C=3.8786881587000437, gamma=0.0017076019229344522; total time=   0.3s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=SVC(),\n",
       "                   param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f5ef8237d68>,\n",
       "                                        'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f5ef82379b0>},\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(svm_clf, param_distributions, n_iter=10, verbose=2, cv=3)\n",
    "rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:15:50.194937Z",
     "iopub.status.busy": "2021-10-23T12:15:50.194041Z",
     "iopub.status.idle": "2021-10-23T12:15:50.197733Z",
     "shell.execute_reply": "2021-10-23T12:15:50.197204Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=3.8786881587000437, gamma=0.0017076019229344522)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:15:50.202693Z",
     "iopub.status.busy": "2021-10-23T12:15:50.201826Z",
     "iopub.status.idle": "2021-10-23T12:15:50.205075Z",
     "shell.execute_reply": "2021-10-23T12:15:50.205512Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8599947252641863"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 점수는 낮지만 1,000개의 샘플만 사용한 것을 기억해야 합니다. 전체 데이터셋으로 최선의 모델을 재훈련시켜 보겠습니다:\n",
    "\n",
    "**경고**: 사용하는 하드웨어에 따라 다음 셀을 실행하는데 몇 시간이 걸릴 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:15:50.209910Z",
     "iopub.status.busy": "2021-10-23T12:15:50.209301Z",
     "iopub.status.idle": "2021-10-23T12:23:33.012784Z",
     "shell.execute_reply": "2021-10-23T12:23:33.013245Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=3.8786881587000437, gamma=0.0017076019229344522)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:23:33.018430Z",
     "iopub.status.busy": "2021-10-23T12:23:33.017675Z",
     "iopub.status.idle": "2021-10-23T12:37:34.046797Z",
     "shell.execute_reply": "2021-10-23T12:37:34.048244Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9978166666666667"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아주 훌륭하네요! 이 모델을 선택하겠습니다. 이제 테스트 세트로 모델을 테스트합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:37:34.056549Z",
     "iopub.status.busy": "2021-10-23T12:37:34.055483Z",
     "iopub.status.idle": "2021-10-23T12:41:48.906672Z",
     "shell.execute_reply": "2021-10-23T12:41:48.907445Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9717"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아주 나쁘지 않지만 확실히 모델이 다소 과대적합되었습니다. 하이퍼파라미터를 조금 더 수정할 수 있지만(가령, `C`와/나 `gamma`를 감소시킵니다) 그렇게 하면 테스트 세트에 과대적합될 위험이 있습니다. 다른 사람들은 하이퍼파라미터 `C=5`와 `gamma=0.005`에서 더 나은 성능(98% 이상의 정확도)을 얻었습니다. 훈련 세트를 더 많이 사용해서 더 오래 랜덤 서치를 수행하면 이런 값을 얻을 수 있을지 모릅니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_문제: 캘리포니아 주택 가격 데이터셋에 SVM 회귀를 훈련시켜보세요._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "사이킷런의 `fetch_california_housing()` 함수를 사용해 데이터셋을 로드합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:41:48.929605Z",
     "iopub.status.busy": "2021-10-23T12:41:48.928837Z",
     "iopub.status.idle": "2021-10-23T12:41:56.625101Z",
     "shell.execute_reply": "2021-10-23T12:41:56.624164Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "X = housing[\"data\"]\n",
    "y = housing[\"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "훈련 세트와 테스트 세트로 나눕니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:41:56.633944Z",
     "iopub.status.busy": "2021-10-23T12:41:56.631444Z",
     "iopub.status.idle": "2021-10-23T12:41:56.652097Z",
     "shell.execute_reply": "2021-10-23T12:41:56.652839Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터의 스케일을 조정하는 것을 잊지 마세요:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:41:56.661233Z",
     "iopub.status.busy": "2021-10-23T12:41:56.659583Z",
     "iopub.status.idle": "2021-10-23T12:41:56.680333Z",
     "shell.execute_reply": "2021-10-23T12:41:56.679431Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 간단한 `LinearSVR`을 훈련시켜 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:41:56.688568Z",
     "iopub.status.busy": "2021-10-23T12:41:56.686962Z",
     "iopub.status.idle": "2021-10-23T12:41:58.281025Z",
     "shell.execute_reply": "2021-10-23T12:41:58.282048Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/handson-ml2/.env/lib/python3.7/site-packages/sklearn/svm/_base.py:1201: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  ConvergenceWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVR(random_state=42)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "lin_svr = LinearSVR(random_state=42)\n",
    "lin_svr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "훈련 세트에 대한 성능을 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:41:58.288921Z",
     "iopub.status.busy": "2021-10-23T12:41:58.288001Z",
     "iopub.status.idle": "2021-10-23T12:41:58.302736Z",
     "shell.execute_reply": "2021-10-23T12:41:58.303411Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9641780189948642"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_pred = lin_svr.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMSE를 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:41:58.307363Z",
     "iopub.status.busy": "2021-10-23T12:41:58.306507Z",
     "iopub.status.idle": "2021-10-23T12:41:58.314586Z",
     "shell.execute_reply": "2021-10-23T12:41:58.315358Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9819256687727764"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "훈련 세트에서 타깃은 만달러 단위입니다. RMSE는 기대할 수 있는 에러의 정도를 대략 가늠하게 도와줍니다(에러가 클수록 큰 폭으로 증가합니다). 이 모델의 에러가 대략 $10,000 정도로 예상할 수 있습니다. 썩 훌륭하지 않네요. RBF 커널이 더 나을지 확인해 보겠습니다. 하이퍼파라미터 `C`와 `gamma`의 적절한 값을 찾기 위해 교차 검증을 사용한 랜덤 서치를 적용하겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:41:58.325863Z",
     "iopub.status.busy": "2021-10-23T12:41:58.324828Z",
     "iopub.status.idle": "2021-10-23T12:52:14.848717Z",
     "shell.execute_reply": "2021-10-23T12:52:14.847262Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] END .....C=4.745401188473625, gamma=0.07969454818643928; total time=  20.6s\n",
      "[CV] END .....C=4.745401188473625, gamma=0.07969454818643928; total time=  19.0s\n",
      "[CV] END .....C=4.745401188473625, gamma=0.07969454818643928; total time=  19.1s\n",
      "[CV] END .....C=8.31993941811405, gamma=0.015751320499779724; total time=  18.2s\n",
      "[CV] END .....C=8.31993941811405, gamma=0.015751320499779724; total time=  17.5s\n",
      "[CV] END .....C=8.31993941811405, gamma=0.015751320499779724; total time=  16.8s\n",
      "[CV] END ....C=2.560186404424365, gamma=0.002051110418843397; total time=  17.4s\n",
      "[CV] END ....C=2.560186404424365, gamma=0.002051110418843397; total time=  16.9s\n",
      "[CV] END ....C=2.560186404424365, gamma=0.002051110418843397; total time=  17.4s\n",
      "[CV] END ....C=1.5808361216819946, gamma=0.05399484409787431; total time=  16.6s\n",
      "[CV] END ....C=1.5808361216819946, gamma=0.05399484409787431; total time=  22.3s\n",
      "[CV] END ....C=1.5808361216819946, gamma=0.05399484409787431; total time=  22.8s\n",
      "[CV] END ....C=7.011150117432088, gamma=0.026070247583707663; total time=  21.4s\n",
      "[CV] END ....C=7.011150117432088, gamma=0.026070247583707663; total time=  18.1s\n",
      "[CV] END ....C=7.011150117432088, gamma=0.026070247583707663; total time=  18.5s\n",
      "[CV] END .....C=1.2058449429580245, gamma=0.0870602087830485; total time=  22.3s\n",
      "[CV] END .....C=1.2058449429580245, gamma=0.0870602087830485; total time=  24.2s\n",
      "[CV] END .....C=1.2058449429580245, gamma=0.0870602087830485; total time=  23.2s\n",
      "[CV] END ...C=9.324426408004218, gamma=0.0026587543983272693; total time=  24.6s\n",
      "[CV] END ...C=9.324426408004218, gamma=0.0026587543983272693; total time=  19.7s\n",
      "[CV] END ...C=9.324426408004218, gamma=0.0026587543983272693; total time=  17.3s\n",
      "[CV] END ...C=2.818249672071006, gamma=0.0023270677083837795; total time=  21.4s\n",
      "[CV] END ...C=2.818249672071006, gamma=0.0023270677083837795; total time=  24.3s\n",
      "[CV] END ...C=2.818249672071006, gamma=0.0023270677083837795; total time=  22.5s\n",
      "[CV] END ....C=4.042422429595377, gamma=0.011207606211860567; total time=  17.3s\n",
      "[CV] END ....C=4.042422429595377, gamma=0.011207606211860567; total time=  16.3s\n",
      "[CV] END ....C=4.042422429595377, gamma=0.011207606211860567; total time=  17.2s\n",
      "[CV] END ....C=5.319450186421157, gamma=0.003823475224675185; total time=  17.0s\n",
      "[CV] END ....C=5.319450186421157, gamma=0.003823475224675185; total time=  17.9s\n",
      "[CV] END ....C=5.319450186421157, gamma=0.003823475224675185; total time=  16.9s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=SVR(),\n",
       "                   param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f5ef9023a20>,\n",
       "                                        'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f5ef5bce8d0>},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(SVR(), param_distributions, n_iter=10, verbose=2, cv=3, random_state=42)\n",
    "rnd_search_cv.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:52:14.856832Z",
     "iopub.status.busy": "2021-10-23T12:52:14.855801Z",
     "iopub.status.idle": "2021-10-23T12:52:14.864258Z",
     "shell.execute_reply": "2021-10-23T12:52:14.864905Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=4.745401188473625, gamma=0.07969454818643928)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 훈련 세트에서 RMSE를 측정해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:52:14.872256Z",
     "iopub.status.busy": "2021-10-23T12:52:14.871274Z",
     "iopub.status.idle": "2021-10-23T12:52:47.813803Z",
     "shell.execute_reply": "2021-10-23T12:52:47.812464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5727524770785358"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "선형 모델보다 훨씬 나아졌네요. 이 모델을 선택하고 테스트 세트에서 평가해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-23T12:52:47.820405Z",
     "iopub.status.busy": "2021-10-23T12:52:47.819446Z",
     "iopub.status.idle": "2021-10-23T12:52:58.118698Z",
     "shell.execute_reply": "2021-10-23T12:52:58.117830Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5929168385528742"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "np.sqrt(mse)"
   ]
  }
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