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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 下載 myfun.py\n",
    "!wget \"https://raw.githubusercontent.com/TA-aiacademy/course_3.0/ML/02_ML/part4/Chapter5/myfun.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, datasets\n",
    "from matplotlib.pylab import rcParams\n",
    "\n",
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = 10, 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from myfun import plot_decision_regions\n",
    "\n",
    "\n",
    "def svm_example(plot_dict, kernel):\n",
    "    # import some data to play with\n",
    "    iris = datasets.load_iris()\n",
    "    X = iris.data[:, :2]  # we only take the first two features.\n",
    "    y = iris.target\n",
    "\n",
    "    # data since we want to plot the support vectors\n",
    "    gamma = 0.7\n",
    "    degree = 3\n",
    "    # Plot the decision boundary. For that, we will assign a color to each\n",
    "    for C in plot_dict:\n",
    "        models = svm.SVC(kernel=kernel, C=C, degree=degree, gamma=gamma)\n",
    "        models.fit(X, y)\n",
    "        accuracy = models.score(X, y)\n",
    "        # prediction = models.predict(y)\n",
    "        plt.subplot(plot_dict[C])\n",
    "        plt.tight_layout()\n",
    "        # plt.figure()\n",
    "        plot_decision_regions(X, y, models)\n",
    "        # Plot also the training points\n",
    "        plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=plt.cm.Paired)\n",
    "        plt.xlabel('Sepal length')\n",
    "        plt.ylabel('Sepal width')\n",
    "        plt.title('Plot for C=%.2f' % C + '\\n' + 'Accuracy:%.2f' % accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# C in Linear SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_dict = {0.01: 141, 0.1: 142, 1: 143, 10: 144}\n",
    "svm_example(plot_dict, kernel='linear')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# C in polynominal SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 試著繼續調大C看看邊界的變化\n",
    "plot_dict = {0.01: 141, 0.1: 142, 1: 143, 10: 144}\n",
    "svm_example(plot_dict, kernel='poly')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# C in RBF SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 試著繼續調大C看看邊界的變化\n",
    "plot_dict = {0.01: 141, 0.1: 142, 1: 143, 10: 144}\n",
    "svm_example(plot_dict, kernel='rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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