{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model, datasets\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 調整C值觀察結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from myfun import plot_decision_regions\n",
    "\n",
    "\n",
    "def regression_example(plot_dict, c):\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",
    "    # create model\n",
    "    logreg = linear_model.LogisticRegression(C=c)\n",
    "\n",
    "    # training\n",
    "    logreg.fit(X, y)\n",
    "\n",
    "    # Plot the decision boundary. For that, we will assign a color to each\n",
    "    if c in plot_dict:\n",
    "        plt.subplot(plot_dict[c])\n",
    "        plt.tight_layout()\n",
    "        # plt.figure()\n",
    "        plot_decision_regions(X, y, logreg)\n",
    "\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: %d' % c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8.5, 6))\n",
    "\n",
    "# 調整 C 值\n",
    "plot_dict = {1: 231, 10: 232, 1e2: 233, 1e3: 234, 1e4: 235, 1e5: 236}\n",
    "for i in plot_dict:\n",
    "    regression_example(plot_dict, i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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