{
 "cells": [
  {
   "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/part3/Chapter4/myfun.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from myfun import plot_decision_regions\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make 3-class dataset for classification\n",
    "centers = [[-5, 0], [0, 1.5], [5, -1]]\n",
    "X, y = make_blobs(n_samples=1000, centers=centers, random_state=40)\n",
    "transformation = [[0.4, 0.2], [-0.4, 1.2]]\n",
    "X = np.dot(X, transformation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "color = \"rbg\"\n",
    "color = [color[y[i]] for i in range(len(y))]\n",
    "plt.scatter(X[:, 0], X[:, 1], c=color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* LogisticRegression 的參數設定可以參考官方文件\n",
    "    * https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for multi_class in ('multinomial', 'ovr'):\n",
    "    clf = LogisticRegression(solver='sag',\n",
    "                             max_iter=100,\n",
    "                             random_state=42,\n",
    "                             multi_class=multi_class).fit(X, y)\n",
    "\n",
    "    # print the training scores\n",
    "    print(\"training score : %.3f (%s)\" % (clf.score(X, y), multi_class))\n",
    "\n",
    "    # plot the decision region\n",
    "    plt.figure()\n",
    "    plot_decision_regions(X, y, clf)\n",
    "    plt.title(multi_class)\n",
    "    # Plot also the training points\n",
    "    colors = \"rbg\"\n",
    "    for i, color in zip(clf.classes_, colors):\n",
    "        idx = np.where(y == i)\n",
    "        plt.scatter(X[idx, 0], X[idx, 1], c=color, cmap=plt.cm.Paired)\n",
    "\n",
    "    # Plot the three one-against-all classifiers\n",
    "    xmin, xmax = plt.xlim()\n",
    "    ymin, ymax = plt.ylim()\n",
    "    coef = clf.coef_\n",
    "    intercept = clf.intercept_\n",
    "\n",
    "    def plot_hyperplane(c, color):\n",
    "        def line(x0):\n",
    "            return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]\n",
    "\n",
    "        plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls=\"--\", color=color)\n",
    "\n",
    "    for i, color in zip(clf.classes_, colors):\n",
    "        plot_hyperplane(i, color)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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