Python52-数据挖掘与机器学习作业（二）.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业1\n",
    "![](https://ws1.sinaimg.cn/large/e59539f0ly1fpvylrsvj6j20nq0gnq6f.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问题1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species\n",
       "0   1           5.1          3.5           1.4          0.2  setosa\n",
       "1   2           4.9          3.0           1.4          0.2  setosa\n",
       "2   3           4.7          3.2           1.3          0.2  setosa\n",
       "3   4           4.6          3.1           1.5          0.2  setosa\n",
       "4   5           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('files/data/python48-data/iris.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w1的期望和方差分别为： 5.005999999999999 0.12424897959183666\n",
      "w1的期望和方差分别为： 2.9739999999999998 0.10400408163265312\n"
     ]
    }
   ],
   "source": [
    "w1=data[data['Species']=='setosa']\n",
    "u1=w1['Sepal.Length'].mean()\n",
    "E1=w1['Sepal.Length'].var()\n",
    "print(\"w1的期望和方差分别为：\",u1,E1)\n",
    "w2=data[data['Species']=='virginica']\n",
    "u2=w2['Sepal.Width'].mean()\n",
    "E2=w2['Sepal.Width'].var()\n",
    "print(\"w1的期望和方差分别为：\",u2,E2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问题2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问题3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Fisher实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2)\n"
     ]
    }
   ],
   "source": [
    "# 1、加载数据\n",
    "from sklearn.datasets import make_multilabel_classification\n",
    "import numpy as np\n",
    "\n",
    "# x, y = make_multilabel_classification(n_samples=20, n_features=2,\n",
    "#                                       n_labels=1, n_classes=1,\n",
    "#                                       random_state=2)  # 设置随机数种子，保证每次产生相同的数据。\n",
    "# # 根据类别分个类\n",
    "# index1 = np.array([index for (index, value) in enumerate(y) if value == 0])  # 获取类别1的indexs\n",
    "# index2 = np.array([index for (index, value) in enumerate(y) if value == 1])  # 获取类别2的indexs\n",
    "\n",
    "# c_1 = x[index1]   # 类别1的所有数据(x1, x2) in X_1\n",
    "# c_2 = x[index2]  # 类别2的所有数据(x1, x2) in X_2\n",
    "com_data=pd.concat([w1,w2])\n",
    "x=com_data[['Sepal.Length','Sepal.Width']]\n",
    "x=x.as_matrix()\n",
    "y=com_data['Species']\n",
    "index1 = np.array([index for (index, value) in enumerate(y) if value == 'setosa'])  # 获取类别1的indexs\n",
    "index2 = np.array([index for (index, value) in enumerate(y) if value == 'virginica'])  # 获取类别2的indexs\n",
    "c_1 = x[index1]   # 类别1的所有数据(x1, x2) in X_1\n",
    "c_2 = x[index2]  # 类别2的所有数据(x1, x2) in X_2\n",
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2、算法实现\n",
    "def cal_cov_and_avg(samples):\n",
    "    \"\"\"\n",
    "    给定一个类别的数据，计算协方差矩阵和平均向量\n",
    "    :param samples: \n",
    "    :return: \n",
    "    \"\"\"\n",
    "    u1 = np.mean(samples, axis=0)\n",
    "    cov_m = np.zeros((samples.shape[1], samples.shape[1]))\n",
    "    for s in samples:\n",
    "        t = s - u1\n",
    "        cov_m += t * t.reshape(2, 1)\n",
    "    return cov_m, u1\n",
    "\n",
    "\n",
    "def fisher(c_1, c_2):\n",
    "    \"\"\"\n",
    "    fisher算法实现(请参考上面推导出来的公式，那个才是精华部分)\n",
    "    :param c_1: \n",
    "    :param c_2: \n",
    "    :return: \n",
    "    \"\"\"\n",
    "    cov_1, u1 = cal_cov_and_avg(c_1)\n",
    "    cov_2, u2 = cal_cov_and_avg(c_2)\n",
    "    s_w = cov_1 + cov_2\n",
    "    u, s, v = np.linalg.svd(s_w)  # 奇异值分解\n",
    "    s_w_inv = np.dot(np.dot(v.T, np.linalg.inv(np.diag(s))), u.T)\n",
    "    return np.dot(s_w_inv, u1 - u2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# 3、判断类别\n",
    "def judge(sample, w, c_1, c_2):\n",
    "    \"\"\"\n",
    "    true 属于1\n",
    "    false 属于2\n",
    "    :param sample:\n",
    "    :param w:\n",
    "    :param center_1:\n",
    "    :param center_2:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    u1 = np.mean(c_1, axis=0)\n",
    "    u2 = np.mean(c_2, axis=0)\n",
    "    center_1 = np.dot(w.T, u1)\n",
    "    center_2 = np.dot(w.T, u2)\n",
    "    pos = np.dot(w.T, sample)\n",
    "    return abs(pos - center_1) < abs(pos - center_2)\n",
    "\n",
    "\n",
    "w = fisher(c_1, c_2)  # 调用函数，得到参数w\n",
    "out = judge(c_1[1], w, c_1, c_2)   # 判断所属的类别\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4、绘制图像\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(c_1[:, 0], c_1[:, 1], c='#99CC99')\n",
    "plt.scatter(c_2[:, 0], c_2[:, 1], c='#FFCC00')\n",
    "line_x = np.arange(min(np.min(c_1[:, 0]), np.min(c_2[:, 0])),\n",
    "                   max(np.max(c_1[:, 0]), np.max(c_2[:, 0])),\n",
    "                   step=3)\n",
    "\n",
    "line_y = - (w[0] * line_x) / w[1]\n",
    "plt.plot(line_x, line_y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- LDA实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import preprocessing\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species\n",
       "0   1           5.1          3.5           1.4          0.2  setosa\n",
       "1   2           4.9          3.0           1.4          0.2  setosa\n",
       "2   3           4.7          3.2           1.3          0.2  setosa\n",
       "3   4           4.6          3.1           1.5          0.2  setosa\n",
       "4   5           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据\n",
    "data=pd.read_csv('files/data/python48-data/iris.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置模型特征X和目标Y\n",
    "w1=data[data['Species']=='setosa']\n",
    "w2=data[data['Species']=='virginica']\n",
    "com_data=pd.concat([w1,w2])\n",
    "# 设置特征X\n",
    "X_Standard=np.array(com_data[['Sepal.Length','Sepal.Width']])\n",
    "# 设置目标Y\n",
    "y=np.array(com_data['Species'].apply(lambda x:1 if x=='setosa' else 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5],\n",
       "       [4.9, 3. ],\n",
       "       [4.7, 3.2],\n",
       "       [4.6, 3.1],\n",
       "       [5. , 3.6],\n",
       "       [5.4, 3.9],\n",
       "       [4.6, 3.4],\n",
       "       [5. , 3.4],\n",
       "       [4.4, 2.9],\n",
       "       [4.9, 3.1],\n",
       "       [5.4, 3.7],\n",
       "       [4.8, 3.4],\n",
       "       [4.8, 3. ],\n",
       "       [4.3, 3. ],\n",
       "       [5.8, 4. ],\n",
       "       [5.7, 4.4],\n",
       "       [5.4, 3.9],\n",
       "       [5.1, 3.5],\n",
       "       [5.7, 3.8],\n",
       "       [5.1, 3.8],\n",
       "       [5.4, 3.4],\n",
       "       [5.1, 3.7],\n",
       "       [4.6, 3.6],\n",
       "       [5.1, 3.3],\n",
       "       [4.8, 3.4],\n",
       "       [5. , 3. ],\n",
       "       [5. , 3.4],\n",
       "       [5.2, 3.5],\n",
       "       [5.2, 3.4],\n",
       "       [4.7, 3.2],\n",
       "       [4.8, 3.1],\n",
       "       [5.4, 3.4],\n",
       "       [5.2, 4.1],\n",
       "       [5.5, 4.2],\n",
       "       [4.9, 3.1],\n",
       "       [5. , 3.2],\n",
       "       [5.5, 3.5],\n",
       "       [4.9, 3.6],\n",
       "       [4.4, 3. ],\n",
       "       [5.1, 3.4],\n",
       "       [5. , 3.5],\n",
       "       [4.5, 2.3],\n",
       "       [4.4, 3.2],\n",
       "       [5. , 3.5],\n",
       "       [5.1, 3.8],\n",
       "       [4.8, 3. ],\n",
       "       [5.1, 3.8],\n",
       "       [4.6, 3.2],\n",
       "       [5.3, 3.7],\n",
       "       [5. , 3.3],\n",
       "       [6.3, 3.3],\n",
       "       [5.8, 2.7],\n",
       "       [7.1, 3. ],\n",
       "       [6.3, 2.9],\n",
       "       [6.5, 3. ],\n",
       "       [7.6, 3. ],\n",
       "       [4.9, 2.5],\n",
       "       [7.3, 2.9],\n",
       "       [6.7, 2.5],\n",
       "       [7.2, 3.6],\n",
       "       [6.5, 3.2],\n",
       "       [6.4, 2.7],\n",
       "       [6.8, 3. ],\n",
       "       [5.7, 2.5],\n",
       "       [5.8, 2.8],\n",
       "       [6.4, 3.2],\n",
       "       [6.5, 3. ],\n",
       "       [7.7, 3.8],\n",
       "       [7.7, 2.6],\n",
       "       [6. , 2.2],\n",
       "       [6.9, 3.2],\n",
       "       [5.6, 2.8],\n",
       "       [7.7, 2.8],\n",
       "       [6.3, 2.7],\n",
       "       [6.7, 3.3],\n",
       "       [7.2, 3.2],\n",
       "       [6.2, 2.8],\n",
       "       [6.1, 3. ],\n",
       "       [6.4, 2.8],\n",
       "       [7.2, 3. ],\n",
       "       [7.4, 2.8],\n",
       "       [7.9, 3.8],\n",
       "       [6.4, 2.8],\n",
       "       [6.3, 2.8],\n",
       "       [6.1, 2.6],\n",
       "       [7.7, 3. ],\n",
       "       [6.3, 3.4],\n",
       "       [6.4, 3.1],\n",
       "       [6. , 3. ],\n",
       "       [6.9, 3.1],\n",
       "       [6.7, 3.1],\n",
       "       [6.9, 3.1],\n",
       "       [5.8, 2.7],\n",
       "       [6.8, 3.2],\n",
       "       [6.7, 3.3],\n",
       "       [6.7, 3. ],\n",
       "       [6.3, 2.5],\n",
       "       [6.5, 3. ],\n",
       "       [6.2, 3.4],\n",
       "       [5.9, 3. ]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对特征进行标准化处理\n",
    "# scaler=preprocessing.StandardScaler().fit(X)\n",
    "# X_Standard=scaler.transform(X)\n",
    "X_Standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置分类平滑度\n",
    "h=0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,\n",
       "              solver='svd', store_covariance=False, tol=0.0001)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建LDA模型并拟合数据\n",
    "clf=LinearDiscriminantAnalysis()\n",
    "clf.fit(X_Standard,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cczdrM75SUnvILbm7cFjztmux3y7V9zG0a3vquE0smXNGJrE3QgEst0fWLs3DMpab/kxxqm35fN2ibfR0buWW2f/IpLGb8w7F6sRfYjJrcZ6/3lr8JSYz8/z1NubkbtaiPCOmvXm2jFmL8fx1A/fczVqKe+s2yD13s5YQnr9ur+Keu1nTG8hctNos4eRu1rSCKWM3cN2s89xbtx04uZs1JV8Cz0qreMxdUqek30taUmDdGEk3SXpY0lJJ06oZpJlt16V+J3YrazgnVD9D8QtffwJ4JiL2BuYDXx1tYGY2VDC+6wWufNeFTuxWVkXJXdKewAeAa4tschywKG0vBt4jSUW2NbNhGuyt/+rYk/xtU6tIpT33y4HPAQNF1u8BPAYQEduAZ4Hdhm4kaa6kZZKW8UJyAqgRKuW5Pbx2o8RRTmeLVJrs6dzKVUdckumtj7aqYCNUNHR79O3Syv6JSDoGWB8Ry0ttVmDZDhXJImJhRMyMiJmMnwjAy5l/F243R7tR4iinP/MObIQ4R9YOvjhjAQdMun/7CvpG2R7t/d1ujHZplfTcDweOlbQG+D5wpKTvDNlmLdALIKkLeA2wseIozGyIoLujzydObcTKToWMiPOB8wEkzQLOjYgTh2x2K/Ax4LfA8cDtkVctYbOm52mONnojnucu6WJgWUTcCnwLuEHSwyQ99hOqFJ9ZW3HtdauWYSX3iLgDuCNtX5BZvgX4cDUDM2s3rr1u1eRvqJrlrEv9jOncwuL3nukyAlY1Tu5mOXJv3WrFyd0sJ669brXkeu5mdTfg2utWc+65m9WRe+tWL+65m9WFr5Rk9eWeu1nN+UtJVn9O7mY1k1wp6az9FjG79+68g7E2k9uwzGBB4Lwr7rlduN1domBzd5Fqi8NVbD+VtLuaoOLjvIPms2TOGZnE3giVBBu9KmRXkXa1tm+ldmm59dwHK880TvU9t7PtvhKVgfqKVFscrmL7qaS9rcErPvZ0bmHGpJU0RvXAUlUFGyGObHtbkXa1tm+ldmk+oWpWVT5xao3BY+5mVTPAvIPm+8SpNQQnd7NRS06cXjfrPPfWrWE4uZuNiqc5WmPymLvZCA1etNqJ3RqRe+5mwxaM73qR+Ydd6mqO1rCc3M2GwVdKsmZRdlhGUo+k30m6X9IqSV8qsM0pkp6SdF/6c2ptwjXLT0/nFq5814VO7NYUKum5bwWOjIjNkrqBuyT9NCLuGbLdTRFxRvVDNMtXl/rp6ujz3HVrKmWTe0QEsDm9Ofjd5RLfXzRrHb5SkjWrimbLSOqUdB+wHvhFRCwtsNmHJK2QtFhSb5H9zJW0TNKyeME9IGtsg7XXnditGVWU3COiPyL2B/YEDpa035BNfgJMi4i3A78EFhXZz8KImBkRMzsmTATyL+7U7u1qzoUdbjGvau2nFu2p4zZxy+zT02GYRigS1S6Fw9yuvF3asGbLRMQmSXcA7wdWZpY/ndnsGuCrw9mvVSZbnCoqGBjLbj9QpMBXBzCK2l+vUqyQ5ECRAlvD3U+9JHPXl7K9SFP2yc4Wbiq23Cx/lcyWmSxpYtoeCxwFPDhkm6mZm8cCq8vt11Uhh9/OqqSiYla2Xt6rqisW3nxEisU03GOMplrkaNoD0c/4rhcKVHMs9giKLW+EioGl2o0Sh9u1rApZSc99KrBIUifJP4MfRMQSSRcDyyLiVuAsSceSvNs3AqdUHIFZA/CJU2s1lcyWWQEcUGD5BZn2+cD51Q3NrD580WprRa4tY21swLXXrWW5/IC1JffWrdW5525txldKsvbgnru1Eddet/bh5G5tILlS0ln7LWJ27915B2NWF07u1uLcW7f25DF3a1m+UpK1M/fcrQUFPZ1b/aUka2tO7tZSfKUks0RuwzJKq0M1QmXEerY7K6iKNdztK9lPteLZqaMxnsdC7Z7OrVx1xCWZxN6Y1fp21Fnn/Y/mGI1QDdHtSigqKS9YA52902P82TfmcmxrRT5xau1h32mPLI+ImeW28wlVa3o+cWq2I4+5WxMLxne9yPzDLvWJU7MhnNytKfnEqVlpTu7WdFx73aw8J3drGl3qp6ujz0W/zCrg5G5Nwb11s+Gp5BqqPZJ+J+l+SaskfanANmMk3STpYUlLJU2rRbDWngZrrzuxm1WukqmQW4EjI+IdwP7A+yUdOmSbTwDPRMTewHzgq9UN09qTa6+bjVQl11ANYHN6c/DrbUO/+XQccFHaXgwskKTI6xtS1vR6OrdwwYwFLtFrNkIVjblL6gSWA3sD/xYRS4dssgfwGEBEbJP0LLAbsGHIfuYCcwG069TRRW4tKqm9ft2s89xbNxuFipJ7RPQD+0uaCNwsab+IWJnZpFCFkh167RGxEFgISfmBEcRrLc0lBMyqZVjlByJiE3AH8P4hq9YCvQCSuoDXABurEJ+1CZcQMKuuSmbLTE577EgaCxwFPDhks1uBj6Xt44Hby423t1NVyKxKPirV87jdRapCZpfXuirk+K4Xa1TNsdijHu7yalVOLPbnVq34K31+srezb4COCpYXM9zXZrjHbYQqjI3WLq1sVUhJbwcWkdQN7QB+EBEXS7oYWBYRt0rqAW4ADiDpsZ8QEX8utV9XhTTYPs3R4+tmlam0KmQls2VWkCTtocsvyLS3AB8ebpDWznzi1KyW/A1Vy4FPnJrVmpO71VHSWz9rv0Wev25WY07uVifurZvVk6/EZDXnaY5m9eeeu9VQ0NO51dUczXLg5G414SslmeXLyd2qzrXXzfLn5G5V5S8lmTUGn1C1qujp3MK0CWud2M0ahHvuNmrurZs1HvfcbRR8pSSzRpVbcm+nqpCt1h68Pe+g+SyZc0YmsTdadbzOUeyzkvsOV/aDcp5VBRuhomEllTAb5flq1HZpuQ3LDBajfHlg+zK3m6PdpX56OvuYMWkl0Ld9ZW7tYvpHsc9K7jtc20YRT62et0aII9veVqTdCLE1Wrs0j7nbsHSpnyvfdaGnOZo1OCd3q5jnr5s1Dyd3q4hnxJg1F8+WaUOvH7uBKWPXU+Aa5jvw/HWz5lS25y6pF7gemAIMAAsj4utDtpkF/B/gkXTRjyPi4uqGaqM1bee1/PPBX2PPCY9DiCdfmsQXfnc2f3z2jQW3d2/drHlV0nPfBpwTEW8FDgVOlzS9wHZ3RsT+6Y8Te4MZ07GVa979v3njLo/R09lHT9fL9E5Yx9XvvoAJ3S8M2drz182aXdnkHhGPR8S9aft5YDWwR60Ds+qatftSduroo0Pbh2I6BJ3q53173pXZcoB5B13GkjmfcmI3a2LDOqEqaRrJxbKXFlh9mKT7gXXAuRGxqsD95wJzAbTr1OHGaqMweexGdurccY7suK6tvG7sBnzBarPWUnFylzQB+BHw2Yh4bsjqe4G9ImKzpKOBW4B9hu4jIhYCCwE6e6eXP5tnVbNy45vpG+imu6P/Vctf6OthxcZ9faUksxZT0WwZSd0kif27EfHjoesj4rmI2Jy2bwO6JU2qaqQ2Kvc9/VZWbtybl7bt9MqyLdu6WfP8Hhzde4cTu1mLKZvcJQn4FrA6Ii4rss2UdDskHZzu9+lqBmqjJT5z9xdZuPoEHnluD/7y/FRueOg4Xu7v5n29d+cdnJlVWSU998OBk4AjJd2X/hwt6TRJp6XbHA+sTMfcrwBOiIiSwy4uHFb/dt9ANzc89N/46O3fYPWmN/HJty7mgMl/2r5RSxSecrt8u1HicLuWhcNUJgfXTGfv9Bh/9o25HLu9+cSpWTPbd9ojyyNiZrntXH6grYRPnJq1CSf3thB0d2zjogOvcGI3axNO7i3PvXWzduTCYS2sS/1O7GZtyj33FuXa62btzcm9xXSpnzGdW1j83jM9G8asjTm5txD31s1skJN7i3DtdTPL8gnVpjfg2utmtgP33JuYe+tmVox77k3JV0oys9Lcc286/lKSmZWXW8/dVSFH1p530Pw0sbuq4Mjbxd72jRCbXz+3K22XllvPfbAY5csD25e5Xbzd07mFKWM3MGPSynRN9pJ5ebUbJY7htjNP8Ks0Qmx+/dyutF2ah2WagE+cmtlw+YRqQ/OJUzMbGffcG9ZAZnzdzGx4KrmGaq+k/5C0WtIqSZ8psI0kXSHpYUkrJM2oTbjtIOmt/3TOqU7sZjZilfTctwHnRMS9knYGlkv6RURkC5jMAfZJfw4Brkp/27B4mqOZVUfZnntEPB4R96bt54HVwB5DNjsOuD4S9wATJU2terQtzLXXzayahjXmLmkacACwdMiqPYDHMrfXpsseH3L/ucBcAO3q3J8Ixne9yPzDLnU1RzOrmoqTu6QJwI+Az0bEc0NXF7hL7LAgYiGwEKCzd/oO69tNl/r50szL3Vs3s6qrKLlL6iZJ7N+NiB8X2GQt0Ju5vSewbvThtS7XXjezWqpktoyAbwGrI+KyIpvdCpyczpo5FHg2Ih4vsm3bG/xSkhO7mdVKJT33w4GTgD9Iui9d9gXgDQARcTVwG3A08DDwIvDx6ofa/AZLCFx1xAX+UpKZ1VTZ5B4Rd1F4TD27TQCnVyuoVuQSAmZWT64KWYf21HGbuGX26Wlib4Rqcq4q2N7tRonD7VpWhVREPpNWOnunx/izb8zl2PXkE6dmVk37TntkeUTMLLeda8vUSJf6GdO5hcXvPdNDMWZWd07uNeDeupnlzcm9ynzi1Mwageu5V82Aa6+bWcNwz70K3Fs3s0bjnvuo+EpJZtaY3HMfMddeN7PG5eQ+bMGUsRs4a79FzO69O+9gzMwKcnIfFvfWzaw5eMy9Qr5Skpk1E/fcK+AvJZlZs3HhsDLt8V0vcsvs05kx+aFM9I1QNMiFp9weabtR4nDbhcNykZw4vW7WeZ7maGYNw4XDRsUnTs2sufmE6hA+cWpmraCSa6j+u6T1klYWWT9L0rOS7kt/Lqh+mPUQjO96gSvfdaETu5k1vUqGZb4NLACuL7HNnRFxTFUiykGX+vnSzMud1M2sZVRyDdVfS5pW+1Dy4WmOZtaKqnVC9TBJ9wPrgHMjYlWhjSTNBeYCaNepVTr0yHSpn66OPhf9MrOWVI3kfi+wV0RslnQ0cAuwT6ENI2IhsBCSqZBVOPaIuLduZq1u1LNlIuK5iNictm8DuiVNGnVkNTJYe92J3cxa2aiTu6QpUvJ9U0kHp/t8erT7rT7XXjez9lF2WEbS94BZwCRJa4ELSb8DGxFXA8cDn5a0DXgJOCHy+tprUQPMO2i+Z8OYWduoZLbMR8qsX0AyVbIBuYSAmbWnFi4/4BICZta+WrYq5PZhmEao3tZq7UaJw22/fu3cLi23nvvgqPzLA9uXVac9wGvHPM2MSYPVEvoyR3W7uu1GicPtkbUbJQ63R9YuraWGZQanOXp83czaXYtUhfQ0RzOzrBboufvEqZnZUE2c3JNpjmftt4jZvXfnHYyZWUNp0uTu3rqZWSlNN+buKyWZmZXXVPPcezq3ctURl2QSeyPMNW3HdqPE4bZfv3Zul6a8ysBIegp4NJeDD88kYEPeQdSRH29r8+NtfntFxORyG+WW3JuFpGURMTPvOOrFj7e1+fG2j6Ybczczs/Kc3M3MWpCTe3kL8w6gzvx4W5sfb5vwmLuZWQtyz93MrAU5uZuZtSAn9xIkdUr6vaQlecdSD5LWSPqDpPskLcs7nlqSNFHSYkkPSlot6bC8Y6oVSfumr+ngz3OSPpt3XLUk6X9KWiVppaTvSerJO6Z685h7CZLOBmYCu0TEMXnHU2uS1gAzI6LVvvSxA0mLgDsj4lpJOwHjIqLl60VL6gT+ChwSEc3wJcJhk7QHcBcwPSJekvQD4LaI+Ha+kdWXe+5FSNoT+ABwbd6xWHVJ2gV4N/AtgIh4uR0Se+o9wJ9aNbFndAFjJXUB44B1OcdTd07uxV0OfA4YKLdhCwng55KWS5qbdzA19EbgKeC6dNjtWknj8w6qTk4Avpd3ELUUEX8Fvgb8BXgceDYifp5vVPXn5F6ApGOA9RGxPO9Y6uzwiJgBzAFOl/TuvAOqkS5gBnBVRBwAvAB8Pt+Qai8dfjoW+GHesdSSpF2B44D/AuwOjJd0Yr5R1Z+Te2GHA8emY9DfB46U9J18Q6q9iFiX/l4P3AwcnG9ENbMWWBsRS9Pbi0nYM9nFAAAA10lEQVSSfaubA9wbEU/mHUiNHQU8EhFPRUQf8GPgnTnHVHdO7gVExPkRsWdETCP5GHt7RLT0f35J4yXtPNgGZgMr842qNiLiCeAxSfumi94DPJBjSPXyEVp8SCb1F+BQSeMkieT1XZ1zTHXXpFdishp4PXBz8rdAF3BjRPws35Bq6kzgu+lQxZ+Bj+ccT01JGge8F/hU3rHUWkQslbQYuBfYBvyeNixD4KmQZmYtyMMyZmYtyMndzKwFObmbmbUgJ3czsxbk5G5m1oKc3M3MWpCTu5lZC/r/m7reT38GerIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制图像\n",
    "#设置X和Y的边界值\n",
    "x_min, x_max = X_Standard[:,0].min() - 1,X_Standard[:,0].max() + 1\n",
    "y_min, y_max = X_Standard[:,1].min() - 1,X_Standard[:,1].max() + 1\n",
    "#使用meshgrid函数返回X和Y两个坐标向量矩阵\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max,h), np.arange(y_min, y_max,h))\n",
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "#设置colormap颜色\n",
    "cm_bright = ListedColormap(['#D9E021', '#0D8ECF'])\n",
    "#绘制分类边界\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.pcolormesh(xx, yy, Z, cmap=cm_bright,alpha=0.6)\n",
    "#绘制数据点\n",
    "plt.scatter(X_Standard[:,0], X_Standard[:,1], c=y, cmap=cm_bright)\n",
    "plt.title('Linear Discriminant Analysis Classifiers')\n",
    "plt.axis('tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业二\n",
    "![](https://ws1.sinaimg.cn/large/e59539f0ly1fpwbqa2754j20nq0hngqk.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 问题1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}