Homework 1 Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv', encoding='big5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>測站</th>\n",
       "      <th>測項</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>AMB_TEMP</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>CH4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>CO</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>NMHC</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>NO</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>NO2</td>\n",
       "      <td>16</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>NOx</td>\n",
       "      <td>17</td>\n",
       "      <td>9.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>8.8</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>O3</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>51</td>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>PM10</td>\n",
       "      <td>56</td>\n",
       "      <td>50</td>\n",
       "      <td>48</td>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>52</td>\n",
       "      <td>51</td>\n",
       "      <td>66</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>63</td>\n",
       "      <td>46</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>26</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>45</td>\n",
       "      <td>42</td>\n",
       "      <td>49</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>RAINFALL</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>...</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>RH</td>\n",
       "      <td>77</td>\n",
       "      <td>68</td>\n",
       "      <td>67</td>\n",
       "      <td>74</td>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>49</td>\n",
       "      <td>56</td>\n",
       "      <td>67</td>\n",
       "      <td>72</td>\n",
       "      <td>69</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>SO2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>THC</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>...</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>WD_HR</td>\n",
       "      <td>37</td>\n",
       "      <td>80</td>\n",
       "      <td>57</td>\n",
       "      <td>76</td>\n",
       "      <td>110</td>\n",
       "      <td>106</td>\n",
       "      <td>101</td>\n",
       "      <td>...</td>\n",
       "      <td>307</td>\n",
       "      <td>304</td>\n",
       "      <td>307</td>\n",
       "      <td>124</td>\n",
       "      <td>118</td>\n",
       "      <td>121</td>\n",
       "      <td>113</td>\n",
       "      <td>112</td>\n",
       "      <td>106</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>WIND_DIREC</td>\n",
       "      <td>35</td>\n",
       "      <td>79</td>\n",
       "      <td>2.4</td>\n",
       "      <td>55</td>\n",
       "      <td>94</td>\n",
       "      <td>116</td>\n",
       "      <td>106</td>\n",
       "      <td>...</td>\n",
       "      <td>313</td>\n",
       "      <td>305</td>\n",
       "      <td>291</td>\n",
       "      <td>124</td>\n",
       "      <td>119</td>\n",
       "      <td>118</td>\n",
       "      <td>114</td>\n",
       "      <td>108</td>\n",
       "      <td>102</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>WIND_SPEED</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014/1/1</td>\n",
       "      <td>豐原</td>\n",
       "      <td>WS_HR</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014/1/2</td>\n",
       "      <td>豐原</td>\n",
       "      <td>AMB_TEMP</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014/1/2</td>\n",
       "      <td>豐原</td>\n",
       "      <td>CH4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          日期  測站          測項     0     1     2     3     4     5     6  ...  \\\n",
       "0   2014/1/1  豐原    AMB_TEMP    14    14    14    13    12    12    12  ...   \n",
       "1   2014/1/1  豐原         CH4   1.8   1.8   1.8   1.8   1.8   1.8   1.8  ...   \n",
       "2   2014/1/1  豐原          CO  0.51  0.41  0.39  0.37  0.35   0.3  0.37  ...   \n",
       "3   2014/1/1  豐原        NMHC   0.2  0.15  0.13  0.12  0.11  0.06   0.1  ...   \n",
       "4   2014/1/1  豐原          NO   0.9   0.6   0.5   1.7   1.8   1.5   1.9  ...   \n",
       "5   2014/1/1  豐原         NO2    16   9.2   8.2   6.9   6.8   3.8   6.9  ...   \n",
       "6   2014/1/1  豐原         NOx    17   9.8   8.7   8.6   8.5   5.3   8.8  ...   \n",
       "7   2014/1/1  豐原          O3    16    30    27    23    24    28    24  ...   \n",
       "8   2014/1/1  豐原        PM10    56    50    48    35    25    12     4  ...   \n",
       "9   2014/1/1  豐原       PM2.5    26    39    36    35    31    28    25  ...   \n",
       "10  2014/1/1  豐原    RAINFALL    NR    NR    NR    NR    NR    NR    NR  ...   \n",
       "11  2014/1/1  豐原          RH    77    68    67    74    72    73    74  ...   \n",
       "12  2014/1/1  豐原         SO2   1.8     2   1.7   1.6   1.9   1.4   1.5  ...   \n",
       "13  2014/1/1  豐原         THC     2     2     2   1.9   1.9   1.8   1.9  ...   \n",
       "14  2014/1/1  豐原       WD_HR    37    80    57    76   110   106   101  ...   \n",
       "15  2014/1/1  豐原  WIND_DIREC    35    79   2.4    55    94   116   106  ...   \n",
       "16  2014/1/1  豐原  WIND_SPEED   1.4   1.8     1   0.6   1.7   2.5   2.5  ...   \n",
       "17  2014/1/1  豐原       WS_HR   0.5   0.9   0.6   0.3   0.6   1.9     2  ...   \n",
       "18  2014/1/2  豐原    AMB_TEMP    16    15    15    14    14    15    16  ...   \n",
       "19  2014/1/2  豐原         CH4   1.8   1.8   1.8   1.8   1.8   1.8   1.8  ...   \n",
       "\n",
       "      14    15    16    17    18    19    20    21    22    23  \n",
       "0     22    22    21    19    17    16    15    15    15    15  \n",
       "1    1.8   1.8   1.8   1.8   1.8   1.8   1.8   1.8   1.8   1.8  \n",
       "2   0.37  0.37  0.47  0.69  0.56  0.45  0.38  0.35  0.36  0.32  \n",
       "3    0.1  0.13  0.14  0.23  0.18  0.12   0.1  0.09   0.1  0.08  \n",
       "4    2.5   2.2   2.5   2.3   2.1   1.9   1.5   1.6   1.8   1.5  \n",
       "5     11    11    22    28    19    12   8.1     7   6.9     6  \n",
       "6     14    13    25    30    21    13   9.7   8.6   8.7   7.5  \n",
       "7     65    64    51    34    33    34    37    38    38    36  \n",
       "8     52    51    66    85    85    63    46    36    42    42  \n",
       "9     36    45    42    49    45    44    41    30    24    13  \n",
       "10    NR    NR    NR    NR    NR    NR    NR    NR    NR    NR  \n",
       "11    47    49    56    67    72    69    70    70    70    69  \n",
       "12   3.9   4.4   9.9   5.1   3.4   2.3     2   1.9   1.9   1.9  \n",
       "13   1.9   1.9   1.9   2.1     2   1.9   1.9   1.9   1.9   1.9  \n",
       "14   307   304   307   124   118   121   113   112   106   110  \n",
       "15   313   305   291   124   119   118   114   108   102   111  \n",
       "16   2.5   2.2   1.4   2.2   2.8     3   2.6   2.7   2.1   2.1  \n",
       "17   2.1   2.1   1.9     1   2.5   2.5   2.8   2.6   2.4   2.3  \n",
       "18    24    24    23    21    20    19    18    18    18    18  \n",
       "19   1.8   1.8   1.8   1.8   1.8   1.8   1.8   1.8   1.8   1.8  \n",
       "\n",
       "[20 rows x 27 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.iloc[:, 3:]\n",
    "df[df == \"NR\"] = 0\n",
    "raw_data = df.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_data = {}\n",
    "\n",
    "for month in range(12):\n",
    "    sample = np.empty([18, 20 * 24])\n",
    "    for day in range(20):\n",
    "        sample[:, day * 24 : (day + 1) * 24] = raw_data[18 * (month * 20 + day) : 18 * (month * 20 + day + 1), :]\n",
    "    month_data[month] = sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_data_pm = {}\n",
    "\n",
    "for month in range(12):\n",
    "    sample = np.empty([1, 20 *24])\n",
    "    for day in range(20):\n",
    "        sample[:, day * 24 : (day + 1) * 24] = raw_data[18 * (month * 20 + day) + 9, :]\n",
    "    month_data_pm[month] = sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 480)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_data_pm[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 計算 18 個特徵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算每 5 個小時為一組\n",
    "x_fiv = np.empty([475 * 12, 18 * 5])\n",
    "y_fiv = np.empty([475 * 12, 1])\n",
    "\n",
    "for month in range(12):\n",
    "    for day in range(20):\n",
    "        for hour in range(24):\n",
    "            if day == 19 and hour > 14:\n",
    "                continue\n",
    "            x_fiv[month * 475 + day * 24 + hour, :] = month_data[month][:, day * 24 + hour : day * 24 + hour + 5].reshape(1, -1)\n",
    "            y_fiv[month * 475 + day * 24 + hour, :] = month_data[month][9, day * 24 + hour + 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算每 9 個小時為一組\n",
    "x = np.empty([471 * 12, 18 * 9])\n",
    "y = np.empty([471 * 12, 1])\n",
    "\n",
    "for month in range(12):\n",
    "    for day in range(20):\n",
    "        for hour in range(24):\n",
    "            if day == 19 and hour > 14:\n",
    "                continue\n",
    "            x[month * 471 + day * 24 + hour, :] = month_data[month][:, day * 24 + hour : day * 24 + hour + 9].reshape(1, -1)\n",
    "            y[month * 471 + day * 24 + hour, :] = month_data[month][9, day * 24 + hour + 9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 計算 PM 2.5 一個特徵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算每 9 個小時為一組，僅使用 PM 2.5 單一特徵\n",
    "x_pm = np.empty([471 * 12, 9 * 1])\n",
    "y_pm = np.empty([471 * 12, 1])\n",
    "\n",
    "for month in range(12):\n",
    "    for day in range(20):\n",
    "        for hour in range(24):\n",
    "            if day == 19 and hour > 14:\n",
    "                continue\n",
    "            x_pm[month * 471 + day * 24 + hour, :] = month_data_pm[month][:, day * 24 + hour : day * 24 + hour + 9].reshape(1, -1)\n",
    "            y_pm[month * 471 + day * 24 + hour, :] = month_data_pm[month][:, day * 24 + hour + 9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 標準化資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Standardization(data):\n",
    "    '''\n",
    "    計算平均值與標準差，並最終輸出標準化後的資料，替換到原本的資料集中。\n",
    "    '''\n",
    "    mean = np.mean(data, axis=0)\n",
    "    std = np.std(data, axis=0)\n",
    "    \n",
    "    for i in range(len(data)): # X 的行數\n",
    "        for j in range(len(data[0])): # X 的列數\n",
    "            if std.any != 0:\n",
    "                data[i][j] = (data[i][j] - mean[j]) / std[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把訓練集的數據標準化\n",
    "Standardization(x)\n",
    "Standardization(y)\n",
    "\n",
    "# 把訓練資料分成 8:2 的訓練資料與驗證資料\n",
    "import math\n",
    "x_train_set = x[: math.floor(len(x) * 0.8), :]\n",
    "y_train_set = y[: math.floor(len(y) * 0.8), :]\n",
    "x_validation = x[math.floor(len(x) * 0.8): , :]\n",
    "y_validation = y[math.floor(len(y) * 0.8): , :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把 5 小時的資料標準化\n",
    "Standardization(x_fiv)\n",
    "Standardization(y_fiv)\n",
    "\n",
    "# 把訓練資料分成 8:2 的訓練資料與驗證資料\n",
    "x_fiv_train_set = x_fiv[: math.floor(len(x) * 0.8), :]\n",
    "y_fiv_train_set = y_fiv[: math.floor(len(y) * 0.8), :]\n",
    "x_fiv_validation = x_fiv[math.floor(len(x) * 0.8): , :]\n",
    "y_fiv_validation = y_fiv[math.floor(len(y) * 0.8): , :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把 5 小時的資料標準化\n",
    "Standardization(x_pm)\n",
    "Standardization(y_pm)\n",
    "\n",
    "# 把訓練資料分成 8:2 的訓練資料與驗證資料\n",
    "x_pm_train_set = x_pm[: math.floor(len(x) * 0.8), :]\n",
    "y_pm_train_set = y_pm[: math.floor(len(y) * 0.8), :]\n",
    "x_pm_validation = x_pm[math.floor(len(x) * 0.8): , :]\n",
    "y_pm_validation = y_pm[math.floor(len(y) * 0.8): , :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Reg_model(x_data, y_data, x_validation, y_validation, learning_rate, iter_time):\n",
    "    dim = len(x_data[0]) + 1\n",
    "    w = np.zeros([dim, 1])\n",
    "    x_ind = np.concatenate((np.ones([len(x_data), 1]), x_data), axis = 1).astype(float)\n",
    "    \n",
    "    # 初始化 adagrad 的參數\n",
    "    adagrad = np.zeros([dim, 1])\n",
    "    eps = 1 * 10 ** -10 # 極小項，避免出現分母為零的狀況\n",
    "    loss_list = [] # 紀錄 Loss function 的遞迴狀況\n",
    "    \n",
    "    for i in range(iter_time):\n",
    "        loss = np.sqrt(np.sum(np.power(np.dot(x_ind, w) - y_data, 2))/len(x_data))\n",
    "        loss_list.append(loss)\n",
    "        \n",
    "        # adagrad 遞迴式\n",
    "        gradient = gradient = 2 * np.dot(x_ind.transpose(), np.dot(x_ind, w) - y_data) #dim*1\n",
    "        adagrad += gradient ** 2\n",
    "        w = w - learning_rate * gradient / np.sqrt(adagrad + eps)\n",
    "    \n",
    "    np.save('weight_LR{0}_i{1}.npy'.format(learning_rate, iter_time), w)\n",
    "    \n",
    "    # 交叉驗證 Loss function\n",
    "    test_x = np.concatenate((np.ones([len(x_validation), 1]), x_validation), axis = 1).astype(float)\n",
    "    \n",
    "    ans_y = np.dot(test_x, w)\n",
    "    loss = np.sqrt(np.sum(np.power(ans_y - y_validation, 2))/len(x_validation)) #rmse\n",
    "    \n",
    "    # 繪圖顯示遞迴效果\n",
    "    plt.plot(np.arange(1, iter_time), loss_list[1:])\n",
    "    plt.ylim(0, 3)\n",
    "    plt.xlim(0, iter_time)\n",
    "    \n",
    "    # 顯示結果\n",
    "    print(\"Loss function of training: {}\".format(loss_list[-1]))\n",
    "    print(\"Loss function of testing:  {}\".format(loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss function of training: 0.3453874167183221\n",
      "Loss function of testing:  0.34175366897247156\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": [
    "Reg_model(x_train_set, y_train_set, x_validation, y_validation, 0.1, 3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss function of training: 0.3457531003878947\n",
      "Loss function of testing:  0.3421259511919242\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": [
    "Reg_model(x_train_set, y_train_set, x_validation, y_validation, 1, 3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss function of training: 0.34579967936661354\n",
      "Loss function of testing:  0.34218603579369555\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": [
    "Reg_model(x_train_set, y_train_set, x_validation, y_validation, 5, 3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss function of training: 0.34581050807351194\n",
      "Loss function of testing:  0.34220023950470535\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": [
    "Reg_model(x_train_set, y_train_set, x_validation, y_validation, 50, 3000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 小時的 Loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss function of training: 0.350019572302232\n",
      "Loss function of testing:  0.3387192119834031\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": [
    "Reg_model(x_fiv_train_set, y_fiv_train_set, x_fiv_validation, y_fiv_validation, 0.1, 3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss function of training: 0.3499924864374308\n",
      "Loss function of testing:  0.33862075832888855\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": [
    "Reg_model(x_fiv_train_set, y_fiv_train_set, x_fiv_validation, y_fiv_validation, 1, 5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9 小時的 PM2.5 預測"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss function of training: 0.37287393375588124\n",
      "Loss function of testing:  0.35193893417615907\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": [
    "Reg_model(x_pm_train_set, y_pm_train_set, x_pm_validation, y_pm_validation, 0.1, 3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss function of training: 0.37287393362571625\n",
      "Loss function of testing:  0.3519378079899781\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": [
    "Reg_model(x_pm_train_set, y_pm_train_set, x_pm_validation, y_pm_validation, 0.5, 3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Machine Learning",
   "language": "python",
   "name": "machine_learning"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}