Assignment2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 2 - Classification of Iris Categories\n",
    "\n",
    "Onur Tokoglu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sl</th>\n",
       "      <th>sw</th>\n",
       "      <th>pl</th>\n",
       "      <th>pw</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sl   sw   pl   pw  c\n",
       "0    5.1  3.5  1.4  0.2  1\n",
       "1    4.9  3.0  1.4  0.2  1\n",
       "2    4.7  3.2  1.3  0.2  1\n",
       "3    4.6  3.1  1.5  0.2  1\n",
       "4    5.0  3.6  1.4  0.2  1\n",
       "5    5.4  3.9  1.7  0.4  1\n",
       "6    4.6  3.4  1.4  0.3  1\n",
       "7    5.0  3.4  1.5  0.2  1\n",
       "8    4.4  2.9  1.4  0.2  1\n",
       "9    4.9  3.1  1.5  0.1  1\n",
       "10   5.4  3.7  1.5  0.2  1\n",
       "11   4.8  3.4  1.6  0.2  1\n",
       "12   4.8  3.0  1.4  0.1  1\n",
       "13   4.3  3.0  1.1  0.1  1\n",
       "14   5.8  4.0  1.2  0.2  1\n",
       "15   5.7  4.4  1.5  0.4  1\n",
       "16   5.4  3.9  1.3  0.4  1\n",
       "17   5.1  3.5  1.4  0.3  1\n",
       "18   5.7  3.8  1.7  0.3  1\n",
       "19   5.1  3.8  1.5  0.3  1\n",
       "20   5.4  3.4  1.7  0.2  1\n",
       "21   5.1  3.7  1.5  0.4  1\n",
       "22   4.6  3.6  1.0  0.2  1\n",
       "23   5.1  3.3  1.7  0.5  1\n",
       "24   4.8  3.4  1.9  0.2  1\n",
       "25   5.0  3.0  1.6  0.2  1\n",
       "26   5.0  3.4  1.6  0.4  1\n",
       "27   5.2  3.5  1.5  0.2  1\n",
       "28   5.2  3.4  1.4  0.2  1\n",
       "29   4.7  3.2  1.6  0.2  1\n",
       "..   ...  ...  ...  ... ..\n",
       "120  6.9  3.2  5.7  2.3  3\n",
       "121  5.6  2.8  4.9  2.0  3\n",
       "122  7.7  2.8  6.7  2.0  3\n",
       "123  6.3  2.7  4.9  1.8  3\n",
       "124  6.7  3.3  5.7  2.1  3\n",
       "125  7.2  3.2  6.0  1.8  3\n",
       "126  6.2  2.8  4.8  1.8  3\n",
       "127  6.1  3.0  4.9  1.8  3\n",
       "128  6.4  2.8  5.6  2.1  3\n",
       "129  7.2  3.0  5.8  1.6  3\n",
       "130  7.4  2.8  6.1  1.9  3\n",
       "131  7.9  3.8  6.4  2.0  3\n",
       "132  6.4  2.8  5.6  2.2  3\n",
       "133  6.3  2.8  5.1  1.5  3\n",
       "134  6.1  2.6  5.6  1.4  3\n",
       "135  7.7  3.0  6.1  2.3  3\n",
       "136  6.3  3.4  5.6  2.4  3\n",
       "137  6.4  3.1  5.5  1.8  3\n",
       "138  6.0  3.0  4.8  1.8  3\n",
       "139  6.9  3.1  5.4  2.1  3\n",
       "140  6.7  3.1  5.6  2.4  3\n",
       "141  6.9  3.1  5.1  2.3  3\n",
       "142  5.8  2.7  5.1  1.9  3\n",
       "143  6.8  3.2  5.9  2.3  3\n",
       "144  6.7  3.3  5.7  2.5  3\n",
       "145  6.7  3.0  5.2  2.3  3\n",
       "146  6.3  2.5  5.0  1.9  3\n",
       "147  6.5  3.0  5.2  2.0  3\n",
       "148  6.2  3.4  5.4  2.3  3\n",
       "149  5.9  3.0  5.1  1.8  3\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "df_iris = pd.read_csv(u'iris.txt',sep=' ')\n",
    "\n",
    "df_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1/(1+np.exp(-x))\n",
    "\n",
    "def classify(category):\n",
    "    sl = np.matrix(df_iris.sl[0:]).T\n",
    "    sw = np.matrix(df_iris.sw[0:]).T\n",
    "    pl = np.matrix(df_iris.pl[0:]).T\n",
    "    pw = np.matrix(df_iris.pw[0:]).T\n",
    "    y = np.matrix(df_iris.c[0:]).T\n",
    "        \n",
    "    for i in range(len(y)):\n",
    "        if y[i,0] == category:\n",
    "            y[i,0] = 1\n",
    "        else:\n",
    "            y[i,0] = 0\n",
    "        \n",
    "    X = np.hstack((sl,sw,pl,pw))\n",
    "    w = np.matrix([1,1,1,1]).T\n",
    "    eta = 0.001\n",
    "    \n",
    "    for i in range(10000):\n",
    "        pr = sigmoid(X*w)\n",
    "        w = w + eta*X.T*(y-pr)\n",
    "\n",
    "    return (w,pr,y)\n",
    "\n",
    "def Error(y,pr,tresh):\n",
    "    fn = 0\n",
    "    fp = 0\n",
    "    for i in range(len(y)):\n",
    "        if (y[i,0] == 1.0 and pr[i,0] < tresh) :\n",
    "            fn += 1\n",
    "        elif (y[i,0] == 0.0 and pr[i,0] > tresh) :\n",
    "            fp += 1\n",
    "\n",
    "    return (fn/len(y),fp/len(y))\n",
    "\n",
    "def ErrorVTresh(y,pr):\n",
    "    tresh = np.matrix(np.linspace(0.2,1,50)).T\n",
    "    fn = np.zeros((len(tresh),1))\n",
    "    fp = np.zeros((len(tresh),1))\n",
    "    for i in range(len(tresh)):\n",
    "        fn[i,0],fp[i,0] = Error(y,pr,tresh[i,0])\n",
    "    \n",
    "    return (tresh,fn,fp)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Category 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w=[[ 0.71883956]\n",
      " [ 3.36466218]\n",
      " [-4.74062616]\n",
      " [-1.61263832]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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2U/oysjGkewpmFwtI2rbweE9gTEQ8q/Rlchvm+RuQvt/lvp42FBEt/Td9+vRBj8FxOk7H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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1197f5438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w,pr,y = classify(1)\n",
    "tresh,fn,fp = ErrorVTresh(y,pr)\n",
    "plt.plot(tresh,fn,\"rx-\")\n",
    "plt.plot(tresh,fp,\"bx-\")\n",
    "plt.xlabel('Treshold')\n",
    "plt.ylabel('Error')\n",
    "plt.title('FalseNegative(red) , FalsePositive(blue) vs Treshold')\n",
    "print('w='+str(w))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Category 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w=[[ 0.73300616]\n",
      " [-1.87866542]\n",
      " [ 1.04693053]\n",
      " [-2.84352623]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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86U/w5Zfwww/5lsRJyAcfwIIFcNllttywoY9ocJwM4YojDTbZBDp3tnnJnQJl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\ncZyU+H/DkHRTx54cQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1195eca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w,pr,y = classify(2)\n",
    "tresh,fn,fp = ErrorVTresh(y,pr)\n",
    "plt.plot(tresh,fn,\"rx-\")\n",
    "plt.plot(tresh,fp,\"bx-\")\n",
    "plt.xlabel('Treshold')\n",
    "plt.ylabel('Error')\n",
    "plt.title('FalseNegative(red) , FalsePositive(blue) vs Treshold')\n",
    "print('w='+str(w))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Category 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w=[[-4.80685745]\n",
      " [-4.96911165]\n",
      " [ 6.35877022]\n",
      " [ 7.88567013]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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XRES3LrpY7SOOKcBWShcjG0w6p+Dm/AyStsw93wUYHBELlS4mt1Y2fU3S9V2e7GhFEVHX\nj3HjxvV6DI7TcTpOx9n66ImqHnFExDKlK6veSdtw3GmSjk/FMRE4WNLRpBOb3qTt7OMNgRuyo4lV\ngSsi4s5qxmtmZl2r+iVHIuIO0sly+WkX557/mHSJjMLlZtF28S8zM6sTddE5PhA0NDT0dgglcZyV\n5Tgry3HWh6qfOV4LkqI/vA8zs1qRRNRp57iZmfUzThxmZgPFrbdCc88v++XEYWY2UIweDaef3uPk\n4T4OM7OBpLkZTj0VTZzoPg4zMyvB0KFwWOHdbMvjxGFmNpA0N8OvftWjKpw4zMwGiubm1Mdx9NFd\nz9sJJw4zs4HivvvgnHNgeWd34u1a1S85YmZmdeJz2e2Oli7tUTU+4jAzG2iWLOnR4k4cZmYDjROH\nmZmVxYnDzMzK4j4OMzMri484zMysLE4cZmZWFjdVmZlZWXzEYWZmZXHiMDOzsjhxmJlZWdzHYWZm\nJVu+HF5/vUdVVD1xSBojabqkGZJOLVJ+gKSpkh6T9JCk0aUua2ZmZXrtNRgypEdVVPXWsZJWAWYA\n+wDzgCnAYRExPTfPkIh4I3u+I3B1RHywlGVzdfjWsWZmpXjxRRg5Es2bV7e3jh0JzIyIORHRAkwC\nxuZnaE0ambWA5aUua2ZmZVqyBNZeu0dVVDtxDAfm5l6/kE1rR9KBkqYBtwBfKWdZMzMrw5IlsM46\nPaqiLm7kFBE3AjdK2h04G/h0uXU0NjaueN7Q0EBDQ0OlwjMz6/OamppoamqCZ5+FBQt6VFe1E8eL\nwGa515tk04qKiHslfUDSsHKXzScOMzNrb8UO9fXXw+uvc9asWd2uq9pNVVOArSSNkDQYOAy4OT+D\npC1zz3cBBkfEwlKWNTOzMlWgj6OqRxwRsUzSScCdpCR1SURMk3R8Ko6JwMGSjgbeAd4EDuls2WrG\na2bW71Wgj6Oqw3FrxcNxzcxKdM458MYb6Nxz63Y4rpmZ1ZM+MBzXzMzqSQWaqpw4zMwGEicOMzMr\ny9KlThxmZlYG93GYmVlZ3FRlZmZlcVOVmZmVxU1VZmZWFjdVmZlZyVpa0mONNXpUjROHmdlA0dq/\noW5daWQFJw4zs4GiAv0b4MRhZjZwVKB/A5w4zMwGjgoMxQUnDjOzgcNNVWZmVhY3VZmZWVmcOMzM\nrCzu4zAzs7K4j8PMzMripiozMyuLm6rMzKwsbqoyM7Oy9JWmKkljJE2XNEPSqUXKj5A0NXvcK2mn\nXNnsbPpjkh6qdqxmZv1ahRLHqhUIpUOSVgEuBPYB5gFTJN0UEdNzsz0H7BERiyWNASYCo7Ky5UBD\nRCyqZpxmZgNCH+njGAnMjIg5EdECTALG5meIiAciYnH28gFgeK5YNYjRzGxg6CN9HMOBubnXL9A+\nMRQ6Frg99zqAuyRNkXRcFeIzMxs4+kJTVTkk7QUcA+yemzw6IuZL2oCUQKZFxL3Flm9sbFzxvKGh\ngYaGhipGa2bWtzTdfTdNzc0wYQIMGtSjuhQRFQqrSOXSKKAxIsZkr08DIiLGF8y3E3AdMCYinu2g\nrnHA0oj4aZGyqOb7MDPr8958E9ZbD956CwBJRES3bgVY7aaqKcBWkkZIGgwcBtycn0HSZqSkcVQ+\naUgaImmt7PmawGeAJ6scr5lZ/1ShZiqoclNVRCyTdBJwJylJXRIR0yQdn4pjInAmMAy4SJKAlogY\nCWwI3CApsjiviIg7qxmvmVm/VcHEUdWmqlpxU5WZWRcefRS++lV47DGgvpuqzMysHlTwiMOJw8xs\nIHDiMDOzslTorHFw4jAzGxgqdNY4OHGYmQ0MbqoyM7OyOHGYmVlZli51U5WZmZXBRxxmZlYWJw4z\nMyuLh+OamVlZPBzXzMzK4qYqMzMrixOHmZmVpYLDcX1ZdTOz/m75clhtNXjnnRW3jfVl1c3MrGOv\nvQZDhvT4XuOtnDjMzPq7Cg7FBScOM7P+r4JDccGJw8ys/6vgiCpw4jAz6/+cOMzMrCwVHIoLThxm\nZv2fjzjMzKwsfS1xSBojabqkGZJOLVJ+hKSp2eNeSTuVuqyZmZWgLw3HlbQKcCGwL7ADcLik7Qpm\new7YIyJ2Bs4GJpaxrJmZdaWPDccdCcyMiDkR0QJMAsbmZ4iIByJicfbyAWB4qcuamVkJ+lhT1XBg\nbu71C7QlhmKOBW7v5rJmZlZMhRPHqhWrqYck7QUcA+zeneUbGxtXPG9oaKChoaEicZmZ9XlLl9I0\nezZNud/Jnqh24ngR2Cz3epNsWjtZh/hEYExELCpn2VaNFfpAzMz6nSVLaBg9moa99lox6ayzzup2\nddVuqpoCbCVphKTBwGHAzfkZJG0GXAccFRHPlrOsmZmVoC81VUXEMkknAXeSktQlETFN0vGpOCYC\nZwLDgIskCWiJiJEdLVvNeM3M+qUKD8f1jZzMzPq7DTeEqVNho41WTPKNnMzMrGO1Ho4raZCkn1Rs\njWZmVjstLemxxhoVq7LLxBERy+jmEFkzM+tlrVfGVbdapYoqtXP8MUk3A9cAr7dOjIjrKxaJmZlV\nXoWbqaD0xPEe4FVg79y0AJw4zMzqWW8ljog4pqJrNTOz2qjwUFwocVSVpE0k3SDp5exxnaRNKhqJ\nmZlVXoWvjAulD8e9lHTW9sbZ45ZsmpmZ1bMqNFWVmjg2iIhLI+Ld7HEZsEFFIzEzs8rrraYq4FVJ\nX8rO6Rgk6UukznIzM6tnvdhU9RXgEOA/wHzgv0iXQDczs3rWG6OqJA0CDoqIAyq6ZjMzq74lS2B4\nZe+BV+qZ44dXdK1mZlYbVejjKPUEwPskXQj8ifZnjj9a0WjMzKyyqtDHUWri+HD2939y04L2Z5Kb\nmVm96aU+jlWAX0XE1RVds5mZVV9vDMeNiOXAKRVdq5mZ1UYvDsf9i6TvStpU0rDWR0UjMTOzyqtC\nU1VJt46VNKvI5IiID1Q0mm7yrWPNzDowbBjMnAnrr99uck9uHet7jpuZ9VcRMHgwvP56+ptTtXuO\nSzol9/yLBWXndmeFZmZWI2+9BYMGrZQ0eqqrPo7Dcs+/X1A2pqKRmJlZZVWhfwO6Thzq4Hmx12Zm\nVk+qMBQXuk4c0cHzYq+LkjRG0nRJMySdWqR8W0n/lPSWpO8UlM2WNFXSY5IeKmV9ZmaWqcJQXOj6\nBMCdJS0hHV2skT0ne/2erirPTh68ENgHmAdMkXRTREzPzfYq8A3gwCJVLAcaImJRV+syM7MCVWqq\n6jRxRMSgHtY/EpgZEXMAJE0CxgIrEkdELAAWSPp8keVF6eeamJlZXi/1cfTUcGBu7vUL2bRSBXCX\npCmSjqtoZGZm/d3Spb3SVNXbRkfEfEkbkBLItIi4t9iMjY2NK543NDTQ0NBQmwjNzOpV7oijqamJ\npqamilRb1RMAJY0CGiNiTPb6NNIZ5+OLzDsOWBoRP+2grg7LfQKgmVkR48fDq6/Cj3+8UlHVTgCs\ngCnAVpJGSBpMOi/k5k7mX/EmJA2RtFb2fE3gM8CT1QzWzKxfqdJw3Ko2VUXEMkknAXeSktQlETFN\n0vGpOCZK2hB4GFgbWC7pZGB7YAPgBkmRxXlFRNxZzXjNzPqVJUtggw0qXm3V+zgi4g5g24JpF+ee\nvwRsWmTR12i7gZSZmZWrj46qMjOz3uLEYWZmZanScFwnDjOz/spHHGZmVpJbb4Xm5vaJo7k5Ta8A\nJw4zs/5m9Gg4/XRYvDgljubm9Hr06IpU7zsAmpn1RwsXwoYbwqOPwq9/DeecA0OHrijuyQmA9X7J\nETMz644ZM2DzzWGnnWDWrHZJo6fcVGVm1h9deSWsu25KGuefn5qrKsRNVWZm/c2iRelo45ZbYI89\n2vo4cs1V9XytKjMzq7XLL09HG5/8ZHo9dGhKGvfdV5HqnTjMzPqbBQvgkENAuQOKoUPhc5+rSPVO\nHGZm/c1118FBB1WteicOM7P+5Omn01DcUaOqtgonDjOz/uSGG+ALX4BVqvfz7sRhZtafVLmZCjwc\n18ys/3j+edhlF5g/H1ZbrdNZPRzXzMxSM9UBB3SZNHrKicPMrL+oQTMVuKnKzKx/eOkl2HZb+M9/\n4D3v6XJ2N1WZmQ10N90EY8aUlDR6yonDzKw/uP56OPjgmqzKTVVmZn3dokUwYgTMmwdrrVXSIm6q\nMjMbyCZPhr32Kjlp9FTVE4ekMZKmS5oh6dQi5dtK+qektyR9p5xlzcyMmjZTQZWbqiStAswA9gHm\nAVOAwyJiem6e9wIjgAOBRRHx01KXzdXhpiozG1huvTXdQ3zVVWHjjWH27HSZkfvuK+kquPXcVDUS\nmBkRcyKiBZgEjM3PEBELIuIR4N1ylzUzG7BGj043Z7ruunRBw1VWSa9Hj676qqt9z/HhwNzc6xdI\nCaHay5qZ9W+tN2f62Mfgy19e6Q5/1VTtxFEzjY2NK543NDTQ0NDQa7GYmdXEggXwyitwxhnp3uKd\nJI2mpiaampoqstpq93GMAhojYkz2+jQgImJ8kXnHAUtzfRzlLOs+DjMbeA4+GObOhauvhvPPL+uI\no577OKYAW0kaIWkwcBhwcyfz599EucuamQ0c99wDd9yRRlRtvnlKGqefDs3NVV911U8AlDQG+Dkp\nSV0SET+SdDzp6GGipA2Bh4G1geXAa8D2EfFasWU7WIePOMxsYNllFzj8cPje99qmNTfXZFSVzxw3\nM+tr/vY3OO44mDYNBg/uVhX13FRlZmaVFAGnnQZnn93tpNFTThxmZn3JddfBu+/CoYf2WghuqjIz\n6yvefRd22AF+8Qv4zGd6VJWbqszMBoLf/Q422QQ+/eleDcNHHGZmfcEbb8DWW8ONN6azxXvIRxxm\nZv3Rrbe2nZdxwQXwiU+k5HHrrb0alo84zMzqVXNzOqnvu9+FkSPh9tvh0ksrck0qn8fhxGFm/dWc\nObDPPvDxj7dd2LACFzLsSeLoNxc5NDPrVyLg2mvh29+G3XeHK6/s8kKGteI+DjOzasv3VbRqbk7T\ni5X961+paeqss+A3v4H1109J4/zza3Itqq44cZiZVVvrTZdaf/Rb+y5Gj25f1tKSksXIkbDffunS\nIpMnp+apGl/IsDPu4zAzq4XmZjjxRNh003Rl2333hTXWSGVvvgl//jMsWpSe33QTfPjDbbeHzTdP\nlXEhw864c9yJw8zq2dtvw/jx8LOfpR/+44+HdddtP8/ixXDxxfDcc7DFFlUPyedxmJnVSkf9FY2N\nxaefdx7svDM8+CDsv3/qqxg0CL7//ZRMxo9PzwcNSmU/+UmvN0V1xYnDzKwcHfVXHHNM++nPPAOj\nRsEvfwlnnpn6KC64YOW+itbl66wfozNuqjIzK1dzM3z963DIIXDFFfB//g+svTYsXQq//nW6ydLZ\nZ8OXv5yOKP7+9477KqBq/RidcR+HE4eZ1cr8+encir/9DV55JTVDrb56W/nbb8PUqXDLLfD5z/de\nnF1wH4eZWbUtWwYXXQQ77QQbbwxf+ELqkxg9Oo2IevDB9Hf06DT99tvrurmpJ5w4zMwKFXaAP/54\nOrfil7+Em29uGyWV75OYM6fP9VV0l5uqzMwKtXZYf//7MGECXHZZOtK49lq4//7ifRITJsC3vlXz\nvoruch+HE4eZVdpVV8HXvpZumjR0KPz0p3VxnahK8UUOzcwq5YUX4JvfhCefTCfkHXlk3VxcsF64\nj8PMBqbCfoxly9LJettvn5ql/v731MxURxcXrBdVTxySxkiaLmmGpFM7mOcCSTMlPS7pI7npsyVN\nlfSYpIeqHauZDSD5E/kefjide3HhhfDXv6a+ih/+cEB0dHdHVfs4JK0CzAD2AeYBU4DDImJ6bp79\ngJMi4nOSPg78PCJGZWXPAR+NiEVdrMd9HGZWvuefT+dazJ+fzse45hpYb72qXlywXtTzeRwjgZkR\nMSciWoBJwNiCecYCfwCIiAeBdSVtmJWpBjGa2UATAdddl5LDdtvBggXw29+mpAEpORT2aQwd2m+S\nRk9V+0d5ODA39/qFbFpn87yYmyeAuyRNkXRc1aI0s8oq98ZFXZV1dAHB7tTXepOkM89MlwfZYAP3\nY5Sp3vfmR0fELsBngRMl7d7RjI2NjSseTU1NNQvQzIoo9cZFpZYVXkCwO/W1tMD//E9KGvvuC01N\ncNttA6Z1kCMyAAAQdklEQVQfo6mpqd3vZE9Uu49jFNAYEWOy16cBERHjc/P8Grg7Iv6UvZ4O7BkR\nLxXUNQ5YGhE/LbIe93GY1ZvmZvjqV2HrrdMIpWI3Ltpjj9LLurNMa9kdd6R4Xn893STpIx8ZEP0Y\nnanbEwAlDQKeJnWOzwceAg6PiGm5eT4LnJh1jo8CJkTEKElDgFUi4jVJawJ3AmdFxJ1F1uPEYVZv\nnngC9twz3dXua18rfuOiiRPLK+vOMvmyGt0kqS/oSeIgIqr6AMaQksdM4LRs2vHA13LzXAg8A0wF\ndsmmbQE8DjwGPNG6bAfriIiIRYsiJk8OM+ttr74aMWJExKc+FTFrVsQJJ6R/0FaLFqVp5ZR1Z5mu\nygaw7Heze7/r3V2wnh7Aiu+GvxNmvaylJaKhIWLnnVf+0V+0KFb6Zy2lbPbs8pfpqmyA60ni6DfX\nqjrhhOCcc3xVALNed8opcNdd6fHe97ZNL+XGRR2VdXYBwe7UN0D6MTpTt30ctSIpmpqCPffs7UjM\nBrhJk+AHP4ApU2D99Xs7GutEPZ8AWDP77gtnnAHvvNPbkZgNUI8/Dt/4Btxwg5NGP9dvEsdDD8Hl\nl6cBE7fd1r6sJ+cdVaOskucyVbPMrFP5L8+CBemOeD/+cbq6rPVv3e0cqacH2aiqhQsjvv3tiDXX\njDjyyIgFCyrTh1bpsmr081WjzKxTrV+WV16J2HvviJNP9penD8Gd4+3P42i9btnTT8Py5bDaaqCs\nJS8inUC66qrw7ru9V1YvcbSWLV8OJ58MS5akK0t7kIGVpLkZGhrSeRM77ADnnusvTx/hGzkV2Gyz\ndFvgLbaAp56CESPal8+Zk77jvV1WL3EAPPgg7LNPOsn3qafSQBSzLt1yS0oeU6fC73/vpDFA9Js+\njrzm5nS9slmz0r3lW1pgzTXTo6UlTevtsnqJo7XsuuvaTqr9r/+CMWPSvIWfq/s+bIVHHoFvfxt2\n280XCRxoutvGVU8Psj6OiPrqI+irfRzHHhtx1FERQ4ZEXHxxxPLl7vuwAi+9FLHJJhFjxriDrI/C\nfRxtfRydXbcM6qOsGucyVaPsPe+Bww6DbbdNTVu//KVbIox0iPqpT8H7358uS+6T6/oknwDoixxW\nzTPPpH6PoUPhO99JJwWvvnpvR2W96hvfgNmz01VmV+mXrd0Dgk8AtKpoboaf/Sw1X++/PzzwQLq7\n5o9+VB/nmnR6PkzjFJrnLG5fNmcxtzZO6XZZvz1pp5wYL70Ubr8djjrKSWMg624bVz09yPVxWGV0\n1C9y+eURw4dHbLddxDPPrDxv3fQVzW6OEz7UFItmN6ey3OvulvXLDq1yYnzggYj11484/HD3Y/QD\nuI/DTVWV1llf0R57wKmnwmWXpVsfPPQQ7Ldf+/vm3H57Gt7f1FTdsk6XWfgmt/9mLg1bz6PpiWHs\n97FXWWNIOjJ/843g9inr07DjqzQ9sX7RsjM/+wiT71mHc77yLEOHrVKlIOukrJRlZs1KN0C68kp3\ndvUD7uNw4ugVkyenJqxjjy1+35zf/rY2ZUWnL1sGDz/M4oem8dt3vsyx297Dumu1/44sfk389uk9\ni5a9umRVLpu5O8duew8/+8yfWWtw7iJoFQuyzspKWWbWrHSbVevz6vpGTrV44KaqmmttwajkPXXK\nLps8OTUl5afPbo4YPz5ihx1i0Z5j44Rt7opZ/5jbrvkpoq0JqrOyKTe9ENus+5/YdPiyuPnmdJOw\nRbOb2wWyaHZzTJ7ccdm4cR0vUz8fZA/qsz4L38jJiaOW6qZpfmpBf8Tjs+OE9a6IRe/bJhb98oo4\nYYfK9HHsv/nU+MDm78bn9n07jt72/rLqnP2P5yvbZ9KbfRzFNr71WT1JHG6qsrLVy7kyEybAt45Z\nzNDx309jhk8/neYvHsd9nzsX/v1vRh+zDUNHtDW5NM9ZzH2Xzkj1lVl298SZPD5rHX5x+1Zsv8Mq\nnHIK/PGPcMLRS1n72akALN1yZy76w9ocdVSu7JarWLr/4Vz0h7U58US45ho455TFDH3y3tp+WNU4\nscjnavRp7uNw4hi4ZsyAL38Z7r8/3QfiwAOrurrp0+Gb30w3t/vQh1Y+p+Xtt+HJJ1cua51+6KFw\n0UUwbFhVwzTrks/jsIHn7bfhrLNg1Kh0PsHMmenXvMrXStpoo3RwM2tWGl32l7/Aww+nx1/+kqYV\nlrVOnzoV/v1v2G67dO8Y7+tYX+UjDiuuXtqjipXdfTccd1x6vf32cMEF6XlzM5x+OtW6+Xxh9fnX\nULzslFPSvY3y0489Nl3yf9CgNFBp112r91GVUtYXLoEzUGOsZmugR1W5c7zy6qXjNl82c2bE0Uen\nMxD32y9i0qSVO2kXLYq2IUuVNXlyx6vrqGzcuOLTb7op4pxzIlZfPeLUUyPeeqvv9Y07xurHWE3U\nc+e4pDHABFKz2CURMb7IPBcA+wGvA1+OiMdLXTabL6r9Pgakl1+GAw6AadPgjTdg8OD2d3965510\nR6iWltqUvf02/Pd/p6ap88/vFyehPfFE+ojnzUs30+qtj7g3NqdjXLls113TrQ0uuqj6X++6PeIg\n/eA/A4wAVgMeB7YrmGc/4Nbs+ceBB0pdNldHBfNwddx99929HUJJVsR5zz0RH/xgxD77REDEk09G\nLF3a/vHkk7Utmz49lc2a1fc+z07MmtX7H/Ftt91d883ZnbLf/e7uuoijs7I0/e6y63viiVS23noR\nZ5wR8cYb1f1u0oMjjmp3jo8EZkbEnIhoASYBYwvmGQv8Ifv1fxBYV9KGJS7bXh1fVK6pqanuYwRo\nuu02OPJIOOgg+MEPYJttUm/vRRele86utVZ6vPtumlbLsgsuWHHDoKY77qAvWLHdO5C/6VhvfsT3\n3NNU881Zblma3tTrcXQdI5x8clPZ9f3qV6nsgAPSkeiOO8LZZ1f/epbd0t2MU8oDOBiYmHv9JeCC\ngnluAT6Re30XsEspy+bK6r4xdFxrY3e9xrhwYcRll8W4IUMidtop7f7UW4y5snG77lr9RuAKGDdu\nXIdldfAxrvg67rrruF6Po5T+g1NPHVf3MS5alLZ7T+v7058iNt00Yuut08F2pWOkXvs4JB0M7BsR\nX8tefwkYGRHfzM1zC3BeRPwze/0X4BRgi66WzdURMWJEGufY2oA4fTpsuSU8+2zbdOi1skaJxoj6\njfGRR2CjjWhcvpzG++5LQzrqYahKB2WNp51G4yc/WfcnoTU2NtLY2Fi0rF4Grk2YAG+91ciPftRY\nkfqqOWJpwoS2z7NeYxw6tG2793Rde+2VDvwvvjjdofe553r+L986fc6cOj0BUNIooDEixmSvTyNl\nufG5eX4N3B0Rf8peTwf2JCWOTpfN1eGecTOzMnU3caxa6UAKTAG2kjQCmA8cBhxeMM/NwInAn7JE\n0xwRL0laUMKyQPffvJmZla+qiSMilkk6CbiTtiG10yQdn4pjYkTcJumzkp4hDcc9prNlqxmvmZl1\nrV+cOW5mZrXTZ65VJWmMpOmSZkg6tUj5EZKmZo97Je1Yp3EekMX4mKSHJI2uxzhz831MUoukg2oZ\nX279XX2ee0pqlvRo9jijHuPM5mnItvuTku6utxglfTeL71FJT0h6V1LNz7IsIc51JN0s6fEszi/X\nOsYsjq7iHCrp+uz//QFJ2/dCjJdIeknSvzqZ5wJJM7PP88MlVdzd4Vi1fFDaiYSjgHWz52PITiSs\nwziH5J7vCEyrxzhz8/0VmAwcVI9xkgZS3Fzr2LoR57rAU8Dw7PV76y3Ggvk/D/ylTj/L75NGYgK8\nF3gVWLUO4/wxcGb2fNte+jx3Bz4M/KuD8qInYHf16CtHHF2eDBgRD0TE4uzlA8DwGscIpcX5Ru7l\nWsDyGsbXqtSTK78BXAu8XMvgckqNs7cHR5QS5xHAdRHxIkBELKjDGPMOB66qSWTtlRJnAGtnz9cG\nXo2Id2sYI5QW5/bA3wAi4mlgc0kb1DLIiLgXWNTJLB2dgN2pvpI4hgNzc69foPPEcCxwe1UjKq6k\nOCUdKGka6eTHr9Qotrwu45S0MXBgRPyK3vthLnW775YdZt/aG80BlBbnNsAwSXdLmiLpqJpFl5T8\nPyRpDdJR+3U1iKtQKXFeCGwvaR4wFTi5RrHllRLnVOAgAEkjgc2ATWoSXekK38eLlLDTXe3huDUn\naS/SyKzdezuWjkTEjcCNknYHzgY+3cshFTMByLfb9vZefUceATaLiDck7QfcSPqRrjerkq6IsDew\nJnC/pPsj4pneDauo/YF7I6ISF6eohn2BxyJib0lbAndJ2ikiXuvtwAr8CPi5pEeBJ4DHgGW9G1Jl\n9JXE8SIpW7faJJvWjqSdgInAmIjo7PCsWkqKs1VE3CvpA5KGRcTCqkfXppQ4dwUmSRKpHXk/SS0R\ncXONYoQS4sz/WETE7ZIuqtPP8wVgQUS8Bbwl6e/AzqR28loo57t5GL3TTAWlxXkMcB5ARDwraRaw\nHfBwTSJMSvluLiXXopDF+VxNoivdi8Cmuded/matUOvOmm528AyirSNqMKkj6oMF82wGzARG1Xmc\nW+ae7wLMrcc4C+a/lN7pHC/l89ww93wkMLtO49yOdB22QcAQ0h7o9vUUYzbfuqTO5jVq/TmW8Vn+\nEhjXuv1JTS3D6jDOdYHVsufHAZf10me6OfBEB2Wfpa1zfBQldo73iSOOKOFEQuBMYBhwUbaX3BIR\nI+swzoMlHQ28A7wJHFLLGMuIs90itY4RSo7zvyR9HWghfZ6H1mOcETFd0p+Bf5GaKyZGxL/rKcZs\n1gOBP0fEm7WKrRtxng1clhtiekrU9giz1Dg/CPxe0nLSiLqv1jJGAElXAg3A+pKeB8aREl3r97Lo\nCdhd1ptlGjMzs5L0lVFVZmZWJ5w4zMysLE4cZmZWFicOMzMrixOHmZmVxYnDzMzK4sRhBkgalruk\n+HxJL+Red/t8J0l/lHRAGfNvKemxDsr+kV0dwaxX9YkTAM2qLTuB7CMAkv4f8FpE/LRwPkmK6p/8\n5JOrrK75iMNsZSsu6JgdATwl6XJJTwIbZTfw+aekhyVdlV1NFknnZzdpelzSebn69pZ0n6RnJI3N\n5pWk/81uRDRV0sErBSGtIenqbP3XAqtX+X2blcRHHGZd2xb4UkQ8lt1P4TRg74h4S9IPgJMl/Q7Y\nLyI+BOkudbnlN4iI0Up3pbwauIl0qZntImLH7P4HUyTdU7Dek4CFEbFDdme2KdV9m2al8RGHWdee\njYjWfodPkG7Q88+sL+II0oXuFgLLJE2UdCCQv2HXjQAR8QSwcTZtNNkVaCPiJeAfpCsS5+0BXJ7N\n8zjpekdmvc5HHGZdez33XMDtEfHfhTNJ2pV0b5VDgK+T7hsB8HbB8sWUcr+Ter0nig0wPuIw61r+\nB/ufwJ6StgCQNETSVpLWIt3z/jbgO6T7PHdW1z+Aw7K+jg1JRzKF95P4O3Bktp6dgR0q8m7MeshH\nHGZdWzHKKSJelvRV4E+SBmdlPyBd0v16SauTksO3C5cteH0t8HHSZdaXA9+OiAWS1s3NeyHpstxP\nkZqpHq3s2zLrHl9W3czMyuKmKjMzK4sTh5mZlcWJw8zMyuLEYWZmZXHiMDOzsjhxmJlZWZw4zMys\nLE4cZmZWlv8Pg/G3xNuUzEIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ba5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w,pr,y = classify(3)\n",
    "tresh,fn,fp = ErrorVTresh(y,pr)\n",
    "plt.plot(tresh,fn,\"rx-\")\n",
    "plt.plot(tresh,fp,\"bx-\")\n",
    "plt.xlabel('Treshold')\n",
    "plt.ylabel('Error')\n",
    "plt.title('FalseNegative(red) , FalsePositive(blue) vs Treshold')\n",
    "print('w='+str(w))"
   ]
  }
 ],
 "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": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}