Code.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f6fdc999",
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   "source": [
    "import numpy as np\n",
    "from numpy import random\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#ALI MUSTAFA I18_0619"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d5e4d5a",
   "metadata": {},
   "source": [
    "# =======Sorting Data======="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe926736",
   "metadata": {},
   "source": [
    "## Reading in Panda "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2507df7",
   "metadata": {},
   "source": [
    "### (Messidor for Clustering and Mammography for k-NN Classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "025f2490",
   "metadata": {},
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     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "messidor = pd.read_csv('messidor_features.data',sep=\",\")#names = arrTitle)\n",
    "messidor"
   ]
  },
  {
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   "execution_count": 3,
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>1.763305</td>\n",
       "      <td>0.137858</td>\n",
       "      <td>0.011221</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.560632</td>\n",
       "      <td>0.129843</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1148</th>\n",
       "      <td>4.740919</td>\n",
       "      <td>1.077570</td>\n",
       "      <td>0.563518</td>\n",
       "      <td>0.326860</td>\n",
       "      <td>0.239568</td>\n",
       "      <td>0.174584</td>\n",
       "      <td>0.485972</td>\n",
       "      <td>0.106690</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>1.750548</td>\n",
       "      <td>0.046597</td>\n",
       "      <td>0.021180</td>\n",
       "      <td>0.008472</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.556192</td>\n",
       "      <td>0.088957</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1150 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        5.27092  0.771761  0.018632  0.006864  0.003923  0.003923.1  0.486903  \\\n",
       "0      3.325423  0.234185  0.003903  0.003903  0.003903    0.003903  0.520908   \n",
       "1     12.687485  4.852282  1.393889  0.373252  0.041817    0.007744  0.530904   \n",
       "2      9.118901  3.079428  0.840261  0.272434  0.007653    0.001531  0.483284   \n",
       "3      0.410381  0.000000  0.000000  0.000000  0.000000    0.000000  0.475935   \n",
       "4      2.305771  0.323724  0.000000  0.000000  0.000000    0.000000  0.502831   \n",
       "...         ...       ...       ...       ...       ...         ...       ...   \n",
       "1145   0.031145  0.003115  0.000000  0.000000  0.000000    0.000000  0.537470   \n",
       "1146   8.067688  0.979548  0.001552  0.000000  0.000000    0.000000  0.516733   \n",
       "1147   1.763305  0.137858  0.011221  0.000000  0.000000    0.000000  0.560632   \n",
       "1148   4.740919  1.077570  0.563518  0.326860  0.239568    0.174584  0.485972   \n",
       "1149   1.750548  0.046597  0.021180  0.008472  0.000000    0.000000  0.556192   \n",
       "\n",
       "      0.100025  1.2  0  \n",
       "0     0.144414    0  0  \n",
       "1     0.128548    0  1  \n",
       "2     0.114790    0  0  \n",
       "3     0.123572    0  1  \n",
       "4     0.126741    0  1  \n",
       "...        ...  ... ..  \n",
       "1145  0.116795    0  0  \n",
       "1146  0.124190    0  0  \n",
       "1147  0.129843    0  0  \n",
       "1148  0.106690    1  1  \n",
       "1149  0.088957    0  0  \n",
       "\n",
       "[1150 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messidor_data = messidor[['5.27092','0.771761','0.018632','0.006864','0.003923','0.003923.1','0.486903','0.100025','1.2','0']]\n",
    "\n",
    "messidor_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c148602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BI-RADS assessment</th>\n",
       "      <th>Age</th>\n",
       "      <th>Shape</th>\n",
       "      <th>Margin</th>\n",
       "      <th>Density</th>\n",
       "      <th>Severity</th>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "      <th>4</th>\n",
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       "      <th>956</th>\n",
       "      <td>4</td>\n",
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       "      <th>957</th>\n",
       "      <td>4</td>\n",
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       "      <th>958</th>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>961 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    BI-RADS assessment Age Shape Margin Density  Severity\n",
       "0                    5  67     3      5       3         1\n",
       "1                    4  43     1      1       ?         1\n",
       "2                    5  58     4      5       3         1\n",
       "3                    4  28     1      1       3         0\n",
       "4                    5  74     1      5       ?         1\n",
       "..                 ...  ..   ...    ...     ...       ...\n",
       "956                  4  47     2      1       3         0\n",
       "957                  4  56     4      5       3         1\n",
       "958                  4  64     4      5       3         0\n",
       "959                  5  66     4      5       3         1\n",
       "960                  4  62     3      3       3         0\n",
       "\n",
       "[961 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mammographic_titles = ['BI-RADS assessment','Age','Shape','Margin','Density','Severity']\n",
    "mammographic_data = pd.read_csv('mammographic_masses.data',sep=\",\",names = mammographic_titles)\n",
    "\n",
    "mammographic_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dec3d08",
   "metadata": {},
   "source": [
    "### Convert Data into Float/Fill Empty and pick only 6 columns (MA detection results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "26f53bf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            MA1       MA2       MA3       MA4       MA5       MA6   EU_Dist  \\\n",
      "0      3.325423  0.234185  0.003903  0.003903  0.003903  0.003903  0.520908   \n",
      "1     12.687485  4.852282  1.393889  0.373252  0.041817  0.007744  0.530904   \n",
      "2      9.118901  3.079428  0.840261  0.272434  0.007653  0.001531  0.483284   \n",
      "3      0.410381  0.000000  0.000000  0.000000  0.000000  0.000000  0.475935   \n",
      "4      2.305771  0.323724  0.000000  0.000000  0.000000  0.000000  0.502831   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "1145   0.031145  0.003115  0.000000  0.000000  0.000000  0.000000  0.537470   \n",
      "1146   8.067688  0.979548  0.001552  0.000000  0.000000  0.000000  0.516733   \n",
      "1147   1.763305  0.137858  0.011221  0.000000  0.000000  0.000000  0.560632   \n",
      "1148   4.740919  1.077570  0.563518  0.326860  0.239568  0.174584  0.485972   \n",
      "1149   1.750548  0.046597  0.021180  0.008472  0.000000  0.000000  0.556192   \n",
      "\n",
      "      Diameter  AM/FM  cLabel  \n",
      "0     0.144414      0       0  \n",
      "1     0.128548      0       1  \n",
      "2     0.114790      0       0  \n",
      "3     0.123572      0       1  \n",
      "4     0.126741      0       1  \n",
      "...        ...    ...     ...  \n",
      "1145  0.116795      0       0  \n",
      "1146  0.124190      0       0  \n",
      "1147  0.129843      0       0  \n",
      "1148  0.106690      1       1  \n",
      "1149  0.088957      0       0  \n",
      "\n",
      "[1150 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "messidor_data = messidor_data.rename(columns={'5.27092' : 'MA1','0.771761': 'MA2','0.018632': 'MA3','0.006864': 'MA4','0.003923': 'MA5', \\\n",
    "                                              '0.003923.1': 'MA6','0.486903': 'EU_Dist','0.100025' : 'Diameter','1.2' : 'AM/FM','0' : 'cLabel'})\n",
    "\n",
    "# water_data['Q-E'] = pd.to_numeric(water_data['Q-E'], errors='coerce')\n",
    "# water_data['Q-E'] = water_data['Q-E'].fillna(water_data['DBO-E'].mean())\n",
    "\n",
    "# water_data['ZN-E'] = pd.to_numeric(water_data['ZN-E'], errors='coerce')\n",
    "# water_data['ZN-E'] = water_data['ZN-E'].fillna(water_data['DBO-E'].mean())\n",
    "\n",
    "# water_data['PH-E'] = pd.to_numeric(water_data['PH-E'], errors='coerce')\n",
    "# water_data['PH-E'] = water_data['PH-E'].fillna(water_data['DBO-E'].mean())\n",
    "\n",
    "# water_data['DBO-E'] = pd.to_numeric(water_data['DBO-E'], errors='coerce')\n",
    "# water_data['DBO-E'] = water_data['DBO-E'].fillna(water_data['DBO-E'].mean())\n",
    "\n",
    "# water_data['DQO-E'] = pd.to_numeric(water_data['DQO-E'], errors='coerce')\n",
    "# water_data['DQO-E'] = water_data['DQO-E'].fillna(water_data['DBO-E'].mean())\n",
    "\n",
    "# water_data['SS-E'] = pd.to_numeric(water_data['SS-E'], errors='coerce')\n",
    "# water_data['SS-E'] = water_data['SS-E'].fillna(water_data['DBO-E'].mean())\n",
    "\n",
    "print(messidor_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c58542f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "messidor_data['MA1'] = pd.to_numeric(messidor_data['MA1'], errors='coerce')\n",
    "messidor_data['MA1'] = messidor_data['MA1'].fillna(messidor_data['MA1'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "93a018c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            MA1       MA2       MA3       MA4       MA5       MA6   EU_Dist  \\\n",
      "0      3.325423  0.234185  0.003903  0.003903  0.003903  0.003903  0.520908   \n",
      "1     12.687485  4.852282  1.393889  0.373252  0.041817  0.007744  0.530904   \n",
      "2      9.118901  3.079428  0.840261  0.272434  0.007653  0.001531  0.483284   \n",
      "3      0.410381  0.000000  0.000000  0.000000  0.000000  0.000000  0.475935   \n",
      "4      2.305771  0.323724  0.000000  0.000000  0.000000  0.000000  0.502831   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "1145   0.031145  0.003115  0.000000  0.000000  0.000000  0.000000  0.537470   \n",
      "1146   8.067688  0.979548  0.001552  0.000000  0.000000  0.000000  0.516733   \n",
      "1147   1.763305  0.137858  0.011221  0.000000  0.000000  0.000000  0.560632   \n",
      "1148   4.740919  1.077570  0.563518  0.326860  0.239568  0.174584  0.485972   \n",
      "1149   1.750548  0.046597  0.021180  0.008472  0.000000  0.000000  0.556192   \n",
      "\n",
      "      Diameter  AM/FM  cLabel  \n",
      "0     0.144414      0       0  \n",
      "1     0.128548      0       1  \n",
      "2     0.114790      0       0  \n",
      "3     0.123572      0       1  \n",
      "4     0.126741      0       1  \n",
      "...        ...    ...     ...  \n",
      "1145  0.116795      0       0  \n",
      "1146  0.124190      0       0  \n",
      "1147  0.129843      0       0  \n",
      "1148  0.106690      1       1  \n",
      "1149  0.088957      0       0  \n",
      "\n",
      "[1150 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "panda_titles = ['MA1' ,'MA2',  'MA3',  'MA4',  'MA5',  'MA6',   'EU_Dist',  'Diameter',  'AM/FM',  'cLabel']\n",
    "\n",
    "for i in range(len(panda_titles)):    \n",
    "    messidor_data[panda_titles[i]] = pd.to_numeric(messidor_data[panda_titles[i]], errors='coerce')\n",
    "    messidor_data[panda_titles[i]] = messidor_data[panda_titles[i]].fillna(messidor_data[panda_titles[i]].mean())\n",
    "\n",
    "print(messidor_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47f8c9f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BI-RADS assessment     4.000000\n",
      "Age                   70.000000\n",
      "Shape                  3.000000\n",
      "Margin                 2.796276\n",
      "Density                3.000000\n",
      "Severity               0.000000\n",
      "Name: 6, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(mammographic_titles)):  \n",
    "    if i == 2:\n",
    "        mammographic_data[mammographic_titles[i]] = pd.to_numeric(mammographic_data[mammographic_titles[i]], errors='coerce')\n",
    "        mammographic_data[mammographic_titles[i]] = mammographic_data[mammographic_titles[i]].fillna(3)\n",
    "        continue\n",
    "    mammographic_data[mammographic_titles[i]] = pd.to_numeric(mammographic_data[mammographic_titles[i]], errors='coerce')\n",
    "    mammographic_data[mammographic_titles[i]] = mammographic_data[mammographic_titles[i]].fillna(mammographic_data[mammographic_titles[i]].mean())\n",
    "\n",
    "\n",
    "print(mammographic_data.iloc[6])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d65c4f18",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>4.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>4.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>5.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>4.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>961 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     BI-RADS assessment   Age  Margin   Density  Severity\n",
       "0                   5.0  67.0     5.0  3.000000         1\n",
       "1                   4.0  43.0     1.0  2.910734         1\n",
       "2                   5.0  58.0     5.0  3.000000         1\n",
       "3                   4.0  28.0     1.0  3.000000         0\n",
       "4                   5.0  74.0     5.0  2.910734         1\n",
       "..                  ...   ...     ...       ...       ...\n",
       "956                 4.0  47.0     1.0  3.000000         0\n",
       "957                 4.0  56.0     5.0  3.000000         1\n",
       "958                 4.0  64.0     5.0  3.000000         0\n",
       "959                 5.0  66.0     5.0  3.000000         1\n",
       "960                 4.0  62.0     3.0  3.000000         0\n",
       "\n",
       "[961 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mammographic_secondary_data = mammographic_data[['Shape']]\n",
    "mammographic_data = mammographic_data[['BI-RADS assessment','Age','Margin','Density','Severity']]\n",
    "mammographic_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1746d5f9",
   "metadata": {},
   "source": [
    "## -----Normalize Data-----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c38792",
   "metadata": {},
   "source": [
    "### Convert to numpy and normalize from 0-1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d6052ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the min and max values for each column\n",
    "def dataset_minmax(dataset):\n",
    "    minmax = []\n",
    "    for i in range(len(dataset[0])):\n",
    "        col_val = [row[i] for row in dataset]\n",
    "        vmin = min(col_val)\n",
    "        vmax = max(col_val)\n",
    "        minmax.append([vmin, vmax])\n",
    "    return minmax\n",
    " \n",
    "# Rescale dataset columns to the range 0-1\n",
    "def normalize_dataset(dataset, minmax,mammographic_bool = False):\n",
    "    for row in dataset:\n",
    "        for i in range(len(row)):\n",
    "            if mammographic_bool == True and i == 2:\n",
    "                continue\n",
    "            else:\n",
    "                row[i] = (row[i] - minmax[i][0])\n",
    "                row[i] = row[i] / (minmax[i][1] - minmax[i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7bc5b253",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.13511843e-02 3.91835702e-03 7.58995821e-05 ... 5.36340697e-01\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [1.19614160e-01 8.11878355e-02 2.71062241e-02 ... 4.37973130e-01\n",
      "  0.00000000e+00 1.00000000e+00]\n",
      " [8.59705203e-02 5.15246422e-02 1.63401124e-02 ... 3.52674946e-01\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " ...\n",
      " [1.66239603e-02 2.30662452e-03 2.18208868e-04 ... 4.46001996e-01\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [4.46960959e-02 1.80297798e-02 1.09584373e-02 ... 3.02455779e-01\n",
      "  1.00000000e+00 1.00000000e+00]\n",
      " [1.65036908e-02 7.79655752e-04 4.11876287e-04 ... 1.92513004e-01\n",
      "  0.00000000e+00 0.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate min and max for each column\n",
    "messidor1_np = messidor_data.to_numpy()\n",
    "minmax = dataset_minmax(messidor1_np)\n",
    "# Normalize columns\n",
    "normalize_dataset(messidor1_np, minmax)\n",
    "print(messidor1_np)\n",
    "\n",
    "messidor_data = pd.DataFrame(messidor1_np, columns=panda_titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cecd87b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           MA1       MA2       MA3       MA4       MA5       MA6   EU_Dist  \\\n",
      "0     0.031351  0.003918  0.000076  0.000194  0.000657  0.001264  0.682302   \n",
      "1     0.119614  0.081188  0.027106  0.018571  0.007043  0.002509  0.726836   \n",
      "2     0.085971  0.051525  0.016340  0.013555  0.001289  0.000496  0.514678   \n",
      "3     0.003869  0.000000  0.000000  0.000000  0.000000  0.000000  0.481936   \n",
      "4     0.021738  0.005417  0.000000  0.000000  0.000000  0.000000  0.601764   \n",
      "...        ...       ...       ...       ...       ...       ...       ...   \n",
      "1145  0.000294  0.000052  0.000000  0.000000  0.000000  0.000000  0.756089   \n",
      "1146  0.076060  0.016390  0.000030  0.000000  0.000000  0.000000  0.663701   \n",
      "1147  0.016624  0.002307  0.000218  0.000000  0.000000  0.000000  0.859281   \n",
      "1148  0.044696  0.018030  0.010958  0.016263  0.040346  0.056559  0.526653   \n",
      "1149  0.016504  0.000780  0.000412  0.000422  0.000000  0.000000  0.839500   \n",
      "\n",
      "      Diameter  AM/FM  cLabel  \n",
      "0     0.536341    0.0     0.0  \n",
      "1     0.437973    0.0     1.0  \n",
      "2     0.352675    0.0     0.0  \n",
      "3     0.407122    0.0     1.0  \n",
      "4     0.426770    0.0     1.0  \n",
      "...        ...    ...     ...  \n",
      "1145  0.365106    0.0     0.0  \n",
      "1146  0.410954    0.0     0.0  \n",
      "1147  0.446002    0.0     0.0  \n",
      "1148  0.302456    1.0     1.0  \n",
      "1149  0.192513    0.0     0.0  \n",
      "\n",
      "[1150 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "print(messidor_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96d65f7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.09090909 0.62820513 5.         0.66666667 1.        ]\n",
      " [0.07272727 0.32051282 1.         0.63691149 1.        ]\n",
      " [0.09090909 0.51282051 5.         0.66666667 1.        ]\n",
      " ...\n",
      " [0.07272727 0.58974359 5.         0.66666667 0.        ]\n",
      " [0.09090909 0.61538462 5.         0.66666667 1.        ]\n",
      " [0.07272727 0.56410256 3.         0.66666667 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "mammographic_data\n",
    "\n",
    "# Calculate min and max for each column\n",
    "mammographic_np = mammographic_data.to_numpy()\n",
    "minmax = dataset_minmax(mammographic_np)\n",
    "# Normalize columns\n",
    "normalize_dataset(mammographic_np, minmax,True)\n",
    "print(mammographic_np)\n",
    "\n",
    "mammographic_data = pd.DataFrame(mammographic_np, columns=['BI-RADS assessment','Age','Margin','Density','Severity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "918e4b53",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     BI-RADS assessment       Age  Margin   Density  Severity\n",
      "0              0.090909  0.628205     5.0  0.666667       1.0\n",
      "1              0.072727  0.320513     1.0  0.636911       1.0\n",
      "2              0.090909  0.512821     5.0  0.666667       1.0\n",
      "3              0.072727  0.128205     1.0  0.666667       0.0\n",
      "4              0.090909  0.717949     5.0  0.636911       1.0\n",
      "..                  ...       ...     ...       ...       ...\n",
      "956            0.072727  0.371795     1.0  0.666667       0.0\n",
      "957            0.072727  0.487179     5.0  0.666667       1.0\n",
      "958            0.072727  0.589744     5.0  0.666667       0.0\n",
      "959            0.090909  0.615385     5.0  0.666667       1.0\n",
      "960            0.072727  0.564103     3.0  0.666667       0.0\n",
      "\n",
      "[961 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "print(mammographic_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "895289a4",
   "metadata": {},
   "source": [
    "### Adding Shape Class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "22da4248",
   "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>BI-RADS assessment</th>\n",
       "      <th>Age</th>\n",
       "      <th>Margin</th>\n",
       "      <th>Density</th>\n",
       "      <th>Severity</th>\n",
       "      <th>Shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.628205</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.320513</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.636911</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.512821</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.128205</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.717949</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.636911</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.371795</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.589744</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.564103</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>961 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     BI-RADS assessment       Age  Margin   Density  Severity  Shape\n",
       "0              0.090909  0.628205     5.0  0.666667       1.0    3.0\n",
       "1              0.072727  0.320513     1.0  0.636911       1.0    1.0\n",
       "2              0.090909  0.512821     5.0  0.666667       1.0    4.0\n",
       "3              0.072727  0.128205     1.0  0.666667       0.0    1.0\n",
       "4              0.090909  0.717949     5.0  0.636911       1.0    1.0\n",
       "..                  ...       ...     ...       ...       ...    ...\n",
       "956            0.072727  0.371795     1.0  0.666667       0.0    2.0\n",
       "957            0.072727  0.487179     5.0  0.666667       1.0    4.0\n",
       "958            0.072727  0.589744     5.0  0.666667       0.0    4.0\n",
       "959            0.090909  0.615385     5.0  0.666667       1.0    4.0\n",
       "960            0.072727  0.564103     3.0  0.666667       0.0    3.0\n",
       "\n",
       "[961 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mammographic_data = mammographic_data.join(mammographic_secondary_data)\n",
    "mammographic_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe9c685",
   "metadata": {},
   "source": [
    "## -----Quartile Method-----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f2adf4",
   "metadata": {},
   "source": [
    "### Removing Outliers via InterQuartile method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb084d0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MA1         0.099304\n",
      "MA2         0.030861\n",
      "MA3         0.003741\n",
      "MA4         0.001916\n",
      "MA5         0.000814\n",
      "MA6         0.001247\n",
      "EU_Dist     0.181749\n",
      "Diameter    0.147843\n",
      "AM/FM       1.000000\n",
      "cLabel      1.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Q1 = messidor_data.quantile(0.25)\n",
    "Q3 = messidor_data.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "print(IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "239eebe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "messidor_data_out = messidor_data[~((messidor_data < (Q1 - 1.5 * IQR)) |(messidor_data > (Q3 + 1.5 * IQR))).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "395a44ef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           MA1       MA2       MA3       MA4       MA5       MA6   EU_Dist  \\\n",
      "0     0.031351  0.003918  0.000076  0.000194  0.000657  0.001264  0.682302   \n",
      "3     0.003869  0.000000  0.000000  0.000000  0.000000  0.000000  0.481936   \n",
      "4     0.021738  0.005417  0.000000  0.000000  0.000000  0.000000  0.601764   \n",
      "5     0.015400  0.000000  0.000000  0.000000  0.000000  0.000000  0.775126   \n",
      "6     0.011536  0.002516  0.000000  0.000000  0.000000  0.000000  0.929166   \n",
      "...        ...       ...       ...       ...       ...       ...       ...   \n",
      "1143  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.756450   \n",
      "1145  0.000294  0.000052  0.000000  0.000000  0.000000  0.000000  0.756089   \n",
      "1146  0.076060  0.016390  0.000030  0.000000  0.000000  0.000000  0.663701   \n",
      "1147  0.016624  0.002307  0.000218  0.000000  0.000000  0.000000  0.859281   \n",
      "1149  0.016504  0.000780  0.000412  0.000422  0.000000  0.000000  0.839500   \n",
      "\n",
      "      Diameter  AM/FM  cLabel  \n",
      "0     0.536341    0.0     0.0  \n",
      "3     0.407122    0.0     1.0  \n",
      "4     0.426770    0.0     1.0  \n",
      "5     0.506339    0.0     1.0  \n",
      "6     0.081622    1.0     0.0  \n",
      "...        ...    ...     ...  \n",
      "1143  0.410898    0.0     0.0  \n",
      "1145  0.365106    0.0     0.0  \n",
      "1146  0.410954    0.0     0.0  \n",
      "1147  0.446002    0.0     0.0  \n",
      "1149  0.192513    0.0     0.0  \n",
      "\n",
      "[813 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "print(messidor_data_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "22cee24d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BI-RADS assessment    0.018182\n",
      "Age                   0.269231\n",
      "Margin                3.000000\n",
      "Density               0.000000\n",
      "Severity              1.000000\n",
      "Shape                 2.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "MQ1 = mammographic_data.quantile(0.25)\n",
    "MQ3 = mammographic_data.quantile(0.75)\n",
    "MIQR = MQ3 - MQ1\n",
    "print(MIQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "29807e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "mammographic_data_out = mammographic_data[~((mammographic_data < (MQ1 - 1.5 * MIQR)) |(mammographic_data > (MQ3 + 1.5 * MIQR))).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "901fea7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     BI-RADS assessment       Age    Margin   Density  Severity  Shape\n",
      "0              0.090909  0.628205  5.000000  0.666667       1.0    3.0\n",
      "2              0.090909  0.512821  5.000000  0.666667       1.0    4.0\n",
      "3              0.072727  0.128205  1.000000  0.666667       0.0    1.0\n",
      "5              0.072727  0.602564  2.796276  0.666667       0.0    1.0\n",
      "6              0.072727  0.666667  2.796276  0.666667       0.0    3.0\n",
      "..                  ...       ...       ...       ...       ...    ...\n",
      "956            0.072727  0.371795  1.000000  0.666667       0.0    2.0\n",
      "957            0.072727  0.487179  5.000000  0.666667       1.0    4.0\n",
      "958            0.072727  0.589744  5.000000  0.666667       0.0    4.0\n",
      "959            0.090909  0.615385  5.000000  0.666667       1.0    4.0\n",
      "960            0.072727  0.564103  3.000000  0.666667       0.0    3.0\n",
      "\n",
      "[786 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "print(mammographic_data_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c522a30",
   "metadata": {},
   "source": [
    "## Divide into training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "20131142",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "550   236\n"
     ]
    }
   ],
   "source": [
    "training_length = int(len(mammographic_data_out)*0.7)\n",
    "test_length = len(mammographic_data_out) - training_length\n",
    "\n",
    "print(training_length,\" \",test_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff11330d",
   "metadata": {},
   "source": [
    "# =======kNN Classification======="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a3f4392",
   "metadata": {},
   "source": [
    "### Training_length = all the indexes inside training_length and same for test_length "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "57ef4ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "# Assign every training example x^(i) to its closest centroid, given the current centroid positions\n",
    "def find_closest_knn_point(data,training_length,test_index,knn_value):\n",
    "    minval = []\n",
    "    picked_index = []\n",
    "    for i in range(knn_value):\n",
    "        minval.append(999999)\n",
    "        picked_index.append(0)\n",
    "    \n",
    "    #MAKE IT GENERIC \n",
    "    \n",
    "    for i in range(training_length):\n",
    "            euc_dist = 0\n",
    "            euc_dist = math.sqrt( (pow(data[test_index][0]-(data[i][0]),2) ) + (pow(data[test_index][1]-(data[i][1]),2) ) \\\n",
    "                                + (pow(data[test_index][2] - (data[i][2]),2) ) + (pow(data[test_index][3]-(data[i][3]),2) ) ) \\\n",
    "                                #+ (pow(data[test_index][4]-(data[i][4]),2) ) )\n",
    "            #LAST 2 COLUMNS NOT PICKED AS THEY ARE CLASSES\n",
    "            \n",
    "            replace_check = False\n",
    "            for k in range(knn_value): \n",
    "                if replace_check == True:\n",
    "                    if euc_dist < minval[k]:\n",
    "                        \n",
    "                        temp1 = picked_index[k-1]\n",
    "                        picked_index[k-1] = picked_index[k]\n",
    "                        picked_index[k] = temp1\n",
    "                        \n",
    "                        temp = minval[k-1]\n",
    "                        minval[k-1] = minval[k]\n",
    "                        minval[k] = temp\n",
    "                    \n",
    "                if euc_dist < minval[k] and replace_check == False:\n",
    "                    picked_index[k] = i\n",
    "                    minval[k] = euc_dist\n",
    "                    replace_check = True\n",
    "                \n",
    "    \n",
    "    return picked_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2d088a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_test_class(data,training_length,test_length,knn_value):\n",
    "    test_classes = []\n",
    "    \n",
    "    for i in range(test_length):\n",
    "        index = training_length + i\n",
    "        picked_index = find_closest_knn_point(data,index-1,index,knn_value)\n",
    "        clabel0 = 0\n",
    "        clabel1 = 0\n",
    "        clabel2 = 0\n",
    "        clabel3 = 0\n",
    "#         print(\"index =  [\",index,\"] \",picked_index)\n",
    "        for j in range(knn_value):\n",
    "            if data[ picked_index[j] ] [5] == 1:\n",
    "                clabel0 += 1\n",
    "            elif data[ picked_index[j] ] [5] == 2:\n",
    "                clabel1 += 1\n",
    "            elif data[ picked_index[j] ] [5] == 3:\n",
    "                clabel2 += 1\n",
    "            elif data[ picked_index[j] ] [5] == 4:\n",
    "                clabel3 += 1\n",
    "        if clabel0 > clabel1 and clabel0 > clabel2 and clabel0 > clabel3:\n",
    "            test_classes.append(1.0)\n",
    "        elif clabel1 > clabel0 and clabel1 > clabel2 and clabel1 > clabel3:\n",
    "            test_classes.append(2.0)\n",
    "        elif clabel2 > clabel0 and clabel2 > clabel1 and clabel2 > clabel3:\n",
    "            test_classes.append(3.0)\n",
    "        elif clabel3 > clabel0 and clabel3 > clabel2 and clabel3 > clabel1:\n",
    "            test_classes.append(4.0)\n",
    "        else:                              #ELSE PICK CLOSEST VALUES CLASS\n",
    "            test_classes.append(data[ picked_index[0] ][5])\n",
    "        \n",
    "    \n",
    "    return test_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17731f12",
   "metadata": {},
   "source": [
    "## -------kNN INPUT HERE-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3200e562",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input K Value : 5\n",
      "\n",
      "kNN Test Values\n",
      " [4.0, 1.0, 4.0, 4.0, 1.0, 1.0, 4.0, 2.0, 4.0, 4.0, 1.0, 1.0, 4.0, 2.0, 4.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 4.0, 4.0, 4.0, 4.0, 4.0, 1.0, 1.0, 1.0, 4.0, 4.0, 4.0, 2.0, 1.0, 2.0, 1.0, 4.0, 2.0, 1.0, 2.0, 1.0, 1.0, 4.0, 3.0, 1.0, 4.0, 1.0, 2.0, 1.0, 1.0, 3.0, 4.0, 1.0, 4.0, 4.0, 1.0, 1.0, 1.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 1.0, 4.0, 4.0, 4.0, 1.0, 2.0, 2.0, 4.0, 1.0, 1.0, 1.0, 4.0, 1.0, 1.0, 1.0, 4.0, 4.0, 1.0, 3.0, 1.0, 1.0, 4.0, 4.0, 1.0, 4.0, 1.0, 1.0, 3.0, 1.0, 4.0, 4.0, 4.0, 4.0, 4.0, 2.0, 4.0, 4.0, 2.0, 4.0, 3.0, 1.0, 1.0, 4.0, 4.0, 2.0, 4.0, 4.0, 4.0, 4.0, 1.0, 4.0, 2.0, 2.0, 1.0, 1.0, 1.0, 4.0, 1.0, 2.0, 1.0, 2.0, 2.0, 1.0, 1.0, 4.0, 4.0, 2.0, 4.0, 1.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 1.0, 1.0, 4.0, 1.0, 4.0, 4.0, 4.0, 1.0, 1.0, 3.0, 2.0, 4.0, 4.0, 4.0, 1.0, 4.0, 4.0, 4.0, 1.0, 4.0, 4.0, 4.0, 4.0, 1.0, 4.0, 4.0, 4.0, 1.0, 2.0, 4.0, 3.0, 2.0, 4.0, 4.0, 1.0, 1.0, 4.0, 4.0, 1.0, 4.0, 1.0, 4.0, 4.0, 2.0, 4.0, 1.0, 4.0, 4.0, 2.0, 1.0, 2.0, 2.0, 4.0, 4.0, 2.0, 2.0, 1.0, 4.0, 3.0, 4.0, 4.0, 2.0, 1.0, 2.0, 2.0, 4.0, 1.0, 4.0, 4.0, 4.0, 4.0, 2.0, 2.0, 4.0, 4.0, 3.0, 1.0, 4.0, 4.0, 4.0, 1.0, 4.0, 2.0, 4.0, 4.0, 4.0, 4.0]\n",
      "\n",
      "\n",
      "Actual Test Values\n",
      " [4.0, 3.0, 4.0, 4.0, 2.0, 2.0, 3.0, 2.0, 4.0, 4.0, 1.0, 1.0, 2.0, 1.0, 4.0, 1.0, 2.0, 2.0, 2.0, 2.0, 1.0, 2.0, 4.0, 4.0, 4.0, 4.0, 1.0, 1.0, 2.0, 4.0, 4.0, 3.0, 4.0, 2.0, 2.0, 3.0, 4.0, 1.0, 2.0, 2.0, 1.0, 2.0, 2.0, 4.0, 1.0, 2.0, 2.0, 2.0, 1.0, 2.0, 3.0, 4.0, 2.0, 4.0, 4.0, 1.0, 2.0, 1.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 3.0, 4.0, 4.0, 4.0, 4.0, 3.0, 4.0, 2.0, 1.0, 4.0, 4.0, 2.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 1.0, 4.0, 2.0, 1.0, 1.0, 3.0, 4.0, 2.0, 3.0, 1.0, 1.0, 4.0, 4.0, 2.0, 4.0, 2.0, 2.0, 2.0, 2.0, 4.0, 1.0, 4.0, 3.0, 3.0, 2.0, 3.0, 1.0, 1.0, 4.0, 2.0, 1.0, 1.0, 4.0, 4.0, 1.0, 4.0, 3.0, 4.0, 4.0, 2.0, 4.0, 2.0, 2.0, 2.0, 1.0, 1.0, 4.0, 1.0, 2.0, 3.0, 2.0, 2.0, 3.0, 1.0, 4.0, 4.0, 1.0, 2.0, 2.0, 4.0, 2.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 1.0, 1.0, 4.0, 2.0, 4.0, 4.0, 3.0, 2.0, 3.0, 2.0, 3.0, 4.0, 4.0, 4.0, 1.0, 4.0, 4.0, 2.0, 1.0, 4.0, 4.0, 3.0, 4.0, 1.0, 3.0, 4.0, 4.0, 2.0, 3.0, 3.0, 4.0, 1.0, 4.0, 4.0, 1.0, 2.0, 3.0, 4.0, 1.0, 4.0, 1.0, 4.0, 4.0, 3.0, 4.0, 1.0, 3.0, 2.0, 1.0, 2.0, 4.0, 2.0, 4.0, 4.0, 2.0, 3.0, 1.0, 4.0, 1.0, 4.0, 4.0, 2.0, 1.0, 1.0, 3.0, 4.0, 1.0, 2.0, 4.0, 4.0, 3.0, 1.0, 2.0, 2.0, 4.0, 4.0, 2.0, 4.0, 4.0, 2.0, 2.0, 4.0, 2.0, 4.0, 4.0, 4.0, 3.0]\n"
     ]
    }
   ],
   "source": [
    "mammographic_np = mammographic_data_out.to_numpy()\n",
    "\n",
    "knn = input(\"Input K Value : \")\n",
    "knn = int(knn)\n",
    "\n",
    "test_knn = find_test_class(mammographic_np,training_length,test_length,knn)\n",
    "\n",
    "print(\"\\nkNN Test Values\\n\",test_knn)\n",
    "\n",
    "test_actual = []\n",
    "\n",
    "for i in range(test_length):\n",
    "    test_actual.append(mammographic_np[training_length+i][5])\n",
    "    \n",
    "print()\n",
    "print(\"\\nActual Test Values\\n\",test_actual)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0419193a",
   "metadata": {},
   "source": [
    "## Compare Actual v.s kNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a0213e48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  0.614406779661017\n"
     ]
    }
   ],
   "source": [
    "accurate = 0\n",
    "\n",
    "for i in range(test_length):\n",
    "    if test_actual[i] == test_knn[i]:\n",
    "        accurate += 1\n",
    "\n",
    "print(\"Accuracy = \",accurate/test_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daaf9a5c",
   "metadata": {},
   "source": [
    "## Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b53f8fb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculating_Metrics(confusion_Matrix,classindex):\n",
    "    nb_precision = float((confusion_Matrix[classindex][classindex])/ \\\n",
    "                         (confusion_Matrix[classindex][0]+confusion_Matrix[classindex][1] + confusion_Matrix[classindex][2]+confusion_Matrix[classindex][3]))\n",
    "    \n",
    "    nb_recall = float((confusion_Matrix[classindex][classindex])/ \\\n",
    "                      (confusion_Matrix[0][classindex]+confusion_Matrix[1][classindex] + confusion_Matrix[2][classindex]+confusion_Matrix[3][classindex]))\n",
    "\n",
    "    nb_f1 = 2*((nb_precision*nb_recall)/(nb_precision+nb_recall))\n",
    "\n",
    "#     nb_precision = \"{:.2f}\".format(nb_precision*100)\n",
    "#     nb_recall = \"{:.2f}\".format(nb_recall*100)\n",
    "#     nb_f1 = \"{:.2f}\".format(nb_f1*100)\n",
    "\n",
    "\n",
    "    print(\"\\nPrecision is : \",\"{:.2f}\".format(nb_precision*100))\n",
    "\n",
    "    print(\"Recall is : \",\"{:.2f}\".format(nb_recall*100))\n",
    "    print(\"F1 is : \",\"{:.2f}\".format(nb_f1*100))\n",
    "    \n",
    "    return nb_precision,nb_recall,nb_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "611e8588",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                  Actual\n",
      "               1   2   3   4\n",
      "           1  [0, 0, 0, 0]\n",
      "Predicted  2  [0, 0, 0, 0]\n",
      "           3  [0, 0, 0, 0]\n",
      "           4  [0, 0, 0, 0]\n",
      "\n",
      "==========Evaluation Matrice==========\n",
      "Total Test Set Length :  236\n",
      "Correct Guesses :  145\n",
      "\n",
      "                  Actual\n",
      "               1   2   3   4\n",
      "           1  [38, 29, 5, 0]\n",
      "Predicted  2  [10, 17, 5, 2]\n",
      "           3  [1, 3, 2, 3]\n",
      "           4  [2, 13, 18, 88]\n",
      "\n",
      "Overall Accuracy is :  61.44\n",
      "\n",
      "------Class 1------\n",
      "\n",
      "\n",
      "Precision is :  52.78\n",
      "Recall is :  74.51\n",
      "F1 is :  61.79\n",
      "\n",
      "------Class 2------\n",
      "\n",
      "\n",
      "Precision is :  50.00\n",
      "Recall is :  27.42\n",
      "F1 is :  35.42\n",
      "\n",
      "------Class 3------\n",
      "\n",
      "\n",
      "Precision is :  22.22\n",
      "Recall is :  6.67\n",
      "F1 is :  10.26\n",
      "\n",
      "------Class 4------\n",
      "\n",
      "\n",
      "Precision is :  72.73\n",
      "Recall is :  94.62\n",
      "F1 is :  82.24\n",
      "\n",
      "----------------------\n",
      "\n",
      "\n",
      "Macro Precision is :  49.43\n",
      "Macro Recall is :  50.80\n",
      "Macro F1 is :  47.43\n",
      "\n",
      "Micro F1/Recall/Precision are :  61.44\n",
      "=======================================\n"
     ]
    }
   ],
   "source": [
    "folders = ['0','1']\n",
    "total = test_length\n",
    "correct = 0\n",
    "#THIS ONE IS FOR EXACTLY 4 CLASSES AND CLASSES value STARTING FROM 1 till 4\n",
    "\n",
    "confusion_Matrix = [ [0,0,0,0] , [0,0,0,0] , [0,0,0,0] , [0,0,0,0] ]  \n",
    "\n",
    "print(\"\\n                  Actual\\n               1   2   3   4\")\n",
    "value = 1\n",
    "for i in confusion_Matrix:\n",
    "    if value == 2:\n",
    "        print(\"Predicted \",value,\"\",i)\n",
    "    else:\n",
    "        print(\"          \",value,\"\",i)\n",
    "    value+=1\n",
    "\n",
    "print()\n",
    "      # FAKE  Real\n",
    "# FAKE              \n",
    "# Real\n",
    "for i in range(test_length):\n",
    "        result = test_knn[i]\n",
    "        actual = test_actual[i]\n",
    "        result = int(result) - 1 \n",
    "        actual = int(actual) - 1\n",
    "        \n",
    "        if result == actual:\n",
    "            correct+=1\n",
    "        confusion_Matrix[result][actual] += 1\n",
    "        \"\"\"\"\"\n",
    "        if actual == 0.0:\n",
    "            if result == actual:\n",
    "                confusion_Matrix[result][actual] += 1\n",
    "            else:\n",
    "                confusion_Matrix[1][0] += 1\n",
    "                \n",
    "        elif result == 1.0:\n",
    "            if result == actual:\n",
    "                confusion_Matrix[1][1] += 1\n",
    "            else:\n",
    "                confusion_Matrix[0][1] += 1\n",
    "        \"\"\"\"\"\n",
    "\n",
    "print(\"==========Evaluation Matrice==========\")\n",
    "print(\"Total Test Set Length : \",total)\n",
    "print(\"Correct Guesses : \",correct)\n",
    "print(\"\\n                  Actual\\n               1   2   3   4\")\n",
    "value = 1\n",
    "for i in confusion_Matrix:\n",
    "    if value == 2:\n",
    "        print(\"Predicted \",value,\"\",i)\n",
    "    else:\n",
    "        print(\"          \",value,\"\",i)\n",
    "    value+=1\n",
    "print()\n",
    "# print(\"\\nTrue Positive : \",confusion_Matrix[1][1])\n",
    "# print(\"False Positive : \",confusion_Matrix[0][1])\n",
    "# print(\"True Negative : \",confusion_Matrix[0][0])\n",
    "# print(\"False Negative : \",confusion_Matrix[1][0])\n",
    "classindex = 0\n",
    "\n",
    "nb_accuracy = float(correct/total)\n",
    "nb_accuracy = \"{:.2f}\".format(nb_accuracy*100)\n",
    "\n",
    "precision = 0\n",
    "recall = 0\n",
    "f1 = 0\n",
    "\n",
    "print(\"Overall Accuracy is : \",nb_accuracy)\n",
    "\n",
    "print(\"\\n------Class 1------\\n\")\n",
    "one,two,three = calculating_Metrics(confusion_Matrix,0)\n",
    "\n",
    "precision += one\n",
    "recall += two\n",
    "f1 += three\n",
    "\n",
    "print(\"\\n------Class 2------\\n\")\n",
    "one,two,three = calculating_Metrics(confusion_Matrix,1)\n",
    "precision += one\n",
    "recall += two\n",
    "f1 += three\n",
    "\n",
    "print(\"\\n------Class 3------\\n\")\n",
    "one,two,three = calculating_Metrics(confusion_Matrix,2)\n",
    "precision += one\n",
    "recall += two\n",
    "f1 += three\n",
    "\n",
    "print(\"\\n------Class 4------\\n\")\n",
    "one,two,three = calculating_Metrics(confusion_Matrix,3)\n",
    "precision += one\n",
    "recall += two\n",
    "f1 += three\n",
    "\n",
    "\n",
    "print(\"\\n----------------------\\n\")\n",
    "print(\"\\nMacro Precision is : \",\"{:.2f}\".format(precision/4*100))\n",
    "\n",
    "print(\"Macro Recall is : \",\"{:.2f}\".format(recall/4*100))\n",
    "print(\"Macro F1 is : \",\"{:.2f}\".format(f1/4*100))\n",
    "\n",
    "print(\"\\nMicro F1/Recall/Precision are : \",nb_accuracy)\n",
    "\n",
    "print(\"=======================================\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f96ffb21",
   "metadata": {},
   "source": [
    "# =======K-Means Clustering======="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c9bcfd95",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_Initial_Clusters(kmean,dataset):\n",
    "    value = 0\n",
    "    initial_clusters = []\n",
    "    initial_centroids = []\n",
    "    data = dataset.to_numpy()\n",
    "    np.random.shuffle(data)\n",
    "    for i in range(kmean):\n",
    "        previous_value = value\n",
    "        value += int((len(dataset)/kmean))\n",
    "        initial_clusters.append(data[previous_value:value,:])\n",
    "        initial_centroids.append(data[random.randint(previous_value,value)])\n",
    "        \n",
    "    \n",
    "    initial_clusters = np.asarray(initial_clusters)\n",
    "    initial_centroids = np.asarray(initial_centroids)\n",
    "\n",
    "    return initial_clusters,initial_centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1c36ec1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "# Measure According to only 2 MA values and  EU Dist and Diameter\n",
    "def find_closest_centroids(data, centroids):\n",
    "    '''returns the array of assigned clusters to each example ''' \n",
    "    m = data.shape[0] # no of datappints\n",
    "    k = len(centroids)  #division size\n",
    "#     print(\"\\n\",centroids)\n",
    "#     print(centroids[0][1])\n",
    "#     print(X[8][1])\n",
    "\n",
    "    idx = np.zeros(m) # array to assign the centriod\n",
    "    \n",
    "    #MAKE IT GENERIC \n",
    "    \n",
    "    for i in range(m):\n",
    "        minval = 99999\n",
    "        pick = 0\n",
    "        for j in range(k):\n",
    "            euc_dist = 0\n",
    "            euc_dist = math.sqrt( (pow(centroids[j][0]-(data[i][0]),2) ) + (pow(centroids[j][1]-(data[i][1]),2) ) \\\n",
    "                                + (pow(centroids[j][2] - (data[i][2]),2) ) + (pow(centroids[j][3]-(data[i][3]),2) ) \\\n",
    "                                + (pow(centroids[j][4]-(data[i][4]),2) ) + (pow(centroids[j][5]-(data[i][5]),2) ) \\\n",
    "                                + (pow(centroids[j][6]-(data[i][6]),2) ) + (pow(centroids[j][7]-(data[i][7]),2) ) )\n",
    "            #LAST 2 COLUMNS NOT PICKED AS THEY ARE CLASSES\n",
    "            \n",
    "            if euc_dist < minval:\n",
    "                pick = j\n",
    "                minval = euc_dist\n",
    "                \n",
    "        idx[i] = pick\n",
    "    \n",
    "    return idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "97d0a6c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update Centroids\n",
    "def compute_centroids(data, idx, k):\n",
    "    m, n = data.shape\n",
    "    centroids = np.zeros((k, n))  #3*4\n",
    "    '''Return Updated Values of all K centroids'''\n",
    "    \n",
    "    totalnumber = np.zeros(n) #number of occurences of centroids (will be divided)\n",
    "    \n",
    "    for i in range(k):\n",
    "        for j in range(m):\n",
    "            if idx[j] == i:\n",
    "                totalnumber[i] += 1\n",
    "                centroids[i] += data[j]\n",
    "        \n",
    "        if (totalnumber[i] != 0):\n",
    "            centroids[i] /= totalnumber[i]\n",
    "#         else:\n",
    "#             centroids[i] = np.ones(n)\n",
    "    \n",
    "    return centroids        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8984c50",
   "metadata": {},
   "source": [
    "### Running K_means Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "607f77fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_k_means(data, initial_centroids, max_iters):\n",
    "    m, n = data.shape\n",
    "    k = initial_centroids.shape[0]\n",
    "    idx = np.zeros(m)\n",
    "    centroids = initial_centroids\n",
    "    \n",
    "    previouscentroids = centroids\n",
    "    for i in range(max_iters):\n",
    "        # find closest centroid\n",
    "        idx = find_closest_centroids(data, centroids)\n",
    "        #update centroids\n",
    "        centroids = compute_centroids(data, idx, k)\n",
    "        \n",
    "#         if centroids.all() == previouscentroids.all() and i > 5:\n",
    "#             print(\"Iterations = \",i)\n",
    "#             break\n",
    "#         else:\n",
    "#             previouscentroids = centroids\n",
    "    \n",
    "    return idx, centroids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f2aa04b",
   "metadata": {},
   "source": [
    "## -------Input Kmean and OutPut-------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fc97983",
   "metadata": {},
   "source": [
    "### Takes some time to compute as 650 iterations are made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "80a5c13a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input K Value : 5\n"
     ]
    }
   ],
   "source": [
    "kmean = input(\"Input K Value : \")\n",
    "kmean = int(kmean)\n",
    "# initial_centroids = np.array([X[random.randint(0,50)], X[random.randint(50,100)], X[random.randint(100,150)]])\n",
    "iClusters,iCentroids = set_Initial_Clusters(kmean,messidor_data_out)\n",
    "messidor_np = messidor_data_out.to_numpy()\n",
    "\n",
    "idx, final_centroids = run_k_means(messidor_np, iCentroids, 650) #BREAK LOOP IF CHANGES ARENT BIG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "88aa71ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#FINAL CLUSTERS\n",
    "fClusters = []\n",
    "for i in range(kmean):\n",
    "    fClusters.append(messidor_np[np.where(idx == i)[0],:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51aac636",
   "metadata": {},
   "source": [
    "## Plot Initial and Final Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3df45a5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e023aeb0d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig ,ax= plt.subplots(figsize=(20,10))\n",
    "#ax = fig.add_subplot(projection='3d')\n",
    "colors = ['r','g','b','c','m','y','k','tan','plum','slategrey']\n",
    "labels = ['Cluster 1','Cluster 2','Cluster 3','Cluster 4','Cluster 5','Cluster 6','Cluster 7','Cluster 8','Cluster 9','Cluster 10']\n",
    "ax.scatter(iCentroids[:,6], iCentroids[:,7], s=200, color='slategrey',label='Center')\n",
    "for i in range(kmean):\n",
    "    ax.scatter(iClusters[i][:,6], iClusters[i][:,7], s=15, color=colors[i], label=labels[i])\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e4ee8fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e023dfe6d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig ,ax= plt.subplots(figsize=(20,10))\n",
    "#ax = fig.add_subplot(projection='3d')\n",
    "colors = ['r','g','b','c','m','y','k','tan','plum','slategrey']\n",
    "labels = ['Cluster 1','Cluster 2','Cluster 3','Cluster 4','Cluster 5','Cluster 6','Cluster 7','Cluster 8','Cluster 9','Cluster 10',]\n",
    "\n",
    "ax.scatter(final_centroids[:,6], final_centroids[:,7], s=200, color='slategrey',label='Center')\n",
    "for i in range(kmean):\n",
    "    ax.scatter(fClusters[i][:,6], fClusters[i][:,7], s=15, color=colors[i], label=labels[i])\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e8184aa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e024133790>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "colors = ['r','g','b','c','m','y','k','tan','plum','slategrey']\n",
    "labels = ['Cluster 1','Cluster 2','Cluster 3','Cluster 4','Cluster 5','Cluster 6','Cluster 7','Cluster 8','Cluster 9','Cluster 10',]\n",
    "for i in range(kmean):\n",
    "    ax.scatter(iClusters[i][:,2], iClusters[i][:,6],iClusters[i][:,7], s=15, color=colors[i], label=labels[i])\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e3e9b02f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e023e6f850>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "colors = ['r','g','b','c','m','y','k','tan','plum','slategrey']\n",
    "labels = ['Cluster 1','Cluster 2','Cluster 3','Cluster 4','Cluster 5','Cluster 6','Cluster 7','Cluster 8','Cluster 9','Cluster 10',]\n",
    "for i in range(kmean):\n",
    "    ax.scatter(fClusters[i][:,2], fClusters[i][:,6],fClusters[i][:,7], s=15, color=colors[i], label=labels[i])\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5df8c49",
   "metadata": {},
   "source": [
    "# Davies-Bouldin Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "846a9624",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter(centroids,clusters):\n",
    "    #ASSUMING P = 2 HERE\n",
    "    euc_dist = 0\n",
    "    for i in range(len(clusters)):   #MEASURE VIA ONLY THE FEATURES SELECTED\n",
    "                euc_dist += math.sqrt( #(pow(centroids[0]-(clusters[i][0]),2) ) + (pow(centroids[1]-(clusters[i][1]),2) ) \\\n",
    "                                #+ (pow(centroids[2] - (clusters[i][2]),2) ) + (pow(centroids[3]-(clusters[i][3]),2) ) \\\n",
    "                                #+ (pow(centroids[4]-(clusters[i][4]),2) ) + (pow(centroids[5]-(clusters[i][5]),2) ) \\\n",
    "                                (pow(centroids[6]-(clusters[i][6]),2) ) + (pow(centroids[7]-(clusters[i][7]),2) ) )\n",
    "    euc_dist = float(euc_dist/len(clusters))\n",
    "    return euc_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f903d5db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure_sep(centroids1,centroids2):\n",
    "    #ASSUMING P = 2 HERE\n",
    "    m = math.sqrt( (pow(centroids1[6]-(centroids2[6]),2) ) + (pow(centroids1[7]-(centroids2[7]),2) ) )\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "62ca334a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def findR(centroids1,centroids2,clusters1,clusters2):\n",
    "    R = ( scatter(centroids1,clusters1) + scatter(centroids1,clusters1) ) / measure_sep(centroids1,centroids2)\n",
    "    return R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d81a2905",
   "metadata": {},
   "outputs": [],
   "source": [
    "def daviesBouldin(centroids,clusters):\n",
    "    dbindex = 0\n",
    "    for i in range(len(centroids)):\n",
    "        maxvalue = -999999\n",
    "        for j in range(len(centroids)):\n",
    "            if i == j:\n",
    "                continue\n",
    "            R = findR(centroids[i],centroids[j],clusters[i],clusters[j])\n",
    "            \n",
    "            if R > maxvalue:\n",
    "                maxvalue = R\n",
    "        \n",
    "        dbindex += maxvalue\n",
    "        \n",
    "    dbindex=float(dbindex/len(centroids))\n",
    "    \n",
    "    return dbindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6c2aefca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Davies Bouldin Index for K [ 5 ] is =  0.9229326517686829\n"
     ]
    }
   ],
   "source": [
    "print(\"Davies Bouldin Index for K [\",kmean,\"] is = \",daviesBouldin(final_centroids,fClusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "305984d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: For 2 Features only And Values are somewhat different\n",
      "because less iterations are taken for less computation time and random points are taken\n",
      "\n",
      "Davies Bouldin Index for K [ 2 ] is =  1.0308430581286718\n",
      "Davies Bouldin Index for K [ 3 ] is =  0.9490719340965689\n",
      "Davies Bouldin Index for K [ 4 ] is =  0.9227711190490653\n",
      "Davies Bouldin Index for K [ 5 ] is =  0.9241947641017271\n",
      "Davies Bouldin Index for K [ 6 ] is =  0.9577093730382175\n",
      "Davies Bouldin Index for K [ 7 ] is =  0.9305420684682704\n"
     ]
    }
   ],
   "source": [
    "print(\"Note: For 2 Features only And Values are somewhat different\\nbecause less iterations are taken for less computation time and random points are taken\\n\")\n",
    "i = 2\n",
    "for k in range(6):\n",
    "    clusterss,icentr = set_Initial_Clusters(i,messidor_data_out)\n",
    "    messidor_np = messidor_data_out.to_numpy()\n",
    "    idxx, fcentr = run_k_means(messidor_np, icentr, 250)\n",
    "    fClust = []\n",
    "    for j in range(i):\n",
    "        fClust.append(messidor_np[np.where(idxx == j)[0],:])\n",
    "    print(\"Davies Bouldin Index for K [\",i,\"] is = \",daviesBouldin(fcentr,fClust))\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "facf5ddc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe98e5a0",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
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}