fcm-semi-part2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Part 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Dataset Preparation & Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3\n",
       "0    5.1  3.5  1.4  0.2\n",
       "1    4.9  3.0  1.4  0.2\n",
       "2    4.7  3.2  1.3  0.2\n",
       "3    4.6  3.1  1.5  0.2\n",
       "4    5.0  3.6  1.4  0.2\n",
       "..   ...  ...  ...  ...\n",
       "145  6.7  3.0  5.2  2.3\n",
       "146  6.3  2.5  5.0  1.9\n",
       "147  6.5  3.0  5.2  2.0\n",
       "148  6.2  3.4  5.4  2.3\n",
       "149  5.9  3.0  5.1  1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# đọc dữ liệu\n",
    "import pandas as pd\n",
    "data = pd.read_csv('resources/iris.data', header = None)\n",
    "value = data.select_dtypes(include=[\"float64\", \"int64\"])\n",
    "label = data.select_dtypes(exclude=[\"float64\", \"int64\"])\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Xử lý nhãn dư, đếm nhãn\n",
    "import numpy as np\n",
    "label_list = pd.unique(label[label.columns[0]])\n",
    "label_list = label_list.tolist()\n",
    "num_class = len(label_list)\n",
    "label_data =label[label.columns[0]].values.tolist()\n",
    "label_count = np.array(label[label.columns[0]].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "X = np.array(value)\n",
    "n = X.shape[0]\n",
    "p = X.shape[1]\n",
    "print(X.shape)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Funtion**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=0\n",
    "def setC(i = 3):\n",
    "    global c\n",
    "    c = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "def generate_M(m, m1, k, c=3):\n",
    "    global M\n",
    "    global index_x_giamsat\n",
    "    index_x_giamsat = random.sample(range(n),k)# CHọn k1 vector x từ n vector x ban đầu  \n",
    "    index_x_giamsat = np.sort(index_x_giamsat)\n",
    "    M = np.zeros((n,c))\n",
    "    for i in range(n):\n",
    "        for j in range(c):\n",
    "            M[i,j] = m\n",
    "        if i in index_x_giamsat:\n",
    "            c1= random.randint(0,c-1)\n",
    "            M[i,c1] = m1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def generate_V(c):\n",
    "    global V, V_truoc\n",
    "    V = np.random.rand(c,p) \n",
    "    V_truoc = V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_U(c):\n",
    "    global U\n",
    "    U = np.zeros((n,c))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def update_D(n,c):\n",
    "    global D \n",
    "    D = np.zeros((n,c))   \n",
    "    \n",
    "    for i in range (n):\n",
    "        for k in range(c):\n",
    "            D[i][k] = math.sqrt(\n",
    "                                sum(  pow(X[i] - V[k], 2))\n",
    "                                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve_mu(sum_mu_i, d_ik, m, m1, epsilon):\n",
    "    mu = 0\n",
    "    vp = pow(1/(m1*d_ik*d_ik),1/(m1-1))\n",
    "    vt = -1\n",
    "    left = 0.0\n",
    "    right = 2.0\n",
    "    while (abs(vt-vp) > epsilon):\n",
    "          mu = (right + left)/2\n",
    "          vt = mu/pow(mu + sum_mu_i, (m1-m)/(m1-1))  \n",
    "          if vt<vp:\n",
    "                left = mu\n",
    "          else:\n",
    "                right =mu\n",
    "    return mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def update_U(m,m1,c,epsilon):\n",
    "    \n",
    "    global U\n",
    "    update_D(n,c)\n",
    "    for i in range(n):\n",
    "        ##Cong thuc 15\n",
    "        if i not in index_x_giamsat:\n",
    "            for k in range(c):\n",
    "                mau_so = sum(pow( D[i][k] / D[i], 2/(m-1)))\n",
    "                U[i][k] = 1/mau_so\n",
    "        ##Cong thuc 16\n",
    "        elif i in index_x_giamsat :\n",
    "            d_min = np.amin(D[i])\n",
    "            d_i = D[i]/d_min   #Cong thuc 17\n",
    "            mu_i = np.zeros(c)\n",
    "            #Cong thuc 18\n",
    "            for j in range(c) :\n",
    "                if (M[i][j] == m):\n",
    "                    mu_i[j] = 1/pow( m *d_i[j]*d_i[j] , 1/(m-1) )\n",
    "            sum_mu_i = sum(mu_i)\n",
    "            #Cong thuc 19\n",
    "            for j in range(c) :\n",
    "                if (M[i][j] == m1):\n",
    "                    mu_i[j] = solve_mu(sum_mu_i, d_i[j], m, m1, epsilon)\n",
    "            U[i] = mu_i / sum(mu_i)\n",
    "                   \n",
    "            \n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_V(c):\n",
    "    V_temp = np.zeros((c,p))\n",
    "    for k in range(c):\n",
    "        temp = pow((U.T)[k],(M.T)[k])\n",
    "        tu_so = np.zeros(p)\n",
    "        for i in range(n):\n",
    "            tu_so += temp[i] * X[i]\n",
    "        V_temp[k] =  tu_so / sum(temp)\n",
    "    return V_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "def thuat_toan(m,m1,c,k,epsilon):\n",
    "    setC(c)\n",
    "    generate_V(c)\n",
    "    generate_U(c)\n",
    "    generate_M(m,m1,k,c)\n",
    "    global V, V_truoc\n",
    "    Epsilon = np.zeros((c,p)) + epsilon\n",
    "    while True:\n",
    "        update_U(m,m1,c,epsilon)\n",
    "        V_truoc =V\n",
    "        V= update_V(c)\n",
    "        delta_V = abs(V- V_truoc)\n",
    "        ktra = np.less_equal(delta_V, Epsilon)\n",
    "        if (np.all(ktra)):\n",
    "            break\n",
    "    print(\"====================================================================================\")\n",
    "    print(\"=======================================Kết quả======================================\")\n",
    "    print(\"====================================================================================\")\n",
    "    print(\"Tâm cụm :\")\n",
    "    print(V)\n",
    "    print(\"\\n\\n\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Evaluate**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# đếm số lượng phần tử mỗi cụm tương ứng với từng lớp nhãn dữ liệu\n",
    "def count_class(num_class, num_cluster):\n",
    "    global count_class_cluster\n",
    "    count_class_cluster = np.zeros((num_class,num_cluster), dtype=\"int64\")\n",
    "    for k in range(n):\n",
    "        k_class = label_list.index(label_data[k])\n",
    "        index_max = np.argmax(U[k])\n",
    "        count_class_cluster[k_class][index_max] +=1   \n",
    "    # Ma trận có num_class dòng và num_cluster cột, phần tử thứ i,j của ma trận thuộc class i, cluster j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Các độ đo\n",
    "def external_validity(num_class, c):\n",
    "    a1 = b1 = c1 = d1= 0\n",
    "    for i in range(num_class):\n",
    "        a1+= sum ( (count_class_cluster[i]-1)*count_class_cluster[i]/2 )\n",
    "        b1+= (pow(sum(count_class_cluster[i]),2) - sum(pow(count_class_cluster[i],2)) )/2\n",
    "    for i in range(num_class-1):\n",
    "        for j in range(i+1, num_class):\n",
    "            for k in range(c):\n",
    "                c1+=count_class_cluster[i][k] * count_class_cluster[j][k]\n",
    "                for h in range(c):\n",
    "                    if h!=k :\n",
    "                        d1+=count_class_cluster[i][k] * count_class_cluster[j][h]  \n",
    "    print(\"====================================================================================\")\n",
    "    print(\"=======================================Đánh giá=====================================\")\n",
    "    print(\"====================================================================================\")\n",
    "    print()\n",
    "    print(count_class_cluster)\n",
    "    print()\n",
    "    print(\"a1= \"+str(a1))\n",
    "    print(\"b1= \"+str(b1))\n",
    "    print(\"c1= \"+str(c1))\n",
    "    print(\"d1= \"+str(d1))\n",
    "    print()\n",
    "    # Rand int\n",
    "    w1 =  (a1 + d1)/ (a1 +b1 + c1 +d1)\n",
    "    # Adjusted Rand Index\n",
    "    M= a1 + b1 +c1 +d1\n",
    "    w2 = (a1 - (a1+c1)*(a1+b1)/M) /( (2*a1+b1+c1)/2 - (a1+c1)*(a1+b1)/M )\n",
    "    #Jaccard Coefficient\n",
    "    w3 = a1 / (a1+b1+c1)\n",
    "    print (\"Rand Index          :\" +str(w1))\n",
    "    print (\"Adjusted Rand Index :\" + str(w2))\n",
    "    print (\"Jaccard Coefficient :\" +str(w3))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Meam_accuraccy():\n",
    "    sum_cluster = sum(count_class_cluster)\n",
    "    mean_accuraccy = sum_cluster/label_count\n",
    "    print(\"Mean accuraccy      :\"+ str(np.amin(mean_accuraccy)))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(num_class, numcluster):\n",
    "    count_class(num_class, numcluster)\n",
    "    external_validity(num_class, numcluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Main**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================================\n",
      "=======================================Kết quả======================================\n",
      "====================================================================================\n",
      "Tâm cụm :\n",
      "[[6.69616969 3.03528326 5.55724176 2.03399357]\n",
      " [5.00303625 3.39066757 1.49135522 0.25361125]\n",
      " [5.90402033 2.77644596 4.37540196 1.39158172]]\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "thuat_toan(2,6,3,150,1e-6)\n"
   ]
  }
 ],
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