A4_120040039.ipynb

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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import numpy as np\n",
    "from random import *\n",
    "\n",
    "data = np.loadtxt(\"seeds_dataset.txt\")\n",
    "new_data = data[:,:-1]\n",
    "rslt = []\n",
    "for i in range(len(data)):\n",
    "    rslt.append(data[i][7])\n",
    "    \n",
    "    \n",
    "def E_D(x, y):\n",
    "    x = np.array(x)\n",
    "    y = np.array(y)\n",
    "    return np.sqrt(np.sum((x-y)**2))\n",
    "\n",
    "\n",
    "data = new_data\n",
    "rslt"
   ]
  },
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     "name": "stdout",
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     "text": [
      "[0.5446838826708948, 0.5718523765752067, 0.16763397876953076, 0.49668806739761784, 0.5816319555993578, 0.19691305661663833, 0.1986893913118857, 0.5946012981513659, 0.6100762808286947, 0.4906790996281364]\n",
      "[0.6636882121412191, 0.3552418206568094, 0.48347057219753825, 0.5613595535978645, 0.3811405506053645, 0.6506460535505723, 0.6310459407807222, 0.606630002426051, 0.5248662004697479, 0.12853859600095213]\n",
      "[0.6335353321012626, 0.3873636081818311, 0.33312793700016946, 0.5174833003343269, 0.6526164334043179, 0.29147577606521247, 0.12853859600095213, 0.039925488562248417, 0.5863922566991563, 0.6243878275677728]\n",
      "[0.5791386122658301, 0.6576563662670486, 0.08430394414297729, 0.039925488562248417, 0.33700699068060636, 0.08430394414297729, 0.23756279717954012, 0.6394635838134515, 0.3744912383815158, 0.6225257517502246]\n",
      "[0.5108224201946612, 0.6945211620213048, 0.6605334353947105, 0.6741950967405964, 0.31001315361704473, 0.3855864976817473, 0.32941014181564354, 0.13078135240254876, 0.6741950967405964, 0.2708935569536805]\n",
      "[0.42115450717861586, 0.6560670155534672, 0.06380151801124968, 0.47747793341045663, 0.3508349623846397, 0.5818363462316446, 0.6394635838134515, 0.6782982672440118, 0.6560670155534672, 0.3864436182500552]\n",
      "[0.5716762190841121, 0.5791386122658301, 0.6782982672440118, 0.16313163573453723, 0.39965896166890275, 0.47280900640132756, 0.5959483295319585, 0.22035705290382543, 0.6831274702026834, 0.3617190956515362]\n",
      "[0.5375954202047196, 0.5984656875241368, 0.4703810675800114, 0.41866073155913686, 0.6673112094527638, 0.5783033320015942, 0.25684078875175087, 0.2576684405019327, 0.3744912383815158, 0.289085244385647]\n",
      "[0.12724060293406367, 0.5056421266113224, 0.559941405585431, 0.5863922566991563, 0.6013736314239502, 0.5156722245457994, 0.49585570258888634, 0.3846680386094104, 0.6958362944782104, 0.6243878275677728]\n",
      "[0.6937607718063621, 0.4603142406389126, 0.24007760721134458, 0.6937607718063621, 0.12575383697635784, 0.4953151075510975, 0.39965896166890275, 0.5174833003343269, 0.5620492230862738, 0.5198763440147909]\n",
      "SE:  [0.44534493875493286, 0.4986627502426841, 0.419484655591725, 0.36563787171864204, 0.46409519135625343, 0.49114447676310596, 0.4725864650688726, 0.4448803160343209, 0.5097010111044004, 0.4708050165094731]\n",
      "RI:  [0.8767800453514739, 0.8749659863945578, 0.8749659863945578, 0.8749659863945578, 0.8749659863945578, 0.8749659863945578, 0.8749659863945578, 0.8749659863945578, 0.8749659863945578, 0.8749659863945578]\n",
      "Iteration  [4, 9, 11, 9, 8, 3, 8, 14, 10, 8]\n",
      "Variance of SE \t 0.0016184817272007848\n",
      "Mean of SE \t 0.4582342693144411\n",
      "Variance of RI \t 2.9617289092508056e-07\n",
      "Mean of RI :\t 0.8751473922902495\n"
     ]
    }
   ],
   "source": [
    "SEs = []\n",
    "RIs = []\n",
    "its = []\n",
    "\n",
    "for p in range (10):\n",
    "    m, n = np.shape(data)\n",
    "    centroids = np.zeros((3,n))\n",
    "    for i in range(3):\n",
    "        index = int(np.random.uniform(0,m)) \n",
    "        centroids[i,:] = data[index,:]\n",
    "    c_ass = np.mat(np.zeros((m, 2)))\n",
    "    check = True\n",
    "    iteration = 0\n",
    "    while check:\n",
    "        iteration += 1\n",
    "        check = False\n",
    "        for i in range(m):\n",
    "            mindis = 1e5\n",
    "            minindex = -1\n",
    "            for j in range(3):\n",
    "                x1 = np.array(centroids[j,:])\n",
    "                x2 = np.array(data[i,:])\n",
    "                distJ = np.sqrt(np.sum((x1 - x2) ** 2))\n",
    "                if distJ < mindis:\n",
    "                    mindis = distJ\n",
    "                    minindex = j\n",
    "            if c_ass[i, 0] != minindex:\n",
    "                check = True\n",
    "            c_ass[i,:] = int(minindex), mindis\n",
    "        for cent in range(3):\n",
    "            ptsinclust = data[np.nonzero(c_ass[:,0].A == cent)[0]]\n",
    "            centroids[cent,:] = np.mean(ptsinclust, axis= 0)\n",
    "    centroids, c_ass, iteration\n",
    "\n",
    "    SE=[]\n",
    "    for t in range(10):\n",
    "        avg1 = []\n",
    "        avg2 = []\n",
    "        avg3 = []\n",
    "\n",
    "\n",
    "        point = randint(0,m-1)\n",
    "        for i in range(3):\n",
    "            if i != c_ass[point, 0]:\n",
    "                clst2 = i\n",
    "                break\n",
    "        for i in range(3):\n",
    "            if i != c_ass[point, 0] and i != clst2:\n",
    "                clst3 = i\n",
    "                break\n",
    "            \n",
    "\n",
    "        for i in range(m):\n",
    "            if c_ass[i, 0] == c_ass[point, 0]:\n",
    "                dis=E_D(data[i,:],data[point,:])\n",
    "                avg1.append(dis)\n",
    "            elif c_ass[i, 0] == clst2:\n",
    "                dis=E_D(data[i,:],data[point,:])\n",
    "                avg2.append(dis)\n",
    "            else:\n",
    "                dis=E_D(data[i,:],data[point,:])\n",
    "                avg3.append(dis)\n",
    "\n",
    "        ai=np.mean(avg1)\n",
    "        a = np.mean(avg2)\n",
    "        b = np.mean(avg3)\n",
    "        if a > b:\n",
    "            bi = b\n",
    "        else:\n",
    "            bi = a\n",
    "        SE.append(((bi-ai)/max(ai,bi)))\n",
    "    print(SE)\n",
    "    avgSE=np.mean(SE)\n",
    "    avgSE\n",
    "\n",
    "    lA = 0\n",
    "    lB = 0\n",
    "    lC = 0\n",
    "    lD = 0\n",
    "    for i in range(m):\n",
    "        for j in range(m):\n",
    "            if rslt[i] == rslt[j] and c_ass[i, 0] == c_ass[j, 0]:\n",
    "                lA += 1\n",
    "            elif rslt[i] != rslt[j] and c_ass[i, 0] != c_ass[j, 0]:\n",
    "                lB += 1\n",
    "            elif rslt[i] == rslt[j] and c_ass[i, 0] != c_ass[j, 0]:\n",
    "                lC += 1\n",
    "            elif rslt[i] != rslt[j] and c_ass[i, 0] == c_ass[j, 0]:\n",
    "                lD += 1\n",
    "\n",
    "    RI = (lA+lB) / (lA+lB+lC+lD)\n",
    "    RI\n",
    "    \n",
    "    SEs.append(avgSE)\n",
    "    RIs.append(RI)\n",
    "    its.append(iteration)\n",
    "    \n",
    "    \n",
    "print('SE: ',SEs)\n",
    "\n",
    "print('RI: ',RIs)\n",
    "print('Iteration ', its)\n",
    "print('Variance of SE \\t',np.var(SEs))\n",
    "print('Mean of SE \\t',np.mean(SEs))\n",
    "print('Variance of RI \\t',np.var(RIs))\n",
    "print('Mean of RI :\\t',np.mean(RIs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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    {
     "name": "stdout",
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     "text": [
      "[0.5183929170440655, 0.3846680386094104, 0.5716762190841121, 0.5174833003343269, 0.07314451872169997, 0.6361782271266846, 0.6328776391186486, 0.586186711074528, 0.690436544684037, 0.13078135240254876]\n",
      "[nan, 0.518051438521512, 0.22556112968748535, 0.18867445814769154, nan, 0.6741007788180128, nan, nan, 0.6214921131832462, nan]\n",
      "[0.5287570017058755, 0.33312793700016946, 0.5411954233426938, 0.22035705290382543, 0.6335353321012626, 0.6155801599626545, 0.22035705290382543, 0.5861825869459808, 0.6231929674460102, 0.6155801599626545]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\StefanM\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3474: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "c:\\Users\\StefanM\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\numpy\\core\\_methods.py:181: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ret = um.true_divide(\n",
      "c:\\Users\\StefanM\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\numpy\\core\\_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.551558358409605, 0.618840013105938, 0.6774504299595935, 0.6611345071070039, 0.6625758984426052, 0.6023182092328819, 0.6313750756498168, 0.6243258813381114, 0.551558358409605, 0.71527698868515]\n",
      "[0.3156789538467262, 0.4117173187994132, 0.5533163146139822, 0.6160511751745656, 0.03838807534419788, 0.44159842570558716, 0.6148465316920346, 0.5679492581061024, 0.5683792151730023, 0.626642464144501]\n",
      "[0.6079359527848196, 0.289085244385647, 0.6888629960291875, 0.6059010012170334, 0.48347057219753825, 0.06283392337624549, 0.6310459407807222, 0.5036612682012369, 0.065325639920682, 0.39387640611575037]\n",
      "[0.4020968136203934, 0.34199921426819013, 0.5439626796486882, 0.1854320673837627, 0.5851100876487334, 0.3535037689484493, 0.4152129271509527, 0.5222847602684114, 0.12226895100516465, 0.1854320673837627]\n",
      "[0.3543693715409194, 0.5918874803307377, 0.5825426817533038, 0.22957792335871838, 0.664879551630484, 0.34139910370654053, 0.20785983082045037, 0.6051068824711496, 0.5871827125481378, 0.6143413581828394]\n",
      "[0.4703810675800114, 0.32941014181564354, 0.6243878275677728, 0.37537541337153235, 0.5419133088922948, 0.4160045327208676, 0.13064355035236874, 0.6457096553547113, 0.6056896445265312, 0.6079359527848196]\n",
      "[0.628373252561684, 0.04691015539409469, 0.5734306761829949, 0.6148465316920346, 0.6317801658942411, 0.6778161660551053, 0.5304627639140183, 0.6778161660551053, 0.547759526296836, 0.3939534622694467]\n",
      "SE:  [0.4741825468200062, nan, 0.4917865674274952, 0.629641372034031, 0.4754567732600113, 0.4331998945008862, 0.36573033373265085, 0.47791468963432815, 0.4747451094966554, 0.532314886631556]\n",
      "RI:  [0.8749659863945578, 0.7322448979591837, 0.8749659863945578, 0.7271655328798186, 0.8719727891156462, 0.8749659863945578, 0.8719727891156462, 0.8719727891156462, 0.8749659863945578, 0.8719727891156462]\n",
      "Iteration  [14, 6, 8, 6, 10, 12, 6, 8, 9, 6]\n",
      "Variance of SE \t nan\n",
      "Mean of SE \t nan\n",
      "Variance of RI \t 0.0033099838030450275\n",
      "Mean of RI :\t 0.8447165532879819\n"
     ]
    }
   ],
   "source": [
    "SEs = []\n",
    "RIs = []\n",
    "its = []    \n",
    "for p in range(10):\n",
    "    c_ass = np.mat(np.zeros((m, 2)))\n",
    "    for i in range(m):\n",
    "        c_ass[i, 0] = -1\n",
    "    check = True\n",
    "    centroids = np.zeros((3,n))\n",
    "    for j in range(3):\n",
    "        index = int(np.random.uniform(0,m)) \n",
    "        centroids[j,:] = data[index,:]\n",
    "    iteration = 0\n",
    "    while check:\n",
    "        iteration += 1\n",
    "        check = False\n",
    "        for i in range(m):\n",
    "            minindex = -1\n",
    "            mindis = 1e5\n",
    "            if c_ass[i, 0] == 0 or 1 or 2:\n",
    "                x = int(c_ass[i, 0])\n",
    "                dis_min = 0\n",
    "                if x == 0:\n",
    "                    dis_min = min(E_D(centroids[0,:], centroids[1,:]), E_D(centroids[0,:], centroids[2,:]))\n",
    "                elif x == 1:\n",
    "                    dis_min = min(E_D(centroids[0,:], centroids[1,:]), E_D(centroids[2,:], centroids[1,:]))\n",
    "                elif x == 2:\n",
    "                    dis_min = min(E_D(centroids[2,:], centroids[1,:]), E_D(centroids[0,:], centroids[2,:]))\n",
    "                if 2 * E_D(centroids[x,:], data[i,:]) <= dis_min:\n",
    "                    minindex = x\n",
    "                    distJ = dis_min\n",
    "                else:\n",
    "                    for j in range(3):\n",
    "                        distJ = E_D(centroids[j,:], data[i,:])\n",
    "                        if distJ < mindis:\n",
    "                            mindis = distJ\n",
    "                            minindex = j\n",
    "            else:\n",
    "                for n in range(3):\n",
    "                    dis_min = 0\n",
    "                    if x == 0:\n",
    "                        dis_min = min(E_D(centroids[0,:], centroids[1,:]), E_D(centroids[0,:], centroids[2,:]))\n",
    "                    elif x == 1:\n",
    "                        dis_min = min(E_D(centroids[0,:], centroids[1,:]), E_D(centroids[2,:], centroids[1,:]))\n",
    "                    elif x == 2:\n",
    "                        dis_min = min(E_D(centroids[2,:], centroids[1,:]), E_D(centroids[0,:], centroids[2,:]))\n",
    "                    if 2 * E_D(centroids[n,:], data[i,:]) <= dis_min:\n",
    "                        minindex = n\n",
    "                        distJ = E_D(centroids[n,:], data[i,:])\n",
    "                        if distJ < mindis:\n",
    "                            mindis = distJ\n",
    "                            minindex = n\n",
    "                        break\n",
    "            if c_ass[i, 0] != minindex:\n",
    "                check = True\n",
    "            c_ass[i,:] = minindex, mindis\n",
    "        for c in range(3):\n",
    "            ptsinclust = data[np.nonzero(c_ass[:,0].A == c)[0]]\n",
    "            centroids[c,:] = np.mean(ptsinclust, axis= 0)\n",
    "    centroids, c_ass, iteration\n",
    "\n",
    "    SE=[]\n",
    "    for t in range(10):\n",
    "        avg1 = []\n",
    "        avg2 = []\n",
    "        avg3 = []\n",
    "\n",
    "\n",
    "        point = randint(0,m-1)\n",
    "        for i in range(3):\n",
    "            if i != c_ass[point, 0]:\n",
    "                clst2 = i\n",
    "                break\n",
    "        for i in range(3):\n",
    "            if i != c_ass[point, 0] and i != clst2:\n",
    "                clst3 = i\n",
    "                break\n",
    "            \n",
    "\n",
    "        for i in range(m):\n",
    "            if c_ass[i, 0] == c_ass[point, 0]:\n",
    "                dis=E_D(data[i,:],data[point,:])\n",
    "                avg1.append(dis)\n",
    "            elif c_ass[i, 0] == clst2:\n",
    "                dis=E_D(data[i,:],data[point,:])\n",
    "                avg2.append(dis)\n",
    "            else:\n",
    "                dis=E_D(data[i,:],data[point,:])\n",
    "                avg3.append(dis)\n",
    "\n",
    "        ai=np.mean(avg1)\n",
    "        a = np.mean(avg2)\n",
    "        b = np.mean(avg3)\n",
    "        if a > b:\n",
    "            bi = b\n",
    "        else:\n",
    "            bi = a\n",
    "        SE.append(((bi-ai)/max(ai,bi)))\n",
    "    print(SE)\n",
    "    avgSE=np.mean(SE)\n",
    "    avgSE\n",
    "\n",
    "    lA = 0\n",
    "    lB = 0\n",
    "    lC = 0\n",
    "    lD = 0\n",
    "    for i in range(m):\n",
    "        for j in range(m):\n",
    "            if rslt[i] == rslt[j] and c_ass[i, 0] == c_ass[j, 0]:\n",
    "                lA += 1\n",
    "            elif rslt[i] != rslt[j] and c_ass[i, 0] != c_ass[j, 0]:\n",
    "                lB += 1\n",
    "            elif rslt[i] == rslt[j] and c_ass[i, 0] != c_ass[j, 0]:\n",
    "                lC += 1\n",
    "            elif rslt[i] != rslt[j] and c_ass[i, 0] == c_ass[j, 0]:\n",
    "                lD += 1\n",
    "    RI = (lA+lB) / (lA+lB+lC+lD)\n",
    "    RI\n",
    "    \n",
    "    SEs.append(avgSE)\n",
    "    RIs.append(RI)\n",
    "    its.append(iteration)\n",
    "   \n",
    "    \n",
    "print('SE: ',SEs)\n",
    "\n",
    "print('RI: ',RIs)\n",
    "print('Iteration ', its)\n",
    "print('Variance of SE \\t',np.var(SEs))\n",
    "print('Mean of SE \\t',np.mean(SEs))\n",
    "print('Variance of RI \\t',np.var(RIs))\n",
    "print('Mean of RI :\\t',np.mean(RIs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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