PCA letter recognition tria.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\r\n",
    "import os\r\n",
    "import  matplotlib.image as mpimg\r\n",
    "from scipy import misc\r\n",
    "from PIL import Image\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "path ='D:\\Collage\\TERM 4\\Arabic handwritten characters\\\\Train Images 13440x32x32\\\\train\\\\id_1_label_1.png'\n",
    "try:  \n",
    "    img  = Image.open(path) \n",
    "    plt.imshow(img)\n",
    "except IOError: \n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def img_to_vector(filename):\n",
    "    img  = Image.open(filename).resize((112, 92)) \n",
    "    img_vector = np.array(img).flatten()\n",
    "    sz = img_vector.shape[0]\n",
    "    #img_vector = img_vector.reshape(sz,1)\n",
    "    return img_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess():\n",
    "    train_set = []\n",
    "    train_set_number = []\n",
    "    test_set = []\n",
    "    test_set_number = []\n",
    "    counter = 0\n",
    "    \n",
    "    for i in range(840):\n",
    "        person_id = (i%28)+1\n",
    "        for j in range(8):\n",
    "                counter = counter + 1\n",
    "                print(person_id,counter)\n",
    "                path =r'D:\\Collage\\TERM 4\\Arabic handwritten characters\\Train Images 13440x32x32\\train\\id_'+str(counter)+'_label_'+str(person_id)+'.png'\n",
    "                img = img_to_vector(path).astype(np.int64) \n",
    "                train_set.append(img)\n",
    "                train_set_number.append(person_id)\n",
    "    counter = 0\n",
    "    for i in range(140):\n",
    "        person_id = (i%28)+1\n",
    "        for j in range(2):\n",
    "                counter = counter + 1\n",
    "                print(person_id,counter)\n",
    "                path =r'D:\\Collage\\TERM 4\\Arabic handwritten characters\\Test Images 3360x32x32\\test\\id_'+str(counter)+'_label_'+str(person_id)+'.png'\n",
    "                img = img_to_vector(path).astype(np.int64)\n",
    "                test_set.append(img)\n",
    "                test_set_number.append(person_id)\n",
    "        \n",
    "        \n",
    "    train_set = np.array(train_set)\n",
    "    train_set_number = np.array(train_set_number)\n",
    "    test_set = np.array(test_set)\n",
    "    test_set_number = np.array(test_set_number)\n",
    "    return train_set.T, train_set_number, test_set.T, test_set_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
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     ]
    }
   ],
   "source": [
    "train_set,train_set_number,test_set,test_set_number = preprocess()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n(10304, 6720)\n"
     ]
    }
   ],
   "source": [
    "print(train_set)\n",
    "print(train_set.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[ 1  1  1 ... 28 28 28]\n(6720,)\n"
     ]
    }
   ],
   "source": [
    "print(train_set_number)\n",
    "print(train_set_number.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n ...\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]\n [0 0 0 ... 0 0 0]]\n(10304, 280)\n"
     ]
    }
   ],
   "source": [
    "print(test_set)\n",
    "print(test_set.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[ 1  1  2  2  3  3  4  4  5  5  6  6  7  7  8  8  9  9 10 10 11 11 12 12\n 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24\n 25 25 26 26 27 27 28 28  1  1  2  2  3  3  4  4  5  5  6  6  7  7  8  8\n  9  9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20\n 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28  1  1  2  2  3  3  4  4\n  5  5  6  6  7  7  8  8  9  9 10 10 11 11 12 12 13 13 14 14 15 15 16 16\n 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28\n  1  1  2  2  3  3  4  4  5  5  6  6  7  7  8  8  9  9 10 10 11 11 12 12\n 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24\n 25 25 26 26 27 27 28 28  1  1  2  2  3  3  4  4  5  5  6  6  7  7  8  8\n  9  9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20\n 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28]\n(280,)\n"
     ]
    }
   ],
   "source": [
    "print(test_set_number)\n",
    "print(test_set_number.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_mean(arr):\n",
    "    M = np.mean(arr, axis = 1)\n",
    "    return M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0.45357143 0.45357143 0.43125    ... 0.43735119 0.70416667 0.70416667]\n"
     ]
    }
   ],
   "source": [
    "M = find_mean(train_set)\n",
    "print(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_normalization(arr):\n",
    "    M = find_mean(arr)\n",
    "    x_range = arr.shape[0]\n",
    "    y_range = arr.shape[1]\n",
    "    for i in range(x_range):\n",
    "        for j in range(y_range):\n",
    "            arr[i][j] -= M[i]\n",
    "    return arr "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[ 0  0  0 ...  0  0  0]\n [ 0  0  0 ...  0  0  0]\n [ 0  0  0 ...  0  0  0]\n ...\n [-3 -3 -3 ... -3 -3 -3]\n [-3 -3 -3 ... -3 -3 -3]\n [-3 -3 -3 ... -3 -3 -3]]\n"
     ]
    }
   ],
   "source": [
    "A = mean_normalization(train_set)\n",
    "A = A\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(10304, 6720)\n"
     ]
    }
   ],
   "source": [
    "print(A.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(6720, 6720)\n[[53975203  9792118 12091406 ... -1956856  1892530  1541165]\n [ 9792118 50406198 20278452 ... -1003969 -4924299  1331570]\n [12091406 20278452 58731550 ... -2302413 -5543609   272647]\n ...\n [-1956856 -1003969 -2302413 ... 47244311 28435372 30213586]\n [ 1892530 -4924299 -5543609 ... 28435372 46691465 21002980]\n [ 1541165  1331570   272647 ... 30213586 21002980 46273421]]\n"
     ]
    }
   ],
   "source": [
    "L = np.dot(A.T, A)\n",
    "print(L.shape)\n",
    "print(L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "eigenvalues shape :  (6720,)\neigenvalues :  [2.41142777e+10 2.33112918e+10 1.45056375e+10 ... 2.23462551e-06\n 2.43291432e-06 2.37620480e-06]\neigenvectors shape :  (6720, 6720)\neigenvectors :  [[ 1.15012036e-03  1.19560343e-02  3.91015320e-03 ...  6.67873058e-05\n  -9.17406759e-05 -2.65076034e-04]\n [-2.03030711e-03  1.74903127e-02  1.06982772e-02 ...  1.13278484e-04\n  -7.93963490e-05  4.96682867e-05]\n [-4.16171469e-03  1.42732474e-02  1.50344408e-02 ...  6.63478356e-07\n  -4.02430758e-06  2.65549236e-06]\n ...\n [ 4.49207700e-03 -2.03135507e-03  3.53033265e-03 ... -4.85676747e-04\n  -2.91318738e-03  8.01136254e-05]\n [ 3.48942132e-03 -1.64378939e-03  3.82320962e-03 ... -4.21813280e-04\n  -1.42015003e-04  1.49496085e-04]\n [ 7.13315513e-03  3.15822749e-03  6.27354067e-03 ...  2.27553896e-04\n   2.44354823e-05 -4.11067374e-05]]\n"
     ]
    }
   ],
   "source": [
    "eigenvalues, eigenvectors = np.linalg.eig(L)\n",
    "# sort eigenvectors by the value of eigenvalues\n",
    "print(\"eigenvalues shape : \", eigenvalues.shape)\n",
    "print(\"eigenvalues : \", eigenvalues)\n",
    "print(\"eigenvectors shape : \", eigenvectors.shape)\n",
    "print(\"eigenvectors : \", eigenvectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(10304, 6720)\n[[-1.67893894e+01 -1.27522421e+01 -8.24104140e+00 ...  1.88484342e-07\n   1.51513779e-07 -2.30628838e-07]\n [-1.67893894e+01 -1.27522421e+01 -8.24104140e+00 ...  1.88484342e-07\n   1.51513779e-07 -2.30628838e-07]\n [-1.50200702e+01 -1.23725678e+01 -7.84954690e+00 ...  4.61837653e-08\n   2.40805536e-07  1.19375388e-07]\n ...\n [-1.09475923e+02 -5.82601206e+01 -2.96431316e+01 ... -7.14281439e-06\n  -1.43631057e-06 -8.01883080e-06]\n [-1.20480325e+02 -6.14309492e+01 -3.13279534e+01 ...  2.80609817e-06\n  -1.75327505e-06 -1.77172704e-06]\n [-1.20480325e+02 -6.14309492e+01 -3.13279534e+01 ...  2.80609817e-06\n  -1.75327505e-06 -1.77172704e-06]]\n"
     ]
    }
   ],
   "source": [
    "U = []\n",
    "sz = L.shape[0]\n",
    "for i in range(sz):\n",
    "    U.append(np.dot(A,eigenvectors[:,i]))\n",
    "\n",
    "U = np.array(U)\n",
    "U = U.T\n",
    "print(U.shape)\n",
    "print(U)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(6720, 6720)\n[[ 2.91195673e+07 -4.78083479e+07 -1.00008344e+08 ...  1.09185066e+08\n   8.47900553e+07  1.73910105e+08]\n [ 2.81306988e+08  4.11591398e+08  3.36200629e+08 ... -4.81061565e+07\n  -3.89693161e+07  7.36989402e+07]\n [ 5.73320432e+07  1.56503444e+08  2.20019332e+08 ...  5.17136380e+07\n   5.59302577e+07  9.19478858e+07]\n ...\n [ 1.45752271e-09  2.08760111e-09  4.46032425e-09 ...  3.21960765e-09\n   2.56031127e-09  8.99147963e-09]\n [-1.58080156e-09 -1.00956085e-09 -1.09793105e-09 ...  3.96941560e-09\n   3.61584842e-10  3.38132078e-09]\n [ 2.45014087e-09 -2.95620021e-09 -4.09326874e-09 ...  1.60235257e-09\n   3.30180960e-09 -3.92609015e-10]]\n"
     ]
    }
   ],
   "source": [
    "weight_vector = np.dot(U.T, A)\n",
    "print(weight_vector.shape)\n",
    "print(weight_vector)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0.45357143 0.45357143 0.43125    ... 0.43735119 0.70416667 0.70416667]\n",
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n",
      "(10304, 280)\n",
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "x_range = test_set.shape[0]\n",
    "y_range = test_set.shape[1]\n",
    "print(M)\n",
    "print(test_set)\n",
    "for i in range(x_range):\n",
    "    for j in range(y_range):\n",
    "        test_set[i][j] -= M[i]\n",
    "print(test_set.shape)\n",
    "print(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(6720, 280)\n[[ 1.35579321e+08  1.11743921e+08  4.02649485e+08 ...  3.60266875e+08\n   6.91544761e+08  3.94945385e+08]\n [ 1.30054033e+08  2.73860391e+08 -8.80300885e+07 ... -1.16760285e+08\n   8.99513605e+07 -3.41647458e+08]\n [-1.40079484e+08 -1.41758767e+08 -8.41840947e+06 ... -4.02967706e+08\n  -1.71523322e+08 -2.16831211e+08]\n ...\n [-2.70456163e-04  5.54740603e-06 -3.85552873e-05 ... -1.91552923e-04\n   1.75593694e-04  3.40719364e-05]\n [-1.90859495e-04 -4.48659212e-05 -3.63009730e-05 ...  2.43872120e-04\n   2.05331798e-04  6.53234319e-05]\n [ 1.66058611e-05  1.67093965e-04 -8.00878309e-06 ... -7.79098931e-05\n   1.85596815e-04 -2.49317410e-05]]\n"
     ]
    }
   ],
   "source": [
    "weight_vector_test = np.dot(U.T, test_set)\n",
    "print(weight_vector_test.shape)\n",
    "print(weight_vector_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
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   ],
   "source": [
    "validation = []\n",
    "count = 0\n",
    "for i in range(280) : \n",
    "    ith_wv = weight_vector_test[:,i]\n",
    "    ans = 0\n",
    "    index = 0\n",
    "    for j in range(6720):\n",
    "        jth_wv = weight_vector[:,j]\n",
    "        diff = ith_wv - jth_wv\n",
    "        diff = np.absolute(diff)\n",
    "        sm = np.sum(diff)\n",
    "        if ans == 0 :\n",
    "            ans = sm\n",
    "            index = j\n",
    "        else :\n",
    "            if sm < ans:\n",
    "                ans = sm\n",
    "                index = j\n",
    "    print(train_set_number[index])\n",
    "    if train_set_number[index] == test_set_number[i]:\n",
    "        count = count + 1\n",
    "        validation.append(1)\n",
    "    else:\n",
    "        validation.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "#print(validation.shape)\n",
    "print(validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "accuracy =  57.5 %\n"
     ]
    }
   ],
   "source": [
    "print(\"accuracy = \", (count*100)/280, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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