4_knn-on-cat-dog-0-1-dataset-X-Y-then-MNIST-Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2)\n",
      "(100,)\n"
     ]
    }
   ],
   "source": [
    "X=np.load(\"Datasets/Data/X.npy\")\n",
    "Y=np.load(\"Datasets/Data/Y.npy\")\n",
    "print(X.shape)     # train data has 2 parameters height and weight\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11.563957    1.3389402 ]\n",
      " [10.06721983  4.08616331]\n",
      " [ 8.77253877  2.33030515]\n",
      " [10.12748869  5.32277484]\n",
      " [ 9.06439343  1.74115688]\n",
      " [ 7.88506245  1.50083021]\n",
      " [ 9.38860303 -1.41342865]\n",
      " [ 9.67284681 -0.20283165]\n",
      " [11.42846765  1.85948594]\n",
      " [ 8.24628674  3.12802959]\n",
      " [ 8.2434749   0.41644891]\n",
      " [ 7.77072231  3.86243349]\n",
      " [10.06393839  0.99078055]\n",
      " [10.50478434  1.18730514]\n",
      " [ 9.05251038  5.19142425]\n",
      " [ 8.69289001  1.54322016]\n",
      " [ 9.76566918  1.27689813]\n",
      " [ 9.86174537  6.02871838]\n",
      " [ 9.37370771  4.94611021]\n",
      " [ 8.7337095   2.49162431]\n",
      " [ 9.40806778  0.21238028]\n",
      " [ 8.18378052  1.29564214]\n",
      " [ 8.77572148 -0.59828407]\n",
      " [11.0329545  -0.16816717]\n",
      " [ 9.37814427  0.98057303]\n",
      " [10.85496409  5.85225779]\n",
      " [ 8.81982906  3.92571768]\n",
      " [ 9.38919979  1.13329119]\n",
      " [ 9.50048972 -0.26430318]\n",
      " [ 9.63773799 -0.01713182]\n",
      " [10.49459357  3.88766956]\n",
      " [11.48586652  4.72216659]\n",
      " [ 9.49232241  3.81547073]\n",
      " [ 8.68937095  1.48709629]\n",
      " [ 8.39622818  1.22550966]\n",
      " [ 9.48375428  3.96014003]\n",
      " [10.6124154   0.29700045]\n",
      " [ 9.53495097  4.0557957 ]\n",
      " [10.58206053  4.47715319]\n",
      " [ 8.19679821  1.45456477]\n",
      " [ 9.30844046  4.80439317]\n",
      " [ 8.56185686  1.1240557 ]\n",
      " [10.06842783  4.16975128]\n",
      " [10.27524684  0.59854311]\n",
      " [11.07092841  4.36429207]\n",
      " [ 9.95926647  0.85665806]\n",
      " [ 8.84477826  0.77318037]\n",
      " [ 8.35760513  0.99907772]\n",
      " [ 8.94530697  5.30230187]\n",
      " [ 8.98102438  4.39258782]\n",
      " [ 9.33427576  3.25149617]\n",
      " [ 9.54991027  0.57020007]\n",
      " [10.46524894  4.08771906]\n",
      " [ 6.96082151  0.33696129]\n",
      " [10.07884389  1.59539821]\n",
      " [11.03256145  4.0891382 ]\n",
      " [ 8.77678625  3.17120528]\n",
      " [ 7.95783483  3.60092296]\n",
      " [11.81718094  1.29698838]\n",
      " [10.29529538  1.46789265]\n",
      " [11.65725122  5.3739542 ]\n",
      " [ 9.10423924  3.06318305]\n",
      " [ 9.67593895  5.34992028]\n",
      " [ 8.60073592  3.19174526]\n",
      " [10.83465533  2.3245668 ]\n",
      " [ 8.03745967  5.17621887]\n",
      " [ 9.1625914   3.40534414]\n",
      " [ 9.1438515   1.78117369]\n",
      " [ 8.43515864  1.37491631]\n",
      " [ 8.92229526 -0.63993225]\n",
      " [ 9.75658923  5.14670674]\n",
      " [10.82828043  3.84527696]\n",
      " [10.17196159  4.81287951]\n",
      " [10.72051688  0.89008628]\n",
      " [ 9.20989214  1.7467316 ]\n",
      " [10.21143856  0.76943446]\n",
      " [ 9.49906632  2.934728  ]\n",
      " [ 7.99313649  0.93528488]\n",
      " [ 9.56860938  4.50869016]\n",
      " [ 9.19237516  5.17306236]\n",
      " [ 9.17044091  4.34579642]\n",
      " [ 9.2825722   4.48060463]\n",
      " [ 8.86330381  2.94628015]\n",
      " [ 8.26530669  2.96705005]\n",
      " [10.13583073  0.96373495]\n",
      " [ 9.03763281  2.14664757]\n",
      " [ 8.97080851  3.31090141]\n",
      " [10.12719906 -0.89623088]\n",
      " [10.98547806  3.70271807]\n",
      " [ 9.81432518  2.78141557]\n",
      " [ 9.20148969  3.01423705]\n",
      " [11.56942304  4.0550612 ]\n",
      " [ 8.526605    4.64006132]\n",
      " [10.97634432 -0.41632061]\n",
      " [ 8.99014223  3.96486732]\n",
      " [ 8.34468785  1.63824349]\n",
      " [ 8.23501274  1.78248134]\n",
      " [10.21779286  3.96675333]\n",
      " [ 9.50886477  2.856055  ]\n",
      " [ 9.85037563  1.24509053]]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 0 1 0\n",
      " 1 1 0 1 0 1 0 1 0 0 0 1 1 1 0 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 0\n",
      " 0 0 0 0 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 0 1 0 0 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(Y)    # 1 for cat and 0 for dog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=X[:,0]      # means take all rows and only first colmn\n",
    "y=X[:,1]      # means take all rows and only 2nd colmn\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(x,y,c=Y)  # c means color saare dogs ek color ke and saare cats ek color ke\n",
    "plt.scatter(8,5.5,s=1000)    # s represents size of that particular point   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now applying KNN algo\n",
    "# here v1 and v2 are 2d coordinates\n",
    "def dist(v1,v2):\n",
    "    return np.sqrt(np.sum((v1-v2)**2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape     # total 100 points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def knn(X,Y,query_point,k=5):\n",
    "    m=X.shape[0]             # n.o of points in training dataset \n",
    "    distances=[]             # calculate distance of all points with query point\n",
    "    for i in range(m):           # for each point\n",
    "        d=dist(query_point,X[i])\n",
    "        distances.append((d,Y[i]))      # appending label too to be able to tell which distance is for dog and which is for cat\n",
    "    \n",
    "    distances=sorted(distances)[:k]      # take first k elements  , writing nothing onleft of semicolon means its 0 by default\n",
    "    distances=np.array(distances)        # convert list to array so that we can apply np operations\n",
    "    labels=distances[:,1]                #  select all rows and second colmn\n",
    "    labels,freq=np.unique(labels,return_counts=True)    # return 2 arrays see below example\n",
    "    pred=labels[np.argmax(freq)]                        # take the highest freq class \n",
    "    return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query=np.array([8,6.5])\n",
    "knn(X,Y,query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.array([0,1,0,0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), array([3, 2], dtype=int64))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(a,return_counts=True)        # 0 element freq =3 and 1 element freq =2\n",
    "# here writing return_counts=True is necessary to return count of every unique element in a separate array\n",
    "# see output , there will be two arrays           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train=pd.read_csv(\"Datasets/MNIST-1/train.csv\").values\n",
    "Test =pd.read_csv(\"Datasets/MNIST-1/test.csv\").values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19999, 785)\n",
      "(9999, 785)\n"
     ]
    }
   ],
   "source": [
    "print(Train.shape)     # 19999 pics and each has 785 parameters , last 785th thing colmn isnt a parameter its the y values\n",
    "print(Test.shape)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=Train[:,:-1]     # take all the cols except last one ,bcoz its the value y , proof -> in  Train[:,s:e] the col e isnt included\n",
    "Y_train=Train[:,-1]      # we are taking the 785th colmn herre\n",
    "\n",
    "X_test=Test[:,:-1]       \n",
    "Y_test=Test[:,-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize(img):\n",
    "    # we plot an image of square or rectangle dimensions\n",
    "    plt.imshow(img.reshape(28,28),cmap=\"gray\")     # why reshape  , ans in copy , bcoz we can only plot square images so \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index=1\n",
    "visualize(X_test[index])\n",
    "knn(X_train,Y_train,X_test[index])    # predicts what number is written in the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.imshow(X_train[1].reshape) \n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(784,)\n"
     ]
    }
   ],
   "source": [
    "visualize(X_train[2])\n",
    "print(X_train[2].shape)\n",
    "#visualize(X_test[30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img=plt.imread(\"Datasets/paint1.png\")    # how to use custom image i.e via paint \n",
    "plt.imshow(img)\n",
    "print(img.shape)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f903099d48>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "#(thresh,img) = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.imshow(img)\n",
    "plt.imshow(img,cmap=\"gray\")\n",
    "print(img.shape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(784,)\n"
     ]
    }
   ],
   "source": [
    "img=img.reshape((784,))\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "\n",
      " \n",
      "\n",
      "ACTUAL MATRIX \n",
      "\n",
      "\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
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      " 0.         0.         0.         0.         0.         0.\n",
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      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.13333334 0.05098039 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.19215687 0.92156863 0.7176471  0.7254902\n",
      " 0.7254902  0.7254902  0.7254902  0.7254902  0.67058825 0.5058824\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.47058827 0.14901961 0.         0.\n",
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      " 0.5058824  0.         0.         0.         0.         0.\n",
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      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.6039216  0.\n",
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      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.11764707 0.44705886 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.3137255\n",
      " 0.10980392 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.5411765  0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.6431373  0.         0.         0.         0.\n",
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      " 0.         0.         0.         0.         0.         0.\n",
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      " 0.         0.         0.         0.         0.         0.5254902\n",
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      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.11372549 0.3764706  0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.3529412  0.11764707 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.72156864 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.57254905 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.16470589\n",
      " 0.36078432 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.57254905 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.58431375 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.5921569\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.22745098 0.36078432 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.41568628 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
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      " 0.         0.         0.         0.         0.         0.\n",
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      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.        ]\n",
      "\n",
      " \n",
      "\n",
      "MODIFIED MATRIX \n",
      "\n",
      "\n",
      "[  0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
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      "   0.         0.         0.         0.         0.         0.\n",
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      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "  34.        13.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.        49.       235.       183.       185.\n",
      " 185.       185.       185.       185.       171.       129.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.       120.00001   38.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      " 129.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.       154.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.        30.000002 114.00001    0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.        80.\n",
      "  28.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.       138.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.       164.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.       134.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.        29.        96.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "  90.        30.000002   0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.       184.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.       146.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.        42.\n",
      "  92.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.       146.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.       149.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.       151.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.        58.        92.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      " 106.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.      ]\n"
     ]
    }
   ],
   "source": [
    "visualize(img)\n",
    "print(knn(X_train,Y_train,img))\n",
    "print(\"\\n \\n\")\n",
    "print(\"ACTUAL MATRIX \\n\\n\")\n",
    "print(img)\n",
    "img=img*255                         # DONT RUN THIS CELL MULTIPLE TIMES IR ELSE MULTIPLE MULRIPLICATIONS WILL HAPPEN\n",
    "print(\"\\n \\n\")\n",
    "print(\"MODIFIED MATRIX \\n\\n\")\n",
    "print(img)\n",
    "\n",
    "#print(X_test[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "   0   0   0   0   0   0   0   0   0   0   0   0   0 169 253 253 253 253\n",
      " 253 253 218  30   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0 169 253 253 253 213 142 176 253 253 122   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0  52 250 253 210  32\n",
      "  12   0   6 206 253 140   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0  77 251 210  25   0   0   0 122 248 253  65   0   0\n",
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      "   0   0   0   0  76 247 253 231  63   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0 128 253 253 144\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
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      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0  25 234 253 233\n",
      "  35   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0 198 253 253 141   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  78 248 253\n",
      " 189  12   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0  19 200 253 253 141   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 134 253\n",
      " 253 173  12   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0 248 253 253  25   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      " 248 253 253  43  20  20  20  20   5   0   5  20  20  37 150 150 150 147\n",
      "  10   0   0   0   0   0   0   0   0   0 248 253 253 253 253 253 253 253\n",
      " 168 143 166 253 253 253 253 253 253 253 123   0   0   0   0   0   0   0\n",
      "   0   0 174 253 253 253 253 253 253 253 253 253 253 253 249 247 247 169\n",
      " 117 117  57   0   0   0   0   0   0   0   0   0   0 118 123 123 123 166\n",
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      "   0   0   0   0   0   0   0   0   0   0]\n",
      "[  0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "  34.        13.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.        49.       235.       183.       185.\n",
      " 185.       185.       185.       185.       171.       129.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.       120.00001   38.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      " 129.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.       154.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.        30.000002 114.00001    0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.        80.\n",
      "  28.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.       138.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.       164.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.       134.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.        29.        96.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "  90.        30.000002   0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.       184.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.       146.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.        42.\n",
      "  92.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.       146.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
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      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.       149.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.       151.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.        58.        92.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
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      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      " 106.         0.         0.         0.         0.         0.\n",
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      "   0.         0.         0.         0.         0.         0.\n",
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      "   0   0  49 235 183 185 185 185 185 185 171 129   0   0   0   0   0   0\n",
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      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0 106   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img.reshape(28,28))\n",
    "print(X_test[0])\n",
    "\n",
    "print(img)\n",
    "img=img.astype(int)\n",
    "print(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize(img)\n",
    "knn(X_train,Y_train,img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}