quick_and_dirty_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a2b4acd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a very simple way to predict digits from MNIST\n",
    "# Christian Sutton\n",
    "# christian(dot)sutton(at)gmail(dot)com\n",
    "# MNIST library: https://github.com/hsjeong5/MNIST-for-Numpy\n",
    "\n",
    "import numpy as np\n",
    "import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eebbee85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading train-images-idx3-ubyte.gz...\n",
      "Downloading t10k-images-idx3-ubyte.gz...\n",
      "Downloading train-labels-idx1-ubyte.gz...\n",
      "Downloading t10k-labels-idx1-ubyte.gz...\n",
      "Download complete.\n",
      "Save complete.\n"
     ]
    }
   ],
   "source": [
    "# download the mnist images\n",
    "\n",
    "mnist.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c5f7943",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load method, seperate into images and labels for training and test sets\n",
    "\n",
    "x_train, t_train, x_test, t_test = mnist.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41b459ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "i,_ = x_train.shape\n",
    "x_train_sq = np.reshape(x_train,([i,28,28]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c93b9144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_sq.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "086fca40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8a8c2ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# nums is a dictionary of numpy arrays - which training positions are 0,1,2..9\n",
    "\n",
    "nums = {}\n",
    "for i in range(10):\n",
    "    nums[i] = t_train == i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe671e7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False, ..., False, False, False])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d69e51fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_train[nums[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d47b3893",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                           | 0/10 [00:00<?, ?it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 10%|████████▎                                                                          | 1/10 [00:00<00:08,  1.04it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 20%|████████████████▌                                                                  | 2/10 [00:01<00:06,  1.33it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 30%|████████████████████████▉                                                          | 3/10 [00:01<00:04,  1.72it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 40%|█████████████████████████████████▏                                                 | 4/10 [00:02<00:03,  1.59it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|█████████████████████████████████████████▌                                         | 5/10 [00:03<00:02,  1.72it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 60%|█████████████████████████████████████████████████▊                                 | 6/10 [00:03<00:02,  1.69it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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TYImk1bl9uE/MzFpMomN/haTmeSG3RMSWzDpDETFe23MAGB8z6RJgb9P39hXLknVATmKFXN3OqVOnOsZy42IdOXIkGT96NDnBcXbux1QnbKpOC2B0dDQZ37t3bzJ+8uTJZHzp0qUdY6l5ISE/d2OuFis1H2iuti9Xm5f7M8mZQXViU54BHCAiQlJPByN7OdmhY+4OSfslPV28ruulEWZWHeODInbzmqLR8cvE4ufBYvl+YE3T9y4tliV104r7ae2YA/hmRGwoXo93sR0zq4kSO/bbeQzYVLzfBDzatPyzxV3KDwFHmi47O8peTkbEjyWtm2JjzayG+jwD+J3AQ5JuBvYANxRffxy4DtgFnAA+380+eukTu1XSZ4HtwFci4tUOv8RmGrdLWbt2bQ+7M7PpUlYS6zADOMA1bb4bwC2T3cdUL2rvAd4FbKBx5+CuTl+MiC0RMRwRwytXrpzi7sxsOvX5crJUUzoTi4i3b2lJ+hbwg9JaZGYDVaUE1Y0pJTFJq5s63D4NPJf6vpnVS52encwmsQ4dc1dL2gAEsBv4Qv+aOD1ydTupsatyNUWpeqVu1s+Nm5Wq1crNWZlrW25+xYsvvjgZX726c8F1bm7H3HyduXhK7kwj9484NydmnZJAOzPqTKxDx9y9fWiLmVXEjEpiZnZuOSf6xMxsZnMSM7Naq1OfnpOYmbXwmZiZ1Zb7xGaoXv5Qc+UbuWFdckP9pMoo5s2bl1w3N21abricoaGhZPzCCy/sGMtNZZcroXj11bZPur0t9YTIggULkusuWrQoGc8dt7qXYDiJmVmtOYmZWa05iZlZbY0PilgXTmJm1sJnYmZWa05iZlZrJY7segXwt02L3gn8e2AJ8EfAoWL57VMd5t5JzMwmKLNOLCJ+SWPwVCTNpjHxxyM0hp7+ZkR8o9d9OIkVehl6JTdczcKFC5Px3NRkuSndUlOXzZ49O7lubrTdXNtz09Ht3995spp9+/Yl183ViS1evDgZX758ecdYrr4ttS7kj0uuTqzql2t9at81wEsRsafM7dfnFoSZTZtJTNm2QtL2ptfmxGZvBL7T9PlWSc8U00J2nqA019aprmhmM9ckxtgfG59Do3i1nf1b0lzgd4H/USzqep6OHF9OmtkEfXp28lPAU+Pzc5Q5T4fPxMysRR9mO7qJpkvJ8RnACz3N0+EzMTNrUeaZmKSFwCeYOBfHn5c1T4eTmJm1KPOxo4g4Diw/a9lnytq+k5iZTeDxxGoq9z9PavyopUvTd4dzNUnLli1Lxnft2pWMp8bVOn78eHLdw4cPJ+O5eqhTp04l46k6stRUcwCrVq1KxteuXZuMr1+/vmNs3bp1yXVXrFiRjPda+1f1JFH19jVzEjOzFk5iZlZrTmJmVmtOYmZWWx4U0cxqz2diZlZrTmJmVmszKolJWgM8AAzReERgS0T8haRlNEZsXEfjsYEbIiI9EWCF5foAUmOG5eqZUvVKAAcOHEjGR0dHk/EXXnihY2zv3r3JdXNjeuXGxcrFU/M35mq1NmzYkIx/4AMfSMavuuqqjrE1a9Yk183Ntzl37txkvE59SmerW7FrN0f6NPCViLgS+BBwi6QrgduAJyNiPfBk8dnMZoBJjCc2cNlWRMRIRDxVvD8G7AQuATYCW4uvbQWu71MbzWya9WEUi76ZVJ+YpHXAe4GfAUMRMVKEDtC43DSzGaAqCaobXScxSRcADwNfjoijzb9kRISk6LDeZmAz5J91M7PBq9JZVje6uqiVNIdGAnswIr5XLB4dH9is+Hmw3boRsWV86NrcpBRmVg11upzMJjE1WnovsDMi7m4KPQZsKt5vAh4tv3lmNgh1SmLdXE5+GPgM8Kykp4tltwN3Ag9JuhnYA9zQlxZOk9ydlnnz5nWM5ab3uuKKK3ra94IFC5Lxiy66qGMsN4xPbjq4XAlFbpihyy+/vGMsVQLRTfyyyy5LxlNn/r1OuVaVO3P9UqffL5vEIuInQKeUe025zTGzQSv7LEvSbuAY8BZwOiKGy6wzrU+6NbNp04fLyd+OiA0RMVx8Lq3O1EnMzFpMQ59YaXWmTmJm1mISSaybGcAD+KGkHU3x0upM/QC4mbWYxFnWWNMlYicfiYj9klYB2yT9ojmYqjPthpOYmU1Q9qCIEbG/+HlQ0iPABynqTCNiJFVn2g1fTppZi7L6xCQtlLRo/D3wSRqzfZdWZ+ozsS7Nnj27YyxXc3TJJZck47kp39797ncn4x//+Mc7xsbGxpLrnjhxIhlP/d6QHmoH0lOf5errFi9enIynhkeCdK1X7kyjKoWcg1Li7z8EPFJs7zzg2xHxd5J+Tkl1pk5iZtairCQWES8Dv9Vm+WFKqjN1EjOzCar0SFE3nMTMrMWMeuzIzM49PhMzs1pzEjOz2nKfmJnVnpPYOSbXCTp//vxkPDVWGeSnD0tNfXbmzJnkurl4Tu4ve+rY5I5br53LdfqHWDV1OnZOYmbWwncnzay23CdmZrXnJGZmteYkZma15iRmZrXmJGZmtVX2oIj95iRWAbn/9XqpxTKbCp+JmVmt1SmJ+b9wM2tR4vDUayT9SNILkp6X9KVi+R2S9kt6unhdN9W2+kzMzCYoudj1NPCViHiqGGt/h6RtReybEfGNXnfgJGZmLcrqZy3mlhwp3h+TtBNITzoxSb6cNLMWJU+eO77NdcB7gZ8Vi26V9Iyk+ySlZ8tJcBIzsxaTSGJjETHc9NrSYXsXAA8DX46Io8A9wLuADTTO1O6aalt9OWlmE5T9ALikOTQS2IMR8T2AiBhtin8L+MFUt589E5uOuwtmVi0l3p0UcC+wMyLublq+uulrn6Yxoe6UdHMm1ve7C2ZWLSWeiX0Y+AzwrKSni2W3AzdJ2gAEsBv4wlR3kE1i03F3wcyqpcS7kz8B2mXEx0vZAZPs2J/K3QVJm8fvXBw6dKi31ppZ33V7KVmVqv6uk9hU7y5ExJbxOxcrV67svcVm1nd1SmJd3Z3s990FM6uWqiSobnRzd7LvdxfMrFpm2plY3+8umFm1VCVBdaObu5N9v7tgZtUhD4poZnU3o87EzOzc4yRmZrVVpU77bjiJmVkLJzEzqzV37JtZrflMzMxqy31iZlZ7TmJmVmtOYmZWa05iZlZbdXvsqD4tNbNpU+YoFpKulfRLSbsk3VZ2W53EzKxFiROFzAb+GvgUcCWN0W+uLLOtTmJm1qLEM7EPArsi4uWIeBP4LrCxzLZOa5/Yjh07xiTtaVq0AhibzjZMQlXbVtV2gds2VWW27R29bmDHjh1PSFrR5dfnS9re9HnLWRPoXgLsbfq8D/jnvbax2bQmsYiYMMi+pO0RMTydbehWVdtW1XaB2zZVVWtbRFw76DZMhi8nzayf9gNrmj5fWiwrjZOYmfXTz4H1ki6TNBe4EXiszB0Muk5sS/4rA1PVtlW1XeC2TVWV29aTiDgt6VbgCWA2cF9EPF/mPhQRZW7PzGxa+XLSzGrNSczMam0gSazfjyH0QtJuSc9Kevqs+pdBtOU+SQclPde0bJmkbZJeLH4urVDb7pC0vzh2T0u6bkBtWyPpR5JekPS8pC8Vywd67BLtqsRxq6tp7xMrHkP4FfAJGoVvPwduiogXprUhHUjaDQxHxMALIyV9DHgNeCAiriqW/TnwSkTcWfwHsDQi/m1F2nYH8FpEfGO623NW21YDqyPiKUmLgB3A9cDnGOCxS7TrBipw3OpqEGdifX8MYaaIiB8Dr5y1eCOwtXi/lcY/gmnXoW2VEBEjEfFU8f4YsJNG5fhAj12iXdaDQSSxdo8hVOkPMoAfStohafOgG9PGUESMFO8PAEODbEwbt0p6prjcHMilbjNJ64D3Aj+jQsfurHZBxY5bnbhjv9VHIuJ9NJ66v6W4bKqkaPQFVKlG5h7gXcAGYAS4a5CNkXQB8DDw5Yg42hwb5LFr065KHbe6GUQS6/tjCL2IiP3Fz4PAIzQuf6tktOhbGe9jOTjg9rwtIkYj4q2IOAN8iwEeO0lzaCSKByPie8XigR+7du2q0nGro0Eksb4/hjBVkhYWHa5IWgh8Enguvda0ewzYVLzfBDw6wLZMMJ4gCp9mQMdOjTFi7gV2RsTdTaGBHrtO7arKcaurgVTsF7eQ/zP/9BjC16e9EW1IeieNsy9oPJL17UG2TdJ3gKtpDNUyCvwZ8H3gIWAtsAe4ISKmvYO9Q9uupnFJFMBu4AtNfVDT2baPAH8PPAucKRbfTqP/aWDHLtGum6jAcasrP3ZkZrXmjn0zqzUnMTOrNScxM6s1JzEzqzUnMTOrNScxM6s1JzEzq7X/D2pKq0+elrRLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 70%|██████████████████████████████████████████████████████████                         | 7/10 [00:04<00:01,  1.85it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 80%|██████████████████████████████████████████████████████████████████▍                | 8/10 [00:04<00:01,  1.99it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 90%|██████████████████████████████████████████████████████████████████████████▋        | 9/10 [00:05<00:00,  2.06it/s]"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [00:05<00:00,  1.77it/s]\n"
     ]
    }
   ],
   "source": [
    "#lets look at the stack of all 0's, 1's ... 9's.\n",
    "\n",
    "for i in tqdm.tqdm(range(10)):\n",
    "    plt.title(f'{i}')\n",
    "    plt.imshow(255 *x_train_sq[nums[i]].mean(axis = 0)/x_train_sq[nums[i]].max(),cmap='binary')\n",
    "    plt.colorbar()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7be4a4eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 27.20it/s]\n"
     ]
    }
   ],
   "source": [
    "# x_train_sq_stk is a dictionary of the normalized stacks of all the digis\n",
    "\n",
    "x_train_sq_stk = {}\n",
    "for i in tqdm.tqdm(range(10)):\n",
    "    x_train_sq_stk[i] = 255 * x_train_sq[nums[i]].mean(axis = 0)//x_train_sq[nums[i]].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c83096c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train_sq_stk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc79c5df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x200423a5b20>"
      ]
     },
     "execution_count": 13,
     "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": [
    "plt.imshow(x_train_sq_stk[0],cmap='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a055bc5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n"
     ]
    }
   ],
   "source": [
    "i,_ = x_test.shape\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "381aa1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_sq = np.reshape(x_test,[i,28,28])\n",
    "x_test_sq.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "361b300e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20042410370>"
      ]
     },
     "execution_count": 16,
     "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": [
    "plt.imshow(x_test_sq[0],cmap='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a4462f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1,3,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "72fdfb1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.index(min(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "00c9e7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# look at the first image in the test dataset..  loss function is the absolute sum of the difference between the 1st image \n",
    "# all the values in the stack.  The lowest value of the loss function corresponds to the \"predicted\" value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d1b19df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = []\n",
    "for i in range(10):\n",
    "    s.append(np.sum((np.abs(x_test_sq[0] - x_train_sq_stk[i])))/784)\n",
    "\n",
    "ind = s.index(min(s))\n",
    "plt.imshow((np.abs(x_test_sq[0] - x_train_sq_stk[ind])),cmap='binary',vmin=0,vmax=250)\n",
    "plt.colorbar()\n",
    "plt.title(f'Predicted value: {ind}, Actual Value: {t_test[0]}')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1cc8e1fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = []\n",
    "for i in range(10):\n",
    "    s.append(np.sum((np.abs(x_test_sq[15] - x_train_sq_stk[i])))/784)\n",
    "\n",
    "ind = s.index(min(s))\n",
    "plt.imshow((np.abs(x_test_sq[15] - x_train_sq_stk[ind])),cmap='binary',vmin=0,vmax=250)\n",
    "plt.colorbar()\n",
    "plt.title(f'Predicted value: {ind}, Actual Value: {t_test[15]}')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8aba10ad",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = []\n",
    "for i in range(10):\n",
    "    s.append(np.sum((np.abs(x_test_sq[1990] - x_train_sq_stk[i])))/784)\n",
    "\n",
    "ind = s.index(min(s))\n",
    "plt.imshow((np.abs(x_test_sq[1990] - x_train_sq_stk[ind])),cmap='binary',vmin=0,vmax=250)\n",
    "plt.colorbar()\n",
    "plt.title(f'Predicted value: {ind}, Actual Value: {t_test[1990]}')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3aebf70c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rr71WN5Z77iPAe97znmx87Nix2XhZH7ff/e53dWNnn312dt2nn346G3/qqaey8Y9+9KN1YxdeeGF23aOOOiobH+yqlMRKL0FIuknSRknLa+aNlXS/pGfSz8P6tphm1p8OOOCAhl6toJFS3Ayc0WHeFcADETENeCC9N7NBoL1NrJFXKyhNYhHxILClw+xzgIVpeiEwu7nFMrOBVKUk1tM2sQkRsT5NvwBMqLegpLnAXIApU6b0cHdm1p9aJUE1otcntVE8YaPuUzYiYkFEtEVEW9lDKcysNVSpJtbTJLZB0kSA9HNj84pkZgNJUtMa9utcGJwnaZ2kx9PrrJrYpyWtlPS0pPyl86SnSeweYE6angPc3cPtmFkLamJN7GY6XxgEuD4iZqbXfWmfM4APAn+e1vknSUPKdlDaJibpNmAWME7SWuCzwDXAHZIuBNYA5zXyaQZS2XMly/qJvfLKK3VjZc+VfOmll7LxZ599Nhs/+eSTe7X9nMMPPzwbL+tft2VLx2s++3rzm99cN/bcc89l133DG96QjZc97zPXPy9XLihvE2qVU6m+0qzPFxEPSpra4OLnALdHxKvA7yWtBE4AfpVbqTSJRcT5dULvbLBgZlYx3Uhi4yQtrXm/ICIWNLDeJZI+DCwFLo+Il4BJwJKaZdameVn7TY99M2tcN5LY5oho6+bmbwA+T3FB8PPAtUD92ytKOImZ2T76+spjRGyo2dc3gB+kt+uAyTWLHpHmZbXGfQNm1lL68raj9p4NyblA+5XLe4APShou6ShgGvDrsu25JmZmnTSrJlbnwuAsSTMpTidXAxcBRMSTku4AngL2ABdHxN6yfTiJmVknTbw62dWFwRszy18NXN2dfQyaJFbWhaKsq8DevfmEv2vXrrqxHTt2ZNc97bTTsvFHHnkkGy8bFibX/WP69OnZdcu6KZR1Hynz61/XPxs45phjsuuWHdeyx8WNGjWqbqxsiKIDD8z/aQzmLhat1Bu/EYMmiZlZ8ziJmVmltcpYYY1wEjOzTlwTM7PKcpuYmVWek5iZVZqTmJlVmpNYC+ptP7LcUD1lw/gsXrw4Gy97ZNt3vvOdbDzXF6xsuJuyfmK5x8EBjBkzJhvP9QUrG2LoZz/7WTY+bty4bPyww+o/hGvkyJHZdYcMyQ9jVaU/8u5qHxSxKvabJGZmjatSknYSM7NOnMTMrNKcxMys0pzEzKyy3NnVzCrPVyfNrNJcE6ugsi8t129o9uzZ2XWvuuqqbHz9+vXZ+Msvv5yNr1q1qm6s7D9q2bbnzZuXjd99d/6Ro0cffXTd2AUXXJBdd8SIEdn4WWedlY3n+okNGzYsu26VaiJ9wUnMzCrLbWJmVnlOYmZWaVU6nXYSM7NOXBMzs8pym5iZVZ6TmJlVmpPYACg76GUNlWXjR+X6LJX1pdq6dWs2nns+IsCrr76ajW/cuDEbz/nUpz6VjX/zm9/MxidMmJCNT5kypW6sbKyyXB+zRvadO67783MlG1Glz196CULSTZI2SlpeM2+epHWSHk+vfK9DM6uM9kERG3m1gkZKcTNwRhfzr4+Imel1X3OLZWYDqb1xv+zVCkpPJyPiQUlT+6EsZtYiWiVBNaI39cFLJP02nW7WvUlN0lxJSyUt3bRpUy92Z2b9pUo1sZ4msRuAY4CZwHrg2noLRsSCiGiLiLbx48f3cHdm1p+qlMR6dHUyIja0T0v6BvCDppXIzAZUKyWoRvQoiUmaGBHt48ecCyzPLW9m1dIqVx4bUZrEJN0GzALGSVoLfBaYJWkmEMBq4KK+K2JzlH0pQ4cOzcZzfY6effbZ7LoHHXRQNv7www9n42984xuz8bFjx9aNnXjiidl1v/CFL2Tjo0ePzsbLPtuhhx5aNzZ8+PDsupMnT87Gy555mRszrLd/pFWqqfRElT5fI1cnz+9i9o19UBYzaxGDKomZ2f6lam1i1TnxNbN+06yrk3Xu+Bkr6X5Jz6Sfh6X5kvQVSStT963jGymrk5iZddLE245upvMdP1cAD0TENOCB9B7gTGBaes2l6MpVXtZGFjKz/UuzamIR8SCwpcPsc4CFaXohMLtm/rejsAQYI2li2T7cJmZm++hmm9g4SUtr3i+IiAUl60yo6aL1AtA+HMkk4Pma5damednHge03SazsSynrYpHrSlB2O9XOnTuz8bJuDtu2bcvG58+fXze2aNGi7Lpl3RyOOuqobLxsCKPcMETHHntsdt2yoXbKunfkTnfKfh+q1LDdF7rx+TdHRFtP9xMRISl6uj74dNLMutDHtx1taD9NTD/bB8RbB9R2DjwizctyEjOzTvo4id0DzEnTc4C7a+Z/OF2lPAnYVnPaWdd+czppZo1pHxSxSdvq6o6fa4A7JF0IrAHOS4vfB5wFrAReAfKPiE+cxMysk2a1Cda54wfgnV0sG8DF3d2Hk5iZdVKlCxtOYmbWiZOYmVVW1e6d3G+SWNmXUtaQmetPdeSRR2bXveuuu7LxWbNmZePbt2/PxkeOHFk3duedd2bXLesHdu+992bjuaF2AK644oq6senTp2fXPeSQQ7JxP3at71Tp2O03SczMGjeoBkU0s/2Pa2JmVlluEzOzynMSM7NKcxIzs0pzw76ZVZbbxCqq7EvLjZv1rW99K7vuyy+/nI0/99xz2fh1112XjZ966ql1Y+vW5Ucy+eUvf5mNv/TSS9l42XGbOLH+wJzjxo3Lrls2XljZWGZ9+YdY3OY3MPvuD1Uqv5OYmXXiJGZmleYkZmaV5iRmZpXVzEER+4OTmJl14pqYmVWak5iZVdqgSmKSJgPfpnjAZVA8HPPLksYC3wOmAquB8yIi36mohfXmSyvrM3T77bdn46+//no2vnv37mx8x44ddWNlfdTKxio7/fTTs/HbbrstGz/jjI5PsP9PL7zwQnbdYcOGZeNV+kOrkqp1dm2k9W4PcHlEzABOAi6WNAO4AnggIqYBD6T3ZjYIHHDAAQ29WkFpKSJifUQ8lqZ3ACsoHi1+DrAwLbYQmN1HZTSzftbHz51sqm61iUmaChwHPAJMqHmw5QsUp5tmNgi0SoJqRMNJTNLBwJ3AxyNie+2HjIiQ1GXDkKS5wFyAKVOm9K60ZtbnWqmW1YiGTmolDaVIYLdExKI0e4OkiSk+EdjY1boRsSAi2iKibfz48c0os5n1sSqdTpYmMRUlvRFYERG1wyncA8xJ03OAu5tfPDMbCFVKYo2cTp4CfAh4QtLjad6VwDXAHZIuBNYA5/VJCVtE7gsr62JRZu/evdn4rl27svFcF4utW7dm1925c2c2PnXq1Gx8y5Yt2fiYMWPqxrZt25Zdty8fyVb2nQ32oXbKtMqVx0aUJrGIeAio9429s7nFMbOB1kq1rEa4x76ZdeIkZmaV5iRmZpXmJGZmleYkZmaV5UERzazyXBPbz5T1KdqzZ082/uqrr2bjZcPp5B6rlutDBuXDAP3iF7/IxkeMGJGNr1y5sm5s1KhR2XXLagNlf2i576W3ffsGu2YmMUmrgR3AXmBPRLQ1cyiv6tQZzazf9EGP/b+MiJkR0ZbeN20oLycxM9tHowmsl7W1pg3l5dNJM+ukGw374yQtrXm/ICIWdFgmgJ+mkW6+nuJNG8rLSczMOulGLWtzzSliPadGxDpJbwDul/TvtcHcUF6N8OmkmXXSzNPJiFiXfm4E7gJOoMGhvBrhJGZm+2hmm5ikUZJGt08DpwPLaeJQXj6dNLNOmtjFYgJwV9regcCtEfFjSY/SpKG8nMQalOtXVNbXqrf9xF555ZUex8v2PXTo0Gz8N7/5TTa+fPnybPxzn/tc3diSJUuy65Y1Lvd2TLC+WncwaFYSi4hVwFu7mP8iTRrKy0nMzDrxbUdmVlkeFNHMKs9JzMwqzUnMzCrNSczMKs1JzMwqy4MiDlK96SdW9lzJ3bt3Z+OvvfZaNp4r27Bhw7LrDhkyJBs///zzs/Ef/vCH2fgnPvGJurGDDz44u+4HPvCBbLxKtYWqqdKxdRIzs06cxMys0pzEzKyy3NnVzCrPDftmVmmuiZlZpTmJmVllDbo2MUmTgW9TDG4WFA8C+LKkecDHgE1p0Ssj4r6+KmiVlf1CHHhg/msYOXJkNn7ooYfWjQ0fPjy7bpmy8cQmT56cjf/85z+vG5s+fXp23RkzZmTjZeOs5cZKK+sf19tnXlZdlT5fIzWxPcDlEfFYGmZ2maT7U+z6iPhi3xXPzAbCoEpi6bFK69P0DkkrgEl9XTAzGzhVujrZrZJKmgocBzySZl0i6beSbpJ0WJ115kpaKmnppk2bulrEzFpIPz08t2kaTmKSDgbuBD4eEduBG4BjgJkUNbVru1ovIhZERFtEtI0fP773JTazPlelJNbQ1UlJQykS2C0RsQggIjbUxL8B/KBPSmhm/a5VElQjSmtiKj7NjcCKiLiuZv7EmsXOpXiWnJkNAoOtJnYK8CHgCUmPp3lXAudLmknR7WI1cFEflK9l5Bo6yx57VnY5v6wbxCGHHJKN5x7LVjYMUJmyX9T58+f3eP2ybZfFy45L7jvb37tQlKnS52/k6uRDQFefyH3CzAYhD4poZpU3qGpiZrb/cRIzs8pqpUb7RjiJmVknTmJmVmlu2DezSnNNbD9T9oWX9RMri5v1J7eJmVnlOYmZWaU5iZlZpTmJmVllVe22o+qU1Mz6TTNHsZB0hqSnJa2UdEWzy+okZmadNCuJSRoCfA04E5hBMfpN/gkw3eQkZmadNLEmdgKwMiJWRcRrwO3AOc0sa7+2iS1btmyzpDU1s8YBm/uzDN3QqmVr1XKBy9ZTzSzbkb3dwLJly34iaVyDi4+QtLTm/YKIWFDzfhLwfM37tcCJvS1jrX5NYhGxzyD7kpZGRFt/lqFRrVq2Vi0XuGw91Wpli4gzBroM3eHTSTPrS+uA2icsH5HmNY2TmJn1pUeBaZKOkjQM+CBwTzN3MND9xBaULzJgWrVsrVoucNl6qpXL1isRsUfSJcBPgCHATRHxZDP3oYho5vbMzPqVTyfNrNKcxMys0gYkifX1bQi9IWm1pCckPd6h/8tAlOUmSRslLa+ZN1bS/ZKeST8Pa6GyzZO0Lh27xyWdNUBlmyxpsaSnJD0p6bI0f0CPXaZcLXHcqqrf28TSbQi/A95N0fHtUeD8iHiqXwtSh6TVQFtEDHjHSElvA14Gvh0Rf5Hm/R9gS0Rck/4BHBYRf98iZZsHvBwRX+zv8nQo20RgYkQ8Jmk0sAyYDXyEATx2mXKdRwsct6oaiJpYn9+GMFhExIPAlg6zzwEWpumFFH8E/a5O2VpCRKyPiMfS9A5gBUXP8QE9dplyWS8MRBLr6jaEVvoiA/ippGWS5g50YbowISLWp+kXgAkDWZguXCLpt+l0c0BOdWtJmgocBzxCCx27DuWCFjtuVeKG/c5OjYjjKe66vzidNrWkKNoCWqmPzA3AMcBMYD1w7UAWRtLBwJ3AxyNie21sII9dF+VqqeNWNQORxPr8NoTeiIh16edG4C6K099WsiG1rbS3sWwc4PL8UURsiIi9EfE68A0G8NhJGkqRKG6JiEVp9oAfu67K1UrHrYoGIon1+W0IPSVpVGpwRdIo4HRgeX6tfncPMCdNzwHuHsCy7KM9QSTnMkDHTsUYMTcCKyLiuprQgB67euVqleNWVQPSYz9dQv4S/3kbwtX9XoguSDqaovYFxS1Ztw5k2STdBsyiGKplA/BZ4PvAHcAUYA1wXkT0ewN7nbLNojglCmA1cFFNG1R/lu1U4JfAE8DrafaVFO1PA3bsMuU6nxY4blXl247MrNLcsG9mleYkZmaV5iRmZpXmJGZmleYkZmaV5iRmZpXmJGZmlfb/AegNv2u79I5aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = []\n",
    "for i in range(10):\n",
    "    s.append(np.sum((np.abs(x_test_sq[3001] - x_train_sq_stk[i])))/784)\n",
    "\n",
    "ind = s.index(min(s))\n",
    "plt.imshow((np.abs(x_test_sq[3001] - x_train_sq_stk[ind])),cmap='binary',vmin=0,vmax=250)\n",
    "plt.colorbar()\n",
    "plt.title(f'Predicted value: {ind}, Actual Value: {t_test[3001]}')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b30beef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 10000/10000 [00:03<00:00, 3213.84it/s]\n"
     ]
    }
   ],
   "source": [
    "# do this for all 10k samples..\n",
    "\n",
    "fin = []\n",
    "for i in tqdm.tqdm(range(t_test.shape[0])):\n",
    "    s = []\n",
    "    for j in range(10):\n",
    "        s.append(np.sum((np.abs(x_test_sq[i] - x_train_sq_stk[j])))/784)\n",
    "    val = s.index(min(s))\n",
    "    fin.append(val == t_test[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1cb74fa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.671"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so you can get roughly 2/3 of the digits right just using this method..  \n",
    "\n",
    "sum(fin)/10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0fd43855",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 10175.41it/s]\n"
     ]
    }
   ],
   "source": [
    "test_num = {}\n",
    "\n",
    "for i in tqdm.tqdm(range(10)):\n",
    "    test_num[i] = t_test == i\n",
    "    #print(len(test_num[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "02d063a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Digit 0 is 81.63%\n",
      "Digit 1 is 99.82%\n",
      "Digit 2 is 42.44%\n",
      "Digit 3 is 61.78%\n",
      "Digit 4 is 67.31%\n",
      "Digit 5 is 32.96%\n",
      "Digit 6 is 78.71%\n",
      "Digit 7 is 76.75%\n",
      "Digit 8 is 44.46%\n",
      "Digit 9 is 77.70%\n"
     ]
    }
   ],
   "source": [
    "ms = {}\n",
    "for i in range(10):\n",
    "    ms[i] = sum(np.array(fin) * test_num[i])\n",
    "    s = sum(np.array(fin) * test_num[i]) / sum(test_num[i])\n",
    "    print(f'Digit {i} is {s * 100:0.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "10ea153b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.671"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(ms.values())/t_test.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "230a4d57",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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