Kmeans_Handwriting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5de8f4a0",
   "metadata": {},
   "source": [
    "# Basic Digit Hand Writing Recognition with KMeans Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3794a5ed",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "277c278d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55598312",
   "metadata": {},
   "source": [
    "#### Introductions to Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0b425a8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "# Inspect Dataset Description\n",
    "# print(digits.DESCR)\n",
    "\n",
    "# Check Digit Data\n",
    "digits_data = digits.data\n",
    "\n",
    "# Check Digit Targets\n",
    "digit_targets = digits.target\n",
    "k = len(np.unique(digits.target))\n",
    "\n",
    "# 5. Check Digit Images\n",
    "digit_images = digits.images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ae70b98",
   "metadata": {},
   "source": [
    "#### Use matplotlib to view image => compare with target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "32dcc8ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "img = 2\n",
    "plt.gray()\n",
    "plt.matshow(digit_images[img])\n",
    "print(digit_targets[img])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7e32eab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=10)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build model\n",
    "kmeans = KMeans(init=\"k-means++\", n_clusters = k)\n",
    "kmeans.fit(digits_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99715ff3",
   "metadata": {},
   "source": [
    "#### Visualise Centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2163ac01",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,3))\n",
    "fig.suptitle(\"Cluster Center Images\", fontsize=14, fontweight=\"bold\")\n",
    "for i in range(k):\n",
    "    ax = fig.add_subplot(2, 5, 1 + i)\n",
    "    ax.imshow(kmeans.cluster_centers_[i].reshape(8,8), cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f473c8b",
   "metadata": {},
   "source": [
    "#### Test model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "576c0aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True values: [5 1 1 9 2 0 7 3 0 0]\n",
      "Test Values: [0 1 2 3 4 5 6 7 8 9] => find the images above that correspond to these indices\n"
     ]
    }
   ],
   "source": [
    "# test predictor\n",
    "\n",
    "range_start = 0\n",
    "range_end = 10\n",
    "\n",
    "x_test = digits_data[range_start:range_end]\n",
    "y_pred = kmeans.predict(x_test)\n",
    "\n",
    "# match indexes of Test values with images manually\n",
    "print(f\"Test values: {y_pred}\")\n",
    "print(f\"True Values: {digit_targets[range_start:range_end]} => find the images above that correspond to these indices\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9e5d47",
   "metadata": {},
   "source": [
    "#### Test model on my own handwriting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a448dc3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test values: [5 8 5 4]) => find the images above that correspond to these indices\n",
      "True Values: 0705\n"
     ]
    }
   ],
   "source": [
    "my_handwriting = np.array([\n",
    "[0.00,0.23,4.12,5.34,5.34,4.27,0.76,0.00,0.00,4.42,7.62,5.72,5.64,7.47,7.17,1.29,0.53,7.24,5.19,0.00,0.00,1.76,7.63,3.81,1.52,7.62,2.44,0.00,0.00,0.00,7.62,3.81,0.84,7.63,3.89,0.00,0.00,0.00,7.62,3.81,0.00,5.64,7.02,1.22,0.00,0.46,7.62,3.81,0.00,2.14,7.32,7.55,6.18,6.79,7.62,2.06,0.00,0.00,0.92,4.04,4.57,4.57,2.59,0.00],\n",
    "[0.00,0.00,0.69,0.76,0.23,0.00,0.00,0.00,3.28,6.63,7.62,7.62,6.79,0.92,0.00,0.00,4.58,5.64,3.13,2.67,7.32,4.57,0.00,0.00,0.00,0.00,0.00,0.00,5.42,6.48,0.00,0.00,0.00,0.00,0.00,0.00,4.57,6.86,0.00,0.00,0.00,3.43,6.10,5.87,6.25,6.86,0.00,0.00,0.00,5.87,7.62,7.62,7.62,7.09,2.36,0.23,0.00,1.37,3.81,3.81,4.42,7.63,7.62,1.90],\n",
    "[0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.07,6.33,7.62,7.62,7.62,7.24,3.51,0.00,4.80,7.32,3.58,3.05,3.20,7.32,5.11,0.08,7.40,4.73,0.00,0.00,0.00,6.10,5.34,0.76,7.62,3.13,0.00,0.00,0.69,7.09,4.80,0.23,7.17,6.48,1.83,0.76,5.49,7.47,1.60,0.00,2.14,7.01,7.62,7.62,7.62,3.74,0.00,0.00,0.00,0.31,2.21,2.29,2.14,0.08,0.00],\n",
    "[1.45,6.41,7.62,7.62,7.62,7.62,7.17,4.88,4.50,7.62,3.28,1.52,1.52,2.67,3.81,2.44,2.98,7.62,4.88,0.76,0.00,0.00,0.00,0.00,0.00,4.35,7.40,7.17,4.50,2.06,0.00,0.00,0.00,0.00,1.45,5.34,7.24,7.62,3.05,0.00,0.00,0.00,0.00,0.00,1.53,7.24,5.34,0.00,2.98,3.81,4.42,6.71,7.62,7.55,3.51,0.00,5.88,6.86,6.86,5.57,3.35,1.07,0.00,0.00]\n",
    "])\n",
    "\n",
    "y_pred = kmeans.predict(my_handwriting)\n",
    "\n",
    "print(f\"Test values: {y_pred}) => find the images above that correspond to these indices\")\n",
    "print(f\"True Values: 0705\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7eed878",
   "metadata": {},
   "source": [
    "Works!!! ...Sometimes... Overall, not bad."
   ]
  }
 ],
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