learning.ipynb

    "It is obvious that this Learner is not very efficient. In fact, it will guess correctly in only 1135/10000 of the samples, roughly 10%. It is very fast though, so it might have its use as a quick first guess."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive-Bayes\n",
    "\n",
    "The Naive-Bayes classifier is an improvement over the Plurality Learner. It is much more accurate, but a lot slower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "# takes ~45 Secs. to execute this\n",
    "\n",
    "nBD = NaiveBayesLearner(MNIST_DataSet, continuous=False)\n",
    "print(nBD(test_img[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual class of test image: 7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f614c37aba8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiL8QFKEH0iK8ANJEX4gKcIPJPVP82g/p9/JjhUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f614c422a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "print(\"Actual class of test image:\", test_lbl[0])\n",
    "plt.imshow(test_img[0].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-Nearest Neighbors\n",
    "\n",
    "We will now try to classify a random image from the dataset using the kNN classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "# takes ~20 Secs. to execute this\n",
    "kNN = NearestNeighborLearner(MNIST_DataSet, k=3)\n",
    "print(kNN(test_img[211]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure that the output we got is correct, let's plot that image along with its label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual class of test image: 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f614c93da58>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f614fce2dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "print(\"Actual class of test image:\", test_lbl[211])\n",
    "plt.imshow(test_img[211].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset."
   ]
  }
 ],
 "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": "3.5.2+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}