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   "source": [
    "> This is one of the 100 recipes of the [IPython Cookbook](http://ipython-books.github.io/), the definitive guide to high-performance scientific computing and data science in Python.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8.3. Learning to recognize handwritten digits with a K-nearest neighbors classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Let's do the traditional imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sklearn\n",
    "import sklearn.datasets as ds\n",
    "import sklearn.cross_validation as cv\n",
    "import sklearn.neighbors as nb\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Let's load the digits dataset, part of the `datasets` module of scikit-learn. This dataset contains hand-written digits that have been manually labeled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "digits = ds.load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "print((X.min(), X.max()))\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the matrix `X`, each row contains the $8 \\times 8=64$ pixels (in grayscale, values between 0 and 16). The pixels are ordered according to the row-major order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Let's display some of the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nrows, ncols = 2, 5\n",
    "plt.figure(figsize=(6,3));\n",
    "plt.gray()\n",
    "for i in range(ncols * nrows):\n",
    "    ax = plt.subplot(nrows, ncols, i + 1)\n",
    "    ax.matshow(digits.images[i,...])\n",
    "    plt.xticks([]); plt.yticks([]);\n",
    "    plt.title(digits.target[i]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Now, let's fit a K-nearest neighbors classifier on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "(X_train, X_test, \n",
    " y_train, y_test) = cv.train_test_split(X, y, test_size=.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "knc = nb.KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "knc.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Let's evaluate the score of the trained classifier on the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "knc.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Now, let's see if our classifier can recognize a \"hand-written\" digit!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's draw a 1.\n",
    "one = np.zeros((8, 8))\n",
    "one[1:-1, 4] = 16  # The image values are in [0, 16].\n",
    "one[2, 3] = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(2,2));\n",
    "plt.imshow(one, interpolation='none');\n",
    "plt.grid(False);\n",
    "plt.xticks(); plt.yticks();\n",
    "plt.title(\"One\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "knc.predict(one.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).\n",
    "\n",
    "> [IPython Cookbook](http://ipython-books.github.io/), by [Cyrille Rossant](http://cyrille.rossant.net), Packt Publishing, 2014 (500 pages)."
   ]
  }
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