Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_names = ('EGY','GLF','LAV','MSA','NOR')\n",
    "colors = ('black', 'turquoise', 'darkorange', 'green', 'blue')\n",
    "marker = (2,3,4,5,6)\n",
    "    \n",
    "lw = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "###\n",
    "#PCA and LDA for given features\n",
    "###\n",
    "X = np.loadtxt('scripts/feats')    \n",
    "y = np.loadtxt('scripts/labels')\n",
    "\n",
    "#LDA\n",
    "lda = LinearDiscriminantAnalysis(n_components=3)\n",
    "X_r2 = lda.fit(X, y).transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: (1524, 400)\n",
      "X_r2 shape: (1524, 3)\n",
      "y shape (1524,)\n",
      "[-1.21458904  0.62681023 -1.84052709]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/disooqi/anaconda2/envs/daID_env/lib/python3.6/site-packages/matplotlib/collections.py:877: RuntimeWarning: invalid value encountered in sqrt\n",
      "  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor\n"
     ]
    },
    {
     "data": {
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tW9XPxWQimj3LRGdOm+ixxx6j3hVMKiWFRcWsKSri73LjjURJSaz06DCZ6uwq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\nKKMSu6IoSpRRiV1RFCXKqMSuKIoSZf4/wLkOIU9tDi4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ffc406c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print('X shape:', X.shape)\n",
    "print('X_r2 shape:', X_r2.shape)\n",
    "print('y shape', y.shape)\n",
    "print(X_r2[0])\n",
    "#np.savetxt('type.lda', X_r2)\n",
    "plt.clf()\n",
    "for color, i, target_name, m in zip(colors, [1, 2, 3, 4, 5], target_names, marker):\n",
    "    plt.scatter(X_r2[y == i, 0], X_r2[y == i, 1], X_r2[y == i, 2], alpha=.8, color=color,\n",
    "                label=target_name, marker=(m, 1))\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('LDA of dialect dataset')\n",
    "plt.show()\n",
    "# Percentage of variance explained for each components\n",
    "# fig.savefig(\"lda.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Unknown property size",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-fbd5af24bb10>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m111\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprojection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'3d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_r2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_r2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_r2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ro'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;31m# plt.scatter(X_r2[y == 2, 0], X_r2[y == 2, 1], X_r2[y == 2, 2], c='b', marker='^')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# plt.scatter(X_r2[y == 3, 0], X_r2[y == 3, 1], X_r2[y == 3, 2], c='g', marker='x')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/daID_env/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3315\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3316\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3317\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3318\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3319\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m~/anaconda2/envs/daID_env/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_grab_next_args\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    405\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m                     \u001b[0;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/daID_env/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0mncx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    394\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mncx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 395\u001b[0;31m             \u001b[0mseg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mncx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mncy\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    396\u001b[0m             \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/daID_env/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_makeline\u001b[0;34m(self, x, y, kw, kwargs)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0mdefault_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getdefaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setdefaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m         \u001b[0mseg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/daID_env/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m         \u001b[0;31m# update kwargs before updating data to give the caller a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[0;31m# chance to init axes (and hence unit support)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpickradius\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickradius\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mind_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/daID_env/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, props)\u001b[0m\n\u001b[1;32m    883\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m             ret = [_update_property(self, k, v)\n\u001b[0;32m--> 885\u001b[0;31m                    for k, v in props.items()]\n\u001b[0m\u001b[1;32m    886\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meventson\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/daID_env/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    883\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m             ret = [_update_property(self, k, v)\n\u001b[0;32m--> 885\u001b[0;31m                    for k, v in props.items()]\n\u001b[0m\u001b[1;32m    886\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meventson\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/daID_env/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m_update_property\u001b[0;34m(self, k, v)\u001b[0m\n\u001b[1;32m    876\u001b[0m                 \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'set_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    877\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 878\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unknown property %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    879\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: Unknown property size"
     ],
     "output_type": "error"
    },
    {
     "data": {
      "image/png": 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uVo2YHXZa6ueXTqeF82S3poUANULI2Z4MNNnFYjHY7XbEYjGMjIxg//79Vf3i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\nEomV8P97/5IIkLkAAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9b384da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "==============\n",
    "3D scatterplot\n",
    "==============\n",
    "\n",
    "Demonstration of a basic scatterplot in 3D.\n",
    "'''\n",
    "\n",
    "\n",
    "# fig = plt.figure(figsize=(15, 12), frameon=False)\n",
    "plt.subplot(111, projection='3d')\n",
    "\n",
    "plt.plot(X_r2[y == 1, 0], X_r2[y == 1, 1], X_r2[y == 1, 2], 'ro')\n",
    "plt.scatter(X_r2[y == 2, 0], X_r2[y == 2, 1], X_r2[y == 2, 2], c='b', marker='^')\n",
    "plt.scatter(X_r2[y == 3, 0], X_r2[y == 3, 1], X_r2[y == 3, 2], c='g', marker='x')\n",
    "plt.scatter(X_r2[y == 4, 0], X_r2[y == 4, 1], X_r2[y == 4, 2], c='c', marker='P')\n",
    "plt.scatter(X_r2[y == 5, 0], X_r2[y == 5, 1], X_r2[y == 5, 2], c='y', marker='D')\n",
    "# ax.set_xlabel('X Label')\n",
    "# ax.set_ylabel('Y Label')\n",
    "# ax.set_zlabel('Z Label')\n",
    "# plt.autoscale(tight=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #PCA\n",
    "# pca = PCA(n_components=4)\n",
    "# X_r = pca.fit(X).transform(X)\n",
    "# #np.savetxt('type.pca', X_r)\n",
    "# fig = plt.figure()\n",
    "# plt.clf()\n",
    "# for color, i, target_name, m in zip(colors, [1, 2, 3, 4, 5], target_names, marker):\n",
    "#     plt.scatter(X_r[y == i, 0], X_r[y == i, 1], color=color, alpha=.8, lw=lw,\n",
    "#                 label=target_name, marker=(m, 1))\n",
    "# plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "# plt.title('PCA of dialect dataset')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1., ...,  5.,  5.,  5.])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  }
 ],
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