{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "c = Counter((w for l in open(\"train.txt\") for w in l.strip().split() if w != \"<unk>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#lp = l.strip().split()\n",
    "ngrams = Counter((w for l in open(\"train.txt\") for lp in [l.strip().split()] for w in zip(lp, lp[1:], lp[2:]) if w != \"<unk>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "ls = np.array([n for _, n in c.most_common(1000)])\n",
    "words = [w for w, _ in c.most_common(1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hist = [0]*100000\n",
    "for w, k in c.most_common():\n",
    "    #print k\n",
    "    hist[k] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nhist = [0]*100000\n",
    "for w, k in ngrams.most_common():\n",
    "    #print k\n",
    "    nhist[k] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 30, 14, 25, 72, 276, 1014, 744, 593, 512]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KRMQBAACUiIgDAAAoEREHAABQIgOLHgDbu2q1mubmpqJntKurG5fa2tqiZwAA8HcScdDL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\nAAAASuT/B74kvDkHYHj3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e61e11b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(ls[:200])\n",
    "sns.set_context(\"talk\", rc={\"lines.linewidth\": 2.5})\n",
    "sns.set_style(\"darkgrid\")\n",
    "f, ax = plt.subplots(figsize=(15, 7))\n",
    "\n",
    "#plt.plot(figsize=(10,10))\n",
    "g = sns.barplot(y=\"total\", x=\"abbrev\", data={\"total\": nhist[1:20], \"abbrev\": range(1,20)},\n",
    "            label=\"Total\")\n",
    "#g.get_axes()[0].set_yscale('log')\n",
    "g.set( yscale=\"log\")\n",
    "\n",
    "#_ = ax.set_yticklabels([ w if i % 10 ==0 else \"\" for i, w in enumerate(words[:200]) ])\n",
    "plt.savefig(\"nhist.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A7VZsbKz8fn9zLAUAAAAAWq2o30Vz48aNuueeezRp0iT9+Mc/1jfffKPq6up6\nj/H7/cHP3/J4PDp8+HDwvpqaGlVXV0f8+Vwul0tut0u1UazZ7XYpJsYV/LqlZkWDLLLCZfn9fhUV\nOf9Mvd69+Uw9AACAliiqgrdx40ZNmzZNs2fPVnp6uiQpOTlZbrdbpaWlSk5OliRt3rxZ/fr1k3Ts\nXTOLi4uVlpYm6dglmzExMUpJSYnoOc89N14dOni0P4p1d+jgUadOCcGvmzNrXzNm7W3GrAPNmLW7\nGbOiQZbzrNJSr37/xxk6/wL7n6m3c0eV7v3P13TFFVdEtR4AAAA0P8cF7/Dhw3rggQfqlTvp2Bm6\njIwM5eXlKTc3VwcOHNCyZcs0ZcoUSdLYsWO1ePFiZWZmKj4+Xnl5ecrKylJsbGxEz7tvX5UqKqK7\nnLOiwq/y8srg12SR1Rqzzr8gXkkOP1Pv+CwAAACcWnW/tG+M44K3cuVK7dixQ88++6yeffbZ4O03\n3XSTZs+erVmzZik9PV1ut1tZWVkaM2aMJGnChAnatm2bxo0bJ8uy1L9/f+Xk5ET8vJZlqbbWcrps\nSVJtraWaGiv4NVlkkeU8CwAAAC2H44KXlZWlrKyskPcvWLAg5H0zZ87UzJkznT41gBbE7/fL53P+\ner7UVF7PBwAA0FyifpMVAK2bz+fVq3+6R10dvJ5v944q3TX2JQ0YMPAkrAwAAKD1oeABiFrXC+KV\n2MvZ6/kAAADQfJrlc/AAAAAAAKcfBQ8AAAAADEHBAwAAAABDUPAAAAAAwBAUPAAAAAAwBAUPAAAA\nAAzBxyQAaDH40HQAAIDoUPAAtBg+n1cL8+9R5+72PzR97/Yq3X0LH5oOAABaNwoegBalc/d4XcCH\npgMAADjCa/AAAAAAwBAUPAAAAAAwBAUPAAAAAAzBa/AAGIl35AQAAK0RBQ+AkXw+r54uuEedEhNs\nb1teVqmHRn/3jpyURQAAcKag4AEwVqfEBHVNif4dOX0+rx57/x6d5aAsHiyrVO5NfHwDAAA4NSh4\nABCBsxIT1PFCPr4BAAC0bLzJCgAAAAAYgoIHAAAAAIbgEk0AOIV4wxYAAHAyUfAA4BTy+bx68P05\nik88x/a2VWXf6tmb5vCGLQAAICQKHgCcYvGJ56jDhZ1P9zLq4cwiAABmoOABAI6dWXz3RSUkdrG9\nbWXZHj076l7OLAIA0AJQ8AAAkqSExC7qcGH3070MAAAQBQoeAJyhuKwSAAA0RMEDgDOUz+fVg+89\no4TEc21BjHCrAAAbJ0lEQVRvW1m2T89mzeSySgAADEPBA4AzWELiuepw4fmnexn1cGYRAIDTh4IH\nAGhWPp9XM9/9tRISu9retrJst54ZNTV4ZrE5yyLFEwDQGlDwAADNLiGxqzqk9Ig6x+fzambBq0pI\ntH+WsrJsp54ZfVewLPp8Xj1U8HslJF7gIGuHnh79Iy5pBQC0eBQ8AECLlpB4vjqkJDVT1gXqkNKz\nWbIAAGiJKHgAANjE5Z4AgJaKggcAgE3HLvdcqoTuDi733L5DT4/+IZd7AgBOCgoeAAAOJHS/QB1S\nep3uZdTDmUUAAAUPAABD+HxePVzw/ymhe6LtbSu3l+mp0WM4swgAZzgKHgAAp1Fzn3VL6J6oc1Iu\nbI6lAQDOQBQ8AABOo2Ov53tTCd272962cvt2PT16PGfdAABBFDwAAE6zhO7d1SEl5XQvAwBgAAoe\nAAA4QbSXjkq8aQsAnA4UPAAAcAKfz6tHCt5VQndnHzJfuX2r5o8epQEDBraKstic+9gavl8ATh4K\nHgAAaFRC9ySdk5IadY7P59XPCv5/nZXY09H2B8u26MnRavbXGjZnkfL5vJrz7hfqkOjs+1VR5tOc\nUcf20efzauG7/1LnxN6OsvaWFenuUc3//QJwZqDgAQCAk+6sxJ46J6VP1DnNXcr+q+BTnZXo7PWP\nB8uK9d/HFc8Oiak6N+XSqNZWp3Nib11wYf9myQLQulDwAADAGcPn8+rRgtU6KzHZ0fYHy0r1xHGl\n7KzEFHVMubgZV4hIcBkqcPJQ8AAAwBnlrMRkdUzpd7qXcUZoqUXK5/PqL/kb1aO7s8tQt20vkm7h\nMlSgMRQ8AAAAQ/l8Xr3x7lc6P9HZ5bE7y7y6/d+v52vustije2+l9rosqjwAJ6LgAQAAGOz8xD5K\nShkQdY7P51V+wUZ1d/jmL9vLinTLSXiznNYi2oJ9fLluziy0PBQ8AAAARKR7Ym+lpJh91q2lFimf\nz6tvFm1Qahf7Bdu3p0ia8l259vm8Knzlc/XucqHtrKI9m6X/+C6LstjyUPAAAABwxmruS0d9Pq/W\nLvtKvc63f1lryU6vlF2/SH3x+6+U0s1+KSveVST9qP4Zz9QuvdU/MfqzsZLUu8uFGpAY/bu++nxe\nFS76VL279rK9bdHuknrFE82DggcAAIAzls/n1Udvfa2eFzh7neGWHV7p1volo9f5fdSvZ/MUqZRu\nvXVJUvNktVS9u/bSgMTo3422NZwNPBX7SMEDAADAGa3nBX3UJ9nsEtUa+HxebXrtQ/Xukmx726I9\npdLkln/pqM/n1abfv60+3RJtb+vdVSb9aFyTZzwpeAAAAABahN5dkjWgR/Qfg+LzebXpd39R7649\nbW9btHuLdOfJu3S0T7dEDehh//WPkaLgAQAAADBO7649NaCHs0t3j9dSzwaGQsEDAAAAgBB8Pq82\nvf6u+nRLsr2td9dW6Y5Rp/SNZCh4AAAAABBGn25JGtAj9XQvIyLu070AAAAAAEDzOC0Fb8OGDZow\nYYKGDx+uzMxM5efnn45lAAAAAIBRTvklmtXV1br33nv16KOPKjMzU1u3btW4ceN08cUXq0+f6F8E\nCQAAAACt1Sk/g7d27Vq5XC5lZmZKkpKSknTDDTfo/fffP9VLAQAAAACjnPKCV1xcrJ49638eRXJy\nsoqKik71UgAAAADAKKe84Pn9fsXFxdW7rV27dgoEAqd6KQAAAABglFP+Grz4+HgdOnSo3m2BQCDi\nD/9zuVxyu10q3LXd0fN7d21XP7dLMTEuSfp31k6HWTtPyNq0a4+jrKJde3RRg6yiXeUOs8p1SYOs\nzbsqHWVt3lWpyxtkFTvMKt5Vqa4Nskp3Ossq3Vmp5AZZWx1mbd1Zqb4Nssp2VjnKKttZpcsaZG3f\n4Wxd23dU6uoGWTscrmvHziq5G2Tt3OEsa+eOE7N2O8za3UjW3u3OsvZur5L7mvpZ5WXOvvflZZVy\np9XPOugw62BZpdyX18+qKvvWUVZV2bcnZFWW7XOUVVm2T+4rGmY5+/lVWbZH7isbZu12mLW7kSxn\nP6Mry3ae8PdYWbbDYdaOE7O2O8zavkPuqxpmOft/WuX27Y1klTnMKpP7qqsbZG11lHUsb6vcVw1U\nTIzr3/+GtjjOOli2Re60i4/LKo0iq1TutAuPyyqOIqtY7rQLglkVZT7HWRVlPrmvvDKYtbfM+ZVN\ne8uK5L7yu+/9zjLnH9C8s8wr95UDglnbo1jX9rIiXZN2aTBr23bnWdu2F+nSa77L2rLD+T5u2eHV\nhe7+9Wa/ZKezvJKdXl3gHtDguMnZfhbvKlJagyzfHmdZvj1FutR9Wf3j3z2bHWUV7dmsi91X1c/a\nXeIsa3eJLnZ3r38svafU4bpKdZE7pX7Wbmc/d4p2b9FF7ovqZf3vLmc/D727tuoi98AGWc5+Tnt3\nleki96BgViguy7IsR8/g0KeffqrHHntMa9asCd72wAMPKDU1Vffdd9+pXAoAAAAAGOWUX6J5zTXX\nqE2bNvrTn/4kSSosLNRnn32m0aNHn+qlAAAAAIBRTvkZPOlYqZszZ47279+v2NhY3X///UpPTz/V\nywAAAAAAo5yWggcAAAAAaH6n/BJNAAAAAMDJQcEDAAAAAENQ8AAAAADAEBQ8AAAAADAEBQ8AAAAA\nDGFUwfv22291//33a/DgwRo2bJheeuklRzmff/65Ro4cWe+/QYMGKScnx1He+vXrNX78eI0cOVKj\nRo3S66+/7iinbm233Xabhg8frhEjRujjjz+2nbF06VINHDhQixYtCt5WXl6uu+++W+np6crIyNCT\nTz6pSN5gtbGsunUOHTpU06dPj2pd27Zt07Rp0zRy5Eilp6dr1qxZqq6udpS1ZcsW/cd//IdGjBih\njIwMPf744zp8+LDjfawzdepUDR06NII9bDxr6NChGjJkSL15W7NmjeN1PfvssxoyZIhuvPFGzZw5\nU36/33ZWdXX1Cf8GhgwZEtHHmTS2rp07d2r69OkaMWKEMjMzNWfOHB06dMhx1owZM5SRkaHBgwfr\nV7/6VZM5a9eu1YQJEzRy5EhlZGTotddek+Rs7kNlSfbnPlSWk7kPleVk7sPtY51I5z5UlpO5D5Vl\nWZbtuW8s68iRIxoxYoTtuQ+1LidzHy7L7tyvWbNG48aN08iRI5WZmRn8f4+TuQ+VJdmf+1BZTuY+\nVJaTuQ+3j3UinftQWU7mPlSWk7lvLMvp3Idal5O5D5dld+7rHDhwQNddd50ee+wxSc6PcxrLkpwd\n5zSW5fQ4p7Esp8c5ofaxjp3jnMaynB7nNJblZO4by3I696HW5fQ4J1ye09mXJFkGmT59uvWLX/zC\nsizL2rdvnzVx4kSrtLQ06tzDhw9bI0aMsL766ivb2/r9fistLc1avXq1ZVmWtXfvXmvQoEHWmjVr\nbGeVl5dbaWlp1qpVqyzLsqy1a9dal112mbVr166IM2bPnm09/PDD1rhx46xFixYFb7/vvvusOXPm\nBNc8duxY64033nCUVVBQYI0fP956+OGHrWnTpkW1rjFjxlgLFiwIrmvcuHFWXl6eo6zRo0dbv/nN\nb4JZt956q/XSSy85yqrz1ltvWcOGDbOGDh3qeB+HDBlirVu3rsntI8l69dVXrfHjx1t+v986evSo\n9fDDD1t//vOfHWU19PDDD1uvvvqqo6wpU6ZYTzzxhGVZllVdXW3deeed1tNPP+0oa+LEicGsXbt2\nWddee631l7/8JWTOnj17rIEDB1pr1661LMuytm7dal1++eXWl19+aXvuw2XZnftwWXbnPlzWzTff\nbGvuw2XViXTuw2XZnftwWa+++qp16623Rjz3kexjnabmPlyW3bkPl+V07r/44otg1hVXXGGtX7/e\n8dw3luV07hvLcjr3jWU5nfvGsurYnfvGspzOfWNZTuc+3D7WiXTuG8tyOveNZdmd++M98sgj1rBh\nw6xHH33Usixnxzmhspwc54TKcnKcEyrLyXFOqKw6do5zQmU5Oc4JleXkOCdUVkORHOeEynJynBMu\nL5rZtyzLMuYM3u7du/XJJ5/o3nvvlSR16tRJb7zxhnr27Bl19ksvvaRBgwbp0ksvtb3tjh07dPDg\nQV177bWSpPPOO0/9+vWTz+eznfXPf/5TCQkJwd+iDBo0SKmpqVq5cmXEGePHj9f8+fPVvn374G2V\nlZVatWqV7rrrLklS+/btlZ2drYKCAttZktSnTx8tWbJEPXr0iGpdlmVp2rRpmjJlSnBd11xzjTZt\n2mQ7q7a2VtOmTdOkSZOCWVdeeaU2b97saB+lYzO3cOFC/fSnP43ot4DhsiLZPpKspUuXaurUqWrf\nvr1iYmI0f/58jRw50vG66nz22WcqLCzUHXfc4Shr06ZNwX8Dbdu21dVXX62ioiLbWZWVlfrHP/6h\n22+/XZLUtWtXjRo1Su+9917InJiYGD311FMaNGiQJKlHjx5KTU3Vhg0bbM99qKxNmzapb9++tuY+\nXNa0adPqraupuQ+XNXXq1OD3K5K5D5Xl9Xol2Zv7cOuS7M19uKxly5Zp2rRpEc99U/tYJ5K5D7cu\nu3Mfblbtzr3b7dYzzzyjyy+/PJjVs2dPFRYW2p77UFmbN2+2Pfehsrxer+25D5XlZO7D7aNkb+5D\nZdX9f9/O3IfLsjv3Te1jnUjmPty67M59uFm1O/d1PvzwQ5WVlWnUqFGSpKqqKkfHOY1lSc6OcxrL\nsixLU6dOtX2c01iW0+OcUPso2T/OCZdl9zgnVJaT45xw66oT6XFOqCwnxzmh8qqqqhzPfh1jCl5h\nYaE6deqkt99+W6NGjdLNN9+spUuXRp37f//3f/rDH/4QLI52JScnKzk5OfhDZOvWrfJ6vfre975n\nO8vlcunIkSP1bmvfvr1KS0sjzrjkkktOuG3Lli2SpKSkpOBtx//PyE6WJPXt21dt2rSJeE2hslwu\nlzIyMhQfHy/p2GWDa9asUVpamu0st9utzMxMxcXFSZLKysr04Ycf6sYbb7SdVWfWrFn6yU9+onPP\nPTdsRiRZr732msaNG6fMzEw999xzJ/w9R5Ll9/u1ZcsW7dy5U9nZ2crIyFBubm6Tl2eEW1edp59+\nWj/96U/ldof/kREq6/rrr9f777+vmpoaVVVV6dNPP9V1111nO8vlcp3w78Dj8aikpCRkTqdOnTRs\n2LDgn7du3aqioiJdfPHFkuzNfaisK6+8Un369LE19+GyMjIylJCQICmyuQ+XlZmZGSzJkcx9qKwr\nrrhCkr25byrLztyHyrrssstUWlpqa+6bWledSOY+3Pfe7tyHyvre975ne+7PPffcepdUrV27Vjt3\n7gweRNuZ+8ayduzYoe9///u25z5U1nXXXWd77kNlXX/99bbnPtw+SvbmPtT3/gc/+IEke3MfKist\nLc323De1j3Uimftw+2h37sPto925l6SKigrl5uYqNzdXLpdLkoLHSXaPcxrLkpwd5zSW5XK5NGLE\nCNvHOY1lOT3OCbWPkv3jnHBZdo9zGssKBAKOjnPCratOpMc5obKcHOeEynNyrNOQMQWvoqJC5eXl\nateund59913Nnz9fzzzzjD777LOocl955RXdfPPNEQ93QzExMcrNzdX8+fM1aNAgjRgxQpMmTVK/\nfv1sZ11++eWqrq5Wfn6+pGM/BDdu3BjxtdqhBAIBtW3btt5tcXFxCgQCUeU2p+rqaj300EPq0qWL\nbrvtNsc55eXlSk9PV2ZmpjIyMiL6rU9j3nrrLcXGxiorK8vxWupkZGTolltu0dtvv61FixZp1apV\n+vWvf2075+DBg5Kkr7/+Wq+//rr+8Ic/6PPPP9fLL78c1fo+/PBDWZalIUOGOM546KGH9PXXX+vq\nq6/W9773PZ111lmaMGGC7Zz4+HilpaVp0aJFqqmp0fbt2/XnP/854n8Du3bt0vTp0/XjH/9YkqKa\n++OzUlNT7e1IhFlO5r6xLKdz3zArmrk/Pqt3795Rzf3xWR07dpTkfO5Dfe+dzH3DrGjmvuH3y+nc\nf/zxxxo8eLB+8pOf6Oc//3lUP++Pz3r88cdtn72INMvu3IfKcjL3jWU5nfuG3/sePXo4nvuGWWed\ndZYkZ3Mf7ntvd+4b20enc98wq1+/fo7mft68ebr99tuVlJRUrxg4mfvGspxqKsvO3IfLsjv3obKc\nzH2oLCdz31jWgQMHJNmf+6a+93bmPlSW07lvLM/j8UR1rCMZVPDOPvtsSQqemu7bt69uuOGGiF/E\n2Ziamhq98847uuWWWxxn7NmzR9OnT9czzzyjv/3tb/r000+1evVqvfHGG7azOnTooJdfflnLli3T\niBEj9Je//EWDBg0K7rtTHo/nhKHx+/3yeDxR5TaX8vJyTZ48WS6XSwsXLmzytyvhdOrUSStXrtSa\nNWvk9Xr1+OOP287YtWuX8vLyNHfuXMfrON7PfvYzDR8+XJLUrVs3TZo0SatXr7adU/c//AkTJig2\nNlbnnHOObrvtNn3yySdRre/tt9/WzTffHFXG9OnTlZ6ers8//1z/+Mc/1LVrV82cOdNR1lNPPaV9\n+/YpMzNTv/zlLzV06NCI/g1s3LhR2dnZGjt2rO65556o5r5hVjRCZTmZ+1BZTua+YVY0c9/YupzO\nfcOsaOY+3N+j3blvLMvp3DeW5XTub7jhBn300UdasmSJnn/+ea1bt87x3DfMiuTyNrtZTuY+VJaT\nuW+Y9c477zie+8bW5XTuG2bV/fLaydyH+3u0O/eNZTmd+8ay7M796tWrtWPHjuBldnWXBTr5eR8q\ny4mmsuzMfVNZduY+VJaTn/fh1mV37kNlOfl5H8nfY6RzHy7LydyHy3P6M7+OvfPLLVhSUpKOHj2q\nqqqq4CUekmyfQj/eunXrFBsbq4suushxxhdffKGzzz47eF1ux44dNXjwYH3yySfBa2vtSEtL07Jl\ny4J/rntnzmgkJyfL7XartLRUycnJkqTNmzc7Ost4vGh/2yUdOzM7efJkDR48WA8++KDjnIMHD2rl\nypUaO3asJOmcc87R+PHjNXv2bNtZH330kaqqqoK/mamurtb+/ft14403avHixTr//PMjzqqurlZx\ncXG973VNTc0Jv2mMhMfj0XnnnRf8DVedmJgY21l1/H6//vrXv+pnP/uZ44zy8nJt2LBBL7zwglwu\nl9q2batRo0Zp6tSpjvK6detW77d1s2fPbvL1sRs3btS0adM0e/bs4DtkOZ37xrKOZ2fuQ2U5mfvG\nspzOfWNZTue+sSync99YltO5D/f3aHfuG8tyOveh1mV37ktKSrRlyxYNHjxYknThhRdq6NCh+uab\nb2zPfaisjz/+WKNHj5YU+dyHyxo8eLCtuQ+V9e6776qmpkZjxoyRFNncN5Y1ZMgQzZs3T23btrU1\n96HWtWLFCvXp08fW3IfK+utf/2p77pv6e7Qz9+G+93bnvql12Zn75cuXa8uWLcHLEg8ePKijR4+q\nsLBQMTExtuY+VFZJSUnwOCzSuQ+X9etf/9rW3IfK+te//qX//M//tDX3obJWrVple+7Dfe9zc3Nt\nzX2475fduW/q79HO3If73hcXF9v+ed/U2uwe69QT8duxnAF++MMfBt+xZtu2bdZVV13V6LtDRerF\nF1+0pkyZEtWaioqKrP79+1sbNmywLOvYuxpNnDjRev75521nVVVVWcOHD7c2btxoWZZlvfPOO9b1\n119vHTp0yHbWpEmTrFdeeSX45wcffDD4zj0VFRVWVlaW9ac//clRVp0FCxbYfnepxtb1y1/+0lZG\nY1l+v9+65pprrLfeesuyLMs6evSo9eijj0a8vlD7aFmW9fe//90aMmSIo3Xt27fPuuyyy6yPPvrI\nsizL+vbbb61bbrkl5HM1ta7nnnvOmjRpknX48GErEAhYt912m/Xiiy86yrIsy1q/fr3Vv3//iLYP\nlVVbW2tde+211muvvRb88xNPPGFNnDjR0boeeOAB6/XXX7csy7K8Xq919dVXW998803I7Q8dOmTd\neOON1ooVK064z+7ch8uqE+ncN7UuO3MfKsvJ3Eeyj5YV2dyHynIy9+HWZXfum9pHO3MfKsvJ3Idb\nl925//LLL63LL7/cKiwstCzr2HyPGjXKWrRoke25D5dVJ9K5b2pdduY+VNaLL75oe+4j2UfLimzu\nQ2W98sortuc+3Lrszn1T+2hn7kNl/fa3v7U99+HWZXfuG3rhhReCsx7NcU7DrDpOjnMaW5fT45zj\nswKBQFTHOQ3XdTy7xznHZ5WXl0d1nNNwXdEc5zTMsiznxzkNs6I5zmks7/77749q9l2WFcU55xam\nrKxMOTk52rZtm9q3b6877rgjqtdrPf744zpy5Ihyc3OjWte7776r3/zmN6qurpZlWfr+97+vRx99\nVO3atbOd9d577+mFF17Q0aNH1blzZ82dO1d9+/aNaNuamhoNHDgw+MLNmJgYud1u3XLLLXrooYc0\na9Ys/e///q/cbreysrJ033332c66+eabtW3bNn3xxReqqamRZVlq06aNunfvruXLl9vOevPNN9W9\ne3fFxsYGH5+UlKS8vDzb+3jrrbfqv//7v7V371653W5ddNFF+vnPf67OnTvbzvrFL34RfNzf//53\n/dd//ZdWrVrl6Hs/cuRIPfXUU/L7/XK73RoxYoTuvffekJdohMuaNWuW5syZo08//VRxcXG67rrr\n9Mgjj9T7/tnZx/fee0/PPfdc2H2LJGvChAl68skntWfPHklSr169lJOTE/I1POGyJk6cqJycHB08\neFAul0sPPPCAMjMzQ67rvffe0yOPPHLCO+redNNNuuOOO2zNfbiszz//3Nbch8q66qqr9Mc//tHW\n3Idb1/XXX29r7sNlHf9mU5HMfbisK6+80tbch8uaOnWqrblvah/tzH24rMGDB9ua+3BZw4YNszX3\nkpSfn69f/epXqq2tlWVZGjZsmB5++GFVVlbamvtQWQ899JCmTJmiL7/8MuK5D5V15513avDgwUpM\nTKz3m/1wcx9uH7/++mtbcx8u6/iZjGTuw32/1q1bZ2vuw63r6NGjtua+qX20M/fhsr755htbcx8u\ny+v12p7747344ovavn27cnNzdeDAAdtzHyrrzjvvtD33jWU9+OCDuu6662zPfah1bdiwwfbch8o6\nXqRzHypr7dq1tuc+VFZ1dbXtuQ+3j3bnPlTW119/bXvuw+UVFhZGNftGFTwAAAAAaM2MeZMVAAAA\nAGjtKHgAAAAAYAgKHgAAAAAYgoIHAAAAAIag4AEAAACAISh4AAAAAGAICh4AAAAAGIKCBwAAAACG\noOABAAAAgCH+H9+43bMkj83HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e67b72a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(ls[:200])\n",
    "sns.set_context(\"talk\", rc={\"lines.linewidth\": 2.5})\n",
    "sns.set_style(\"darkgrid\")\n",
    "f, ax = plt.subplots(figsize=(15, 7))\n",
    "\n",
    "#plt.plot(figsize=(10,10))\n",
    "sns.barplot(y=\"total\", x=\"abbrev\", data={\"total\": hist[6:50], \"abbrev\": range(6,50)},\n",
    "            label=\"Total\")\n",
    "#_ = ax.set_yticklabels([ w if i % 10 ==0 else \"\" for i, w in enumerate(words[:200]) ])\n",
    "plt.savefig(\"hist.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "image/png": 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F06JFKzuNSkRERMQxFCANSFk776oNZHHOSRaMmoW3t7edRiUiIiLiGAqQBky5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\nRERERHpEAVJEREREekQBUkRERER65P8BQERdGvm5ebQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3497817be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(ls[:200])\n",
    "sns.set_context(\"talk\", rc={\"lines.linewidth\": 2.5})\n",
    "sns.set_style(\"darkgrid\")\n",
    "f, ax = plt.subplots(figsize=(10, 7))\n",
    "\n",
    "#plt.plot(figsize=(10,10))\n",
    "sns.barplot(x=\"total\", y=\"abbrev\", data={\"total\": ls[:200], \"abbrev\": words[:200]},\n",
    "            label=\"Total\")\n",
    "_ = ax.set_yticklabels([ w if i % 10 ==0 else \"\" for i, w in enumerate(words[:200]) ])\n",
    "plt.savefig(\"zipf.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.        , -1.        , -1.        , -1.        , -1.        ,\n",
       "       -1.        , -1.        , -1.        , -1.        , -1.        ,\n",
       "       -1.        , -1.        , -1.        , -1.        , -1.        ,\n",
       "       -1.        , -1.        , -1.        , -1.        , -1.        ,\n",
       "       -1.        , -1.        , -1.        , -1.        , -1.        ,\n",
       "       -1.        , -1.        , -1.        , -1.        , -1.        ,\n",
       "       -1.        , -1.        , -1.        , -1.        , -1.        ,\n",
       "       -1.        , -1.        , -1.        , -1.        , -1.        ,\n",
       "       -1.        , -1.        , -1.        , -1.        , -1.        ,\n",
       "       -0.99999997, -0.99999856, -0.99991793, -0.99534489, -0.76580379,\n",
       "        0.76580379,  0.99534489,  0.99991793,  0.99999856,  0.99999997,\n",
       "        1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "        1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "        1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "        1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "        1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "        1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "        1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "        1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "        1.        ,  1.        ,  1.        ,  1.        ,  1.        ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aLhYwM2AANkYBw3ZaOi/OgClgAPZFAcN2WrvGd0BXUsAAbIwChu00dwVVUpil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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e68205890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "x = np.linspace(-5, 5, 1000)\n",
    "y = x * x * x\n",
    "plt.plot(x, y)\n",
    "#plt.gcf().subplots_adjust(top=0.75)\n",
    "plt.ylim(-5.2, 5.2)\n",
    "plt.xlim(-5, 5)\n",
    "plt.savefig(\"cube.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A77W6HMARCFnAIru2RBVp8+u3J8ZUqVjf/ek382G/f3evxZUAzkHIAhbxeAxd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\nELIAADQIIQsAQIMQsgAANMj/B8w87klvDZDmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e680d7310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = np.gradient(y)\n",
    "plt.plot(x, 100*y2)\n",
    "#sns.regplot(y=\"y\", x=\"x\", data={\"x\":x, \"y\":y},  scatter=False, order=5)plt.ylim(-0.2, 1.2)\n",
    "plt.xlim(-5, 5)\n",
    "plt.ylim(-0.2, 5.2)\n",
    "#plt.xlim(-5, 5)\n",
    "\n",
    "\n",
    "plt.savefig(\"cubegrad.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z+tsFnHADQGQoamAYbdpZo3c+/VKFuWl66L4Zcjr4iAGIDH9FgGGy42C9/vje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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e68655a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 1000)\n",
    "y = np.tanh(x)\n",
    "plt.plot(x, y)\n",
    "#plt.gcf().subplots_adjust(top=0.75)\n",
    "plt.ylim(-1.2, 1.2)\n",
    "plt.xlim(-5, 5)\n",
    "\n",
    "#sns.regplot(y=\"y\", x=\"x\", data={\"x\":x, \"y\":y},  scatter=False, order=5)\n",
    "plt.savefig(\"tanh.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D10CjEbDmunSMjomTI/aJggmDmmgWqg57u03XXJcucyU0F6tLUhCu16LqUCs8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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e68715a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = np.gradient(y)\n",
    "plt.plot(x, 100*y2)\n",
    "#sns.regplot(y=\"y\", x=\"x\", data={\"x\":x, \"y\":y},  scatter=False, order=5)plt.ylim(-0.2, 1.2)\n",
    "plt.xlim(-5, 5)\n",
    "plt.ylim(-0.2, 1.2)\n",
    "#plt.xlim(-5, 5)\n",
    "\n",
    "\n",
    "plt.savefig(\"tanhgrad.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xTo5zA6MKtvVo8bxiLVtCUScbP8fJ56YxjquoA4GAOjo6Jh93dnYqELj4Se73+y+6gr7c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\nANjY/wPdO5CSJET2yQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e68b32450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 1000)\n",
    "y = np.maximum(-1, x)\n",
    "y = np.minimum(1, y)\n",
    "plt.plot(x, y)\n",
    "#plt.gcf().subplots_adjust(top=0.75)\n",
    "plt.ylim(-1.2, 1.2)\n",
    "plt.xlim(-5, 5)\n",
    "\n",
    "\n",
    "#sns.regplot(y=\"y\", x=\"x\", data={\"x\":x, \"y\":y},  scatter=False, order=5)\n",
    "plt.savefig(\"hardtanh.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y69evLz3Ux8EVn0ftoovtfd7T06Pt27eX/ozl6VdmujZeu3atbr/9dknSz3/+\nc9XW1mrlypXVLi+WLrZPPyqPsw/s/OEPf9CCBQu0dOlSvfbaa0GXUzEE9WWYbu/zv/3tbxoZGVF7\ne7ukcz2U1atX67e//a0WLFhQzRIj72L7y0vS3r17NTAwoF/96ldVqij+UqmURkdHS5+z2axSqVSA\nFcXX5Z59gMvz5ptv6uWXX9bAwIDOnDmj9957T+vXr9dPfvKToEvzhZ3JDNx+++3au3ev5s6dG3Qp\nsTI4OKjHHntMTz/99LSHwODyTUxMqLW1VU8++aTq6+t19913a+vWrbGajBMGhUJBP/rRjzR37lxt\n3Lgx6HJi7/XXX9f27du1bdu2oEvxjR61AYYSbTzyyCM6e/asvvvd70qSbrzxRm3evDnYomIgkUio\nq6tLnZ2dyufz6ujoIKQNvPHGG9q3b59uuOEG3XXXXZI4+wCXhh41AAAhFp9pcQAAxBBBDQBAiBHU\nAACEGEENAECIEdQAAIQYQQ0AQIgR1AAAhBhBDQBAiP1/egSROzYmbPYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e68632050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = np.gradient(y)\n",
    "plt.plot(x, 100*y2)\n",
    "#sns.regplot(y=\"y\", x=\"x\", data={\"x\":x, \"y\":y},  scatter=False, order=5)\n",
    "plt.ylim(-0.2, 1.2)\n",
    "plt.xlim(-5, 5)\n",
    "\n",
    "plt.savefig(\"hardtanhgrad.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZXRLgM4IaQMAzDEPr/3JE/+/9E0qMDdeDS2YoK51pihEcCGoAAc1rGHr+9QPasbtaGcnR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\nhRHUAABYGEENAICF/X8SmgXK+DpYkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e6828e250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 1000)\n",
    "y = 1./(1 + np.exp(-x)) \n",
    "plt.plot(x, y)\n",
    "#plt.gcf().subplots_adjust(top=0.75)\n",
    "plt.ylim(-0.2, 1.2)\n",
    "plt.xlim(-5, 5)\n",
    "\n",
    "\n",
    "#sns.regplot(y=\"y\", x=\"x\", data={\"x\":x, \"y\":y},  scatter=False, order=5)\n",
    "plt.savefig(\"sigmoid.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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65OkOL4xf6dS5hi798WOP/vixR1L4bIDlRQ7d4M7SDUVZKi9ysNZtAIJ6Ghjy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\n9v8Bv8C/4ru9590AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e6836fed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = np.gradient(y)\n",
    "plt.plot(x,100*y2)\n",
    "plt.ylim(-0.2, 1.2)\n",
    "plt.xlim(-5, 5)\n",
    "\n",
    "plt.savefig(\"sigmoidgrad.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import IPython\n",
    "IPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qeHKsHl5YILuNc1hENiILwCdOdfXr2Ypq2W02LS1xKjmBc1iAyAK4YF6vpWcr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e687cba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 1000)\n",
    "\n",
    "y = np.array([max(a, 0) for a in list(x)])\n",
    "plt.plot(x, y)\n",
    "#sns.regplot(y=\"y\", x=\"x\", data={\"x\":x, \"y\":y},  scatter=False, order=5)\n",
    "plt.ylim(-0.2, 5)\n",
    "plt.xlim(-5, 5)\n",
    "plt.savefig(\"Relu.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e67f4fe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = np.gradient(y)\n",
    "plt.plot(x, y2*100)\n",
    "#sns.regplot(y=\"y\", x=\"x\", data={\"x\":x, \"y\":y},  scatter=False, order=5)plt.ylim(-0.2, 1.2)\n",
    "plt.ylim(-0.2, 1.2)\n",
    "plt.xlim(-5, 5)\n",
    "\n",
    "plt.savefig(\"Relugrad.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e686ad610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 1000)\n",
    "\n",
    "y = x > 0\n",
    "plt.plot(x, y)\n",
    "#sns.regplot(y=\"y\", x=\"x\", data={\"x\":x, \"y\":y},  scatter=False, order=5)\n",
    "plt.ylim(-0.2, 1.2)\n",
    "plt.xlim(-5, 5)\n",
    "plt.savefig(\"01.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e680eb310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = np.gradient(y)\n",
    "plt.plot(x, y2*100)\n",
    "#sns.regplot(y=\"y\", x=\"x\", data={\"x\":x, \"y\":y},  scatter=False, order=5)\n",
    "plt.ylim(-0.2, 1.2)\n",
    "plt.xlim(-5, 5)\n",
    "plt.savefig(\"01grad.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}