{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from KMR_class import KMR, set_pay"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "payoff = [[[4,4],[0,3]],[[3,0],[2,2]]]\n",
      "set_pay(payoff)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OK. one_payoff is\n",
        "[[4 0]\n",
        " [3 2]]\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = 10\n",
      "epsilon = 0.05\n",
      "mode = 'simultaneous'\n",
      "x0_p = 0\n",
      "T = 10**5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmr = KMR(N, x0_p, epsilon, mode)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmr.plot(T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SGAZ+IiKFYeAnIlIYBn4iIoUxOfAnJCQgMjISnTp1wgMPPICioqJqy7BXDxGR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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7790940>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmr.hist(T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MCyIiksWwICIiWQwLIiKSxbAgIiJZDAsiIpLFsCAiIlkMCyIiksWwICIiWQwL\nIiKSxbAgIiJZDAsiIpLFsCAiIlkMCyIiksWwICIiWQwLIiKSxbAgIiJZDAsiIpLFsCAiIllWh0VC\nQgKCgoIwaNAg3H///aiurkZJSQnCwsLg7++PCRMmoKyszGx+g8GAgIAA7Nu3Txr/ww8/YNCgQTAY\nDFi0aJE0vrq6GjNnzoTBYMDIkSORm5trbVOJiKiDrAqLnJwcbNiwAUePHsXJkydRX1+PlJQUJCYm\nIiwsDNnZ2QgNDUViYiIAICsrC1u3bkVWVhbS0tKwcOFC6WHhCxYsQFJSEoxGI4xGI9LS0gAASUlJ\ncHNzg9FoxOLFi7FkyZJOWmQiImovq8JCqVTCwcEBlZWVqKurQ2VlJby8vJCamorY2FgAQGxsLHbu\n3AkA2LVrF6KiouDg4AC9Xg8/Pz9kZGSgqKgIFRUVCA4OBgDExMRIZZrWFRERgfT09A4vLBERWceq\nsFCr1Xjqqafg4+MDLy8vuLi4ICwsDMXFxdBoNAAAjUaD4uJiAEBhYSF0Op1UXqfTwWQyNRuv1Wph\nMpkAACaTCd7e3gAAe3t7qFQqlJSUWLeURETUIfbWFDp9+jTWrFmDnJwcqFQq3Hfffdi0aZPZPAqF\nAgqFolMaKSc+Pl76OyQkBCEhIVflfYmIuoeDfw3Wsyosjhw5gttvvx1ubm4AgOnTp+P777+Hp6cn\nzp49C09PTxQVFcHDwwPA5SOG/Px8qXxBQQF0Oh20Wi0KCgqajW8sk5eXBy8vL9TV1aG8vBxqtdpi\ne5qGBRERXSnkr6HRinbXYNVpqICAABw6dAhVVVUQQmD//v0IDAzE5MmTkZycDABITk7G1KlTAQDh\n4eFISUlBTU0Nzpw5A6PRiODgYHh6ekKpVCIjIwNCCGzcuBFTpkyRyjTWtX37doSGhlrTVCIi6gRW\nHVkMGTIEMTExGDFiBOzs7HDLLbfgoYceQkVFBSIjI5GUlAS9Xo9t27YBAAIDAxEZGYnAwEDY29tj\n/fr10imq9evXY86cOaiqqsLEiRNxzz33AADmzZuH6OhoGAwGuLm5ISUlpZMWmYiI2kshGq9h7aYU\nCgW6+SIQ0XXk8hdlW++z2r/f5B3cREQki2FBRESyGBZERCSLYUFERLIYFkREJIthQUREshgWREQk\ni2FBRESyGBZERCSLYUFERLIYFkREJIthQUREshgWREQki2FBRESyGBZERCSLYUFERLIYFkREJIth\nQUREshhZYX2PAAAQk0lEQVQWREQki2FBRESyGBZERCSLYUFERLIYFkREJIthQUREshgWREQki2FB\nRESyGBZERCSLYUFERLIYFkREJIthQUREshgWREQki2FBRESyGBZERCSLYUFERLIYFkREJMvqsCgr\nK8OMGTMwYMAABAYGIiMjAyUlJQgLC4O/vz8mTJiAsrIyaf6EhAQYDAYEBARg37590vgffvgBgwYN\ngsFgwKJFi6Tx1dXVmDlzJgwGA0aOHInc3Fxrm0pERB1kdVgsWrQIEydOxKlTp3DixAkEBAQgMTER\nYWFhyM7ORmhoKBITEwEAWVlZ2Lp1K7KyspCWloaFCxdCCAEAWLBgAZKSkmA0GmE0GpGWlgYASEpK\ngpubG4xGIxYvXowlS5Z0wuISEZE1rAqL8vJyfP3115g7dy4AwN7eHiqVCqmpqYiNjQUAxMbGYufO\nnQCAXbt2ISoqCg4ODtDr9fDz80NGRgaKiopQUVGB4OBgAEBMTIxUpmldERERSE9P79iSEhGR1awK\nizNnzsDd3R1xcXG45ZZb8OCDD+LixYsoLi6GRqMBAGg0GhQXFwMACgsLodPppPI6nQ4mk6nZeK1W\nC5PJBAAwmUzw9vYG8HcYlZSUWLeURETUIVaFRV1dHY4ePYqFCxfi6NGjcHR0lE45NVIoFFAoFJ3S\nSCIisi17awrpdDrodDrceuutAIAZM2YgISEBnp6eOHv2LDw9PVFUVAQPDw8Al48Y8vPzpfIFBQXQ\n6XTQarUoKChoNr6xTF5eHry8vFBXV4fy8nKo1WqL7YmPj5f+DgkJQUhIiDWLRUR0jTr412A9q8LC\n09MT3t7eyM7Ohr+/P/bv34+goCAEBQUhOTkZS5YsQXJyMqZOnQoACA8Px/33348nn3wSJpMJRqMR\nwcHBUCgUUCqVyMjIQHBwMDZu3IjHH39cKpOcnIyRI0di+/btCA0NbbE9TcOCiIiuFPLX0GhFu2tQ\niMbLktrpxx9/xPz581FTUwNfX1988MEHqK+vR2RkJPLy8qDX67Ft2za4uLgAAFauXIn3338f9vb2\nWLt2Le6++24Aly+dnTNnDqqqqjBx4kSsW7cOwOVLZ6Ojo3Hs2DG4ubkhJSUFer2++QIoFLByEYiI\nrrrLp+dtvc9q/37T6rDoKhgWRNSddNew4B3cREQki2FBRESyGBZERCSLYUFERLIYFkREJIthQURE\nshgWREQki2FBRESyGBZERCSLYUFERLIYFkREJIthQUREshgWREQki2FBRESyGBZERCSLYUFERLIY\nFkREJIthQUREshgWREQki2FBRESyGBZERCSLYUFERLIYFkREJIthQUREshgWREQki2FBRESyGBZE\nRCSLYUFERLIYFkREJIthQUREshgWREQki2FBRESyGBZERCSLYUFERLIYFkREJIthQUREsjoUFvX1\n9Rg2bBgmT54MACgpKUFYWBj8/f0xYcIElJWVSfMmJCTAYDAgICAA+/btk8b/8MMPGDRoEAwGAxYt\nWiSNr66uxsyZM2EwGDBy5Ejk5uZ2pKlERNQBHQqLtWvXIjAwEAqFAgCQmJiIsLAwZGdnIzQ0FImJ\niQCArKwsbN26FVlZWUhLS8PChQshhAAALFiwAElJSTAajTAajUhLSwMAJCUlwc3NDUajEYsXL8aS\nJUs60lQiIuoAq8OioKAAe/fuxfz586Udf2pqKmJjYwEAsbGx2LlzJwBg165diIqKgoODA/R6Pfz8\n/JCRkYGioiJUVFQgODgYABATEyOVaVpXREQE0tPTrV9KIiLqEKvDYvHixXj11VdhZ/d3FcXFxdBo\nNAAAjUaD4uJiAEBhYSF0Op00n06ng8lkajZeq9XCZDIBAEwmE7y9vQEA9vb2UKlUKCkpsba5RETU\nAVaFxZ49e+Dh4YFhw4ZJRxVXUigU0ukpIiLq3uytKfTdd98hNTUVe/fuxaVLl3D+/HlER0dDo9Hg\n7Nmz8PT0RFFRETw8PABcPmLIz8+XyhcUFECn00Gr1aKgoKDZ+MYyeXl58PLyQl1dHcrLy6FWqy22\nJz4+Xvo7JCQEISEh1iwWEdE16uBfQweIDjp48KCYNGmSEEKIp59+WiQmJgohhEhISBBLliwRQgiR\nmZkphgwZIqqrq8Vvv/0mbr75ZtHQ0CCEECI4OFgcOnRINDQ0iHvvvVd89tlnQggh3nrrLfHII48I\nIYTYsmWLmDlzpsX374RFICK6agAIQNh4aP9+06ojiys1nm5aunQpIiMjkZSUBL1ej23btgEAAgMD\nERkZicDAQNjb22P9+vVSmfXr12POnDmoqqrCxIkTcc899wAA5s2bh+joaBgMBri5uSElJaUzmkpE\nRFZQ/JV03ZZCoWjxdxMioq7m8hdlW++z2r/f5B3cREQki2FBRESyGBZERCSLYUFERLIYFkREJIth\nQUREshgWREQki2FBRESyGBZERCSLYUFERLIYFkREJIthQUREshgWREQki2FBRESyGBZERCSLYUFE\nRLIYFkREJIthQUREshgWREQki2FBRESyGBZERCSLYUFERLIYFkREJIthQUREshgWREQki2FBRESy\nGBZERCSLYUFERLIYFkREJIthQUREshgWREQki2FBRESyGBZERCSLYUFERLIYFkREJIthQUREsqwK\ni/z8fIwdOxZBQUEYOHAg1q1bBwAoKSlBWFgY/P39MWHCBJSVlUllEhISYDAYEBAQgH379knjf/jh\nBwwaNAgGgwGLFi2SxldXV2PmzJkwGAwYOXIkcnNzrV1GIiLqIKvCwsHBAW+88QYyMzNx6NAhvPXW\nWzh16hQSExMRFhaG7OxshIaGIjExEQCQlZWFrVu3IisrC2lpaVi4cCGEEACABQsWICkpCUajEUaj\nEWlpaQCApKQkuLm5wWg0YvHixViyZEknLTIREbWXVWHh6emJoUOHAgCcnJwwYMAAmEwmpKamIjY2\nFgAQGxuLnTt3AgB27dqFqKgoODg4QK/Xw8/PDxkZGSgqKkJFRQWCg4MBADExMVKZpnVFREQgPT29\nY0tKRERW6/BvFjk5OTh27Bhuu+02FBcXQ6PRAAA0Gg2Ki4sBAIWFhdDpdFIZnU4Hk8nUbLxWq4XJ\nZAIAmEwmeHt7AwDs7e2hUqlQUlLS0eYSEZEV7DtS+MKFC4iIiMDatWvh7OxsNk2hUEChUHSocW0V\nHx8v/R0SEoKQkJCr8r5ERN3Dwb8G61kdFrW1tYiIiEB0dDSmTp0K4PLRxNmzZ+Hp6YmioiJ4eHgA\nuHzEkJ+fL5UtKCiATqeDVqtFQUFBs/GNZfLy8uDl5YW6ujqUl5dDrVZbbEvTsCAioiuF/DU0WtHu\nGqw6DSWEwLx58xAYGIgnnnhCGh8eHo7k5GQAQHJyshQi4eHhSElJQU1NDc6cOQOj0Yjg4GB4enpC\nqVQiIyMDQghs3LgRU6ZMaVbX9u3bERoaak1TiYioEyhE42VJ7fDNN9/gzjvvxODBg6VTTQkJCQgO\nDkZkZCTy8vKg1+uxbds2uLi4AABWrlyJ999/H/b29li7di3uvvtuAJcvnZ0zZw6qqqowceJE6TLc\n6upqREdH49ixY3Bzc0NKSgr0en3zBVAoYMUiEBHZxOV9pq33We3fb1oVFl0Jw4KIupPuGha8g5uI\niGQxLIiISBbDgoiIZDEsiIhIFsOCiIhkMSyIiEgWw4KIiGQxLIiISBbDgoiIZDEsiIhIFsOCiIhk\nMSyIiEgWw4KIiGQxLIiISBbDgoiIZDEsiIhIFsOCiIhkMSyIiEgWw4KIiGQxLIiISBbDgoiIZDEs\niIhIFsOCiIhk2du6AUREV4NSqUZFRamtm9FtKYQQwtaN6AiFQoFuvghEdBUoFAoAXWFf0RXa0f79\nJk9DERGRLIYFERHJYlgQEZEshgUREcliWBARkSyGBRERyWJYEBGRLIYFERHJYlgQEZEshgUREcnq\n8mGRlpaGgIAAGAwGrFq1ytbNISK6LnXpsKivr8djjz2GtLQ0ZGVlYcuWLTh16pStm9VlHTx40NZN\n6BK4Hv7GdUGdpUuHxeHDh+Hn5we9Xg8HBwfMmjULu3btsnWzuizuGC7jevgb1wV1li4dFiaTCd7e\n3tJrnU4Hk8lkwxYRkTWUSjUUCoVNB+qYLh0Wbf2Abb0RfvLJJ//wmiDq3i4/R0LYeKAOEV3Y999/\nL+6++27p9cqVK0ViYqLZPL6+vrbeAjlw4MChWw2+vr7t3h936Ycf1dXVoX///khPT4eXlxeCg4Ox\nZcsWDBgwwNZNIyK6rnTpx6ra29vjzTffxN133436+nrMmzePQUFEZANd+siCiIi6hi79A3dreLPe\nZfn5+Rg7diyCgoIwcOBArFu3ztZNsrn6+noMGzYMkydPtnVTbKqsrAwzZszAgAEDEBgYiEOHDtm6\nSTaTkJCAoKAgDBo0CPfffz+qq6tt3aSrZu7cudBoNBg0aJA0rqSkBGFhYfD398eECRNQVlYmW0+3\nDAverPc3BwcHvPHGG8jMzMShQ4fw1ltvXbfrotHatWsRGBh43V8uuWjRIkycOBGnTp3CiRMnrttT\nuDk5OdiwYQOOHj2KkydPor6+HikpKbZu1lUTFxeHtLQ0s3GJiYkICwtDdnY2QkNDkZiYKFtPtwwL\n3qz3N09PTwwdOhQA4OTkhAEDBqCwsNDGrbKdgoIC7N27F/Pnz8f1fIa1vLwcX3/9NebOnQvg8u9/\nKpXKxq2yDaVSCQcHB1RWVqKurg6VlZXQarW2btZVM2bMGLi6upqNS01NRWxsLAAgNjYWO3fulK2n\nW4YFb9azLCcnB8eOHcNtt91m66bYzOLFi/Hqq6/Czq5bbtqd5syZM3B3d0dcXBxuueUWPPjgg6is\nrLR1s2xCrVbjqaeego+PD7y8vODi4oLx48fbulk2VVxcDI1GAwDQaDQoLi6WLdMt/6Ou99MLlly4\ncAEzZszA2rVr4eTkZOvm2MSePXvg4eGBYcOGXddHFcDly86PHj2KhQsX4ujRo3B0dGzTqYZr0enT\np7FmzRrk5OSgsLAQFy5cwObNm23drC6jrXe4d8uw0Gq1yM/Pl17n5+dDp9PZsEW2VVtbi4iICMye\nPRtTp061dXNs5rvvvkNqair69euHqKgoHDhwADExMbZulk3odDrodDrceuutAIAZM2bg6NGjNm6V\nbRw5cgS333473NzcYG9vj+nTp+O7776zdbNsSqPR4OzZswCAoqIieHh4yJbplmExYsQIGI1G5OTk\noKamBlu3bkV4eLitm2UTQgjMmzcPgYGBeOKJJ2zdHJtauXIl8vPzcebMGaSkpGDcuHH46KOPbN0s\nm/D09IS3tzeys7MBAPv370dQUJCNW2UbAQEBOHToEKqqqiCEwP79+xEYGGjrZtlUeHg4kpOTAQDJ\nyclt+5JpdV8cNrZ3717h7+8vfH19xcqVK23dHJv5+uuvhUKhEEOGDBFDhw4VQ4cOFZ999pmtm2Vz\nBw8eFJMnT7Z1M2zq+PHjYsSIEWLw4MFi2rRpoqyszNZNsplVq1aJwMBAMXDgQBETEyNqamps3aSr\nZtasWaJPnz7CwcFB6HQ68f7774s///xThIaGCoPBIMLCwkRpaalsPbwpj4iIZHXL01BERHR1MSyI\niEgWw4KIiGQxLIiISBbDgoiIZDEsiIhIFsOCiIhkMSyIiEjW/wdEzIinNO/LBwAAAABJRU5ErkJg\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa1a6908>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmr.equilibrium()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6ScKGPXv2iPj4ePP40qVLxfLlyy3apKamiszMTPN4aGioOHv2bItlRUZGOu6w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\nEmJ4ExFJiOFNRCQhhjcRkYQY3kREEvo/bF2tIooZKyQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x136eadd8>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}