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   "cell_type": "markdown",
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   "source": [
    "#  algorithme\n",
    "\n",
    "\n",
    "1.  Posons $X^{(0)}$ un reseau quelconque.\n",
    "\n",
    "2.  copie $X^{(0)}$ dans $X^{(1)}$\n",
    "\n",
    "3.  pour change antenne dans $X^{(1)}$\n",
    "    \n",
    "    1. si `antenne` peut etre allume et $U(0, 1) < 0.5$\n",
    "        1. On allume `antenne` dans $X^{(1)}$ \n",
    "    2. sinon\n",
    "        1. On eteint `antenne` dans $X^{(1)}$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from network.network import (is_valid_network,\n",
    "                             get_number_of_activated_antenna,\n",
    "                             pp_network,\n",
    "                             switch_antenna_in_network,\n",
    "                             turn_off_antenna,\n",
    "                             can_antenna_be_turned_on,\n",
    "                             turn_on_antenna)\n",
    "from numpy import mean\n",
    "from random import Random\n",
    "from itertools import cycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "random = Random(666)\n",
    "\n",
    "def get_all_antennas(box_size):\n",
    "    return range(box_size * box_size)\n",
    "    \n",
    "def get_new_network_with_gibbs(network, box_size):\n",
    "    for antenna in get_all_antennas(box_size):\n",
    "        if can_antenna_be_turned_on(antenna, network, box_size) and random.choice([True, False]):\n",
    "            network = turn_on_antenna(antenna, network, box_size)\n",
    "        else:\n",
    "            network = turn_off_antenna(antenna, network)\n",
    "    return network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "networks = [0]\n",
    "box_size = 5\n",
    "\n",
    "for i in range(100):\n",
    "    last_network = networks[-1]\n",
    "    networks.append(get_new_network_with_gibbs(last_network, box_size))\n",
    "\n"
   ]
  }
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