First commit

importance_sampling.ipynb

@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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",
+    "                             create_network,\n",
+    "                             get_number_of_activated_antenna,\n",
+    "                             get_activated_antennas,\n",
+    "                             can_antenna_be_turned_on,\n",
+    "                             pp_network,\n",
+    "                             turn_off_antenna,\n",
+    "                             turn_on_antenna)\n",
+    "\n",
+    "from network.impSamp import AntennaGenerator\n",
+    "from numpy import mean\n",
+    "from random import Random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "random = Random(666)\n",
+    "\n",
+    "def get_random_network(box_size, random):\n",
+    "    antenna_generator = AntennaGenerator(box_size, random)\n",
+    "    antennas = []\n",
+    "    antenna, network_prob = antenna_generator.get_random_antenna()\n",
+    "\n",
+    "    while antenna != -1:\n",
+    "        antennas.append(antenna)\n",
+    "        antenna, p = antenna_generator.get_random_antenna()\n",
+    "        network_prob *= p\n",
+    "        \n",
+    "    return create_network(antennas), network_prob\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "box_size = 4\n",
+    "antannas = [0,1,2,3]\n",
+    "\n",
+    "for i in range(1000):\n",
+    "    network = get_random_network(box_size)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "box_size = 5\n",
+    "for i in range(100000):\n",
+    "    network, p = get_random_network(box_size)\n",
+    "#     print(\"network\", network)\n",
+    "#     print(pp_network(network, box_size))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ds = list(reversed(range(10)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "all_networks = set()\n",
+    "\n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.582214353409138"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_estimation(1000000, random)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25318"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(all_networks)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3+"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}