180 lines
3.7 KiB
Plaintext
180 lines
3.7 KiB
Plaintext
{
|
|
"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
|
|
}
|