@ -54,23 +54,11 @@
},
{
"cell_type": "code",
"execution_count": 25 ,
"execution_count": 1 ,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named 'matplotlib'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-25-7de2b31ae930>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnxsim\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mNetworkSimulation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnetworkx\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msettings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mImportError\u001b[0m: No module named 'matplotlib'"
]
}
],
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from nxsim import NetworkSimulation\n",
@ -102,7 +90,7 @@
},
{
"cell_type": "code",
"execution_count": null ,
"execution_count": 2 ,
"metadata": {
"collapsed": true
},
@ -133,11 +121,23 @@
},
{
"cell_type": "code",
"execution_count": null ,
"execution_count": 3 ,
"metadata": {
"collapsed": false
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting simulations...\n",
"---Trial 0---\n",
"Setting up agents...\n",
"Written 50 items to pickled binary file: sim_01/log.0.state.pickled\n",
"Simulation completed.\n"
]
}
],
"source": [
"sim = NetworkSimulation(topology=G, states=init_states, agent_type=ControlModelM2,\n",
" max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)\n",
@ -164,11 +164,19 @@
},
{
"cell_type": "code",
"execution_count": null ,
"execution_count": 4 ,
"metadata": {
"collapsed": false
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done!\n"
]
}
],
"source": [
"for x in range(0, settings.number_of_nodes):\n",
" for empresa in models.networkStatus[\"agente_%s\"%x]:\n",
@ -199,7 +207,7 @@
},
{
"cell_type": "code",
"execution_count": null ,
"execution_count": 5 ,
"metadata": {
"collapsed": false
},
@ -251,6 +259,13 @@
"plt.savefig('control_model.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![alt text](https://raw.githubusercontent.com/gsi-upm/soil/master/control_model.png \"Control model\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -267,7 +282,7 @@
},
{
"cell_type": "code",
"execution_count": null ,
"execution_count": 6 ,
"metadata": {
"collapsed": true
},
@ -310,7 +325,7 @@
},
{
"cell_type": "code",
"execution_count": null ,
"execution_count": 7 ,
"metadata": {
"collapsed": true
},
@ -373,7 +388,7 @@
},
{
"cell_type": "code",
"execution_count": null ,
"execution_count": 8 ,
"metadata": {
"collapsed": true
},
@ -514,87 +529,6 @@
"source": [
"This file contains all the variables that can be modified from the simulation. In case of implementing a new spread model, the new variables should be also included in this file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# settings.py\n",
"def init():\n",
"\n",
" network_type=1\n",
" number_of_nodes=1000\n",
" max_time=50\n",
" num_trials=1\n",
" timeout=2\n",
"\n",
" #Zombie model\n",
" bite_prob=0.01 # 0-1\n",
" heal_prob=0.01 # 0-1\n",
"\n",
" #Bass model\n",
" innovation_prob=0.001\n",
" imitation_prob=0.005\n",
"\n",
" #Sentiment Correlation model\n",
" outside_effects_prob = 0.2\n",
" anger_prob = 0.06\n",
" joy_prob = 0.05\n",
" sadness_prob = 0.02\n",
" disgust_prob = 0.02\n",
"\n",
" #Big Market model\n",
" ##Names\n",
" enterprises = [\"BBVA\",\"Santander\", \"Bankia\"]\n",
" ##Users\n",
" tweet_probability_users = 0.44\n",
" tweet_relevant_probability = 0.25\n",
" tweet_probability_about = [0.15, 0.15, 0.15]\n",
" sentiment_about = [0, 0, 0] #Valores por defecto\n",
" ##Enterprises\n",
" tweet_probability_enterprises = [0.3, 0.3, 0.3]\n",
"\n",
" #SISa\n",
" neutral_discontent_spon_prob = 0.04\n",
" neutral_discontent_infected_prob = 0.04\n",
" neutral_content_spon_prob = 0.18\n",
" neutral_content_infected_prob = 0.02\n",
"\n",
" discontent_neutral = 0.13\n",
" discontent_content = 0.07\n",
" variance_d_c = 0.02\n",
"\n",
" content_discontent = 0.009\n",
" variance_c_d = 0.003\n",
" content_neutral = 0.088\n",
"\n",
" standard_variance = 0.055\n",
"\n",
" #Spread Model M2 and Control Model M2\n",
" prob_neutral_making_denier = 0.035\n",
"\n",
" prob_infect = 0.075\n",
"\n",
" prob_cured_healing_infected = 0.035\n",
" prob_cured_vaccinate_neutral = 0.035\n",
"\n",
" prob_vaccinated_healing_infected = 0.035\n",
" prob_vaccinated_vaccinate_neutral = 0.035\n",
" prob_generate_anti_rumor = 0.035\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
jesusmsanchez
8 years ago