mirror of
https://github.com/gsi-upm/soil
synced 2024-12-22 00:08:12 +00:00
Tasio Mendez
parent
5a2fcb5564
commit
cd9b7e096a
5
.gitignore
vendored
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5
.gitignore
vendored
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@ -0,0 +1,5 @@
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__pycache__/
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.idea/
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.ipynb_checkpoints/
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*.png
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*.gexf
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Binary file not shown.
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Before Width: | Height: | Size: 35 KiB |
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2
soil.py
2
soil.py
@ -85,7 +85,7 @@ def results(model_name):
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cured_line = ax1.plot(x_values, cured_values, label='Cured')
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vaccinated_line = ax1.plot(x_values, vaccinated_values, label='Vaccinated')
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ax1.legend()
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fig1.savefig(model_name+'.png')
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fig1.savefig(model_name + '.png')
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# plt.show()
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@ -32,10 +32,10 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"SOIL is based in 3 main files:\n",
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"SOIL is based in 2 main files:\n",
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"* __soil.py__: It's the main file of SOIL. The network creation, simulation and visualization are done in this file.\n",
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"- __models.py__: All the spread models already implemented are stored in this file.\n",
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"+ __settings.py__: This file contains every variable needed in the simulation in order to be modified easily."
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"+ __settings.json__: This file contains every variable needed in the simulation in order to be modified easily.\n",
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"- __models__: All the spread models already implemented are stored in this directory as modules."
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]
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},
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{
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@ -80,24 +80,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"from models import *\n",
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"from nxsim import NetworkSimulation\n",
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"import numpy\n",
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"# import numpy\n",
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"from matplotlib import pyplot as plt\n",
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"import networkx as nx\n",
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"import settings\n",
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"import models\n",
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"from models import *\n",
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"import math\n",
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"import json\n",
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"\n",
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"settings.init() # Loads all the data from settings\n",
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"models.init() # Loads the models and network variables"
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"import json"
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]
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},
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{
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@ -116,62 +113,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"if settings.network_type == 0:\n",
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" G = nx.complete_graph(settings.number_of_nodes)\n",
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"if settings.network_type == 1:\n",
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" G = nx.barabasi_albert_graph(settings.number_of_nodes,10)\n",
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"if settings.network_type == 2:\n",
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" G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)\n",
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"if settings.network_params[\"network_type\"] == 0:\n",
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" G = nx.complete_graph(settings.network_params[\"number_of_nodes\"])\n",
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"if settings.network_params[\"network_type\"] == 1:\n",
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" G = nx.barabasi_albert_graph(settings.network_params[\"number_of_nodes\"], 10)\n",
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"if settings.network_params[\"network_type\"] == 2:\n",
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" G = nx.margulis_gabber_galil_graph(settings.network_params[\"number_of_nodes\"], None)\n",
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"# More types of networks can be added here"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Simulation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The simulation starts with the following code. The user can provide the network topology, the maximum time of simulation, the spread model to be used as well as other parameters."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Starting simulations...\n",
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"---Trial 0---\n",
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"Setting up agents...\n",
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"Written 50 items to pickled binary file: sim_01/log.0.state.pickled\n",
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"Simulation completed.\n"
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]
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}
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],
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"source": [
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"sim = NetworkSimulation(topology=G, states=init_states, agent_type=ControlModelM2,\n",
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" max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)\n",
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"\n",
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"\n",
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"sim.run_simulation()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -213,38 +169,31 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Done!\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"for x in range(0, settings.number_of_nodes):\n",
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" for enterprise in models.networkStatus[\"agent_%s\"%x]:\n",
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" emotionStatusAux=[]\n",
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" for time in models.networkStatus[\"agent_%s\"%x][enterprise]:\n",
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" prec = 2\n",
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" output = math.floor(models.networkStatus[\"agent_%s\"%x][enterprise][time] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo\n",
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" emotionStatusAux.append((output,time,None))\n",
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" attributes = {}\n",
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" attributes[enterprise] = emotionStatusAux\n",
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" G.add_node(x, attributes)\n",
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"def visualization(graph_name):\n",
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"\n",
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" for x in range(0, settings.network_params[\"number_of_nodes\"]):\n",
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" for attribute in models.networkStatus[\"agent_%s\" % x]:\n",
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" emotionStatusAux = []\n",
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" for t_step in models.networkStatus[\"agent_%s\" % x][attribute]:\n",
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" prec = 2\n",
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" output = math.floor(models.networkStatus[\"agent_%s\" % x][attribute][t_step] * (10 ** prec)) / (10 ** prec) # 2 decimals\n",
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" emotionStatusAux.append((output, t_step, t_step + settings.network_params[\"timeout\"]))\n",
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" attributes = {}\n",
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" attributes[attribute] = emotionStatusAux\n",
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" G.add_node(x, attributes)\n",
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"\n",
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"print(\"Done!\")\n",
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" print(\"Done!\")\n",
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"\n",
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"with open('data.txt', 'w') as outfile:\n",
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" json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))\n",
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" with open('data.txt', 'w') as outfile:\n",
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" json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))\n",
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"\n",
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"nx.write_gexf(G,\"test.gexf\", version=\"1.2draft\")"
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" nx.write_gexf(G, graph_name+\".gexf\", version=\"1.2draft\")"
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]
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},
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{
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@ -256,70 +205,111 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"x_values = []\n",
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"infected_values = []\n",
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"neutral_values = []\n",
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"cured_values = []\n",
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"vaccinated_values = []\n",
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"def results(model_name):\n",
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" x_values = []\n",
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" infected_values = []\n",
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" neutral_values = []\n",
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" cured_values = []\n",
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" vaccinated_values = []\n",
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"\n",
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"attribute_plot = 'status'\n",
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"for time in range(0, settings.max_time):\n",
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" value_infected = 0\n",
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" value_neutral = 0\n",
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" value_cured = 0\n",
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" value_vaccinated = 0\n",
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" real_time = time * settings.timeout\n",
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" activity= False\n",
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" for x in range(0, settings.number_of_nodes):\n",
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" if attribute_plot in models.networkStatus[\"agent_%s\" % x]:\n",
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" if real_time in models.networkStatus[\"agent_%s\" % x][attribute_plot]:\n",
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" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 1: ##Represent infected\n",
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" value_infected += 1\n",
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" activity = True\n",
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" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 0: ##Represent neutrals\n",
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" value_neutral += 1\n",
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" activity = True\n",
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" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 2: ##Represent cured\n",
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" value_cured += 1\n",
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" activity = True\n",
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" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 3: ##Represent vaccinated\n",
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" value_vaccinated += 1\n",
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" activity = True\n",
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" attribute_plot = 'status'\n",
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" for time in range(0, settings.network_params[\"max_time\"]):\n",
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" value_infectados = 0\n",
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" value_neutral = 0\n",
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" value_cured = 0\n",
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" value_vaccinated = 0\n",
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" real_time = time * settings.network_params[\"timeout\"]\n",
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" activity = False\n",
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" for x in range(0, settings.network_params[\"number_of_nodes\"]):\n",
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" if attribute_plot in models.networkStatus[\"agent_%s\" % x]:\n",
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" if real_time in models.networkStatus[\"agent_%s\" % x][attribute_plot]:\n",
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" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 1: ## Infected\n",
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" value_infectados += 1\n",
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" activity = True\n",
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" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 0: ## Neutral\n",
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" value_neutral += 1\n",
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" activity = True\n",
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" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 2: ## Cured\n",
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" value_cured += 1\n",
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" activity = True\n",
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" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 3: ## Vaccinated\n",
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" value_vaccinated += 1\n",
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" activity = True\n",
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"\n",
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" if activity:\n",
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" x_values.append(real_time)\n",
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" infected_values.append(value_infected)\n",
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" neutral_values.append(value_neutral)\n",
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" cured_values.append(value_cured)\n",
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" vaccinated_values.append(value_vaccinated)\n",
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" activity=False\n",
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" if activity:\n",
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" x_values.append(real_time)\n",
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" infected_values.append(value_infectados)\n",
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" neutral_values.append(value_neutral)\n",
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" cured_values.append(value_cured)\n",
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" vaccinated_values.append(value_vaccinated)\n",
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" activity = False\n",
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"\n",
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"infected_line = plt.plot(x_values,infected_values,label='Infected')\n",
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"neutral_line = plt.plot(x_values,neutral_values, label='Neutral')\n",
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"cured_line = plt.plot(x_values,cured_values, label='Cured')\n",
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"vaccinated_line = plt.plot(x_values,vaccinated_values, label='Vaccinated')\n",
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"plt.legend()\n",
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"plt.savefig('control_model.png')"
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" fig1 = plt.figure()\n",
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" ax1 = fig1.add_subplot(111)\n",
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"\n",
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" infected_line = ax1.plot(x_values, infected_values, label='Infected')\n",
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" neutral_line = ax1.plot(x_values, neutral_values, label='Neutral')\n",
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" cured_line = ax1.plot(x_values, cured_values, label='Cured')\n",
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" vaccinated_line = ax1.plot(x_values, vaccinated_values, label='Vaccinated')\n",
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" ax1.legend()\n",
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" fig1.savefig(model_name + '.png')\n",
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" # plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"![alt text](https://raw.githubusercontent.com/gsi-upm/soil/master/control_model.png \"Control model\")"
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"### Simulation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Models.py"
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"The simulation starts with the following code. The user can provide the network topology, the maximum time of simulation, the spread model to be used as well as other parameters."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"agents = settings.environment_params['agent']\n",
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"\n",
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"print(\"Using Agent(s): {agents}\".format(agents=agents))\n",
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"\n",
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"if len(agents) > 1:\n",
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" for agent in agents:\n",
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" sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params[\"max_time\"],\n",
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" num_trials=settings.network_params[\"num_trials\"], logging_interval=1.0, **settings.environment_params)\n",
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" sim.run_simulation()\n",
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" print(str(agent))\n",
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" results(str(agent))\n",
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" visualization(str(agent))\n",
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"else:\n",
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" agent = agents[0]\n",
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" sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params[\"max_time\"],\n",
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" num_trials=settings.network_params[\"num_trials\"], logging_interval=1.0, **settings.environment_params)\n",
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" sim.run_simulation()\n",
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" results(str(agent))\n",
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" visualization(str(agent))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Models"
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]
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},
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{
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@ -331,31 +321,22 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from nxsim import BaseNetworkAgent\n",
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"import numpy as np\n",
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"import random\n",
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"import settings\n",
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"\n",
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"settings.init()\n",
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"networkStatus = {} # Dict that will contain the status of every agent in the network\n",
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"\n",
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"##############################\n",
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"# Variables initializitation #\n",
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"##############################\n",
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"def init():\n",
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" global networkStatus\n",
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" networkStatus = {} # Dict that will contain the status of every agent in the network\n",
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"\n",
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"sentimentCorrelationNodeArray=[]\n",
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"for x in range(0, settings.number_of_nodes):\n",
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" sentimentCorrelationNodeArray.append({'id':x})\n",
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"sentimentCorrelationNodeArray = []\n",
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"for x in range(0, settings.network_params[\"number_of_nodes\"]):\n",
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" sentimentCorrelationNodeArray.append({'id': x})\n",
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"# Initialize agent states. Let's assume everyone is normal.\n",
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"init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys as as necessary, but \"id\" must always refer to that state category"
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"init_states = [{'id': 0, } for _ in range(settings.network_params[\"number_of_nodes\"])]\n",
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" # add keys as as necessary, but \"id\" must always refer to that state category"
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]
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},
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{
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@ -374,13 +355,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import settings\n",
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"from nxsim import BaseNetworkAgent\n",
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"\n",
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"\n",
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"class BaseBehaviour(BaseNetworkAgent):\n",
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"\n",
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" def __init__(self, environment=None, agent_id=0, state=()):\n",
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" super().__init__(environment=environment, agent_id=agent_id, state=state)\n",
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" self._attrs = {}\n",
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@ -399,19 +385,19 @@
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" def run(self):\n",
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" while True:\n",
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" self.step(self.env.now)\n",
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" yield self.env.timeout(settings.timeout)\n",
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" yield self.env.timeout(settings.network_params[\"timeout\"])\n",
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"\n",
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" def step(self, now):\n",
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" networkStatus['agent_%s'% self.id] = self.a_json()\n",
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" networkStatus['agent_%s'% self.id] = self.to_json()\n",
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"\n",
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" def a_json(self):\n",
|
||||
" def to_json(self):\n",
|
||||
" final = {}\n",
|
||||
" for stamp, attrs in self._attrs.items():\n",
|
||||
" for a in attrs:\n",
|
||||
" if a not in final:\n",
|
||||
" final[a] = {}\n",
|
||||
" final[a] = {}\n",
|
||||
" final[a][stamp] = attrs[a]\n",
|
||||
" return final\n"
|
||||
" return final"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -437,58 +423,71 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import settings\n",
|
||||
"import random\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class ControlModelM2(BaseBehaviour):\n",
|
||||
" #Init infected\n",
|
||||
" init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}\n",
|
||||
" init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}\n",
|
||||
"\n",
|
||||
" # Init infected\n",
|
||||
" init_states[random.randint(0, settings.network_params[\"number_of_nodes\"]-1)] = {'id': 1}\n",
|
||||
" init_states[random.randint(0, settings.network_params[\"number_of_nodes\"]-1)] = {'id': 1}\n",
|
||||
"\n",
|
||||
" # Init beacons\n",
|
||||
" init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}\n",
|
||||
" init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}\n",
|
||||
" init_states[random.randint(0, settings.network_params[\"number_of_nodes\"]-1)] = {'id': 4}\n",
|
||||
" init_states[random.randint(0, settings.network_params[\"number_of_nodes\"]-1)] = {'id': 4}\n",
|
||||
"\n",
|
||||
" def __init__(self, environment=None, agent_id=0, state=()):\n",
|
||||
" super().__init__(environment=environment, agent_id=agent_id, state=state)\n",
|
||||
"\n",
|
||||
" self.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance)\n",
|
||||
" self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
"\n",
|
||||
" self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance)\n",
|
||||
" self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
"\n",
|
||||
" self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance)\n",
|
||||
" self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance)\n",
|
||||
" self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
" self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
"\n",
|
||||
" self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance)\n",
|
||||
" self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance)\n",
|
||||
" self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance)\n",
|
||||
" self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
" self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
" self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
"\n",
|
||||
" def step(self, now):\n",
|
||||
"\n",
|
||||
" if self.state['id'] == 0: #Neutral\n",
|
||||
" if self.state['id'] == 0: # Neutral\n",
|
||||
" self.neutral_behaviour()\n",
|
||||
" elif self.state['id'] == 1: #Infected\n",
|
||||
" elif self.state['id'] == 1: # Infected\n",
|
||||
" self.infected_behaviour()\n",
|
||||
" elif self.state['id'] == 2: #Cured\n",
|
||||
" elif self.state['id'] == 2: # Cured\n",
|
||||
" self.cured_behaviour()\n",
|
||||
" elif self.state['id'] == 3: #Vaccinated\n",
|
||||
" elif self.state['id'] == 3: # Vaccinated\n",
|
||||
" self.vaccinated_behaviour()\n",
|
||||
" elif self.state['id'] == 4: #Beacon-off\n",
|
||||
" elif self.state['id'] == 4: # Beacon-off\n",
|
||||
" self.beacon_off_behaviour()\n",
|
||||
" elif self.state['id'] == 5: #Beacon-on\n",
|
||||
" elif self.state['id'] == 5: # Beacon-on\n",
|
||||
" self.beacon_on_behaviour()\n",
|
||||
"\n",
|
||||
" self.attrs['status'] = self.state['id']\n",
|
||||
" super().step(now)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" def neutral_behaviour(self):\n",
|
||||
"\n",
|
||||
" # Infected\n",
|
||||
" infected_neighbors = self.get_neighboring_agents(state_id=1)\n",
|
||||
" if len(infected_neighbors)>0:\n",
|
||||
" if len(infected_neighbors) > 0:\n",
|
||||
" if random.random() < self.prob_neutral_making_denier:\n",
|
||||
" self.state['id'] = 3 # Vaccinated making denier\n",
|
||||
"\n",
|
||||
@ -514,7 +513,6 @@
|
||||
" if random.random() < self.prob_cured_healing_infected:\n",
|
||||
" neighbor.state['id'] = 2 # Cured\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" def vaccinated_behaviour(self):\n",
|
||||
"\n",
|
||||
" # Cure\n",
|
||||
@ -523,7 +521,6 @@
|
||||
" if random.random() < self.prob_cured_healing_infected:\n",
|
||||
" neighbor.state['id'] = 2 # Cured\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Vaccinate\n",
|
||||
" neutral_neighbors = self.get_neighboring_agents(state_id=0)\n",
|
||||
" for neighbor in neutral_neighbors:\n",
|
||||
@ -539,7 +536,7 @@
|
||||
" def beacon_off_behaviour(self):\n",
|
||||
" infected_neighbors = self.get_neighboring_agents(state_id=1)\n",
|
||||
" if len(infected_neighbors) > 0:\n",
|
||||
" self.state['id'] == 5 #Beacon on\n",
|
||||
" self.state['id'] == 5 # Beacon on\n",
|
||||
"\n",
|
||||
" def beacon_on_behaviour(self):\n",
|
||||
"\n",
|
||||
@ -557,7 +554,6 @@
|
||||
" if random.random() < self.prob_generate_anti_rumor:\n",
|
||||
" neighbor.state['id'] = 2 # Cured\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Vaccinate\n",
|
||||
" neutral_neighbors = self.get_neighboring_agents(state_id=0)\n",
|
||||
" for neighbor in neutral_neighbors:\n",
|
||||
@ -569,7 +565,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Settings.py"
|
||||
"## Settings.json"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -579,6 +575,78 @@
|
||||
"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": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"[\n",
|
||||
" {\n",
|
||||
" \"network_type\": 1,\n",
|
||||
" \"number_of_nodes\": 1000,\n",
|
||||
" \"max_time\": 50,\n",
|
||||
" \"num_trials\": 1,\n",
|
||||
" \"timeout\": 2\n",
|
||||
" },\n",
|
||||
"\n",
|
||||
" {\n",
|
||||
" \"agent\": [\"BaseBehaviour\",\"SISaModel\",\"ControlModelM2\"],\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" \"bite_prob\": 0.01,\n",
|
||||
" \"heal_prob\": 0.01,\n",
|
||||
"\n",
|
||||
" \"innovation_prob\": 0.001,\n",
|
||||
" \"imitation_prob\": 0.005,\n",
|
||||
"\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",
|
||||
" \"enterprises\": [\"BBVA\", \"Santander\", \"Bankia\"],\n",
|
||||
"\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],\n",
|
||||
"\n",
|
||||
" \"tweet_probability_enterprises\": [0.3, 0.3, 0.3],\n",
|
||||
"\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",
|
||||
"\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",
|
||||
" }\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -837,7 +905,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.2"
|
||||
"version": "3.6.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
BIN
spread_model.png
BIN
spread_model.png
Binary file not shown.
|
Before Width: | Height: | Size: 37 KiB |
Loading…
Reference in New Issue
Block a user