def run_simu(parameters, synergy): import numpy as np import networkx as nx from complex_systems.dygraph import DyGraph from complex_systems.pgg import PublicGoodGames from complex_systems.quasi_unit_disk_graph import gen_quasi_unit_disk_weight from complex_systems.quasi_unit_disk_graph import remove_edges_from_graph number_of_node = parameters['number_of_node'] size_of_simulation_area = parameters['size_of_simulation_area'] outer_radius = parameters['outer_radius'] inner_radius = parameters['inner_radius'] alpha_quasi_unit_disk = parameters['alpha_quasi_unit_disk'] coop_ratio = parameters['initial_cooperator_ratio'] simulation_length = parameters['simulation_length'] sampling_interval = parameters['sampling_interval'] alpha_levy = parameters['alpha_levy'] noise_var = parameters['noise_variance'] beta = parameters['beta'] f_min = parameters['f_min'] f_max = parameters['f_max'] s_min = parameters['s_min'] s_max = parameters['s_max'] velocity = parameters['velocity'] G = DyGraph(time_stop=simulation_length, time_step=sampling_interval) G.generate_mobility_levy_walk( alpha=alpha_levy, beta=beta, size_max=size_of_simulation_area, f_min=f_min, f_max=f_max, s_min=s_min, s_max=s_max, b_c=2, radius=outer_radius, nb_node=number_of_node, velocity=velocity, ) first_run = True resultats = [] for g in G: g = gen_quasi_unit_disk_weight(G=g, outer_radius=outer_radius, inner_radius=inner_radius, alpha=alpha_quasi_unit_disk) g = remove_edges_from_graph(g) if first_run == True: PGG = PublicGoodGames(G=g, synergy=synergy, cooperator_ratio=coop_ratio, noise_var=noise_var) nb_coop = PGG.run_game(sampling_interval) resultats.append(nb_coop) strategies = PGG.get_strategies() first_run = False else: PGG = PublicGoodGames(G=g, synergy=synergy, cooperator_ratio=coop_ratio, noise_var=noise_var) PGG.set_strategies(strategies) nb_coop = PGG.run_game(sampling_interval) strategies = PGG.get_strategies() resultats.append(nb_coop) return (synergy, nb_coop, np.mean(G.avg_degree()))
def main(parameters): import networkx as nx from complex_systems.dygraph import DyGraph from complex_systems.pgg_diffusion import PGG_diffusion from complex_systems.quasi_unit_disk_graph import gen_quasi_unit_disk_weight from complex_systems.quasi_unit_disk_graph import remove_edges_from_graph import pylab as plt import numpy as np nb_node = 400 xmax = 1000 ymax = 1000 coop_ratio = 0.5 synergy = 9 nb_game_per_round = 1 nb_def = [] nb_coop = [] number_of_node = parameters['number_of_node'] nb_seeder = parameters['number_of_seeder'] size_of_simulation_area = parameters['size_of_simulation_area'] buffer_size = parameters['buffer_size'] outer_radius = parameters['outer_radius'] inner_radius = parameters['inner_radius'] alpha_quasi_unit_disk = parameters['alpha_quasi_unit_disk'] coop_ratio = parameters['initial_cooperator_ratio'] simulation_length = parameters['simulation_length'] sampling_interval = parameters['sampling_interval'] alpha_levy = parameters['alpha_levy'] #noise_var = parameters['noise_variance'] noise_var = 0.1 beta = parameters['beta'] f_min = parameters['f_min'] f_max = parameters['f_max'] s_min = parameters['s_min'] s_max = parameters['s_max'] velocity = parameters['velocity'] G = DyGraph(time_stop=simulation_length, time_step=sampling_interval) G.generate_mobility_levy_walk( alpha=alpha_levy, beta=beta, size_max=xmax, f_min=f_min, f_max=f_max, s_min=s_min, s_max=s_max, b_c=2, radius=outer_radius, nb_node=nb_node, velocity=velocity, ) first_run = True for g in G: g = gen_quasi_unit_disk_weight(G=g, outer_radius=outer_radius, inner_radius=inner_radius, alpha=alpha_quasi_unit_disk) g = remove_edges_from_graph(g) if first_run == True: PGG = PGG_diffusion(G=g, synergy=synergy, cooperator_ratio=coop_ratio, noise_var=noise_var, buffer_size=buffer_size, nb_seeder=int(nb_seeder)) PGG.run_game() nb_def.append(PGG.defector_counter()) nb_coop.append(PGG.cooperator_counter()) strategies = PGG.get_strategies() time_stamps = PGG.get_time_stamps() states = PGG.get_states() #sent_update = PGG.get_sent_update() seeder = PGG.get_seeder() #print time_stamps[0] #print sent_update #print strategies #print states first_run = False else: PGG = PGG_diffusion(G=g, synergy=synergy, cooperator_ratio=coop_ratio, buffer_size=buffer_size, noise_var=noise_var) PGG.set_strategies(strategies) PGG.set_time_stamps(time_stamps) PGG.set_nodes_states(states) #PGG.set_sent_update(sent_update) PGG.set_seeder(seeder) res = PGG.run_game() nb_def.append(PGG.defector_counter()) nb_coop.append(PGG.cooperator_counter()) strategies = PGG.get_strategies() time_stamps = PGG.get_time_stamps() states = PGG.get_states() #print states #print strategies #print time_stamps[0] #sent_update = PGG.get_sent_update() #seeder = PGG.get_seeder() #print seeder print synergy, res, np.mean(G.avg_degree()) plt.figure() plt.plot(nb_def,'b-*') plt.plot(nb_coop,'r-*') #plt.figure() #print np.mean([val for key, val in G.degree().iteritems()]) #nx.draw_networkx(G, node_size=20, pos=p, with_labels = False) time_stamps_dist = PGG.get_distribution_time_stamps() x = [] y = [] for key,val in time_stamps_dist.iteritems(): x.append(key) y.append(val) plt.figure() plt.bar(x,y) plt.show()
def main(parameters): import networkx as nx from complex_systems.dygraph import DyGraph from complex_systems.pgg_diffusion import PGG_diffusion from complex_systems.quasi_unit_disk_graph import gen_quasi_unit_disk_weight from complex_systems.quasi_unit_disk_graph import remove_edges_from_graph import pylab as plt import numpy as np nb_node = 400 xmax = 1000 ymax = 1000 coop_ratio = 0.5 synergy = 9 nb_game_per_round = 1 nb_def = [] nb_coop = [] number_of_node = parameters['number_of_node'] nb_seeder = parameters['number_of_seeder'] size_of_simulation_area = parameters['size_of_simulation_area'] buffer_size = parameters['buffer_size'] outer_radius = parameters['outer_radius'] inner_radius = parameters['inner_radius'] alpha_quasi_unit_disk = parameters['alpha_quasi_unit_disk'] coop_ratio = parameters['initial_cooperator_ratio'] simulation_length = parameters['simulation_length'] sampling_interval = parameters['sampling_interval'] alpha_levy = parameters['alpha_levy'] #noise_var = parameters['noise_variance'] noise_var = 0.1 beta = parameters['beta'] f_min = parameters['f_min'] f_max = parameters['f_max'] s_min = parameters['s_min'] s_max = parameters['s_max'] velocity = parameters['velocity'] G = DyGraph(time_stop=simulation_length, time_step=sampling_interval) G.generate_mobility_levy_walk( alpha=alpha_levy, beta=beta, size_max=xmax, f_min=f_min, f_max=f_max, s_min=s_min, s_max=s_max, b_c=2, radius=outer_radius, nb_node=nb_node, velocity=velocity, ) first_run = True for g in G: g = gen_quasi_unit_disk_weight(G=g, outer_radius=outer_radius, inner_radius=inner_radius, alpha=alpha_quasi_unit_disk) g = remove_edges_from_graph(g) if first_run == True: PGG = PGG_diffusion(G=g, synergy=synergy, cooperator_ratio=coop_ratio, noise_var=noise_var, buffer_size=buffer_size, nb_seeder=int(nb_seeder)) PGG.run_game() nb_def.append(PGG.defector_counter()) nb_coop.append(PGG.cooperator_counter()) strategies = PGG.get_strategies() time_stamps = PGG.get_time_stamps() states = PGG.get_states() #sent_update = PGG.get_sent_update() seeder = PGG.get_seeder() #print time_stamps[0] #print sent_update #print strategies #print states first_run = False else: PGG = PGG_diffusion(G=g, synergy=synergy, cooperator_ratio=coop_ratio, buffer_size=buffer_size, noise_var=noise_var) PGG.set_strategies(strategies) PGG.set_time_stamps(time_stamps) PGG.set_nodes_states(states) #PGG.set_sent_update(sent_update) PGG.set_seeder(seeder) res = PGG.run_game() nb_def.append(PGG.defector_counter()) nb_coop.append(PGG.cooperator_counter()) strategies = PGG.get_strategies() time_stamps = PGG.get_time_stamps() states = PGG.get_states() #print states #print strategies #print time_stamps[0] #sent_update = PGG.get_sent_update() #seeder = PGG.get_seeder() #print seeder print synergy, res, np.mean(G.avg_degree()) plt.figure() plt.plot(nb_def, 'b-*') plt.plot(nb_coop, 'r-*') #plt.figure() #print np.mean([val for key, val in G.degree().iteritems()]) #nx.draw_networkx(G, node_size=20, pos=p, with_labels = False) time_stamps_dist = PGG.get_distribution_time_stamps() x = [] y = [] for key, val in time_stamps_dist.iteritems(): x.append(key) y.append(val) plt.figure() plt.bar(x, y) plt.show()