def __init__(self, model, color_nodes, adj_list):
# The maximum number of rounds to run the simulation. Randomly chosen
# number between 100 and 200.
self.max_rounds = randint(100, 200)
# A mapping of the colors and the nodes of that color.
self.color_nodes = color_nodes
# The adjacency list of the graph.
self.adj_list = adj_list
# Import the correct module for the model.
if model == "majority_all":
from models.majority_all import MajorityAll
self.model = MajorityAll(self.adj_list)
elif model == "majority_colored":
from models.majority_colored import MajorityColored
self.model = MajorityColored(self.adj_list)
elif model == "most_common_colored":
from models.most_common_colored import MostCommonColored
self.model = MostCommonColored(self.adj_list)
elif model == "random_p":
from models.random_p import RandomP
self.model = RandomP(self.adj_list)
elif model == "weighted_random":
from models.weighted_random import WeightedRandom
self.model = WeightedRandom(self.adj_list)
else:
print "Not a valid model. Random model selected."
from models.weighted_random import WeightedRandom
self.model = WeightedRandom(self.adj_list)
def __init__(self, model, color_nodes, adj_list):
# The maximum number of rounds to run the simulation. Randomly chosen
# number between 100 and 200.
self.max_rounds = randint(100, 200)
# A mapping of the colors and the nodes of that color.
self.color_nodes = color_nodes
# The adjacency list of the graph.
self.adj_list = adj_list
# Import the correct module for the model.
if model == "majority_all":
from models.majority_all import MajorityAll
self.model = MajorityAll(self.adj_list)
elif model == "majority_colored":
from models.majority_colored import MajorityColored
self.model = MajorityColored(self.adj_list)
elif model == "most_common_colored":
from models.most_common_colored import MostCommonColored
self.model = MostCommonColored(self.adj_list)
elif model == "random_p":
from models.random_p import RandomP
self.model = RandomP(self.adj_list)
elif model == "weighted_random":
from models.weighted_random import WeightedRandom
self.model = WeightedRandom(self.adj_list)
else:
print "Not a valid model. Random model selected."
from models.weighted_random import WeightedRandom
self.model = WeightedRandom(self.adj_list)
class Simulation:
"""
Class: Simulation
-----------------
Simulates the epidemic using different epidemic models.
"""
def __init__(self, model, color_nodes, adj_list):
# The maximum number of rounds to run the simulation. Randomly chosen
# number between 100 and 200.
self.max_rounds = randint(100, 200)
# A mapping of the colors and the nodes of that color.
self.color_nodes = color_nodes
# The adjacency list of the graph.
self.adj_list = adj_list
# Import the correct module for the model.
if model == "majority_all":
from models.majority_all import MajorityAll
self.model = MajorityAll(self.adj_list)
elif model == "majority_colored":
from models.majority_colored import MajorityColored
self.model = MajorityColored(self.adj_list)
elif model == "most_common_colored":
from models.most_common_colored import MostCommonColored
self.model = MostCommonColored(self.adj_list)
elif model == "random_p":
from models.random_p import RandomP
self.model = RandomP(self.adj_list)
elif model == "weighted_random":
from models.weighted_random import WeightedRandom
self.model = WeightedRandom(self.adj_list)
else:
print "Not a valid model. Random model selected."
from models.weighted_random import WeightedRandom
self.model = WeightedRandom(self.adj_list)
def run(self):
"""
Function: run
-------------
Runs the epidemic simulation for each generation of the epidemic until there
is no more change (i.e. it is stable) or we reach the maximum number of
generations.
returns: A tuple (output, results) where the output is a dictionary object
containing a mapping of each generation to the diff generated and
results is the final mapping of colors to their nodes.
"""
# Stores a mapping of nodes to their color.
node_color = dict([(node, None) for node in self.adj_list])
# Output to be written to file.
output = OrderedDict()
# Choose the initial colors for the nodes.
diff = init(self.color_nodes, node_color)
color_mappings = to_color_mapping(diff)
# Make sure all colors are in the initial diff.
for color in self.color_nodes.keys():
if color not in color_mappings:
color_mappings[color] = []
output["0"] = color_mappings
generation = 1
# Keep calculating the epidemic until it stops changing.
nodes = self.adj_list.keys()
while not is_stable(generation, self.max_rounds, output):
print ".",
diff = {}
# Get the next color for the nodes.
node_color_copy = deepcopy(node_color)
for node in nodes:
(changed, color) = self.model.update(node_color, node)
# Store the node's new color only if it changed.
if changed:
diff[node] = color
node_color_copy[node] = color
node_color = node_color_copy
# Convert the mapping of a node to its color into colors and
# corresponding nodes.
output[str(generation)] = to_color_mapping(diff)
generation += 1
print "\nTotal:", generation, "generations."
return (output, final_color_mapping(self.color_nodes.keys(),
node_color))
class Simulation:
"""
Class: Simulation
-----------------
Simulates the epidemic using different epidemic models.
"""
def __init__(self, model, color_nodes, adj_list):
# The maximum number of rounds to run the simulation. Randomly chosen
# number between 100 and 200.
self.max_rounds = randint(100, 200)
# A mapping of the colors and the nodes of that color.
self.color_nodes = color_nodes
# The adjacency list of the graph.
self.adj_list = adj_list
# Import the correct module for the model.
if model == "majority_all":
from models.majority_all import MajorityAll
self.model = MajorityAll(self.adj_list)
elif model == "majority_colored":
from models.majority_colored import MajorityColored
self.model = MajorityColored(self.adj_list)
elif model == "most_common_colored":
from models.most_common_colored import MostCommonColored
self.model = MostCommonColored(self.adj_list)
elif model == "random_p":
from models.random_p import RandomP
self.model = RandomP(self.adj_list)
elif model == "weighted_random":
from models.weighted_random import WeightedRandom
self.model = WeightedRandom(self.adj_list)
else:
print "Not a valid model. Random model selected."
from models.weighted_random import WeightedRandom
self.model = WeightedRandom(self.adj_list)
def run(self):
"""
Function: run
-------------
Runs the epidemic simulation for each generation of the epidemic until there
is no more change (i.e. it is stable) or we reach the maximum number of
generations.
returns: A tuple (output, results) where the output is a dictionary object
containing a mapping of each generation to the diff generated and
results is the final mapping of colors to their nodes.
"""
# Stores a mapping of nodes to their color.
node_color = dict([(node, None) for node in self.adj_list])
# Output to be written to file.
output = OrderedDict()
# Choose the initial colors for the nodes.
diff = init(self.color_nodes, node_color)
color_mappings = to_color_mapping(diff)
# Make sure all colors are in the initial diff.
for color in self.color_nodes.keys():
if color not in color_mappings:
color_mappings[color] = []
output["0"] = color_mappings
generation = 1
# Keep calculating the epidemic until it stops changing.
nodes = self.adj_list.keys()
while not is_stable(generation, self.max_rounds, output):
print ".",
diff = {}
# Get the next color for the nodes.
node_color_copy = deepcopy(node_color)
for node in nodes:
(changed, color) = self.model.update(node_color, node)
# Store the node's new color only if it changed.
if changed:
diff[node] = color
node_color_copy[node] = color
node_color = node_color_copy
# Convert the mapping of a node to its color into colors and
# corresponding nodes.
output[str(generation)] = to_color_mapping(diff)
generation += 1
print "\nTotal:", generation, "generations."
return (output, final_color_mapping(self.color_nodes.keys(), node_color))
