def update(self):
"""Execute the action"""
if self.skip_update():
return
levels = self.experiment.data['resources'][self.resource]['levels']
row = [self.experiment.epoch, mean(levels), std(levels), int(self.res.available)]
self.writer.writerow(row)
def update(self):
"""Execute the Action"""
if self.skip_update():
return
g = self.experiment.population.topology.graph
cluster_counts = [0] * len(self.types)
cluster_sizes = {}
for i in range(len(self.types) + 1):
cluster_sizes[i] = []
unvisited = g.nodes()
while len(unvisited) > 0:
node = random.choice(unvisited)
type = g.node[node]["cell"].type
c = cluster(g, node)
c_size = len(c)
cluster_counts[type] += 1
cluster_sizes[type].append(c_size)
cluster_sizes[len(self.types)].append(c_size)
[unvisited.remove(x) for x in c]
row = { 'epoch' : self.experiment.epoch,
'total_clusters' : sum(cluster_counts),
'total_size_mean' : mean(cluster_sizes[len(self.types)]),
'total_size_std' : std(cluster_sizes[len(self.types)]) }
for index, t in enumerate(self.types):
if cluster_counts[index] == 0:
row['%s_clusters' % (t)] = 0
row['%s_size_mean' % (t)] = 0
row['%s_size_std' % (t)] = 0
else:
row['%s_clusters' % (t)] = cluster_counts[index]
row['%s_size_mean' % (t)] = mean(cluster_sizes[index])
row['%s_size_std' % (t)] = std(cluster_sizes[index])
self.writer.writerow(row)
def update(self):
"""Execute the action"""
if self.skip_update():
return
levels = self.experiment.data['resources'][self.resource]['levels']
row = { 'epoch' : self.experiment.epoch,
'mean' : mean(levels),
'standard_deviation' : std(levels),
'available' : int(self.res.available)}
self.writer.writerow(row)
def update(self):
"""Execute the action"""
if self.skip_update():
return
levels = self.experiment.data['resources'][self.resource]['levels']
row = [
self.experiment.epoch,
mean(levels),
std(levels),
int(self.res.available)
]
self.writer.writerow(row)
def update(self):
"""Execute the Action"""
if self.skip_update():
return
g = self.experiment.population.topology.graph
cluster_counts = [0] * len(self.types)
cluster_sizes = {}
for i in range(len(self.types) + 1):
cluster_sizes[i] = []
unvisited = g.nodes()
while len(unvisited) > 0:
node = random.choice(unvisited)
type = g.node[node]["cell"].type
c = cluster(g, node)
c_size = len(c)
cluster_counts[type] += 1
cluster_sizes[type].append(c_size)
cluster_sizes[len(self.types)].append(c_size)
[unvisited.remove(x) for x in c]
row = [self.experiment.epoch]
row.append(sum(cluster_counts))
row.append(mean(cluster_sizes[len(self.types)]))
row.append(std(cluster_sizes[len(self.types)]))
for t in range(len(self.types)):
row.append(cluster_counts[t])
row.append(mean(cluster_sizes[t]))
row.append(std(cluster_sizes[t]))
self.writer.writerow(row)
def update(self):
"""Execute the Action"""
if self.skip_update():
return
g = self.experiment.population.topology.graph
degrees = list(nx.degree(g).values())
row = { 'epoch' : self.experiment.epoch,
'nodes' : nx.number_of_nodes(g),
'edges' : nx.number_of_edges(g),
'avg_degree' : mean(degrees),
'std_degree' : std(degrees),
'avg_clustering_coefficient' : nx.average_clustering(g),
'diameter' : nx.diameter(g),
'num_connected_components' : nx.number_connected_components(g)}
self.writer.writerow(row)
def update(self):
"""Execute the Action"""
if self.skip_update():
return
g = self.experiment.population.topology.graph
degrees = list(nx.degree(g).values())
row = [
self.experiment.epoch,
nx.number_of_nodes(g),
nx.number_of_edges(g),
mean(degrees),
std(degrees),
nx.average_clustering(g),
nx.diameter(g),
nx.number_connected_components(g)
]
self.writer.writerow(row)
def update(self):
"""Execute the Action"""
if self.skip_update():
return
g = self.experiment.population.topology.graph
degrees = list(nx.degree(g).values())
row = [self.experiment.epoch, nx.number_of_nodes(g), nx.number_of_edges(g), mean(degrees), std(degrees), nx.average_clustering(g), nx.diameter(g), nx.number_connected_components(g)]
self.writer.writerow(row)
