def update(self):
"""Update the Population: update the topology stochastically
During each update, a number of nodes in the topology are selected at
random, and the update method of the Cells in the selected nodes is
called. By default, the number of nodes selected is equal to the
number of nodes in the topology (so each node's Cell will be updated,
on average, each epoch. This number can be changed by setting the
events_per_epoch parameter in the Experiment section of the
configuration.
"""
# Reset the transitions count. Long-term transitions data can be
# obtained by using the PrintCellTypeTransitions action
num_types = self._cell_class.max_types
self.experiment.data['population']['transitions'] = [
[0] * num_types for i in range(num_types)
]
# Select a set of cells to update and update them
events = self.experiment.config.getint(section=self.config_section,
name='events_per_epoch',
default=len(
self.topology.graph))
nodes_to_update = sample_with_replacement(self.topology.graph.nodes(),
k=events)
[self.topology.graph.node[n]['cell'].update() for n in nodes_to_update]
def update(self):
"""Update the Resource
During each update, a number of nodes in the topology are selected at
random, and the update method of the ResourceCell in the selected nodes
is called. By default, the number of nodes selected is equal to the
number of nodes in the topology (so each node's ResourceCell will be
updated, on average, each epoch. This number can be changed by setting
the events_per_epoch parameter in the Experiment section of the
configuration.
"""
events = self.experiment.config.getint(
section=self.config_section, name="events_per_epoch", default=len(self.topology.graph)
)
nodes_to_update = sample_with_replacement(self.topology.graph.nodes(), k=events)
[self.topology.graph.node[n]["resource"].update() for n in nodes_to_update]
def update(self):
"""Update the Population: update the topology stochastically
During each update, a number of nodes in the topology are selected at
random, and the update method of the Cells in the selected nodes is
called. By default, the number of nodes selected is equal to the
number of nodes in the topology (so each node's Cell will be updated,
on average, each epoch. This number can be changed by setting the
events_per_epoch parameter in the Experiment section of the
configuration.
"""
# Reset the transitions count. Long-term transitions data can be
# obtained by using the PrintCellTypeTransitions action
num_types = self._cell_class.max_types
self.experiment.data['population']['transitions'] = [[0]*num_types for i in range(num_types)]
# Select a set of cells to update and update them
events = self.experiment.config.getint(section=self.config_section,
name='events_per_epoch',
default=len(self.topology.graph))
nodes_to_update = sample_with_replacement(self.topology.graph.nodes(), k=events)
[self.topology.graph.node[n]['cell'].update() for n in nodes_to_update]
