Simple simulations with minibn

Simple simulations with minibn#

The module minibn implements basic methods for computing simulation runs with usual synchronous, fully-asynchronous, and (general) asynchronous update modes. For the two latter, the seed argument ensures the reproducibility of traces. The implementation also enables defining custom update modes.

from colomoto import minibn
import pandas as pd # for displaying traces

bn = minibn.BooleanNetwork({
    "a": "c",
    "b": "!a",
    "c": "!b"
})

Run synchronous simulation from zero state, for at most 10 iterations:

pd.DataFrame(minibn.SyncRun(bn, bn.zero(), 10))

	a	b	c
0	0	0	0
1	0	1	1
2	1	1	0
3	0	0	0
4	0	1	1
5	1	1	0
6	0	0	0
7	0	1	1
8	1	1	0
9	0	0	0
10	0	1	1

Run fully-aynchronous simulation from zero state, for at most 10 iterations:

pd.DataFrame(minibn.FAsyncRun(bn, bn.zero(), 10, seed=23892830))

	b	c
0	0	0
1	0	1
2	1	1
3	1	0

Run (general) aynchronous simulation from zero state, for at most 10 iterations:

pd.DataFrame(minibn.GAsyncRun(bn, bn.zero(), 10, seed=12323892))

	b	c
0	0	0
1	1	1
2	1	0

We define a custom update mode by extending from the general asynchronous mode. The method select_for_update is called to select which nodes to update from the given list of nodes which can change of value in the current state.

In this example, we implement a simple priority mecanisms which will select the nodes with the highest priority, and then use the general asynchronous mode, i.e., picking randomly a subset of them:

class PrioGAsyncRun(minibn.GAsyncRun):
    def __init__(self, *args, priorities=[], **kwargs):
        super().__init__(*args, **kwargs)
        self.priorities = priorities
    def select_for_update(self, nodes):
        # select nodes by priority order
        for m in self.priorities:
            mn = set(m).intersection(nodes)
            if mn:
                nodes = list(mn)
                break
        # use parent class to select the nodes to update
        return super().select_for_update(nodes)

priorities = [{"c"},{"b"},{"a"}]
pd.DataFrame(PrioGAsyncRun(bn, bn.zero(), 10, priorities=priorities, seed=12323892))

	b	c
0	0	0
1	0	1
2	1	1
3	1	0