BooleanState#

class graph_tool.dynamics.BooleanState(g, f=None, p=0.5, r=0, s=None)[source]#

Bases: DiscreteStateBase

Boolean network dynamics.

Parameters:

gGraph: Graph to be used for the dynamics
fVertexPropertyMap (optional, default: None): Vertex property map of type vector<bool> containing the Boolean functions. If not provided, the functions will be randomly chosen.
pfloat (optional, default: .5): Output probability of random functions. This only has an effect if f is None.
rfloat (optional, default: 0.): Input random flip probability.
sVertexPropertyMap (optional, default: None): Initial global state. If not provided, a random state will be chosen.

Notes

This implements a Boolean network model.

If a node \(i\) is updated at time \(t\), the transition to state \(s_i(t+1)\) is given by

\[s_i(t+1) = f^{(i)}_{\sum_{j\in \partial i}2^{\hat s_j(t)}}\]

where \(\partial i\) are the (in-)neighbors of \(i\), indexed from \(0\) to \(k-1\), and \(\hat s_i(t)\) are the flipped inputs sampled with probability

\[P(\hat s_i(t)|s_i(t)) = r^{1-\delta_{\hat s_i(t),s_i(t)}}(1-r)^{\delta_{\hat s_i(t),s_i(t)}}.\]

Examples

>>> g = gt.random_graph(50, lambda: (2,2))
>>> state = gt.BooleanState(g)
>>> ret = state.iterate_sync(niter=1000)
>>> s0 = state.s.copy()
>>> ret = state.iterate_sync(niter=1)
>>> l = 1
>>> while any(state.s.a != s0.a):
...     ret = state.iterate_sync(niter=1)
...     l += 1
>>> print("Period length:", l)
Period length: 9

Methods

`copy`()	Return a copy of the state.
`get_active`()	Returns list of "active" nodes, for states where this concept is used.
`get_state`()	Returns the internal `VertexPropertyMap` with the current state.
`iterate_async`([niter])	Updates nodes asynchronously (i.e. single vertex chosen randomly), niter number of times.
`iterate_sync`([niter])	Updates nodes synchronously (i.e. a full "sweep" of all nodes in parallel), niter number of times.
`reset_active`()	Resets list of "active" nodes, for states where this concept is used.
`set_active`(active)	Sets the list of "active" nodes, for states where this concept is used.

copy()#: Return a copy of the state.

get_active()#: Returns list of “active” nodes, for states where this concept is used.

get_state()#: Returns the internal VertexPropertyMap with the current state.

iterate_async(niter=1)#: Updates nodes asynchronously (i.e. single vertex chosen randomly), niter number of times. This function returns the number of nodes that changed state.

iterate_sync(niter=1)#: Updates nodes synchronously (i.e. a full “sweep” of all nodes in parallel), niter number of times. This function returns the number of nodes that changed state.

Parallel implementation.

If enabled during compilation, this algorithm will run in parallel using OpenMP. See the parallel algorithms section for information about how to control several aspects of parallelization.

reset_active()#: Resets list of “active” nodes, for states where this concept is used.

set_active(active)#: Sets the list of “active” nodes, for states where this concept is used.

BooleanState

Contents

BooleanState#