ExhaustiveSweepState#

class graph_tool.inference.ExhaustiveSweepState(entropy_args={})[source]#

Bases: EntropyState

Base state that implements exhaustive enumerative sweeps.

Methods

`exhaustive_sweep`([entropy_args, callback, ...])	Perform an exhaustive loop over all possible network partitions.
`get_entropy_args`()	Return the current default values for the parameters of the function `entropy()`, together with other operations that depend on them.
`reset_entropy_args`()	Reset the current default values for the parameters of the function `entropy()`, together with other operations that depend on them.
`update_entropy_args`(**kwargs)	Update the default values for the parameters of the function `entropy()` from the keyword arguments, in a stateful way, together with other operations that depend on them.

exhaustive_sweep(entropy_args={}, callback=None, density=None, vertices=None, initial_partition=None, max_iter=None)[source]#

Perform an exhaustive loop over all possible network partitions.

Parameters:

entropy_argsdict (optional, default: {}): Entropy arguments, with the same meaning and defaults as in graph_tool.inference.BlockState.entropy().
callbackcallable object (optional, default: None): Function to be called for each partition, with three arguments (S, S_min, b_min) corresponding to the the current entropy value, the minimum entropy value so far, and the corresponding partition, respectively. If not provided, and hist is None an iterator over the same values will be returned instead.
densitytuple (optional, default: None): If provided, it should contain a tuple with values (S_min, S_max, n_bins), which will be used to obtain the density of states via a histogram of size n_bins. This parameter is ignored unless callback is None.
verticesiterable of ints (optional, default: None): If provided, this should be a list of vertices which will be moved. Otherwise, all vertices will.
initial_partitioniterable of ints (optional, default: None): If provided, this will provide the initial partition for the iteration.
max_iterint (optional, default: None): If provided, this will limit the total number of iterations.

Returns:

statesiterator over (S, S_min, b_min): If callback is None and hist is None, the function will return an iterator over (S, S_min, b_min) corresponding to the the current entropy value, the minimum entropy value so far, and the corresponding partition, respectively.
Ss, countspair of numpy.ndarray: If callback is None and hist is not None, the function will return the values of each bin (Ss) and the state count of each bin (counts).
b_minVertexPropertyMap: If callback is not None or hist is not None, the function will also return partition with smallest entropy.

Notes

This algorithm has an \(O(B^N)\) complexity, where \(B\) is the number of groups, and \(N\) is the number of vertices.

get_entropy_args()#: Return the current default values for the parameters of the function entropy(), together with other operations that depend on them.

reset_entropy_args()#: Reset the current default values for the parameters of the function entropy(), together with other operations that depend on them.

update_entropy_args(**kwargs)#

Update the default values for the parameters of the function entropy() from the keyword arguments, in a stateful way, together with other operations that depend on them.

Values updated in this manner are preserved by the copying or pickling of the state.

ExhaustiveSweepState

Contents

ExhaustiveSweepState#