MulticanonicalState#

class graph_tool.inference.MulticanonicalState(state, S_min, S_max, nbins=1000)[source]#

The density of states of a multicanonical Monte Carlo algorithm. It is used by graph_tool.inference.multicanonical_equilibrate().

Parameters:

stateBlockState or OverlapBlockState or NestedBlockState: Block state to be used.
S_minfloat: Minimum energy.
S_maxfloat: Maximum energy.
nbinsint (optional, default: 1000): Number of bins.

Methods

`get_allowed_energies`()	Get allowed energy bounds.
`get_bin`(S)
`get_density`([B])	Get density of states, normalized so that total sum is \(B^N\), where \(B\) is the number of groups, and \(N\) is the number of nodes.
`get_energies`()	Get energy bounds.
`get_entropy`(S[, B])
`get_flatness`([h, allow_gaps])	Get energy histogram flatness.
`get_hist`()	Get energy histogram.
`get_perm_hist`()	Get permanent energy histogram.
`get_posterior`([N])	Get posterior probability.
`get_range`()	Get energy range.
`reset_hist`()	Reset energy histogram.
`sweep`(**kwargs)

get_density(B=None)[source]#: Get density of states, normalized so that total sum is \(B^N\), where \(B\) is the number of groups, and \(N\) is the number of nodes. If not supplied \(B=N\) is assumed.

get_flatness(h=None, allow_gaps=True)[source]#: Get energy histogram flatness.

MulticanonicalState