penaltymodel.PenaltyModelCache.retrieve¶
- PenaltyModelCache.retrieve(samples_like, graph_like, *, linear_bound: Tuple[float, float] = (- 2, 2), quadratic_bound: Tuple[float, float] = (- 1, 1), min_classical_gap: float = 2) Tuple[dimod.binary.binary_quadratic_model.BinaryQuadraticModel, float] [source]¶
Retrieve a penalty model from the database.
- Parameters
samples_like –
The set of feasible states that form the ground states of the generated binary quadratic model.
’samples_like’ is an extension of NumPy’s array_like. See
dimod.as_samples()
.graph_like –
Defines the structure of the desired binary quadratic model. Each node in the graph represents a variable and each edge defines an interaction between two variables. Can be given as a
networkx.Graph
, aint
, or as a sequence of variable labels.If given as a sequence of labels, the structure will be fully-connected, with the variables labelled according to the sequence.
If given as an int, the structure will be fully-connected with the variables labelled
range(n)
.The nodes of the graph must be a superset of the labels of
samples_like
.If not provided, defaults to a fully connected graph with nodes that are the variables of
samples_like
.linear_bound – The range allowed for the linear biases of the binary quadratic model.
quadratic_bound – The range allowed for the quadratic biases of the binary quadratic model.
min_classical_gap – This is a threshold value for the classical gap. It describes the minimum energy gap between the highest feasible state and the lowest infeasible state.
- Returns
A 2-tuple of the binary quadratic model and the classical gap. Note that the binary quadratic model always has vartype
'SPIN'
.