dimod.AdjVectorBQM¶

class AdjVectorBQM(vartype=None, *args)¶

A binary quadratic model where the neighborhoods are c++ vectors.

Can be created in several ways:

AdjVectorBQM(vartype)

Creates an empty binary quadratic model.

AdjVectorBQM(bqm)

Creates a BQM from another BQM. See copy and cls kwargs below.

AdjVectorBQM(bqm, vartype)

Creates a BQM from another BQM, changing to the appropriate vartype if necessary.

AdjVectorBQM(n, vartype)

Creates a BQM with n variables, indexed linearly from zero, setting all biases to zero.

AdjVectorBQM(quadratic, vartype)

Creates a BQM from quadratic biases given as a square array_like or a dictionary of the form {(u, v): b, …}. Note that when formed with SPIN-variables, biases on the diagonal are added to the offset.

AdjVectorBQM(linear, quadratic, vartype)

Creates a BQM from linear and quadratic biases, where linear is a one-dimensional array_like or a dictionary of the form {v: b, …}, and quadratic is a square array_like or a dictionary of the form {(u, v): b, …}. Note that when formed with SPIN-variables, biases on the diagonal are added to the offset.

AdjVectorBQM(linear, quadratic, offset, vartype)

Creates a BQM from linear and quadratic biases, where linear is a one-dimensional array_like or a dictionary of the form {v: b, …}, and quadratic is a square array_like or a dictionary of the form {(u, v): b, …}, and offset is a numerical offset. Note that when formed with SPIN-variables, biases on the diagonal are added to the offset.

Notes

The AdjVectorBQM is implemented using an adjacency structure where the neighborhoods are implemented as c++ vectors.

Advantages:

Supports incremental construction
Fast iteration over the biases

Disadvantages:

Only supports float64 biases

Intended Use:

When performance is important and the use case requires incremental construction
This should be the default BQM type for large problems where arbitrary types are not needed

Examples

>>> import numpy as np
>>> from dimod import AdjVectorBQM

>>> # Construct from dicts
>>> AdjVectorBQM({'a': -1.0}, {('a', 'b'): 1.0}, 'SPIN')
AdjVectorBQM({a: -1.0, b: 0.0}, {('a', 'b'): 1.0}, 0.0, 'SPIN')

>>> # Incremental Construction
>>> bqm = AdjVectorBQM('SPIN')
>>> bqm.add_variable('a')
'a'
>>> bqm.add_variable()
1
>>> bqm.set_quadratic('a', 1, 3.0)
>>> bqm
AdjVectorBQM({a: 0.0, 1: 0.0}, {('a', 1): 3.0}, 0.0, 'SPIN')

Attributes¶

`dtype`	The data type of the linear biases, float64.
`itype`	The data type of the indices, uint32.
`ntype`	The data type of the neighborhood indices, varies by platform.
`num_interactions`	The number of interactions in the model.
`num_variables`	The number of variables in the model.
`offset`	The constant energy offset associated with the model.
`shape`	A 2-tuple, the `num_variables` and `num_interactions`.
`variables`	The variables of the binary quadratic model.
`vartype`	The variable type, `Vartype.SPIN` or `Vartype.BINARY`.

Views¶

`adj`	Quadratic biases as a nested dict of dicts.
`linear`	Linear biases as a mapping.
`quadratic`	Quadratic biases as a flat mapping.
`binary`	The binary-valued version of the binary quadratic model.
`spin`	The spin-valued version of the binary quadratic model.

Methods¶

`add_offset`(offset)	Add specified value to the offset of a binary quadratic model.
`add_interaction`(u, v, bias)	Add an interaction and/or quadratic bias to a binary quadratic model.
`add_interactions_from`(quadratic)	Add interactions and/or quadratic biases to a binary quadratic model.
`add_variable`(self[, v])	Add a variable to the binary quadratic model.
`add_variables_from`(linear)	Add variables and/or linear biases to a binary quadratic model.
`change_vartype`(self, vartype[, inplace])	Return a binary quadratic model with the specified vartype.
`contract_variables`(u, v)	Enforce u, v being the same variable in a binary quadratic model.
`copy`([deep])	Return a copy.
`degree`(self, v)
`degrees`([array, dtype])
`empty`(vartype)	Create a new empty binary quadratic model.
`energies`(self, samples[, dtype])	Determine the energies of the given samples.
`energy`(sample[, dtype])
`fix_variables`(fixed)	Fix the value of the variables and remove them.
`flip_variable`(v)	Flip variable v in a binary quadratic model.
`from_coo`(obj[, vartype])	Deserialize a binary quadratic model from a COOrdinate format encoding.
`from_file`(type cls, file_like)	Construct a BQM from a file-like object.
`from_ising`(h, J[, offset])	Create a binary quadratic model from an Ising problem.
`from_networkx_graph`(G[, vartype, …])	Create a binary quadratic model from a NetworkX graph.
`from_numpy_matrix`(mat[, variable_order, …])	Create a binary quadratic model from a NumPy array.
`from_numpy_vectors`(linear, quadratic, …[, …])	Create a binary quadratic model from vectors.
`from_qubo`(Q[, offset])	Create a binary quadratic model from a QUBO problem.
`get_linear`(self, v)	Get the linear bias of v.
`get_quadratic`(self, u, v[, default])	Get the quadratic bias of (u, v).
`has_variable`(v)	Return True if v is a variable in the binary quadratic model.
`iter_interactions`()	Iterate over the interactions of the binary quadratic model.
`iter_linear`(self)
`iter_neighbors`(u)	Iterate over neighbors of a variable in the binary quadratic model.
`iter_quadratic`(self[, variables])
`iter_variables`()	Iterate over the variables of the binary quadratic model.
`normalize`([bias_range, quadratic_range, …])	Normalizes the biases of the binary quadratic model such that they fall in the provided range(s), and adjusts the offset appropriately.
`relabel_variables`
`relabel_variables_as_integers`	Relabel the variables in the BQM to integers.
`scale`(scalar[, ignored_variables, …])	Multiply all the biases by the specified scalar.
`set_linear`(self, v, Bias b)	Set the linear biase of a variable v.
`set_quadratic`(self, u, v, Bias b)	Set the quadratic bias of (u, v).
`shapeable`()
`to_coo`([fp, vartype_header])	Serialize the binary quadratic model to a COOrdinate format encoding.
`to_file`(self)	View the BQM as a file-like object.
`to_ising`()	Converts a binary quadratic model to Ising format.
`to_networkx_graph`([node_attribute_name, …])	Convert a binary quadratic model to NetworkX graph format.
`to_numpy_matrix`([variable_order])	Convert a binary quadratic model to NumPy 2D array.
`to_numpy_vectors`(self[, variable_order, …])	Convert to numpy vectors.
`to_qubo`()	Convert a binary quadratic model to QUBO format.
`remove_interaction`(self, u, v)	Remove the interaction between variables u and v.
`remove_interactions_from`(interactions)	Remove the given interactions from the binary quadratic model.
`remove_offset`()	Set the binary quadratic model’s offset to zero.
`remove_variable`(self[, v])	Remove a variable and its associated interactions.
`remove_variables_from`(variables)	Remove the given variables from the binary quadratic model.
`update`(other)	Update the binary quadratic model, adding biases from another.