Setting a default sampler

Allows the user to specify a binary quadratic model sampler in a single place.

API Definition

A sampler is a process that samples from low energy states in models defined by an Ising equation or a Quadratic Unconstrained Binary Optimization Problem (QUBO). A sampler is expected to have a ‘sample_qubo’ and ‘sample_ising’ method. A sampler is expected to return an iterable of samples, in order of increasing energy.

Example sampler

First we can create a stand in for a binary quadratic model sampler that can be used in the following examples.

>>> class ExampleSampler:
...     # an example sampler, only works for independent set on complete
...     # graphs
...     def __init__(self, name):
...         self.name = name
...     def sample_ising(self, h, J):
...         sample = {v: -1 for v in h}
...         sample[0] = 1  # set one node to true
...         return [sample]
...     def sample_qubo(self, Q):
...         sample = {v: 0 for v in set().union(*Q)}
...         sample[0] = 1  # set one node to true
...         return [sample]
...     def __str__(self):
...         return self.name

No default sampler set

If the user wishes to specify which sampler each binary quadratic model sampler algorithm uses, the user can provide the sampler directly to the functions.

>>> sampler = ExampleSampler('sampler')
>>> G = nx.complete_graph(5)
>>> dnx.maximum_independent_set(G, sampler)
[0]

Setting a default sampler

Alternatively, the user can specify a sampler that will be used by default.

>>> sampler0 = ExampleSampler('sampler0')
>>> dnx.set_default_sampler(sampler0)

This sampler will now be used by any function where no sampler is specified

>>> dnx.maximum_independent_set(G)
[0]

A different sampler can still be provided, in which case the provided sampler will be used instead of the default.

>>> sampler1 = ExampleSampler('sampler1')
>>> dnx.set_default_sampler(sampler0)
>>> dnx.maximum_independent_set(G, sampler1)
[0]

Unsetting a default sampler

The user can also unset the default.

>>> dnx.unset_default_sampler()
>>> print(dnx.get_default_sampler())
None

Functions

set_default_sampler(sampler)	Sets a default binary quadratic model sampler.
unset_default_sampler()	Resets the default sampler back to None.
get_default_sampler()	Gets the current default sampler.