"""
A random sampler for unit testing and debugging.
"""
from random import choice
from dimod.core.sampler import Sampler
from dimod.response import Response, SampleView
__all__ = ['RandomSampler']
[docs]class RandomSampler(Sampler):
"""A sampler that gives random samples for testing.
Examples:
This example provides random samples for a two-variable QUBO model.
>>> import dimod
>>> response = dimod.RandomSampler().sample_qubo({(0, 0): -1, (1, 1): -1, (0, 1): 2}, num_reads=5)
>>> len(response)
5
>>> print(next(response.data())) # doctest: +SKIP
Sample(sample={0: 1, 1: 0}, energy=-1.0)
"""
properties = None
parameters = None
def __init__(self):
self.parameters = {'num_reads': []}
self.properties = {}
[docs] def sample(self, bqm, num_reads=10):
"""Give random samples for a binary quadratic model.
Args:
bqm (:obj:`~dimod.BinaryQuadraticModel`):
Binary quadratic model to be sampled from.
num_reads (int, optional):
Number of reads.
Returns:
:obj:`~dimod.Response`: A `dimod` :obj:`.~dimod.Response` object.
Notes:
For each variable in each sample, the value is chosen by a coin flip.
Examples:
This example provides samples for a two-variable Ising model.
>>> import dimod
>>> sampler = dimod.RandomSampler()
>>> h = {0: -1, 1: -1}
>>> J = {(0, 1): -1}
>>> bqm = dimod.BinaryQuadraticModel(h, J, -0.5, dimod.SPIN)
>>> response = sampler.sample(bqm, num_reads=3)
>>> len(response)
3
>>> response.data_vectors['energy'] # doctest: +SKIP
array([ 0.5, -3.5, 0.5])
"""
values = tuple(bqm.vartype.value)
def _itersample():
for __ in range(num_reads):
sample = {v: choice(values) for v in bqm.linear}
energy = bqm.energy(sample)
yield sample, energy
samples, energies = zip(*_itersample())
return Response.from_dicts(samples, {'energy': energies}, vartype=bqm.vartype)