def _sample(self,
type_,
linear,
quadratic,
params,
undirected_biases=False):
"""Internal method for `sample_ising`, `sample_qubo` and `sample_bqm`.
Args:
linear (list/dict):
Linear terms of the model.
quadratic (dict[(int, int), float]):
Quadratic terms of the model.
params (dict):
Parameters for the sampling method, solver-specific.
undirected_biases (boolean, default=False):
Are (quadratic) biases specified on undirected edges? For
triangular or symmetric matrix of quadratic biases set it to
``True``.
Returns:
:class:`Future`
"""
args = dict(type_=type_,
linear=linear,
quadratic=quadratic,
params=params)
dispatch_event('before_sample', obj=self, args=args)
# Check the problem
if not self.check_problem(linear, quadratic):
raise InvalidProblemError(
"Problem graph incompatible with solver.")
# Mix the new parameters with the default parameters
combined_params = dict(self._params)
combined_params.update(params)
# Check the parameters before submitting
for key in combined_params:
if key not in self.parameters and not key.startswith('x_'):
raise KeyError(
"{} is not a parameter of this solver.".format(key))
# transform some of the parameters in-place
self._format_params(type_, combined_params)
body_data = json.dumps({
'solver':
self.id,
'data':
encode_problem_as_qp(self,
linear,
quadratic,
undirected_biases=undirected_biases),
'type':
type_,
'params':
combined_params
})
logger.trace("Encoded sample request: %s", body_data)
body = Present(result=body_data)
computation = Future(solver=self,
id_=None,
return_matrix=self.return_matrix)
logger.debug("Submitting new problem to: %s", self.id)
self.client._submit(body, computation)
dispatch_event('after_sample',
obj=self,
args=args,
return_value=computation)
return computation
def _sample(self, type_, linear, quadratic, params):
"""Internal method for `sample_ising` and `sample_qubo`.
Args:
linear (list/dict):
Linear terms of the model.
quadratic (dict[(int, int), float]):
Quadratic terms of the model.
**params:
Parameters for the sampling method, solver-specific.
Returns:
:class:`Future`
"""
args = dict(type_=type_,
linear=linear,
quadratic=quadratic,
params=params)
dispatch_event('before_sample', obj=self, args=args)
# Check the problem
if not self.check_problem(linear, quadratic):
raise InvalidProblemError(
"Problem graph incompatible with solver.")
# Mix the new parameters with the default parameters
combined_params = dict(self._params)
combined_params.update(params)
# Check the parameters before submitting
for key in combined_params:
if key not in self.parameters and not key.startswith('x_'):
raise KeyError(
"{} is not a parameter of this solver.".format(key))
# transform some of the parameters in-place
self._format_params(type_, combined_params)
body = json.dumps({
'solver': self.id,
'data': encode_problem_as_qp(self, linear, quadratic),
'type': type_,
'params': combined_params
})
logger.trace("Encoded sample request: %s", body)
future = Future(solver=self,
id_=None,
return_matrix=self.return_matrix)
logger.debug("Submitting new problem to: %s", self.id)
self.client._submit(body, future)
dispatch_event('after_sample',
obj=self,
args=args,
return_value=future)
return future