dwave.preprocessing.composites.ScaleComposite.sample#
- ScaleComposite.sample(bqm, *, scalar=None, bias_range=1, quadratic_range=None, ignored_variables=None, ignored_interactions=None, ignore_offset=False, **parameters)[source]#
Scale and sample from the provided binary quadratic model.
If
scalaris not given, the problem is scaled based on bias and quadratic ranges. SeeBinaryQuadraticModel.scale()andBinaryQuadraticModel.normalize()- Parameters:
bqm (
dimod.BinaryQuadraticModel) – Binary quadratic model to be sampled from.scalar (number) – Value by which to scale the energy range of the binary quadratic model. Overrides bias_range and quadratic_range.
bias_range (number/pair, default=1) – Value/range by which to normalize the all the biases, or if quadratic_range is provided, just the linear biases. Overridden by scalar.
quadratic_range (number/pair) – Value/range by which to normalize the quadratic biases. Overridden by scalar.
ignored_variables (iterable, optional) – Biases associated with these variables are not scaled.
ignored_interactions (iterable[tuple], optional) – As an iterable of 2-tuples. Biases associated with these interactions are not scaled.
ignore_offset (bool, default=False) – If True, the offset is not scaled.
**parameters – Parameters for the sampling method, specified by the child sampler.
- Returns: