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 scalar is not given, the problem is scaled based on bias and quadratic ranges. See BinaryQuadraticModel.scale() and BinaryQuadraticModel.normalize()

  • 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.