# dimod.reference.composites.scalecomposite.ScaleComposite.sample_ising¶

ScaleComposite.sample_ising(h, J, offset=0, scalar=None, bias_range=1, quadratic_range=None, ignored_variables=None, ignored_interactions=None, ignore_offset=False, **parameters)[source]

Scale and sample from the problem provided by h, J, offset

if scalar is not given, problem is scaled based on bias and quadratic ranges.

Parameters: h (dict) – linear biases J (dict) – quadratic or higher order biases offset (float, optional) – constant energy offset scalar (number) – Value by which to scale the energy range of the binary quadratic model. bias_range (number/pair) – Value/range by which to normalize the all the biases, or if quadratic_range is provided, just the linear biases. quadratic_range (number/pair) – Value/range by which to normalize the quadratic biases. 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. dimod.SampleSet