sample_ising(h, J, offset=0, scalar=None, bias_range=1, quadratic_range=None, ignored_variables=None, ignored_interactions=None, ignore_offset=False, **parameters)¶
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.
- 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.