dimod.higherorder.utils.poly_energies

poly_energies(samples_like: Sequence[float | floating | integer] | Mapping[Hashable, float | floating | integer] | Tuple[Sequence[float | floating | integer], Sequence[Hashable]] | Tuple[ndarray, Sequence[Hashable]] | ndarray | Sequence[Sequence[float | floating | integer]] | Tuple[Sequence[Sequence[float | floating | integer]], Sequence[Hashable]] | Sequence[Sequence[float | floating | integer] | Mapping[Hashable, float | floating | integer] | Tuple[Sequence[float | floating | integer], Sequence[Hashable]] | Tuple[ndarray, Sequence[Hashable]] | ndarray] | Iterator[Sequence[float | floating | integer] | Mapping[Hashable, float | floating | integer] | Tuple[Sequence[float | floating | integer], Sequence[Hashable]] | Tuple[ndarray, Sequence[Hashable]] | ndarray], poly: Mapping[Sequence[Hashable], float | floating | integer] | BinaryPolynomial) → ndarray

Calculates energy of samples from a higher order polynomial.

Parameters:

• samples_like – A collection of raw samples. samples-like is an extension of NumPy’s array_like structure. See as_samples().

• poly – Either a polynomial, as a dict of form {term: bias, …}, where term is a tuple of one or more variables and bias the associated bias, or a BinaryPolynomial. Variable labeling/indexing here must match that of samples_like.

Returns: Energies of the samples.

Examples

>>> poly = dimod.BinaryPolynomial({'a': -1, ('a', 'b'): 1, ('a', 'b', 'c'): -1},
...                               dimod.BINARY)
>>> samples = [{'a': 1, 'b': 1, 'c': 0},
...            {'a': 1, 'b': 1, 'c': 1}]
>>> dimod.poly_energies(samples, poly)
array([ 0., -1.])