dimod.binary.BinaryQuadraticModel.add_quadratic_from_dense¶

BinaryQuadraticModel.add_quadratic_from_dense(quadratic: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]])[source]¶

Add quadratic biases from a square 2d array-like.

Parameters: quadratic – Quadratic biases as a square 2d array_like.
Raises: ValueError – If any self-loops are given; i.e., the array contains a non-zero value on its diagonal, which would set a bias for interaction (u, u).

Examples

>>> bqm = dimod.BinaryQuadraticModel("BINARY")
>>> bqm.add_quadratic_from_dense([[0, -0.4, 0.2],[0, 0, 0], [0, 0, 0]])
>>> print(bqm)
BinaryQuadraticModel({0: 0.0, 1: 0.0, 2: 0.0}, {(1, 0): -0.4, (2, 0): 0.2}, 0.0, 'BINARY')