dimod.binary.BinaryQuadraticModel.from_numpy_vectors¶

classmethod BinaryQuadraticModel.from_numpy_vectors(linear: typing.Union[typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[typing.Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype], typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], typing.Sequence[typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]], typing.Sequence[typing.Sequence[typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]]], typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]]]], bool, int, float, complex, str, bytes, typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]], typing.Sequence[typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]]], typing.Sequence[typing.Sequence[typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]]]], typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]]]]]], quadratic: typing.Union[typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[typing.Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype], typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], typing.Sequence[typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]], typing.Sequence[typing.Sequence[typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]]], typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]]]], bool, int, float, complex, str, bytes, typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]], typing.Sequence[typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]]], typing.Sequence[typing.Sequence[typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]]]], typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]]]]]], offset: float, vartype: dimod.vartypes.Vartype, *, variable_order: typing.Optional[typing.Iterable] = None, dtype: typing.Union[numpy.dtype, None, type, numpy.typing._dtype_like._SupportsDType[numpy.dtype], str, typing.Tuple[typing.Any, int], typing.Tuple[typing.Any, typing.Union[typing_extensions.SupportsIndex, typing.Sequence[typing_extensions.SupportsIndex]]], typing.List[typing.Any], numpy.typing._dtype_like._DTypeDict, typing.Tuple[typing.Any, typing.Any]] = <class 'numpy.float64'>) → BinaryQuadraticModel[source]¶

Create a binary quadratic model from NumPy vectors.

Parameters

linear – Linear biases.
quadratic – Quadratic biases.
offset – Offset of the binary quadratic model.
vartype –
Variable type for the binary quadratic model. Accepted input values:
- SPIN, 'SPIN', {-1, 1}
- BINARY, 'BINARY', {0, 1}
variable_order – Variable order for the binary quadratic model’s labels.
dtype – Data type for the returned binary quadratic model.

Returns

A binary quadratic model.

Examples

>>> import numpy as np
>>> linear = np.ones(10)
>>> quadratic = (np.arange(0, 10), np.arange(1, 11), -np.ones(10))
>>> bqm = dimod.BQM.from_numpy_vectors(linear, quadratic, 0, "BINARY")