dimod.DiscreteQuadraticModel.from_numpy_vectors#

classmethod DiscreteQuadraticModel.from_numpy_vectors(case_starts, linear_biases, quadratic, labels=None, offset=0)[source]#

Construct a DQM from five numpy vectors.

Parameters:

case_starts (array-like) – A length num_variables() array. The cases associated with variable v are in the range [case_starts[v], cases_starts[v+1]).
linear_biases (array-like) – A length num_cases() array. The linear biases.
quadratic (tuple) –
A three tuple containing:
- irow: A length num_case_interactions() array. If the case interactions were defined in a sparse matrix, these would be the row indices.
- icol: A length num_case_interactions() array. If the case interactions were defined in a sparse matrix, these would be the column indices.
- quadratic_biases: A length num_case_interactions() array. If the case interactions were defined in a sparse matrix, these would be the values.
labels (list, optional) – The variable labels. Defaults to index-labeled.
offset (float) – Energy offset of the DQM.

Example

>>> dqm = dimod.DiscreteQuadraticModel()
>>> u = dqm.add_variable(5)
>>> v = dqm.add_variable(3, label='3var')
>>> dqm.set_quadratic(u, v, {(0, 2): 1})
>>> vectors = dqm.to_numpy_vectors()
>>> new = dimod.DiscreteQuadraticModel.from_numpy_vectors(*vectors)