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)
See also