dimod.DiscreteQuadraticModel.to_numpy_vectors#

DiscreteQuadraticModel.to_numpy_vectors(return_offset: bool = False)[source]#

Convert the DQM to five numpy vectors and the labels.

Parameters:

return_offset – Boolean flag to optionally return energy offset value.

Returns:

A named tuple with fields [‘case_starts’, ‘linear_biases’, ‘quadratic’, ‘labels’].

case_starts: A length num_variables() array. The cases associated with variable v are in the range [case_starts[v], cases_starts[v+1]).
linear_biases: A length num_cases() array. The linear biases.
quadratic: A named tuple with fields [‘row_indices’, ‘col_indices’, ‘biases’].
- row_indices: A length num_case_interactions() array. If the case interactions were defined in a sparse matrix, these would be the row indices.
- col_indices: A length num_case_interactions() array. If the case interactions were defined in a sparse matrix, these would be the column indices.
- biases: A length num_case_interactions() array. If the case interactions were defined in a sparse matrix, these would be the values.
labels: The variable labels in a Sequence.

If return_labels=True, this method will instead return a tuple (case_starts, linear_biases, (irow, icol, qdata), labels) where labels is a list of the variable labels.

Return type:

DQMVectors