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