def from_pandas_dataframe(bqm_df, offset=0.0, interactions=[]):
"""Build a binary quadratic model from a pandas dataframe.
Args:
bqm_df (:class:`pandas.DataFrame`):
A pandas dataframe. The row and column indices should be that variables of the binary
quadratic program. The values should be the coefficients of a qubo.
offset (optional, default=0.0):
The constant offset for the binary quadratic program.
interactions (iterable, optional, default=[]):
Any additional 0.0-bias interactions to be added to the binary quadratic model.
Returns:
:class:`.BinaryQuadraticModel`
"""
bqm = BinaryQuadraticModel({}, {}, offset, Vartype.BINARY)
for u, row in bqm_df.iterrows():
for v, bias in row.iteritems():
if u == v:
bqm.add_variable(u, bias)
elif bias:
bqm.add_interaction(u, v, bias)
for u, v in interactions:
bqm.add_interaction(u, v, 0.0)
return bqm
def from_numpy_matrix(mat, variable_order=None, offset=0.0, interactions=[]):
"""Build a binary quadratic model from a numpy matrix.
Args:
mat (:class:`numpy.matrix`):
A square numpy matrix. The coefficients of a qubo.
variable_order (list, optional):
If variable_order is provided, provides the labels for the variables in the binary
quadratic program, otherwise the row/column indices will be used. If variable_order
is longer than the matrix, the extra values are ignored.
offset (optional, default=0.0):
The constant offset for the binary quadratic program.
interactions (iterable, optional, default=[]):
Any additional 0.0-bias interactions to be added to the binary quadratic model.
Returns:
:class:`.BinaryQuadraticModel`
"""
import numpy as np
if mat.ndim != 2:
raise ValueError("expected input mat to be a square matrix") # pragma: no cover
num_row, num_col = mat.shape
if num_col != num_row:
raise ValueError("expected input mat to be a square matrix") # pragma: no cover
if variable_order is None:
variable_order = list(range(num_row))
bqm = BinaryQuadraticModel({}, {}, offset, Vartype.BINARY)
for (row, col), bias in np.ndenumerate(mat):
if row == col:
bqm.add_variable(variable_order[row], bias)
elif bias:
bqm.add_interaction(variable_order[row], variable_order[col], bias)
for u, v in interactions:
bqm.add_interaction(u, v, 0.0)
return bqm
