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Use np.ndarray for var_types, basis_status

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Alinson S. Xavier
2021-08-08 07:36:57 -05:00
parent 7d55d6f34c
commit 45667ac2e4
6 changed files with 31 additions and 23 deletions
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4
miplearn/solvers/tests/__init__.py
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@@ -44,7 +44,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
names=np.array(["x[0]", "x[1]", "x[2]", "x[3]", "z"], dtype="S"),
lower_bounds=np.array([0.0, 0.0, 0.0, 0.0, 0.0]),
upper_bounds=np.array([1.0, 1.0, 1.0, 1.0, 67.0]),
types=["B", "B", "B", "B", "C"],
types=np.array(["B", "B", "B", "B", "C"], dtype="S"),
obj_coeffs=np.array([505.0, 352.0, 458.0, 220.0, 0.0]),
),
)
@@ -85,7 +85,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
solver.get_variable_attrs(),
Variables(
names=np.array(["x[0]", "x[1]", "x[2]", "x[3]", "z"], dtype="S"),
basis_status=["U", "B", "U", "L", "U"],
basis_status=np.array(["U", "B", "U", "L", "U"], dtype="S"),
reduced_costs=np.array(
[193.615385, 0.0, 187.230769, -23.692308, 13.538462]
),