def test_linear_sum_assignment_input_validation():
assert_raises(ValueError, linear_sum_assignment, [1, 2, 3])
C = [[1, 2, 3], [4, 5, 6]]
assert_array_equal(linear_sum_assignment(C),
linear_sum_assignment(np.asarray(C)))
assert_array_equal(linear_sum_assignment(C),
linear_sum_assignment(np.matrix(C)))
def assignment_score_slow(cm, normalize=True, rpad=False, cpad=False):
"""Calls Python/Numpy implementation of the Hungarian method
Testing version (uses SciPy's implementation)
"""
cost_matrix = -cm.to_array(rpad=rpad, cpad=cpad)
ris, cis = linear_sum_assignment(cost_matrix)
score = -cost_matrix[ris, cis].sum()
if normalize:
score = _div(score, cm.grand_total)
return score
def test_linear_sum_assignment():
for cost_matrix, expected_cost in [
# Square
([[400, 150, 400],
[400, 450, 600],
[300, 225, 300]],
[150, 400, 300]),
# Rectangular variant
([[400, 150, 400, 1],
[400, 450, 600, 2],
[300, 225, 300, 3]],
[150, 2, 300]),
# Square
([[10, 10, 8],
[9, 8, 1],
[9, 7, 4]],
[10, 1, 7]),
# Rectangular variant
([[10, 10, 8, 11],
[9, 8, 1, 1],
[9, 7, 4, 10]],
[10, 1, 4]),
# n == 2, m == 0 matrix
([[], []],
[]),
]:
cost_matrix = np.array(cost_matrix)
row_ind, col_ind = linear_sum_assignment(cost_matrix)
assert_array_equal(row_ind, np.sort(row_ind))
assert_array_equal(expected_cost, cost_matrix[row_ind, col_ind])
cost_matrix = cost_matrix.T
row_ind, col_ind = linear_sum_assignment(cost_matrix)
assert_array_equal(row_ind, np.sort(row_ind))
assert_array_equal(np.sort(expected_cost),
np.sort(cost_matrix[row_ind, col_ind]))