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from munkres import Munkres, DISALLOWED, UnsolvableMatrix
import munkres
import pytest
m = Munkres()
def _get_cost(matrix):
indices = m.compute(matrix)
return sum([matrix[row][column] for row, column in indices])
def test_documented_example():
'''
Test the matrix in the documented example.
'''
matrix = [[5, 9, 1],
[10, 3, 2],
[8, 7, 4]]
cost = _get_cost(matrix)
assert cost == 12
def float_example():
'''
Test a matrix with float values
'''
matrix = [[5.1, 9.2, 1.3],
[10.4, 3.5, 2.6],
[8.7, 7.8, 4.9]]
cost = _get_cost(matrix)
assert_almost_equal(cost, 13.5)
def test_5_x_5():
matrix = [[12, 9, 27, 10, 23],
[7, 13, 13, 30, 19],
[25, 18, 26, 11, 26],
[9, 28, 26, 23, 13],
[16, 16, 24, 6, 9]]
cost = _get_cost(matrix)
assert cost == 51
def test_5_x_5_float():
matrix = [[12.01, 9.02, 27.03, 10.04, 23.05],
[7.06, 13.07, 13.08, 30.09, 19.1],
[25.11, 18.12, 26.13, 11.14, 26.15],
[9.16, 28.17, 26.18, 23.19, 13.2],
[16.21, 16.22, 24.23, 6.24, 9.25]]
cost = _get_cost(matrix)
assert cost == pytest.approx(51.65)
def test_10_x_10():
matrix = [[37, 34, 29, 26, 19, 8, 9, 23, 19, 29],
[9, 28, 20, 8, 18, 20, 14, 33, 23, 14],
[15, 26, 12, 28, 6, 17, 9, 13, 21, 7],
[2, 8, 38, 36, 39, 5, 36, 2, 38, 27],
[30, 3, 33, 16, 21, 39, 7, 23, 28, 36],
[7, 5, 19, 22, 36, 36, 24, 19, 30, 2],
[34, 20, 13, 36, 12, 33, 9, 10, 23, 5],
[7, 37, 22, 39, 33, 39, 10, 3, 13, 26],
[21, 25, 23, 39, 31, 37, 32, 33, 38, 1],
[17, 34, 40, 10, 29, 37, 40, 3, 25, 3]]
cost = _get_cost(matrix)
assert cost == 66
def test_10_x_10_float():
matrix = [[37.001, 34.002, 29.003, 26.004, 19.005, 8.006, 9.007, 23.008, 19.009, 29.01],
[9.011, 28.012, 20.013, 8.014, 18.015, 20.016, 14.017, 33.018, 23.019, 14.02],
[15.021, 26.022, 12.023, 28.024, 6.025, 17.026, 9.027, 13.028, 21.029, 7.03],
[2.031, 8.032, 38.033, 36.034, 39.035, 5.036, 36.037, 2.038, 38.039, 27.04],
[30.041, 3.042, 33.043, 16.044, 21.045, 39.046, 7.047, 23.048, 28.049, 36.05],
[7.051, 5.052, 19.053, 22.054, 36.055, 36.056, 24.057, 19.058, 30.059, 2.06],
[34.061, 20.062, 13.063, 36.064, 12.065, 33.066, 9.067, 10.068, 23.069, 5.07],
[7.071, 37.072, 22.073, 39.074, 33.075, 39.076, 10.077, 3.078, 13.079, 26.08],
[21.081, 25.082, 23.083, 39.084, 31.085, 37.086, 32.087, 33.088, 38.089, 1.09],
[17.091, 34.092, 40.093, 10.094, 29.095, 37.096, 40.097, 3.098, 25.099, 3.1]]
cost = _get_cost(matrix)
assert cost == pytest.approx(66.505)
def test_20_x_20():
matrix = [[5, 4, 3, 9, 8, 9, 3, 5, 6, 9, 4, 10, 3, 5, 6, 6, 1, 8, 10, 2],
[10, 9, 9, 2, 8, 3, 9, 9, 10, 1, 7, 10, 8, 4, 2, 1, 4, 8, 4, 8],
[10, 4, 4, 3, 1, 3, 5, 10, 6, 8, 6, 8, 4, 10, 7, 2, 4, 5, 1, 8],
[2, 1, 4, 2, 3, 9, 3, 4, 7, 3, 4, 1, 3, 2, 9, 8, 6, 5, 7, 8],
[3, 4, 4, 1, 4, 10, 1, 2, 6, 4, 5, 10, 2, 2, 3, 9, 10, 9, 9, 10],
[1, 10, 1, 8, 1, 3, 1, 7, 1, 1, 2, 1, 2, 6, 3, 3, 4, 4, 8, 6],
[1, 8, 7, 10, 10, 3, 4, 6, 1, 6, 6, 4, 9, 6, 9, 6, 4, 5, 4, 7],
[8, 10, 3, 9, 4, 9, 3, 3, 4, 6, 4, 2, 6, 7, 7, 4, 4, 3, 4, 7],
[1, 3, 8, 2, 6, 9, 2, 7, 4, 8, 10, 8, 10, 5, 1, 3, 10, 10, 2, 9],
[2, 4, 1, 9, 2, 9, 7, 8, 2, 1, 4, 10, 5, 2, 7, 6, 5, 7, 2, 6],
[4, 5, 1, 4, 2, 3, 3, 4, 1, 8, 8, 2, 6, 9, 5, 9, 6, 3, 9, 3],
[3, 1, 1, 8, 6, 8, 8, 7, 9, 3, 2, 1, 8, 2, 4, 7, 3, 1, 2, 4],
[5, 9, 8, 6, 10, 4, 10, 3, 4, 10, 10, 10, 1, 7, 8, 8, 7, 7, 8, 8],
[1, 4, 6, 1, 6, 1, 2, 10, 5, 10, 2, 6, 2, 4, 5, 5, 3, 5, 1, 5],
[5, 6, 9, 10, 6, 6, 10, 6, 4, 1, 5, 3, 9, 5, 2, 10, 9, 9, 5, 1],
[10, 9, 4, 6, 9, 5, 3, 7, 10, 1, 6, 8, 1, 1, 10, 9, 5, 7, 7, 5],
[2, 6, 6, 6, 6, 2, 9, 4, 7, 5, 3, 2, 10, 3, 4, 5, 10, 9, 1, 7],
[5, 2, 4, 9, 8, 4, 8, 2, 4, 1, 3, 7, 6, 8, 1, 6, 8, 8, 10, 10],
[9, 6, 3, 1, 8, 5, 7, 8, 7, 2, 1, 8, 2, 8, 3, 7, 4, 8, 7, 7],
[8, 4, 4, 9, 7, 10, 6, 2, 1, 5, 8, 5, 1, 1, 1, 9, 1, 3, 5, 3]]
cost = _get_cost(matrix)
assert cost == 22
def test_20_x_20_float():
matrix = [[5.0001, 4.0002, 3.0003, 9.0004, 8.0005, 9.0006, 3.0007, 5.0008, 6.0009, 9.001, 4.0011, 10.0012, 3.0013, 5.0014, 6.0015, 6.0016, 1.0017, 8.0018, 10.0019, 2.002],
[10.0021, 9.0022, 9.0023, 2.0024, 8.0025, 3.0026, 9.0027, 9.0028, 10.0029, 1.003, 7.0031, 10.0032, 8.0033, 4.0034, 2.0035, 1.0036, 4.0037, 8.0038, 4.0039, 8.004],
[10.0041, 4.0042, 4.0043, 3.0044, 1.0045, 3.0046, 5.0047, 10.0048, 6.0049, 8.005, 6.0051, 8.0052, 4.0053, 10.0054, 7.0055, 2.0056, 4.0057, 5.0058, 1.0059, 8.006],
[2.0061, 1.0062, 4.0063, 2.0064, 3.0065, 9.0066, 3.0067, 4.0068, 7.0069, 3.007, 4.0071, 1.0072, 3.0073, 2.0074, 9.0075, 8.0076, 6.0077, 5.0078, 7.0079, 8.008],
[3.0081, 4.0082, 4.0083, 1.0084, 4.0085, 10.0086, 1.0087, 2.0088, 6.0089, 4.009, 5.0091, 10.0092, 2.0093, 2.0094, 3.0095, 9.0096, 10.0097, 9.0098, 9.0099, 10.01],
[1.0101, 10.0102, 1.0103, 8.0104, 1.0105, 3.0106, 1.0107, 7.0108, 1.0109, 1.011, 2.0111, 1.0112, 2.0113, 6.0114, 3.0115, 3.0116, 4.0117, 4.0118, 8.0119, 6.012],
[1.0121, 8.0122, 7.0123, 10.0124, 10.0125, 3.0126, 4.0127, 6.0128, 1.0129, 6.013, 6.0131, 4.0132, 9.0133, 6.0134, 9.0135, 6.0136, 4.0137, 5.0138, 4.0139, 7.014],
[8.0141, 10.0142, 3.0143, 9.0144, 4.0145, 9.0146, 3.0147, 3.0148, 4.0149, 6.015, 4.0151, 2.0152, 6.0153, 7.0154, 7.0155, 4.0156, 4.0157, 3.0158, 4.0159, 7.016],
[1.0161, 3.0162, 8.0163, 2.0164, 6.0165, 9.0166, 2.0167, 7.0168, 4.0169, 8.017, 10.0171, 8.0172, 10.0173, 5.0174, 1.0175, 3.0176, 10.0177, 10.0178, 2.0179, 9.018],
[2.0181, 4.0182, 1.0183, 9.0184, 2.0185, 9.0186, 7.0187, 8.0188, 2.0189, 1.019, 4.0191, 10.0192, 5.0193, 2.0194, 7.0195, 6.0196, 5.0197, 7.0198, 2.0199, 6.02],
[4.0201, 5.0202, 1.0203, 4.0204, 2.0205, 3.0206, 3.0207, 4.0208, 1.0209, 8.021, 8.0211, 2.0212, 6.0213, 9.0214, 5.0215, 9.0216, 6.0217, 3.0218, 9.0219, 3.022],
[3.0221, 1.0222, 1.0223, 8.0224, 6.0225, 8.0226, 8.0227, 7.0228, 9.0229, 3.023, 2.0231, 1.0232, 8.0233, 2.0234, 4.0235, 7.0236, 3.0237, 1.0238, 2.0239, 4.024],
[5.0241, 9.0242, 8.0243, 6.0244, 10.0245, 4.0246, 10.0247, 3.0248, 4.0249, 10.025, 10.0251, 10.0252, 1.0253, 7.0254, 8.0255, 8.0256, 7.0257, 7.0258, 8.0259, 8.026],
[1.0261, 4.0262, 6.0263, 1.0264, 6.0265, 1.0266, 2.0267, 10.0268, 5.0269, 10.027, 2.0271, 6.0272, 2.0273, 4.0274, 5.0275, 5.0276, 3.0277, 5.0278, 1.0279, 5.028],
[5.0281, 6.0282, 9.0283, 10.0284, 6.0285, 6.0286, 10.0287, 6.0288, 4.0289, 1.029, 5.0291, 3.0292, 9.0293, 5.0294, 2.0295, 10.0296, 9.0297, 9.0298, 5.0299, 1.03],
[10.0301, 9.0302, 4.0303, 6.0304, 9.0305, 5.0306, 3.0307, 7.0308, 10.0309, 1.031, 6.0311, 8, 1.0312, 1.0313, 10.0314, 9.0315, 5.0316, 7.0317, 7.0318, 5.0319, 1.032],
[2.0321, 6.0322, 6.0323, 6.0324, 6.0325, 2.0326, 9.0327, 4.0328, 7.0329, 5.033, 3.0331, 2.0332, 10.0333, 3.0334, 4.0335, 5.0336, 10.0337, 9.0338, 1.0339, 7.034],
[5.0341, 2.0342, 4.0343, 9.0344, 8.0345, 4.0346, 8.0347, 2.0348, 4.0349, 1.035, 3.0351, 7.0352, 6.0353, 8.0354, 1.0355, 6.0356, 8.0357, 8.0358, 10.0359, 10.036],
[9.0361, 6.0362, 3.0363, 1.0364, 8.0365, 5.0366, 7.0367, 8.0368, 7.0369, 2.037, 1.0371, 8.0372, 2.0373, 8.0374, 3.0375, 7.0376, 4.0377, 8.0378, 7.0379, 7.038],
[8.0381, 4.0382, 4.0383, 9.0384, 7.0385, 10.0386, 6.0387, 2.0388, 1.0389, 5.039, 8.0391, 5.0392, 1.0393, 1.0394, 1.0395, 9.0396, 1.0397, 3.0398, 5.0399, 3.04]]
cost = _get_cost(matrix)
'''
Here, it becomes mandatory to set "places" argument, otherwise test might
fails. It happens because float values in this example have more number of
digits after decimal point than other float examples.
'''
assert cost == pytest.approx(20.362, rel=1e-3)
def test_disallowed():
matrix = [[5, 9, DISALLOWED],
[10, DISALLOWED, 2],
[8, DISALLOWED, 4]]
cost = _get_cost(matrix)
assert cost == 19
def test_disallowed_float():
matrix = [[5.1, 9.2, DISALLOWED],
[10.3, DISALLOWED, 2.4],
[8.5, DISALLOWED, 4.6]]
cost = _get_cost(matrix)
assert cost == pytest.approx(20.1)
def test_profit():
profit_matrix = [[94, 66, 100, 18, 48],
[51, 63, 97, 79, 11],
[37, 53, 57, 78, 28],
[59, 43, 97, 88, 48],
[52, 19, 89, 60, 60]]
import sys
cost_matrix = munkres.make_cost_matrix(
profit_matrix, lambda cost: sys.maxsize - cost
)
indices = m.compute(cost_matrix)
profit = sum([profit_matrix[row][column] for row, column in indices])
assert profit == 392
def test_profit_float():
profit_matrix = [[94.01, 66.02, 100.03, 18.04, 48.05],
[51.06, 63.07, 97.08, 79.09, 11.1],
[37.11, 53.12, 57.13, 78.14, 28.15],
[59.16, 43.17, 97.18, 88.19, 48.2],
[52.21, 19.22, 89.23, 60.24, 60.25]]
max_ = 2**32
cost_matrix = munkres.make_cost_matrix(
profit_matrix, lambda cost: max_ - cost
)
indices = m.compute(cost_matrix)
profit = sum([profit_matrix[row][column] for row, column in indices])
assert profit == pytest.approx(392.65)
def test_irregular():
matrix = [[12, 26, 17],
[49, 43, 36, 10, 5],
[97, 9, 66, 34],
[52, 42, 19, 36],
[15, 93, 55, 80]]
cost = _get_cost(matrix)
assert cost == 43
def test_irregular_float():
matrix = [[12.01, 26.02, 17.03],
[49.04, 43.05, 36.06, 10.07, 5.08],
[97.09, 9.1, 66.11, 34.12],
[52.13, 42.14, 19.15, 36.16],
[15.17, 93.18, 55.19, 80.2]]
cost = _get_cost(matrix)
assert cost == pytest.approx(43.42)
def test_rectangular():
matrix = [[34, 26, 17, 12],
[43, 43, 36, 10],
[97, 47, 66, 34],
[52, 42, 19, 36],
[15, 93, 55, 80]]
padded_matrix = m.pad_matrix(matrix, 0)
padded_cost = _get_cost(padded_matrix)
cost = _get_cost(matrix)
assert padded_cost == cost
assert cost == 70
def test_rectangular_float():
matrix = [[34.01, 26.02, 17.03, 12.04],
[43.05, 43.06, 36.07, 10.08],
[97.09, 47.1, 66.11, 34.12],
[52.13, 42.14, 19.15, 36.16],
[15.17, 93.18, 55.19, 80.2]]
padded_matrix = m.pad_matrix(matrix, 0)
padded_cost = _get_cost(padded_matrix)
cost = _get_cost(matrix)
assert padded_cost == pytest.approx(cost)
assert cost == pytest.approx(70.42)
def test_unsolvable():
with pytest.raises(UnsolvableMatrix):
matrix = [[5, 9, DISALLOWED],
[10, DISALLOWED, 2],
[DISALLOWED, DISALLOWED, DISALLOWED]]
m.compute(matrix)
def test_unsolvable_float():
with pytest.raises(UnsolvableMatrix):
matrix = [[5.1, 9.2, DISALLOWED],
[10.3, DISALLOWED, 2.4],
[DISALLOWED, DISALLOWED, DISALLOWED]]
m.compute(matrix)
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